esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Accuracy  
Calculating the accuracy is a standard way to evaluate the anomaly detection algorithms. It is a single-number summary of the performance of the proposed algorithm and can be calculated as follows:  
\[Accuracy=\left(\frac{\sum TP+\sum TN}{TotalPopulation}\right), \tag{20}\]  
where true positive \((TP)\) corresponds to the samples that the proposed algorithm detects as positive samples and that are, in fact, positive. Similarly, true negatives \((TNs)\) are the points that the proposed algorithm detects as negative samples and that are, in fact, negative.  
Figure 10 shows the accuracy of the proposed schemes (EDADS-1 and EDADS-2) for various IEEE standard bus systems according to a varying number of training samples. The efficiency of the learning algorithm can be improved by increasing the amount of learning data. We compare the performance of the proposed scheme with that of the statistical model-based anomaly detection method [7] in which the FE technique for dimensionality reduction was utilized and further that of the neighborhood component analysis (NCA) technique, respectively. NCA is a supervised learning method for classifying multivariate data into distinct classes according to a given distance metric over the data. The results show that the proposed FS-based schemes (EDADS-1 and EDADS-2) have higher CCD assault detection accuracy. EDADS-2 exhibits slightly higher performance than EDADS-1 since it only employs the normal features for training, utilizing the labeled data. Hence, the average of all  
Figure 8: The ROC curves of the proposed EDADS-1 in standard IEEE 14-, 39-, 57- and 118-bus systems. (**a**) IEEE 14-bus system; (**b**) IEEE 39-bus system; (**c**) IEEE 57-bus system; (**d**) IEEE 118-bus system.  
the distances of test samples is close to the value that is required to accurately separate the normal class from the compromised one. It is also shown from Figure 10 that accuracy in detection increases with the increasing number of training data samples. In addition, the NCA-based FS technique has low detection accuracy as compared to the proposed schemes. However, the NCA-based FS scheme performs better than the statistical-based method [7].  
Figure 9: The ROC curves of the proposed EDADS-2 in standard IEEE 14-, 39-, 57- and 118-bus systems. (**a**) IEEE 14-bus system; (**b**) IEEE 39-bus system; (**c**) IEEE 57-bus system; (**d**) IEEE 118-bus system.
## 3 Proposed Method
### Local Channel Spatial Attention (LCSA)  
The architecture of the local channel spatial attention is depicted in Figure 3.  
Figure 3: The architecture of the local channel spatial attention (LCSA) module.  
Although the overall feature map can be effectively enhanced via global channel attention, the local feature enhancement is even more important when RSI is considered. Therefore, as shown in Figure 3, we propose local channel spatial attention (LCSA), a local feature enhancement module, to explore the local area of RSI. In the experiments, the proposed LCSA proved to be more beneficial than global channel attention and could further improve prediction accuracy.  
The operation of the LCSA is addressed below. First, as depicted in Figure 3, the input global feature map \(F\) is split into four local feature maps \(f_{11},f_{12},f_{21}\), and \(f_{22}\), as described in Equation (1). \(F\) is the original feature map of size \(H\times W\times C\), where \(H\), \(W\), and \(C\) are the height, width, and channel number of the feature map, respectively. The split local feature \(f_{x,y}\) has the size \(\frac{H}{2}\times\frac{W}{2}\times C\), where \(x=1,2\) and \(y=1,2\).  
\[F=\begin{bmatrix}f_{11}&f_{12}\\ f_{21}&f_{22}\end{bmatrix} \tag{1}\]  
Then, the split local feature \(f_{x,y}\) enters the squeeze and excitation (SE) block [26], and the split local feature \(f_{x,y}\) is added with the SE block output \(S_{x,y}\) to generate the residual SE (RSE) block output \(R_{x,y}\), where \(x=1,2\) and \(y=1,2\). As shown in Equation (2), the \(S_{x,y}\) is the feature after the SE block, and \(\sigma\) denotes sigmoid. The RSE block output \(R_{x,y}\) is the sum of the SE output and the input feature, as shown in Equation (3), and each has a size of \(\frac{H}{2}\times\frac{W}{2}\times C\).  
\[S_{x,y}=\sigma\Bigg{(}\frac{1}{h\times w}\sum_{i=1}^{h}\sum_{j=1}^{w}f_{x,y} \Bigg{)} \tag{2}\]  
\[R_{x,y}=f_{x,y}+S_{x,y} \tag{3}\]  
Next, the four RSE block outputs are combined to restore the original spatial size, as shown in Equation (4).  
\[F_{c}=\begin{bmatrix}R_{11}&R_{12}\\ R_{21}&R_{22}\end{bmatrix} \tag{4}\]  
The restored global feature \(F_{c}\) is sent to the spatial attention for further feature enhancement. The architecture of our designed spatial attention is depicted in Figure 4.  
