esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparative Evaluation of HCI Adsorption Efficiency  
Our study achieved a hydrogen chloride (HCI) adsorption capacity of 77.99% using activated carbon pellets treated with nitric acid and microwave energy. This result is significant, but when compared with the existing literature, it shows that while our method is effective, there are materials and methods with higher adsorption capacities.  
For example, [PERSON] et al. reported an HCl adsorption capacity of 1626.20 mg/g using lyocell-based activated carbon fibers (ACFs), which is considerably higher than what we achieved [32]. This high capacity is attributed to the specific surface area (1403 m\({}^{2}\)/g) and optimal pore structure tailored through steam activation at specific temperatures and times.  
Similarly, [PERSON] et al. demonstrated that Na\({}_{2}\)CO\({}_{3}\)-impregnated activated carbon could achieve an adsorption capacity of 3.3 \(\times\) 10\({}^{-4}\) mol/g under specific conditions, showing the importance of impregnation and activation techniques in enhancing adsorption [33]. [PERSON] et al. reported an HCl adsorption capacity of 130-150 mg/g using commercial activated carbon, indicating that even standard materials can offer reasonable adsorption when appropriately treated [34].  
Our results, while competitive, suggest that further optimization of activation methods or material selection could enhance the HCl adsorption capacity of our activated carbon pellets. The studies mentioned above show that tailoring the pore structure, surface area, and activation process can lead to significantly higher adsorption capacities, which is a potential area for future research.
## 4 Research on Platform Performance Testing
### Dynamic Stiffness Analysis and Testing of the Platform  
According to the test results of the subchain tensile stiffness test, the finite element model of the subchain with equivalent stiffness was established, as shown in Figure 17a, and the finite element model of the whole mechanism was modified. The elastic modulus of the shaft corresponding to the axial stiffness was approximately 45 GPa. In order to simulate the vibration characteristics of the parallel mechanism installed with the third mirror, the finite element model used a 30 kg mass block to represent the load. In addition, due to the high stiffness of the base platform of the parallel adjustment mechanism, it did not contribute to the overall low-order natural frequency of the mechanism. In order to use the limited computing power to obtain a higher-precision fundamental frequency, the finite element model removes the base platform. The finite element simulation of the parallel adjustment mechanism is shown in Figure 17b,c. Through finite element simulation, it can be obtained that the fundamental frequency of the parallel adjustment mechanism was 94.5 Hz with the 30 kg load.  
To measure the natural frequency of the parallel platform at the zero position with a simulated load of 30 kg, an acceleration sensor was attached to the platform at the position with the highest vibration amplitude according to finite element simulation results and the platform is then tapped with a hammer. The natural frequency test system is pictured in Figure 18. The test system uploaded the vibration response data from the platform under test that were collected using the sensor to the computer and then generated the vibration response curve of the platform, as shown in Figure 19.  
Figure 16: Static stiffness characteristics of the parallel platform.  
Figure 17: Finite element simulation of the third mirror adjustment mechanism: (**a**) Equivalent finite element model of subchain; (**b**) Finite element model of the whole mechanism; (**c**) Result of the finite element modal analysis.  
Referring to the finite element simulation shown in Figure 17, the results of the analysis indicated that the natural frequency, which corresponds to the low-frequency peak at less than 50 Hz in Figure 19, was the natural frequency of the vibration isolation platform where the test system was placed, and the frequencies at the three peaks denoted by A, B, and C correspond to the natural frequencies of the entire platform (with a 30 kg load). The platform's fundamental frequency reached 86.25 Hz. The difference between the test data and the simulation data was mainly caused by the difference between the finite element model of the motion pair and its actual physical characteristics.
## 5 Experimental Results
### Dataset Analysis  
Based on traffic data, this work extracts key features including source/destination IPs, ports, and protocol types to construct a unique, reversible flow identifier, supporting downstream analysis and feature engineering. A time window mechanism is introduced to dynamically aggregate packets, capturing both the continuity and burst characteristics of traffic. In addition to basic attributes like the duration, packet count, and total length, the model distinguishes upstream and downstream byte counts. A deduplication mechanism ensures that each bidirectional flow is recorded once, minimizing redundancy and computational load.  
