esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion  
### Characteristics of Cutting Force in Jointed Rock Mass  
#### 3.1.1 Cutting Force in the Direction Perpendicular to the Joint Plane  
The variation in the cutting force with cutting distance for intact rock and jointed rock mass specimens, when the cutting direction is perpendicular to the joint plane (\(\alpha\) = 90\({}^{\circ}\)), is shown in Figure 7. Figure 7a illustrates \(FC\) in a single cut within a series of cuts performed with a cutting depth of 3 mm and a line spacing of 9 mm on an intact rock and a jointed rock mass specimen with a joint spacing of 30 mm. For the jointed rock mass specimens, \(FC\) increased to a maximum of approximately 3 kN during the cutting process and then decreased to nearly zero, exhibiting a consistent cycle. Additionally, FC approached zero near the joint planes, and this cycle had a length of approximately 30 mm, corresponding closely to the joint spacing. In contrast, for the intact rock specimen, \(FC\) peaked at about 5 kN, but no distinct periodic pattern was observed. Figure 7b shows the cutting force with a cutting depth of 6 mm and a line spacing of 18 mm. In the jointed rock mass (\(J_{s}\) = 60 mm), it also exhibited a periodic variation with a cycle of approximately 60 mm, fluctuating between a maximum of about 9 kN and nearly 0. It can be observed from Figure 7c that similar behaviour occurs in cutting on jointed rock mass with a joint spacing of 90 mm (\(p\) = 9 mm and \(s\) = 24 mm).  
This behaviour of \(FC\) can be explained through the rock chip formation process. In rock cutting, the cutting force exhibits some repetitive patterns as it builds up to a peak, then drops, and builds up again [31]. Rock chips tear off as soon as these sudden drops occur [32]. Figure 8 depicts rock chip formation in both intact rock and jointed rock mass based on the theory by [PERSON] [33]. As seen in Figure 8a, in intact rock, the fracture path required for rock chip formation extends from the tip of the pick to the free surface of the rock. Here, the peak value of \(FC\) required for rock chip formation can be calculated as follows:  
\[FC=L_{\text{AB}}\times\sigma_{t} \tag{2}\]  
where \(L_{\text{AB}}\) is the length of the path from point A to point B.  
On the other hand, in jointed rock mass (Figure 8b), rock chips are generated once the fracture path reaches the joint plane. This suggests that in jointed rock mass, the fracture path is restricted by the joint spacing. As a result, \(L_{\text{AB}}\) is reduced, ultimately leading to a decrease in \(FC\). This phenomenon can be observed in Figure 9. Consequently, \(FC\) peaks are lower in jointed rock mass than in intact rock and exhibit periodic variations corresponding to the joint spacing.  
Figure 6: The tool forces recorded during a single cut in the LCM test.  
Figure 8: Rock chip formation based on the theory by [PERSON] [31]: (**a**) intact rock; (**b**) jointed rock mass.  
Figure 9: Jointed rock mass specimen after cutting at an \(\alpha\) of 0.
# UAV-Borne Profiling Radar for Forest Research
## 2 FGI-Tomoradar Development
### Signal Processing  
An RF low-noise amplifier (LNA) is used to pre-amplify the received signal before it is down-converted to an IF signal by the mixer. Then, the pre-amplified signal is amplified with a cascade IF LNA amplifier to the level required by the digitizer. The received power decreases as a function of range at a rate of R\({}^{4}\) as Equation (5) presents  
\[P_{r}=\frac{P_{t}G_{t}A_{r}\sigma F^{4}}{(4\pi)^{2}R^{4}} \tag{5}\]  
where \(P_{r}\) is the received power; \(P_{t}\) is the transmit power; \(G_{t}\) is the transmitter gain; \(A_{r}\) is the antenna aperture; \(\sigma\) is the reflecting cross-section (RCS) of the target; \(F\) is the propagation factor and; \(R\) is the target range.  
Meanwhile the illuminated area proportionally increases to the distance between the radar and the target by R\({}^{2}\) as Equation (6) presents  
\[A_{i}=\pi r^{2}=\pi R^{2}tg\left(\frac{\theta_{FOV}}{2}\right) \tag{6}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-System** & **Quantity** & **Mass (gram)** \\ \hline Antenna with OMT & 2 & 900 \\ RF + IF Unit & 1 & 1800 \\ Georeferencing System & 1 & 4600 (helicopter)/600 (UAV) \\ Digitizer & 1 & \(<\)5000 (helicopter)/1000 (UAV) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mass of the Tomoradar Sub-systems.  
where, \(A_{i}\) is the illuminated area; \(\theta_{FOV}\) is the field of view, and \(R\) is the target range.  
