esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Crushed Rock Properties on the Productivity of a Hydraulic Excavator
## 3 Calculation of the excavator's Productivity  
Calculation of the technical excavator's productivity consisted of determining the volume of material in the bucket and the duration of the cycle. The amount of material in the bucket depends on its geometric volume and the bucket fill factor while the duration of the cycle is a function of the excavator size and working conditions. The general formula for calculation of the excavator's technical productivity can be expressed by (1):  
\[\text{Q}=(3600~{}\text{V}~{}\text{k}_{\text{Q}})/\text{T}_{\text{c}}, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  
* A\({}_{\text{T}}\)—slew angle of the excavator; Vm—measured volume of material in the bucket; Ar—angle of repose; Tc—mean duration of the excavator cycle; n—coefficient of uniformity of the particle size distribution; Xe—characteristic particle size; D50—50% of particles are smaller than this dimension.  
\end{table}
Table 1: Results of field measurements and granulometric analysis.  
Figure 3: Determination of rock size distribution parameters: (**a**) digital image; (**b**) rock particle size diagram.  
Geometrical Volume of the excavator's Bucket  
The method for calculating the geometrical volume of buckets for hydraulic excavators published by the Society of Automotive Engineers (SAE) [30] is representative of the methods used by most heavy equipment manufacturers. This method is technically equivalent to methods described in ISO standard 7451 [31]. Buckets are rated on both their struck and heaped capacities. Struck capacity is the volume actually enclosed inside the outline of the sidelines and rear and front bucket enclosures without any consideration of any material supported or carried by the spillplate or bucket teeth. Heaped capacity is the volume in the bucket under the strike-off plane plus the volume of the heaped material above the strike-off plane, having an angle of repose of 1:1 (45\({}^{\circ}\)) regardless of the type of material (Figure 4). The Committee on European Construction Equipment (CECE) rates heaped bucket pay loads on a 1:2 (\(\sim\)27\({}^{\circ}\)) angle of repose for material above the strike-off plane. These standard procedures of the excavator bucket volume calculation presume certain errors in results because dissimilar materials have different angles of repose.
# Automated Identification of Sensitive Financial Data Based on the Topic Analysis
## 2 Methodology
### K-Means Clustering
#### 2.2.1 Choice of Distance Measurement Method  
Commonly used distance measurement methods include Euclidean distance [14; 15], Manhattan distance [15; 16], cosine similarity [17; 18], etc. The following factors need to be considered when a distance measurement method is selected in K-means clustering [19; 20].  
Data type. The distance measurement method should apply to the data type. For example, Euclidean distance, Manhattan distance, and cosine similarity can be used separately for continuous numerical data, binary or discrete data, and text data.  
Data characteristics. The distance measurement method should capture the data characteristics. For instance, Euclidean distance is suitable for data considering numerical differences in various dimensions, while cosine similarity is suitable for vector data considering the direction and angle relationship.  
Data distribution. The distance measurement method should be capable of processing data under different data distributions. Some distance measurement methods are sensitive to outlier values, while others can better cope with data with skewed or long-tail distributions.  
Algorithm performance. The algorithm performance can be influenced by the computational complexity of the distance measurement method. The time-consuming nature of certain distance measurement methods necessitates careful consideration of algorithm efficiency, particularly when dealing with large-scale data sets.  
The cosine similarity measure is particularly suitable for text-like data, as it disregards the text length and instead focuses on capturing the directional similarity between vectors, rather than specific numerical differences. Therefore, cosine similarity is selected as the distance measurement method for K-means clustering, which is obtained from Equation (13).  
\[S_{cosine}(v_{1},v_{2})=\frac{v_{1}\cdot v_{2}}{\|v_{1}\|\cdot\|v_{2}\|} \tag{13}\]  
where \(v_{1}\) and \(v_{2}\) represent two nonzero vectors and \(\|\cdot\|\) represents the modulus calculation of the vector.
