esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport
## 3 Methodology and Results
### Communication Range
#### 3.1.4 Comparison of Radio Propagation Planning and Radio Measurement  
The comparison of the radio propagation planning with the radio measurement brought forth two major findings: the validation of the planning tool and the specific evaluation of neuralgic points of interest. The image below, Figure 2, compares the radio propagation planning with real radio measurements using the example of the intersection \"Eckenheimer Landstr./Eschenheimer Anlage\". The levels that were assessed during a measurement around this RSU location can be found on the left side, while the prediction results (radio propagation planning) are shown over a larger area on the right side. The gray dashed line serves as a comparison axis between both views, highlighting the RSU location.  
In the second step, the reporting points assigned to the route and the traffic lights were added as an additional display layer. At this point, the task consisted of identifying  
Figure 2: Comparison of the measurement results with prediction results from a selected location (code ELS) for radio coverage, taking into account the buildings. Please note that the measurements on the left side were obtained while driving on the road.  
neuralgic points of interest, thus clarifying the question of where the registration points were and which reports no longer reached the assigned traffic light in terms of technological communication ability. To estimate the radio accessibility at the registration points, the calculated reception values were examined by radio measurements at intersections where the radio propagation planning tool outputs critical accessibility. This is shown as an example in Figure 3.  
The tool for radio propagation planning determined rather poor radio coverage for both the pre-registration points in the inbound direction and the pre-registration points in the outbound direction. However, the real measurements showed that these areas were not critical and that there was corresponding coverage. These so-called RSSI (received signal strength indicator) values can be seen as color-coded hexagons. The lighter the color value, the better the reception conditions. If there is no hexagon shown, there are no values because they were not measured or estimated. In the first case, no values could be received, or the measuring position was not sought. In the latter case, the values were not determined because they were outside the range or because of architectural shadowing caused by buildings around the site of testing.  
Result 2:  
From the combination of both tools, coverage with C-ITS cooperation could be demonstrated. On the one hand, the combined approach always provides new scientific knowledge, and on the other hand, it enables a much more time-saving evaluation compared with the conventional singular approach.
# M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders
## 5 Simulation and Analysis
### Results  
#### 5.4.1 Parameters of the Stacked DSAE Network  
Using the proposed DSAE model with noisy and noiseless training data, we obtained the detection performance for different numbers of nodes in the hidden layers and different ratios of training and testing sets. The most appropriate ratios of training and testing sets were selected, as well as the stacked network set of the hidden layers.  
For 2 PPSK, \(M=2\), the ratio of training and testing set is 60,000:10,000 and the corresponding number of nodes in the input layer is 1050 with 2 of these nodes in the output layer. For 4 PPSK, \(M=4\); the ratio of training and testing set is set to 60,000:100,000, and the corresponding number of nodes in the input layer is 100 with 4 of these nodes in the output layer.  
The detection performances are compared, assuming different numbers of nodes in the hidden layers. The training datasets are noisy and noiseless, as shown in Tables 1 and 2. H1 is the first hidden layer; H2 is the second hidden layer; H3 is the second hidden layer; '\(-19\)' means the channel \(SNR=-19\) dB; '\((-18)\) -\(19\)' means the training data \(\text{SNR}=-18\) dB with channel \(SNR=-19\) dB; and '\((-3)\) -\(19\)' means training data \(SNR=-3\) dB with channel \(SNR=-19\) dB. The main parameters of the improved MPPSK system are \(M=2\); \(K=30\); \(N=105\); \(f_{\text{c}}=21\times 10^{6}\) Hz; sampling rate \(f_{\text{s}}=210\times 10^{6}\) Hz; and the ratio of training and testing set is 60,000:10,000. The receiver consists the SIF followed by the stacked DSAE network.  