We employ the t-SNE to reduce the dimensionality and map the high-dimensional traffic data onto a two-dimensional space. Figure 5 illustrates the distribution differencesbetween malicious and benign traffic, where the red triangles represent malicious traffic and the blue points represent benign traffic. Malicious traffic shows tight clustering with narrow horizontal spread and pronounced vertical variation, reflecting tool generated regularity. For instance, DDoS traffic exhibits periodic bursts; port scans reveal protocol anomalies, and brute force attacks concentrate around failed session attempts. Furthermore, attack types differ in vertical positioning; low-frequency scans cluster in negative ranges, while high-frequency brute force appears in the positive axis.  
Benign traffic exhibits radial diffusion, reflecting its inherent heterogeneity. For instance, short HTTP sessions from web browsing coexist with long duration video streams, periodic email client interactions differ from the bursty patterns of instant messaging, and SSH tunnels and DNS queries display distinct payload characteristics across application layer protocols. The slight overlap between benign and malicious traffic is attributed to the ambiguity introduced by encrypted communication, which blurs distributional boundaries.  
From a feature engineering perspective, relying on a single dimension such as the packet count is insufficient to distinguish APT attacks from normal encrypted behavior. However, constructing a feature space that integrates timing, protocol, and statistical characteristics enables the natural clustering of attack traffic during dimensionality reduction. This validates the effectiveness of multi-dimensional fusion in amplifying the visibility and separability of malicious behaviors.
# Content and Privacy Protection in JPEG Images by Reversible Visual Transformation
## 3 Proposed Algorithm for Content Protection in JPEG Images
### Generating the Visually Transformed JPEG Images
#### 3.1.2 User Interaction to Select the Regions to Be Protected  
A JPEG image is divided into \(8\times 8\) blocks and each block is assigned with an index ranging from 0 to \(n-1\), as there are \(n\) blocks in the JPEG image. When a user selects one or more rectangular regions to be protected, the location information of the selected regions will be recorded. For each selected region, the index of top left block, and the height and width in blocks are recorded. Suppose there are \(w\) blocks per row and \(h\) blocks per column in a JPEG image with \(n\) blocks. The location information of each block consists of three parts with the lengths as follows:  
\[l=\left[log_{2}n\right] \tag{1}\]  
\[l_{1}=\left[log_{2}w\right] \tag{2}\]  
\[l_{2}=\left[log_{2}h\right] \tag{3}\]  
where \(l\), \(l_{1}\), \(l_{2}\) represent the lengths in bits for block index, region width and height, respectively. The recorded information of all selected regions is combined together to form a location map. Note that the length of the generated location map is calculated, which is denoted by \(len\) and embedded into the JPEG image prior to the location map.
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 6 Discussion
### Limitation  
One main limitation of our work is that we only analyze and predict the IPv6 traffic based on the traffic volume data of universities in Shanghai. Therefore, our analysis results could only present the situation of universities in Shanghai, and the generalization of our proposed prediction model remains to be further validated.
