esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of Deep Learning for Video Classification
## 3 Video Classification
### Comparison of Some Existing Approaches on UCF-101 Dataset  
UCF-101 is a benchmark action recognition dataset published by the researchers of University of Central Florida in the year 2012 [124], and the videos in the dataset were collected from YouTube. The total videos in the dataset are 13,320, with 101 action categories. The dataset is challenging because of the uncontrolled environment in the captured videos,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **\# of Videos** & **\# of Classes** & **Year** & **Background** \\ \hline KTH & 600 & 6 & 2004 & Static \\ Weizmann & 81 & 9 & 2005 & Static \\ Kodak & 1358 & 25 & 2007 & Dynamic \\ Hollywood & 430 & 8 & 2008 & Dynamic \\ Hollywood2 & 1787 & 12 & 2009 & Dynamic \\ MCG-WEBV & 234,414 & 15 & 2009 & Dynamic \\ Olympic Sports & 800 & 16 & 2010 & Dynamic \\ HMDB51 & 6766 & 51 & 2011 & Dynamic \\ CCV & 9317 & 20 & 2011 & Dynamic \\ UCF-101 & 13,320 & 101 & 2012 & Dynamic \\ THUMOS-2014 & 18,394 & 101 & 2014 & Dynamic \\ MED-2014 (Dev. set) & 31,000 & 20 & 2014 & Dynamic \\ Sports-1M & 1,133,158 & 487 & 2014 & Dynamic \\ ActivityNet & 27,901 & 203 & 2015 & Dynamic \\ EventNet & 95,321 & 500 & 2015 & Dynamic \\ MPII Human Pose & 20,943 & 410 & 2014 & Dynamic \\ FCVID & 91,223 & 239 & 2015 & Dynamic \\ UCF11 & 1600 & 11 & 2009 & Dynamic \\ YouTube Celebrities Face & 1910 & 47 & 2008 & Dynamic \\ Kinetics & 300,000 & 400 & 2017 & Dynamic \\ YouTube-8M & 6.1 M & 3862 & 2018 & Dynamic \\ JHMDB & 928 & 21 & 2011 & Dynamic \\ Something-something & 110,000 & 174 & 2017 & Dynamic \\ \hline \hline \end{tabular}
\end{table}
Table 4: Benchmark datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Metric** & **Year of Publication** & **Reference** \\ \hline Accuracy & 2020–2021 & [116–120] \\ Precision & 2020–2021 & [116,118,119] \\ Recall & 2020–2021 & [116,118,119] \\ F1 Score & 2020–2021 & [116,118,119] \\ Micro F1 & 2020 & [121,122] \\ K-Fold & 2019 & [123] \\ Top-k & 2018,2021 & [111,114] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Commonly used evaluation metrics for video classification.  
and it is widely being used by researchers working on the video classification problem. Therefore, it is easy to compare most of the existing literature based on this dataset. The existing works employing UCF-101 are compared in Table 6, where the methods are arranged in ascending order based on the performance. The results reported in Table 6 are taken from the existing studies in the literature.
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 4 Markov Decision Process  
We formulate an MDP model to maximize the data rate for the mobile node. The MDP is defined by five tuples: (1) decision epoch, (2) state space, (3) action space, (4) reward and constraint function, and (5) transition probability. In the MPD model, the mobile node is the agent. The important notation for the model is summarized in Table 1.  
Figure 2: System model.
## 4 Materials and Methods
### The problem of Identifying Hidden Markov Models  
The identification of hidden Markov models consists in estimating the parameters of the model, namely the initial distribution \(\pi\), the transition probability matrix between the hidden states \(P\) and the output probability matrix \(G\), based on the observations \(O=\{O_{1},O_{2},\ldots,O_{T}\}\), for \(O_{j}\in V\), \(1\leq j\leq T\). Determination of the structure of HMM \(\lambda=(\pi,P,G)\) consists in solving three problems [8; 40].  
