esa-sceva/satcom-chunk-collection huggingface dataset:
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Implementation Details
#### 3.2.3 Training Settings  
To accelerate convergence during model training, the Adam optimizer was adopted, with an initial learning rate of 0.001 for model training. A learning rate adjustment strategy combining \"Warmup\" and \"Cosine Annealing with Warm Restarts (CosAWR)\" was designed to improve the convergence speed and stability of model training.  
1. Warmup Stage: In the initial \(\mathit{warmup\_steps}\) epochs of training, we used a linearly increasing learning rate scheduler--warmup_lr--to adjust the learning rate, gradually increasing the learning rate from a value close to zero to the base learning rate \(\mathit{base\_lr}\). The specific formula is \[lr=\mathit{base\_lr}\cdot\min(1.0,\frac{\mathit{step}}{\mathit{warmup\_steps}})\] (8) where \(\mathit{step}\) is the current training step; \(\mathit{warmup\_steps}\) is the total number of steps in the Warmup stage; and \(\mathit{base\_lr}\) is the base learning rate. This strategy helps the model avoid gradient explosion caused by a too-high learning rate in the early training stages and accelerates convergence.
2. Cosine Annealing with Warm Restarts (CosAWR) Stage: After the Warmup stage, we switched to the cosine annealing restart strategy to maintain the model's ability to jump out of local optima by periodically adjusting the learning rate. The CosineAnnealingWarmRestarts scheduler provided by PyTorch can achieve this operation, and its key parameters include the following: T0: The initial cycle length is set to 100 epochs. T\({}_{\text{mult}}\): The multiplication coefficient of each cycle is set to 2. T\({}_{\text{min}}\): The minimum value of the learning rate is set to \(10^{-7}\).  
Whenever a cycle ends, the learning rate gradually decreases to \(\eta_{\text{min}}\) in the form of a cosine function, and then a new cycle restarts, but the function cycle length is multiplied by T\({}_{\text{mult}}\).  
Through the two-stage learning rate strategy, the model can converge quickly in the early training stages and reduce the risk of falling into local optima through periodic learning rate adjustment in the subsequent stages. This strategy substantially enhances the model's training stability and final performance in experimental validations.  
Training cycle: A total of 300 epochs were trained, and an early stopping mechanism was adopted. If the test set performance did not improve for 10 consecutive epochs, the training was stopped.
## 2 Methodology
### Workflow of the State Recognition Method  
The basic flow of the state recognition method is illustrated in Figure 2. This process involves several steps, including signal acquisition, feature extraction and training, real-time recognition, and result output.  
The first step of this methodology involves the installation of triaxial accelerometers on the key loaded structures of the HBM to monitor its critical working states. Subsequently, the signals from the three directions are merged into a single group of signals, and any anomalous signals are filtered. The obtained sensor signals are then partitioned into multiple data segments. During the signal preprocessing stage, various features are extracted from each data segment either in the time-domain, frequency-domain, or time-frequency-domain.  
Following this, a kNN model is constructed, and the feature information is used for training and testing the state recognition classifier. Finally, during the actual operation of HBM, the vibration signal features are collected in real time. Based on the kNN classifier results, the decision tree is utilized to classify and obtain the HBM working state.  
Figure 2: Process of identifying the working states of the HBM.
## 4 Game Model
### Nash Equilibrium Analysis  
_Theorem 1_: For a dynamic routing game \(G\), there are \(n\) nodes participating in the game. Then in \(G\) there exists a pure strategy Nash equilibrium.  
_Proof_: For a dynamic routing game, since the number of participants n is finite, and no matter how large the communication radius of a node is, the number of neighbor nodes is finite within the communication range. Therefore, for the pure strategy space routing game, the strategy of a participant is finite and \(G\) is a finite game. Since in \(G\), we think that every node participating in a current game knows the strategy of previous participating nodes, \(G\) is a perfect information game. Furthermore, we can conclude that there exists at least one pure finite Nash equilibrium in \(G\) and \(G\) is a finite perfect information game. Besides the existence of a Nash equilibrium, we conclude that the relationship among combination optimization transmission reliability grade, energy consumption and network lifetime in a routing game.  
