esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations  
### Radio Transmission Model  
In this study, we adopt the radio model stated in [4] to calculate the energy consumed by radio transmissions. There are two primary factors that involve in the radio model: \(E_{elec}\) and \(\varepsilon_{amp}\). \(E_{elec}\) represents the energy consumption per bit by either the electrical circuits of the transmitter node \(s_{t}\) or the receiver node \(s_{r}\) in \(S\), and \(\varepsilon_{amp}\) is the energy consumption per bit by the signal amplifier of the transmitter node \(s_{t}\). The radio model is formulated by:  
\[E_{Tx}(k,d)=k(E_{elec}+\varepsilon_{amp}d^{\gamma}) \tag{10}\]  
\[E_{Rx}(k)=kE_{elec}\,, \tag{11}\]  
where \(E_{Tx}\) is the energy consumption for transmitting data, \(E_{Rx}\) denotes the energy consumption by receiving data, \(d\) is the distance between the transmitter node \(s_{t}\) and the receiver node \(s_{r}\), and \(\gamma\) is the path loss exponent.  
For the nodes that serve as intermediate nodes, besides energy consumption of transmitting data and receiving data, extra energy consumption is required to complete the tasks of aggregating and compressing the sensed data. When an intermediate node receives a packet of length \(k\)-bits, the energy consumption can be formulated by \(E_{Rx}\). The packet is then compressed into a packet of \(\mu\times k\) bits, and the energy consumption can be formulated by \(k\times E_{DA}\), where \(E_{DA}\) is the energy consumption per bit in data aggregation, and \(\mu\) is the compression coefficient. After the data aggregation, the node transmits the aggregated data to the next-hop node. Hence, the total energy consumption of an intermediate node for receiving and transmitting a packet, denoted by \(E_{int}\), is:  
\[E_{int}=E_{Rx}+kE_{DA}+\mu E_{Tx}\,. \tag{12}\]  
Figure 3: Data transmission paths using the ECHR algorithm for a specific network topology.  
It is worth noting that this radio transmission model may not be consistent with the specification of some radio-frequency transceivers. However, this radio transmission model has been widely utilized in many previously proposed studies. In order to conduct a fair comparison between the performances of the proposed approach and other existing methods, as well as to focus the scope of this study on the routing issue, the aforementioned radio transmission model is utilized.
# A Review of Seatbelt Technologies and Their Role in Vehicle Safety
## 3 Results
### Autonomous Vehicles and Adaptive Seatbeltls
The findings underscore the trade-offs in restraint design and highlight the need for targeted adaptations to protect reclined occupants [48]. The investigation in [73] explored the potential of pre-impact repositioning strategies to mitigate injury risks for reclined occupants in autonomous vehicle crash scenarios. Using active and passive human body models (HBMs), simulations demonstrated that repositioning the seatback or utilizing occupant torso inertia during braking could restore upright posture and reduce head excursion. However, submarining risks persisted due to incomplete pelvic repositioning, primarily influenced by lumbar spine flexibility. The study emphasized the potential role of active musculature in influencing crash outcomes and called for more nuanced restraint strategies that consider biomechanical variability. Also, with the advent of highly automated driving systems, the study in [74] assessed the biomechanical implications of reclined and rearward-facing postures through twelve finite element simulations. Key parameters such as seatback angle, rotational stiffness, and crash velocity were varied to evaluate their influence on injury metrics. While reclining did not significantly elevate injury risk values, it altered occupant interaction with restraint systems, especially under high-speed impacts. Stiffer seatbacks were found to reduce brain and thoracic injury criteria but resulted in increased neck loads, highlighting complex trade-offs in future seat design. A real-world crash analysis using German In-Depth Accident Study (GIDAS) data evaluated in [75] showed the injury severity among reclined versus upright belted occupants, focusing on body-region-specific outcomes. Odds ratios for Maximum Abbreviated Injury Scale (MAIS)2+, MAIS3+, and MAIS4+ injury levels were significantly elevated in the reclined group, with particularly high risks for the pelvis/lower extremity region. At MAIS4+, a statistically significant increase in head/face/neck injury severity was also observed. These findings establish a strong association between reclined posture and increased injury risk, reinforcing the urgency for restraint system redesign in autonomous vehicle contexts. These insights underscore the need for intelligent, human-centered restraint systems, such as deployable lap belt anchors and posture-aware pretensioning algorithms, that dynamically adapt to occupant posture and context, meeting the evolving safety demands of autonomous mobility.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 5 Conclusions  
