esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 2 Methods
### Arrival Time Prediction for Geoeffective CMEs
#### 2.2.3 Cost-Sensitive Regression Loss Function  
In the training phase, each observed image uses the deep residual regression network based on group convolution to predict the transit time. But we find that the loss between different images is quite different. We hope that the network can focus more attention on hard samples and reduce the proportion of simple samples in the loss function. In this paper, we propose a cost-sensitive regression loss function through the combination of L1 Loss and L2 Loss. The formula is as follows:  
\[Loss=\left\{\begin{array}{l}|y_{pred}-y_{true}|\ if\ |y_{pred}-y_{true}|\ \leq\beta\ hours\\ (y_{pred}-y_{true})^{2}\ otherwise,\end{array}\right. \tag{5}\]  
where \(y_{pred}\) represents the prediction result of our network, \(y_{true}\) represents the actual label, and \(\beta\) is manually set as a positive number greater than 1. We define the sample with an error less than or equal to \(\beta\) as an easy sample, and the sample with an error greater than \(\beta\) as a hard sample. In the training phase, the error of almost all samples is greater than 1. For these easy samples with a loss greater than 1, using L1 Loss can reduce its proportion in the loss function, and when the gradient is updated by backpropagation [47], the absolute value of the gradient contributed by these easy samples is only 1, which reduces the impact on the network. For hard samples, the value of the loss function is much larger than easy samples, thus avoiding the situation where the loss function is dominated by easy samples. Meanwhile, the absolute value of the gradient contributed during backpropagation is greater than \(2\times\beta\), which is also much larger than the simple sample, and the greater the error, the greater the absolute value of the gradient. Compared with L1 Loss, our loss distinguishes samples with different errors on the gradient of backpropagation. Compared with L2 Loss, our loss reduces the proportion of most easy samples in the loss function and the contribution of gradient updates, so that the network can pay more attention to hard samples.  
Figure 4: The illustration of the residual block based on group convolution.
# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
## 2 Materials and Methods
### Risk of Collision
#### 2.2.2 Collision Scenario  
In order to obtain data, it is necessary to develop a scenario with a risk of collision. To develop this scenario, we constructed the CRI values as matrices by entering numerical conditions to find situations where the risk of collision exists. The matrix's horizontal axis used the TS's relative bearing based on the OS. Here, the relative bearing was based on the head of the OS, with a value of \(-27\) degrees to \(27\) degrees. Moreover, the longitudinal axis of the matrix was the aspect. Here, the aspect is the angle at which the TS looks at the OS, and a value from \(-25\) degrees to \(25\) degrees was used. The scenario for the questionnaire was constructed by calculating the CRI of \(0.5\) or more at randomly designated values of the above two variables at regular intervals. The figures are reported in Table S1. The distance between the OS and the TS was set at \(6\) miles reported to commence the collision avoidance operation [27; 28]. The collision risk matrix was constructed using the collision risk index described in Section 2.2.1. for three cases when the OS is faster than TS, the speed of the OS and TS are the same, and the OS is slower than the TS based on the relative bearing, aspect, and fixed distance of \(6\) miles.