As described in Equation (5), it first performs max-pooling and average-pooling on \(F_{c}\), and \(MaxPool(F_{c})\) and \(AvgPool(F_{c})\) are concatenated. Then, it performs convolution on the concatenated feature \(Cat[MaxPool(F_{c}),AvgPool(F_{c})]\) and is activated by the sigmoid function. The sigmoid-activated feature is multiplied with the input feature \(F_{c}\) to generate the output feature \(F_{s}\).  
\[F_{s}=\sigma(Conv2D[MaxPool(F_{c}),AvgPool(F_{c})])\times F_{c} \tag{5}\]  
Figure 4: The architecture of the spatial attention (SA).  
Finally, as shown in Equation (6), the feature maps \(F_{\text{s}}\) and \(F\) are added to obtain the LCSA output \(F_{lcsa}\).  
\[F_{lcsa}=F_{\text{s}}+F \tag{6}\]
## 5 Discussion  
This study considered a security attack scenario on a non-terrestrial FSO link between a satellite (Alice) and a ground station (Bob). Due to the long link length (e.g., 500 km to 500 million km), the beam radius expands in the range of meters to kilometers [16]. This beam radius expansion allows for the UAV-born passive eavesdropper ([PERSON]) to interrupt the broad optical beam without notifying the legitimate peers [PERSON] and [PERSON] under the hypothesis that [PERSON] is a sufficiently sensitive device that can collect a fraction of leak power \(<\) 10\({}^{-2}\)[5]. Hence, an upper-layer data encryption technique that uses the algorithm (Xsalsa20) within NaCl library was implemented to secure FSO systems. GNU radio simulation platform with SDR hardware was used to prove the effectiveness of the proposed data encryption technique to prevent eavesdropping.  
Simulation results of a realistic non-terrestrial FSO link showed that when [PERSON] receives a fraction of leak power of 10\({}^{-2}\) (i.e., 0.1 W of 10 W) and boosts the received signal using an amplifier with a gain of 20 dB, it can decipher the received packets with PDR of 96%. This is consistent with the literature [5] which reported a failure of the physical layer security to prevent eavesdropping at this level of leak power. The results also showed that [PERSON] failed to decrypt any encrypted messages.  
The immunity of the cryptographic encryption techniques to eavesdropping attacks was demonstrated experimentally using GNU Radio simulation platform with Red Pitaya SDR and optical hardware. The results also showed that combining encryption with FSO preserved data security.  
The results of this study showed that more work is required to secure non-terrestrial optical communication systems. The proposed Xsalsa20 provides sufficient data security against flying eavesdropper that has limited computing power to break the key. However, advanced encryption techniques are required to prevent more powerful malicious security attacks with higher computing capability.
# Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
## 3 Experimental Results and Analysis
### Crab Detection
#### 3.2.4 Comparison of Detection Algorithms  
To evaluate the performance of the improved model, it was compared with representative models such as Faster R-CNN, YOLOv8n, and YOLOv10n. The improved model achieved mAP50 and mAP50-95 scores of 98.5% and 88.5%, respectively, significantly outperforming the other models. Additionally, the improved model demonstrated lower computational complexity with GFLOPs of 4.2 and 2.11M parameters, outperforming Faster R-CNN and YOLOv8n in terms of efficiency. Moreover, the improved model achieved an FPS of 309, significantly higher than Faster R-CNN's 10.8 and YOLOv8n's 275, showcasing exceptional real-time detection capability. Overall, the improved model achieves an excellent balance between accuracy, efficiency, and computational cost. The results of comparative experiments are shown in Table 3.
## 3 Case Study
### Data Pre-Processing  
The Texas NBI data, published by the Federal Highway Administration, is utilized in this study. The NBI database encompasses over 130 types of bridge characterization information, coded and stored according to the uniform American bridge inspection standards. Each bridge component is rated on a scale from 0 to 9, and the final rating of the entire bridge is derived from the lowest condition rating among its components. Studies have indicated that predicting the condition of individual bridge components has a minimal impact on the overall bridge condition. Consequently, the Elman neural network will be employed to directly predict the bridge's overall condition.  