## 3 Joint Power Control and Resource Allocation
### Power Allocation with Power Control Consideration
\begin{cases}\alpha_{c1}^{*},\text{Case 1}:|l_{2}|^{2}(Q-\sigma_{u,r} ^{2}|l_{r}|^{2})-\eta\rho Q|l_{r}|^{2}|h_{2}|^{2}\leq 0\\ \alpha_{c2}^{*},\text{Case 2}:|l_{2}|^{2}(Q-\sigma_{u,r}^{2}|l_{r}|^{2})- \eta\rho Q|l_{r}|^{2}|h_{2}|^{2}>0\end{cases}  \tag{43}\]  
Next, we assume a fixed \(t\) and \(\rho\) to derive the optimal power allocation ratio using the derived optimal power control parameters and the peak power limit of the source nodes. The derived theorem is described in Theorem 1. Theorem 1: _For fixed parameters \(t\) and \(\rho\), the optimal power allocation ratio that satisfies OP1 is obtained as_  
\[(P_{1}^{*},P_{2}^{*})= \begin{cases}(P_{1,\max},P_{2,\max}),\;\;\text{if }A_{1}\leq \alpha^{*}\leq A_{2}\\ (P_{1,max},\frac{(1-\alpha^{*})Q}{l_{2}^{2}}),\;\;\text{if }\alpha_{1}\leq \alpha^{*}\leq 1\\ (\frac{\alpha^{*}Q}{l_{1}^{2}},\frac{(1-\alpha^{*})Q}{l_{2}^{2}}),\;\;\text{ if }A_{2}\leq\alpha^{*}\leq A_{1}\end{cases}  \tag{44}\]  
\[P_{R}^{*}=\min(\eta\rho(\tau_{\Sigma}^{*}+\sigma_{u,r}^{2})\frac{t}{1-t}, \frac{Q}{|l_{r}|^{2}}) \tag{45}\]  
_where \(\alpha^{*}\) is the optimal power control parameter that maximizes the minimum cognitive user transmission rate without considering the source peak power limits, \(A=\frac{|l_{1}|^{2}P_{1,\max}}{Q}\), \(A_{2}=1-\frac{|l_{2}|^{2}P_{2,\max}}{Q}\), \(\alpha_{1}=\max(A_{1},A_{2})\), \(\alpha_{2}=\min(A_{1},A_{2})\), and \(\gamma_{\Sigma}^{*}=|h_{1}|^{2}P_{1}^{*}+|h_{2}|^{2}P_{2}^{*}\)._  
Proof: When a limit on the source's peak power is not enforced, the source transmit power can be written as \(P_{1}=\frac{\alpha^{*}Q}{|l_{1}|^{2}}\), \(P_{2}=\frac{(1-\alpha^{*})Q}{|l_{2}|^{2}}\) with optimum value \(\alpha^{*}\). With a peak power constraint, \(P_{1}\) and \(P_{2}\) are separated into four cases based on the value of \(\alpha^{*}\) according to Equations (21)-(22):  
1. when \(A_{1}\leq\alpha^{*}\leq A_{2}\), \(P_{1}=P_{1,\max}\), \(P_{2}=P_{2,\max}\)
2. when \(\max(A_{1},A_{2})\leq\alpha^{*}\leq 1\), \(P_{1}=P_{1,\max}\), \(P_{2}=\frac{(1-\alpha^{*})Q}{|l_{2}|^{2}}\)
3. when \(0\leq\alpha^{*}\leq\min(A_{1},A_{2})\), \(P_{1}=\frac{\alpha^{*}Q}{|l_{1}|^{2}}\), \(P_{2}=P_{2,\max}\)
4. when \(A_{2}\leq\alpha^{*}\leq A_{1}\), \(P_{1}=\frac{\alpha^{*}Q}{|l_{1}|^{2}}\), \(P_{2}=\frac{(1-\alpha^{*})Q}{|l_{2}|^{2}}\). Thus, if \(\alpha^{*}\in\{\text{B1},\text{B2},\text{B3},\text{B4}\}\), then the power control for such cases is guaranteed. Further, by substituting the obtained \(P_{1}\) and \(P_{2}\) into Equation (23), \(P_{R}\) can be derived as shown in Equation (45). The power allocation algorithm with fixed \(t\) and \(\rho\) is given in Algorithm 1.
