esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
## 3 Methods
### Radiomic Features  
Radiomic features are quantitative descriptors extracted from images using several algorithms. They express different levels of complexity, from local characteristics to global ones. [PERSON] et al. distinguished radiomic features into two main categories, i.e., \"semantics\" and \"agnostics\" [34]. Semantics are those features commonly used by radiologists to describe ROIs visually. Agnostic features, instead, are quantitative descriptors obtained by mathematical operations on the data, which do not necessarily have a meaning in terms of imaging characteristics. These descriptors include first-, second-, or higher-order statistical indicators and shape features.  
Radiomic features can be divided into different classes, i.e., intensity-, morphological- and textural-based characteristics. The intensity-based features, also known as first-order features, describe the distribution of the intensity values in the ROIs based on the computation of the intensities histogram; such features do not take into consideration the spatial relationship between pixels, or voxels in the case of 3D Volumes of Interest (VOIs). Morphological features, instead, describe the geometric characteristics of the region. Textural-based features, also known as second-order statistics, describe the spatial composition of the intensity levels; they are defined starting from several data structures, such as the Gray Level Co-occurrence Matrix (GLCM) [35], the Gray Level Size Zone Matrix (GLSZM) [36; 37], the Gray Level Run Length Matrix (GLRLM) [38], the Neighboring Gray Tone Difference Matrix (NGTDM) [39] and the Gray Level Dependence Matrix (GLDM) [40].  
In this work, we used only agnostic features; in particular, we considered only 3D features extracted from VOIs. After having been segmented manually in CT images, VOIs of the tumoral area were reconstructed as a 3D volume before the features extraction step. To extract the radiomic features, we exploited the open-source Python framework _PyRadiomics_, which implements methods for extracting radiomic features in compliance with the definition present in IBSI [13]. The image processing and features extraction steps were performed in accordance with the analogous procedures described in [PERSON] et al. [32]. We extracted features from both the original VOIs and the filtered VOIs. In particular, we considered both the image after having applied a wavelet transform, where eight decompositions were obtained with Coiflets from 3D volumes, and the image after filtering with the Laplacian of Gaussian (LoG). The sigma parameter for the LoG filter varied from 0.5 to 5, with ticks spaced by 0.5 each.
## 4 Proposed Approach
### Priority Computing Unit  
The PCU logical block is the core element of the proposed approach, since it is responsible for the detection and classification of all considered EoI. Actually, the PCU unit will dynamically access a predefined _Twitter_ data stream looking for Events of Interest, which will then be classified and associated to priority indexes. Every detected event will be associated to a single numerical priority value within a predefined range of priority indexes.  
The operation of the PCU unit is complex and thus it is divided into two distinct modules: (1) Event Detector; and (2) Event Grader. Both modules are required and they must operate in sequence, as described in the following.  
* **Event Detector**: This module receives a real-time data flow from _Twitter_ and extracts information from events that are currently happening. This article focuses on _Twitter_, due to its aforementioned real-time nature and high volume of data, but other social media could be considered in future works. _Input_: JSON formatted data from _Twitter_ Streaming API. _Output_: List of detected events (\(E\)).
* **Event Grader**: After detection, the extracted events data go to this module for classification, where the information is analyzed and, based on a set of predefined parameters (e.g., events related to natural disasters may be more important than party-related events, depending on the applications monitoring requirements), it receives a priority level within a predefined range. _Input_: List of detected events (\(E\)). _Output_: List of events associated to priorities (\(EoI\)).
# Sedimentary Model of Sublacustrine Fans in the Shahejie Formation, Nanpu Sag
## 4 Results and Discussion
### Characteristics of Sand Body Profile  
The identification of sublacustrine fan distributions through seismic sedimentology holds significant implications for establishing depositional models. Based on seismic profiles (Figure 10), distinct seismic reflections and sedimentary characteristics can be identified. Fan deltas exhibit medium-amplitude reflective seismic facies, characterized by stable sedimentary patterns in cross-sections [32]. These are manifested as subparallel seismic reflections with moderate continuity, where reflection events often downlap onto the basal surface, indicating degradation of sediments into the lake basin influenced by sediment supply or tectonic activity. The underlying surfaces of clinoforms show a slight inclination, with proximal onlap and distal downlap observed. Sublacustrine fans predominantly develop in Es\({}_{1}\), displaying wedge-shaped or base-flat-top-convex seismic geometries that thin bilaterally and pinch out laterally. Lenticular reflection structures are observed in sublacustrine low-relief highs and slope zones.  
