esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 4 Proposed Methodology
### Stream-2: Sensor Stream Methodology--Bi-LSTM Integration with Channel Attention (CA) Model
#### 4.5.1 Bi-LSTM and Channel Attention Integration  
The data are first processed by a Bi-LSTM layer. Bi-LSTM, a type of recurrent neural network (RNN), is designed to capture both past and future temporal dependencies in time-series data, making it more effective than unidirectional LSTMs for handling sequences with long-term dependencies. The Bi-LSTM processes the input data from both the past and future, providing a richer context for more accurate predictions at each time step [65; 66; 67; 68; 69].  
The output from the Bi-LSTM layer is considered Feature-1. This feature is then passed through a CA mechanism, which selectively refines the feature representations by highlighting important channels and suppressing irrelevant ones. This attention mechanism helps the model focus on the most significant parts of the data, improving the overall feature extraction process. After applying CA, the refined feature is passed through a linear layer, followed by ReLU activation and another linear layer. A sigmoid activation function is applied to this layer to produce a final scaled output. To further enhance model performance, we incorporate a residual (skip) connection by multiplying Feature-1 with the output from the sigmoid layer. This step helps retain important information from earlier layers, improving the model's ability to capture relevant patterns. The final result is passed through a linear layer followed by a Softmax activation function to output the classification probability.
## 5 Application to Image Contour Closure  
We investigated the effect of segmentation using different superpixels in image contour closure applications. Superpixel is a preprocessing step to reduce the complexity of subsequent image tasks. [PERSON] et al. [6] proposed an energy framework to search for a subset of superpixels to obtain the contour of strong edge support in an image. The energy framework proposed by [PERSON] et al. was used to compare six superpixel algorithms. The energy framework was applied to the Weizmann Segmentation Evaluation Database (WSED) [35], which consists of 100 images with human-annotated ground truth segmentations for each image. The average F-measure, precision, and recall are summarized in Table 3. Figure 11 also shows the qualitative results of the four examples. Both quantitative and qualitative results show that the proposed method has good foreground segmentation performance.  
Figure 11: Visual comparison of contour closure results on four examples in Weizmann Segmentation Evaluation Database (WSED). The superpixel boundaries are shown in green, and the closure contours are shown in red.
# Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
## 4 Numerical Simulation
### Simulation Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{S_{T1}}\) & \(\mathbf{S_{T2}}\) & \(\mathbf{S_{T3}}\) & \(\mathbf{S_{T4}}\) \\ \hline Position/km & 0.6181 & 0.6108 & 0.7004 & 0.7072 \\ Velocity/(m/s) & 0.4487 & 0.4197 & 0.4594 & 0.4514 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean RMSE of the Monte Carlo simulation. Figure 6: Time history of the RMSEs. the observations and the targets are matched. The proposed method is successfully applied into solving an angle-only four-spacecraft tracking problem. The simulated scenario is specifically designed to increase the difficulty of the date association, with the orbits of the four targets crossing with each other. Simulations show that all the targets are well tracked. The data association accuracy of the proposed method is higher than 99.99%, meaning that the proposed second-order analytical solution is accurate enough to predict the measurement. The mean one-step run time is only 25.25 ms. The position and velocity estimated errors are around 0.6 km and 0.4 m/s when the standard deviation of the measurements is 0.01\({}^{\circ}\). Furthermore, as the proposed second-order analytical solution can provide precise prediction, the targets are correctly identified and the orbits are accurately estimated even after the one-hour invisible arcs. In the future, the multi-spacecraft tracking problem considering an unknown maneuver can be further studied. Moreover, the initial orbit determination method for the multi-spacecraft scenario can be investigated. Conceptualization, T.Q. and S.W. ; methodology and software, X.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Defense Basic Scientific Research Project (Grant No. JCKY2020903B002) and the National Natural Science Foundation of China (Grant No. 51827806). Not applicable. The authors thank [PERSON] for the helpful guide. The authors declare no conflict of interest.
