esa-sceva/satcom-chunk-collection huggingface dataset:
# Calculation of Blasting Damage Zone Radius of Different Charge Structures in Burnt Rock
## 3 The Introduction of the Damage Factor in Burnt Rock  
### Damage Factor in Damage Mechanics  
Under the action of an external load, the internal microstructure of rock is further deteriorated, which is called rock damage. Damage variables or damage factors are usually used to represent the degree of deterioration of rock. There are two kinds of damage variables commonly used in rock mechanics:  
1. Defining the damage variable according to the crack area  
The meso-defects inside the rock reduce the effective bearing area of the rock. If the bearing area of the intact rock is \(A\) and the effective bearing area of the damaged rock is reduced to \(A_{1}\), the damage factor defined by the fracture area can be expressed as  
\[D=\frac{A-A_{1}}{A} \tag{8}\]  
In Equation (8), parameter \(D\) represents the degree of rock damage. It should be noted here that when \(D=0\), the rock is complete; when \(D=1\), the rock is completely destroyed.
2. Defining the damage variable according to the decrease in the elastic modulus  
The elastic modulus of intact rock is defined as \(E_{0}\), and the elastic modulus of damaged rock is defined as \(E_{1}\); then, the damage factor \(D\) is  
\[D=1-\frac{E_{1}}{E_{0}} \tag{9}\]  
After defining the damage factor \(D\), the stress \(\sigma/\sigma^{\prime}\) relationship in the material before and after damage can be obtained:  
\[\sigma^{\prime}=\frac{\sigma}{1-D} \tag{10}\]The equivalent effect becomes  
\[\varepsilon=\frac{\sigma}{E_{1}}=\frac{\sigma}{(1-D)E_{0}}=\frac{\sigma^{\prime}} {E_{0}} \tag{11}\]  
The damage factor in rock can be obtained by direct or indirect measurement methods. Direct measurement refers to the direct observation of the number, shape, size and distribution of various microdefects in the rock by means of an optical microscope, X-ray imaging and an infrared camera. An indirect measurement method involves determining the damage factor of rock according to the principle that the microstructure of rock determines the macroscopic behavior by measuring the resistance, wave velocity, stiffness, strength and plastic deformation of rock.  
At present, there are two simple methods used in many test methods:  
1. Elastic modulus measurement method By indirectly measuring the elastic modulus of rock before and after damage, the damage factor of rock can be obtained according to the calculation of Formula (9) above. This method is simple and easy to operate.
2. Wave velocity measurement method The relationship between the elastic wave velocity \(c\), elastic modulus \(E\) and rock density \(\rho\) can be expressed by the following formula:  
\[E=\rho c^{2} \tag{12}\]  
The relationship between the rock elastic wave velocity and damage factor can be obtained by combining Formula (9) and Formula (12):  
\[D=1-\frac{\rho_{1}c_{1}^{2}}{\rho_{2}c_{2}^{2}} \tag{13}\]  
In this formula, \(\rho_{1}/\rho_{2}\)--density of rock before and after the damage, kg/m\({}^{3}\), and \(c_{1}\)/\(c_{2}\)--wave velocity before and after the rock damage, m/s.
## 3 Interrogation Techniques
### Polarimetric Detection
#### 3.1.2 Dual Quadrature Scheme  
Another improvement of the processing scheme presented above is shown in Figure 4. In this case, the output light is divided into two orthogonal polarizations, through the use of a Wollaston prism, and these two signals are detected by two independent photodetectors and processed by an analog circuit that computes the output signal \(S\), given by Equation (4).  
Instead of using a Wollaston prism, it is possible to use a fiber coupler to divide the signal into two polarized states at \(\pm 45^{\circ}\) with respect to the input polarizer, and allowing the generation of two output  
Figure 4: Schematic of the dual-quadrature polarimetric detection incorporating optical fiber links. Legend: GRIN (GRadient INdex); P\({}_{\rm D}\) (Photodetector); P\({}_{i}\) (\(I\) = x,y) optical power in polarizations states.  
Figure 3: Transfer function of two polarizers showing the transmitted power as a function of their relative angle.  
signals with opposite phase. This dual-quadrature scheme offers better common noise rejection than the previous one.  
