esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of the Load Ratio \(R\)  
The load ratio is one of the important parameters that has a significant impact on the fatigue performance of a structure. In this section, its effect on the fatigue characteristics of the spot-welded structure was analyzed, where the load ratio \(R\) was varied between 0.0 and 0.75. Figure 5 shows the obtained \(N\)/ values. It was observed that they were 23,185; 43,778; 212,620; and 600,580 cycles when \(R\) equaled 0.0, 0.25, 0.50, and 0.75, respectively. It was noticed that when the \(R\) got smaller, the fatigue life decreased; the change in \(N\)/ was very dramatic with values of 85% and 50.0% when \(R\) decreased from 0.75 to 0.5 and later to 0.25, respectively. No significant change was observed when \(R\) reduced to 0.0 from  
Figure 4: Number of cycles to failure (\(N\)/) for Cases 1–3 obtained experimentally [19] and numerically.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Nugget Diameter (mm) (\(F_{\min}\)/\(F_{\max}\) (kN))** & _da/dN (mm/Cycle)_ \\ \hline Case 1 & 4.5 (2000/4000) & \(1.88\times 10^{-8}\) \\ \hline Case 2 & 4.5 (1800/3600) & \(1.39\times 10^{-8}\) \\ \hline Case 3 & 4.5 (1600/3200) & \(0.69\times 10^{-8}\) \\ \hline Case 4 & 4.5 (0/4000) & \(17.2\times 10^{-8}\) \\ \hline Case 5 & 4.5 (1000/4000) & \(9.13\times 10^{-8}\) \\ \hline Case 6 & 4.5 (3000/4000) & \(0.66\times 10^{-8}\) \\ \hline Case 7 & 3.5 (2000/4000) & \(6.33\times 10^{-8}\) \\ \hline Case 8 & 4.0 (2000/4000) & \(4.59\times 10^{-8}\) \\ \hline Case 9 & 5.0 (2000/4000) & 0.621 \(\times 10^{-8}\) \\ \hline Case 10 & 5.5 (2000/4000) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerically obtained crack propagation rates (\(da\)/\(dN\)) for different cases studied.  
0.25. The smaller value of \(R\) reduces the fatigue life of a structure sensibly. This was due to increased stress levels during the cyclic loading, leading to accelerated fatigue crack growth and overall durability. This can be seen from the comparison of _da/dN_ values presented in Table 3. Its value for \(R\) = 0.0 increased by more than 25 times when compared to that of \(R\) = 0.75.
# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 4 Collaborative Filtering Algorithm Based on Graph Embedding Model  
Traditional collaborative filtering recommendation algorithms suffer from severe data sparsity and cold start problems. Social network-based collaborative filtering algorithms assume that friends have common interest preferences, and usually improve traditional collaborative filtering algorithms by exploiting the explicit trust relationship between users in social networks. However, traditional collaborative filtering algorithms based on social networks have the following problems: (1) the trust relationships between users in social networks are coarse-grained, that is, only 0 or 1 can be used to indicate the trust level between users; (2) only explicit trust relationships are considered, while implicit trust relationships are ignored. Explicit trust relationships for the observable, trust relationships with direct connections, it captures the local structure of the social network information. In addition, many users do not have a direct trust relationship, but they have many common neighbors and are likely to have a high level of trust between them. Even if two users do not establish a direct trust relationship, if they have a similar neighbor structure, it is likely that there is a strong trust relationship between these two users. In this paper, the trust relationship observed by the social network is referred to as first-order trust and the trust relationship derived from the neighbor structure is referred to as second-order trust. The second-order trust relationship captures information about the global structure of the social network. Considering both explicit first-order trust relationships and implicit second-order trust relationships when modeling a recommendation model can effectively improve the performance of the recommendation model.  
Therefore, in this paper, the GraphSAGE model is used to derive the user trust relationships from the original social network that hold both local and global information of the social network, and a graph-embedded model-based collaborative filtering recommendation algorithm is proposed. In the following, we first describe the process by which the GraphSAGE model learns the low-dimensional embeddings of a node that preserves both local and global information, and then explain in detail the model of the collaborative filtering recommendation algorithm based on the graph embedding model.
