esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 8 Simulation
### Wi-Fi Wireless Network
#### 8.1.3 Main Tank Sector 3  
The results showed that wells T/1/257/0/96 and T/1/656/0/96 have relatively high RSSI at 15 dBm. T/1/677/0/96 slightly less, reaching the 10 dB threshold at 17 dBm. On the other hand, the RSSI of wells T/1/235/0/96, T/1/657/0/96, and T/1/678/0/96 is significantly lower. They only meet the threshold at full transmitter power. The differences in their performance can be attributed to their distance and terrain impact. Therefore, using the same approach implemented in sector 2, it was found that by increasing the height of the antennas, the RSSI improved as the path loss reduced. All three wells achieved good results at 7.5 m, reaching the RSSI margin at a TX power between 16-17 dBm, as shown in Figure 16. The final link parameters are summarized in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sector/Link** & **Distance** & **Antenna Type** & **Antenna Type** & **TX Power** & **Antenna Height** & **RSSI** & **Fresnel** & **Status** \\  & **(km)** & **(Base)** & **(Remote Node)** & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** & **(\%)** & **Status** \\ \hline Main Tank & & & & & & & & \\ Sector 2 & & ANT793-6 DG & & & 5 & & & & \\ \hline ww1 & 2.39 & ANT793-6 DG & ANT793-8 DK & 17 & 7 & 10.5 & \(\sim\)80 &
\begin{tabular}{c} Height \\ adjusted, \\ feasible \\ \end{tabular} \\ \hline ww2 & 1.33 & ANT793-6 DG & ANT793-8 DK & 15 & 5 & 11.8 & \(\sim\)80 & Feasible \\ \hline T/1/676/0/96 & 1.74 & ANT793-6 DG & ANT793-8 DK & 15 & 5 & 11 & \(\sim\)80 & Feasible \\ \hline \hline \end{tabular}
\end{table}
Table 5: Link Parameters for Sector 2.  
Figure 15: Transmit Power vs. RSSI for wells connected to Sector 2, with height adjustments.
## 4 Discussion
### Correlation between Soil-Sediment Organic Matter and Nitrogen  
It can be seen from the correlation analysis in Table 7 that, overall, organic matter was positively correlated with soil-sediment total nitrogen and nitrogen forms. In terms of classification (Tables 3-6), the organic matter contents of inland soil, lagoon sediments and sand bar soil were positively correlated with all nitrogen forms and significantly and positively correlated with ammonium, which is similar to the conclusion of [PERSON] et al. [25] that offshore sediment organic matter was negatively, but not significantly, correlated with all nitrogen species and was significantly and positively correlated with nitrous nitrogen. This is because most of the nitrogen in the soil-sediment surface exists in the organic matter, and the colloidal adsorption by the organic matter can also improve the nitrogen preservation ability in the soil-sediment. The ammonium obtained by the ammonification of organic matter is the substrate source of the nitrification process and affects the activity of nitrifying bacteria in soil. In general, soil organic matter helps to improve the adsorption and retention capacity of ammonium ions and ammonia and reduce the volatilization of soil nitrogen. This result shows that organic matter is the main factor influencing the soil-sediment total nitrogen and organic nitrogen, which is consistent with the research results of [PERSON] et al. [26].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **pH** & \begin{tabular}{c} **Organic** \\ **Matter** \\ \end{tabular} & **CEC** & **Clay** & \
\begin{tabular}{c} **Total** \\ **Nitrogen** \\ \end{tabular} &
\begin{tabular}{c} **Organic** \\ **Nitrogen** \\ \end{tabular} & **Inorganic** & **Ammonium** & **Nitrate** & **Nitrite** \\ \hline pH & 1.00 & & & & & & & & \\ organic matter & \(-\)0.23 & 1.00 & & & & & & & \\ CEC & \(-\)0.23 & 0.828 ** & 1.00 & & & & & & \\ clay & \(-\)0.295 * & 0.560 ** & 0.670 ** & 1.00 & & & & & \\ total nitrogen & \(-\)0.271 * & 0.240 * & 0.18 & 0.14 & 1.00 & & & & \\ Organic nitrogen & \(-\)0.267 * & 0.23 & 0.17 & 0.13 & 1.000 ** & 1.00 & & & \\ inorganic nitrogen & \(-\)0.21 & 0.370 ** & 0.360 ** & 0.380 ** & 0.08 & 0.06 & 1.00 & & \\ ammonium & 0.18 & 0.514 ** & 0.388 ** & 0.360 ** & -0.07 & \(-\)0.08 & 0.413 ** & 1.00 & & \\ nitrate & \(-\)0.284 * & 0.253 * & 0.274 * & 0.301 * & 0.11 & 0.09 & 0.962 ** & 0.15 & 1.00 & \\ nitrite & 0.06 & 0.293 * & 0.340 ** & 0.420 ** & 0.01 & \(-\)0.01 & 0.714 ** & 0.533 ** & 0.596 ** & 1.00 \\ \hline \hline \end{tabular}  
* At the 0.05 level (two-tailed), the correlation is significant. ** At the 0.01 level (two-tailed), the correlation is significant. is significant.  
