esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optical Experimental Setup and Results
From the results, it can be seen that the use of a 1D scanning system with two galvanometers has shown an increase in the effective scan length, improved beam spot at the work plane, and increased telecentricity. Through the results and based on the studies of other researchers [24; 29], we have also reached further conclusions that the ability to extend the effective scan length is also attributed to the structure of the f-theta lens. With this approach, when the laser beam hits the pivot point on mirror one, and because mirror two is deflected by a suitable angle, the beam leaving the f-theta lens will be perpendicular to the workpiece plane, which keeps the laser beam more circular in the scan area of the f-theta lens [20]. In other words, for an f-theta lens design, the lower the telecentricity, the more uniform the laser spot size will be. In our experimental setup, the f-theta lens had a large scanning area of 53 \(\times\) 53 mm\({}^{2}\), with telecentricity = 1.1 degrees, as claimed by the manufacturer using the UV wavelength. Experimental results show an increase in working length of 8.1%. The evaluation results of the methods using the additional optical components are compared with each other, as shown in Figure 8:1 A commercial scanning system incorporating an f-theta lens with a field of view of 170 \(\times\) 170 mm\({}^{2}\), with telecentricity = 12.7 degrees, showed an increase in effective working length of 63 mm at UV wavelength [29]. Another experimental setup's results used a scan lens with a working area of 10 \(\times\) 10 mm\({}^{2}\) and telecentricity = 11.5 degrees for an increased working length of 200 \(\upmu\)m [27]. A similar method, using an AOD as an added optical element, shows an increase in accuracy and improves the roundness of microholes to industry standard, with an f-theta lens with a focal length of 100 mm, a scan area of 35 \(\times\) 35 mm\({}^{2}\) and telecentricity = 1.5 degrees [24] at visible wavelength. In our study, the f-theta lens, with the advantage of an extensive scanning area and small telecentricity, showed an improvement in effective working length.
## 2 Research on Visualization of Landslide Monitoring Literature
### Keyword Analysis
By accurately controlling the geometric shape, geological and hydrological conditions, as well as the characteristics of large-scale landslide hazards, early warning and prevention of landslide hazards can be achieved. Due to the advantages of convenient implementation, reliable accuracy, and intuitive results, GIS plays an important role in landslide monitoring. In addition, it can be seen that UAV technology and DFOS technology, as emerging technologies, have unique advantages in the field of landslide monitoring. They play an increasingly important role in this field [20; 21; 22]. However, in actual landslide monitoring projects, it is necessary to combine the advantages and adaptability of various technologies considering the different monitoring environments and conditions. It is worth noting that about 10.93% of the above-mentioned indexed studies involve the Three Gorges Region in China.
## 3 The Case Study  
The hybrid energy systems of two non-interconnected Italian minor islands (Ventotene and Pantelleria), both located in the Mediterranean Sea, are assessed. Firstly, the reference scenario for each island is analyzed, corresponding to the current configuration. For the sake of clarity, Table 3 lists the current energy system of each island as given in Reference [41].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Percent Recovered** \\ \hline Cobalt & 80 \\ Nickel & 80 \\ Iron & 75 \\ Copper & 90 \\ Carbon & 85 \\ Lithium & 85 \\ Manganese & 75 \\ Separator & 87 \\ Aluminum & 85 \\ Steel & 95 \\ Electrolyte & 80 \\ Wire board & 90 \\ Plastics & 77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage of recycling [37; 38; 39].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Annual Production**} & \multicolumn{4}{c}{**Installed Power**} \\ \hline \multirow{2}{*}{**Island**} & **PV** & **Wind** & **Diesel** & **PV** & **Wind** & **Diesel** \\  & **(MWh)** & **(MWh)** & **(MWh)** & **(kW)** & **(kW)** & **(kW)** \\ \hline Ventotene & 119 & 5.5 & 2700 & 97 & 3 & 1920 \\ Pantelleria & 810 & 45 & 44,170 & 647 & 32 & 29,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Actual energy mix of the islands.  
Then, two alternative scenarios are considered. The first one (named \"first scenario\") expects only the addition of batteries. This solution provides a better exploitation of the already existing renewable energy plants and, consequently, an increment of their load factor. In particular, the batteries' global size, resulting from an in-depth analysis of the islands' power generation system production, is assumed to be equal to 1 MWh. From a preliminary simulation of the annual system operation, batteries could reduce the diesel consumption by about 2% in Pantelleria and 7% in Ventotene. The simulation is conducted on hourly basis over a year by means of the software HomerPro [42]. The electric load of the islands and wind and solar variability are considered. This simulation is used to find the annual production of each source (RESs and diesel) and the charge-discharge cycles of batteries.  