## 5 Diagnostic Methods and Techniques
### Computer Aided Diagnosis
#### 5.3.1 Computational Fluid Dynamics  
CFD achieves high temporal and spatial resolution of flow field parameters by solving the Navier-Stokes (NS) equations over a user-defined fluid domain. A typical CFD simulation pipeline consists of reconstructing a 3D geometric model from medical images, generating a mesh, and setting the simulation parameters. CFD can be utilized to analyze the hemodynamic characteristics of the LA and LAA in AF patients [14; 16; 17; 128], simulate pre- and post-operative conditions [18; 127; 129; 130] (see Figure 3), investigate the mechanisms behind thrombus formation in the LAA [16; 39; 124], and identify new metrics for stroke risk assessment [14; 16; 18]. The indexes in the latter part of Table 3 were derived from CFD simulations. As a modern tool, CFD provides high temporal and spatial resolution, comprehensive coverage, and accuracy.  
However, the limitations of CFD must also be acknowledged. Firstly, different CFD simulation studies often employ varying assumptions regarding models and computational settings, such as boundary conditions, initial conditions, and mesh resolution. These differences lack reliable clinical validation, complicating the assessment of their impact on simulation results [18; 39; 127]. Currently, there is no consensus on the most appropriate CFD simulation parameters and pipeline for the LA and LAA. Commonly used inlet boundary conditions typically include the velocities, pressures, and flow rates of the four pulmonary veins, often sourced from assumed values, clinical measurements, or flow balance calculations [127]. Similarly, typical outlet boundary conditions involve velocities, pressures, and flow rates at the mitral valve, which are also derived from fixed values or clinical measurements [127]. [PERSON] et al. [39] compared the effects of rigid versus flexible wall assumptions, pulmonary vein boundary conditions, and laminar versus Large Eddy Simulation (LES) models on simulation outcomes, concluding that laminar flow and rigid wall assumptions are considered reliable.  
Secondly, the high cost of CFD calculations poses a significant challenge to CFD's development. Due to resource limitations, CFD studies are often restricted to small patient cohorts. This limitation, combined with a lack of reliable clinical validation, hampers  
Figure 3: The CFD results of LA/LAA (streamlines of blood flow during a representative cardiac cycle in the posterior view) for two patients (case 1 and case 2) with AF history [131].  
the widespread application of CFD methods in clinical settings. Nonetheless, the rapid advancement of CFD technology remains notable; for instance, the HeartFlow software (Version: Heartflow One; Website: [[https://www.heartflow.com/](https://www.heartflow.com/)]([https://www.heartflow.com/](https://www.heartflow.com/))) for assessing coronary artery disease has received approval from the U.S. Food and Drug Administration [132], demonstrating the potential for CFD tools in clinical applications.
## 2 Materials and Methods
### Regional Patch Extraction and Classification Using ResNet Model  
A sliding window mechanism was adopted to extract regional patches of varying size (\(10\times 10\times 10\times 20\times 20\times 20\),...,60\(\times 60\times 60\)) from MRI, and each patch was assigned the label of its central voxel, i.e., a positive label was assigned to it if the central voxel belonged to WML, and vice versa. A deep ResNet architecture was trained to assign WML and normal label to each patch. ResNet is an advanced artificial neural network, which mimics the working principal of cerebral cortex in the brain. ResNet utilizes several skip connections in between the adjacent layers. These skip connections between layers add the outputs from previous layers to the outputs of stacked layers. This results in the ability to train much deeper networks than what was previously possible. More detail on ResNet can be found in the pioneering study [30].  
We used a modified version of ResNet with architecture of 50 convolutional layers (\(3\times 3\) kernels, stride of 1, no padding), a max pooling layer with a stride of 2 after every two convolutional layers, followed by a fully connected layer of 512 nodes with a ReLu and a dropout rate of 70%. The network was optimized using stochastic gradient descent optimization method with binary cross-entropy loss. A differential learning rate starting at \(5\times 10^{-5}\) with cosine annealing was adopted in the beginning; the network kept on varying the learning rate until convergence was achieved or the validation loss began to increase.
