esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 4 Model Performance and Results Analysis
### Model Generalizability Through Cross-Validation  
The inclusion of K-fold cross-validation provided robust evidence of the models' generalizability. The DTA-IRT model demonstrated stable performance across folds, with low standard deviations in AUC and AIC values, reinforcing its validity for practical applications. This suggests that subtractive time adjustments, when appropriately scaled, offer a promising approach for improving ability estimation in timed testing environments. In the third approach involving K-fold cross-validation, only the ability parameters (\(\theta\)) were re-estimated on the test set, while item difficulties (b) and time sensitivity parameters (\(\lambda\)) were fixed, using estimates from the training set. Consequently, when computing AIC and BIC for the test data, the number of free parameters (k) was set equal to the number of students in the test fold, as only \(\theta\) was optimized. This ensures consistency with the principles of information-theoretic model evaluation and allows for a fair comparison of model generalization performance under fixed b and \(\lambda\) assumptions.
## 3 Architecture of the Platform and Related Software and Hardware Aspects
### Implementation Aspects and Associated Configuration and Monitoring Processes
#### 3.2.4 Routing Protocols Implemented in the Platform
In addition to the protocols previously mentioned, it is readily possible to provide implementations for other protocols too. As already noted, the required changes in the software are confined within a small part of the codeused for defining the routing metric.
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 3 Results
### Satellite Data Sensitivity to Soil Texture
#### 3.1.1 Sentinel-2 Sensitivity Analysis

Table 4: Coefficients of determination for the linear regressions computed between clay content and optical reflectance, in bands B11 and B12 of the S-2 images recorded on eight different dates during the summer of 2017; bolded numbers correspond to the best accuracy. Figure 5: Soil clay content as a function of the SWIR bands of S-2, analyzed on 4 August 2017: (**a**) Band 11 (B11); (**b**) Band 12 (B12).
## 4 CPS Framework Implementation and Experimentation
### Experiment and Result Analysis
CNN has more channels than LeNet and can extract more feature information, so the accuracy of CNN is greater than LeNet. The BiLSTM designed in reference [38] can not only extract spatial feature information through convolution, but also extract temporal information. The accuracy of BiLSTM is greater than CNN. BiLSTM can extract bidirectional temporal features, and its accuracy is greater than LSTM. MSCNN can extract multi-scale features, which significantly improves the feature extraction ability, and the performance of MSCNN is better than that of BiLSTM, CNN and LeNet. With the network depth increases, ResNet can overcome the phenomena of gradient disappearance and explosion, which makes the network easy to train; thus, ResNet has the best accuracy. In the spindle and bearing datasets, ResNet18 performs better than ResNet10. In gear datasets, ResNet10 performs better than ResNet18. This may be due to the slight overfitting of the gear datasets. Through the above analysis, the method proposed in this paper successfully monitors the condition of CTC in SPL. The experimental results of STFT, WPD, DMT and different DL algorithms show the necessity of CPS hybrid modeling and the feasibility of algorithm implementation. To better understand the classification effect of these models for each label, Figure 12 shows the confusion matrices of LeNet, CNN, LSTM, BiLSTM, MSCNN, and ResNet18 in bearing datasets with STFT signal processing. In Figure 12, it is evident that almost all the categories in the ResNet18 model are easier to diagnose than those of other models, except for label 4, where ResNet18 is slightly less easy to diagnose than MSCNN.
## 2 Proposed Approach to Modeling POS Combinations in MUPSA
### Step 3: Development of a Multi-Unit Model Combining Integrated Single-Unit Models  
In the third step, the integrated single-unit models are combined into a MUPSA model, which is also in the form of a single-top fault tree.  
Figure 5 shows an example of a four-unit case. The right side of Figure 5 is a single-top fault tree logic for the MUPSA model, where the top event, 4 ULOOP-TOP, represents core damage in at least one of the four units, and the number of failed units distinguishes the fault trees under it. For each number of failed units, a tag event with a probability of 1 (e.g., #4 UNITS) is employed to prevent cut sets involving more units from being subsumed by cut sets involving fewer units. This kind of modeling approach enables the calculation of a given risk metric for each number of failed units (e.g., 1-, 2-, 3-, 4-unit CDF) as well as for the top event (e.g., site CDF) with a single quantification [5; 16].  
One of the essential tasks in this step is to add a fault tree logic for the POS basic events so that one of these events must occur for each unit, as shown in Figure 6. This gate logic is necessary because each unit should be in a particular POS at a certain time. Without this logic, the quantification of the model can generate nonsense cut sets, such as a cut set where core damage occurred only in one unit for which a POS basic event occurred and not in another unit for which a POS basic event did not occur, even though core damage would likely occur in both units. This is because, as in the example in Figure 3, the fault tree logic for each POS is modeled under an AND gate with the corresponding POS basic event with a probability of less than 1.  
