esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 4 Conclusions  
The zero-thickness bonding element method is an effective tool for simulating the fracture behavior of rocks under the impact of spherical cutters. Using this simulation approach, we conducted a comprehensive comparative analysis of the penetration displacements, fracture zones, stress influence areas, and specific energy consumption of 16 mm and 22 mm spherical cutters under different drilling pressures and impact energy conditions. The results indicate that, at an impact energy of 35 Joules and a drilling pressure of 1.4 kN, the performance of the 22 mm spherical cutter significantly surpasses that of the 16 mm cutter, achieving a stress influence area of 43.2 mm and improving energy efficiency. By analyzing the stress superposition effects between adjacent spherical cutters, we identified the optimal arrangement for the cutters. Under the same impact energy and drilling pressure, the optimal spacing and arrangement for the 22 mm cutters were determined to be 47.2 mm and 51.2 mm, respectively, placing the stress influence area above the stress threshold. This arrangement optimizes drilling parameters and the configuration of the cutters, reducing their number while maintaining rock fragmentation efficiency and lowering costs. In summary, the proposed layout achieves a good balance between fragmentation efficiency  
Figure 12: The influence range of stress under different spacing.  
and operational costs, providing valuable insights for the rational arrangement of different diameter spherical cutters in large-diameter pneumatic down-the-hole hammers used in granite formations.  
Methodology, J.Z., K.L. and B.X.; Software, J.Z.; Validation, H.W.; Data curation, J.Z. and K.L.; Writing--review & editing, J.Z. and K.L.; Funding acquisition, H.W. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on efficient pneumatic drilling tools and rapid hole forming technology and equipment for life rescue holes in mine disaster accidents grant number [Qiankehe support [2023] general 422] And The APC was funded by Research on efficient pneumatic drilling tools and rapid hole forming technology and equipment for life rescue holes in mine disaster accidents.  
The data presented in this study are available on request from the corresponding author ([PERSON]).  
We extend our sincere thanks to the China University of Geosciences (Beijing) for providing essential facilities and resources. This work was financially supported by research on efficient pneumatic drilling tools and rapid hole forming technology and equipment for life rescue holes in mine disaster accidents. In addition, we would like to thank previous researchers for their excellent work, which has greatly helped academic research.  
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.
## 4 Methodology
### Mathematical and Geometrical Models  
Each category above defined was simulated in the EnergyPlus using simple models, by adopting small cubic samples (3 \(\times\) 3 \(\times\) 3 m) with 15 cm concrete walls. The concrete cubic sample was used as reference model in the comparisons with each category. The analysis of these models allowed the comparison between VGS categories and the execution of a large number of tests, avoiding the simulation of complex buildings. The dynamic simulations were performed in a free running mode, without plants. The meteorological data used refer to the Brescia-Ghedi weather file [66].  
From the geometric point of view, GBS were modeled as shading objects: simplified trees for GTB and rectangular surfaces for GCB, both placed at a distance of 2 m from the walls of the sample. A transmittance schedule was associated with each shading object, to represent the different transmission of solar radiation in each season.  
GCS were simulated adding two layers on the outer sample's surfaces: an anti-root membrane and a vegetated layer. The subcategory GMC also has a small layer of substrate, but its contribution can be negligible in terms of thermal insulation. In addition to the modification of the layers' properties, the behavior of the plants was reproduced through the modification of the solar absorptance, of the sensible heat flux and by adding the latent heat contribution. The latter, for the GCC only results from foliage transpiration, while for the GMC results from the whole process of evapotranspiration, due to the presence of soil.  
The GW have the common feature of functioning as ventilated facades, and they were simulated as described in 4.2.4. The MV has a defined stratigraphy, and the outer layer was simulated respecting the characteristics of the system patented by [PERSON] [12]. The HS and LS can be composed of different types of plants and substrates, which determine a great variability in the results. Therefore, a parametric study was carried out to identify the most effective combination of variables in improving the thermohygrometric behavior of the sample. The parameters were analyzed one at a time, keeping the other characteristics of the system unchanged and applying only the setting concerning that specific variable. The variation of the extinction coefficient \(k_{s}\), the thickness of the air gap and the type of soil appears to be almost irrelevant in influencing the external surface temperatures; while the variation of LAI, as shown in Figure 3, influences the external surface temperatures which is reduced by about 2 \({}^{\circ}\)C in summer for a LAI equal to 5 compared to a LAI equal to 1, comparable to the results obtained by [43].  