As shown in Tables 1 and 2, the SER values based on training data with \(SNR=(E_{b}/N_{0})\times(R_{b}/W)\)\(=-3\) dB are better than that based on the training dataset of \(-18\) dB, the case shown as '\(-3\)', '\(-18\)'. Given this, all experiments below will use the \(-3\)dB noised training data. It is also observed that the detection performance with three hidden layers is better than that with two layers. Therefore, we adopt the combination of hidden layers 600-300-150. This means 600 nodes in the first hidden layer, 300 in the second hidden layer and 150 nodes in the third hidden layer.
# A Cross-Modal Hash Retrieval Method with Fused Triples
## 5 Experiment
### Experimental Results and Analysis
Figure 3 demonstrates that on the MIRFLICKR-25K data set, the accuracy of this paper's method is much greater than the accuracy of the other methods, both in the case of retrieving text based on picture information and obtaining images based on text information and from beginning to end. It shows that the proposed method is adaptable and has high retrieval accuracy on this data set. It can also be seen from Figure 3 that the unsupervised cross-modal retrieval-based methods CMFH and CCA-ITQ continue to have significantly lower PR curves than the other methods. From this, it is clear that supervised cross-modal retrieval methods are more applicable to this data set than unsupervised cross-modal retrieval methods. Furthermore, the precision of CMFH and CCA-ITQ remained between 0.55 and 0.6 regardless of the change in the recall, surrounded by the PR curves of the other methods, so the unsupervised cross-modal retrieval methods do not generalize well on this data set. From the experimental results in Figure 4, it can be seen that on the NUS-WIDE dataset, when retrieving text based on image information, the precision of this paper's method  
Figure 4: PR curve for cross-modal retrieval on NUS-WIDE. (**a**) NUS-WIDE on image retrieval text; (**b**) text retrieval images on NUS-WIDE. is higher than that of other methods when the recall is less than 0.8. Still, the method's precision starts to drop faster or even later than other methods when the recall is 0.8. The method also shows faster precision decline when retrieving images based on textual information. According to the principle of the PR curve, it can be seen that although the precision of this method is lower than other methods, it does not mean that the precision is not good, but only means that the method of this paper is not good at adapting to the dataset in the later stage. Thus, we analyze the reason for this phenomenon because the dataset is too large. The network training time is long, meaning that in this paper's method on the dataset with the increase of training time, the adaptive ability cannot obtain the best results, and has certain limitations. Therefore, the method in this paper needs to go through operations such as splitting samples to improve the retrieval performance when experimenting on large-scale datasets. Meanwhile, it can be seen that the unsupervised cross-modal retrieval methods CMFH and CCA-ITQ have a slight advantage in terms of accuracy on this dataset, which can indicate that the unsupervised cross-modal retrieval methods are more suitable for over-large-scale datasets. In future research, unsupervised cross-modal retrieval methods can be considered for experiments on oversized datasets.
## 3 Results  
### Spatial Patterns of NDVI and NPP  
The spatial distributions of the perennial average values of NDVI and NPP in the Sanjiangyuan National Park are shown in Figure 2. Overall, the NDVI was high in the southeast and low in the northwest. Similar results have been reported in previous research [35]. For the entire region, the mean annual maximum NDVI from 2000-2016 was 0.31, ranging from 0.06 to 0.82. The Yangtze River headwater region had the lowest NDVI, which was mostly concentrated between 0 and 0.3, and the Lancang River headwater region had the highest NDVI, which was mostly concentrated between 0.4 and 0.8. It was clear to see that the NPP had similar spatial distribution patterns of high in the southeast and low in the northwest, and the mean annual NPP from 2000-2016 was 93 gC/m\({}^{2}\), ranging from 0.01 to 410.62 gC/m\({}^{2}\). The NPP of the Yangtze River headwater region was predominantly 0-100 gC/m\({}^{2}\), and 100-250 gC/m\({}^{2}\) for the Lancang River headwater region.  
