esa-sceva/satcom-chunk-collection huggingface dataset:
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 5 Implementation Issue
### Complexity  
In the proposed scheme, the additional implementation complexity in typical NOMA is not needed since SIC is performed for the own user's data. However, even though the superposed signals are the user's own data, the symbols with small allocation power in superposed signal have poor BER performance. To solve this problem, a detection scheme with better performance should be used. However, a non-linear algorithm has high complexity. For this problem, a complexity-reduced detection algorithm can be considered. The main consideration is achieving higher throughput with lower complexity.
# Application of Bonded-Block Models to Rock Failure Analysis
## 2 Discrete Element Modeling of Rock Fracture
### Bonded-Block Models
The application of bonded-block models in rock mechanics has swiftly extended to more complex physical problems and diverse site conditions, including time-dependent damage evolution in sandstone [42]; hydro-mechanical problems [43]; dynamic fracture in the simulation of wave propagation experiments in a modified split Hopkinson pressure bar [44]; and thermo-mechanical problems [45; 46; 47]. Practical engineering applications have become more widespread in the design of rock bolt support in underground mines [48]; in the study of the stability of deep mining pillars [49]; in the investigation of design issues in underground works [50]; and coal-mining problems [51; 52]. An issue of major importance in research using bonded-block models remains the calibration of input parameters usinglab or field experiments in order to provide contributions for the progressive validation of these models [46; 53]. The scope of bonded-block models has extended outside rock mechanics to include the fracture of other geomaterials and structures, for example, in the detailed analysis of lab tests on masonry specimens, prisms and wallettes [54; 55; 56]. Concrete fracture has also been approached with bonded-block models [57] and related numerical techniques employing polygonal or polyhedral elements [58]. Bonded-particle and bonded-block models are both effective tools to address rock fracture. The early circular-particle models were known to underestimate frictional effects [3]. Further developments, e.g., using particle clumps [11; 13] or special contact formulations [14; 15; 16; 17], allowed a more realistic simulation of the grain structure. Block models represent, in a natural way, polygonal grains using standard DEM contact formulations, which also allow the extension of the analysis into the large-displacement regime without the need to resort to special-purpose techniques. From a computational point of view, block models tend to be more demanding, given the additional complexity of the contact detection and update tasks. Thus, particle models may allow more refined analyses with reasonable run times. In summary, both types of modeling remain active fields of research and application in rock engineering, as discussed in various review articles [7; 9; 10; 58].
## 3 Aim, Materials, and Methods
### Rescue Team Scheduling Mode of Multi-Dimensional Resource Integration
#### 4.5.2 Professional Rescue Team  
In China, frequent occurrences of various natural disasters such as earthquakes, floods, and landslides pose significant threats to the safety of people's lives and property. Professional rescue teams possess extensive experience in emergency response. In situations where local professional rescue forces are insufficient due to disasters in a specific area, the government can promptly request cross-regional support through the local emergency management platform. Upon receiving requests from the cross-regional emergency platform, relevant departments intelligently allocate rescue personnel based on analysis results obtained from the cross-regional platform. These highly skilled and experienced personnel are swiftly deployed for rescue operations. During cross-regional support efforts, an intelligent distribution system optimizes resource allocation to enhance overall efficiency; for instance, by analyzing team capabilities and disaster severity across different regions, resources are allocated reasonably to prevent wastage while ensuring timely and effective support is provided to each affected region.  
Figure 6: Rescue team scheduling mode of multi-dimensional resource integration. Source: Own processing according to the literature.
# Machine Learning and Bagging to Predict Midterm Electricity Consumption in Saudi Arabia
## 2 Related Work  
Numerous studies in the literature have used machine learning approaches to estimate electricity consumption. This section focuses on published studies on electricity consumption prediction and covers international-based research before focusing on Saudi-based research. A summarization of the past literature regarding electricity consumption prediction can be seen in Table 1.  
