esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### SAT-Based Model Counting  
The well-known SAT solver [35; 36] is commonly used to solve the model counting problem. An instance of the SAT-based model counting problem is a Boolean formula made up of different clauses. Each clause is a disjunctive or conjunctive form of Boolean variables. A Boolean formula, which can be expressed using the conjunction or disjunction of different clauses, is categorized into the conjunctive normal form (CNF) and the disjunctive normal form (DNF). Clearly, there are different truth assignments for a Boolean formula. Essentially, the model counting problem is to find truth assignments for a specific Boolean formula.
## 7 A case Study in Antarctica
### Costs and Benefits of Renewable Energy Applications in Antarctica  
Mawson Station has built a wind farm which costs about 8.9 million Australian dollars. The cost of a wind turbine was 0.74 million Australian dollars. Undiscounted simple payback period of the wind farm in Antarctica is estimated to be from 5 to 12 years. For South Pole Station, the project of installing nine 100 KW wind turbines was estimated to cost approximately 4.3 million US dollars. In McMurdo Station, a 1 MW wind turbine has cost 2-3 million US dollars. Net savings of this project remain 1-4 million US dollars over a 20-year life span. Similarly, SANAE IV Station of South Africa has installed a 100 KW turbine with a simple undiscounted payback period of about 10 years. The simple undiscounted payback period of PV system is shorter than wind turbine. For example, a solar thermal system at SANAE IV Station has a payback period of 6 years by saving 10,000 L fuel annually. In the preliminary research stage of the standalone renewable energy system in Zhongshan Station, it is hard to accurately estimate cost savings after the introduction of renewable energy. However, based on the cost savings estimate of other stations, the use of the standalone renewable energy system was estimated to save approximately 1.43 million US dollars in one year.
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Image Sensors  
Image sensors are devices that convert light into electrical signals to create images. The image sensors can be divided into two categories, such as CCD (charge-coupled device) and CMOS (complementary metal-oxide-semiconductor). In CCD sensors, the electrical charges are transferred across the sensor and read at one corner. Although CCD sensors are often more costly and power-intensive, they are renowned for their excellent image quality and minimal noise. Because CMOS sensors feature separate amplifiers and ADCs for every pixel, readout speeds can be increased while power consumption can be decreased. CMOS sensors are widely deployed in consumer electronics as they are cost-effective and versatile. Image sensors can be used to capture real-time images or video of a person's sitting posture. The data from these images can be analyzed using computer vision algorithms to detect and categorize various sitting postures. When compared to other sensor approaches, the use of image sensors for posture recognition offers many advantages, such as:  
* Non-Intrusive monitoring
* Detailed visual information allows for a more comprehensive analysis of posture.
* It captures many frames, and it is easier to identify postures.
* Video cameras enable real-time monitoring and recognition.  
Figure 9: Example of an ultrasonic sensor-based posture recognition system [106].  
Individuals do not need to wear any sensors and devices.  
It is more robust against different environmental conditions.
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Research Methodology  
This review follows a structured approach to literature analysis. Peer-reviewed publications were retrieved from the Scopus database using keyword combinations such as \"machine learning,\" \"genetic algorithm,\" \"combustion,\" \"engine,\" and various fuel types (e.g., \"biomass,\" \"hydrogen,\" \"biodiesel\"). The search was limited to English-language articles published between January 2015 and March 2024. Abstracts were screened manually to exclude unrelated works, duplicates, or purely theoretical studies lacking combustion context. Only studies with a clear application of ML or GA methods to combustion-related problems were retained. A total of 165 papers were analyzed and subsequently categorized by fuel type, algorithmic approach, and target application (e.g., prediction, classification, optimization). This thematic classification forms the foundation for the structured analysis presented in the subsequent sections.  
A variety of research strategies were used in the analyzed work, combining numerical simulations, laboratory experiments, industrial tests, and processing of large datasets. In many cases, the experimental data was used to train ML models or validate numerical results.
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 4 Blockchain Copyright Protection Model Based on Vector Map Unique Identification
### Copyright Verification Process
#### 4.2.1 Unique Identification Matching  
The unique identification matching process ensures reliable copyright verification by sequentially comparing the constructed unique identifications from suspicious data with the records stored in the blockchain. Initially, the identifications are constructed from suspicious data based on topological and geometric characteristics, as described in Section 4.1. These constructed identifications are then compared against the unique identification repository recorded on the blockchain. The comparison is performed by calculating the similarity between each constructed identification and every blockchain-stored identification using the bit error rate (BER), defined as the proportion of differing bits between two standardized bit strings. For each constructed identification, the blockchain-stored identification with the highest similarity (i.e., the lowest BER) is selected as the most probable match. This approach not only ensures accurate matching but also enhances the robustness of the system against potential alterations in suspicious data, as the identification process prioritizes the closest match, reducing the risk of false negatives in cases of the partial distortion or modification of the embedded identifiers.  
