esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Possibility of Expanding Power Output of Future PV-Powered EVs  
In this study, the PV potential of a PV-powered EV with silicon solar cells was produced in a short driving time of approximately 10 min. However, the amount of power output that can be expected from potential PV-powered EVs may expand in various aspects owing to the developments in solar cell efficiency, vehicle charging in various cases, diversification of solar mobility, and expansion of production.  
First, various types of high-efficiency solar cells have been developed, and the efficiency of the module used in this study is approximately 15%. Recently, the power output efficiency of solar cells has increased, and various types of solar cells, such as transparent and flexible, are being developed. Perovskites, which have a power output efficiency of approximately 30%, can be integrated with PV-powered EVs to double the power output potential. Second, the power output time of PV-powered EV is maximized. Herein, the PV potential was investigated for a vehicle traversing 3135 m of the study area once in 564 s. However, PV potential can be enhanced by increasing the driving time or using 100% of the sunlight time, including non-driving outdoor stops and parking time. Finally, there is a diversification of solar mobility and expansion of production. Herein, only one vehicle was used; however, approximately 24.37 million cars have been registered in Korea. More power output can be expected if the field of mobility utilization combining sunlight into taxis, shuttle buses, and trains, which are driven regularly throughout the year, is diversified and the production is increased.  
The PV potential was estimated used the aforementioned conditions (Table 4). Assuming that 243,000 units, or 1% of the vehicles registered in Korea, use PV modules with efficiencies of 15% and 30% and generate power for 6 h a day (9:00 to 15:00) annually, 53,783 GWh and 107,556 GWh of power can be produced. This is a large figure and efforts must be made to expand the supply of PV-powered EV and develop high-efficiency solar cells.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**PV Potential per Car (kWh)**} & \multicolumn{2}{c}{**PV Potential for 243,000 Cars (MWh)**} \\ \cline{2-5}
**Time** & \multicolumn{2}{c}{**Cell Efficiency**} & \multicolumn{2}{c}{**Cell Efficiency**} \\ \cline{2-5}  & **15\%** & **30\%** & **15\%** & **30\%** \\ \hline
564 s & 0.0158 & 0.0316 & 3.84 & 7.68 \\
1 h & 0.1011 & 0.2022 & 24.57 & 49.13 \\
1 day & 0.6064 & 1.2128 & 147.36 & 294.71 \\
1 year & 221.3298 & 442.6596 & 53,783.14 & 107,556.28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Estimation of PV potential from PV-powered EV based on a hypothetical scenario.
###### Abstract  
Accurate rainfall estimates are important in many hydrologic activities. Rainfall data are retrieved from rain gauges (RGs), satellites, radars, and re-analysis products. The accuracy of gauge-based gridded precipitation products (GbGPPs) relies on the distribution of RGs and the quality of rainfall data records obtained from these. The accuracy of satellite-based precipitation products (SbPPs) depends on many factors, including basin climatology, basin topography, precipitation mechanism, etc. The hydrologic utility of different precipitation products was examined in many developed regions; however, less focused on the developing world. The Huai Bang Sai (HBS) watershed in north-eastern Thailand is a less focused but an important catchment that significantly contributes to the water resources in Thailand. Therefore, this research presents the investigation results of the hydrologic utility of SbPPs and GbGPPs in the HBS watershed. The efficiency of nine SbPPs (including 3B42, 3B42-RT, PERISANN, PERISANN-CCS, PERISANN-CDR, CHIRPS, COMRPH, IMERG, and MSWEP) and three GbGPPs (including APHRODITE_V1801, APHRODITE_V1901, and GPCC) was examined by simulating streamflow of the HBS watershed through the Soil & Water Assessment Tool (SWAT), hydrologic model. Subsequently, the streamflow simulation capacity of the hydrological model for different precipitation products was compared against observed streamflow records by using the same set of calibrated parameters used for an RG simulated scenario. The 3B42 product outperformed other SbPPS with a higher Nash-Sutcliffe Efficiency (\(NSE_{monthly}>0.55\)), while APHRODITE_V1901 (\(NSE_{monthly}>0.53\)) performed fairly well in the GbGPPs category with closer agreements with observed streamflow. In addition, the COORPH precipitation product has not performed well in capturing observed rainfall and subsequently in simulating streamflow (\(NSE_{monthly}<0\)) of the HBS. Furthermore, MSWEP and CHIRPS products have performed fairly well during calibration; however, they showcased a lowered performance for validation. Therefore, the results suggest that accurate precipitation data is the major governing factor in streamflow modeling performances. The research outcomes would capture the interest of all stakeholders, including farmers, meteorologists, agriculturalrists, river basin managers, and hydrologists for potential applications in the tropical humid regions of the world. Moreover, 3B42 and APHRODITE_V1901 precipitation products show promising prospects for the tropical humid regions of the world for hydrologic modeling and climatological studies.  
