esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation
### Numerical Model Establishment  
The deformation of the tunnel support structure under different working conditions was calculated using FLAC3D numerical simulation software. To eliminate the boundary effect on the test results, the left, right, and lower boundaries of the model are taken to be three times the diameter of the tunnel, and upper boundaries are determined in accordance with the actual situation. Figure 9 displays the model of a shallow unsymmetrical loading section based on the newly built Shimentangshan super-large-span highway tunnel. Fixed boundaries are applied on the left and right sides, as well as the front and back sides, while the bottom of the model is constrained. The top of the model is left free for deformation. The model does not include secondary lining during the construction process.  
Numerical simulation process of double-side drift method: (1) Simulation of initial ground stress \(\rightarrow\) (2) Construction of reinforcement area \(\rightarrow\) (3) Excavation at section 1, cycle length of 0.6 m, construction of initial support, temporary support and anchor bolt \(\rightarrow\) (4) Excavation at section 2, cycle length of 0.6 m, Steps in length of 3 m, construction of initial support, temporary support and anchor bolt \(\rightarrow\) (5) Excavation at section 3, Cycle length of 0.6 m, construction of initial support, temporary support and anchor bolt \(\rightarrow\) (6) Excavation at section 4, cycle length of 0.6 m, b temporary support and anchor bolt \(\rightarrow\) (7) Excavation at section 3.2.2.2, cycle length of 0.6 m, benches in length of 3 m \(\rightarrow\) (8) Remove the temporary support.
###### Abstract  
The precise point positioning (PPP) service via the B2b signal (PPP-B2b) on the BeiDou Navigation Satellite System (BDS) provides high-accuracy orbit and clock data for global navigation satellite systems (GNSSs), enabling real-time atmospheric data acquisition without internet access. In this study, we assessed the quality of orbit, clock, and differential code bias (DCB) products from the PPP-B2b service, comparing them to post-processed products from various analysis centres. The zenith tropospheric delay (ZTD) and precipitable water vapour (PWV) were computed at 32 stations using the PPP technique with PPP-B2b corrections. These results were compared with post-processed ZTD with final orbit/clock products and ZTD/PWV values derived from the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) and radiosonde data. For stations between 30\({}^{\circ}\) N and 48\({}^{\circ}\) N, the mean root mean square error (RMSE) of ZTD for the PPP-B2b solution was approximately 15 mm compared to ZTD from the International GNSS Service (IGS). However, accuracy declined at stations between 30\({}^{\circ}\) N and 38\({}^{\circ}\) S, with a mean RMSE of about 25 mm, performing worse than ZTD estimates using Centre National d'Etudes Spatiales (CNES) products. The mean RMSES of PWV derived from PPP-B2b were 3.7 mm and 4.4 mm when compared to PWV from 11 co-located radiosonde stations and ERA5 reanalysis, respectively, and underperformed relative to CNES solutions. Seasonal variability in GNSS-derived PWV was also noted. This reduction in accuracy limits the global applicability of PPP-B2b. Despite these shortcomings, satellite-based PPP services like PPP-B2b remain viable alternatives for real-time positioning and atmospheric applications without requiring internet connectivity.  
