esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Analysis
### Informal analysis
#### 4.2.5 Replay Attack  
In the replay attack, it is possible for attackers to eavesdrop and obtain communication messages between the UAV and the GCS and replay the authentication information or communication messages in the session. Therefore, using the timestamp mechanism to protect the freshness of the sent message and signing the timestamped message before sending it not only ensures the freshness of the received message, but also the integrity of the message. Attackers are unable to sign the timestamp and cannot achieve a replay attack. When the recipient receives the message, it first checks the freshness of the timestamp so that it can detect the replayed message, so the protocol is resistant to replay attacks.
## 3 Model Construction and Prediction Results
### Comparison and Evaluation of Algorithm Model  
In order to investigate the reliability and accuracy of the prediction results of the DNN model established in this study, under the same training and testing conditions, this paper uses three other commonly used algorithm models to compare them, which are extreme gradient boosting (XGBoost), random forest (RF), and back-propagation (BP) neural networks with only one hidden layer. After several iterations of training, the prediction of these four models on the training set and the testing set is obtained, as shown in Figure 3. It can be seen from Figure 3 that the DNN model has the best prediction effect. Except for individual sample points, most of the sample points can be closely distributed on both sides of the \(y=x\) line, followed by XGBoost and RF algorithm models, while the BP neural network model has a slightly weaker prediction ability.  
There are many criteria for evaluating the performance of deep learning models. In this study, the widely used root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (\(R^{2}\)) are selected to evaluate the performance of the model. The calculation results are shown in Table 3. The closer the \(R^{2}\) of the model is to 1, the smaller the RMSE, MAPE, and MAE, the better the fitting effect of the model and the smaller the prediction error. According to the evaluation index of the model, the DNN model proposed in this paper demonstrates the best predictive performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Optimization Scope** & **Final Value** \\ \hline Number of hidden layers & 1\(\sim\)10 & 3 \\ Number of neurons & 8\(\sim\)200 & [32; 136; 168] \\ Activation function & relu, tanh, Lrelu & relu \\ Optimizer & SGD, Adam, RMSProp, AdaGrad & Adam \\ Rate of learning & [1,0.01,0.001,0.0001] & 0.001 \\ Regularization & - & Dropout \\ Epoch & - & 3500 \\ Batch\_size & 1\(\sim\)50 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hyperparameters of the model.
## 6 Simulation Results
### Sensitivity Tests
#### 6.2.2 Varying Strategy Indexes among Different Proportions of PIRs
Varying proportions of opinion leaders  
Figure 11 shows the sensitivity of the market penetration at each step to varying proportions of opinion leaders from 0.02 to 0.2 with different proportions of PIRs. More opportunities to generate opinion leaders contribute to the increase in market penetration, while the effect of opinion leaders seems to increase at first and then decrease with the increase in proportions of PIRs. We used a regression test to further investigate the relationship between the positive impact of opinion leaders on final market penetration with different proportions of PIRs. To obtain a more precise analysis, we varied the proportions of PIRs from 30% to 70%, with a 10% increase each time. As indicated by Table 5, the effect of cultivating proportions of opinion leaders on final penetration increases when the proportions of PIRs increase from 30% to 50% and then stop increasing. These results may occur because cultivating opinion leaders contributes to higher self-efficacy and more precise personal judgments of service quality and then induces PIRs to join the platform before most of their friends join. Considering that such a mechanism works mainly on PIRs, it exerts a larger effect with the increase in proportions of PIRs to a certain degree. However, if the proportions of PIRs are too large, there are many PIRs who lack an opportunity to be educated by opinion leaders, thus diluting its effect. Figure 11: Market penetration at each step. Figure 10: Market penetration at each step.
## 3 Results
### Comparison Experiments
#### 3.2.3 Scene III  
Figure 8 displays the experimental outcomes obtained from various networks in Scene III. The water distribution in Scene III is irregular, with a complex background and details, which places high demands on the network's feature extraction ability. From the results, it can be seen that MF2 AM and Deeplabv3+ have a large number of missed detections and false alarms, resulting in a poor detection performance for small water areas. U-Net can roughly detect small and slender water bodies, but there are too many false alarms and it cannot effectively suppress noise interference, reflecting the problem of inaccurate semantic information extraction. GCAFF-Net can extract more detailed water bodies and detect much fewer false alarms than other networks, indicating its excellent ability to extract global semantic information about water bodies, effectively suppress noise interference, and extract the detailed boundaries of water bodies. Scene III requires a high capacity for the extraction of detailed water information. According to Table 4, the overall water detection accuracy of all four networks decreased, while the other networks did not reach 89%. GCAFF-Net achieved a detection accuracy of 89.59%, which is significantly improved compared to other networks and over 7% higher than DeepLabV3+. We can also see that GCAFF-Net outperforms other networks in this study across various indicators: PA (89.59%), IoU (82.10%), Recall (92.93%) and F1-Score (90.17%). These findings indicate that GCAFF-Net is highly effective for segmentation tasks with an irregular water morphology, complex backgrounds, and rich details.
