esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Energy Efficiency in WSNs  
### Energy Efficient Routing Protocols for WSN  
In the domain of WSNs, routing protocols are evolving rapidly. The application needs and the underlying network architecture are considered when developing routing protocols. The amount of energy required to capture by the sensors will affect the lifetime of the underlying network. [PERSON] et al. [41] proposed a DCHS for WSNs to extend network lifetime and reduce energy consumption. The analytical results demonstrate that this strategy can balance network efficiency in two phases and has a longer network surviving life span. [PERSON] and [PERSON] [42] proposed an approximation factor-based Fixed Parameter Tractable (FPT) solution for the Load-Balanced Clustering Problem (LBCP). The energy-balanced routing strategy and an energy-efficient technique for routing between the sink and the CHs have been developed. Combining routing and clustering models into a precise chromosome has resulted in a genetic algorithm-based technique for calculating overall energy consumption [43]. Load parameters were assigned using the fitness function to adjust energy consumption among nodes. According to the findings of this study, appropriate load balancing across the CHs was achieved in a range of circumstances. The UCRA-GSO for WSNs has also been used to address the problem of energy balance. To choose the best clustering strategy, the GSO model considers the CH proximity distance, the CH energy, the CH density, and the cluster compactness [44].  
The Stable Election Protocol (SEP) was the second generation of HWSN [45]. It is made up of two distinct nodes. Within SEP, CH selection is based on weighted election probability (WEP). However, when it came to multi-level heterogeneous WSNs, SEP revealed a lack of SN efficiency. Differences in data creation rate (traffic) determine the computational heterogeneity Energy Dissipation Forecast and Clustering Management (EDFCM) [46] incorporates both traffic and energy heterogeneity in highly specialized two-level WSNs. Furthermore, EDFCM uses extra nodes (management nodes) to limit cluster growth, which throws into doubt its intrinsic distributed localized decision-making
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 2 Proposed Method
### Structured Spectrum of Periodically Modulated Signals  
Actually, the spectrum of periodically modulated signals exhibits the following properties. First, only few coefficients around the carrier frequency and its integer multiple harmonics are non-zero where the remaining coefficients can be considered as zero. Second, the spectrum is quasi-symmetrical around the carrier frequency. Third, the position of non-zero coefficients around the different harmonics of the carrier frequency remains almost identical. Consequently, given that the carrier frequency \(f_{c}\) is known, the target spectrum can be modeled to be a block-sparse signal with partially-known active block positions. In fact, the center location of active blocks is known and equal to the carrier frequency, but the locations of non-zero coefficients inside the active block are unknown.  
Let \(B\) be the number of carrier harmonics, i.e., the number of active groups. Each active group \(b\) is centered on the harmonic \(b.f_{c}\). We further assume that active blocks are not overlapping and are quasi-symmetrical, i.e., each active block \(b\) contains the same number \(Q\) of coefficients on the left and the right side of \(b.f_{c}\). This also means that we assume non-overlapping modulations, which is almost the case when the carrier frequency is very large when compared to the fundamental modulations frequencies. We also denote, by \(\mathcal{A}\), the union of all active \(B\) groups, then \(card(\mathcal{A})=B(2Q+1)\). This means that we assume, at most, \(B(2Q+1)\) non-zero coefficients (\(B\) carrier harmonics and \(2Q\) non-zero coefficients around each carrier. It is worth noting that the number of non-zero coefficients around each carrier is actually much lower than \(2Q\). Let \(\bar{\mathcal{A}}\) denote the set of the remaining non-active coefficients, i.e., zero coefficients. In the following, for every \(b\in\{1,\,\ldots,\,B\}\), we denote, by \(x_{0,b}\), the coefficient at the frequency bin \(b.f_{c}\) and, for every \(q\in\{1,\,\ldots,\,Q\}\), we denote, by \(x_{-q,b}\) and \(x_{q,b}\), the \(q^{th}\) coefficient at the left of \(b.f_{c}\) and its symmetric with respect to \(b.f_{c}\), respectively (see Figure 1). Note that each coefficient \(x_{q,b}\) can be easily obtained from \(\mathbf{x}\) by applying a suitable sparse matrix \(\mathbf{P}_{q,b}\) containing one appropriate line of a permutation matrix i.e., \(x_{q,b}=\mathbf{P}_{q,b}\mathbf{x}\).  
