esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
1. The simplified particle flow model successfully replicates the theoretical fracturing lengths of the strata, with the initial stress levels observed in subsequent model monitoring being in good agreement with experimental measurements. This indicates that the model effectively simulates deep mining conditions, and the approach of simplifying the particle flow model is both practical and valid.
2. Mining of the 3 upper coal seam primarily induces fracture propagation within the primary and secondary key strata, as well as compressive damage between rock blocks. The integrity of the siltstone interlayer layer remains largely unaffected by mining of the overlying coal seams. The mining of the 3 lower coal seam leads to limited damage, primarily deepening the collapse of the overlying strata in the mined-out area and increasing the fragmentation of rock blocks, with minimal damage occurring to the surrounding rocks.
3. Mining of the 3 upper coal seam results in significant stress redistribution within the surrounding rock, allowing clear differentiation between high-stress zones, low-stress zones, and areas exhibiting original rock stress levels. Mining of the 3 lower coal seam generates a localized, multi-point high-stress distribution, and the stress patterns in the siltstone interlayer largely mirror the global stress behavior in the surrounding rock.
4. The porosity in fractured surrounding rock regions correlates with the compressive interactions between rock blocks. In areas with severe surrounding rock damage, an inverse relationship between stress and porosity is observed. Monitoring of the siltstone interlayer's porosity further confirms that its integrity remains unaffected by the mining of the 3 upper coal seam, with damage occurring only as a result of mining activities in the 3 lower coal seam.  
Conceptualization and methodology, J.X., G.L. and C.S.; software, J.X., B.T. and H.R.; chart design, J.X. and B.T.; writing--original draft preparation, all authors; writing--review and editing, J.X., G.L. and B.T. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (U22A20165); Special Project Support for Key Research and Development Tasks of Xinjiang Uygur Autonomous Region (2023B01010-1); Key R&D Program of Shandong Province (2024 CXGC010322) and National Science and Technology Major Project (2024 ZD1004104).  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
The following abbreviations are used in this manuscript:  
PFC2D Particle Flow Code 2D
## 4 Conclusions  
The realistic static and kinematic tests conducted in this study with the robust hand held GNSS GPSMap 66 unit capable of recording GNSS raw code, carrier phase and Doppler measurements demonstrate its capability under a variety of conditions. The benefit of its helix antenna over that of a smartphone leads to better multipath rejection and generally continuous carrier phase measurements under line-of-sight conditions. The Huawei P10 (operating with the Android 8 operating system)  
Figure 28: GPSMap 66 GPS C/N\({}_{0}\) values measured inside sculpture shown in Figure 29.  
Figure 27: GPSMap 66 was located inside sculpture in front of The Bow (**left**). South and west skyline left of sculpture (**right**).  
Figure 29: GPS epoch-by-epoch coordinate variations inside sculpture shown in Figure 27.  
carrier phase measurement continuity was poor in most instances. The 7 to 8 dB-Hz gain advantage of the GPSMap 66 over the P10 is one reason for its relative better performance. The differential positioning accuracy performance of the GPSMap 66 in vehicular mode was slightly better than 1 m RMS in each of the three coordinate components. The use of an external geodetic antenna in a high multipath environment demonstrated a further advantage over a helix antenna through reduction of multipath and signal attenuation, and better single point positioning. In an indoor environment with C/N\({}_{0}\) values of 15 to 35 dB-Hz, the GPSMap 66 was able to acquire and track sufficient signals to maintain one sigma accuracy of the order of 10 m RMS in each coordinate component. Although Galileo coverage is still limited, the unit produced Galileo only positions that were at the same general level of performance as that of GPS. The PPP approach was able to deliver sub-metre accuracy in each of the coordinates using 75-min data sets. The level of performance obtained in this study is similar to that obtained with the Garmin Rino 750 as described in [6]. Raw GNSS data with this unit is not however commercially available. Another analysis reported in [7] using GNSS watches with custom software to provide raw data show results close to those of the GPSMap 66 but under more ideal field conditions.  
All authors have cooperated in the data collection, processing, analyses and writing of the paper.  
Fund for this research was provided by iCORE/Alberta Innovates Technology Futures.  
The authors declare no conflict of interest.
