esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
From the results of the study, insights for the design of compact gearboxes can be gained. The NSGA-II method was also applied by [PERSON] et al. [21] to solve the multi-objective optimization problem of a two-stage helical gearbox for minimum volume and maximum load. The results of the multi-objective optimization of the tooth surface in helical gears using the response surface method were presented by [PERSON]. [22]. The best or optimal level of the selected criterion is determined by single-objective optimization, which is an absolute optimization. A multi-objective optimization problem is one with two or more simple goals (or criteria). As a result, the solution to the multi-objective optimization problem cannot best satisfy all criteria simultaneously. For instance, it is not possible to meet both the efficiency and cost requirements of the gearbox. In simple terms, determining a solution to a problem that is both \"white\" and \"black\" is impossible; only a \"gray\" solution can be determined. The gray solution is the one that falls between the best and worst solutions, or between \"white\" and \"black\" in the multi-objective optimization problem. As a result, it is known as optimization based on gray relation analysis. The original Taguchi method is used to solve the single-objective problem, and the Taguchi method and gray relation analysis are required to solve the multi-objective optimization problem. While numerous studies have focused on multi-objective optimization for helical gearboxes, the identification of optimal main design factors for such gearboxes has not received adequate attention. Furthermore, previous research on multi-objective optimization for helical gearboxes has not demonstrated the relationship between optimal input factors and total gearbox ratio. This is a critical issue to consider when designing a new gearbox. In this paper, we present a multi-objective optimization study for a two-stage helical gearbox, considering two single objectives: minimizing gearbox mass and maximizing gearbox efficiency. The proposal of five optimal main design factors for the two-stage helical gearbox is the most significant result of this research. These variables include the CWFW for both stages, the ACS for both stages, and the first stage's gear ratio. Furthermore, by combining the Taguchi method and the GRA in a two-stage process not previously described, we present a novel approach to addressing the multi-objective optimization problem in gearbox design. Additionally, a link between optimal input factors and the total gearbox ratio was proposed.
# Efficient Processing of \(k\)-Hop Reachability Queries on Directed Graphs
## 4 Experiment

Table 2: Comparison of algorithms with respect to kHRQ processing on directed graphs with SCCs, where \(G_{k}(u)\) is the subgraph of \(G\) that can be reached from \(u\) in \(k\) hops, \(D\) is the subgraph of \(G\) obtained by removing vertices in \(S\), \(\hat{a}_{max}^{in}(\hat{a}_{max}^{out})\) is the maximum in-degree (out-degree) for all vertices, and \(S=S\cup S_{1}\cup S_{2}\subseteq V\). \(S_{1}\) is the set of vertices with out-degree greater than \(d\), and \(S_{2}\) is the set of vertices with in-degree greater than \(d\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & 1 \(V1\) & 1 \(V1\) & \(deg_{avg}\) \\ \hline Human & 38,811 & 39,576 & 1.02 \\ WikiTalk & 2,281,879 & 2,311,570 & 1.01 \\ Twitter & 18,121,168 & 18,359,487 & 1.01 \\ Web-uk & 22,753,644 & 38,184,039 & 1.68 \\ BerKStan & 685,230 & 7,600,595 & 11 \\ EX6 & 6545 & 147,840 & 23 \\
3 dtube & 45,330 & 1,584,144 & 35 \\ Bio & 36,417 & 1,884,276 & 52 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of real datasets, where \(d\) denotes the average degree (\(deg_{avg}=|E|/|V|\)). downloaded from the same web page ([[https://networkrepository.com/](https://networkrepository.com/)]([https://networkrepository.com/](https://networkrepository.com/)), accessed on 13 March 2021) with large densities (\(deg_{avg}>20\)). These dense graphs were used to test the performance when \(deg_{avg}\) increased. **Workloads:** We tested the performance of kHRQ answering using an equal workload. The workload in our study consisted of 1,000,000 queries of which 500,000 were considered reachable and of which 500,000 were considered unreachable. In order to avoid bias towards unreachable queries, we did not use a random workload that would select pairs of vertices at random. This approach is known to heavily favor unreachable queries in practice [1]. Furthermore, it is unlikely that most queries in a real-world scenario would be unreachable, as vertex pairs tend to have a certain level of connectivity [9]. To generate our equal workload, we adopted the following procedure: First, we generated the set of unreachable queries by sampling vertex pairs with equal probability until we reached the required ratio, and then we tested each query using one of the existing algorithms. For the reachable queries, we randomly selected a vertex, \(u\), in each iteration and then selected a vertex, \(v\), from the transitive closure of \(u\). This process continued until we generated the required number of reachable queries.
