esa-sceva/satcom-chunk-collection huggingface dataset:
# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 2 Related Work
### Edge-Guided Segmentation  
Accurate boundary delineation remains a challenge, particularly for complex textures or slender objects. While deep networks excel at semantics, the hierarchical pooling operations can progressively dilute fine-grained edge information. Some semantic segmentation works explore edge preservation or refinement, often using edge detection as an auxiliary task or incorporating graphical models like CRFs as post-processing. However, explicitly integrating robust edge guidance \"within\" the panoptic segmentation pipeline, especially for enhancing instance boundaries, is less explored. Existing decoders often lack sufficient capability to process high-frequency edge information effectively. Our proposed Edge Guidance Module (EGF) directly addresses this by explicitly encoding multi-scale edge information, fusing it with backbone features via attention and gating, and employing an auxiliary edge supervision loss to force the network to learn precise boundary features, thereby improving segmentation accuracy, especially around object borders.
## 2 Main Contributions of This Work  
In this paper, we formulate a joint 3-dimension optimization problem of user pairing, subcarrier mapping and backhaul resource sharing between different pairs (subcarriers). A low complexity but efficient algorithm based on alternating optimization strategy and perfect mapping is proposed to solve this mixed integer programming problem. Simulations show that this allocation algorithm can improve the system capacity significantly compared with the blind alternatives. The contributions of this manuscript can be summarized as follows:  
1. In this work, we divide the complex integer programming problem into three steps: The user pairing, resource allocation and compression of the system noise. This operation helps reduce the dimensionality and the complexity of the algorithm without losing system performance significantly. Related results of this work could be easily extended to the IoT scenario;
2. We have proved the optimal result of the joint optimization problem using the three dimension optimization approach, and the theoretical results indicate that our proposed method could solve the multi-steps optimization problem at a satisfactory speed, so the system performance is improved significantly compared to reference methods;
3. To evaluate the availability of our proposed method, we first propose a system level simulation to check the effectiveness of our method. Results indicate that our given method could be used in real systems and the performance meets the expectations studied in the theoretical analysis.
# Effect of Processing on Volatile Organic Compounds Formation of Meat--Review
## 4 Thermal Processing
### Cooking Techniques  
The aroma characteristic of meat after heat treatment is obtained from VOCs derived from thermally induced reactions occurring during heating, mainly as a result of Maillard reactions, lipid oxidation, interaction between Maillard reaction products with lipid-oxidized products, and vitamin degradation [77]. Proteins react with sugars, resulting in Maillard reaction products such as carbonyls, nitrogen and sulfur containing compounds, which can interact each other or with other reactive compounds to yield compounds such as pyrazines, thiazoles, and thiophenes [16].  
[PERSON] et al. [78] analyzed chicken meat after heat treatment using three methods (frying, boiling, and roasting). VOCs were analyzed using the GC-IMS method. A total of 26 VOCs were detected. Phenylacetaldehyde were the main volatile flavor compounds in boiled chicken; 3-butanedione, etc., were the main volatile flavor compounds in fried chicken; while 3-methylbutyraldehyde, etc., were the main volatile flavor compounds in roasted chicken.  
[PERSON] et al. [68] evaluated the impact of temperature and time _in sous vide_ method on the quality parameters of duck meat. The flavor profile of meat was analyzed using GC-MS. The concentrations of hexanal, heptanal, octanal, (E)-2-octenal, nonanal, cis-4-decenal, decanal, decanal, (E,E)-2,4-nonadienal, 2,4-decadienal, (E,E)-2,4-decadienal and 1-octen-3-ol in samples cooked at 70 \({}^{\circ}\)C for 6 h or 12 h were relatively higher than in other samples.
