esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Results with the PV-GIS Tool  
Having concluded in the previous section that a constant inclination angle of either 25\({}^{\circ}\) S or 30\({}^{\circ}\) S for solar panels in Greece sufficed to obtain the maximum energy gain, a comparison was planned between the monthly \(G_{\epsilon,255}\) or \(G_{\epsilon,205}\) sums in the present study and those derived from the PV Geographical Information System (PV-GIS) tool [67] using the latest Surface Solar Radiation Data Set--Heliostat (SARAH) 2005-2016 database. The purpose of the comparison was to simply evaluate the solar energy derived in this study with some standard tool, such as the PV-GIS. For this reason, four stations were selected from Table 5 to represent each PV\({}_{\text{ctts}}\) zone, namely Souda for PV\({}_{\text{ctts}}\) zone A, Kozani for PV\({}_{\text{ctts}}\) zone B, Chios for PV\({}_{\text{ctts}}\) zone C, and Skyros for PV\({}_{\text{ctts}}\) zone D. Figure 12 presents the above comparisons. In all cases, the PV-GIS-SARAH estimates were in excellent agreement with those from the TMY-PV-based ones of the present study, if one takes into account the different time periods used for the estimates (2005-2016 for the PV-GIS tool and 1985-2014 for the TMYs-PV). The excellent agreement in the comparisons was confirmed by their really high correlation coefficients (see the legend of Figure 12). Any slight difference between the two curves may have been due to the different time periods considered in each case (SARAH and TMY-PV), as well as the need for further development of the algorithms used for estimating the solar radiation from the satellites (SARAH database) [68].  
Figure 12: Intra-annual variation of the monthly \(G_{\epsilon,255}\) or \(G_{\epsilon,305}\) sums for Souda (**a**, PV\({}_{\text{ctts}}\) zone A), Kozani (**b**, PV\({}_{\text{ctts}}\) zone B), Chios (**c**, PV\({}_{\text{ctts}}\) zone C), and Skyros (**d**, PV\({}_{\text{ctts}}\) zone D). The red curves are estimates based on the corresponding photovoltaic typical meteorological year (TMY-PV) of the site, while the blue ones come from the Surface Solar Radiation Data Set—Heliostat (SARAH) 2005–2016 database using the PV Geographical Information System (PV-GIS) tool. The agreement between the two approaches is remarkable. In all cases, the correlation coefficient between the two curves is high: (**a**) 0.9736, (**b**) 0.9852, (**c**) 0.9679, and (**d**) 0.9883.
## 3 Impact of Mining on Withdrawal Roadway
### Exavation Timing of Withdrawal Roadway  
The majority of mines aim to excavate the withdrawal roadway later in order to concentrate the construction efforts on the preparation and extraction of the working face. To mitigate the impact of the mining activities on the working face, the determination of the latest feasible time for excavating the withdrawal roadway has emerged as a significantly crucial concern in this scenario. Based on the law of rock pressure and the numerical simulation results, the following can be elucidated:  
(1) The influence range of the front abutment pressure on the working face should not exceed 1.0-1.2 times that of the width of the working face [17; 18].  
(2) From the analysis of the abutment pressure characteristics, the influence range of the front abutment pressure on working face 21405 is 142.7 m (0.47 times more than that of the width of the working face), while the influence range of the abutment pressure on the withdrawal roadway is 28.2 m (5.9 times more than that of the width of the withdrawal roadway).  
(3) The stress superposition occurs when the distance between the working face and the withdrawal roadway is 115 m.  
(4) At 130 m in front of the working face, the stress fluctuation of the coal body significantly increases.  
From the perspective of facilitating the maintenance and control of the withdrawal roadway, the maximum distance of 171 m (which is the sum of the influence ranges of the abutment pressure between the working face and the roadway) can be selected as a safe distance to avoid significant disturbances. In other words, reasonable timing should be selected to ensure that the distance between the coal face and the withdrawal roadway is greater than 171 m when the excavation of the withdrawal roadway is completed. Considering that the excavation speed \(v_{1}\) and the mining speed \(v_{2}\) are not consistent, the following function can be used to determine the excavation timing:  
\[L=\frac{305v_{2}}{v_{1}}+171 \tag{1}\]  
Figure 7: The evolution of vertical stress during mining: (**a**) The evolution curves of vertical stress at different measuring points in front of the working face; (**b**) The evolution curves of stress fluctuation value; (**c**) The amplitude of stress variation.  