## 3 Results and Discussion
### Biochar Characterization
#### 3.1.2 Elemental Analysis and Molar Ratios  
The proximate analysis, ultimate composition, and molar ratios of biochars samples are presented in Table 2, and confirmed with the EDS elemental composition which is illustrated in Table 3. The ultimate and proximate analyses of the date palm feedstock are also given in Table S1 in the Supplementary Materials. The yield of produced biochar decreased with increased pyrolysis temperature from 43% for BC300 and reached 32% for BC700. It was found that the carbon content and ash content of all biochar samples increased with increasing pyrolysis temperature, whereas the elemental composition (H, N, and O) decreased with increased carbonization temperature. However, these trends are expected and known in the literature, and occur as a result of the accelerated and intensified degree carbonization with increasing of pyrolysis temperature [18; 19; 20]. This higher pyrolysis temperature induce more cleavage and cracking of the functional groups of within the biomass structure that consists of hydrogen, oxygen, and nitrogen [24]. Also, the moisture content of biochar samples was found to decrease with increased pyrolysis temperature. These results are consistent with those obtained by other researchers who used the same type of biomass feedstock for the production of biochar [25]. On the other hand, elemental molar ratios, i.e., H/C, O/C, and (O+N)/C, were found to sharply decrease with increasing of charring temperature. In general, the decrease in the (H/C) ratio means less original organic matter, such as polymeric CH\({}_{2}\) and fatty acid, lignin, and some cellulose is preserved, and more biomass has been pyrolysed. While the declining trend of the (O/C) ratio suggests that the surfaces of biochars produced at high temperatures become less hydrophilic. Also, the decrease in the ((O+N)/C) ratio, which is known as the polarity index, indicates a reduction of the biochars surface polar functional groups with increased pyrolytic temperatures [26]. The H/C ratio of biochar samples is more specifically related to the degree of aromaticity, while the (O/C) and (O+N)/C ratios are related to hydrophilicity and polarity, respectively. These molar ratios are known to be very important factors, and can play a major role in the interface compatibility between the biochar surface and the polymer matrixes (filler-matrix interactions). Figure 1: Fourier-transform infrared spectroscopy (FTIR) of biochar samples.
# Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare
## 4 Results and Performance Evaluation
### Performance by Estimation of Missing Values  
Several missing value estimation methods exist, including collaborative filtering (CF), regression model (RM), k-nearest neighbor (KNN), and deep learning (DL) [10; 31]. The missing value estimation method proposed in this study uses RNN-based multimodal deep learning (RMD). CF, RM, and KNN are widely used as traditional prediction methods and are commonly used in data mining. A large amount of data are required to train a deep learning model, and in the real world, data analysts or companies mainly use traditional methods before data are obtained for this situation. These methods were compared in terms of accuracy and turnaround time. The healthcare data are the test data selected by the mean-based representative value selection method. To assess the accuracy, the RMSE metric was applied to evaluate the degree of error. To assess the turnaround time, the mean of the time taken from the input time to the output time was evaluated. Table 6 shows the RMSE and turnaround time of each missing value estimation method. In this table, bs is blood sugar, hr is heart rate, sb is systolic blood pressure, db is diastolic blood pressure, ns is the number of steps, tm is the average temperature, It is the lowest temperature, ht is the highest temperature, and hu is the average relative humidity. The turnaround time is the time taken from the data input to the end of the operation, and to the calculation of an output value [4]. According to the evaluation, the proposed RMD-based method had the lowest error overall. Nevertheless, RMD requires a great number of operations because of the large number of weights, and therefore it consumes a high turnaround time.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 2 Signal Representations
### Fixed Signal Transformations  
Most neural network architectures applied for ASC require multi-dimensional input data (compare Section 5). The most commonly used time-frequency transformations are the short-time Fourier transform (STFT), the Mel spectrogram, and the wavelet spectrogram. The Mel spectrogram is based on a non-linear frequency scale motivated by human auditory perception and provides a more compact spectral representation of sounds compared to the STFT. ASC algorithms process only the magnitude of the Fourier transform while the phase is discarded.  
