esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Result Analysis
### Experimental Result Analysis
#### 4.4.2 Comparison of Existing Algorithms  
We conducted a comparative experiment between some methods mentioned in the literature and our method proposed in this paper. The experimental results are shown in Table 5. Through a comparison of the classification accuracy in this table, we can find that the method mentioned in this paper can achieve ideal results when applied to the experimental data set and results in a greater improvement in classification accuracy than the traditional machine learning method. Compared with other deep learning methods, it also achieved an accuracy improvement of more than 1%. In terms of the false alarm rate for bad images, the advantages of this method are more obvious. The traditional method has a high false alarm rate for bad images, which also proves that some special attributes of bad images are not unique; thus, they cannot be fully used as the classification basis for bad images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Method** & **Accuracy (\%)** & **FAR (\%)** \\ \hline
[3] & RGB-skin & 61.0 & - \\
[4] & RGB-skin + svm & 75 & 35 \\
[6] & YCbCr-skin & 75.4 & 10.13 \\
[7] & texture & 87.6 & 14.7 \\
[9] & BoVWS & 87.6 & - \\
[23] & CNN & 86.9 & - \\
[26] & ResNet101 + CBAM & 93.2 & - \\ Ours & RepVGG-SimAM & 94.5 & 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of existing algorithms.  
Figure 9: The variation curves of loss during the training process and accuracy during the verification process.
# Optimized Isolation and Characterization of the Major Polysaccharide from Grape Pomace
###### Abstract  
Grape pomace is the major component in grape fruits and is mostly wasted after wine and juice making processes. To recycle the residual biomass in grape pomace, extraction conditions of polysaccharides from grape pomace (GPP) were investigated. Three parameters affecting the crude GPP extraction, material to solvent ratio, extraction time, and extraction temperature were determined through single parameter optimization and then further optimized by orthogonal test. Results showed that the optimum extraction conditions were material to solvent ratio of 1:25, extraction temperature of 75 \({}^{\circ}\)C, and extraction time of 40 min, with extraction time as the most significant factor among them. Crude GPP was purified by gel column chromatography and chemically characterized. UV-Vis spectra analysis indicated that the GPP fraction did not contain any proteins or nucleic acids. FT-IR analysis implied that GPP consisted of \(\alpha\)- and \(\beta\)-pyranose with carboxyl groups. Monosaccharide composition analysis indicated that GPP was composed of arabinose, glucose, galactose, and mannose with a molar ratio of 18.4:14.1:10.8:3.0. These results provide a theoretic basis for the production and utilization of GPP.  
g rape pomace; polysaccharides; orthogonal test; monosaccharide composition; biomass recycling +
Footnote †: journal: 10  
In this study, single parameter optimization was applied to optimized parameters of material to solvent ratio, extraction times, and extraction temperature first. Subsequently, the orthogonal test was employed to evaluate the synergistic effect of these parameters. The isolated crude GPP was further purified with chromatography, and its chemical properties were primarily characterized. This research provides an initial chemical account of the polysaccharide from grape pomace and lays the foundation for its future utilization in food and healthcare.
###### Abstract  
Time-of-flight (ToF)-based 3-D target localization is a very challenging topic because of the pseudo-targets introduced by ToF measurement errors in traditional ToF-based methods. Although the influence of errors in ToF measurement can be reduced by the probability-based ToF method, the accuracy of localization is not very high. This paper proposes a new 3-D target localization method, Iterative Maximum Weighted Likelihood Estimation (IMWLE), that takes into account the spatial distribution of pseudo-targets. In our method, each pseudo-target is initially assigned an equal weight. At each iteration, Maximum Weighted Likelihood Estimation (MWLE) is adopted to fit a Gaussian distribution to all pseudo-target positions and assign new weight factors to them. The weight factors of the pseudo-targets, which are far from the target, are reduced to minimize their influence on localization. Therefore, IMWLE can reduce the influence of pseudo-targets that are far from the target and improve the accuracy of localization. The experiments were carried out in a water tank to test the performance of the IMWLE method. Results revealed that the estimated target area can be narrowed down to the target using IMWLE and a point estimate of target location can also be obtained, which shows that IMWLE has a higher degree of accuracy than the probability-based ToF method.  
target localization; Iterative Maximum Weighted Likelihood Estimation +
Footnote †: journal: Applied
# Investigating the Secondary Thermal Neutron Intensity of Neutron Capture-Enhanced Proton Therapy
## 2 Materials and Methods
### Neutron Fluence Measurement  
The neutron fluence from proton irradiation was measured using thin gold wire activation analysis [19; 20; 21]. A 13.6 cm water-equivalent phantom (RW3, PTW) was used, with a multi-well plate placed beneath it. The cells in the multi-well plate contained 1.0 mL of water and one thin gold wire (diameter 0.25 mm; length 10 mm; 99.95% purity; The Nilaco Corporation, Tokyo, Japan) positioned at the isocenter depth (Figure 2).  