## 2 Background
### Direct-Energy-Loss Method  
Chemical effects within the cell must be considered as they can influence heat release. One is the reversible heat effect. It is caused by the intercalation of lithium ions in the lattice [3]. At the beginning of a charging cycle, it is endothermic and becomes exothermic during most of the charging cycle. During discharge, this effect is the opposite. If the cell is charged and discharged symmetrically, the reversible heat effect of the cell is cancelled [7]. To measure the internal resistance with the direct-energy-loss method, a charge-neutral profile is applied to the cell/battery. The charged energy \(E_{dbg}\) and the discharged energy \(E_{ddg}\) were determined with the measured values of the charging voltage \(U_{dbg}\) and discharging voltage \(U_{ddg}\), as well as the charging current \(I_{chg}\) and the discharging current \(I_{ddg}\). The energy loss for a single charge/discharge step results from Equations (2) and (3).  
\[Q_{loss}=E_{chg}-E_{ddg} \tag{2}\]  
\[Q_{loss}=\left(U_{chg}I_{clg}\Delta t_{chg}\right)-\left(U_{ddg}I_{dcg} \Delta t_{dchg}\right) \tag{3}\]  
With the energy loss, the internal resistance \(R_{i}\) can be calculated, i.e., dividing the energy loss \(Q_{loss}\) through the integrated current that was applied during a measurement cycle (see Equation (4)),  
\[R_{i}=\frac{Q_{loss}}{\int_{I2}^{t1}\left(I(t)\right)^{2}dt} \tag{4}\]  
The cells's SoC must not change during the measurement. Otherwise, the values of the charged energy and the withdrawn energy cannot be compared.
## 3 Methodology
### Model Optimization and Deployment Strategies  
Although each component of the MSWT-DN model can effectively complete the task, its overall complexity may limit its deployment in resource-constrained environments, especially in scenarios with limited computing power, such as edge devices or mobile platforms. To address this challenge and ensure the efficient operation of the model in these environments, the following optimization strategies can be adopted:Model pruning: by removing redundant or unimportant parameters, model pruning can significantly reduce storage requirements and improve inference speed without significantly affecting performance. This makes the model more suitable for real-time deployment, especially in devices with limited computing resources.
* Quantization: quantization reduces memory usage and computational overhead by reducing the precision of weights and activations (for example, converting floating point numbers to low-precision integers). It is particularly suitable for devices with limited hardware capabilities, which can speed up computation and save memory.
* Adaptive computing: adaptive computing allows the model to dynamically adjust the amount of computation based on the complexity of the input. For simple inputs, the model uses fewer layers or modules, thereby reducing computational requirements; for complex inputs, the full model is used to maintain high accuracy, which helps optimize the use of computing resources.
* Hardware acceleration: using GPU, TPU or AI-specific hardware (such as NPU) to accelerate the inference process can significantly improve computing efficiency, especially for real-time applications and resource-constrained devices.  
In summary, although the MSWT-DN performs well in rainfall tasks, its large number of parameters and complexity pose challenges when deployed in resource-constrained environments. Through optimization strategies such as model pruning, quantization, adaptive computing, and hardware acceleration, its computing efficiency and deployment feasibility can be significantly improved, ensuring that the model runs efficiently in a variety of practical application scenarios.
## 4 Results
### Variations in Feedback Effectiveness Across Subjects  
An ANOVA test evaluated if AI-driven feedback created different levels of effectiveness between students studying STEM subjects versus students studying Language Education. Figure 16 shows the results of post-test scores among STEM students as well as Language students.  
