esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analytical Method Validation  
The method was validated in terms of the linearity of the calibration function, accuracy, precision, limits of detection (LOD), limits of quantitation (LOQ), and ME. Briefly, six TMA and DMA concentrations points were used (2-20 \(\mu\)g/mL range). Linearity was evaluated by the suitability of the function model, comparing the experimental and theoretical Fisher value at 95%. If the dataset expressed a linear function, the condition \(\mathrm{F_{theo}}>\mathrm{F_{exp}}\) is fulfilled [40]. In this work, the described  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **CMP 1** & **LSL 2** & **USL 3** & **CpK 4** \\ \hline \multirow{2}{*}{HS-SPME} & TPA 5  & TPA & 0.03 & 0.18 & 1.47 \\  & IP & 0.02 & 0.20 & 1.49 \\ GC-MS & Resolution & 0.04 & 0.17 & 1.38 \\  & TF 7  & 0.02 & 0.19 & 1.36 \\  & SF 8  & 0.05 & 0.17 & 1.37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Method control results.  
Figure 4: Established limits and results of the capability analysis, for total peak area and intermediate precision.  
condition was achieved for both amines with a polynomial equation of second degree. The ME was studied using the standard additions method. Repeatability and intermediated precision were used for precision determination, through inter-and intraday variation measurements.  
LOQ and LOD were calculated through the regression curve slope and standard deviation of the interception. Table 3 summarizes the obtained results for all the evaluated parameters and confirms that the results are under the stipulated values of the guidelines (ME: 70-125%; intermediate precision and repeatability under 15% RSD; recovery: 80-115%) [25; 26].  
Figure 5 shows the chromatographic profile of targeted amines obtained with the optimal method conditions.  
Figure 5: Chromatographic profile of targeted amines with the optimal method conditions (**a**) total ion chromatogram results; (**b**) TMA ion selection 58 m/z. (**c**) DMA ion selection 44 m/z.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **TMA** & **DMA** \\ \hline
**Ion (m/z)** & 58 & 44 \\
**Retention time (min)** & 4.12 & 4.23 \\
**Concentration range (\(\mu\)g/mL)** & 2-20 & 2-20 \\
**Regression equation** & Y = \(11143\)x\({}^{2}\) + \(2\times 10^{7}\) - \(1\times 10^{7}\) & Y = \(11153\)x\({}^{2}\) + \(2\times 10^{7}\) - \(3\times 10^{7}\) \\ \(R^{2}\) & 0.9504 & 0.9989 \\
**Linearity test (F\({}_{\text{theo}}\)/F\({}_{\text{exp}}\))** & 3.0 1 & 15.4 1 \\
**LOD (\(\mu\)g/mL) 5** & 0.4 & 0.6 \\
**LOQ (\(\mu\)g/mL) 6** & 1.3 & 2.0 \\ Matrix effect (\%) & 105 & 109 \\
**Repeatability (\% RSD 7)** & 8.2 2 6.0 3 9.6 4 ** & 8.3 2 11.2 3 7.4 3 \\
**Intermediate Precision (\% RSD 7)** & 8.4 2 7.1 3 9.7 4 ** & 9.6 2 13.6 3 8.8 4 \\
**Recovery (\%)** & 93 - 98 3 99 4  & 90 3 93 2 95 ** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters results of the HS-SPME/GC-MS method for quantification of TMA and DMA in fish.