**Problem 1**.: _Estimation of the probability of the sequence of realizations \(O=\{O_{1},O_{2},\ldots,O_{T}\}\) for the hidden model of the Markov model \(\lambda=(\pi,P,G)\), which is the sum of all possible probabilities for the sequences of hidden states \(Q=\{q_{1},q_{2},\ldots,q_{T}\}\), \(q_{i}\in S\) for \(1\leq i\leq T\)._  
\[\begin{array}{ll}P(O|\lambda)&=\sum\limits_{Q}P(O,Q|\lambda)=\sum\limits_{Q} P(O|Q,\lambda)P(Q|\lambda)\\ &=\sum\limits_{Q}\pi_{q_{1}}g_{q_{1}}O_{p_{1}}q_{q_{2}}g_{q_{2}}O_{p_{2}}q_{q_{ 2}}g_{q_{3}}g_{q_{3}}O_{\cdots}g_{q_{T}O_{T}}.\end{array} \tag{10}\]  
**Problem 2**.: _Determination of the sequence of hidden states \(Q=\{q_{1},q_{2},\ldots,q_{T}\}\) (Viterbi path), which is best explained for the sequence of observations \(O=\{O_{1},O_{2},\ldots,O_{T}\}\), where \(q_{i}\in S\) and \(O_{j}\in V\) for \(1\leq j\leq T\). Determining the sequence \(Q\) for the hidden model of the Markov model \(\lambda=(\pi,P,G)\) consists in solving the problem_  
\[\underset{Q}{max}P(Q|O,\lambda)=\underset{Q}{max}\frac{P(Q,O|\lambda)}{P(O| \lambda)}. \tag{11}\]  
**Problem 3**.: _Determination of the parameters \(\lambda=(\pi,P,G)\) for the hidden Markov model consists in solving the problem_  
\[\underset{\lambda}{max}P(O|\lambda). \tag{12}\]We use the forward-backward algorithm to estimate the values defined by Equation (10). We use the Viterbi algorithm to determine the sequence of hidden states with the highest probability (11). The values \(\lambda=(\pi,P,G)\) are determined by maximizing the probability (12) of the occurrence of the series of observations \(O\) using the Baum-Welch algorithm [6; 40; 41].
## 3 Results  
### Engineering Background and Challenges  
A significant project in Chongqing is the Yuzhong tunnel in the Xiaoshizi district. Sandstone dominates the tunnel, with surrounding rocks of class IV. The cross-section is 11.5 m in width, 9.5 m in height, and 19-25 m in burial depth. Excavation used the benching tunneling method. The operation environment is complicated. As shown in Figure 3, an ancient arhat temple is now on the grounds surrounded by skyscrapers. The top of the underground tunnel is tangent to subway Line 1 and subway Line 6 runs below it. The ground vibration velocity of the area along the tunnel cannot exceed 1.0 cm\(\cdot\)s\({}^{-1}\).  
The demand was difficult for conventional technology to meet because there were two challenges. One was a small number of available detonator delays. A single delay needs to initiate lots of holes and charges. The vibration velocity is bound to exceed the limit. The other was that using the short-advance partial-excavation method would significantly reduce efficiency and increase costs. It used the new technology of precision-controlled blasting to control blast-induced vibrations in urban underground tunnels while ensuring blasting efficiency.  
We customized a new high-precision NONEL detonator series with 25 delays to solve the problems of conventional detonators such as delays being too short and delay errors being significant. We detected 10 samples per delay to test and record the range of the initiation time \(\delta t_{\text{m}}\) and renumbered the remaining after removing any that may have jumped the queue. To control the vibration while keeping the regular advance, we arranged eight inclined cut holes during the stage of cut blasting. Each delay of the detonators only initiated one cut hole. The initiation times for the first eight delays were within the time range shown in Figure 4[26]. There was no error in the first delay. After the cut blasting, the difficulty of breaking the rock was reduced and replaced with one delay to initiate multiple holes.  
Figure 4: Initiation time range of detonators per delay.  
Figure 3: Buildings around the tunnel.  
Using this idea, the problem transformed into how to predict the vibration induced by cut blasting and how to maximize the single hole charge/advance per round without exceeding the blast vibration limit. The linear superposition method can predict the wave for the entire duration of cut blasting but does not consider the changes caused by the formation process of the new free surface, which will inevitably lead to inaccurate vibration prediction results after the new free surface's formation at the later stage of cut blasting. Thus, it was best to identify the new free surface's formation time using the method described in this paper, then the optimal parameters were selected by using waveform superposition.