_Theorem 2_: For a dynamic routing game \(G\) that has n nodes participating, the optimal path \(P\)* regarding energy consumption and network lifetime as aims is a Nash equilibrium result of \(G\).  
_Proof_: According to the description of payoff function, in the routing game the benefit of every participant gained has a relationship between combination optimization transmission reliability grade, energy consumption and network lifetime. It means that the higher the optimization, the larger the payoff value for a participant. Apparently, the path \(P\)* is the optimal one for all paths. In path \(P\)*, the nodes have the largest payoff. Here for all the nodes located at \(P\)*, their strategies are optimum. In the case that the strategy of other participants does not change, even if some node located at \(P\)* changes its strategy, the payoff would not augment. It means that the participants located at \(P\)* have no appetite to change their strategies. For the nodes not located at \(P\)*, they do not participate in forwarding packet, thus their payoff is zero. In this case, for all the participants, if they do not change their strategies, the participants not located at \(P\)* cannot join the set of intermediate nodes even though they change their strategy. This is because the node located at \(P\)* would not select the node not located at \(P\)* as next hop. Therefore, the nodes not located at \(P\)* have no appetite to change their strategies. Based on the above description, in the routing game, there are no participants having appetite to change their strategies. Therefore, \(P\)* is a Nash equilibrium result of \(G\).
## 5 Future Applications with Emphasis in Agriculture  
### Water & Agriculture  
As already stated at the start of the manuscript, it is estimated that agriculture consumes up to 70% of the world's fresh water supply [1]. [PERSON] et al. [97] now estimate that up to 273 M ha of land are irrigated with the majority deployed in south Asia (77 M ha), east Asia (60 M ha) and the United States (29 M ha). The need to increase water use efficiency is seen as a key driver of the sustainable intensification of agriculture [3; 98]. This recognizes the key challenge of the need to develop novel technologies that can provide food for an expanding global population, whilst minimising impacts on the environment. Operational estimation of SSM and ET has the potential to underpin water use efficiency across global agricultural applications.  
Despite this overwhelming global need, EO operational products approaches in relation to SSM and ET have not been widely adopted by the agricultural community. This is surprising as there is now a global trend towards the development of precision irrigation technology [99]. Ultimately, precision irrigation technology drives water use efficiency by carefully targeting moisture application directly to the needs of individual plants rather than for a whole crop. A key technical barrier to the wide scale adoption of precision irrigation techniques include the availability of operational EO estimates of ET and SSM estimates at high spatio-temporal resolutions.
# Research on a Partial Discharge Expert System for the Diagnosis of Damaged Transformation Equipment
## 2 Materials and Methods
### Hardware Architecture
#### 2.1.1 Embedded ARM Processor  
The PD raw discharge waveform acquisition system in this study utilizes the Texas Instruments TI Sitara Processor AM3358 processor, which is based on the ARM Cortex-A8 architecture. The processor specifications and development board are depicted in Figure 2.
