esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This work uses FLAC3D numerical simulations, based on the in situ stress distribution law, to investigate the influence of the angle between the directions of maximum horizontal principal stress and the roadway orientation, as well as the lateral pressure coefficient, on the horizontal stress and deformation of the roadway. The study then optimizes the roadway support scheme. The optimized scheme, derived from simulation and analysis, was implemented in the corresponding roadway, and its effectiveness was evaluated by monitoring roof subsidence, floor heave, and side convergence over six months. The conclusions are as follows:  
1. By using the hollow body stress relief method, the in situ stress distribution at three measurement points is obtained, showing that \(\sigma_{\rm H}>\sigma_{\rm v}>\sigma_{\rm h}\), with the maximum principal stress being horizontal.
2. The angle between the directions of maximum horizontal principal stress and the roadway orientation and the lateral pressure coefficients are directly proportional to the horizontal stress and deformation of the surrounding rock.
3. The original support scheme is optimized. Simulations indicate that the roadway deformation was significantly reduced: the roof subsidence was reduced by 37.3%, the floor heave was reduced by 49.5%, the side convergence was reduced by 34.7%, and the stability of the surrounding rock was greatly improved.
4. The optimized support scheme was applied to the 6A# Left 2 nd Roadway in the Ronghua No. 1 Mine. On-site monitoring shows that the roadway deformation has significantly decreased, and stability has improved.  
**Author Contributions:** Conceptualization, F.X.; validation, S.L.; formal analysis and investigation, B.W., Y.W. and F.X.; data curation, H.L., L.S. and K.X.; writing--original draft preparation, B.W. and H.L.; writing--review and editing, S.L., F.X. and K.X.; supervision, F.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Natural Science Foundation of Heilongjiang Province (ZD2021E006) and the Scientific and Technological Key Project of \"Revealing the List and Taking Command\" in Heilongjiang Province (2021 ZXJ02A03, 2021 ZXJ02A04).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Implementation of DFDLOF in Personalizing Educational Pathways
### Integration of Machine Learning Algorithms  
#### 4.2.1 Selection and Application of Machine Learning Algorithms  
It is crucial to select and apply the most appropriate machine learning algorithms in the DFDLOF, as it dictates the system's effectiveness [46]. The algorithms chosen can handle complicated educational data and offer practical information. The algorithms should possess capabilities for pattern recognition, adaptability, scalability, and real-time processing.  
DFDLOF mainly uses supervised learning algorithms in tasks such as predictive analytics, which entails utilizing records on student performance to forecast future results. The approach helps to identify early students who may need more support, thereby intervening timely. For instance, for content recommendations that require personalization, unsupervised learning algorithms such as clustering are used to group students according to their preferences and achievements and help create individual learning paths.  
DFDLOF is a bidirectional fusion architecture that heavily relies on reinforcement learning, especially in adjusting the learning pathway after student interaction. The algorithm learns from its environment and continually adapts to improve policies that will maximize a specified reward in this context, optimizing the learning experience.  
In addition, DFDLOF incorporates deep learning strategies that are more effective in handling natural language and complex decision making [47]. With the largely unstruc  
Figure 4: Architecture of the DFDLOF showing interrelations among key components.  
tured nature of data, deep learning algorithms that use layered neural networks are useful in acquiring subtle details on student learning behaviors and preferences.  
