Orthogonal time frequency space (OTFS) modulation is emerging as a strong candidate for Low Earth Orbit (LEO) satellite communication systems due to its ability to counter Doppler and delay impairments, enabling reliable performance in high-mobility environments. However, OTFS-based LEO Satcom still faces critical challenges in physical layer security (PLS) and peak-to-average power ratio (PAPR) reduction, particularly in Frequency Division Duplex (FDD) systems where channel reciprocity is only partial. To address these issues, this paper proposes a chaos-based secure OTFS scheme that jointly enhances secrecy and reduces PAPR. The scheme exploits the slow-varying delay-Doppler channel characteristics of the legitimate link to derive a common chaos sequence at both the satellite and ground receiver. This sequence is used to generate secure phase rotations, eliminating the need for side-information exchange and simultaneously enabling PAPR reduction through a selective mapping (SLM) process. A closed-form approximation of the ergodic secrecy rate is derived, and performance is validated through simulation. Results show that the proposed method significantly improves secrecy rate while maintaining a bit error rate (BER) close to 0.5 for the eavesdropper, and achieves notable PAPR reduction without additional signaling overhead. These findings confirm that the proposed scheme effectively addresses both the security and energy efficiency requirements of LEO Satcom.

Dense multi-target sensing in orthogonal time–frequency space (OTFS)-based integrated sensing and communication (ISAC) is challenged by sub-resolution target spacing, unknown target cardinality, and inter-target radar-cross-section (RCS) variation. This paper develops a neural-assisted detector (NAD) that combines convolutional-neural-network candidate generation with continuous delay–Doppler refinement, physics-based validation, active splitting, and explicit model-order control. The CNN generates candidates from the nominal delay–Doppler grid while predicting fractional offsets, so that the final target positions are continuous-valued rather than confined to grid centers. Isolated candidates are refined individually, whereas closely spaced candidates are jointly optimized through alternating position and complex-gain updates, with active splitting used to resolve dense target pairs. For unknown target number, NAD-Hybrid combines conservative candidate generation with information-criterion-based weak-target rescue and backward pruning to control false alarms under RCS mismatch. On a frozen 8000-frame blind test covering two scenes and four RCS standard deviations, NAD-Hybrid achieves a macro-averaged $F_1$ score of 0.9338 and reduces false alarms relative to the unknown-order OMPFR-BIC baseline, at the cost of lower detection probability under severe RCS mismatch. The proposed detector therefore provides a conservative operating point that prioritizes low false alarms and reliable target-count estimation. The sensing framework is further demonstrated to coexist with an index-modulated OTFS communication link using a shared frame.