esa-sceva/satcom-chunk-collection huggingface dataset:
# Olfactory Capacity and Obesity: A Narrative Review of the Literature
## 3 Results
### Eating Behavior Disorders  
A study by [PERSON] [63] also investigated the association between olfactory ability and ghrelin levels. The study recruited 239 women, including 64 with anorexia nervosa, 80 age-matched healthy-weight controls, 59 women with obesity, and 36 women with normal weight. The results showed that olfactory ability was differentially associated among the groups. Olfactory function was impaired in participants with obesity and enhanced in those with anorexia nervosa (hyposmia prevalence was 54.3% in obesity and 6.4% in anorexia nervosa). Ghrelin levels were significantly reduced in participants with obesity and were associated with impaired olfactory function. Olfactory ability and ghrelin may act as moderators of emotional eating and BMI.  
The association between eating behavior disorders and olfactory function has also been studied. In [PERSON]'s study [64], 19 patients with bulinia nervosa, 18 patients with anorexia nervosa, and 19 healthy controls were recruited. Olfactory function was evaluated using the 'Sniffin' Sticks' method to assess olfactory threshold. Both bulinia nervosa and anorexia nervosa patients exhibited poorer olfactory and gustatory function compared to controls. Olfactory stimulus discrimination and overall olfactory function were reduced in both groups, while the olfactory threshold was specifically altered in patients with bulinia nervosa. In both groups, olfactory function scores fell within the range of hyposmia.  
In [PERSON]'s study [65], 17 hospitalized patients with anorexia nervosa, 24 with bulinia, and 23 controls underwent olfactory tests to assess the effects of treatment on these measures. The findings showed that, upon discharge, anorexic patients had an increased BMI and significant improvement in overall olfactory and gustatory function.  
In a study similar to the previous one, [PERSON] et al. [66] compared the olfactory sensitivity of 12 women with anorexia nervosa to 24 healthy controls. No differences were found in olfactory sensitivity to n-butanol. However, patients with anorexia nervosa had a significantly lower detection threshold for food-related odors, but only in the fasting condition. They also exhibited significant deficits in odor discrimination and identification and underestimated the pleasantness of isoamyl acetate. This reduced enjoyment of isoamyl acetate suggests a diminished olfactory response to food stimuli in anorexia nervosa.  
However, some studies have not found such associations. For instance, in the study by [PERSON] [67], 19 participants with anorexia nervosa were compared to 21 controls, and no associations were found between BMI and olfactory ability.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Software Level
#### 3.5.2 Vulnerability Management  
Vulnerability management is a necessary measure to secure software [72]. Vulnerability management allows for an effective update policy to ensure that the device's firmware and software are kept up to date, reducing the risk of known vulnerabilities. For example, security patches are pushed to devices through a remote update mechanism after the product's release.  
Regular vulnerability scanning helps to find and fix potential vulnerabilities in software [73]. Network application layers are regularly scanned for vulnerabilities, and any discovered vulnerabilities are fixed in a timely manner to optimize the security of the IoT device.
## 5 Experimental Verification and Analysis
### The Experimental Results  
The experimental results are shown in Figure 6. Table 6 can be derived from Figure 6, in Table 6, the accuracy rate is equal to the number of correct identification results divided by the number of test samples.  