The second scenario (named \"alternative scenario\"), which can be considered a more advanced one, assumes an increase of the RES penetration in the islands by means of the installation of new wind turbines and PV modules.  
The solar and wind potentialities on the two island and the geographic and landscape constraints are considered to properly size the new installations [41], while the size of the batteries is computed by means of an annual simulation by means of HomerPro to avoid the dissipation of energy from RES plants. A preliminary economic assessment guarantees a payback time lower than 10 years but, in this work, the economic optimization is neglected, since it is not the focus of the LCA.  
The main results of the annual energy simulation are listed in Table 4. The annual consumption of diesel decreases by about 23% for Pantelleria and 12% for Ventotene with respect to the reference scenario.
## 5 Analyses on Dynamic Behavior of Test FPSE
### Natural Frequencies of PP and DP  
Figure 6 shows the predicted DNFs of PP and DP with increasing charge pressures in the range 0-50 bar for the reference, case 3. As the charge pressure increases, the DNF of PP increases rapidly, and that of DP decreases slowly, which are related to the increases and decreases in the direct system stiffness of \(K_{pp}\) and \(K_{dd}\), respectively. The two DNFs of pistons are equal and crossing at the charge pressure of 16 bar with a frequency of 35 Hz, forming the resonance condition of the FPSE.  
Figure 6: Predicted PP and DP damped natural frequencies with charge pressure increasing from 0 to 50 bar for case 3.  
In particular, the DNF curve for DP is relatively constant with slight decreases from the DP spring stiffness. Therefore, the constant DNF of DP determines the vertical position of the crossing point, i.e., the operating frequency of the FPSE. On the other hand, the DNF curve of PP shifts up and down depending on the PP spring stiffness and mass values, so that the horizontal position of the crossing point moves to the left and the right, determining the operable charge pressures. Consequently, the simple vibration analysis predicts operable charge pressures and working frequencies of the FPSE in a simple and direct way.  
Theoretically the use of damped natural frequency is more accurate than that of natural frequency. However, the level of damping magnitude in this study yields a minor effect on the natural frequencies. For example, with the damping ratio of 0.17 in Figure 4, the difference is 1.4% from the definition of \(\omega_{pp,d}=\omega_{pp}\sqrt{1-\zeta_{pp}}^{2}\). Considering difficulties and errors in estimation of damping, the use of natural frequencies makes predictions much easier and more applicable.
## 4 Cell Selection Games
### Congestion Game with ICI
: _For CGI, define:_  
\[g_{2}(n_{i})=n_{i}+\frac{B_{i}}{B_{i}+B_{0}}\sum_{\alpha\
eq i}n_{\alpha}+\frac{B_ {i}B_{0}}{B_{i}+B_{0}}wI_{0i}(n_{i}) \tag{18}\]  
\[A_{2,i}=\frac{B_{i}}{B_{i}+B_{0}}(N_{0}+1+B_{0}wI_{i0}) \tag{19}\]  
_(1) if \(g_{2}(N_{i})<A_{2,i}\), \(n_{i}^{*}=N_{i}\) forms the PSNE; (2) if \(g_{2}(1)>A_{2,i}\), \(n_{i}^{*}=0\) forms the PSNE; (3) otherwise, \(n_{i}^{*}\) satisfying \(g_{2}(n_{i}^{*})\leq A_{2,i}\leq g_{2}(n_{i}^{*}+1)\) forms the PSNE._  
**Proof. ** Based on Definition 1, we obtain the conditions for the PSNE of CGI as follows:  
\[C_{k}(B\!S_{i},p_{-k}^{*})\leq C_{k}(B\!S_{0},p_{-k}^{*}) \tag{20}\]  
\[I_{0i}(n_{i})=\frac{GP_{0}}{N_{0}-\sum_{\alpha}n_{\alpha}}\big{(}\sum_{j\in \mathbf{D}\mathbf{V}_{all}}\frac{1}{d^{\gamma}_{ij}}-\sum_{\alpha}\sum_{j\in \mathbf{D}\mathbf{V}_{\alpha}}\frac{1}{d^{\gamma}_{\alpha j}}\big{)} \tag{12}\]0  
Equations (20) and (21) indicate separately that no device selecting \(B\!S_{i}\) and \(B\!S_{0}\) feels like changing the CS preference. Taking Equations (14)-(17) into (20) and (21), we get:  
\[I_{0i}(n_{i})=\frac{GP_{0}}{N_{0}-\sum_{\alpha}n_{\alpha}}\big{(}\sum_{j\in \mathbf{D}\mathbf{V}_{all}}\frac{1}{d^{\gamma}_{ij}}-\sum_{\alpha}\sum_{j\in \mathbf{D}\mathbf{V}_{\alpha}}\frac{1}{d^{\gamma}_{\alpha j}}\big{)} \tag{12}\]1  
To sum up,  
