esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
## 7 Conclusions  
The stability of slope with tensile crack is analyzed in this study, and the influence of optimal crack depth and slope parameters on stability are also investigated. The feasibility of the analytical method is verified by comparing with the results of earlier cases, and the applicability of the analytical method is also verified by numerical simulation. To sum up, the main conclusions are obtained as follows:  
(1) The safety factor of slope with tensile crack is small compared with the slope without crack, and the value decreases by about 10%. Neglecting the tensile crack will largely overestimate the stability of slope.  
(2) The safety factor first decreases as the crack depth increases and then increases as the crack depth further increases. The minimum safety factor, corresponding to the optimal crack depth, is treated as the basis for slope stability evaluation.  
(3) The internal friction angle and cohesion of soil play a significant role in the stability of slope subjected to the tensile cracks. The safety factor increases with an increase in cohesion and internal friction angle. As these parameters increase the safety factor will become sensitive, especially when the safety factor is larger than 1.0. Additionally, the safety factor decreases with the increase of slope angle and height, and the larger these parameters are, the more minor the effect of tensile crack on the safety factor is.  
(4) The tensile crack depth should not be overestimated, so it is advised to set a reasonable value, that is, the optimal crack depth, to ensure the rationality of slope stability analysis. The results of optimal crack depth show that the maximum, \(\delta^{\prime}/H=0.28\), does not exceed one-third of the slope height.  
Conceptualization, investigation, methodology, Y.L. and X.C.; data processing, L.W.; original draft, Y.L.; writing and editing draft, Y.L. and X.C. All authors have read and agreed to the published version of the manuscript.  
Project supported by Hebei Natural Science Foundation (No. E2021512002), Key Laboratory of Building Collapse Mechanism and Disaster Prevention, China Earthquake Administration (No. FZ211104); Langfang Science and Technology Research and Development Plan Project (No. 2021011065).  
NOT applicable.  
The data used to support the finding of this study are available from the corresponding author upon request.  
The authors express appreciation to [PERSON] for editing and English language assistance.  
The authors declare no conflict of interest.
## 4 Implementation
### Datasets
#### 4.1.1 BoT-IoT Dataset  
In previous studies, different datasets, such as KDD99, ISCX, and CICIDS2017, have been used to evaluate ML models; however, few datasets have been produced to reflect realistic IoT network traffic. These datasets were either not diverse enough in terms of attacks or not realistic in terms of the testbed [19]. Therefore, [PERSON] et al. [49] designed the BoT-IoT dataset to address these limitations. The BoT-IoT dataset is used in forensic analysis and to evaluate IDS. The dataset contains normal IoT traffic and different types of attack traffic with subcategories for each type, which are listed in Table 2. Reconnaissance  
Figure 3: Illustration of the details of the proposed model.  
is one of the privacy threats, and it allows a threat actor to collect data about a victim via port scanning and OS fingerprinting, among other ways. Information theft includes data theft by unauthorized access and keylogging. On the other hand, a DoS threat affects the availability of services and can damage systems, which make it one of the biggest threats to smart cities. In this dataset, UDP, TCP, and HTTP protocols were used to perform both DoS and DDoS attacks.
## 3 System Model
### The Data Rate  
A receiver receives multiple signals from different source users/humans/machines simultaneously. For the target-estimated signal, the Gaussian noise and other non-target signals are both background noise.  
The attenuation in the fiber link is linear, which can be seen in Equation (4). Therefore, whether data computing occurs at the place of ONUs, OLT, or Internet, the received signal to interference plus noise ratio (SINR) for user/human/machines \(i\) is  
\[SINR_{i}=\frac{\alpha^{2}p_{0}\sum\limits_{j}^{K}|\mathbf{h}_{ki}|^{2}}{\alpha^{2} p_{0}\sum\limits_{k\
eq j}|\mathbf{h}_{ki}|^{2}+\left(\frac{1-\alpha^{K-1}}{1- \alpha}\right)^{2}\sigma^{2}} \tag{5}\]  
where \(\sigma^{2}\) is the noise power of the Gaussian noise, \(\mathbf{n}\), in Equation (1).  
