esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 SSFXG Architecture and Basic Interactions
### NW Subscription Identity Issuance
#### 5.1.2 NW Broker Generating the NW Identity Credentials for the New NW Subscribers  
This phase involves the NW broker issuance of unique NW identification credentials for a new subscriber (Step 4 Figure 2). The process begins with generating two identification numbers for the requesting NW subscriber. The first number is private and uniquely tied to the subscriber. It serves as an authentication mechanism, allowing NW providers to verify the subscription status of the individual and grant them the necessary privileges to access the NW's services. The second number is public and designed for broader communication. This public identifier enables other NW subscribers to recognize and interact with the holder, facilitating seamless communication.  
After the NW broker generates these subscription identity credentials, it proceeds to record the information on the blockchain. This step ensures the transparency and security of the subscriber's identity within the federated NW. Specifically, the NW broker creates a blockchain transaction encapsulating all public information associated with the requesting subscriber (Step 5 Figure 2). This transaction is formalized as \(DIDDoc_{SUB}\), a digital document detailing the subscriber's public credentials. The address of \(DIDDoc_{SUB}\) in the blockchain ledger is represented as \(DID_{SUB}\). This unique address allows other NW participants to access and verify the subscriber's public credentials in a decentralized and tamper-proof manner. This process ensures the integrity and security of subscriber identities within the federated wireless communication NW while promoting trust and transparency.
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 2 Related Work
### rPPG Extraction with Screening Method  
In the context of infrared-environment-acquired rPPG signals, a noise-resistant heart rate estimation was conducted by assessing signal quality. To evaluate the quality of the signal, frequency domain analysis was employed, accompanied by the incorporation of hand-crafted features. Specifically, three features were defined. Firstly, the signal-to-noise ratio (SNR) was computed by dividing the power of the maximum value in the powerspectral density (PSD) and its two first harmonics by the remaining power. Secondly, bandwidth (BW) was determined as the range encompassing 99% of the power, centered around the maximum value of the PSD. Lastly, ratio peaks (RP) represented the ratio between the power of the highest peak in the PSD and the power of the second highest peak. By utilizing these characteristics, the performance was compared to the classic rPPG algorithm using the mean absolute error (MAE) metric. Furthermore, different video segments were evaluated using the same quality algorithm to compare performance. While the proposed quality-based rPPG method exhibited improved performance compared to the classic rPPG algorithm, it demonstrated relatively poor performance in cases where the video segments were longer [16].  
As shown in Table 1, previous studies mainly extracted rPPG signals through ROI detection, chrominance-based methods, or deep learning techniques. However, there are limitations in extracting signals from NIR environments using DistancePPG, CHROM, POS, and 3D CNN approaches. Moreover, previous studies except for [PERSON], [PERSON] et al. [16], did not incorporate rPPG screening methods. The proposed method in this study enables rPPG signal extraction in NIR environments and allows for more robust pulse signal extraction through a noise-assessment-based rPPG screening method. Additionally, unlike [PERSON], [PERSON] et al. [16], our method utilizes motion, illumination, and pulse signal quality as noise assessment indicators. The proposed method enhances the accuracy of the first-step rPPG extraction result through the second-step screening method, and can be applied to all existing rPPG extraction methods. To confirm this, we checked the screening results using Green, DistancePPG, which is applicable to the NIR environment. As can be seen in the result section, the application of the screening method confirmed performance improvements in the two rPPG methods.
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### CubeSat ACS Actuators  
The most common ACS systems for modern satellites use ion propulsion, reaction wheels, or thrusters. Even though satellites may have magnetic torque coils, they also choose another propulsion method with a higher specific impulse. Our CubeSat is different because it only uses magnetic torque coils. But, since our CubeSat is small and light and has stabilization requirements, magnetic torque coils could be enough. The benefit of using magnetic torque coils is that they are small, cheap, very easy to control, and relatively low in energy.  
