esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Electronics
### Power Board
#### 9.1.3 Logic Signal Control Circuit  
This segment of the design is responsible for driving the laser beacon and also monitoring the battery levels. The circuit includes two functional blocks:  
Block A: Four logic gates are employed for selecting between internal and external triggers. This choice is determined by a control bit from the microcontroller. When the control bit is low, the internal trigger signal is utilized; otherwise, the external trigger is used. These logic gates exhibit a response time of 4 ns, ensuring minimal distortion of the trigger signal, which must remain stable.  
Block B: This subsystem adjusts the battery voltage levels to be compatible with the PIC ADC. An amplifier configured in differential mode, coupled with a resistive divider, enables approximating the battery charge values. The design of the resistive divider emphasizes high impedance to minimize the current flow, thereby reducing battery consumption.
## 3 Results and Discussion
### In Vitro Disintegration Test  
#### 3.8.1 Disintegrating Time  
According to the results obtained in Table 3, the images show the formation of a gel associated with starch and chitosan polymers [54]. Extensive research has discussed the applications of starch-based films as edible coatings, which can form gels upon contact with water. The polymer structure loses compaction (increases in free volume) due to the exchange of kinetic energy of the water molecules at the time of interaction. This is because this system exceeds the energy of the hydrogen bonds present in the macromolecule. At this point, the starch gradually absorbs water until the granules complete their destructuring, burst, and form a new mixture of viscous consistency and flow [55]. Similarly, in the case of chitosan, water hydroxyls penetrate the structure, reducing the availability of hydroxyl groups, weakening intramolecular hydrogen bonds, and affecting solubility. Water acts as a strong hydrogen bond donor and acceptor, making it an excellent agent for gelling chitosan. Consequently, water molecules integrate into the chitosan network, disrupting its three-dimensional structure [56]. The breakdown of hydrogen bonds increases molecular mobility, which, in turn, enhances the flexibility of the films [57]. The disintegration time test was conducted by placing the film in a Petri dish containing water at 37 \({}^{\circ}\)C, as studied by other researchers [21; 58; 59; 32]. To minimize subjectivity in determining the endpoint of the film, API release was measured gradually in solution. Additionally, the test considered visible film rupture and swelling tendencies in the sample holder. In this case, fracture was observed, along with a significant increase in diameter from the initial time point (0 min) to 5 min. Furthermore, the film became translucent, indicating increased fluidity of the polymeric matrix.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Contact Angle (\({}^{\circ}\)) and Picture** & **AW (\%)** \\ \hline TPS–CH–M & 62.18 \(\pm\) 0.12 & 321.28 \(\pm\) 2.85 \\ TPS–CH–M–A & 62.91 \(\pm\) 0.03 & 410.52 \(\pm\) 3.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contact angle and water absorption (AW) at the surface of TPS-CH-M and TPS-CH-M-A films.
# Parallel Complex Quadrature Spatial Modulation
## 2 System Model and Related Works
### Quadrature Spatial Modulation  
In QSM, the incoming bits stream is divided into three parts, where one part modulates a signal symbol \(s_{a}\) and the remaining two parts are used to activate two transmit antennas indexed \(l\) and \(k\). The real part \(s_{a_{\rm B}}\) and the imaginary part \(s_{a_{\rm 2}}\) are then transmitted through the \(l\)-th and \(k\)-th transmit antenna, respectively. The received signal vector is given by  
\[\mathbf{y}=\mathbf{h}_{l}s_{a_{\rm B}}+j\mathbf{h}_{k}s_{a_{\rm 2}}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{h}_{l}\) and \(\mathbf{h}_{k}\) correspond to the \(l\)-th and \(k\)-th column of \(\mathbf{H}\), respectively, with \(l,k\in\{1,2,\cdots,N_{l}\}\). The spectral efficiency of QSM is \(\log_{2}(N_{t}^{2}M)\) bits\(/\mathrm{s}/\mathrm{Hz}\).  