# Anti-Biofilm Agents to Overcome _Pseudomonas aeruginosa Antibiotic Resistance_
## 4 Conclusions  
Bacterial biofilms are an aggravating factor in _P. aeruginosa_ infections as they provide optimal conditions for (i) bacterial cell growth, (ii) protection against the immune system and antibiotics and (iii) the acquisition of resistance phenotypes, which make infections very hard to treat. The understanding of biofilm communities and underlying mechanisms is a first step towards the development of anti-biofilm strategies. It brings out relevant targets for drug development. In fact, the discovery of anti-biofilm agents could help to fight infections while reducing the emergence of resistances and restoring the efficiency of conventional ATBs when used in dual therapy. The rational design of molecules offers easy access to efficient compounds with improved physiochemical properties and reduced cytotoxicity. In particular, some QSIs targeting the _pqs_ system appear very interesting. The AQ analogues **ABA-12** and **ABA-14** possess very potent anti-biofilm activity (IC\({}_{50}\) = 4.5 \(\upmu\)M for **ABA-12** and IC\({}_{50}\)\(\leq\) 0.5 \(\upmu\)M for **ABA-14**). **M64** and its derivative **D88**, biaromatic competitive PqgR inhibitors, can reduce biofilm formation and show synergistic activity with conventional ATBs against preformed biofilm. Some AMPs such as the D-AMP **DJK6** and the repetitive peptide **KDEON WK-11** have also revealed very promising anti-biofilm properties at low concentrations (IC\({}_{50}\) = 0.3 \(\upmu\)M for **DJK6** and 75% inhibition at 0.8 \(\upmu\)M for **KDEON WK-11**). It should be noted that there is as yet no mutual agreement on biofilm infection models, which limits effective comparisons between potent anti-biofilm agents. Finally, the emergence of new biotechnologies such as nanocarriers could solve problems of bioavailability or toxicity. Progress in research into anti-biofilm agents appears very encouraging for future therapies.  
**Funding:** [PERSON] was the recipient of a Grant from the \"Ministere de l'Education Superieure et de la Recherche\".  
**Acknowledgments:** The authors thank the \"Ministere de l'Education Superieure et de la Recherche\" for the doctoral fellowship of [PERSON]. We are thankful for the collaborative support of <<FHU RESPIRE>> (<<Federation Hospitalo-Universitaire RESPIRE>> certified by GCS G4 and AVIESAN, France).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Carbon Emission Analysis of RC Core Wall-Steel Frame Structures
###### Abstract  
The development of super high-rise building projects has become crucial for mitigating land shortages in rapidly growing urban areas. Super high-rise steel structures, particularly RC core wall-steel frame systems, have become the preferred choice due to their superior performance, high prefabrication level, and construction efficiency. Despite their benefits, super high-rise buildings face challenges related to higher energy consumption and carbon emissions. Consequently, it is important to analyze the carbon emissions of these buildings throughout their lifecycle and propose low-carbon construction strategies. A carbon emission analysis focused on super high-rise buildings with RC core wall-steel frame structures is conducted in this study. A carbon emission analysis model is constructed based on BIM-enabled LCA through a real-world case study. The emission factor method is combined with the BIM model to calculate carbon emission. Furthermore, carbon emissions across various construction strategies are compared, with a particular focus on the manufacturing processes of the main materials. The results indicate that incorporating admixtures in concrete, along with adopting the electric arc furnace (EAF) method and utilizing recycled scrap steel in steel manufacturing, significantly reduces the carbon emissions of the buildings. Lastly, effective low-carbon approaches for these buildings are proposed.  
carbon emissions; super high-rise buildings; RC wall-steel frame; life cycle assessment; sustainability
# Automotive Cybersecurity Application Based on CARDIAN
## 4 Case Study  
In the previous sections, the main vulnerabilities in the automotive sector have been shown, as well as the main related countermeasures; the main techniques for intrusion detection have also been analyzed. Furthermore, it has recently been demonstrated how  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Advantages** & **Disadvantages** \\ \hline Data frequency-based approach & Simplicity of implementation &
\begin{tabular}{c} Cannot identify all types \\ of attacks \\ \end{tabular} \\ Machine learning approach & Effectiveness in identifying anomalous situations & Computational complexity \\ Statistical approach & High accuracy & Does not recognize replay-type attacks \\ Approach based on the electrical analysis of the CAN bus & Direct analysis of the operating state of the bus & High levels of False Positives \\ \hline \hline \end{tabular}
\end{table}
Table 3: Approaches for IDS development.  
easy and possible it is to break in and inject malicious messages into the CAN bus of a vehicle, accessing it remotely through the infotainment system.  
