esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results
### Optimization Process with In-Laboratory Measurements
#### 7.1.3 Impact Angle Influence  
Two separate studies were conducted to evidence the influence of the two impact angles from Figure 2 to the DS results. In Figure 12, the results in terms of fit parameters are reported.  
In the two cases, increasing the impact angles leads to a \(R^{2}\) reduction, while the confidence interval (\(\delta s\)) generally increases. The DS estimation value instead remains almost constant under \(15^{\circ}\), but it is subjected to more important oscillation in larger angles.  
Figure 11: Trend of dynamic stiffness as a function of the applied force. In red, the trend on the specimen fixed with concrete is shown, and in blue, the trend on the specimen fixed with plaster is indicated.  
Figure 12: Influence on DS (s), associated confidence interval (\(\delta s\)), and the \(R^{2}\) coefficient based on the two angles of impact.
# Overview on Different Types of Solar Cells: An Update
## 5 Thin-Film Solar Cells
### CdTe Film Solar Cells  
Cadmium telluride (CdTe) films show high degradation durability, near-optimum band-gap value (1.45 eV), a low production cost, and high direct absorption coefficient (10\({}^{4}\) cm\({}^{-1}\)), and can be used to replace silicon-based solar cells. Generally, CdTe-based solar cells consist of glass, transparent conducting oxide (serving as the front contact), a CdS window layer, a CdTe absorber layer, and back contact. However, this type of solar cell has many problems such as glass breakage (during the production process) and poor thermal conductivity (causing very poor performance). The preparation of CdTe films onto flexible metal foils and polymer substrate can solve these problems. The power conversion efficiency was shown to be 11% for devices prepared on polyimide foils [174], indicating the superstrate and substrate configurations. Several deposition methods have been reported to produce cadmium-tellurium thin films (Table 2). The photovoltaic properties of the obtained films were studied.
## 3 Results and Discussion
### Optical Properties  
The reflectance of Mo bilayer film is also a crucial parameter for the photo-conversion efficiency of a solar cell. Maximizing the light reflectance of the Mo bilayer film allows more photons to be absorbed [31]. Figure 5 shows that the reflectance of Mo bilayer film increased with decreasing bottom layer sputtering pressures and increasing annealing temperatures. This can be attributed to the fact that the Mo bottom layer films prepared at lower bottom layer sputtering pressure were more uniform and compact, and had lower RMS roughness (Table 1). Therefore, the top layer Mo film deposited on the Mo bottom layer film prepared at lower bottom layer sputtering pressure at the same sputtering conditions had the lower RMS roughness; accordingly, the reflectance of the Mo bilayer film was higher. Similar conclusions have been discussed in earlier reports [21]. For Mo bilayer films annealed at different temperatures, with increasing annealing temperatures, the particle energy of Mo bilayer films increased gradually, and the prepared Mo bilayer films were more uniform and compact having lower RMS roughness (Table 1), which reduced light scattering and absorption of Mo bilayer films. This brought about higher reflectance.  
Figure 4: Resistivity of Mo bilayer films deposited at different bottom sputtering pressures (**a**) and annealed at different temperatures (**b**).  
The experimental results show that the Mo bilayer film prepared at lower bottom layer sputtering pressure and higher annealing temperature has the better properties. CIGS absorbers were deposited on these Mo bilayer films with bottom layer sputtering pressure of 0.4 Pa, top layer sputtering pressure of 0.3 Pa and annealed at different temperatures via a 3-stage co-evaporation [32]. Then, cadmium sulphide (CdS) buffer layers were prepared by chemical bath deposition (CBD), intrinsic zinc oxide (i-ZnO)/n-type indium tin oxide (ITO) window layers were obtained through the sputtering method, and the Al grid electrode was evaporated in order. Figure 6 shows the current-voltage (I-V) parameters as a function of annealing temperature of Mo electrodes. The results show that the open circuit voltages (Voc), short circuit current densities (Jsc), the fill factors (FF) and photo-conversion efficiencies of the CIGS solar cells increased with increasing annealing temperature. This mainly originated from the fact that the reflectance and light absorption of Mo bilayer film increased and the resistivity decreased with increasing annealing temperature. The Mo bilayer film annealed at 400 \({}^{\circ}\)C demonstrated excellent comprehensive performance: its resistivity reached to 0.92 \(\times\) 10\({}^{-5}\)\(\Omega\).cm and the reflectance was above 55%, the maximum photo-conversion efficiency of 13.5% was achieved for the CIGS solar cell.  