## 2 Preliminaries and Problem Setup  
### Configuration Space and Sampling-Based Path Planning  
In sampling-based path planning, configuration space \(\mathbf{Q}\) (also called joint space for robot manipulators) represents the space of possible joint angles and is a subset of \(n\)-dimensional Euclidean space \(\mathbb{R}^{n}\) where \(n\) denotes the number of the joints of the manipulator. The values of the joint angles of a robot manipulator are denoted as a point in \(\mathbf{Q}\)[1; 2; 34]. The configuration space consists of two subsets: the collision-free space \(\mathbf{Q}_{\text{\tiny{true}}}\) and \(\mathbf{Q}_{\text{\tiny{outside}}}\) in which the robot manipulator collides with other obstacles or itself. For motion planning, a discrete representation of the continuous \(\mathbf{Q}_{\text{\tiny{true}}}\) is generated by random sampling. Then a connected graph (roadmap) is obtained. The nodes in the graph denote admissible configuration of the robot manipulators, and the edges connecting any two nodes mean feasible paths (trajectory) between the corresponding configurations. Finally, when the starting and goal configurations \(q_{0}\) and \(q_{\text{\tiny{goal}}}\) are given, any graph search algorithm is employed to find the shortest path connecting \(q_{0}\) and \(q_{\text{\tiny{goal}}}\). There exists a shortest path between \(q_{0}\) and \(q_{\text{\tiny{goal}}}\) since they are two nodes on the connected graph.
# Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?
## 3 Methods
### Regression with AutoRegressive Integrated Moving Average Errors
#### 3.1.3 Trigonometric Seasonality  
Seasonal ARIMA models can be cumbersome when the seasonal period is long, as in the case of weekly data, leading to a substantial reduction in fitting sample. In retailing it is typical that time series are relatively short, and therefore we often cannot afford to lose that many data points.  
Trigonometric encoding of seasonality can be helpful to overcome this. With trigonometric seasonality we express the seasonality as \(m/2\) pairs of sines and cosines of increasing frequency. More specifically:  
\[s_{t}^{\zeta_{l}}=\sum_{l=1}^{m/2}\biggl{[}\xi_{l}\sin\biggl{(}\frac{2\pi lt}{ m}\biggr{)}+\xi_{l+m/2}\cos\biggl{(}\frac{2\pi lt}{m}\biggr{)}\biggr{]}, \tag{2}\]  
where the coefficients \(\xi_{l}\) are the amplitudes of the sines and cosines. Observe that for \(l=m/2\) the \(\sin(2\pi lt)/m=\sin(\pi t)\) that is constant for all \(t\) and can be ignored. This makes the usual formulation of seasonality with binary indicators and trigonometric variables equivalent, as the same information is modelled by the same number of variables [32]. The trigonometric encoding is advantageous when we want a sparse representation of seasonality. Observe that (2) is the Fourier decomposition of the seasonal pattern. Therefore, eliminating terms, typically of higher frequency, controls the quality of the approximation. On the contrary, when we eliminate binary indicators then we set these seasonal periods to zero, degrading the approximation of seasonal patterns substantially.  
The elimination of terms in \(s_{t}^{\zeta_{l}}\) can be done as with usual variable selection. Therefore it is easy to augment models with trigonometric seasonality to overcome the substantial reduction in fitting sample.
## 4 Evaluation of Prediction Results
### Analysis of Prediction Results  
In this paper, rainfall data and ponding data from 1 August 2017 to 12 August 2017 are selected as the test set, with 240 sets of ponding-rainfall data, and the models are trained with MSE, MAE and MSLE as loss functions, denoted as LSTM (mse), LSTM (mae) and LSTM (msle). The prediction results for each time-step are shown in Figure 3.  
Figure 3: _Cont_.  
According to Figure 3, the prediction results obtained from the LSTM models trained by the three loss functions are synchronous with the measured values in general, but all of them have different degrees of backward shift; the prediction results after 35 min in particular have a significant backward shift. For the prediction of the extreme values of ponding depth, LSTM (mae) predicted the extreme values over 40 mm close to the measured values, but the prediction for the general values of ponding depth (less than 30 mm) was poor; LSTM (mse) and LSTM (msle) predicted the general values better.  