Among the different vegetation types (Figure 3), the perennial average NDVI ranged from 0.19 to 0.67, and the NPP ranged from 39.77 to 352.36 gC/m\({}^{2}\). The coniferous forests had the highest values of NDVI and NPP, followed by the shrub and meadow, while the prairie had the lowest values of NDVI and NPP.  
Figure 3: Perennial average NDVI (**a**), NPP of different vegetation types (**b**).  
Figure 2: Spatial distribution of normalized difference vegetation index (NDVI) (**a**), net primary productivity (NPP) during 2000–2016 (**b**).
## 3 Results and Discussion
### Enthalpy Change in the \"Multi Walled Carbon Nanotubes-Hydrocarbon Plasticizer\" System
It is also noteworthy that the minimum MWCNT diameter of 21 nm is achieved in the dispersion system with Residual Extract. The obtained data (Table 3) allowed for the calculation of the total change in interaction surface area for the ensemble (aggregate) of multi-walled carbon nanotubes after ultrasonic dispersion, as well as the enthalpy, presented in Figures 4 and 5. The results obtained in this study (Figure 4) allow for a detailed analysis of changes in the interaction surface area of multi-walled carbon nanotubes (MWCNTs) in hydrocarbon plasticizers after ultrasonic dispersion. The corresponding enthalpy changes (Figure 5) were also calculated to assess the energy required for dispersion. The interaction surface area (\(\Delta A\)) increased significantly after ultrasonic treatment, indicating enhanced dispersion of MWCNTs within hydrocarbon plasticizers. For example: for Selective Purification Extract, Residue at 5\(\cdot\)10\({}^{-2}\)% MWCNT content, the surface area increased from 16.91 m\({}^{2}\) to 152.70 m\({}^{2}\) (\(\Delta A\) = 135.79 m\({}^{2}\)). The concentration of MWCNTs plays a critical role in both the surface area changes and enthalpy contributions. Higher concentrations result in larger \(\Delta A\) and \(\Delta H\), highlighting the challenges in dispersing densely packed MWCNTs. As mentioned earlier, the primary task in developing stable dispersion systems containing carbon nanotube nanoclusters is to minimize the enthalpy (\(\Delta H\)) by improving the interaction between the MWCNTs and the hydrocarbon plasticizer. As can be seen from the obtained data (Table 4), the main contribution to the enthalpy in our case is made by the concentration of MWCNT. A significantly positive \(\Delta H\) value indicates that the dispersion of MWCNT requires an energy input, which is typically observed when there are strong intermolecular interactions within the MWCNTs, such as van der Waals forces, which hinder their separation (deagglomeration). At this stage of the research, the Selective Purification Extract (distillate and residue) demonstrated the most significant increases in interaction surface area and enthalpy, suggesting superior dispersion efficiency compared to Industrial Oil and II Vacuum Distillate.
## 3 Results
### Effects of the Anterior Wedge on the Activity Level of Thigh Muscles  
Overall, the meta-analysis showed no significant effect of the anterior wedge on the thigh muscles' activity levels (Hedges' g = \(-\)0.028; SE = 0.081; 95% CI = \(-\)0.188, 0.131; Z = \(-\)0.345; \(p\) = 0.730; seven studies), but, as a significant heterogeneous distribution was observed (Q\({}_{\rm T}\) = 38.402, df = 17, \(p\) = 0.002, I\({}^{2}\) = 55.732), a subgroup analysis assessment was conducted.  