## 4 Experiments and Results
### Pre-Training Effect Evaluation
#### 4.1.1 Datasets and Image Preprocessing  
The first dataset was provided by the Brain Tumor Segmentation (BraTS) Challenge 2021 [1; 2; 3]; it contained MRI scans from 1251 cases of patients. The dataset was divided into 1000, 200, and 51 for pre-training, fine-tuning, and testing, respectively. A 5-fold cross-validation strategy was used to evaluate the segmentation performance after pre-training. M instances were randomly selected for fine-tuning from the fine-tuning dataset of 200 instances, assuming that only these M instances were labeled. Multiple M values (e.g., 10, 20, and 40) were tested in the experiments to observe how the size of the training set during the fine-tuning stage affected the segmentation performance. The segmentation accuracy was evaluated by the average Dice Score for three regions (enhancing tumor, tumor core, and whole tumor region). The second dataset was provided by the Brain Tumor Segmentation Challenge (BraTS) 2020 [1; 2; 3]. It contained images of 369 patients split into 300, 50, and 19 for pre-training, fine-tuning, and testing, respectively. Other experiment settings were the same as the BraTS Challenge 2021. Image enhancement included translation, rotation, and scale operations.
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 4 Experimental Results
### The Results of CNN Model Training
The FPR represents the proportion of normal traffic incorrectly classified as attacks, which can lead to alert fatigue and increased operational overhead for security analysts in production environments. Conversely, the FNR indicates the proportion of actual attacks misclassified as normal traffic, which poses more severe security risks as these attacks would go undetected. It is noteworthy that this FNR of 1.25% was obtained from a balanced dataset with approximately a 5:5 ratio of normal to malicious samples. In real-world network environments where normal traffic constitutes the vast majority of total traffic, while the FNR percentage is expected to remain similar, the absolute number of missed attacks would be significantly lower due to the substantially reduced volume of actual attack traffic compared to our test conditions. Our model demonstrates excellent performance with both low false positive and relatively low false negative rates, representing an effective balance between minimizing alert fatigue and maintaining comprehensive attack detection capabilities. Figure 8: The confusion matrix for binary classification. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Precision** & **Recall** & **F1-Score** & **FPR (\%)** & **FNR (\%)** & **Accuracy (\%)** \\ \hline
0.9875 & 0.9930 & 0.9902 & 0.65 & 1.25 & 99.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The results of evaluation metrics.
## 3 Simulation Experimental Design
### Vehicle Operating Rules
#### 3.1.2 Lane-Changing Rules  
The lane-changing model used in the simulation in this paper is LC2013 in SUMO. This model fulfills two main purposes: It computes the change decision of a vehicle for a single simulation step based on the route of the vehicle and the current and historical traffic conditions in the surrounding vehicles. Furthermore, it computes changes in the velocity for the vehicle itself and for obstructing vehicles, which promotes the successful execution of the desired lane change maneuver.  
This model put forward four motivations for a lane change, three of which are involved in this paper and are sorted by priority as follows. Specific lane change rules can be found in the relevant literature for clarification [45].  
1. Strategic change. The vehicle should change to the target lane, which is connected to the next edge of its route. If the vehicle fails in the strategic change, it will not reach the next road, let alone the destination. The variable lcStrategic describes the urgency of the strategic change. A higher value means a more forward lane-change position;
2. Cooperative change. When vehicle A obtains the information that vehicle B will change to [PERSON]'s lane, [PERSON] may decelerate to keep a safe distance or change to another lane if there is enough gap. The latter change behavior is defined as a cooperative change in this model, as the only motivation of lane-change of A is to help B change lane successfully. The attribute lcCooperative is used here to measure the willingness to perform cooperative lane changing;
3. Tactical change. Tactical change describes the vehicles changing lanes to avoid following a slow leader. It needs to compute and compare the speed-gaining after lane change and the cost of the lane change. IcSpeedGain is the parameter used to compute the driver's willingness to change lanes and the speed-gain value.
## 5 Conclusions  
We developed a campus SWMM rain-flood model, using the west campus of CUG as the study area. By analyzing campus waterlogging under various rainstorm scenarios and return periods in terms of runoff and network performance (\(P\) = 1 y, 2 y, 5 y, 10 y, 20 y, and 50 y), we identified patterns in the spatiotemporal evolution of waterlogging and proposed improvement measures. The key findings are as follows:  
1. Simulating two actual rainfall events revealed that the calculated ponding depth closely matched the observed ponding depth and distribution, with an error of less than 10%. This indicates that the model is reliable and accurately reflects campus waterlogging.  