The prediction results of each model were evaluated by RMSE, MAE, MAPE and NSE for accuracy calculation, and the average value of prediction accuracy of each model at each time step is shown in Figure 4.  
Figure 3: Predicted results of water depth at each time-step (**a**-**l**) are the prediction results of 5 min, 10 min, 15 min, 20 min, 25 min, 30 min, 35 min, 40 min, 45 min, 50 min, 55 min and 60 min.  
Figure 4: _Cont._  
According to Figure 4 and Table 1, LSTM (msle) is the model with the best accuracy indicators among the three models, in which the average RMSE is 5.34, MAE is 3.45, MAPE is 53.93% and NSE is 0.35. Compared with LSTM (mse) and LSTM (mae), full time average MAPE of LSTM (msle) decreased by 13.15% and 8.88%, respectively, and full time average NSE increased by 0.21 and 0.20, respectively.  
The comparison results of LSTM (mse) and LSTM (mae) precision indicators show that the average RMSE, MAE and MAPE of LSTM (mse) in the first 30 min (the first 6 moments) were higher than those of LSTM (mae), and the NSE was relatively smaller, indicating that the prediction effect of LSTM (mae) was better than that of LSTM (mse) in the first 30 min; while the average RMSE, MAE and NSE of LSTM (mse) in the next 30 min (the last 6 moments) are slightly better than those of LSTM (mse).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{4}{c}{**LSTM (msle) vs. LSTM (mse)**} & \multicolumn{4}{c}{**LSTM (msle) vs. LSTM (mae)**} & \multicolumn{4}{c}{**LSTM (msle) vs. LSTM (mae)**} \\ \cline{2-13}  & **RMSE** & **MAE** & **MAPE (\%)** & **NSE** & **RMSE** & **MAE** & **MAPE (\%)** & **NSE** & **RMSE** & **MAE** & **MAPE (\%)** & **NSE** \\ \hline \(5\min\) & \(-1.92\) & \(-1.43\) & \(-18.91\) & 0.43 & \(-0.67\) & \(-0.84\) & \(-15.90\
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Existing Energy-Saving Techniques
#### 2.3.5 Optimizing Machine Learning  
Machine learning algorithms can dynamically adjust equipment power settings and forecast equipment failures, based on real-time conditions and historical information, thereby harvesting energy savings. For example, by intervening before the occurrence of equipment problems and preventing equipment from running in an inefficient or lossy state, the use of machine learning algorithms can yield energy savings. Likewise, machine learning is capable of fine-tuning the equipment runtime and task scheduling, aligning the energy consumption closely with the real demand.
# Cloud Detection and Tracking Based on Object Detection with Convolutional Neural Networks
## 3 Results
The orange line shows the DNI values during the test and the blue line represents the cloud transient remaining time if the cloud speed is assumed to be constant. Note that clouds moving with a nonconstant speed also change their shape, which affects their position and speed. Some instants (numbered in black in Figure 7) were selected and the resulting images are shown in Figure 8, where the trajectory (white line), the cloud number, the cloud position, and the estimated remaining time for the transient are shown. As shown in Figures 7 and 8, the remaining time and the trajectory predicted were validated by subsequent events. DNI values and image 0 in Figure 8 show a good agreement between the prediction and the reality. Except for small errors, the calculated trajectory and the remaining time prediction were the same as in the real events. The root mean squared error between the remaining time prediction and the remaining time assuming a constant cloud speed was less than 1.7 sec. Note that at the beginning the absolute error was greater than the error at the final point. As mentioned before, this may have been due to the fact that the cloud not only moved, but also changed its shape, which affected the calculation of its position and, therefore, the calculation of its speed, especially at the beginning of the detection when the cloud formed or was close to the horizon line. However, during the rest of the validation, the predicted remaining time and the estimated constant cloud speed were very similar. Finally, the new approach predicted the moment when the cloud began to create the transient without error (0 s in Figure 7). Although the best model predictions were expected with the image taken using the hardware used to generate the dataset, this model can be used with images from other  
Figure 7: Remaining time prediction and direct normal irradiation evolution during the test. hardware, for instance, sky cameras (see Figure 9). The model can also be retrained with additional images to adapt it better to other kinds of hardware. Figure 8: Zoomed images of the selected instants of prediction. Figure 9: Object detection results from a Mobotix Q25 camera image.
