esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Halitosis and submandibular abscesses are examples of mouth-related diseases with the possible bacterial origin. Salivary volatile organic compounds (VOCs) are potential biomarkers of them, once they can be addressed as metabolites of bacterial activity. Healthy patients (n = 15), subjects with submandibular abscesses located in fascial deep space (n = 10), and subjects with halitosis (n = 5) were enrolled in the study. Saliva samples were subjected to headspace solid-phase microextraction (HS-SPME) and gas chromatography coupled to mass spectrometry (GC/MS) analysis. A total number of 164 VOCs was detected by the developed methodology, 23 specific for halitosis and 41 for abscess. Halitosis' profiles were characterized by a larger number of sulfur compounds, while for abscess they had a higher variety of alcohols, aldehydes, and hydrocarbons--biomarkers of inflammatory processes. Principal components analysis allowed visualization of clusters formed according to the evaluated conditions. Kruskal-Wallis test indicated that 39 VOCs presented differentiated responses between the studied groups, with statistical relevance (p < 0.05). Random forest was applied, and a prediction model based on eight VOCs (2-butanone, methyl thioacetate, 2-methylbutanoic acid, S-methyl pentanetthioate, dimethyl tetrasulfide, indolizine, pentadecane, and octadecan) provided 100% of sensitivity, 82% of specificity, and 91% of balanced accuracy, indicating the specific presence of submandibular abscess.  
saliva; halitosis; submandibular abscess; VOCs; GC/MS; diagnosis Article
###### Abstract  
Climate-induced changes in precipitation and temperature can have a profound impact on watershed hydrological regimes, ultimately affecting agricultural yields and the quantity and quality of surface water systems. In India, the majority of the watersheds are facing water quality and quantity issues due to changes in the precipitation and temperature, which requires assessment and adaptive measures. This study seeks to evaluate the effects of climate change on the water quality and quantity at a regional scale in the Nagavali and Vamsadhara watersheds of eastern India. The impact rainfall variations in the study watersheds were modeled using the Soil and Water Assessment Tool (SWAT) with bias-corrected, statistically downscaled models from Coupled Model Intercomparison Project-6 (CMIP-6) data for historical (1975-2014), near future (2022-2060), and far future (2061-2100) littermates using three Shared Socioeconomic Pathways (SSP) scenarios. The range of projected changes in percentage of mean annual precipitation and mean temperature varies from 0 to 41.7% and 0.7 \({}^{\circ}\)C to 2.7 \({}^{\circ}\)C in the future climate, which indicates a warmer and wetter climate in the Nagavali and Vamsadhara watersheds. Under SSP245, the average monthly changes in precipitation range from a decrease of 4.6% to an increase of 25.5%, while the corresponding changes in streamflow and sediment yield range from \(-\)11.2% to 41.2% and \(-\)15.6% to 44.9%, respectively. Similarly, under SSP370, the average monthly change in precipitation ranges from \(-\)3.6% to 36.4%, while the corresponding changes in streamflow and sediment yield range from \(-\)21.53% to 77.71% and \(-\)28.6% to 129.8%. Under SSP585, the average monthly change in precipitation ranges from \(-\)2.5% to 60.5%, while the corresponding changes in streamflow and sediment yield range from \(-\)15.8% to 134.4% and \(-\)21% to 166.5%. In the Nagavali and Vamsadhara watersheds, historical simulations indicate that 2438 and 5120 sq. km of basin areas, respectively, were subjected to high soil erosion. In contrast, under the far future Cold-Wet SSP585 scenario, 7468 and 9426 sq. km of basin areas in the Nagavali and Vamsadhara watersheds, respectively, are projected to experience high soil erosion. These results indicate that increased rainfall in the future (compared to the present) will lead to higher streamflow and sediment yield in both watersheds. This could have negative impacts on soil properties, agricultural lands, and reservoir capacity. Therefore, it is important to implement soil and water management practices in these river basins to reduce sediment loadings and mitigate these negative impacts.  
climate change; sediment yield; shared socioeconomic pathways (ssps); streamflow; swat; watershed +
Footnote †: journal: Applied Sciences  
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Footnote †: journal: Applied Sciences  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 3 SDMob Architecture
### Enhanced SDMob
#### 3.2.2 Handling Multiple MNs (Smart Buffer Management)  
One of the most important features of enhanced SDMob is supporting multiple MNs that require multiple instances of the filter to run in parallel. On the other hand, with multiple MNs the control traffic increases dramatically sometimes even more than the capacity of the network. SDMob will require multiple independent buffers at the border router each associated with an individual MN. These buffers assign a priority to the newest reports and discard the old ones.  
Another additional feature focuses on improving the efficiency of the inter-process communication between the border router and the filter. In the enhanced SDMob, the buffers (between the border router and the particle filter) are implemented using shared memory, instead of using Linux pipe files that were utilized in the basic SDMob. This feature drastically decreases the time spent for predicting the future parent and handoff delay, thus improving network reliability.  