In this study, the effectiveness of a proposed H-Bridge circuit in rectifying voltage and power from PEH system under low frequency, human induced excitation is investigated. Based on the experimental results, the use of MOSFETs with low threshold voltage in the H-Bridge circuit has achieved higher rectified voltage and output power in comparison with the conventional FBR circuit. The experimental results have shown that H-Bridge circuit significantly outperforms the FBR circuit under all testing scenarios, namely, shaker, cycling, and walking tests. In the walking test, under the same source of excitation, the proposed H-Bridge circuit produces a maximum rectified voltage of 3.8 Vd and an output power of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Testing Scenarios** & **Sources of Excitation** & **Controlling Parameters** & **Rectifier Circuits** & **Output Power (\(\mu\)W)** \\ \hline \multirow{3}{*}{Shaker test} & \multirow{3}{*}{Mechanical shaker} & \multirow{3}{*}{Frequency: 2 Hz} & H-Bridge & 15.2 \\ \cline{3-5}  & & & FBR & 4.9 \\ \hline \multirow{3}{*}{Cycling test} & \multirow{3}{*}{\
\begin{tabular}{c} Human performing cycling motion \\ \end{tabular} } & \multirow{3}{*}{Speed: 80 RPM} & H-Bridge & 10.1 \\ \cline{3-5}  & & & FBR & 5.01 \\ \hline \multirow{3}{*}{Walking test} & \multirow{3}{*}{
\begin{tabular}{c} Human performing walking motion \\ \end{tabular} } & \multirow{3}{*}{Speed: 8 km/h} & H-Bridge & 21.05 \\ \cline{3-5}  & & & FBR & 5.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of maximum output power generated by all three tests.  
21.05 uW, while the FBR circuit yielded 1.6 Vdc and 5.01 uW of rectified voltage and output power, respectively. The voltage rectified by H-Bridge also shows higher stability. Thus, it is concluded that H-Bridge is a suitable rectifier circuit for harvesting various human motions induced mechanical energy from PD. For future work, study into dual-stage H-Bridge circuit for human motions application is recommended.  
Conceptualization, M.E.; Data curation, M.E.; Formal analysis, M.E.; Funding acquisition, Y.Y.L.; Investigation, M.E.; Methodology, M.E.; Project administration, M.E.; Resources, M.E.; Software, M.E.; Supervision, Y.Y.L., R.V.P. and M.D.; Validation, M.E.; Visualization, M.E.; Writing--original draft, M.E.; Writing--review & editing, M.E., Y.Y.L. and R.V.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available on request due to privacy and restrictions.  
The authors declare no conflict of interest.
## 2 Pixel-Level Equalized Memory Matching Network
### Intelligent Inspection Segmentation and Tracking  
Once the target of the electrical equipment to be tracked is identified, the intelligent inspection device begins the segmentation and tracking process. Our network is based on the segmentation tracker SiamMask, the structure of which is shown in Figure 3. The whole framework is divided into three parts, which are the pixel-level equalized memory matching network (PEMMN), the mask quality evaluation module (MQEM), and the process for generating mask and tracking results. First, the feature extraction network of the base segmentation tracker (i.e., ResNet50) is utilized to extract the reference feature \(X^{k}\) and the query feature \(X^{i}\) from the reference and query frame images, respectively. Then, the downsampled reference frame mask \(m^{k}\), the reference features \(X^{k}\), and the query features \(X^{i}\) are introduced as inputs to a pixel-level equalized memory matching method. By comparing the reference features with the current frame features and retrieving target-related information from the features of the stored frames, the obtained matching score is concatenated with the query frame feature \(X^{i}\) to generate a composite feature map \(X^{\prime}\). Further, the composite feature \(X^{\prime}\) is fed into three branches of the base segmentation tracker, namely, a classification branch (Cls), a regression branch (Reg), and a segmentation branch (Segmentation), which generates a binary mask. The segmentation branch introduces the low-level features of the query frame during the up-sampling process to make the edges of its segmentation result finer. From this process, the query frame mask \(M^{i}\) and the bounding box of the target object (i.e., tracking result) can be inferred. Finally, the current query frame mask \(M^{i}\) is input into the mask quality evaluation module (MQEM) together with the query frame image to obtain the evaluation score \(S_{t}^{A}\), and if it is higher than the threshold \(\sigma\), the query frame mask \(M^{i}\) is added to the memory bank to dynamically update the memory bank.
