esa-sceva/satcom-chunk-collection huggingface dataset:
# Miniaturized Antenna Array-Based Novel Metamaterial Technology for Reconfigurable MIMO Systems
## 3 MTM Analysis and Discussion
### Antenna Reconfiguration Study  
The proposed antenna array was designed for reconfigurable MIMO systems based on sub-6 GHz to suit the applications of 5G networks. Therefore, it is very obvious that such systems require antenna terminals of configurable performance [19]. In our design, we introduced four LDR switches to control the antenna performance. The location of positioning the proposed LDR switches was selected to be between the meander line structure and the antenna ground plane. Such a location is considered to reflect out of phase the surface wave, at 90\({}^{\circ}\) phase difference, and to cancel the capacitive part inductively; this realizes the frequency shift through imaginary part consolation [20].  
Figure 9: The obtained S-parameters spectra for the proposed antenna array with and without MTM defects.  
Our target was to control the surface current on the antenna element by controlling the real impedance at a certain point, in which most of the charge accumulation could accrue [20]. It is well known from the literature that such an accumulation of electric chargers happens at the meander lines corners [21]. Therefore, it is very wise to consider these locations for introducing the LDR switches. Nevertheless, the ground plane is a very saturated conductive surface that realizes an excellent phase reflection to obtain the imaginary part consolation for frequency reconfiguration [22]. In such a manner, each antenna element is introduced to four LDR switches to control the antenna performance in terms of frequency bandwidth and gain, as listed in the antenna truth Table 3. It was found from the study that a significant change could accrue when switching scenarios of the proposed LDR terminals change. Such a variation in the antenna response is attributed to the fact of varying the surface current on the antenna path and the inductive/capacitive behaviors to realize a significant bandwidth variation. It is good to mention that the proposed work was conducted only for these cases due to space limitations. For more details, the authors present the variation in the obtained S\({}_{11}\) and S\({}_{12}\) spectra in Figure 12, which shows non-remarkable variation in the antenna mutual coupling. This is because S\({}_{12}\) spectra are usually a measure of coupling between the adjacent antennas that has no response to LDR-switching scenarios. This observation was discussed earlier in [22].  
Figure 10: The obtained S-parameters spectra for the proposed antenna array with changing MTM defects number: (**a**) S\({}_{11}\) and (**b**) S\({}_{12}\) spectra.  
Figure 11: Array performance in terms of (**a**) the obtained TRAC spectra at different signal phase excitations and (**b**) radiation efficiency spectra.  
Figure 12: The obtained S-parameters spectra for the proposed antenna array with changing switching scenarios: (**a**) S\({}_{11}\) and (**b**) S\({}_{12}\) spectra.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 3 Proposed Speech Inpainting Methods and Model Architecture
### Series Prediction LSTM Model in Speech Inpainting  
An LSTM model was constructed, as shown in Figure 2. The left side of the figure shows the proposed speech inpainting process applied in this work, and the right side shows the structure of the LSTM model. Note that only a five-layer LSTM model is depicted here.  
The utilisation of LSTM in speech inpainting was motivated by its exceptional ability to capture complex temporal dependencies in spoken language. For processing and predicting time-series data such as speech signals, the capability of LSTM in modelling long-term temporal relationships, adapting to variable-length contexts, preserving contextual information, and learning hierarchical representations renders it well suited for this work.  
During the speech inpainting process, the original speech signals are first split into multiple windows, labelled as \(window\_i,\forall i\in[1,N]\), where \(N\) is the total number of windows split from the original speech signal, corresponding to the blue blocks in Figure 2. The length of each window is a fixed value of 640 speech samples, corresponding to a 40 ms speech signal when the signal is sampled at 16 kHz.  
Figure 1: LSTM cell structure.  
Figure 2: The inpainting process and neural network structure of the proposed speech inpainting model.  