The experimental results show that the recognition method combining HMM and multi-information fusion technology has a greater advantage than single-sensor recognition. The recognition rate reached 84.21%, and a new method was provided to identify the direction of the single-crystal diamond circular arc tool in the indexing sharpening process online. Moreover, if the model orientation identification result is the difficult grinding direction, according to the angle relationship between the difficult grinding direction and easy grinding direction of the typical crystal surface, as shown in Figure 2[13], the angles of the tool rotation to reach the easy grinding direction can be calculated, thus providing effective information for the control strategy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**(a)**} & \multicolumn{1}{c}{**(a)**} & \\ \multicolumn{1}{c}{**Sharpening direction**} & **Number of test samples** & **Correct number of recognition results** & **Accuracy rate (\%)** \\ \hline Non-difficult grinding direction & 19 & 15 & 78.94 \\ Difficult grinding direction & 19 & 12 & 63.15 \\ Total & 38 & 27 & 71.05 \\ \hline \multicolumn{1}{c}{**(b)**} & \multicolumn{1}{c}{**(b)**} & \\ \multicolumn{1}{c}{**Sharpening direction**} & **Number of test samples** & **Correct number of recognition results** & **Accuracy rate (\%)** \\ \hline Non-difficult grinding direction & 19 & 13 & 68.42 \\ Difficult grinding direction & 19 & 11 & 57.89 \\ Total & 38 & 24 & 63.16 \\ \hline \multicolumn{1}{c}{**(c)**} & \multicolumn{1}{c}{**(c)**} & \\ \multicolumn{1}{c}{**Sharpening direction**} & **Number of test samples** & **Correct number of recognition results** & **Accuracy rate (\%)** \\ \hline Non-difficult grinding direction & 19 & 17 & 89.47 \\ Difficult grinding direction & 19 & 15 & 78.94 \\ Total & 38 & 32 & 84.21 \\ \hline \hline \end{tabular}
\end{table}
Table 6: (**a**) The recognition results of a single acoustic emission sensor. (**b**) The recognition results of a single vibration sensor. (**c**) The recognition results of the method in this paper.  
Figure 6: The experimental results.
## 5 Discussion
### Superior Performance of Rubber Packer Than Geotextile Packer  
It was anticipated that when pressure is applied to grouting after packing the borehole using the packer, the entire pressure is transferred into the borehole. However, the experimental results showed that there was a different pullout capacity and creep behavior of the pressure-grouted micropiles installed using the geotextile packer and the rubber  
Figure 7: Micropile with pressure grouting in fractured zone.  
packer (Figure 5). This can be attributed to the limitations of the geotextile packer and the superior performance of the rubber packer. The geotextile packer is manually tightened on the reinforcing bar close to the entrance of the borehole (Figure 3a). Then, the packing of the borehole takes place as the packer expands by injecting cement slurry. However, there are practical problems with incomplete packing of the borehole. In this experiment, it was observed that the pressure grouting frequently leaked between the geotextile packer and the reinforcing bar, or the geotextile packer and the casing during grout filling. The leakage stopped when the internal pressure was dissipated. The level of leakage was highly dependent on the packability of the packer. The packability (i.e., manual tightening) varied depending on the practitioner's level of proficiency.  
The micropile installed using the rubber packer showed marginal leakage or no leakages during pressure grouting. The rubber packer seemed to bring a tight contact with the reinforcing bar and the casing after expanding with air. Consequently, the applied pressure was maintained during pressure grouting, which induces higher bonding stress between the micropile and soils. In addition, the rubber packer enabled us to eliminate the slime that is transferred to the top of the borehole during grouting via the nozzle attached on the packer (Figure 3d). Although the presence of the slime was not exactly evaluated in this study, purer grouting in the borehole should lead to higher bonding stress. The use of the nozzle should be investigated further.
## 3 Packet Detection Algorithms
### Preamble-Based Algorithms  
A general communication system engineering principle is that the receiver should use all the available information to its advantage. If the structure of the preamble is known, it should be incorporated into the packet detection algorithms to achieve better detection performance. The following two algorithms, auto-correlation and cross-correlation, use the correlation of the preamble to obtain the decision variable \(m_{n}\).
## 2 Materials and Methods
### Image Acquisition  
All patients in this study underwent standard 3D PET/CT imaging using a GE PET/CT-16 slice and a Discovery STE (GE Medical System, Milwaukee, WI, USA). In all cases, 370 MBq [18]FFDG was administered to the patients 40 min prior to 3D PET/CT imaging. Three-dimensional CT was conducted at 120 kV in the helical mode with a Smart/Auto mA (range, 30-210 mA), followed by conduction of 3D-PET imaging. In the PET imaging process, image collection of each section took 1.5 min to complete for the set field size. Three-dimensional PET image reconstruction was performed using the expectation-maximization algorithm (20 subsets and 2 iterations), and the images were post-filtered with a 7-mm 3D Gaussian filter.  