Figure 3: Effect of LAI on the surface external temperature of the walls. Model properties: Substrate in Loam (9 cm); \(k_{s}\) = 0.70; air gap thickness = 4 cm.
# High Performance IoT Cloud Computing Framework Using Pub/Sub Techniques
## 2 Background and Related Works  
MQTT is an Organization for the Advancement of Structured Information Standards (OASIS) standard messaging protocol for the Internet of Things (IoT) [4]. It is designed as an extremely lightweight publish/subscribe messaging transport that is ideal for connecting remote devices with a small code footprint and minimal network bandwidth. The MQTT is a protocol to publish/subscribe messaging transport protocols designed to be open, simple, and easy for clients to implement. These characteristics are used in many contexts, including limited environments such as machine-to-machine (M2M) communications and the Internet of Things (IoT) [5]. Today, MQTT is used in a wide variety of industries, such as automotives, manufacturing, telecommunications, oil, gas, etc. [6].  
Apache Kafka is an open-source distributed event streaming platform for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. It provides a publish/subscribe messaging model for data production and consumption and supports the ability to access data in real-time for stream processing by allowing long-term storage of data [7; 8]. Kafka was designed from the ground up to provide long-term data storage and data replay. Apache Kafka has a unique approach to data persistence, fault tolerance, and replay [9]. Therefore, this can be seen in how it handles scalability by allowing data access using cross-partition data sharing, topics/partitions, data offsets, and consumer group names for data replication persistence in clusters, increased data volume, and load. Apache Kafka is also well suited for real-time stream processing applications because it is designed to act as a communication layer for real-time log processing. This makes Apache Kafka suitable for applications running on communications infrastructure that process large amounts of data in real-time.  
Reliability of message delivery is important for many IoT use cases [10]. Therefore, MQTT has three defined quality of service (QoS) levels: \(0-\)at most once, \(1-\)at least once, and \(2-\)exactly once. The QoS refers to a level that guarantees the quality of service. An appropriate QoS level should be selected according to the type of service. In this study, the QoS level was set to \(0\) because speed is prioritized over the reliability of data generated by sensors.  
Our research work is based on the IoT cloud-based framework structure. In the process of designing the system structure, we gained considerable insight from the overall concept and structure presented in the review paper [11; 12; 13]. The paper focuses on providing a comprehensive overview of what the IoT cloud is as well as the most relevant use cases, tradeoffs, and implementation considerations.
## 3 Results and Discussion  
### Comparison of Changes in the Porosity of the Electrolyte Layer and Molar Concentration  
The H\({}_{2}\) and O\({}_{2}\) electrodes are composed of layers in which electric charge and chemicals are transferred. These porous layers consist of randomly linked electron and ion carriers for the electrochemical reaction. The performance is highly dependent on the characteristics of the electrode's morphology, such as the porosity and dimensions of the electrode layers. Figure 5 shows that the hydrogen concentration from the hydrogen electrode interface to the hydrogen electrode surface gradually decreases with changes in H\({}_{2}\) concentration. The distribution of H\({}_{2}\) in the anode due to different porosities causes a limitation in the mass concentration occurring in the SOFC, with an anode porosity of 0.25. This is most obvious when the H\({}_{2}\) surrounding the anode is clearly distributed and fully consumed. In addition, a higher electrode porosity could enhance the fuel mass transfer, as it can be seen that the H\({}_{2}\) concentration is almost equal at an electrode porosity of 0.75. However, the influence of the anode thickness on the operation of the exchange cells is minimal because of the excessive difference in thickness between electrode layers. Figure 6 shows the simulation results for the molar concentration of hydrogen in the anode layer at 650 \({}^{\circ}\)C. The greatest value in the molar concentration results is 0.75, which is evenly spread on the left side of the model. The input value ranges from 0.25 to 0.75 because of the influence of the electrochemical reaction in the fuel cell, causing the molar concentration of H\({}_{2}\) in the electrolyte layer to gradually increase and reach the highest output value of 0.75, as shown in the diagram of the simulation results. The results are compared with those of previous studies.  