Magnetic torque coils are basically electromagnets. We made the torque coils for the CubeSat ourselves. A thin copper wire is coiled five hundred times around a mu metal ferromagnetic core. The core is an inch long and three millimeters wide. It works as a booster. When the electromagnet is on, the polarized dipoles of the ferromagnetic electrons line up. Unlike a permanent magnet, because the core is made from a softer metal, it does not keep its magnetization and almost quickly loses its alignment after the coil is off. The core adds a kind of gain to the magnetic torque coil. The magnetic field strength becomes about 15 times bigger with the mu metal ferromagnetic core. Please see Figure 30 below:  
The ACS controller decides how much the torque coils are magnetized. The controller works on the Teensy 3.5 flight computer. The flight computer uses an IMU to measure the actual rotational velocity. Based on the rotational velocity input, the controller gives a pulse width modulation (PWM) value to the coil. Since the flight computer has low power, an H bridge is required. The H bridge powers the torque coils in proportion to the PWM value from the flight computer. The PWM level controls the strength of the magnetic torque coils.  
Besides the coils themselves, all the other components are cheap, commercial off-the-shelf (COTS) parts that can be easily bought online. They are not space-rated but rather hobbyist microcontrollers, electronics, and sensors. However, similar products have been used in space with very dependable results. The system, including the flight computer, can be built for less than USD 100. Compared to the cost of other ACS systems on comparable missions, our system was very cost-effective.
# Transmission Performance Analysis of RV Reducers Influenced by Profile Modification and Load
## 2 Analytical Model of RV Reducer with Tooth Profile Modifications
### Contact Ratio  
In theory, half of the cycloid teeth without a TPM will participate in the meshing simultaneously. However, because the cycloid profile will be thinned with a TPM, which inevitably reduces the meshing area, in order to study the variation in the meshing area caused by a TPM the contact ratio is defined, which may be calculated using the ratio of the actual meshing area length with the cycloid profile length of a single tooth. The contact ratio may be expressed as follows:  
\[R_{c}=\frac{l_{c}}{l_{0}} \tag{32}\]
# RL-Based Sim2 Real Enhancements for Autonomous Beach-Cleaning Agents
## 1 Introduction  
The degradation of coastal ecosystems due to pollution is a pressing issue, particularly on beaches where waste accumulation adversely impacts both human and marine life [1, 2]. In Chile, the severity of this issue is compounded by inadequate waste management practices, leading to significant shoreline pollution [3]. This paper introduces the deployment of autonomous beach-cleaning robots, equipped with advanced artificial intelligence, as a novel solution to mitigate these effects and contribute significantly to marine conservation.  
The motivation for this work stems from the urgent need to preserve marine biodiversity and improve the health of coastal ecosystems. Previous efforts to tackle beach pollution have primarily focused on manual cleanup operations, which are often inefficient and unsustainable. Moreover, existing robotic technologies have not yet fully adapted to the dynamic and complex nature of beach environments, where fluctuating conditions present substantial operational challenges.  
Building upon existing knowledge in the fields of robotics and artificial intelligence, this paper employs DRL, an advanced AI technique that integrates deep learning with reinforcement learning principles, to develop effective control systems for autonomous robots [4, 5]. Through extensive training in simulated environments, these robots master essential skills for navigating complex terrains, avoiding obstacles, and maintaining precise position control, crucial for their effective operation in real-world scenarios [6].  
Utilizing the Khepera IV robot within the CoppeliaSim environment, this paper pioneers a systematic approach to controller design. Robots are trained in a Gym-simulated environment, carefully designed to mirror real-world conditions. This methodology ensuresa seamless transition from simulation to actual deployment, with the robots demonstrating robustness and adaptability in their behaviors.  
The contributions of this work are twofold: it establishes a robust framework for developing autonomous systems capable of efficiently operating in unpredictable beach environments, and it sets a benchmark in the methodology for transitioning from simulated to real-world applications, enhancing the practical applicability of reinforcement learning in environmental conservation efforts.  
