esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware Model Checking Algorithms and Techniques
## 8 Comparative Analysis and Experimental Evaluation
As mentioned before, there is not currently a definitive winning strategy when it come to bit-level hardware model checking, so portfolio approaches are the de facto standard in the field. Since its introduction, IC3/PDR has proven itself time and time again as one of the most proficient strategies in the state of the art, as also shown by the results here provided. Nevertheless, its contribution need to be complemented with interpolation-based strategies in order to provide better coverage. This is far more evident when we shift the focus to harder-to-solve instances, in which the subset of benchmarks solved by interpolation-based strategies stays basically the same, whereas the number significantly decreases for IC3. IC3 proves itself a high-performing engine, with a significant edge on easier and mid-tier instances. Interpolation-based techniques, in turn, complement it providing orthogonal approaches. The same consideration holds for BMC-based approaches, that focus on falsification with the aim of finding an actual counterexample to the property under verification. Their targets stay basically unchanged, with a scope on instances with deeper unrollings of the corresponding transition relation, which other engines may struggle to tackle. When taking into account BDD-based approaches, which are not directly the focus of this paper, one can take into account both similarities and differences with their SAT-based counterparts. On the one hand, they both try to address problems encoded in Boolean form but where SAT checkers just need to focus on finding a single satisfying assignment for the formula under verification, BDD-based ones need to encode a function describing all the satisfying assignments. Once such an encoding is available, BDDs are capable of performing tasks which are not supported by SAT-based solutions, such as focusing on optimality given a cost function, provide counting, or supporting variable quantification, at the cost of being able to build such a data structure, though. In general SAT checkers relying on some variant of the DPLL algorithm outperform BDD-based solutions; nevertheless, there are niche scenarios in which BDDs are still the winning strategies, such as specific instances of equivalence and parity checking. Complementary to all of the techniques referenced above, we have logic synthesis, simulation and combinatorial-based approaches which tackle verification problems from a more circuital and structural point of view. They tend to be applicable to smaller instances  
\begin{table}
\begin{tabular}{l l r r r r r r r} \hline \hline
**Engine** & **Result** & **Solutions** & \(avg_{i}\) & \(avg_{i}\) & \(avg_{g}\) & \(avg_{i}\)**[s]** & \(avg_{g}\)**[s]** & \(avg_{m}\)**[MB]** \\ \hline \multirow{3}{*}{BDD} & SAT & 10 & 18.30 & 334.50 & 10,029.30 & 1217.21 & 210  
or instances with very specific patterns and a large number of equivalent elements, which can be collapsed in a few equivalence classes and/or abstracted away from the actual model under verification.
## 2 Literature Overview
### Industry 4.0
They combine signal acquisition and simultaneous data evaluation in a decentralized system to reach their goal, avoiding the use of external cloud solutions, which could be unnecessarily time-consuming [8]. Improvement of factories to be suitable for Industry 4.0 includes development in the field of Flexible Manufacturing Systems (FMSs) which serve as a base for digital manufacturing. [PERSON] and [PERSON] in their study (2020) analyze some of the key elements of FMS, in order to offer a solution for optimizing material flows in advanced manufacturing systems. The resulting model provides an overview of the whole manufacturing system and allows rapid error detection, which is what enables early corrective actions [9]. Among the challenges preceding the successful transformation to Industry 4.0, we need to also count with implementing a clear roadmap by manufacturing enterprises. While the task is usually manageable for big companies, small and medium companies can encounter certain economical, technical, and cultural difficulties. [PERSON], [PERSON] and [PERSON] in their paper (2021) propose two contributions to help software engineers of small and medium manufacturing enterprises to incorporate Industry 4.0 in the context of customer life cycle. The first contribution takes the form of a methodology to design new software services to change the interaction between customers and companies. The second contribution is an implementation of said methodology as a system in a real manufacturing scenario [10]. [PERSON] in a study (2018) on complexity-aware economics points out the unintended consequences of Industry 4.0 and Digital economy. The author formulates basic principles for a new economic governance and recommends gradualism during Industry 4.0 transition [11]. Changing role of human employees is another aspect of the progress. In a study (2021) published by [PERSON] and [PERSON], the authors present the advantages and challenges provided to maintenance operators in a digitalized environment. As the greatest advantage, accessibility of important information and predictions calculated by AI is stated. On the other hand, the increasing demand on certain abilities of the operator--such as the ability to communicate with computers, robots, and databases, as well as to rapidly adapt one's skills--is stated to be the biggest challenge [12].