Subsequently, the split windows are fed into the LSTM model to obtain a series of prediction blocks, labelled as _pre_\(\lambda\)_, where \(\forall\lambda\in[1,N]\), and \(\lambda\) is the number of predicted blocks in the inpainted speech signal, corresponding to the orange blocks in Figure 2. The length of each predicted block is 80 speech samples, corresponding to a 5 ms speech signal at a 16 kHz sampling rate. During each prediction process, the LSTM model predicts and outputs the results according to each input window in turn. After predicting a block, the window will shift 80 speech samples to form a new window of 640 speech samples and then repeat the prediction process. At the end of the inpainting process, all the predicted blocks (the orange-coloured ones in the figure) are concatenated to obtain the resulting inpainted speech signal, as shown at the bottom of the left-hand side of the figure.
## 3 Materials and Methods
### Particle Swarm Optimization Methods (PSO)
In recent times, the PSO algorithm has received a lot of attention due to its several benefits. In addition, it works well for global search and does not depend on the size of design variables. It is easy to set up and only needs a few parameters to be set; it can be easily parallelized for simultaneous processing. The PSO algorithm's strengths are as follows [33]:  
1. It is very resilient and adaptable to many application contexts with minimal change. 2. It is easy to perform parallel computations because the algorithm is the swarm evolutionary algorithm. This gives it a strong ability to be spread out. 3. It can quickly move close to the optimal value. 4. It is simple to hybridize or combine with other algorithms to enhance its performance. The PSO algorithm generates a swarm of particles at random locations to represent possible solutions to the optimization task. In order to achieve the optimal value, the particle positions are repeated until they reach the maximum of a fitness value. Then, the PSO checks the positions of each particle and stores the best solution for each particle. Furthermore, the PSO keeps a record of the best solutions at the end of each iteration. Particle positions and velocities are calculated in each subsequent iteration, using optimal swarm positions, the best position of the particle itself, and previous particle velocities. The computational process is carried out for 25 iterations. Based on the use of this algorithm, 4699 optimal features were obtained from the sum of features of the CCCS-CIC-AndMal-2020 dataset. Figure 5 shows how the particle swarm optimization shapes the optimization for feature selection.
###### Abstract
We therefore implement DQN-EC, which leverages erasure coding to provide optimized routing paths based on deep reinforcement learning to address the throughput, bandwidth, and transmission link cost issues that arise when transferring blocks. Instead of building an accurate mathematical model for the underlying network, DQN-EC solves this problem through the experience of deep reinforcement learning. DQN-EC applies reward values designed for the collection of comprehensive network information for the state, configuring one-to-many networks to select nodes and switches for the next link, and optimizing the network throughput, bandwidth, and transmission costs. As the learning progresses, the agent learns a policy that predicts the future behavior of the underlying network and finally proposes improvements to the routing path between the source and destination nodes. Section 2 describes the basic principles behind the erasure coding-based distributed file system as well as DQN and deep reinforcement learning. Section 3 discusses research examples related to the methodology applied to solve the problem of machine learning (supervised and reinforcement learning)-based network routing paths and the methodology for network optimization of erasure coding. Section 4 discusses the data items used in the proposed DQN-EC learning, the design of neural network models, and the configuration of the hyperparameters [9]. In Section 5, we describe evaluation results showing the improvements when applying DQN-EC to the erasure coding network topology. Finally, some concluding remarks are provided.