Let \(B_{i}^{X}\) be the bandwidth assigned to user/human/machine \(i\) via \(X\) link. \(X\in\{W,F,Int\}\). \(W\) is the wireless link from user/human/machine to ONU (MEC-1). \(F\) is the fiber link from ONU to OLT (MEC-2). \(Int\) is the fiber link from OLT to Internet (MEC-3). Note that the carrier wavelengths of \(F\) and \(Int\) are different. The data rate of user/human/machine \(i\) via wireless links is  
\[R_{i}^{X}=B_{i}^{X}log(1+SINR_{i}) \tag{6}\]  
Each user/human/machine has a task to execute. The task of the user/human/machine \(i\) is denoted as task \(i\). The data size of task \(i\) is denoted as \(D_{i}\), and the CPU frequency cycles of task \(i\) data computing that are required by ONU, OLT, or Internet, are proportional to \(D_{i}\) with the coefficient \(\eta_{i}\). The CPU frequency cycles required for data computing for task \(i\) are \(\eta_{i}D_{i}\).
## 4 Literature Review on DAS+PRS
### Experimental Procedure
#### 4.3.2 Multi-Class Classification
The length of the optical fiber cable is 1 km, and the two sensors were placed at 665 m and 756 m. 360 signals (40 signals for each class) were used for ANN training, and 90 signals (10 signals for each class) were used for testing. The classification accuracy was 95%. The proposal described in [16] aimed to classify different ethanol quantities in water. The length of the optical fiber cable was 500 m. 5 different sensors were employed for signal acquisition, located each at 87 m, 215 m, 307 m, 397 m, and 436 m. A combination of 9 ethanol quantities from the 5 sensors was intended to be detected, which derives in a 9-class classification problem. 675 signals which correspond to 75 signals per class were employed for ANN training, and 135 signals which correspond to 15 signals per class were employed for testing. The classification accuracy was 92%. Other works focused on machine+activity classification and threat/non-threat classification. For example the works presented in [23; 24] shared the same experimental setup, and aimed to two different types of classification: (1) machine+activity classification, where the same classes as those recorded in [22] were analyzed, and (2) threat detection. These works share the same recording protocol, optical fiber cable configuration, and cross-validation experimental setup that [22]. In [24], the same data employed for GMM training were used for MLP training. Results presented in [23] showed an accuracy of 45% on machine+activity classification, and 80% of TDR and 40% of FAR for threat detection. Results presented in [24] showed an accuracy of 55% on machine+activity classification, and 81% of TDR and 35% of FAR for threat detection. The authors of [25] aimed to classify a series of blind machines carrying out certain activities and threat detection occurring in a long pipeline. A database that comprises 45 machine+activity pairs (30 of them are threats, and the rest are non-threats) with 22.5 h of signal recordings was employed for GMM training. The testing was done in two different stages: (1) the first blind tests were carried out one day in a 400-m pipeline section where the sensor was placed at the beginning of this section, and (2) the second round of blind tests was carried out a different day in a 5 km pipeline section placed 35 km far from the sensor. Results showed a 46% of accuracy when doing machine+activity classification, and 80% of TDR with 10% of FAR in threat detection. Regarding the statistical significance of the results, the same considerations described at the end of Section 4.3.1 also apply here. Consider for example the case of [15], where the tests were done on 90 signals and the reported accuracy was 95%. The confidence interval in this case would be of \(\pm\)4.5% (for a confidence level of 95%), which is rather high.
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 4 Simulation
### Multicast Capacity Comparison  
Multicast capacity refers to the number of multicast groups that a network can accommodate. The reason why there is a limit to the number of multicast groups that a network can accommodate is mainly due to the limit of the forwarding state that nodes can accommodate and the limit of the link bandwidth. The experiment will terminate when the network can no longer accommodate more multicast groups. At this point, the number of multicast groups that the network can accommodate is the multicast capacity.  
In this experiment, we assume that each node can accommodate up to 1k forwarding states, and each link can accommodate up to 1.5 k multicast flows. This experimental setup is based on commercial switching device, such as the Huawei CloudEngine S16700 series and CloudEngine S8700 series switches [39].  