gauge-based gridded precipitation products; Huai Bang Sai (HBS) watershed; hydrologic utility; satellite-based precipitation products +
Footnote †: journal: Hydrology  
0000-0002-0001-9500-0002-0000][PERSON]  
0000-0002-0002-0002-0002-0000][PERSON]  
0000-0002-0002-0002-0000][PERSON]  
0000-0002-000
## 4 Numerical Examples
### Example 1--Cantilever Beam
In the RBTO analysis, the PMA converges with 9 iterations in all cases, while the optimal topology is reached at iteration 41. In the classical RBTO methodology, there is a total of 369 iterations until convergence. Figure 7 shows the graphics of the structure's performance during the optimization procedure in each case studied. It is verified that the PI has the ability to monitor the iterative process because a peak in the OF, probably due to the breakage of a bar in the structure and/or the removal of many elements at once, causes a sharp drop in this structure's performance. Therefore, at this point, it is necessary that the designer has a critical eye with the ability to assess whether the structure is collapsing or not. In the example analyzed, there is a peak in the OF, but the structural performance is not compromised because the PI value is not less than 1. It is also observed that for the peak in the SERA, the OF is smoother than in ESO and SESO. This can be explained by observing the material removal and addition heuristics used by SERA, that is, in the region where the compliance is high, there is the addition of \"virtual\" elements, softening the OF's behavior, and consequently, the structure's performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Vol \%** (mm\({}^{3}\))** & **OF (N\(\cdot\)mm)** & **Displacement (mm)** & **Computational Cost (sec)** \\ \hline
**RBTO-ESO** & 6.4 & 0.3236 & 0.00322 & 2142.51 \\ \hline
**RBTO-SESO** & 6.4 & 0.3216 & 0.00320 & 2181.61 \\ \hline
**RBTO-SERA** & 6.4 & 0.3214 & 0.00320 & 2354.01 \\ \hline
**RBTO-SIMP** & 6.4 & 0.3612 & 0.00360 & 2512.45 \\ \hline
**RBTO-SRSM** & 7.8 & - & 0.05274 & - \\ \hline
**DTO-ESO** & 6.8 & 0.3031 & 0.00303 & 2180.25 \\ \hline
**DTO-SESO** & 6.8 & 0.3031 & 0.00303 & 2166.45 \\ \hline
**DTO-SERA** & 6.8 & 0.3029 & 0.00303 & 2412.76 \\ \hline
**DTO-SIMP** & 6.8 & 0.3379 & 0.00338 & 2695.03 \\ \hline
**DTO-SRSM** & 6.8 & - & 0.059648 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reliability-based topology optimization using evolutionary methods. Figure 6: RBTO: contour, topology and surface: (**a**) ESO, (**b**) SERA, (**c**) SESO and (**d**) SIMP.
## 5 Analysis
### Safety Evaluation  
The actual conditions of the wall are likely to exhibit behavior with characteristics of both failure patterns. The support conditions suggested by the model calibration are of significantly good quality, though unlikely to be completely translationally fixed. For the purpose of evaluating the safety of the structure under seismic loading, however, the analysis in this case is sufficient. Direct comparisons can be made between the expected peak ground acceleration (PGA) from local design codes and the values resulting from the analyses. Spanish seismic codes use probabilistic seismic hazard analysis to estimate the expected PGA for a given location, integrating data from historical and instrumental records of seismicity, which typically reference magnitude in terms of the moment magnitude scale (Mw). Spanish seismic codes outline Lanjaron as one of the more seismically susceptible regions in Spain, with an expected PGA value of 0.18 g [9]. At minimum, the wall was able to achieve a value of 0.25 g before collapse. This strength can be considered sufficient to withstand local peak ground accelerations according to the code. A safety factor can be obtained from the ratio of the minimum peak value achieved to the PGA of the area, defined as 0.25 g/0.18 g = 1.39. However, the positioning of the castle atop a steep rocky slope and the local aquifers of the surrounding region could destabilize the ground around the structure as suggested in [35].
