esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
During an emergency rescue operation, rescuers have to deal with many different health complications like cardiovascular, respiratory, neurological, psychiatric, etc. The identification process of the common health complications in rescue events is not very difficult or time-consuming because the health vital symptoms or primary observations are enough to identify, but it is quite difficult with some complications related to neurology e.g., schizophrenia, epilepsy with non-motor seizures, or retrograde amnesia because they cannot be identified with the trend of health vital data. The symptoms have a wide spectrum and are often non-distinguishable from other types of complications. Further, waiting for results from medical tests like MRI and ECG is time-consuming and not suitable for emergency cases where a quick treatment path is an obvious necessity after the diagnosis. In this paper, we present a novel solution for overcoming these challenges by employing artificial intelligence (AI) models in the diagnostic procedure of neurological complications in rescue situations. The novelty lies in the procedure of generating input features from raw rescue data used in AI models, as the data are not like traditional clinical data collected from hospital repositories. Rather, the data were gathered directly from more than 200,000 rescue cases and required natural language processing techniques to extract meaningful information. A step-by-step analysis of developing multiple AI models that can facilitate the fast identification of neurological complications, in general, is presented in this paper. Advanced data analytics are used to analyze the complete record of 273,183 rescue events in a duration of almost 10 years, including rescuers' analysis of the complications and their diagnostic methods. To develop the detection model, seven different machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbor (KNN), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were used. Observing the model's performance, we conclude that the neural network and extreme gradient boosting show the best performance in terms of selected evaluation criteria. To utilize this result in practical scenarios, the paper also depicts the possibility of embedding such machine learning models in hardware like FPGA. The goal is to achieve fast detection results, which is a primary requirement in any rescue mission. An inference time analysis of the selected ML models and VTA AI accelerator of Apache-TVM machine learning compiler used for the FPGA is also presented in this research.  
neuralogy; machine learning; TVM; VTA; artificial intelligence; support vector machine; random forest; logistic regression; naive bayes; artificial neural network; K-nearest neighbour; prediction; rescue patient +
Footnote †: journal: _Algorithms_
## 1 Introduction
We focus on related work that analyses the economics of the concept. [PERSON] et al. [25] introduced a stochastic programming approach to optimize power flow in the face of renewable energy intermittency, showing that using demand-side flexibilities can substantially reduce redispatch costs. [PERSON] et al. [26] explored bidding strategies in energy and reserve markets for aluminium smelters, utilising stochastic programming to effectively manage energy costs and demonstrating the benefits of demand-side management for industrial plants. [PERSON] et al. [24] presented an an optimization strategy for aluminium smelting, focusing on balancing power bids and spot market prices, demonstrating that this approach can lead to significant cost savings of 5-20% in weekly production. [PERSON] et al. [27] investigated how the aluminium industry can integrate variable renewable energy resources while remaining competitive. They evaluated the economic and environmental benefits of different scenarios of renewable energy integration. [PERSON] et al. [28] explored how EnPot technology can be used to adjust smelters' power use with renewable energy in different scenarios to increase profits and reduce emissions. [PERSON] et al. [29] evaluated a real-world example of a virtual battery and its flexibility in providing balancing services to the grid, as well as for adjustment of their load according to short-term prices. [PERSON] et al. [30] analysed aluminium production pathways in India from an environmental-techno-economic perspective, focusing on the potential of carbon capture and renewable energy integration. They suggested that using wind and solar power can offer positive economic implications for aluminium production due to their lower emissions, although the overall benefits depend on other factors. Recently, [PERSON] et al. [31] analysed the economics of integrating solar photovoltaic (PV) panels into the aluminaproduction process. On-site PV electricity generation offers significant potential for economic improvement, with a payback period of 4.5 years. While others have explored the concept of energy flexibility, there has not been an economic study that combines and extrapolates actual power load data and distributed PVproduction into long-term prospects on a national level. Our paper goes a step further by also comparing the costs and uses of traditional batteries versus energy flexibility services in actual scenarios. This approach gives us a clearer understanding of the economic side, which is presented in this study. The remainder of this paper is structured as follows. The next section describes the utilised methodology in detail, while the results and a discussion of the economic analysis are presented in Section 3. The final section concludes this paper.
# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 2 Material and Methods  
### Study Sites  
The study was conducted as part of the Soil Moisture Active Passive Validation Experiment in 2016 (SMAPVEX16) during the growing season (i.e., day of year (DOY) 145-269; total: 125 days) in four neighboring counties in North Central Iowa: Story, Franklin, Hamilton, and Hardin County [19; 20]. The overall experiment was dedicated to exploring the land surface soil moisture conditions across a homogeneous landscape to improve vegetation characterization and soil moisture remote sensing algorithms via the Soil Moisture Active Passive Mission. Within each county, two field sites under a corn-soybean rotation, one each planted in corn and the other in soybeans (Figure 1). The sites are located on the Des Moines Lobe and soils belong to the Clarion-Nicollet-Webster association [21]. The site characteristics are summarized in Table 1. The sites reflect well the dominating crop and soil management conditions of the four counties, with 98% of the harvested cropland used for corn and soybean production, and only 13% of the cropped area under no-till and 3% planted with cover crops [5]. In terms of climatic parameters, the 30-year (1986-2016) minimum and maximum temperature between June and September averaged (\(\pm\) SE) over four nearby weather stations was 15.38 \(\pm\) 0.16 and 26.94 \(\pm\) 0.08 \({}^{\circ}\)C. All agricultural sites are rain-fed systems with a seasonal 30-year precipitation of 508 \(\pm\) 17 mm [22].  
Figure 1: Land use map of four counties in Iowa (red marked counties in small map) in 2016 [18]: Story County (lower), Hamilton County (center left), Hardin County (center right), and Franklin County (upper). Corn fields are marked dark green, and soybean fields are light green (each land use area \(>\) 55%). The red triangles and yellow circles show the location of the eddy-covariance (EC) stations in soybean and corn fields, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
## 4 Analysis and Discussion of Experimental Results
### Result Analysis
#### 4.3.2 Evaluation Analysis of Normalized Discounted Cumulative Gain  
Figure 11 illustrates the results of normalized discounted cumulative gain (NDCG@5) for the five job recommendation methods. The graph clearly demonstrates that among these methods, JRSR attains the highest NDCG@5 score, reaching 0.938. Among the four benchmark methods, TF-C achieves the lowest NDCG@5 score at 0.675, while GERS reaches the highest score at 0.859. When compared with TF-C and GERS, the proposed JRSR method exhibits a remarkable improvement in NDCG@5, with enhancements of 38.96% and 9.19%, respectively. This emphasizes the effectiveness of the JRSR job recommendation method presented in this paper in aligning the preferences and requirements of college graduates with job opportunities.  
The experimental data were non-normally distributed. In this paper, the Wilcoxon signed-rank test was used to assess whether there was a significant difference in the NDCG@5 assessment values of the JRSR, TF-C, MS, GERS, and RSCF methods. As evident in Table 4, with \(p<0.05\), there is a significant difference between the assessment values of JRSR and the other four methods.
## 4 Stimulation Process of UV-C  
### Effect of UV-C on Cell Physiology and Mechanical Properties  
Several studies suggested that UV-C radiation is effective not only because of its disinfecting effect, but because it may also stimulate plant defenses against mechanical damages. Exposing UV-C (or hormetin) in food crops has an important effect, called the hormetic effect [101; 102], that is responsible for the production of phenylalanine ammonia-lyase (PAL), which induces the formation of a phenolic compound referred to as phytoalexins. The phytochemicals released from PAL having UV absorbing characteristics are found to be chlorogenic acid, gallic acid, epicatechin, and quercetin. This phenolic compound is capable of improving the resistance of fruits and vegetables to microorganisms and defense mechanisms. Phytoalexins give rise to the accumulation of other inducible defenses, such as cell wall modifications [103]. The phenolic content of different crops is discussed as it is the secondary metabolites that are directly connected to plant defense responses. Despite the sophisticated hormetic response process, the role of the hormetic effect in the fruits and their cellular mechanisms by the respective enzymes was demonstrated by a few researchers. The enzymes and genes can be seen in three types based on their response to the UV-C irradiation [104].  
