esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 4 Challenges and Opportunities
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Fog** & **Edge** & **Cloud** \\ \hline Operators & Users and cloud provider & Local enterprise or network & Cloud provider \\ Participating Nodes & Fog devices (switches, etc) & Edge devices & Fewer nodes spanning cloud to IoT \\ Service Type & Less global & Local & Global \\ Management & Distributed /centralized & Local business and service provider & Centralized \\ Hardware & Devices with virtualization facility (access points, routers, switches, servers) & Edge devices with compute capacity & Massive data centers and equipment \\ Computation Device & Any device capable of computation, networking, and storage & Edge devices with virtualization potential \\ Available Computing & Moderate & Moderate & High \\ Resources & Nature of Failure & Highly diverse & Highly diverse & Predictable \\ Main Driver & Academia/ Industry & Academia/industry & Academia/industry \\ User Connectivity & Mostly wireless & Mostly wireless & High speed (Both wired and wireless) \\ Distance from Users & Relatively close & Close & Far \\ \multirow{2}{*}{ \begin{tabular}{c} Internal Connectivity \\ \end{tabular}  } & Operate autonomously with intermittent & Operate autonomously with intermittent or no internet connectivity & Requires internet connectivity \\  & or no internet connectivity & intermittent or no internet connectivity & throughout service duration \\  & & & National Institute of Standards and Technology (NIST), Cloud Security \\ Main & & & Alliance (CSA), Distributed \\ Standardization & OpenFog Consortium, IEEE & - & Management Task Force (DMFT), \\ Entity & & & Open Commons Consortium (CCC), \\  & & & Global Inter-Cloud Technology Forum (GICTF) \\ Power Source & Battery/green energy/ & Battery/green energy/direct power & Direct power \\ Power Consumption & Low & Low & High \\ Application Type & High computation with lower latency & Low latency computation & Ample computation \\ Architecture & Decentralized/hierarchical & Localized/distributed & Centralized/hierarchical \\ Computation Capacity & Moderate & Moderate & High \\ Storage Capacity & Limited & Limited & Massive storage capacity \\ Availability & High & Average & High \\ Latency & Low & Low & Relatively high \\ Node mobility & High & High & Very low \\ Security/Vulnerability & Must be provided on & Must be provided along & Must be provided along \\  & participant nodes & Cloud-to-Things continuum \\ Server Location & Can be deployed at edge or dedicated & Near edge devices & Stationed in huge \\  & locations & & dedicated buildings \\ Number of & One/few & One & Multiple \\  & Hardware & WAN, LAN, WLAN, Wi-Fi, cellular & WAN, LAN, WLAN, Wi-Fi,...,.
## 4 Discussion
### Time-Varying Demand  
Given the actual practice at Svanehoj during the study period, the results at Figure 3 show Svanehoj's current inventory practice is the worst performing for, respectively, item 1 and item 2, while ranking as the second best for item 3. Looking at the ML models, Q-Learning delivers the best performance in terms of the total profit for all three products. Looking at the tables, one of the reasons why Q-learning is outperforming all the other methods is because it is extremely efficient at keeping the inventory levels very low by balancing order quantities and for the given demand. Ultimately leading to a much higher profit than the other methods. Lastly, the most complex method, DQN, does not show any improvements compared to Q-Learning. One issue that DQN seems to have is the model tends to procure higher quantities than the actual demand. From these comparisons, it is clear that different inventory management techniques have unique impacts on cost components and total profit for non-deteriorating items with time-varying demand. Furthermore, it is also visible that the Q-Learning model is superior to the DQN, EOQ, and actual practice.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models  
The physical model is constructed as illustrated in Figure 1. Specifically, Figure 1 presents the three-dimensional model of the PV panel cleaning robot, which primarily consists of the box structure, the main framework, and the sweeping component; Figure 1 illustrates the physical model of the PV panels for the most complex scenario regarding their relative positioning based on data collected from fieldwork. By analyzing the motion laws of photovoltaic panel cleaning robots during their operational processes, this study establishes a trajectory model, a smoothing processing model, and an optimization model to achieve the design and optimization of non-coplanar trajectories.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 4 Proposed Protocol
