esa-sceva/satcom-chunk-collection huggingface dataset:
# Producing Synthetic Dataset for Human Fall Detection in AR/VR Environments
## 3 Proposed Digital Human Falling Dataset Generation Pipeline
### Physical Modeling  
We used a skeleton containing the 22 main bones of the body and legs and 30 bones of the fingers, which has a standard tree structure. Figure 1b,c shows the anatomy of 3D scanned people models corresponding to the hierarchy of skeletal bones. It is outlined that the human skeleton has the freedom degrees (DOF) for each joint--red 6 DOF, yellow 3 DOF, green 2 DOF, blue 1 DOF. The physical model of a digital human is based on a hinge system of primitives with corresponding constraints for each joint.  
As shown in Figure 1b, certain bones have their own DOF. Below in the experiments, we will discuss the process of a person's fall as closely as possible. Such imitations are possible using a system based on physical capsules corresponding to the skeleton hierarchy [47]. Figure 1c shows a schematic diagram of the physical model. It is a person modelled as an articulated rigid body system consisting of primitives such as capsules. Each bone is assigned a capsule with the appropriate weight and simulation parameter value.  
The physical model for simulating the rigid body dynamics of a human model based on capsules was used. The idea is based on the movement simulation of the rigid body, which takes into account the applied forces and moments. However, we used local constraint, where the obtainable angular transformation is limited at each stage by the upper and lower limits [48]. This minimizes and avoids unrealistic joint displacements during physical simulation.
## 1 Introduction
### Dosimetry Formalism  
According to TRS-398, absorbed dose to water (\(D_{w}\)) at a reference beam quality can be determined using Equation (1):  
\[D_{w,Q}=M_{Q}N_{D,w,Q_{o}}k_{Q,Q_{o}} \tag{1}\]  
where \(N_{D,w,Q_{o}}\) is the absorbed dose to water calibration factor and \(k_{Q,Q_{o}}\) is the beam quality correction factor that accounts for the differences between beam quality \(Q_{o}\) (from a reference calibration laboratory) and beam quality \(Q\) (from a user hospital). Field measurements were performed at a user hospital. \(M_{Q}\) refers to corrected hospital measurements. The uncorrected hospital measurements, \(M_{raw}\), are corrected using Equation (2):  
\[M_{Q}=M_{raw}k_{TP}K_{elec}k_{pol}k_{s} \tag{2}\]  
Here, \(k_{TP}\) corrects for the nonreference ambient temperature and pressure during hospital measurements. Moreover, \(K_{elec}\) is the electrometer calibration factor, \(k_{pol}\) is the polarity correction factor, and \(k_{s}\) is the ion recombination correction factor. Using the TG-51 protocol, \(D_{w,Q}\), at reference depth dref, is determined using Equation (3):  
\[D_{w}^{Q}=Mk_{Q}D_{D,w}^{60\text{C}o} \tag{3}\]  
Here, M refers to corrected hospital measurements and k\({}_{\text{Q}}\) is the beam correction factor that translates \(N_{D,w,Q_{o}}\) for a \({}^{60\text{C}o}\) beam into \(N_{D,w,Q}\) for the user beam quality \(Q\). As in the German protocol DIN 6800-2 [5], the absorbed dose to water, Dw (P\({}_{\text{eff}}\)), is determined using Equation (4):  
\[\text{D}_{w}(\text{P}_{\text{eff}})=\text{kNM} \tag{4}\]  
Here, k is the product of all factors that account for the nonreference influence quantities that affect the measurement results. The effective point of measurement (P\({}_{\text{eff}}\)) approach shifts the chamber axis toward the radiation source by approximately 0.5 r\({}_{\text{cyl}}\) (radius of a cylindrical chamber).