When the Faraday rotation is relatively small and we include the linear birefringence (\(\beta\)) effect in the sensing element, Equation (4) becomes:  
\[\begin{array}{cc}S_{AC}{=}2\theta_{F}\frac{sin\beta}{\beta}&\beta{\gg}2\theta_ {F}\\ S_{AC}{=}2\theta_{F}&\beta{\ll}2\theta_{F}\end{array} \tag{5}\]  
The two methods presented above do not take into account the linear birefringence effect, which as can be seen from Equation (5), can greatly reduce the sensitivity of the sensor. Furthermore, because the linear birefringence (\(\beta\)) is usually temperature dependent, severe measurement errors can be introduced. To account for linear birefringence, it is necessary to introduce other detection methods that require more complex signal processing strategies that will be described in the following section [1].
## 5 Discussion
Through this integration, the process is monitored with greater efficiency and optimization, which means the total integration of digital and physical systems. AR and VR in conjunction with DTs are innovative technologies that offer realistic environments, thus generating an immersive and interactive experience for the operator and clients, increasing the understanding of the systems, and therefore having greater precision for decision-making and reducing errors by the operator, which has a direct impact on planning and improving productivity. In addition, these systems can improve the training of operators and be used in the classroom where they allow for greater interaction and simulation with real parameters, which favors learning in technical areas. Another aspect of the results analyzed was the transportation stage between stations; this is carried out with a mobile robot that has a Lidar sensor for mapping. It was observed that the information received from the physical environment can be used for different characteristics to obtain the trajectory through simulations that are more optimal and, in this way, reduce the time required. It should be considered that one of the factors that affects transport time is the angle of vision that the robot has toward the obstacles. This angle depends on the initial and final positions in which the robot is located at the beginning and end of the process. Some limiting factors that were revealed during the results of the study were the high consumption of computational resources and the latency presented in the cloud connection because it directly depends on the quality of the Internet. This is due to the large amount of data that the MES sends and because these data are collected from the cloud to develop AR and VR applications. To solve this problem, the main parameters that will be sent for monitoring and control from the immersive environments were selected and, in this way, communication times were reduced; also, when controlling from the virtual environment, this was immediately reflected in the physical environment and vice versa. Within the protocols used for communication between the different levels and platforms, robustness and bidirectionality were mainly considered. Furthermore, it should be considered that the choice of a protocol also depends on the characteristics of the physical equipment. In our case, it is advantageous to use the OPC UA industrial standard because it guarantees compatibility and variability in addition to having fluid interfaces and communication in I4.0 processes, allowing communication between different devices. However, the implementation of DTs in Industry 4.0 still presents several challenges that must be addressed, among which are: (1) the complexity and distribution of the networks that communicate the management levels due to the integration of technologies and security that the data must have, (2) performance problems that the simulation of immersive DTs has due to the high consumption of computational resources that they present, and (3) interoperability and the integration of systems and data between platforms.
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 2 Related Work
### wav2 vec  
wav2 vec is an unsupervised pre-training method for speech recognition that learns representations of raw audio. It trains on a large amount of unlabeled data to learn speech representations. The resulting representations are then put into an acoustic model instead of traditional acoustic features such as log-mel filterbank features. wav2 vec consists of two main modules: the _encoder_ network and the _context_ network. First, give an original voice signal \(x_{i}\in X\), using the _encoder_ to generate a low-frequency feature representation \(z_{i}\in Z\)  
Figure 1: Overview of Contrastive Predictive Coding, audio as input.  
Then mix multiple latent representations \(z_{i}...z_{i-v}\) as the _context_ inputs produce a single contextualized tensor \(c_{i}=g(z_{i}...z_{i-v})\). The framework of the model is shown in Figure 2.  
The model takes the sample \(z_{i+k}\) from the next \(k\) steps as a positive sample and \(z\) from \(Z\) with a random distribution \(p_{n}\) as a negative sample. For each step \(k=1,...,K\), the following contrastive loss function is proposed:  
\[\mathcal{L}_{k}=-\sum_{i=1}^{T-k}(log\sigma(z_{i+k}^{\top}h_{k}(c_{i}))+ \lambda\operatorname*{\mathbb{E}}_{z\sim p_{n}}[log\sigma(-z^{\top}h_{k}(c_{i} ))]) \tag{2}\]  
where \(\sigma(x)=1/(1+exp(-x))\), \(h_{k}(\cdot)\) is a step-specific affine transformation for every step \(k\), \(T\) is the length of the audio sequence, and \(\lambda\) is the number of negative samples.
