esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Ray Tracing Methods
### Equal Gradient Ray Tracing Method  
In the equal gradient ray tracing method, it is assumed that the relationship between the velocity of sound and the depth is linear, and the trajectory of a sound wave in each layer is considered as an arc segment with the radius \(r\)[28], as shown in Figure 4. In Figure 4, \(S_{i}\) is the path of the arc segment through the \(i\)th layer, and \(r_{i}\) is the radius of the circle corresponding to the radius of the \(i\)th layer; \(P\) is the Snell constant which is defined by Equation (13).  
The sound speed gradient \(g_{i}\) in the \(i\)th layer can be expressed as  
\[g_{i}=\frac{c_{i+1}-c_{i}}{z_{i}} \tag{18}\]  
where \(g_{i}\) is the sound velocity gradient of the \(i\)th layer.  
The trajectory of a sound wave in each layer can be regarded as a circular arc with a constant curvature; the radius \(r_{i}\) and the length \(S_{i}\) of the arc corresponding to the \(i\)th layer arc can be expressed as  
\[r_{i}=-\frac{1}{pg_{i}} \tag{19}\]  
\[S_{i}=\frac{2\pi\cdot r_{i}}{2\pi}(\theta_{i+1}-\theta_{i})=r_{i}(\theta_{i+1 }-\theta_{i})=\frac{\theta_{i}-\theta_{i+1}}{pg_{i}} \tag{20}\]  
The horizontal displacement \(x_{i}\) and time \(t_{i}\) of the acoustic wave passing through the \(i\)th layer are  
\[x_{i}=r_{i}(\cos\theta_{i+1}-\cos\theta_{i})=\frac{\cos\theta_{i}-\cos\theta_ {i+1}}{pg_{i}} \tag{21}\]  
\[t_{i}=\int\limits_{z_{i}}^{z_{i+1}}dz/c^{i}(z)=(1/g_{i})\ln(c_{i+1}/c_{i}) \tag{22}\]  
Figure 4: Equal gradient ray tracing method.  
The total horizontal displacement \(X\) of the signal received by the transmitter from the underwater target is  
\[X=\sum_{i=1}^{n}x_{i}=\sum_{i=1}^{n}\frac{\cos\theta_{i}-\cos\theta_{i+1}}{pg_{i}} \tag{23}\]  
The ray path calculated by this method is in line with the actual ray path, and the calculation accuracy is higher than that of the constant sound speed ray tracing method [29]. However, the number of iterations is large, making the calculation process time-consuming.
## 3 Cherry Phenols Bioavailability and Physiological Roles
### Bioactivity of phenolic compounds from sour and sweet cherry
#### 3.1.2 Anti inflammatory effect  
Several studies have demonstrated that cherry intake inhibits inflammatory pathways. [PERSON] _et al_[46] have shown a decrease of blood levels of C-reactive protein (CRP) and nitric oxide (NO) in healthy subjects after intake of sweet cherries (280 g/die) for 28 days [46]. These results are in agreement with other studies [44]. Elevated levels of serum CRP is one of the most important indicators of inflammation and it is a significant risk factor for cardiovascular disease (CVD) [47]. The  
Figure 2: Protective effects exerted by cherry phenols and implication in chronic diseases.  
decrease in plasma CRP levels after cherry intake suggests a reduction in inflammation that may affect the risk for cardiovascular diseases. Also the increased production of NO and their reaction product peroxynitrrite (ONOO-) contribute to oxidative stress, tissue injury and an increase of their plasma concentrations are implicated in a variety of rheumatic diseases, including systemic lupus erythematosus, rheumatoid arthritis and osteoarthritis [48,49]. It has been suggested that the decrease in plasma NO level probably results from inhibition of the enzyme nitric oxide synthase-inducible (iNOS) activity [46]. Studies _in vitro_ have demonstrated that anthocyanins are able to inhibit nitric oxide (NO) production and other pro-inflammatory factors such tumor necrosis factor-alpha (TNF-\(\alpha\)) in activated macrophages [50,51]. Therefore, it has been suggested that the anti-inflammatory properties of cherries could be mainly related to anthocyanins.  
