esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This research paper addresses the problems of fiberless optical communication, known as free space optics, in predicting RSSI (Received Signal Strength Indicator) parameters necessary for hard switching in a hybrid FSO/RF (Free Space Optics/Radio Frequency) system. This parameter is used to determine the intensity of the transmitted signal (in our case, a light beam) from one FSO head to another. Since we want to achieve almost 100% reliability, it is important to know the parameters of the transmission environment for the FSO and RF lines. Each of them has its limitations and, as a result, a weather monitoring station is required. The FSO is mostly affected by fog and the concentration of particles in the air, while the RF line is affected by rain and snow. It is precisely due to these influences that it is necessary (based on the mentioned RSSI parameter) to switch using the hard switching method from the primary FSO line to the backup RF line by correctly predicting this value. If the value of the RSSI parameter falls below the critical level--42 dBm--the system automatically switches to the backup RF line. There are several ways we can predict this parameter. One of them is machine learning methods such as decision trees. Our research focused on the prediction of the RSSI parameter, the methods of decision trees and decision trees using the AdaBoost regressor. Since we want to correctly predict the RSSI parameter, it is also necessary to choose the right way to predict it based on the recorded weather conditions. If we want to correctly use the hard switching method in hybrid FSO/RF systems, it is necessary to choose the correct method of predicting the RSSI parameter, which serves as an indicator for switching from the primary FSO line to the secondary RF line. Therefore, we decided to investigate methods of machine learning--the decision tree and the decision tree with the use of an AdaBoost Regressor. The main benefit of this paper is the improvement of existing machine learning methods (decision trees and decision trees using the AdaBoost regressor) for the correct prediction of the RSSI parameter for the needs of hard switching in a hybrid FSO/RF system. The method chosen in this manuscript has very good results. As can be seen in the attached graphs, over a longer period and using correctly selected training data, it is possible to achieve ideal results for the prediction of the RSSI parameter. The tables also show the effectiveness of the prediction, and the fact that it is best to train on either the first- or third-minute data. In the future, it would be appropriate to implement weather prediction or to consider other methods, such as random forests or neural networks.  
FSO; hybrid FSO/RF; machine learning; RF link; weather conditions +
Footnote †: journal: _Photonics_
## 5 Simulation Verification
The rise time is the time required from the steady-state value to the peak value for the first time; the overshoot is the absolute value of the difference between the peak value and the steady-state value divided by the steady-state value; the adjustment time is the time required from the beginning to the oscillation reaching the 5% error of the steady state value for the first time. The comparison results show that when the system is disturbed by the load, without any control, the power angle amplitude of the synchronous machine increases continuously, and the system becomes unstable; under the traditional PSO algorithm, the POD control can reduce the power angle oscillation amplitude, and finally maintain the power angle stability; POD control under the improved PSO algorithm makes the power angle oscillation curve rise longer, the overshoot smaller, the adjustment time shorter, and the fast recovery power angle stability stronger. It is verified that the POD control under the improved PSO algorithm is better than the POD control under the traditional PSO algorithm for suppressing low-frequency oscillations. The Figure 15 shows the angular velocity variation curve of the rotor of a single synchronous machine without POD control and POD control with POD control and parameter optimization after adding a small load disturbance at the 4 s. Due to the limited power regulation effect of the wind storage system without POD control, the generator speed changes significantly, and amplitude oscillation occurs. Under the traditional PSO algorithm, POD control can reduce the angular velocity oscillation amplitude, increase the oscillation frequency, and finally maintain the stability of the generator speed. Under the improved PSO algorithm, the POD control can further reduce the oscillation amplitude, the oscillation frequency is higher, and the speed stability ability is stronger. The effectiveness of the proposed controller and optimization algorithm in maintaining speed stability is verified. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **The Rise Time (s)** & **The Overshoot (s)** & **The Adjustment Time (s)** \\ \hline No control & 0.5 & / & / \\ With POD control & 0.2 & 41\% & 4.7 \\ With NIPSO POD & 0.4 & 31\% & 3.9 \\ control & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of System Time Domain Performance Indexes under Three Controls. Figure 14: Comparison of the change of power angle of synchronous machine under three controls.
## 7 Results
### Evaluation Framework
#### 7.5.3 Performance Evaluation  
The DLPS was used for standardized benchmarking and to analyze influential variables and the performance of Fabric. The tool was chosen because it clearly defines how it determined throughput and latency, which are the key performance metrics in this study. The DLPS is open-source and facilitates the testing of various configurations through its cloud network support. The benchmarking covered several variables that potentially influence the performance of Hyperledger Fabric. Since DLPS did not support all Fabric properties by default, the latter was upgraded to Fabric 2.0 to support multiple channels, private transactions, and complex queries. Moreover, the CouchDB and ordering node docker were set on separate or the same peer nodes to increase the support for architectural parameters. Splitting or joining tasks with different peers potentially enhances performance by lowering cross-instance latencies.  