# Multi-Controller Deployment in SDN-Enabled 6G Space-Air-Ground Integrated Network
## 2 Problem Formulation and Solution
### Problem Formulation
#### 2.1.2 Hierarchical Multi-Controller Deployment Modeling
The formula is as follows (Equation (8)):  
\[\delta(t)=\mu\times\frac{\max\left(\frac{1}{\mathbb{C}}d_{ij}*e_{ij}\right)}{ L_{\max}}+\lambda\times\frac{\sum_{j=1}^{m}\left[B_{j}-\frac{1}{m}\sum B_{j}(t) \right]^{2}}{m} \tag{8}\]  
where \(\{\mu+\lambda=1|\mu,\lambda\in(0,1)\}\) is the weight of the delay and load, variable parameters, and \(B_{j}(t)=\max\{B_{j}|j=1,2,\ldots,m\}\) is the maximum load. To sum up, the packet-IN packet rate \(q_{i}(t)\) and transmission delay \(L_{ij}(t)\) of switch nodes \(s_{i}\) are optimized by multi-objective optimization. The resulting multi-objective optimization function is described as follows (Equation (9)):  
\[\left(x_{cj},y_{cj},z_{cj}\right)=r*\left(\cos\theta_{cj}\cos\varphi_{cj},\sin \theta_{cj}\cos\varphi_{cj},\sin\varphi_{cj}\right) \tag{1}\]0  
The solution space \(X\) is the controller position matrix and the corresponding relation matrix between the switch and the controller. The objective function condition is described as follows:  
* Condition 1: Communication link status of switch and controller: \(e_{ij}\in\{0,1\}\). * Condition 2: The number of controllers is greater than 2. * Condition 3: The controller to the jurisdiction of the switch node delay is less than the maximum delay: \(L_{ij}(t)<L_{max}\). * Condition 4: Controller resource capacity greater than 0: \(Q_{c_{i}}>0\). * The solution space, i.e., the latitude and longitude of the controller is limited in China (latitude: 3.86-53.55\({}^{\circ}\); longitude: 73.66-135.05\({}^{\circ}\)).
###### Abstract  
For the Jiawula lead-zinc deposit, as easily accessible resources become depleted, mines are becoming deeper to replenish ore reserves. Identifying large, continuous, and high-grade ore bodies in deep areas has become a daunting problem. Moreover, separating lead-zinc-bearing complex ore bodies from waste material and extracting them from associated minerals are also difficult. Thus, pioneering exploratory strategies and technological methodologies are required to make breakthroughs in mineral discovery. Based on extensive-scale structural lithofacies mapping, this paper uses short-wave infrared (SWIR) spectroscopy technology to investigate hydrothermal alteration minerals in the mining area. It has identified a total of 16 hydroxyl-bearing alteration minerals, including chlorite, muscovite, illite, calcite, ankerite, kaolinite, and smectite. These minerals establish zoning characteristics around the ore bodies and on their flanks. They comprise a segmented assemblage that follows the pattern of comb-textured quartz-illite-chlorite-carbonate \(\rightarrow\) muscovite-illite-chlorite-ankerite \(\rightarrow\) illite-smectite-chlorite \(\rightarrow\) chlorite-kaolinite-calcite. Deep-zone illitization with a lower Al-OH absorbance peak wavelength (<2206 nm) and higher crystallinity indices (>1.1) and chloritization with higher Fe-OH absorbance peak wavelengths (>2254) and higher crystallinity indices (>3.0) are indicators of potential hydrothermal centers in the deeper regions. By finding hydrothermal centers and connecting their spatial distribution with existing ore bodies, a pertinent relationship between diabase + andesite, Fe-chlorite + illite, and high-grade mineralization has been established. They correspond well with the lithology-alteration mineralization. This research provides a basis for predicting the positioning of concealed ore bodies deep inside a mine or at the periphery.  
technic lithofacies; shortwave infrared; hydrothermal alteration; alteration zone; hydrothermal center; Jiawula +
Footnote †: journal: Applied Sciences
## 3 Results
### Interobserver Variability  
The interobserver variability in the phantom lesions expressed by the mean of the \(SR\) medians showed no significant difference between the two observers (\(p\) = 0.937 ANOVA). The IQR/median \(SR\) ranged from 0.063 to 0.156 (mean: 0.101) for observer A who had the least experience, and 0.025-0.073 (mean 0.043) for observer B who had more experience, but the difference was not significant (\(p\) = 0.055, ANOVA).  