## 1 Introduction
A goodness of fit approach using Kolmogorov-Smirnov (KS) was suggested to solve the problem of modulation classification in different channels, like the AWGN channel, the flat-fading channel, and channels that have unknown phase and/or unknown frequency offsets [14]. In [15], the authors proposed a KS test for the classification of signals of higher order by comparing the difference between the empirical cumulative distribution function (ECDFs) from received signals and CDFs of the signal under different candidate modulation format to classify modulation type. The decision statistic results in the smallest value of the greatest distance between the two CDFs mentioned above [16]. Comparing the KS test with LB and FB methods, the KS test has less computational complexity and performs better at various channels than the LB method. It is more accurate than the FB method. However, the KS test is used only for the small datasets and is more complex than the FB method. In this study, an in-depth survey is conducted on FB approaches for digital MR, which can be useful for readers who are looking for lucid information about the features and classifiers used in FB-AMR. The remainder of this paper is organized as follows: Section 2 presents the AMR signal model. Section 3 discusses the FB approach and its features, which are used during classification. Section 4 investigates the classifier algorithms of pattern recognition (PR). Section 5 presents a performance analysis. Section 6 provides the conclusions.
## 6 Experimental Evaluation and Discussion
### Open World Evaluation  
When we introduced the composition of the dataset, we mentioned the background traffic, SMART-BACKGROUND, which we had previously collected to apply to the open world environment, and for the foreground dataset, SMART-DEFENDED, we assumed that the attacker could collect sufficient traces of both kinds to weaken the potential negative effects, quantifying the adversary with specific data. The collection capacity can be expressed as the ratio has exceeded 110:1, reflecting the realism of the open world through this high ratio. All of our experiments employ confidence intervals to ensure that four attacks strike a good balance between TPR and FPR.  
If the classification outcome falls inside the confidence interval, we conclude the traffic pattern and the foreground traffic belong to the same category. In addition, we also list the features that affect classification in a larger proportion one by one, as shown in Figure 6. The time-interval sequence of cells had the highest proportion of classification, with a proportion of 0.025.  
We will focus on evaluating the effectiveness of Var-CNN, DF, K-F, and k-NN for SMART defense attacks in an open-world environment, and we will apply a simulator to artificially split these datasets (using BWR splitting, where s = 4 and m = 5) and use both undefended and defended traffic to calculate the ROC curves for each classifier. As  
Figure 5: Impact of different s and m values on the classification accuracy.  
depicted in Figure 7, we observe that the difference between the undefended and defended ROC curves is significant, with Var-CNN having a very high recognition accuracy for the undefended dataset, with an area under the AUC curve of over 0.85. While Var-CNN is close to the random guess curve (AUC = 0.5) for the defended dataset (AUC = 0.58), the adversary cannot successfully carry out WFP attacks in the open world as a result of our defense.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 5 Simulation Results
### Complexity Analysis  
The proposed resource control scheme complexity is divided into two parts.  
In the joint power allocation method, the complexity is almost determined by the preprocessing step. The full search method complexity is directly proportional to the PAR search range. If a search range of \(R\) is assumed with a search step of 0.05, the search will preprocess \(20\times R\) times, including \(20\times R\times M\) times distortion computing of reference viewpoints and \(20\times R\times L\) times virtual view synthesis. However, the estimation method will preprocess only 6 times, including \(6\times 2\) times distortion computing of the leftmost and rightmost reference views and \(6\times L\) times virtual view synthesis. Only the first frame of each sequence is preprocessed in the proposed estimation method, whereas the whole sequence is preprocessed in the full search method. The significance becomes clear as the sequence frames number grows. Thus, the estimation method complexity is 96.25% lower than that of the full search method, which is trivial.  
In the greedy search algorithms (Algorithms 1-3), all possible chunk (\(n\)) or channel (\(r\)) values are traversed iteratively, and for each \(n\) or \(r\), all the operations involved are linear. Thus, Algorithms 1-3 all have an \(O(N)\) complexity, which is negligible. The \(N\) chunks energy sorting process in each GOP is the major source of complexity, and is \(O(NlogN)\). However, instead of strictly sorting all the chunks according to their energy distribution, the sorting process can be avoided, as chunks can be sorted in a zigzag scanning manner (i.e., JPEG image compression). Furthermore, the complexity can be further reduced by considering the calculation of all the optimal values for the first GOP only and then applying the result to the remaining GOPs. This is possible if the video content of consecutive frames does not change greatly.