This paper will describe the creation of an intrusion detection system based on the use of a Bayesian network, which, connected directly to the CAN bus, controls the flow of messages in order to detect anomalies and intrusions. The Bayesian network was structured starting from the analysis of the automotive domain ontology.  
The data used for training and validation of the network are real data, acquired from the CAN bus of a heavy vehicle, in SAE J1939 format. This dataset was modified by inserting attacks at regular time intervals, as described in [23].
## 6 Discussion
In such areas, geological data remain critical for monitoring geological hazards (e.g., subsidence, surface water leakage) and environmental concerns (e.g., groundwater contamination, wastewater treatment, ecological restoration). Three-dimensional geological models, reconstructed from historical data using BME, can aid in assessing disaster risks and guiding environmental remediation efforts. For example, accurate coal seam data can support groundwater flow and subsidence monitoring, providing essential data for long-term environmental management after mine closure. Furthermore, integrating soft and hard data through BME can help evaluate environmental risks and support mine reclamation and pollution remediation. Thus, while real-time updates may not be as crucial in closed mines, updating three-dimensional geological models based on historical data still provides valuable insights for disaster monitoring, environmental protection, and reclamation efforts. In particular, regions like Europe, with closed coal mines, will benefit from these advanced modeling techniques in post-mining environmental restoration and management.
## 5 Experiment Validation
### Experiment Description  
The installation of the experimental devices is shown in Figure 20. The intermediate frequency acquisition system, ADC system, Hall Effect sensor, and the single patch antenna spun with the vehicle together. When the Hall Effect sensor is activated by the magnet, it produces a low-level pulse as a zero-phase mark point. The magnet is settled below the spinning part of the rotary machine when the center of the antenna is facing the ground, it activates the Hall Effect sensor. The patch antenna was inserted into the vehicle by cutting off the side surface of it, and we adjusted the radiation characteristics of the antenna by integral design with the vehicle, so that the antenna has similar radiation characteristics to the unmounted patch antennas.  
The experimental procedure was as follows:  
1. Initialize the Flash of both systems with PC via the serial port. Send signals to start the IF acquisition system (\(t_{0}\)) and the ADC system (\(t_{1}\)) and record their start time;
2. Ensure the antenna's boresight is oriented to the zenith and record 90 s of IF data in static situation, record the start time (\(t_{2}\)) and the end time (\(t_{3}\)) as well;
3. Turn on the rotary machine, the antenna and Hall effect sensor rotates with the vehicle for 120 s with three different speeds listed in Table 3 and record the start time (\(t_{3}\)) and end time (\(t_{4}\));
4. Connect the serial port, stop IF signals acquisition system (\(t_{5}\)) and the ADC system (\(t_{6}\)), record the end time of both recording systems, and read the data of them.
# Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
## 3 Spatiotemporal Models for Continuous Emotion Recognition
### Pre-Processing
#### 3.2.2 Sequence Learning  
The target dataset contains long video sequences showing a variety of emotions along with records of facial expressions from single subjects. The duration of emotions is not clearly established, and it varies for each individual. Several studies were previously carried out to obtain expression intensity variations by pointing peak and non-peak expressions along with sequences. However, while whole video sequences represent multiple annotations at a specific sampling rate and not a single label to describe a succession of diverse emotional states, the video sequences are split into several clips of fixed length with a specific overlapping ratio. This has two main advantages: (i) it increases the amount of data for training CNNs; (ii) it allows the investigation of which window settings are better for training spatiotemporal CNNs to learn from long sequences of valence and arousal annotations. Based on an exploratory study, two sequence lengths (16 and 64 consecutive frames of a single video) and three overlapping ratios for each sequence length (0.2, 0.5,  
Figure 4: Exhaustive MTCNN architecture composed of a stack of CNNs. A single meaningful bounding box and five facial landmarks are extracted from face images. Adapted from [70].  
and 0.8) were chosen. Thus, for instance, a window of 16 consecutive frames with an overlap of 0.5 contains the last eight frames of the previous window.  