To further elucidate, Table 3 below is a summary of the key machine learning algorithms used in DFDLOF and their contributions to personalized learning:
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 2 Related Work
### BLE-Based Positioning  
BLE-based positioning has been extensively explored over the past few years. Currently, the most commonly-used BLE includes Apple's iBeacon and Google's URIBeacon [20]. BLE is not designed from the ground up as a positioning technique, but it is feasible for estimating the user's position based on RSS measurements. Though the principles of BLE-based and WiFi-based positioning methods are basically identical, it has been shown that under the same circumstance, BLE-based positioning tends to be more reliable than WiFi due to its lower transmission power and a unique channel hopping mechanism [43]. Moreover, BLE-based positioning has the advantage that the BLE beacon tags can be placed as required, and it provides good signal coverage. Depending on whether or not the distance information to an anchor tag is required, two derivatives of BLE-based indoor positioning algorithms are considered: range-based and range-free algorithms. The range-based algorithm first uses the path loss model to estimate the distance to nearby anchor tags and then adopts trilateration and least-square estimation to obtain the estimated location. Due to the multipath fading and background interference, the range estimates are inaccurate. Furthermore, additional efforts are needed to obtain the empirical path loss model. Over time, researchers have proposed various methods to improve the accuracy of a range-based algorithm utilizing various propagation models and machine learning [7; 44; 45]. However, all proposed approaches have limitations affecting RSS values and, consequently, distance and location estimates, which arise from attenuation from obstacles, radio interference, aggregation or low frequency of measurements, and their strong dependence on the environment [46]. For range-free methods, some adopt fingerprint-based methods similar to WiFi positioning [10; 24]. However, it is labor-intensive to build and maintain the fingerprint database, and the positioning accuracy greatly depends on the quality and scale of the database.
## 4 The Prediction Model When Signal Approaches the Ground Station (Section B)
### Training the Learning System  
The learning system that predicts the channel attenuation was trained in this stage. Two different methods were used to find a faster and more accurate process. We used the LSTM (long short-term memory) model using Python libraries. LSTM is an artificial neural network designed to recognize patterns in data sequences, such as numerical time series data emanating from sensors, stock markets, and government agencies. RNNs (recurrent neural networks) and LSTMs are different from other neural networks as they have a temporal dimension.
## 1 Introduction
conducted kinetic experiments on Colombian heavy oil using a self-designed one-dimensional model and validated the historical matching with a kinetic model [39]. In recent years, there has been a greater emphasis on obtaining kinetic parameters and establishing kinetic models. It has been learned that kinetic parameters almost always depend on the composition of the oil. However, there is currently a lack of research on the impact of the magnitude of kinetic parameters for oil samples on the effectiveness of in situ combustion in heavy oil reservoirs, that is, the adaptability of oil samples to ISC. To address these issues, it is crucial to take into account the oxidation characteristics of oil samples and incorporate changes in kinetic parameters into numerical simulation analysis. In this study, we chose two different heavy oil samples from the Liaohe oilfield and the Huabei oilfield, with variations in viscosity, density, and composition. Experimental analyses were conducted using RTO, PDSC, and TGA to examine the oxidation characteristics of the oil samples. The experimental equipment and procedures are described in detail. In the RTO experiments, online measurements of the exhaust gas composition were conducted to analyze the thermal conversion process of heavy oil and the influence of operating conditions. This allowed for the determination of the combustion state of the oil and the evaluation of the feasibility of in situ combustion. Additionally, PDSC and TGA experiments were employed to obtain the thermal flow and thermogravimetric characteristics of the oil samples under different pressure and heating rate conditions. Combining these results with the Arrhenius equation, the reaction kinetic parameters were calculated. Finally, utilizing a CMG-STARS simulator, a combustion drive model was established to analyze the impact of changes in crude oil oxidation kinetic parameters on the combustion drive effect. Previous research primarily derived kinetic parameters from thermal analysis experiments to provide reaction models for reservoirs' ISC numerical simulations. Our study explores the impact of changing pre-exponential factors and activation energies on reservoir recovery rates through numerical simulation. This approach yields suitable crude oil reaction kinetic parameters for ISC, thereby enabling the selection of oil reservoirs for ISC adaptability from the perspective of their oil characteristics. This series of studies serves as a reference for combustion drive experiments under various conditions and provides a theoretical basis for field engineering practices. It holds certain guiding significance for the ISC development of heavy oil reservoirs.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 3 5G Overview
