esa-sceva/satcom-chunk-collection huggingface dataset:
Selection of an Optimum Anchoring Method of Composite Rock Stratum Based on Anchor Bolt Support Prestress Field  
[PERSON]  
1 Coal Mining Branch, China Coal Research Institute, Beijing 100013, China; [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (B.H.)
2 CCTEG Coal Mining Research Institute, Beijing 100013, China; [EMAIL_ADDRESS]  
[PERSON]  
2 CCTEG Coal Mining Research  
pointed out that roadway deformation decreases with the increase in anchor bolt prestress. [PERSON] [12] simulated and studied the influence of different composite components on the bolt support stress field through a large-scale similar material test bed, obtained the influence law of different bolt combination components on the diffusion effect of the bolt support prestress field, and presented the order of advantages and disadvantages. [PERSON] et al. [13] studied the support effect of bond stiffness on prestressed anchor bolts through numerical simulation. [PERSON] et al. [14] proposed a reasonable range of bolt pretightening torque for commonly used anchor construction pretightening machines in coal mines, effectively eliminating the phenomenon of bolt breakage. [PERSON] [15] analyzed the deformation and failure characteristics of fractured roofs under the influence of bedding and proposed that high prestressed anchor rods have a good effect on supporting fractured roof tunnels with deteriorating bedding. [PERSON] et al. [16] explored the real-time monitoring of the load status of service anchor bolts in tunnels under the influence of high-intensity mining and analyzed the real-time safety status of tunnel anchor bolts based on on-site monitoring data.  
The above research results have played a significant role in driving the large-scale promotion of bolt support in China. As most of the coal measure strata are sedimentary rocks and laminar strata, composite rock strata exist widely in the underground surrounding rocks of coal mines and are common in the roof structure of roadways [17]. The common performance of such roadways is a weak interlayer roof, soft-hard intercalated roof, and so on. Due to the large difference in physical and mechanical parameters among the layers and the low cohesion between layers, it is easy for deflection and subsidence to occur after mining, which leads to mutual separation and falling off between strata, and even further causes roof fall accidents. In view of the common composite rock strata in the mine, the thickness and lithology of each layer vary greatly under different conditions. However, the anchoring parameters currently used in the design of bolt supports are relatively singular, and the anchoring parameters used in different situations of composite roofs are basically the same. Therefore, this paper hopes to optimize the anchorage parameters for a composite rock stratum by studying the distribution law of the prestress field of anchor bolt supports under different rock lithologies and anchorage lengths from the perspective of the prestress field of anchor bolt supports.
## 2 Materials and Methods  
### Experimental  
The experimental setup (depicted in Figure 1) consisted of a silver (99.999% of purity) target placed at the bottom of a Griffin beaker and submerged in 2 mL of either pure water or aqueous solutions containing one of two different stabilizers: CTAB or PVP. Laser irradiation of the silver target was performed at room temperature, with the beaker rotating continuously to avoid crater formation on the target due to the laser ablation. Laser pulses of 50 fs centered at 800 nm were provided by a Ti:Sapphire amplified laser system (Spectra Physics, Santa Clara, CA, USA) working at a repetition rate of 1 kHz. The laser beam was focused on the target with a 25 cm focal length lens and impinged from the top of the beaker with an angle of incidence almost normal to the target's surface and a spot size of about 100 \(\upmu\)m. The irradiation time for all the experiments was set to 10 min.  
Figure 1: Experimental setup for pulsed laser deposition in liquid environment.  
Three different aqueous solutions were used: pure water, a solution with 0.1 M of CTAB, and a solution with 0.25 mM of PVP. Additionally, the laser pulse energy was varied with an attenuator wheel, and three cases were studied: 0.8, 1.5, and 2.0 mJ/pulse, corresponding to a laser fluence of 10.2, 19.1, and 25.5 J/cm\({}^{2}\), respectively. Considering the spot size, \(D\), of 100 \(\upmu\)m and the pulse energy, \(E\), the laser fluence can be estimated as \(F=4E/\pi D^{2}\).  