## 3 Results  
### Laboratory Test Results  
The stiffness modulus was estimated based on the PN-EN 12697-26:2018-08 test [49], considering the previously described assumptions. The applied strain amplitude was 50 \(\mu\varepsilon\). The peak-to-peak value was 100 \(\mu\varepsilon\). The stiffness modulus was established in the 100-test cycle. The results were recorded in real-time using the Testlab software. An exemplary result was presented in Figure 6.  
Based on the test results, a degradation index was developed. It expresses a change in the stiffness modulus caused by an appropriate impact, such as water, frost, or road salt. The ratio made it easier to analyze asphalt mixture degradation caused by water, frost, and road salt impact. The formula was presented in the Equation (2).  
\[DI_{(S)}=\left(\frac{E_{0,2}^{*}}{E_{0,1}^{*}}-1\right)\cdot 100\% \tag{2}\]  
where:  
\(DI_{(S)}\)--degradation ratio expressed by the change in the stiffness modulus of the mixture [%],  
\(E_{0,1}^{*}\) --stiffness modulus value before the corresponding impact (fresh) [MPa],  
\(E_{0,2}^{*}\) --stiffness modulus value after the corresponding impact (degradated) [MPa].  
Test results due to the excessive data amount are presented and discussed as an average of at least 5 markings (independently tested beams) per each mix recipe. The  
Figure 6: Sample result registered by testlab software.  
dispersion of the results (expressed by the classical coefficient of variation), considering the specificity of the method used (the four-point bending test (4-PB)), was very low--it did not exceed 5%. Data was featured in Table 3.  
The following symbols are used in Table 3:  
ref--reference set,  
f-t--set impacted by water and frost,  
rs--set impacted by road salt,  
\(V_{b}\)--percentage of binder content added to MMA (by weight) [%],  
\(V_{m}\)--air void content in MMA [%],  
\(E^{*}_{0,1}\)--stiffness modulus value before the corresponding impact (fresh) [MPa],  
\(E^{*}_{0,2}\)--stiffness modulus value after the corresponding impact (degradated) [MPa],  
\(DI_{(S)}\)--degradation ratio expressed by the change in the stiffness modulus of the mixture [%].  
Four-point bending (4-PB-PR) test results (Table 3) expose the degradation of various asphalt mixtures caused by freeze-thaw and road salt impact. Only the binder type and production technology differentiated the mixes. At first glance, the material appeared to have a significant environmental impact. Results were discussed in a further part of the manuscript.
## 4 Discussion and Conclusions  
### Discussion  
(1) The analysis results of acoustic emission energy and two-dimensional spectrum show that the step method can realize the step-by-step release of rockburst energy and achieve a good rockburst control effect. The method is in the form of \"rupture bands\" or \"logarithmic spirals\" to resist rockbursts.  
(2) There are two disadvantages of this method. First, the step method can control rockbursts at the cost of expanding the plastic zone, which may cause safety hazards such as roof fall, collapse and sliding instability, or lead to more serious secondary disasters. Second, the method increases the buried depth of rockburst resistance to less than 1350 m. In view of this, supporting measures will be adopted to carry out explosion-proof support design, and relevant experimental studies will be further carried out to meet the needs of site safety construction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Signal Proportion (\%)**} \\ \cline{2-5}
**Stages** & \begin{tabular}{c} **Low** \\ **Frequency** \\ **(100–200 kHz)** \\ \end{tabular} & \begin{tabular}{c} **Intermediate** \\ **Frequency** \\ **(250–350 kHz)** \\ \end{tabular} & \
\begin{tabular}{c} **High** \\ **Frequency** \\ **(450–600 kHz)** \\ \end{tabular} &
\begin{tabular}{c} **Ultra High** \\ **Frequency** \\ **(600–700 kHz)** \\ \end{tabular} \\ \hline I & 19.33 & 67.54 & 9.43 & 0.00 \\ II & 18.60 & 65.12 & 4.65 & 0.00 \\ III & 33.65 & 56.09 & 4.53 & 5.97 \\ IV & 41.38 & 54.17 & 1.43 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main frequency diagram in the whole process of lower step specimen.