Before predicting future bridge deterioration, it is necessary to process the bridge features. Initially, records with missing indicators are excluded from the NBI data, as well as data coded with 'N', which applies to culverts and other structures without deck slabs, and bridges with a rating of less than 3. The latter are considered failed in the bridge inventory description. Subsequently, the characteristics of each bridge are extracted and organized into a time series format that correlates bridge age with its condition.  
To accommodate computer performance and ensure the completeness of the time series for each bridge, this study focuses on data for Texas bridges from 1992 to 2022.
## 4 Computational Simulations and Experimental Results
### System Simulation Design  
The critical parameters of the communication system are defined. The system's symbol rate (R) is set at 2.5 ksps, with each symbol characterized by a width (W) of 2 bits. Consequently, the system's overall transmission rate (S) stands at 5 kbps. The symbol slicing frequency is established to be eight times the carrier frequency. In the 254 nm wavelength band of the mercury lamp emission, solar background light radiation ranges from approximately 1 \(\times\) 10\({}^{4}\) to 5 \(\times\) 10\({}^{4}\) photons per second. Therefore, the background radiation intensity is fixed at 2.5 \(\times\) 10\({}^{4}\) photons/s. In this context, the impact of electronic noise on the optical communication system is disregarded. Electronic noise primarily emanates from two sources: the dark current introduced by the PMT and the internal electronic noise introduced by the operational amplifier circuit. Testing both aspects of electronic noise using an oscilloscope revealed a magnitude ranging from 50 mV to 130 mV. In comparison, environmental light noise, tested between 1.1 V and 1.3 V, exhibits a magnitude difference of over tenfold.  
Initially, the transmission of data through attenuated paths to the receiver is simulated. At the receiver end, a Poisson distribution is employed to model the probability distribution of photon reception by the PMT. Subsequently, a signal decoding algorithm based on 4 FSK  
Figure 4: Ultraviolet weak signal communication system block diagram.  
Figure 5: Hardware implementation module of UV communication system.  
modulation is utilized to process the received data, followed by an analysis of the system's communication capabilities.
# An Efficient Finite-Difference Stencil with High-Order Temporal Accuracy for Scalar Wave Modeling
## 5 Numerical Experiments
### Seismic Modeling of the 2D Inhomogeneous Velocity Model  
We used a modified 2D Sigsbee velocity model to test the new stencil in the complex velocity model. The modified Sigsbee model (Figure 6) has \(500\times 1200\) grid nodes, with the velocities varying from 1550 to 4500 (m/s). We set \(M=8\), \(N=8\), grid spacing \(h=8.5\) (m) and the time step \(\Delta t=0.0009\) (s) for all FD schemes. The source with the peak frequency \(f_{p}=30\) (Hz) (Ricker wavelet) was injected at the center of the surface. For convenience, we ignored the influence of the discrete grid representation on the strong material interfaces [25; 26].  
Figure 7 shows the snapshots and seismic records of different FD methods. The TE-C method had obvious dispersion errors in the low-velocity layers, while the corresponding  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Cases** & **Methods** & **M** & **N** & **Total Relative Errors (10\({}^{-1}\) Pa)** & **Execution Times (s)** \\ \hline
1 & TE-C & 12 & \(\backslash\) & 42.63832 & 41.22905 \\
2 & TE-CR & 12 & 6 & 2.02534 & 80.51018 \\
3 & TE-R & 12 & 6 & 3.86926 & 53.72843 \\
4 & TE-CR & 12 & 8 & 1.48282 & 109.32694 \\
5 & TE-R & 12 & 8 & 2.51165 & 61.30650 \\
6 & TE-CR & 12 & 10 & 0.85564 & 156.39934 \\
7 & TE-R & 12 & 10 & 1.52663 & 65.83632 \\
8 & TE-CR & 12 & 12 & 0.24282 & 204.95636 \\
9 & TE-R & 12 & 12 & 0.72446 & 70.14191 \\ \hline \end{tabular}
\end{table}
Table 2: Relative errors and CPU execution times of the TE-CR and TE-R methods with different orders \(N\).  
Figure 6: The 2D Sigsbee model had \(500\times 1200\) grid nodes with a grid spacing \(h=8.5\) (m) and variation in the velocities from 1550 to 4500 (m/s).  
dispersion errors were reduced in the TE-R and DRP-R methods. Figure 8 shows the seismic records at position \((2125,0)\) (m), at which the reference trace **r** used a high-order temporal and spatial FD scheme (\(M=12,N=12\)) with the DRP-based coefficients [7]. Traces **a-r** **b-r** and **c-r** represent the relative errors of the TE-C, TE-R and DRP-R methods compared with the reference method, respectively. The dispersion errors of the TE-C method were serious for the first arrival waves and reflection waves. The corresponding errors were significantly alleviated in the TE-R and DRP-R methods. Table 3 lists the relative errors of all FD methods. It is clear that the relative errors of the TE-R and DRP-R methods were far less than that of the TE-C method. The numerical experiments with the Sigsbee model demonstrated that the new stencil also had better performance in the complex velocity model for mitigating dispersion errors.  