Within the literature, many studies have attempted to predict electricity consumption using machine learning techniques. These techniques include a self-adaptive grey fractional weighted model [9], Holt-Winters smoothing [10], ANNs [3; 11; 12; 13], a self-adaptive screening and vector error correction model [3], support vector regression [12], a least-squares support vector machine [14], linear regression [15; 16], recurrent neural networks using long short-term memory [17], an abductory induction mechanism [18], and the auto regressive integrated moving average [19]. From the literature, it can be seen that publications regarding predicting electricity consumption are scarce when compared to publications in other areas. One of the main reasons for this is the lack of specific datasets that represent accurate consumption. In Saudi Arabia, the investigated experiments obtained their datasets from local institutions that have physical or soft copies of the electricity consumption for said institutes [15; 17], general statistics that are shared publicly [13], or the Saudi Consolidated Electric Company of the Eastern Province [15; 17; 18]. The majority of past studies attempted electricity consumption forecasting in the eastern region alone [15; 17; 18]. However, the specific cities included were unclear, and the region was treated as a unit. Qassim City was also investigated in [17], but the investigation was based on data from the College of Computer Science at Qassim University and not the actual city. Therefore, this research deployed a new dataset collected from the Saudi Electrical Company (SEC) for investigating how machine learning techniques perform in forecasting electricity consumption. Furthermore, the dataset utilized in this research includes the electricity consumption of all areas covered by the SEC in Saudi Arabia divided by service offices, 18 of which were included in this study (i.e., the service offices that cover the same area as tier 1 monitoring stations). The literature implies the need to attempt electricity consumption forecasting with methods that have not been investigated before, such as instance-based machine learning methods, where there is room for investigation. Hence, this work studied the performance of the K* algorithm in predicting Saudi Arabia's electricity consumption. Since ensemble techniques have been shown to provide good performance, the bagging ensemble method is also investigated in this work as a potential tool for predicting electricity consumption in Saudi Arabia. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Year** & **Aina** & **Location** & **Dataset Science** & **Method** & **Results** \\
**Ref.
# A Predictive Model of Chlorophyll a in Western Lake Erie Based on Artificial Neural Network
## 2 Materials and Methods
from Erieau and Windsor stations, located on the north shore of the central basin. Wind speed and air temperature data were collected from two sources. Most data were obtained from the National Ocean and Atmospheric Administration's (NOAA) National Data Buoy Center's buoy located in the central basin (Station ID: 45005) of Lake Erie and these data were only available during months without ice cover. Data from the Cleveland Burke airport were used for the months with ice cover. For information on the data resources of the Detroit and Maumee Rivers, see Appendices A and B.  
Observed surface water temperature data from 2002 to 2015 were obtained from NOAA. The observed Chl-a concentrations from 2002 to 2015 were retrieved from the Great Lakes Environmental database system (GLENDA), and they were sampled through the water column during spring and in the epilimnion (from 2 to 4 m depths) during summer. The observed bio-volumes (\(\upmu\)m\({}^{3}\)L\({}^{-1}\)) were converted to Chl-a biomass (\(\upmu\)g L\({}^{-1}\)) using the conversion formula: \(\log_{10}(biovolume)=a+b\log_{10}(chlorophyll\ a)\) with species-specific values of \(a\) and \(b\)[36].
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 3 System Modeling
### System Model
The main interference in the integrated network includes inter-system interference and intra-group user interference, among which, the interference of satellite receiving signal mainly comes from the satellite users in the group and BS users with beam coverage, and the receiving signal is expressed as  
\[ \begin{array}{l}y_{s,l,J}=\left(\sqrt{\rho_{S,J,I}^{(1)}P_{S,I},J,H_{S,J}^{(1)} }X_{S,J,J}^{(1)}+\sqrt{\rho_{S,J,I}^{(2)}P_{S,I},[PERSON]_{S,J}^{(2)}}X_{S,J,J}^{(2)} \right)\\ +\sum\limits_{i=1}^{I}\sum\limits_{j=1}^{N}\left(\sqrt{\rho_{S,I,j}^{(1)}P_{S, i,j}H_{S,J}^{(1)}}X_{S,J,j}^{(1)}+\sqrt{\rho_{S,i,j}^{(2)}P_{S,i,j}H_{S,i,j}^{(2)}}X_{S,i,j}^{(2)}\right)+n\end{array}  \tag{1}\]  