As can be seen from Table 3, SPT has the minimum multicast capacity because it establishes long multicast paths and cannot perceive the network load status, making it easy to cause load congestion. WAVE has the largest multicast capacity because it can detect the network load most accurately. The multicast capacity of RSPT is similar to that of WAVE, reaching 94%, 87%, 82%, and 89% of WAVE performance in four different topologies, respectively. At the same time, the multicast capacity of RSPT is 2.34 times that of SPT and 1.94 times that of Near. The reason why RSPT can have such a similar performance as WAVE and significantly surpass SPT and Near is because it obtains the load status that can effectively detect the multicast path established downlink, thereby avoiding high-load links and nodes.  
It is worth noting that the performance gap between RSPT and WAVE first increases and then decreases. This is because as the topology increases, RSPT's perception error will become larger, and therefore the performance gap with WAVE will increase. However, in the topology of 2 million points, RSPT's detection range has been increased to 4, so the perception accuracy has been improved, and the gap with WAVE has been reduced. This is in line with our previous analysis.  
It can be seen from Table 3 that the larger the network, the greater its multicast capacity. This is easily understandable. With a larger network, there is more room for adjustment in the multicast tree, which is more conducive to avoiding high-load areas. Therefore, larger networks are less likely to experience congestion compared to smaller networks, and can accommodate more multicast groups.
# Experimental Study of 6 LoPLC for Home Energy Management Systems
## 5 Results and Discussion
### Network Capacity  
This section investigates the maximum network capacity in terms of raw throughput. The throughput measures the amount of data (sensor data, control message, luminance instruction and all communication overheads) successfully delivered over the network per unit time. Figure 9 illustrates the variation of system throughput with data size. This simulation is based on one controller and one node.  
Figure 9: System throughput as a function of application data size.  
By increasing the network load from 256-9216 bytes (steps of 256 bytes), the throughput increases until it attains its peak value and begins to drop. That upper threshold signifies the maximum achievable throughput. Although the Hanadu solution is envisioned to provide a maximum data rate of 250 kbps (assumed to be symmetric), Figure 9 shows that the current implementation used in the test bed can only deliver a maximum of 68.198 kbps and 76.2275 kbps in uplink and downlink, respectively. As an illustration, in the 76 bytes contained in the sniffed packet (Figure 5), data payload accounts for only 14 bytes (18.42%), and the rest is communication overhead. Since the overhead (which represents 81.52% of the message) is fixed and the payload is typically small, the current throughput is sufficient for HEMS monitoring and control applications.
## 5 Conclusions  
This work analyzes the performance of various deep CNN architectures for the classification of over-volume vehicles that can act as supporting blocks for implementing over-volume vehicle detection prototypes in real-time. The primary goal of this work is to evaluate the capabilities of cutting-edge deep convolutional neural networks for over-volume vehicle classification into non-over-volume goods carriers, over-volume-goods carriers, and non-goods carriers. A thorough analysis was performed with pre-trained models, namely MobileNetV2, VGGNet19, ResNet50, and EfficientNet[B0][B3][B4]. To the best of our knowledge, this is the first study that presents a quantitative analysis of the above-mentioned deep architectures for over-volume vehicle classification. The performance of these models was analyzed with metrics including the ROC curve, AUC score, confusion matrix, mean square error, and test accuracy. Observations with different architectures revealed that ResNet and EfficientNet architectures shows the best results in the required classification with Resnet50, EfficientNet [B3], and EfficientNet [B4] having an average accuracy of 95%. The future scope of this research may involve specificity in the data so that they are classified in terms of goods, as well as the addition of new classes that may help us to obtain a generic model. This study represents the first step in building a real-time prototype for over-volume vehicle detection.  