The well that contained the gold wire was centered and irradiated with a dose of 120.0 Gy with a 4.0 \(\times\) 4.0 cm\({}^{2}\) irradiation field and a 6.0 cm Spread-out Bragg Peak (SOBP). The activity of the gold wire was measured using a high-purity germanium spectrometer LVis version 3.3.57.0 (ORTEC, AMETEK GmbH, Germany), and the reaction rate (RR) was calculated using the following equation:  
\[\mathrm{RR}=\frac{\lambda\mathcal{C}}{\mathcal{E}\gamma\mathrm{N}_{0}\mathrm{e }^{-\lambda\mathrm{t}_{e}}(1-\mathrm{e}^{-\lambda\mathrm{M}_{\mathrm{m}}}) \sum_{i=1}^{n}(\frac{\Delta}{\Delta t}(1-\mathrm{e}^{-\lambda\mathrm{M}t}) \mathrm{e}^{-\lambda(\mathrm{n-i})\mathrm{At}})} \tag{1}\]  
Figure 1: Nozzle structure of PROBEAT M1 (HITACHI).  
where \(\mathcal{E}\) is the detector efficiency for gamma rays emitted from \({}^{198}\)Au, \(\gamma\) is the gamma emission rate due to the decay of \({}^{198}\)Au, \(\lambda\) is the decay constant of \({}^{198}\)Au, \(t_{\text{c}}\) is the time from the end of irradiation to the start of measurement, \(t_{\text{m}}\) is the measurement time, C is the peak count of gamma rays from \({}^{198}\)Au measured by the detector, and Q\({}_{i}\) represents the charge delivered to the target at each interval \(\Delta\)t. Assuming a constant charge during irradiation, the RR was determined using the following modified equation:  
\[\text{RR}=\frac{\lambda\text{C}}{\mathcal{E}\gamma\text{N}_{0}\text{e}^{- \lambda t_{\text{c}}}(1-\text{e}^{-\lambda t_{\text{m}}})(1-\text{e}^{-\lambda t _{\text{b}}})} \tag{2}\]  
where \(t_{b}\) is the irradiation time.  
The thermal neutron fluence \(\phi_{th}\) in the water phantom was subsequently calculated from the RR data using the following equation:  
\[\phi_{th}=\text{RR}/\overline{\sigma}_{act}\text{f} \tag{3}\]  
where \(\overline{\sigma}_{act}\) is the average activation cross-section for thermal neutrons in gold (85.35 barns), and f represents the self-shielding factor for thermal neutrons (0.87). The measured value primarily indicates the thermal neutron fluence, owing to the predominance of thermal neutrons in the neutron spectrum generated through proton irradiation, as confirmed by prior studies using PHITS simulations [9].
# Green Synthesis Method and Application of NaP
###### Abstract  
In view of the current and urgent environmental protection needs, the use of industrial solid waste in China's Ningdong is becoming more and more important. In this paper, NaP zeolite with good physical properties is synthesized by using coal gasification coarse slag (CGCS) as the raw material, without the addition of a silicon and aluminum source, without the addition of a template agent, and without high-temperature calcination. Add a small amount of NaOH and deionized water to the CGCS to adjust the molar ratio to SiO\({}_{2}\):Al\({}_{2}\)O\({}_{3}\):Na\({}_{2}\)O:H\({}_{2}\)O = 5.2:1:0.5:0.100. The effects of aging time, crystallization temperature, and crystallization time parameters on synthetic zeolite were studied. The raw materials and the obtained zeolite were tested by XRF, XRD, SEM, FT-IR, TG-DSC, BET, and other technologies. The results show that the specific surface area of the synthesized NaP zeolite can reach 161.06 m\({}^{2}\)/g, which has the characteristics of large specific surface area, regular morphology, and high crystallinity. We obtained NaP zeolite through a simple and low-cost synthesis method. The synthesized NaP zeolite was used to simulate the removal of ammonia nitrogen in wastewater, and the optimal removal rate was 92.67%. Among them, Na\({}^{+}\) plays an important role in the synthesis of NaP zeolite and ion exchange with NH\({}_{4}\)\({}^{+}\). Our research provides new ideas for solving the large-scale accumulation of CGCS and treating ammonia nitrogen in industrial wastewater. Thus, it is a promising green environmental protection and \"treating waste by waste\" route.  
