esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Concept of Operations
### Local Rate Regulators for Communications and Clock Data Services
#### 2.4.1 Performance Specifications  
The novelty of this work lies in its control performance analysis, which explicitly addresses the dynamic variations in both communication requirements and pLEOT implementation. It effectively adapts the increments of the total CN0 available at the \(i\)th orbital platform. Specifically, the scenario examined in this study involves the participation of subsystems (8) and (9), which are subject to the constraint (23). This approach ensures satisfactory performance with minimal transient and steady-state errors, as described in Equations (14), (16) and (17).  
In this context, each local agent \(i\) autonomously determines an optimal decision sequence for resource sharing, denoted as \(u^{i}(l)\). This decision-making process aims to minimize a performance index governed by the following quadratic cost function over an infinite optimization horizon  
\[J_{i}(l_{0})=\sum_{l=l_{0}+1}^{\infty}\left\{x_{i}^{T}(l)Q_{i}x_{i}(l)+u_{i}^{ T}(l)R_{i}u_{i}(l)\right\}, \tag{24}\]  
where \(l_{0}\) denotes an initial system state whereas \(Q_{i}\in\mathbb{R}^{6\times 6}\) and \(R_{i}\in\mathbb{R}^{2\times 2}\) are, respectively, symmetric, positive semi-definite and positive definite weighting matrices representing the degrees of freedom; e.g., \(w_{11}^{i}\), \(w_{22}^{i}\), \(w_{33}^{i}\), \(w_{44}^{i}\), \(r_{11}^{i}\), and \(r_{22}^{i}\)  
\[Q_{i}\triangleq\left[\begin{array}{cccccc}w_{11}^{i}&w_{11}^{i}&0&0&0&-w_{1 1}^{i}\\ w_{11}^{i}&w_{11}^{i}&0&0&0&-w_{11}^{i}\\ 0&0&w_{22}^{i}&0&0&0\\ 0&0&0&w_{33}^{i}&0&0\\ 0&0&0&0&w_{44}^{i}&0\\ -w_{11}^{i}&-w_{11}^{i}&0&0&0&w_{11}^{i}\end{array}\right]\geq 0 \tag{25}\]  
\[R_{i}\triangleq\left[\begin{array}{cc}r_{11}^{i}&0\\ 0&r_{22}^{i}\end{array}\right]>0\,. \tag{26}\]  
By properly selecting the parameters \(Q_{i}\) and \(R_{i}\), the stability of the closed-loop system described in Equations (21) and (22) can be ensured. When the first quadratic term in Equation (24) is small, most transient errors related to tracking the desired CIR and CNR thresholds and the CN0 allowance available at the \(i\)th orbital platform are effectively addressed. Furthermore, it is advisable to reinforce these guarantees by allocating just enough clock data and communication power resources, even if it means incurring some costs as indicated by the second quadratic term in Equation (24).
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
## 5 Hand Gesture Recognition Evaluation
### Experimental Setup  
We trained three separate models to evaluate how annotation quality affects gesture recognition performance. Each model was trained on the same set of gesture-labeled sequences, using annotations derived from one of the following sources: our manually refined IPN HandS dataset, MediaPipe Hands [12], and MediaPipe Holistic [13]. All annotations were applied to the same 200 videos, ensuring consistency in gesture class distribution and video content across models.  
To ensure subject-independent evaluation, and following the IPN Hand [5] configuration, we split the 50 subjects in the dataset into 37 for training and 13 for testing, resulting in 148 and 52 videos, respectively. From these, we obtained a total of 3797 gesture instances for training and 1380 for testing, excluding No-Gesture segments. Each instance corresponds to a gesture-specific temporal interval annotated with hand landmarks for a single active hand. Only the 14 gesture classes were used for training and evaluation; background frames were excluded to focus the classification task on intentional gesture recognition.  
We prepared gesture sequences using a fixed maximum length of 64 frames and a sliding window with a stride of 32. Thus, longer gestures, particularly for pointing classes (P1F and P2F), were fragmented into overlapping clips to ensure temporal consistency without exceeding the sequence length constraint. Conversely, no padding or interpolation was applied to shorter sequences, which represent most of the non-pointing gestures, as shown in Table 4. In other words, this approach avoids artificial stretching or padding of variable-duration gestures, preserving the natural dynamics of the gesture.  
