esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Breakage Rate of Broken Rock Mass in Different Size Grading Conditions
In the early period of Stage I, the stress in the broken rock mass approaches zero, resulting in an almost negligible breakage rate. In the later period of Stage I, stress begins to slowly increase, leading to a gradual increase in the breakage rate. In Stage II, the interaction force between rock blocks is intensified, initiating the formation of penetrating cracks and causing a slow increase in the breakage rate. Combined with Figure 8, it can be concluded that during Stage II, the erosive weakening effect of impact strength predominantly governs the generation of microcracks in the broken rock mass. Upon entering Stage III, the interlocking action between rock blocks rapidly increases, accelerating the breakage rate, thereby causing a rapid increase in the breakage rate in Stage III. Combining with Figure 9a, take the broken rock sample with a size grading of G1 as an example, this study explored the influence of impact-induced strength corrosion on the breakage rate. In Stage I, there are few early AE events in the G1 broken rock sample, indicating that no small-sized impacting rock particles have been formed yet. At this stage, the two breakage rate curves, with or without impact influence, overlap with each other. In the later period of Stage I, impacting rock particles start to appear, and the strength corrosion effect begins to affect the breakage rate, causing a gradual separation between the two curves. In Stage II, the number of impact particles increases, and the separation trend between the two curves becomes more pronounced. During this stage, the impact of erosion on the breakage rate reaches its maximum. In Stage III, the growth rates of both curves become similar. In this stage, microcracks are caused by two factors: firstly, the mutual compression and interlocking between rock blocks lead to direct fracture; secondly, particle impacts reduce the local strength of rock blocks, making fracture phenomena more likely to occur. The difference in incremental AE events decreases in Stage III, indicating that mutual interlocking between rock blocks becomes the dominant factor for rock fragmentation at this stage. Combined with Figure 8, according to the trend similarity of the stress-strain curve, these five kinds of grading size broken rock masses can be divided into two groups: Group 1 with G1, G2, and G5; Group 2 with G3 and G4. To discuss the two groups' differences in breakage rate characteristics, we take G1 vs. G3, for example, as shown in Figure 10. The difference value of the breakage rate between G1 and G3 increases until Stage III, then decreases during Stage III, and the maximum difference value is 0.094. Figure 10: Relationship between breakage rate and strain of broken rock mass with different particle size grades.
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 5 Numerical Results
### Performance Comparison  
Figure 6 displays a performance comparison between the authentication scheme proposed in [16] and our scheme. From Figure 6, we can see that the performance (\(P_{f}\), \(P_{d}\)) of our scheme jointly using AAoA, EAoA and path gain outperforms that of thescheme only using path gain [16]. This is because the scheme in [16] uses coarse-grained channel information while our scheme uses multi-dimensional mmWave MIMO channel features (i.e., AAoA, EAoA and path gain) to effectively utilize the fine-grained channel information. It is also noted that when \(P_{f}\) increases, the detection rates of both schemes increase. Moreover, the authentication performance (\(P_{f}\), \(P_{d}\)) of our scheme can better satisfy the practical authentication requirements. This is beneficial for exploiting both the AAoA and EAoA features, and allows us to gain important insights on the impacts of angle domain features on authentication performance.