Conceptualization, V.D.M.V. and D.S.P.K.; methodology, V.D.M.V., P.V.S.N. and D.S.P.K.; software, V.D.M.V. and D.S.P.K.; validation P.V.S.N., S.S.R. and P.V.; formal analysis, V.D.M.V., S.S.R. and P.V.; investigation, V.D.M.V., P.V.S.N. and D.S.P.K.; data curation, V.D.M.V., P.V.S.N. and D.S.P.K.; writing--original draft preparation, V.D.M.V., P.V.S.N. and D.S.P.K.; writing--review and editing, S.S.R., P.V. and V.R.; visualization, S.S.R. and P.V.; supervision, S.S.R., P.V. and V.R.; project administration, S.S.R., P.V. and V.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**S. No.**} & **Neural** & **Input** & **Test** & \multirow{2}{*}{**Flop**} & **Auc Score** \\  & **Network** & **Dimensions** & **Accuracy** & & \\ \hline
1. & MobileNetV2 & 224 \(\times\) 224 & 86.71 & 1.3 b & 0.9001 \\ \hline
2. & VGG 19 & 226 \(\times\) 226 & 83.38 & 19.6 b & 0.875 \\ \hline
3. & ResNet 50 & 224 \(\times\) 224 & 96.01 & 16.4 b & 0.9701 \\ \hline
4. & EfficientNet [B0] & 240 \(\times\) 240 & 94.01 & 0.7 b & 0.9554 \\ \hline
5. & EfficientNet [B3] & 300 \(\times\) 300 & 96.013 & 1.8 b & 0.9700 \\ \hline
6. & EfficientNet [B4] & 380 \(\times\) 380 & 96.34 & 4.2 b & 0.9726 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of performance measures for different deep learning models.
## 3 Results
### Device Accuracy and Reliability of Data Collection during Field Implementation on Sheep  
For an estimate of the accuracy of the device when placed on animals, the paddock fence lines drawn based on GNSS positional data were placed on the top of the fitted line as shown in Figure 8a which indicates the high accuracy of the GNSS device in the paddock. The measurement errors orthogonal to the fence lines varied from 0 m to a maximum of 0.25 m for the north-west fence line due to the large and complex gate structure which made the line harder to estimate (Figure 8b). In addition to accuracy, the reliability of continuous data collection is another criterion required from a GNSS device. Overall, the RTK rovers worked well and recorded across the study duration, except for a temporary recording issue with rover C on the first day. Rover G was not used on day 1 due to accidental damage, and rover F did not record on day 4 due to operator error. Of 28 total sampling days (7 rovers \(\times\) 4 study days), continuous data were obtained for 25 of these available (Figure 9).  
Figure 8: Graphs of (**a**) the fence line coordinates based on GNSS positional data with an optimally fit line (black line) and the GNSS positional points (coloured lines), and (**b**) the measurement errors of each fence line (north-east, north-west, south-east, and south-west). Note: To normalise the count, the frequency of observations in each bin was divided by the total number of observations.  
Figure 7: Results of laboratory testing the RTK rovers before placement on animals. Each figure shows the measured location of six rovers that were rigidly fixed on a 1 m \(\times\) 2 m grid. (**a**) Before RTK fix was achieved and showing differential GNSS accuracy. (**b**) Rovers stationary with a clear view of the sky. (**c**) Rovers moving with a clear view of the sky. (**d**) Rovers moving underneath tree foliage and losing view of GNSS satellites and during the walk behind the trees which led to blocking of the satellite signals and the RTK correction link (heading NE). Note: The grid size is 2 m and the centre of the plot tracks to the centre of the array of RTK rovers. The large dots indicate the current location of the RTK rovers with the smaller dots displaying their movement history. Plots b and c show the situations similar to that expected in the real trial in the field.