## 2 Review
### Machine Learning-Based Techniques
In [26], OCC was done by using generative adversarial network (to generate defect patterns and to train normal/defective pattern discriminator) and the introduction of additional blocks to map the image analyzed to latent space and back. A convolutional denoising autoencoder (CDA) [27] was used for extraction of essential features and producing a representation of defect-free fabric patches; the autoencoder (AE) tries to generate an output image as similar as possible to the input image by producing an intermediate internal form (i.e., feature vector, which is a compressed representation of the input, also called a signature). The assumptions are: (a) small differences between the input and reconstructed output images correspond to tolerable variations, and (b) if the autoencoder was trained only on normal samples, it would extract knowledge relevant only to these defect-free samples, hence, it reconstructs anomaly with a high error. Thus, based on the value of residual between the original input and response output, a decision about a defect presence could be performed, and the location of the defect can be predicted because there a large reconstruction error occurs. For PCBs defect detection, usually a PCB does not have a small size repeatable pattern, thus, reconstruction of the whole PCB by autoencoder might produce a large error. In [28], a set of signatures (the output of the \"bottleneck\" network layer) was used instead of calculation of the reconstruction error of the AE. It is assumed that normal and defective classes of samples are separated in the multidimensional feature space of signatures, and a set of normal signatures forms a compact cluster or several clusters in this space. A test sample is considered a defect if the distance between its signature and the cluster of normal signatures is high, otherwise it may be considered normal. The AE built had 6 convolutional layers and small number of parameters (less than 30,000). A custom-based architecture was designed for solder defect detection. It showed high accuracy of 89%. The training set contained 1240 normal solder samples. The key distinction of our method of technical defect analysis from traditional image classification lies in the questions: \"Does the input look like an object of a certain class (e.g., PCB)?\" vs. (as in our work) \"Does the input comply to the defect-less PCB design?\". We were motivated by the approach presented in [28], based on a small amount of training data, consisting of only normal samples, to form a compact set of normal signatures. A combination of unsupervised learning and transfer learning approaches was employed to create signatures. We analyze the accuracy of the method for different number of training samples and discuss the applicability of the approach and what to expect when the training data are small.
## 3 System Model
### Service Coverage Model  
The heterogeneous nature of the SEC network necessitates distinct coverage models for UAVs and LEO satellites, reflecting their different mobility patterns and communication characteristics.
## 3 Results and Discussion
### Application of Artificial Intelligence, Predictive Analytics, and Digital Twins
#### 3.4.1 AI-Based Forecasting and Dispatch Optimization  
Accurate forecasting of renewable generation and load demand is critical for optimizing microgrid operations. AI-based models, including machine learning (ML) and deep learning techniques, have demonstrated superior performance in handling complex forecasting tasks under uncertain and dynamic conditions [12; 13; 79]. Studies report successful applications of neural networks, support vector machines, and reinforcement learning algorithms for short-term load and renewable generation forecasting, achieving higher accuracy compared to traditional statistical models [14; 80; 81].  
AI-driven dispatch optimization strategies contribute to more efficient energy management by minimizing operational costs and maximizing the utilization of RES [24; 25; 26]. Furthermore, hybrid models that combine AI methods with metaheuristic algorithms, such as genetic algorithms and particle swarm optimization, have been developed to improve dispatch efficiency in multi-objective optimization scenarios [50; 51; 82]. Despitethese advances, challenges related to model interpretability and limited data availability persist [41; 52; 53].
## 4 Energy Consumption of Different Transport Schemes
### Energy Consumption per Unit Coverage
#### 4.2.2 Urban Area  
Figure 11a,b show the power consumption per square km for the urban areas. Compared to Figure 10a,b, the results are significantly different even though they follow a similar trend. In the urban areas, the baseband-over-fiber scheme is still the most energy inefficient. Due to the high density of mobile users in the urban areas, micro base stations are commonly used with smaller cell size and lower transmitting power (\(<\)25 dBm) [45]. This falls on the left side of the red dashed line (transmitting power = 25 dBm) in Figure 11a,b. Therefore for the more dense urban areas where microcells are used, analog transport schemes are more energy-efficient compared to digitized transport; with analog RF-over-fiber being the most energy-efficient among the five schemes investigated.  
Figure 11: Power consumption per square km coverage for urban area for the five different transport schemes plotted as a function of (**a**) base station transmitting power and (**b**) cell size (With kind permission from Springer Science and Business Media).  
Figure 10: Power consumption per square km coverage for rural area for the five different transport schemes plotted as a function of (**a**) base station transmitting power and (**b**) cell size (With kind permission from Springer Science and Business Media).