Optical absorption spectra were measured at room temperature using a deuterium-halogen continuous light source (DH-2000 from Ocean Optics, Largo, FL, USA) and a compact spectrometer (USB2000+ from Ocean Optics) configured with a multichannel array detector for measuring the whole spectrum in the range 200-1100 nm with a spectral resolution better than 1 nm. Integration time was set to 100 ms, and 10 scans were averaged for each measurement. Optical measurements were performed for each colloidal solution using a glass cuvette with an optical path of 1 cm. Morphology, size, and size distribution of the resulting nanocrystals were analyzed by means of transmission electron microscopy (TEM) on a JEM-1400 PLUS (JEOL, Akishima, Tokyo, Japan) transmission electron microscope operating at an acceleration voltage of 120 kV. Carbon-coated 400 square mesh copper grids were used. All the samples were centrifuged at least once before blotting on the grid.
# SDN Anomalous Traffic Detection Based on Temporal Convolutional Network
## 2 Related Work  
Aiming at the threat of anomalous traffic attacks faced in SDN architectures, in order to meet the challenges of the complex network attack methods and the dynamic traffic patterns, researchers at home and abroad have proposed a variety of detection methods. Currently, the research on SDN anomalous traffic detection methods mainly focuses on the following aspects: detection techniques based on traditional statistical methods, which realize anomaly identification by analyzing the differences in the statistical distribution of traffic feature parameters. Machine learning methods show strong adaptability, and typical algorithms include supervised learning models such as Support Vector Machine (SVM), Decision Tree and K-Nearest Neighbor (KNN). With the breakthrough of deep learning technology, neural network-based detection methods have gradually become the mainstream of research, which can be categorized into supervised learning, unsupervised learning, and semi-supervised learning.
## 2 System Model
The four possible cases of sensing outcomes and PU activities, i.e., \(P_{ON}P_{D}\), \(P_{ON}P_{MD}\), \(P_{OFF}P_{FA}\), and \(P_{OFF}(1-P_{FA})\), are considered for the proposed analysis, and are assumed to be identical for all channels. In detail, \(\rm P_{ON}P_{D}\) refers to a scenario where the activity of a PU on a channel is represented by the \(ON\) state, and the state is correctly detected by a SU. \(P_{ON}P_{MD}\) refers to the case where the PU is in the \(ON\) state, but the state is incorrectly detected by the SU, and a missed detection occurs. \(P_{OFF}P_{FA}\) refers to the case where the PU is in the \(OFF\) state, but the state is incorrectly detected by the SU, and a false alarm occurs. Similarly, \(P_{OFF}(1-P_{FA})\) refers to the scenario where the PU is in the \(OFF\) state, and the state is correctly detected by the SU. A CR transmitter, i.e., SU, switches to the next channel for the two cases, i.e., \(P_{ON}P_{MD}\) and \(P_{OFF}P_{FA}\). In our work, we assume that the sum utilization of spectrum is contributed only when a SU detects the state of the considered channel correctly, i.e., \(P_{OFF}(1-P_{FA})\) and \(P_{ON}P_{D}\). The access contention for the SUs is not considered.
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results
### Security Evaluation
#### 4.2.1 Vulnerabilities  
There are several imperfections and security issues of the LoRaWAN technology (specification 1.0.x):  
* The preshared key AppKey cannot be updated. The key update problem is discussed in [70].
* The keys are persistently stored on a LoRaWAN device and could be subject to physical attacks. Using a tamper-resistant storage (i.e., secure element, HSM) improves the security of the stored key, but it also increases the costs.
* The paper [71] demonstrates that LoRaWAN transmissions are prone to jamming attacks.
* The paper [72] shows that the OTAA approach enables attackers to conduct a replay attack.
* The operator's NS knows the application keys and can decrypt the end-to-end communication, as noted in [73] (fixed in 1.0.4).
* The paper [74] shows potential vulnerabilities to denial of service (DoS) attacks during the join procedure.
## 3 Observations and Analysis
### Impact of Interventions on Affective Disorders  
Out of the 37 studies reviewed, 7 showed no significant impact of the serious game intervention as compared to the control group. The affective disorders for these were  
Figure 2: Distribution of affective disorders that were investigated by the selected studies.  
either anxiety (5 out of 37; 4 ASD and 1 neurotypical) [58; 62; 63; 64; 67], PTSD (1 out of 37; 1 neurotypical) [45] and fear of falling (1 out of 37; 1 neurotypical) [57]. There were no obvious similarities between these studies and hence no conclusive reasoning could be synthesized for the lack of positive impact from serious game interventions for these studies, apart from the following:  
* Both [62; 64] mention that MindLight and CBT interventions were effective on their own but together they do not have any additional positive impact on the outcome.