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 6 Simulation Results  
In this section, we will use simulation experiments to evaluate the performance of the DOTO algorithm. In order to be in line with reality, the parameters are set as follows in this paper [8; 20]. In all simulation experiments, the transmitter parameter of the AP server is set to \(P=50W\), the UAV energy harvesting efficiency \(\eta_{i}\) is uniformly distributed in the [0.6, 0.8] interval, and the distance \(d_{i}\) from each UAV to the AP is uniformly distributed in the range of (5,8) meters, \(i=1,\ldots,N\). The channel gain \(\widetilde{c}_{i}\) follows the free-space path loss model \(\widetilde{c}_{i}=A_{d}(\frac{3\times 10^{8}}{4\;\pi\;h\;d_{i}})^{d_{e}}\), where \(A_{d}=4.11\) denotes the antenna gain,\(d_{e}\) denotes the path loss index, generally \(d_{e}=2.8\), and \(h=915\;M\;H\;z\) represents the carrier frequency. In the \(t\) - th time block, the wireless channel gain \(c_{t}=[c_{t}^{1},c_{t}^{2},\ldots,c_{t}^{N}]\) of \(N\) UAVs can be obtained from the Rayleigh fading channel model \(c_{t}^{i}=\widetilde{c}_{i}\alpha_{t}^{i}\), where \(\alpha_{t}^{i}\) represents an independent random channel fading factor and obeys the exponential distribution of unit mean. We use the TDMA protocol for computing task offloading. To ensure the generality of the experiment, the length of the time block is set to be less than the time of the channel interference--that is, \(T=1\). It is assumed that the wireless channel gain remains constant in the same time block, but changes in different time blocks. We assume that the computing efficiency \(k_{i}\) of all UAVs is equal, which is \(k_{i}=10^{-22}\), \(i=1,\ldots,N\), and the period \(O=200\) cycles/bit. The data communication bandwidth is \(B=2\;M\;H\;z\), the noise power is \(G=10^{-10}\), and the offload task communication overhead coefficient \(\theta=1.1\). The DNN model consists of two hidden layers, an input layer and an output layer, and the training environment is Tensorflow2. Below, we analyze the experimental results.
# Extending the Coverage of IEEE 802.15.4z HRP UWB Ranging
###### Abstract  
Currently, the ultra-wideband (UWB) technology commercialized in smartphones and smart keys is a high-rate pulse repetition frequency (HRP) UWB of the IEEE 802.15.4z standard, which aims to accurately determine the distance between UWB devices located within tens of meters using two-way ranging (TWR). However, in order for UWB ranging technology to be spread to various location-based services or positioning services, it must be able to measure the distance between UWB devices that are hundreds of meters apart. Fortunately, UWB technology can freely change physical layer parameters, as long as they comply with the UWB local regulations worldwide. Therefore, in this study, we analyzed the method of configuring the packet structure, length, and transmission power from the link budget perspective to enable longer UWB ranging of hundreds of meters within UWB local regulations. As a result of the analysis, we theoretically confirmed that UWB ranging is possible even at hundreds of meters by selecting the optimal physical layer parameters. In addition, the experimental results using the Qorvo DW3000 module were confirmed to be consistent with the results analyzed in this study. The results of this study can be used as basic data for the introduction of wide-area UWB technology and services in the future.  