\end{table}
Table 7: Correlation analysis between the basic soil–sediment physical and chemical properties and nitrogen forms (69 samples).
# Patterns of Arctic Tundra Greenness Based on Spatially Downscaled Solar-Induced Fluorescence
## 2 Data and Methods  
### Study Area  
The study area (Figure 1) covers the tundra region as defined by the Circumpolar Arctic Vegetation Map (CAVM) [39]. Within the spatial extent of the CAVM, the vegetated area (86%) contains erect shrublands (26%), graminoid tundras (18%), mineral graminoid tundras (11%), prostate-shrub tundras (11%), mountain complexes (13%), and wetlands (7%). The Low Arctic, predominantly low-shrub tundra, is mainly located in Russia, whereas abundant barren types and prostrate dwarf-shrub tundra are located in the High Arctic, Canada [39].  
Figure 1: Spatial extent of the tundra region of the study. The green pentagrams denote the eddy-covariance (EC) flux sites within the study area.
## 6 Case Study
### Case study--Functionality  
#### 6.1.1 The Motivation  
The need for mechanical ventilation is the primary reason for admission to intensive care units. Critical aspects in managing mechanically ventilated patients are related to unsuitable cuff pressure of the endotracheal tube. Underinflation of the cuff may lead to air leakage and improper ventilation. Consequently, contaminated subglottic secretions might aspirate into the lungs, possibly leading to ventilator-associated pneumonia (VAP) [31]. Besides being a significant cause of morbidity and mortality, VAP is known as one of the healthcare-associated infections (HAI) which burden the health system and can be preventable. Several studies have attempted to estimate VAP's economic burden, reporting costs ranging from USD 10,000 to USD 40,000 per treated patient [32]. On the other hand, the cuff's over-inflation might cause local mechanical injuries such as edema and ulcerations due to excessive pressure on the tracheal tissue.
# Experimentation in 5G and beyond Networks: State of the Art and the Way Forward
## 2 Overview of Published Papers
### 5G Enhancements  
Two papers belong to this research area, with the primary goal of proposing enhancements for current 5G systems.  
Contribution 6 by [PERSON] et al. focuses on channel coding for 5G [18]. Considering that Low-Density Parity-Check (LPDC) codes are the 3 GPP-standardized coding scheme for 5G NR data channels, this work discusses the design of a Quasi-Cyclic low-latency LPDC (QC-LPDC) scheme, which significantly reduces encoding complexity and resource utilization. By leveraging existing studies, the work showcases a new power-efficient QC-LPDC design, compatible with 5G NR standards and achieving significantly lower encoding latency thanks to a multi-channel parallel coding structure.  
Contribution 7 by [PERSON] analyzes 5G cybersecurity and discusses security concerns for both 5G networks and connected devices [19]. A method aimed at accurately analyzing 5G vulnerabilities is proposed, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and hexagonal fuzzy numbers theory. Such a method finds the network graph where attacks might propagate and quantifies both attack costs and network security levels. This work validates the method on a 5G testbed, using a classical TOPSIS-based approach and a vulnerability scanner tool called Nessus as benchmarks [20].
## 4 Sentinel-1: SAR simulation
Conversely, the microscopic behavior, which determines the presence of speckle, is introduced via a statistical model: in this paper, we assume a fully developed speckle [32, 33], and the amplitude-signal value obtained for each facet is multiplied by one specific realization of a Rayleigh random variable [30]. In Table 2, the parameters used for the simulation of Sentinel-1 and COSMO-SkyMed images are reported. With these parameters, we obtain a pixel spacing in azimuth-ground range on the final images of \(4.66\) m \(\times\)\(5\) m for Sentinel-1 and \(2.08\) m \(\times\)\(2.33\) m for COSMO-SkyMed.