# User Experience of 5G Video Services in Indonesia: Predictions Based on a Structural Equation Model
## 4 Analysis
### The Results
#### 4.1.1 Correlation Value Analysis of \(\alpha\) and \(\beta\)
Through this value, it appears that stimulus is in the form of a new device with new technology, in this case, the 5G technology that provides a change in experience rather than an increase in features. Factor #6: Novelty  
A new thing, especially a new technology, will of course have an influence, whatever its form. This is also true of a technology that is applied to run video streaming. However, it appears that the novelty of proven 4G technology can still provide a significant value with a measure of \(\alpha_{6}=0.5\), while as a new technology that is highly anticipated, 5G provides a higher significance value of \(\beta_{6}=0.62\). Factor #7: Satisfaction  
A user of a feature, especially a feature related to information and entertainment such as video streaming, experiences a value of satisfaction with the features used, either in terms of the technology or the content contained in the features. Video streaming with 4G technology currently has a significance value of \(\alpha_{7}=0.72\) compared to the value of \(\beta_{7}=0.67\) that is a prediction of satisfaction with 5G technology. This is a logical outcome because 4G technology that has proven its ability will appear superior to technology that has not proved itself. Factor #8: Price  
Price-related factors describe the willingness of users to pay more for a technology when there is a novelty about it. The effect of price on 4G technology gives a significance value of \(\alpha_{8}=0.27\), while the value for 5G is \(\beta_{8}=0.5\). This shows that users in Indonesia are more willing to pay for a new technology that is more favourable and influential than the technology that they currently enjoy. Factor #9: Social  
The possibility of a social estrangement factor due to focusing on a feature provided by a technology is an assessment of social aspects due to video streaming. The significance values of \(\alpha_{9}=0.16\) in 4G technology and \(\beta_{9}=0.23\) in 5G technology illustrate that, according to video streaming users, social estrangement due to a technology is possible. However, it does not have a big impact as the significance value is still below a quarter of the scale. Factor #10: Economy  
In addition to satisfaction, price, and social aspects, an additional factor to assess for the significance of its effect on video streaming UX with 4G and 5G technology is the economic aspect. The economic aspect here includes the influence of video streaming as a medium for promotion, online shopping, and other independent economic activities. The significance values of \(\alpha_{10}=0.47\) for 4G and \(\beta_{10}=0.52\) for 5G indicate that there is a fairly optimistic economic potential, especially when using newer technologies.
## 3 Principles of Signal Transmission and Processing
### Analysis of Transmission Rate and Transmission Bandwidth  
Based on the above orthogonality principle, the minimum frequency interval between frequencies is \(1/T_{s}\). The emission frequency of the mercury lamp exhibits high flatness within the 40 to 60 kHz range, encompassing a bandwidth of 20 kHz. When the symbol rate is \(R\), adhering to the relationship \(T_{s}=1/R\), the maximum number of carrier frequencies within this band is limited to \(20k/R\). Therefore, the width of each symbol, denoted as \(W\), which represents the mean number of bits, can be expressed as  
\[W=\log_{2}\biggl{(}\frac{20k}{R}\biggr{)} \tag{11}\]  
The system transmission rate, denoted as \(S\), can be represented as  
\[S=R\times W=R\times\log_{2}\biggl{(}\frac{20k}{R}\biggr{)} \tag{12}\]  
Figure 3 illustrates the correlation between symbol rate and transmission rate concerning bandwidths of 20 kHz and 40 kHz. The red circles and blue triangles signify admissible values of \(R\) for integer symbol widths. It is observed that within a 20 kHz bandwidth, the maximal achievable transmission rate stands at 10 kbps, while a bandwidth of 40 kHz  
supports a maximum rate of 20 kbps. Larger symbol widths entail heightened computational demands for decoding and may induce waveform distortions during modulation, potentially augmenting the likelihood of decoding inaccuracies. To ensure the stability of signal transmission, critical parameters have been judiciously determined. Specifically, the communication system parameters encompass a symbol rate of 2.5 ksps alongside a symbol width of 2 bits, thereby yielding an effective transmission rate of 5 kbps. Concurrently, the research team has meticulously selected carrier frequencies set at 40 kHz, 45 kHz, 50 kHz, and 55 kHz to serve as the pivotal components of the system's communication infrastructure.
# Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users'
## 2 Theoretical Background and Research Hypotheses
### Effects of the Space Type on Self-Disclosure to IoT-CAs
#### 2.2.2 Self-Awareness and Self-Disclosure  
The space in which IoT-CA users are located may invoke changes in their self-awareness (either private or public), which could be used to predict their self-disclosure. Self-awareness describes one's state of attention and can be directed either inward to various aspects of the self (private) or outward to consider external things (public) [32; 33; 34]. Heightened private self-awareness is associated with increased attention to physical or emotional states [35] and results in increased self-disclosure [36]. Similarly, [PERSON] [37] argued that heightened private self-awareness and a lowered feeling of the social presence of other users heighten one's willingness to disclose personal information and identities in online interpersonal communication. Therefore, people are more willing to disclose more personal information to others when they are more focused on themselves (combined with a lower awareness of outside factors). In a living space, people may focus foremost on themselves, followed by outside factors. In the workspace, people primarily work under pressure and scrutiny and need to pay attention to the social context. Previous studies have shown that making participants identifiable by an audience [38] or accountable for their own behavior can enhance public self-awareness [38; 39; 40]. Thus, users experience lower public awareness in the living space compared with the workspace, where users may experience higher public awareness, which results in more self-disclosure. Hence, we have the following final hypothesis:  
**Hypothesis 3**.: _Self-awareness mediates the effect of the space on the self-disclosure of users to IoT-CAs._
# Classification of EEG Signals for Prediction of Epileptic Seizures
## 6 Classification  
For classification, we propose LSTM, a version of a recurrent neural network [79]. After concatenating statistics and CNN features, the combined feature set is transformed to a sequence length of 50 and fed into an LSTM for classification. The LSTM has many gates, including a forget gate and input gates, for storing and forgetting prior cell information. Forget (\(f(t)\)), input (\(i(t)\)), and previous LSTM layer (\(H_{t-1}\)) weights [80], plus cell states and new weights are computed as follows:  
\[f(t)=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{12}\]  
\[i(t)=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{13}\]  
\[\hat{C}(t)=tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{14}\]  
\[C(t)=f_{t}*C_{t-1}+i_{t}*\hat{C}_{t} \tag{15}\]  
\[o(t)=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{16}\]  
\[h_{t}=o_{t}*tanh(C_{t}) \tag{17}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Layer Name** & **Output Shape** & **Parameters** \\ \hline conv2d (Conv2D) & (None, 65, 117, 16) & 9216 \\ maxpooling2d (MaxPooling2D) & (None, 32, 58, 16) & 0 \\ batch-normalization (BatchNormNo) & (None, 32, 58, 16) & 64 \\ conv2d1 (Conv2D) & (None, 32, 58, 32) & 4640 \\ maxpooling2d1 (MaxPooling2) & (None, 31, 57, 32) & 0 \\ batch-normalization1 (Batch No) & (None, 31, 57, 32) & 128 \\ conv2d2 (Conv2D) & (None, 31, 57, 64) & 18,496 \\ maxpooling2d2 (MaxPooling2) & (None, 30, 56, 64) & 0 \\ flatten (Flatten) & (None, 107,520) & 0 \\ dense (Dense) & (None, 256) & 27,525,376 \\ dense1 (Dense) & (None, 1) & 257 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model summary of convolutional neural network designed to extract features.  