(1) If \(g_{2}(N_{i})<A_{2,i}\), all of the devices under the coverage of \(B\!S_{i}\) select it, and \(n_{i}^{*}=N_{i}\) forms the PSNE. (2) If \(g_{2}(1)>A_{2,i}\), all of the devices under the coverage of \(B\!S_{i}\) select \(B\!S_{0}\), and \(n_{i}^{*}=0\) forms the PSNE. This case cannot be indicated by Equation (22) because the integral for \(I_{0i}(n_{i})\) is equivalent to an  
\[I_{0i}(n_{i})=\frac{GP_{0}}{N_{0}-\sum_{\alpha}n_{\alpha}}\big{(}\sum_{j\in \mathbf{D}\mathbf{V}_{all}}\frac{1}{d^{\gamma}_{ij}}-\sum_{\alpha}\sum_{j\in \mathbf{D}\mathbf{V}_{\alpha}}\frac{1}{d^{\gamma}_{\alpha j}}\big{)} \tag{12}\]2 Monotonicity of payoff functions in Theorems 2 and 3. Obtained by setting \(B_{i}=2/3B_{0}\), \(R_{i}=300\) m, \(w=3\times 10^{6}\) and other parameters commonly configured as in Section 5, for a scenario with one picocell located within the macrocell. Curves are obtained by searching for the PSNE, so the number of devices selecting the picocell gradually decreases during iterations. **(a)** Cost in Theorem 2; **(b)** capacity in Theorem 3.  
extremely large number of devices deployed in the area. In such a case, there is absolutely one device collocated with \(B\!S_{i}\), leading to infinity for \(g_{2}(0)\). (3) Otherwise, \(n_{i}^{*}\) satisfying Equation (22) forms the PSNE. Since \(I_{0i}(n_{i})\) is a decreasing function with quite a small value, as long as \(w\) is kept below a certain threshold, the gap between \(g_{2}(n_{i}^{*})\) and \(g_{2}(n_{i}^{*}+1)\) is smaller than one, so there is always at least one \(n_{i}^{*}\) satisfying it.
## 4 Experimental Setup
### Experimental Design
#### 4.5.2 Experiment for RQ2  
The proposed method uses two steps that affect the accuracy of bug report summarization: sentence significance factor assessment in Step 3 and feature preservation in Step 7.  
The sentence significance factors to be assessed in our method are sentence-to-sentence believability, sentence cohesion, and topic association. For feature preservation, we use the beam search algorithm, which will select the final output summary \(\mathit{Selected\_Summary}\). To analyze the effects of these two steps on the summary quality, we conducted two experiments.  
In the first experiment, we analyzed the summary quality changes resulting from the selection of two steps. We intended to investigate whether each step actually contributes to summary quality improvement. If the step for feature preservation is excluded and the beam search algorithm does not apply, we selected the top 30% sentences with the highest sentence assessment scores.  
In the second experiment, we identified the optimal weights to calculate the sentence assessment scores in Step 4 and analyze the summary quality changes based on the three sentence significant factors. Each factor is represented as a percentage. If the weight is 0, the corresponding assessment is skipped. When experimenting with only two of the three sentence significance factors, we determined the optimal weights by increasing the weights by 0.25. When experimenting with all three sentence significance factors, we determined the optimal weights by increasing the weights by 0.1.
# Predicting Runoff from the Weigan River under Climate Change
## 2 Data Sources and Methods
### Research Methodology
#### 2.3.3 Building A SWAT Hydrological Model  
A SWAT (soil and water assessment tool) model is a distributed watershed hydrological model based on GIS [27]. In recent years, this type of model has been developed rapidly and has been widely used at home and abroad. [PERSON] et al. [28] constructed a drought assessment model, SWAT-PDSI, based on the Palmer Drought Severity Index (PDSI) and a SWAT model, and analysed the spatial and temporal distribution characteristics and frequency characteristics of drought disasters in the Weihe River basin. This study constructed a hydrological model of the mountainous area of the Weigan River based on the SWAT platform and simulated the runoff from the Weigan River out of the mountain to recreate its hydrological process. On this basis, the SWAT model was combined with the CMIP6 climate model data to predict the future runoff evolution trend.  