Figure 5: Distribution of H\({}_{2}\) in the hydrogen electrode of the SOFC at T = 650 \({}^{\circ}\)C, V = 0.19 V, and porosities (\(\varepsilon\)) of 0.25, 0.375, 0.55, and 0.75.
## 2 Related Work
### Sparse Subgraph Generation Method  
In order to mitigate the complexity of graph structures, researchers have explored the generation of sparse subgraphs as a means to reduce the scale of graph-structured data. The methods for generating sparse subgraphs [37] primarily involve pruning strategies and graph sampling. Pruning strategies employ approaches such as degree-based pruning and distance-based pruning, which trim the graph based on topological structure or node features. Structural Deep Network Embedding (SDNE) [38] is a technique that can be utilized to learn low-dimensional embeddings of nodes in a network. SDNE achieves this by extracting sparse subgraphs through pruning, targeting the removal of low-weight edges in the network to reduce noise and redundant information, thereby enhancing the quality and effectiveness of the embeddings. The pruning process of SDNE can be seen as a strategy that reduces the complexity of the graph while preserving its structural information. Sampling methods typically involve selecting nodes or edges based on their importance. Random walk sampling [39] simulates random walks between nodes, occasionally restarting at specific nodes, and constructs a probability transition matrix that governs the movement between nodes. By iteratively calculating the transition matrix, the stable distribution of nodes can be obtained. Subsequently, sparse subgraphs with high-probability connections can be extracted based on the stable distribution among nodes. These approaches not only preserve the structural information of the graph but also reduce its complexity, thereby decreasing the computational costs associated with prediction and classification tasks in large-scale graph-structured data. Figure 1 illustrates an example of extracting sparse subgraphs from a large graph using random walk and k-hop neighbor sampling methods.
## 5 Simulation Results and Analysis
### System Identification under Stochastic Constant Control  
Firstly, in order to verify the effectiveness of the identification method, simulation analysis is carried out under the randomly generated constant control signals. A total of 500 times Monte Carlo simulations is performed. The constant control quantity of 500 times is shown in Figure 1, and the root mean square (RMS) values of the errors of the true system and identification system are shown in Figure 2. It is seen from Figure 2 that the RMS value of the final error is less than one, indicating that the identification model has high precision.  
Figure 4: Outputs and errors of true system (TS) and identification system (IS) under stochastic constant control law \(u\).  
Figure 3: Control signals—stochastic constant control law \(u\).
# Siamese Tracking with Adaptive Template-Updating Strategy
## 4 Experiments and Results
### DARPA Dataset Results  
#### 4.2.1 Qualitative Evaluation  
As mentioned earlier, the images in our program were taken from airborne-sensor platforms that were far away from targets. This led to a low resolution and small-size targets. Therefore, similar datasets collected by DARPA were utilized for our testing. Images on the left-hand side below are results for the Siamese tracking network without template updating on the DARPA datasets; ones on the right-hand side are results for our Siamese tracking with an adaptive template-updating strategy on the same frames.  
Figure 11 are the results of two different sequences of video images from the DARPA dataset. To be specific, images in Figure 11a-c are from video sequence \"egtest02\", and images in Figure 11d-f are from video sequence \"egtest03\". In Figure 11a,d, both of the tracking vehicles were making a turn and this lead to target deformation. These appearance changes could not match previous templates with high similarities according to the heat maps. As a result, the left images in Figure 11b,c,e,f show that the tracker without template-updating failed to track the target, while the right images in the corresponding group prove that our latest method was able to solve the problem through template updating.  