It is important to note that when the indices of the two active transmit antennas are the same, that is, \(l=k\), there will only be one non-zero element in the transmission vector and (1) becomes  
\[\mathbf{y}=\mathbf{h}_{l}(s_{a_{\rm B}}+js_{a_{\rm 2}})+\mathbf{n}. \tag{2}\]
# Effect of Aggregate Type on the Shear Behavior of Reinforced Lightweight Concrete Beams
###### Abstract  
Despite extensive research on lightweight aggregate concrete (LWAC), the precise effects of different coarse aggregate types and their physical properties on the shear capacity of reinforced all lightweight aggregate concrete (ALWAC) beams remain unclear. A comprehensive understanding of how aggregates influence the shear behavior of reinforced concrete (RC) beams is essential for accurately predicting shear strength and effectively designing ALWAC structures. To advance this understanding, experiments were conducted on twelve RC beams: four made of normal-weight concrete (NWC) and eight of ALWAC. ALWAC beams exhibited more extensive and wider flexural cracks compared to NWC beams under the same loading conditions. ALWAC beams demonstrated structural performance similar to NWC beams under identical loading conditions. The cracking loads of ALWAC can be estimated through measured concrete strength, with the post-cracking behavior predominantly influenced by the tensile reinforcement. All considered design codes underestimated the shear capacity of the tested ALWAC beams, and the shear resistance estimated by EC2 corresponded more closely than other existing codes. Lastly, the limitations and future work based on the results of this study were discussed and summarized.  
shear capacity; lightweight aggregate concrete; reinforced concrete beam; structural design Article Effect of Aggregate Type on the Shear Behavior Lightweight Concrete Beams  
[PERSON] and [PERSON] \({}^{\ast}\)\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +82-2-970-6514
## 2 Matricial Power-Flow Formulation
To relate the currents \(\mathbf{J}\) in all the branches with the voltage drops, it is possible to apply [PERSON]'s law as presented below:  
\[\mathbf{E}=\mathbb{Z}\mathbf{J}, \tag{4}\]  
where \(\mathbb{Z}=\mathbf{diag}(Z_{1},Z_{2},\dots,Z_{b})\). To determine the relation between the currents injected at the nodes (i.e., \(\mathbf{I}\)) and all the branch currents, let us apply [PERSON]'s second law at each node while considering the current directions defined in Table 1. We thus obtain  
\[\mathbf{I}_{1} =\mathbf{J}_{1}+\mathbf{J}_{2},\] \[\mathbf{I}_{2} =-\mathbf{J}_{1}+\mathbf{J}_{3},\] \[\mathbf{I}_{3} =-\mathbf{J}_{2}+\mathbf{J}_{4}+\mathbf{J}_{5},\] \[\mathbf{I}_{4} =-\mathbf{J}_{3}-\mathbf{J}_{4}+\mathbf{J}_{6},\] \[\mathbf{I}_{5} =-\mathbf{J}_{5}-\mathbf{J}_{6}.\]  
It should be noted that \(\mathbf{I}\) can also be split as  
\[\mathbf{I}=\left[\mathbf{I}_{s}\;\mathbf{I}_{d}\right]^{T}, \tag{5}\]  
where \(\mathbf{I}_{s}\) and \(\mathbf{I}_{d}\) are the current of generators and demands, respectively. Using the relation between the branch and nodal currents presented above, it is possible to find that  
\[\mathbf{I}_{s} =\mathcal{A}_{s}^{T}\mathbf{J}, \tag{6}\] \[\mathbf{E}=\mathbb{Z}\mathbf{J}, \tag{4}\]0  
Now if expressions (3), (4), and (7) are combined based on \(\mathbb{Y}=\mathbb{Z}^{-1}\), then the following result is obtained:  
\[\mathbf{E}=\mathbb{Z}\mathbf{J}, \tag{4}\]1  
To find the relation between the injected currents and powers, it is possible to apply the [PERSON]'s theorem, which defines apparent power as follows:  
\[\mathbf{E}=\mathbb{Z}\mathbf{J}, \tag{4}\]2 \[\mathbf{E}=\mathbb{Z}\mathbf{J}, \tag{4}\]3  