## 4 Coordination of PSS and BESS Controllers to Enhance Oscillatory Stability Using Proposed Method
### Power-System-Controller Modeling  
#### 4.1.1 Power-System Stabilizers  
PSS structure is well-established in the literature [6; 32], and its primary function is to modulate excitation-system voltage to improve dynamic stability. A typical PSS connected with a \(i^{th}\) generator with rotor-speed deviation as input constitutes a gain of \(K_{i,pss}\), lead-lag constant with parameters \(T_{1i,pss}\), \(T_{2i,pss}\), \(T_{3i,pss}\), \(T_{4i,pss}\), and a washout block with time constant \(T_{i,pssw}\), as depicted in Figure 4[6]. The values of maximum voltage \(V_{max}\), minimum voltage \(V_{min}\), and \(T_{i,pssw}\) are 0.2 and \(-0.2\) p.u. and 10 s, respectively [6]. The range of other PSS parameters is provided in Section 4.2.1.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 7 Conclusions and Future Research  
This paper is a systematic review of the literature in wearable sensor technology for analysis of gait, balance, and RoM. There is a high demand for wearable sensor technology in these analyses due to the high level of portability that they afford. Wearable sensor systems allow clinicians and researchers to analyze gait, balance and RoM not only in the research lab, but also in the clinic and out the community. But, before wearable sensors can be useful as clinical devices, methods must be valid and reliable, and the data provided by these systems should be accurate, informative and practical. At present, most body worn sensor systems are not affordable for most clinics and require experience in data analysis to interpret the results.  
The studies reviewed in this paper have contributed to information on the use of wearable sensors in gait, balance and RoM analysis, however, further validation and improvements in these systems are needed. Based on this review of the literature, future research should focus on the design of wearable sensor systems that are affordable and more simple to use clinically or in the community. Calculations for gait parameters such as step length, step width, and walk deviation using wearable sensors need to be improved upon. Balance assessment using wearable sensors could be further developed with parameters that are correlated with gait parameters. Lastly, the integration of values extracted from time-series symmetry calculation, such as dynamic time warping and autocorrelation, to measure gait variability using wearable sensors should be investigated.  
[PERSON] contributed to the writing and organization of the manuscript. [PERSON] revised the manuscript providing insightful suggestions and made necessary English corrections. [PERSON] provided suggestions and supervision. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by the National Science Foundation Florida-Georgia Louis Stokes Alliance for Minority Participation Bridge to the Doctorate under Grant No. 1400837 and the National Science Foundation under grants No. 1458928 and 1645025, An REU Site on Ubiquitous Sensing.  
The authors declare no conflict of interest.
###### Abstract  
As the number of controllers and devices increases in Industrial Internet of Things (IIoT) applications, it is essential to provide a secure and usable user authentication system for human operators who have to manage tens or hundreds of controllers and devices with his/her password. In this paper, we propose a _formally verified_ certificate-based authentication system using a secondary network device for such IIoT applications. In the proposed system, a user's sign key is encrypted with a secret key that can be computed with his/her password and a secret parameter in a secondary device to securely protect the sign key. To demonstrate the feasibility of the proposed system, we implemented a prototype with standard cryptographic algorithms (AES-256, RSA-3072, and ECDSA-256). The experiment results demonstrated that the execution time overhead of the sign key recovery process was 0.039 and 0.073 s, respectively, for RSA-3072 and ECDSA-256, which was marginal compared with the total execution time (0.383 s for RSA-3072 and 0.319 s for ECDSA-256) of the conventional system. We also verified the security of the proposed protocol using a formal verification tool called ProVerif.  
industrial controller; key management; key protection; user authentication
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 2 Materials and Methods
### Data Preprocessing  
A shipborne radar image was collected in the Cartesian coordinate system and then transformed into the polar coordinate system. The morphological features of targets are easier to recognize in the Cartesian coordinates system. For example, the co-frequency interferences caused by the radars of other ships show high-bright linear noise in a vertical direction (Figure 4), and therefore, we first transformed the image back to the Cartesian coordinate system with the azimuth as the horizontal axis and the detection distance as the longitudinal axis. The flow of data preprocessing is shown in Figure 5. Firstly, the Laplace operator of Equation (1) was used to enhance the gray value of the highlighted pixels (Figure 6a). The image was then segmented according to the mean value (Figure 6b). The corresponding highlighted pixels in the original image were smoothed by a linear mean filter (Figure 6c). The isolated targets were extracted by the Otsu [38] threshold and the area threshold \(T_{\text{area}}\) (Figure 6d), and then removed with a median filter (Figure 6e). The window of the median filter must be larger than \(T_{\text{area}}\).  