Figure 4: Proposed CNN.  
The suggested method uses an LSTM with 256 neurons at the input and 02 neuron at the output to classify preictal state and interictal state EEG patterns. For classification using LSTM, the proposed approach includes 775,682 trainable parameters.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Simulation Parameters  
This section presents the main configuration parameters used in the developed simulation model.
## 3 Proposed Analog RF Beamforming
### Source Analog Precoder
\begin{bmatrix}e^{iX_{1i}}\\ e^{iX_{2i}}\\ \vdots\\ e^{iX_{N_{t}}}\end{bmatrix} . \tag{24}\]  
Algebraic manipulations after substituting \(\textbf{F}_{opt}(\cdot;i)\) and \(\textbf{V}_{RF}(\cdot;i)\) in \(f_{i},\ i\in\big{\{}1,2,\ldots,N_{t}^{RF}\big{\}}\) transforms it into another useful form that helps in finding the phase-shift values that lead to the function at its local minimum. The final simplified form of \(f_{i}\) can be expressed as  
\[f_{i}=\sum_{m=1}^{N_{t}}\Big{\{}1+r_{mi}^{2}-2r_{mi}\cos(X_{mi}-\theta_{mi}) \Big{\}}. \tag{25}\]  
It is clear from (25) that \(f_{i}\) attains minimum when \(X_{mi}=\theta_{mi},\forall i,m\).
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### Feature Fusion and Classification Head  
After processing independently, the global average pooled features from the U-Net decoder are concatenated with the transformer outputs to form a detailed feature vector that captures local spatial and global contextual information. The outputs of the U-Net model are 2D local feature maps. The use of GlobalAveragePooling2D computes the average of all the values in each feature map (channel) across both the height and width dimensions. Essentially, it reduces each 2D feature map (spatial grid) to a single scalar value, representing the average of all the values in that feature map. The output is a 2D tensor of shape (batch_size, channels), where each channel has a single average value computed from its corresponding 2D spatial grid. On the other hand, the Transformer outputs are 1D global feature sequences. The use of GlobalAveragePooling1D computes the average of all the values along the sequential dimension, resulting in a single value for each channel. The output is a 2D tensor of shape (batch_size, channels), where each channel contains the average of the values along the steps dimension. The fusion of the local features from U-Net and the global features from Transformer is then concatenated into single feature vectors, which are used for the classification layer.  
This combined vector is passed through fully connected layers with dropout regularization and ReLU activation, leading to a final dense layer that yields logits for binary classification. A fully connected layer featuring 64 units and ReLU activation is applied to this integrated feature vector. To avoid overfitting, dropout regularization is incorporated with a dropout rate of 0.2. Ultimately, the final output is produced by a dense layer with two units (representing binary classification) and no activation function, intended for use with a binary cross-entropy loss function. Figure 8 presents this feature fusion and classification head module.  
Feature fusion concatenates features from the U-Net and Transformer branches:  
\[F\_fused=[F\_U-Net+F\_Transformer] \tag{7}\]  
where _F_U-Net_ and _F_Transformer_ are the features from the U-Net and Transformer branches, respectively. The classification head computes the final output:  
\[y=softmax(W\_class\ *\ F\_fused+b\_class) \tag{8}\]  
where _W_class_ is the weight matrix for classification, and _b_class_ is the bias term for classification.