Figure 14: Comparison of pre- and post-intervention cognitive load.  
The study findings revealed that STEM students received better outcomes from AI-generated feedback as their post-test scores reached 81.4% while Language Education student scores settled at 78.2%. Both disciplines received lower scores from the control group participants because they scored 67.2% in STEM and 64.8% in Language. Subject domain emerged as a significant factor that affected feedback effectiveness according to the ANOVA evaluation (\(p=0.028\)).  
Figure 17, a Sankey diagram, visualizes how AI-driven feedback complexity--categorized as low (basic error identification), moderate (hints and guided corrections), and high (adaptive learning path suggestions)--is distributed between STEM and Language Education students. The straight and curving lines represent the flow of feedback instances from the AI system to each subject domain, with line width proportional to the volume of  
Figure 16: Post-test scores of students with AI feedback and without it for STEM and language education students.  
Figure 15: Relationship between initial post-test scores and delayed recall test scores, with average weekly AI feedback interactions indicated for the experimental group and traditional feedback for the control group.  
feedback delivered (e.g., STEM students received a higher proportion of high-complexity feedback, approximately 50% of total instances, due to the analytical nature of topics like programming and physics). The curving reflects transitions between complexity levels and subject areas, illustrating that STEM students received more varied and complex feedback, while Language Education students predominantly received moderate feedback (e.g., 60% of instances focused on grammar and composition). This distribution aligns with the ANOVA findings (\(p\) = 0.028), highlighting subject-specific tailoring of AI feedback to address conceptual demands.  
The AI-driven feedback engine produced more complex feedback content consistently for students taking STEM classes because it used the subject matter to adapt feedback depth accordingly. Language education students received standardized feedback which dedicated most attention to linguistic aspects of their work.
## 3 Results
### Microwave-Assisted Extraction Optimization Results
#### 3.1.2 Extraction Temperature  
In subsequent experiments, the efficiency of the extraction method was studied at three temperature levels, 60, 80, and 100 \({}^{\circ}\)C, when using ACN and AcOEt as extraction solvents. The temperatures of 60 and 80 \({}^{\circ}\)C with AcOEt resulted in the acceptable extraction for 57% of the compounds. The percentage was reduced to 53% when the extraction temperature was set at 100 \({}^{\circ}\)C. On the other hand, the extraction with ACN at 60 \({}^{\circ}\)C gave satisfactory  
Figure 1: Percentage of pesticides and mycotoxins with acceptable recoveries in the range 70–120% when using acetonitrile (ACN), ethyl acetate (AcOEt), methanol (MeOH), and water as extraction solvents.  
results for 80% of the compounds. This percentage decreased to 62.5% when the extraction was performed at 80 \({}^{\circ}\)C, while at 100 \({}^{\circ}\)C the obtained RE% were adequately low, possibly due to degradation. The percentages of analytes with acceptable RE% at different extraction temperatures are graphically presented in Figure 2. The obtained RE% of all individual analytes using ACN and AcOEt as extraction solvents are presented in Tables S3 and S4, respectively.  