Wavelets can be computed in a one-step [11; 12] or cascaded fashion [13] to decompose time-domain signals into a set of basis function coefficients. The deep scattering spectrum [13] decomposes a signal using a sequential cascade of wavelet decompositions and modulation operations. The scalogram [14; 15] uses multiple parallel wavelet filters with logarithmically spaced support and bandwidth to provide invariance to both time-warping and local signal translations. Time-averaged statistics based on computer vision algorithms like local binary patterns (LPB) or histogram of oriented gradients (HOG) can be used to summarize such two-dimensional signal transformations [16]. In addition to basic time-frequency transformations, perceptually-motivated signal representations are used as input to deep neural networks. Such representations for instance characterize the distribution (e.g., Mel-frequency cepstral coefficients (MFCC) [17], sub-band power distribution [18], and gammatone frequency cepstral coefficients [19]) and modulation of the spectral energy (e.g., amplitude modulation bank features [20] and temporal energy variation [21]). Feature learning techniques based on hand-crafted audio features and traditional classification algorithms such as support vector machines (SVM) have been shown to underperform deep learning based ASC algorithms [22; 23].  
High-dimensional feature representations are often redundant and can lead to model overfitting. Therefore, before being processed by the neural network, the feature space dimensionality can be further reduced: One approach is to aggregate sub-bands of spectrograms using local binary pattern (LBP) histograms [24] or sub-band power distribution (SBD) features [18]. A second approach is to map features to a randomized low-dimensional feature space as proposed by [PERSON] et al. [25].  
The best performing ASC algorithms from the recent DCASE challenges used almost exclusively spectrogram representations based on logarithmic frequency spacing and logarithmic magnitude scaling such as log Mel spectrograms as the network input. Occasionally, end-to-end learning based on raw waveforms was used.
## 3 Case Study--Description of Variants
### Variant 1  
The first variant replaced the planned 1000 kW compression water chiller CWCh1 with a dual-stage absorption water chiller with equivalent cooling power. It used deionized water as a refrigerant and a lithium bromide (LiBr) water solution as the absorbent. The refrigerant, i.e., water vapor, flowed from evaporator to the absorber due to the pressure difference. The resulting decrease of lithium bromide concentration necessitated the regeneration of absorbent, which was directed to the generator. A schematic of the proposed variant of system retrofitting is shown in Figure 3. The source was equipped with a dual-stage 1000 kW AWCh paired with an open-type cooling tower. The technical characteristics of the setup described in this variant are given in Table 1.  
Figure 3: Schematic of chilled water system retrofitting Variant 1.  
Figure 2: Schematic of a basic solution for chilled water system retrofitting—Variant 0.  
The implementation of Variant 1 required: (i) the development of a steam and condensate network from the steam CHP plant to the absorption water chiller room adjacent to the production building; (ii) the development of a steam connection station to the absorption water chiller with an automatic regulation system; (iii) the development of a service water network connecting the water chiller with the open cooling tower; and (iv) the development of a network supplying the cooling water volume in the system, along with a treatment station.  
Other elements of the system were in line with the accepted basic variant of modernization for a chilled water system. A schematic of the connections of the 1.0 MW dual-stage absorption water chiller is shown in Figure 4[28].  
\begin{table}
\begin{tabular}{l l l} \hline Cooling power & [kW] & 1000 \\ \hline Generator power & [kW] & 850 \\ \hline Refrigerant & [-] & H\({}_{2}\)O \\ \hline Absorbent & [-] & LiBr-H\({}_{2}\)O \\ \hline Concentration of absorbent strong solution & [\%] & 62.8 \\ \hline Concentration of absorbent weak solution & [\%] & 55.3 \\ \hline EER & [-] & 1.19 \\ \hline SEER (including auxiliary energy consumption) & [-] & 1.09 \\ \hline External cooling system power & [kW] & 1850 \\ \hline Chilled water temperature & [\({}^{\circ}\)C] & 7/15 \\ \hline Pressure of refrigerant in evaporator (absolute) & [MPa] & 0.00083 \\ \hline Temperature of refrigerant in evaporator & [\({}^{\circ}\)C] & 4.2 \\ \hline Chilled water volumetric flow & [m\({}^{3}\)/h] & 123.0 \\ \hline Steam pressure in the generator (gauge) & [MPa] & 0.90 \\ \hline Steam mass flow in the generator & [Mg/h] & 1.24 \\ \hline Cooling water temperature—absorber and condenser & [\({}^{\circ}\)C] & 29.0/35.0 \\ \hline Cooling water volumetric flow—absorber and condenser & [m\({}^{3}\)/h] & 267.0 \\ \hline Pressure of refrigerant in condenser (absolute) & [MPa] & 0.0066 \\ \hline Temperature of refrigerant in condenser & [\({}^{\circ}\)C] & 38.0 \\ \hline \end{tabular}
\end{table}
Table 1: Technical data of AWCh for Variant 1–1.0 MW water chiller.  
