esa-sceva/satcom-chunk-collection huggingface dataset:
# CRC-Based Reliable WiFi Backscatter Communication for Supply Chain Management
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tag Data Length \(N\) (Bit)** & **Brute-Force Search \(T_{b}\) (s)** & **CRC Reverse Algorithm \(T_{c}\) (s)** \\ \hline
4 & 0.0026 & 0.0104 \\
6 & 0.0049 & 0.0095 \\
8 & 0.0194 & 0.0103 \\
10 & 0.0586 & 0.0110 \\
12 & 0.224 & 0.0118 \\
14 & 0.896 & 0.0099 \\
16 & 3.529 & 0.0111 \\
18 & 38.707 & 0.0097 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of two decoding methods. Figure 8: Comparison of the decoding BER between tag data with four duplicate parity bits and tag data without check bits. (**a**) shows the relationship between BER and SNR, and (**b**) shows the relationship between BER and tag data length. Adding redundancy check bits can significantly reduce the decoding BER of the CRCScatter system. scheme will facilitate product quality inspection, cargo classification, and other operations in supply chain management. With the improvement of throughput, low-power video communication will also be available.
## 2 Materials and Methods
### Bi-LSTM Classifier Model  
After establishing the unsupervised feature learning model, the classifier autonomously learns discriminative features to classify the pre-ictal and inter-ictal data. Recurrent neural networks (RNNs) differ from other types of neural networks as they can maintain the state between different sequential inputs and can independently process a set of time series data. A complete RNN consists of the same feedforward neural network (RNN cells), and each step corresponds to a time.  
LSTM is a special type of RNN used to overcome the problems of gradient vanishing and information distortion during neural network training. It can easily learn long-term dependencies. LSTM introduces the concept of memory cells with control gates. LSTM not only maintains gradient values during training but also preserves the temporal dependencies between inputs. Figure 6 illustrates the structure of a single LSTM cell.  
In this context, \(x_{t}\) represents the input at time \(t\), while \(f_{t}\), \(\widetilde{c}_{t}\), and \(o_{t}\) represent the forget gate, input gate, and output gate, respectively. \(h_{t-1}\) and \(c_{t-1}\) are the shadow and cell states from the previous step. \(h_{t}\) and \(c_{t}\) represent the next state transferred to the next cell.  
An LSTM block consists of one or more LSTM cells, and each cell processes a time sequence continuously in one direction. At a particular time point, the LSTM block only calculates new state information based on the previous state and current input and outputs it to the next LSTM block until the last input is processed and the output is computed, completing the task. This type of LSTM network is called unidirectional LSTMs.  
The Bi-LSTM network is an optimized version based on the LSTM. In the Bi-LSTM, a single layer consists of two LSTM blocks that process two sequences of inputs in opposite directions, as shown in Figure 7. The results of the two LSTM blocks in a single layer are combined to compute the final output for the task at hand. The Bi-LSTM takes into account  
Figure 5: Network structure of the unsupervised feature learning model (WGAN-GP).  
the temporal dependency of the input data, which makes it promising for achieving higher classification results.  
Figure 8 illustrates the construction of the classifier module: the classification network flattens the features extracted by the feature extractor and connects them to a fully connected layer. To reduce the computational complexity of the classification network, the feature vector is mapped to a 36-dimensional space. Furthermore, a Bi-LSTM layer is added to extract temporal features, followed by two additional fully connected layers for further feature extraction. The former uses a sigmoid activation function with an output size of 256, while the latter uses a soft-max activation function with an output size of 2.  
Figure 8: Network structure of the classification module ((**A**): feature extractor after unsupervised training; (**B**): classifier).  
Figure 6: Structure of a single LSTM cell.  
Figure 7: Diagram illustrating the principle of Bi-LSTM.