## 2 Technical Backgrounds
### Security Considerations and Requirements
#### 2.4.2 Flexibility  
Service-based architectures through network virtualization and network slicing are critical to the flexibility of 5G communication environments. These technologies enable the 5G network to provide various services at the network level, and network devices can provide various types of services through services implemented in the form of network functions. Many of the devices in a heterogeneous network are devices with limited resources. They are sensitive to the consumption of resources used in key generation and management, the operation of cryptographic algorithms, and mutual authentication. Some devices may not support these mechanisms. Therefore, a communication environment composed of heterogeneous networks should provide a basis for satisfying different performance and security requirements for each device by providing various protocols and communication mechanisms in the form of network services [17; 18; 19]. The communication architecture for 5G must be able to support a variety of protocols and devices based on this virtualization.
## 2 Data and Methods
### Research Methods
#### 2.4.2 Method for Response Degrees Verification  
To verify the responsiveness of the multisource SIF satellite products to the main influencing factors of GPP in arid regions, Pearson correlation analysis was used to calculate their correlation, and t-tests were performed to determine the confidence level. Our data used for comprehensive calculations have complete and stable time series, with a clear number of variables and the exclusion of outliers and outliers, which avoids bias and inconsistency in comprehensive analysis. We set the SIF satellite product to be applicable in arid regions when it exhibits a high correlation with influencing factors under different ecosystems. The parameter calculation formula is as follows:  
\[r=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\sum_{i= 1}^{n}\left(x_{i}-\overline{x}\right)^{2}}\sqrt{\sum_{i=1}^{n}\left(y_{i}- \overline{y}\right)^{2}}} \tag{7}\]  
\[t=r/\sqrt{(1-r^{2})/(n-2)} \tag{8}\]  
where \(n\) represents the sample size, \(x_{i}\) represents the satellite values, and \(y_{i}\) represents the observed values of influencing factors.
## 2 State of Art  
The study which is the basis of the present research in the band 6425-7125 MHz [1] does not contain an interference scenario with the shared location of 5G networks and FS, additionally, that the study doesn't contain adjacent channel interference simulation and doesn't refer to the national level regulation focusing solely on the cross-border scenario. Thus, this research complements the previous study as well as takes into account conditions that were not considered in the related studies for the millimeter-waves band.  
When it comes to simulation and spectrum management techniques and approaches, this work takes into account various works on that topic [4,5] that are commonly used in the studies in such organizations like ITU-R, CEPT and many others.. Earlier works on the matter of sharing studies have introduced the need defining compatibility conditions between 5G networks or networks of legacy standards and fixed service stations [6, 7, 8, 9, 10, 11, 12, 13, 14], at the same time these studies cannot be fully applied to 6425-7125 MHz because of the entirely different deployment between those two bands since propagation losses between 6 GHz and millimeter waves are significantly different, also millimeter-wave base stations have shorter antenna heights and the different number of antenna configurations which affect dramatically on the interference levels. In addition, these works include only interference study from BS but do not include the study of interference from UE, usually, commonly it is explained that since 5G uses TDD there is no need to study UE interference since it's normally negligible compared to the interference from BS, the authors of this paper believe that interference from UE needs to be studied anyway for the reason that in many cases operation of BS may be restricted depending on the regulators' needs, whereas UEs normally cannot be regulated, and in some local scenarios the interference from UEs can be comparable with BS interference.
## 3 Underwater Routing Protocols
### Rrss  
A routing protocol based on received signal strength (RRSS) is proposed in [61]. A source node establishes a vector from itself to the sink node. The distance of the source node from the sink node is used as a measure of the intensity of the beacon signal. The beacon signal is broadcast by the sink node to communicate with the sensor nodes. A source node decides the selection of a forwarder candidate inside the vector based on the intensity of the hello packet and the intensity of the beacon signal at the forwarder. Nodes periodically broadcast hello packets to share vital information with one another. Forwarder nodes that are in the void regions are avoided to forward data packets. The protocol achieves improved energy efficiency and end-to-end delay. However, nodes inside the vector die rapidly due to data overloading.
