esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 4 Connectivity Properties of Ship MANET
### Analysis of Movement Situation between Nodes
#### 4.3.1 The Scenario of Approaching and Then Leaving  
As shown in Figure 5, fix Ship 1 at the origin of the coordinate axis, its motion vector is \(\overrightarrow{OA}\), and for Ship 2, its motion vector is \(\overrightarrow{PB}\). Using the knowledge of the relative motion of objects, we can derive the relative motion line \(\overrightarrow{PC}\) of Ship 2 when Ship 1 is stationary. In addition, \(d_{0}\), \(d_{1}\), and \(d_{2}\) are the distance between Ship 1 and Ship 2 in the different stages of the process of navigation. It can be seen that \(d_{1}\) is gradually decreasing, \(d_{2}\) is gradually increasing, and \(d_{0}\) is a minimum value. That is, the distance relationship between Ship 1 and Ship 2 is to approach first and then move away, and point \(Q\) is the closest point. According to the situation of ship encounters, almost all encounter scenarios, such as head-on, crossing, and overtaking situation, are approaching then leaving scenario.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Electrolysis
#### 2.3.4 Anion Exchange Membrane Water Electrolysis  
AEMEL is a developing technology, not yet scaled up to commercial applications, that presents good potential to be used as a way to produce hydrogen due to its low cost and high performance when compared with other electrolysis technologies. Despite presenting a similar configuration and working principle to AEL, AEMEL possesses an anion exchange membrane (such as ammonium ion exchange membranes) instead of the gas-tight diaphragm and does not require a highly concentrated electrolyte [44], as shown in Figure 11.  
Researchers are working on scaling up the production capacity, increasing the operational stability, which at the moment is 35,000 h, and lowering the hydrogen production cost [44]. In [49], the state of the art of the present technology is described, as well as the challenges that it faces.  
In all the previously described cases, despite not originating any process-related GHG emissions, these emissions may arise from the water and electricity supply chains, among others. The hydrogen produced through electrolysis is only classified as green hydrogen if renewable energy resources are used to generate the electricity required for the electrolysis process.  
Figure 11: Schematic representations of the working principle of anion exchange membrane water electrolysis (adapted from [44]).
## 2 Materials and Methods
### Overview of the Study Area and Data Sources
#### 2.1.2 Data Sources  
In this study, data on multiple key factors influencing road collapse hazard susceptibility--such as topography, vegetation cover, road distribution, population size, precipitation, and lithology--were selected to support the model by comprehensively analyzing their spatial characteristics. These data were sourced from authoritative geospatial and meteorological platforms and processed to meet the requirements of the study area. Specific data sources and descriptions are presented in Table 1.  
The data utilized in this study are derived from multiple sources. Topographic data, including a 30-meter resolution digital elevation model (DEM), are obtained from the Geospatial Data Cloud (Chinese Academy of Sciences, Beijing, China, [[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)) (accessed on 9 March 2025)), with slope and aspect maps for Guangxi generated through cropping and projection transformation. Vegetation cover data, consisting of 30-meter resolution Normalized Difference Vegetation Index (NDVI) data, are sourced from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), and adapted to the Guangxi region via projection transformation and resampling. Road data are acquired from the National Catalogue Service for Geographic Information, with the road network and distance-from-road maps for Guangxi produced  
Figure 1: Elevation distribution map of China and the Guangxi Zhuang Autonomous Region.  
through projection conversion and cropping. Population data are retrieved from the LandScan dataset, and the population distribution map for Guangxi is created following projection and resampling. Precipitation data are obtained from the China Meteorological Data Service Center, with monthly average precipitation data projected and converted to compute annual precipitation. Lithology data, also sourced from the National Catalogue Service for Geographic Information, are transformed and cropped to extract lithology information for Guangxi. Collapse disaster data, provided by the Institute of Geographic Sciences and Natural Resources Research, CAS, are processed through attribute filtering and projection for model training and validation.
## 3 Results
### Prototype Characteristics
#### 3.1.2 Leaf Mass Estimation  
Based on leaf mass influence on trunk frequency, identifying trunk frequency is a prerequisite for leaf mass estimation. Figure 4a,b illustrates the frequency response  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**East–West Direction** & **North–South Direction** & \(D_{b}\) (cm) & \(L_{b}\) (m) & \(\alpha_{b}\) (°) & \(\beta_{b}\) (°) & **Simplification for FEM** \\ \hline \end{tabular}
\end{table}
Table 2: Dimensions of branches and simplifications for FEM.  
functions (FRF) of the prototype in the north-south and east-west directions, and the mode shapes corresponding to the first three peaks of the FRFs are further identified, as shown in Figure 4c,d. Because the mode shapes present typical shape characteristics of a cantilever, the first three frequencies and damping are associated with the trunk. The first frequencies of the trunk in the orthogonal directions are 5.18 Hz and 5.25 Hz, while the corresponding damping for both is 5.38%, which verifies the symmetry of the prototype.  