For the sub-group assessment, the meta-analysis showed that the only significant effect of the anterior wedge during squatting was on the activity level of the rectus femoris (Hedges' g = 0.399; SE = 0.190; 95% CI = 0.026, 0.771; Z = 2.098; \(p\) = 0.036; three studies) (Figure 3). No significant effect was seen on the activity level of the vastus medialis (Hedges' g = \(-\)0.074; SE = 0.124; 95% CI = \(-\)0.318, 0.169; Z = \(-\)0.599; \(p\) = 0.549; six studies), vastus lateralis (Hedges' g = \(-\)0.201; SE = 0.135; 95% CI = \(-\)0.466, 0.063; Z = 1.495; \(p\) = 0.135; five studies), or biceps femoris (Hedges' g = \(-\)0.023; SE = 0.150; 95% CI = \(-\)0.318, 0.272; Z = \(-\)0.150; \(p\) = 0.881; four studies) (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Papers** & **Q1** & **Q2** & **Q3** & **Q4** & **Q5** & **Q6** & **Q7** & **Q8** & **Q9** & **Q10** & **Q11** & **Q12** & **Q13** & **Q14** & **Sum** \\ [PERSON] et al. (2022) [32] & 1 & 1 & 1 & 1 & 1 & 0.17 & 1 & 0.6 & 0.8 & 0 & 1 & 1 & 1 & 1 & 0 & 10.57 \\ [PERSON] et al. (2022) [9] & 1 & 0.75 & 0.5 & 1 & 0.17 & 1 & 0.8 & 1 & 0 & 0 & 1 & 1 & 1 & 0.5 & 9.72 \\ [PERSON] et al. (2021) [14] & 1 & 1 & 1 & 1 & 0.5 & 1 & 0.8 & 1 & 0 & 0 & 1 & 1 & 1 & 1 & 0.5 & 10.8 \\ [PERSON] et al. (2019) [16] & 1 & 0.75 & 1 & 1 & 0.67 & 1 & 0.5 & 1 & 0 & 0 & 1 & 1 & 1 & 0.5 & 10.42 \\ [PERSON] et al. (2018) [5] & 1 & 1 & 0.5 & 0.5 & 0.5 & 1 & 0.8 & 1 & 0 & 0 & 1 & 1 & 1 & 0.5 & 9.8 \\ [PERSON] et al. (2017) [13] & 1 & 1 & 0.5 & 1 & 0.67 & 1 & 0.6 & 0.8 & 0 & 0 & 1 & 1 & 1 & 0.5 & 10.07 \\ [PERSON] et al. (2017) [27] & 1 & 1 & 0.5 & 1 & 0.17 & 1 & 0.6 & 0.8 & 0 & 0 & 1 & 1 & 1 & 0.5 & 9.57 \\ [PERSON] et al. (2015) [10] & 1 & 0.25 & 0.5 & 0 & 0 & 0.5 & 0.6 & 0.8 & 0 & 0 & 1 & 1 & 1 & 0.5 & 7.15 \\ [PERSON] et al. (2012) [12] & 1 & 0.75 & 0.5 & 1 & 0.5 & 1 & 1 & 1 & 0 & 0 & 1 & 1 & 1 & 1 & 10.75 \\ [PERSON] et al. (2008) [15] & 1 & 1 & 0.5 & 0.5 & 0.17 & 0.5 & 0.6 & 0.4 & 0 & 0 & 1 & 1 & 1 & 0.5 & 8.17 \\ [PERSON] et al. (2007) [11] & 1 & 1 & 0 & 0.5 & 0.5 & 0.5 & 0.6 & 0.4 & 0 & 0 & 1 & 1 & 0 & 1 & 7.5 \\ [PERSON] et al. (2007) [18] & 1 & 1 & 0.5 & 0 & 0.83 & 1 & 0.8 & 1 & 0 & 0 & 1 & 1 & 1 & 0.5 & 9.63 \\ [PERSON] et al. (2006) [19] & 1 & 1 & 0.5 & 0.5 & 0.8 & 1 & 0.8 & 0.8 & 1 & 0 & 1 & 1 & 1 & 0.5 & 10.9 \\ [PERSON] et al. (2005) [17] & 1 & 1 & 0.5 & 0.5 & 0.67 & 1 & 0.8 & 1 & 1 & 0 & 1 & 1 & 1 & 0 & 10.47 \\ [PERSON] (1999) [20] & 1 & 1 & 0.5 & 1 & 0.67 & 1 & 0.8 & 1 & 1 & 0 & 1 & 1 & 1 & 0 & 10.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quality appraisal of eligible studies.