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 4 Discussions
### Hydrogen Distribution and Infrastructure Challenges  
A key component of the hydrogen value chain is the infrastructure required for distributing and storing hydrogen. This infrastructure is just as important as production costs. Unlike traditional fuels like natural gas, hydrogen has unique physical and chemical properties that make it harder to handle. For example, it has low energy density, a small size, and easily diffuses [72; 73]. As a result, specialized infrastructure is required to transport and store it efficiently, safely, and cost-effectively [74].  
**Hydrogen Transport:** Transporting hydrogen from production sites to end-users involves various methods, depending on the distance, volume, and geographic location. It can be transported via pipeline, sea, or road [74]. For shorter distances, truck or rail transport is efficient; for longer distances, pipelines or sea transport are used. In the long run, pipelines are more economical, but they require specialized infrastructure [72; 75]. The existing natural gas pipelines can be repurposed for hydrogen use; however, hydrogen can cause embrittlement to these natural gas pipelines [75]. Measures to prevent embrittlement include coating, using materials highly resistant to hydrogen embrittlement, the use of inhibitors, and enclosing the pipeline within another pipeline [72; 75].  
**Hydrogen storage:** Hydrogen can be stored in various forms, including compressed gas, liquid hydrogen, or chemically bound in materials such as metal hydrides and liquid organic hydrogen carriers (LOHCs) [76]. The most suitable storage method depends on various factors, including the desired storage capacity, energy density, and intended use application [76; 77]. Hydrogen has great potential and versatility, but faces storage challenges due to low density and volatility [74]. Developing cost-effective, efficient storage systems is essential for the sustainable use [74].
# Approximate Networking for Universal Internet Access
## 6 Conclusions  
The Utopian goal of providing \"ideal networking\" service universally is an elusive target (due to the moving target nature of \"ideal networking\" and the lack of affordability of advanced technologies in challenging markets). A lot of experience has highlighted the _fidelity-convenience trade-off_, which is seen when users are willing to tradeoff considerable fidelity for convenience (in terms of accessibility and affordability). In this paper, we have described \"approximate networking\" as a philosophy that understands that there will no one-size-fits-all, ideal networking solution that will be universally applicable--approximation networking proposes to adopt appropriate context-specific trade-offs to provide \"good-enough\" service. In addition, we have also provided an overview of approximate networking technologies and have highlighted how a number of existing Internet technologies can be seen as instances of the larger approximation networking vision. Approximate networking, as envisioned in this paper, is a powerful abstraction that has significant promise for Global Access to the Internet for All (GAIA). Realizing approximate networking will require multidisciplinary research in diverse domains such as algorithms, protocols, operating systems, and hardware architectures. We believe that approximate networking can provide a novel fundamental tuning knob that can facilitate context-appropriate trade-offs (by adopting the extra degree-of-freedom of approximate computing at the hardware level) that can help realize the egalitarian vision of GAIA.  
All authors contributed equally to this work. [PERSON] and [PERSON] suggested the skeleton of the paper and also provided the supplementary material for paper write up. [PERSON], [PERSON] and [PERSON] wrote the main paper. [PERSON] and [PERSON] provided the technical guidance for 5G and Approximate Computing respectively. All authors provided comments on the manuscript at all stages.  
The authors declare no conflict of interest.
## 4 PSO Based on the PSO-BP Model
### Results and Analysis
#### 4.5.4 Comparison of RSM and PSO-BP Optimization Methods  
Both response surface methodology (RSM) and the PSO-BP model exhibited prediction errors for the angle of repose below 5%, confirming the accuracy and reliability of optimized parameters for rolling friction coefficient, static friction coefficient, and JKR surface energy. This indicates that both methods are suitable for angle of repose prediction [33]. However, the PSO-BP model demonstrated superior prediction accuracy, with a post-optimization error (1.5%) significantly lower than RSM (3.2%). As shown in Figure 16, both models displayed strong fitting precision between measured and predicted values. The evaluation metrics of the PSO-BP model (R\({}^{2}\) = 0.943, MSE = 0.1367) outperformed RSM (R\({}^{2}\) = 0.931, MSE = 0.1666), with R\({}^{2}\) increasing by 1.29% and MSE decreasing by 17.95%. This confirms the PSO-BP model's enhanced predictive capability and higher accuracy over RSM.  