Wells NP4-80, NP4-81, and G129X2 primarily consist of light gray siltstone interbedded with multiple thin mudstone layers. Well NP4-80 exhibits serrated-box and funnel-shaped well-logging curves, while NP4-81 shows predominantly serrated-box patterns. In contrast, Well G129X2 displays box-shaped and serrated curves. Well NP4-92 contains thick mudstone sequences intercalated with minor siltstone, with logging curves characterized by serrated and serrated-box morphologies (Figure 10). Integrated seismic and well-log analysis reveals that Es\({}_{1}\) in the study area features both sublacustrine fans and fan delta deposits. Fan delta systems exhibit early progradational and late retrogradational stacking patterns. Vertically, multiple phases of sublacustrine fan sand bodies are developed, each separated by thick mudstone layers [33].  
Figure 9: Grain size probability curve of sand body in Es1 of Well NP4-68. (**a**) NP4-68, 4606.93 m; (**b**) NP4-68, 4607.22 m.
## 4 Results and Discussion
### Major Cracks and Displacement
#### 4.1.1 Factors Affecting the Development of Cracks in Clay Slopes  
There are many factors affecting the development of cracks in clay slopes, and these have been preliminarily explained in the introduction. Soil is a complex granular material, and its mechanical behavior is closely related to its stress state. From the perspective of micromechanics, fissures are ubiquitous in the soil, and there is closure and connection of fissures in soils during the stress variation process. Although there may be plenty of fissures in the slope, the primary cracks are generally related to the failure mechanism [28]. This study focused on the initiation and development of major cracks of a clay slope under vertical static loads.  
At present, with regard to the influence of open cracks and formation cracks on slope stability, the literature on the subject is mostly divided into three considerations, namely situations where cracks are of known depth and unknown location, situations where cracks are of known location and unknown depth, and situations where cracks are of unknown depth and unknown location. This study considered the most unfavorable scenario where both the location and the depth of the crack are unknown [26]. In addition, the precise measurement and implications of the particular width of a crack is a very complex issue [54], and this study did not do any research on this subject.
###### Abstract  
It is important to protect assets located within cavities vulnerable to incident shock waves generated by explosions. The aim of the present work is to explore if closed cell aluminum foams can mediate and attenuate incident shocks experienced by cavities. A small cavity of 9 mm diameter and 2 mm length was created within the steel end-wall of a shock tube and exposed to shocks, directly or after isolating by aluminum foam liners. Shock waves with incident pressure of 9-10 bar travelling at a velocity of 1000-1050 m/s were generated in the shock tube. Compared to the no-foam condition, the pressure induced in the cavity was either equal or lower, depending on whether the foam density was low (0.28 g/cc) or high (0.31 to 0.49 g/cc), respectively. Moreover, the rate of pressure rise, which was very high without and with the low density foam barrier, reduced substantially with increasing foam density. Foams deformed plastically under shock loading, with the extent of deformation decreasing with increasing foam density. Some interesting responses such as perforation of cell walls in the front side and densification in the far side of the foam were observed by a combination of scanning electron microscopy and X-ray microscopy. The present work conclusively shows that shocks in cavities within rigid walls can be attenuated by using foam liners of sufficiently high densities, which resist densification and extrusion into the cavities. Even such relatively high-density foams would be much lighter than fully dense materials capable of protecting cavities from shocks.  