## 4 Proposed Multi-Feedback Interference Cancellation
### Comparison of Soft Interference Cancellation and MAP Detection  
To show the differences between the proposed MBMF-SIC, the MAP and the conventional soft ICI cancellation algorithm, the LLRs of different methods are shown below. For the MAP detection algorithm, the LLR of the \(i\)-th particular bit \(s_{k}(i)\) can be found as:  
\[\text{L}(s_{k}(i))=\ln\frac{\sum_{\mathbf{s}_{k}\in\mathbb{S}_{i+}}\text{Pr}( \mathbf{s}_{k}|\mathbf{y}_{k})}{\sum_{\mathbf{s}_{k}\in\mathbb{S}_{i-}}\text{ Pr}(\mathbf{s}_{k}|\mathbf{y}_{k})}, \tag{21}\]  
where \(\mathbb{S}_{i^{\pm}}\) denotes the set of different symbol vector combinations \(\mathbf{s}_{k}\) with \(s_{k}(i)=\pm 1\). For soft interference cancellation based on Equation (10), the LLR can be evaluated by:  
\[\text{L}(s_{k}(i))=\ln\frac{\sum_{s_{k}\in\mathbb{C}_{i+}}\text{Pr}(s_{k}| \mathbf{\tilde{y}}_{k})}{\sum_{s_{k}\in\mathbb{C}_{i-}}\text{Pr}(s_{k}| \mathbf{\tilde{y}}_{k})}, \tag{22}\]  
where \(\mathbb{C}_{i^{\pm}}\) denotes the set of different symbol combinations \(s_{k}\) with \(s_{k}(i)=\pm 1\). Given Equations (21) and (22), Equation (18) can be rewritten in the form of an LLR as:  
\[\text{L}(s_{k}(i))\approx\ln\frac{\sum_{\mathbf{s}_{k}\in\mathbb{S}_{k}}\sum _{s_{k}\in\mathbb{C}_{i+}}\text{Pr}(s_{k}|\mathbf{\tilde{y}}_{k})\text{Pr}( \mathbf{\tilde{y}}_{k}|\mathbf{s}_{\bar{k}})\text{Pr}(\mathbf{s}_{\bar{k}})}{ \sum_{\mathbf{s}_{\bar{k}}\in\mathbb{S}_{\bar{k}}}\sum_{s_{k}\in\mathbb{C}_{i- }}\text{Pr}(s_{k}|\mathbf{\tilde{y}}_{k})\text{Pr}(\mathbf{\tilde{y}}_{k}| \mathbf{s}_{\bar{k}})\text{Pr}(\mathbf{s}_{\bar{k}})}, \tag{23}\]  
where \(\mathbb{S}_{\bar{k}}\) denotes the set of symbol vector combinations \(\mathbf{s}_{k}\) omitting the symbol \(s_{k}\), the vector \(\mathbf{\tilde{y}}_{k}\) denotes the received signals suppressing the interference from \(\mathbf{s}_{\bar{k}}\) and the vector \(\mathbf{y}_{\bar{k}}\) denotes the received signals canceling the desired symbol \(s_{k}\). Note that the quantity \(B\) actually constrains the set size of \(\mathbb{S}_{\bar{k}}\). However, the probability \(\text{Pr}(\mathbf{\tilde{y}}_{k}|\mathbf{s}_{\bar{k}})\) may not be tractable in the implementation, so \(\text{Pr}(\mathbf{\tilde{y}}_{\bar{k}})\approx\text{Pr}(\mathbf{\tilde{y}}_ {k}|\mathbf{s}_{\bar{k}})\text{Pr}(\mathbf{\tilde{s}}_{\bar{k}})\) is assumed. Hence, Equation (23) can be considered as another way of implementing the conventional MAP detection in two steps. The first step performs MAP detection for the interference symbols \(\mathbf{s}_{\bar{k}}\), and the last step performs interference cancellation for one particular bit \(s_{k}(i)\).
## 2 Results
### Structural Analysis of EPS  
The analysis of the EPS structure involved pullulanase hydrolysis, TLC, and FTIR. Pullulanase specifically hydrolyzes pullulan to produce maltorios [14]. After purification, the purified EPS, when hydrolyzed by pullulanase, released a significantly higher quantity of reducing sugars, increasing by 110.7-fold compared with the control. In contrast, the reducing sugars from a commercial pullulan increased by 3.83-fold (Figure 3a). Moreover, the migration heights of components in hydrolysates from the purified EPS and commercial pullulan on a TLC plate matched that of maltoriose (1%) (Figure 3b), affirming the presence of maltoriose in the purified EPS. These results indicate that the EPS produced by strain ZH27 was pullulan.  