# Rapid and Cost-Effective Fabrication and Performance Evaluation of Force-Sensing Resistor Sensors
## 5 Conclusions  
This research successfully demonstrated the feasibility of fabricating FSR sensors using cost-effective materials such as pencil graphite and silicone sealant. The hand-made sensors, particularly those utilizing a mixture of 9B pencil graphite and silicone sealant, showed performance characteristics that were comparable to commercial sensors. The results validate the potential of these alternative materials in producing efficient and reliable force-sensing devices. The cost-effectiveness and accessibility of the materials used in this study make the proposed fabrication method highly advantageous for various applications, particularly in fields where large-scale deployment is necessary. Future research should focus on refining the fabrication process to reduce noise levels and further enhance sensor performance.  
In conclusion, the study provides a promising outlook for the development of low-cost FSR sensors, highlighting the potential for widespread adoption of these alternative materials in the force-sensing industry. The findings pave the way for innovative approaches to force measurement, which could significantly impact the design and implementation of sensors in diverse applications.  
Conceptualization, B.K. and K.L.; methodology, J.J.; software, K.L.; validation, K.L. and J.J.; formal analysis, K.L.; investigation, J.J.; writing--original draft preparation, J.J.; writing--review and editing, B.K.; supervision, B.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by research grants from Daegu Catholic University in 2022.  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
All authors declare that they have no conflicts of interest.
## 3 Algorithm Design  
### Game Theory Model  
Since the MCRCS is in the regional communication mode, we pay more attention to the overall communication capability in the region rather than the communication efficiency of a single SU. Therefore, we used the game theory for spectrum allocation.  
The game theory model is often used to analyze the situation in which the users compete with each other in the cognitive radio system in order to maximize the benefits. The users participate in the competition to make decisions on their own known information. The spectrum allocation model based on the game theory is expressed as below:  
\[G=\left\{N;\left\{S_{i}\right\}_{i\in N};\left\{U_{i}\right\}_{i\in N}\right\} \tag{1}\]  
where \(N\) represents the collection of game participants; \(S_{i}\) represents the policy set for the game participant \(i\); and \(U_{i}\) represents the profit of the game participant \(i\), which involves the strategy of the game participant \(i\) and other user strategy functions (i.e., \(U=\left(S_{i},S_{-i}\right)\)). In this paper, the SUs are the game participants, whose strategies are applying for leasing in the free spectrum. SUs gain benefit in spectrum competition through their own strategies (\(U_{i},i\in N\)). Furthermore, SUs' earnings are not only related to their own strategies, but also are affected by other SUs.  
In the game \(G\)[13], we use \(s_{i}=\left(s_{1},s_{2},\ldots,s_{N}\right)\) to represent a combination of strategies from one game to another. If the benefit for each participant (\(U_{i}(s_{i},s_{-i})\geq U\left(s_{i}^{*},s_{-i}\right)\)) is established for all strategies (\(s_{i}\in S_{i}\)), the strategy combination \(s_{i}=\left(s_{1},s_{2},\ldots,s_{N}\right)\) is a Nash equilibrium of the game, where the \(s_{i}^{*}\) represents that participant \(i\) is different from it in strategy \(s_{i}\). The \(s_{-i}\) represents a policy mix among participants in addition to participant \(i\).  
The Nash equilibrium is understood as the standard behavior of rational participants in fragmented situations. In this paper, when all SUs cannot change their strategies in order to enhance their rational benefit, a Nash equilibrium will be achieved and the strategy combination of each user is the final spectrum allocation scheme. As a result, the overall communication efficiency in the region of MCRCS is improved to the greatest extent.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Algorithmic Approaches
#### 4.5.7 Constrained Markov Decision Process (CMDP)  
Two reviewed studies [22; 36] tackle the Online 3D Bin Packing Problem (3D-BPP) using Constrained Markov Decision Processes (CMDPs). The CMDP is an extension of traditional Markov Decision Processes (MDPs) that accommodates constraints. In the context of 3D-BPP, these constraints relate to the order dependence of items and the imperative for physical stability during the packing process.  