## 4 Discussion
### Lipid Production in Nitrogen-Limited Media
When the nitrogen dose was increased, with the phosphorus dose unchanged (M2), yeast growth was improved, but the efficiency of the biosynthesis of lipids was significantly reduced. The decreased nitrogen-to-phosphorus ratio (N/P = 3.8:1) in M3 medium, compared to M2 medium (N/P = 4.3:1), resulted in half the biomass yield with 32% cellular lipid accumulation efficiency. Under different conditions, when the reduced nitrogen rate was accompanied by increased cell access to phosphorus sources in the culture medium (M4--N/P = 1:1), growth needs were met, resulting in one of the best biomass yields of 7.45 g\({}_{\mathrm{d.m. }}\)/dm3 with satisfactory SCO production efficiency. It can be summarized that phosphorus is needed by cells for growth, but in this context nitrogen is crucial. Nevertheless, a greater addition of phosphorus source to a nitrogen-deficient medium may lead to the accumulation of higher storage lipid yields without negative effects on growth caused by reduced access to nitrogen. Both bioreactor experiments and preliminary studies emphasized the role of phosphorus as a substrate component in metabolic processes related to the basic metabolism of the yeast cell and SCO biosynthesis. Still, the role of phosphorus in combination with the issue of media supplementation with a nitrogen source needs further studies. Using a C/N ratio of 30:1 led to an increase in saturated fatty acid production and a decrease in polyunsaturated fatty acids in _Y. lipolytica_ yeast cells. The application of additional phosphorus limitation (C/P = 1043:1) in the medium did not alter the fatty acid group content of total lipid acids observed previously. Similar to the current experiment, total lipid content increased under limiting conditions, but did not exceed 30%. The highest biomass yield (over 7 g\({}_{\text{d.m. }}\)/dm\({}^{3}\)) was achieved for culture in medium with C/N = 30:1, without limiting phosphorus source (C/P = 6:1) [33]. Microbial oil accumulated in lipid bodies consists mainly of TAG and steryl-esters in smaller amounts (neutral fractions), which are enclosed by polar fractions, e.g., phospholipids, glycolipids, and sphingolipids. Generally, the growth of oleaginous microorganisms may be divided into two phases. When cells grow in media rich in all the nutrients necessary for growth, biomass, also free of lipids, is produced. During this phase, lipids with a higher proportion of polar fractions, corresponding to cell membrane lipids, are accumulated. In a situation of limited access to sources such as nitrogen, phosphorus or sulfate, the lipid accumulation phase is stimulated, mainly of neutral fractions [40].
## 2 Preliminaries
### Error Propagation Formula  
Let \(Y=\{y_{1},y_{2},\ldots,y_{n}\}\) be a set of random variables and \(Z=f(y_{1},y_{2},\ldots,y_{n}),y_{i}\in Y\) be a random function. Assume that \(\xi_{y_{i}}^{2}\) is the random error of the variable \(y_{i}\), then the random error of \(z\) is  
\[\xi_{z}^{2}=\sum_{i=1}^{n}\biggl{(}\frac{\partial f}{\partial y_{i}}\biggr{)}^ {2}\xi_{y_{i}}^{2}+2\sum_{1\leq i\leq j\leq n}\frac{\partial f}{\partial y_{i}} \frac{\partial f}{\partial y_{j}}\zeta_{ij}\zeta_{y_{i}}\zeta_{y_{j}}, \tag{8}\]  
where \(\zeta_{ij}\) is a correlation coefficient of the variable \(y_{i}\).  
Specifically, the random error \(\xi_{z}^{2}\) is mutual independent when \(\varrho_{ij}=0\), for all \(i,j=1,2,\ldots,n\), then Equation (8) reduces to the following form [26; 30]:  
\[\xi_{z}^{2}=\sum_{i=1}^{n}\biggl{(}\frac{\partial f}{\partial y_{i}}\biggr{)}^ {2}\xi_{y_{i}}^{2}. \tag{9}\]Using the error ranges \(\Delta y_{i}(i=1,2,\ldots,n)\) to replace the standard random errors \(\xi_{y_{i}}(i=1,2,\ldots,n)\), Equation (9) can be rewritten as the following famous error propagation formula  
\[(\Delta z)^{2}=\sum_{i=1}^{n}\biggl{(}\frac{\partial f}{\partial y_{i}}\biggr{)} ^{2}(\Delta y_{i})^{2} \tag{10}\]