When the distance between the coal mining face and the designated position of the withdrawal roadway reaches \(L\), the excavation of the withdrawal roadway must commence. During on-site implementation, if there are changes in mining production (for example, if the excavation speed of the withdrawal roadway is slowed down or the mining speed of the working face is accelerated), this distance can be appropriately enlarged.
# Adaptive Attention-Enhanced Yolo for Wall Crack Detection
## 4 Performance Evaluation
### Comparison with State of the Art  
Many models and methods with good detection results have been investigated in recent years. [PERSON] et al. [48] developed a method that combines pixel values with geometry to generate features using an optical gradient lifter. The CrackTree [49] method identifies cracks through geodesic shadow removal, tensor voting, and graph models. [PERSON] et al. [50] combined a 2D Gaussian kernel and Brownian motion processes in the generation of crack images, and the prediction of actual and simulated cracks was based on an image segmentation network approach. In order to recognize and segment different types of cracks, [PERSON] et al. [51] tried to train the network at the pixel level, while the morphological features of the cracks are represented by a single pixel-width skeleton structure, which is a full convolutional network-based detection method.  
During our experiments, we obtained Precision, Recall, and F1-Score, among other evaluation metrics, for the AAEY model. In order to more fully demonstrate its excellent performance, we also compared AAEY with a variety of existing methods, and the detailed comparison results are shown in Table 3.  
Figure 4: Recognition effects of visualization in the ablation study.  
The metric F1-Score, which is a reconciled average of Precision and Recall, enables a comprehensive assessment of the model's performance, through which we can specifically describe the advantages of the AAEY model over other existing techniques. As can be seen from the table, the F1-Score of the model proposed in this study is 84.22%, which is the maximum value among the models. The comparison shows that it is 14.72% and 13.42% higher than the F1-Score of the LightGBM model and the CrackTree model, respectively. It has a somewhat smaller difference of 8.98% and 4.27% with the F1-Score of CSN and FCN, respectively.  
Based on the data analysis above, it is obvious to conclude that the AAEY model outperforms many existing models on the detection and recognition task, significantly improving the prediction of object recognition. In the practical application of wall crack detection, the AAEY model does further enhance both detection efficiency and accuracy.
## 4 Proposed Binary Field Multiplication
### Implementation Results on an 8-Bit ATmega128 MCU and Comparison to Previous Work  
We have implemented our methods on a target board containing ATmega128 MCU. Table 2 shows the timing results of the proposed methods and compare them with those of [PERSON] et al.'s method [10]. For efficiency, the proposed methods are developed in AVR assembly language. A 64-bit wise proposed secure Block-Comb method requires 1213 \(cc\) (resp. 1050 \(cc\)) with (resp. without) multiplier encoding process, and these timing results are an 8.8% (resp. 21.1%) improvement compared with [PERSON] et al.'s 64-bit wise multiplication method. In the case of 128-bit multiplication, the proposed method with (resp. without) multiplier encoding requires 3816 \(cc\) (resp. 3490 \(cc\)), which are improvements of 32.8% and 38.5% compared with [PERSON] et al.'s 128-bit wise multiplication method. The improvement ratio in 128-bit multiplication has been increased compared with that in 64-bit multiplication. This is because we implement 128-bit multiplication by the enhanced Karatsuba technique with assembly language while [PERSON] et al.'s 128-bit multiplication method is implemented with \(C\) and assembly language. Furthermore, the partial product at step 8 in Algorithm 4 does not require a multiplier encoding process because the encoded multiplier can be obtained by XORing the precomputed multipliers as \((EB[15\ldots 8]\oplus EB[7\ldots 0])\).
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology
### Consensus Functions
#### 2.4.2 Meta-Clustering Algorithm  
MCLA defines the similarity between two clusters in terms of the number of objects grouped in them using the extended Jaccard index [55]. After that, the similarity matrix between the clusters is created and then a graph is formed by considering clusters as nodes and assigning a weight equal to the similarity between clusters to the edge between two nodes. Finally, this graph is partitioned using the METIS algorithm and the final partition known as \"meta-clustering\" is obtained.