\[F_{Laplace}=\left[\begin{array}{ccc}0&-1&0\\ -1&4&-1\\ 0&-1&0\end{array}\right] \tag{1}\]  
Figure 4: Image generation mechanism.  
Figure 5: Flow of data preprocessing.
## Materials and Methods
### HS-GC-IMS Analysis Conditions  
The HS-GC-IMS system (Flavourspec(r), G.A.S, Dortmund, Germany) was equipped with an autosampler (CTC Analytics AG, Zwingen, Switzerland) and an FS-SE-54-CB-1 capillary column (15 m \(\times\) 0.53 mm ID, 1 \(\upmu\)m, CS-Chromatopaphie Service GmbH, Germany). A 0.2 g sample of SS was accurately weighed into a 20 mL headspace bottle and incubated at 75 \({}^{\circ}\)C for 20 min at 500 r/min. The volume of the extracted headspace air was 100 \(\upmu\)L, and the syringe temperature was 45 \({}^{\circ}\)C. The column temperature was 60 \({}^{\circ}\)C, and the carrier gas consisted of 99.99% pure nitrogen and its flow rate was first set at 2 mL/min for 2 min, then increased to 10 mL/min within 15 min, increased to 100 mL/min over 25 min and increased to 120 mL/min over 30 min. The pre-separated compounds driven into an ionization chamber and ionized by a 3H ionization source with 300 MBq activity in positive ion mode. The resulting ions were driven to a drift tube (9.8 cm in length), which was operated on a constant temperature (45 \({}^{\circ}\)C) and voltage (5 kV). The flow rate of the drift gas (nitrogen gas) was set at 150 mL/min. The retention index (RI) of each compound was calculated using n-ketones C4-C9 as external references. VOCs were identified based on an IMS database of the HS-GC-IMS Library Search application software. The mobility Ko is also involved in the identification of compounds. It is a normalized expression of the ionic mobility (K) at standard temperature and pressure, and its calculation formula is based on references [42].
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 3 Problem
These facts necessitate a dynamic and adaptive mechanism to disseminate commands and receive responses in a multihop IoT network, without careful manual parameter tuning, to satisfy both reliability and low-latency requirements.
## 6 Security Analysis
### Bfbsa  
Security of BFBSA depends on the probability of false positive. Given the number of basic packets, \(N\), and a bit array of the Bloom filter with \(m\) bits, the probability \(p\) of false positive, with optimal \(k=(m/N)\)ln 2, is as follows [25]:  
\[p\ =\ (0.6185)^{\frac{m}{N}} \tag{10}\]  
Obviously, \(p\) decreases with lower \(N\) and higher \(m\) as shown in Figure 8. In code dissemination in WSNs, the number of basic packets, \(N\), is predetermined according to the size of a new code image. Given \(N\), the probability of false positive with regard to \(m\) is determined using (10). For example, we assume that \(N\) is set to 1000 packets. When \(m\) is configured as 1000 bits, \(p\) becomes 0.6185 which is very high. However, when \(m\) increases from 1000 bits to 10,000 bits, \(p\) becomes 0.0082 which is much less than 0.6185. Furthermore, when \(m\) is set to 100,000 bits, \(p\) is 1.36 \(\times\) 10\({}^{-21}\). This means that approximately 2\({}^{69}\) elements have to be generated for an attacker to fabric a false positive packet. Therefore, the BFBSA scheme can authenticate every packet securely with a proper configuration of \(m\). We set \(m/N\) to 92 targeting at the false positive probability of 6.36 \(\times\) 10\({}^{-20}\) which is assumed to be secure in [26]. In addition, even if a false positive occurs, it can be detected by the page-level hash. Regarding node capture attacks in BFBSA, compromised nodes cannot do anything but to discard packets since it is infeasible for them to build forged packets to pass the Bloom filter and the page-level hash. Moreover, BFBSA is resilient to DoS attacks since it provides an immediate authentication per packet.