## 3 Modeling the Temporary Facility Layout Problem
### ANP-Based Modeling for Layout Planning
#### 3.1.4 Step 4: Supermatrix and Global Priority Calculation  
Through pairwise comparisons, the relative importance is obtained. However, this is not enough for demonstration of the differences between clusters and elements. Therefore, the supermatrix, as shown in Equation (4), is calculated for further evaluation. The supermatrix represents the relative importance between the elements in the matrix, e.g., \(W_{12}\) indicates the relative importance between the \(C_{1}\) element on the row and \(C_{2}\) element on the column in Equation (4). The local priority vectors are derived from the pairwise comparison matrices, which are inserted into the supermatrix. As illustrated in Table 5, the supermatrix is not normalized and the sum of the elements on each column does not equal to one, which is defined as the unweighted supermatrix:  
\[\text{W}=\begin{array}{c}C_{1}\\ C_{2}\\ \vdots\\ C_{n}\end{array}\left[\begin{array}{cccc}C_{1}&C_{2}&\cdots&C_{n}\\ W_{11}&W_{12}&\cdots&W_{1n}\\ W_{21}&W_{22}&\cdots&W_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ W_{n1}&W_{n2}&\cdots&W_{nn}\end{array}\right] \tag{4}\]  
In order to derive the weighted supermatrix, normalization is required to be conducted on the unweighted supermatrix. The normalized weighted supermatrix \(\overline{\text{W}}\) can be calculated by multiplying the unweighted supermatrix W shown in Equation (4) and the weighting matrix. The weighting matrix is determined by the comparisons between the criteria of elements in the Strategic Level shown in Figure 2. To ensure that the weighted supermatrix reaches stabilization or convergence, the limit relative ordering vector of the supermatrix is calculated as Equation (5):  
\[W_{limit}=\lim_{x\rightarrow\infty}(W_{weighted})^{x} \tag{5}\]  
The weighted supermatrix can be raised to large powers to obtain the global priority vector. As illustrated in Table 6, the limit supermatrix represents the resulting matrix, which reflects all effects of relationships and interdependence in the network structure.  
The limit supermatrix is the final resultant matrix from the decision-making process of ANP model, which shows the overall rankings of all criteria. The relative importance between the criteria and priorities for each criterion are illustrated clearly. From Table 6, it is concluded that the most importance criterion is the LPE criteria, while the least importance criterion is the FCR criterion. As the weight changes may affect the final performance rankings of different alternatives, a sensitivity analysis is conducted to assess the stability of the rankings of potential layout planning alternatives in the following sections.
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 2 Description of the Mechanism
### System Design
#### 2.1.3 Refueling Pipe  
As Figure 2 and 3 show, the refueling pipe is connected to the drogue and stretched along with the docking subassembly under berthing docking; this pipe is generally made of hard metal. Thus, the refueling pipe generates an additional force on the driving subassembly during the docking process, as mentioned in Section 1. A helical configuration for the refueling pipe is an excellent choice for linear tension and compression motion to decrease this additional force. In the context of this paper, the refueling pipe needs to stretch and retract repetitively, meaning that its initial and terminal states must be designed to correspond with compression and tension, respectively, for an equal axial deformation; this is to ensure that the maximum additional force of the two states is equal and the lowest. The additional force generated by the refueling pipe also influences the design of the driving force and spring pins, as explained in Section 2.3. Given the necessary compactness of on-orbit mechanisms, the design space of the refueling pipe is limited and irregular; hence, an elliptical-helical configuration with bias loading is proposed to replace the circular-helical configuration and decrease the additional force, as shown in Figure 7. The expressions of axial stiffness and strength checking of this type of pipe are derived in Section 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Dimensions** \\ _Y_-Direction** \\ \end{tabular} & \begin{tabular}{c} **\(Z\)-Direction** \\ \end{tabular} & \begin{tabular}{c} **Power** \\ \end{tabular} & \begin{tabular}{c} **Driving** \\ **Force** \\ \end{tabular} & \begin{tabular}{c} **Travel** \\ \end{tabular} & \
\begin{tabular}{c} **Speed** \\ \end{tabular} &
\begin{tabular}{c} **Redundancy** \\ \end{tabular} \\ \hline
40 mm & 157 mm & 166 mm & 12 W & 1323 N & 76 mm & 1 mm/s & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of the driving subassembly.  
Figure 6: Components of the driving subassembly.