# Electrical Stimulation May Improve the Feeding and Nutritional Status of Children with Dysphagia
## 8 Technical Parameters of NMES
NMES effectiveness depends on key biophysical parameters such as waveform type, pulse duration, frequency, electrode placement, and tissue resistance. Proper adjustment of these factors ensures safe and efficient electrical stimulation tailored to clinical needs. In addition to the widely used neuromuscular electrical stimulation (NMES) in the treatment of dysphagia, sensory stimulation using interferential current (IFC) is also applied in clinical practice. Its mechanism of action is primarily based on afferent modulation and sensitization of structures involved in the initiation of the swallowing reflex, rather than on direct muscle strengthening. IFC appears to be potentially beneficial, particularly in the pharyngeal phase of swallowing, where sensory stimulation may enhance reflex \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameter** & **Range/Example** & **Clinical Significance** & **Source** \\ \hline Frequency (Hz) & 20–50 Hz & Higher frequencies produce stronger contractions but lead to faster muscle fatigue & \\ Pulse width (\(\upmu\)s) & 200–400 \(\upmu\)s & Wider pulses activate more motor units, increasing effectiveness & [PERSON], 1992; [PERSON], 2009 [105; 106] \\ \hline Contraction/rest time & 5–10 \(\upmu\)s/10–30 s & Rest periods reduce muscle fatigue and improve patient tolerance & [PERSON] et al., 2005 [107] \\ \hline Intensity (mA) & Individually adjusted & It should be sufficient to elicit a functional contraction without excessive discomfort & [PERSON], 2018 [108] \\ \hline Waveform type & \multirow{2}{*}{Symmetrical/asymmetrical} & Symmetrical impulses are generally more comfortable, especially for superficial muscles & [PERSON] and [PERSON], 2002 [109] \\ \hline Therapy duration & 15–30 min per session & Therapeutic effects are observed with regular sessions of 15–30 min & [PERSON] et al., 2013 [104] \\ \hline Therapy frequency & 3–5 times per week & More frequent sessions enhance the cumulative effect of muscle training & [PERSON], 2018 [108] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: NMES technical parameters in therapy. initiation through facilitation of neural centers [110]. It was shown that interferential electric stimulation applied to the neck increases swallowing frequency [111]. However, there is a lack of reliable clinical data supporting the effectiveness of this method in the oral phase of dysphagia. Unlike NMES, which was confirmed in numerous studies as an effective tool for improving the function of muscles responsible for bolus transport and airway protection, sensory stimulation remains supplementary and experimental in nature [112].
# An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN
## 2 The Proposed Method and Its Theories
### The ANN Structure  
A three-layer artificial neural network (ANN) was applied to conduct fault classification in this approach as shown in Figure 3. The ANN is composed of three layers, input layer, hidden layer, and output layer. The input layer has 8 neurons corresponding to the extracted feature vector involving the comprehensive gradients, \(\beta_{c}\) and \(\beta_{c}\), and the vertical projection vector \(vp\). The composition of the input vector is shown in Table 1. The hidden layer has 15 neurons. The number of hidden neurons is decided by means of Manual optimization in the range of 10 to 20. Since the faultless condition and 7 faulted conditions were seeded, the output layer is a full connected layer with 8 neurons, after that is a softmax function. The softmax function is commonly exploited in output layer in multi-classification problems [19]. Further, scaled conjugate gradient was used to train this network based on cross entropy loss function.
## 3 Design and Development of the Measuring System
### Software Design and Data Algorithm  
#### 3.3.1 Measuring System Software  
The upper computer software is programmed using LabVIEW R2015. The program has the functions of data acquisition, data processing, visual display, and storage. It includes four modules, i.e., self-check, calibration, measurement, and data analysis. Their main functions are briefly introduced as follows:  
(1) Self-inspection module: to check whether the connection between each sensor and the upper computer is successfully established.  
(2) Calibration module: to obtain the distance between each group of sensor probes.  
(3) Measurement module: to get the distance from each sensor probe to the measured surfaces, and calculate the distance between the two surfaces at each measuring point.  
(4) Data analysis module: to display the distribution coordinates of each measuring point, process the obtained data, and calculate the flatness of the tested surface.  
The measurement processing workflow diagram is shown in Figure 10.
## 4 Experiments
### Datasets  
* DTU dataset [38]: This dataset leverages an adjustable industrial robot arm to capture 129 scenes in a laboratory setting. Each scene comprises object views from 64 or 49 different angles under seven distinct lighting conditions, with recorded intrinsic and extrinsic camera parameters. The dataset is partitioned into 79 training scenes, 18 validation scenes, and 22 test scenes. It is noteworthy that we adopt the same dataset partitioning method as CasMVSNet [6].