## 3 Results and Discussion
### Functional Characteristics of T. catapa and Its Blends with Erythritrol and Cane Sugar
The breakdown viscosity (3.5-12 mPas) indicates good paste stability during cooking, while the setback viscosity (41.5-126 mPas) suggests a moderate retrogradation tendency. The pasting temperature range (53.5-87 \({}^{\circ}\)C) is typical for nut flours and indicates good gelatinization properties suitable for various food applications. Figure 1 illustrates the pasting profiles of _T. catappa_ powder and its blends with erythiritol and cane sugar over time under controlled temperature conditions. The viscosity curves demonstrate distinct patterns among the different formulations during the heating-cooling cycle. Pure _T. catappa_ and the 5% cane sugar blend exhibited the highest viscosity along the profiles, reaching approximately 170-190 mPa-s during the heating phase. These samples do not show the characteristic pasting behavior with precise peak formation, followed by a breakdown phase and subsequent increase during cooling. Oppositely, they resemble more high protein sample pasting profiles with a prolonged phase of building viscosity. These samples' relatively high (compared to initial) final viscosities (around 270-300 mPa-s) suggest some retrogradation tendencies. Figure 1: Pasting profiles of _T. catappa_ and its blends with erythroid and cane sugar. Adding higher concentrations of sweeteners (15% of erythroid and cane sugar) resulted in even lower viscosity profiles throughout the entire pasting cycle. These blends do not show isolated peaks with a viscosity of approximately 100 mPa\(\cdot\)s during the heating and maintaining phase, resembling the profile of pure _T. catappa_. The final viscosities of these blends were also notably lower (130-140 mPa\(\cdot\)s), suggesting a dilution effect to the control and lower concentration blends. The 5% erythroid blend showed an intermediate pasting profile, with a peak viscosity of around 104 mPa\(\cdot\)s and a relatively stable viscosity throughout the heating-holding phase. All samples showed relatively stable viscosity during the holding phase at 95 \({}^{\circ}\)C, indicating good thermal stability of the formed pastes. The cooling phase demonstrated varying degrees of setback, with pure _T. catappa_ and the 5% cane sugar blend showing the most pronounced increases in viscosity, indicating a stronger tendency for viscosity formation. These pasting profiles align with the numerical data presented in Table 3, confirming that both the type and concentration of sweeteners significantly influence the pasting behavior of _T. catappa_ powder blends. The pasting profiles demonstrate characteristic behavior for high fat high protein nut-like flours, showing slowed transitions through gelatinization, peak viscosity, and setback phases [37]. The addition of different sweeteners notably affects the viscosity development, with erythroid and cane sugar showing distinct impacts on the pasting behavior. This aligns with [PERSON] et al. 's (2023) [38] findings regarding the influence of additives on flour pasting properties.
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 5 Experimental Results and Analysis
### Data Preprocessing
#### 5.2.1 Data Preprocessing for ClaMP Dataset  
We preprocessed the ClaMP dataset using the following steps:  
1. Firstly, all features need to be of a numerical type in order to be recognized by ML algorithms. As a unique characteristic feature in ClaMP, \"Packing type\" was converted into a numerical feature using the _factorize()_ function from the Pandas library.
2. The _StandardScaler()_ function was called in the Scikit-learn library to normalize the data in ClaMP.
3. The PCA technique was utilized to select the first 20 principal features of the ClaMP dataset, with the aim of lowering the original dataset's dimensionality while retaining more data variability.
4. After randomly disrupting the dataset, random sampling was executed to divide it into an 80% training set and a 20% testing set.
## 3 Case Studies
### AC/DC Interface  
The AC/DC interface consists of an LCL filter, a bidirectional AC/DC inverter and a bidirectional full-wave DC/DC converter.  
An LCL filter is crucial at the interface of a three-phase bidirectional AC/DC inverter, as it significantly reduces the harmonic voltages and currents produced during the conversion between DC and AC current. Harmonics can cause voltage and current distortions, which can impair power quality and affect devices connected to the grid. The LCL filter, made up of coils and capacitors, smoothes the waveform, ensuring that the energy exported or imported by the DCMG meets the quality standards required by the distribution network.  
The three-phase bidirectional AC/DC inverter is essential at the interface of DCMG with AC distribution networks, as it enables efficient conversion between three-phase AC current and DC current, both when importing and exporting energy. Control of the three-phase bidirectional AC/DC inverter is essential to ensure efficient and stable conversion between AC and DC current. Good control optimizes power quality, adjusts power flow and ensures synchronization with the AC grid to avoid harmonic distortion and ensure that the transferred power is compatible with grid conditions. Figure 8 shows the control structure applied to the bidirectional three-phase AC/DC inverter.  