It is also important to check the integrity of contiguous video frames. Indeed, some frames were discarded because no face was detected within them, damaging the continuity of the temporal information of emotion between them. Furthermore, the proposed strategy to divide videos into clips may introduce critical temporal gaps between two consecutive frames. Therefore, a tolerance (a temporal difference between close frames) was applied to select clips that give sense to a unique emotion unit. Globally, the MTCNN can detect faces in videoclips, and on average, 90% of the frames are kept, depending on the sequence length, overlapping ratio, and frame rate. Figure 5 presents the number of clips available in training, validation, and test sets, according to such parameters. Finally, the last preprocessing step is to fuse annotations of multiple frames in one clip to obtain a single emotion label for each window. For such an aim, either the average or the extremum value of the labels is used to obtain a single label for each continuous emotion (single valence and arousal values).
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 5 Potential Challenges in Estimating Energy Efficiency For End-Device
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**State** & **Process** & **Time (ms)** & **Avg. Current (mA)** & **Energy (mJ)** \\ \hline
1 & Initialization & 5390 & 8.46 & 167.52 \\
2 & Module activation with service provider & 11,030 & 57.63 & 2326.22 \\
3 & Connection & 6930 & 28.07 & 717.94 \\
4,6 & eDRX & 31,000 & 9.58 & 1089.65 \\
5 & Tx mode & 373 & 46.12 & 63.48 \\
7 & deep sleep & variable & 0.10 & variable \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Current composition split into different states of operation for NB-IoT. There is a trade-off between available energy and the application requirement that can be achieved by managing the frequency of message transmissions and optimal management of the computation.
# Analysis of _In Vivo_ Plant Volatiles Using Active Sampling and TD-GC\(\times\)GC-TOFMS
## 2 Materials and Methods
### Plant Sampling System  
The plant sampling system has several components, including Teflon(tm) tubing (McMaster-Carr, Elmhurst, IL, USA), a 64 oz. glass chamber (Richards Packaging, Winnipeg, MB, Canada), a Teflon(tm) plate (McMaster-Carr, Elmhurst, IL, USA), and carbon traps (Scientific Glassblowing, University of Alberta Department of Chemistry) (Figure S1) [48]. Prior to sampling the plants, the system was cleaned and dried in an oven to remove any residual impurities and to avoid carryover from previous samples. The Teflon(tm) tubing and plate were cleaned with 50% ethanol in water. The Teflon(tm) tubing and plate were dried at 150 \({}^{\circ}\)C for 2 h and 1 h, respectively. The 64 oz. glass chamber was washed sequentially with hexane, acetone, methanol, and water and then dried in an oven for 1 h at 150 \({}^{\circ}\)C. Solvents were selected due to their range in polarities to remove contaminants also with a range of polarities. All solvents were ACS-grade (Fisher Scientific, Ottawa, ON, Canada). 18.2 M\(\Omega\cdot\)cm\({}^{-1}\) water from a PURELAB Dispenser (ELGA, High Wycombe, UK) was used.  
The sampling system was assembled as in Figure S1. Clean air at a flow rate of 300 mL/min enters the chamber via Teflon(tm) tubing (1/4\({}^{\prime\prime}\) diameter) with an inline hydrocarbon trap containing activated carbon. The clean air then enters a 64 oz. glass chamber containing the plant being sampled. The glass chamber is placed inside grooves specific to the glass chamber's dimension on top of a Teflon(tm) plate (Machine Fabrication, University of Alberta Department of Chemistry, Canada) that encloses the plant around its stem. Glass wool (Corning Inc., Corning, NY, USA) is used between the stem and the Teflon(tm) plate to close the system. Headspace air from the chamber is pulled through a second Teflon(tm)tube and into a TD tube using a GilAir 3 air sampling pump (Sensidyne, Clearwater, FL, USA) calibrated to 50 mL/min, as recommended by the Environmental Protection Agency (EPA) [23]. The selection of materials for the sampling system was based on minimal sorption and leaching capabilities, ease of cleaning, cost, and availability. Prior to the thermal desorption of the samples, collected plant samples underwent an initial dry purge step to remove water from the tube without removing trapped VOCs. The dry purge was conducted using the TC-20 unit at a temperature of 50 \({}^{\circ}\)C for 10 min with a nitrogen flow of 50 mL/min. Tubes were then capped immediately and kept at 4 \({}^{\circ}\)C until analysis. In this study, the whole plant, excluding the soil, was sampled.