Figure 15: Fitness evolution curve of the particle swarm optimization process.
## 3 Node Key Construction Technology
### Cantilever Truss Node
#### 3.3.4 Node 4 Structural Design  
The upper chord SXG1, oblique belly FG2 and vertical ZFG1 form Node 4, as shown in Figure 9. The upper chord SXG1 uses a box steel plate with a size of 1500 mm \(\times\) 1500 mm \(\times\) 80 mm \(\times\) 80 mm. The diagonal rod FG2 is made of 700 mm \(\times\) 700 mm \(\times\) 60 mm \(\times\) 60 mm box steel. The vertical rod ZFG1 is composed of 700 mm \(\times\) 700 mm \(\times\) 40 mm \(\times\) 40 mm box-shaped steel plate. Both of them are shown in Table 4. In the design of Node 4, the upper chord SXG1 is mainly responsible for transferring and supporting the horizontal loads. Meanwhile, it is connected by the diagonal rod FG2 and the vertical rod ZFG1. The local tearing problem caused by excessive steel plate thickness should be considered. The diagonal bar FG2 is used to transfer the internal force. It can also increase the anti-lateral movement ability and stability of the node. The vertical ZFG1 is mainly responsible for providing vertical support to enhance the overall stiffness of Node 4. The design idea of this node is basically the same as Node 2, taking full account of the impact of rare earthquake actions. The oblique belly rod forms a single K-node structure with a clear force transmission path, and the node form is optimized to ensure the coordination between components and effective force transmission. The above operation can effectively absorb and disperse the seismic energy when an earthquake occurs. Meanwhile, the above strategy can also reduce the degree of damage to the structure and can improve the seismic performance of the overall structure.
# Research Progress on the Influence of Thermo-Chemical Effects on the Swelling Pressure of Bentonite
## 3 Influence of Thermo-Chemical Effect on the Swelling Pressure
### Coupled Thermo-Chemical Effect on the Swelling Pressure  
[PERSON] et al. [28] conducted alternating wetting tests using distilled water and NaCl solutions of different concentrations on Na-GMZ bentonite with an initial dry density of 1.7 g/cm\({}^{3}\) at 20 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, as shown in Figure 8. The results showed that when the thermo-chemical effect was coupled, the increase of temperature and electrolyte concentration was not conducive to the development of swelling pressure, and in the multi-step salination-desalination process, the initial swelling pressure of the sample could not be fully restored after saline circulation. The possible causes were the irreversible interpore collapse and the further homogenization of the clay structure at the macrostructural level.  
Similar results were obtained with the swelling pressure test of monovalent bentonite with the same dry density, in India [58]. In addition, that study showed that a high temperature can promote the expansion of bivalent bentonite. In general, the swelling pressure of the divalent bentonite increased monotonically and then stabilized, while the collapse occurred in the middle of the monovalent bentonite and increased again after equilibrium. When the thermo-chemical effect was coupled, the swelling pressure of monovalent bentonite was greatly reduced, but that of divalent bentonite was very small. Compared with the equilibrium swelling pressure when using distilled water wetting at 25 \({}^{\circ}\)C, the inhibition effect of a 0.1 M NaCl solution was less than the promotion effect of 95 \({}^{\circ}\)C temperature on the swelling pressure. Therefore, under the condition of 95 \({}^{\circ}\)C and 0.1 M NaCl solution infiltration, the equilibrium swelling pressure of bivalent bentonite still increased. However, when the sample was soaked in a CaCl\({}_{2}\) solution with the same concentration and at the same temperature, the equilibrium swelling pressure of bivalent bentonite was reduced. The authors attributed this to the combined effect of high temperature, leading to the expansion of the soil particles, and the increase of microscopic pore water pressure. At the same time, when low-concentration Na\({}^{+}\) was exchanged with Ca\({}^{2+}\), the exchanged Na\({}^{+}\) was hydrated until the exchange process was saturated, which promoted permeability and expansion [46]. However, this study did not take into account the phenomenon of mineral alteration occurring in bentonite. The microscopic mechanism of bentonite swelling pressure variation under coupled thermo-chemical effects needs to be further refined and studied.