zenith total delay (ZTD); precipitable water vapour (PWV); BDS-3 PPP-B2b service; ERA5; radiosonde +
Footnote †: journal: atmospheric
## 4 Proposed Security Algorithm for Patient Data in WBSN and IoMTs Applications
### QR Code Generation  
In this phase, confidential patient information is saved in a text file named \"Patient_Info.txt\". Then, this file is concatenated with the \"Feature_vectors.txt\" file to produce a single text file named \"FILE.txt\", which is converted into a QR code. Figure 6 shows an example of a QR code obtained with the proposed method. When this code is scanned, confidential patient information is obtained with the feature vectors. We used the \"qrcode 7.3.1\" python module for producing the QR code image in our scheme.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 4 Experiments
### Ablation Study  
This section evaluates different network variables and analyses the reasons that affect network performance. We used DeepLabv3+ as the baseline, and the backbone of the baseline uses ResNet-101 with dilated convolution. We conducted improvement experiments with different network structures. (1) Baseline + A: indicates that the SEM attention module is embedded into the baseline method. (2) Baseline + B: indicates that DRN-D-54 replaces the backbone of the baseline method. (3) Baseline + D: indicates that our improved decoder structure is used. We first conducted experiments with single-variable improvement. Then, we conducted three groups: Baseline + A + B, Baseline + A + D, and Baseline + B + D.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Low-ASPP Rate** & **Spacecraft A** & **Spacecraft B** & **Spacecraft C** & **MIoU** \\ \hline
1 & - & 78.56 & 86.64 & 91.46 & 85.55 \\
2 & [1,6,12,18] & 80.88 & 86.77 & 91.98 & 86.54 \\
3 & [1,6,12] & 70.31 & 87.77 & 86.66 & 81.58 \\
4 & [12,24,36] & 85.66 & 87.25 & 91.02 & 87.98 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The MIoU results of the second ASPP module comparative experiments on the test set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone** & **Spacecraft A** & **Spacecraft B** & **Spacecraft C** & **MIoU** \\ \hline Mobilenet-v2 [56] & 74.02 & 84.29 & 91.78 & 83.37 \\ ResNet-50 [17] & 81.01 & 77.1 & 90.09 & 82.74 \\ ResNet-50 [D] & 78.17 & 82.74 & 88.42 & 83.11 \\ ResNet-101 [17] & 77.64 & 82.71 & 88.7 & 83.02 \\ ResNet-101 [D] & 83.15 & 81.26 & 88.43 & 84.28 \\ Xception-65 [13] & 67.61 & 72.5 & 88.34 & 76.15 \\ Densenet121 [57] & 51.62 & 64.97 & 83.38 & 66.66 \\ DRN-D-54 [16] & 78.56 & 86.64 & 91.46 & 85.55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The MIoU results of backbone comparative experiments on the test set.  
Finally, the experiment of our proposed SISnet method, namely Baseline + A + B + D, is conducted. We performed the quantitative evaluation on the test set for each experiment, and the experimental results are shown in Table 7 below.  
As can be seen from Table 6, the segmentation results of our method compared to the baseline have been steadily improved. The DRN-D-54 backbone we used is optimized for the gridding artifacts and has a strong feature extraction ability. It can achieve better results than ResNet-101 with fewer layers. The channel attention mechanism we designed in the network can also improve the metric by about 1% by assigning channel weights. By enhancing the contextual information of low-level features, our decoder structure significantly improves the segmentation results of Spacecraft A, which is worth significantly improving the final MIoU. The ablation study validates the improvements we made. These improvements can improve the accuracy of spacecraft semantic segmentation to varying degrees. In terms of MIoU metrics, our approach achieves the best segmentation results.
## 4 IoT Applications
### Smart Agriculture  
The use of IoT in agriculture targets traditional agricultural practices to satisfy rising demands and reduce output losses. IoT utilizes robotics, drones, remote sensors, and computer vision in agricultural industries together with rapidly evolving machine-learning and analytical instruments for field observation, farm surveys, and monitoring, which provide farmers with knowledge regarding appropriate farm management strategies to save both time and energy. In indoor planting, IoT allows micro-climate monitoring and management, which effectively increases efficiency. For outdoor planting, soil humidity and nutrients can be sensed in conjunction with temperature to enable smart irrigation and fertilizer systems through devices using IoT technology. For instance, when water is provided only by sprinkler systems, this may prevent a useful resource for being lost [78]. Some smart agriculture features are shown in Figure 11.
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 2 Background
### Open RAN Architecture
#### 2.2.1 CAPEX and OPEX Efficiency  
Cloud-RAN is a promising access network technique that can address increased mobile traffic and optimize network performance, offering scalability and heterogeneity management [22].  
By centralizing baseband processing, c-RAN can reduce overall CAPEX and OPEX through efficient utilization of resources and the ability to scale computational resources based on demand.  
In addition, the integration of software-defined networking and network virtualization in c-RAN and v-RAN architectures can further improve the flexibility and efficiency of the network, leading to significant benefits in terms of cost savings [23].  