After completion of 3D PET/CT imaging, the patients underwent 4D PET/CT imaging using an instrument similar to that employed for 3D PET/CT imaging, but equipped with an additional Varian Real-Time Position Management (RPM, Varian Medical Systems, Palo Alto, CA, USA) system. The process began with conduction of 4D-CT imaging. Unlike 3D-CT, which continuously captures images using helical mode scanning, 4D-CT uses Cine mode scanning (an axial scan of generally more than one gantry rotation) to acquire a series of images from an anatomical portion of the chest for at least the temporal length of one breathing cycle, and then moves to the next portion of the anatomy to take images not covered by the previous acquisition. The process was complete when the images acquired spatially and temporally covered the entire chest. In this way, for each portion  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Patient Number** & **Sex** & **Age (years)** & **Histology** & **Location** \\ \hline
1 & M & 64 & NSCLC & RLL \\
2 & M & 59 & NSCLC & LLL \\
3 & M & 63 & NSCLC & RLL \\
4 & F & 57 & NSCLC & LUL \\
5 & M & 61 & NSCLC & RLL \\
6 & M & 64 & NSCLC & LLL \\
7 & F & 53 & NSCLC & LUL \\
8 & M & 77 & NSCLC & LUL \\
9 & F & 69 & NSCLC & RUL \\
10 & F & 53 & NSCLC & RLL \\
11 & F & 79 & NSCLC & RUL \\
12 & M & 72 & NSCLC & LUL \\
13 & F & 68 & NSCLC & RML \\ \hline \end{tabular} Abbreviations-M: male; F: female; LUL: left upper lobe; LLL: left lower lobe; RUL: right upper lobe; RML: right middle lobe; RLL: right lower lobe.  
\end{table}
Table 1: Clinical features of patients with non-small cell lung cancer (NSCLC).  
of the anatomy, a series of images that covers the entire breathing cycle was obtained for subsequent 4D-CT image reconstruction. The 4D-CT scan conditions for the chest region were 120 kV and 100 mA, and the respiratory cycle was divided into 10 phases, named T5%, T15%, T25%,..., 7, 195% (the T5% phase corresponding to the end-inspiration and the T55% phase to the end-expiration). In order to obtain sufficient images to resolve the problem caused by respiratory movement of patients, an 8-min series of images was used to reconstruct the 4D-PET images. Reconstruction was performed using the expectation-maximization algorithm with 2 iterations and 20 subsets, and a post-reconstruction Gaussian filter (7 mm) was applied.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 4 Results and Discussion
### Accuracy  
A method's accuracy is the ratio of correctly predicted sign language sentences to total predictions. Overall, it validates the model.  
\[\text{Accuracy}=\frac{\text{Number of Correctly Recognized Sentences (C)}}{\text{Total Number of Sentences (N)}}\times 100 \tag{12}\]  
In Equation (12), C stands for the successfully predicted sentences, and N denotes the total number of sentences in the test dataset.  
Cost criterion: The cost criterion in a model is defined using the cross-entropy loss function, which quantifies the classification error between the predicted sign language sentence and the actual ground truth.  
Accuracy is a performance metric, while the cost function is used for model optimization. The model's accuracy is evaluated using Equation (12), which assesses the proportion of correctly predicted sentences. The training of the model minimizes a cost function based on the categorical cross-entropy loss, penalizing incorrect predictions more heavily when the model is highly confident about an incorrect class. Cross-entropy is suitable for classification tasks like sign language sentence recognition as it improves sentence prediction accuracy, directly impacting performance in Equation (12). Softmax normalization ensures a probability distribution over classes, stabilizing training and preventing divergence. Gradient-based optimization (Adam or SGD) ensures convergence towards a model that maximizes classification performance. The cost criterion used during training is the categorical cross-entropy loss function, while accuracy is a post-training evaluation metric. This distinction should be explicitly mentioned when presenting the model to avoid ambiguity.  