The FEMs of Nos. 1-10 branches were simplified according to branch dimensions, as shown in Table 2. Based on the geometric dimensions of the prototype (Tables 1 and 2) and material properties, the FEM of the prototype was established. The leaf clusters were modelled as lumped masses attached uniformly to the branches of order 2 and 3 according to the visual observation. Figure 5 illustrates the relationship between fundamental trunk frequency and total leaf mass. The fundamental trunk frequencies decrease with increasing leaf mass. The uncertainty of the estimated leaf mass is based on the fact that the minimum change in leaf mass no longer affects the fundamental trunk frequency. When leaf mass is 15.41 kg, the fundamental trunk frequency of the FEM is consistent with that derived from SISO. Therefore, the estimated total leaf mass is 15.41 kg.  
It is necessary to evaluate the effect of variations of MOE and mass on the effective mass fraction. According to Equation (1) to Equation (5), the effective mass fraction is determined by relative distributions of stiffness and mass of the system. To keep the same frequency, the total mass of the tree (including leaf, branches and trunk) varies linearly with MOE based on the frequency formula (\(f=\sqrt{k/m}\), \(k\)\(\propto\) MOE, where \(k\) is the stiffness and \(m\) is the mass). This indicates that the relative distributions of stiffness and mass keep constant. Based on that, the variations of MOE and mass have little effect on the effective mass fraction. On the other hand, each specific MOE requires a matched mass to keep the frequency constant. Therefore, the FEM of the prototype with the specific MOE (\(7.2\times 10^{9}\) Pa/m\({}^{2}\)) and the matched leaf mass (15.41 kg) can reflect the contributions of branch modes to the overall dynamic response.  
Figure 4: FRFs of the prototype at (**a**) north–south and (**b**) east–west directions, and first three mode shapes of the trunk at (**c**) north–south and (**d**) east–west directions.
# Tuning of Graphene-Based Optical Devices Operating in the Near-Infrared
## 2 Graphene Optical Properties
### Graphene Conductivity
#### 2.1.2 Graphene Conductivity Formula  
The analysis of the Kubo equations was carried out following References [5, 13, 15, 17, 18, 19, 20, 21, 25] (based on its greater prevalence in the considered works). The calculated curves of the complex permittivity obtained from the combination of Kubo Formulas (1) and (2) are shown in Figure 5.  
Two situations were analyzed at \(\Gamma\) = Se13 s\({}^{-1}\) and \(\Gamma\) = 2e12 s\({}^{-1}\) (since this parameter depends on the quality of graphene (Section 2.1.1 (c)) and it was interesting to look at the behavior of the permittivity curves when it changes). In this regard, the changes in the Kubo model curve when this parameter changes are one of the most important criteria when choosing a formula option. In this regard, it can be noted that the [13, 25] and [17] options do not react to changes in the relaxation rate. The opposite case reflects the permittivity curve obtained by the formula from [15], demonstrating the highest sensitivity. The model from [18, 19, 20, 26, 58] is \"smoothed\" at \(\Gamma\) = 8e13 s\({}^{-1}\) (experimentally obtained result [9]), which makes it difficult to analyze the transient process. As can be seen from  
Figure 5: (**a**) Real (blue curves) and imaginary (red curves) parts of the permittivity, (**b**) refractive index and (**c**) extinction coefficient as a function of the chemical potential from various publications, when \(\lambda\) = 1.55 \(\upmu\)m, \(t_{\text{G}}\) = 0.34 nm, \(\mu_{\text{c}}\) = 0 \(\div\) 1 eV, \(\Gamma\) = 8e13 s\({}^{-1}\) and \(T\) = 300 K. (**d**) Real and imaginary parts of the permittivity, (**e**) refractive index and (**f**) extinction coefficient as a function of the chemical potential from various publications, when \(\lambda\) = 1.55 \(\upmu\)m, \(t_{\text{G}}\) = 0.34 nm, \(\mu_{\text{c}}\) = 0 \(\div\) 1 eV, \(\Gamma\) = 2e12 s\({}^{-1}\), \(T\) = 300 K.  