Additionally, the design supported multiple data center deployments and network delay simulation. The benchmarking processes initiated automatic crashes of orders and peers and assessed traffic statistics and single-core CPU usage. The variables considered for benchmarking are summarized in Table 9.  
The experimental testing was incremental to enhance the reliability of outcomes. A configuration file specified the particularities of the Hyperledger Fabric, and the DLPS utilized the file to set the client network and blockchain before the benchmarking processes. In a single DLPS run, requests were sent from clients to the network for a specific duration and rate, which is portrayed as the gradient of requests on a curve. A response curve was generated to confirm successful transaction processing. Seven incoming transaction rates were considered, including creating the patient record, reading a patient record, updating a patient record, deleting a patient record, creating an appointment, updating or deleting an appointment, and setting an access policy. Figure 19 illustrates the single benchmarking run, and Figure 20 portrays the ramp series.
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 2 Construction of On-Orbit Reconstruction Indicators for a Giant Satellite Swarm
### The Introduction of the Reconstruction Indicators
However, the remaining fuel of adjacent satellites may be insufficient, resulting in excessive fuel consumption of the satellite, a shorter working life, and negative effects on the health and mission performance of the satellite swarm. The constraint of fuel consumption balance is introduced to avoid such a situation. The variance of the satellite speed increment involved in the reconstruction is used to characterize the fuel consumption balance:  
\[P=\frac{\sum\left(J_{i}-\overline{J}\right)}{N_{H}-1},i\in N_{H} \tag{8}\]  
In the formula, \(\overline{J}\) is the average energy consumption of all nonfaulty satellites participating in the reconstruction.
## 3 Materials and Methods  
The study commenced by measuring magnetic susceptibility using the Kappameter KM-7 device on one profile in Slovacka Cave and two profiles within the Crnopac Cave System (see Figure 2). Subsequently, another round of magnetic susceptibility measurements on the same samples were conducted in the laboratory using the Bartington MS2B System. Additionally, for three selected samples, each from a different profile, the X-ray powder diffraction (XRD) method was applied. The research methods are delineated in chronological order as they were utilized in the preparation of this study.  
Sampling locations were selected based on accessibility and the presence of suitable vertical profiles within soft sediments. The chosen sites represented two distinct cave systems in the Dinaric Karst, with varying material sources (northern and southern Velebit Mountain). Sampling intervals were set to minimize errors from sediment layer variations: 0.5 m for KG1 and 0.2 m for KG2. For the SL1 profile, intervals were denser in the upper section (0.05 m), intermediate in the middle (0.1 m), and wider in the lower section (0.2 m). The varying intervals account for the significant variation in readings, with denser spacing in areas with greater variability.
# Voltage-Based Braking Controls for Electric Vehicles Considering Weather Condition and Road Slope
###### Abstract  
This article addresses the braking controls for an electric vehicle with DC motors such that the voltage in the motors is used for controlling the wheel angular velocity. Other papers on the anti-lock braking system (ABS) handled how to derive the braking torque (or braking pressure) for controlling the wheel angular velocity. However, heavy or prolonged braking can cause brake fade or wear. According to EURO 7 regulations, brake fade or wear is not desirable, since the regulations refer to the reduction in particles emitted from brake pads. For avoiding heavy or prolonged braking, this paper does not use a brake unit, such as electro-mechanical brake units or hydraulic brake units, for vehicle stop. Instead, the motor voltage is used for controlling the wheel angular velocity. While a vehicle moves, the goal of this paper is to provide automatic braking controls in real time, so that the vehicle stops safely and smoothly without slippage before colliding with an obstacle. In practice, road conditions can change depending on weather conditions, such as rain or snow. Moreover, road slope can have an effect on the braking distance for the vehicle. Thus, this article introduces automatic braking controls, while considering both road slope and road conditions. This article is unique in presenting automatic braking controls for the smooth stop of electric vehicles with DC motors, while considering both road slope and road conditions. In addition, this article is unique in controlling the motor voltage for controlling the wheel angular velocity, while not requiring any brake units.  