coal gasification coarse slag; NaP zeolite; ammonia nitrogen; green environmental Article
# Study on Fluctuating Wind Characteristics and Non-Stationarity at U-Shaped Canyon Bridge Site
## 4 DWT Reconstruction and Analysis
### Optimal Decomposition Level for DWT  
To investigate the changes in the autocorrelation and partial autocorrelation of pulsating wind after wavelet decomposition at each level, the ACF and PACF test methods introduced in Section 4.1 were employed for calculation and analysis. Figure 11 illustrates the correlation between the wavelet-decomposed signal at each level and its adjacent data points.  
Figure 10: DWT reconstruction results for the sample: (**a**) hour, (**b**) day.  
The pulsating wind characteristics under different wavelet decomposition levels exhibit notable differences: Level 3 showed rapid attenuation and periodic oscillations in ACF, approaching 0 after 4 s, while levels 5, 7, and 9 displayed a more gradual attenuation trend and stabilized at a certain value. The PACF results indicate that all levels decay rapidly to nearly 0 within a very short lag, suggesting that the correlation of adjacent data persists for only about two sample intervals, or approximately 0.5 s. Comparison of ACF and PACF results suggests that the wind retains a large-scale pulsating structure at levels 5-9, which is presumed to correspond to a coherent structure of turbulence. However, level 3 disrupts the coherent structure in the turbulent flow, making it unsuitable for analyzing pulsating wind characteristics.  
Figure 12 presents the power spectral density analysis results for each wavelet decomposition level. Power spectral density analysis reveals that reconstruction results for level 5 and above converge near 0.05 Hz, with these wavelets significantly influencing wind above this frequency. In contrast, reconstruction at level 3 primarily affects the medium- and high-frequency components of pulsating wind. Thus, improper level selection significantly alters the characteristics of reconstructed pulsating wind.  
Comparison with the original data power spectral density reveals that levels 5-9 consistently preserve high-frequency wind components, effectively remove low-frequency large-scale wind, and retain the middle- and high-frequency structures. Therefore, selecting levels 5-9 in the measured data is considered reasonable.  
Additionally, comparative analysis of Figures 13 and 14 indicates that levels 7 and 9 exhibit a gradual rising trend in the power spectral density and retain some low-frequency components, whereas the level 5 wavelet eliminates low-frequency wind more effectively and better preserves the coherent structures of turbulence. Thus, the level 5 wavelet is recommended for decomposing and reconstructing the time-varying mean wind in this study.  
Figure 11: Analysis of ACF and PACF.  
Figure 12: PSD at different levels across different decomposition levels.
# Formal Modeling of IoT-Based Distribution Management System for Smart Grids
###### Abstract  
The smart grid is characterized as a power system that integrates real-time measurements, bi-directional communication, a two-way flow of electricity, and evolutionary computation. The power distribution system is a fundamental aspect of the electric power system in order to deliver safe, efficient, reliable, and resilient power to consumers. A distribution management system (DMS) begins with the extension of the Supervisory Control and Data Acquisition (SCADA) system through a transmission network beyond the distribution network. These transmission networks oversee the distribution of energy generated at power plants to consumers via a complex system of transformers, substations, transmission lines, and distribution lines. The major challenges that existing distribution management systems are facing, maintaining constant power loads, user profiles, centralized communication, and the malfunctioning of system equipment and monitoring huge amounts of data of millions of micro-transactions, need to be addressed. Substation feeder protection abruptly shuts down power on the whole feeder in the event of a distribution network malfunction, causing service disruption to numerous end-user clients, including industrial, hospital, commercial, and residential users. Although there are already many traditional systems with the integration of smart things at present, there are few studies of those systems reporting runtime errors during their implementation and real-time use. This paper presents the systematic model of a distribution management system comprised of substations, distribution lines, and smart meters with the integration of Internet-of-Things (IoT), Nondeterministic Finite Automata (NFA), Unified Modeling Language (UML), and formal modeling approaches. Non-deterministic finite automata are used for automating the system procedures. UML is used to represent the actors involved in the distribution management system. Formal methods from the perspective of the Vienna Development Method-Specification Language (VDM-SL) are used for modeling the system. The model will be analyzed using the facilities available in the VDM-SL toolbox.  