Studies in _vitro_ and _in vivo_ have demonstrated that anti-inflammatory properties of cherry phenols could also due to inhibition of the activity of the cyclooxygenase II (COX II) [42,52,53]. COXs are pro-inflammatory enzymes that play an important role in processes such as inflammation, carcinogenesis, apoptosis, cell proliferation and angiogenesis. It has to be stressed that the cyclooxygenase inhibitory activities of cherry anthocyanins are comparable to those of ibuprofen and naproxen at 10 \(\upmu\)M concentrations [52,53].  
The inhibitory effect of inflammatory pathways has been hypothesized to be the main molecular mechanism to explain the relationship between the consumption of cherries or cherry products and the alleviation of arthritic pain and gout [54-58].  
Obesity is associated with inflammation. Using model animals ([PERSON] fatty rat model of obesity and metabolic syndrome and Dahl Salt-Sensitive rat) it has been demonstrated that tart cherry-enriched diets reduce plasma and tissue inflammation. In fact it has been reported that cherry intake for 90 days significantly reduced the levels of proinflammatory molecules in plasma (interleukin-6, TNF-\(\alpha\)) and the activity nuclear factor kappa and increases peroxisome proliferatorator-activated receptors (PPARs) in abdominal fat and in hepatic tissues. These modifications were associated with a decrease of levels of serum glucose, triglycerides and cholesterol [59,60].
## 4 Experiments  
### Datasets and Evaluation Metrics  
**Datasets.** In this study, the proposed method was evaluated on the HELEN dataset [32], which is a challenging face parsing dataset that includes 11 semantic facial categories such as skin, left/right eyes, left/right eyebrows, upper/lower lips, inner mouth, nose, and hair. To ensure consistency and comparability with existing studies, the \"hair\" category was excluded from segmentation in our experiments. The dataset consists of 2330 high-quality images, among which 2000 were used for training, 230 for validation, and 100 for testing.  
The model was also applied to the CelebAMask-HQ dataset [33], a large-scale facial semantic segmentation dataset with a resolution of \(512\times 512\). It contains 30,000 manually annotated images covering 19 semantic categories, including both facial components and accessories, such as skin, nose, eyes, eyebrows, ears, mouth, lips, hair, hat, earrings, necklace, neck, and clothing. According to the official data split, 24,183 images were used for training, 2993 for validation, and 2824 for testing.  
**Evaluation Metrics.** For the CelebAMask-HQ dataset, two widely adopted evaluation metrics were employed: mean Intersection over Union (mIoU) [34] and FI-score [35]. For the HELEN dataset, the overall F1-score was used to assess the model's performance, where the metric is computed over merged facial components: eyebrows (left + right), eyes (left + right), nose, and mouth (upper lip + lower lip + inner mouth). The formulas for the above metrics are defined as follows:  
\[\text{mIoU}=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum _{j=0}^{k}p_{ji}-p_{ii}} \tag{16}\]  
\[F1=2\times\frac{\text{precision}\cdot\text{recall}}{\text{precision}+\text{ recall}} \tag{17}\]  
**Implementation Details.** In this study, all experiments were conducted using PyTorch version 1.12.1. The hardware environment consists of an Intel Xeon Platinum 8352V CPU and an NVIDIA GTX 4090 GPU (NVIDIA Corporation, Santa Clara, CA, USA). MobileNetV2 [36] was adopted as the backbone for the proposed network architecture. The model was optimized for 200 epochs using the Adam optimizer [37], with an initial learning rate (base lr) set at \(4\times 10^{-4}\) and a momentum of 0.9. A cosine annealing schedule was applied to decay the learning rate during training. The batch size was set to 16. In addition, standard data augmentation techniques were employed to prevent overfitting. The loss function used was the standard pixel-wise cross-entropy loss. To enhance generalization, data augmentation includes random horizontal flipping, random scaling (ranging from 0.75 to 1.25), random rotations within \(\pm 10\) degrees, random cropping, and color jittering (adjustments to brightness, contrast, and saturation). Weight decay was set to \(1\times 10^{-4}\) to regularize the training process.
# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 2 Perceived Emotion in Humans
### Association between Nonverbal Cues  
Cues are presented simultaneously, and humans rely on multiple cues to decode emotion. Therefore, instead of designing an approach that focuses on a specific cue, looking at the entire scene may be more informative, including the context in which the person is placed. The context has a direct influence on emotion, and the amount of influence is usually divided into three broad categories by the literature on psychology, discerning personal features, such as demographics and traits, situational features, such as goals and social norms, and cultural features, such as interdependence and nation wealth [38].  