# Coherent Image Source Modeling of Sound Fields in Long Spaces with a Sound-Absorbing Ceiling +
###### Abstract  
Sound-absorbing boundaries can attenuate noise propagation in practical long spaces, but fast and accurate sound field modeling in this situation is still difficult. This paper presents a coherent image source model for simple yet accurate prediction of the sound field in long enclosures with a sound absorbing ceiling. In the proposed model, the reflections on the absorbent boundary are separated from those on reflective ones during evaluating reflection coefficients. The model is compared with the classic wave theory, an existing coherent image source model and a scale-model experiment. The results show that the proposed model provides remarkable accuracy advantage over the existing models yet is fast for sound prediction in long spaces.  
long space; coherent image source method; sound-absorbing boundary; sound field modeling; scale-model experiment +
Footnote †: This paper is an extension of a conference paper presented in INTER-NOISE 2014.
# An Emergency Alert Broadcast Based on the Convergence of 5G and ATSC 3.0
## 3 The Novel Emergency Alert Broadcast Approach Based on the Convergence
### Latency Performance for the Proposed Emergency Alert Broadcast Approach  
In order to investigate the performance enhancement, we derive the latency performance for the proposed emergency alert broadcast approach. For the derivation, assume a random variable \(\eta\), which denotes the number of total broadcasts for successful transmission of an emergency alert message to a mobile terminal. Thus, the expected value of \(\eta\) denotes the latency performance, and is expressed as follows:  
\[E[\eta]=\sum_{k=0}^{\infty}{(k+1)\Pr(K=k)}, \tag{1}\]  
where \(K\) denotes the number of attempts required to successfully transmit the emergency alert message. In (1), \(\Pr(K=k)\) defines the probability that the first \(k\) attempts will fail and the \((k+1)\)th attempt will succeed. Since \(K\) follows a geometric distribution in (1), \(\Pr(K=k)\) can be expressed as follows:  
\[\Pr(K=k)=p_{e}^{k}(1-p_{e}), \tag{2}\]  
Figure 7: The specific framework for the interface between AMF and ATSC 3.0 core network using the N3 IWF.  
where \(p_{e}\) denotes the probability of an erroneous transmission of the emergency alert message to the mobile terminal in the case of the proposed convergence. After the substitution of (2) into (1), \(E[\eta]\) of (1) can also be expressed as follows:  
\[E[\eta]=\frac{1}{1-p_{e}}. \tag{3}\]  
Since the emergency alert broadcast method relies on the convergence network, \(p_{e}\) of (3) can be expressed as follows:  
\[p_{e}=p_{e,5g}p_{e,atsc3}, \tag{4}\]  
where \(p_{e,5g}\) and \(p_{e,atsc3}\) denote the probabilities of an erroneous transmission of the emergency alert message to the mobile terminal in the cases of the 5G and the ATSC 3.0, respectively. Therefore, the latency performance for the cases of the proposed convergence, the 5G, and the ATSC 3.0 can be given in (5), (6), and (7), respectively, as follows:  
\[E[\eta]=\frac{1}{1-p_{e,5g}p_{e,atsc3}}. \tag{5}\]  
\[E[\eta]=\frac{1}{1-p_{e,5g}}. \tag{6}\]  
\[E[\eta]=\frac{1}{1-p_{e,atsc3}}. \tag{7}\]  
Since \(p_{e,5g}<1\) and \(p_{e,atsc3}<1\), \(p_{e}\) of (4) is less than \(p_{e,5g}\) and \(p_{e,atsc3}\). Therefore, it is revealed that the latency performance for the proposed convergence is better than that for the 5G and the ATSC 3.0 in the comparison of (5), (6), and (7).
## 3 Results  
### Rcv  
The RCV results for the CHB-MIT data set are presented in Table 1. The findings show that chb01 had the highest ACC (91.33%) and SEN (100%), indicating that the model could accurately identify the correct preictal and interictal states of this patient. Furthermore, three patients had 100% SPE, reflecting the ability of the model to correctly identify interictal states. On average, the model achieved an ACC of 78.75%, SEN of 64.48%, and SPE of 78.20% in all patients. In contrast, chb04 had the lowest ACC (68.85%), and chb05 had the lowest SEN (33.28%), indicating that the model's performance was relatively lower for these patients. For chb01, the model failed to identify the interictal states, probably due to the very short interictal time interval, leading to an SPE of 0%. It is interesting to notice that the prediction performance can significantly differ among patients. One possible explanation is that the considered cohort might contain patients with heterogeneous types of seizures, which can manifest with a variety of EEG signatures that might be more or less challenging to detect.  