In conclusion, by implementing this innovative technology the issues of beach pollution are addressed effectively, establishing a precedent for the application of advanced AI in environmental protection and setting the stage for future initiatives in autonomous robotic systems for ecological restoration.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Reconfigurable Intelligent Surfaces
#### 3.2.4 Array Configuration  
In order to attain peak performance, RIS designs use advanced array structures. A popular approach is patch arrays, which are built with periodic unit cells modeled as lumped-element circuits (refs. [60; 62]). These unit cells, on the rear side, have varactor diodes to control the load impedance, enabling precise tunability (ref. [60]). The goal is to have compact designs with enhanced beam steering capabilities. This feature is achieved with waveguide-based structures, making them necessary for wireless communicationsin 6G because, with this technique, we can control directional signals (refs. [62; 63]). Patch arrays have independent operations too, in innovations such as planar spiral inductors (PSIs) or suspended electromagnetic bandgap (EBG), that result in minimized interference and maximized functionality (ref. [63]).
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 3 Materials and Methods
### Experimental Set-Up
The central node hardware was the Nordic Semiconductor nRF52840 Discovery Kit (Nordic Semiconductor, Trondheim, Norway), while Adafruit Feather nRF52840 Express (Adafruit, New York, NY, USA) was used for each peripheral node. Both development kits feature nRF52840 System-on-Chip (SoC) from Nordic Semiconductor, which contains a 64 MHz Cortex-M4 central processing unit and a floating-point unit [31]. BLE version 5.3 was used. The peripheral nodes were placed in a semicircle fashion equidistant from the central node, as shown in Figure 2, initially at a radius of 0.5 m (with the central node in the center). Radii of 4.5 m and 9 m were also tested, though only when studying the maximum network size for each configuration. All measurements were performed in a home office to take into consideration a typical environment (containing possible sources of interference, such as cellular devices, laptops, smart TVs, etc.) in which a prosthesis/orthosis may be used. Figure 2: Photograph of the experimental set-up with 4 peripherals. The peripheral nodes are shown on the left-hand side, and they are wirelessly connected to one central node placed in the center of the semicircle. The central node is connected to a PC using the serial port. The Nordic Semi PPKII (Nordic Semiconductor, Trondheim, Norway) is connected to the first in-use peripheral to source and  
measure the node's power consumption. The PPKII is connected to the PC to configure and log measurements. Note, to capture this photograph, the distance between the peripheral and central devices was reduced. Additionally, not shown (to minimize the cabling shown) is the signal generator connected to the ADC input for each peripheral. The 12-bit ADC of each peripheral node was enabled and configured to sample ADC channel 0 at a rate of 1 kHz. Each ADC input was connected to a 70 Hz sine wave with an amplitude of 1.5 V\({}_{\mathrm{PP}}\) and DC offset of 0.850 V (HP 33120A Arbitrary Function Generator, Keysight, Santa Rosa, CA, USA). For each trial, 10 min of ADC data were streamed from each connected peripheral node to the central node. The first in-use peripheral node (P0) was powered (5 V) by the Nordic Semiconductor Power Profiler Kit (PPKII). The remaining peripheral nodes were powered by a standard 5V USB connection powered from a wall adapter. The central node was powered from the USB connection of the PC.
## 3 Neighbor Verification through Low-Power Beacons
### Neighbor Check through Low-Power Beaconing
```
1:procedureWhisper-Check(\(W_{V}\))\(\triangleright\)\(U\) received whisper \(W_{V}\) from \(V\)
2: Extract \(L_{V}\), \(I_{V}\), and \(dig(C_{V})\) from \(W_{V}\)
3:if\(I_{U}\in L_{V}\)then\(\triangleright\) Did \(V\) hear my whisper? 4:if\(B_{U}(I_{V})==\emptyset\)then\(\triangleright\)\(B\) is the WID-certificate binding set; No binding exists for \(V\) yet
5:\(B_{U}(I_{V})\gets dig(C_{V})\)\(\triangleright\) Make one for \(V\); \(C_{V}\) is the link to BSM from \(V\)
6:\(L_{U}\gets L_{U}\cup I_{V}\)\(\triangleright\) Store \(V\)'s whisper ID (WID) for rebroadcast
7:else\(\triangleright\) Binding exists for \(I_{V}\)
8:if\(B_{U}(I_{V})==dig(C_{V})\)then\(\triangleright\) Binding confirmed? 10:\(C(I_{V})\)++\(\triangleright\) Credit up