Figure 10: Results of different trackers on vehicle videos in OTB100: (**a**) one-pass evaluation (OPE) precision plots; (**b**) OPE success plots.  
Figure 11: _Cont._
# CVE2 ATT&CK: BERT-Based Mapping of CVEs to MITRE ATT&CK Techniques
## 1 Introduction
### Automated Mapping to ATT&CK: The Threat Report ATT&CK Mapper (TRAM) Tool  
Threat Report ATT&CK Mapping (TRAM) [16] is an open-source tool developed by _The Center for Threat-Informed Defense_ that automates the process of mapping MITRE ATT&CK techniques on cyber-threat reports. TRAM utilizes classical pre-processing techniques (i.e., tokenization, stop-words removal, lemmatization) [17] and applies Logistic Regression on the bag-of-words representations. Since the tool maps any textual input on MITRE ATT&CK techniques, it could, in theory, be adapted to link the CVE list to the MITRE ATT&CK Framework by simply using it on the CVE textual description. However, due to its simplicity, the tool has serious limitations when it comes to its capacity to learn the right association between text descriptions and techniques. In addition, TRAM labels each sentence individually, failing to capture dependencies in textual passages. In this way, the overall meaning of the text is lost.  
The main contributions of this paper are as follows:  
* Introducing a new publicly available dataset of 1813 CVEs annotated with all corresponding MITRE ATT&CK techniques;
* Experiments with classical machine learning and Transformer-based models, coupled with data augmentation techniques, to establish a strong baseline for the multi-label classification task;
* A qualitative analysis of the best performing model, coupled with error analysis that considers Lime explanations [18] to point out limitations and future research directions. We open-source our dataset on TagTog [19] and the code on GitHub [20].
## 1 Introduction
[PERSON] [22] proposed a leg structure combining a piezoelectric actuator and a metal spring for vibration control, which can isolate residual vibration under multi-frequency combination, and the average amplitude attenuation exceeds 20 dB. [PERSON] [23] combined Magnetorheological (MR) dampers, piezoelectric ceramics, and rubber components to develop a hybrid vibration isolator, where the vibration excitation amplitude was attenuated in the frequency range of 5-300 Hz, with a maximum attenuation of 9.17 dB. The vibration frequency band of sensitive instruments is wide, from a rigid body mode to thousands of Hertz [24]. The frequency range of concern is mainly from the bandwidth frequency of the attitude control system to the upper-limit frequency of payload sensitivity [25]. Therefore, passive control cannot meet the vibration isolation requirements of sensitive spacecraft loads, and it is also difficult for independent active control to meet the requirements due to the bandwidth limitation of actuators, energy consumption, and stability [26]. The technology of active and passive vibration isolation is the best method to suppress the vibration of sensitive spacecraft loads [27]. The vibration isolation performance of the existing vibration isolation platform is mainly reflected in the resonance frequency, and the effect of random disturbances is not ideal. The robustness of the control algorithm needs to be improved [28]. The robust control has limitations, and the control results are relatively conservative for structural uncertainties, such as parameter perturbation [29]. In this paper, an active-passive integrated six-dimensional orthogonal vibration isolation platform is presented, which is used to support sensitive space instruments and restrain the disturbances. The dynamic model of the proposed platform is established based on the Lagrange equation approach. The simulation analysis is performed with an H\({}_{\infty}\) controller-based u-synthesis method to verify the vibration isolation effect of the APIVI platform. Finally, experimental studies are conducted on the APIVI platform.
## 8 Current Challenges and Future Prospects
### The IoT and Wireless Sensors (IoTWS)  
Installing IoTWS appropriately across farmland can provide growers with the latest knowledge and information and enable them to change accordingly, which helps enhance crop yields. For sustainable agriculture, the IoT can be core and forefront for farming operations. This comprises the efficient use of electricity, water, transportation of crops, alerts for the maintenance and operation of agriculture machinery, and updated market prices. It can simplify and streamline these tasks by identifying crop requirements at each step. It has proven to be advanced and will provide farmers with unprecedented control over land and assets, thereby further changing our perceptions of various agricultural activities to maximize their efficiency and effectiveness. Besides, the upcoming Internet technology can be formed by significant improvements in wireless sensor networks and 5 th generation (5G) of smartphone communication technology, providing growers important updates in real-time and anywhere. Based on recent successes, by 2021, it is assessed that there will be more than 75 million internet technology-based devices working in the agriculture sector. Furthermore, the average farm is expected to produce 4.1 million data points regularly by 2050 [186].
