esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Design, Analysis, and Simulation of RC-NB-LDPC Codes
#### 4.1.1 Design of RC-NB-LDPC Codes  
The LDPC code in the DVB-S2 standard has two kinds of code length, 64800-bit long code, and 16200-bit short-code, and 11 and 10 code rates are specified for long-code and short-code, respectively. The information bit length, check bit length and code-word length of the LDPC code in the DVB-S2 standard are fixed and cannot be arbitrarily selected. Regardless of long-code or short-code, they have the same code-word length and different information n bit length, respectively. Table 2 shows the specific parameters of six typical rates of short-code in the DVB-S2 standard.  
Through density evolution optimization analysis, the distribution of variable node degrees for all rate designs is (2, 3, 4), which can achieve excellent waterfall performance. On the finite fields, the designed mother matrix rate is 7/8, the size is \(4\times 32\), and the expansion factor is \(p=127\)  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Code Rate** & **Information Bitlength** & **Code-Word Length** & **Variable Node Degree** & **Variable Node Degree** \\ \hline
4/9 (\(\approx\)1/2) & 7200 & & (1, 2, 3, 8) & (1, 8999, 5400, 1800)/16,200 \\
2/3 & 10,800 & & (1, 2, 3, 13) & (1, 5399, 9720, 1080)/16,200 \\
11/15 (\(\approx\)3/4) & 11,880 & 16,200 & (1, 2, 3, 12) & (1, 4319, 11,520, 360)/16,200 \\
7/9 (\(\approx\)4/5) & 12,600 & & (1, 2, 3) & (1, 3599, 12,600)/16,200 \\
37/45 (\(\approx\)5/6) & 13,320 & & (1, 2, 3, 13) & (1, 2879, 12,960, 360)/16,200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the short codes in the DVB-S2 standard.  
The RCNBQC-LDPC code matrix is designed based on the concealment puncturing technique, and other sub-rate matrixes are obtained from the mother matrix. The particular design is shown in Figure 12. Each time the four columns of the matrix of the current rate are deleted, the number of columns at the same time as 2, 3, and 4 is reduced by 1, 2, and 1. The above process makes the designed matrix of each rate have a superior degree distribution, and the structure is similar, so that codec is simple to implement. The specific parameters of the designed code are shown in Table 3.
# Source Rock Prediction Using Well Log and Seismic Data:
## 4 Results
### Types of Organic Matter in Source Rocks  
The type of organic matter is an important factor when evaluating the type and quantity of hydrocarbon generation; its type can reflect the ability of organic matter to generate oil or gas [27]. Here, the maximum pyrolysis peak temperature (T\({}_{\text{max}}\)) and hydrogen index (HI) are used to identify the organic matter types of the source rocks [6; 7]. A total of 116 (\(n\) = 116) samples were selected from nine wells in the study area. The results show that the hydrogen index of the Lower-Cretaceous-Albian-stage source rocks in the Cote d'Ivoire Basin ranges from 100 to 550 mg/g, with an average value of 263.6 mg/g. The maximum pyrolysis peak temperature ranges from 420 to 445 \({}^{\circ}\)C, with an average value of 439 \({}^{\circ}\)C, and the values are concentrated in the range of 430-445 \({}^{\circ}\)C. Figure 3 shows the characteristics of mixed-type kerogen, mainly type II kerogen, and some sample sites were type III kerogen. This reflects the fact that the parent material of kerogen has the ability to generate oil and gas, and the organic matter source is mainly a mixture of lower aquatic organisms, such as algae, and higher terrestrial plants [28; 29].
# Improvement of Intake Structures in a Two-Way Pumping Station with Experimental Analysis
## 2 Overview of the Two-Way Pumping Station  
Due to the different demands for water resources in different periods, the two-way pumping station was constructed to undertake the tasks of both drainage and diversion. Depending on the different water demands, the pumping station needs to transfer water between the two rivers. For example, when water needs to be transferred from river 1 to river 2, keep gate 1, 3 open and gate 2, 4 closed, and otherwise gate 2, 4 open and gate 1, 3 closed. The pumping station consists of six vertical axial flow pump units in total. Figure 1 is the elevation structure diagram of one pump unit. The pumping station is axisymmetric. The operating conditions are determined by the difference in water levels of the rivers, which were obtained from the annual hydrologic statistics. The pumping station consists of six pump units with a total design flow of \(Q=150\) m\({}^{3}\)/s and a total nominal power of \(P=18\) MW. Each unit consists of a vertical axial flow pump with a four-blade impeller and two-way flow passages. Table 1 shows the main parameters and operating conditions of the pumping station.  