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 2 Related Works
### Sparse Convolution in Point Cloud Processing  
Due to the sparse nature of the data, the direct application of convolutional networks is often inefficient, and sparse convolution is therefore used to process point cloud data. [PERSON] et al. [10] proposed the SSCN to efficiently process spatially sparse data by introducing a new sparse convolutional operation, which greatly saves computational resources. [PERSON] et al. [8] proposed Minkowski-Net along with a generalized sparse convolution of sparse tensors and a self-differentiating library. For the efficient processing of large amounts of data, [PERSON] et al. [11] proposed LatticeNet, which back-projects grid features onto point clouds via a data-dependent interpolation module, DeformsSlice. To reduce the calculation effort, [PERSON] et al. [30] proposed a sparse grid network based on bilateral convolutional layers to compute hierarchical and spatially perceptive features. To extract structural information from the point cloud, [PERSON] et al. [31] proposed DownBCL, Up-BCL, and CorrBCL based on bilateral convolutional layers (BCL).  
In summary, the original point-based method is usually applied to indoor point clouds, which is difficult to adapt to large-scale data. Although the graph model-based method has a higher segmentation accuracy, the computational efficiency cannot meet the requirements of real-time applications of the system. Although the projection-based method has a higher computational efficiency, some spatial information will be lost in the projection transformation. The voxelization-based method can extend the 2D model to 3D space, but the corresponding sparse operations need to be designed to solve the computational efficiency problem. Therefore, to achieve real-time semantic segmentation for unmanned vehicle systems with limited computing power and memory capacities, we designed two attention modules based on the voxelization method. A sparse tensor-based implementation is also introduced to improve the accuracy and real-time performance of semantic segmentation.
## 4 Discussion
### fCover Retrieval Using Remote Sensing Dataset
While the RedEdge-M sensor outperformed PlanetScope in terms of model accuracy and variability detection, it is important to note that both sensors still have limitations in their ability to perfectly predict fCover across all treatments. Factors such as environmental variability, differences in nutrient uptake efficiency, and potential sensor-specific biases may have contributed to the observed discrepancies. For example, the RedEdge-M model, while effective in detecting general variability, may still face challenges in capturing extreme treatment responses due to spectral limitations.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 5 Performance Analysis
### Bit Error Rate Computation
#### 5.3.2 Case 2: Emitter Placed at 1.90 m from the Source and the Receiver at 1.80 m  
We now increase the distance between the emitter and the source, as well as the receiver and the source. We have the same situations as for the first configuration, i.e., the LOS and NLOS cases. The obtained results are detailed in Figures 10 and 11. By comparing the results to those obtained in the first experimental scenario, we can notice that the increase of the distance does not affect the BERs much. This remark is also valuable for the voltage. However, we can note a degradation of performance when the impulsive noise is present whatever the voltage used compared to the absence of impulsive noise. In Figure 10, a target BER of \(10^{-4}\) is achieved at an EbNo of 3.2 dB and 6 dB for the 12-kV and 14-kV noise samples, respectively. When the channel is affected by 16-kV and 18-kV noise, the target BER of \(10^{-4}\) is approximately 6.2 and 6.5 dB, respectively. Nevertheless, without an impulsive noise, a target BER of \(10^{-4}\) is achieved at an EbNo of 4.4 dB, as depicted in Figure 11. The degradation is approximately 2.1, 1.6, 2.1 and 3.1 dB for 12-, 14-, 16- and 18-kV impulsive noise, respectively.  
Figure 8: BER performance for different voltages in the LOS situation.  
Figure 10: BER performance for different voltages in the LOS situation.  
Figure 9: BER performance for different voltages in the NLOS situation.