## Nomenclature  
\(\mathcal{F}_{B}\): Satellite coordinate system  
\(\mathcal{F}_{A}\): Antenna coordinate system  
\(\boldsymbol{\phi}\): Relative rotation joint angle vector of the antenna with respect to the satellite \(\boldsymbol{I}_{c}\): Inertia of the whole satellite around the center of mass of the whole satellite \(\boldsymbol{I}_{ac}\): Inertia matrix of the antenna around the center of mass of the whole satellite \(N\): Elements of dividing the control input \(t_{f}\): Settling time of satellite and antenna \(\boldsymbol{X}\): Decision variable of Nonlinear programming problem
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Deep-Learning-Based Fault Diagnosis Method
#### 5.3.3 Recurrent Neural Network (RNN)
(2018) [111] \\ \cline{2-6}  & \multirow{4}{*}{Centralized} & A supervised SAE model is proposed to achieve higher accuracy at a lower number of iterations & Temperature & Soft fault & [PERSON] et al. (2020) [112] \\ \hline \multirow{4}{*}{Deep Learning-based} & \multirow{4}{*}{DBN} & A method based on DBN and generalized likelihood ratio test is proposed & \multirow{4}{*}{Thermocouple} & Soft fault & [PERSON] et al. (2017) [118] \\ \cline{2-6}  & & The extended DBN model uses the strategy of repeatedly stacking the original data in the training phase to extract useful information fully & - & - & [PERSON] et al. (2020) [119] \\ \hline \multirow{4}{*}{RNN} & \multirow{4}{*}{distributed} & RNN models nodes, the dynamics of nodes, and the coupling with other nodes with a low false positive rate & Temperature & Drift fault & [PERSON] et al. (2008) [123] \\ \cline{2-6}  & & LSTM and data fusion methods for multi-sensor fault diagnosis & Angle, speed & Stuck and biased fault & [PERSON] et al. (2019) [124] \\ \cline{2-6}  & \multirow{4}{*}{-} & A fault diagnosis scheme based on RNN is proposed and combined with linear parameter changes to improve robustness & \multirow{4}{*}{Angle} & Soft fault & [PERSON] et al. (2020) [125] \\ \cline{2-6}  & & A fault diagnosis scheme based on RNN is proposed and combined with linear parameter changes to improve robustness & \multirow{4}{*}{Angle} & Soft fault & [PERSON] et al. Due to the fact that sensor data are collected at various time points with a high temporal correlation, the RNN model has been extensively studied in sensor fault diagnosis. [PERSON] et al. [132] utilized RNN to model sensor nodes, node dynamics, and sensor node coupling to achieve sensor node fault diagnosis. This method had a lower early false alarm rate than the Kalman filter. [PERSON] et al. [133] adopted LSTM for wind turbine sensor fault diagnosis. The method can be extended to multi-sensor fault diagnosis through data fusion. In addition, the method was robust under limited data conditions, and its performance was superior to SVM and other approaches. [PERSON] et al. [134] proposed an RNN-based satellite sensor fault diagnosis scheme. They combined the linear parameter change method with the based model method to improve the robustness of fault diagnosis and the accuracy of the scheme. To solve the overfitting problem of deep neural networks in fault diagnosis, [PERSON] et al. [135] implemented dropout in the LSTM model to prevent overfitting and enhance the training process's efficacy. Since deep learning automatically extracts features from the original data, there is no mapping between the extracted features and the fault mechanism. Although the deep learning model is suitable for resolving the big data characteristics of large-scale sensors, its training speed is slow and consumes computing resources. Therefore, the deep learning model is not typically executed directly on sensors but on edge nodes or the cloud.