Evaluation was conducted at the gesture instance level. Each input sequence was classified as a whole, and predictions were compared against the ground-truth labels for the corresponding gesture intervals. We report the overall classification accuracy, average recall (the unweighted mean of per-class recall values), and normalized confusion matrices. Average recall reflects the classification performance averaged across all gesture classes, regardless of class frequency, allowing for a more balanced assessment. Each model was trained for 30 epochs with a batch size of 64 sequences, a learning rate of 0.001, and a dropout rate of 0.3. No additional temporal smoothing or spatial augmentation was applied. In addition, to ensure statistical robustness, all experiments were repeated over three independent runs. Hence, we report the mean and standard deviation for both accuracy and average recall.
# Modeling Polymer Microencapsulation Processes Using CFD and Population Balance Models
## 3 Results and Discussion
### CFD + PBM Model Performance  
Due to the computational expense of solving PBM equations simultaneously with multiphase flow and turbulence equations, only five bins were used to compute particle number densities. Although this limitation can introduce discretization errors, the literature suggests that five bins are sufficient for satisfactory results [27]. To validate the model, simulated droplet size distributions at different impeller speeds were compared to the experimental data from DLS analysis.
## 4 Results
### Risks Related to Vessel Traffic in the Area Covered by the VTS Systems
The calculation was based on the model and a set of causation factors [57], which can be understood as the probability that a ship will not take evasive action to avoid running around or a collision (Table 6). Once the assumed waterway model has been defined, the IWRAP MK II software can calculate the resulting annual average collisions, groundings and shoal entries. The results can be displayed in tabular or graphical form using color coding [57]. While creating the model for the described VTS systems, some simplifications have been applied: historical data on collisions, groundings and incidents have not been considered; specific local conditions related to harbor traffic have not been taken into account; and the traffic of small units such as yachts and fishing vessels has not been considered. The authors' intention was to build a model generated for the same conditions for each VTS system. Collisions in head on and overtaking situations were considered. Historically available AIS data were collected for the period 1 January 2021 to 30 September 2021. Figures 6-8 show the results achieved from the IWRAP MK II software [57]. The layout of the defined recommended shipping lane is divided into sections (LEGs) and the probabilities of collision at each section (LEG) for the described VTS systems were calculated. The color of the lines have the following meanings: blue--very high intensity/probability, red--high intensity/probability, orange--medium intensity/probability, yellow--low intensity/probability. The summary of the calculated probabilities of collision in head on and overtaking situations at each LEG of the described VTS systems is shown in Figure 9.  
A comparison of the VTS systems taking into account the criterion probability of collision related to default causation factors is presented in Table 7. Table 7 shows the LEGs of the VTS systems, where the probability of collision was assessed to be higher than the set probability value \(p\). Based on data presented in Figure 9 and Table 7, it can be concluded that the highest probability of analyzed collisions is observed in the case of vessels' movements within the Swinoujsce-Szczecin fairway. Figure 8: Area covered by the VTS Swinoujsce–Szczecin system. (**a**) Density map of ships’ movements on each individual LEG. (**b**) Probability of collision on each individual LEG (our own elaboration based on [57]). Figure 7: Area covered by the VTS Lawica Slupska system. (**a**) Density map of ships’ movements on each individual LEG. (**b**) Probability of collision on each individual LEG (our own elaboration based on [57]).