Figure 8: Bidirectional Three-Phase AC/DC Inverter Control Scheme.  
\begin{table}
\begin{tabular}{c c} \hline 1 HVM module & 2.76 kWh, 51.2 V \\ \hline Number of modules & 7 \\ \hline Capacity & 19.32 kWh \\ \hline Maximum output current & 50 A \\ \hline Peak output current & 75 A, 5 s \\ \hline Nominal voltage & 358 V \\ \hline Operating voltage & 280–403 V \\ \hline \end{tabular}
\end{table}
Table 8: Technical features of BYD HVM 19.3.  
The full-wave DC/DC converter with insulation is essential for ensuring safety and efficiency in DCMG. The galvanic insulation offered by the converter eliminates the need for a common neutral point between the AC and DC sides, which is particularly important to avoid leakage currents or ground loops that can cause safety problems and electromagnetic interference. Insulation also prevents the propagation of faults between network components, which ensures greater reliability and resilience of the DCMG.
## 3 Results
### Functional Assessment and Verification of DEGs  
The FPKM method was employed to compute the unigenes expression level. DEGs were screened out by comparison between the two samples, the parameters (FDR < 0.05 and \(|\log_{2}\) (fold change) \(|\geq\) 1) were used as the threshold to judge the significance of the DEGs, which resulted in a set of 24,570 unique DEGs. Among them, 15,889 were up-regulated and 8681 were down-regulated in the high-scent (TH) relative to light-scent flowers (TL) (Figure 4). To determine the functions associated with the DEGs changes between two groups of samples, GO and KEGG pathway analysis were performed.  
In total, 24,570 DEGs were classified into 57 GO terms; there are 28, 18, and 11 terms in the three major categories, respectively. Among them, the terms 'cellular process' and 'cell' are dominant in the biological process and cellular component categories, respectively, while 'binding' is dominant in the category of molecular function (Figure S3). To further understand the function of DEGs in biological processes, 140 KEGG pathways showed hits with DEGs, of which 36 pathways were significantly enriched with DEGs. The top-20 enriched pathways were visualized on a dot bubble. The'metabolic pathways' (ko01100) and 'biosynthesis of secondary metabolites' (ko01110) pathway were the top 2 remarkably enriched KEGG pathways (Figure 5).  
The main components of floral scent are terpenids, phenylpropanoids/benzenoids, and volatile fatty acid derivatives [10]. Based on these divisions, a total of 525 DEGs were annotated in seven pathways related to the floral scent synthesis (Table S3). Twelve key DEGs were identified and selected to verify the reliability of RNA-Seq data and explore the expression patterns of genes related to synthesis of floral scent using qRT-PCR analysis(Figure 6). The result revealed that in the terpenids-related pathways, _ECHS_ (limonene and pinene degradation, ko00903) and _GPPS_ (terpenoid backbone biosynthesis, ko00900) had higher expression and _FLDH_ (ko00900) displayed lower expression in the TH. In the phenylpropanoid biosynthesis pathway (ko00940), _CCR_ and _POX_ were up-regulated in TH, while the expression level of _PAL_, _C4H_, _CAD_, and _TOGT_ were enhanced in TL. In the alpha-linolenic acid metabolism (ko00592), _ACAA_ was up-regulated in TH, and TL displayed a higher mRNA level of _AOC_ and _LOX_. Overall, qRT-PCR results indicated that  
Figure 4: Volcano plot of DEGs in TH vs. TL. The abscissa represented \(\log_{2}\) (fold change), and the ordinate represented \(-\log_{10}\) (adjusted \(p\)-value). Red dots were the up-regulated DEGs, blue dots were the down-regulated DEGs. Gray dots were genes of nonsignificant difference.  
the genes performed the same expression trends as the RNA-seq data, suggesting the high level of reliability of the RNA-seq data.
# CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning
## 1 Introduction  
Respiratory disease is one of the leading causes of death worldwide [1; 2] long before the advent of Coronavirus Infectious Disease 19 (COVID-19) [3], the main symptom of which is lung failure. According to a report by the World Health Organization (WHO), the top five major causes of lung related severe illness are chronic obstructive pulmonary disease (COPD), asthma, acute lower respiratory tract infections, tuberculosis (TB), and lung cancer [4]. According to their statistics, each year, 4 million people die prematurely from chronic respiratory disease [5], with infants and young children particularly susceptible [6].  
