esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mapping Test Model
### Multi-Channel 1D-CNN Model  
The mapping test model adopts 1D-CNN architecture [33; 34]. CNNs perform exceptionally well in processing sequential data due to their ability to capture temporal information and local correlations within the data [35]. The convolutional operation in CNNs involves scanning the entire input with filters, which allows weight parameters to be shared across different positions. The mapping test model consists of three main components: the input, the filter, and the output. The output is generated by convolving the input sequence with the filter.  
As shown in Figure 7, multiple sets of simulated impact tests were conducted on the simple dummy to test the acceleration signals of the simple dummy's chest and head, which were used as inputs of the 1D-CNN model. The measured load signal is taken as the finite element model boundary condition, and the head acceleration and upper neck force of Hybrid III 50 th FEM are calculated. Optimize the 1D-CNN model filter and bias until the model output responds to match the Hybrid III 50 th FEM. To reduce the number of optimization parameters, a pooling layer is set after the input layer, and the data after maximum pooling participates in the convolution operation. (For pooling formula, see (12), and for convolution formula, see (13).) Then, the error of the output layer with Hybrid III 50 th FEM under the same loading is calculated. When the error is within the acceptable range, the optimal parameters are output. Otherwise, the NAGA-II generates a new parameter sample of the convolution layer:  
\[\left\{\begin{array}{l}y_{ij}^{1}=\underset{u=1}{\overset{w}{\max}}(a_{i,j+ u}^{1})\\ y_{ij}^{2}=\underset{u=1}{\overset{w}{\max}}(a_{i,j+u}^{2})\end{array}\right. \tag{12}\]  
\[\left\{\begin{array}{l}a_{ij}^{3}=\underset{s=1}{\overset{k}{\sum}}(y_{i,j+ s}^{1}\cdot h_{s}^{11}+y_{i,j+s}^{2}\cdot h_{s}^{21})+b_{j}^{1}\\ F_{ij}=\underset{s=1}{\overset{k}{\sum}}(y_{i,j+s}^{1}\cdot h_{s}^{12}+y_{i,j+ s}^{2}\cdot h_{s}^{22})+b_{j}^{2}\\ j=1,2,\ldots,n-k;\ s=1,2,\ldots,k\end{array}\right. \tag{13}\]  
\(a_{ij}^{1}\) represents the \(j\)-th data point of the chest acceleration input of the model in the \(i\)-th group of experiments, \(a_{ij}^{2}\) represents the \(j\)-th data point of the head acceleration input of the model in the \(i\)-th group of experiments, \(a_{ij}^{3}\) represents the \(j\)-th data point of the head acceleration output of the model in the \(i\)-th group, \(F_{ij}\) represents the \(j\)-th data point of the axial upper neck force output of the model in the \(i\)-th group of experiments, \(n\) is the length of the data, \(w\) is the size of the pooled window, \(k\) is the length of the filter, \(h_{s}^{tv}\) represents the \(s\)-th data point of filter in the \(t\) input channel and the \(v\) output channel, and \(b_{j}^{v}\) represents the \(j\)-th data point of bias in the v output channel.
## 3 Mathematical Formulation
### Second Stage: Joint Restoration Scheme
#### 3.2.3 Time-Space Network Constraints  
The TSN model is used to represent the movement of fleet vehicles along the grid during a disaster [10]. The binary variable \(G_{f,e^{\prime},t}\) is one if the vehicle \(f\) is on an arc from FEVCS \(e\) to \(e^{\prime}\) during time \(t\). Constraint (25) models undergo movement during a period of time. Equation (26) models the relationship between the state of a vehicle and the progression of time. Equation (27) defines the initial position of the vehicles.  