Another important task here is to reflect inter-unit dependencies in the MUPSA model. Shared components, inter-unit common cause failures (CCFs), inter-unit seismic correlation, and dependencies between human failure events (HFEs) in different units are representatives to be considered. Detailed approaches for this task can be found in many recently published documents (see for example [3; 5]).  
From the POS perspective, different types of inter-unit dependencies may need to be considered depending on the operational strategies used at the plant or site being analyzed. As an example of such strategies, refueling outages for twin units at the same time would not be planned, or even when two or more units are in refueling outages, it may be planned that no more than one unit can be in a specific POS containing a high-risk operation such as a mid-loop operation at the same time.  
If the integrated single-unit models developed in Step 2 are very complex or the number of units is large, the generation of a MUPSA model in the form of a single-top fault tree by combining the single-unit models may fail before moving on to Step 4 for  
Figure 5: Example multi-unit model combining integrated single-unit models (4-unit case).  
the quantification of the MUPSA model. In this case, as in Step 2, the screening step is performed, after which Step 3 recommences.
## 3 Results and Discussion
### Comparison of Relative Effects of Critical Factors on ELUE by Path Analysis
** represents significant level \(p<0.01\). NDVI), even shortening the growing season, and thus reduces the seasonal cumulative ecosystem productivity and the consequent ELUE [88]. However, those effects of water stress seldom or never occurred in long-term flooded paddy rice croplands. Therefore, T\({}_{\text{a}}\) might exert more effect on ELUE than water availability in paddy rice.
## 1 Introduction
However, although RNNs including LSTMs were used for modeling sequential data for a few decades, their success in system identification was limited due to their inability to capture significantly long-time delays in-process data. Moreover, these are heuristic methods, and their performance is highly dependent upon the experience of users and they are challenging to implement [19]. Another NN method widely used in system identifications of complex non-linear processes is the modeling method based on the radial basis function (RBF) NN due to its simple structure, straightforward training, and fast learning convergence, and ability to approximate arbitrary non-linear function. However, its time consuming and slow application is its main disadvantage in real-time applications [20]. In [21; 22], two research works have been conducted on the modeling of the fineness of particles in the milling circuit to control a cement mill better. These research works have tried to explore the relationship between the particle size of cement produced (a quality performance indicator) and various mill parameters and consequently provide a model for predicting Blaine (finiteness of particles). Different data-driven non-linear models including LSTM and multilayer perceptron (MLP) results on a cement mill dataset have been compared and concluded that predictive models' accuracy highly depended on the cement mill dynamics. The contribution of this research lies first in the prediction of coarse return for process control in cement plants which is not yet delivered in the literature. This study aims to leverage recent advances in deep learning techniques to propose an FCDNN model to predict the upcoming coarse return in a cement grinding process. In the next section, we describe a real-world cement industry's closed grinding circuit system use case and the data collection campaign alongside the proposed FCDNN model. This will be followed by a review of the deployed model's behavior in the dynamics of the complex operations and prediction results. Section 4 discusses the results and comparison and evaluation with two other methods. Finally, this study is concluded by a conclusion and future research direction.
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 2 System Design  
As demonstrated in Figure 1, the wireless optical signals, i.e., the FSO links, are transferred from an optical transmitter (Tx) located in a defined optical receiver (Rx) at an optical line terminal (OLT) over the vacuum in space as well as different climate conditions in earth's atmosphere suitable for satellite-to-earth station communications. For this, an unconstrained line of sight (LOS) is required between the Txs and Rxs for transmission [20].
## 3 Experimental Results
### Data Comparison  
The drone LiDAR measures a surface that changes according to the banking process. Therefore, a time zone was established where no banking is carried out, and was then compared between the two measurements from the drone and the conventional method. A total of 89-137 manual measurements were obtained from 18 January 2021 to 16 June 2022, as shown in Table 2. The measurements were carried out daily or once every three days. A total of 14-22 drone measurements were obtained for the same time range, as shown in Table 3. The survey was carried out once or twice per month. The surface settlements were sampled for data comparison with the drone survey.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Location** & **Measurements** & **Num of Data** \\ \hline
1. & K-SK-10 & Daily or 1 per 3 days & 116 \\
2 & K-SK-11 & Daily or 1 per 3 days & 120 \\
3 & K-SK-14 & Daily or 1 per 3 days & 89 \\
4 & K-SK-19 & Daily or 1 per 3 days & 137 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Surface settlements.  
Figure 8: A cross section profile AA’ in Figure 3 (Red: June 2020; Yellow: July 2020; Green: January 2021).  
Figure 9: Differential DTM (February 2022 to February 2021).  
Figure 10 shows the embankment height in red and the ground-measured cumulative settlement in blue, with drone-surveyed data in yellow dots for the target spots. The drone survey data well describe the settlement pattern. Note that the comparison was conducted for the period when no more banking was carried out.  