# A Proposal for IoT Dynamic Routes Selection Based on Contextual Information
## 2 Related Work
It is observed that MRHOF offers better performance than OF0 in terms of network quality. The MRHOF is suitable for use in sensor network that require data delivery in a reliable network. In turn, OF0 is suitable for use in sensors network that require fast network connection training and low power consumption. A Smart Energy Efficient Objective Function (SEEOF) is proposed in [7]. It was designed to create an IPv6 mesh topology for IoT based on smart metering applications. The SEEOF is based on RPL and it is designed to use energy efficiently and extend the network lifetime. It mainly takes into account the node energy. The energy consumption in RPL using SEEOF was compared with MRHOF [6]. Simulation results show that an improvement up to 27% is attained in the network life time when using the proposed objective function. More important, the results show that SEEOF balances the energy consumption more uniformly among the battery powered devices keeping them alive for longer duration. In [8], an Energy Efficient and Path Reliability Aware Objective Function (ERAOF) for IoT applications that requires energy efficiency and reliability in data transmission is proposed. The ERAOF is based on the composition of the following metrics: node energy and link quality. Different from the proposal presented in [7], ERAOF considers not only the energy consumed but also the quality of the link in the process of route selection. This characteristic motivates its inclusion in the comparative analysis performed in this work. The proposal of this work was based mainly on the works presented in [7; 8; 14; 19; 20]. These works motivated the authors to propose a new approach for five main reasons: (i) to use appropriate metrics for 6 LoWPAN environments; (ii) to perform a weighted average of the metrics to provide quality message transmission through a sensor network without wire; (iii) to use the fuzzy logic objective function; (iv) to use an energy efficient objective function; and (v) to use Context-Aware Objective Function. The proposed approach is presented in the next section.
###### Abstract  
Earthquake-induced structural bounding may cause major damages to structures, and therefore it should be prevented. This study is focused on using an artificial neural network (ANN) method to determine the sufficient seismic gap in order to avoid collisions between two adjacent buildings during seismic excitations. Six lumped mass models of structures with a different number of stories (from one to six) have been considered in the study. The earthquake characteristics and the parameters of buildings have been defined as inputs in the ANN analysis. The required seismic gap preventing bounding has been firstly determined for specified structural arrangements and earthquake records. In order to validate the method for other structural parameters, the study has been further extended for buildings with different values of height, mass, and stiffness of each story. Finally, the parametric analysis has been conducted for various earthquakes scaled to different values of the peak ground acceleration (PGA). The results of the verification and validation analyses indicate that the determined seismic gaps are large enough to prevent structural collisions, and they are just appropriate for all different structural arrangements, seismic excitations, and structural parameters. The results of the parametric analysis show that the increase in the PGA of earthquake records leads to a substantial, nearly uniform, increase in the required seismic gap between structures. The above conclusions clearly indicate that the ANN method can be successfully used to determine the minimal distance between two adjacent buildings preventing their collisions during different seismic excitations.  
seismic gap; structural bounding; earthquakes; artificial neural network +
Footnote †: journal: Applied Sciences
## 4 Radial-Shaped Geo Clustering and Angular Routing  
The sensing nodes are deployed randomly over the deployment area, and the network area is divided into a circular-shaped virtual concentric \(m\) number of rings. Moreover, each virtual ring has \(k\) number of clusters. The sectors of radial-shaped virtual concentric rings \(\theta\) are calculated from the midpoint of the network area _plot_\((x_{Center},y_{Center})\) to the radius of each ring based on the sine and cosine transformations as given in algorithm lines 1 to 4. The nodes are deployed randomly and each node identifies its polar coordinates in line no. 8. The node identifies its own sector from lines 9 to 16 in Algorithm 1.  
Figure 1: Deployment structure of radial-shaped clustering.  
```
1:Input:\(r\), \(m\), \((x_{Center},y_{Center})\)
2:Output: Total number of clusters
3:\(k=(2*i-1)*n\)
4:for\(i=1\) to \(k\)
5:for\(j=1\) to \(i\)
6: Generate the number of sectors according to the Equations (1)-(3)
7:\(plot(x,y)\)
8:endfor
9:endfor
10: Deploy the nodes randomly and measure the polar coordinates by using the formula \(r=sqrt(x^{2}+y^{2})\)\(\theta=\text{atan}\Big{(}\frac{y}{x}\Big{)}\)
11:for\(i=1\) to s.no
12:if\(node_{layer(i)}==r\)
13:\(\text{k}=(2*i-1)*n\)
14:for\(j=1\) to \(k\_\text{nodes}\)
15:if\(node(i)_{angle}\leq sector(i)_{angle}\)
16:\(node(i).positionsector(i).position\)