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
## 6 Mathematical Model
\begin{split}\mathrm{d}&=226.5+0.1493\mathrm{t}^{2 }+0.3006\mathrm{t}\times\sin\left(0.04688-\mathrm{t}-69.33\mathrm{f}\right)+0.1 493\mathrm{sin}\left(0.04688-\mathrm{t}-69.33\mathrm{f}\right)^{2}+\\ &\qquad\qquad\sin\left(\tan\left(226.5-\mathrm{t}-69.33\mathrm{f} \right)\right)-69.33\mathrm{xf}-1.979\mathrm{f}\left(\mathrm{t}-\sin\left(0.04688 -\mathrm{t}-69.33\mathrm{f}\right)\right),\end{split}  \tag{8}\]  
where \(\mathrm{d}\) is the distance in cm, \(\mathrm{f}\) is the frequency in GHz and \(\mathrm{t}\) the temperature in \({}^{\circ}\)C. Equation (8) presents a correlation coefficient of 0.9238 and its average absolute error is 0.63 cm. Figure 22 shows the estimation of maximum distances for OFDM depending on the working frequency and temperature. These approximations are obtained from Equation (8). The maximum distance (26 cm) is obtained for 21 \({}^{\circ}\)C working at 2412 MHz. We can also see that for temperatures of 22 \({}^{\circ}\)C, the maximum reached distance is about 25 cm and for temperatures of 16 \({}^{\circ}\)C, we can obtain distances between 15 cm to 21 cm. Finally, CCK transmission scheme can be modeled by Equation (9) which relates the distance with the working frequency and the environmental temperature:  
\[\mathrm{d}=1538\mathrm{f}+4.057\mathrm{ft}^{2}+0.003264\mathrm{t}^{4}-2981-7.8 57\mathrm{t}-0.2975\mathrm{t}^{3}-9.28\mathrm{f}^{3}\mathrm{t}, \tag{9}\]  
where \(\mathrm{d}\) is the distance in cm, \(\mathrm{f}\) is the frequency in GHz and \(\mathrm{t}\) the temperature in \({}^{\circ}\)C. The correlation coefficient of Equation (9) is 0.8635 meanwhile the average absolute error is 1.16 cm. Figure 21: Estimated maximum distances for QPSK modulation. Figure 22: Estimated maximum distances for OFDM modulation. Figure 23 shows the estimation of maximum distances for CCK transmission scheme as a function of the water temperature and working frequency. These approximations are obtained from Equation (9). The maximum distance (26 cm) is obtained for 22.5 \({}^{\circ}\)C working at 2412 MHz. In addition, we can see that for temperatures of 27 \({}^{\circ}\)C, the maximum reached distance is between 10 cm to 15 cm and for temperatures of 16 \({}^{\circ}\)C, we can obtain distances between 17 cm to 22 cm.
# Cell-Laden Thermosensitive Chitosan Hydrogel Bioinks for 3D Bioprinting Applications
## 1 Introduction
However, most hydrogels use crosslinkers that could induce cell death [11]. In contrast, thermosensitive hydrogels, which are liquid at room temperature and transit into gels as the temperature increases (Sol-gel transition) [12], could be crosslinked physically (without chemical crosslinkers). Thus, thermosensitive hydrogels could overcome the limitations associated with cell viability and cytotoxicity. Biomaterials derived from natural resources are preferred over those made of synthetic polymers for bioprinting. Among these, chitosan, which has been widely used in tissue engineering owing to its biocompatibility and osteeoconductivity, could be used as a thermosensitive hydrogel. In fact, chitosan hydrogels are presently used for the 3D printing of biomaterials [13; 14]. The gelling agent and solvent in chitosan-based thermosensitive hydrogels play an important role in the sol-gel transition [15]. [PERSON] et al. suggested that hydrochloric acid, lactic acid, and acetic acid are suitable solvents for chitosan-based thermosensitive hydrogels [16]. Furthermore, the use of \(\beta\)-glycerophosphate (\(\beta\)-GP), potassium phosphate (K\({}_{2}\)HPO\({}_{4}\)), and sodium bicarbonate (NaHCO\({}_{3}\)) as gelling agents has been reported previously [17; 18]. However, there is no study on the comparative suitability of these gelling agents or solvents for 3D bioprinting. The objectives of this study were to develop bioinks from chitosan hydrogels for the purpose of 3D bioprinting (Figure 1A) and to compare the potential suitability of the developed chitosan hydrogel as a bioink. In addition, the optimal solvents and gelling agents for the fabrication, characterization, printability, and biocompatibility of chitosan bioinks (CBIs) were explored.