The interference of BS receiving signal mainly comes from the BS users in the group and BS users with coverage, and the receiving signal is expressed as  
\[ \begin{array}{l}y_{B,i,J}=\left(\sqrt{\rho_{B,i,I}^{(1)}P_{B,i,J}H_{B,i,J}^{ (1)}}X_{B,i,J}^{(1)}+\sqrt{\rho_{B,i,J}^{(2)}P_{B,i,J}H_{B,i,J}^{(2)}}X_{B,i,J }^{(2)}\right)\\ +\sum\limits_{j=1}^{K_{i}/2}\left(\sqrt{\rho_{S,I,j}^{(1)}P_{S,I,j}H_{S,I,j}^{ (1)}}X_{S,J,j}^{(1)}+\sqrt{\rho_{S,J,j}^{(2)}P_{S,I,j}H_{S,J}^{(2)}}X_{S,j,j}^ {(2)}\right)+n\end{array}  \tag{2}\]
## 5 Calculation of Load Carrying Capacity
### Accuracy Analysis of Load Carrying Capacity Calculation Method  
According to the section reduction method, the comparison between the load carrying capacity calculation results \(N_{\text{cs}}\) by the above three specifications and the test results \(N_{\text{t}}\) of specimens are shown in Figure 14. It can be found that the ultimate load carrying capacities of specimens calculated based on section reduction are close to the test results, and have a certain safety margin, indicating the feasibility of this method. Considering that \(N_{\text{cm}}\) and \(N_{\text{cs}}\) are basically the same, the method of material degradation for load carrying capacities can also be proved feasible. After comparison, the conservative degree of calculation results of three specifications indicates a descending order of ANSI/AISC 360-16, GB50017-2017, and EN 1993-1-1.
# Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
## 2 Related Work  
There are several approaches to conduct streaming under QoE assurance requirements depending on network conditions [8; 9; 10]. In this section, we review the previous and well-known video streaming techniques and discuss corresponding limitations. In the following, the progressive download (PD) [11; 12], HTTP-based adaptive streaming (HAS) [13], Scalable Video Coding (SVC), and Dynamic Adaptive Streaming over HTTP (DASH) [14] methods are presented.
# New Methods for Assessing External Sulfate Attack on Cement-Based Specimens
## 2 Materials and Methods  
### Materials  
The cement paste used in this study was composed of CEM 152,5 N CE CP2 NF with a water-to-cement ratio of 0.6. Cement composition and anhydrous content based on [PERSON]'s equations are shown in Tables 1 and 2, respectively.  
Cylindrical specimens were used for the first proposed method (i.e., the drying method developed to evaluate the penetration depth of sulfate ions). The cement paste specimens were cast in a rigid PVC mold with a diameter of 10 cm and a length of 15 cm (see Figure 1a). Plastic covers were attached to both ends of the mold. The purpose of using such molds was to reduce the risk of desiccation that might occur during the hydration process, particularly with high-water content pastes. Vibration was used to compact each layer of filling as it was poured into the molds during the casting process. Voids were eliminated as a result of this process.  
To ensure that the cement paste cylinders were homogeneous and to avoid any separation of the solid particles and water, the cylinders were placed after casting in a large cylindrical device (see Figure 1b) and rotated at a low speed (1 cycle/min) for 16 h. A similar technique was used in [6,10].  
One day after casting, 1 cm slices were removed from both sides of the cylinders to eliminate the edge effects, and to obtain a homogeneous paste in cylinders (see Figure 1c). Two identical cylindrical slices (length = 5 cm and diameter = 10 cm) were cut from each remaining cylinder (see Figure 1c). The specimens (slices) were intended to be immersed in an attack solution (see description of exposure conditions below). However, before immersion, each slice was coated on its lateral surface with epoxy resin (see Figure 1d) and allowed to cure for 24 h. With this type of protection, the sulfate ions present in the immersion solution are only allowed to penetrate into the cement paste through the bottom circular base of the cylindrical slice (the upper part of the slice is out of the solution as explained in the description of exposure conditions below).  
\begin{table}
\begin{tabular}{c c} \hline
**Components** & **wt \%** \\ \hline CaO & 62.79 \\ SiO\({}_{2}\) & 20.38 \\ Al\({}_{2}\)O\({}_{3}\) & 4.30 \\ TiO\({}_{2}\) & 0.24 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.80 \\ MgO & 1.25 \\ \hline SO\({}_{3}\) & 3.46 \\ S & Traces \\ K\({}_{2}\)O & 0.73 \\ Na\({}_{2}\)O & 0.35 \\ Chlorides & 0.04 \\ MnO & 0.05 \\ LOI (loss on ignition) & 2.04 \\ Insoluble & 0.54 \\ \(\overline{\Sigma}\) & 99.97 \\ Free lime & 1.39 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of cement CEM I 52.5 N CE CP2 NF provided by the manufacturer.  