### Greedy Forwarding Route Establishment Strategy Based on Weight  
The weight of the strategy proposed in this section changed according to the changes in the neighbor table in VANET; thus, the proposed routing protocol was adaptive. This section comprehensively considered the distances between nodes and the direction of the movement of nodes, combined with the reliable node density and the cumulative communication duration of nodes, with the weight calculation method proposed as follows:  
\[G_{n}=w_{1}(\frac{D_{sd}-D_{nd}}{D_{sd}})+w_{2}\cos\theta+w_{3}\frac{RN_{n}}{S }+w_{4}T_{i} \tag{4}\]  
In Formula (4), G\({}_{\text{n}}\) defines the weight value of the node n. D\({}_{\text{nd}}\) is the Euclidean distance between the node n and the destination node d, while D\({}_{\text{sd}}\) is the Euclidean distance between the source node s and the destination node d. \(\theta\) is the angle between the speed vector V\({}_{\text{n}}\) of the node n and the direction vector L\({}_{\text{nd}}\) of the node n pointing to the destination node. RN\({}_{\text{n}}\) is the number of reliable neighbor nodes of the node n, while S is the area of the communication range of node n. w\({}_{\text{i}}\) is the weight coefficient, w\({}_{1}\)+w\({}_{2}\)+w\({}_{3}\)+w\({}_{4}\)\(=1\) and w\({}_{1}\), w\({}_{2}\), w\({}_{3}\), w\({}_{4}\)\(\in[0,1]\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Size** \\ \hline The package type & 2 bytes \\ The x co-ordinate of the destination & 8 bytes \\ The y co-ordinate of the destination & 8 bytes \\ The exact time when the location was last updated & 4 bytes \\ The x co-ordinate of entering the perimeter mode & 8 bytes \\ The y co-ordinate of entering the perimeter mode & 8 bytes \\ The perimeter mode flag & 1 byte \\ The x co-ordinate of the previous hop & 8 bytes \\ The y co-ordinate of the previous hop & 8 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 3: A format table of packet header.  
The Euclidean distance D\({}_{\text{sd}}\) between the two nodes is defined as follows:  
\[D_{sd}=\sqrt{\left(x_{s}-x_{d}\right)^{2}+\left(y_{s}-y_{d}\right)^{2}} \tag{5}\]  
The angle \(\theta\) between the two nodes is defined as follows:  
\[\theta=\cos^{-1}(\frac{V_{n}\times L_{nd}}{\parallel V_{n}\parallel\times \parallel L_{nd}\parallel}) \tag{6}\]  
In Formula (6), V\({}_{\text{n}}\) is the speed vector of node n, while L\({}_{\text{nd}}\) is the direction vector of node n pointing to the destination node d. The larger the \(\cos\theta\) value, the closer the direction of movement of the node is to the direction of movement of the destination node.  
The cumulative communication duration of node n for node s is defined in Formula (1), i.e., the larger the T\({}_{\text{i}}\) value, the closer the communication between the nodes n and s.  
Formula (7) can be used to determine whether a neighbor node is a reliable node:  
\[D_{sd}-D_{nd}\geq 0 \tag{7}\]  
We calculated the reliable node density via formula (8):  
\[\frac{RN_{n}}{S}=\frac{RN_{n}}{\pi r^{2}} \tag{8}\]
# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 3 Preliminaries
### Transfer Learning  
Transfer learning is a type of machine learning technique. Here, transfer learning aims at extracting the knowledge from one or more source tasks, and applies the knowledge to a target task. The key to transfer learning is achieving a remarkable improvement in performance by learning from similar tasks. In addition, it can avoid \"starting from scratch\" when training on an individual task, and can reduce the training time. Therefore, we can achieve better performance and reduce training time by using a small number of training samples for a new task. Due to this advantage, in our study, we use transfer learning to overcome the limited-data problem. We obtain a large source data and learn  
Figure 1: The structure of long short-term memory (LSTM) block.  
knowledge from them before transferring the knowledge into the target data (i.e., where data from only the target stock are used). A large source of data can be formed by mixing other stock data, and Section 5 will discuss this in detail.  