In contrast, the traditional Distributed-RAN architecture may not be as cost-effective, as it requires the deployment of dedicated hardware at each cell site, resulting in higher CAPEX and OPEX [22; 24; 25].  
Although the Open RAN architecture aims to enable the interoperability of open hardware, software, and interfaces, it also introduces additional complexity in the management and coordination of the distributed components, which may impact overall cost-efficiency [25].
## 3 Results and Discussion
### Numerical Validation with Experimental Tests  
To validate the numerical method, a series of experimental tests were conducted under pristine conditions. The objective was to compare the ToA, signal amplitude, and wave velocity between the numerical and experimental signals.  
Figure 5 presents a schematic diagram that illustrates the essential components required for an experimental platform that is dedicated to damage detection applications using UGWs. The platform necessitates a minimum of five elements, namely a a signal generator (_No._ 1 in the figure), a power amplifier (_No._ 2), an acquisition system (i.e., oscilloscope) (_No._ 1), one or more transducers (_No._ 3), and a personal computer (PC) (_No._ 4). Specifically, the experimental step-by-step process from signal generation to acquisition was as follows:  
Figure 4: Simulation flowchart.  
* The signal was generated with a NI-PXI wave generator;
* The actuation signal was amplified using the amplifier module;
* The resulting signal was applied by an Olympus A413S transducer to the specimen to initiate the desired response;
* Another transducer, functioning as a sensor, was employed to capture the resulting signals;
* The signal was acquired via an NI-PXI oscilloscope;
* For data acquisition and manipulation, a PC equipped with dedicated acquisition software was utilized.  
A 50 [mV] peak-to-peak amplitude, amplified 50 times by the amplifier with a center frequency of 500 [kHz], was applied with the actuation probe. The sampling frequency was set at 60 [MHz]. The total recording duration for the experimental signals was \(t_{tot}\ =100\ [\upmu\text{s}]\), and each measurement was recorded 50 times and averaged to enhance the signal-to-noise ratio. The transducer configuration employed was a pitch-catch arrangement, where the positions of the actuator and sensor were switched to account for the uncertainties based on the measurements, as depicted in Figure 6.  
The material under investigation was steel with the dimensions of \(l=100\) [mm], \(h=50\) [mm], and \(w=75\) [mm]. Moreover, the material properties assumed were \(E\ =\ 200\) [GPa], \(\
u=0.30\), and \(\rho=8000\ [\frac{\text{kg}}{\text{m}^{3}}]\)[38].  
Figure 5: Experimental setup.  
Figure 6: Pitch–catch configuration.
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 5 Discussion  
In this work, we proposed a novel DFormer-Based Cross-Attention Network (DBCAN) for RGB-D semantic segmentation, which addresses several key challenges in integrating RGB and depth modalities. Our asymmetric network design effectively utilizes DFormer as an encoder to extract both RGB and spatial features. By introducing a cross-modal feature fusion module and a symmetric hybrid decoder, our approach improves feature integration and segmentation performance, achieving state-of-the-art results on NYUDepthv2 and SUNRGBD datasets.  
One of the key innovations of our model is the cross-modal feature fusion module, which promotes effective interaction between the RGB and spatial modalities. Previous methods have often treated these two modalities separately, leading to suboptimal performance. In contrast, our approach leverages the complementary nature of RGB and spatial features, thereby enhancing the model's ability to capture fine-grained details and object boundaries.  
Despite these advancements, some limitations remain. First, the computational complexity of the proposed method, especially in terms of training and inference time, could be a challenge when deploying the model in real-time applications. While our experiments demonstrate significant performance gains, further research is needed to optimize the model for resource-constrained environments, such as the embedded systems used in autonomous driving. Additionally, although our model shows strong performance on RGB-D datasets, extending this approach to other modalities may require re-pretraining of the backbone networks to handle different types of inputs effectively.  
Future work could explore techniques to reduce the model's complexity without sacrificing performance, as well as expand its applicability to a wider range of input modalities and real-world tasks. Moreover, the integration of self-supervised learning techniques could further enhance the model's generalization capabilities, especially in scenarios with limited labeled data.
# Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models
The SC models are more cost-effective to give authentic relationships in comparison with numerical and empirical models. Hence, SC techniques are reliable alternatives for estimating the scour depth in hydraulic engineering [24; 25; 26]. The potential of SC models in predicting the scour depth have been extensively discussed over the previous studies [22]. A number of investigations have been conducted to improve the accuracy of depth scouring prediction using SC models such as genetic programming [27; 28; 29] support vector machines [10; 11; 30], adaptive neural fuzzy inference systems [31; 32] and artificial neural networks [33]. Those techniques exhibit accurate performances to predict scour depth downstream of different types of hydraulic structures such as spillway, chutes, etc. In recent years, the potential of SC models for prediction of scouring depth downstream of weirs have been assessed using different models like genetic expression programming, self-adaptive extreme learning machine and neuro-fuzzy [7; 34; 35; 36]. The research findings indicated that the SC models provided better performance in comparison with regression formulas. The adequacy observed in SC models is originated from evolutionary algorithms and iterative procedures. However, the standalone SC models have been recently integrated with meta-heuristic optimizations to provide better predictive models [22]. The meta-heuristic algorithms which are inspired from nature such as ant colony optimization and particle swarm optimization are efficient tools for enhancing standalone models [37]. [PERSON] [38] combined the neural networks with the fuzzy logic technique to provide the adaptive neural fuzzy inference systems (ANFIS), which includes the benefits of both methods. A number of studies indicated that the ANFIS model is a viable alternative to solve hydraulic engineering problems in comparison with the regression technique [32; 39; 40; 41]. However, parameters tuning is a major limitation of ANFIS. To solve this problem, the standalone ANFIS has been integrated with meta-heuristic optimization algorithms to tackle this limitation [9]. This study aims to develop different hybrid ANFIS models integrated with meta-heuristic optimization algorithms for estimating the scour depth downstream of weirs. To the best of the authors' knowledge, meta-heuristic algorithms (e.g., Cultural, BBO, TLBO and IWO) have not been combined yet with standalone ANFIS to estimate the scour depth around the different types of structures such as bridges, pipelines, dam spillways, grade control structures and piles. The approach proposed in the present study enhances the training phase of standalone ANFIS and provides better performance in this application of area.
# Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks +
## 2 Preliminaries
### Hybrid Message Passing Inference
#### 2.1.3 Message Passing Inference for Continuous BNs  
Type 14 in Figure 1 is a BN in which all nodes are continuous. The MP algorithm can be extended to this case by defining Pi/Lambda messages as follows [29]. These messages can be computed exactly for linear Gaussian networks. For general Gaussian networks, the messages can be approximated using the unscented transformation [31] to project mean and covariance estimates through nonlinear transformations.  
The Pi and Lambda functions for a continuous node \(X\) in a Gaussian network can be written as follows:  
\[\pi(X)=\int\limits_{\mathbf{U}}P(X|\mathbf{U})\prod_{i}\pi_{X}(U_{i})\,dU \tag{7}\]  
and  
\[\lambda(X)=\prod_{j}\lambda_{Y_{j}}(X), \tag{8}\]  
where \(\,dU\) is the m-dimensional differential with \(\mathbf{U}=\{U_{1},U_{2},\,\ldots,\,U_{m}\}\), \(\pi_{X}(U_{i})\) denotes the Pi message from the continuous parent \(U_{i}\) to \(X\) and \(\lambda_{Y_{j}}(X)\) denotes the Lambda message from the continuous child \(Y_{j}\) to \(X\). These Pi and Lambda messages can be written as follows:  
\[\pi_{Y_{j}}(X)=\alpha\left[\prod_{k\
eq j}\lambda_{Y_{k}}(X)\right]\pi(X) \tag{9}\]\[\lambda_{X}(U_{j})=\int\limits_{X}\lambda(X)\int\limits_{\overline{\mathbf{U}}}P(X| \mathbf{U})\prod\limits_{k\
eq j}\pi_{X}(U_{k})\,d\overline{\mathbf{U}}\,dX, \tag{10}\]  
where \(d\overline{\mathbf{U}}\) is the \((m-1)\)-dimensional differential with \(\overline{\mathbf{U}}=\{s|s\in\mathbf{U}\text{ and }s\
eq U_{j}\}\).
# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
## 3 Neural Networks
### Gcnn-Lstm-At Network
#### 3.3.3 Sequential Modeling with LSTM  
In the third block, a two-layer bidirectional long short-term memory (Bi-LSTM) is applied to capture features based on wavelength sequences. The Bi-LSTM processes the input both forward and backward in sequence steps to capture context from both directions. For each element in the input sequence, each LSTM layer computes the following functions:  
\[i_{t}=\sigma(W_{ii}x_{t}+b_{ii}+W_{hi}h_{t-1}+b_{hi}) \tag{6}\]  
\[f_{t}=\sigma\Big{(}W_{if}x_{t}+b_{if}+W_{hf}h_{t-1}+b_{hf}\Big{)} \tag{7}\]  
\[g_{t}=\tanh\Big{(}W_{ig}x_{t}+b_{ig}+W_{hg}h_{t-1}+b_{hg}\Big{)} \tag{8}\]  
\[o_{t}=\sigma(W_{io}x_{t}+b_{io}+W_{ho}h_{t-1}+b_{ho}) \tag{9}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot g_{t} \tag{10}\]  
\[h_{t}=o_{t}\odot\tanh(c_{t}) \tag{11}\]  
where \(x_{t}\), \(h_{t}\), and \(c_{t}\) are the input, hidden state, and cell state at step \(t\), respectively. \(f_{t}\), \(g_{t}\), \(o_{t}\) are the forget, cell, and output gates, respectively. \(\sigma\) is the sigmoid function and \(\odot\) is the Hadamard product. In a multi-layer LSTM, the input \(x_{t}^{l}\) of the \(l\)th layer (\(l\geq 2\)) is the hidden state \(h_{t}^{l-1}\) of the previous layer.  
In a Bi-LSTM, the hidden states of the forward and backward LSTM are concatenated at each time step, creating a more expressive feature representation. This allows the model to capture and utilize information from both directions, leading to a better representation and understanding of the sequence. In the implementation of this block, the input tensor is squeezed into the shape of (\(n\), 13, 156). Each Bi-LSTM layer has 64 hidden units, so the output shape is (\(n\), 13, 128).
# Predicting Student Academic Performance by Means of Associative Classification
###### Abstract
The rules applied in the prediction of the exam success rate of each student are known. Hence, they give an insight into the actual motivation behind rate assignment (i.e., local explanations). Assessing the appropriateness of the generated predictions could help teachers to trust the data-driven model, to decide whether to collect new data or not, and to tailor the subsequent actions to specific student profiles. For example, if at-risk profiles are shown to rarely access the online course materials then teachers could foster the use of online materials in order to prevent exam failures. Motivated by the aforesaid model characteristics, this paper aims at exploring the potential of associative classification techniques to support early student performance prediction. Specifically, the following research questions will be addressed:  
* Are associative models as accurate as the best performing classifiers in predicting the exam success rates of university-level students? * What are the most discriminating features to forecast the exam success rates at different time points? * Which combinations of feature values have frequently been used to assign the exam success rates? To answer question (RQ1), we analyze learner-generated data acquired by our technical university, which adopts a blended learning model, using a variety of classification models with various levels of explainability. Specifically, as a case study, we considered about 5000 students enrolled in B.S. engineering courses, and their personal, admission, and schalastic career data. Besides, the learning platform traces students' interaction with educational material, where a massive educational video service, which delivers video recordings of the in-class lectures, has a central role [14]. The achieved results show that the performance of \(L^{3}\), a state-of-the-art associative classifier [15] is comparable to that of the best performing (not explainable) models and, on average, superior to than that of decision tree models. To answer questions (RQ2) and (RQ3), we thoroughly analyze the characteristics of the rules applied by the associative classifier to the students under consideration. Furthermore, we monitor profile changes over time. In the considered case study, the performed analyses have allowed us to better understand the career progress of the students enrolled to our university. The rest of the paper is organized as follows. Section 2 overviews the literature related to early student performance prediction. Section 3 thoroughly describes the proposed methodology. Section 4 presents the main experimental results. Section 5 draws conclusions and discusses the future research directions.