**Keywords:** aluminum foam; shock wave; porous material; cavity pressure
## 2 Materials and Methods
### Power Saving Strategies
#### 2.4.1 Reducing the Number of Packets  
Reducing the duration of the active broadcast window presents an opportunity to decrease the overall volume of transmitted data packets. This optimization can be achieved through the utilization of BLE APIs, which enable precise control over the timing between individual data packets within a single active broadcast window. By reducing the number of data packets transmitted during each window, we can effectively lower power consumption. Data packet transmissions typically constitute the highest power consumption segments throughout the system's operation timeline. However, it is important to note that decreasing the number of transmitted packets may pose challenges, particularly in noisy environments. In such cases, receivers may encounter difficulties in reliably detecting the transmitted packets, potentially leading to broadcast failures. Therefore, it is crucial to conduct thorough investigations and establish clear limitations when reducing the number of transmitted packets.
## 2 Design and Transfer Function Acquisition of MRB  
### Structure of MRB  
Some transmission devices need to meet the requirements of having a compact structure, long service life and low maintenance cost. MRB should have excellent sealing performance to ensure no leakage of MRF. In addition, for reducing the magnetic leakage phenomenon, the direction of magnetic induction line should be perpendicular to the effective working area of the MRF as far as possible, to ensure that the magnetic field meets the normal working requirements of the transmission device.  
As shown in Figure 1, a kind of disk-shaped MRB was designed. In order to make the magnetic induction lines more distributed in the working area of the MRF and reduce the leakage of the magnetic induction lines and the magnetic resistance of the magnetic circuit, the coil was designed to be arranged inside the shell. When the coil is not energized, the MRF between the drive disks is in a Newton fluid state, and the driving and driven disks are in a separate state. When the coil is energized, magnetic particles in MRF are magnetized, and attract each other to form columnar structures parallel to the applied field. The structures restrict the flow of the fluid, which makes the driving disc and the brake disc of the MRB integrated. The braking torque of MRB is mainly produced by the field-induced yield stress of MRF, which can be adjusted by the exciting coil current.  
The torque calculation principle of the MRB is shown in Figure 2; the effective working area is the area enclosed by \(R_{1}\) and \(R_{2}\). Taking the micro ring of width \(dr\), the shear stress in the magnetized MRF in this micro ring region can be calculated by the following formula.  
\[dT\ =\ dF\cdot r\ =\ dA\cdot\tau\cdot r\ =\ 2\pi r\cdot dr\cdot\tau\cdot r\ =\ 2\pi r^{2}\cdot\tau\cdot dr \tag{1}\]  
where \(\tau\) is the shear stress in MRF.  
Figure 1: Structural diagram of MRB. 1—Input shaft, 2—bearing end cover, 3—deep groove ball bearing, 4—left shell, 5—input disk, 6—sealing ring, 7—magnetic spacer ring, 8—excitation coil, 9—outer shell, 10—MRF, 11—liquid inlet, 12—driven disk, 13—right shell, 14—positioning pin.  
The formula for calculating the shear strain rate \(\gamma\) of MRF can be expressed as follows.  
\[\gamma\ =\frac{r(\omega_{1}\!-\!\omega_{2})}{L} \tag{2}\]  
where \(\omega_{1}\) is the input disc speed; \(\omega_{2}\) is the brake disc speed; and \(L\) is the distance between the input disc and the brake disc.  
The constitutive relation of MRF is expressed by the Bingham model [10].  
\[\begin{array}{l}\tau=\tau_{0}(H)sgn(\gamma)+\eta\gamma\ \ \ \ \ \ \ \ |\tau|>|\tau_{0}|\\ \tau=0\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \
## 6 Simulation and Analysis
Through the calculation of the three indicators of SNR, NCC and RMSE, the noise reduction effects of the above four methods are further compared. The results can be seen in Figure 10:0  
It can be seen from Table 8 that the evaluation indexes of ISVD-MVMD are the most ideal. The SNR value of the noise-reduced PD signal is the largest, at about 13.0791, the NCC value is the closest to 1, at about 0.9769, and the RMSE value is the closest to 0, at about 0.0326. Furthermore, in order to evaluate the running efficiency of the proposed algorithm, the running efficiency index of the algorithm is constructed by SNR, NCC, RMSE and algorithm duration, as shown in (35). For the field-measured PD signal, the algorithm  
Figure 10:1 Spectrum analysis after noise reduction of the PD signals. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SNR (dB)** & **NCC** & **RMSE** \\ \hline S\_VMD & 6.8573 & 0.8904 & 0.0667 \\ IIE-MVMD & 7.1333 & 0.8974 & 0.0646 \\ SVD-EWT & 8.1723 & 0.9220 & 0.0573 \\ ISVD-MVMD & 13.0791 & 0.9769 & 0.0326 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Noise reduction performance evaluation of different methods. operation efficiency index is constructed by NRR, ARR and algorithm duration, as shown in (36). The time-consuming and operating efficiency indicators of different algorithms are shown in Table 9.  