According to the availability of labels in the source dataset and the target dataset, transfer learning approaches are divided into three categories: inductive transfer learning, transductive transfer learning, and unsupervised transfer learning [49]. Table 1 shows the differences between these categories in transfer learning.  
In our case, inductive transfer learning is applied because labeled data in both the source data and the target data are available. Moreover, in this study, we first obtained a large source data from a large number of different stocks. Then, a base deep neural network was trained with obtained source data, and we attained a well-trained deep network based on validation performance. In the next step, a prediction model was constructed that has the same architecture as the base model. Thus, we transferred the trained parameters of the base model including weights and biases to initialize the prediction model. Finally, the prediction model was fine-tuned by using a small amount of target data and we optimized these parameters to achieve final prediction performance. Therefore, among the different approaches for inductive transfer learning setting (i.e., instance-transfer, feature-representation-transfer, parameter-transfer, and relational-knowledge-transfer) [49], parameter-transfer was chosen for our approach.
# Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
## 2 Methodologies
### The Proposed Bi-SCQLSTM for Dam Deformation Interval Pre Diction  
Based on the theoretical background described above, this section proposes a novel distribution-free dam deformation interval prediction model that integrates Bi-LSTM with the SCQP, termed the Bi-SCQLSTM. The Bi-LSTM architecture can convey hidden states in different time dimensions and deeply explore the nonlinear feature of dam deformation. On the other hand, the SCQP algorithm generates high-quality deformation PIs that meet both high valid coverage and ideal interval coverage width, which can fully adapt to dam deformation sequences with different distributions.  
The proposed Bi-SCQLSTM model is described in detail and shown in Algorithm 1, and the framework of the proposed model is summarized in Figure 4:  
Figure 4: The framework of proposed Bi-SCQLSTM for dam deformation PI forecasting.  
1. First, a raw dam monitoring series is interpolated, and the resulting dataset is divided into training samples and test samples, and min-max normalization is performed on the two sample sets independently.
2. Secondly, the training samples are used as the input data of the Bi-SCQLSTM. During this process, the training samples are further divided into two disjoint subsets, a training subset \(\mathcal{L}_{1}\) and a calibration subset \(\mathcal{L}_{2}\). Two function curves \(\hat{q}_{aUB}\) and \(\hat{q}_{aLB}\) are fitted to the training subset \(\mathcal{L}_{1}\) using the Bi-LSTM. The number of neurons in the output layer of the Bi-LSTM is set to 2, and the cost function is defined as pinball loss. The main idea behind this process stems from quantile regression.
3. The Bi-LSTM is then used as the underlying evaluator for the SCQP algorithm, which is used to calculate the consistency score \(E_{i}\). For finite samples \(Y_{i}\) from the calibration subset \(\mathcal{L}_{2}\), the consistency score \(E_{i}\) and corresponding empirical quantile \(Q_{1-\alpha}(R,\mathcal{L}_{2})\) will be obtained by Equation (14) and Equation (16), respectively. Then, the deformation PIs constructed by the model for the new exogenous variable \(X_{n+1}\) will be optimized by the \(Q_{1-\alpha}(R,\mathcal{L}_{2})\) to further adapt to the dam deformation series with variance variability and random fluctuation.
4. Finally, the deformation PIs are evaluated and compared by using the interval evaluation indicators PICP, MPIW, and CWC.