\begin{table}
\begin{tabular}{c c} \hline
**Components** & **Mass Content Based on Bogue Equation \%** \\ \hline C\({}_{3}\)S & 57.05 \\ C\({}_{2}\)S & 14.99 \\ C\({}_{3}\)A & 7.91 \\ C\({}_{4}\)AF & 8.9 \\ \hline \end{tabular}
\end{table}
Table 2: Main cement clinker phases calculated by Bogue’s equations.
# Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning
## 2 Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning
### Transmission Control by Deep Reinforcement Learning
However, despite action \(a\), \(L_{t+1}\) sometimes exceeds \(L_{ideal}\). In this situation, the RL server should not allocate the bandwidth to the UE. Thus, reward \(r\) is zero value when the RL server outputs QoS4 as action \(a\) in this situation. Furthermore, we weight reward \(r\) by the elapsed time. In the proposed method, it is preferable to delay the mobile data communication and maintain the eNB load within \(L_{ideal}\). However, the control becomes increasingly difficult as the elapsed time increases in the case of delaying and controlling the mobile data communication. Hence, in Algorithm 1, the longer the control elapsed time, the greater the positive reward when the RL server gets a reward. Conversely, the shorter the control elapsed time, the greater the negative reward. The episode end time is set by assuming that the RL server periodically controls within a certain time range, such as 1 day. Accordingly, the RL server can balance the load by considering the content delay tolerance while not exceeding \(L_{ideal}\), since reinforcement learning learns the action to maximize the reward. ```
1 Current load of connecting eNB: \(L_{t+1}\)
2 Ideal load that is control target value: \(L_{ideal}\)
3 Available bandwidth of connecting eNB: \(ABW\)
4 Select action at time \(t\): \(a_{t}\)
5 Episode end time: \(t_{end}\)
6 Normalization variable: \(\beta\)
7 if\(L_{t+1}\leq L_{ideal}\)then
8\(r\gets 1+\frac{t}{\beta}\)
9 else
10 if\(ABW\ =\ 0\) and \(a_{t}\ =\ 4\)then
11\(r\gets 0\)
12 else
13\(r\leftarrow-(\frac{L_{t+1}-L_{ideal}}{L_{ideal}}+\frac{t_{end}-t}{\beta})\)
14 endif
15 endif
16 return\(r\)
```  
**Algorithm 1** Reward function of the proposed method
## 3 Available Photovoltaic Software
### Solar Radiation Maps  
Solar radiation maps allow users to understand solar resources for every spot on Earth with a simple visual. There are two software programs available online for this purpose, which are PVGIS and SolarGIS. Both programs provide geographical information based on maps or satellite imaginary. Photovoltaic Geographical Information System (PVGIS) is online software that estimates solar radiation, provides solar radiation maps and calculates annual energy generated from grid connected and standalone PV system. The system was founded in Institute of Energy and Transport--European Commission and it is available on the Internet. This software provides simulation for grid connected PV system only. It is easy to handle, and as PVsyst, it able to import meteorological data. However, this software is exclusively used in Europe and Africa only and has no PV component database. This software also does not include energy de-rating factor and economic analysis. Other than that, no other software could interconnected with this program [3; 67]. SolarGIS is also a free solar radiation map that is available online. SolarGIS's authors are also the main co-authors for the previous PVGIS. There are six applications for SolarGIS, which is iMaps, estimate energy output, monitor performances for existing power plants, purchase time series of meteorological data, receive real time performance monitoring for solar energy system, and data visualization on poster maps. Its database is a high resolution, and continuously updated on daily basis. The data is generated using in-house developed algorithms [68].