The results demonstrate that ACN is the most appropriate solvent and the temperature of 60 \({}^{\circ}\)C is optimal. The determination of patulin was not successful. Even though LC-MS analysis demonstrates several advantages in the analysis of mycotoxins, offering high sensitivity, the presence of interfering compounds competing with the analytes in the ionization process prevents accurate quantification [28]. In general, apples, regardless of their variety, have a high content of sugars and pigments, which are extracted simultaneously with patulin and interfere with chromatographic analysis, infecting the system [29]. Thus, a clean-up method should be included to avoid the significant effects of the matrix. Considering that a clean-up method was not used in the present study, patulin was not included in the next experiments. In addition, CIT RE% were found to be adequately low in most trials. The results show that when the extraction is carried out at 60 \({}^{\circ}\)C with AcOEt, the highest recovery value is 63%. It was shown, however, that AcOEt is not an appropriate solvent for the extraction of the majority of the studied contaminants, and for this reason CIT was also not included in the subsequent assay experiments.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
###### Abstract  
Recently, non-orthogonal multiple access (NOMA) has become prevalent in 5G communication. However, the traditional successive interference cancellation (SIC) receivers for NOMA still encounter challenges. The near-far effect between the users and the base stations (BS) results in a higher bit error rate (BER) for the SIC receiver. Additionally, the linear detection algorithm used in each SIC stage fails to eliminate the interference and is susceptible to error propagation. Consequently, designing a high-performance NOMA system receiver is a crucial challenge in NOMA research and particularly in signal detection. Focusing on the signal detection of the receiver in the NOMA system, the main work is as follows. (1) This thesis leverages the strengths of deep neural networks (DNNs) for nonlinear detection and incorporates the low computational complexity of the successive interference cancellation (SIC) structure. The proposed solution introduces a feedback deep neural network (FDNN) receiver to replace the SIC in signal detection. By employing a deep neural network for nonlinear detection at each stage, the receiver mitigates error propagation, lowers the BER in NOMA systems, and enhances resistance against inter-user interference (IUI). (2) We describe its algorithm flow and provide simulation results comparing FDNN and SIC receivers under MIMO-NOMA scenarios. The simulations clearly demonstrate that FDNN receivers outperform SIC receivers in terms of BER for MIMO-NOMA systems.  
Keywords:NOMA; DNN receiver; multi-user system; MIMO +
Footnote †: journal: Electronics Electronics
## 2 System Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(\mathbf{\Delta}\) & UAV trajectory coordinates, denoted by \([\mathbf{\Delta}_{1},\mathbf{\Delta}_{2},\cdots,\mathbf{\Delta}_{n}]\) \\ \(\mathcal{H}\) & UAV’s altitude in meters (m) \\ \(\mathbf{V}\) & Number of vehicles \(\{\mathbf{V}_{1},\ldots,\mathbf{V}_{n}\}\) \\ \(\mathbf{\Delta}_{(x,y)}\) & UAV trajectory; is a set of sequential coordinates \\ \(\mathcal{T}\) & UAV’s flying time \\ \(\tau\) & Slot duration for communication between vehicles and UAV \\ \(\mathcal{g}_{u\to v}^{(t)}\) & Downlink channel gain from UAV to a vehicle \\ \(\left|h_{u\to v}^{(t)}\right|^{2}\) & Channel coefficient \\ \(\sigma_{u\to v}^{(t)}\) & Large-scale fading component \\ \(\mathbf{x}_{v}^{(t)}\) & Location of vehicle \(v\) at slot \(t\) \\ \(\phi_{u\to v}^{(t)}\) & Standard deviation in log-normal shadowing \\ \(\gamma_{0}^{(t)}\) & SINR at receiver at time slot \(t\) \\ \(\mathbf{b}_{u}^{(t)}\) & Communication bandwidth available in a transmission window \\ \(\mathbf{p}_{u}^{(t)}\) & UAV’s available power in a TTI \\ \(\mathcal{N}^{2}\) & Power spectral density of noise \\ \(\mathcal{C}_{v}^{(t)}\) & Downlink spectral efficiency of vehicle \(v\) at time \(t\) \\ \(\kappa_{u}\) & Rician K-factor \\ \(c_{q}\) & Bin set capacity \\ \(a_{\mathbf{v}}\) & Packet set transmitted from a vehicle \\ \(\mathbf{\Psi}_{i}\) & Size of each packet comprising PFL model hyper-parameters \\ \(\zeta_{k}\) & Decision variable to denote if packet \(i\) is assigned to