where the superscript \(\star\) represents the conjugate operator, \(\mathbf{S}_{s}^{\star}\) is the apparent power generation in the slack node, and \(\mathbf{S}_{d}^{\star}\) are the apparent power consumption in the demand nodes. Now, if expressions (8) and (10) are combined to obtain \(\mathbf{V}_{d}\), the following result is obtained:  
\[\mathbf{E}=\mathbb{Z}\mathbf{J}, \tag{4}\]4  
where \(\mathbf{Z}_{dd}\) is defined as \(\left[\mathcal{A}_{d}^{T}\mathbb{Y}\mathcal{A}_{d}\right]^{-1}\), and \(\mathbf{b}=+\mathbf{Z}_{dd}\mathcal{A}_{d}^{T}\mathbb{Y}\mathcal{A}_{s} \mathbf{V}_{s}\). It should be noted that to solve expression (11), an iterative procedure is required, as this expression has a recursive structure. For this purpose, let us to add an iterative counter \(t\) that starts from the initial point \(t=0\) (i.e., \(\mathbf{V}_{d}^{0}\) is perfectly known), which allows the rewriting of (11) as presented below:  
\[\mathbf{E}=\mathbb{Z}\mathbf{J}, \tag{4}\]5  
**Remark 1**. : _The iterative procedure in (12) is performed until the convergence error \(\epsilon\) is reached, i.e., \(\max\left\{\mathbf{V}_{d}^{t+1}-\mathbf{V}_{d}^{t}\right\}\leq\epsilon\) or the total number of iterations \(t_{\max}\) is completed._
# A Survey of Structure of Atmospheric Turbulence in Atmosphere and Related Turbulent Effects
## 3 Theoretical Studies of Turbulent Effect
### Theoretical Studies of Kolmogorov Turbulent Effect
By using the extended Rytov theory, the examination of various fourth-order statistical quantities, such as the scintillation index and the irradiance covariance function, were extended to the strong fluctuations regimes. The scintillationsfor plane wave, spherical wave, and Gaussian-beam wave were developed. Moreover, the covariance functions of irradiance models for plane wave and spherical wave were also deduced. The temporal spectrum of irradiance for plane wave was examined in the strong fluctuations condition [1]. [PERSON] and [PERSON] developed a new method. They derived the Boltzmann-Langevin kinetic equation from the first principles of quantum optics and the scintillation index was obtained for the practically important range of weak and moderate atmospheric turbulence [30]. After more than half a century of efforts, the theory of optical wave propagation in atmospheric turbulence has made great progress. In recent years, major research efforts have been devoted to the theoretical study of propagation of special beams in atmospheric turbulence, including vortex laser beam, vortex cosine hyperbolic Gaussian beam, airy prime beam, lowest order Bessel Gaussian beams, high power partially coherent laser beams, low order Laguerre-Gaussian beams, Bessel-Gaussian-Shell model beam, vortex Bessel-like beams, optical bottle beam, radial phased-locked rotating elliptical Gaussian beam array, airy Gaussian vortex beam array, flat-topped Gaussian vortex beam, generalized Bessel-Laguerre-Gaussian beams, and so on. Furthermore, the atmospheric turbulent effects involve mean intensity, scintillation, coherence, beam spreading, wander, and other propagation properties [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. To sum up, the theoretical research on optical wave propagation in the turbulent atmosphere starts from plane waves, spherical waves, and Gaussian beams, which are conventional optical waves. Currently, people are expanding to special beams to explore the statistical law of the impact of atmospheric turbulence on special beams in order to obtain a better solution to overcome the influence of atmospheric turbulence on optical wave propagation.