autonomous vehicle; autonomous braking; weather condition; slippery road; longitudinal maneuvers; road slope; automatic braking control; electric vehicles; DC motors; smooth stop of autonomous vehicle
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 2 Materials and Methods
### Output Layer  
To enhance the propagation of features and gradients throughout our model and to counteract potential problems of gradient vanishing or explosion, we implement skip connections using a standard convolution layer with an \(1\times 1\)-sized kernel. These skip connections play a crucial role in preserving vital information from earlier layers by bypassing intermediate layers directly to later ones. This method not only facilitates a smoother and more stable training process by ensuring that gradients flow freely through the network but also helps in retaining critical information necessary for accurate prediction over long-range dependencies. By integrating these connections, our model is better equipped to produce precise and reliable traffic flow predictions, leveraging both deep and surface-level features effectively. It transforms the output, \(X_{FNN}\), into \(X_{skip}\in\mathbb{R}^{T\times N\times d_{skip}}\), where \(d_{skip}\) is the skip dimension. Then, the output of each skip connection layer is summed to obtain the final hidden state, \(X_{fs}\in\mathbb{R}^{T\times N\times d_{skip}}\). It can flexibly adjust the representational dimensions of features, ensuring their efficient propagation in the deeper layers of the network and preserving temporal and spatial features simultaneously.  
To achieve multi-step traffic flow prediction, the output layer is directly employed to obtain the prediction result as follows:  
\[X_{Pred}=Conv_{3}(Conv_{2}(X_{f_{8}})) \tag{20}\]  
where \(X_{Pred}\in\mathbb{R}^{T^{\prime}\times N\times F}\) represents the \(T^{\prime}\) time steps' prediction result and \(Conv_{2}\) and \(Conv_{3}\) are two standard convolution layers, each characterized by a kernel size of \(1\times 1\). The convolution layers are used to transform the time steps and the skip dimension of \(X_{fs}\), respectively. Considering cumulative errors and computational efficiency, we use a direct way instead of adopting a recursive manner to obtain prediction values.  
STFEformer sets a new benchmark for the prediction of spatial and temporal traffic data in Intelligent Transportation Systems. The modular and adaptable design of STFEformer serves as a foundation for future predictive models in the field. By demonstrating how dynamic and long-range spatial-temporal correlations patterns can be effectively captured and integrated, STFEformer offers a blueprint for future developments in traffic management, urban planning, and beyond. Researchers and engineers can build upon this architecture to create more nuanced models that accommodate the increasing complexity of traffic datasets and drive advancements in real-time, adaptive traffic management systems.
## 4 Numerical Examples  
In this section, the proposed ST-GFDM is validated by testing four numerical examples. These examples include the Fornberg-Whitham equation and its modified form, the modified Camassa-Holm equation, and the modified Degasperis-Procesi equation. The following symbols are defined: \(T\) is the end time of the numerical problem, \(N_{D}\) is the number of times that the ST domain has been moved by the time-marching method, and is written as \(N_{D}=T/\text{dt}\). The distribution of nodes for each example is displayed in Figure 1a. To analyze the accuracy of the proposed ST-GFDM, the numerical errors are defined in this study as follows:  
\[\left\{\begin{array}{l}L_{\infty,\text{n}}=\max_{\begin{subarray}{c}1\leq i \leq N_{T}\end{subarray}}\left|u_{i}^{ext}-u_{i}^{num}\right|\right|^{n},\\ L_{\infty}=\max\{L_{\infty,\text{n}}\},\\ 1_{\leq\text{n}\leq N_{D}}\end{array} \tag{21}\]  
Figure 3: The flowchart of the numerical process of the proposed ST-GFDM scheme.  
\[\left\{\begin{array}{l}\text{RMSE}_{\text{n}}=\sqrt{\sum\limits_{i=1}^{N_{T}}{{{{({{u _{i}^{ext}}-{{u_{i}^{num}}})}^{2}}}/{N_{T}}}}}\Bigg{|}^{\text{n}}},\\ \text{RMSE}=\text{max}\{\underset{1\leq n\leq N_{D}}{{{\text{RMSE}_{\text{n}}}} }\},\end{array}\right. \tag{22}\]  
where superscript _ext_ is the exact solution; _num_ denotes numerical solutions. In Equation (21), \(L_{\infty,\text{n}}\) is the maximum absolute errors (MAEs) in \(\text{Dn}\), and \(L_{\infty}\) is the maximum MAEs in the entire simulation. Meanwhile, the root-mean-squared error (RMSE) in each ST domain is defined as \(\text{RMSE}_{\text{n}}\) in Equation (22), and RMSE is the maximum RMSE in the whole simulation. In addition, the initial guesses are the initial conditions of each numerical problem for iteration in D1, and the numerical solutions in \(\text{Dn}-1\) are initial guesses of numerical processes in \(\text{Dn}\).
