esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Preprocessing Steps
The equations used in SVM [43] are as follows: \[f(x)=sign\,\left(\,\sum_{i=1}^{N}\alpha_{i}\,y_{i}\,K(x,x_{i})+b\right)\] (8) where \(f\) (\(x\)) is the predicted class label, \(\alpha_{i}\) is the Lagrange multiplier for the \(i\)-th training sample, \(y_{i}\) is the class label of the \(i\)-th training sample (either \(+1\) or \(-1\)), \(K(x,x_{i})\) is the kernel function that maps the input features \(x\) and \(x_{i}\) to a higher-dimensional space, and \(b\) is the bias term. * Kernel: A kernel function is a function that maps the input data into a higher-dimensional space, where it is easier to find a separating hyperplane. The equation of linear Kernal [44] is as follows: \[K\big{(}x_{i}\,,\ x_{j}\big{)}=x_{i}^{T}\,x_{j}\] (9) where \(x_{i}\) and \(x_{j}\) are the input features of the \(i\)-th and \(j\)-th training samples, respectively. * KNN: \(K\)-Nearest Neighbors (KNN) is a simple, yet effective machine learning algorithm used for classification and regression tasks. The basic idea behind KNN is to find the \(K\)-nearest training samples to a given test sample based on a distance metric, and then use the labels of the \(K\)-nearest neighbors to predict the label of the test sample. The equation of KNN can be represented as follows [42]: \[d\,\left(xi,\ xj\right)=\sqrt{\sum_{k=1}^{p}\left(x_{ik}-x_{jk}\right)^{2}}\] (10) where \(p\) is the number of features in each sample. * Neural Network: Neural networks are a powerful class of machine learning algorithms that are inspired by the structure and function of the human brain. A neural network consists of multiple layers of interconnected processing units called neurons, and the input data are processed through the network in a forward pass, with the output of each layer serving as the input to the next layer. After applying different ML algorithms, using the cross-validation \(K\) = 10, the accuracy results were very low, which is to be expected. The results are presented in Table 3.  
The accuracy results proved the importance of preprocessing steps since it is a constructive and essential step for obtaining the correct data required to build a classifier, as shown in several types of research such as [44; 45; 46; 47; 48]. Data preprocessing, which aims to convert the raw data into a simpler and more efficient format for subsequent processing steps, is a crucial step in the knowledge discovery process because quality decisions must be based on quality data. Thus, the preprocessing procedures were carried out on the AWID3 dataset. The AWID3 consists of 13 CSV files with 36,913,503 instances, 30,387,099 of normal traffic, and 6,526,404 malicious ones. That has been studied and is well understood.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 5 Simulation Results
### Results and Analysis
#### 5.3.3 End-to-End Delay  
Figure 9 presents the temporal variations in WLQRP, VBF, and HH-VBF as the node count increases. The graphical depiction indicates that WLQRP experiences a gradual reduction in latency as the node count grows. This trend is congruent with the observations in Figure 4, where WLQRP's hop count follows a comparable trajectory with respect to the node count. This congruence arises due to the strong positive correlation between packet hop count and latency. In the context of WLQRP, packet latency encompasses the transmission time within the channel and the duration expended at relay nodes for reception and subsequent forwarding. This implies that the time spent at relay nodes for reception and forwarding contributes significantly to the overall latency. Consequently, as the number of relay nodes traversed by the packet increases, the latency proportionately rises.  
Figure 8 reveals a decline in the packet hop count as the node count increases, and correspondingly, the latency of the packets decreases in line with node count escalation. The graph further suggests that WLQRP outperforms the VBF protocol and HH-VBF in terms of latency. One contributing factor to this phenomenon is that VBF and HH-VBF entail a waiting period during next-hop selection, thereby introducing a fraction of its latency. However, the substantial discrepancy in latency between the two routing protocols can be primarily attributed to WLQRP's lower average hop count compared to VBF. The reduction in relay transmission time achieved by the WLQRP protocol significantly diminishes latency, leading to a considerable reduction compared to VBF and HH-VBF.