2. The flow process curve for the main outlet aligned well with the rainfall pattern curve, exhibiting a lagging effect consistent with the rainfall-runoff response. As the return period increased, the peak lag time gradually decreased. When the rainfall intensity declined, the outlet runoff volume did not drop immediately, and some rainwater remained stored, indicating sustained pressure in the drainage pipes. The model results show that a 2-year return period rainfall event could cause pipeline overload.
3. The proportions of overflow nodes and overloaded conduits reached 72.22% and 57.94%, respectively, with overload duration exceeding 1 h and increasing with the number of watersheds. The ponding area expanded from scattered to widespread, with overflow node and conduit counts, overload duration, ponding depth and area, and watershed counts all increasing accordingly.
4. Using SWMM, we assessed the waterlogging risk grades under different rainstorm scenarios, based on submerged ponding depth criteria. The area and number of medium-high waterlogging risk zones expanded as the storm intensity increased, with severe ponding primarily concentrated in the campus southwest region. The maximum ponding depth reached 0.5 m, and high-risk waterlogging zones covered up to 21.25% of the campus.
5. Campus waterlogging is driven by climate change, suboptimal land use and design, aging infrastructure, and other factors. Applying the sponge city concept in future campus redevelopment can improve adaptability to rainfall, reduce waterlogging pressure, and enhance resilience by integrating strategies for infiltration, detention, storage, purification, drainage, and absorption.  
C.Z.: writing--original draft preparation, conceptualization, and methodology; B.Y.: data curation, writing--review and editing; F.T.: conceptualization, supervision, and project administration; Z.M.: data curation; S.G.: data curation; X.Q.: investigation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hubei Natural Science Foundation (No. 2023 AFD217) and the China Geological Survey Project Foundation (Grant No. DD20221734).  
Not applicable.  
Some or all of the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses
## 3 Results and Discussion
### Case Study Description
#### 3.1.3 Estimation/Control Flux Model  
Three different kinetic flux model structures are studied as estimation/control flux model, a constant, a linear, and a non-linear model.  
The constant model considers the fluxes to be constant:  
\[\mathbf{u}(t)=\mathbf{p}_{\mathrm{u}} \tag{27}\]  
with \(\mathbf{p}_{\mathrm{u}}\in\mathbb{R}^{d\times 1}\), thus three parameters to be identified.  
In the linear model, the fluxes are just a linear function of the extracellular concentrations:  
\[\mathbf{u}(t)=\mathbf{C}\cdot\mathbf{c}_{\mathrm{ext}}(t) \tag{28}\]  
with \(\mathbf{C}\) as the (\(d\times m_{\mathrm{ext}}\)) matrix of parameters that define the linear relationship between fluxes and metabolite concentrations. This results in nine parameters to be identified.  
The non-linear kinetics model that is used in this work is the general biological reaction kinetics model of [25]. This model can describe both positive and negative effects of components on reaction rates, and uses only one parameter per component to describe both effects, and one maximum rate parameter per flux, i.e., \((m_{\mathrm{ext}}+1)\times d\) parameters:  
\[u_{i}(t)=u_{\mathrm{max,i}}\cdot\prod_{j=1}^{m_{\mathrm{ext}}}\alpha_{ij}(c_{ \mathrm{ext,j}}(t)) \tag{29}\]  
with  
\[\alpha_{ij}(c_{\mathrm{ext,j}}(t))=\begin{cases}\frac{c_{\mathrm{ext,j}}}{c_ {\mathrm{ext,j}}+D_{ij}^{2}}&\text{if }D_{ij}\geq 0\\ \frac{1}{1+c_{\mathrm{ext,j}}-D_{ij}^{2}}&\text{if }D_{ij}<0\end{cases} \tag{30}\]  
In these equations, \(\mathbf{u}_{\mathrm{max}}\) is the (\(d\times 1\)) vector of maximum rate parameters, and \(\mathbf{D}\) is the (\(d\times m_{\mathrm{ext}}\)) matrix of modulation parameters. If these parameters are positive, they describe a positive activation/saturation effect of the concentrations on the fluxes, if they are negative, an inhibition effect is represented. Hence, 12 parameters need to be identified i.e., 4 parameters per free flux.