The modelling process of the SWAT model was mainly divided into the following steps: Firstly, we imported the DEM data, determined the catchment threshold of the study area and performed operations such as watershed and water system extraction and sub-watershed division. Secondly, we imported data from a pre-constructed soil-typedatabase and land-use database for easy association with the constructed model in the later stage. Finally, we divided the hydrological response units in the study area and completed the model construction work. In terms of model tuning, the optimal operating parameters for localising the SWAT model in the Weigan River Basin have been described in detail in a research article by [PERSON] et al. [21], and the same parameters were used in this study. After completing the above steps, it was necessary to import the pre-downloaded and processed historical temperature and precipitation model data into the SWAT model for operation to verify its feasibility in order to obtain a SWAT model suitable for the prediction of future runoff.
## 3 Modeling, Implementation, and Numerical Simulation
### Post-Processing Approach  
The locations of the data extracted from the model were selected considering the positions where, on the real test rig, it was feasible to place the piezoelectric accelerometers to collect the signals (gearbox housing), so that it would be possible to validate the simulation results with experimental data. Since the tangential surface of the shaft was the nearest location possible for the housing or bearing seats, this position provided the best-fit option to simulate this signal acquisition [57]. The points that represent the location of each data object extracted are shown in Figure 11. Subsequently, the Fast Fourier Transform (FFT) was applied to each of the data objects--a post-process analysis to check the most common frequencies of the system. In this way, the signal in the time domain represents the vibration responses in a waveform; however, to better visualize the problem, the system behavior was also analyzed in the frequency domain for the classification and multi-class classification problems. Moreover, FFT generates results that allow the visualization of the frequency spread in a spectrum, the energy of which that is carried by a signal can be studied at the frequency determined by the system. Therefore, when some divergence and increment occur in a frequency, damage indicators can be considered in the analysis [58]. Indeed, the FFT can be a good approach for predicting faults. However, it is difficult to determine severity using FFT alone [8]; thus, other sources and techniques, such as ANN, can be used for this purpose.  
Figure 10: Simulation data organization for the third case (second multi-class classification approach).  
Moreover, a single numerical simulation took 45 h, approximately, considering 6000 steps, and each object extraction took 4 h. Two seats of the T3D software were used, in which each seat required one computer (RAM: 32 GB; Processor: Intel Core i7-11700K) for simulating the system.
# Solar Filament Detection Based on an Improved Deep Learning Model
## 4 Method
### Transformer Encoder  
The Transformer Encoder is responsible for extracting global features from the solar filament features obtained by the CNN Encoder. Since the Transformer layer requires vectors as input, the filament features need to be serialized before being fed into the Transformer layers. First, the input filament features are sliced into N feature blocks based on a preset slicing size parameter \(p\), where \(N=\frac{HW}{p^{2}}\), and each feature block has a dimension of \(P^{2}\times C\). Here, (\(C\), \(W\), \(H\)) represents the shape of the input solar filament features. Subsequently, a trainable linear projection is applied to each feature block to linearly map the feature into a series of feature sequences. Additionally, since the self-attention mechanism does not capture spatial positional relationships, the positional information of each feature block needs to be encoded and embedded into the corresponding position. This process can be represented by the Formula (1).  
\[z_{0}=\left[x_{p}^{1}E;x_{p}^{2}E;\cdots;x_{p}^{N}E\right]+E_{pos} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **In Channels** & **Out Channels** & **Blocks** \\ \hline Group 1 & 3 & 64 & MRB\(\times\)1 \\ Group 2 & 64 & 256 & MRB\(\times\)2 \\ Group 3 & 256 & 512 & MRB\(\times\)3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed structure of the CNN Encoder.  
Figure 8: The structure of the MRB.  
Here, \(z_{0}\) represents the output sequence, \(x_{p}^{N}\) represents the \(N\)th slice, \(E\) is the trainable linear projection, and \(E_{pos}\) is the embedded positional encoding. For each slice, it is transformed into a vector form through a trainable matrix \(E\). \(E_{pos}\) is used to record the original position of the slices.  
12 stacked Transformer layers are used in this part. The outputs are then reshaped for the CNN Decoder.