## 4 Experiment Verification
### Path Planning  
We built the dynamic model of the flight simulation platform according to Equation (12), and the controller calculated the required actuator force or torque to make the robot follow the desired translation and orientation trajectory. We used the translation and orientation variable as the coordinate of the platform defined by \(X=[x\;\theta]^{T}\), in which the translation is represented by \(x=\left[x_{p}\;y_{p}\;z_{p}\right]^{T}\), while the moving platform orientation is represented by \(\theta=\left[\theta_{x_{p}}\;\theta_{y_{p}}\;\theta_{z_{p}}\right]^{T}\). Both of the translation and orientation planning were built using a sinusoidal motion given by:  
\[r=\left[\begin{array}{c}x_{plan}\\ \theta_{plan}\end{array}\right]=\left[\begin{array}{c}x_{r}+A_{r}sin\left( \omega_{plan}t\right)\\ A_{o}sin\left(\omega_{plan}t\right)\end{array}\right] \tag{29}\]  
We used the twist velocity variable as the coordinate of the platform defined by \(\dot{r}=\left[v_{plan}\omega_{plan}\right]^{T}\), and the twist velocity planning was obtained after differentiating the Equation (30) given by:  
\[\dot{r}=\left[\begin{array}{c}v_{plan}\\ \omega_{plan}\end{array}\right]=\left[\begin{array}{c}A_{r}\omega_{plan} cos\left(\omega_{plan}t\right)\\ A_{o}\omega_{plan}cos\left(\omega_{plan}t\right)\end{array}\right] \tag{30}\]  
The values used in path planning of the 6-DOF Stewart-Gough flight simulation platform simulations are given in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Variable** & **Value** & **Units** \\ \hline radius\({}_{lour}\) & 0.504 & m \\ radius\({}_{upper}\) & 0.504 & m \\ \(\theta_{A_{i}}\) & [30,30,150,150,270,270] & deg \\ \(\theta_{B_{i}}\) & [0,60,120,180,240,300] & deg \\ \(m_{p}\) & 28.7 & kg \\ \(m_{i_{1}}\) & 2.77 & kg \\ \(m_{i_{2}}\) & 0.54 & kg \\ \(c_{i_{1}}\) & 0.5456 & m \\ \(c_{i_{2}}\) & 0.305 & m \\ \(I_{xx,p}\) & 1.13 & kg m\({}^{2}\) \\ \(I_{yy,p}\) & 1.13 & kg m\({}^{2}\) \\ \(I_{zz,p}\) & 2.23 & kg m\({}^{2}\) \\ \(I_{xx,c_{i_{1}}}\) & 0.21 & kg m\({}^{2}\) \\ \(I_{yy,c_{i_{1}}}\) & 0.01 & kg m\({}^{2}\) \\ \(I_{zz,c_{i_{1}}}\) & 0.001 & kg m\({}^{2}\) \\ \(I_{xx,c_{i_{2}}}\) & 0.0677 & kg m\({}^{2}\) \\ \(I_{yy,c_{i_{2}}}\) & 0.0077 & kg m\({}^{2}\) \\ \(I_{zz,c_{i_{2}}}\) & 0.000114 & kg m\({}^{2}\) \\ \(g\) & [0,9.8,0] & N/kg \\ \hline \end{tabular}
\end{table}
Table 1: Platform geometric and inertial parameters.
## 2 Developed Multiscale PCA-Based NN  
### Feature Extraction Using PCA Technique  
PCA is a multivariate statistical analysis technique. It is utilized for information extraction from data and has been employed in a wide range of disciplines. In the operation, monitoring, and control of chemical processes, it is also used to complete a number of tasks, such as data rectification, fault detection and isolation, and disturbance and fault diagnosis, due to its effectiveness in extracting abnormal changes from the information in the system. Therefore, the foundation of PCA is the projection of large amounts of multivariate data obtained from measurable process variables onto a reduced dimensional space of uncorrelated principal components [38]. Consider the data that were gathered in the form of a \(X\in\Re^{n\times m}\) matrix from a process that was working normally with \(n\) samples of \(m\) variables, and store them in an \(X\) matrix with zero mean and unity [38].  