The RCV results in our data set were similar, as shown in Table 2. The best accuracy of 86.23% was achieved for p8, whereas p4 had the highest sensitivity of 83.40%. Eight patients obtained a specificity of 100%. The mean values for ACC, SEN, and SPE in all patients were 81.68%, 64.66%, and 96.12%, respectively. Among the patients, p10 had the smallest ACC and SEN with 73.24% and 49.80%, respectively, while p7 had the minimum SPE with 72.39%. These results demonstrate that the RCV validation method can achieve high prediction performance in both data sets.  
Figure 3 compares the RCV performance metrics between the two data sets. The results indicate that the model achieved similar performance in both data sets, with a noticeable difference of almost 18% only for the SPE metric.
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
## 5 Conclusions  
This paper investigated the design of hybrid precoders for an mmWave-channel-based URLLC communication system and explored a low-complexity, high-performance, high-reliability, and low-latency communication architecture. An achievable rate maximization problem was proposed by considering the total power, the precoder design, and the constant mode constraint of the subarray. To solve the nonconvex optimization problem, we employed two efficient iterative algorithms, DPD and HPD. This model can be applied to small-scale point-to-point communication scenarios and can effectively guarantee the communication in some delay-sensitive scenarios. The simulation results of our proposed algorithm demonstrated its ability to successfully solve the optimization problem in the new scenario, and our algorithm had better convergence and calculation results. Compared with another algorithm MM, it was found that the proposed algorithm had a better performance in the achievable rate and energy efficiency. Meanwhile, in the URLLC scenario with a single user and single antenna, the approximate solution of the MRT can also be used to solve the digital precoding. However, the research in this paper also had certain limitations. The algorithm used in this paper is only applicable to a URLLC with a single user and a  
Figure 5: Relationship between the number of RF chains and the energy efficiency.  
single antenna. In multiuser scenarios, due to the interference between users, it is necessary to deal with interference items. In view of the characteristics of millimeter-wave fading, other technologies such as reconfigurable intelligent surface technology [26] can be used in addition to precoding technology to improve the quality of communication.  
Conceptualization, Z.L. and G.W.; methodology, Y.S. and J.-B.W.; software, Y.S.; validation, M.Q., G.W. and J.-B.W.; formal analysis, Z.L.; investigation, Y.S.; resources, Z.L.; data curation, M.Q.; writing--original draft preparation, Y.S.; writing--review and editing, G.W. and J.-B.W.; visualization, M.Q.; supervision, G.W.; project administration, J.-B.W.; funding acquisition, J.-B.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Grid Jiangsu Electric Power Co., Ltd. grant number J2021210 and The APC was funded by State Grid Jiangsu Electric Power Co., Ltd. under grant number J2021210.  
In our paper, no new data were created.  
The authors declare no conflict of interest.
# Planar Delaunay Mesh Smoothing Method Based on Angle and a Deep Q-Network
## 3 Parameter Setting and Data Preparation of the Constrained DQN Model
### Neural Network and Network Parameter Setting  
The structure of the Q-network changes depending on the number of hidden layers and the number of neural units. In this paper, we set the number of hidden layers to one and two, and the number of neural units with one hidden layer is 10, 20, 30, 40, 50, and 60. Q-networks with two hidden layers are set with the same number of neural units in the hidden layers and fully connected between the two hidden layers. Q-networks with different structures are used to train the dataset. The numerical convergence of the average Q-value and the average reward with the number of training rounds is output after the training is completed. The convergence using different Q-networks for training is shown in Figure 8.  
Figure 8a shows the variation of the average Q-values of different Q-networks as the number of training epochs increases. The average Q-value of the Q-network with one hidden layer and 20 neural units grows more slowly when the number of training epochs is 0-5; it grows rapidly when the number of training epochs is 5-10. It grows slowly when the number of training epochs is 10-15; after 15 epochs, it stays around 39.174, and the average Q-value changes more smoothly compared with other Q-networks. Figure 8b shows the variation of the average reward with increasing training epochs using different Q-networks. The Q-network with one hidden layer and 20 neural units has a smooth change in the average reward when compared with other Q-networks at 10-20 training epochs; after 20 epochs, there is a significant decrease in the average reward. Considering the stability of the DQN model, the number of training epochs is set as 16. Since the reward of \(-\)100 given by the environment when the agent moves out of the polygon is much smaller than the reward obtained by the improved node quality after the agent moves, the average reward is \(<\)0. This is set so that the DQN model does not produce inverted elements when smoothing the mesh. According to the experimental results, such a reward setting is effective and does not negatively affect the performance of the DQN model.  