## 3 Results and Discussion
### Degree of Retting  
The Fried test score of the six retting treatments is presented in Table 2. The results reveal that the uninoculated SW and FW treatments had a score of 1.5 and 1.0, indicating inferior separation feasibility and retting achievements. APPB with solvents scored from 3.5 to 4.75, clearly indicating the superior degree of kenaf fiber separation feasibility from its intact bindings. The highest score of APPB1 with SW might attributed to the compatibility of bacteria and solvents. However, the visual observations of kenaf-reverted stems (Figure 4) offered comparable findings to the results of the Fried test score and to the previous work reported by [PERSON] et al. [34]. However, the high test score supports that the inoculants successfully invaded the plants' cuticle, thereby being admitted into the internal polysaccharide matrix [61]. The advancement of retting score in the APPB with SW treatment might have the combined positive effect of maceration and bacterial enzymes. The progression of retting was achieved by inundating the harvested plants with solvents where soluble cell wall compounds, such as sugars, nitrogenous substances, and proteins, are dissolved, allowing the microbial community to develop [52].  
\begin{table}
\begin{tabular}{c c} \hline
**Retting Treatments** & **Test Score** \\ \hline USW & 1.50 (2,2,1,1) \\ UFW & 1.0 (1,1,1,1) \\ APPB1 + SW & 4.75 (5,5,4,5) \\ APPB1 + FW & 4.25 (5,4,4,4) \\ APPB2 + SW & 3.75 (4,4,4,3) \\ APPB2 + FW & 3.5 (4,4,3,3) \\ \hline \end{tabular}
\end{table}
Table 2: Fried test scores of the kenaf retting treatments.  
Figure 5: Pectinase enzyme activity during non-sterile kenaf retting using alkalophilic pectinolytic bacteria combined with retting solvents.  
In this study, eye-estimated numerical values coupled with centrifugation forces were used to find the Fried test score. Although the chance of person-biased scores was higher to differentiate among the treatments effectively, it offered a clear insight into the retting achievement. In contrast, the expert growers confirmed the CWR achievement by touching and feeling the retted plants' surfaces, rather than using any precise testing method.
## 2 Related Works
The authors go into great detail about the different design difficulties that IRS in 6G networks faces, such as channel estimation, beamforming optimisation, and hardware limitations. It would have been beneficial to have heard more about standardisation initiatives and their possible effects on the practical application of the technology. The article [24] provides a complete account of current developments in IRS-assisted wireless power transfer. The study discusses the core concept of IRS. The paper [25] introduces IRS and some of their potential uses for them in wireless communication systems. There is no consideration of the potential benefits of the IRS in multi-antenna systems, such as massive MIMO. Future studies should go deeper into the potential benefits of IRS in multi-antenna systems. A thorough summary of current developments in IRS technology is given in the work [26]; this paper's limits and look for additional sources for more in-depth study or review of particular problems. For wireless communication systems that employ IRS, the study of the paper [27] proposes a hybrid active and passive beamforming technique (IRS). A comparison with already-existing alternatives is necessary to assess the inventiveness and efficacy of the proposed solution. The authors in [28] proposed an innovative iterative estimation method for reconfigurable intelligent surface (RIS)-assisted near-field localisation. However, the complexity of the algorithm is quite high compared to other similar methods. The work in [29] proposes an interesting idea where IRS phase is optimised first and then a fair allocation of resources (both subcarrier and power per subcarrier) for an Orthogonal Frequency Division Multiplexing (OFDM) system. The performance analysis demonstrates that the algorithm maximises the date rate in a multiuser scenario. In order to achieve that, it develops a practical beamforming codebook to select the RIS configuration that maximises the SNR which in turn maximises the achievable rate. However, the proposed iterative method's performance is inferior to well known semi-definite relaxation (SDR) method. The work in [30] proposes two IRS elements grouping mechanism to reduce complexity of channel estimation with the aim of improving the achievable rate of the the uplink of a communication system. It also improves the energy efficiency of the system. In the first method, it combines the elements of sub-matrix as a single entity (such as 2 by 2 or 6 by 6). However, it neither provides insights on accuracy of such assumption, nor provides the robust argument for sub-matrix size. In the second method, using LoS between users transmission and reception, it reconstructs the channel matrix which reduces the estimation complexity and overhead. However, such assumption of LoS between users may not always be realistic.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### Image Data Collection
#### 5.1.3 Hyperspectral Sensor  
The hyperspectral sensor analyzes a broad spectrum of light, instead of assigning primary colors (red, green, and blue). These sensors can record hundreds of narrow radiometric spectral bands from visible to infrared, sometimes up to microwave ranges. Its ability in providing narrow radiometric spectral bands can detect specific field concerns. Thus, users can compute narrowband indices, such as the chlorophyll absorption ratio index (CARI), transformed chlorophyll absorption ratio index (TCARI), triangular vegetation index (TVI), and photochemical reflectance index (PRI) [51].  