# Motion Generation for Crane Simulators Using Streamlined Motion Blending Technology
## 3 Streamlined Motion Blending Technology for Crane Motion Simulation  
### Analysis of Streamlined Motion Blending Technology  
The crane motion simulation scheme based on motion blending shown in Figure 6 can also be further optimized according to the characteristics of crane motion. In the motion components of the crane, the large-scale low-frequency motion caused by the acceleration and deceleration of the trolley needs to be separated by low-pass filtering, and replaced by the tilt coordinate method with the equivalent rotation motion. The motion platform can reproduce other small-scale high-frequency motions. Since the low-frequency motion and high-frequency motion are naturally separated from each other from the moment they are generated, it is only necessary to combine the low-frequency channel motion components converted by the tilt coordinate method with the high-frequency channel motion components as the final driving instructions issued to the motion platform to realize the motion simulation of the crane motion with high fidelity.  
The structural diagram of the motion simulation system of the streamlined crane proposed in this paper is based on the improvement of the traditional motion blending technology (as shown in Figure 7). Though, for this way to develop a dynamic simulation system, the technical route is clearer, the workload is greatly reduced, and the real-time performance is stronger.  
Figure 6: Crane motion simulation based on motion blending technology.  
Figure 7: Motion simulation of crane based on streamlined motion blending technology.  
Table 2 makes a detailed comparison between the existing motion blending technology in motion simulation and the streamlined motion blending technology proposed in this paper. It can be seen that the streamlined motion blending technology is consistent with the existing motion blending technology in principle, so it has a reliable theoretical basis. At the same time, the latter fully combines the characteristics of crane motion so that the calculation efficiency of the algorithm is higher.
# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 3 Proposed Method
### Evaluation Metrics  
The performance metrics of the classification are mean class accuracy (mAcc) and overall accuracy (OA). The performance metrics of the segmentation are overall accuracy (OA) and mean Intersection over Union (mIoU). Assuming a total of k categories, TP denotes the number of true positives, FP denotes the number of false positives and FN denotes the number of false negatives.  
\(mAcc\) is defined as  
\[mAcc=\frac{1}{k}\sum\limits_{i=0}^{k-1}\frac{TP}{\sum\limits_{j=0}^{k-1}FN}, \tag{6}\]  
\(OA\) is defined as  
\[OA=\frac{\sum\limits_{i=0}^{k-1}TP}{\sum\limits_{i=0}^{k-1}\sum\limits_{j=0}^ {k-1}FN}, \tag{7}\]  
\(mIoU\) is defined as  
\[mIoU=\frac{1}{k+1}\sum\limits_{i=0}^{k}\frac{TP}{FN+FP+TP}, \tag{8}\]  
For the point cloud classification, in order to describe the classification performance more intuitively, we use the classification result of each object to calculate the classification accuracy of each object, as described in  
\[\text{CA}=\frac{\sum\limits_{i=1}^{m}\delta(r_{i},s_{i})}{m}, \tag{9}\]  
where \(r_{i}\), \(s_{i}\) are the actual and classified object points, respectively, \(m\) is the number of points of each actual object and \(\delta\) is the indicator function.  
\[\delta(r_{i},s_{i})=\begin{cases}1&r_{i}=s_{i}\\ 0&r_{i}\
eq s_{i}\end{cases} \tag{10}\]
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 4 IoTDS: A Host-Based Botnet Detection System for IoT
### Process Details
#### 4.4.2 Continuous Analysis Phase  
The Continuous Analysis phase, presented in Figure 3, consists of using the induced model to monitor the device and try to prevent IoT botnets from running in the system. The IoTDS Agent first checks whether there is a behaviour model in the system. If the Agent does not have a behaviour model induced by the Management Console, it starts the Induction Model phase. After this check, the system starts a trust factor counter \(n\) with zero. Next, the Agent collects the observations to produce a new data instance according to the \(s\)-second time-window used to induce the behaviour model. This new instance undergoes the same normalisation step described in Equation (10), except that this time, the \(max(x[j])\) and \(min(x[j])\) are the same as the ones used when the behaviour model was induced. After that, the approach decides whether this new instance is a legitimate behaviour or an anomaly. If it is the latter, the device raises an alert.  
The alert is sent to the IoTDS Management Console with information about the device's IP address, information about the resources utilisation and the timestamp of the alert. Once the IoTDS Management Console receives the alert, it is stored and presented in the user interface for the network administrator analysis. After the alert is sent, the IoTDS Agent increments the trust factor \(n\) by one and resumes the host data analysis.  