The distributions of median \(SR\) and IQR/median were not significantly different between observers A and B. The mean IQR alone was significantly different between observer A, at 0.125 (SD: 0.050), and observer B 0.050 (SD: 0.018) (\(p\) = 0.027 ANOVA). The range was not significantly different between the observers. The interobserver agreement assessed by intra-class correlation (ICC) for average measures between observer A and B was 0.661 (\(p\) = 0.040).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Lesion** & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**4**} \\
**Background 25 kPa** & \multicolumn{2}{c}{**8 kPa**} & \multicolumn{2}{c}{**14 kPa**} & \multicolumn{2}{c}{**45 kPa**} & \multicolumn{2}{c}{**80 kPa**} \\ \hline Observer & A & B & A & B & A & B & A & B \\ Median \(SR\) & 0.52 & 0.68 & 0.96 & 0.82 & 1.91 & 1.35 & 2.50 & 2.82 \\ Range & 0.09 & 0.08 & 1.04 & 0.10 & 0.18 & 0.11 & 0.91 & 0.24 \\ IQR 1 25–75 & 0.06 & 0.03 & 0.15 & 0.06 & 0.12 & 0.04 & 0.17 & 0.07 \\ IQR/Median & 0.118 & 0.044 & 0.156 & 0.073 & 0.063 & 0.030 & 0.068 & 0.025 \\ \hline \hline \end{tabular}  
* IQR: Inter Quartile Range. Mean IQR/median for phantom lesions: 0.07.  
\end{table}
Table 1: Elastography strain ratio (\(SR\)) in four inclusions in a tissue-mimicking phantom.  
Figure 2: Elastogram from a tissue-mimicking phantom displaying a spherical inclusion (80 kPa) with a diameter of two cm in a background of 25 kPa. Right side: B-mode image with area markings. A (lesion) and B (reference). Left side: elastogram in color coding. The stress source was working from above in the axial direction. The lesion is speckled and green-blue, whereas the background material is mostly homogeneously green. Between the lesion and the probe, the red color indicates strain concentration between the stress source and the harder lesion. The strain ratio (\(SR\)) is mean strain in B/mean strain in A = 1.80.
# Impact of LGE-MRI in Arrhythmia Ablation
## 4 Use of the Processed LGE-MRI for Ablation Procedures
### Integration of LGE-MRI and Electroanatomical Map (EAM) during Ablation  
Several electroanatomical advanced mapping systems that display bipolar voltage maps and activation maps on a 3D reconstruction of the intracardiac chamber of interest have become available over the past few decades. Each system uses a different technology to generate a 3D image, record electrograms, and localize the electrode catheter in space. Current ablation techniques are heavily reliant on EAM systems (Carto (New York, NY, USA), Biosense Webster, Inc. (Irvine, CA, USA); NAVX (Paris, France), St Jude Medical (Saint Paul, MN, USA); Rhythmia Mapping, Boston Scientific Inc., (Boston, MA, USA).  
Electroanatomical systems have been validated for anatomical and electrical accuracy in the atria as well as the ventricles [64; 65; 66]. However, there is an important limitation: the 3-dimensional reconstructions from catheters can provide inaccurate data on scar characteristics and could under- or overestimate the extent of scarring and arrhythmogenic substrate. This is due to different reasons: the influence of the electrode size, interelectrode spacing, angle of the incoming wavefront to the mapping catheter [60; 67], and contact of the catheter with tissue (especially for those without a contact force sensor). Henceforth, EAMs can hardly be considered the gold standard of substrate definition. Moreover, it must be considered that low voltage detected in the EAM is not always equivalent to fibrosis and vice versa. Fibrosis distribution and fibrosis architecture and the possibility of far-field detection of the healthy tissue in the border of the fibrosis could affect the amplitude of the electrogram detected in the EAM.  
Integration of imaging data into EAM systems provides more information about the arrhythmogenic substrate. Therefore, it is possible to merge the EAM with the 3D LGE-MRI reconstruction. Successful integration (with high accuracy) between EAM and MRI has been demonstrated in several studies [53; 66; 68].  