Figure 5: Reflectance of Mo bilayer films (**a**) deposited at different bottom layer sputtering pressures and (**b**) annealed at different temperatures.  
Figure 6: _Cont._
## 4 Simulation Results
### Robust Schedules with Different Robust Levels  
In this part, the load schedules with different robust levels and the corresponding electricity payment are presented. The various robust schemes are solved by CPLEX under difference levels of uncertainties. Six typical robust schemes, where the robust level pairs (\(\Gamma_{d}\),\(\Gamma_{\theta}\)) are set as (0, 0), (2, 2), (2, 8), (8, 2), (8, 8), and (10, 10), respectively, are extracted from the 121 robust schemes as representatives and are shown in Figure 6. It can be seen that all of the typical schemes strictly satisfy the users' comfort constraints under corresponding uncertain levels. It demonstrates the great robustness of the schemes while facing different levels of uncertainties. As the uncertain levels increasing, the interval of hot water temperature becomes wider.  
Figure 5: The average temperature differences under crossed influence the uncertainties.  
Figure 6: Hot water temperature under six typical robust schemes.  
Observing the six schemes in Figure 6 and the RTP prices in Figure 1, the schemes indeed pre-heat the water during the period that the RTP prices are generally low and shut down the DEWH when the prices are extremely high. When compared with the six schemes, it can be found that the working periods with high level of uncertainties are much longer to maintain the temperature in the comfort zone. Figure 7 gives the values of electricity bill under different robust levels settings. As shown in Figure 7, to meet the constraints under higher level of uncertainties, the users have to pay more. It proof that the users can also gain the completely robust and comfortable schemes at the cost of higher electricity bill.
## 5 Experiments and Discussion  
This section provides the experimental outcomes used to assess the proposed EIAFM model. Considering that FM-based models have been established as the most highly endorsed models in the field, we contrasted EIAFM against models containing AFM, basicFM, deepFM, and xdeepFM. In addition, we compared the outcomes of various parameters of EIAFM and studied the performance of its different components. The experimental results of EIAFM are summarized as follows. EIAFM is implemented using TensorFlow. We first describe the original dataset for the experiments, then introduce the evaluation of the indicators, and finally present the experimental findings. The experimental configurations and versions are as follows: Numpy-1.15.4, Pandas-1.1.5, Tensorflow-1.4.0, Python-3.6.13.