The approach presented by [PERSON] et al. [36] introduces a novel application of CMDP, leveraging it to guide the reinforcement learning process. Their use of CMDP facilitates the modelling and resolution of the intricate 3D-BPP with inherent constraints. The integration of a 2D height-map representation enhances the learning process, enabling effective policy optimisation. Additionally, the incorporation of Monte Carlo Tree Search (MCTS) for handling multiple look-ahead items contributes to the adaptability of their proposed approach.  
Conversely, [PERSON] et al. [22] adopt a model-based strategy inspired by AlphaGo. Their CMDP formulation assumes a deterministic transition model, representing the bin state through a height map. The policy network is central to their approach, providing placement probabilities based on the height map and item dimensions. The method addresses single-item, item reorientation, and multi-item scenarios with a buffer, showcasing its versatility.
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 3 Dynamic Buckling Criteria
### Relationship between Static Force and Dynamic Force
#### 3.3.2 Parametric Effect Due to Velocity  
Table 4 presents the statistical data of the dynamic forces under different velocities. The results of \(\Delta t\), the maximum force, and the average force are calculated as described above.  
Figure 12 shows the relationships between forces and velocity simulated under various cases of thickness at 0.04 m and 0.06 m. Figure 13 shows the time history of forces for the cases of impact velocities of 12 m/s, 14 m/s, and 16 m/s, respectively. It should be noted that when the velocity is larger than 14, the shell structure can be considered to be buckled already. According to Figure 13 and Table 4, the duration and impulse of collision contact is almost the same before buckling, and the skewness ratio of \(k_{s}\) is almost the same as well, which implies that the contact forces have the same shape functions under a variety of impact velocities.  
In addition, it can be observed that the maximum force and the mean force during collision are proportional to the velocity before dynamic buckling incurs, and the impulse is also proportional to the velocity. To identify the skewness ratio, both the maximum force and the mean force can be written in the same function as Equation (8) with different coefficients:  
\[f_{p}=k_{v}\cdot v,f_{m}=km_{v}\cdot v \tag{8}\]  
where \(k_{v}\) and \(km_{v}\) are the proportional ratio. The results of regression analysis are shown in Table 5. The skewness can be descripted by the ratio of \(k_{s}=k_{v}/km_{v}\), which can be considered as a constant value of 1.70. On this ground, it can be concluded that the maximum force and the average force comply with the same rules, and the ratio is dependent on the thickness. Moreover, the ratios of \(k_{v}\) and \(km_{v}\) in different cases of \(t=0.06\) m and 0.04 m are 1.5 and 1.6, respectively, which are almost the same as the identical ratio over the corresponding thickness. It is important to note that Equation (8) is independent of the other parameters.
## 5 Experiment Results and Analysis  
### Performance Comparison Experiment (Answer to RQ1)  
This section thoroughly addresses and verifies RQ1. To ensure the reliability of the verification results, a benchmark consistent with SOTA methods [10] is selected, with accuracy used as the primary metric. Table 3 shows the performance differences between the traditional FT method and LePB-SA4 RE with three separate domain training data of AWARE. Table 4 shows the comparative experimental results of the current models in the ABSA field, using the entire AWARE dataset as the training set, with the accuracy and F1-Score as the evaluation metrics. Confusion matrices are provided in Figure 4 for more detailed error analysis.  