Preparing hyperspectral data is more complicated than RGB and multispectral sensors because its radiometric and atmospheric calibration workflows are more complex. Sensor calibration approaches are generated from the UAV's hyperspectral platforms, which use simulated targets to check data quality, correct radiance, and provide high-quality reflectance information [52]. Therefore, typical steps in acquiring and preparing hyperspectral data captured by UAV remote sensing are: (1) setting up a flight plan, (2) image size and data storage, and (3) quality assessment [41]. Table 2 summarizes the characteristics of each sensor alongside its advantages and disadvantages.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sensors/Details** & **RGB** & **Multispectral** & **Hyperspectral** \\ \hline Resolution (Mpx) & 16–42 & 1.2–3.2 & 0.0025–2.2 \\ Spectral range (nm) & 400–700 & 400–900 & 300–2500 \\ Spectral bands & 3 & 3–10 & 40–660 \\ Weight (approx.) (kg) & 0.5–1.5 & 0.18–0.7 & 0.032–5 \\ Price (approx.) (USD) & 950–1780 & 3560–20,160 & 47,434–59,293 \\ \multicolumn{4}{c}{High-quality images} \\ \multirow{2}{*}{Advantages} & Low-cost operational needs & \multirow{2}{*}{Have more than three bands} & \multirow{2}{*}{Hundreds of narrow radiometric bands} \\  & No need for radiometric and & & Can generates more \\ \multicolumn{4}{c}{atmospheric calibration} \\ \multirow{2}{*}{Disadvantages} & Only have three bands & & \\ A limited number of & & & \\ vegetation indices can be & & & \\ computed & & & \\ \end{tabular}
\end{table}
Table 2: Characteristic of RGB, multispectral, and hyperspectral sensors.
## 1 Introduction
In this study, spectrum entropy, approximate entropy, sample entropy and fuzzy entropy can be used to describe the features about the power spectrum, periodicity and approximation degree of a time series signals. In order to discuss these features based on EEG signals under the driving condition of fatigue and normal states, the above four different entropies had been used to discuss and analyze the EEG signals. The difference between the four entropy features and the comparison of the average accuracy with respect to single entropy and combined entropy were all calculated in this paper. The good results show that the performance based on the combined entropy is superior to that of the single entropy. For single entropy, we also found that the fuzzy entropy obtained a better performance.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 6 Discussion  
This paper has described a methodology for deriving accurate SSH from the initial untreated post-processed shipborne GNSS measurements (detailed in [23]). The results indicate a 4.1 cm height determination accuracy, in terms of standard deviation (cf. Section 5.1), for the unfiltered SSH data. Since sea state conditions should not influence this estimate (i.e., the estimate represents a rigid system), it can also describe the final SSH accuracy, assuming sufficiently well-estimated vessel sailing-related corrections (cf. Section 5.3). Note that the application of the low-pass filter may have improved the accuracy by reducing data noise. Further data validation demonstrated standard deviation estimates of 1.8 cm to 5.5 cm for the determined residual values of individual campaigns (cf. Figure 14a), whereby the combination of all campaigns provided an estimate of 5.0 cm (cf. Figure 16b). The standard deviation estimates of campaign-internal intersections varied between 1.8 cm and 3.4 cm (cf. Figure 14b). By comparing the residuals of various campaigns to each other at intersections, the standard deviation estimates also remained within 5 cm (cf. Figure 15).  
These results suggest that the developed methodology can provide at least a 5 cm accuracy for the SSH. For comparison, Baltic Sea region geoid validation studies (using NKG2015 model) have indicated the following agreement (in terms of standard deviation) with shipborne GNSS measurements: 3.4 cm [11], 3.8-6.8 cm [12], 4.2-12.0 cm [24], and 1.2-27.9 cm [32]. Other previous studies that investigated shipborne GNSS-determined SSH demonstrated 5-15 cm [10], 4.0-5.3 cm [33], and 7.4-11.9 cm [38] accuracies, in terms of standard deviation. The results of this study are, hence, in good agreement with the previous results, or surpass them.  