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### Obtaining Data for the Model  
The ANN was designed using visual images of buildings damaged in the devastating Kahramanmaras earthquake of 6 February 2023. It was of magnitudes 7.7 and 7.6 and severely affected 11 districts in Turkiye. The damage levels for 194 buildings were classified based on their visual appearance after the earthquake as no damage, slight damage, moderate damage, or severe damage or collapse. No earthquake damage means a structure was unharmed. There is no risk in using the building. Slight damage implies the earthquake caused paint, plaster, and tiny fractures on the building's walls. These are buildings with plaster. Moderate damage is defined as structures that may develop fine fractures in the carrying components and walls during an earthquake. Severe damage or collapse means that the earthquake damaged the building's load-bearing materials. Large and extensive shear fractures/separations occur in structures.  
In the model, depending on their appearance following the earthquake, structures were classified as no damage, slight damage, moderate damage, severe damage, or collapse. After that, the consensus value for each parameter was used to generate the risk index (\(RI\)). Risk indices range from 0 to 1, whereas damage levels are 0.25, 0.50, 0.75, and 1. Except for building apparent quality, which is rated between 0 (good) and 1 (bad), building vulnerability parameters like soft stories, heavy overhangs, short columns, bounding effect, topography effect, vertical irregularity, and plan irregularity are quantified as 0 or 1. The details of the modeling are given in detail in the following sections.
## 3 Materials and Methods
### Vibration Signal Analysis  
Signal analysis plays a crucial role in understanding the dynamic behavior of drilling operations. Time series and frequency analysis were conducted to identify temporal patterns and frequency components in the vibration signals [36]. These patterns and components provide insights into various conditions affecting the drill bit's performance. This study carried out a comparative analysis between a standard drill bit and a faulty one to underscore differences in their vibration characteristics. The analysis focused on significant frequencies and distinctive peaks, indicating notable variations in vibration characteristics between the two drill conditions. Figures 4 and 5 display the time-series waveforms and frequency spectra comparisons of the two conditions. Time series analysis revealed no discernible trends, with both normal and faulty conditions exhibiting fluctuations in acceleration amplitude over time. Subsequently, the frequency analysis of the two-bit conditions was carried out, as depicted in Figures 4(b) and 5(b). Generally, it was noted that both conditions exhibited similar dominant frequencies within the range of 0-6000 Hz. A consistent feature emerged, with the highest peaks consistently clustered around the 4000-5000 Hz frequency range. A distinct trend occurred in terms of amplitude; normal drill bits consistently exhibited a lower amplitude compared to the faulty drill bit. The presence of larger peaks indicated increased vibrational energy at the dominant frequency due to structural damage to the abnormal drill bit, reduced cutting efficiencies, and uneven contact of the bit with the rock. The analysis process required a detailed examination of the signals, making it labor-intensive. Therefore, it is essential to implement techniques that can improve the efficiency and accuracy of the process, such as deep learning models and XAI.  
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{2}{c}{**Drilling Specifications**} \\ \hline Striking frequency (Hz) & 52 \\ Impact pressure (MPa) & 13.5–13.7 \\ Rotation pressure (MPa) & 4–6 \\ Feed pressure (MPa) & 4 \\ Hits per minute & 3120 \\ \hline \end{tabular}
\end{table}
Table 1: Rock drill machine specifications.  
Figure 3: Drill bits used in the experiments: (**a**) normal and (**b**) abnormal.