Recently, due to the COVID-19 epidemic, many studies have been conducted around the world to predict the occurrence of cardiopulmonary diseases [7]. Artificial intelligence (AI) [8] has been rapidly being applied to healthcare such as pharmaceuticals [9] and precision medicine [10]. Its application to medical imaging in the recent medical industry [11] is especially used in terms of lung disease analysis, because most of the medical data obtained from lung examinations include medical images.  
Standardized big data related to lung examinations are required for such studies to be properly conducted. Many data related to pulmonary functions in the past were stored in hard copies. Even if the data were stored in electronic medical records (EMRs), they were stored either in a non-standardized format or in such a rudimentary form from which it is impossible to conduct proper statistical analysis. This makes it difficult for researchers to make proper use of lung-related big data for applied research.  
Therefore, to solve this problem, this study proposed a deep-learning-based medical image AI processing system to classify and itemize medical image (CIMI). The proposed AI algorithm in CIMI not only classifies and itemizes medical images obtained from lung testing, but also de-identifies information for security purposes and reads medical images in a standardized medical data form for big data or AI researchers to use. The actual pulmonary function testing (PFT) data obtained from patients at Seoul St. Mary's Hospital were used for evaluation. CIMI is envisioned to be used as an impactful tool for future research on the big data pulmonary disease analysis and future solutions to predict respiratory diseases.
## 3 Results
### Experimental Validation  
We focused on the lesion morphology and the maximum temperature difference reached in the experiment and simulation. Figure 3a illustrates the temperature fields recorded by the thermal imager and the simulations for the MEU and MEB modes at 10 s of RPCA. The morphologies of the temperature fields in the experiment and simulation agreed closely. The maximum temperatures reached in the stimulations were relatively higher, but the occupancies of the highest temperature field in each mode were less than those in the experiment. Because the temperature (green zone in the image) from the experimental result in each mode acted as an obvious borderline separating the red and blue zones of the temperature distribution, we chose this temperature and set it as the minimum value in the simulation result. Based on this adjustment, we defined and measured the maximum width \(W_{\text{max}}\) of the temperature field on the tissue surface generated by each electrode, as shown in Figure 3b. Table 2 compares the width \(W_{\text{max}}\) of the experiment and the simulation. The maximum temperature differences between the experiment and the simulation were 2.9% and 1.5% for the MEU and MEB modes, respectively. The temperature field under each active electrode in the MEU mode merged and formed an arch-shaped damaged path. The lesion produced by the MEB mode was smaller and narrower, and it closely resembled a series of individual temperature zones around each electrode, with only a small interconnection between adjacent electrodes.
## 3 Results and Discussion
### Slip Lines and Active Slip Systems  
The critical resolved shear stress (CRSS) of pure titanium has been discussed in several papers, based on experimental results [26; 27; 28]. However, it is still difficult to assess the values for each slip system accurately (basal, prismatic, pyramidal, and 2 nd order pyramidal) and for tensile/compressive twins, for the following reasons: (1) It is difficult to make single-crystal specimens for tensile tests in order to realize the uniform stress-strain state and to obtain the CRSS value for each individual slip system; (2) the stress in each grain of a polycrystalline specimen is significantly affected by theshape, size, and crystal orientation of neighboring grains. In addition, the stress is not uniform, as is often assumed when evaluating the Schmid factor; (3) the stress and deformation of surface grains are influenced by subsurface and interior grains. Therefore, the Schmid factor, normalized by CRSS, gives only a rough indication of the slip activity of polycrystalline metals. A combination of experiments and the crystal plasticity finite-element method is known to be one of the best methods [29; 30; 31; 32; 33; 34]. However, since it is not possible to know the geometries and crystal orientation of subsurface and interior grains, modeling is limited to two dimensions or quasi-three dimensions.  