## 5 Conclusions and Future Works  
Industrial wireless sensor networks (IWSNs) exploited in various industries have been designed with a focus on mitigating the drawbacks of wired networks. Therefore, when network topology changes frequently, problems such as increased management costs and performance degradation due to graph reconstruction and resource reallocation occur. Recently, various mobile devices have been exploited in IWSNs, which causes frequent topology changes. Therefore, a strategy is required to address issues like path disconnection and reliability degradation caused by mobile devices during graph reconstruction and resource reallocation. In this paper, we propose a mobility support scheme to prevent packet loss caused by device mobility. The main idea is to employ a decision tree for classifying mobility patterns (linear, non-linear, and random), followed by regression to predict the actual trajectories for each identified pattern. By adopting this scheme, the network manager could proactively detect and respond to network changes, thereby achieving a higher transmission success ratio than traditional methods while also managing resources efficiently. In the performance evaluation results, the proposed scheme achieved high prediction accuracy and a high transmission success ratio even in scenarios where multiple mobile devices continuously changed the network topology. Moreover, by employing prediction methods tailored to each classified mobility pattern, resources can be allocated more efficiently compared to existing schemes, thereby ensuring network scalability.  
However, certain limitations remain. Support for devices with random movement patterns remains insufficient. Due to the inherent unpredictability of random mobility, it is very challenging to make accurate forecasts using regression or RNN-type models. Such devices with truly random mobility are expected to increase in future industrial environments, necessitating further research to address this issue.  
As part of future research, integrating fuzzy decision tree methodologies, such as those proposed by [PERSON] et al. [23], could provide enhancements. These techniques allow for better handling of uncertainty and variability in attribute importance, offering a more refined evaluation of factors influencing decision-making processes. This approach is expected to improve the prediction of random mobility patterns and optimize resource allocation even under highly dynamic conditions.  
C.K. and S.K. contributed to protocol design and detailed algorithms. They also conducted the performance analyses. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Soonchunhyang University Research Fund (No. 20220441).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Review of Photodetectors for Space Lidars
## 3 Space Lidar Detectors from Short-Wave to Mid-Wave Infrared  
There is an increasing demand for spectroscopic lidars at SWIR (1-2.5 \(\upmu\)m) and MWIR (from 2.5 to 5 \(\upmu\)m) wavelengths. There are many spectral absorption lines of interest in this wavelength region for both atmospheric chemistry and surface geology and geochemistry. The solar background illumination is much lower. The laser eye-safety threshold is much higher above 1.4 \(\upmu\)m wavelength. InGaAs APDs are currently the detector of choice for SWIR lidars. There has been no MWIR lidar in space to date due to the lack of high-speed and high-sensitivity detectors. HgCdTe APDs have recently emerged as new lidar detectors for wavelengths from SWIR to MWIR. They have much better performance than InGaAs APDs in SWIR. They fill the gap of single-photon detectors in MWIR. NASA GSFC has been working with industry over the past several years to develop the HgCdTe APD technology for future space lidars.