## 5 Emerging Challenges and Opportunities
### Mobile Sensing and Cloud-Enabled Decisions
Since the data and emergency response resources (e.g., hospitals, rescuers, volunteers, polices and emergency management organizations) may belong to different organizations and need to collaborate with each other in case of emergencies, [92] introduces the concept of social networking to construct a community-based emergency management system on the cloud. In this system, the cloud centre has two major components, _information repository_ and _social networking_. The information repository manages the emergency response resources and collects the data of emergent situations from social networking, while the social networking provides users with access interfaces to interact with each other. _QoS in the cloud:_ For scalability and fault-tolerance reasons, MapReduce provides a parallel and distributed programming model to process massive amount of data. Hadoop is one of the most commonly-applied implementations of MapReduce to enable the virtualisation in the cloud. As a multi-core CUPis involved, Hadoop exploits virtualisation technologies to deploy multiple virtual machines (VMs) in a single physical machine, so as to speed up data processing in cloud. Work [93] exploits the above advantages to divide each database query into several subqueries in MapReduce for quick recovery from mid-query faults. However, I/O tasks between VMs may incur overheads, especially for I/O-intensive applications. Thus, how to reduce I/O tasks becomes a critical challenge in the cloud. Reference [94] allocates data across all VMs based on the data locality to reduce I/O overheads. To relieve the waste of network bandwidth, due to communications between VMs for I/O-intensive applications, [95] proposes a _decentralized affinity-aware migration_ technique that monitors fingerprints of traffic exchanges between VMs and dynamically adjusts/migrates VM placement for communication optimization. In addition to optimization of data distribution in the cloud, some efforts address the QoS issue on virtual machine provisioning, which emphasises how many virtual machine instances should be allocated to an application for achieving QoS requirements (e.g., response time). Obviously, virtual machine provisioning without over/under-provisioning and having unacceptable QoS is a challenge, because workload and runtime situations are highly dynamic. Based on an analysis of workload for each application, [96] proposes a prediction-based approach to determine the number of allocated virtual machines.
# Exploring Structural Changes in Ge-Te Amorphous Films Through Small-Angle Neutron Scattering
## 2 Materials and Methods  
In this work, we studied amorphous films of the Ge\({}_{x}\)Te\({}_{1-x}\) system with \(x<0.5\). The use of films, rather than materials prepared by melt-quenching techniques, is justifiedby the inherently limited amorphous domain of this system. Thermal co-evaporation offers a significant advantage by enabling access to a broader amorphous region, making it a more suitable technique for investigating amorphous GeTe mixtures.  
The use of the SANS technique offers certain advantages over X-rays. The neutron scattering lengths of Ge (8.185 fm) and Te (5.80 fm) are comparable, allowing a balanced analysis of both elements in the Ge-Te glass structure. This contrasts with X-rays, where the higher atomic number of tellurium (Z = 52) leads to a dominant contribution, overshadowing that of germanium (Z = 32). In X-ray studies, the atomic form factor serves as the equivalent to the neutron scattering length, but its dependence on atomic number makes X-rays less suited for studying systems with significant differences in Z.
## 3 Example Analysis
### Performance Verification of Large Samples
#### 3.2.4 Analysis of Fault Detection Performance  
With the influence of network attacks, the fault detection results of the CIKS-MFS-FLGB method are shown in Table 4. The average detection precision of the CIKS-MFS-FLGB method for faults is 93.62%, and the average F1 score is 93.41%. The CIKS-MFS-FLGB method not only distinguishes the causes of various faults but also realizes the fuzzy localization of fault points. This makes it easier for operators to remove faulty devices in time after network attacks, avoiding or mitigating losses caused by transient processes. In addition, it achieves the stable and efficient operation of the power system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attack Type** & **Class** & **Traditional Precision** & **Improved Model Accuracy** & **Traditional F1** & **Improved Model F1** \\ \hline Data injection (FDIA) & 7–12 & 0.7518 & 0.9385 & 0.7084 & 0.9376 \\ Remote tripping & & & & & \\ command & 15–20 & 0.7473 & 0.9466 & 0.7326 & 0.9388 \\ injection (TRCJ) & & & & & \\ Relay setting & 21–30, 34–36 & 0.8286 & 0.9376 & 0.8211 & 0.9321 \\ change (RSC) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Large sample network attack detection performance.  
Figure 9: Large sample classification report.
## 2 Hardware Composition of Performance Testing System for Space Harmonic Reducer  
As shown in Figure 2, the test system mainly includes drive module, high vacuum and low temperature component, tested component, and load component. For different test items, the appropriate components are selected to couple into the test system. When the test is carried out under normal temperature and pressure, the driving component, the tested component, and the loading component are directly connected.  
1. The drive assembly includes servo motors, input torque and angle encoder, which are installed on the fixed bearing. The servo motor power is output after input torque sensor and angle encoder in turn.