### Enhanced Mobile Broadband (eMBB)  
eMBB is a communication service that enables high data rate communications, which is crucial for numerous human-oriented applications, such as video streaming, high-traffic mobile broadband service, virtual reliability. Moreover, online gaming and the scenarios, including 3D/Ultra HD video live broadcasts that can be shared and accessed anytime and anywhere, dramatically increase wireless traffic. Indeed, the massive demand for an extended data rate was the primary motivation for the earlier evolution of the cellular systems' generations, including 3G and 4G. 5G has certain advantages in large bandwidth and low latency requirements. It intends to offer a peak data rate of 20 Gbps and assure a decent data rate (50-100 Mbps) of ubiquity adverse provisions, e.g., in a dense area under high mobility. Additionally, the target for user data traffic latency is 4 ms.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Multi-Channel MAC Protocols
#### 4.3.4 CaSSaM  
The author in [79] proposed a context-oriented information-based system. It is a dissemination-based protocol that works in a decentralized environment. This protocol considers the channel busy ratio (CBR), collision, number of neighbors, speed, and inter-vehicular distance. The CaSSaM system decides which parameter is adjusted for performance enhancement. The slotted 1-persistence protocol is used for the dissemination mechanism. When a node receives a packet for the first time, a time slot is allocated to the node and the node is then re-broadcasted with a probability of 1; for re-broadcast, priority is given to the farther node.  
It is mostly used for guaranteed safety applications with reduced delay and enhanced throughput. The power consumption of nodes is not analyzed; the protocol has a decentralized approach, and performance can be improved by implementing a centralized approach or by a combination of both.
## 7 Conclusions and Future Work  
In this paper, we have presented an AUV-aided energy-efficient routing scheme for UWSNs. We have also presented a model for efficient data gathering and proposed a mobility model using CDS for the sub-optimal trajectory of the AUV. We addressed the problems of the low data delivery ratio, the energy hole and high energy consumption. We have calculated the sub-optimal value of \(\beta\) by using the Monte Carlo simulation method. Simulation results have proven that our protocol performs better than the AURP and the AEERP (in harsh oceanic condition(s)) in terms of data gathering and energy consumption. The throughput of the AEDG is 35% more than the AEERP and 41% more than the AURP. However, the end-to-end delay of the AEDG is 30% more than the AEERP and 38% more than the AURP.  
In the near future, we will extend our work to a 3D network area with multiple AUVs having different trajectories. Moreover, we will exploit the physical layer to enhance the data rate in a harsh underwater environment.
## 2 Materials and Methods
### Sensory Analysis
Then, the panelists performed a modified RATA test in which they were asked to rate the intensity of each attribute from 0 to 5, where 0 was \"does not apply\", 1 was \"weak\" and 5 was \"strong\". The wine samples were analyzed in duplicate in two different MRATA sessions. The bottles were opened 30 min before the session. An aliquot (30 mL) of wine was poured into ISO glasses labelled with a random three-digit number. The testing order of the wine samples was randomized using a complete Latin square balance design among the panelists to reduce carry-over effects [23; 24]. In addition, a sensory test was also performed by local German panel in an accredited laboratory for wine testing (DIN EN ISO/IEC 17025:2018). The analysis results were provided by the company. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Wine** & **Attributes** & **Descriptors** & **Description** \\ \hline \multicolumn{3}{c}{VISUAL} \\ \hline GREENISH & & From pale grass to an intense green \\ YELLOWISH & & From pale straw to a rich yellow \\ \hline \multicolumn{3}{c}{OLFACTORY} \\ \hline Pome tree fruit-a & Green apple, pear & Tree fruit (green apple, pear) \\ Caramelized-a & Chestnut honey & Chestnut-honey \\ Woody-a & Oak wood & Fresh or burnt wood \\ Pungent-a & Alcohol & Strong sensation from alcohol \\ Tropical fruit-a & Banana, pineapple & Tropical fruit (banana, pineapple) \\ Vegetative-a & Green bell pepper & Cut green bell pepper \\ Spicy-a & Clove, white pepper & Cloves and white pepper \\ PINOT GRIS & Floral-a & Rose, Jasmine green tea & Rose water and fresh scent of wildlowers, \\ \hline \multicolumn{3}{c}{GUSTATORY} \\ \hline Warmness & Alcohol & Burning sensation in the mouth \\ Sourness & Acid (tartaric, malic, lactic acid) & Acid taste resembling vinegar, ripe apple \\ Bitterness & Caffeine & Bitter taste typical for coffee beverage \\ Saltiness & Sodium chloride & Salty taste typical for salt \\ \hline \multicolumn{3}{c}{FLAVOR} \\ \hline Pome tree fruit-f & Apple/pear & Typical of pome tree fruits (apple, pear) \\ Stone fruit-f & Apricot/peach & Typical of stone fruits (apricot, peach) \\ \hline \multicolumn{3}{c}{_OVERALL QUALITY JIDGEMENT (OQI)_} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Visual, aromatic, and gustatory attributes for Pinot Gris. The abbreviations ‘a’ refers to aroma and ‘f’ refers to flavor.