11:else\(\triangleright\) Conflict
12: return\(\triangleright\) Ignore this whisper
13:endif
14:endif
15:else
16: return\(\triangleright\) Ignore this unverifiable neighbor
17:endif
18:endprocedure
```  
**Algorithm 1** Whisper check at \(U\).
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 4 Results
### The Median RMS of 9-day Solutions  
The median RMS of 9-day solutions are shown in Table 6, where the median values correspond to the direction of RTN and 3D-RMS respectively. In the statistical results of the median RMS, the accuracy of orbit overlaps is better than that of external orbit comparison for all BDS satellites. We also find that there exists a huge gap between the two modes of orbital comparisons for GEO C01, whose 3D-RMS is 100.6 and 2641.8 cm, with a maximum difference in their T direction. For BDS-2 IGSO and MEO, these indicators are at the same accuracy level as that of results by [PERSON], [PERSON] and [PERSON] (2015). The orbit overlap 3D-RMS for IGSO C13 is 114.4 cm and 130.1 cm for comparison with WUM orbit. Overall, the 3D-RMS of BDS-2 MEO, IGSO, and GEO are in the decimetre-, metre-, and more than ten-metre levels respectively. The geometric distribution and number of the visible SLR observation sites, as well as the inaccuracy of the dynamic model, are important factors leading to this situation. Meanwhile, the median RMS of the new four BDS-3 MEO satellites are slightly lower than those of BDS-2 C11. The main reason may be influenced by the number and quality of SLR observations. The orbit overlaps median RMS of BDS-3 in R direction can reach 5 cm, and better than 10 cm, for comparison with the WUM orbit.  
Figure 15: The overall average 3D-Root Mean Square (RMS) of SLR-only orbit determination for BDS satellites.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**3D-RMS (Min/Max)** & **3-Day** & **5-Day** & **7-Day** & **9-Day** \\ \hline \multirow{4}{*}{**Overlaps**} & **GEO** & 2.61 & 4.80 & 5.28 & 5.77 \\  & **IGSO** & 1.38/4.70 & 2.61/3.07 & 2.56/3.60 & 1.89/3.49 \\  & **MEO** & 1.55/2.00 & 1.05/1.80 & 0.69/0.99 & 0.49/0.75 \\ \hline \multirow{4}{*}{**Vs WUM**} & **GEO** & 8.75 & 21.57 & 23.88 & 23.95 \\  & **IGSO** & 3.71/4.89 & 3.28/4.97 & 2.42/6.21 & 1.85/4.95 \\ \cline{1-1}  & **MEO** & 1.54/2.26 & 1.07/1.81 & 0.73/1.14 & 0.55/1.04 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The minimum and maximum values of 3D-RMS for BDS satellites (Unit: m).
## 3 Case Study
### Construction of Numerical Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Lithology**} & \multirow{2}{*}{\begin{tabular}{c} **Normal Stiffness** \\ **(GPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Shear Stiffness** \\ **(GPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Tensile Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Cohesion (MPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Internal Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} } \\ \hline Loose strata & 1.59 & 0.60 & 0.4 & 0.01 & 14 \\ Sand–mud & 4.89 & 1.93 & 4.3 & 0.12 & 32 \\ interbedding & 6.74 & 2.72 & 8.7 & 0.22 & 37 \\ Silstone & 6.55 & 2.60 & 9.2 & 0.20 & 35 \\ Medium-grained & 5.18 & 2.07 & 6.0 & 0.16 & 32 \\ Coarse sandstone & 4.91 & 1.98 & 5.5 & 0.13 & 35 \\ Mudstone & 4.47 & 1.75 & 2.3 & 0.07 & 37 \\ Sandy mudstone & 3.91 & 1.58 & 2.2 & 0.09 & 36 \\ Coal seam & 2.08 & 0.75 & 1.3 & 0.05 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Physical–mechanical parameters of joints. In order to avoid abnormal phenomena such as slurry leakage and ground heave caused by excessive injection of slurry, there is an ultimate injection-extraction ratio in the grouting into overburden separation project, which can be calculated as follows [11]:  
\[\alpha_{L}=(1-\frac{h_{ic}}{W\tan\varphi})\bigg{[}1-\frac{H_{c}(k_{p}-1)}{M} \bigg{]} \tag{28}\]  
Based on Equation (28) and the specific geological and grouting conditions at the engineering site, an ultimate injection-extraction ratio of 41.5% was determined for the 8006 working face. To explore the effects of varying ratios on overburden behavior, numerical simulation tests were conducted using injection-extraction ratios of 0%, 10%, 20%, 30%, and 40%.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 6 Performance Evaluation  
A performance evaluation of the proposed multi-tier data relaying scheme is conducted next, with focus on two main significant aspects: \((i)\) efficiency of relaying data through the tiers in terms of energy expenditure, and \((ii)\) efficiency of deploying an UAV to provide on-demand wireless communication in disaster scenarios. Specifically, experimental results are obtained through trace-driven simulations in a realistic disaster scenario. Each simulation is replicated ten times and the average values along with the related standard deviations are then reported when meaningful.