In this paper, we set that, for every \(k\in\bar{\mathcal{A}}\), \(x_{k}=0\). The following parts propose a model for the coefficients in active blocks \(\mathcal{A}\) that takes the structure of spectrum into account.
## 3 Method
### Graph Construction
#### 3.2.3 Feature Representation  
The graph structure describes the relationship between objects. And the nodes of the graph aim to describe the features of each object, where we select hand-crafted feature, intensity distribution, and texture feature as the representation of objects. Inspired by [PERSON] [14], we calculate and analyze the image intensity characteristics on image datasets EmotionROI and FI. In detail, we quantify the intensity of each pixel to 0-10 and make histograms of intensity distribution. As shown in Figure 4, we find that the intensity of positive emotions (joy, surprise, etc.) is higher than that of negative emotions (anger, sadness, etc.) when the brightness is 4-6, while the intensity of negative emotions is higher on 1-2.  
The result shows that the intensity distribution can distinguish the sentimental polarity of the images to some extent. At the same time, we use the Gray Level Co-occurrence Matrix(GLCM) to describe the texture feature of each object in the image as a supplement to the image detail feature. Specifically, we quantified the luminance values as 0-255 and calculated a 256-dimensional eigenvector with 45 degrees as the parameter of GLCM. The node feature in the final graph model is a 512-dimensional eigenvector.  
Figure 4: The distribution curve of the number of brightness pixels of different emotion categories in the EmotionROI and Flickr and Instagram (FI) dataset.
## 4 Conclusions and Future Directions  
A PSO-RVFLNS foam flotation performance identification method combining CNN and LBP was proposed to improve the identification accuracy. The method can be used in practical situations to solve problems such as difficult flotation agent dosing state detection, low identification efficiency with existing methods, and even excessive reliance on manual experience.  
In this study, an NSST-CNN feature extraction network was constructed on the basis of a VGG16 model. The original foam images were decomposed into different frequency scales through NSST decomposition to better extract the high- and low-frequency features of foam. The low frequency of NSST contains a lot of energy of the foam, and the high frequency contains a lot of information about the boundary and detail texture of the bubble, which can characterize the foam more comprehensively and is beneficial for identification of the final working condition. With the same number of training samples, the NSST-CNN network developed here has higher recognition accuracy than the single-mode CNN feature recognition model. The proposed LBP-TOP model can better extract time-frequency features of foam videos and further improve the recognition accuracy. The combination of the CNN model and LBP-TOP model overcomes the deficiency of CNN in dynamic features. The results show that the recognition accuracy of the multi-scale CNN and LBP-TOP method was improved by 4.97% and 6.55%, respectively, compared with CNN and the traditional LBP algorithm. The average recognition accuracy of flotation states with PSO-RVFLNS reached 95.17%.  
In addition, this method is also useful for the flotation of other ones. At this stage, only the working conditions of foam flotation are identified and classified; reagents and machine parameters are not considered. The next step is to control the reagent concentration, type, dosage and parameters of machines for foam flotation classification.  
The conceptualization, algorithm, and code development were carried out by [PERSON], [PERSON], [PERSON] and [PERSON]. Experiments, manuscript writing, arrangement of resources, planning, design of experiments, and state of the art review were carried out by [PERSON], [PERSON], [PERSON] and [PERSON]. Part of writing, review, artwork, and code development were carried out by [PERSON], [PERSON] and [PERSON]. The day-to-day supervision and manuscript review were carried out by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData sharing is not applicable to this article due to the privacy of participants.  
Figure 17: Performance recognition results of three modes. (**a**) Foam flotation condition identification combining multi-scale CNN and LBP features; (**b**) Multi-scale NSST-CNN condition identification; (**c**) LBP-TOP condition identification.