## 4 Parameter Identification
### Parameter Identification Strategy  
The critical challenge involves establishing precise mappings between the state-space model, thermal network model, and optimization algorithm--translating the thermal parameter identification problem into a multidimensional optimization framework. In this strategy, thermal parameters requiring identification are stored in set \(p\), which is equivalent to candidate solutions \(\overset{\rightarrow}{X_{i}}\). During each generational iteration, updated parameters are propagated into the LPTN model within the VML through the NOA's positional update mechanisms.  
The LPTN model is expressed in state-space form with input (time-varying rotational speed and radial load) and output temperature \(T\). For the transient-state or high thermal gradient conditions, outputs correspond to real-time temperatures at reference nodes. Under the steady-state or low thermal gradient conditions, outputs represent domain-averaged temperatures. As shown in the Equation (19), inputs and outputs of the LPTN model directly map to the state-space model's \(U\) and \(Y\), respectively. The system matrix \(A\) and input matrix \(B\) embed unknown parameters \(p\), which are treated as optimization variables in the NOA. This equivalence allows NOA's \"food location\" search mechanism to correspond to parameter identification, where identifying unknown thermal parameters is analogous to seeking the best position in the algorithm's logic. To accelerate NOA convergence and improve results' accuracy, the MATLAB R2021a implementation employs parallel computing techniques leveraging multi-core processors, significantly reducing computational time while maintaining solution accuracy. Owing to the nonlinear characteristics of bearing contact TRs and the complexity of convective heat transfer within the bearing chamber, recursive formulations designed for linear systems, such as least squares and maximum likelihood methods, yield suboptimal identification performance. To address this limitation, a fitness function based on cumulative temperature error aggregation is adopted for solution quality evaluation, expressed as:  
\[J=\sum_{i=1}^{N}\sum_{j=1}^{M}\left|T_{i,j}^{VML}-T_{i,j}^{PEL}\right| \tag{25}\]  
where \(T_{i,j}^{VML}\) and \(T_{i,j}^{PEL}\) are the reference node temperatures for the virtual datasets and sample datasets, respectively, while \(N\) and \(M\) refer to the total number of sample data and reference nodes, respectively. Through key thermal parameter identification, the most accurate thermal parameters are ultimately obtained, ensuring that the virtual temperature datasets at all reference nodes closely approximate the experimental temperature datasets. This process achieves determination of unknown parameters within the LPTN in the VML, thereby enabling high-fidelity thermal prediction for bearing systems under complex operational conditions.