Figure 5a,d, the curves for the cases [5,21] show the stability of the real part of the dielectric constant to a change in the relaxation rate on the one hand, but the high sensitivity of the imaginary part on the other hand, which may be more effective, in comparison with other formulas according to the Kubo model, for modeling and analyzing graphene.
## 2 System Model
We use the following notation conventions:  
\[s_{nm}(t)\xrightarrow{STFT}\mathcal{T}(s_{nm})\in\mathbb{C}^{N_{f}\times N_{ t}}\xrightarrow{norm}T(s_{nm})\in\mathbb{R}^{N_{f}\times N_{t}}\]  
The comparison between the transmitted signal real valued spectrogram \(T(s_{nm})\) and the received signal real valued spectrogram \(R(s_{nm})\) is at the heart of the training of the Denoising Autoencoders described in the next section. Notice that sometimes, to reduce the dimensionality of the problem, the values of each spectrogram are encoded to binary values using a comparison to the average value of the pixel. For instance, one can define  
\[[B(s_{nm})]_{ij}\equiv \begin{cases}1&if\ [R(s_{nm})]_{ij}>\langle R(s_{nm})\rangle \\ 0&otherwise\end{cases}  \tag{2}\]  
so that \(B(s_{nm})\in\{0,1\}^{N_{f}\times N_{t}}\). In this work, we assume that each spectrogram captures a single emission of each of the active RAT types. This assumption is motivated by the common practice of programming SDRs to scan multiple frequency bands [25; 28]. Our model can be expanded upon to include wideband spectrograms that can span multiple emissions by using spectrum localization [46], and other time-frequency representations based on sweeping carrier frequency [58]. Having calculated the spectrogram \(R(s_{nm})\), and possibly its binarized version \(B(s_{nm})\), the receiver passes it to the emitter classification algorithm that outputs an estimate of the operating RAT type. Figure 2 shows samples of the received spectrograms for some combination of channel scenarios and SNR levels. The transmitted spectrograms for each IEEE 802.11 protocol and LTE are presented in the first row of Figure 2. The LTE spectrogram could be corrupted easily under _Scenario 2_ for SNR 20 dB. Channel _Scenario 1_ and SNR 0 dB damage the preamble's visual pattern in the captured spectrograms. The detailed description about data generations and the implemented Channel _Scenarios_ are discussed later in Section 4. In real environments, it is hard to know the channel condition and to model it by a precise equation, especially in a harsh dynamic environment. To perform the identification task and have a system with a robust and high accuracy, the problem of the channel and noise effects in the received spectrograms should be solved. In the following section, our proposed methodology to achieve a robust identification task is detailed.
## 4 Results and Discussion
### Development of Predictive Models for Estimating Bolme Abrasion Value (BAV)
#### 4.2.2 M5 Model Tree Algorithm (M5P)  
The M5 algorithm, originally developed by [PERSON] [39], is a powerful and hybrid method for handling complex problems. It combines the advantages of decision trees with the predictive capabilities of linear regression. The M5P analyses were performed in the WEKA (Waikato Environment for Knowledge Analysis) environment. The number of if-then rules was set to between four and six in the M5P analyses. As a result, a simple M5P algorithm was developed for assessing BAV. The decision tree and corresponding linear models (Lm1-Lm3) are presented in Figure 6.
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 5 Design and Implementation
### Latency Optimization Strategy
#### 5.2.1 Single Rx Queue  
When there is only one Rx queue in the data plane, all packets of the Tx queue come from that Rx queue. Suppose the CPU finishes processing the packets from the Rx queue, of which \(P_{out}\) packets are put into the Tx queue, the \(\overline{P_{out}}\) is the average number of packets entering the Tx queue per round in \(T_{observed}\) and the maximum Tx batch size is \(\theta_{MAX}\), then we adjust the \(\theta_{Tx}\) of the Tx queue to \(f(\overline{P_{out}})\):  
\[f(\overline{P_{out}})=\left\lfloor\frac{\overline{P_{out}}}{2}\right\rfloor*2,f(P_{out})\in[2,\theta_{MAX}] \tag{8}\]  
\(f(\overline{P_{out}})\) is the largest even number that is not greater than \(P_{out}\). Although adjusting \(\theta_{Tx}\) to \(P_{out}\) is an intuitive approach, we choose to adjust \(\theta_{Tx}\) to \(f(\overline{P_{out}})\) considering factors such as memory alignment. Note that we have limited the minimum value of \(f(\overline{P_{out}})\) to 2 and the maximum value to \(\theta_{MAX}\). This means that if the computed result of \(f(\overline{P_{out}})\) is less than 2, it will be corrected to 2, and if the computed result is greater than \(\theta_{MAX}\), it will be corrected to \(\theta_{MAX}\). Assuming that the CPU utilization is \(\eta\), then our packet latency optimization strategy can be expressed by following formula:  
\[\theta_{Tx}=\begin{cases}P_{out},\eta\leqslant H\\ f(\overline{P_{out}}),\eta>H\end{cases} \tag{9}\]  
where \(H\) is a settable CPU utilization threshold. When \(\eta\leqslant H\), we set \(P_{out}\) as the value of \(\theta_{Tx}\), which means that the packets will not wait in the Tx queue, but will be forwarded directly. When \(\eta>H\), we set \(f(\overline{P_{out}})\) as the value of \(\theta_{Tx}\).