## 2 Bibliometric Analysis
### The Louvain Algorithm for Community Detection in Graphs
As exact modularity maximization is computationally hard, an approximation algorithm is needed to deal with large graphs. One such approximation algorithm that implements modularity maximization for weighted graphs is the _Louvain_ algorithm. This is an agglomerative clustering method, as communities are formed by merging similar nodes, and thus it is an unsupervised data mining method. The authors of the algorithm suggest that the complexity is linear on sparse graphs [28]. The algorithm consists of two phases, which are repeated iteratively (see Figure 6). The initial state is a weighted graph of n nodes where each node forms its community. In the _first phase_, for each node, all its \(j\) neighbors are considered. The gain in modularity is calculated for all neighbors when node \(i\) would leave its community and would join the community of node \(j\). The node \(i\) is then placed into the community where the modularity gain is maximal, on the condition that the modularity gain is positive. Figure 6: Steps of the Louvain algorithm, adapted from [28]. Otherwise, the node \(i\) stays in its community. This is applied repeatably for all nodes until no further gain in modularity can be achieved [28]. In the _second phase_, a new graph is created, having the communities formed in phase one as its nodes. The weight of an edge between two nodes in the new graph is calculated by summing up the weights of the edges between their communities. Similarly, the weights of the edges between nodes in the same community are summed up, and the result is used as the weight of a self-loop edge. After the second phase, the next pass is done. Following this, the algorithm starts again with the first phase by replacing the initial graph with the one formed in phase two. This is repeated until no additional modularity gain is achieved, i.e., by finding the local maximum modularity. After each repetition of the two phases, a graph of communities is created, holding fewer but larger communities from repetition to repetition. Finally, every data point is assigned to a community [28]. An example visualization of a similarity network is shown in Figure 7. It shows 3000 academic publications. Each color represents a community, computed by the Louvain method. The (x,y) coordinates of the publications were computed with the Fruchterman-Reingold algorithm, which was run for 100 iterations. Every cluster is a research front candidate, e.g., it could depict an upcoming topic of research. With this technique, research fronts may be identified that were not known by the researcher previously.
## 3 Results and Discussion
### Model Training and Evaluation Index  
Model training details: 32 images were loaded in each iteration and Forward Propagation (FP) process was completed in 16 batches. After 32 images completed the forward propagation process, the parameters were updated by Back Propagation (BP). The maximum number of iterations was set as 10,000, and the initial learning rate, weight attenuation coefficient, and momentum factor were set as 0.001, 0.0005, and 0.9, respectively. The multi-scale training strategy was used to randomly select the input size of the image from (320,352,384,416,448, 480,512,544,576,608) every 10 iterations [38]. Flip, zoom, translation, rotation, noise, and other methods were randomly combined to generate more target samples that could be trained by the network, so as to enhance the robustness and generalization of the network [39; 40].  
Evaluation index: the performance of the model was evaluated by Precision (P), Recall (R), Average Precision (AP), mean Average Precision (mAP), and detection speed (FPS). The calculation formula is shown in (15)-(18):  
\[\mathrm{P}= \frac{TP}{TP+FP} \tag{15}\] \[\mathrm{R}= \frac{TP}{TP+FN} \tag{16}\]  
Figure 8: Partial dataset pictures. (**a–c**) Clear; (**d–f**) blurred; and (**g–i**) deblurred pictures.  
\[\text{AP}=\int_{0}^{1}P(R)dR \tag{17}\]  
\[\text{mAP}=\frac{1}{c}c_{i}\text{AP} \tag{18}\]  
where _TP_ indicates the number of positive samples identified as positive samples, _FP_ indicates the number of positive samples mistakenly identified as positive samples, _FN_ indicates the number of positive samples misjudged as negative samples, and c represents the number of categories in the sample.
# MSWI Bottom Ash Application to Resist Sulfate Attack on Concrete
## 4 Conclusions  
In this research, the durability of concrete with added BA was evaluated by immersing specimens in a solution containing 10% sodium sulfate. Then, capillary rise, crystallization rate, and solution absorption tests were performed to investigate the intrusion of concrete by sodium sulfate solution. Moreover, the crucial type of porosity was found with the help of porosity measurements. The following conclusions can be drawn:  
1. BA has some hydration activity due to its chemical constitution. Even if 30% of the cement is replaced with BA, the compressive and flexural strength values are still greater than 42% and 50%, respectively, of those of the mortar without BA.
2. The compressive strength of the concrete decreased with increasing BA content; this is due to the weak pozzolanic reactivity of BA. However, BA can improve the performance of concrete against sulphate attack because BA has a very high surface area and fills in the pores of concrete.
3. The coarse capillary porosity plays a key role in the capillary transport and crystallization of sulfate solution in concrete.
4. The W/C and BA content have a certain impact on the porosity of concrete and further affect the capillary height, absorption amount, and crystallization rate of sulfate solution in concrete.  