Table 3 illustrates that LePB-SA4 RE has an 8-14% better average performance across all three domains (Social, Productivity, Games) compared to traditional ML and DNN models [8]. Compared with the fine-tuning method (SOTA), there is also a performance improvement of 0.6-1.9%. The minor enhancement in \"Games\" may be attributed to the fact that the BERT is more inclined to handle a wide range of linguistic expressions; this \"Games\" set contains a myriad of game-related specialized terms. These undefined terms in the vocabulary are tokenized as [UNKOWN], consequently hindering the semantic comprehension of the B4 MLM. Moreover, the annotation errors within the \"Games\" dataset  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Models** & **Social** & **Productivity** & **Games** \\ \hline Dataset baseline (ML) & - & 69.7 & 68.7 & 67.5 \\ \hline \multirow{8}{*}{Machine learning} & SVM & 80.9 & 79.2 & 78.4 \\  & MLP & 80.0 & 77.6 & 76.9 \\ \cline{1-1}  & DT & 73.2 & 70.6 & 70.3 \\ \cline{1-1}  & GNB & 74.8 & 74.1 & 72.0 \\ \cline{1-1}  & LR & 81.4 & 79.5 & 77.7 \\ \cline{1-1}  & KNN & 76.7 & 76.8 & 75.7 \\ \hline \multirow{8}{*}{Deep learning} & LSTM & 69.3 & 68.9 & 65.2 \\ \cline{1-1}  & CNN & 51.4 & 51.4 & 60.0 \\ \cline{1-1}  & FT+BERT (SOTA) & 81.9 & 78.5 & 75.1 \\ \cline{1-1}  & **LePB-SA4 RE** & **83.7** & **81.4** & **76.2** \\ \hline \hline \end{tabular} The bold means high-performance results.  
\end{table}
Table 3: Performance of LePB-SA4 RE in three domains compared to SOTA (accuracy).  
might be comparably elevated, resulting in less accurate learning signals during the training process and affecting the final performance. Table 4 provides a comparative analysis of the LePB-SA4 RE model against other ABSA methods. LePB-SA4 RE demonstrates superior performance compared to other syntax-based and attention-based models. It achieves the highest accuracy (80.51%) and F1-Score (79.17%). The Lexicon-based and PT strategies allow LePB-SA4 RE to integrate sentiment-specific knowledge and contextual understanding, leading to superior performance. The confusion matrix diagram shows that the model made more mistakes while detecting positive reviews across all dataset categories.
# Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell +
## 1 Introduction
Various experimental conditions and devices, such as different types of fuel cells, their remaining lifespan, operating frequencies, converter topologies, and power levels, complicate the systematic comparison of these controllers. However, a macroscopic evaluation can be performed by considering factors like the number of sensors used, stability confirmation, trials conducted with actual fuel cells, as well as the derivation and verification of controller parameters [11]. Through the analysis of these macrofactors, the controller offers robust theoretical support, encompassing stability certification, parameter derivation, and verification, and can also confirm favorable experimental outcomes with the fuel cell. Enabled by its linearity, this controller is easily implementable, representing an additional advantage facilitated by a fair comparative experiment. The novelty of the proposed controller lies in the design of a new state-feedback control structure, which can provide strong robustness and highly dynamic characteristics. Based on the above analysis, the main contributions of this article can be summarized as follows. 1. A simple robust controller is proposed by combining two linear control algorithms. Additionally, a simple theoretical proof is provided to certify the corresponding stability. 2. The LQR controller is employed to synthesize the best parameters by considering the performance index. The robustness of the controller can be evaluated by analyzing its behavior under variations in circuit parameters and its ability to maintain a stable output voltage, even in the presence of input voltage fluctuations. The controller demonstrates excellent performance in both dynamic and steady-state conditions compared to other linear and nonlinear controllers reported in the literature. The rest of this article is organized as follows. Section 2 presents the mathematical model of the PEMFC, simplified using rational assumptions, along with the model of the four-phase IBC derived from its operating principle. Section 3 discusses the controller design, including the development of the inner and outer loop controllers based on state-feedback control, as well as the selection and verification of controller parameters. Section 4 presents robustness testing against variations in circuit parameters, along with comparisons to other controllers. Simulation results and further comparisons are discussed in Section 5. Finally, Section 6 concludes the article.
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 2 The STEP Cube Lab-II CubeSat's Overview
### Satellite's Configuration and Design Descriptions

Table 1: Detailed system specifications of the STEP Cube Lab-II. and ground contact. For mission accomplishment, the onboard ADCS roll maneuvers the satellite back to the nadir point attitude.