# Harnessing Agri-Food Waste as a Source of Biopolymers for Agriculture
## 2 Sources of Biopolymers
### Pectin: Importance and Applicability as Biopolymer
Pectin is also a very important polysaccharide for fruit coating; [PERSON] et al. [58] used cocoa shell powder as a raw material to recover the pectin, that was then used to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entry** & **Biomass** & **Extraction Method** & \
\begin{tabular}{c} **Yield on Biomass** \\ **d.w. (\%)** \\ \end{tabular} & **Ref.** \\ \hline \multirow{2}{*}{1} & Apple (_Malus_ sp.) pomace & \multirow{2}{*}{SWE} & 16.68 & [45] \\  & _Citrus reticulata_ peel & & 21.95 & [45] \\ \hline \multirow{2}{*}{2} & Pomeragronate (_Punica_ & \multirow{2}{*}{UAE + citric acid} & \multirow{2}{*}{31.89} & [46] \\  & _granatum_) peel & & & \\ \hline \multirow{2}{*}{3} & Grape (_Vitis vinifera_) pomace & \multirow{2}{*}{UAE + citric acid} & 25.46 & [47] \\ \hline \multirow{2}{*}{4} & _Citrus maxima_ peel (albedo & \multirow{2}{*}{MAE + HCl} & Albedo: 5.57 & [48] \\  & and flavedo) & & Flavedo: 3.09 & [48] \\ \hline \multirow{2}{*}{5} & _Ananas comosus_ (L.) peel & \multirow{2}{*}{
\begin{tabular}{c} MAE + H\({}_{2}\)SO\({}_{4}\) \\ Hot bath extraction + H\({}_{2}\)SO\({}_{4}\) \\ \end{tabular} } & 2.43 & [49] \\ \hline \multirow{2}{*}{6} & Orange (_Citrus_\(\times\) _sincnsis_) & \multirow{2}{*}{Hot bath extraction + HCl} & 18.73 & [50] \\  & peel (albedo) & & & \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{Onion (_Allium cpa_) waste} & PUAE + HAE & 20.32 & [51] \\  & & HAE & 16.22 & [51] \\ \multicolumn{2}{c}{} & PUAE & 9.83 & \\ \hline \hline \end{tabular}  
d.w.: dry weight.  
\end{table} 
Table 2: Pectin extraction methods and yields. coat tomatoes and was found to be more successful in maintaining the fruit characteristics compared to both the control and edible coating without pectin. Edible pectin films for fruit protection can also be functionalized with antimicrobial agents to enhance the protection against fruit pathogens, because the pectin does have bacteriostatic properties; in plants they serve as a barrier that protects them from biotic and abiotic stress [59] and pectin-derived saccharides have been found to exert antimicrobial activity [60]. Thus, pectin coating functionalized with _trans_-cinnamaldehyde was able to reduce weight loss, keep the firmness of the fruit, and reduce pericarp browning while maintaining the taste of rambutan [61].
# Digital Twin Conceptual Model within the Context of Internet of Things
## 6 Data Analytics and Visualization Layer (DAVL)
### Supervised Machine Learning Using Classification and Regression Techniques on Sensor Data  
Supervised machine learning is largely used in industrial automation since it has a prior knowledge of input data and target labels that are instrumental in performing predictive quality analytics, root cause analysis, etc. A potential use case for applying classification algorithm is to detect component failure via labels of failure vs. no failure in the sensor readings. Common classification algorithmsinclude naive Bayes, logistic regression, k-means classification, random forest, support vector machines, and artificial neural networks. Using one of these classification techniques, predictive maintenance using multiclass classification can be conducted since there are multiple causes that can be attributed to a potential failure of sensors or machinery component [22]. Another important supervised machine learning (ML technique is regression to predict future sensor readings. Using the regression techniques, remaining useful time (RUL) can be estimated for a sensor/machine component, i.e., the number of remaining days before the next failure.  
