esa-sceva/satcom-chunk-collection huggingface dataset:
# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 3 Processing Capability
### Higher Level: Highly Analysis Ready Data  
#### 3.3.1 General Concept  
All higher-level FORCE routines follow the same general concept and act on the Level 2 ARD data cubes. The processing is tile based, i.e., the tiles are processed in sequential order (see Figure 6). Parallelization is implemented within the tile using multithreading.  
FORCE reads and processes necessary information only. The square extent needs to be defined (red rectangle in Figure 6). Additionally, a tile white-list can be provided to restrict the number of tiles for non-square areas of interest (colored tiles in Figure 6). The data fusion functionalities (see Section 3.3.5) require additional data from neighboring tiles to produce seamless products; only pixels on the edge of the tiles are read (in dependence on the prediction radius). Only relevant products are pulled (in most cases, these are BOA and QAI products). The same applies to sensors, i.e., any combination of Landsat 4, 5, 7, 8, Sentinel-2A, and -2B can be chosen. A waveband mapping procedure is used to generate multi-sensor products, i.e., only matching bands are used (Table 1; for details see [42]). Spectral bands are only read when required (e.g., red and near-infrared bands for the Normalized Difference Vegetation Index (NDVI)). Temporal filters restrict the amount of data to the time period (and/or season) that is required. Output products can freely be selected, which in turn trigger the respective processing. Output is tile based; the FORCE auxiliary module (FORCE AUX) includes a tool for mosaicking generated products using the Geospatial Data Abstraction Library (GDAL) Virtual Format.  
As multiple spatial resolutions are permitted within a data cube, the target resolution must be defined. Resolution adjustment can be performed using nearest-neighbor resampling (pixel decimation/replication) or reduction using approximated point spread functions (PSF). On-the-fly resolution enhancement is not implemented, but the spatial resolution of ARD can be improved beforehand (see Section 3.3.5).  
Quality control is completely under the user's control. All provided quality flags (Table 2) can be used individually.  
Figure 6: General concept of higher-level FORCE processing.
## 3 Numerical Model and Simulation Scheme of the Strain-Structural Plane
### Numerical Simulation Scheme  
To explore the influence of the structural plane geometric parameters on the information response characteristics of the surrounding rock, three working conditions are set up. The structural plane in Figure 2 is taken as a reference, and the geometric parameters are adjusted on this basis. The length and relative distance of the structural plane are fixed. Taking the center point of the structural plane as the horizontal line, the angle is set to be 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\), respectively. The angle and relative distance of the structural plane are fixed; the extension lengths of the structural plane to both sides are set at 6.5 m, 8.5 m, and 10.5 m, respectively. The angle and length of the structural plane are fixed. The relative distance between the structural plane and tunnel was set to 0 m, 1.7 m, and 3.4 m, respectively. The specific forms of each condition are shown in Figure 3.  
Considering that this paper mainly studied the influence laws of structural plane geometric parameters on the multivariate information field within the surrounding rock during rockburst, the influence of excavation mode, support, and other conditions is not considered. The excavation method is full-section excavation, and no support is carried out after excavation. The multivariate information characteristics of the surrounding rock under different conditions are studied by controlling variables. The simulation scheme is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{3}{c}{**Geometric Parameters of Structural Plane**} \\ \cline{2-4}  & **Dip Angle (\#)** & **Length (\(L\))** & **Relative Distance (\(D\))** \\ \hline a & 30 & 8.5 & 1.7 \\ b & 45 & 8.5 & 1.7 \\ c & 60 & 8.5 & 1.7 \\ d & 45 & 6.5 & 1.7 \\ e & 45 & 8.5 & 1.7 \\ f & 45 & 10 & 1.7 \\ g & 45 & 8.5 & 0 \\ h & 45 & 8.5 & 1.7 \\ i & 45 & 8.5 & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The numerical calculation scheme.  
Figure 3: Different structural plane geometry parameter setting forms: (**a**) Dip angle; (**b**) Length; (**c**) Relative distance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Contact Properties**} & **Normal Stiffness** & **Shear Stiffness** & **Internal Friction Angle** & **Cohesion Force** \\  & **(GPa/m)** & **(GPa/m)** & **(°)** & **(MPa)** \\ \hline Fictitious joint & 70.45 & 52.20 & 53.51 & 24.67 \\ Structural plane & 10 & 10 & 30 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the block contacts.  