* Tanks&Temples Dataset [39]: The dataset encompasses 14 indoor and outdoor scenes with varying resolutions. Due to the absence of intrinsic camera parameters in this dataset, we employ OpenMVG [40] (open multiple view geometry) to compute and generate sparse point clouds. Evaluation of the reconstructed point clouds is conducted using an F1 score that combines precision and recall for a comprehensive assessment.
## 3 Result and Discussion
### Preparation of Concentrated Aqueous Solutions from Pre-Solutions  
In the end, the practically useful polymer-dispersant solutions should preferably have a much higher concentration than the solution we have prepared above. To further verify the feasibility of the DEG pre-solution methods for application on coloration materials, we prepared 4.4 and 8.0 wt% BL-05 aqueous solutions, where 0.893 g and 1.786 g of DEG solution of 28 wt% BL-05 were weighed and mixed with 5 mL alkaline solutions in a screw vial under the irradiation of an ultrasonic wave. As a result, although the solution containing 8.0 wt% of BL-05 seemed to be somewhat cloudy, we successfully obtained the molecularly dissolved aqueous solution containing 4.4 and 8.0 wt% of BL-05. It should be further noted that these solutions did not make sediments, and the dominant size-distribution of the dispersed polymers were observed at 10-20 nm by DLS measurements, even for the 8.0 wt% polymer solutions. To the best of our knowledge, this would be the first report of obtaining a solution with as high a concentration as 4.4 and 8 wt% of polymer solutions, where the polymer has quite a low acid-value of 150, further emphasizing a benefit of our proposed methods using the DEG pre-solution. Because studied polymers with low acid-value would have an advantage regarding the loading capacity of hydrophobic molecules such as the pyrene as studied above, these polymers are used as a dispersant even at high pigment concentrations of water-based ink of around 10 wt%. Our proposed methods are beneficial for a polymer dispersant of coloration materials such as those usually used as concentrated pigments. If the dissolved polymer aqueous solution is in equilibrium with the pigment surface, the polymer molecules would be efficiently supplied to the pigment surface even in a concentrated-ink system. A water-based ink is an ink that has pigments in a colloidal suspension in a solvent. Although the main solvent in water-based inks is water, there can also be other co-solvents present. These co-solvents typically are volatile organic compounds (VOCs). The goal of using water-based inks is just to reduce the VOCs that are present. Concentrated aqueous dispersant-solutions prepared from concentrated dispersants dissolved in water-soluble organic solvents meet this goal well due to their low VOC-content. Although there is a need for continuing research into improvement of this, including the optimization of polymers' hydrophobic structures and further control of the dispersion kinetics of DEG in the aqueous phase, this method would extend the variety of polymers applicable for use as a dispersant.  
Figure 8: (**a**) Transparency at 600 nm and (**b**) particle size (number mean) of BL-01, BL-03 and BL-05 alkaline solution with various concentrations in pre-treated solution.