Step 1: Convert the constructed unique identifications and blockchain identifications into bit string representations. Let \(u_{i}\) and \(b_{j}\) be bit strings of length \(k\). Assume \(u_{i}\) and \(b_{j}\) are standardized to bit strings of the same length.  
Step 2: For each pair of identifications (\(u_{i}\), \(b_{j}\)), calculate the bit error rate (BER). The BER is defined as the proportion of differing bits between two bit strings. The specific formula is as follows:  
\[BER(u_{i},b_{j})=\frac{1}{k}\sum_{l=1}^{k}\delta(u_{i}^{l},b_{j}^{l}) \tag{16}\]  
Step 3: Calculate \(BER(u_{i},b_{j})\) for each pair of identifications (\(u_{i}\), \(b_{j}\)). If \(BER(u_{i}\), \(b_{j})<BER_{threshold}\), consider it a successful match and add the pair (\(u_{i}\), \(b_{j}\)) to the result set \(M\).  
Step 4: Traverse all combinations of constructed identifications \(u_{i}\) and stored identifications \(b_{j}\), and record the matching pairs (\(u_{i}\), \(b_{j}\)) for all successful matches. Output the matching result set \(M\).  
\[M=\left\{(u_{i},b_{j})\middle|BER(u_{i},b_{j})\,<BER_{threshold},\forall u_{i} \in U,b_{j}\in B\right\} \tag{17}\]
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### AI-Enabled Interference Mitigation
#### 4.4.2 AI-Based Interference Prediction and Traffic Steering  
3 GPP specifications (e.g., technical specifications 28.104 and 28.105) outline how AI-driven data analytics can be used to predict interference events before they occur [195]. This proactive approach allows networks to apply traffic steering, FR optimization, or antenna tuning to avoid potential interference zones. Such functionality is particularly effective in UDNs and supports enhanced QoE [196].
# Antipodal Vivaldi Antenna Arrays Fed by Substrate Integrated Waveguide Right-Angled Power Dividers
## 3 Results
### SIW Three-Way Power Divider as Feed System  
For designing an antenna array with high gain at K-band frequencies, the three-way SIW divider of Section 2.1 is combined with three Vivaldi antennas (Section 2.3) as presented in Figure 15. The entire circuit is designed and fabricated on a single substrate layer which makes it a compact, planar and easy to fabricate system. The circuit is simulated in the full wave simulator CST 2017, and fine-optimization is applied for improving the performance. As shown in Figure 16, the measured reflection coefficient  
Figure 11: Measured and simulated reflection coefficient and gain of H-plane SIW antenna array with two-way out-of-phase power divider including the s-parameters of the power divider at both ports.  
Figure 12: Comparison between measured and simulated far-field radiation pattern of H-plane SIW antenna array with two-way 180-degree out-of-phase power divider at **(a)** 26.5 GHz **(b)** 27 GHz, and **(c)** 27.5 GHz.  
of the system is better than \(-10\) dB over a bandwidth of 2.14 GHz centered at 22.6 GHz. The maximum measured gain is 14.39 dB at 20 GHz and the minimum gain is 11.69 dB at 25 GHz.  
Figure 14: Simulated radiation pattern of Vivaldi antenna array with two-way 120-degree SIW power divider as feed system at 27 GHz.  
Figure 13: Vivaldi antenna array fed by two-way SIW power divider with 120-degree phase difference; (**a**) structure, (**b**) reflection coefficient of antenna system and s-parameters of two-port power divider.  
Figure 15: Vivaldi antenna array with three-way in-phase SIW power divider feed system.  
Due to the quasi in-phase power divider feed, this design has a directive radiation pattern (as used, e.g., in communication systems) over the entire operating frequency range as depicted in the patterns of Figure 17. It is observed that the measured main lobe shows very good agreement with simulations while there is some disagreement towards the back lobe. We attribute these discrepancies to the fact that during a full rotation of the array, not all metal parts could be completely covered by absorber material. The measured and calculated radiation efficiency of this system vs. frequency is presented in Figure 18. As is shown, they have a good agreement over the entire frequency band. The polarization efficiency of this system is measured versus frequency and is presented in Table 2.  