## 3 Results and Discussion
### Results for AT&T Face Dataset
#### 3.1.1 Results for All Experiment Designs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Exp.**} & **Number of** & **SVM** & \multirow{2}{*}{**Accuracy**} & **Precision** & **Recall** & **F1-Score** \\  & & **Images** & **Kernel** & & & \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{10} & Quadratic & 98.5 & 98.5 & 98.64 & 98.57 \\  & & Cubic & 98.5 & 98.5 & 98.64 & 98.57 \\  & & Quadratic & 93 & 93 & 93.87 & 93.43 \\  & & Cubic & 93.5 & 93.5 & 94.82 & 94.16 \\  & & Quadratic & 97.75 & 97.75 & 97.93 & 97.84 \\  & & Cubic & 97.75 & 97.75 & 97.95 & 97.85 \\
6 & & Quadratic & 94 & 94 & 95.16 & 94.57 \\  & & Cubic & 94 & 94 & 95.04 & 94.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The confusion matrix performance results (%) for the AT&T Face Dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Exp.**} & **Coefficients** & \multirow{2}{*}{**1-NN**} & \multicolumn{4}{c}{**SVM**} \\ \cline{3-6}  & & & **Linear** & **Quadratic** & **Cubic** & **NN-ReLU** \\ \hline \multirow{4}{*}{1} & AL & 97.0 & 97.5 & 97.8 & 97.8 & 96.2 \\  & HG & 96.8 & 95.8 & 97.8 & 98.0 & 96.5 \\  & VG & 96.5 & 96.0 & 97.5 & 97.2 & 96.5 \\  & DG & 96.2 & 96.8 & 98.0 & 98.2 & 97.0 \\
2 & (c-AL+HG+VG+DG) & 97.2 & 97.5 & 98.2 & 98.2 & 97.8 \\
3 & AL & 97 & 97.8 & 98.2 & 98.2 & 97 \\  & HG & 96.8 & 96.2 & 97.8 & 97.8 & 96.8 \\  & VG & 97 & 96.2 & 97.8 & 97.5 & 96.2 \\  & DG & 96 & 96.8 & 98.2 & 97.5 & 97 \\
4 & (c-AL+HG+VG+DG) & 96.2 & 97.5 & 98.2 & 98.2 & 97.5 \\
5 & I’ & 96.8 & 98.2 & 99.2 & 99.2 & 97.2 \\
6 & I’ & 96 & 97.2 & 98 & 97.8 & 95.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average accuracy results (%) for AT&T Face Dataset from all experiments.
## 4 Case Study
### Discussion  
The study of cases 2, 3 and 4 above shows that the designed DRNN-LADRC controller can stably track the given signal and effectively reduce the adverse effects of interference on the radar position servo system. At the same time, compared with the LADRC and BPNN-LADRC, DRNN-LADRC has a more significant signal tracking ability, interference suppression ability and better system adjustment ability. In addition, Case 1 and Case 5 show that the introduction of ATD effectively solves the contradiction between tracking speed and filtering performance, and achieves the purpose of energy saving. It consumes less energy than the TD, successfully eliminates the peak value of the control signal, reduces the peak impact of the control signal on the actuator, and has a more practical application value.
# Effects of Cement Treatment on Mechanical Properties and Microstructure of a Granite Residual Soil
## 2 Experimental Procedures
### Uniaxial Compression Tests  
Uniaxial compression tests are conducted to determine the uniaxial compressive strength (UCS) and secant modulus (\(E_{50}\)) of the untreated and cement-treated GRSs following water saturation. The specimens for the uniaxial compression test are 50 mm in diameter and 100 mm in height. Four series of specimens with volumetric cement contents of 0% (untreated), 1%, 2%, 3%, 4%, and 5% in each series are cured to 1 d, 7 d, 14 d, and 28 d, respectively. A duplicate specimen is prepared for each cement content and curing time. Prior to uniaxial compression, the cured specimen is vacuum-saturated under confined conditions to ensure an identical saturation state (i.e., full saturation) and the surfaces of the saturated specimens are coated with a thin film of Vaseline to avoid air-drying. An electronic universal testing machine is utilized for the uniaxial compression tests. A loading speed of 0.1 mm/min is employed, as was suggested by ISRM [35]. Each test is ended after a peak state is achieved or when the axial strain exceeds 20%.
## 4 Discussion
### Limitations and Future Research  
This study has several limitations. First, the results of this study cannot be extended to women, other age groups, or athletes from different sports, as the participants were limited to healthy adult male university runners. Second, the relatively small sample size, the lack of a control group, and the fact that the study did not restrict normal running training mean that the results must be interpreted with caution. However, in research involving athletes, the sample size tends to be small because long-term observation is necessary [51]. Thirdly, because we recruited the runners of a university track and field club, selection bias cannot be denied. Finally, this study did not examine how these results affect the actual running performance. Despite the above-mentioned limitations, this study used a highly reliable method of randomized controlled trials, carefully evaluated the pre-intervention state, and conducted a longitudinal analysis. As a result, we were able to clearly show the effects of each exercise. This study suggests that toe tube training may improve the IFMs and their function. Incorporating toe tube training into daily training may be effective in preventing disability and promoting a return to play.