Peroxidase and reductases: responsible for oxidative burst and formation of lignin polymers creating boundaries against microorganisms.  
Glucanases and chitinases: responsible for lytic activities towards major fungal cell wall components.  
L-Phenylalanine ammonia-lyase (PAL): responsible for the biosynthesis of phenolic that led to cell wall modification.  
A multitude of studies have shown that UV-C irradiation, depending on its dose, induces the production of phenolic components and other related defense responsive chemicals. The amount of UV-C energy per unit time (dose) released from the light source is an important factor when dealing with surface treatment [105].
## 3 Numerical Simulation
### Influence of Bearing Raceway Waviness on Dynamic Rotational Error  
Time-varying restoring bearing force can be generated due to the surface waviness of the bearing parts [25]. The excitation caused by bearing waviness is applied on the SRB system. The spindle displacement response is represented as dynamic rotational error when the spindle rotates at high speed. In the simulation, only the effect of a rear bearing waviness is taken into account. Assuming that the bearing inner ring raceway is ideally circular, the rolling elements do not have any shape or dimensional errors. The outer raceway surface has a single order of radial and axial sinusoidal waviness. The amplitude of the bearing waviness is 1-2 \(\upmu\)m, and the harmonic order is 11-20 UPR (Undulations Per Revolution). To show the relationships more distinctly between radial errors and the number of undulations and rolling elements, values that are larger than actual errors are selected. The number of rear bearing rolling elements is 20. The length of numerical simulation is set to  
Figure 7: Schematic diagram of radial error motion measurement.  
20 revolutions. The discrete time response signals and the polar plots of the radial error motion are shown in Figure 8.  
As can be seen from Figures 8 and 9, when the bearing waviness has the same amplitude, the radial displacement response in the V\({}_{\mathrm{s}}\)-axis is related to the order of the bearing waviness. When the order of the waviness and the number of rolling elements are the same, the waviness has almost no effect on the rotational error motion. However, when the difference between the order and the number of rolling elements is 1, a very obvious asynchronous error motion can be generated in the SRB system. Meanwhile, waviness hardly causes synchronous error motion. The same trends can be obtained by comparing the influence of the waviness on the rotational error of a single bearing in the literatures [32; 33]. In the comparing literature, an angular contact ball bearing (SKF, 7012/CD) is chosen for the calculation. The comparison of the effects on radial error motion is shown in Figure 10.  
When the order of surface waviness is the same, the magnitude of the bearing waviness determines the magnitude of the rotational error. The rotational error motion value, especially for asynchronous error, increases with the increase of the waviness amplitude.  
Figure 8: Responses and rotational errors with the amplitude (1 \(\upmu\)m) and different orders of the surface waviness.
## 3 Experimental Phenomena and Results  
### Experimental Phenomena  
In uniaxial pull-out tests, the failure modes of the specimens primarily encompass bond anchor failure (steel bar pulled out) and anchorage intact (steel bar fracture), as illustrated in Figure 8.  
Of the specimens, 11 exhibited bond anchor failure, characterized by the corrugated pipe being pulled out. These specimens had relatively shorter bond anchor lengths and showed no visible cracks on their surfaces throughout the loading process. For the five specimens that utilized high-strength grouting materials with strengths exceeding 60 MPa, slip occurred at the loading end, increasing incrementally with the load. Six specimens with anchor lengths of 0.4 and 0.6 L\({}_{\text{ab}}\) experienced bond failure between the corrugated pipe and the concrete before reaching yield strength. Similarly, five specimens with anchor  
Figure 8: Failure modes of the specimens. (**a**) Pull-out failure of steel bar; (**b**) tensile failure of steel bar.  