* Both [63; 67] mention that their study protocols were not entirely observed by the participants and hence the findings could not be conclusive.  
However, there was ample evidence that the serious game interventions in the rest of the 30 studies did produce a net positive impact as a therapy tool or as a mediator to associated therapies for affective disorders. In total, 3 of these studies were for the autistic population [65; 66; 68] while the other 27 were for the neurotypical population. In fact, the authors of [66; 68] argued that these two together could be highly effective in reducing patients' phobias, which is in contrast to what the authors of [62; 64] found about applying serious games with CBT for anxiety therapy, as often phobias are classified as anxiety disorders [101].  
There were 4 studies that had more than or equal to 100 participants among the 37 selected studies, which were 180 for the study that employed phobia as a therapy for acrophobia [54], 174 for the study that employed MindLight as a therapy for anxiety [55], 117 for the study that also employed MindLight as a therapy for anxiety [35] and 100 for the study that employed VIMSE as a therapy for spider phobia; all of them showed a positive impact of the serious game intervention on their participants.  
Games such as MindLight seem to be most effective for the younger population as a sole anxiety therapy, as was evident from the relatively large populations for the studies [35; 49; 55] where the mean ages of the participants were 12 and 9.5, respectively. On the other hand, several VR games showed a significant positive impact as therapies for different types of anxieties, phobias and depression [34; 38; 39; 41; 43; 46; 47; 48; 54; 65; 66; 68] and a majority of these cases employed a therapeutic VR with considerably good outcomes. Table 4 provides greater details on the methodology, data collection techniques and interventions that were employed in the studies along with their outcomes.
## 7 Long-Range Underground Monitoring  
### Technology Selection  
Long-Range Underground Monitoring applications require long ranges, low power consumption and low cost. These properties can be met by Low Power Wide Area Network (LPWAN) technologies. There are a lot of LPWAN technologies on the market to choose from and the technologies differ in a lot of points. Regarding underground mining operations, the difference between operator-based and private LPWA networks is the most crucial. Operator-based LPWAN networks (e.g., Sigfox or NB-IoT) are maintained and hosted by network carriers, usually in extensive surface networks covering whole regions or countries. For mining operations in dynamic environments, the network infrastructure has to be added, adapted and moved. This task makes it less feasible to have the infrastructure of the network maintained by another party chosen from a very limited list and cannot be done by the mine operator or a wide list of contractors. Furthermore, licensing is limiting the possibility of open and independent research and tests. Therefore, in underground mining operations, privately hosted LPWAN technologies should be preferred. However, in surface mining the usage of operator-based LPWANs is feasible. A hybrid approach of multiple technologies (operator-based and private) can be of an advantage for combined surface and underground operations.  
An overview and comparison of current LPWAN technologies regarding technical, implementation and functional factors is given in [10]. With that publication, a first selection and evaluation of LPWAN technologies can be conducted.  
When considering only private LPWAN networks, LoRa, Weightless, Wi-SUN, DASH7, IQRF and MIOTY technologies should be considered (see [10]). Evaluation and possible potentials of these technologies regarding mining operations will be derived in this paper.  
\begin{table}
\begin{tabular}{l|c c c} \hline \hline Bluetooth & 3 & 3 & 1 & 2 \\ ZigBee & 2 & 3 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summarized Comparison and Evaluation of Bluetooth and ZigBee.
## 2 Materials and Methods
### The Moderate Resolution Imaging Spectroradiometer-Based Vegetation Photosynthesis Model
#### 2.4.3 Model Evaluation  
In order to test VPM's applicability for coastal wetland and to check the consistency between the modeled and the observed results, four statistical criteria were used for the model evaluation: (i) the coefficient of determination (R\({}^{2}\)). Due to temporal autocorrelation within simulated GPP and EC-based GPP, generalized least square regression was conducted by using _gls()_ function in R package _nlme_ to adjust the residuals. Accordingly, the R\({}^{2}_{\text{Cox and Snell}}\) was instead used as pseudo-R\({}^{2}\) by performing _nagelkerke()_ function in package _rompanion_; Equation (2), the root of mean square error (RMSE, Equation (14)); Equation (3), the relative mean deviation (RMD, Equation (15)); Equation (4), the relative error (RE) [55]. The RMSE provides the prediction error of model by heavily weighting high errors. The RMD can smooth out the differences between the simulated and measured results by weighting all of the errors in the same way, which when close to 0 indicate the absence of bias for the model [15, 56]. We use SPSS version 20.0 (SPSS Inc., Chicago, IL, USA) and R package 3.2.1 [57] to conduct all of the statistical calculations.  