\[X=[x_{1},x_{2},...,x_{m}]\in\Re^{n\times m} \tag{1}\]The following equation describes the linear transformation that turns the data matrix into another novel matrix called the score matrix of uncorrelated variables, \(T=[t_{1},t_{2},...,t_{n}]^{T}\in\Re^{n\times m}:\)  
\[T=XP \tag{2}\]  
The score matrix is \(T\), and the loading matrix is \(P\). The singular value decomposition (SVD), which is used to create an orthogonal transformation of \(X\)'s covariance matrix, results in \(P\) as depicted in the following equation:  
\[C=\frac{1}{n-1}X^{T}X=P\Lambda P^{T} \tag{3}\]  
The eigenvalues for the diagonal matrix \(\Lambda=diag(\lambda_{1},\lambda_{2},...,\lambda_{m})\) are arranged in decreasing order to indicate the variance \(\lambda_{1}\geq\lambda_{2}\geq...\geq\lambda_{m}\). \(P\) and \(\Lambda\) are divided into modeled and non-modeled variants (Figure 1) to diminish the dimensionality of the dataset. As a result, \(\Lambda_{l}\in\Re^{l\times l}\) and \(P_{l}\in\Re^{m\times l}\) span the first subspace, which is called principal subspace. Otherwise, \(\Lambda_{m-l}\in\Re^{(m-l)\times(m-l)}\) and \(P_{m-l}\in\Re^{m\times(m-l)}\) span the residual subspace (second subspace). Numerous methods, such as parallel analysis, cumulative percent variance (CPV), scree plot, kaiser criterion, and cross-validation, are used in the literature to choose principal components (PCs), \(l\). The CPV criterion is used in the current paper in order to select the PCs, and it is depicted as the following equation:  
\[CPV(l)=100\left(\begin{array}{c}\sum\limits_{j=1}^{l}\lambda_{j}\\ \sum\limits_{j=1}^{m}\lambda_{j}\end{array}\right)\% \tag{4}\]
# Identification of Location and Camera Parameters for Public Live Streaming Web Cameras
## 3 Public Camera Parameters Estimation
#### Site-Specific Calibration  
If the unified calibration procedure data are unavailable, we can estimate camera position and orientation parameters (\(R\),\(O\)) separately from others. Resolution \(N\times M\) of images/video from a camera is available with the images/video. As noted earlier, usually  
\[c_{u}=N/2,c_{v}=M/2. \tag{14}\]  
Many photo cameras (including phone cameras) add the EXIF metadata to the image file, which often contain focal length \(f\). The image sensor model description can contain a pixel width \(w\) and height \(h\), so (4) gives \(f_{u}\) and \(f_{v}\) values. The distortion coefficients are known for high-quality photo lenses. Moreover, the raw image processing programs can remove the lens radial distortion. If lucky, we can obtain intrinsic camera parameters and radial distortion coefficients and apply a pose computation algorithm (PnP, [12; 13]) to sets \(\{P_{emu}\}\), \(\{P_{im}\}\) to obtain \(R\),\(O\) estimates. When EXIF metadata from the camera are of no help (this is typical for most public cameras), small sets \(\{P_{emu}\}\) and \(\{P_{im}\}\) and Equation (5) can help to obtain \(f_{u}\) and \(f_{v}\) estimations. Suppose we know the installation site of the camera (usually, the place is visible from the camera's field of view). In that case, we can estimate GNSS/ENU coordinates of the place (point \(O\) coordinates) by the method listed earlier. The camera orientation (matrix R) can be detected with the point \(G\) coordinates (Figure 1) and the estimation of the camera horizon tilt. Horizontal or vertical 3D lines (which can be artificial) in the camera's field of view can help evaluate the tilt.
# Adaptive Feature Fusion for Small Object Detection
## 2 Materials and Methods
### Methods  
#### 2.2.1 MMF-YOLO Algorithm  
The YOLO series network has a simple structure and improves the speed while ensuring the accuracy. Therefore, it has received extensive attention and application. In order to take into account the accuracy and speed at the same time, inspired by the YOLOX [38] network, this paper proposes the MMF-YOLO algorithm for the problems of overlapping small objects and large scale changes in complex scenes. It uses the backbone network of YOLOX, adds the MMF-Net multi-scale fusion module to the neck layer, and modifies the size of the object detection head. The overall algorithm structure is shown in Figure 1.  
From Figure 1, we can see that the network structure is roughly divided into three main bodies: the feature extraction network (backbone), the feature fusion network (neck), and the detection head (head). The whole process of the algorithm can be simply summarized as follows: the training images are extracted to obtain different levels of features, and then the features of different levels are output to the detection head through feature fusion, and the detection head completes the position adjustment of the prediction box to perform the detection task.  