Since the units were brought into operation in 1991, it has created huge economic benefits. However, severe flooding occurred in 2016, and the units encountered serious problems such as blade breakage during the drainage process. The accident investigation showed that due to the unreasonable design of the intake, the impeller inflow was unstable, and the blades were subjected to alternating loads. The blades were prone to fatigue damage when operating under severe working conditions for a long time. Therefore, the optimization of the intake is of great significance to the safe and stable operation of the pumping station.
## 3 Research Design
### Forecasting Project Performance Based on RCF and RBF  
#### 3.2.1 Reference Class Forecasting Technique  
This paper presents an RBF neural network model based on the RCF technique to accurately assess the level of project performance subjected to a disruptive event. Specifically, we use a combination of RCF and RBF neural networks to construct the assessment mode. In the implementation of the model, we use the RCF technique as the core framework to incorporate external perspectives.  
To achieve this, an appropriate reference class must be selected, consisting of historical events similar to the current event to be forecasted. An extensive collection of historical data relevant to the forecasting objective should cover the outcomes and influencing factors of similar projects in the past. The following step involves extracting features from the reference class, which may include factors such as cost, schedule, and risk related to the forecasted event. The purpose of feature extraction is to establish a correlation between the reference class and the forecasted event. The occurrence of the current event is predicted through the established forecasting model. Ultimately, forecasts are generated using the validated model. It is also necessary to compare the predictions with the actual results in order to verify the accuracy of the model and to adjust and optimize it as needed. It should be noted that the effectiveness of the RCF technique depends on the accuracy and completeness of the historical data, as well as on the extent to which changes in the external environment affect project results. Therefore, these factors should be fully considered when applying the RCF technique, and corresponding measures should be taken to improve the accuracy and reliability of the forecast.
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 2 Satellite Constellations in IoT
#### Traditional Satellite vs. IoT Satellite
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Satellite IoT Communications** & \multicolumn{1}{c}{**Traditional Satellite Communications**} \\ \hline Main objective & Facilitate connectivity among IoT devices and gather small volumes of data (e.g., sensors) to provide specialized services. & Provide global telecommunications services, such as television, telephony, and high-speed data transmission, to large audiences. \\ \hline Type of transmitted data & Small data and control messages from IoT devices, such as sensors and meters. & Larger volumes of data, such as video, voice transmissions, and high-speed data. \\ \hline Bandwidth & Lower bandwidth is required for transmitting low-speed and low-volume IoT data. & Higher bandwidth is required to handle high-speed transmissions and large data volumes. \\ \hline Latency & Tolerant to higher latencies, as IoT data are often less time-sensitive. & Requires lower latency to ensure high quality of real-time transmissions, such as video conferencing and television broadcasts. \\ \hline Network design & Oriented towards wide-area networks (WANs) to cover extensive geographical areas and connect distributed devices. & Designed for wide-area networks (WANs) or local area networks (LANs) to transmit data globally or regionally. \\ \hline Power requirements & Focus on energy efficiency to meet the limitations of battery-powered and processing-constrained IoT devices. & Higher power supply capacity for traditional satellites and ground terminals with higher power requirements. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between IoT satellite and traditional satellite communications.
## 4 Conclusions  
In this study, the effect of piston bowl geometry on the combustion, performance, fuel oil consumption, and emission formations of a four-stroke direct injection diesel marine engine was investigated to specify the most effective geometry for the piston bowl of the engine.  
The primary results of the present study are summarized below:  
1. The TKE of the turbulent flow in the engine cylinder when using the re-entrant and \(\omega\)-type piston bowls was higher than that of the U-type bowl type. The re-entrant type of bowl produced the highest TKE.
2. The \(\omega\)-type and re-entrant piston bowls reduced the in-cylinder peak temperature due to more uniform temperature distributions inside the engine cylinder. The re-entrant piston bowl produced the lowest peak temperature.
3. The \(\omega\)-type and re-entrant piston bowls enhanced the cylinder power by 1.26% and 2.67%, respectively, while reducing the ISFOC of the engine by 1.06% and 1.60%, respectively, compared to the U-type bowl.
4. The \(\omega\)-type and re-entrant piston bowls reduced NO emissions by up to 25.61% and 46.09%, respectively, in comparison to the U-type piston bowl.
5. For soot and CO\({}_{2}\) emissions, the computed results pointed out that changing the type of piston bowl has a negligible effect on the engine-out soot and CO\({}_{2}\) emission characteristics of the engine.  