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 4 Experiments
### Implementation Details  
The present study utilized the open-source DTU dataset for both model training and testing. In stages of the sampling process, a uniform deep sampling method with dimensions of [64; 24; 4] was employed. Specifically, the second and third stages of the deep sampling process incorporated eight and four sampling values, respectively, based on neighborhood normal information. Optimization procedures were conducted by implementing the Adam algorithm in conjunction with a weight decay coefficient of 0.0  
Figure 8: Several images sourced from the DTU dataset are presented herein, accompanied by their corresponding depth truth and the associated normal vector truth, as derived from our analysis.  
Figure 9: Various perspective images of some scenes on the Tanks and Temples dataset.  
and beta parameters of (0.9, 0.999). A customized learning rate adjustment method was also integrated, which included a warm-up mechanism in which the learning rate gradually increased from 0 to a maximum rate of 0.0016 during the initial 500 training batches. Beyond the 20 th epoch, the learning rate underwent periodic reduction to 0.625 times its prior value with an interval of ten epochs. To facilitate the model training process, five RTX 3090 GPUs were used with a batch size of 30. Throughout the training phase, each target image was equipped with source images from two adjacent views.
## 3 Results
### Comparing Polar-Orbit Constellation Detection Capability with the Spire Constellation
The UCI(1) parameter of the 12 RS/multi-GNSS system was comparable to that of the 100 RS/GPS RO system; however, the UCI(m) parameter of 12 RS/multi-GNSS was smaller than that of the 100 RS/GPS RO system. This indicates that although the global coverage fraction of 12 RS/multi-GNSS RO events basically reached the level of 100 RS/GPS RO system, the global uniformity of the 12 RS/multi-GNSS system was worse than that of the 100 RS/GPS RO system. In summary, in the multi-GNSS combination scenario, the occultation constellation composed of polar-orbit RS satellites, with the increase in the number of RS satellites, the number and GCF values of RO events increased significantly. When the number of RS satellites increased to 12, the RO detection capability of the RS12/multi-GNSS RO system could reach that of the 100 RS/GPS RO system; specifically, the 24 h RO events GCF could reach 100%, and the global coverage uniformity could reach more than 40%, which could meet the basic requirements of global numerical weather prediction in terms of the RO event number, timeliness, and global coverage fraction.
###### Abstract  
In recent years, aerial vehicles have allowed exploring scenarios with harsh conditions. These can conduct reconnaissance tasks in areas that change periodically and have a high spatial and temporal resolution. The objective of a reconnaissance task is to survey an area and retrieve strategic information. The aerial vehicles, however, have inherent constraints in terms of energy and transmission range due to their mobility. Despite these constraints, the Data Foraging problem requires the aerial vehicles to exchange information about profitable data sources. In Data Foraging, establishing a single path is not viable because of dynamic conditions of the environment. Thus, reconnaissance must be focused on periodically searching profitable environmental data sources, as some animals perform foraging. In this work, a data-foraging-oriented reconnaissance algorithm based on bio-inspired indirect communication for aerial vehicles is presented. The approach establishes several paths that overlap to identify valuable data sources. Inspired by the stigmergy principle, the aerial vehicles indirectly communicate through artificial pheromones. The aerial vehicles traverse the environment using a heuristic algorithm that uses the artificial pheromones as feedback. The solution is formally defined and mathematically evaluated. In addition, we show the viability of the algorithm by simulations which have been tested through various statistical hypothesis.  