The objective of the trust factor is to avoid generating an excessive number of alerts during an infection. If a device is compromised, the resource consumption data changes its behaviour for as long as the malware operates. Consequently, the Agent detects this unusual behaviour every time it analyses the devices' data, generating an alert at every new iteration. Then, the trust factor takes place to control this situation. Most of IoT botnets, such as Mirai, BashLite, and Hajime run in the device's RAM and, as a consequence, are not persistent. Thus, a system reboot is effective in removing the malware. When the trust factor reaches \(\theta\), a threshold that can be set by the network administrator, the IoTDS Agent sends a reboot warning to the IoTDS Management Console informing the device's IP address and the timestamp, and then it reboots the device. When the device restarts, the Agent resumes the Continuous Analysis phase.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 5 MASU Empirical Evaluation
### Evaluation Results
#### 5.4.2 Battery Consumption of Producers and Consumers  
Some additional tests were performed to evaluate the energy costs of Producer and Consumer roles. We compared the performance of Producers and Consumers in case of high and low intensity data sharing processes, when sharing data using AllJoyn. That is, the cost of sensing from those sensors that generate low and high amounts of data, including the costs of sharing such data at low and high data rate, respectively.  
The high intensity data process was established by sensing GPS data continuously and sharing it every time geolocation value changed. This implied that the GPS data was sent continuously due to the high precision of the GPS sensor and also because the devices were constantly moving. On the other hand, the low intensity data sharing process was established by sending an audio file captured from the smartphone microphone.  
Figure 17 shows the results obtained. In the case of high intensity data sharing process, the cost of sensing is slightly smaller than the cost of sensing and transmitting the data, but higher than the cost of receiving it. Therefore, in case of high data-intensive Producers we can confirm the usefulness of sharing the sensed data using AllJoyn, even when the data is shared solely with one Consumer.  
Contrarily, the cost of using AllJoyn for sharing small amounts of data, at a low transmission rate, is very high and considerably higher than the cost of sensing. For this reason, in case of low data-intensive Producers it would make much more sense to collect the information directly in the devices that are going to consume it. However, it seems reasonable to think that if the number of devices is high enough, this energy cost could be compensated.
## 3 Numerical Examples
### Cantilever Beam
#### 3.1.3 Sensitivity of Stress Solution to Number of Field Nodes  
Different numbers of regularly distributed field nodes (126, 175 and 403) were carried out, respectively, for numerical examinations to test the sensitivity of stress solution to the number of field nodes. Stress solutions at coordinate position (24 m, 6 m) were plotted in Figure 9. A model of regularly distributed 175 field nodes and 175 distributed irregularly field nodes was also carried out to test the sensitivity of stress solution to the distribution of field nodes. We found that the stress solution of based on improved RPIM and improved EFG were both insensitive to the field node distribution and the number of field nodes. The results also show that there are good agreements among the stress solution of improved RPIM, improved EFG and the analytical results.
## 3 Routing
### Routing in CRSN: Rising Issues
#### 3.2.2 Spectrum Sensing/Licensed User Interference  
Spectrum sensing introduces silent periods in the nodes and hence reduces the time required for a duty-cycled node to attempt a data transfer. This reduction arises because nodes cannot perform spectrum sensing and data transfer at the same time. Thus, reducing the sensing period increases the probability of interfering with primary user activity and also increases channel access duration. However, based on the licensed user activity and the route management algorithm, the sensing period can be varied. The other option is to use multiple front-ends for dedicated activities. This option, in addition to its cost implications, compromises the simplicity of the nodes.
## 4 Performance Evaluation in Edge Scenarios
### System Call Overhead  
A core challenge faced by SGX is how to manage its relationship with OS. In Section 2, two approaches to interacting OS are introduced. The SGX SDK fully distrusts OS, which means that system calls cannot be made within the enclave. While this approach enhances security, it presents challenges in terms of performance and code portability. On the other hand, when allowing for a moderate increase in TCB, LibOS-based solutions can perform most system calls within the enclave. Through security checks, these solutions can also avoid attacks from OS, such as Iago attacks [70]. We primarily tested the system call overhead of process creation and inter-process communication in Gramine and Occlum to observe SGX performance in multi-process tasks.