Therefore, situational features should be encoded in datasets, given that their reflection of the situation will impact someone's emotions. The current placement of the person will have a direct impact on their emotions. Examples of placements that could positively impact emotions could be parties, public parks, and natural landscapes, while obscure, dark scenarios could indicate negative emotions. In addition, the presence of other people in the scene could be used as a cue for perceiving emotion. For example, in samples with group interaction, the individual emotions of the group members influence each other [39]. Studies show that perceivers automatically encode such contextual information to make a judgment [6], and that altering this background information could lead to an altered perception of the same facial expression [40].
## 3 Experiment Setup
### In-Vehicle Testing of the System  
To test the system performance and to understand the influences of motion artifacts from various sources, we conducted a series of in-vehicle tests, both on the highway as well as local roads. Our cascaded tests comprise 4 scenarios. Between each scenario, we add one extra source of interference and bring the test scenario closer to a realistic use case. In the first scenario, participants are asked to sit in the lab with their faces toward the camera. In scenario two, we change the viewing angle of the ToF camera and add the vibration from the engine to the test. Participants are asked to sit in a parked car with the engine running. In the third scenario, participants sit on the passenger seat while being driven on a highway/local road (participants asked to stay still). Compared to the previous scenario, we add the \"passive motion artifacts\" such as driving on an uneven road as well as making turns. Finally, in the last scenario, we add the voluntary motion from the driver such as checking the traffic conditions and adjusting their seating position while driving. Participants are asked to drive the car themselves on a highway/local road, mimicking the real-life operation scenario for which the system intended to be used. In total, 6 of the study team members participated in scenarios 1-3, while only 5 participated in scenario 4. During the measurement of each team member, roughly 25 measurements of HR/RR were taken (actual number of samples depends on time the participants spend to drive the designated route), leading to more than 125 measurements for each scenario. In terms of  
Figure 5: Processes to extract RR while driving using depth and grayscale information from a ToF camera.  
RR measurements, we measure the RR of the participants as they drive on the highway while wearing the respiration belt. Table 2 shows the details of the cascaded driving test. To protect the privacy of the study team members, all data recorded in this study are only stored in local hard disk drives, and only study team members in charge of data analysis have access to the hard disk drive.
# Crack Initiation and Propagation Fatigue Life of Ultra High-Strength Steel Butt Joints
###### Abstract  
The division of the total fatigue life into different stages such as crack initiation and propagation is an important issue in regard to an improved fatigue assessment especially for high-strength welded joints. The transition between these stages is fluent, whereas the threshold between the two phases is referred to as technical crack initiation. This work presents a procedure to track crack initiation and propagation during fatigue tests of ultra high-strength steel welded joints. The method utilizes digital image correlation to calculate a distortion field of the specimens' surface enabling the identification and measurement of cracks along the weld toe arising during the fatigue test. Hence, technical crack initiation of each specimen can be derived. An evaluation for ten ultra high-strength steel butt joints reveals, that for this superior strength steel grade more than 50% of fatigue life is spent up to a crack depth of 0.5 mm, which can be defined as initial crack. Furthermore, a notch-stress based fatigue assessment of these specimens considering the actual weld topography and crack initiation and propagation phase is performed. The results point out that two phase models considering both phases enable an increased accuracy of service life assessment.  
Keywords:fatigue strength; ultra high-strength steel; butt joints; weld topography; crack detection and tracking; crack propagation +
Footnote †: journal: Applied Sciences
###### Abstract  
Background: The use of natural feed supplements in rabbit farming can enhance the nutritional value of meat and improve its properties as a functional food. Aim: This study aimed to analyze the effects of supplementing rabbit feed with linseed meal, fodder yeast, and a combination of linseed meal and selenium yeast on meat quality. The study included 45 rabbits kept in household farms, distributed into three groups, namely, the control group (CG--fed a basic diet consisting of alfalfa pellets); group 1 (G1--supplemented with linseed meal and fodder yeast), and group 2 (G2--supplemented with linseed meal and selenium yeast). Following the three-month study period, rabbit meat samples were analyzed for nutritional composition. Results: The results revealed reduced rabbit meat caloricity in G1 and G2 (ME = 111.74 and 112.58 kcal/100 g, respectively), when compared to CG datasets. Peak polyunsaturated fatty acid content was observed in G2, with omega-3 and omega-6 fatty acids content at 12.326% and 18.382%, respectively. Total mineral content of rabbit meat peaked in G2 (720.99 mg/g), whereas CG (603.71 mg/g) had the lowest content. Conclusions: These results demonstrate that feed supplements such as linseed meal, fodder yeast, and selenium yeast can significantly enhance the nutritional and functional properties of rabbit meat, offering a sustainable approach to producing nutrient-rich animal-derived foods.  