## 1 Introduction
We categorize prediction methods into three major classes: statistical methods, traditional machine learning, and deep learning. With the enhancement of computational resources and advancement in data acquisition techniques, deep learning has gradually become mainstream due to its outstanding performance in prediction accuracy. In Section 3, we introduce traffic data and its unique characteristics, summarize the abstract description of traffic prediction problems, and then discuss the impact of input data representation on RNNs and their variants for traffic prediction. This includes the form of input sequences (including time series, matrix/grid-based sequences, and graph-based sequences) and sliding window techniques. This section emphasizes the importance of accurately representing input data to improve the prediction accuracy of RNN models. It discusses the performance differences when handling different data representations, ranging from simple RNNs to complex models combining CNNs and GNNs. In Section 4, we delve into RNNs and their variants, sequentially introducing classical methods and hybrid models (such as RNNs combined with machine learning techniques, CNNs, GNNs, and attention mechanisms). This section showcases RNNs' advantages in capturing temporal dependencies and methods for overcoming limitations and enhancing prediction performance through integration with other technologies. In Section 5, we delve into the application of RNNs and their variants in traffic prediction, covering seven aspects as follows: traffic flow prediction, passenger flow prediction, OD (Origin-Destination) demand prediction, traffic speed prediction, travel time prediction, traffic accidents and congestion prediction, and occupancy prediction. This section provides a detailed exposition of the performance of relevant models in various domains. In Section 6, we discuss the comparison between RNNs and the most popular models, i.e., transformer series models, as well as other classical prediction models, such as convolutional-based time series prediction models. This section also explores the challenges faced, such as model interpretability, accuracy in long-term prediction, the lack of standardized benchmark datasets, issues with small sample sizes and missing data, and the challenge of integrating heterogeneous data from multiple sources.
## 3 Results and Discussions
### Influence of the Synthesis Temperature on the Adsorption Performance of SSO-MIPs  
Figure 9 presents a graph that shows the effect of the variation of the synthesis temperature on the adsorption performance of the polymer, wherein the amount of solanesol was 0.025 mmol, the amount of MMA was 0.2 mmol, the amount of cross-linking agent was 7.5 mmol, and the amount of KPS was 10 mg. It is evident that the adsorption capacity of the SSO-MIPs first increased and then decreased with the increase of the synthesis temperature from 65 to 85 \({}^{\circ}\)C. When the synthesis temperature was 70 \({}^{\circ}\)C, the adsorption capacity of the SSO-MIPs reached the maximum. This is because the initiation efficiency of the initiator is low at a lower temperature. When the reaction temperature increases, the Brownian motion of latex particles intensifies; this increases the rate of coalescence caused by the collision between latex particles, ultimately leading to the decrease of emulsion stability. When the reaction temperature increases to be equal to or greater than the cloud point of the emulsifier, the emulsifier will lose its stabilizing effect, which will lead to emulsion breaking. It can be seen from the figure that the effect of temperature on adsorption effect is higher than the pore volume, which may be because the temperature affects the pore structure of SSO-MIPs rather than the pore volume to a large extent. An inappropriate synthesis temperature leads to the increase of incomplete stereo holes; thus, SSO-MIP's recognition ability for solanesol decreases, resulting in poor adsorption effect.
# Body-to-Body Cooperation in Internet of Medical Things: Toward Energy Efficiency Improvement
## 2 WBSN Networks Architecture
### Proposed Inter-WBSN Cooperation Network Architecture  
In the traditional WBSN network architecture, many sensors homogeneously spread over the human body to observe any important vital signs, and the attached sensor gathers and transmits the data to the CN. Thus, a WBSN is based on the single-hop star topology, whereby all sensors transmit their gathered data over a wireless medium or HBC to the _CN_. The _CN_ then sends the data to the next tier, as explained in the previous section (shown in Figure 1).  
In fact, WBSN systems are quite likely to overlap each other, and this makes the cooperation between WBAN systems possible. In this paper, we proposed a new network architecture, known as inter-WBSN cooperation in the IoMT environment. We assumed that two WBSNs were co-located in the same transmission range (area). Each WBSN consists of a single _CN_ and several sensors distributed on the human body. The operation of the proposed inter-WBSN cooperation is summarised as follows:  
* Each sensor gathers the data then forwards it to the second tier (_T_2) over two phases, as depicted in Figure 2.
* In the first phase, sensor 1 of the WBAN1 broadcasts the gathered data to _CN_1 and _CN_2. In the second phase, _CN_1 and _CN_2 transmit what is received from sensor 1 to the tier 2 device (_T_2).
* The devices in \(T\)2 combine the received signals via maximal ratio combing (MRC) [23].  