The specific process is as follows: the image goes through the backbone network CSPDarknet for feature extraction. First, the image goes through a Focus network structure to reduce the width and height to half the original size, so the width and height information is concentrated on the channel, and the channel is expanded to four times the original size. Then, the number of channels is further expanded through a convolution block, which consists of a conventional convolution, normalization, and Silu activation functions. Then, residual blocks of four different convolution kernels are used to filter the image information features, and to obtain four feature maps feat0, feat1, feat2, feat3 of the middle layer, respectively. These four features are input into the MMF-Net module for multiscale fusion.  
Meanwhile, because the proportion of small objects in the picture is small, it is equivalent to local information relative to the entire picture, and the detection heads with a size of 20 \(\times\) 20 in the three detection heads of the original YOLOX network are considered global relative to small objects, which is not conducive to the identification of small objects. Therefore, it is discarded, and in order to increase the classification score of small object detection, the feature fusion module through which feature feat0 passes is pulled out of a detection head.  
Figure 1: Overall framework of the MMF-YOLO algorithm.  
The following will introduce the MMF-Net module structure and the improved attention mechanism and the size-changed object detection head structure, respectively.
# Characteristics of HEMP Radiation Field on Typical Transport Aircraft with Frequency Sweep Method
## 2 Models and Setup
### HEMP Parameters  
In accordance with IEC 61000-2-9, mathematical expressions for the double exponential waveforms used to describe each component of the HEMP waveform are provided, as shown in Equation (1) ([PERSON] and [PERSON], 2013).  
\[E(t)=kE_{0}\left(\mathrm{e}^{-at}-\mathrm{e}^{-bt}\right) \tag{1}\]  
where \(E_{0}\) is the initial peak electric field intensity, \(E_{0}=50\) kV/m; \(k\), \(a\), \(b\) are the constants, \(k=1.3\), \(a=4\)\(\times\)\(10^{7}\) s\({}^{-1}\), \(b=6\)\(\times\)\(10^{8}\) s\({}^{-1}\).  
In addition to the characteristics of the radiation field, factors that affect the coupling effect are also related to the incident angle and polarization direction. As shown in Figure 1, the radiation field in relation to the object (cabin hole, cable, antenna, etc.) defines the incident angle, polarization direction and other parameters. In Figure 1, \(\overset{\rightarrow}{k}\) is the wave vector which represents the direction of the incoming wave; \(\alpha\) is the polarization angle which defines the angel between the electric field direction and the incident plane; \(\beta\) and \(\gamma\) represent the incident angles in the spatial coordinate system.
## 4 Experimental Results and Analysis
### Verification for Automatic Language Identification Using R-Vector  
In experiments on language corpora, it was successfully verified that the automatically extracted rhythm metrics preserve language-discriminative information. Next, we attempted to verify whether the metrics play a role in automatic language identification. For this work, we developed two types of the conventional LID approaches including i-vector-based LID and CNN-based LID, and then we compared them with our proposed approach in terms of LID performance and computation complexity.  
The first baseline based on i-vector was constructed in accordance with the description mentioned in Section 2.1. Figure 8 demonstrates the standard procedure of the i-vector-based LID approach.  
The second baseline is an up-to-date LID approach employing CNN. As this approach uses two different DNN structures to operate consecutive procedures (feature extraction and classification), it can be regarded as an end-to-end LID approach. Figure 9 shows the configuration of layers for this operation. A total of 64 packs of 13-dimensional MFCCs extracted from each speech frame enter into an input layer as initial data, consisting of a (\(64\times 13\))-matrix. Then, the input data pass through three convolutional and max pooling layers sequentially. The results of each convolution operation are multiplied by the weights of the corresponding convolution filter. Then, the dimension of the weighted values is reduced by a pooling layer. Among representative pooling ways including max pooling and average pooling, we use the max pooling approach to reflect only the highly highlighted value. After passing the pooling layer, dropout is subsequently performed to exclude some data for reducing overfitting. While selecting hyperparameters, we empirically set the optimal filter size as (\(2\times 2\)) and changed the number of filters from 64 to 256, maintaining the dropout rate to 0.5.  
Figure 8: Procedure of the conventional i-vector-based LID approach.  
Figure 7: Tendency of distribution of rhythm metric values for Chinese, Spanish, and English.  
Next, outputs of the convolutional layers enter into a fully connected layer that connects every node in one layer to all nodes in another layer. Three fully connected layers are followed by a final layer called'softmax' in which a language is determined as an LID result by normalizing the final output values based on a softmax function. While learning CNN-based LID models, we empirically set other hyperparameters: the batch size of input features as 700, learning rate as 0.001, and the number of epochs as 400. The entire learning process was executed under the Pytorch framework [34].