Figure 10: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Location** & **Measurements** & **Num of Data** \\ \hline
1 & K-SK-10 & 1\(\sim\)2 per month & 21 \\
1 & K-SK-11 & 1\(\sim\)2 per month & 22 \\
1 & K-SK-14 & 1\(\sim\)2 per month & 19 \\
1 & K-SK-19 & 1\(\sim\)2 per month & 14 \\ \hline \end{tabular}
\end{table}
Table 3: Drone survey.  
Table 4 presents the difference between the two measurements, showing 56-254 mm in RMSE. Overall, a 13 cm vertical accuracy was observed in the study. Considering that the settlement is up to 10 m, the accuracy was decent to measure the elevation change.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Averaged Difference over Time (mm)** & **RMSE Difference over Time (mm)** \\ \hline K-SK-10 & \(-\)84 & 109 \\ K-SK-11 & 6 & 56 \\ K-SK-14 & \(-\)185 & 254 \\ K-SK-19 & \(-\)90 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Differences between drone-survey settlement and the surface settlement.  
Figure 10: Embankment height and cumulative settlement graphs with drone surveying data (surface settlement, K-SK-10–K-SK-19).
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 4 Performance Evaluation
### Statistical Experimental Indicators  
There are several experimental indicators studied in this paper.  
(1) Average success rate  
Average success rate is the proportion of successfully-received packets to the total number of sent packets, as illustrated in Equation (20).  
\[S=\frac{N_{r}}{N_{s}} \tag{20}\]  
Here,\(N_{r}\) is the sum of packets successfully received. \(N_{s}\) is the sum of sending packets.  
(2) Average end-to-end delay  
Average end-to-end delay represents the average undergoing time from a data packet being sent to its arrival at the destination node, as illustrated in Equation (21).  
\[D=\frac{\sum t_{j}}{N_{r}} \tag{21}\]  
Here, \(t_{i}\) indicates the average undergoing time from the \(j\)-th data packet being sent to its arrival at the destination node. \(N_{r}\) is the sum of packets which were successfully received.  
(3) Network lifetime  
The average residual energy of nodes or average number of dead nodes in network can clearly indicate the length of network lifetime.  
(4) Average hop count  
Average hop count indicates the average number of hops from source to destination.  
(5) Average frequencies of preferred parent changing  
Average frequencies of the preferred parent changing indicate the stability of the network's topology. Low frequencies of preferred parent changing can change network topology little, but the optimal route selection is affected. The opposite is true for high frequencies of preferred parent changing. Therefore, the appropriate frequencies of preferred parent changing should be used.  
(6) Weighting factors (\(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\), \(a_{5}\))  
The relationship between weighting factors (\(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\), \(a_{5}\)) and simulation time illustrates the changes of weighting factors of these five routing metrics with the simulation running. That is, RPL-CGA can dynamically adjust the values of \(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\), and \(a_{5}\) according to network changes, choose the optimum parents as preferred parents, and improve network performance effectively.  
### Statistical Experimental Indicators  
We conduct extensive simulation experiments under different conditions. The experiment parameters and some related experiment rules are illustrated in Table 2.  
\(E(y,d)\), listed in Table 1, can be computed according to Equation (22) [28].  
\[E(y,d)=\left\{\begin{array}{ll}2E_{elec}\times y+\varepsilon_{amp}\times y \times d^{2},\ d<d_{0}\\ 2E_{elec}\times y+\varepsilon_{fs}\times y\times d^{4},\ d>d_{0}\end{array}\right. \tag{22}\]  
In Equation (22), \(E_{elec}\) represents the energy loss for relaying unit bit message. \(\varepsilon_{amp}\) and \(\varepsilon_{fs}\) are the energy needed for each unit bit message sent by the amplifier in these two circumstances, respectively. Table 3 shows the values of these parameters.