Figure 6 indicates that the hybrid HGE-ACN model, which uses adaptive convolution and graph-based embeddings, recognizes sign language better than DDSTGCNN, MSeqGraph, and BAE. It outperforms DDSTGCNN, MSeqGraph, and BAE with 94.8% accuracy under normal settings. HGE-ACN remains accurate despite noise or compounding obstructions. Excellent performance is due to its spatiotemporal dynamics recording and sign language motion resilience.
## 2 Problem Description and Analysis  
For special railway transport lines for hazardous chemicals, such as coal-to-oil in a certain region, in the case of an emergency in the railway transport of hazardous chemicals under extreme conditions, or in the case of an extreme emergency, such as earthquake, landslide, mud rock flow, and other natural disasters, leading to the leakage of hazardous chemicals on the railway and damage along some transport networks, as well as production safety accidents in the hazardous chemicals' production area, the regional emergency management department needs to quickly study the disaster and carry out emergency disposal.  
Under scenarios such as the leakage of hazardous chemicals, including coal-to-oil and damage to railway network lines, the initial weight and emergency weight calculation methods for the allocation of safety production emergency rescue teams are proposed according to the trapped personnel, disaster situation, accident type, and the scope of the ripple effect at the initial moment, and a multi-objective optimization model with the shortest team response time and the smallest cost of transporting emergency materials along the railway transport channel of hazardous chemicals as the objective function is constructed.  
In the area of the transportation of hazardous chemical products such as coal-to-oil by rail, there are \(h\) collections of disaster areas that need to be involved in the rescue \(I_{1},I_{2},\ldots,I_{h}\). There are \(m\) collections of rescue points \(J_{1},I_{2},\ldots,J_{m}\) and \(b\) command center collections \(K_{1},K_{2},\ldots,K_{b}\) at the rescue point \(J_{m}(m=1,2,\ldots,m)\) on the stockpile of the first \(c(c=1,2,\ldots,C)\). The number of rescue emergency supplies is \(number(c_{cj})\) in the command center \(K_{b}(b=1,2,\ldots,b)\) on the first \(c(c=1,2,\ldots,C)\). The number of emergency supplies stored at the rescue point is \(number(c_{ck})\). Here, h represents the total number of disaster points, m represents the total number of rescue centers, and b represents the total number of command centers.  
The type of accident determines the type of rescue material. In this paper, the rescue material is divided into three types of rescue squads for configuration according to the number of people trapped in the disaster, the level of hazardous material leakage, and the priority of the line.
# One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation
## 2 Related Work
### Simultaneous Detection and Segmentation  
People have made a lot of effort to achieve simultaneous detection and segmentation. [PERSON] and [PERSON] [40] recruited the Edgelet feature to capture objects' local shape and then built detection and segmentation simultaneously on top of it. Meanwhile, they utilized boosting algorithms to enhance performance. However, due to the dependence on hand-engineered features, their model only worked on simple datasets. [PERSON] et al. [41] resorted to the convergent message-passing algorithm to inference their joint object detection, scene classification, and semantic segmentation model, which performed better than previous work. [PERSON] et al. [42] attempted to use ConvNets do simultaneous detection and segmentation. Their model was built on R-CNN [7] and appeared as multi-stage for they just used ConvNets as a fixed feature extractor. OFA-Net model described in this paper can also use detection and segmentation simultaneously but the feature extractor module was unfixed. Making the feature extractor unfixed has many advantages as it can behave not only as a feature extractor module but a bridge allowing the detection and segmentation to borrow features from each other.