Furthermore, due to BA adjusting the strength and durability of the concrete, the optimum range of BA in concrete is from 10% to 15%. In this range, BA can not only improve the durability of concrete but also ensure the strength of concrete to meet design requirements.  
The deficiency of this study is that no suitable treatment measures were found to enhance the hydration activity of BA. The objective of future research studies in this area is to improve the activity of BA by wet and high-temperature treatment. On this basis, the effect of BA on the strength and durability of concrete can be studied.  
[PERSON] proposed the research method for analyzing the intrusion of sodium sulfate solution to reveal the durability of concrete. The experiments were performed and the manuscript was written by [PERSON], [PERSON], [PERSON] and [PERSON]. [PERSON] proposed revision suggestions for this manuscript.  
This research was funded by the Construction System Science and Technology Project in Jiangsu, China (Grant No. 2018 ZD045), and the Training Program on Entrepreneurship and Innovation for University Students (Grant No. 2019 JGY)003 and 201911049023Y).  
The authors declare that there are no conflicts of interest regarding the publication of this paper.
# Mask R-CNN-Based Stone Detection and Segmentation for Underground Pipeline Exploration Robots
## 2 Materials and Methods
### Training and Loss Function  
In this study, Mask R-CNN was implemented based on the feature pyramid network (FPN), which extracts low-level features as well as high-level features, and ResNet101 was applied as a backbone. ResNet101 makes use of a residual learning process, which decreases the number of parameters that must be adjusted and minimizes the total computing cost. However, the learning time taken by ResNet101 is too long, as the structure has 101 layers, and every layer requires considering so many things while solving a new problem. Therefore, we applied transfer learning by employing a pretrained model trained with an MS-COCO (Microsoft Common Objects in Context) dataset to lessen the learning time and determine the number of images required to train the whole network [23]. The RPN, classifier, and mask heads of the network were trained up to 100 epochs to fine-tune the weights for detecting stones. The stochastic gradient descent with a momentum of 0.9 was used to train the model, where the learning rate was set to 0.001, and the details of the training specification are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute Name** & **Value** \\ \hline CPU & AMD Ryzen 7 5800H at 3.2 GHz \(\times\) 16 \\ Memory & 40 GB \\ GPU & NVDIA RTX3060 \\ OS & Windows 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification of the training and testing platform.  
Figure 4: Framework of the Mask R-CNN based stone segmentation and distance measurement system.  
The loss function of the Mask R-CNN [21] model is calculated as Equation (1) in the following:  
\[L=L_{class}+L_{bbox}+L_{mask} \tag{1}\]  
where \(L\) represents the total validation loss and is calculated with the combination of \(L_{class}\) (classification loss), \(L_{bbox}\) (bounding box loss), and \(L_{mask}\) (mask loss). Classification loss takes into account the model's confidence in predicting the true class, and the bounding box loss function provides the error between the actual bounding box and the predicted bounding box. Finally, the mask loss function generates a pixel-to-pixel mask for all detected classes.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 2 Related Research and Background  
This Section considers how researchers have tackled data overload within IoT/IoMT networks. It then goes on to consider video streaming practices and technologies designed for resource-constrained devices. IoT security and encryption is also discussed in this Section. Lastly, it covers research in video compression and motion detection, which are the main contributors to this paper's proposal.
## 5 Testbed Demonstration Setup
### Summary and Outlook of the Testbed Demonstrations  
The tests carried out have shown that the 5G integration of satellites currently depends very strong on the satellite ground and edge equipment that is used. New technologies such as network slicing can only be utilized in a satellite backhaul if satellite modems have already taken concepts such as SDN and NFV into account in their hard- and software architectures. On the one hand, the developed edge node concept allows independently of the modem technology the use of different backhaul technologies. For example, part of the data can be transmitted on top of an existing terrestrial MNO network and another part via satellite. On the other hand, legacy point-to-point modems (as used in Setup 2 in Section 5.2) without a specific API to control satellite resources will be difficult to align with the 5G concepts.  