## 2 Materials and Methods
### Analysis of Antioxidative Activity  
The antioxidativeative potentials of various extracts or fermented extracts were determined using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline)-6-sulphonic acid (ABTS). First, a 100 \(\upmu\)M DPPH solution was freshly prepared in pure ethanol. Next, extracts at various concentrations (1 mL) were individually added to 1 mL of ethanol and 500 \(\upmu\)L of the DPPH solution and maintained in the dark at room temperature. After 30 min, the absorbance was measured at 517 nm by using an Epoch enzyme-linked immunosorbent assay (ELISA) reader (BioTek Instruments, Santa Clara, CA, USA) [19]. BHT was used as a positive control. All experiments were conducted in triplicate. The DPPH scavenging activity of the extract was calculated as follows:  
\[\text{DPPH scavenging activity}(\%)=\left[1-\left(\frac{OD_{517}\ of\ sample}{OD_{517}\ of\ control}\right)\right]\times 100 \tag{1}\]  
The ABTS solution was prepared by mixing 7 mM ABTS with 2.45 mM K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) (1:1) in the dark, and the ABTS solution was diluted with methanol before use until the absorbance at 734 nm reached 0.7. Next, 50 \(\upmu\)L of the sample was mixed with 950 \(\upmu\)L of the ABTS solution for 30 min, and the absorbance was measured at 734 nm by using an Epoch ELISA reader [13]. BHT was again used as the positive control. All experiments were conducted in triplicate. The ABTS scavenging activity of the extract was calculated as follows:  
\[\text{ABTS scavenging activity}(\%)=\left[1-\left(\frac{OD_{734}\ of\ sample}{OD_{734}\ of\ control}\right)\right]\times 100 \tag{2}\]  
The IC\({}_{50}\) value of the ABTS scavenging activity was determined at 50% scavenging activity.
## 8 Introduction to Proprietary and Standards-based Solutions
### Standards-Based Technologies
#### 8.2.2 Weightless  
Weightless Special Interest Group (Weightless-SIG) proposed Weightless, an open standard offering LPWAN connectivity. There are three versions of weightless: Weightless-W, Weightless-N, and Weightless-Weightless-P. W is designed to operate in the TV white space (TVWS) spectrum (470 MHz to 790 MHz band), and it has better signal propagation as compared with Weightless-N and It supports a wide range of spreading codes and modulation techniques like DBPSK and quadrature amplitude modulation (QAM). The data packet size can be up to 10 Bytes with throughput ranging from 1 kbps to 10 Mbps, subject to link budget, and other settings [30]. To enhance energy efficiency, end nodes communicate with the gateway with a narrow spectrum and at low power [31]. As the use of TVWS is not permitted in many countries, Weightless-SIG has introduced two other variations, viz. Weightless-N and Weightless-P. Weightless-N (nWave) is similar to Sigfox and uses slotted ALOHA in the unlicensed band, supporting only unidirectional communication for end-devices to the base station. It achieves a communication range of up to 3 km with a maximum data rate of 100 kbps. Weightless-P uses Gaussian minimum shift keying (GMSK) and quadrature phase shift keying (QPSK) in the unlicensed band and offers bi-directional communication with support for acknowledgments. It achieves a data rate around 100 kbps, has a comparatively shorter communication range (2 km), and has a shorter battery life.
## 2 Spoilage Characteristics and Shelf Life of Fresh Fruit and Vegetables
### Respiration  
Postharvest fruit and vegetables are living tissue with continuing respiration that leads to further ripening and eventually over-ripeness and senescence. Respiration describes the process of metabolizing O\({}_{2}\) from the air together with organic molecules (e.g., sugar) from the tissue to intermediate compounds and eventually CO\({}_{2}\) and water [19; 20]. Many metabolic processes that have direct influence on quality attributes such as firmness, sugar content and aroma are depended on respiration [19]. If these quality parameters deteriorate beyond a certain acceptance threshold, the product is spoiled. Therefore, respiration rate and shelf life are mostly inversely correlated, meaning that a higher respiration rate implies a shorter shelf life [19]. A comprehensive table with respiration rates for fruit and vegetables can be found in [PERSON] et al. [21]. Some authors classify the respiration rates relatively to each other to indicate whether a product has a \"high\" or \"low\" respiration rate and accordingly a longer or shorter shelf life [5; 16; 19]. Respiration is strongly influenced by the ambient temperature and the gas atmosphere surrounding the product [20]. Lowering respiration rates via temperature control and/or modified gas atmosphere (e.g., reduction of oxygen concentration) can lead to an extended storage life for some commodities [16; 22].  
A special type of respiratory behavior can be observed in climacter fruits during ripening. This also includes some products which are commonly termed as \"vegetables\" but are physiologically fruit-vegetables, such as tomatoes. For non-climacter commodities hardly any changes are observed in their respiration pattern, climacter fruits show a sudden strong increase in respiration rate to a maximum (climacter peak) that is followed by a rapid decline [5]. Examples of climacter fruits are apples, bananas, mangoes, melons, pears and tomatoes. Climacteric behavior can be advantageous if products are transported over long distances, such as overseas transport, because it allows for continued ripening after harvest. It is common practice that climacter fruits are harvested mature but preclimacteric and are ripened under controlled conditions at their destination [9]. The gaseous plant hormone ethylene strongly influences the onset and progression of climacter ripening [23]. Therefore, optimized conditions regarding temperature and ethylene concentration in the surrounding atmosphere are necessary for preventing product loss by enhanced respiration or sudden climacter ripening.