Figure 9 shows slip lines observed in the intensity image of the digital holographic microscope, using an objective with 5 magnification, at \(P\) = 635 N. Slip lines were found in grains 5, 8, and 19, within a square observation area of 242 \(\times\) 242 \(\upmu\)m\({}^{2}\). The angles with respect to the loading direction were \(-\)49.39, 36.50, and \(-\)45.00\({}^{\circ}\), respectively, where the anticlockwise direction was positive. Plastic deformation occurred by slip in these surface grains, and other grains were thought to have maintained elastic conditions. Trace analysis was performed based on the slip-line angles and crystal orientation of slipped grains. The results suggest that pyramidal slip was activated in grains 5 and 19, which had high Schmid factors of 0.487 and 0.440, respectively, and that second-order pyramidal slip was activated in grain 8, which had a very low Schmid factor of 0.094. Since the CRSS value of the pyramidal-slip system is much higher than that of the basal- and prismatic-slip systems [26; 27; 28], the second-order pyramidal slip in grain 8 would barely have been activated if the Schmid law were simply applied under a uniform stress assumption. Thus, we found that the deformation of surface grains did not follow the Schmid law, as previously shown. The effects of neighboring and subsurface grains, as well as non-uniform stress-strain conditions in grains, were considered to be the primary reasons for this.
# A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing
## 3 Design of Golay Complementary Coded Radar  
The block diagram of the Golay complementary coded radar for a multi-observation point detection system is shown in Figure 2. There are six key components: a digital transmitter, a dual-channel receiver, a clock manager, a network clock, a self-positioning module and a wireless communication module. A Xilinx Artix-7 Field Programmable Gate Array (FPGA) is used as the main controller unit to manage the peripherals of the radar. To reduce the system cost and improve the spurious-free dynamic range, the equivalent-time sampling technique is adopted. A 16-bit analog to digital converter (ADC) with a maximal sampling rate of 160 Mbps and a full power bandwidth of 1.4 GHz is used as the receiver. A pair of bow-tie antennas are used for electromagnetic radiation and reception. A W5300 chip is used to exchange the raw radar data and commands with the control host by the TCP network protocol, and the wireless bridge realizes the wireless communication. In the following, the Golay complementary coded signal transmitter (marked A in Figure 2), the network clock module (marked D in Figure 2), the self-positioning module (marked E in Figure 2) and antennas are described in detail.  
Figure 1: The wireless-netted multi-observation point detection architecture.
###### Abstract  
Lightweighting is a critical focus in the transportation sector, directly enhancing efficiency and significantly reducing costs. In electric vehicle (EV) design, the body surrounding the battery must effectively absorb impact, especially during crashes. This study aims to improve the crash performance of the side profiles in the battery box of an M1 category vehicle. It is based on the crash test in Annex 8D of the ECE R100 regulation. In this study, the safe displacement at which the battery will not deform is set as 20 mm, and the maximum force and energy absorption at this displacement are compared. In total, 33 different electric and hybrid vehicle models were benchmarked in this study. L-shaped geometry and aluminum materials are generally preferred; this study focuses on using glass-fiber-reinforced polymer (GFRP) pultruded profiles to make batteries more durable and lighter. The GR800 material was selected for its superior mechanical strength among glass fiber composites. A virtual tensile test verified its properties. A unique hybrid model combining honeycomb and auxetic geometries was developed, showing a crash performance improvement of ~360% over honeycomb structures and ~88% over auxetic structures. Through multi-objective optimization using artificial neural networks (ANNs), 27 models were analyzed, leading to an optimized design. The final design resulted in the battery box side profile being 23.9% lighter and 38.6% cheaper, and exhibiting a performance 3% higher. This study demonstrates significant advancements in EV safety and cost efficiency, highlighting the practical benefits of innovative material and design approaches.  
hybrid structure; multi-objective optimization; glass-fiber-reinforced polymer (GFRP); battery box; crashworthiness; pultrusion Article