# Study on the Ash Deposition Characteristics for Co-Combustion of Zhundong Coal with Cotton Stalk
## 3 Results and Discussion  
### Effects of the Cotton Stalk Blending Ratio on the Ash Melting Ratio  
Based on the blending ratio and calorific value of cotton stalks, the mass of cotton stalks at different blending ratios was calculated. The ash composition of the blended system under varying blending ratios was calculated, and the results are shown in Table 3. As indicated in Table 3, the K\({}_{2}\)O content in the ash significantly increased with the blending ratios of cotton stalk, rising from 0.31% to 9.31% when the blending ratio was 25%. Additionally, the content of Na\({}_{2}\)O increased from 6.41% to 7.02%. There was little difference in the contents of the other components under different blending ratios.  
Based on the ash composition data in Table 3, the Factsage calculations were performed to determine the solid phase and liquid phase contents of ash under different blending ratios and temperatures. The molten fraction under corresponding conditions was further calculated, and the results were illustrated in Figure 2. It can be seen that no molten phase was formed at any blending ratio when the temperature was lower than 1573 K. In the temperature range from 1573 K-1658 K, the melting ratio increased sharply from 0 to about 0.38. Because this temperature range was quite close to the temperature range of the high-temperature zone of pulverized coal combustion, this indicated that coal ash will melt under high temperature during the combustion process. Within this temperature range, higher cotton stalk blending ratios resulted in a larger melting ratio at the same temperature. When the temperature was higher than 1658 K, the melting ratio exhibited an exponential relationship with the temperature, and the ash was fully molten at 2523 K. The melting ratio-temperature curves were segmented and fitted, and the fitting equations for each blending ratio were obtained. The R square of each fitting equation was more than 0.97, indicating that the fitting equation can well describe the trend of liquid phase proportions under different blending ratios and temperatures.
## 5 Use Case: Dynamic Radio/MEC Resource Allocation and Slicing Management in High Mobility Scenarios
### Setting up the Case Study Scenario  
The use case scenario simulates a smart city comprising four nodes, each representing an instance of a near-RT RIC (along with their associated RAN and MEC systems) that manages distinct traffic profiles specific to the area of the city it serves. Each of these four nodes hosts two models deployed as xApps within the near-RT RICs:  
* Model 1: Manages the admission control of the radio and MEC resource slices.
* Model 2: Guarantees the quality of service of the processed slices through the execution of configuration actions, as described in [7].  
Additionally, a separate node is defined to implement a non-RT RIC. This node hosts an aggregation server deployed as an rApp, which is responsible for managing the aggregation of DQN models from client nodes using FL. The FL framework is utilized to collaboratively train the Model 1 instances of each client node while preserving data privacy.
## 3 Methodology
### System Model
#### 3.1.10. Integration Approach  
The variable \(y\_d\) represents the combined output signal at the destination node D in Equation (10). It is the sum of the instant signal-to-noise ratio (SNR) of the link between the sender node S and the receiver node \(D(y\_SD)\) and the instant SNR of the link between the NHN R and the receiver node \(D(y\_RD)\), respectively. Maximum Ratio Combining(MRC) is the hybrid combining method used here, which combines the two signals at the destination node to enhance the system's efficiency.  
\[y_{d}=y_{SD}+y_{RD} \tag{10}\]  
The pseudo code of the proposed work is given in Algorithm 1.  