\[RMSE=\frac{100}{\overline{O}}\sqrt{\frac{\sum_{i=1}^{n}\left(P_{i}-O_{i} \right)^{2}}{n}} \tag{14}\]  
\[RMD=\frac{100}{\overline{O}}\sum_{i=1}^{n}\frac{P_{i}-O_{i}}{n} \tag{15}\]  
where \(O_{i}\) represent the EC estimated values and \(P_{i}\) represent the model-simulated values. \(\overline{O}\) is the mean of the EC estimated value and \(\overline{P}\) is the mean of the model-predicted value. \(n\) is the number of observations.
# Three-Dimensional Object Recognition Using Orthogonal Polynomials: An Embedded Kernel Approach
## 4 Results and Discussion  
The performance of the recognition system based on the proposed technique is evaluated in this section. The evaluation is performed using the McGill dataset formed in [54]. Figure 5 shows samples of the object in the dataset. The dataset (McGill) serves as a benchmark dataset and includes nineteen classes expressed as 3D objects. These classes are categorized as planes, chairs, dinosaurs, fishes, cups, birds, spiders, spectacles, snakes, octopus, teddies, pliers, dolphins, fours, ants, humans, tables, and craps. For the SVM, we utilized the LIB-SVM version 3.24 [55]. It is noteworthy that the experiment is performed using Matlab 2019 on an 8-core core i7-4700 MQ CPU with a frequency of 2.4 HGz and 16 GB RAM.  
In this paper, we focused on 19 different objects and applied a range of translation effects to enrich the original dataset. The augmentation process starts by shifting each object within a range from \(-10\) to 10 along the x, y, and z axes, using a step size of 5. This systematic shifting creates a diverse array of translations, which helps the recognition system generalize better across various object positions. This augmentation approach guarantee that the dataset captures a wide range of spatial arrangements. A total of 5902 samples are generated as a result of the utilized augmentation techniques. To evaluate the performance of the proposed technique, we implemented different evaluation metric, which are discussed in the following sections.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 2 Related Work
### Counterpart Schemes in Brief
#### 2.1.1 AUV Visits Path Nodes  
The designed UWSN is three-dimensional, where the nodes' depth is the same by anchoring them to the ocean floor. The AUV is also moving at a constant depth, _i.e._, \(D_{raw}<D\). In this way, the authors simplified the problem, and they simulated the two-dimensional field, with a random uniform node distribution at a fixed depth \(D\). This two-dimensional Plane A is divided into several sub-regions, which are named clusters. Initially, when the network starts, the AUV partitions the network into clusters through a Voronoi generator point and broadcasts the cluster information. Nodes use this information to identify the cluster to which they belong. After the cluster and node association phase, MNs select a delegate node known as the CH. The selected CH in each cluster further divides the cluster into several sub-clusters. In each sub-cluster, a PN is selected through the CH, which gathers primary data from MNs and relays them to the AUV. The CH broadcasts the PN information throughout the cluster. After this phase, the AUV takes another tour, which is called the data gathering tour; it visits each PN for which the CH informs of its address.  
Data gathering is completed in three steps: MNs send their sensed data to the PN; in the next step, PNs send the received data to the AUV; and in the final step, the AUV sends all of the received data from the PNs to the sink. PNs gather data continuously, except the interval in which they transfer their data to the AUV. At the start of data gathering tour, the AUV visits each cluster and gets the list of PNs in that cluster. After obtaining the lists from each cluster, the AUV visits each PN in the order of the list. After completing one tour, the AUV returns to the start point and sends data to the surface sink. TDMA is used for multiplexing the link between the MNs and PN. The PN assigns the time slots to the MNs for data sending. To avoid the interference between the clusters, CDMA is employed. CHs with the AUV and the PN with the CH only exchange the control information, and they share the contention-based control channel of \(F_{control}\). Between the AUV and sink, CDMA with dedicated orthogonal code is employed.