## 4 Experiments and Results
### Distributed Control Strategy Comparison Experiments
#### 4.3.2 Test of the Scalability  
To verify the scalability of the learning-based strategies, we apply the strategies learned in the small-scale swarm directly to the larger-scale swarm (\(N=20,30,40\)) while keeping other parameters related to the network structure unchanged. The quantitative evaluation results are shown in Table 5.  
Table 5 shows that the distributed APF strategy is entirely unable to maintain connectivity as the swarm scale increases. In all episodes, individuals split out of the swarm uncontrollably, so its other consistency metrics are of little significance.  
A longitudinal comparison of different swarm scales under the control of IDGCNs-based strategy shows that the strategy trained on small-scale swarms can be directly migrated to larger-scale flocking control. The SR and other consistency evaluation metrics are maintained at a high level, indicating that the learning strategy based on IDGCNs has high scalability.  
A horizontal comparison of the control strategies based on IDGCNs and DAGNNs shows that although the DAGNNs-based strategy is also scalable, it is significantly weaker than the IDGCNs-based strategy in terms of the consistency evaluation metrics and the success rate. As the swarm scale increases, the SR based on DAGNNs strategy increases significantly since the redundancy of communication links increases, and therefore the individuals are less likely to split from the swarm. The smaller the size of the swarm and the more restrictive the communication, the more harmful the destruction of a single node and link is to the whole swarm. This is the reason why this paper focuses on improving the robustness of small-scale swarms under restricted communication environments.
# Investigation of Using CAPTCHA Keystroke Dynamics to Enhance the Prevention of Phishing Attacks
## 6 Evaluation Metrics  
There are several metrics that were used to evaluate the effectiveness of the model. The false positive (FP), false negative (FN), true positive (TP), and true negative (TN) are parameters often used by any phishing solution researchers to judge the performance of solutions. Let true positive (TP) indicate the number of phishers correctly classified as phishing attackers; true negative (TN) indicates the number of genuine users correctly classified as genuine, false positive (FP) indicates the number of genuine users who are incorrectly classified as phishers, and false negative (FN) indicates the number of phishers who are incorrectly classified as genuine users. This study employs five different metrics based on these parameters, as follows:  
True positive rate (TPR): it is the rate of phishers who are correctly classified as phishers of the total phishers. The equation of the TPR is shown in Equation (3):  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
True negative rate (TNR): it is the rate of genuine users who are correctly classified as genuine users of the total genuine users; the equation of calculating TNR is shown in Equation (4):  
\[\text{TNR}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{4}\]  
False positive rate (FPR): it is the rate of genuine users who are incorrectly classified as phishers of the total genuine users. The Equation (5) defined a FPR equation:  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{5}\]  
False negative rate (FNR): it is the rate of phishers who are incorrectly classified as genuine users of the total phishers. Equation (6) shows how to compute FNR:  
\[\text{FNR}=\frac{\text{FN}}{\text{FN}+\text{TP}}. \tag{6}\]  
Accuracy refers to the total number of correctly classified attempts (true accept/true reject) in relation to the total number of all users' completed attempts, and computes as shown in Equation (7):  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{7}\]