## 7 Performance Evaluation of Multicast R-GOOSE over Public WAN
### Results and Discussion
In other words, the communication reliability is calculated by counting the number of R-GOOSE messages that do not fulfill the defined constraints and comparing this number with the total number of messages exchanged during the recording period. Table 6 also shows the reliability numbers for the example applications (Logic Selectivity and LOM). These values should be read as the amount of time the communication would work for the said application, meaning that the communication delay is below the threshold limit for the application. The results show that, e.g., the 4G communication would perform perfectly in the LOM application and adequately in Logic Selectivity whereas 5G communication would perform very poorly in Logic Selectivity and poorly (or unacceptably) in LOM. This conclusion is based on the idea that Logic Selectivity reliability does not have to be very high, and LOM reliability on the other hand has to be. The reasoning behind this idea is that bad communication quality in LOM leads to not only loss of protection functionality but may also be a safety hazard for utility field personnel due to unintentional islanding. In this paper, Logic Selectivity and LOM were explained as the examples showing how to interpret the results, and this way can be applied for studying reliability level in other similar protection scenarios. In a nutshell, for each communication-based protection application, it depends on the system designer to decide the adequate reliability level based on the type of application and the associated consequences which could occur in the event of communication delay below the threshold. In addition, in practice, the selected configuration must also provide at least a LOW level of security (as defined in Table 7) to ensure trustworthiness of R-GOOSE data and consequently authentic operation of the applications. In the table below, Confidentiality, Integrity, and Availability are used as the benchmarks for evaluating the level of communication security. As was discussed, public WAN communication can provide the coordinated interactions required by wide-area protection logic in communication-based protection systems.
## 3 Results
### Adsorption Equilibria
#### 3.2.2 Effect of Adsorbent Dosage  
The dependence of the MC adsorption efficiency on the dosage of SSS (1-3 g/L) for an initial concentration of 60 mg/L, a solution pH 6 and a contact time of 120 min is illustrated in Figure 4. The adsorption rate increases with the dose of SSS and stabilizes from 2.5 g/L. A significant improvement in the adsorption of MC from 66.39% to 89.04% is observed when the SSS dosage changes from 1 to 2.5 g/L. This result is due to the increase in functional groups and adsorption sites, which is linked to the properties of the adsorbent [7; 17].  
On the other hand, the increase in sorbent dose from 1 g/L to 3 g/L caused a decrease in the uptake of MC from 39.72 mg/g at 17.60 mg/g, respectively, which is entirely logical given that \"q\" is inversely proportional to the total quantity of adsorbent in suspension [39].  
Figure 3: Evolution of adsorption efficiency (**a**) and uptake (**b**) of MC at equilibrium in terms of contact time and initial MC concentration (conditions: SSS dose: 2 g/L, pH 6.0, flask shaking at 300 rpm at 25 \({}^{\circ}\)C).
## 3 Leveraging Machine Learning in a TDTSP Heuristic  
In Section 1 we have recalled the reasons around the growing interest in applying Machine Learning models to optimization problems. The aim of this section is show how to embed some information gained through a ML algorithm into a simple constructive heuristic. The goal is to improve its performance in those settings in which instances with similar features have to be solved over and over again, as it is customary in distribution management. Instead of starting every time from scratch, we want to insert a learning mechanism inside the heuristic in such a way it can benefit from previous runs on other instances with similar features.  
The constructive heuristic we are going to use as a baseline is the well-known _nearest neighbor procedure_ (NNP) that is suitable to be used in a real-time setting due to its speed. Algorithm 1 reports its pseudocode, where \(V_{1}\) is the set of already visited customers.  