Another notable result is that all GNSS data collection was autonomous [23], implying that GNSS devices can be installed on various vessels and for extended periods. Here, CORS-based post-processing of GNSS data were primarily used, but distant offshore locations may similarly be of interest for SSH measurements. It has been demonstrated in earlier studies that, for instance, the CSRS-PPP approach can provide reasonable height determination accuracy on such occasions, and even for transoceanic scenarios [24; 37; 38]. In future studies, it could be interesting to investigate the potential of the developed method exactly in distant offshore locations (using, e.g., CSRS-PPP instead of relying on CORS-based post-processing). This could provide essential SSH datasets with satisfactory accuracy for marine-related studies.  
As is evident from the previous analyses and discussions, the presented joint SSH derivation and validation entails several steps and datasets that can influence the results. The following sections now examine the impact of some of these components. For that purpose, a single step or dataset was exchanged for an alternative, whereby all other data processing was conducted as described above.
## 4 The DCPVP Protocol
### Phase D: Cluster Head Routing Phase  
Data forwarding to the BS is multi-hop and done by other CHs. Routing between CHs is done before the steady state phase using the \"Most Forwarding Progress within Radius\" technique (Figure 7). CHs implement this technique by knowing their distance to the BS and share it with other CHs. Any CH transmits its data to the CH which is closer to the BS and is in its radius. The flowchart of phases C and D is shown in Figure 8.
## 4 Improvement of Thermal Performance
### Thermal Conductivity Enhancement Techniques
#### 4.3.3 Enhancement of Thermal Conductivity with Metallic Foams and Expanded Graphite  
Another perspective for thermal conductivity enhancement is the fabrication of a composite material consisting of the PCM and metallic foam as a carrier. Metallic foams that have been used for this purpose are copper foam [76], nickel foam [77], and porous graphite foam [78]. The composites can be prepared with vacuum infiltration or vacuum impregnation. These structures have plenty of advantages, such as high thermal conductivity, high thermal and chemical stability, high porosity, low density, and high aspect ratio. Their low density and high aspect ratio are the properties that make metallic foams prevail over metallic nanoparticles. Taking into account that thermal conductivity depends on the arrangement of the two components in the composite, it is mentioned that thermal conductivity is higher when the porosity is low because there are less interfacial interactions, and, as a result, the thermal resistance is lower. Although the energy storage capacity is improved when the porosity is higher, it is highlighted that both of those effects have to be considered for the optimization of the final material. Porous graphite and copper foam composite PCMs exhibit higher thermal conductivity in comparison with nickel foam composites [45].  
Expanded graphite (EG) is a structure that comes from the oxidation and desiccation of graphite that has all the desired properties to be used for enhancement of thermal conductivity: chemical stability, high thermal conductivity, low density, and aspect ratio. The fabrication of the form-stable composite PCM can be achieved by impregnation of the molten PCM in the EG. The thermal conductivity of the composite depends on mass fraction, packing density, aspect ratio, surface area, and thickness of EG. High mass fraction and packing density leads to higher thermal conductivity [45; 79; 80].
## 2 The Proposed Methods
### Extension Neural Network (ENN)
#### 2.3.2 Weight Training Phase of ENN  
The weights of ENN are trained mainly by selecting the set \(X\equiv\left\{x_{1},x_{2},\ldots,x_{N_{p}}\right\}\) to be trained from the samples, where \(N_{p}\) is the number of all training data. The number of training data includes the samples and sample features, \(X_{i}^{p}=\left\{x_{i1}^{p},x_{i2}^{p},\ldots,x_{in}^{p}\right\}\), where n is the number of training data and \(p\) is the feature of the \(i^{th}\) training data. ENN's training steps are, as follows:  
Step 1: Set the weight value between the input node and the output node according to the feature-matter element model.  
\[R_{k}=\left[\begin{array}{ccc}N_{k},&c_{1},&V_{k1}\\ &c_{2},&V_{k2}\\ &\vdots&\vdots\\ &c_{j},&V_{kn}\end{array}\right] \tag{10}\]  
where, \(R_{k}\) is a multi-dimensional matter-element; \(k=1,2,\ldots,n_{c};j=1,2,\ldots,n;c_{j}\) is the characteristic of \(N_{k}\); \(V_{kj}\) are the classical domains of the \(k^{th}\) cluster (\(N_{k}\)).  
Step 2: Calculate the middle value of the weight of each feature.  