## 2 Results and Discussion  
### Germination Studies  
Imbibition, or the seed's absorption of water, stimulates metabolic activity and the release of chemicals from storage, which causes the embryo to grow and the radicle (or other organs) to penetrate the surrounding tissues [26]. In our study, we observed that the germination rate is significantly affected by temperature. At 30 \({}^{\circ}\)C, seed radicle emergence is accelerated and occurs within 48 h, in contrast to 48-72 h at 25 \({}^{\circ}\)C and \(>\)72 h at 35 \({}^{\circ}\)C (Supplementary Figure S1a). In this case, some of the growth metabolites, including carbohydrates, polyols, and enzyme proteins, may be under the control of temperature. With the rising temperature, a considerable decrease in seedling length was observed (\(p<0.05\)) (Figure 1) (Supplementary Figure S1b). PKM-1, however, exhibited significantly longer shoot lengths at 30 \({}^{\circ}\)C (11.56 cm) than at 25 and 35 \({}^{\circ}\)C (7.93 and 3.21 cm, respectively). In the current study, a temperature of 35 \({}^{\circ}\)C impacted root length. As the temperature rose, moringa seedling leaves increased in mass, accounting for 48.1 and 73.4 percent of the seeding dry weight (Supplementary Figure S1c). Response surface methodology also indicated that 25 \({}^{\circ}\)C and 24 h were the optimal conditions to enhance the accumulation of riboflavin, phenolics, and antioxidant activity of moringa sprouts. In comparison, the total GLS contents were observed at 30 \({}^{\circ}\)C for 96 h, and thiamine achieved the maximum content at 30 \({}^{\circ}\)C for 24 h [27]. Higher temperatures drastically decreased the PKM-1 seedling vigor. The results show that seedling vigor I and II peaked at around 30 \({}^{\circ}\)C (Supplementary Figure S1d,e); in contrast, the seedlings performed poorly at 35 \({}^{\circ}\)C. _M. oleifera_ showed higher growth under field conditions regarding height, spread, leaf number, leaf area, and leaf dry weight at 25 and 30 \({}^{\circ}\)C [28]. The dry weight, root length, and germination index comprise the most important factors to consider when determining temperature tolerance, as these factors determine biomass accumulation [29]. The early seed radicle emergence at 30 \({}^{\circ}\)C may be due to rapid hydrolysis and mobilization of the seed reserve due to enzymatic activities, such as the \(\alpha\)-amylase conversion of the essential reserve constituent starch into glucose units [1]. On the other hand, seeds that are sensitive to low temperatures during the early stage of imbibition have a lower percentage of germination and are predicted to have poor seedling development and lower plant productivity [14]. The current results align with the finding of [PERSON] [20], in which the highest germination rates of _Moringa oleifera_ were observed at temperatures of 30 \({}^{\circ}\)C during both the day and night. At this temperature, the seed may become metabolically active during the germination process, which may be linked to controlling the accumulation of substrates for respiration [19].
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 4 IP Mobility Management in MANET and Comparative Summary
### Proactive Gateway Discovery-Based Mobility Management Solutions
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Features**} & **Mobility** & **MANET** & **Handoff** & **Gateways** & **Evaluation\(\backslash\)Simulation** \\  & & **Scheme** & **Routing** & **Metrics** & **Gateways** & **Evaluation\(\backslash\)Simulation** \\ \hline [PERSON] et al. [35] & No modification to MIP. & & \begin{tabular}{l} AODV- \\ AODV- \\ DSDV \\ \end{tabular} & - & 4 foreign agents & \begin{tabular}{l} Evaluated using NS2, 175 \\ nodes. \\ \end{tabular} \\ \hline Alsaied and & \begin{tabular}{l} No modification to \\ MIP. \\ \end{tabular} & \begin{tabular}{l} MIPv4 \\ \end{tabular} & \begin{tabular}{l} DSDV \\ \end{tabular} & - & \
\begin{tabular}{l} 3 routers \\ covering 4 fixed \\ foreign agents. \\ \end{tabular} &
\begin{tabular}{l} Evaluated using NS2 - 1, \\ 5, 10, 15, 20 and 25 nodes. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Proactive gateway discovery-based mobility management solutions. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Scheme** & **Features** & **Mobility Scheme** & **MANET Routing** & **Handoff Metrics** & **Gateways** & **Evaluation\(\backslash\)Simulation** \\ \hline [PERSON] et al. [37] & Uses route update message to propagate gateway existence. Gateway acts as foreign agent & MIPsv4 & Eff- DSDV & Gateway sends De-registration message to HA when the route entry to a node is lost. & Evaluated using NCTUns simulator, 20 nodes, compared to [48]. \\ \hline MIPAD [40] & Divides network into service subnets, each is covered by an access router. A mobile node with high mobility not register with a new router, stays with old router to exchange signaling. & Registration cost with HA and FA, hop counts, residence time, traffic and packet arrival rate. & Registration cost with HA and FA, access router and 1 mobile node moving between the service regions, compared to Hierarchical MIP. \\ \hline [PERSON] [41] & DSR routing message is piggybacked with MIF signaling message. Designs express