It is obvious that the decrease of the received power cannot be proportionally compensated by the increase of illustration area, resulting in the decrease of system dynamic range when the range measurement increases. However, this decrease can be compensated for by an amplifier with \"distance corrector\" functionality, whose gain increases 6 dB/octave with increasing measured range/frequency. The correction corresponds to the reduction of the backscattering coefficient as a function of range at a 0 degree incident angle (i.e., the NADIR). Due to the turnover of DDS triangular modulation intensive noise will be detected in the IF spectrum. This noise should be filtered out by omitting the first hundred samplings in the digitizer.
_applied sciences_  
_Article_  
**Valorization of Pineapple Residues from the Colombian**  
**Agroindustry to Produce Cellulose Nanofibers**  
**[PERSON] 1, [PERSON] 2,*(c) and [PERSON] 3,*(c)**  
Footnote 23 optical, rheological, and film-forming properties [5; 10]. Because of these properties, cellulose nanomaterials hold different applications such as adsorbents in water treatment and antimicrobial materials. In fact, cellulose nanofibers have been used to turn contaminated water into drinking water; they have also been shown to be antimicrobial materials in protective healthcare equipment, food packaging, and ultrafiltration membranes [8].  
Cellulose nanofibers have had a recent impact on industrial applications [5; 7], mainly in the Japanese market. The cellulose nanofiber market size was valued at USD 346 million in 2021 and is projected to reach USD 1424 million by 2030, growing at a compound annual growth rate (CAGR) of 19% from 2022 to 2030 [11].  
Cellulose nanofibers are generally obtained from agricultural sources such as wheat straw, cotton, coconut husk fibers, sisal fiber, bamboo waste, and sugarcane bagasse, among others [5; 12]. Agro-industrial waste could also be a good source of cellulose nanofibers [13; 14; 15]. In this regard, circular economy [16; 17] and revalorization strategies [18; 19] are capable of revalorizing abundant and biodegradable agro-industrial waste that could play an important role in a globally sustainable future economy.  
Colombia is one of the ten pineapple-producing countries in the world. Colombian pineapple production has increased at an annual rate of 12%, reaching over 1.2 million tons in 2021 [20]. Pineapple leaves are an abundant waste during pineapple transformation since leaves represent between 65% and 80% of the total crop; most of these come from the crown (leaves from the upper part of the fruit) and the stem. A pineapple plant in the harvested state contains 25 to 30 leaves, with diameters ranging from 45 \(\upmu\)m to 205 \(\upmu\)m. These leaves contain 2% to 3% fiber and are composed of approximately 70-80% cellulose, 17% hemicellulose, and 5% lignin [21].  
Concentrated chemicals and mechanical methods over long periods of time degrade the cellulose during the isolation of nanofibers, reducing the degree of chain polymerization. The use of combined methods could decrease cellulose degradation and optimizes morphology and aspect ratio [14]; therefore, this work aimed to obtain cellulose nanofibers from pineapple waste using chemical methods combined with ultrasound. Then, to demonstrate the efficiency of these methods, fibers were characterized by scanning electron microscopy (SEM), infrared analysis (FTIR), thermogravimetric analysis (TGA), and transmission electron microscopy (TEM).
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 3 Network Model
### Multihop Cooperative Structure  
Let \(V_{RCR}(j)\) denote the set of \(k=|V_{RCR}(j)|\) cooperative nodes (i.e., RCR) at the \(j\)-th hop; let \(V_{RCR}(j+1)\) denote the adjacent cooperative group of \(V_{RCR}(j)\) a hop closer to the sink. Let \(m_{j}\) and \(m_{j+1}\) denote the reference nodes of \(V_{RCR}(j)\) and \(V_{RCR}(j+1)\), respectively. According to Definition 1, for given \(u\in V_{RCR}(j)\), and \(v\in V_{RCR}(j+1)\), \(|uv|\leq R\), and for \(u_{1},u_{2}\in V_{RCR}(j)\) and \(v_{1},v_{2}\in V_{RCR}(j+1)\), we get \(|u_{1}v_{2}|\leq R\).