Further confirmation of the structure and elemental composition of the purified EPS was obtained via FTIR analysis. The FTIR spectrum of the purified EPS closely resembled that of a commercial pullulan (Figure 3c). The absorption peak at 3423.03 cm\({}^{-1}\) for the purified EPS was close to that (3396.03 cm\({}^{-1}\)) of commercial pullulan (Table S2), signifying the presence of the stretching vibration of hydroxyl groups (-OH) in the EPS [2]. The signal  
Figure 3: Structural identification of the purified EPS using hydrolysis, TLC, and Fourier-transform infrared spectroscopy (FTIR). (**a**) Reducing sugars released from the purified EPS and commercial pullulan. CPO and CPW marked with black denote the commercial pullulan (1%) hydrolyzed without and with pullanase, respectively. EPO and EPW marked with red represent the purified EPS (1%) treated without and with pullulanase, respectively. (**b**) Lines 1 and 2 show the purified EPS (1%) treated without and with pullulanase, respectively. Lines 3 and 4 represent glucose (1%) and maltoriose (1%), respectively. Lines 5 and 6 show commercial pullulan (1%) treated with and without pullulanase, respectively. (c) FTIR spectra of purified EPS and commercial pullulan. “**” represents the probability value below 0.01.  
near 2923.60 cm\({}^{-1}\) from both samples was attributed to the stretching vibration of the C-H bonds in methylidyne and methylene of sugar rings [10]. Additionally, the characteristic bonds O-C-O, C-O-C, C-O, and C-O-H in the purified EPS were identified on the basis of absorption peaks near 1637.29 cm\({}^{-1}\), 1159.03 cm\({}^{-1}\), 1025.96 cm\({}^{-1}\), 1427.09 cm\({}^{-1}\), and 1371.16 cm\({}^{-1}\), respectively (Table S2). Notably, the presence of \(\alpha\)-1,6-D-glucosidic linkages, \(\alpha\)-D-glucopyrananose, and \(\alpha\)-1,4-glucosidic linkages was verified by the absorption peaks at 933.38 cm\({}^{-1}\), 852.40 cm\({}^{-1}\), and 755.97 cm\({}^{-1}\), respectively. The functional groups in the EPS closely resembled those in the commercial pullulan, providing additional confirmation that the EPS derived from strain ZH27 was indeed pullulan.
## 4 Related Work
### Simulation-Based Energy Consumption  
[PERSON] et al. performed a simulation-based energy consumption calculation [29]. The objective of this study was to develop an accurate model for estimating the energy consumption of electric vehicles for a given driving cycle. The BMW i3 was chosen as a case study to demonstrate the concept. An advanced vehicle simulation model was developed in MATLAB/Simulink, including the powertrain system and the longitudinal dynamics of the vehicle. The powertrain includes the transmission and battery, using the Thevenin equivalent circuit battery model. The model was validated using available BMW data and other reliable literature sources. The consumption of auxiliaries was only estimated based on average values found in the literature and it was concluded that these could have a significant impact on energy consumption. The error between the simulation results and test data was less than 6% for both the Environmental Protection Agency (EPA) and New European Driving Cycle (NEDC) tests. The authors noted that additional information about the road, such as traffic and weather conditions and driver characteristics, should be added to reduce the prediction error bias. They also predicted more accurate data for the battery model, which has a significant impact on battery efficiency and power consumption. In addition, the inertia of the vehicle's rotating components can be calculated and incorporated into the model to improve accuracy. The preliminary objectives of the aforementioned parties were found to be analogous to our own, yet their methodology diverges from ours. It is our expectation that greater accuracy can be achieved if we not only estimate but also calculate the power consumption of the largest auxiliary loads. This phenomenon may be attributed to the fact that these electrical loads function as dynamic subsystems within the vehicle, and the energy consumption of these devices can vary significantly depending on the specific use case. Nevertheless, our objective is to implement a realistic battery model, as opposed to a simplified Thevenin equivalent circuit.
## 5 Discussion and Future Directions
### Limitations of SCI Device-Based Therapies
#### 5.2.3 Surgical Risk and Anatomical Constraints  
Implantable devices mentioned in this review pose significant surgical challenges. EES and BCI systems require precise electrode placement near spinal lesions or cortical regions, which increases the risk of infection, device migration, and tissue damage. Similarly, SMART implants and circumferential arrays must conform to spinal anatomy, yet long-term biocompatibility and fibrotic encapsulations remain as unresolved issues. There is a further surgical risk when surgically implanting devices, as the patient may reject the implant, causing it to malfunction and creating bigger problems. Patient viability in spinal cord morphology, injury level, and comorbidities complicates device standardisation and surgical planning.