Figure 7: Snapshots and corresponding seismic records of the 2D Sigsbee velocity model. Here, \(M=8\), \(N=M\), \(\Delta t=0.0009\) (s) and \(h=8.5\) (m). (**a,b**) The TE-C method. (**c,d**) The TE-R method. (**e,f**) The DRP-R method.
## 3 Results
### Environmental Conditions  
Figure 7 reports the daily air temperatures recorded throughout the growing season in this study. The maximum temperature registered in the control plots (48 \({}^{\circ}\)C) exceeded that observed beneath the PV panels (42 \({}^{\circ}\)C), with a maximum absolute difference of 8 \({}^{\circ}\)C. The temperature profile in both plots showed four distinct phases: an increase until 24 July, a subsequent decrease until 18 August, another increase from 19 August to 28 August, and a final decrease in the last 4 days of observation.  
The maximum air temperature differences were also reflected in the soil temperature measured in each pot for both control and PV panel plots. The control plots showed higher soil temperatures, averaging 36.8 \(\pm\) 4.7 \({}^{\circ}\)C in the LW-Control pots and 35.7 \(\pm\) 3.7 \({}^{\circ}\)C in the HW-Control pots. In contrast, the average soil temperature under the PV panels was 29.2 \(\pm\) 2.5 \({}^{\circ}\)C and 29.2 \(\pm\) 3.1 \({}^{\circ}\)C for both the HW- and LW-PV panel plots. In particular, the difference was not significant between the LW- and the HW-Control plots, nor between the HW-PV panel and the LW-PV panel. However, it was significant between the two light conditions (control plots vs. PV panel plots) the regardless of irrigation regime (Figure 8a).  
Soil humidity showed a lower average value in the pots located in the control plots (HW-Control and LW-Control) than the average humidity recorded under the pots beneath the PV panel (HW-PV panel and LW-PV panel groups). However, a significant difference was observed between the HW-Control and both the LW-Control and HW-PV panel conditions. No significant difference was noted between the HW-Control and LW-PV panel (Figure 8b).  
The illuminance values recorded during the maximum shadow expansion beneath the panel were around 3999.00 \(\pm\) 573.38 lx, while those measured in full sunlight reached approximately 125,125.00 \(\pm\) 2904.05 lx.  
Figure 7: The maximum temperature reached in the open-field sunlight control plots (HW Control and LW Control groups) and under the PV panel (HW-PV panel and LW-PV panel groups). The dashed line represents the moving average with a 10-day time window.
## 2 Modeling of Tethered UAV Dynamics
### Dynamics Modeling of UAV Platforms
#### 2.1.3 Force Analysis of the UAV Platform  
When the UAV is working, the brushless motor usually rotates, driving the rotors to generate air flow to drive the UAV to complete various actions. There are three primary sources of force acting on a UAV. These include the vertical upward lift \(F_{T}\) generated by the rotors, the gravity \(G\), and the bollard pull \(T\). Figure 4 is a schematic diagram of the force of the UAV platform.  
Figure 3: Coordinate system of the UAV platform.  
In Figure 4, \(F_{1}\), \(F_{2}\), \(F_{3}\), and \(F_{4}\) are the lifts generated by the four rotors; \(M_{1}\), \(M_{2}\), \(M_{3}\), and \(M_{4}\) are the torques generated by each rotor; \(T\) is the tension of the tethered cable, and \(G\) is the gravity of the UAV platform. Since UAVs are mostly hovering when working in high-altitude environments, the angles of \(\alpha\) and \(\varphi\) are approximately 0 degrees when analyzed. The amount of lift and torque is related to the speed of the rotor.  
Changing the rotational speed of the rotor allows for different flight attitudes.  
When \(F_{1}=F_{2}=F_{3}=F_{4}\), the UAV platform is in an ascending, descending, or hovering state;  
When \(F_{2}=F_{4}\) and \(F_{1}\
eq F_{3}\), the system is in the pitch state;  
When \(F_{1}=F_{3}\) and \(F_{2}\
eq F_{4}\), the system is in a roll state;  
When \(F_{1}=F_{3}\
eq F_{2}=F_{4}\), the system is in yaw.