## 4 Spatiotemporal Feature Extraction Techniques
### Hybrid Spatiotemporal Feature Extraction Models  
In recent years, the hybrid model of CNN and LSTM to extract spatial and temporal features has been widely studied [61], thereby providing a more comprehensive understanding of complex attack patterns. For instance, in extracting spatiotemporal features from network traffic, this hybrid network has an accuracy rate of over 99.50% in determining the main types of attacks [8]. This hybrid model uses CNN and LSTM to extract spatial and temporal features of traffic data, demonstrating extremely high processing efficiency.  
Table 5 shows a comparison of existing deep learning models for spatiotemporal feature extraction in IDS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Model** & **Spatiotemporal Feature Extraction** & **Advantages** & **Challenges** & **Empirical Support** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{\begin{tabular}{c} Convolutional \\ Neural Network \\ (CNN) \\ \end{tabular} } & \begin{tabular}{c} Exects in capturing spatial \\ features; limited in temporal \\ feature extraction. Effective for \\ image-like data but less so for \\ time series without \\ modifications (e.g., with \\ Temporal \\ Convolution Networks). \\ \end{tabular} & \begin{tabular}{c} Strong spatial feature \\ extraction, adaptable to \\ various data formats \\ (e.g., images, video). \\ \end{tabular} & \begin{tabular}{c} High computational cost, \\ struggles with temporal \\ dependencies without \\ chitectural modifications. \\ \end{tabular} & [7; 8; 9; 29; 34; 62; 63; 64; 65] \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{\begin{tabular}{c} Automatic Encoder \\ (Autoencoder) \\ \end{tabular} } & \begin{tabular}{c} Extract essential features \\ through data reconstruction; \\ can learn latent spatiotemporal \\ representations in \\ sequence-based AEs (e.g., \\ LSTM Autoencoders). \\ \end{tabular} & \begin{tabular}{c} Reduces dimensionality, \\ effective noise removal. \\ \end{tabular} & \begin{tabular}{c} Sensitive to anomalies \\ and outliers, potentially \\ requiring large datasets. \\ \end{tabular} & [24; 55; 66; 67] \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{\begin{tabular}{c} Long Short-Term \\ Memory (LSTM) \\ \end{tabular} } & \begin{tabular}{c} It specializes in capturing \\ temporal dependencies, ideal \\ for sequential data where \\ timing is crucial (e.g., sensor \\ data, IoT). \\ \end{tabular} & \
\begin{tabular}{c} Handles long-term \\ dependencies well, \\ resilient to abrupt \\ changes in data streams. \\ \end{tabular} &
\begin{tabular}{c} It is computationally \\ expensive, prone to \\ gradient vanishing, \\ requires \\ extensive tuning. \\ \end{tabular} & [8; 65; 68] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of existing deep learning models for spatiotemporal feature extraction in IDS.
# An Efficient Document Retrieval for Korean Open-Domain Question Answering Based on ColBERT
## 1 Introduction
The ColBERT approach demonstrates a performance surpassing term-based retrieval BM25 and exhibits significantly faster search times compared to KoBERT. Furthermore, utilizing various pre-trained Korean language models for ODQA tasks, we identify KoSBERT as the most effective and efficient pre-trained Korean language model. Lastly, through performance comparisons based on tokenizers in the ablation study, we underscore the necessity for efficient models tailored to the complexities of the Korean language. This paper's main contributions are outlined as follows:  
* We adapt and propose the application of ColBERT to Korean document retrieval, demonstrating its efficiency and effectiveness in open-domain Korean question answering. * We construct and analyze a large-scale Korean open-domain question answering dataset, filling a gap in Korean natural language processing resources. * Through extensive evaluations using various pre-trained language models, we demonstrate that KoSBERT significantly outperforms other models in document retrieval tasks, emphasizing the effectiveness of KoSBERT specifically for Korean language retrieval tasks. The rest of this paper is organized as follows: Section 2 discusses the related work, providing a background and context for this study. Section 3 delves into the adaptation of ColBERT for Korean open-domain question answering, including details on the query encoder, document encoder, and late interaction mechanism. Section 4 covers the Korean open-domain question answering dataset construction, detailing the creation of both the training and evaluation datasets. Section 5 presents the experimental setup and results, including the datasets used, baseline comparisons, evaluation metrics, computational details, results, and an ablation study. Section 6 discusses the implications of the study, highlighting how the ColBERT model's effectiveness in Korean language processing can be extended to various languages. Finally, Section 7 concludes the paper with a summary of the findings and suggestions for future research directions.