# Analysis of Cultivated Land Productivity in Southern China: Stability and Drivers
## 3 Results
### Factors Affecting the Stability of Cultivated Land Productivity from 2001 to 2022  
Multiple linear regression analysis was used to explore the factors affecting the stability of cultivated land productivity. County-level _MAP_, _MAT_, _field-road distance_, _AREA_MN_, _field-river distance_, _ENN_, and _FRACT_ values were set as the independent variables, and the CV was set as the dependent variable. The R\({}^{2}\) value of the regression model was 0.915, reflecting that the selected independent variable explained 91.5% of the CV of the dependent variable, indicating that the model had a high degree of fitting (Table 1). At a 95% significance level, _field_river distance_, _AREA_MN_, _FRACT_, _ENN_, and _MAP_ significantly affected the stability of cultivated land productivity. However, the influence of _MAT_ and _field_road distance_ were not statistically significant (Table 1).  
Specifically, _field_river distance_, _AREA_MN_, and _FRACT_ were positively correlated with the stability of cultivated land productivity; that is, an increase in these factors improved the stability of cultivated land productivity. Conversely, _ENN_ and _MAP_ were negatively correlated with the stability of cultivated land productivity, indicating that an increase in these factors may reduce the stability of cultivated land productivity.
# A Comparative Analysis of High Availability for Linux Container Infrastructures
###### Abstract  
In the current era of prevailing information technology, the requirement for high availability and reliability of various types of services is critical. This paper focusses on the comparison and analysis of different high-availability solutions for Linux container environments. The objective was to identify the strengths and weaknesses of each solution and to determine the optimal container approach for common use cases. Through a series of structured experiments, basic performance metrics were collected, including average service recovery time, average transfer rate, and total number of failed calls. The container platforms tested included Docker, Kubernetes, and Proxmox. On the basis of a comprehensive evaluation, it can be concluded that Docker with Docker Swarm is generally the most effective high-availability solution for commonly used Linux containers. Nevertheless, there are specific scenarios in which Proxmox stands out, for example, when fast data transfer is a priority or when load balancing is not a critical requirement.  
high availability; containers; Linux; Kubernetes; Proxmox; Docker; Docker Swarm 1
## 6 Simulation Results and Discussions
### Simulation Scenarios and Results
#### 6.3.3 Simulation Scenario 3: Energy Consumption Evaluation  
In this subsection, we dive into the simulation results that reveal how energy consumption varies across different protocols. Specifically, we compare the total energy consumption of our cutting-edge protocol with its counterparts: LDIWPSOC, PSO-EEC, OFCA, and NPSOP. As shown in Figure 12, it is clear that energy consumption rises as data transmission and distances increase. While these other protocols have made strides, they still fall short of what our proposed protocol offers, both with and without routing options, and especially when using multi-hop routing. Our protocol goes above and beyond to minimize energy consumption by integrating a host of innovative features. From a novel scheduling strategy to an optimal DFG formation process that secures the ideal number of PDFHs, it is designed to maximize efficiency. One of our key contributions is regrouping after a set of rounds, triggered when a PDFH's energy falls below the DFG's average. This approach significantly extends the network's lifespan by ensuring SNs with lower energy are less likely to become PDFHs, effectively conserving their resources. By tackling network impairments, our protocol delivers a noticeable boost in overall performance, truly setting a new standard for energy-efficient networking. In essence, backup CHs (i.e., FSDFH, SSDFH, FSDFR, and SSDFRs) play a crucial role in maintaining the stability, reliability, and efficiency of data transmission. These backups are responsible for aggregating data from SNs and relaying it to the sink node or other PRs. However, IoT networks are often prone to SN failures due to battery depletion, connectivity issues, or environmental factors, which can disrupt communication and data flow. Implementing backup CHs provides a fail-safe mechanism that ensures continuous network operation, even when PDFHs fail. FSDFH
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 3 Deep-Space Telemetry
### Giotto  
Giotto, the first deep-space mission conducted by the European Space Agency [55], was launched in July 1985 on a mission to explore Comet Haley. Because the exploration involved a high-velocity fly-by close the the comet nucleus, there was a high probability that the spacecraft would be destroyed. Consequently, the standard approach of measuring, storing, and then transmitting the measurements, was replaced by a system that transmitted the telemetry in real-time on an 8.428 GHz downlink [56]. The engineering telemetry link (called the \"housekeeping\" link by the _[PERSON]_ team [57]) was 350 bits/s. The bit rate for each instrument is listed in Table 1 of [57]. The composite telemetry bit rates ranged  
Figure 12: The encoder for the \((7,\
icefrac{{1}}{{2}})\) convolutional code used by _Voyager_ 1 and _Voyager_ 2. Adapted from [6]. The box with a “D” is a single-bit delay.  