Keywords:data foraging; reconnaissance; bio-inspired indirect communication; graph exploration; interruptibility +
Footnote †: journal: Applied Sciences
# An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers
## 2 The Resource Scheduling Model in Containerized Microservice
### Microservice Resource Constraints  
In the beginning, we use \(P\) as the set of physical hosts, where \(P=\{P_{1}\), \(P_{2}\),..., \(P_{n}\}\). \(n\) is the number of physical hosts. \(P_{i}=\{0,1\}\) is a binary variable. When it is \(1\), it indicates that the physical host \(i\) is activated. Otherwise, it shows that the physical host \(i\) is in the sleep state. When deploying microservices, we firstly consider that each container can only be deployed on one physical host, as shown in Equations (1) and (2). The binary parameter, \(k_{ij}\), indicates that the container \(j\) will be deployed on the host \(i\), where the amount of containers is \(m\).  
\[\sum_{i=1}^{n}k_{i,j}=1 \tag{1}\]  
\[k_{ij}=\left\{\begin{array}{rl}1,&\text{the container $j$ is placed on the physical host $i$}\\ &0,&\text{else}\end{array}\right. \tag{2}\]  
At the same time, there is also a comprehensive resource constraint, which means that the sum of the resources required by all microservice containers on the same host \(i\) cannot exceed the physical host's resources, as shown in Equation (3).  
\[\sum_{j=i}^{m}k_{ij}\times r_{j,l}\leq c_{i,l}\quad 1\leq l\leq\text{s} \tag{3}\]  
In Equation (3), the set of all resources requested by the container is \(R_{j}=\left\{r_{j1},r_{j2},\ldots,r_{js}\right\}\), where the represents the type \(l\) resource requested by the container \(j\). In this article, we use \(C_{is}\) to indicate the set of the type \(s\) resources that the physical host \(i\) can provide. In this article, we only consider four types of resources, namely CPU, memory, disk, and bandwidth, so the maximum of \(s\) is \(4\).
## 3 Results
### Optimization of pH  
The pH is a critical factor that affects both the formation of metal complexes and their extraction processes. This study examined the influence of pH on the preconcentration of Hg(II) ions within a pH range of 2.0 to 9.0, as depicted in Figure 2. To adjust the pH of the extraction medium, 1 mL of various buffer solutions was used: 0.2 mol/L KCl and 0.2 mol/L HCl for pH 2; 0.1 mol/L potassium hydrogen phthalate and 0.1 mol/L hydrochloric acid for pH 3 to 4; 0.1 mol/L potassium hydrogen phthalate and 0.1 mol/L sodium hydroxide for pH 5 to 6; and 0.1 mol/L tris(hydroxymethyl) aminomethane and 0.2 mol/L HCl for pH 7 to 8. Optimal extraction of the complex was achieved at pH 4.0, leading to the decision to buffer all subsequent samples to pH 4.0 for further analysis. At pH values lower or higher than the optimal pH of 4.0, it is likely that the ion-associate complex (Hg(II)-HMIMPF6) becomes charged, preventing its transfer to the organic phase. Based on the solubility product constant (Ksp) of mercury hydroxide, it is expected that at pH values above 5.8, mercury precipitates as its hydroxide compound.  
Conditions: C\({}_{0}\): 2.5 \(\upmu\)g/L Hg(II); 1 mL buffer solution; ionic liquid amount: 10 \(\upmu\)L. HMIMPF6; extraction time: 20 min; extraction temperature: 26 \({}^{\circ}\)C; extraction speed: 750 rpm; added dodecanol amount: 80 \(\upmu\)L; final volume: 150 \(\upmu\)L.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Organic Solvent** & \
\begin{tabular}{c} **Melting Point** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} &
\begin{tabular}{c} **Extraction Efficiency of Hg** \\ **(\%)** \\ \end{tabular} \\ \hline
1-dodecanol & 21–24 & 98.8 \\ \hline
2-dodecanol & 17–18 & 97.9 \\ \hline
1-undecanol & 16 & 97.2 \\ \hline
1-bromohexadecane & 17–18 & 97.8 \\ \hline n-hexadecane & 18 & 96.5 \\ \hline
1,10-dichlorodecane & 14–16 & 96.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Melting temperatures and extraction efficiencies of extraction solvents used in SFODME.  
Figure 2: Effect of pH.