## 1 Introduction
However, in the case described here, only the accuracy of the dimensions and shape of the part was not considered, omitting other possible criteria for assessing technological quality, such as surface roughness and mechanical strength. A general definition of the concept of product quality in the literature refers to technological quality and service quality. Technological quality depends on the choice of method, parameters, material, and equipment used in the manufacturing process. It is characterized by the degree of reproduction of the finished product in relation to technical requirements or customer expectations. Factors characterizing technological quality include dimensional accuracy, shape accuracy, accuracy of mutual position, stress state, or surface roughness. Service quality, on the other hand, is a set of characteristics that can be characterized using the product and its applicability, such as functionality, durability, wearing, and reliability [29]. Hence, technological quality can be characterized in a measurable way by quality indicators such as accuracy of dimension, shape, or surface roughness, while service quality is a set of subjective characteristics about the product and its properties [30]. The characteristics of current trends in the development of the FFF method indicate that no uniform methodology for assessing technological quality in this sense has been developed. Attempts to generalize the evaluation of the quality of the FFF process and its results are characterized by a selective nature and are not sufficiently multi-criteria. Studies focus on strength, quality, or material aspects with limited integration of these aspects into a coherent evaluation system. This is mainly due to the complexity of the issue of creating models using the FFF technique and the multitude of directions for modifying this process. For the reasons mentioned above, it seems purposeful to develop a methodology for assessing technological quality, which, in the long perspective, will allow the development of a universal set of evaluation criteria to facilitate the comparison of the results of research conducted by different research teams. The present work is a case study that proposes an original approach to multi-criteria evaluation of the technological quality of products manufactured with additive techniques. It describes an original procedure for assessing the technological quality of the FFF process using a specially developed reference component from PLA, considering dimensional deviations, shape deviations, and surface roughness of the model.
## 3 Results and Discussion
### Effect of Blue Light on Carotenoid Content in Maize Kernels
It was shown that illumination did not affect the antioxidant capacity of fruits and the content of \(\beta\)-carotene, ascorbic acid, and lycopene, while the content of flavonoids increased 1.9 times. More than 95% of \(\beta\)-carotene could be retained in tomato juice by using a constant illuminance of 2476 lux for 24 h at 4 \({}^{0}\)C [60]. These results substantiate the potential of using LEDs in photosensitization to reduce the microbial load on the maize surface without affecting carotenoids. Previous studies have reported a reduction in mycotoxin accumulation in the presence of carotenoids [15; 16]. However, no significant differences were observed in AFB1 accumulation between white maize having a relatively low carotenoid content of 1.74 \(\upmu\)g:g\({}^{-1}\) DW and popcorn maize with a relatively high content of 33.44 \(\upmu\)g:g\({}^{-1}\) DW. In addition, the inhibition of mycotoxin accumulation by carotenoids is strain-dependent [14; 15]. Furthermore, it has also been reported that the reduction in mycotoxin production depends on the individual carotenoids present, particularly \(\beta\)-carotene and \(\beta\)-cryptoxanthin have a significant effect on the aflatoxin synthesis [16]. In the present study, only the popcorn maize had detectable levels of \(\beta\)-cryptoxanthin and \(\beta\)-carotene, while zeaxanthin was the predominant carotenoid in all varieties. Furthermore, technical challenges such as developing the most efficient procedure to disperse the curcumin solution, need to be addressed, when upscaling this new green preservation technique to farm/industry-scale.