queue \(k\) \\ \(\phi_{k}\) & Decision variable to denote whether bin \(k\) is utilized \\ \(\mathbf{\mathcal{D}}^{(t)}\) & Total delay encountered by a packet \\ \(d_{v}^{t}\) & Delay experienced by a packet communicated from vehicle \(v\) \\ \(\varsigma_{i,j}^{(k)}(t)\) & Binary variable to identify a vehicle scheduled for transmission or waiting state \\ \(\mathbf{\psi}^{(t)}\) & Vector consisting of different packets comprising PFL model hyper-parameters \\ \(\tau_{d}\) & Delay experienced by a vehicle while downloading global model hyper-parameters \\ \(\tau_{u}\) & Delay experienced by a vehicle while uploading local model hyper-parameters \\ \(t_{i}\) & Time duration of the TTI under consideration \\ \(p_{i,j}\) & Power consumption of a UAV in a TTI \\ \(a,b\) & Major and minor axes of UAV’s elliptical path \\ \(\alpha\) & Tuning factor to expand or contract the elliptical trajectory \\ \(\mathcal{D}_{n}\) & Dataset with \(n\) samples \\ \(T_{r}(x)\) & Trajectory storage vector \\ \(\mathcal{A}\) & Set of available actions for UAV \\ \(V^{*}(s_{t})\) & State value \\ \(\mathcal{Q}^{*}(s_{t},a_{t})\) & Action value that implies the expected cumulative reward \\ \(\mathcal{R}(s_{t},a_{t})\) & Accumulated reward for state-action pair \\ \(\mathcal{H}(\tau_{\phi})\) & Policy learned by the UAV at height \(\mathcal{H}\) \\ \(\gamma_{d}\) & Discount coefficient \\ \(\varphi\) & Personalization parameter \\ \(\mathbb{E}_{a_{t}\sim\pi}\) & Expectation over state \(s\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of symbols used in this paper.
## 3 Results and Discussion
### Optimization of HS-SPME
#### 3.2.2 The Effect of Extraction Time  
The extraction time affects the distribution of VOCs between the sample solution and SPME coating and hence directly affects the analytical performance. The extraction time is related to the volatility of compounds and the adsorption dynamics of the SPME fiber [28]. A longer extraction time is conducive to the volatiles' occupying more space on the fiber, but prolonging the occupation time of all positions will not improve the preconcentration efficiency. Conversely, it could even trigger desorption [29]. However, an extraction equilibrium is not necessarily reached in order in an effective extraction, and a balance between the sensitivity and extraction time must be considered. Figure 4b shows that TA, SA and TN increase steadily with the extension of extraction time from 10 to 30 min. There was no significant difference in SA values obtained from 30, 40 and 60 min. Therefore, 30 min was considered to be the optimal extraction time.
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 4 Implementation and Experimental Results
### Experiment Setup and Results
#### 4.2.1 Single Fog Node and Single Consumer Experiment
In our experiments, we normalized these values given that the offloading time to a specific fog node is approximately equal to the offloading time to a specific CDN. 2. Similar to the cloud CDNs, the fog node can serve more than one edge device, making the offloading time cost negligible to the overall enhancement provided. 3. The offloading process itself is an ongoing process. Whenever a resource is updated, the \(FW\) is notified and pulls the updated resources again. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Website URL** & **Resource Size (Approx.)** & **Website Classification** \\ \hline [[https://www.gampeedia.com/](https://www.gampeedia.com/)]([https://www.gampeedia.com/](https://www.gampeedia.com/)) & \(\approx\)7 MB & Video Gaming \\ \hline [[https://www.foxnews.com/](https://www.foxnews.com/)]([https://www.foxnews.com/](https://www.foxnews.com/)) & \(\approx\)21.5 MB & News \\ \hline [[https://www.espn.com/](https://www.espn.com/)]([https://www.espn.com/](https://www.espn.com/)) & \(\approx\)22.1 MB & Sport News \\ \hline [[https://www.bbc.com/](https://www.bbc.com/)]([https://www.bbc.com/](https://www.bbc.com/)) & \(\approx\)8.4 MB & News \\ \hline [[https://flickr.com/](https://flickr.com/)]([https://flickr.com/](https://flickr.com/)) & \(\approx\)28 MB & Photo Sharing \\ \hline [[https://www.youtube.com/feed/explore](https://www.youtube.com/feed/explore)]([https://www.youtube.com/feed/explore](https://www.youtube.com/feed/explore)) & \(\approx\)12 MB & Video Streaming \\ \hline \hline \end{tabular}  
Website accessed on 14 December 2021.  
\end{table} 
Table 2: List of the websites studied.