## 3 Results and Discussion
According to Figure 12a,b, the Barium Ferrite W-type sample compacted at 100 MPa and heated in a microwave field presents major cracks and extended melt areas. Moreover, the thermal runaway was intense in terms of high polarization due to the electromagnetic field followed by arc discharge from inside to the surface. The high temperature of 1010 \({}^{\circ}\)C was recorded at the beginning of the arc discharge. Also, due to compaction, more than half of the sample suffered an exfoliation. The sample compacted at 200 MPa had a similar behavior in the microwave field, but without the initiation of arc discharge, which led to less damage to the sample. Figure 12c,d presents top and side views of the sample compacted at 200 MPa after microwave processing. No arc discharge was reported, but the local overheating at the upper edge of the sample led to micro-melting of the surface. The highest temperature recorded was 934 \({}^{\circ}\)C below sintering temperature, which, for Barium Ferrite W-type materials, is usually between 1050 \({}^{\circ}\)C and 1200 \({}^{\circ}\)C. This level of heating explains the color of the sample, which, in the case of the sintered product, is typically shiny. The samples compacted at 300 MPa presented a similar distribution to those compacted at 200 MPa in terms of temperature distributions and local overheating. No thermal runaway or arc discharge was reported, but the samples suffered from cracking and exfoliation. Figure 12e,f presents the products coded as \(P_{3}\) which were heated in a microwave field. The samples presented cracks on the surface as well as local overheating in the areas where cracks were observed. In addition, around 20% of the sample suffered an exfoliation from the top of the product, which was not reported for the samples compacted at 100 and 200 MPa. The densities of the samples could provide valuable information related to the sintering process. Following the MHH process for sintering the samples, the density of the barium ferrite cylindrical materials increased, and this increase was more significant for products compacted at higher pressures (200 MPa and 300 MPa), because the initial porosity was lower and the material was better prepared to fuse and become denser. At a pressure of 100 MPa, the final density was lower, due to a higher initial porosity. Table 5 contains the measured densities of samples before and after the MHH process. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Densities**} \\  & \multicolumn{2}{c}{**(g/cm\({}^{3}\))**} \\ \cline{2-3}  & **Before MHH** & **After MHH** \\ \hline \(P_{1}\) & 2.26 & 2.99 \\ \hline \(P_{2}\) & 2.97 & 3.76 \\ \hline \(P_{3}\) & 3.09 & 4.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Measured densities before and after the MHH process for \(P_{1}\), \(P_{2}\) and \(P_{3}\) samples.
## 2 Materials and Methods
### Connection Assembly and Manufacturing Process  
The manufacturing process of the full-scale prototype obeyed conventional processes for the manufacture of metal structures. The processes implemented were plasma cutting, inspection, polishing with hand tools, mechanized drilling, and welding with the semi-automatic MIG process. The following points are worth mentioning: the welds that join the diaphragm and the column were made with the full penetration MIG process, while the weld between the cutting plate and the column was a fillet weld; the diaphragm holes were made to a 1/16-inch (1.5875 mm) tolerance in diameter with the bolt; and the contact  
Figure 2: Components and main dimensions of the connection obtained from the design process. The action of the maximum capacity force \(F_{\mathrm{pr}}\) and the maximum probable moment \(M_{\mathrm{f}}\) are also illustrated. surfaces between the beam skids and the diaphragms were prepared to meet critical sliding contact and thus were not painted. The test was carried out at the homologation framework lab (MaPH for its acronym in Spanish) located at the School of Civil Engineering of the University of Valle. Figure 3 shows the experimental setup, including the mounting details of the beam-column connection. As shown in Figure 3a, two supports were installed at each end of the column under the following constraint conditions: at end 1, translations in the plane and out of the plane were restricted; at end 2, vertical translation and translation outside the plane were also restricted; rotation in the plane was allowed for both ends. Figure 3b illustrates the beams that provide lateral support to the framework. The test specimen components were transported to the MaPH and assembled on site. Manually pretensioned bolted joints were utilized, thus providing a pre-tensioning force of 107 kN. In addition, direct tension indicator washers were utilized for the 5/8 in A490 bolts. The MaPH has two auxiliary beams that provide lateral support to the IPE360 beam of the connection, which is located at a fixed distance: \(H=2550\) mm. This distance is greater than the maximum permissible length of the lateral support that guarantees the possibility of developing ductility: \(L_{\text{bmax}}=1860\) mm. Therefore, it was necessary to implement a geometry in the supports such that the distance \(H\) was less than or equal to \(L_{\text{bmax}}\). In this case, the length of the beam measured from the axis of the column was \(L=3563\) mm; the length of the column was \(W=3468\) mm and \(L_{\text{b}}=1800\) mm.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 8 Future Directions in 6G Technology Development
### High-Speed Mobile Experience  
The 6G wireless networks are expected to yield higher data rates, approximated to Tbps, which vary with the type of technology employed. Such technologies may include THz communication, high beamforming, better coding methods, and channel access plans. This area of 6G KPIs needs to be explored in detail. The data preparation and storage facility required for connecting future 6G wireless networks to edge intelligence devices can be achieved by integrating cloud/edge/mist platforms with wireless platforms. In addition, the metric of quality of physical experience (QoPE) supports the linking of AI technology-inferred human insights, QoS, and QoE, which offers support in various ways. In this regard, it is imperative to mention that QoPE supports XR and holographic communication to allow the human brain to practically sense any experience. With the introduction and use of 6G networks and technology, the current network security and protection measures will also be enhanced. Thus, efforts are being invested in integrating new techniques, such as  
Figure 12: Future trends and directions in 6G technology development.  
secure channel coding, channel-based adaptation, quantum key distribution, and quantum teleportation, with future 6G wireless networks [136].