Multivariate Catastrophe Information Characteristics of the Strain-Structural Plane Slip Rockburst under Different Conditions
## 5 Modification of the HBFT Algorithm
### Latency  
In this section, we compare the latency of the mHBFT algorithm with the HBFT and PBFT algorithms by varying the number of nodes. We recorded the time interval from transaction initiation to the client receiving the completion of consensus, which serves as the measure of latency. The experiments were conducted multiple times, and the average values were taken for comparison.  
The system latency is defined as the average time interval from the activation of the protocol in the first node to all nodes reaching consensus and obtaining the result. Additionally, we also considered the baseline latency in the experiments, which refers to the delay required for each node to propose a single transaction throughout the protocol execution process. Figure 11 illustrates the comparison of latency between PBFT, HBFT, and the proposed improved algorithm in terms of the number of nodes.  
We measure the time interval from when the data are sent from the client to when the corresponding data are received as our benchmark for latency. As shown in Figure 11, the proposed improved algorithm, the PBFT algorithm, and the HBFT algorithm all experience a slight increase in latency with the increase in the number of nodes. However, it is evident that the improved algorithm exhibits a much slower growth rate in latency compared to both the HBFT and PBFT algorithms. Specifically, the PBFT algorithm's latency reaches 180 ms. Therefore, from these experiments, we can conclude that our algorithm reduces latency by 66% compared to PBFT and by 33% compared to HBFT. This improvement in latency allows our algorithm to handle company transactions more efficiently, enabling companies with multiple functional departments to achieve consensus more quickly and complete tasks assigned by upper management in real-life scenarios.
## 2 Resolution, Scale, Scope and Other Properties of Descriptions
### Symbols, Alphabet and Encoding  
The alphabet is only indirectly connected to a description. An exact definition of our conception of the alphabet is justifiable to properly depict some notions about scale. The alphabet is the set of elementary symbols used to create the compound symbols that may be part of our interpretation of a message. In other words, a symbol may be formed by several alphabetic symbols--usually regarded as characters. When reading an English text on a computer screen, the symbols of the description could be the words of the text, while the alphabet should be considered as the set of letters used to form larger symbols or words. While these larger symbols are useful due to their capacity to store meaning or semantic content, the symbols forming the alphabet are tools with no meaning by themselves. Even though alphabet symbols can be represented as the conjunction of even smaller symbols, at a certain point of our interpretation, we are not interested in the sub-symbols of the alphabet symbols. Thus, when talking about a natural language, for example, the characters and not the pixels, are considered as the elementary symbols which form the corresponding alphabet.  
The symbols of an alphabet are useful to create larger and probably more meaningful, symbols. We refer to the rules which allow the formation of alphabet symbols as the 'encoding'. With this schema, an English text can be regarded as a description formed by words, which in turn are formed by letters. These letters are elements of the set considered as the English alphabet. Now, each one of these letters may be written by a single drawing, as with a pencil and paper, or by another 'letter-forming system' as a group of pixels with color and shape resembling the alphabetic symbol or, if the encoding Morse system is used, the set of dashes and dots. This reasoning leads to treating the alphabet and encoding as artifacts, which enables us to construct an observation scale. However, the alphabet and the encoding do not directly link to the description, though they are essential to any written communication system. Whenever the alphabet is confused with the most relevant set of symbols used in a description, the freedom to form effective interpretations of the message, disappears. In this regard, exploring the impact the alphabet has over the effectiveness of a language seems to be an interesting branch of research. However, it lies beyond the scope of the present study.
## 1 Introduction
Third, for real-time traffic, _i.e._, video traffic, we demonstrate the effect of DRRF with deadline (maximum allowable delay) enforcement _versus_ Earliest Deadline First (EDF) [12]. The results show that DRRF outperforms EDF, and maintains fairness under overload scenario [13]. This paper is organized as follows. In Section 2, we describe a simple scheduling technique imitating Generalized Processor Sharing, or GPS, along with Deficit Round Robin (DRR). In Section 3, we discuss a modification of DRR by allowing packet fragmentation, called DRRF. Section 4 presents a concept of Generalized Weighted Fairness (GWF) _versus_ temporal fairness, or slot fairness, and throughput fairness, or byte fairness. Section 5 shows how to derive the quantum size for each mobile user with minimum reserved traffic rate constraint; and how to apply maximum sustained traffic rate and traffic priority parameters to DRRF. We describe scheduling techniques for real-time traffic in Section 6. Section 7 shows simulation results based on DRRF in both real-time and non real-time traffic scenarios. Finally, conclusions are drawn in Section 8.