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
## 3 Materials and Methods  
A comprehensive literature search on the threat models and countermeasures for the 2 FA scheme in mobile money was conducted using digital library databases. Some of the databases used include IEEE Xplore, MDPI, Emerald Insight, Hindawi, ACM, Elsevier, Springer, Google Scholar, and Specific and International Journals. Specific keywords such as \"2 FA scheme for mobile money\", \"2 FA protocol for mobile money\", \"2 FA framework for mobile money\", \"2 FA scheme for mobile wallet\", \"2 FA protocol for mobile wallet\", \"2 FA framework for mobile wallet\", \"2 FA scheme for mobile banking\", \"2 FA protocol for mobile banking\", and \"2 FA framework for mobile banking\" were used to identify the literature for the study. The researchers only reviewed papers related to the 2 FA scheme, and each collected source was evaluated against relevance, originality, eminence, influence, and year of publication. Furthermore, the researchers omitted some papers whose content was outside 2 FA schemes and not written in English. The selected collection of literature comprised the most relevant papers in the area of the 2 FA scheme for mobile money as the primary objective. Ninety-seven papers were reviewed, of which 28 were from the IEEE Xplore, three from MDPI, two from Emerald Insight, three from Hindawi, six from ACM, three from the Elsevier, six from Springer, 16 from Google Scholar, and 30 from Specific and International Journals, as summarised in Figure 7. Out of the reviewed papers, 39 papers focused on the threat models in the 2 FA scheme for mobile money, and 58 focused on the countermeasures against the attacks in mobile money's 2 FA schemes. The literature search started in January 2020 and continued until the date of the submission of the paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S/No** & **Countermeasure** & **Scheme** \\ \hline
1 & Asymmetric encryption function & ECC & [66; 67; 68; 69; 70] \\ \cline{3-4}  & & RSA encryption algorithm & [72] \\ \hline
2 & Symmetric encryption function & DES & [73; 74; 75] \\ \cline{3-4}  & & AES & [56; 76] \\ \hline
3 & Secure hash function & SHA-1 & [77; 78] \\ \hline
4 & Number-based countermeasures & Password & [79; 80; 81] \\ \cline{3-4}  & & PIN & [52; 83; 84; 85; 86; 87; 88; 89; 90; 91] \\ \cline{3-4}  & & OTP & [71; 79; 86; 87; 90; 97; 98] \\ \cline{3-4}  & & QR Code & [56; 71; 100; 102; 103] \\ \hline
5 & & Fingerprint recognition & [52; 81; 85; 88; 104; 105] \\ \cline{3-4}  & & Facial recognition & [90] \\ \cline{3-4}
5 & Biometric-based countermeasures & Iris recognition & [89] \\ \cline{3-4}  & & Retina recognition & [68] \\ \cline{3-4}  & & Voice recognition & [80; 86] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the countermeasures against the attacks in mobile money authentication schemes.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 2 M2D2 System Framework
### Baseline Model: Self-Supervision and Contrastive Learning  
Once the data have been preprocessed, M2D2 typically assumes that the collected offline training set is primarily composed of \"normal\" data. Leveraging self-supervised or unsupervised methods, it then learns a baseline model that represents a \"normal operational state\". In contexts where strong correlations or physical proximity exist across multiple machines (e.g., shared pipelines, adjacent production lines), a GNN may be introduced to encode cross-machine dependencies. This baseline model commonly integrates two complementary strategies:  
1. Reconstruction-Based Learning: Autoencoders and similar architectures reconstruct multi-modal data samples assumed to be normal. The system then derives a distribution of reconstruction errors, such that high reconstruction error may indicate emerging anomalies or drifts.
2. Contrastive Learning: When certain pairs or subsets of samples are known to stem from the same operational regime, contrastive objectives bring their latent representations closer together. Conversely, those from distinct regimes are pushed apart. In this way, the learned embedding space becomes more distinct for subsequent drift detection, improving generalization to new, unseen variations.  
At the conclusion of this training phase, M2D2 acquires one or more baseline distributions--e.g., a \"reconstruction error distribution\" or a \"latent embedding distribution\"--which serve as references when evaluating any shifts in newly arriving data.
## 3 Challenges in FSO Communication
### FSO System and Channel Models  
FSO channel models are essential for understanding and predicting system performance. These models account for various factors that affect the propagation of light, such as AT, absorption, scattering, and weather conditions like fog or rain. Accurate channel modeling helps in designing robust FSO systems, optimizing link availability, and ensuring reliable data transmission. By analyzing different channel models, researchers and engineers can develop strategies to mitigate adverse effects, enhancing the efficiency and reliability of FSO communication systems.