## 6 Research Areas
### Autonomous Navigation
#### 6.1.2 Path Planning  
Path planning involves finding the optimum path or a series of control points (called waypoints) from the robot's current location to its target location while avoiding collision with the different elements of the structure [207]. Besides the positions of the control points, the path information may also include the velocity and yaw of the robot [135]. BIM has been used as a planning tool for robot path planning [207; 208]. Most research on path planning focuses on single-building inspection. However, a multi-building path planning approach has also been performed with UAVs by [PERSON] et al. [209]. Another technique used for path planning for UAVs is discrete particle swarm optimization [31]. Dynamically changing site conditions in construction projects also require the robot to re-adjust its path, which means any pre-programmed path may not work in the long run or must be revised each time the robot is expected to complete its mission. Therefore, path planning should consider the current state of the structure. Therefore, 4D BIM is used for path planning because it captures the state of the structure with time during construction [81]. However, the success of the path planning will depend on how accurately the 4D BIM represents the actual temporal state of the structure. BIM also provides information about the openings (e.g., doors) that needs to be considered in indoor path planning [126; 210]. When BIM is not available, a 3D point cloud can also be used in its place for path planning [211].  
[PERSON] et al. [121] used reinforced learning (RL) to train a robot to find the optimum path. RL is one of three types of machine learning models, other than supervised and unsupervised learning. It is based on finding the correct solution based on a positive and negative reward system. [PERSON] et al. [180] used an embedded Nvidia GPU on the robot body for mapping and navigation using deep-learning-based semantic segmentation of images captured by the robot's camera. This eliminates the continuous need for connectivity and improves the autonomy of the robot.
# On Applicability of Network Coding Technique for 6 LoWPAN-based Sensor Networks
## 3 Network Coding for 6 LoWPAN-Based Wireless Sensor Networks
### Linear Network Coding Technique
Node 3 can decode the original packets by performing the following operations  
\[P_{1\times 2}^{T}=V_{2\times 2}^{-1}\times C_{1\times 2}^{T}, \tag{4}\]  
\[V_{2\times 2}^{-1}=\left[ \begin{array}{cc}1&1\\ 0&1\end{array} \right]^{-1}=\left[ \begin{array}{cc}1&-1\\ 0&1\end{array} \right], \tag{5}\]  
\[P_{1\times 2}^{T}=\left[ \begin{array}{c}p_{1}\\ p_{2}\end{array} \right]=\left[ \begin{array}{cc}1&-1\\ 0&1\end{array} \right]\times\left[ \begin{array}{c}c_{1}\\ c_{2}\end{array} \right]=\left[ \begin{array}{c}c_{1}-c_{2}\\ c_{2}\end{array} \right]. \tag{6}\]  
The following equation is valid in the field GF(2):  
\[c_{1}-c_{2}=c_{1}\oplus c_{2}. \tag{7}\]  
Thus, in node 3, the packet \(p_{1}\) is extracted by the calculation \(p_{1}=c_{1}-c_{2}=c_{1}\oplus c_{2}\) (bitwise XOR operation), and the packet \(p_{2}=c_{2}\) is buffered in node 3. The broadcast nature of wireless networks becomes a natural NC opportunity, since nodes can simultaneously receive broadcasted transmissions. Nodes that perform an encoding operation have to know which packets can be encoded (depending on the decoding possibility of the nodes that will receive coded packet). Even more, each node should also remember the packets that have already been sent, in order to use them for potential decoding. Also important is that inter-session network coding operations have to be performed in intermediate nodes. We expect that, while using the NC in the 6 LoWPAN-based network, we can decrease the overall traffic volume that is generated by the WSN's nodes, what leads to the decrease in the energy consumption in the whole network.
## 2 TVR Problem
### Analysis Methods  
Specific sample \(\mathbf{Z}\) is generated based on the probabilistic properties of input random variable \(\mathbf{Z}\). In the case of sample determination, the LSF can be viewed as a unitary function \(g(\mathbf{Z},t)\) only with regard to time \(t\). The TVR analysis methods can be classified into extreme value method and FCTP method based on how Equations (3) and (4) are calculated.  