# Graphene Nanoplatelets Impact on Concrete in Improving Freeze-Thaw Resistance
## 4 Conclusions  
This paper researched the effect of GNP on the freeze-thaw performance of concrete. A series of experimental investigations were carried out. Concrete samples consist of 0%, 0.02%, 0.05%, 0.1%, 0.2%, 0.3%, and 0.4% graphene nanoplatelets were prepared for the rapid F-T tests. The workability, compressive strength, and durability graphene of concrete subjected to F-T cycles of the specimens were studied, and the main conclusions were as follows.  
1. The SEM micrographs indicate that the effect of GNP enhanced the freeze-thaw resistance of concrete. It can be concluded that graphene nanoplatelets with the appropriate additional acts effectively in enhancing the durability of concrete under freezing-thawing cycles.
2. Adding graphene nanoparticles could reduce the workability of graphene concrete, and the slump of the specimen decreases with the increasing nanoparticles addition.
3. Concrete with GNP displayed less mass loss during F-T actions, particularly the concrete with the 0.05% GNP addition. In addition, GNP concrete exhibited fewer damages on the sample's surfaces than the control concrete.
4. Adding graphene nanoparticles up to 0.3% could improve the compressive strength of concrete; before F-T test, the maximum increase of compressive strength in GC0.05 relative to that of GC0 was 22.40%. However, above 0.4%, the incorporation of GNP had a negative influence on enhancing the compressive strength of concrete. Furthermore, concrete with the appropriate additional GNP showed less compressive strength loss after 200 F-T cycles compare to the control specimens.  
[PERSON] wrote the manuscript, [PERSON] designed the theoretical framework, [PERSON] and [PERSON] performed the experiments, [PERSON] corrected the language. This research was funded by the National Natural Science Foundation of PR. China (Grant No.51308166), Shandong Provincial Natural Science Foundation (Grant No. ZR2019 MEE090) and Weihai Science and technology Development Plan Project (Grant No.2015 DXGJMS011). We thank the Analysis and Testing Center of Harbin University of Technology (Weihai) for assistance with the SEM analysis. The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**F-T Cycles** & **GC0** & **GC0.02** & **GC0.05** & **GC0.1** & **GC0.2** & **GC0.3** & **GC0.4** \\ \hline
0 & 0.00188 & 0.00224 & 0.00232 & 0.00229 & 0.00220 & 0.00209 & 0.00189 \\
10 & 0.00194 & - & - & 0.00236 & 0.00214 & 0.00203 & 0.00185 \\
20 & 0.00178 & - & - & 0.00217 & 0.00214 & 0.00199 & 0.00180 \\
30 & 0.00175 & - & - & 0.00195 & 0.00207 & 0.00195 & 0.00184 \\
50 & 0.00173 & 0.00187 & 0.00192 & 0.00185 & 0.00184 & 0.00174 & 0.00167 \\
100 & 0.01051 & 0.00163 & 0.00171 & 0.00161 & 0.00158 & 0.00154 & 0.00158 \\
150 & 0.00152 & 0.00156 & 0.00166 & 0.00157 & 0.00161 & 0.00151 & 0.00154 \\
200 & 0.00139 & 0.00152 & 0.00164 & 0.00151 & 0.00147 & 0.00144 & 0.00133 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Peak strain of concrete after F-T cycles.
## 4 Modeling Business Rules
### Representation of Regulatory Constraints in SPARQL  
We aim to model the way experts use CSTB guides and try to automate their know-how. It forces us to follow their interpretation and to establish a formal representation of regulations. SBVR describes the concepts and requirements regardless of their implementation. We chose to represent constraints into \"ASK\" SPARQL queries. This formalization allows the automation of the conformance of a document against these constraints (see Figure 5).  
To perform a transformation of SBVR rules into SPARQL queries we followed a number of defined steps and used a controlled-vocabulary, in our case we used OntoDT.  