Figure 16: Measured and simulated reflection coefficient and gain of H-plane SIW antenna array with quasi in-phase three-way right-angled power divider.  
Figure 17: Far-field radiation pattern of H-plane SIW antenna array with three-way in-phase right-angled power divider at \(\varphi\) = 0 and \(\theta\) between 0 and 360 degrees at (**a**) 22 GHz, (**b**) 22.5 GHz, and (**c**) 23 GHz.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 3 Achievable Rates, Outage Probabilities and Bit-Error-Rates Analysis
### Transmission between RS and UE  
#### 3.2.1 Application of AF Coding Scheme  
The relay amplifies the incoming signal in the second stage using an AF coding scheme and an amplification factor of (18). Thus, the received signal (\(y_{k}^{\text{AF}}\)) at the \(k\)th user (UE\({}_{k}\)) is  
\[y_{k}^{\text{AF}} =y_{RS}\mathbf{h}_{R,k}\sqrt{\beta_{k}}G+n_{k} \tag{17}\] \[=\mathbf{h}_{R,k}\sqrt{\beta_{k}}G\sqrt{\alpha_{R}}\mathbf{h}_{ TX}\mathbf{x}+\mathbf{h}_{R,k}G\sqrt{\beta_{i}}\eta_{R}+\eta_{k}\]  
where \(n_{k}\) is the complex additive Gaussian noise at \(k\)th user with \(\mathbb{CN}(0,\sigma^{2})\), \(\mathbf{h}_{R,k}\) is a 3D channel model following (3) and the \(\beta_{k}\) is path loss between the RS and \(k\)th UE. \(G\) is the amplifying factor [107] given by  
\[G=\sqrt{\frac{\alpha_{S}^{2}}{P_{s}\left|\mathbf{h}_{TX}^{2}\right|+\sigma_{R} ^{2}}} \tag{18}\]where \(a_{g}^{2}=\varrho(1-d)^{-k}\) is a non-fading variable path gain, \(\varrho=1\) is the reference distance's path-loss exponent, \(d_{0}\) is the reference distance, \(d=d_{e}/d_{0}\) is the normalized relay distance from the source, and \(k\) is the path-loss exponent of the TX\(-\)RS channel. The effective distance \(d_{e}\) is the actual distance of TX\(-\)RS in meters, where the cell radius is \(d_{0}\). To reduce the impact of inter-user interference on each user, SIC at the destination's receiver [108]. The effective users' channel gains determine SIC decoding order in ascending order, that is \(|\mathbf{h}_{R,1}|^{2}\geq|\mathbf{h}_{R,2}|^{2}\). The signal-to-interference noise ratio (\(\gamma_{1,2}^{\mathrm{AF}}\)) for the strong user (UE\({}_{1}\)) to decode \(x_{2}\) by applying SIC processing is given in [109] as  
\[\gamma_{1,2}^{\mathrm{AF}}=\frac{\alpha_{2}\gamma^{2}|\mathbf{h}_{TX}|^{2}| \mathbf{h}_{R,2}|^{2}}{\gamma^{2}|\mathbf{h}_{TX}|^{2}|\mathbf{h}_{R,1}|^{2} \alpha_{1}+\gamma\Big{(}|\mathbf{h}_{TX}|^{2}+|\mathbf{h}_{R,1}|^{2}\Big{)}+1} \tag{19}\]  
After decoding the message of UE\({}_{2}\) and subtracting it, UE\({}_{1}\) decodes its signal with the following SINR (\(\gamma_{R,1}^{\mathrm{AF}}\)) between the RS and UE\({}_{1}\)[109].  
\[\gamma_{R,1}^{\mathrm{AF}}=\frac{\alpha_{1}\gamma^{2}|\mathbf{h}_{TX}|^{2}| \mathbf{h}_{R,1}|^{2}}{\gamma\Big{(}|\mathbf{h}_{TX}|^{2}+|\mathbf{h}_{R,1}|^ {2}\Big{)}+1} \tag{20}\]  
The SINR (\(\gamma_{R,2}^{\mathrm{AF}}\)) between the RS and the UE\({}_{2}\) to detect its own message is expressed in [109] as  
\[\gamma_{R,2}^{\mathrm{AF}}=\frac{\alpha_{2}\gamma^{2}|\mathbf{h}_{TX}|^{2}| \mathbf{h}_{R,2}|^{2}}{\gamma^{2}|\mathbf{h}_{TX}|^{2}|\mathbf{h}_{R,2}|^{2} \alpha_{1}+\gamma\Big{(}|\mathbf{h}_{TX}|^{2}+|\mathbf{h}_{R,2}|^{2}\Big{)}+1} \tag{21}\]  
where \(\gamma\) gives the average signal-to-noise ratio (SNR).