Article
## 5 Conclusions  
This study has examined electrical treeing performance and has conducted a morphological analysis of plasma-treated silicone-rubber nanocomposites under different filler loadings. In addition, we have made various comparisons with existing filler treatment methods. The paper has presented the experimental results, the main findings of which are as follows:  
* The plasma-treated SiO\({}_{2}\) nanofillers showed smoother and smaller amounts of agglomeration nanofillers with an increase in oxygen content after treatment among the three different treatment methods of the nanocomposites.
* The plasma treatment improved the electrical tree initiation time, tree propagation time, and tree growth rates. In addition, based on the bridging times of the electrical trees, ANOVA analysis results showed a signifcant difference between the plasma-treated nanocomposites and silane-treated as well as direct-mixed nanocomposites. In addition, the plasma-treated nanocomposites exhibited the longest tree bridging times, implying that the plasma treatment of the SiO\({}_{2}\) nanofiller improved the electrical tree performance of the silicone rubber nanocomposite. The increase in filler concentration also resulted in better electrical treeing performance.
* The plasma-treated nanocomposites possessed the least agglomeration among the three different treatment methods of the nanocomposites due to the well dispersed SiO\({}_{2}\) nanofillers.  
The authors gratefully acknowledge the financial support by Universiti Teknologi Malaysia and the Malaysia Ministry of Education under the Fundamental Research Grant Scheme (FRGS), Vot. No. 4F398, 4F599, 4F751, 13H98, and 10J87 to carry out this work.  
[PERSON] (MPhil student) conducted the experiments and drafted the manuscript; [PERSON] and [PERSON] supervised the experiments and study as well as finalized the manuscript; [PERSON] assisted in the design plasma chamber and plasma treatment experiments; [PERSON] provided technical assistance in the course of this study.  
The authors declare no conflict of interest.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Chemical Vapor Deposition (CVD)  
CVD refers to the segregation or chemical reactions of gaseous reactants near a heated substrate surface in an activated situation (heat, light, plasma), which will finally form a stable solid product [157]. Its history goes back to 1893 when [PERSON] (an electrical engineer and inventor) deposited tungsten onto carbon lamp filaments through the reduction of tungsten hexchloride (WCl\({}_{6}\)) by hydrogen (H\({}_{2}\)). This initiated the pedestal of the industrial utilization of CVD [158]. Typically, the CVD method is applied to deposit conductive polymers on the textile substrate (fiber, yarn, fabric) [159]. The fundamental concept of CVD is a chemical reaction that forms a layer on the substrate from the vapor of the reagents, leaving the by-products in a volatile form.  
Researchers experiment with different kinds of materials by using the CVD method to make textile materials conductive. FeCl\({}_{3}\)-coated cotton yarns were exposed to pyrrole vapor, thereby producing a coated layer of PPy on cotton yarns [160]. The CVD method is adopted for PPy deposition in various fabrics such as viscose, cupro, lyocell, nylon, PET, etc. [161; 162; 163].  
The CVD process is a recognized technique for making highly conductive polymeric layers on different substrates [164]. To get high conductivity and a uniform polymeric layer on a flexible and rigid substrate, a novel method called Oxidative Chemical Vapor Deposition (OCVD) was implemented [165]. OCVD is a solvent-free technique that results in more homogenous, thin, and highly conductive polymer layers on different substrates. The steps for OCVD of PEDOT include: (i) First, fiber penetration with oxidant (FeCl\({}_{3}\)) solution and sub-drying; (ii) then, oxidant-enriched fibers were exposed to EDOT monomer vapor; and last, (iii) PEDOT-coated fiber doping. It is reported that the CVD technique could successfully coat PET fabrics with PEDOT, but PEDOT-coated PET fabrics showed reduced conductivity due to poor fiber contact [166]. PEDOT-coated PET yarn fibers with good electrical and mechanical characteristics were developed, which could be converted into woven structures with good electrical properties. Furthermore, viscose yarn was tested in both the CVD and OCVD processes. Although the OCVD process found a high conductivity level of 14.2 Scm\({}^{-1}\), fewer mechanical properties were obtained at 15 wt% oxidant concentration on viscose yarn [167].