Figure 3: Extension neural network (ENN) architecture.  
Different training samples are denoted by \(N_{k}\), \(c_{j}\) are the various features of training samples, where \(V_{kj}=\left<W_{kj},\,U_{kj}\right>\) is the interval of the features (weight interval)  
\[w_{kj}^{u}=\underset{i\in\text{{N}}p}{\text{Max}}\left[x_{ij}^{k}\right] \tag{11}\]  
\[w_{kj}^{u}=\underset{i\in\text{{N}}p}{\text{Min}}\left[x_{ij}^{k}\right] \tag{12}\]  
\[Z_{k}=\left\{z_{k1},z_{k2},\ldots,z_{kn}\right\} \tag{13}\]  
\[Z_{kj}=\frac{\left(w_{kj}^{L}+w_{kj}^{u}\right)}{2} \tag{14}\]  
where, \(i=1,\,2,\,\ldots,\,Np;j=1,\,2,\,\ldots,\,n;k=1,\,2,\,\ldots,\,n_{c}\).  
Step 3: Read \(i^{th}\) training samples and eigenvalue \(p\).  
\[X_{i}^{p}=\left\{x_{11}^{p},x_{12}^{p},\ldots,x_{in}^{p}\right\},\,\,\,\,p\in n _{c} \tag{15}\]  
Step 4: Calculate the distance between each training data and the \(k^{th}\) cluster using the extension distance.  
\[ED_{ik}=\sum_{j=1}^{n}\left[\frac{\left|x_{ij}^{p}-z_{kj}\right|-\left(U_{kj}- W_{kj}\right)/2}{\left|\left(U_{kj}-W_{kj}\right)/2\right|}\right] \tag{16}\]  
where, \(k=1,\,2,\,\ldots,\,n_{c}\).  
Step 5: Look for \(k\), so that \(ED_{ik}=min\{ED_{ik}\}\). If \(k=p\), jump to step 7; otherwise, go to step 6.  
Step 6: Update the weights of the \(p^{th}\) and \(k^{th}\) clusters, as follows:  
\[\left\{\begin{array}{l}W_{pj}^{new}=W_{pj}^{old}+\eta\left(x_{ij}^{p}-z_{pj} ^{old}\right)\\ U_{pj}^{new}=U_{pj}^{old}+\eta\left(x_{ij}^{p}-z_{pj}^{old}\right)\end{array}\right. \tag{17}\]  
\[\left\{\begin{array}{l}W_{kj}^{new}=W_{kj}^{old}+\eta\left(x_{ij}^{p}-z_{kj }^{old}\right)\\ U_{kj}^{new}=U_{kj}^{old}+\eta\left(x_{ij}^{p}-z_{kj}^{old}\right)\end{array}\right. \tag{18}\]  
where \(\eta\) is the learning rate. The learning process is just to adjust the weights of the \(p^{th}\) and \(k^{th}\) clusters in this step.  
Step 7: Repeat Step 3 to Step 6 until all samples have been sorted.  
Step 8: After training, the ideal weight interval and weight center can be obtained. Let ENN classify all the data to be measured.
## 4 Results and Discussions
### Preliminary Tests of Chlorophyll a Measurements
#### 4.4.2 Readout Electronics  
The major problem associated with the fluorescence signal is its low amplitude and, consequently, low current in the photodiodes (tens of nA were measured for low chlorophyll concentrations), so it is easily affected by surrounding noise. The implementation of a high-sensitivity, noise-free reading circuit capable of making viable readings is required. This mechanism can be implemented using synchronous detectors as the lock-in amplifier, which extracts the signal embedded in noise, allowing a higher signal-to-noise ratio, since noise decreases as frequency increases [26]. Shifting the useful detection signal to higher frequencies, the signal-to-noise ratio is increased, allowing the detection of smaller frequency signals, such as those expected from phytoplankton fluorescence, by modulating the excitation light (LED). The simplest approach for modulating the LED is to pulse it in the order of kHz, shifting the signal of interest to frequencies where lower noise is expected. The original DC signal (from fluorescence) is recovered through a synchronous demodulator that will shift the signal from the modulation frequency to DC, attenuating all signals that are not synchronized with excitation reference.  
The representative block diagram of a lock-in amplifier is shown in Figure 12.  
Figure 11: Photograph of the developed device showing the several subsystems.  
Figure 12: Lock-in amplifier functional block diagram.