Based on the results of this study, it is highly recommended to use the re-entrant piston bowl for four-stroke direct injection diesel engines because it not only improves engine performance and fuel oil consumption, but also decreases NO emissions. In another word, this type of piston bowl helps to enhance both economic and environmental benefits. As the spray angle of the fuel injector of direct injection ICEs has an influence on the squish flow inside the engine cylinder, which affects in-cylinder air-fuel mixing quality, further investigations on the combined effects of piston bowl geometry along with injector spray angle should be performed in future works.  
Conceptualization, V.C.P., J.K.K. and J.-H.C.; methodology, V.C.P., V.V.L. and W.-J.L.; software, V.C.P. and W.-J.L.; validation, J.K.K. and J.-H.C.; formal analysis, V.V.L., S.-J.C. and W.-J.L.; data curation, V.C.P., S.-J.C. and W.-J.L.; writing--original draft preparation, V.C.P.; writing--review and editing, V.C.P. and J.-H.C.; project administration, J.-H.C.; funding acquisition, J.-H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Development Program (S3273720), funded by the Ministry of SMEs and Startups (MSS, Korea); and by the Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (20220568).  
Not applicable.  
The authors declare no conflict of interest.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### NFC
NB-IoT  
Narrow-band IoT (NB-IoT) is a new wireless communication technology stipulated in Release 13 of the 3 rd Generation Partnership Project [60] to permit a wide variety of cellular devices and services. It utilizes the licensed long-term evolution (LTE) frequency bands and can be utilized in the 2G/3G/4G spectrum [61]. It employs a QPSK modulation scheme, which allows the signal to carry twice as much information using the same bandwidth. It offers low cost and low power and can cover a distance up to 10 km in the rural settlements and 1 km in urban settlements. SigFox  
SigFox is a low-power wide area network similar to LoRa and LoRaWAN but with an improved communication coverage. It utilizes unlicensed ISM band, which varies between regions, with 868 MHz in Europe, 915 MHz in North America, and 433 MHz in Asia. SigFox wireless communication technologies use a binary phase shift keying (BPSK) modulation in an ultra-narrow band sub-GHz ISM band carrier. Thus, by utilizing the ultra-narrow band, SigFox offers very low power consumption, and low-cost antenna design [58]. In comparison to LoRa and LoRaWAN, SigFox has a very low data rate (<10 kbps), though with very good communication coverage with 10 km in urban environments and 15 km in rural areas [58]. A summary and comparison of these wireless communication technologies are presented in Table 1. As shown in Table 1, most of these communication technologies are low-power with varying coverage area and data rates. For instance, Bluetooth and Wi-Fi cover short distances with high data rates. Data rate refers to the amount of data transmitted per unit time. Amongst the low-power wireless communications, SigFox offers long-range capability especially in the rural settings (up to 50 km) and it consumes less power. However, its data transmission rate is relatively low. Likewise, LoRaWAN can cover long distances up to 20 km in rural areas but with a low data rate. Moreover, 3G/LTE and LTE-A cover long distances up to 30 km and have a relatively high data rate, however, their power usage is very high when compared to some other wireless communication technology. Therefore, for water supply system infrastructure monitoring, long-range communications with a relatively good data rate is a requirement. In CPS, the short-range communications are currently utilized. However, to provide reliable monitoring to water distribution networks (which are large-scale), future CPSs should consider integrating the long-range wireless communication technologies into the system to improve communication coverage.
## 4 Applications of the TPCT
### Railways
[54] used heated TPCTs to manage the frost heave disaster of high-speed railway subgrade in seasonal permafrost zone, and based on the working mechanism of the TPCTs, they established a coupled heat transfer models between heated TPCTs and the fill soil, and compared and analyzed the calculation results with the on-site monitoring data, which proved the reasonableness of the model. The simulation results show that the heated TPCT can regulate the temperature distribution inside the roadbed, thus weakening the frost damage of a high-speed railway roadbed in seasonal permafrost zone.
# Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review
## 4 Considerations on the State of the Art and Future Perspectives
Currently, most of the networks are built using commercial standalone components that incur low overall cost. The current miniaturized solutions for sensor networks require ad hoc solutions that are, in most cases, not yet industrialized [122; 123]. The harvesting technique employed in geohazard monitoring can surely benefit from miniaturized solar cells [124] to reduce the nodes' dimensions. The employment of machine learning and artificial intelligence has various application possibilities in geohazard monitoring and is currently a very studied field [125]. The predictive algorithms help to enhance susceptibility models [126] and are useful for the differentiation of warning events based on the movement patterns recorded by the nodes. More capillary integration of AI edge computing in networks, like those that have been reported, would be an effective tool for risk assessment and manutention operations.