```
1. import networking_library
2. import communication_library
3.Fi = forwarder nodes
4.rmn = minimum radius
5.St = sink node
6.SJ = neighbor head node (NHN)
7.rJ = advertisement packet
8.Sn = all candidates
9.p = routing path
10.#Function to select neighbor node based on a specific criteria
11.def select_neighbor (nodes):
12.Implement neighbor node selection logic here
13.selected_neighbor = None
14.Select neighbor node based on desired criteria
15.return selected_neighbor
16.Function to perform cooperative data transmission
17.def transmit_data (source, destination, data):
18.Implement cooperative data transmission logic here
19.Pass
20.while (TTL > 0) and (Fi! = St):
21.Fi.Sn = \(\mathcal{O}\)
22.Networking_library.Transmit (Qi, broadcasting packet with neighbor head node min)
23.for all SJ with \(\delta\) (i, j) \(<\)rmn:

1.ifcos (TTL) < 0:

1.communication_library.Sleep (SJ)
2.else:
3.St.Sn.Add (SJ)
24.Networking_library.transmit (SJ, broadcasting packet with neighbor head node rJ)
25.for all SJ k with \(\delta\) (j, k) \(<\)rJ:
26.if (\(\epsilon\)resk \(<\)\(\epsilon\) resj):
27.communication_library.Sleep (S k)
28.else:
29.Fi.Sn.add (S k)
30.Data transmission from NHN to sink
31.for NHN in St. Sn:
32.data = communication_library.Collect_data (NHN)
33.selected_neighbor = select_neighbor (Fi. Sn)
34.transmit_data (NHN, selected_neighbor, data)
35.endif
36.endfor
37.endif
38.endfor
39.endwhile
```  
**Algorithm 1**For the proposed NBEER routing protocol for UWSN  
The node parameter table lists the specifications of the simulation's sensor nodes. This consists of the principal energy of normal nodes (22 joules), the transmission range of each node (250 m), and the quantity of NNS/relay nodes (20).  
parison to state-of-the-art protocols, namely Co-UWSN and CEER. This study focused on evaluating the performance of these protocols using key parameters, including end-to-end delay, total energy consumption, packet delivery ratio, alive nodes, and the number of packets received (NPR). By examining these metrics, we gain valuable insights into the effectiveness and efficiency of NBEER in relation to its counterparts. The subsequent discussion delves into the implications of the results, highlighting the strengths and weaknesses of each protocol and shedding light on potential avenues for further enhancements in underwater wireless sensor network (UWSN) routing protocols.
# Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image
## 2 Materials and Methods
### Image Pre-Processing  
We utilized an imaging tool specifically designed for tongue image acquisition, which included a camera and a chin support instrument. However, we encountered some image distortion due to poor user control and minor movements, leading to several unfocused and blurred images. In particular, images that were out of focus could result in distortions of the extracted image features, potentially compromising the validity of our analyses. To address this issue, we implemented an unsharp masking algorithm to enhance the clarity of the unfocused images. This method involves subtracting a blurred version of the original image from the original image itself, multiplying the resulting difference by a predetermined weight value, and subsequently adding this adjusted value back to the original image. The mathematical representation of this process is as follows [22]:  
\[\text{Sharpened image = original image + weight \times (original image - blurred image)}\]  
The blurred image was generated using a two-dimensional Gaussian low-pass filter, which effectively reduced high-frequency noise while preserving essential image details. The weight value applied in this process was determined through a series of empirical tests, with the final value set to 4.  
We proceeded to extract a variety of features by converting the images into different color channels. Specifically, the original RGB (red, green, blue) sample images were transformed into several color spaces, including CMYK (cyan, magenta, yellow, and black), HSV (hue, saturation, and value), LAB (lightness, red/green, and blue/yellow), YCbCr (luminance and chrominance), and grayscale images. Principal component analysis (PCA) was then applied to 17 color channel images to extract three principal components. The principal components were calculated by multiplying the difference image by the eigenvector. The difference image was obtained by subtracting the normalized image from the average value of the normalized image. The eigenvector was derived from the covariance matrix of the difference image. For our feature extraction process, we specifically utilized the RGB channel, the magenta channel from CMYK representation, and the three principal components derived from PCA (Figure 2). The choice to focus on the magenta channel was based on the hypothesis that it would contain valuable information regarding the condition of the tongue, given its prominent red hue, which may reflect vascular and structural characteristics pertinent to salivary function.  
Figure 2: Image generation for image feature extraction.