Figure 4: Schematic of dual-stage absorption water chiller connections—Variant 1.
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 3 System Model and Problem Statement
### Related Parameters  
The parameters used to represent the model shown in Figure 1 include data parameters, transmission parameters, and bandwidth parameters. Data parameters include data size \(S\) and data tolerance delay \(\tau^{\prime}\) measured by MB and ms, respectively. Transmission parameters, including transmission delay \(\widetilde{\tau}_{i,j}\) (in ms) and forwarding delay \(\widehat{\tau}_{i}\) (in ms), indicate the delay of the data stream from one node to another and the delay of data stream forwarding on the node. The bandwidth parameters include the bandwidth of the cloud \(\mathcal{B}\) (in MB/s), bandwidth between the edge node and the cloud \(B_{i,C}\) (in MB/s), and bandwidth between  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline \begin{tabular}{c} **Optimization** \\ **Objective** \\ \end{tabular} & \begin{tabular}{c} **Reference** \\ \end{tabular} & \begin{tabular}{c} **Strategy** \\ \end{tabular} & \begin{tabular}{c} **Processing Node** \\ **End** \\ \end{tabular} & \
\begin{tabular}{c} **Cloud** \\ \end{tabular} &
\begin{tabular}{c} **Offloading Type** \\ \end{tabular} \\ \hline \multirow{3}{*}{Throughput} & [PERSON] et al. [36] & Iterative optimization & \(\surd\) & \(\surd\) & & Joint scheduling \\  & [PERSON] et al. [37] & trust-region-dogle search algorithm & \(\surd\) & & WiFi offloading \\  & [PERSON] et al. [38] & Iterative optimization & \(\surd\) & & \(\surd\) & Communication scheduling \\ \hline Data loss & [PERSON] [39] & Heuristic & \(\surd\) & & \(\surd\) & Fault tolerant \\ \hline QoS & [PERSON] et al. [35] & Game theory & \(\surd\) & & QoS \\ \hline WFO & Our proposed & Heuristic & \(\surd\) & \(\surd\) & Content delivery \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: An illustration of the data offloading scenario integrated edge computing and cloud computing nodes.  
edge nodes \(B_{i,j}\) (in MB/s). These important notations used throughout this article are listed in Table 2.
## 1 Introduction
The LoRa Alliance has defined the higher layers and network architecture known as LoRaWAN, with the medium access control protocol (MAC) layer essentially being an ALOHA variant of random access. Generally, a massive number of LoRa devices may be connected to a GW because of the vast coverage area factor, which creates a near-far fairness problem due to the heavy route loss as the bottleneck in overall system performance in LoRa networks. Moreover, LoRa networks also suffer from collisions of concurrent transmissions on the same channel and SF in closely packed deployment situations. In this regard, the conventional protocol for pure ALOHA in IoT LoRa networks is proven to be oversimplified because it fails to account for channel fading, power control, and aggregate interference [12]. To overcome this issue in LoRa, previous studies of [13; 14; 15] proposed multihop communication schemes using relay devices and a programmed e-node that acted as a range extender to increase the reliability of data forwarding from LoRa devices with duty-cycle constraints to a GW in IoT LoRa networks. In this case, class C devices acted as relay devices, intercepting data transmissions from LoRa devices by overhearing and relaying them to the GW. Furthermore, each relay device alternated between overhearing the end devices' transmissions during the receive window and forwarding the contents of the overheard packets to the GW on a regular basis. However, these studies focused on only increasing coverage extension probability while ignoring fairness in terms of network area success probability.