17:endif
18:endfor
19:endif
20:endfor
```  
**Algorithm 1** Radial-shaped clustering (RSC).
## 4 Experiments and Results
### Experiments on Real Datasets  
In this section, the performance of the CPDD-ID algorithm is further evaluated against six other algorithms on ten real datasets, all of which are taken from the UCI machine learning repository, and the parameters of all the real datasets on the seven different algorithms are given in Table 7.  
In particular, Table 8 shows the clustering results of the selected real datasets on the different algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **K-Means** & **DBSCAN** & **DPC** & \begin{tabular}{c} **RNN-** \\ **DBSCAN** \\ \end{tabular} & **LDP-** & \
\begin{tabular}{c} **HCDC** \\ **MST** \\ \end{tabular} & **HCDC** &
\begin{tabular}{c} **CPDD-** \\ **ID** \\ \end{tabular} \\ \hline Thyroid & k = 3 & EPS = 5500, MinPts = 3 & dc = 0.2 & k = 4 & NC = 3 & NC = 3 & k = 17 \\ Wine & k = 3 & EPS = 30,000, MinPts = 2 & dc = 0.2 & k = 4 & NC = 3 & NC = 3 & k = 7 \\ Ionosphere & k = 2 & EPS = 0.78, MinPts = 0.78 & dc = 2 & k = 50 & NC = 2 & NC = 2 & k = 3 \\ Wireless & k = 4 & EPS = 0.14, MinPts = 11 & dc = 2 & k = 10 & NC = 4 & NC = 4 & k = 36 \\ Ecoli & k = 8 & EPS = 0.5, MinPts = 5 & dc = 2 & k = 20 & NC = 8 & NC = 8 & k = 11 \\ Pima & k = 2 & EPS = 7, MinPts = 7 & dc = 2 & k = 10 & NC = 2 & NC = 2 & k = 7 \\ Yeast & k = 10 & EPS = 0.09, MinPts = 4 & dc = 3 & k = 15 & NC = 10 & NC = 10 & k = 3 \\ Balance & k = 15 & EPS = 100, MinPts = 2 & dc = 3 & k = 15 & NC = 15 & NC = 15 & k = 40 \\ Satimage & k = 6 & EPS = 18, MinPts = 3 & dc = 2 & k = 10 & NC = 6 & NC = 6 & k = 6 \\ Breast & k = 2 & EPS = 1.5, MinPts = 2 & dc = 2 & k = 45 & NC = 2 & NC = 2 & k = 31 \\ \hline \hline \end{tabular}  
* In particular, Table 8 shows the clustering results of the selected real datasets on the different algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & & **K-Means** & **DBSCAN** & **DPC** & \begin{tabular}{c} **RNN-** \\ **DBSCAN** \\ \end{tabular} & **LDP-** & **HCDC** & **CPDD-ID** \\ \hline Thyroid & \
\begin{tabular}{c} ACC \\ NMI \\ ARI \\ \end{tabular} &
\begin{tabular}{c} 0.87 \\ 0.57 \\ 0.59 \\ 0.59 \\ 0.59 \\ 0.31 \\ 0.31 \\ 0.20 \\ 0.31 \\ 0.20 \\ 0.43 \\ 0.39 \\ 0.24 \\ 0.18 \\ 0.35 \\ 0.42 \\ 0.43 \\ 0.39 \\ 0.43 \\ 0.42 \\ 0.43 \\ 0.55 \\ 0.73 \\ 0.31 \\ 0.3  
The results show that the CPDD-ID algorithm ranks first in ACC, NMI, and NRI on the Ionosphere and Pima datasets and also ranks first in two metrics on the Wine, Satimage, and Balance datasets, and it has top-three performance on the remaining datasets. Overall, the CPDD-ID algorithm shows excellent performance in dealing with low-dimensional datasets and highlights the effectiveness of the CPDD-ID algorithm's strategy of reasonable partitioning and merging based on the correlation between all dimensions when dealing with high-dimensional datasets. As a result, the clustering performance of the CPDD-ID algorithm on high-dimensional datasets, such as Ionosphere and Satimage, is better than that of other algorithms.