The deployment of state-of-the-art satellite ground equipment and 5G elements such as RAN and core network alone will not be enough to successfully manage the satellite integration for 5G. A particular satellite network slice can differ from other slices within that network based on latency, bandwidth, reliability, security or functionality provided. The orchestration of the specific satellite ground equipment will be crucial for a smooth satellite 5G integration for example to adjust the satellite bandwidth. Concepts such as the SatCore shown in Setup 1 in Section 5.1 together with network slicing could enable a satellite network operator to segment its network dedicated to individual businesses such as 3 rd party MNOs, other market segments or specific 5G services.  
With the successful development of the satellite emulator, a solution for the high speed data plane processing within the satellite data processing pipe will be further optimized. The optimization of the TCP PEP functionality addressing specifically the 5G data path (primarily in respect to GTP optimization) will be carried out during the next phase of the testbed development. Moreover, the successful modem and satellite hub integration with the core network control plane elements via APIs for configuration and data path control will be very important to make network slicing on the satellite layer reality. The deployment of all the specific nodes (see Figure 6) for the SATis5 testbed will be completed by 2019. Afterwards, a measurement campaign will provide results and data for the capabilities of the various concepts that have been developed.
## 2 Materials and Methods
### Determination of VFCs  
VFCs were determined via HS-SPME/GC-MS with a Shimadzu GC/MS-QP 2010 Plus (Shimadzu Co., Kyoto, Japan). Here, 1.0 g of BLS was put into 20 mL headspace sampling bottles and equilibrated at 70 \({}^{\circ}\)C for 34 min on a magnetic stirrer. After stabilization, the solid-phase microextraction (SPME) fiber (65 \(\upmu\)m polydimethyl siloxane/divinylbenzene, PDMS/DVB, Supelco, Inc., Bellefonte, PA, USA) was inserted into the vials to absorb the volatile components at 70 \({}^{\circ}\)C. After 30 min, the fiber was retracted and inserted into the injection GC-MS port for 5 min at 250 \({}^{\circ}\)C to desorb the volatile components.  
Gas chromatography parameters were as follows: Rtx-5 MS column (30.0 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Shimadzu Co., Kyoto, Japan), helium was used as a carrier gas at a flow rate of 1.0 mL/min in split mode with a 1:20 ratio. The temperature of the injection port was 250 \({}^{\circ}\)C. The oven temperature was maintained at 40 \({}^{\circ}\)C for 3 min, increased to 150 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, held for 4 min, and then increased to 230 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and held for 5 min. Mass spectrometry parameters were as follows: the interface and ion source temperature were maintained at 220 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, respectively. Using an electron ionization source, the scanning range was from 50 to 500 m/z. Solvent delay time was 5 min.  
Qualitative analysis of volatile components was based on comparisons of the National of standards and Technology (NIST17) mass spectral libraries and by matching the retention index (RI) values. Qualification results are only used when the match is greater than 80% (up to 100%). The peak area normalization method was used to quantitatively calculate the relative concentration of VFCs in pickled BLS. The ROAV method was used to evaluate VFCs. ROAV was calculated according to the formula:  
\[\text{ROAV}_{\text{a}}=100\ \times\text{OAV}_{\text{a}}/\text{OAV}_{\text{max}} \tag{4}\]  
where \(\text{ROAV}_{\text{a}}\) is the relative odor activity value of substance\({}_{\text{a}}\); \(\text{OAV}_{\text{max}}\) is the highest odor activity value (OAV) among the VFCs; and \(\text{OAV}_{\text{a}}\) is the OAV of substance\({}_{\text{a}}\). OAV was calculated via the equation \(\text{OAV}_{\text{a}}=\text{C}_{\text{a}}/\text{T}_{\text{a}}\), where \(\text{C}_{\text{a}}\) was the relative concentration and \(\text{T}_{\text{a}}\) was the odor threshold in the air [31; 32; 33; 34].