For the merging process, landmarks in both the 3D reconstruction and EAM must be placed (one or more, depending on the system). The selected points to be used as the landmarks or fiducials must be in an identifiable and distinguishable place in the mapped anatomies (for example, the mitroaortic union, LV apex, the ostium of the pulmonary veins, etc.) with a certain angulation that enables them to be placed in the same direction. Once these points are selected, the estimated corresponding location of this endocardial point is marked on the imported 3D MRI surface reconstruction, thus creating a 'landmark pair'. At this point, the navigation system superimposes the 3D MRI surface reconstruction onto the real-time electroanatomic map with different algorithms, depending on each system (visual alignment, surface registration, etc.). Once the integration is completed, the user is able to navigate with the catheters over the 3D MRI reconstruction, visualizing and localizing the scar and the fibrotic tissue.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 3 Traffic-Assisted BOF Vulnerability Mining and Exploitation Method in ADASs  
This article proposes a traffic-assisted BOF vulnerability mining and exploitation method in Figure 4 to tackle the complexities of BOF vulnerability mining in ADAS devices. The first step is firmware extraction. Firmware extraction uses methods such as console commands or the reading of flash chips, and this is discussed in Section 3.1. The next step is a firmware and system analysis. We capture the communication traffic to obtain field-s/features, collect system running information (such as listening status and background processes), and locate the ADAS manufacturer's application. This step is discussed in Section 3.2. Next, we identify the BOF risk points and mine vulnerabilities using available information, such as traffic. This step is discussed in Section 3.3. For detected BOF vulnerabilities, we validate their effectiveness, which is discussed in Section 3.4. Finally, Section 3.5 discusses the exploitation of BOF vulnerabilities, such as obtaining console access.
## Results
### Transcript Levels of Key Genes Involved in C/N Metabolisms  
In the roots, Pe was more responsive to the N supply than Ps (Figure 9A). The mRNA levels of _CWI_, _NiR_, _Fd-GOGAT_, and _SUS2_ in the roots were fairly consistent at all three N supply levels in both poplars (Figure 9A). The transcript levels of _VI_, _AMT1.2_, _GS1.3_, _GS2_, _HxK_, _NRT1.1_, _SUT1_, and _NRT2.4B_ were upregulated in the Ps roots when exposed to low N supply, but the responses of these genes to the N supply varied in Pe (Figure 9A). For instance, the transcript levels of _VI_ and _HxK_ in the Pe roots were not altered in response to the N level (Figure 9A), but the mRNA levels of _NRT2.4B_ were increased in the Pe roots with low N supply (Figure 9A). However, _GS1.3_, _GS2_, and _NRT1.1_ were suppressed in the Pe roots in response to low N supply (Figure 9A). The transcript levels of _SPS_, _SUS1_, _NRT3.1A_, and _NR_ increased with N supply in the roots of both Ps and Pe (Figure 9A).  
In leaves, increased transcription of _CWI_, _SUT1_, _GS1.3_, and _NRT1.1_ were detected in Ps in response to N deficiency, but no such effects were found in Pe (Figure 9B). The mRNA levels of _SUS1_, _SUS2_, _HxK_, _NRT2.4B_, _NiR_, _Fd-GOGAT_, _NADH-GOGAT_, and _AMT1.6_ were decreased in the leaves of Ps in response to low N supply, but in the leaves of Pe, the transcript levels of these genes showed differing responses to N deficiency (Figure 9B). The transcript levels of _SUS1_, _SUS2_ and _HxK_ showed a regulatory pattern in the leaves of Pe similar to that in the leaves of Ps (Figure 9B). However, the mRNA levels of _NRT2.4B_, _NiR_, _Fd-GOGAT_, _NADH-GOGAT_, and _AMT1.6_ in the leaves of Pe were largely unchanged in response to the N supply (Figure 9B).  
Figure 9: Cluster analysis of transcriptional fold-changes of key genes involved in N uptake and assimilation in roots (**A**) and leaves (**B**) of _P. simonii_ (Ps) and _P. euramericana_ (Pe) under 0.01, 1, and 10 mM NH4 NO3. The colour scale indicates fold-changes of mRNAs. For each gene, the expression levels in roots or leaves of Pp exposed to 1 mM NH4 NO3 were defined as 1, and the corresponding fold-changes under 0.01 and 10 mM NH4 NO3 were calculated.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 6 Performance Evaluation  
In this section, the simulation parameters are firstly provided. Then, the experiments are conducted to evaluate the performance of the proposed CCRA algorithm.