## 6 Application to Real Data  
In this section, we applied the EEP model to three real data sets to assess its ability in fitting reliability data. For comparison, we choose two models with Pareto tails. The first one is the modified heavy-tailed Pareto model [26]. This is a model that was recently developed to fit the data with UBT-shaped hazard rates. The other one is the Weibull-Pareto composite model [20,23]. This model was previously used to model unimodal survival and reliability data.  
We compared the performance of the proposed EEP model against the original EP model, generalized in terms of Akaike information criterion (AIC) [27], AIC3 [28], corrected Akaike information criterion (AICc) [29], and consistent Akaike information criterion (cAIC) [30]. In addition, to justify that the EEP model is proper compared to its parent EP model, LRT and Wald's test were conducted with the hypothesis in (18). In addition, the Kolmogorov-Smirnoff (KS) test was used to assess the goodness-of-fit of the EEP and the EP model.
# From Symptom Tracking to Contact Tracing:
## 3 Results
### Features and App Uses  
Contact-tracing apps have received the bulk of media attention. However, the top-100 returned apps on an app store search for \"COVID-19\" offered a range of diverse features and functions (see Figures 2 and 3). In both May and June, the most common features in coronavirus-related apps were educational materials related to the virus (74.1%, 79.7%), updated statistics (56.9%, 53.6%), updated local news (39.7%, 44.9%), and a screening tool to determine whether the user should get further testing (37.9%, 40.6%). Less common in both May and June were apps for telehealth (3.4%, 7.2%), research studies (5.2%, 10.1%), community service efforts (6.9%, 5.8%). There was a notable increase in apps that provided content or support for mental health between May and June, rising from 13.8% to 23.2% of the apps having resources or support available to users. In terms of use of the apps, 23 were patient-facing, 42 were reference, and 6 were for first responder/essential worker use. We did not identify strong correlations between app origins and app features, but the apps from non-profit developers appeared most strongly correlated with symptom tracking (R = 0.504, \(p=0.00025\)) and research study (R = 0.504, \(p=0.00025\)) offerings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **May** & **June** \\ \hline Government & 49 & 53 \\ For-Profit & 20 & 28 \\ Non-Profit & 1 & 3 \\ Trusted healthcare company & 13 & 7 \\ Academic Institution & 10 & 5 \\ \hline \hline \end{tabular} \({}^{1}\) We removed eight apps for the analysis in May and four apps for the analysis in June, as either the apps became unavailable within the one week that we analyzed them or the app’s name was in a language that required a different script to notate.  
\end{table}
Table 1: Origin of COVID Apps in May and June 1.
# A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision
## 1 Introduction
This method calculates disparity from different viewpoints to directly obtain depth information, which is then used to determine fragment sizes. The application of binocular measurement technology can effectively advance the process of intelligent fragmentation recognition. In summary, this study employs YOLOv8 and binocular vision technology for blasting fragment size recognition, establishing a recognition method based on YOLOv8 and binocular vision, which is applied to the Husab Uranium Mine in Namibia. YOLOv8 is used for accurate rock detection and mask segmentation, and a series of machine learning algorithms are integrated to achieve high-precision size recognition, followed by computing actual rock dimensions using binocular vision. This method not only improves the automation of blasting fragment recognition but also maintains high recognition accuracy under complex environmental conditions, supporting blasting evaluation and production optimization. Compared to existing methods, this study integrates binocular vision with YOLOv8 for high-precision fragment distribution recognition during haul truck transportation, offering a new path toward intelligent and automated blasting in mining.