# Applicable Conditions of Room-and-Pillar Mining Goal Treatment Methods under a Traffic Load
## 5 Discussion  
In this study, the evaluation of the treatment methods for room-and-pillar mining goat treatments under traffic load conditions was analyzed. The novelty of the research lies in the following areas: firstly, the disturbance depth of the traffic load was coupled, the calculation results were analyzed by multiple linear regression, and the calculation formula for the disturbance depth of the traffic load was fitted. Secondly, according to the relationship between the disturbance depth of the traffic load and the mining depth and the stability of the roof, coal pillar, and base plate, the applicability of the treatment method was analyzed.  
The research presented in this study contributes to the research concerning the treatment methods used for goat areas under highways, as the previous research mostly focused on the treatment method used and did not extensively study the applicability of these methods.  
From the multiple regression analysis of the numerical simulation results, it can be observed that, although the disturbance depth of traffic load is related to the pavement elastic modulus, subgrade height, unit weight of foundation soil, vehicle speed and vehicle load, the pavement elastic modulus, subgrade height, vehicle speed, and vehicle load are significant influencing factors, while the unit weight of foundation soil is a non-significant influencing factor. From the evaluation of the treatment method, it can be observed that a disturbance depth of 1.5 times that of the traffic load does not reach the roof depth of the room-and-pillar mining goat areas and only requires the laying of a geogrid on the subgrade for treatment; grouting is unnecessary. When the disturbance depth of the subgrade load exceeds the roof depth of the room-and-pillar mining goat areas by 1.5 times, it is necessary to calculate the stability of the roof, coal pillar, and base plate. If they are all in a stable state, the geogrid can be used for treatment purposes; otherwise, grouting treatment must be conducted. From the subsidence monitoring results of the engineering cases, it can be seen that the subsidence values of the goat areas treated by these two treatment methods, respectively, all met the requirements of the post-construction subsidence, indicating that the evaluation process of the treatment methods can be applied to practical projects.  
As mentioned above, the research content of this paper provides a scientific basis for the applicability of room-and-pillar mining goat treatment methods. It also provides  
Figure 11: The subsidence of geogrid treatment section (K1627 + 145–K1627 + 423).  
a reference for similar engineering constructions. However, this paper only verified the analysis results through an actual project. In future studies, similar simulation tests can be used to provide more samples to verify the analysis results of this study.
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 5 Experiments
### Baseline Models
#### 5.1.2 PAN  
PAN [42] is a state-of-the-art model for semantic segmentation that incorporates Feature Pyramid Attention and Global Attention Upsample modules to enhance feature representation and localization accuracy. We re-implemented PAN using the semantic models library [45], employing ResNet-34 as the backbone with ImageNet pretrained weights for fair comparison.
## 5 Numerical Parametric Study on Tunnel Displacement
### Effect of Length-to-Depth Ratio  
Figure 17 presents the relationship between the tunnel heave, horizontal displacement and varying \(L_{p}/H_{p}\) ratios, with a final \(H_{p}\) value of 8 m. The \(L_{p}\) values along the transversal tunnel direction are assumed to be from 8 m to 56 m, so as to explore geometrical effects of an excavation on tunnel responses. At a given \(H_{p}\) value, the tunnel heave and horizontal displacement are found to increase with increase in the \(L_{p}/H_{p}\) ratio, but at a reduced rate. The maximum heave and horizontal displacement are obtained when the \(L_{p}/H_{p}\) ratio is 7. In this case, the tunnel heave and horizontal displacement at stage 1 increases from 5.16 and 0.45 mm, to 14.5 and 3.33 mm at stage 8 (181% and 640% change, respectively; note only the larger values of these two indicators in twin-tube tunnels are presented), which proves a more significant effect on horizontal displacement increasement. Moreover, the maximum incremental tunnel heave and horizontal displacement are less than 4.1% and 2.4% when the \(L_{p}/H_{p}\) ratio varies from 6 to 7. It is demonstrated that the increased \(L_{p}\) values contributing to developments of tunnel heave and horizontal displacement are marginal when the \(L_{p}/H_{p}\) ratio larger than 6. Due to a more positive response of tunnel horizontal displacement, countermeasures may be necessary to minimize the adverse impact of excavation on existing tunnels.  
Figure 16: Influence of \(H/H_{p}\) ratio on tunnel displacement.  
Figure 17: Influence of \(L_{p}/H_{p}\) ratio on tunnel displacement.