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 3 Threat Model
### Protocol Tampering
#### 3.2.1 Hijacking UE Capability  
In protocol tampering attacks, hijacking UE capability involves intercepting and altering the capability information of the UE transmitted in plaintext format. This information, defined by the integration of hardware, software, and applications in a terminal device according to 3 GPP standards, includes the supported frequency bands, data transmission rates, and carrier aggregation technologies. The plaintext transmission method makes the UE capability data vulnerable to interception and modification by MitM relay attacks. The hijack UE capability attack [65; 66] proceeds through the following four stages, as shown in Figure 4:  
* To initiate the attack, the MitM relay uses a different tracking area code (TAC) from the device's registration area, which prompts a tracking area update (TAU) procedure. The relay then rejects this with a TAU request message, erasing the current security context and temporary identities, and a new registration is triggered via an attach request to the MitM relay.
* By forwarding the messages, the nonaccess stratum (NAS) security setup for the UE can be completed. As this involves the creation of a new registration, the AMF prompts the next generation node B (gNB) to obtain the capabilities of the UE.
* Before the UE capability information is sent from the UE to the gNB, the MitM relay changes the S1 mode from supported to unsupported. The modified message is then sent to the network, allowing the RRC security and registration processes for the UE to be completed.
* The UE is released to the network with an RRC release message. The modified capabilities are forwarded by the gNB to the AMF, eliminating the need to repeat the capability transaction.  
After the attack, the piece of UE is recognized as a device without supporting the S1 mode when it next connects to the gNB, resulting in reduced service quality. The S1 mode enables it to be served by non-standalone networks, whereas the N1 mode allows the UE to be served in standalone networks. To transition into a non-standalone network region, the network checks the UE's previously stored capabilities to ascertain whether it supports the S1 mode. However, if the UE's S1 mode support status is maliciously changed from \"supported\" to \"unsupported\", the UE will encounter a service interruption when entering the non-standalone network service area.
###### Abstract  
The deformation of the image plane due to gravity, clamping forces, and surface shape errors can significantly impact the exposure accuracy and imaging quality of the doubly telecentric projection optical system. To address the issues of resolution and image quality degradation resulting from non-conjugate image planes, this paper proposes a high-precision method for compensating image plane deformation using a dual-prism approach, establishes an analytical compensation model, and the theoretical derivation and quantification of the effects of compensation devices on image plane defocus, tilt, and aberration are provided. Compared to traditional simulation methods, the proposed approach offers a more predictable and precise compensation framework. The optimized design of the dual-prism compensation device has been validated through simulations, demonstrating the ability to control image plane deformation within 3.011 \(\upmu\)m, which is smaller than the system's depth of focus. The results indicate that this method significantly enhances the imaging accuracy of doubly telecentric projection optical systems and presents a novel theoretical tool for optimizing the design of optical compensation devices, thereby advancing the development of high-precision optical compensation technologies.  
abrration theory; image deformation compensation; dual-prism Article
## 4 Methods  
### Scalable Performance Analysis  
In parallel computing, several metrics can be used to evaluate the performance of a program after parallelization optimization to determine the effectiveness of the optimization:  
Speedup: The ratio of the running time of a serial algorithm to the running time of a parallel optimised algorithm is referred to as the speedup ratio. The higher the speedup ratio, the better the parallel optimisation results. In parallel programs, the running time t1 of a program running on a low core p1 is usually divided by the running time t2 of a program running on a high core p2, expressed by the formula:  
\[S=\frac{t1}{t2} \tag{1}\]  
Efficiency: The speed-up ratio of the parallel optimised algorithm divided by the number of parallel processes is known as the efficiency (E). The higher the efficiency, the more effective the parallelisation. In parallel programs, the acceleration ratio S is usually divided by the ratio(N) of the number of high cores p2 to the number of low cores p1, expressed by the following formula:  
\[E =\frac{S}{N} \tag{2}\] \[N =\frac{p2}{p1} \tag{3}\]  
Scalability is the ability to maintain performance gain when system and problem size increase. For parallel programs, suppose we run a parallel program with a fixed number of processes or threads, a fixed problem size, and an efficiency value E. Now, we increase the number of processes or threads used in the program, and if the program's efficiency value remains E while the problem size increases proportionally, then we say that the program is scalable. A program is called strongly scalable if it can maintain a fixed efficiency when increasing the number of processes or threads without increasing the size of the problem. If the efficiency value can only be maintained by increasing the problem size by the same factor when increasing the number of processes or threads, then the program is called weakly scalable.