\[G_{f,e^{\prime},t}=1,\ \forall f\in S_{F},t\in T \tag{25}\]\[\sum_{(m,e^{\prime})\in K^{m}_{m}}G_{f,ee^{\prime},t}=\sum_{(m,e^{\prime})\in K^{m}_ {m}}G_{f,ee^{\prime},t+1},\;\forall f\in S_{F},e\in S_{EVCS}\cup S_{V},t\in T \tag{26}\]  
\[\sum_{(m,e^{\prime})\in K^{\pm}_{m}}G_{f,ee^{\prime},t}=G^{0}_{f,e^{\prime}},\; \forall f\in S_{F},e\in S_{EVCS}\cup S_{V} \tag{27}\]
# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
## 2 Materials and Methods  
### Orchard Robotic Vehicle Design  
#### 2.1.1 Hardware Integration of the Orchard Robotic Vehicle  
Utilized in complex orchards, our robot features a tracked chassis, an industrial computer, GNSS antennas (T1 Receiver), a receiver (FRII-D-Plus-INS), servo motors, a motor driver (KYDBL4875-2E), LiDAR (RS-LiDAR-16), and a wireless radio (HX-DU1601D). The chassis has a 0.32 m track gauge and a 1.2 m track length. The T1 receiver, enabling RTCM3.3 and centimeter-level RTK positioning, provides full RTCM differential data. Equipped with a high-performance MEMS inertial device, the FRII-D-Plus-INS receiver supports differential positioning with under 1.5 cm accuracy. The HX-DU1601D boasts a robust design and supports features such as full-duplex communication, channel viewing, and power adjustment. It enables users to set frequencies between 410 and 470 MHZ, fitting for varied outdoor scenarios. The RS-LiDAR-16, employing hybrid solid-state LiDAR technology, includes 16 laser transceivers. It offers a maximum range of 150 m, precision within \(\pm\)2 cm, a capacity of 300,000 points per second, 360\({}^{\circ}\) horizontal angles, and \(-\)15\({}^{\circ}\) to 15\({}^{\circ}\) vertical angles. The RS-LiDAR-16 projects high-frequency lasers through 16 rotating emitters to scan the environment continually, yielding 3D point cloud data and reflectivity via its ranging algorithms. The overall hardware assembly composition of the robotic vehicle is shown in Figure 1.
# Detection of Sensitive Data to Counter Global Terrorism
## 2 Research Methodology
### Term Frequency-Inverse Document Frequency (TFIDF)  
After the successful implementation of Lemmatization and StopWord Remover, we had an array of terms corresponding to the document. The next challenge was to compute the frequencies of the terms within the document, as well as in the entire corpus so that the importance of the words can be found. For this, we implemented Term Frequency-Inverse Document Frequency. The term frequency counts the number of terms and defines the weight of the terms in the document. The mathematical derivation for the calculation of the term frequency is as follows:  
Term frequency is used to extract features from raw data. It is as follows:  
\[tf(t,d)=\sum_{i\in d}f(i,t) \tag{1}\]  
where \(f\) (\(i,t\)) is a function which is defined as  
\[f(i,t)=\begin{cases}1&if\ i=t\\ 0,&Otherwise\end{cases} \tag{2}\]  
The mathematical representation of inverse document frequency is as follows:  
\[idf(t)=\log\frac{|D|}{1+|[d:t\in d]|} \tag{3}\]where \(|\{d:t\in d\}|\) represents the term \(t\) which is present in document \(d\) when \(\textit{tf}(t,d)\
eq 0\), and \(1\) is added to the formula to avoid zero division.  
The _tf-idf_ combines term frequency and inverse document frequency and its mathematical derivation is as follows:  
\[tf-idf\ (t)=tf(t,d)\ x\ idf(t) \tag{4}\]  
The CountVectorizer algorithm selects top-frequency words to form vocabulary and generates a sparse representation for the documents over the vocabulary [53]. CountVectorizer is chosen against HashingTF, as HashingTF is dependent on hashing of terms, and collision may occur between terms while hashing. Vocabulary size is limited, with numTerms being variable. In our work, we implemented CountVectorizer API to find the term frequencies and pseudocodes, which are presented in Appendix E, to describe the steps to find the term frequencies. For the generation of a sparse representation of the document and to calculate TF-IDF, the snippets of codes are presented in Appendix E. IDF is an estimator which takes the vectorized counts produced by the CountVectorizer (TF algorithm used) and gives a weighting to the terms which are frequently used in the document.
## 2 Materials and Methods
### Datasets
Earlier studies have indicated that time series of greenness-related GPP, vegetation indices (e.g., NDVI, EVI), and water-related vegetation indices (LSWI) can be used to identify the green-up phase (from the beginning of leaf budding to the completion of full leaf expansion) and the senescence/leaf-drop phase at the canopy level ([PERSON] _et al._, 2019; [PERSON] _et al._, 2019). In this study, GPP, NDVI, EVI, and LSWI threshold values (GPP \(\geqslant\) 1 g\(\cdot\) C\(\cdot\) m\({}^{-2}\). day\({}^{-1}\), NDVI \(\geqslant\) 0.3, EVI \(\geqslant\) 0.2, LSWI \(\geqslant\)\(-\)0.1) were used to delineate vegetation phenology ([PERSON] _et al._, 2019; [PERSON] _et al._, 2019). The above three vegetation indices calculated from the reflectance of spectral bands have been proven to effectively monitor vegetation phenology. Furthermore, the time series of NDVI, EVI, and LSWI provided valuable insight into the processes (e.g., growing season length and water condition) that regulate ecosystem carbon exchange. Therefore, it is important to select these vegetation indices and to evaluate their biophysical and phenological performance in wetland ecosystems for more accurate GPP prediction.
# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 4 Experimental Result
### Vehicle Re-Identification Based on Image Features  
In this chapter, we conducted experiments using the Xi'an Ring Expressway Re-identification Dataset, which was established on the Xi'an Ring Expressway in Shaanxi Province. The purpose of these experiments was to evaluate the robustness of the improved vehicle re-identification model built in this paper.  
In the dataset, we collected continuous video footage captured at different time intervals along various mileposts on the expressway. The selected segments have no entrances or exits. A total of 100 vehicles were captured from different perspectives, with milepost locations including K32 + 900, K33 + 700, and K34 + 600 for the upstream direction; and K51 + 610, K51 + 400, K51 + 000, and K50 + 300 for the downstream direction. An example is illustrated in Figure 11.  
The comparative algorithms selected for evaluation included models that performed well on the VeRi-776 public dataset, namely: the StRDAN model utilizing attribute-based methods [32], the VAnet model based on viewpoints [33], the PRND model based on vehicle components [34], the SAVER model using GANs [35], and the HPGN model based on graph networks [36]. The Cumulative Matching Characteristic (CMC) curves of the experimental results are shown in Figure 12.  
From the results depicted in Figure 12, it is evident that the vehicle re-identification model designed in this chapter, which is based on improved attribute information, outperforms the models utilizing different methods for vehicle re-identification. Furthermore, the experimental results indicate that the vehicle re-identification model based on attribute information exhibits robust performance, making it suitable for the task of re-identifying vehicles across different cameras on the Xi'an Ring Expressway.  
Figure 11: Schematic diagram of vehicle behavior trajectory.
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 4 Experiment
### Experiment Description  
#### 4.1.1 Data and Computation Source  
A total of 500 original images were taken by the camera (Hikvision DS-IPC-B12V2-1), and the original image size was 1920 \(\times\) 1080 pixels. First, the original image was cut manually. Then, the number of images in the data set increased to 1500 through horizontal flipping, vertical flipping, and expansion. Finally, the labelme software marked the surface ice and icicles to generate a mask map. Manually labelled images were used as a benchmark to evaluate accuracy. The method was divided into two stages. In the first stage, 1500 preprocessed images were divided into training sets and test sets according to 82. The training set of the second stage included 1500 mask images of U-Net, divided into training sets according to 82, as shown in Table 3.  
Figure 12: The stay cable icing recognition framework.  
The hardware configuration used in this research for training and testing is as follows: AMD Ryzen7 5800 H (3.20 GHz), 16 GB memory, NVIDIA GeForce RTX3060, CUDA11.6, and Pytorch1.2.0. In order to avoid the impact of hyperparameters on the experimental results, the hyperparameters of each network were configured uniformly. After trial and error, the hyperparameters were determined: learning rate \(1\times 10^{-4}\), number of epochs 300, number of iterations in each epoch 90, batch size 5, and optimizer Adam.