from 360 bits/s to 46,080 bits/s in 4 selectable formats [56]. The channel code was the same concatenated coding scheme used by _Voyager_ 2 illustrated in Figure 11c [56]: the outer code was the (255,223) Reed-Solomon code and the inner code was the rate \((7,\
icefrac{{1}}{{2}})\) convolutional code.  
The _Giotto_ mission was a success; in March 1986 it fleew within 610 km of the comet nucleus according to [PERSON] [56] or at 596 km from the comet nucleus according to [PERSON] [58] (p. 360) and survived the encounter. Because _Giotto_ survived the encounter, it was directed to a fly-by of Comet Grigg-Skjellerup at 200 km in July 1992 [58].
## 4 Practical Explanations and Implications
### Early Estimations  
Users initiate the intended SAGE-based process by sending a small number of training symbols to the BS. The first predictions \(\widehat{\alpha}(u,0)\), \(\widehat{\Omega}(u,0)\), and \(\widehat{\widehat{\mathbf{h}}}^{(u)}(0)\) get more accurate as the number of training symbols increases. In contrast, boosting the number of training symbols decreases the energy accessible for data symbols and raises the required bandwidth. Therefore, the ratio of training symbols to information symbols should be as little as practicable. The suggested methodology makes use of the soft information included within the soft information symbols that are delivered by the channel decoder. This allows us to produce excellent identification and estimation accuracy while having a minimum loss in throughput. The early estimations of the parameters under consideration are derived from (18) and (19) by limiting the items in \(\mathbf{Q}_{\alpha(u),\Omega(u)}^{(u)}\) to the influence made by training symbols only. The proposed approach is summarized in Algorithm 1.  
Figure 2: The proposed structure of the BS.  
For each user \(u\):  
* Use a few training symbols to construct the matrix \(\mathbf{Q}_{\alpha(u),\Omega(u)}^{(u)}\) as shown in (5) for each possible combination of STBC format \(\alpha(u)\) and modulation type \(\Omega(u)\). Here, we set the unknown data symbols to zeros.
* Use the proposed time-domain multiple access interference cancellation algorithm, as indicated in (9).  
For \(\zeta=1:\mathcal{I}\), where \(\mathcal{I}\) is the number of iterations:  
1. Use (17) to determine the channel estimate under each possible pair of \(\alpha(u)\) and \(\Omega(u)\).
2. Use (18) to determine the estimates of \(\alpha(u)\) and \(\Omega(u)\).
3. Use (19) to determine the final channel estimate.
4. Perform soft data detection under the estimates of channel parameters, modulation type, and STBC format.
5. Use the soft decoder outputs to compute the a posterior expectations of the transmitted data symbols as reported in (22).
6. Use the a posteriori expectation of the transmitted data symbols to construct \(\mathbf{Q}_{a(u),\Omega(u)}^{(u)}\), as shown in (5). Here, we set the unknown data symbols to their a posteriori expectations.