###### Abstract  
Location data collected from mobile devices via global positioning system often lack semantic information and can form sparse trajectories in space and time. This study investigates whether user age groups can be accurately classified solely from such sparse spatial-temporal trajectories. We propose a feature extraction method based on a Gaussian mixture model (GMM), which assigns representative points (RPs) by clustering the location data and aggregating user trajectories into these RPs. We then construct three machine learning (ML) models--support vector classifier (SVC), random forest (RF), and deep neural network (DNN)--using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. In our experiments, we introduced a missing value ratio \(\theta_{\text{th}}\) to quantify trajectory sparsity and analyzed the effect of trajectory sparsity on the classification accuracy and generalizability performance of the ML models. The results indicate that GMM-based features outperform IDNN-based features in both classification accuracy and generalization performance. Notably, the RF model achieved the highest accuracy, whereas the SVC model displayed stable generalizability. As the missing value ratio \(\theta_{\text{th}}\) increases, the IDNN becomes more susceptible to overfitting, whereas the GMM-based approach preserves accuracy and robustness. These findings suggest that sparse trajectories can still offer meaningful classification performance with appropriate feature design and model selection even without semantic information. This approach holds promise for domains where large-scale, sparse trajectory data are common, including urban planning, marketing analysis, and public policy.  
2020 IEEEexample:BSTcontrolcontrol
## 3 Results and Discussion
### Effects on Nitrite and Nitrate Contents  
Cold plasma is an effective source of reactive oxygen and nitrogen species (RONS) [32]. Potential accumulation of nitrites and nitrates in plasma-treated foods may affect food safety. Thus, nitrite and nitrate concentrations in plasma-treated food products should be monitored to ensure acceptable levels [32; 33; 34]. To evaluate the production of nitrate and nitrite following ADCP treatment, changes in their contents before and after ADCP treatment were examined. Nitrate content before and after ADCP treatment showed an increase from 6.31 \(\pm\) 0.21 to 9.31 \(\pm\) 0.20 \(\mu\)g/mL (\(p\) < 0.05), whereas nitrite content changed from 1.51 \(\pm\) 0.13 to 1.40 \(\pm\) 0.07 \(\mu\)g/mL without a significant difference (Table 2). Nitrate may be produced via oxidation of nitrite, and the increase of nitrate in this study is thought to be due to nitrite oxidation induced by the reactive species generated during cold plasma treatment [35; 36]. [PERSON] et al. reported that nitrite in meat batter increased according to the duration of atmospheric pressure plasma treatment [33]. [PERSON] et al. also reported an increase in nitrite when dielectric barrier discharge plasma treatment was applied to pork jerky [34]. According to [PERSON] et al., nitrite produced during cold plasma treatment may lower the pH of liquids [37]. In this study, however, there was no significant difference between nitrite levels before and after ADCP treatment, while the pH of CBCs immediately following the treatment did not show a significant difference either. Thus, these results suggest that nitrite and nitrate contents would not pose a safety threat for CBCs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Microorganisms and Nitrite/Nitrate Concentrations** & **Untreated** & **ADCP-Treated** \\ \hline _Salmonella_ (CFU/cube) & 5.04 \(\pm\) 0.13 \({}^{\text{a}}\) & 3.59 \(\pm\) 0.06 \({}^{\text{b}}\) \\ Indigenous mesophilic bacteria (CFU/cube) & 5.78 \(\pm\) 0.19 \({}^{\text{a}}\) & 5.08 \(\pm\) 0.16 \({}^{\text{b}}\) \\ Tulane virus (PFU/cube) & 3.68 \(\pm\) 0.29 \({}^{\text{a}}\) & 2.60 \(\pm\) 0.34 \({}^{\text{b}}\) \\ Nitrite (\(\mu\)g/mL) & 6.31 \(\pm\) 0.21 \({}^{\text{b}}\) & 9.31 \(\pm\) 0.20 \({}^{\text{a}}\) \\ Nitrate (\(\mu\)g/mL) & 1.51 \(\pm\) 0.13 \({}^{\text{a}}\) & 1.40 \(\pm\) 0.07 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} The ADCP treatment voltage and time were 24 kV and 3 min, respectively; values are meaning mean and standard deviations (n = 4); different letters mean significant differences between data in the same row (\(p<0.05\)).  