_Article_  
**Reconstruction of PC Display from Common-Mode**  
**Noise Emitting on Electrical Power Line**  
**[PERSON] 1,*(r) and [PERSON] 2**  
Footnote 1: footnotemark:  
Received: 3 April 2019; Accepted: 3 June 2019; Published: 6 June 2019  
**Abstract:** This paper presents a method for reconstruction of a personal computer (PC) display image from common-mode noises coupling with monitor signals on a PC power cable. While the signal cable, which connects the PC and the monitor, is usually near the user, the power cable is connected to the outside electrical network of the office or the building. Thus, the power cables may become dominant gateways and/or antennas for emission and conduction of the common-mode noise, which may lead to a serious security issue. The measured common-mode was found to include both the monitor signal and undesired beats, which were caused by step responses of the signal and conceal the meaningful information. The original monitor signal was then calculated by excluding the beats, which could be measured by using standard up-step and down-step responses, from the measured common-mode noise and using an inverse function of the noise current level. The experimental results show that the beats were removed almost completely from the noise waveform for a monochromatic image. Alphabetic character strings, each of which were composed of, at most, \(9\times 9\) dots, were confirmed to be reconstructed clearly both in the monitor resolutions of \(800\times 600\) pixels and \(1280\times 1024\) pixels from the common-mode noise.  
**Keywords:** common-mode noise; electromagnetic compatibility; information security; PC display; step response; power cable
# Analysis of Production Process Parameters Using Computer Simulation at the Planning Stage
## 5 Discussion  
Computer simulation is one of the tools that allows for the low-cost analysis of manufacturing process parameters. Scientific publications describe methods of using simulation to verify selected solutions in the field of manufacturing process manage  
Figure 12: Percentage of use of inter-operational warehouses (queue) after implementing additional production stations.  
Figure 13: Percentage of production station utilization after implementing additional production stations.  
ment [1; 8; 14; 15; 17; 18; 23; 29] or logistics [7; 9; 16; 20; 22], as well as those related to the analysis of bottlenecks [8; 9; 14; 15; 19]. In each case, computer simulation was an important tool for conducting analyses related to the implemented production tasks.  
This paper analyzes the parameters of the manufacturing process at the production planning stage in terms of selecting the appropriate number of production resources using the FlexSim 22.2.1 simulation tool, which is often used by researchers to solve this type of manufacturing problem [1; 8]. The analytically determined numbers of stations necessary to perform a given operation were implemented in the FlexSim simulation software, which enables the verification of the correctness of the determination of the number of production stations. Based on the analysis, it was found that it was not possible to produce the assumed production plan within the designated time period in every case. Failure to execute the assumed production plan does not indicate improper use of the method in the analytical determination of the number of production stations. However, it does indicate failure to take into account all factors affecting the duration of production (i.e., means of transport). As a result of the high complexity of manufacturing processes and many factors that are difficult to predict, it seems justified to use computer simulation as a tool to support the planning of production processes.  
It should be noted, however, that there are many other solutions in the field of production process management that allow for the elimination or reduction of bottlenecks in the production process, which in turn allows for shortening the time and reducing the costs of production processes. The most commonly used methods for balancing the production line include the following [35]:  
* Redistribution of all or part of the observed operations to the previous or next operation;
* Improvement of the working methods and thereby, the reduction of the time per unit;
* Analysis of data regarding the duration of individual production operations using statistical distributions, allowing for the identification of the actual duration of production operations (especially at the planning stage of the production process).  
The activities described above may constitute subsequent stages of research, allowing for the selection of the best among the analyzed solutions using computer simulation tools.
# Computing RF Tree Distance over Succinct Representations
## 3 Methodology  
Our approach consists in using succinct data structures, namely encoding each tree using balanced parentheses and represent it as a bit vector. Each tree is then represented by a bit vector of size \(2n\) bits, where \(n\) is the number of nodes present in the tree. This space is further increased by \(o(n)\) bits to support efficiently primitive operations over the bit vector [29]. For comparing trees, we need to also store the label permutation corresponding to each tree. Each permutation can be represented in \((1+\varepsilon)n\log n\) bits while answering permutation queries in constant time and inverse permutation queries in \(O(1/\varepsilon)\) time [23]. Hence, each tree with \(n\) nodes can be represented in \(2n+o(n)+(1+\varepsilon)n\log n\) bits.