## 2 Related Work
### Mouse Dynamics Datasets
#### 2.3.1 Balabit Dataset  
Published in 2016, the Balabit dataset falls in the category of 'non-guided environment' datasets and includes mouse pointer positioning and timing information for 10 users working over remote desktop clients connected to a remote server. During data collection, users were asked to perform their regular daily activities. Mouse events were stored in tuples containing the following data: timestamp, pressed button, mouse state and mouse pointer coordinates. The primary purpose of collecting the Balabit dataset was to learn how the involved users utilize their mouse so as to be able to protect them from unauthorized usage of their accounts. Both training and test data are presented as sessions in the dataset; however, the test sessions are much shorter than the training sessions.
## 3 Results and Discussion
### Antibacterial Activity  
The antibacterial activity of the extract was assayed in vitro using the Mueller-Hinton broth microdilution method against eight types of resistant bacteria. The extract showed the smallest MIC value only to _S. aureus_ and _S. typhimurium_ in concentrations of 640 ug/mL with less activity toward  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sample**} & **Total Phenolic Content** & **Antiv oxidant (IC\({}_{50}\), ug/mL)** & **FRAP (mM** & **Tyrosinase Inhibitory Activity** \\  & **(mg GAE/100 g Extract)** & **DPPH** & **ABTS** & **AAE/g Extract)** & **(mg KAE/g Extract)** \\ \hline _Z. kerrii_ & 2.2 \(\pm\) 0.1 & 143.6 \(\pm\) 2.3 & 169.7 \(\pm\) 41.0 & 0.3 \(\pm\) 0.01 & 22.7 \(\pm\) 0.8 \\ Ascorbic acid & 1.6 \(\pm\) 0.8 & 5.2 \(\pm\) 0.8 & - & - \\ \hline \end{tabular} Note: GAE: gallic acid equivalence; AAE: ascorbic acid equivalence; KAE: kojic acid equivalence; DPPH: 2.2-diphenyl-1-picryhydrazyl; ABTS: 2.2-azino-bis(3-ethylbenzothiazazine-6-sulfonic acid) diammonium salt; FRAP: ferric ion reducing antioxidant power. Values are the mean \(\pm\) SD, \(n=3\).  
\end{table}
Table 2: Total phenolic content, antioxidant activity, and tyrosinase inhibitory activity of _Z. kerrii_ rhizome extract.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Concentration (ug/mL)**} & **\% Cell Viability** \\ \hline
12.5 & 98.7 \(\pm\) 6.0 \\
25 & 95.9 \(\pm\) 6.9 \\ s50 & 99.9 \(\pm\) 0.3 \\
100 & 97.5 \(\pm\) 16.0 \\
200 & 23.1 \(\pm\) 10.4 \\ \hline \end{tabular} Note: values are the mean \(\pm\) SD, \(n=3\).  
\end{table}
Table 3: Cytotoxicity of _Z. kerrii_ rhizome extract on HaCaT cells after 24 h treatment.  
other bacterial strains in concentrations 1280 \(\upmu\)g/mL or more compared with the standard antibiotics vancomycin, gentamycin, and ampicillin, respectively (Table 4).  
We found that the antimicrobial activity of _Z. kerrii_ extract was different from other _Zingiber_ species, such as the _n_-hexane solvent extract of the _Z. officinal_ rhizome, which exhibited sensitivity to Gram-positive and Gram-negative bacteria, including _S. epidermidis, S. aureus, and E. coli._ It was clear from the results that its extract contained fewer phenolic compounds and oxygenated sesquiterpenes than indicated by previous studies, as these compounds showed antimicrobial potency [40].