# Study on Classification of Fishing Vessel Operation Types Based on Dilated CNN-IndRNN
## 5 Discussion  
Marine resources are one of the most important resources in China and play an important role in China's economic development. How to make sustainable use of marine resources is a difficult problem we need to face together. Among them, accurate identification of fishing vessel operation types can combat illegal fishing and help relevant departments in formulating fishery policies, which is the key to protecting marine resources.  
In this study, a dilated CNN-IndRNN model combining a dilated convolution (dilated CNN) and an independently recurrent neural network (IndRNN) was built for the first time. The CNN is improved to a dilated CNN, and the dilation rate is added so that it can focus on a wider range of features in long-sequence tasks. The improvement of LSTM to IndRNN changes the connection mode of neurons so that it can avoid gradient disappearance or explosion when capturing long-term dependencies. The experimental results show that the accuracy rate of the improved model is 93.18%, the precision rate is 93.17%, the recall rate is 93.22%, and the F1 score is 93.18%.  
According to the experimental results, it can be found that the performance of the model is improved after the CNN is improved to a dilated CNN. This shows that the introduction of the dilation rate and the expansion of the receptive field improve the model's ability to extract features. After improving LSTM to IndRNN, the performance of the model is greatly improved. This shows that the simple structure of the IndRNN is more suitable for long time series tasks and is important for capturing long-term dependencies. The IndRNN is more important than the dilated CNN for improving the performance of the combined model because the performance of the IndRNN alone is already relatively high. However, the introduction of a dilated CNN can further improve the performance of the model, which shows that in this combination, each part contributes to the performance of the model in a unique way, in which the contribution of the IndRNN is more prominent, but the role of the dilated CNN cannot be ignored, and the two together constitute a powerful combination model.  
Although the model has high accuracy in the identification of fishing vessel operation types, there are still some deficiencies. First of all, because the classification of fishing vessels by data sources is limited to three categories, future research can increase the types of fishing vessels. Secondly, future research can increase the data verification of field fishing vessel operation types in multiple regions and increase the persuasiveness of the article.
## 3 Methods
### Regression Method  
SST coefficients are generally derived by applying linear least squares regression that minimizes the sum of square errors of the satellite-derived SSTs from the in situ temperatures. However, if the sub-pixel clouds that survived in the cloud detection process are present in the matchup database or if the outliers of in situ temperatures remain due to an incomplete QC process, then the linear least square method may be less accurate for estimating the SST coefficients [18]. The present regression, on the other hand, has a relatively low sensitivity to outliers compared to least square regression. Thus, the coefficients of MCSST and NLSST were derived by the robust regression between the BTs of COMS/MI and in situ temperatures.  
For robust regression, the M-estimator is defined by minimizing the following function to obtain robust estimates of \(\beta\)[54; 55; 56]:  
\[\beta=\text{argmin}\sum_{i=1}^{n}\rho\Big{(}\frac{\epsilon_{i}}{\tau}\Big{)} \tag{5}\]  
where the \(\epsilon_{i}\) is the \(i\)-th residual of the regression; \(\tau\) is a scale factor; \(\rho(\cdot)\) is related to the maximum likelihood function. Assuming the derivative of \(\rho\) with respect to \(\beta\) is \(\psi(\cdot)\), then the robust estimator \(\beta\) can be obtained by solving as follow:  
\[\sum_{i=1}^{n}x_{i}\psi\Big{(}\frac{\epsilon_{i}}{\tau}\Big{)}=0, \tag{6}\]  
where \(x_{i}\) is satellite-observed temperature and detailed derivations are in [54; 55; 56]. To facilitate solving this equation easily, \(\psi\) should be replaced to weighting function such that:  
\[w_{i}=\frac{\psi(\epsilon_{i}/\tau)}{\epsilon_{i}/\tau} \tag{7}\]  
It is not possible to obtain the solution directly, because the weighting function depends on the residuals of the regression. Therefore, the method of iteratively reweighted least squares is generally used until a stopping criterion is met. In this study, the coefficients for SST retrieval were determined by applying this method, which is the most common method in robust regression [57; 58; 59] with bisquare weighting functions.