## 5 Discussion
### Ablation Studies
#### 5.2.2 Impact of the SE-Residual Blocks  
The second ablation study evaluates the effect of SE-Residual blocks on the model's effectiveness by comparing the ConvResBiGRU-SE architecture with a version that excludes these blocks, as shown in Table 6. The findings reveal that SE-Residual blocks are essential for enhancing the model's ability to determine human movements across both datasets.  
For the UCI-HAR dataset, incorporating SE-Residual blocks significantly improved performance. Accuracy increased from 96.80% to 99.18%, an absolute gain of 2.39%. The F1-score also improved, rising from 96.97% to 99.24%. Additionally, the standard deviation for accuracy decreased from \(\pm\)1.06% to \(\pm\)0.20%, indicating that SE-Residual blocks boost performance and contribute to more stable and consistent predictions.  
The impact on the WISDM dataset was similarly significant. The model with SE-Residual blocks achieved an accuracy of 98.78%, compared to 96.03% without them. The F1-score increased from 94.57% to 98.09%, showing enhanced balanced performance across all activity classes. The reduction in standard deviation from \(\pm\)1.20% to \(\pm\)0.24% further confirms the stabilizing effect of these blocks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Recognition Performance**} \\ \cline{2-5}  & \multicolumn{2}{c}{**UCI-HAR**} & \multicolumn{2}{c}{**WISDM**} \\ \cline{2-5}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline ConvResBiGRU-SE without the SE-Residual blocks & 96.80\% (\(\pm\)1.06\%) & 96.97\% (\(\pm\)1.02\%) & 96.03\% (\(\pm\)1.20\%) & 94.57\% (\(\pm\)1.27\%) \\ \hline Our ConvResBiGRU-SE with the SE-Residual blocks & 99.18\% (\(\pm\)0.20\%) & 99.24\% (\(\pm\)0.19\%) & 98.78\% (\(\pm\)0.24\%) & 98.09\% (\(\pm\)0.53\%) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect the SE-Residual blocks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Recognition Performance**} \\ \cline{2-5}  & \multicolumn{2}{c}{**UCI-HAR**} & \multicolumn{2}{c}{**WISDM**} \\ \cline{2-5}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline ConvResBiGRU-SE without the convolutional block & 97.29\% (\(\pm\)0.16\%) & 97.34\% (\(\pm\)0.15\%) & 97.91\% (\(\pm\)0.16\%) & 97.11\% (\(\pm\)0.29\%) \\ \hline Our ConvResBiGRU-SE with the convolutional block & 99.18\% (\(\pm\)0.20\%) & 99.24\% (\(\pm\)0.19\%) & 98.78\% (\(\pm\)0.24\%) & 98.09\% (\(\pm\)0.53\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of convolution blocks.
###### Abstract  
Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea's current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined.  
machine learning; wildfires; susceptibility
# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 3 QoS Analysis
### Comparison of the Proposed Approach with Published Literature  
At Table 6 we compare the proposed approach presented in this paper with recent published literature (since 2019), using 3 GPP 5G V2X scenarios. These articles fall into different categories. Most of these papers present specific 3 GPP scenarios using the latest Release-16 of the LTE based services aiming at highly reliable and real-time communications for automotive safety use cases. Physical layer structure, resource allocations [15], security, RSC methods [17] and QoS optimization triggered by dynamic radio conditions [16] are the main challenges for LTE V2X communications. However, none of these papers considers channel coding parameters (e.g., different decoding algorithms, number of iterations) in the optimization process for the realistic QoS indicators to be satisfied. The idea of focusing on physical layer channel coding process and particularly using turbo coding in 5G V2X systems has been initially discussed in [20; 21; 22; 23] without, however, the channel coding parameters being optimized for realistic QoS indicators. An initial approach has been investigated in [20; 21], where only a small frame length of 128 bits is examined without the message size to be investigated in the simulation results and not enough vehicle density values are considered. In [21] different message frame lengths of 256 and 512 bits are also investigated. In [23], the authors add more realistic QoS parameters in the simulated scenarios such as different vehicle speeds (60 km/h and 100 km/h) and data rates for the V2V communication. Only the authors in [23] use turbo coding for V2V doubly \(-\)dispersive channels using IEEE 802.11p wireless standard with a QoS optimization procedure. From the above comparison presented in Table 6, only our proposed approach investigates LTE turbo coding performance for all the most common real traffic conditions such as (different message sizes, vehicle speeds, data rates and traffic densities). The aim is the specific QoS performance requirements for basic 3 GPP 5G V2X scenarios to be met.