The extreme value method considers whether the condition is satisfied for the minimum value of LSF response in the time interval \([0,\tau]\). Hence, Equation (3) can be transformed as follows:  
\[P_{f}(\tau)=P\Big{\{}\min_{t\in[0,\tau]}g(\mathbf{Z},t)\leq 0\Big{\}},0\leq\tau\leq T \tag{5}\]  
The FCTP considers the location relationship between time interval \([0,\tau]\) and moment point of unitary function \(g(Z,t)\) from safety domain to failure domain based on specific samples. As shown in Figure 1, there are three intersection points between \(g(Z,t)\) and the time axis, \(t_{1}\), \(t_{2}\), and \(t_{3}\), defining \(t_{1}\) as FCTP. Whether structure fails or not depends on the position relationship of \(t_{1}\) and the time interval \([0,\tau]\).  
From the structure of the function of \(g(\mathbf{Z},t)\), sample points and FCTPs correspond to each other.  
TP \(t_{1}\) is function of the input random variable \(\mathbf{Z}\), denoted \(ct=\varphi(\mathbf{Z})\). The failure probability of structure in the time interval \([0,\tau]\) can be expressed as follows:  
\[P_{f}(\tau)=P\{0\leq ct\leq\tau\} \tag{6}\]  
For Equation (6), the LSF can be transformed to be solved. As shown in Figure 2, the two curves and gray shaded portions contained represent all possible performance functions, i.e., clusters of performance functions. The first intersection of the cluster of performance functions with time axis constitutes the FCTP set. The set can be represented as a function of input variables, i.e., \(ct=\varphi(\mathbf{Z})\). The black shaded portion can represent the PDF of FCTP, \(f(\omega)\). Equation (6) can be transformed as follows:  
\[P_{f}(\tau)=\int_{0}^{\tau}f(\omega)d\omega \tag{7}\]  
Traditional methods of calculating crossing rates need to be based on certain assumptions about stochastic processes. However, the assumptions remain somewhat controversial. And the methods can only be used for a few special forms of performance functions. The  
Figure 1: Schematic diagram of FCTP: (**a**) the safe situation and (**b**) the failure situation.  
Figure 2: Schematic diagram of transformation of \(g(\mathbf{Z},t)\) and \(ct=\varphi(\mathbf{Z})\).  
MCS method is highly inefficient in calculating crossing rates. Therefore, in this paper, a novel TVR analysis method [30] is used to calculate the failure probability of structure.
# New Detection Paradigms to Improve Wireless Sensor Network Performance under Jamming Attacks
## 4 Analysis of Results
### Extrapolation of the Proposed Mechanism for Irregular Traffic  
In this case, we analyze a bigger topology network with 225 nodes where these have different traffic generation functions. Traffic of each node is obtained from a given interval between low and medium traffic function (1-10 packets per second).  
Figure 6 presents the shaded area for the three performance metrics the Scenario 3. Thanks to the simulation results obtained so far, it is important to scale the model to have a reproduction of a medium network and be able to analyze the behavior of each of the performance metrics: retransmissions, CSMA retries, and energy. After careful observation and analysis of a matrix of 225 nodes we noticed that the method of detection and mitigation used can be extrapolated to a greater number of nodes. We expose the Extended technique for jamming detection when the jammer node is in the middle of the topology, jamming is reactive, and the percentage of packet loss per link is between 0.5 and 2.5%. The simulations are run for three characteristic protocols that represent different approaches to know routes in the network: reactive protocols (AODV and DSR), proactive protocol (MPH). In this way, we have a broader analysis of the Extended Mechanism's impact on different communication protocols and a greater number of nodes. The darker area shows the possibility of finding the jammer  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Type of Energy** & **Connected Mechanism Energy (J)** & **Extended Mechanism Energy (J)** \\ \hline \(E_{Starting}\) & 0.0172 & 0.0144 \\ \(E_{MC}\) & 0.1338 & 0.0960 \\ \(E_{CSMA}\) & 0.8021 & 0.7982 \\ \(E_{Switching}\) & 0.0509 & 0.0411 \\ \(E_{RX}\) & 0.2156 & 0.2268 \\ \(E_{TX}\) & 0.1062 & 0.0923 \\ \(E_{Shutdown}\) & 0.0809 & 0.0749 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy consumption for each detection mechanism.  
node there. Please note that in the proactive protocol, such as MPH, the delimitation of that area is reduced to 10% of the network, whereas in reactive protocols, such as AODV and DSR, it is reduced to 25% of the complete topology.