# Beyond Lexical Boundaries: LLM-Generated Text Detection for Romanian Digital Libraries
## 5 Discussion
### Human-Machine Textual Similarity  
For a better understanding of the similarity between human-written and artificially generated texts, a series of statistical similarity metrics were computed, such as cosine similarity [63], BLEU scores [33], and ROUGE scores [34], followed by non-parametric statistical tests to determine the differences or patterns in the similarity scores, while also extracting and comparing various textual complexity indices between human and machine-generated sets of documents. Cosine similarity measures the relatedness of documents using a bag-of-words representation that considers term frequency. The main advantages of this approach are its efficiency for high-dimensional data, such as text, and its insensitivity to document length, while as for disadvantages, it ignores semantic meaning and word order. BLEU is a standard machine translation metric that measures the precision of n-grams (word sequences) in the generated text. The main benefit is the penalization of overusing common words,while the shortcoming is represented by its sensitivity to the reference length, as well as its disregard for recall and semantic meaning. The ROUGE scores are a set of metrics used for evaluating text quality by comparing it to a reference text by measuring n-gram overlap (ROUGE-N) and the longest common subsequence (ROUGE-L). As an advantage, ROUGE is sensitive to content overlap and partial matches, while as a disadvantage, it may not capture the semantic meaning well due to its limitation to the recall and precision of n-grams. Therefore, analyzing the three scores together provides a more grounded view of text similarity, covering different aspects, such as incorporating structural and distributional aspects of the text (cosine), adding a layer of specificity via analyzing the presence of key phrases and content overlap (ROUGE), and offering a view on how well machine-generated texts align with human references in terms of specific word sequences (BLEU). The previous three similarity scores (i.e., cosine similarity, BLEU score, and ROUGE score) were computed between human-written documents and artificially generated texts. Cosine similarity measures the cosine of the angle between two vectors and denotes the similarity between a pair of texts, while BLEU and ROUGE scores evaluate the quality of machine-generated texts by comparing them to reference (human) texts. The scores distribution for each domain is illustrated in Appendix C, Figures A3-A7 for each dataset (e.g., books, news, medical, legal, and scientific/RoCHI), and are further correlated with mean and standard deviation presented in Table 5 for a comprehensive similarity overview. Models with higher mean scores are generally better at capturing textual similarity according to the respective metrics. Nevertheless, we should also analyze the standard deviations to understand the consistency of each model's performance. Lower standard deviations suggest more consistent performance, while higher standard deviations indicate greater score variability.
# An Adaptive 24 GHz PSO-Based Optimized VCO in Next-Generation Wireless Sensor Networks
###### Abstract
[PERSON] [13] and [PERSON] [14] introduced the space mapping concept for engineering optimization, while engineers have used Bayesian optimization for RF design [15]. Modeling and designing RF systems with artificial neural networks have also become popular. Additionally, the shape-preserving response, FSRFT, Pade approximation[16], and simplified real frequency technique (SRFT) [17] are notable optimization techniques. Automated optimization techniques have also been focused on by several studies. For instance, [PERSON] et al. [18] utilized TSEMO, a deep learning-based approach, to optimize the number and value of passive components in circuit design. Similarly, Bayesian optimization has been used to design a high-efficiency transmitter [19; 20]. However, these techniques can be complex when implemented in real-world scenarios. According to [PERSON] et al. [21], the challenges of nonlinearity in transmitters result in convergence problems that can be resolved through simulation annealing PSO analysis, which is a method of analyzing a PSO-based transmitter. In [22], [PERSON] introduced how a fractional-order PSO can be used to optimize a transmitter design and achieve 26.3% efficiency at 2.4 GHz, as well as a holistic optimization strategy at 65-85 GHz that assisted in achieving 26.3% efficiency. In this paper, an improved optimization strategy is proposed to enhance the performance of VCOs. Our approach in this paper is to introduce an adaptive scaling term into traditional particle swarm optimization (PSO) in order to improve the speed of the optimization process, as well as to simulate a stochastic simulation of the optimization process. The proposed approach is integrated into the design of a VCO, which results in fine-tuning of the component values by the adaptive PSO, which delivers faster optimization and better overall performance. Figure 2: Optimization algorithm integrated with an RF circuit block.
## 4 Mobility and Environmental Data Integration: System Overview
### System Architecture  
The architecture of the proposed system is composed of three principal components. Those are data collector, spatial join processor, and data aggregator. The system receives mobility, pollution and meteorological data and geographical maps input from corresponding online databases and websites. It then generates a unified view of the data and returns the result of a particular sophisticated spatial-oriented query submitted by the user in a tabular format that can be easily exploited for further analytics, e.g., for generating interactive region-based aggregate geo-maps such as choropleth maps and heatmaps.  
From an abstract point of view, the mechanism by which EMDI system operates is depicted in Figure 5.