\[E_{s}=\frac{\text{SNR}\cdot\text{NCC}}{\text{RMSE}}\cdot\frac{1}{\text{Time}} \tag{35}\]  
\[E_{m}=\frac{\text{NRR}}{\text{ARR}\times 10^{-2}}\cdot\frac{1}{\text{Time}} \tag{36}\]  
From Table 9, it can be seen that the algorithm time of this method is smaller than that of the S_VMD method and slightly larger than that of the IIE-MVMD method and SVD-EWT method. However, due to the good noise reduction performance, the algorithm efficiency of this method is obviously better than the other algorithms. For the purposes of studying the noise reduction effect of ISVD-MVMD on the noisy PD signal under different SNRs, WGN with SNRs of \(-3\), \(0\) and \(3\) dB and narrowband pulses with the same parameters are superimposed on the pure PD signal shown in Figure 1a. Through the calculation of SNR, NCC and RMSE, the noise reduction effects of the above four methods are further compared. The results are shown in Table 10.  
According to Table 10, the three indicators of ISVD-MVMD perform best under WGN with different SNRs.
A Multi-Source Early Warning System of MEMS Based Wireless Monitoring for Rainfall-Induced Landslides  
[PERSON]  
1 Key Laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China; [EMAIL_ADDRESS] (J.Q.); [EMAIL_ADDRESS] (D.H.); [EMAIL_ADDRESS] (H.T.); [EMAIL_ADDRESS] (X.L.) College of Hydrometeorology, Nanjing University of Information Science and Technology, Nanjing 210044, China; [EMAIL_ADDRESS] Department of Civil & Environmental Engineering, Saitama University, Saitama 338-8570, Japan; [EMAIL_ADDRESS]  
2 October  
Despite the applicable equipment and system, an accurate criterion to predict the slope instability is necessary to quantify the threshold of rainfall-triggered landslides, and it requires an appropriate formulation of the mechanism of slope instability. High-intensity precipitation is the most commonly attributed trigger of landslides in the Wenchuan earthquake-affected region [5]. Recent studies on the initiation of rainfall-induced shallow landslides have broadly indicated that the slip surface may be located above the water table, without the development of positive pore pressures, and the slope failure can be caused by the infiltration of rainwater that decreases the matric suction under unsaturated conditions [6; 7; 8; 9]. According to large-scale field investigations following the Wenchuan earthquake, shallow rainfall-induced landslides less than 1 m thick are the typical form of gravitational erosion in the depression areas of debris flows [5; 10; 11]. These landslides dominate the geomorphologic processes. The landslides in the upstream areas play an important role in the initiation and formation of debris flows. Consequently, rainfall-induced mass embolization has gained more significance in terms of disaster relief in earthquake-affected regions in China. To predict the slope instability for partially saturated landslides, the criteria as a function of multiple parameters based on a real-time landslide monitoring system has been demonstrated for years [12; 13; 14], however, to date, the multivariate thresholds criteria that combine the unsaturated hydro-mechanical methods and in-situ measurement for rainfall-induced landslides early warning is not well documented in the literature [15; 16].  
In this study, a low-cost and portable monitoring equipment for real-time monitoring and early warning system of rainfall-induced landslides is developed. A wireless sensor unit composing of a MEMS tilt sensor, as well as tensiometer, soil moisture sensor and rain gauge was chosen to establish a monitoring system for rainfall-induced landslide prediction. This sensor unit, together with all the equipment inside can be easily installed into the slope ground. The proposed system allows a low-cost and easy installation of the instrument in landslide-prone slopes. The in situ real-time monitoring big data in a landslide-prone slope and catchment in Wenchuan earthquake region in China was used as input for hydro-mechanical analysis, and a multiple thresholds criteria method was used to predict the rainfall-triggered landslide.