The artificial neural networks (ANN) is a very significant machine learning algorithm that is loosely based on analysis of the human brain modeled as a network of billions of neurons. Since sensor readings are highly dimensional in nature sampled over a small frequency, ANNs are highly suitable for sensor data that contain multiple distinctive parameters over a large time span. Using ANN technique, if the interdependence between the sensor readings is sufficiently strong, the sensor readings for dysfunctional or lost sensors can be predicted from the remaining ones.
## 3 Results and Discussion  
### Effect of Carbonation by SCD on Converter Slag  
#### 3.1.1 Mineralogical Properties  
XRD analysis was performed to investigate the effect of SCD on mineralogical changes of converter slag after reaction. Figure 3 shows the XRD patterns of the converter slag before and after the reaction with SCD. Portlandite was observed in the XRD pattern of as received converter slag. This indicates that some amount of free lime in converter slag has already reacted. The converter slag used in this work has experienced some period of aging, but it is not considered so significant because there was only small indication of calcite from as received converter slag due to the carbonation by atmospheric CO\({}_{2}\). It is also noticeable that a small hint of free lime was observed. Periclase peaks were also clearly observed from XRD pattern.  
After reaction with SCD, the lime hump that was observed from as received converter slag was disappeared, and the periclase peak was significantly reduced. Indeed, magnesite (MgCO\({}_{3}\)) formation was observed, which can be the reaction product between MgO and SCD. Portlandite disappeared from the XRD pattern. Calcite peak increased and aragonite was observed after reaction with SCD. Calcium carbonate minerals were produced as results of reaction between portlandite and SCD or lime and SCD. This means that the converter slag has undergone significant level of carbonation during the reaction with SCD.
## 4 Experimental Result
### Application Results of the Deep Neural Network Model  
In order to further verify the prediction effect of the deep neural network model on the percentage of movable fluid in unconventional reservoirs, we performed the prediction of the percentage of movable fluid in 10 unconventional reservoir cores from Changqing Oilfield. Firstly, the core was saturated with oil, and the nuclear magnetic resonance T\({}_{2}\) spectrum data of the saturated oil core were measured by the laboratory's nuclear magnetic resonance instrument. Then the laboratory method of measuring the core movable fluid percentage was used to measure the true movable fluid percentage of this 10 cores. Secondly, the trained deep neural network used the nuclear magnetic resonance T\({}_{2}\) spectrum data of oil-saturated cores to predict the percentage of movable fluid. The predicted value of the model and the true value obtained in the laboratory are shown in Figure 5. From the Figure 5, it can be drawn: (1) the prediction result obtained by the deep neural network model (DNN) was the closest to the result measured by the laboratory method, followed by the BP neural network model, and the worst prediction model was the SVR; (2) on the whole, the predicted value of the machine learning model was greater than the movable fluid percentage measured in the laboratory; (3) compared with the BPNN, KNN, and SVR models, the prediction RMSE of the DNN model was reduced by 39.65%, 51.18%, and 54.35%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Data Set** & **RMSR** & **R\({}_{2}\) Correlation Coefficient** \\ \hline \multirow{3}{*}{DNN} & Training set & 1.487 & 0.9926 \\ \cline{2-4}  & Testing set & 2.901 & 0.9745 \\ \hline \multirow{3}{*}{BPNN} & Training set & 4.359 & 0.9371 \\ \cline{2-4}  & Testing set & 5.362 & 0.9158 \\ \hline \multirow{3}{*}{KNN} & Training set & — & — \\ \cline{2-4}  & Testing set & 7.583 & 0.8316 \\ \hline \multirow{3}{*}{SVR} & Training set & 5.822 & 0.8878 \\ \cline{2-4}  & Testing set & 7.602 & 0.8308 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of different models.
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 3 Results and Analysis  
### Evaluation Metrics  
#### 3.1.1 Accuracy Calculation Process  
This study analyses the performance of the algorithm regarding the accuracy. As shown in (6).  
\[A_{c}{=}\sum_{t=1}^{n}\left\{\begin{array}{l}1,\hat{y}_{t}=y_{t}\\ 0,\hat{y}_{t}\
eq y_{t}\end{array}\right.\times\frac{100}{n} \tag{6}\]  
where \(A_{c}\) represents the classification accuracy, \(n\) is the total number of samples, \(t\) is the sample number, \(y_{t}\) is the true label of the sample, and \(\hat{y}_{t}\) is the inferred label of the sample.