# Dielectric Characterization of Chinese Standard Concrete for Compressive Strength Evaluation
## 1 Introduction
With the increasingly international widespread use of GB concrete, the demand for novel NDT/E techniques and those combining multiple techniques [22] for concrete-based structures, in terms of compressive strength prediction, evaluation, and other mechanical properties, is highly expected. Thus, this topic is of importance in the areas of cross-disciplinary research. One of the most important physical properties that governs electromagnetic wave propagation, is the relative dielectric constant (\(\epsilon_{r}\)\({}^{\prime}\)) of the concrete. Thus far, studies on the dielectric properties of cementitious materials have received great attention in civil engineering [23; 24; 25; 26; 27; 28; 29; 30; 31]. The use of real-time dielectric constant measurements to evaluate the corresponding variation in the compressive strength of concrete, is a technique that will be well suited for SHM. In this paper, the authors present experimental results on the dielectric characterization of standard concrete. Regression modeling of the measured values and analysis, against the water-to-cement ratio of the 28-day concrete, will be considered. Figure 1 illustrates a big picture of the dual-disciplinary experimental work on the dielectric constant and compressive strength of standard concrete. The objective of this study is to develop a data set of nominal values of the dielectric constant for the GB concrete, in accordance with the Chinese standard JGJ55-2011 [2]. Correlations between the dielectric constants and compressive strengths will be considered. Ordinary concrete is a heterogeneous porous material. When the maximum size of the coarse aggregates (31.5 mm used in this study) are comparable to the signal's wavelength, extreme heterogeneous dielectric characteristics may arise. Therefore, a signal frequency of 10 GHz is the highest frequency of interest in this study.
## 2 Material and Algorithm
### Maximum Power Point Tracking (MPPT) Algorithm  
The current-voltage curve of PV is non-linear, and algorithms are used to keep PV operating at its maximum power point, which varies with light conditions and temperature. Sensors, MCU, and actuators are used in conventional PVs to keep harvested energy at the maximum [1]. In the traditional MPPT algorithm and controller, the open-circuit voltage of the PV cell is measured periodically. MPP can be set at a certain fixed level using simple circuits. For low-power PV applications, MPPT techniques are proposed by [PERSON] et al. [53].  
Ambient light conditions in the smart device under usage may change rapidly depending upon the location or orientation (the alignment with a light source). These variables are random and hard to predict, so a simple algorithm is required in the scheme presented in Figure 1, which can attain a completely new maximum power point after a change in orientation or from ALS data. A simple MPTT algorithm can be used when the smart device is placed stationary under ambient light.
# Low Discrepancy Sparse Phased Array Antennas
## 6 Conclusions  
Sparse arrays have a significantly smaller number of elements compared to traditional dense arrays (with \(\lambda/2\) element spacing) which minimizes their cost and complexity. A comprehensive study of sparse phased arrays using low-discrepancy sequence (LDS) element distribution is presented. We show that these LDS element distributions remove the grating lobes associated with large element spacing in sparse arrays, while at the same time avoid undesirable and impractical element distributions in random arrays. The mathematical formulation for implementing LDS for sparse planar arrays is presented, along with numerical studies on their performance. Our studies considered sparse arrays with 86% less elements than a fully populated array. The performance factors compared equidistribution on the aperture of the array by looking at average minimum element spacing, as well as array pattern peak SLL, directivity, and aperture efficiency. Different array aperture configurations were also studied, and we show that the performance of LDS distributions is not impacted by the type/shape of the array. Our studies concluded that in comparison between the LDS techniques, the Poisson disk sampling technique outperforms all other approaches and is the recommended LDS technique for sparse arrays.  
Conceptualization, P.N., P.R., R.H.; writing--original draft preparation, T.T., P.N.; writing--review and editing, T.T., N.A., P.N., P.R., R.H.; supervision, P.N., P.R., R.H.; project administration, R.H.; funding acquisition, P.N. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the National Institute for Occupational Safety and Health (NIOSH) grant 75D30119C05413.  
Not applicable.  
The authors wish to thank the reviewers whose thoughtful comments helped improve the quality and efficacy of this paper. The authors declare no conflict of interest.