In addition, we investigated two more protocols (traditional protocols): direct transmission in an IoMT environment (DT-IoMT) and two hops in an IoMT environment (TH-IoMT). In the DT-IoMT, sensor 1 transmits directly to \(T\)2; however, in the TH-IoMT, sensor 1 transmits to the CN, and the _CN_ forwards the data to \(T\)2.  
Figure 2: Network architecture of the Inter-WBSN cooperation.
# Improved DeepSORT Algorithm Based on Multi-Feature Fusion
## 5 Experimental Results and Analysis
Compared with Our algorithm, the number of FP in Our-1 is reduced by 403 (3.1%), and the number of FN is reduced by 587 (0.02%), which is the best evaluation parameter. The number of IDS has been reduced by 32 (0.49%). The final tracking accuracy (MOTA) of Our-1 tracking algorithm is 0.2% higher than that of Our algorithm. It is improved by 0.31%, but the tracking accuracy (MOTP) is reduced by 0.61%, decreased by 0.5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **IDS\(\downarrow\)** & **\% MOTA\(\uparrow\)** & **\% MOTP\(\uparrow\)** \\ \hline SORT & 7318 & 32,615 & 1423 & 33.4 & 72.1 \\ DeepSORT & 12,852 & 36,747 & 781 & 61.4 & 81.7 \\ Our & 11,746 & 28,461 & 659 & 63.7 & 81.5 \\ Our-1 & 11,343 & 27,874 & 627 & 63.9 & 81.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithmic performance comparison. From the comparative analysis of the above experimental data, it can obviously be seen that using ResNet50 combined with the feature pyramid network to extract pedestrian appearance features can improve the MOTA of the DeepSORT algorithm by 3.7%. Using GIOU instead of IOU as the matching measure for detection frames and trajectory prediction frames, MOTA is improved by 4.1% compared with DeepSORT.
# Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method
###### Abstract  
Due to the errors of the servo system and the errors of the ball screw drive system, the positioning errors inevitably occur in the process of CNC machine tools. The measurement of traditional equipment is limited by a fixed measurement radius and a single degree of freedom, which can only be measured within a fixed plane. In this paper, four different positioning errors of CNC machine tools are first measured at full scale by using J-DBB (a modified double ball bar with one spherical joint connecting two bars) method. The J-DBB device uses a three-degree-of-freedom spherical joint as a connecting part, which realizes that the measurement radius can be continuously changed, and the measurement space is a spatial sphere. First, the principle of the J-DBB method is briefly introduced. Next, four typical positioning errors of CNC machine tools are analyzed and examined, which contain the uniform contraction error of ball screw and linear grating, periodic error of the ball screw and linear grating, interference of measurement devices error, and opposite clearance error. In the end, the trajectories of the CNC machine tool spindle with a single positioning error are simulated by using the J-DBB method. The results reveal that this method can be used for the positioning error of machine tools, which helps to better understand the spatial distribution of CNC machine tool errors and provides guidance for the reasonable selection of working areas to improve the machining accuracy of parts.  
positioning errors; full-scale measurement; machine tool; double ball bar with a spherical joint; double ball bar +
Footnote †: journal: Applied Sciences
## 2 Coding Metamaterials for RCS Reduction
### Coding Metamaterial Parametric Study  
Coding metamaterials can manipulate EM waves, whereby a normally incident beam can reflect in more than one oriented direction under various coding sequences. To assess the aforementioned physical phenomenon, a general square metamaterial design composed of N \(\times\) N equal-sized lattices was adopted by occupying the proposed elements. Then, the adopted 1-bit coding metamaterials were investigated and analyzed based on five different numbers of lattices: 4, 6, 8, 12, and 20, respectively. Each analysis of the lattices comprised three different coding sequences, and the obtained RCS values were compared. Therefore, an arrangement of the elements '0' and '1' based on the number of lattices was designed by adopting the unit cells selected in the previous subsection. Moreover, every coding sequence was designed with distinct patterns to analyse the performance of RCS reduction. For each number of lattices, the arrangement of coding sequences in each row was tabulated. Meanwhile, the coding metamaterial design for 20 lattices was illustrated in only graphical form. The bistatic scattering patterns of the proposed coding metamaterials at 8 GHz were also analyzed in this work.