## 3 Methodology
### Scene Decomposer
#### 3.2.2 Implementation of COS Prompting Method
```
1 Input: a list of scenes \(S\) Output: a list of relation sets \(\mathcal{R}\) Initialize: \(\mathcal{R}=\{\}\);
2 forscene\(s_{i}\) in \(S\)do
3 Initialize an empty relation set: \(R_{i}=\{\}\)
4 Step 1: Scene Type Classifying
5\(\rhd\)Use QRAN to predict a type for a scene: \(\hat{l}_{i}=QRAN(h(s_{i}))\);
6 Step 2: Explicit Relation Extracting
7\(\rhd\) Load the model pool \(\mathcal{M}\);
8 forsentence\(u_{k}\) in \(s_{i}\)do
9 formodel\(m_{j}=(L_{i},P_{i},K_{j},Q_{j};R_{j})\) in \(\mathcal{M}\)do
10 if\(mat(\hat{l}_{i},L_{i})\)then
11\(\rhd\) Use N-LT to obtain the POS tags of \(u_{k}\);
12\(\rhd\) Match \(u_{k}\) with POS to \((P_{j},K_{j})\) by using rule \(Q_{j}\);
13 ifmatch successfullythen
14\(\rhd\) Use matched item in \(u_{k}\) to instantiate \(R_{j}\): \(R_{j}\to R_{j}(u_{k})\);
15\(\rhd\) Let \(R_{i}\cup R_{j}(u_{k})\);
16
17 Step 3: Implicit Relation Extracting
18\(\rhd\) Load the implicit template pool \(\mathcal{T}_{j}\)
19\(\rhd\) Index a template for the scene: \(\mathcal{T}(\hat{l}_{i})=\{\mathcal{C}C,TP\}\);
20\(\rhd\) Remove the numbers and operators in \(R_{i}\) to obtain an entity set \(E_{i}\)
21 forconcept\(c_{j}\) in \(C\)do
22 forentity\(c_{k}\) in \(E_{i}\)do
23 if\(mat(c_{j},\epsilon_{k})\)then
24\(\rhd\) Replace \(c_{j}\) in \(TP\) with \(\epsilon_{k}\)
25 else
26\(\rhd\) Use a binary classifier: \(o=BCN([h(c_{j}):h(\epsilon_{k})])\)
27 if\(o=1\)then
28\(\rhd\) Replace \(c_{j}\) in \(TP\) with \(\epsilon_{k}\)
29
30\(\rhd\) Let \(R_{i}=R_{i}\cup TP\);
```
**Procedure 2**Relation extractor using Scene-Aware \(S^{2}\) method. ### Symbolic Solver

In this algorithm, the symbolic solver is primarily responsible for reasoning an input list of relation sets and outputting a solution that includes a series of answers. For each individual relation set, this component follows the same method as [17] to perform relation inference. First, it replaces entities within the relation set \(R_{i}\) with algebraic symbols to form a symbolic equation system \(E_{i}\) and an entity-symbol mapping table \(F_{i}\). Then, it solves for the unknowns in the equation system \(E_{i}\) using SymPy [36]. Finally, it restores the solutions to the relations using the entity-symbol mapping table \(F_{i}\). This component differs from previous research by employing an answer buffer \(A^{*}\) to assist in reasoning across multiple relation sets. Since the order of the relation sets depends on the sequence in which the LLM generates scenes, the answer buffer sequentially records the inference results produced by reasoning each relation set in this order. It allows subsequent relation sets to share results from the answer buffer as conditional relations for their reasoning. The complete reasoning steps of the symbolic solver are shown in Procedure 3.
## 4 Conclusions  
Aiming at the problem of image registration with parallax, an image registration algorithm based on deep learning and local homography transformation is proposed. A sample and label generation method suitable for local homography matrix estimation is designed by using DLT and MDLT, so as to obtain an effective image registration model through supervised learning. The proposed algorithm overcomes the defect that the existing learning-based image registration algorithm cannot be used for local homography matrix estimation and improves the weak robustness of traditional image registration algorithms. Experimental results show that the proposed algorithm achieves high image registration accuracy; low time complexity; and good robustness to illumination, color, and brightness. In particular, the combination of the proposed algorithm and a better CNN architecture can significantly improve the accuracy of image registration.  
In this paper, the MDLT algorithm is adopted to generate samples with local matching points. The perturbation value cannot be set very large, otherwise it will cause unnatural deformation and dislocation of the image. Therefore, the proposed algorithm is more suitable for the sample with weak locality. In addition, compared with the traditional algorithms, the proposed algorithm has higher requirements on hardware and takes a longer time to generate samples and train neural networks; this will be improved in further work.  
Conceptualization, Y.W., M.Y. and G.J.; methodology, Y.W., M.Y. and G.J.; software, Y.W.; investigation, Z.P. and J.L.; Writing--Original draft preparation, Y.W., M.Y. and G.J.; Writing--Review and editing, M.Y. and G.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant No. 61671258, 61871247, 61931022. It was also sponsored by the K. C. Wong Magna Fund of Ningbo University.  
The authors declare no conflict of interest.