\end{table}
Table 2: Effects of in-package atmospheric dielectric barrier discharge cold plasma (ADCP) treatment on the inactivation of _Salmonella_, natural mesophilic aerobic bacteria, and Tulane virus, and the concentrations of nitrite, and nitrate in chicken breast cubes.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### Machine Learning/Deep Learning Based HO Approaches
They developed a mathematical model to evaluate the HO cost, which incorporates latency, signaling overhead, call dropping, etc. In addition to this analytical method, they also proposed data-driven deep learning-based HO prediction algorithms to further enhance the HO cost parameters. Simulation results of MLP and deep-stacked LSTM frameworks show the dominance of LSTM over MLP for handover prediction. The enhanced performance can be due to LSTM's inherent recurring nature, which facilitates a more accurate representation of time series data. The authors in [115] proposed a two-layer approach for HO optimization in an ultra-dense network. Initially, they performed a two-tier operation to form clusters of similar mobility types and get the optimal controller for each cluster by using k-means clustering algorithm. Then they used a model-free asynchronous advantage actor-critic (A3C) reinforcement learning technique to get HO information for each cluster. Lastly, they generate the generalized weights for every cluster on the basis of available information about clusters. In terms of handover rate and throughput, simulated results revealed that the proposed method outperforms the existing techniques. Various studies have been carried out in recent years to improve handover performance in cellular networks through the use of transfer learning, reinforcement learning, deep neural networks, and other techniques. The approaches summarized in Table 9 demonstrate the effectiveness of using machine learning algorithms in improving handover performance by predicting user mobility, optimizing network parameters, and balancing network load. However, more research is needed to further optimize these techniques and find new ways to improve handover performance in 5G HetNets.
## 3 Experimental Investigation
### Experimental Tests  
The testing matrix consists of 27 different experiments: for each of the 3 different bottom geometries (Figure 5c-e), 9 different levels of water depth, from 4 to 12 cm, with an increasing step of 1 cm, were analysed, allowing to investigate the sensibility of the liquid damping to its volume (mass). The bottom shapes were selected in order to compare and analyse results and relative behaviours.  
In the carried experiments, the tanks were excited with an impulsive kinematic input, i.e., a little displacement imposed at the base, to make the inner liquid in free oscillations. In order to analyse the fluid response in a steady-state motion, the initial transient period  
Figure 5: Experimental apparatus. (**a**) Scheme of the tanks dimensions (measurements in mm). (**b**) 3D printed blocks used to change the bottom shape. Images of the tanks with flat (**c**), sloped (**d**) and circular (**e**) bottom shape.  
was not considered. The data, sampled for a total duration of 80 s, were acquired with a fairly high sampling frequency equal to 1 kHz. A postprocessing procedure was then applied, including a detrend function and a low-pass filter with a cutoff frequency of 5 Hz.
# Permeability Enhancement Induced by Fracture Shear Dilation During Close-Range Coal Seam Mining
## 3 Permeability Model of Mining-Induced Fracture  
After coal mining, the overlying strata gradually collapse and form a mining-induced fracture field (Figure 1), included in the goat. After the overlying coal seam mining, there is fracture development both above and under the caving zone. The fractures are subjected to combined shear/tensile stress [38]. The shear and tensile deformation of fractures result in a dramatic change in the permeability, which is related to the change in the fracture aperture. There are two interactive behaviors, including shear dilation aperture, caused by shear slippage and the normal aperture change due to tensile failure [39].  
Based on the fractal characteristics of fracture surfaces, the permeability model for the fractures subject to shear and tension is given [32; 40]:  
\[b=\mathrm{b_{r}}+\Big{(}\mathrm{b_{f}}+\mathrm{A(\delta_{s})^{H}exp}\big{(}- \mathrm{kW_{p}}\big{)}\Big{)}\mathrm{exp}(-\mathrm{\sigma_{n}C_{f}})\]  
\[\frac{\mathrm{k_{f}}}{\mathrm{k_{f}^{0}}}=\frac{\mathrm{(b)}^{3}}{\mathrm{(b_ {0})}^{3}} \tag{13}\]  
where b is the fracture aperture, b\({}_{r}\) is the \"hard\" part of the fracture aperture or the residual fracture aperture that does not change with stress, b\({}_{f}\) is the stress-sensitive part, and C\({}_{f}\) is the fracture compressibility. Where \(\delta_{n}\) is the normal displacement induced by shear, \(\delta s\) is the shear displacement, H is the Hurst coefficient of fracture surfaces which characterizes the fracture roughness, Wp is the plastic work done by shear on the fracture, A and k are two constants, and k0 fi is the initial permeability. This model is based on the two-part Hooke's model (TPHM) proposed by [PERSON] et al. [41]. The permeability model has been validated against experimental data [40]. A comparison between model results and experimental data was performed to validate the proposed model [33]. The complex coupling of damage and shear dilation on gas flow in the process of uniaxial compression has been revealed.  
Figure 1: Schematic diagram of the caving zone of coal mining.  
Numerical results show that the accumulation of damaged elements largely controls the failure of rock, and the location of permeability change is highly related to the damage zone.