Figure 7: Physical diagram of the loading device. (**a**) Schematic diagram of the loading device; (**b**) physical diagram of the loading device.  
lengths of 0.8 L\({}_{\rm{ab}}\) demonstrated signs of bond failure at the yield stage, accompanied by faint crackling sounds. Continued loading resulted in rapid decreases in load value as the bond anchorage was destroyed, and the corrugated pipe was pulled out, leading to specimen failure. At this point, the specimens displayed no visible cracks and maintained overall integrity.  
The failure mode for the six specimens with anchorage lengths of L\({}_{\rm{ab}}\) and 1.2 L\({}_{\rm{ab}}\) was tensile fracture of the steel bar, accompanied by minimal slip. These specimens had larger anchorage lengths and relatively thicker protective layers. No discernible cracks were observed on their surfaces throughout the loading process. All specimens utilized medium- and low-strength grouting materials with strengths below 80 MPa. The slip occurred at the loading end and increased as the load escalated. When the loading value was between 60 and 70 kN, steel bar yielding commenced, accompanied by faint crackling sounds and the onset of bond failure between the corrugated pipe and the concrete. After the steel bar yielding, slip at the loading end primarily resulted from steel bar elongation, with negligible slip at the free end. Upon reaching the ultimate load, the steel bar fractured, culminating in specimen failure. No visible cracks were evident at this stage, and the specimens largely remained intact.
## 5 Mathematical Modeling of EPATH
### Concept Description  
The EPATH solution utilizes elastic expansion to fit the intermediate inside composite materials. Figure 6 shows a cross-section of an intermediate pressed inside a composite tube. The internal tube's radius \(R_{i2}\) is smaller than the external radius of the intermediate \(R_{o1}\). This difference in diameters allows the composite material to expand elastically and grip into the intermediate, which increases the resistance to axial forces.  
Based on Lame equations for thick-walled cylinders under pressure, the interference fit assembly is modeled [18]. The intermediate is considered a cylinder under external pressure, and the composite part is a cylinder under internal pressure.  
EPATH modeling consists of identifying the maximum axial force that holds the assembly. Several factors affect the value of this force: material resistance, manufacturing defects, surface finishing, temperature effect, aging, and leak prevention. This research article deals with 11 parameters that revolve around material resistance. Consequently, the parameters controlling the model are classified into four categories:  
* Material: The elastic property of the composite material \(E_{comp}\) plays a significant role in defining its maximum allowable expansion at the interference fit zone;
* Geometric: The model takes into consideration dimensional parameters that include the intermediate's radii (internal \(R_{i1}\) and external \(R_{o1}\)) as well as the composite material (internal \(R_{i2}\) and external \(R_{o2}\)). They also include the interference fit contact length \(l\);
* Friction coefficient: The composite material and the intermediate are in full surface-to-surface contact at the interference fit zone. The maximum allowable thrust force is then a function of the friction coefficient \(\mu_{K}\) between the two materials in contact;
* Composite material fill rate \(k\): One of the main challenges in the composite molding process is the air bubbles. The high viscosity of the mixture traps the bubbles inside, weakening the structure and decreasing the effective working area.
# Evaluation of Local Security Event Management System vs. Standard Antivirus Software
## 2 Review and Background Knowledge
### Actual Antivirus Threat Detection and Classification Systems  
The main detection and classification software for this type of malware is the antivirus software, that have evolved in techniques, using mechanisms based on signature, heuristics, rules, and currently the use of artificial intelligence [16; 17].  