# Partially Auxetic Structure of Rotating Rectangles
###### Abstract  
The subject of the analysis is a modified auxetic structure in the form of rotating rectangles, which exhibit large negative Poisson's ratio values. The structure modification consisted of introducing rotational axes on the surfaces of structural units near the corners of the rectangles. Such a system had previously unknown relationships between Poisson's coefficient and parameters in the form of the quotient of the lengths of the sides of the rectangle, the position of the axis of rotation, and the degree of opening of the structure. Poisson's coefficient is always \(-1\) for square units, while it takes numerical values in a wide range of negative and positive values for rectangular units. Based on geometric relationships, formulas were determined to calculate the linear size of the structure and the values of Poisson's ratio. It is shown that depending on the quotient of the lengths of the sides of the rectangle, the structures can lose their auxetic properties. For elongated rectangles (structures with a/b = 2.5), extremely high negative values of Poisson's ratio are obtained but only for small changes in linear dimensions, and with greater stretching, the structure loses auxetic properties. Such a tendency occurs for a large group of structures of systems composed of rotating rectangles. The physical structures built provide an experimental complement to the research conducted.  
negative Poisson's ratio; auxetic structures; rotating rectangles; phase transition Article
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 8 Proposed Framework: Multi-Layer Threat-Defense Alignment Framework
### Applications of MLTDAF in Counter-Terrorism
#### 8.4.4 Enabling Policy-Level Counter-Terrorism Strategy  
The modularity of the framework enables the creation of contingency procedures tailored to individual layers. MLTDAF enables the design of robust incident response tactics  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Objective** & **MLTDAF Benefit** \\ \hline Prevent terrorist access & Layered defense, strong authentication \\ \hline Detect covert operations & Behavioral monitoring, ML-driven detection \\ \hline Protect against hybrid attacks & Combined physical–cyber safeguards \\ \hline Enhance response to attacks & Layer-specific contingency and incident plans \\ \hline Guide strategic infrastructure policy & Prioritization of critical components and investments \\ \hline \hline \end{tabular}
\end{table}
Table 35: Summary of counter-terrorism benefits.  
by providing insight into the propagation of specific attacks (such as malware insertion, jamming, or Sybil attacks) throughout the system. Layer-level vulnerability evaluations enable counter-terrorism units and infrastructure operators to allocate resources more effectively, practice emergency responses, and simulate attack scenarios.
## 3 Simulation Results  
Three parts are presented here: the performance evaluation of LASM(B) and LASM(P), comparisons with MDS-MAP algorithm [23], and an initial experiment using sensor nodes designed by us. The simulation software is Matlab, which is the same as in [23]. Simulation environment: \(10r\times 10r\) square area, with 200 sensor nodes randomly deployed in it, where \(r\)=1 is the unit length. The distance error is presented as follows: If the distance error is \(e\) and the true distance is \(d\), the measured distance is a random value with normal distribution \(d(1+N(0,e))\)[23]. The position estimation error \(err\) is normalized to \(R\), where \(R\) is the transmitting range of each node  
\[err=\frac{\sum\limits_{i=1}^{n}\sqrt{\left(x_{ei}-x_{i}\right)^{2}+\left(y_{ei }-y_{i}\right)^{2}}}{n}\]  
where \(n\) is the number of blind nodes, \(\left(x_{ei},y_{ei}\right)\) is the estimated position of sensor node \(i\), \(\left(x_{i},y_{i}\right)\) is the actual position of sensor node \(i\).
# Effect of Laser Surface Treatment on the Corrosion Resistance of Zircaloy-4 at High Temperature
## 1 Introduction
[31] reported that LST can enhance the nodular corrosion resistance of Zircaloy-2 and Zircaloy-4 corroded in steam at 500 \({}^{\circ}\)C. [PERSON] et al. [32] found that melting Nb on the surface of Zircaloy-4 using a laser can enhance the hardness of Zircaloy-4 and the corrosion resistance of Zircaloy-4 in chloride solution, but it can deteriorate the corrosion resistance of Zircaloy-4 corroded in steam at 400 \({}^{\circ}\)C. In addition, [PERSON] et al. [33; 34], [PERSON] et al. [35], [PERSON] et al. [36] and [PERSON] et al. [37] have reported the effect of LST on the hardness, abrasive resistance and microstructures of the zirconium alloys, but they did not carry out more studies on LST's effect on corrosion resistance in high-temperature steam. Based on the above discussion, LST can affect the corrosion resistance of zirconium alloys, but the effect of LST on corrosion resistance in high-temperature steam during LOCAs has rarely been reported. In our work, LST was conducted on the Zircaloy-4 plate specimens, which were then corroded in steam at 1100 \({}^{\circ}\)C. The morphology and phase constituents, the element distributions of the treatment layer and the morphology of oxide film have been observed, and the mechanism of the effect of LST on the corrosion resistant of Zircaloy-4 in high-temperature steam has also been discussed.