UWB; HRP UWB; positioning; ranging; IEEE 802.15.4z; link budget; IoT 2025  
25, 3058
# Assessment of the Baking Properties of Rye Flour Based on the Polysaccharide Content and Properties
## 3 Results
### The Parameters Related to the Starch-Amplyolytic Complex
#### 3.1.2 Amyloid Properties  
The results of the amylograph and DSC tests are presented in Table 3. The amylograph maximum peak viscosity (APV) ranged from 410 to 940 AU. It was found that only the type of flour had a statistically significant impact on the values of the above parameter. The low-extract rye flour samples were characterized by a significantly higher value of the APV (on average, of 579 AU) compared to high-extract rye flour (on average, of 478 AU). This indicates the significantly lower activity of amyloid enzymes in low-extract rye flour than in the high-extract rye flour samples. This is due to the greater share of particles originating from the outer parts of the grain in high-extract rye flour, which were characterized by a higher content of amylolytic enzymes compared to particles originating from the endosperm [32]. Also, the study by [PERSON] et al. [7] showed that suspensions from rye flour samples with an extract of 70% were characterized by a significantly lower maximum peak viscosity than suspensions from flour with an extract above 90%. In our study, the impact of the year of flour production was not significant. Rye flour samples produced in 2019 and 2020 were characterized by a similar level of APV (average 510 and 547 AU, respectively). The initial temperature of starch gelatinization (T\({}_{\text{I}}\)) was in the range of 50.5-58.0 \({}^{\circ}\)C. In the earlier study by [PERSON] [9], a low-extract rye flour samples with a low extract content were characterized by a T\({}_{\text{I}}\) coefficient in the range from 51.5 to 54.0. Our study revealed that only the type of flour had a significant impact on the analyzed parameter (Table 2). The low-extract rye flour samples were characterized by a significantly lower T\({}_{\text{I}}\) compared to high-extract rye flour (on average, 53.1 and 54.7 \({}^{\circ}\)C, respectively). The observed differences are probably connected to differences in the starch structure, e.g., granule size [33], the share of amylose and amylopectin [34], phosphorus content [35], the length of the amylopectin side chains [36], and differences in the molecular weight of starch [37]. Our study showed that the rye flour samples produced in the 2019 year were characterized by a T\({}_{\text{I}}\) of a similar level to the rye flour samples produced in the 2020 year (on average, 54.2 and 53.5 \({}^{\circ}\)C, respectively). The final temperature of starch gelatinization (TF) ranged from 67.5 to 83.0 \({}^{\circ}\)C. Moreover, above 90% of the study rye flour samples were above the optimal parameters for rye flour, which are characterized by an optimal baking quality, i.e., 65-69 \({}^{\circ}\)C [38]. The low-extract rye flour samples were characterized by a higher TF (on average, 72.2 \({}^{\circ}\)C) compared to high-extract rye flour (on average, 70.0 \({}^{\circ}\)C). However, the differences were not
## 4 Analysis of Differential Deformation Control
### Analysis of Differential Deformation Control during Construction  
#### 4.1.1 Variable Stiffness Method  
After foundation reinforcement, due to the effect of piles in the reinforcement area, the additional stress decreases the modulus of composite foundation increases, and the deformation value of each measuring point decreases. Compared with natural foundation, differential settlement is also reduced. However, the differential settlement at the foot of the wall is still very large (9.399 mm), and a plastic zone is formed at the foot of the wall. To reduce the uneven settlement of the foundation, it is necessary to increase the pile stiffness in the area with large additional stress or reduce the pile stiffness in the area with small additional stress. By increasing the displacement of the wall foot and reducing the displacement from the middle of the arch, the displacement can be reduced. For the convenience of analysis, the piles are arranged from left to right and numbered as shown in Figure 10. It can be seen from the figure that the stiffness of 1 #, 2 #, 3 #, 11 #, 12 #, and 13 # piles at the foot of the wall should be reduced, or the stiffness of other piles should be increased. The elastic modulus E of the pile foundation is reduced to 0.9 E, 0.8 E, 0.7 E, 0.6 E, and 0.5 E respectively, and the pile stiffness in the middle area is increased to 1.1 E, 1.2 E, 1.3 E, and 1.4 E to analyze the uneven settlement of each part, as shown in Figure 11.  