## 4 Results and Discussion
### Plan for Constructing and Renewing Digital Twin with Spatial Object Type
#### 4.2.2 Spatial Object Update Plan  
To ensure the precision of spatial information for each object in the underground utility tunnel, a method must be devised for updating the information and specifying update times. Given the characteristics of the underground tunnel, objects such as fire extinguishers are usually situated at specific points, and their locations must be updated if they are relocated or if they disappear. The decision on whether an update is required depends on the update interval. Too small intervals result in excessive event data because even momentarily used objects prompt updates. Similarly, displacement information for individuals patrolling the tunnel can produce errors if updated too frequently. Therefore, an appropriate interval is crucial, and the update periods were broadly classified into long-term and short-term. The short-term period was presumed to be (nearly) real-time, as underground utility tunnels undergo minimal changes owing to their unique characteristics, which encompass underground space, security facilities, and other facilities. Consequently, spatial information should be updated from a long-term viewpoint to maintain accuracy.  
Descriptions of various update types are provided below, and Figure 8 illustrates these based on the period, event type, and update method.  
* Underground Tunnel Update: This pertains to the update of spatial information for disaster/facilities management. Shapes and properties are updated using data gathered with sensors, which are distinct from a precise update.
* Shape Update: This relates to long-term changes to an object/displacement size of 10 cm or more. Objects that have been moved, such as fire extinguishers, would be exceptions.
* Property Update: This involves updates that generate property information of objects, including information about the object itself, displacement, location value, and time.
* Event Range: This includes movements (point), deflections (line), and cracks (plane).
# Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression
###### Abstract  
Image colorization assigns colors to a grayscale image, which is an important yet difficult image-processing task encountered in various applications. In particular, grayscale aerial image colorization is a poorly posed problem that is affected by the sun elevation angle, seasons, sensor parameters, etc. Furthermore, since different colors may have the same intensity, it is difficult to solve this problem using traditional methods. This study proposes a novel method for the colorization of grayscale aerial images using random forest (RF) regression. The algorithm uses one grayscale image for input and one-color image for reference, both of which have similar seasonal features at the same location. The reference color image is then converted from the Red-Green-Blue (RGB) color space to the CIE L*a*b (Lab) color space in which the luminance is used to extract training pixels; this is done by performing change detection with the input grayscale image, and color information is used to establish color relationships. The proposed method directly establishes color relationships between features of the input grayscale image and color information of the reference color image based on the corresponding training pixels. The experimental results show that the proposed method outperforms several state-of-the-art algorithms in terms of both visual inspection and quantitative evaluation.  
colorization; random forest regression; grayscale aerial image; change detection Article
## 5 Proposed Framework
### Integration of ZTA and FL  
Our framework achieves deep security integration between ZTA and FL through three enforcement layers, as visualized in Figure 9. This integration addresses the unique security challenges of O-RAN's distributed AI paradigm.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **Value** & **O-RAN Requirement** & **Compliance** \\ \hline FL Round Duration & 320 \(\pm\) 45 ms & \(\leq\)1 s (Near-RT) & Yes \\ \hline LSTM Inference Latency & 1.2 ms (mean) & \(\leq\)5 ms (URLLC) & Yes \\ \hline Token Verification & 0.8 ms & \(\leq\)10 ms (E2 interface) & Yes \\ \hline Total Control Loop Latency & \textless{}10 ms & 10 ms\textless{}1 s & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 10: Training and inference latency compliance.  
Figure 9: ZTA-FL integration architecture showing security enforcement points across FL stages. Blue ellipses represent ZTA components (annotated above), green rectangles denote FL phases, and security metrics (FAR, Isolation, Detection) are highlighted below. Labels and spacing align with Figures 10 and 11.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 2 State-of-The-Art FC-AE-1553 Space Network
### FC-AE-1553 Space Network Architecture  
The existing space application information network is designed with a multi-hierarchy architecture, using the FC-AE-1553 protocol. This network comprises three LAN networks, each operating as an independent FC-AE-1553 system, as shown in Figure 1. Within this system, there are two stages of FC-AE-1553 networks: the upper stage is the indoor backbone FC-AE-1553 network located inside the spacecraft, and the lower stage is the outdoor access FC-AE-1553 network. Considering the challenging space environment, these two-stage FC-AE-1553 networks are interconnected using different topologies.  
The indoor backbone FC-AE-1553 network employs a star topology, employing active switches as connectors. This configuration ensures efficient communication within the spacecraft. Conversely, the outdoor access FC-AE-1553 network utilizes a bus topology, where passive optical splitters serve as connectors. This topology is suitable for external access and facilitates connectivity between different components. Notably, the Network Controller (NC) in the access FC-AE-1553 network also functions as the Network Terminal (NT) in the backbone FC-AE-1553 network, providing seamless integration and control between the two stages of the network.