## 3 Results and Discussion
### Tri-Generation Versus Standalone Efficiency  
The RTE is defined as the ratio of the useful energy output (i.e., the utilized energy provided by the LAES plant during the discharge stage) to the power input (or the energy consumed by the LAES plant during the charge stage). For scenario A, the sole useful energy output is the power supplied to the grid during discharging. For scenario B, the useful energy output is the sum of the useful electrical output (power supplied to the grid), the useful heating output and the useful refrigeration output (heat and cold supplied to the food production line).  
A comparison of the RTEs for the two scenarios is shown in Table 3. For the same power input into the LAES system, the RTE is boosted from 47.30% in scenario A (standalone power generation) to 55.63% in scenario B (tri-generation).  
The RTE can potentially be increased further by a serial manner of refrigeration supply to the consumers of cold, unlike the parallel one illustrated in Figure 6. Although the serial organisation of cold energy distribution is generally more energy saving, it appears to be too hard to implement from the viewpoints of functionality and hygienic design. Another RTE-enhancing approach might be to recirculate the cold airflows outgoing from each unit operation (Figure 6) in a smart way. Nevertheless, such a fine and intelligent air-circulation control complicates the overall system, thereby involving a substantial increase in the capital costs.
# Review of Fault-Tolerant Control Systems Used in Robotic Manipulators
## 4 Other Robot-Dedicated FTCS
From the information provided by the FDI, the magnitude of the error was evaluated using singular value decomposition (SVD) [61]. The system in this solution was tested on a single-link flexible manipulator. In earlier works, i.e., before 2019, the algorithms described in the literature for the control of robots in the case of communication faults dealt with two control types, i.e., state observers or estimators. The works in [62; 63] presented a completely different approach to fault-tolerant control. The authors of these works dealt with a problem involving interference in the communication path through which information is sent between the robot's actuators and the controller. The paper proposed to re-construct data corrupted during transmission using a splicing code. The transmission was carried out using the Controller Area Network (CAN) protocol [64]. The authors of [65] presented as many as four techniques for dealing with controlled oscillation and axis damage. These techniques were sinh-cosh, neural compensation, PID gain scheduling, and sinh-cosh gain scheduling. All of them were tested on a manipulator in which the failure was detected by measuring the currents through the robot axis drives. At the moment of axis failure, the current dropped to zero and then the axis was treated as passive, i.e., not participating in control. In the results of the paper, all four methods were compared. The comparison of the methods was conducted using a presentation of the trajectory-tracking error for each method. The best results in terms of the position errors of the individual axes were obtained for neural compensation, whereas the worst results were obtained by the sinh-cosh gain-scheduling method.
## 2 End-of-Life of CRC
### LCAs of EoL of CRC  
Only some LCA studies considering the EoL of CRC are available. [PERSON] et al. (2010) [49] conducted an LCA of waste recycling in the case of cement. As the functional unit (FU), the binding equivalent value BE (kg/m\({}^{3}\)) = cem + k\({}^{*}\)SCM was used and the system boundaries were set from cradle to gate [49]. [PERSON] et al. (2018) [50] researched the environmental damage of concrete production with LCAs. The functional unit was defined as 1 m\({}^{3}\) of concrete, with a specific weight of 2400 kg/m\({}^{3}\), and the system boundaries were included the production phase [50]. [PERSON] et al. (2020) [51] analyzed the environmental impact of concrete with recycled aggregates and geopolymer mixtures. They used 1 m\({}^{3}\) of reinforced concrete with a compressive strength of 30 Mpa as their functional unit and cradle-to-grave as their system boundaries [51]. Furthermore, [PERSON] et al. (2017) [52] investigated the key questions regarding the LCA of carbon-reinforced concrete, wherein several experiments were conducted [52]. Moreover, [PERSON] et al. (2018) [53] present the results of recent experiments on the recyclability of the textile components in textile-reinforced concrete [53]. Finally, [PERSON] et al. (2013) [54] analyzed the life cycle impacts of 12 recycled concrete mixtures with two different cement types and compared them with corresponding conventional concretes for three structural applications. The functional unit was set to 1 m\({}^{3}\) of concrete of a specific strength class with system boundaries, including the construction, use, and demolition stages [54].  
For the above reasons, we will consider the EoL of CRC in the following case study and compare it with that of an equivalently functional steel-reinforced concrete component. The aim is to show EoL considerations already in the design phase, search for suitable solutions, and compare the long-term emissions of complete life cycles of the composite materials.