## 4 Experiments and Results
### Basic Settings
#### 4.1.3 Evaluation Metrics  
In this paper, the root mean square error (termed as RMSE), mean absolute error (termed as MAE), and symmetric mean absolute percentage error (termed as SMAPE) were used as evaluation metrics to evaluate the short-term OD prediction performance, in which RMSE, MAE, and SMAPE are, respectively, formulated as  
\[RMSE =\sqrt{\frac{1}{\phi^{2}}\sum_{i=1}^{\phi}\sum_{j=1}^{\phi}(\hat{ M}_{i,j}-g(\hat{O}_{i,j}))^{2}}, \tag{20}\] \[MAE =\frac{1}{\phi^{2}}\sum_{i=1}^{\phi}\sum_{j=1}^{\phi}\left|\hat{ M}_{i,j}-g(\hat{O}_{i,j})\right|,\] (21) \[SMAPE =\frac{1}{\phi^{2}}\sum_{i=1}^{\phi}\sum_{j=1}^{\phi}\frac{\left| \hat{M}_{i,j}-g(\hat{O}_{i,j})\right|}{(\hat{M}_{i,j}+g(\hat{O}_{i,j}))/2+c}, \tag{22}\]  
where the notations \(\hat{M}_{i,j}\) and \(\hat{O}_{i,j}\) are the same as those in Equation (19), \(\left|\cdot\right|\) denotes \(L_{1}\) norm, and \(c\) is a very small positive constant to prevent the denominator from being 0, which is set as \(10^{-9}\). Since \(\hat{O}_{i,j}\) usually contains decimals and negative numbers, in order to evaluate the model performance more accurately, \(\hat{O}_{i,j}\) was evaluated after rounding and non-negative integer processing. Thus, \(g(\cdot)=g_{2}(g_{1}(\cdot))\) is a composite function, in which \(g_{1}(\cdot)\) denotes the rounding function and \(g_{2}(\cdot)\) denotes the function of taking nonnegative integers. \(g_{2}(g_{1}(\hat{O}_{i,j}))\) can be expressed as:  
\[g_{2}(g_{1}(\hat{O}_{i,j}))=\begin{cases}0,g_{1}(\hat{O}_{i,j})<0\\ g_{1}(\hat{O}_{i,j}),g_{1}(\hat{O}_{i,j})\geq 0.\end{cases} \tag{23}\]  
According to Equations (20) and (21), RMSE and MAE are two different evaluation metrics that can measure the absolute error between the predicted OD matrix and the  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **BJSubway2021** & **BJSubway2022** \\ \hline \multirow{2}{*}{Date} & 14 September 2021 to 17 October 2021 & 1 January 2022 to 9 January 2022 \\  & 14 December 2021 to 29 December 2021 & 18 January 2022 to 21 February 2022 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Data record \\ Station Number \\ Matrix Dimension \\ Time \\ Time Interval \\ \end{tabular} } & 137 million & 98 million \\  & 369 & 372 \\  & 369 \(\times\) 369 & 372 \(\times\) 372 \\  & 6:00 to 23:00 & 6:00 to 23:00 \\  & 5 min & 5 min \\ \hline \end{tabular}
\end{table}
Table 2: Data description.  
real OD matrix but cannot reflect whether the predicted OD matrix is underestimated or overestimated relative to the real OD matrix. According to Equation (22), SMAPE as a relative error metric can reflect whether the predicted OD matrix is underestimated or overestimated relative to the real OD matrix. Therefore, the prediction performance of the network can be comprehensively evaluated by using RMSE, MAE, and SMAPE.