distribution management system; fault detection; formal methods; smart grid; unified modeling language; VDM-SL +
Footnote †: journal: Sustainable
# Electroencephalography (EEG) Technology Applications and Available Devices
### EEG Devices with Several Sensors  
Some EEG devices are designed to capture both psychological (brain) and physiological (blood pressure, muscle activity, heart rate, etc.) data. These devices have one or more extra channels for capturing physiological signals such as Electrocardiogram (ECG), Electrooculography (EOG), Photoplethysmogram (PPG), and Electromyography (EMG).  
ECG sensors record the heart's response during resting or physical activity.  
EOG sensors measure human eye movements.  
PPG sensors monitor blood volume changes.  
EMG sensors collect muscle activity data.  
Some EEG devices are equipped with motion sensors such as gyroscopes and accelerometers to capture head and body motion data. These can be used to measure, e.g., orientation, acceleration, and speed.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 4 Results and Discussion
### Data Packet Loss  
During a handover process, data packets sent from the CN to MN tend to get missing. Figure 9 depicts the packet loss in the course of the handover process. For comparison purposes, the values of \(\lambda_{S}\) and \(E(S)\) were set to 1 and 10, respectively, whereas the value of \(\rho_{f}\) varied from 0 to 0.7. As can be seen in Figure 9, the proposed TFMIPv6 had the lowest amount of data packet loss when compared to other protocols. It was observed from the previous section that the handover latency was directly proportional to the packet loss during the handover process. To illustrate, MIPv6 took more time to complete its handover process, which resulted in more packet loss in contrast with the other protocols. The proposed TFMIPv6 outperformed the rest of the protocols in terms of data packet loss because it had the lowest handover latency.
# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 3 An Overview of the Recent Development for Predictive Control Methods of PMSM
### Deadbeat Predictive Control of PMSM
#### 3.1.1 Delay Compensation  
In [49; 69], a deadbeat multi-step prediction compensation scheme is proposed. Multi-step prediction was carried out according to the current and voltage, and the voltage at time \(k+2\) was obtained as the reference voltage, and the voltage calculation formula is shown in Equation (11). Calculate the required current value according to time \(k\). After the first step of prediction is completed, the predicted current value at time \(k+1\) needs to be saved in the register, and the second step prediction parameters are performed according to the current given value, motor inductance, resistance, flux linkage, and other related parameters to obtain the voltage value of \(u_{dq}(k+2)\). Therefore, the delay existing in the current loop is compensated, and the bandwidth of the current loop is improved [70].  
\[\left[\begin{array}{c}u_{d}^{*}\\ u_{q}^{*}\end{array}\right]=\left[\begin{array}{c}u_{d(k+2)}\\ u_{q(k+2)}\end{array}\right]=\left[\begin{array}{cc}\frac{L_{q}}{I_{s}}&0\\ 0&\frac{L_{q}}{I_{s}}\end{array}\right]\left[\begin{array}{c}i_{d}^{*}\\ i_{q}^{*}\end{array}\right]+\left[\begin{array}{cc}R_{s}-\frac{L_{q}}{I_{s} }&-L_{s}\omega_{e}\\ L_{s}\omega_{e}&R_{s}-\frac{L_{q}}{I_{s}}\end{array}\right]\left[\begin{array}[ ]{c}i_{d(k+1)}\\ i_{q(k+1)}\end{array}\right]+\left[\begin{array}{c}0\\ \psi_{f}\omega_{e}\end{array}\right] \tag{11}\]  
where \(u_{d}^{*}\), \(u_{q}^{*}\) are reference voltages; \(i_{d}^{*}\), \(i_{q}^{*}\) are reference currents.  
Based on deadbeat predictive speed control, a control strategy considering the inherent delay of the speed loop with a disturbance observer is proposed in [71]. Velocity observers were proposed for cascading PSCs to alleviate this inherent latency problem. The problem of increasing robustness is also the most researched in deadbeat predictive control.
# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 4 Collaborative Filtering Algorithm Based on Graph Embedding Model
### GraphSAGE  
As a classic algorithm of the graph embedding model, GraphSAGE proposes an algorithm framework that can easily obtain the embedding of new nodes. Its basic idea is to learn how a node's information is aggregated through the characteristics of its neighbor nodes. After learning the aggregation function, use the known characteristics and neighbor relationships of each node to quickly obtain the embedding of the new node.  
Select Aggregator  
**Mean Aggregator.** Since the neighbors of the nodes in the graph are irregular and disordered, it is necessary to construct an aggregate function that is symmetric, that is, changes the order of inputs but the output of the function remains the same, while having high expressive power.  
\[h_{v}^{k}\leftarrow\sigma(W\cdot MEAN(\{h_{v}^{k-1}\}\cup\{h_{u}^{k-1},\forall u \in N(v)\})). \tag{3}\]  
Once the vector representation of the node is generated, the mean aggregator splices the \(k\)th-1 st layer vectors of the target node and the neighbor node, and then averages each dimension to obtain the result and conducts a nonlinear transformation to generate the \(k\)-th layer vector representation of the target node.  
**Pooling Aggregator.** The pooling aggregator performs a nonlinear transformation on the neighbor representation vector of the target vertex, followed by a pooling operation to splice the result with the representation vector of the target vertex, and finally a nonlinear transformation to obtain the kth level representation vector of the target vertex.  
\[h^{k}_{N(v)}=max(\{\sigma(W_{pool}h^{k}_{u_{i}}+b),\forall u_{i}\in N(v)\}), \tag{4}\]  
\[h^{k}_{v}=\sigma(W^{k}\cdot CONCAT(h^{k}_{v}k-1^{\prime},h^{k-1}_{N(u)})). \tag{5}\]  
**LSTM Aggregator.** LSTM is more expressive than simple averaging operations; however, since the LSTM function is not symmetric with respect to the input, it requires a disorder operation on the neighbors of the vertices when used.
## 3 Materials and Methods
### Framework Architecture
#### 3.3.2 Route Management Methods  
The methods \"Server.addRoute\" and \"Server.removeRoute\" are used to add and remove routes via a connection layer to the C++ library for the web server. In the native function, data transfer of the handler occurs through a \"RequestHandlerObject\" instance with a reference to \"RequestHandler\" for the web server and a value for the function that will be called in user code. After calling \"addRoute\" the callback function's value is transferred to the handler via closure, and upon receiving a request, the procedure adds the callback to the stack, invokes it, and processes its instructions. Instructions are processed until the call stack is emptied, i.e., until the last method with a specified return value exits. The method takes three arguments: the route name, the HTTP method index by the corresponding enumeration, and the callback. If the method is not defined, the default value \"HTTP_ANY\" is used, meaning the route is not restricted by a specific method that must match in the request.  
The returned \"RequestHandlerObject\" can also be used In user code to invoke the handler or dynamically remove it through the \"removeRoute\" method, which also deallocates memory for the passed instance, rendering it unusable after the call. If it is necessary to re-register the same callback, creating a new instance with a separate global or lambda function is recommended to avoid code repetition. Removal with a deallocated instance is safe and has no effect.  
A greater number of integrated functions for managing parts of HTTP requests and responses are also present in the Server class implementation. When retrieving values, the default return type is a string for compatibility and simplicity. The methods are modelled after standard functions from the ESP8266 WebServer class. For example, see the following:  
* \"header(headerName)\" returns the value of a specified HTTP header field (for example, \"Server.header('Content-Type')\" will return \"application/json\" if JSON content is sent in the request body).
* \"pathArg(i)\", based on the URL parameter index, returns its value. Parameters can be defined in routes using curly braces (for instance, if the route \"/items/{}/{}\" is defined and accessed via the URL \"/items/create/3\", the call \"Server.pathArg(0)\" will return \"create\", while the argument value for \"1\" will return \"3\");
* \"inputField(fieldName)\" returns the value associated with a form field with the given name or a query string part. If the specified field is not present in either location, the function returns an empty string.  
* \"send(status = 200, contentType, content)\" defines the HTTP response body with the status code, content type, and content itself. For readability, several derived variants of this method were created:
* \"plain()\" with type \"text/plain\";
* \"json()\" with type \"application/json\";
* \"html()\" with type \"text/html\".