The occurrence of post-sunset equatorial spread-F (ESF) could have detrimental effects on trans-ionospheric radio wave propagation used in modern communications systems. This problem calls for a simple but robust model that accurately predicts the occurrence of post-sunset ESF. Logistic regression was implemented to model the daily occurrence of post-sunset ESF as a function of the evening upward plasma drift (v). The use of logistic regression is formalized by $\widehat{y}=1/\left[1+\exp \left(-z\right)\right]$, where $\widehat{y}$ represents the probability of post-sunset ESF occurrence, and $z$ is a linear function containing $v$. The value of $v$ is derived from the vertical motion of the bottom side of the F-region in the evening equatorial ionosphere, which is observed by the ionosondes in Southeast Asia. Data points (938) of v and post-sunset ESF occurrence were collected in the equinox seasons from 2003 to 2016. The training set used 70% of the dataset to derive $z$ and $\widehat{y}$ and the remaining 30% was used to test the performance of $\widehat{y}$. The expression $z=-2.25+0.14v$ was obtained from the training set, and $\widehat{y}\ge 0.5$ (v ≥ ~16.1 m/s) and $\widehat{y}<0.5$ (v < ~16.1 m/s) represented the occurrence and non-occurrence of ESF, respectively, with an accuracy of ~0.8 and a true skill score (TSS) of ~0.6. Similarly, in the testing set, $\widehat{y}$ shows an accuracy of ~0.8 and a TSS of ~0.6. Further analysis suggested that the performance of the z-function can be reliable in the daily F_10.7 levels ranging from 60 to 140 solar flux units. The z-function implemented in the logistic regression ($\widehat{y}$) found in this study is a novel technique to predict the post-sunset ESF occurrence. The performance consistency between the training set and the testing set concludes that the z-function and the $\widehat{y}$ values of the proposed model could be a simple and robust mathematical model for daily nowcasting the occurrence or non-occurrence of post-sunset ESFs.

Human-Computer Interaction (HCI) has witnessed rapid advancements in signal processing research within the health domain, particularly in signal analyses like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). ECG, containing diverse information about medical history, identity, emotional state, age, and gender, has exhibited potential for biometric recognition. The Random Forest method proves essential to facilitate gender classification based on ECG. This research delves into applying the Random Forest method for gender classification, utilizing ECG data from the ECG ID Database. The primary aim is to assess the efficacy of the Random Forest algorithm in gender classification. The dataset employed in this study comprises 10,000 features, encompassing both raw and filtered datasets, evaluated through 10-fold cross-validation with Random Forest Classification. Results reveal the highest accuracy for raw data at 55.000%, with sensitivity at 46.452% and specificity at 63.548%. In contrast, the filtered data achieved the highest accuracy of 65.806%, with sensitivity and specificity at 67.097%. These findings conclude that the most significant impact on gender classification in this study lies in the low sensitivity value in raw data. The implications of this research contribute to knowledge by presenting the performance results of the Random Forest algorithm in ECG-based gender classification.

The automatic detection of shapes or patterns represented by signals captured from spacecraft data is essential to revealing interesting phenomena. A signal processing approach is generally used to extract useful information from observation data. In this paper, we propose an image analysis approach to process image datasets produced via plasma wave observations by the Arase satellite. The dataset consists of 31,380 PNG files generated from the dynamic power spectra of magnetic wave field data gathered from a one-year observation period from March 2017 to March 2018. We implemented an automatic detection system using image analysis to classify the various types of lightning whistlers according to the Arase whistler map. We successfully detected a large number of whistler traces induced by lightning strikes and recorded their corresponding times and frequencies. The various shapes of the lightning whistlers indicate different very-low-frequency propagations and provide important clues concerning the geospace electron density profile.

Homelab is a discussion platform on course materials and assignments for students and is packed in an Android application product and website. The Homelab website is built using Laravel. For Android-based Homelab application development, a special Application Programming Interface (API) with JWT security is made in this research. In Homelab, besides the question and answer feature, a virtual conversation agent (chatbot) based on deep learning with a retrieval model that uses multilayer perceptron and a special text dataset for conversations about Homelab products is also created. The virtual conversation agent at Homelab is made by utilizing the Sastrawi library and natural language processing to facilitate the processing of user messages in Indonesian. The output of this research is the response from the chatbot and the probability value from the classification results of the available response classes. The system made has an accuracy rate of 96.43 percent with an average processing time of 0.3 seconds to get a response.

We present new insights into post-sunrise ionospheric irregularities in Southeast Asia during the intense geomagnetic storm of 19–20 April 2024. By utilizing Total Electron Content (TEC) and Rate of TEC Change Index (ROTI) maps, along with ionosondes, we identified the emergence of post-sunset Equatorial Plasma Bubbles (EPBs)—plasma depletion structures and irregularities—in western Southeast Asia on 19 April. These EPBs moved eastward, and the irregularities dissipated before midnight after the EPBs covered approximately 10° of longitude. Interestingly, plasma density depletion structures persisted and turned westward after midnight until post-sunrise the following day. Concurrently, an increase in F-region height from midnight to sunrise, possibly induced by the storm’s electric field, facilitated the regeneration of irregularities in the residual plasma depletions during the post-sunrise period. The significant increase in F-region height was particularly pronounced in western Southeast Asia. As a result, post-sunrise irregularities expanded their latitudinal structure while propagating westward. These findings suggest that areas with decayed plasma depletion structures from post-sunset EPBs that last past midnight could be sites for creating post-sunrise irregularities during geomagnetic storms. The storm-induced electric fields produce EPBs and ionospheric irregularities at longitudes where the surviving plasma depletion structures of post-sunset EPBs are present.