## 5 Sensors
### Tissues Detection Sensor
In addition, it can be possible to create a huge database of optical responses like these for each tissue. Some of them should be unhealthy tissues, for example cancer cells at different stages or cells affected by diseases such as diabetes [45]. Thus, there are already some studies that report how the diseases affect the optical properties of biological tissues [45]. Then, the spectral response will vary and it is possible to monitor cells and tissues in real time, in vitro or in vivo, in order to create a regular report of spectra like these [10; 24; 25; 27]. It is useful to detect some diseases or to control substance levels [10; 24; 25; 26; 46; 47]. For instance, if optical properties vary with the concentration of some substances, it is possible to take conclusions regarding the spectral analysis. It is the case for insulin levels (diabetes), blood pressure or oxygen in circulation. Nonetheless, it is very important to have a huge database, in order to create a complex dielectric function model to use on these applications. These real models are not easily available.
# Vulnerability of Buildings to Meteorological Hazards:
## 2 Materials and Methods
### The Selection of Indicators
#### 2.2.3 Indicators for Hail  
In the following paragraphs, indicators related to the impact of hail that will be considered in this study are described (Table 1).  
_Material of the roof_: [PERSON] et al. [51], using insurance claims and policy-in-force data to assess the resilience of various roofing materials to hail effects, have demonstrated that the material of a building's roof is one of the most important indicators affecting the resiliency of buildings towards hail. They also mention that the hail diameter limit for damage in asphalt shingles was found to be as low as 2.54 cm (1 in) according to laboratory impact tests, but the threshold for other items such as concrete tiles was significantly higher (5.08 cm or 2 in), which shows lower resistance of asphalt shingles in comparison to concrete tiles. It is noteworthy that metal and wooden roofs had the highest claim rates [51].  
_Presence and type of shutters_: Shutters are important because they protect the windows from breaking when exposed to hail of a large diameter. This protection varies according to the material of the shutter.  
_State of non-structural elements_: Non-structural elements that include roof features (antennas, chimneys, etc.) can increase the overall vulnerability of a building to the impact of hail storms [51] and consequently the damage and the associated costs.  
_Rool shape_: The shape of the roof also affects the vulnerability of the building and should be considered in the index. In more detail, roofs with a slope and especially a slope perpendicular to the hail direction have demonstrated increased damages during past events.  
_Presence and length of overhang_: The presence and length of overhang are included in the set of indicators due to the assumption that since the overhang protects the building from wind-driven rain it can also offer some protection to the building and its features (e.g., glass windows) when exposed to hailstorms.  
_Window glazing_: The glazing (single, double, triple) of the window is a decisive factor in whether a window will break or not. Reports from a severe hail event in Germany in 2013 showed that most of the damage concentrated on roofs and windows [52].  
_Presence and state of the balcony_: The presence and state of the balcony are included in the index as a sensitive protruding part of a building (see windstorm indicators).  
_Number of floors_: The number of floors is also considered for buildings exposed to hail. Since hail strikes at an oblique angle (due to wind), we assume that buildings with more floors are more vulnerable when exposed to hail.