# Comparison of Multi-Agent Platform Usability for Industrial-Grade Applications
## 3 Selected Sample Agent Platforms
### JADE Agent Platform  
JADE is arguably the most recognised agent platform implemented in Java, known for its strong adherence to standards. It is fully aligned with the FIPA specification [51] and supports distributed agent systems by running agents in containers instantiated on different hosts. JADE agents are mobile, meaning they can move between containers; however, this feature has recently lost some of its appeal. Agent lifecycle management is handled by an Agent Management System (AMS) agent. Additionally, following FIPA standards, JADE includes a built-in Directory Facilitator (DF) agent, which serves as a yellow pages service, allowing agents to register and discover services offered by other agents.  
To facilitate testing and debugging, JADE provides a Graphical User Interface (GUI), which displays key information about currently running agents and messages exchanged between them. Furthermore, to ensure secure inter-platform communication, JADE supports plugins such as (1) JADE Security, based on the Java Security Model, which includes authentication for system components (agents and containers), and (2) JADE Public Key Infrastructure (JADE-PKI), which allows for the encryption of agent communication channels. As of late 2024, JADE continues to be actively developed and maintained.  
One noticeable limitation of JADE is the lack of built-in support for agent knowledge representation and reasoning methods. To address this, several extended JADE-based platforms have been developed, including Jadex.
# Lithium Metal: The Key to Green Transportation
### Lithium in Electric Vehicles  
Lithium was first used in batteries in the 1970s [47], and it has since been used in a wide variety of applications. Those batteries are categorized as: (1) Primary batteries that are single-discharge. They have high charge density, light, and long life. However, their high cost per unit is still a burden to the consumers; (2) secondary batteries that are rechargeable. These rechargeable batteries are those used in electric vehicles, which are in high demand, necessitating a large amount of lithium production.  
Electric vehicles are defined as vehicles powered by an onboard battery that can be charged from an external source of electricity. It includes plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) [48]. Electric vehicles are often referred to as \"plug-in electric vehicles\" (PEVs). Since climate change has become a global concern, carbon emissions has been identified as one of the biggest contributors to global warming. The electric vehicles have become a major project introduced to reduce carbon emitted from road transportation. In this process, lithium plays a significant role in making batteries capable of saving electric power (Figure 3).  
Currently, there are three types of electric vehicles (Table 2) that use lithium-ion batteries, and they are classified based on their technology and the type of connection to the power grid.  
\begin{table}
\begin{tabular}{c c} \hline
**Types of Electric Vehicles** & **Descriptions** \\ \hline \multirow{6}{*}{All-electric Vehicles} & All-electric vehicles are the first generation of EVs that use the energy stored in batteries to power electric motors and provide propulsion power. They are called non-polluting vehicles or zero pollution vehicles. They are plug-in electric vehicles (PEVs). Although they have the advantage of being charged to the power grid \\  & either at home or in public places, their batteries are of limited capacity and cannot \\  & run long distances. They do not give off any greenhouse gases at all and can be \\  & charged with energy from renewable sources. \\ \hline \multirow{2}{*}{Hybrid Electric Vehicles (HEVs)} & They have both a fuel engine and an electric motor. Their battery capacity is \\  & sufficient to save energy from the fuel engine and brakes. About 1.5 million HEVs \\  & have been sold in the last decade. However, they are dependent on the fossil fuel \\  & consumption engine. \\ \hline \multirow{2}{*}{Plug-in Hybrid Electric Vehicles (PHEVs)} & They are combinations of Hybrid Electric Vehicles (HEVs) and All-Electric Vehicles. Their batteries require more capacity than HEVs because they are rechargeable \\  & from the power grid. The PHEV battery must be capable of fast discharge and fast \\  & recharge. It is therefore possible to travel longer distances. \\ \hline \end{tabular}
\end{table}
Table 2: Types of electric vehicles based on battery technology. Data from [29].  
Figure 3: The simplified scenario of lithium to electric vehicles.