Signature detection [18; 19; 20] is the traditional method used by antivirus systems and is based on a database generated by the vendor. Any file downloaded to the system is compared to the database and if there is a match, it is malware. The problem with signature-based detection is that it will only detect those samples that have been previously identified and their signature is stored in the antivirus system database.  
To complement signature-based detection and provide a solution to its limitations, heuristic detection techniques were developed [21; 22; 23]. The operation of heuristic algorithms is based on different criteria, each of them with a score, which determine whether a file is malicious or not. The most common three ways of performing this analysis are [24; 25]:  
**Generic:**: Compares the actions of a file with another already identified as malicious.
**Passive**: Analyzes the file individually and tries to determine how it works.
**Active**: Runs the sample in a safe environment (sandbox) and determines if its activity is malicious. This strategy is difficult to implement, imposes significant delays, and it can be avoided by implementing payload activation delays.  
Heuristic analysis has two fundamental problems, the first is the large number of false negatives and the second is the workload on the system. However, it improves the ability of antivirus software to detect new samples of malware.  
Recently, to increase the capabilities of antivirus systems, the use of machine learning algorithms using artificial intelligence (AI) has been introduced [26; 27; 28]. The inclusion of these techniques allows for a large-scale data analysis, the identification of patterns and trends, as well as the automatic and rapid formulation of predictions. These systems are called Endpoint Detection and Response (EDR), also known as Endpoint Thread Detection and Response (ETDR), and implement and endpoint security mechanism [29] at the clients that collects data and sends it to a centralized console for processing, as in a distributed computing environment [30]. The information collected is correlated in real time to detect and analyze suspicious activity and processed in a centralized database. However, these systems have some weaknesses, as exemplified by [PERSON] and [PERSON] [31], for example, the comprehension of the artificial intelligence of the model created or the huge data ingestion required for accurate decision process.
## 2 Design of Detection System  
Due to the complexity of the production site environment, image acquisition is not easy to achieve [21]. To solve this problem, an image acquisition system was designed based on the existing winding machine. Figure 2 shows a picture of the site, which includes an industrial area camera, a high-resolution lens, a light source, a background plate, and an adjustable fixing device.  
Since most of the non-circular coils are used in production, and the enameled wire surface is smooth, the reflection of light presents irregularity, which has restrictions on the location of the light source. If the light source and the camera irradiate in the same direction, there will be many highlighted areas in the image, resulting in feature loss. If backlighting illumination is used, it makes the coil profile and the background have a very high contrast effect but also causes the coil surface texture to be poorly imaged, so the surface features cannot be effectively identified. Backlighting with a lampshade combines the advantages of the above two methods [22; 23], but the main axis space is limited, and the lampshade is not easy to install. After considering the position of the coil to be photographed, a 90-degree illumination method was designed. As shown in Figure 3, the camera and the light source are installed at an angle of 90 degrees, and the axis is fixed at the position where their midlines intersect. This structure can effectively reduce the reflected disturbing light, and in addition, the background plate can improve the contrast between the winding edge contour and the background.  
Figure 3: Installation structure of camera and light source.  
Figure 2: Image acquisition system.
## 4 Discussion
### Correlation between the E-Nose and GC-MS Analysis  
As GC-MS is widely used in volatiles analysis, it is thus necessary to evaluate the correlation between the E-nose and the GC-MS results. A comparative evaluation of E-nose and GC-MS allowed for an assessment of the contribution of the compounds detected by the E-nose. Different types of E-nose instruments and sensors have various sensitivities to each component [6]. Data were collected by a particular E-nose, and a prediction model was then established to provide a basis for future research on the same material. In this study, we discovered that specific sensors were highly correlated with majority of compounds in crabapple, thereby producing different sensor behaviors and leading to successful scent type differentiation. For example, N-containing compounds, terpenes, and S-containing compounds were highly associated with sensors W5C, W1W, W2S, respectively. This provided a reference for the establishment of the rapid detection of crabapple flower fragrance in the future.