7. Update the estimation of channel parameters, modulation type, and STBC format, as shown in (17), (18), and (19).  
End (iterations)  
* Perform a hard-decision criterion to recover the original transmitted data symbols. End (users)  
**Algorithm 1**Summary of the proposed algorithm
## 5 Computational Results
However, an overlap does not mean that there is no difference. There may be statistically significant difference even if there is an overlap [63]. To determine the difference, the paired \(t\)-tests should be used [63]. The results of the paired \(t\)-tests are given in Table 4. If the 95% confidence interval for the mean difference between the two compared algorithms does not include zero values, it indicates that there is a difference between the two algorithms compared. In addition, \(p\)-values, which are smaller than \(\alpha\) = 0.05 level, further suggest that the compare algorithms perform differently. As can be seen from Table 4, the IVLS, VBIH1, VBIH2, VBIH3 and VBIH4 algorithms are statistically better than the DABC*_RCT algorithm. However, the VBIH5 and VBIH 6 algorithms are statistically equivalent. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm vs. algorithm** & **95\% CI for Mean Difference** & \(p\)**-Value** \\ \hline IVLS \(-\) DABC*\_RCT & (0.0041, 0.0875) & 0.032 \\ VBIH1 \(-\) DABC*\_RCT & (\(-\)0.1162, \(-\)0.0298) & 0.001 \\ VBIH2 \(-\) DABC*\_RCT & (\(-\)0.1242, \(-\)0.0486) & 0.000 \\ VBIH3 \(-\) DABC*\_RCT & (\(-\)0.1193, \(-\)0.0639) & 0.000 \\ VBIH4 \(-\) DABC*\_RCT & (\(-\)0.1258, \(-\)0.0474) & 0.000 \\ VBIH5 \(-\) DABC*\_RCT & (\(-\)0.0843, 0.0117) & 0.137 \\ VBIH6 \(-\) DABC*\_RCT & (\(-\)0.0671, 0.0252) & 0.371 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Paired \(t\)-test for variable block insertion heuristic (VBIH) variants versus discrete artificial bee colony (DABC)*_RCT algorithm. Figure 12: Interval plot of algorithms compared. Finally, in Table 5, we provide the best solutions found by each algorithm. 52 out of 150 problems instances are further improved together with 15 solutions being equal during in this study. All the results and permutations are available based on request.
## 3 Application of Proposed Method on Nanchang Subway Line 3  
### Project Overview and Monitored Data  
Figure 5 shows a plan view of Nanchang Subway Lines. Construction began on Subway Line 1 in 2010, then Lines 2 and 3 were designed in turn. Subway Line 3 was completed in 2020. The total length of Subway Line 3 is 28.5 km, with 22 subway stations from Yin San Jiao Bei Station to Jing Dong Road Station. The stations are mainly excavated by using the cut and cover method and are basically located in medium sand and moderately weathered argillaceous siltstone.  
To validate our proposed method, we chose the running tunnel from Jing Jia Shan Station to Shi Zi Street Station. Figure 6 shows a plan view of the running tunnel of Nanchang Subway Line 3. The total length of the running tunnel is 1369.16 m (from DK34+260 to DK35+630). The running tunnel consists of left and right tunnels, excavated by using the shield method. The left and right shield tunnels excavate adjacent to numerous existing buildings and parallel beneath reinforced concrete pipelines. A region from \(-50\) m to 50 m is used in comparison between the analytical and monitoring results.  
Figure 7 illustrates a cross-sectional view of the existing pipeline and the new shield tunnels, part of the Nanchang Subway Line 3. The external and the internal radius of the segmental lining are 3.0 m and 2.7 m, respectively. The overburden depth of the shield tunnels is about 15.6 m. The vertical clearance between the shield tunnels and the existing pipeline is about 11.3 m. The external diameter and the width of the existing reinforced concrete pipeline are 0.96 m and 0.08 m, respectively.  
After launching from the Shi Zi Street Station, the deformation of the ground surface and pipeline was monitored. The ground surface deformation was measured by total station and bolt-like mark, type TCR1201+ and No. 667556. A magnetic extensometer and probe measured the deformation of the existing pipeline. The monitoring points arrangements for the ground surface and existing pipeline can also be found in Figure 7. The monitoring points are set up every 5 m along the tunnel direction.