# Study on Cyber Common Operational Picture Framework for Cyber Situational Awareness
## 2 Related Works
### Definition of Cyberspace Operations and Cyber Awareness
#### 2.1.2 Definition of Cyber Awareness  
Situational awareness is the most active area of research in the military field. It refers to the perception of an entity in its environment, an understanding of its meaning, and a prediction of its near-future state [13; 14; 15]. Several studies have been conducted to implement situational awareness in cyberspace. [PERSON] et al. [16] explained the following seven aspects of cyber situational awareness.  
1. You should be aware of the current situation: you must be able to identify and recognize attacks, which goes beyond intrusion detection.
2. Pay attention to the impact of an attack: impact assessments should be performed now and in the future.
3. Be aware of how things are going: situation tracking is a key component of this aspect.
4. Pay attention to the behavior of the threat actor: focus on the actions of the attacker or threat actor in the situation rather than the situation itself.
5. Recognize why and how the current situation occurred: causal analysis and forensics are included.
6. Recognize the quality of the collected context-aware information items and derived knowledge decisions: this can be seen as part of a concrete perception.
7. Evaluate the plausible future of the present situation: predict the paths and actions a threat may take in the future.  
[PERSON] et al. [17] defined cyber situational awareness to include an awareness of all kinds of suspicious activities occurring in cyberspace and providing additional insight into the overall situation.  
Figure 1: Joint Cyberspace Operational Planning Process based on Joint Publication 3–12.  
[PERSON] et al. [18] defined cyber situational awareness as assisting decision makers by recognizing events, along with their origins, outcomes, and future predictions, through the collection, fusion, and visualization of data.  
Summarizing the above studies [13; 14; 15; 16; 17; 18], cyber situational awareness is defined as recognizing the current situation in detail and predicting the future situation, focusing on the behavior of the threat actor.
## 2 The Construction of ECA-YOLOX-Tiny Network Model
### Adjustment of Input Image Resolution  
In general, a lot of detailed information on small targets can be retained in high-resolution images. In order to further improve the detection accuracy of the YOLOX-Tiny model for small target defect areas of insulators and multi-scale multi-target insulators, the input image resolution of the YOLOX-Tiny model is adjusted appropriately. The input image resolution of the original YOLOX-Tiny model is reshaped from 416 \(\times\) 416 to 640 \(\times\) 640.
## 4 Current Studies Focused on PPO Activity
### Influence on PPO Activity
#### 4.4.4 Pre-Harvest Conditions  
As explained in previous sections concerning fruit and vegetable characterization, these conditions have been widely studied, as they present a high influence on some characteristics of fruits and vegetables both whole and minimally processed, including firmness, flavor, functional compounds, water loss, etc.  
Regarding cultivar selection, for instance, the composition or characteristics of different cultivars might considerably vary in fruits or vegetables (both whole or minimally processed), especially concerning PPO and polyphenol content, turgence or water loss. Specifically, PPO activity seems to play an important role in potato, whose susceptibility to blackspot can be predicted depending on the geometric parameter of the microstructure of tuber parenchyma, determined at harvest [70]. Indeed, depending on the cultivar, different potato samples presented long term storage stabilities such as \"Purple\" and \"Satsumo Imo\" varieties [71]. Another study pointed out the influence of cultivar on water loss in fresh-cut fruits during storage, pointing out that other factors such as temperature could equally play an important role [72].  
On the other hand, other pre-harvest conditions such as climate, including irrigation or rain conditions, can influence on the characteristics of fruits or vegetables during storage. Thus, in the case of two potato cultivars with different drought sensitivity, different respiration rates and glucose or fructose content were found during storage [73].  
Finally, maturity of the collected sample is another key factor affecting quality of fresh-cut produce, as in the case of minimally processed pomeranate girls, where maturity (as well as cultivar selection) had an important influence on flavor after processing and during storage, with considerable changes volatile profile during fruit maturation [74].