## 3 Control Protocol Design and Stability Analysis
Remark 3: _Compared with existing observer-based prescribed-time consensus studies such as [36; 37], which have divided the proof into three different parts, i.e., \([t_{0},t_{0}+T_{1}]\), \([t_{0}+T_{1},t_{0}+T_{1}+T_{2}]\), and \([t_{0}+T_{1}+T_{2},\infty]\), the current study requires \(T_{1}<T_{2}\)[36] and the construction of different Lyapunov functions for different parts [37]. In this paper, the unified Lyapunov function is constructed to consider the above three different time intervals at the same time within a unified time (T)._  
Let us suppose the situation where we have consensus in the absence of external disturbances, specifically when \(\sigma=0\). In order to realize the prescribed-time bipartite output consensus of multi-agent systems (1) of the undirected topology with state observers, the following control protocol is proposed:  
\[\zeta(t)=(S^{-1}LS\otimes I_{m})Y(t), \tag{15}\]6  
with state observer expressed as  
\[\zeta(t)=(S^{-1}LS\otimes I_{m})Y(t), \tag{15}\]7  
where \(\alpha\) is a constant that is yet to be specified, \(k_{1}>\frac{1+\rho^{2}}{\lambda_{min}(C\mathbb{T}^{\text{T}})}\), \(k_{2}>0\), \(\mu(t)\) is designed in (5), \(\widehat{x}_{i}(t)\) is the estimation of state \(x_{i}\), and \(G_{1}\) is an observer gain matrix to be determined. **Corollary 1**. : _Under Assumptions 1 and 2, for any predefined \(T>0\), prescribed-time group output consensus can be achieved for a multi-agent system (1) with \(\sigma=0\) under a distributed control protocol (24) if the following apply: (1) Control parameter \(\alpha\geqslant\frac{\lambda_{max}(C\mathbb{T}^{\text{T}})+\lambda_{min}(C \mathbb{T}^{\text{T}})}{2\lambda_{2}(L)}\); (2) Observer gain matrix \(G_{1}=-(k_{1}+k_{2}\frac{\dot{\mu}(t)}{\mu(t)})C^{\text{T}}\)._  
The proof of the corollary is a simplification of the process for Theorem 1.
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 5 Android Vulnerability Detection
### Image Analysis-Based Method  
DeepVisDroid is an amalgam of Image fusion with a one-dimensional convolutional-layer neural network. Multiple image-based local and global features are extracted and used to train a convolutional neural network in multiple scenarios. Multiple files from the Android app's contents are converted into grayscale images, such as the Manifest.xml file-based dataset Manifest, DEX code file-based dataset, Manifest and Resources.arsc file-based dataset, and Manifest and Dex file-based image dataset, to construct four grayscale image datasets. Then, the four different image-based local features and three different image-based global features were extracted to train the proposed model. After that, a lightweight, one-dimensional, convolutional layers-based CNN model was proposed and trained using the extracted image-based global and local features. Furthermore, two classical CNN models were proposed, and two well-known deep learning models, namely ResNet and Inception V3, were tested to compare the results of the proposed DeepVisDroid with the results of the state-of-the-art deep learning model [33]. The obtained results show that the proposed model outperformed the classical CNN models in terms of classification accuracy and computational cost.  
Based on attributes collected from images and a manifest file, the authors in [34] offer a unique mobile botnet detection system. The approach combines features derived from the manifest files with a Histogram of Oriented Gradients and byte histograms produced from images representing the executable app. The best features are then used for categorization with ML algorithms after feature selection. The suggested system was tested on the ISCX botnet dataset, and the findings show that it works, with F-1 varying from 0.923 to 0.96, employing standard Machine-Learning techniques. Furthermore, employing an 80:20 train-test split and 10-fold cross-validation, the Extra Trees model [35] achieved up to 97.5 percent overall accuracy and 96 percent overall accuracy.
## 2 Edge Computing
### PUF-Based Authentication Mechanism
#### 2.2.3 Temperature and Voltage Variations  
Process-induced variations in devices are leveraged by PUFs to generate unique and unclonable authentication keys. However, fluctuations in temperature and voltage can compromise the reliability of their outputs. In particular, the error rate in APUFs can reach up to 10% under varying environmental conditions, which may cause protocol failures when PUFs are integrated with cryptographic functions [86]. Temperature changes affect transistor current levels, significantly altering signal propagation delays and, consequently, PUF responses. Researchers have proposed a Temporal Majority Voting (TMV) technique to reduce response variability, coupled with a reusable fuzzy extractor and associated helper data for error correction [87]. This approach achieved Bit Error Rates (BERs) of less than 1.5% under optimal temperatures and 8% under extreme conditions.  
[PERSON] et al. evaluated their XOR-APUF design under temperature variations ranging from 0 \({}^{\circ}\)C to 85 \({}^{\circ}\)C and supply voltage variations between 0.95 V and 1.05 V, demonstrating perfect reliability (100%) through the use of the TMV scheme. Additionally, an Online Reliability Evaluation (ORE) method for APUFs implemented using a 40 nm process achieved 100% reliability across a voltage range of 0.8 V to 1.4 V and an operating temperature range of 0 \({}^{\circ}\)C to 80 \({}^{\circ}\)C [88]. Another PUF design, based on a Current Mirror Inverter, maintained a uniformity of 0.49 and an inter-Hamming distance (inter-HD) of 0.50 while operating under temperatures from \(-30\,^{\circ}\)C to 60 \({}^{\circ}\)C and voltage variations between 0.7 V and 1.5 V [89].