```
1:functionNN(\(V\))
2:\(i\gets 0\)
3:\(t\gets 0\)
4:\(V_{1}\leftarrow\{0\}\)
5:while\(|V_{1}|\leq|V|\)do
6:\(j^{*}\gets arg\min\limits_{\begin{subarray}{c}j\in V\setminus V_{1}\\ V_{1}\leftarrow V_{1}\cup V_{2}\end{subarray}}\tau_{ij}(t)\)
7:\(V_{1}\gets V_{1}\cup V_{2}\)
8:\(t\gets t+\tau_{ij^{*}}(t)\)
9:\(i\gets j^{*}\)
10:endwhile
11:\(t\gets t+\tau_{ij}(t)\)
12:return\(t\).\(V_{1}\)
```  
**Algorithm 1** Baseline heuristic.  
In order to take advantage of the predictive capabilities of supervised ML techniques, the nearest neighbor heuristic has been modified by the introduction of two parameters: \(\alpha\) and \(f_{j}\). Parameter \(f_{j}\) is a prediction (obtained through a supervised ML method) of the _expected time of arrival_ (ETA) at customer \(j\) in an optimal solution, and \(\alpha\in[0,1]\) is a convex combination coefficient. At each iteration, a node is selected using a modification of instruction 6 in Figure 1 as follows:  
\[j^{*}\gets arg\min\limits_{j\in V\setminus V_{1}}\alpha\cdot[t+\tau_{ij} (t)]+(1-\alpha)\cdot|f_{j}-t-\tau_{ij}(t)| \tag{3}\]  
Hence the choice of the next customer to be visited is guided by a convex combination of two terms: the arrival time \(t+\tau_{ij}(t)\) at the next customer (as it is typical of the NNP) and an error measure \(|f_{j}-t-\tau_{ij}(t)|\) of forecast \(f_{j}\), if \(j\) is chosen as the next customer. Parameter \(\alpha\in[0,1]\) is used to balance the cost function \(t+\tau_{ijh}\) with the correction factor depending on \(f_{j}\). In few words, if \(\alpha\) is zero the heuristic takes its own decisions only based on the correction factor; on the other hand, if \(\alpha\) is 1 the heuristic is the well-known NNP, no matter how \(f_{j}\) looks like. In the latter case, the heuristic takes no advantage from previous runs. Hence, \(\alpha\) can be seen as a _learning factor_.
## 4 Robust Design Based on Cutting-Set Method  
In this section, we give a cutting-set method to degrade the influence from the uncertainty of the users' locations. The robust design is achieved by alternating between optimization steps and pessimization steps. In the optimization step, joint resource allocation and trajectory planning are optimized under a given finite subset of worst-case users' locations by Algorithm 1. Then, in the pessimization step, the subset of worst-case users' locations is updated according to the UAV trajectory obtained in the optimization step.
# Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency
## 2 Encoding of LDPC Codes
### Straightforward Encoding With Generator Matrix Multiplication  
As previously mentioned, LDPC codes are linear block codes. They are completely defined by their parity check matrices, which are designed to be sparse so as to obtain smaller decoding complexity. The parity check matrix \(\mathbf{H}\) and the codeword vector \(\mathbf{x}=[x_{1}x_{2}\ldots x_{n}]\) must satisfy the following equation:  
\[\mathbf{x}\mathbf{H}^{\mathrm{T}}=0. \tag{1}\]  
Most practical LDPC codes are systematic, meaning that their codeword can be represented as follows:  
\[\mathbf{x}=[\begin{array}{cc}\mathbf{i}&\mathbf{p}\end{array}], \tag{2}\]  
where the vector \(\mathbf{i}=[i_{1}i_{2}\ldots i_{k}]\) represents a sequence of information bits and the vector \(\mathbf{p}=[p_{1}p_{2}\ldots p_{m}]\) represents a sequence of parity bits. The encoding process implies mapping the vector \(\mathbf{i}\) to the vector \(\mathbf{x}\). However, in general, the code does not have to be systematic, in which case information and parity bits are not divided into separate groups. In any case, the straightforward encoding method is to multiply the information bits vector with the generator matrix \(\mathbf{G}\) as  
\[\mathbf{x}=\mathbf{i}\mathbf{G}. \tag{3}\]  
The generator matrix can be obtained by solving Equation (4) using the Gaussian elimination.  
\[\mathbf{GH}^{\mathrm{T}}=0 \tag{4}\]  
The obtained generator matrix is generally dense, which causes quadratic encoding complexity (\(O\big{(}n^{2}\big{)}\)) [20]. However, when the generator matrix is precalculated, the requiredmatrix multiplication for specific codes can be efficiently achieved in hardware. Therefore, several previous encoders have been based on this method [12; 13; 14; 15; 16].