As an example the following SBVR rules:  
* If the tile _has_ a slope equal to 70% then it is obligatory that the implementation is in Zone 1, in protected situation with a roof covering greater than 8 cm  
**Step 1:** We decompose the SBVR rule into a set of single sentences in order to decompose the different constraints expressed. So we can identify the antecedent and the consequent of our \"if-then\" rules as follows (Table 1):  
Figure 5: Transforming SBVR into SPARQL queries  
**Step 2:** We transform, into triple pattern, each formulation expressed in the antecedent and consequence. We used all concepts and properties of OntoDT (Table 2).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**BVR** & **SPARQL Triple pattern** \\ \hline  & minus \{ \\ The tile _has_ a slope &?tile dt:hasSlope?slope \\ The slope equal to 70\% & FILTER (xsd:integer(?slope)!= 70) \\ \hline The tile _has_ an implementation &?tile dt:hasImplementation?implementation \\ It is obligatory that implementation _is_ in an area &?implementation dt:hasZone?area \\ \hline The area types _is_ Zone! &?area rdf:type dt:Zone! \\ The implementation _has_ a situation &?implementation dt:hasSituation?situation \\ \hline The situation types _is_ protected &?situation rdf:type dt:Protégé \\ The implementation _has_ a roof covering greater &?situation ldt:hasRecovery rl \\ than 8 cm & FILTER (xsd:integer(?r1)\(>\)=8) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transforming SBVR rules into SPARQL triple pattern.  
Figure 6: SPARQL query of the antecedent (Example 1).
## 4 Results
In Table 10, the differences in the rejected hypothesis are presented, indicating which age group the differences favored. To determine this, the rank test was applied again. The results indicate that, in general, students aged 21-30 years exhibited the highest acceptance of the technology analyzed in this study. \begin{table}
\begin{tabular}{c c c} \hline  & **Age Group** & **N** & **Mean Rank** \\ \hline  & 18–20 years & 57 & 96.65 \\  & 21–30 years & 99 & 106.37 \\ EE (Effort Expectancy) & 31–40 years & 21 & 63.62 \\  & 41–50 years & 8 & 90.94 \\  & Over 50 years & 9 & 90.22 \\  & Total & 194 & \\ BI (Behavioral Intention) & 18–20 years & 57 & 84.88 \\  & 21–30 years & 99 & 104.93 \\  & 31–40 years & 21 & 79.74 \\  & 41–50 years & 8 & 114.06 \\  & Over 50 years & 9 & 122.44 \\  & Total & 194 & \\ UB (Usage Behavior) & 18–20 years & 57 & 91.21 \\  & 21–30 years & 99 & 107.09 \\  & 31–40 years & 21 & 76.26 \\  & 41–50 years & 8 & 61.81 \\  & Over 50 years & 9 & 113.11 \\  & Total & 194 & \\ \hline \end{tabular}
\end{table} 
Table 10: Rank test between “Usage Behavior”, “Behavioral Intention”, and “Effort Expectancy” dimensions and students’ age. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dimension** & **Gender** & **N** & **Mean Rank** & **Sum of Ranks** \\ \hline  & Male & 70 & 107.90 & 7553.00 \\ BI (Behavioral Intention) & Female & 124 & 91.63 & 11,362.00 \\  & Total & 194 & & \\ UB (Usage Behavior) & Male & 70 & 109.64 & 7675.00 \\  & Female & 124 & 90.65 & 11,240.00 \\ \hline \end{tabular}
\end{table} 
Table 8: Rank test between “Behavioral Intention” and “Usage Behavior” dimensions and students’ gender. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **PE** & **EE** & **SI** & **FC** & **BI** & **UB** \\ \hline Kruskal-Wallis H & 1.826 & 11.092 & 7.760 & 6.437 & 9.552 & 11.126 \\ df & 4 & 4 & 4 & 4 & 4 \\ Sig. & 0.768 & 0.026 (*) & 0.101 & 0.169 & 0.049 (*) & 0.025 (*) \\ Kruskal-Wallis H & PE & EE & SI & FC & BI & UB \\ \hline \end{tabular} Note: * = significant differences at the 0.05 level.  