It can be seen that increasing the pile stiffness at the bottom of the wall or reducing the pile stiffness at the middle of the inverted arch has a poor control effect on uneven settlement. Therefore, it is not recommended to use the variable stiffness method to control uneven settlement during construction.
# Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires
## 2 Related Works
### Underground Personnel Positioning System  
When various underground disasters occur, underground personnel positioning and communication are the key to timely understanding the development of underground disasters and implementing rescue efficiently. Especially when disasters such as fires and floods occur, the reliability of traditional wired communication methods is not high, so it is necessary to use stable and reliable wireless communication methods to locate underground personnel [16]. Underground personnel positioning is an indoor positioning technology. Due to factors such as signal scattering caused by the complex indoor environment, indoor positioning requires higher accuracy than outdoor positioning technology [17]. Traditional GPS, manual reporting, IEEE802.11b-based, and IP-based wireless video positioning methods have shortcomings such as being unsuitable for underground confined spaces and low accuracy [18]. The literature [19] demonstrates that high-accuracy indoor positioning is achievable in large realistic environments, using wearable sensors without the need for beacons, with performance evaluated through empirical testing across various competition tracks, yielding accuracy scores between 0.90 m and 11.70 m. The Ultra-Wide Band (UWB) refers to signals with a relative bandwidth greater than or equal to 0.20 or a bandwidth greater than or equal to 500 MHz. UWB technology offers high positioning accuracy in 2D and 3D, lightweight positioning tags, and the ability to track multiple objects simultaneously in 3D space. The use of UWB technology to locate underground personnel in real time is the basis for accurate disaster route planning.
# Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry
## Materials and Methods
### Satellite Remote Sensing Data  
Five PlanetScope images at the level 3B were used in the current research (Figure 2, Table 1). Before the application of the empirical bathymetric models, all the images had been cropped to the defined study area. Images have been obtained for free under the Planet Education and Research program [20]. PlanetScope level 3B image is an orthorectified Ortho Scene Product, while the pixel value is scaled to Top-of-Atmosphere (TOA) radiance (at-sensor); a surface reflectance (SR) product is also available, reducing the need for atmospherically correcting the orthorectified PlanetScope images, projected to a Universal Transverse Mercator (UTM) cartographic projection [21]. The use of five PlanetScope images was necessary as a control for variations in the radiometric quality of PlanetScope images, and in the atmospheric and water surface conditions (sunglint, wave formation). While signal-to-noise ratio defines the quality of an image band, the current distribution of the PlanetScope CubeSats lacks this information in the metadata, thus we are not able to evaluate the quality of each selected image based on this metric. A multi-image assessment approach limits the chance factor in the conclusions regarding the performance of PlanetScope images--due to the radiometric quality and the atmospheric/water surface conditions of a single image. From the satellite images, the spectral bands blue (455-515 nm), green (500-590 nm), and red (590-670 nm) have been used for the regressions, while the near-infrared (780-860 nm) has been used for masking the land.  
Figure 1: [PERSON] study site at Apokoronas area; with red dots the collected in-situ data using a single beam echosounder.
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 4 Mobile Radiation Detection Systems
### Gamma Imaging  
Gamma cameras can be divided accordingly to three different principles: Pinhole, Coded-aperture, or Compton scattering (also referred to as Compton camera) [19; 178].  
Pinhole cameras (Figure 10a) are based on thick and heavy collimators (Pb or W) to prevent the entry of gamma-rays outside the detector field of view (FOV). In order to achieve good angular resolution, the pinhole geometric area must be as small as possible, therefore reducing the detection efficiency [22]. These collimators also make the detection system too heavy and difficult to handle [19].  
Compton cameras (Figure 10b) use the kinematics of Compton scattering to estimate the source location (Compton cone), which can be derived from the angle made by the first gammas interactions in the scatter array (first layer of detectors) and in the absorber array of detectors [178]. Since this type of camera do not use any collimation or coded mask, it features a wide FOV [22].  