## 1 Introduction
### Modelling Climate Scenarios with ARPEGE-Climat
#### 2.2.3 Choice of RCP 8.5  
The Representative Concentration Pathway 8.5 scenario (RCP 8.5) is characterized by increasing greenhouse gas concentration levels (>1370 eq-CO\({}_{2}\) in 2100). This scenario is the most extreme and corresponds to a radiative forcing of +5 W/m\({}^{2}\) in 2050 (only +4 W/m\({}^{2}\) for RCP 4.5) [7]. The RCP 8.5 scenario represents a \"pessimistic\" or \"conservative\" vision of what the climate could be like in 2050. In this scenario, the energy demand is high, with the highest greenhouse gas emissions, corresponding to a high population and modest technological improvements. In France, RCP 8.5 corresponded to a temperature increase of 2.2 \({}^{\circ}\)C in 2050 compared to the 1976-2005 period, and a temperature increase of 1.7 \({}^{\circ}\)C for RCP 4.5 in 2050 [44; 45]. According to the IPCC, RCP 8.5 corresponds today to historical paths since 1992.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Annual Index Period Computation** & **Extreme Event Threshold** & **Minimum Cultivation Area** \\ \hline Grassland & 3 rd 10 days in April–2 nd 10 days & \(-200\) mm & \(2\times 10^{4}\) \\  & in September & \(3\)rd 10 days in April–1 st 10 days & \(-200\) mm & \(2\times 10^{4}\) \\ Winter barley & 3 rd 10 days in April–1 st 10 days & \(-200\) mm & \(9\times 10^{4}\) \\ Soft winter wheat & 3 rd 10 days in April–1 st 10 days & \(-200\) mm & \(1.1\times 10^{4}\) \\ North of France & 3 rd 10 days in August & \(-200\) mm & \(1.0\times 10^{4}\) \\ South of France & & in August & \\ \hline \end{tabular}
\end{table}
Table 1: Best parameters used for the calibration of the model by crop.
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 4 Experimental Validation
### PSD Effect on Ambiguity Fixing Performance
Figure 6: Ambiguity fixing time with different TPSDs expressed by the scale factor applied to the empirical TPSD (the value of horizontal axis represents n in the scaled multiple of 2\({}^{n}\)). Figure 7: Ambiguity success rate with different TPSDs expressed by the scale factor applied to the empirical TPSD (the value of horizontal axis represents n in the scaled multiple of 2\({}^{n}\)). As can be seen from Figure 7, consistent with the impact on the ambiguity fixing time, the best TPSD did not have the highest ambiguity success rate. The empirical TPSD cannot adjust according to the actual observation data, so a larger TPSD makes the ambiguity fixing performance of the reference station not optimal. It is necessary to establish a method to determine the TPSD associated with the actual observation data.
## 7 Experimental Section
### Algorithm Properties Validation
#### 7.1.2 Cluster Radius \(R_{a}\) and Network Lifetime  
On network lifetime, there is no clear definition. According to the definitions given in [25], the lifetime of a WSN can be quantified using the following three kinds of metrics: (1) the time from the deployment of the network to the death of the first node (first node dies, FND); (2) the time when a certain percent of nodes are alive (percentage nodes alive, PNA); (3) the time when all of the nodes are dead in the network (last node dies, LND).  
Here, we define the network lifetime as the percentage nodes alive (PNA). The network lifetime is defined as the time when 90 percent of nodes are alive. To verify the conclusion obtained in Theorem 3, we choose low density Scenario 2 and high density Scenario 4 to run our algorithm. As can be seen from the Figure 8, in Scenario 2, the network lifetime increases gradually with the incrementof \(R_{a}\) and gets its maximum value when \(R_{a}\) is \(45\) m, after which, there is a slight decrease, which is consistent with our theoretical analysis results, \(45.5698\) m, obtained from Theorem 3. In our theoretical analysis, when the \(R_{a}\) is set as \(45.5698\) m, the energy consumption of the entire network is the minimum. Furthermore, as shown in Figure 8, in Scenario 4, when \(R_{a}\) is set as \(40\) m, the network lifetime achieves the maximum value, likewise, which is coincident with the optimal value \(39.1604\) m. On the basis of these cases, the conclusion of Theorem 3 is proven to be correct.