## 4 Properties
### Biodegradation Properties
#### 4.2.3 Factors Influencing Hydrolysis
However, different and sometimes contradictory results on the effect of nanoparticles on AP degradation have been reported in the literature. These apparently contradictory results are in our opinion due to the fact that in the analysis of the nanocomposite degradation not only the presence of the nanofillers has to be considered, but additional key points are the chemical properties of the nanofillers, their surface properties (that affect the interface adhesion with the polymer), and the final polymer nanocomposite properties. Some papers reported that nanofillers have an accelerating effect on enzymatic degradation, mainly in nanocomposites based on layered silicate, since hydroxyl groups, which could catalyze the polyester hydrolysis, are present on the edges of the clay. However, other works report that the addition of nanofillers delays the enzymatic degradation of polyesters, because the nanoparticles have high surface volume and act as nucleating agents, thus increasing the degree of crystallinity. During degradation, the crystalline domains are less susceptible to attack, hence the rate of nanocomposite degradation becomes lower as compared to neat polyesters. Furthermore, it must be underlined that the addition of nanoparticles increases the barrier properties of the polyesters, thus reducing water penetration [105]. Nanoparticle surface modification, e.g., achieved through the addition of functional groups, that can not only enhance the compatibility between the polymer matrix and filler, but also influence the degradation rate: for example, the introduction of organically modified layered silicates in PLA matrix resulted in a significant improvement of the hydrolysis rate, as due to the presence of hydroxyl groups of the organic modifier and of the terminal hydroxylated edge groups of the silicate [106]. Also the addition of COOH-functionalized SWCNTs to PLGA matrix contributed to accelerate the hydrolysis kinetic of neat PLGA, due to selective interaction at the interface between nanotubes and polymeric matrix [107]. On the other hand, pristine SWCNTs did not have any effect on the degradation profile of PLGA [107]. Last, but not least, enzyme-filler interactions can play a role in determining the degradation rate. [PERSON] et al. reported on the formation of SWCNTs--proteinase K conjugates that resulted in high enzyme loading due to the large surface area and nanometric size of the SWCNTs [108].
## 4 Video Compression Sub-Application Layer Protocol for WVSN
### M-Frame Transmission in Dynamic Periods
#### 4.3.1 Spmf  
This function is employed to find total number of pixels, which will be sent, with applying M-Frame compression method. It is defined as follows:  
\[SPMF=\left(\frac{X\times Y}{64}\right)\times\sum_{i=1}^{N-1}\left(PP_{Mi(i)} \right)\times\left(1-P_{i}\right) \tag{2}\]  
In this equation, \(X\) and \(Y\) are video frame resolution in the columns and rows of video frame respectively. \(PP_{Mi(i)}\) is pixel numbers in priority level (\(i\)) of each M-Frame block, and \(P_{i}\) is probability of dropping data packets with priority level (\(i\)).
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 4 Evaluation
### Experiment I: Direction Recognition  
The purpose of the first experiment was to determine whether a group of voluntary subjects could recognize navigational instructions displayed to their feet.
# Knowledge Embedding Relation Network for Small Data Defect Detection
## 4 Experiments
### Small Training Sets
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Num of Positive Samples**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**5**} & \multicolumn{2}{c}{**10**} & \multicolumn{2}{c}{**15**} & \multicolumn{2}{c}{**20**} & \multicolumn{2}{c}{**25**} \\ \cline{3-10}  & & **AP/\%** & **FP + FN** & **AP/\%** & **FP + FN** & **AP/\%** & **FP + FN** & **AP/\%** & **FP + FN** \\ \hline baseline & \multirow{2}{*}{DAGM} & 85.58 & 43 + 12 & 91.02 & 43 + 5 & 98.09 & 2 + 3 & 99.27 & 2 + 2 & 99.18 & 1 + 2 \\ Our & & 90.12 & 23 + 16 & 96.23 & 3 + 7 & 98.04 & 2 + 3 & 99.65 & 1 + 1 & 99.11 & 1 + 1 \\ \hline baseline & \multirow{2}{*}{KSDD} & 95.82 & 2 + 2 & 97.25 & 2 + 2 & 97.96 & 1 + 2 & 98.74 & 1 + 1 & 99.78 & 0 + 1 \\ Our & & 97.35 & 1 + 2 & 97.84 & 1 + 2 & 99.15 & 1 + 1 & 99.29 & 0 + 1 & 100.00 & 0 + 0 \\ \hline baseline & \multirow{2}{*}{STEEL} & 58.45 & 490 + 25 & 54.27 & 508 + 18 & 64.81 & 334 + 65 & 62.87 & 419 + 56 & 67.04 & 401 + 61 \\ Our & & 63.28 & 467 + 31 & 60.13 & 438 + 53 & 65.57 & 346 + 70 & 68.13 & 349 + 48 & 75.46 & 315 + 83 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: mAP on Three Methods (DAGM, KSDD, STEEL). Figure 5: Smaller training set size results of DAGM, KSDD, Steel. The three figures above are the change curve of map with the number of positive samples, and the three figures below are the corresponding FP + FN.