Coded-aperture cameras (Figure 10c) work with a passive or active mask to modulate the incident gamma flux that reaches a position sensitive detector to generate gamma images [179].  
According to Reference [19], gamma cameras are not suitable for the rapid detection of weak sources, being more suited to be used in non-dynamic scenarios with large amounts of radioactive materials.  
Despite the fact that gamma cameras are a very powerful tool to identify hot spots or map regions with concentrations of radioactive materials, as in the aftermath of a nuclear accident, their images are devoid of source-camera distance information, as well as depth information of radionuclides embedded in materials (e.g., sand, concrete). Some methods may be used to determine these missing information; for example, [PERSON] et al. [180] proposed methods to obtain the depth information in materials analyzing the spectra of the radionuclides and checking the ratio of scattered to direct gamma-rays.  
The use of gamma cameras for the autonomous mapping and hotspot localization is limited by their long acquisition time and poor angular resolution. In order to reduce these sensor limitations, [PERSON] et al. [181] implemented some exploration algorithms based on two exploration approaches, the behavior-based and multi-criteria decision making (MCDM). For the analysis of the influence of FOV and angular resolution, parameters in the localization of a few sources (e.g., one or two), as well as the number of stops the gamma camera needed to create a radiation image, were considered. It was concludedthat MCDM features the best results especially in complex environments. Moreover, a gamma camera with higher FOV and poor angular resolution (compared to a camera with poor FOV and good angular resolution) would be preferable to explore more areas over a given time. It was also referred the importance of multi-robot systems to improve hotspots localization due to their advantages, like information sharing (faster surveys), robustness, and fault tolerance.
# Defining Blood Plasma and Serum Metabolome by GC-MS
## 5 Workflow of GC-MS Analysis of Blood Metabolome
### Gas Chromatography
#### 5.2.3 Solid and Liquid Stationary Phases  
The chromatographic column can be packed or capillary. A packed column is a metal or glass tube up to 5 m long and about 3 mm in diameter, filled with a finely powdered stationary phase. Packed columns are rarely used today because the separation efficiency they can provide is hundreds of times less than the efficiency of more advanced capillary columns [120], prepared from high-purity silica. Capillary columns are generally much longer (5-150 m) and thinner (0.05-0.53 mm). This geometry provides a high-speed movement of the sample along the column, allows one to work with microvolumes, and obtain chromatograms with better resolution. In modern capillary columns, stationary phases are applied only on the walls in four ways (Figure 5): wall-coated open tubular (WCOT), porous-layer open tubular (PLOT), support-coated open tubular (SCOT), and fused silica open tubular (FSOT). In the WCOT variant, column walls are coated with a liquid polymer, acting as a stationary phase [121]. In the PLOT column, conglomerates of porous particles are deposited in the inner wall. The inner walls of SCOT columns are lined with a layer of supporting material onto which the stationary phase is attached. FSOT column is a modification of WCOT, in which the capillary is made of fused silica, making it stronger, more flexible, and more inert than the WCOT predecessors. The introduction of crosslinking technology allowed the use of stable thick films in WCOT columns and their modifications, making the SCOT type columns practically irrelevant.  
In addition to the column geometry and the method of applying the stationary phase, the stationary phase's composition also affects the analyte's behavior in the chromatographic system. Therefore, classical polyethylene glycols and polysiloxanes of various compositions, polarities, and thicknesses (Figure 5) are used. Moreover, ionic liquid SPs exhibit a \"dual-nature\", allowing the separation of polar and nonpolar compounds and extending the temperature range at which the column can be operated [122].  
A variety of stationary phases are used for blood analysis [12]: e.g., 95% dimethyl/5% diphenyl polysiloxane is popular low-polarity phase [123], and 50% phenyl/ 50% dimethyl-polysiloxane often used as middle-polarity one [124]. When targeting specific metabolites, the phases are selected based on the target compounds. For example, in the analysis of fatty acids in plasma, polyethylene glycol was used [125].