2. The high vacuum and low temperature experimental component includes the input end magnetic fluid sealing shaft, vacuum tank, optical platform fixedly arranged in the vacuum tank, output end magnetic fluid sealing shaft, vacuum pumping system, temperature system, monitoring and control system, etc.
3. The load assembly includes the output end angle encoder, torque sensor, and torque motor which are respectively installed on the fixed support, and the power is input to the torque motor through the output end angle encoder and torque sensor in turn.
###### Abstract  
Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R\({}^{2}\) of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.  
remote sensing; agriculture; crop monitoring; Sentinel-2; wheat 202012278; doi:10.3390/rs12142278
# Optimal Fleet Transition Modeling for Sustainable Inland Waterways Transport
## 6 Conclusions  
This study presents an optimization model for the transition to sustainable inland waterways transport in local lakes, with a specific focus on Orta lake in Italy. The model aims to determine the minimum number of electric vessels required to replace the existing diesel-based fleet, taking into account various parameters such as capacity, range, charging time, and costs of the available electric vessel models.  
The results obtained from applying the model to the case study of Orta lake provide valuable insights into the feasibility and implications of the transition. They highlight the importance of the charging infrastructure, specifically the charging power of the charging stations, in determining the optimal fleet configuration and operation.  
In scenarios with higher charging powers (1000 kW and 500 kW), the entire fleet of boats can be substituted with the same number of electric boats without any change to the routes or to the timetable. However, in the scenario with 500 kW charging power, the state of charge of the boats can drop under 50%, representing a risk for the safe management of the fleet. In scenarios with lower charging powers (200 kW and 100 kW), the minimum number of vessels increases to four, indicating the need for more electric boats to cover the same routes. These findings contribute to the growing body of knowledge on sustainable inland waterways transport and offer a practical tool for planning the transition in local lakes or other maritime settings.
## 5 Implementation and Results  
In this work we conducted three experiments. In the first experiment, we applied the four predictive classifiers (NB, KNN, SVM, and DT) without using the optimized wrapper selection method for the sake of comparison. In the second experiment, we evaluated the performance of the 16 proposed models and compared the obtained results with the first experiment and previous works [3; 27]. In the third experiment, we employed the CNN deep learning approach to compare its results with the proposed models.
# LaRoAwan and Urban Waste Management--A Trial
## 5 Evaluation
### Deployment Conditions Impact
#### 5.2.1 Iglo  
Four tests were performed on different glass containers, reflecting the combinations of the state of the container, full and completely empty, and test conditions, inside and outside. Table 2 shows an analysis on the signal level (RSSI--Received Signal Strength Indicator) of the messages sent by the probe when installed inside and outside each one. The values result from the tests, where measured RSSI values distribution is presented in Figure 17. We can observe the difference on received signal strength indication samples distribution, where inside measurements present a higher statistical variance than when the probe was outside. This behavior is consistent on both container states.  
In terms of impact on the signal, it is observable that the Iglo introduces an average attenuation of 6 dB when empty and about 2 dB when full. It should be noted that the measurements were made in containers whose model is identical but were in different positions within ISEL. The measurements were also made on different days and at different times of the day, so the comparative analysis should focus on inside/outside conditions, that is, the difference in the RSSI value, representing the impact of the waste container build material.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**RSSI Value (dBm)**} & \multirow{2}{*}{**Probe Message Count**} \\  & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} & \multirow{2}{*}{**Avg**} & \multirow{2}{*}{**StdDev**} \\  & & & & & \\ \hline Iglo full & & & & & \\ \hline Inside & \(-91\) & \(-66\) & \(-73.75\) & \(5.18\) & \(60\) \\ Outside & \(-83\) & \(-65\) & \(-71.82\) & \(3.69\) & \(60\) \\ Difference & \(-8\) & \(-1\) & \(-1.93\) & & \\ Iglo empty & & & & & \\ \hline Inside & \(-85\) & \(-62\) & \(-70.07\) & \(5.18\) & \(59\) \\ Outside & \(-72\) & \(-59\) & \(-63.98\) & \(3.38\) & \(59\) \\ Difference & \(-13\) & \(-3\) & \(-6.09\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: RSSI measurements on Iglö containing glass.  
Figure 16: Received message SNR versus RSSI.