## 1 Introduction
The LSTM neural network, as an optimized and improved algorithm, not only addresses the challenge of vanishing gradients during backpropagation but also preserves both long and short-term sequence dependencies. Therefore, LSTM offers a significant advantage when dealing with foundational time series data like land subsidence. However, the prediction accuracy of the LSTM neural network depends on the determination of its parameters, necessitating the optimization of the LSTM neural network [12; 13; 14]. To enhance the accuracy of the LSTM model in forecasting land subsidence, this study employs the SSA to optimize the LSTM's learning rate, hidden layer nodes, and regularization coefficient. SSA is a bio-inspired optimization method innovatively introduced by [PERSON] et al. [15] from Donghua University in 2020. It boasts exceptional convergence properties and high precision. This algorithm has showcased its superior performance in model parameter adjustments. [PERSON] et al. [16] developed an advanced LSTM forecasting model to boost the utilization of clean energies like wind power in integrated energy systems, resulting in enhanced economic and environmental benefits. [PERSON] et al. [17] introduced a high-accuracy time series forecasting method for assessing the lifespan of insulated gate bipolar transistors (IGBTs) based on an LSTM network optimized using the sparrow search algorithm. [PERSON] et al. [18] presented a greenhouse environment forecasting model based on an advanced LSTM network, achieving precise predictions of greenhouse environment data. [PERSON] et al. [19] proposed a model based on the sparrow search algorithm and LSTM neural network, demonstrating its significant advantage in precisely predicting soil oxygen content changes after SSA optimization. Differing from existing research, this study introduces the SSA into the parameter optimization of the LSTM neural network forecasting model, aiming to find the optimal hyperparameter combination. The sparrow search algorithm, by optimizing parameters including the node count in hidden layers and initial learning rates, substantially diminishes the manual parameter tuning workload traditionally seen in LSTM neural network model predictions, thereby improving the precision of land subsidence forecasting. Based on current literature review results, there are still few related research reports available. In this study, the SSA-LSTM model showed good prediction accuracy in the land subsidence prediction, which provided a new and scientific idea for the monitoring and control of land subsidence in the region.
## 4 SnZn-Based Solders
### Wettability  
The SnZn solder has a poor wettability due to its poor oxidation resistance, which decreases the reliability of the solder joints. Figure 14[88] shows that the addition of ZrC nanoparticles has a significant impact on the wettability of the SnZn solder. Compared with the plain SnZn solder, the spreading area of the SnZn-0.06 ZrC composite solder could be increased from 143.64 mm\({}^{2}\) to 190.92 mm\({}^{2}\). However, when the content is excessive, ZrC nanoparticles would agglomerate at the surface of the molten solder, which inhibited the wetting and spreading of the liquid solder on Cu substrate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solders** & **UTS (MPa)** & **Elongation (\%)** & **Microhardness** & **References** \\ \hline Sn3.0 Ag0.5 Cu & 46.0 & 29.5 & / & [83] \\ Sn0.3 Ag-0.5 Cu/0.1 Mn & 56.5 & / & / & [31] \\ Sn3.5 Ag0.25 Cu/1 TiO\({}_{2}\) & 70.1 & 25.2 & 18.5 HV & [32] \\ Sn3.0 Ag0.5 Cu/0.05 La\({}_{2}\)O\({}_{3}\) & 79.0 & 14.1 & 13 HV & [23] \\ Sn3.0 Ag0.5 Cu/0.03 GNSs & 50.3 & 23.5 & / & [83] \\
3 Sn3.0 Ag0.5 Cu/0.1 GNSs & 50.7 & 21.5 & / & [83] \\ Sn3.0 Ag0.5 Cu/0.2 Ni-GNSs & 58.4 & / & 14.6 HV & [84] \\ Sn3.0 Ag0.5 Cu/0.05 Ag-GNSs & 50.1 & 14.7 & / & [85] \\ Sn3.8 Ag0.7 Cu/UNCNS & 56.7 & 24.4 & / & [86] \\ Sn58 Bi & 81 & 21 & 0.30 GPa & [63] \\ Sn58 Bi/0.01 GNSs & 84 & 32 & 0.26 GPa & [63] \\ Sn58 Bi/0.1 GNSs & 93 & 17 & 0.41 GPa & [63] \\ Sn58 Bi/0.03 CNTs & 94.2 & 21.7 & / & [60] \\ Sn9 Zn & 41.0 & 43.4 & 13.6 BHN & [87] \\ Sn9 Zn/1.0 Ag & 43.6 & 19.4 & 16 BHN & [87] \\ Sn9 Zn/1.0 Sb & 53.6 & 21.6 & 15.9 BHN & [81] \\ Sn9 Zn/1.0 Al\({}_{2}\)O\({}_{3}\) & 43.4 & 44.9 & 27 HV & [78] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mechanical properties of nanoparticle-reinforced lead-free solders.  
Figure 13: Effect of Sb nanoparticles on the tensile properties of the Sn–9 Zn–xSb composite solders (x = 0, 0.5, 1.0, 1.5) [81].  
Generally, the larger the width of wetting ring is, the better wettability the solder has. Figure 15 illustrates that with the addition of Al\({}_{2}\)O\({}_{3}\) nanoparticles, the spreading area and the width of precursor film increased [89]. Therefore, the wettability was improved by adding small amount of Al\({}_{2}\)O\({}_{3}\) nanoparticles.