# A General Framework for Visualization of Sound Collections in Musical Interfaces
## 3 Proposed Framework
### Segmentation  
For music creation applications, it is often convenient to work with short segments (e.g., in the order of a few seconds or shorter). From a creative point of view, shorter sounds afford more flexibility for recombination into new sounds or patterns. From a technical point of view, the meaningful description of short time series using feature vectors is also easier, while longer series may contain diverse events which may be more difficult to summarize. Segmentation is thus an important step, unless the source material is already a collection of sounds with a desired length. A common strategy is segmentation by onset detection [23]. Onset detection techniques are very established in MIR and can be used to detect either percussive events or pitch changes, based on different onset detection functions (ODF). Hence they are mostly indicated for musical signals and sounds similarly containing events or short-term spectral changes.  
A more versatile strategy is to use [PERSON]'s classic novelty algorithm [24]. This algorithm computes a novelty function by convolving a distance matrix obtained from audio descriptors with a two-dimensional checkerboard kernel representing change. Peaks in the novelty function above a user-specified threshold result in segment boundaries. This means that by choosing different input features, [PERSON]'s novelty can be used to detect changes in different aspects of the signal (like in the case of different ODFs for onset detection).  
Figure 1: Block diagram of the framework. Solid lines indicate core components, while dashed lines indicate optional components.  
However, unlike in the onset detection, the size of the kernel can also be adjusted, which means that changes can be detected at different time scales. In this sense, [PERSON]'s novelty can be used as an onset detector but also for segmentation at other time scales.  
Our implementation currently focuses on [PERSON]'s novelty, with a choice of input features between pitch, chroma features and mel frequency cepstral coefficients (MFCC). The main segmentation parameters are then input feature, kernel size, novelty threshold, and minimum slice length.
# Precision Analysis and Design of Rotating Coil Magnetic Measurements System
## 2 Mathematical Modeling
### Mathematical Model of the DC Motor  
This study used a DC motor to rotate a cylinder rod which contained a coil to the control speed at a range of 60-120 rpm. The working principle of the DC motor is that it changes electrical energy into mechanical force that can be controlled by armature control using the adjustment of voltage for armature circuit to control the speed of rotation. The control will modify the voltage given to the armature circuit starting from the motor torque control equation as shown in Equation (8):  
\[J_{A}\dot{\omega}=-b\cdot\omega+K_{T}\cdot i_{a}, \tag{8}\]  
where \(J_{A}\) is the armature inertial (kg\(\cdot\)m\({}^{2}\)), \(\omega\) is the angular velocity of the rotating coil (rad/s), \(b\) is the coefficient of viscous friction (N\(\cdot\)m/(rad/s)), \(K_{T}\) is the motor torque constant (N\(\cdot\)m/A), and \(i_{a}\) is the armature current (A).  
The electromotive force equation is as shown in Equation (9):  
\[V_{M}-V_{b}=L_{A}\frac{di}{dt}+R_{A}\cdot i_{A}, \tag{9}\]  
where \(V_{b}=K_{B}\cdot\omega\), \(K_{B}\) is the motor voltage constant (V/(rad/s)), \(V_{M}\) is the electromotive force of the motor (V), \(L_{A}\) is the armature inductance (H), and \(R_{A}\) is the armature resistance (\(\Omega\)).  
The diagram of the armature cycle is shown in Figure 4.  
Figure 3: Block diagram of the DC motor speed control system.  
Equations (7) and (8) can be written as a mathematical equation of the DC motor in a state-space model as follows:  
\[\left[\begin{array}{c}\dot{\omega}\\ \dot{i}_{A}\end{array}\right]=\left[\begin{array}{cc}-\frac{b}{I_{A}}&\frac{K_ {T}}{I_{A}}\\ -\frac{K_{B}}{L_{A}}&-\frac{R_{A}}{L_{A}}\end{array}\right]\left[\begin{array}[ ]{c}\omega\\ i_{A}\end{array}\right]+\left[\begin{array}{c}0\\ \frac{1}{L_{A}}\end{array}\right]V_{M}, \tag{10}\]  
In practice, the variables of some DC motors cannot be measured directly without knowing the value of such variables used in the controller's design. Identification of the DC motor uses a function in the MATLAB-Simulink program (MATLAB 2020b License No.199467, Suranaree University of Technology), in relation to parameter estimation. The procedure for estimating the value of variables of a DC motor is the response value of the voltage and the speed of rotation of the motor which is obtained from the experiments taken as input data (\(V_{M}\)) and output data (\(\omega\)) in the MATLAB program for comparing the output (\(\hat{\omega}\)) of mathematical model. Then, the PI controller is designed using the MATLAB-Simulink program for signal constraints. By setting the percentage overshoot value, rise time, and steady state, a controller can now work within the required scope.