\end{table} 
Table 9: Significant differences based on students’ age across the different UTAUT dimensions. Finally, we tested H\({}_{0}\), referring to the existence or absence of significant differences across the various UTAUT2 dimensions based on the students' field of study. The Kruskal-Wallis values presented in Table 11 indicate that none of the H\({}_{0}\) hypotheses can be rejected at \(p\leq 0.05\). Consequently, we can conclude that there are no significant differences between the various UTAUT dimensions regarding the acceptance of ChatGPT and the students' field of study.
# Experimental Verification of a Compressor Drive Simulation Model to Minimize Dangerous Vibrations
## 2 Materials and Methods
### Description of the Mechanical System  
The subject of the research is a laboratory mechanical system driving a compressor by an electric motor (Figure 2). It is a mechanical system operating with variable torque at constant load. Between the rotating masses there is a pneumatic flexible coupling whose dynamic torsional stiffness can be varied during operation by changing the air pressure [27].  
The revolutions of the electric motor (1) are continuously vector controlled by a frequency converter. The electric motor drives a three-cylinder ORLIK 3 JSK-75 piston compressor (6) through a 1:1 gearbox (2) and a 4-1/70-T-C type pneumatic flexible coupling (5). The compressor is mounted on a rubber layer and has no flywheel; therefore, its impact on dynamics is higher. The compressed air from the compressor flows into an air pressure vessel with a capacity of 300 L. The compressed air is fed to the pneumatic coupling via a rotation supply (7). The magnitude of the torsional vibrations is measured by a torque sensor type 7934 MOM KALIBERGYAR (3). Table 1 describes the technical parameters of some members of the mechanical system.  
The air pressure in the compression chamber of the pneumatic flexible coupling is measured by a pressure sensor. The signals from both sensors are amplified and processed by a universal eight-channel measuring device MX840 from the manufacturer HBM, and the data are subsequently sent to a PC (Figure 3) [43]. The operating revolutions of the mechanical system are \(n_{P}=600\) min\({}^{-1}\).  
Sensors used:  
1. Torque sensor type 7934, manufacturer MOM KALIBERGYAR, measuring range 0-500 N\(\cdot\)m, accuracy of the torque sensor is 0.1% of its measuring range, i.e., 0.5% (combined failure--nonlinearity, hysteresis, and reproducibility).
2. pressure sensor type MBS 3000, manufacturer Danfoss, overpressure measurement range 0-1 MPa, accuracy of the MBS 3000 sensor with metal diaphragm is 0.5% of its measurement range, i.e., 5 kPa (combined failure--non-linearity, hysteresis, and reproducibility).  
Figure 2: Experimental model of a mechanical system: 1—electric motor, 2—gearbox, 3—torque sensor, 4—bearing housing, 5—pneumatic flexible coupling, 6—three-cylinder piston compressor, 7—rotation supply.  
\begin{table}
\begin{tabular}{c c} \hline
**Elements of the Mechanical System** & **Parameters** \\ \hline Three-phase asynchronous motor & \\
1 LE1001-1 DB234 AF4-Z & \\ SIEMENS & Nominal revolutions \(n_{N}\) = 1470 min\({}^{-1}\) pri 50 Hz \\ Mass moment of inertia \(I_{AM}\) = 0.065 kg\(\cdot\)m\({}^{2}\) & \\ Synchronous revolutions \(n_{S}\) = 1500 min\({}^{-1}\) & \\ Nominal power \(P_{N}\) = 11 kW & \\ \hline \end{tabular}
\end{table}
Table 1: Basic parameters of some members of the mechanical system.