## 7 Results
### Scenario PSC 3  
As shown in Table 20, InC + GA achieved the best performance, with the lowest MAE (0.0738) and IAE (0.7924), confirming its high precision under dynamic shading. Fuzzy and InC + P&O also maintained excellent efficiency (above 98.5%) with minimal errors. In contrast, the ANN recorded the lowest efficiency (91.42%) and significantly higher error metrics. This lower performance is attributed to the neural network's limited ability to generalize to the specific irradiance profile of PSC 3, which was underrepresented in the training dataset. Enhancing the ANN performance in this scenario would require expanding or adapting the dataset to better reflect such dynamic conditions. Meanwhile, the DQN demonstrated moderate accuracy (96.28%) but exhibited noticeable tracking fluctuations, indicating reduced control stability in this scenario.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **MAE** & **IAE** & **MSE** & **ISE** & **Avg. Eff. (\%)** & **Conv. Time (s)** \\ \hline P\&O & 1.2470 & 12.7990 & 2.4616 & 27.0702 & 94.3909 & 0.2261 \\ InC & 1.2474 & 12.8019 & 2.4625 & 27.0762 & 94.3890 & 0.2254 \\ Fuzzy & 0.6244 & 6.4607 & 0.8937 & 11.1054 & 96.3035 & 0.1891 \\ InC + P\&O & 1.2473 & 12.8012 & 2.4623 & 27.0758 & 94.3897 & 0.2259 \\ InC + GA & 0.6362 & 6.5798 & 0.7684 & 9.5713 & 96.2199 & 0.4076 \\ P\&O + GA & 0.6361 & 6.5806 & 0.7636 & 9.5218 & 96.2209 & 0.4116 \\ ANN & 0.7540 & 7.8210 & 0.9840 & 13.8583 & 94.7047 & 0.1582 \\ DQN & 0.9174 & 9.1121 & 1.5140 & 16.2580 & 93.0104 & 0.1973 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Performance metrics for Scenario PSC 1 (partial shading conditions).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **MAE** & **IAE** & **MSE** & **ISE** & **Avg. Eff. (\%)** & **Conv. Time (s)** \\ \hline P\&O & 1.0843 & 11.5210 & 1.9253 & 22.4451 & 83.5914 & 0.4561 \\ InC & 1.1220 & 11.5138 & 2.0071 & 22.4282 & 82.9441 & 0.4557 \\ Fuzzy & 1.0036 & 10.4573 & 1.3136 & 15.5130 & 84.0660 & 0.9092 \\ InC + P\&O & 1.0929 & 11.5257 & 1.9405 & 22.4593 & 83.4341 & 0.4559 \\ InC + GA & 0.6162 & 6.3335 & 0.4283 & 4.7474 & 89.3252 & 0.5825 \\ P\&O + GA & 0.6160 & 6.3309 & 0.4308 & 4.7676 & 89.3285 & 0.5809 \\ ANN & 0.5115 & 5.2931 & 0.3810 & 4.9916 & 92.7658 & 0.1816 \\ DQN & 0.2014 & 2.0017 & 0.0645 & 0.6281 & 93.9154 & 0.2866 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Performance metrics for Scenario PSC 2 (severe partial shading conditions).
## 4 Results and Discussion
### Overall Cycle Time  
Given the mine expansion in 2023, it was observed in Figure 12 that the overall cycle time increased from 30.4 min in January to 36 min in December. Another variation came from the months of August and October, which was the result of an increase of 30 s in the waiting time at the loading equipment and destinations assigned during those months. On the one hand, for the stochastic model in August, the queues generated at the loading equipment were 25 s longer compared with the deterministic model, and 10 additional seconds were added to the overall cycle time in that month for the stochastic model compared with the deterministic model. On the other hand, the queues at the loading fronts in October were 34 s longer for the stochastic model, and 6 additional seconds were added to the cycle time at the destination points for the stochastic model during that period. In both cases, the increments were the result of the bunching of trucks due to shorter distances to travel and the fall in planned tonnages.