The total number of layers in the neural network is two--the first is hidden, and the second is an output layer. The state space of the dataset is used as the input, and the number of neural units in the input layer is \(n_{1}=2\times(1+k+l)\), where the central node needs to be recorded as a feature, \(k\) is the number of polygon nodes, and \(l\) is the number of inner polygon nodes. The number of neural units in the hidden layer is \(n_{2}=20\). The action space of the dataset is taken as the output, and the number of neural units in the output layer is \(n_{3}=4\). The structure of the neural network is shown in Figure 9.  
Figure 8: Neural network performance analysis chart. (**a**) The growth curve of average Q-value with training epochs; (**b**) the growth curve of average reward with training epochs.  
Figure 9: Schematic diagram of neural network structure.
## 2 Related Work  
### Supervised Depth Estimation  
Since the ground truth is used as the supervising signal for the network in the supervised approaches, the deep network directly learns the mapping between the RGB map and the depth map. [PERSON] et al. [5] first use CNNs for the monocular depth estimation, the architecture consists of two components: coarse-scale networks to extract global information, and fine-scale networks to refine local information. Based on [5], they propose a unified multiscale network for three tasks: depth estimation, semantic segmentation, and normal vector estimation [7]. [PERSON] et al. [8] propose a fully convolutional network, using residual networks to extract global information and designing residual up-sampling blocks to improve the quality of depth estimations.  
Many methods have converted depth estimation to a classification problem. [PERSON] et al. [9] discretize continuous depth values into several bins and use a CNN to classify them. [PERSON] et al. [10] propose a spacing-increasing discretization strategy to discretize the depth, reconstructing deep network learning as an ordered regression problem to prevent over-strengthened loss.  
Supervised methods require annotated datasets, while the acquisition of these datasets are costly. To alleviate the burden of the annotation task, weakly supervised methods in the form of supervised appearance matching terms [11], parse ordinal depths [12], and  
\begin{table}
\begin{tabular}{l l l l} \hline  & **Supervised** & **Unsupervised** & **PDANet** \\ \hline Ground Truth & Needed & Not Needed & Not Needed \\ Acquisition of Depth Map & Hard & - & - \\ Generalization ability & Low & - & - \\ Color Constancy Hypothesis Problems & No & Yes & No \\ low-texture regions Problems & No & Yes & No \\ Quality of Prediction & High & Low & Relatively High \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of different approaches.  
Figure 2: Examples of PDANet’s results on KITTI2015 [6]. From left to right: input images, ground truth disparity maps, and results of PDANet.  
known object sizes [13] have been proposed. Since research on these tasks have reached a bottleneck, self-supervised monocular depth estimation has been explored in recent studies.
## 3 State Evaluation and Fault Prediction of Protection System Based on Digital Twin Technology
### Fault Prediction of Protection System Based on Clustering Algorithm  
The protection system fault prediction process is initiated when the equipment status is assessed as severe and has a tendency to develop into a fault in order to predict the type of fault that may occur in the equipment. In this section, nine common types of faults in protection systems are summarized and organized, based on which the idea of improved K-means clustering algorithm [31] is used to study the fault prediction of protection systems. Clustering is the process of classifying and organizing similar data, and the K-means algorithm is a partition clustering method with the average value as the clustering center, which is simple and efficient. The time complexity and spatial complexity of the K-means clustering algorithm is O (\(n\times k\times t\)) and O (\(n\times t\)), where \(n\) represents the number of objects in the dataset, \(t\) represents the number of iterations of the algorithm, and \(k\) represents the number of clusters. They can all be considered to be constants, so the time and space complexity can be simplified to be O(\(n\)), i.e., linear.  
The clustering algorithm is used to cluster the characteristic sequences of common faults, and the fault prediction is realized by calculating the correlation degree between the current characteristic sequences of the protection system and the clustering center. The flow of fault prediction based on the clustering algorithm is shown in Figure 6. According to this process, the common faults of the protection system are discussed in Section 3.3.1, and the samples for clustering are discussed in Section 3.3.2, and their clustering centers are calculated. Finally, it explains how to use Euclidean distance to calculate the correlation between the current state of the equipment and various fault types, so as to judge the possible fault types of the equipment.