## 4 Experiments and Analysis of Results
### Evaluation Metrics  
To quantify model performance, standard object detection metrics were employed:  
Precision (P): Ratio of correctly detected positive samples to all predicted positives.  
\[P=\frac{TP}{TP+FP} \tag{7}\]  
Recall (R): Ratio of correctly detected positives to all actual positives.  
\[R=\frac{TP}{TP+FN} \tag{8}\]  
Average Precision (AP): Integral of precision-recall curves across confidence thresholds.  
\[AP=\int_{0}^{1}P(R)\mathrm{d}R \tag{9}\]  
Mean Average Precision (mAP0.5): Averaged AP values across all classes with an Intersection over Union (IoU) threshold of 0.5.  
\[mAP=\frac{1}{n}\sum_{i=0}^{n}AP(i) \tag{10}\]  
Accuracy is defined as the proportion of correctly classified samples.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{11}\]  
The F1-score is the harmonic mean of precision and recall.  
\[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{12}\]  
Figure 6: Selected crop image dataset.  
where true positive (TP) is the number of correctly predicted positive samples; false positive (FP) is the number of incorrectly predicted positive samples; and false negative (FN) is the number of incorrectly predicted negative samples. The precision rate is the ratio of the number of correctly predicted positive samples to the number of all predicted positive samples. Recall, also known as the check all rate, is the ratio of the number of correctly predicted positive samples to the number of all actual positive samples. AP is a metric that describes the comprehensive performance of a model and is used to measure the average accuracy of the model on a single category. mAP is the Mean Average Precision, which is used to measure the average accuracy of the model on multiple categories.
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 4 Effective \"Green\" Technologies  
It is well-established that natural predators, such as copepods, water bugs, fish, and amphibians (e.g., frog tadpoles), that feed on larval and pupal aquatic stages of mosquitoes play a role in the ecology of these arthropod vectors. Larvivorous fish, including _Gambusia_ and _Poecilia_, have been used in over 60 countries. In spite of their potential threat to native aquatic fauna, these predatory fish have been effective in decreasing larval and pupal populations, thereby lowering the adult mosquito burden and, by extension, disease transmission in the regions where they have been employed [119; 120].  
More targeted eco-friendly mosquito control strategies include the classical Sterile Insect Technique (SIT), which utilizes a large-scale release of irradiated adults; the Incompatible Insect Technique (IIT), in which artificially increasing _Wolbachia_ infection levels in adults impose fitness costs; and the Release of Insect carrying a Dominant Lethal gene technique (RIDL), which selects against daughter progeny [121; 122; 123]. A CRISPR/Cas9 gene drive system has also been proposed for pest and vector insect population control [125]. In addition, naturally occurring bacterial varicides that destroy larval midgut have proven to be successful as biocontrol agents. The two most notable bacteria are _Lysinibacillus__sphericus_ (Ls, formerly _Bacillus__sphericus_) and _Bacillus__thuringiensis_ subsp. _israelensis_ (Bit). The larvicidal proteins are the binary toxin Trp1 Aa1/Trp2 Aa1 (formerly BinA/BinB) of Ls, and Cry4 Aa1, Cry4 Ba1, Cry11 Aa1, and lipophilic Cyt1 Aa1 [126] of Biq packaged in a unique composite prokaryote insect larvicidal organelle (PILO) [127]. Each of these control strategies is intended to ultimately prevent the production of viable offspring, thereby suppressing mosquito populations and the subsequent transmission and dissemination of viral and parasitic agents of disease.
## 3 DC Bus Voltage Control Strategy
### Improved Control Strategy Combined with the Variable Step-Size Algorithm
#### 3.3.1 The Instantaneous Error-Based Variable Step-Size Algorithm  
To address the constraints of the fixed-step algorithm, this paper incorporates a variable step-size algorithm based on instantaneous error into the model, leading to the combination of the instantaneous error adaptive filter and LADRC (IVA-LADRC). This adjustment replaces the fixed step-size with a variable one, facilitating more accurate control of voltage. The instantaneous error variable step-size algorithm dynamically adjusts the step-size to optimize both control accuracy and response speed. When the system error is large, the step-size is increased to accelerate the convergence rate; conversely, when the error is small, the step-size is reduced to enhance precision and avoid overshoot. The mathematical expression is as follows:  
\[\mu(n)=a\mu(n-1)+be^{2}(n) \tag{19}\]  
The parameters \(a\) and \(b\) play complementary roles in the instantaneous error-based variable step-size algorithm: \(a\) determines the smoothness and stability of the step-size; when \(a\) is large, the step-size changes more smoothly but adapts more slowly to environmental variations. Meanwhile, \(b\) governs the sensitivity of the step-size to the error; when \(b\) is large, the step-size responds more rapidly to error changes but may lead to oscillations. In this paper, \(a=0.7\) and \(b=0.01\).
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## Abbreviations  
AIS Automatic Identification System  
BP Backpropagation  
GC General Cargo