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Cervical and Ovarian Cancer  
Piperine and mitomycin-C (MMC) co-treatment resulted in inactivating STAT3/NF-kB, leading to suppression of the Bcl-2 signaling pathway in human cervical cancer. Also, this compound, together with its analogs demonstrated significant potential against Hela cervix cell line [63]. A recent study showed that piperine (8, 16, and 20 uM) inhibited cell viability and caused apoptosis in human ovarian A2780 cells via JNK/p38 MAPK-mediated intrinsic apoptotic pathway [64]. Further analysis on the mechanism of its action demonstrated increased levels of cytt-c from mitochondria and consequently increased caspase (caspase-3 and -9) activities and also decreased phosphorylation of JNK and p38 MAPK following piperine treatment.
## 5 Conclusions  
To determine whether a tested frame contains secret information in low-bit-rate speech streams, this paper proposes a frame-wise steganalysis method that integrates a mask-gating attention mechanism with a bilinear interaction codeword feature extraction strategy. The framework includes several components: an embedding layer, a mask-gating attention layer, a bilinear interaction layer, and deep residual network layers. Experimental results show that the proposed method achieves better performance than existing steganalysis techniques in frame-wise detection for low-bit-rate speech streams. Nevertheless, the proposed framework has some limitations. First, it analyzes frames independently and does not model temporal dependencies across speech frames, which may reduce its effectiveness in detecting steganographic schemes that distribute hidden information over multiple frames. Second, the method is designed with specific codec structures in mind and may require modifications to adapt to different speech coding standards or variable bit rates. In future work, we aim to address these limitations and improve our approach in several ways: first, by developing a more universal frame detection framework that can generalize to diverse codecs and conditions through adjustments to the architecture or feature extraction techniques; and second, by designing an explainable frame-wise detection method that makes the detection process more transparent and increases trust in real-world applications.  
All authors contributed to the preparation of this paper. [PERSON] proposed the method, conducted the literature review, designed and performed the experiments, analyzed the results, and wrote the manuscript. [PERSON] and [PERSON] supervised the research, offering guidance and suggestions for improvement. [PERSON] assisted in analyzing the experimental results and provided recommendations for revisions. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are publicly available upon request from the authors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Network Description** & **Accuracy** \\ \hline \#1 & No MGA mechanism & 0.7616 \\ \#2 & No BI mechanism & 0.7573 \\ \#3 & No residual mechanism & 0.7622 \\ \#4 & The complete & 0.7664 \\  & proposed model & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ablation study comparing the performance of different components in the HPS method.
# Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny
## 2 Materials and Methods
### Dataset  
This study utilizes the Nighttime-Yawning-Microsleep-Eyeblink-Distraction (NITYMED) dataset [26]. This dataset is a valuable resource containing video recordings of male and female drivers showing signs of drowsiness through eye and mouth movements during nighttime vehicle operations. The importance of this dataset is heightened by its focus on nighttime driving scenarios, a time during which the likelihood of drowsiness-induced vehicle accidents increases significantly. The dataset carefully collects examples of drivers operating actual vehicles under nighttime conditions, thereby enhancing the ecological validity of the study.  
Drivers represented within the NITYMED dataset comprise 11 Caucasian males and 8 females, carefully chosen to represent a broad spectrum of physical attributes (including variations in hair color, the presence of facial hair, and the usage of optical aids), thereby ensuring a diverse sample that could potentially affect the detection of drowsiness-related symptoms.  
For the purpose of this study, video recordings featuring 14 drivers (consisting of 8 males and 6 females) were selected to generate a substantial pool of training and validation data, amassing a total of 3440 images. This study adopts a binary classification approach, delineating the data into two distinct categories: \"Open Eyes\" and \"Closed Eyes\". From the total dataset, 2955 images (constituting 86% of the dataset) were allocated for training purposes, with the remaining 485 images (representing 14% of the dataset) set aside for validation. Furthermore, video content featuring an additional 5 drivers was incorporated as test data to rigorously evaluate the model's efficacy.  
A critical aspect of this research involves the analysis of ocular activities to infer the driver's state of alertness. A driver is considered to be in an awake state if the duration of eye closure ranges between 100 and 300 milliseconds; should this duration extend beyond 300 milliseconds, the driver is deemed to be in a state of drowsiness [27; 28]. Given that the NITYMED dataset operates at a frame rate of 25 frames per second, a detection of the driver's eyes remaining closed for more than 8 frames is indicative of drowsiness.