To increase the reception ratio of the control traffic, we implement a buffer timer that specifies the time that the border router waits for RSSI measurements. This timer can be tuned in accordance with the hop-distance of the mobile node, congestion in the network, congestion timer, and the interval for sending the control beacons. Each MN will have a buffer timer that can be tuned independently.
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 2 Related Work
### Deficiencies in Existing Methods  
Generally, the following three deficiencies are exhibited by existing stateful network protocol fuzzing methods.  
(1) Frequent auxiliary message interaction  
In stateful network protocol fuzzing, messages consume time waiting and processing at the node and transmission in the network, which is a problem that cannot be ignored. Existing methods usually only focus on improving the validities of test cases without considering the optimization of the fuzzing process. As a result, only a tiny portion of the messages in the test sequence belong to the test cases, while others called auxiliary messages belong to the pre-lead sequence and post-end sequence. These auxiliary messages generate additional time overhead [21], which results in a reduction in the number of test cases participating in fuzzing per unit time and then a reduction in the probability of raising exceptions in the protocol entity, thereby, affecting the vulnerability discovery efficiency of such approaches.  
(2) No in-depth state space exploration  
In stateful network protocol fuzzing, in addition to code coverage, state-space coverage should also be considered. When the code coverage is roughly constant, the higher the state-space coverage is, the deeper the state-space exploration, and the more comprehensive the fuzzing process [22]. However, the existing methods can only explore a small part of the state space of the protocol entity, which reduces the probability of raising exceptions in the protocol entity and affects the efficiency of vulnerability discovery.  
(3) A high share of invalid interaction time A test case raises exceptions in the protocol entity only if the protocol entity can receive it [23]. The existing methods exhibit blindness; it is uncertain whether a test case can be received by the protocol entity before fuzzing. Most test cases are directly discarded by the protocol entity due to their invalidity; they do not meet the protocol specification or match the state of the protocol entity. The above reason results in a high share of invalid interaction time, which reduces the probability of raising exceptions in the protocol entity, thus affecting the vulnerability discovery efficiency of these methods.  
Based on the above analysis, this paper is dedicated to compensating for the above shortcomings to improve the efficiency of vulnerability discovery.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 5 Simulations
### Moving Target over a Forward Scatter Receiver Array  
Doppler information is present, provided the target is not at the baseline crossing point. Therefore, Doppler shifts will be present in the signal as the target approaches and departs the baseline.
# Exceptional Points in Non-Hermitian Photonic Crystals Incorporated With a Defect
## 3 Exceptional Points and Sharp Change in Phase
The extreme value in transmittance meant that there was complex phase uncertainty of the transmission coefficient. The sharp change in the complex phase of the transmission coefficient can also induce the lateral GH shift of the transmitted light. As the light incident from the right, the reflection coefficient was denoted by \(r_{2}=|r_{2}|\exp(i\varphi_{r2})\). Figure 3a gives the reflectance in the parameter space. There were also two extreme points as the normalized frequency and gain-loss coefficient increased. The minimum was labeled by EP\({}_{2}\) and located at \([(\omega_{\rm EP1}-\omega_{0})/\omega_{\rm gap}=0.0654\), \(\sigma_{\rm EP2}=0.1]\), while the maximum was the CPA-LP and located at \([(\omega_{\rm LP}-\omega_{0})/\omega_{\rm gap}=0.0654\), \(\sigma_{\rm LP}=-0.79]\). The positive gain-loss coefficient represented loss and negative gain-loss coefficient represented gain, respectively. When light was incident from the left, the position of EP\({}_{1}\) showed that gain in materials was required, and while the light was incident from the right, loss was required to carry out the singularity of EP\({}_{2}\). These properties indicate that theEPs were dependent on the directions of light incident form the left and right. The dependence on directions for EPs resulted from the asymmetry of the defect and the non-Hermiticity of PCs. As the gain-loss coefficient \(\sigma\) changed, the real and imaginary parts of eigenvalues are depicted in Figure 4a,b, respectively. For the incident and scattered waves, the scattering matrix in PCs can be written as \(S=[t\,r_{1};r_{2}\,t]\), which is the counterpart of Hamiltonian in quantum systems [51,52]. The eigenvalues of \(S\)-matrix were \(\lambda_{1,2}=t\pm(r_{1}r_{2})^{1/2}\) and the eigenvectors were \((r_{1}^{1/2},\pm r_{2}^{1/2})\). The eigenvalues coalesce and the eigenvectors degenerated at \((r_{1}r_{2})^{1/2}=0\), which was defined as EPs of eigenvalues. That is, the EPs could be derived by \(r_{1}\) or \(r_{2}=0\). We generally searched for EPs in the parameter space on conditions of \(R_{1}\) or \(R_{2}=0\). The reflection coefficient complex phase \(\varphi_{r1}\) and \(\varphi_{r2}\) were bound to experience a dislocation as \(R_{1}R_{2}=0\), which is an unique characteristic of EPs. The zero reflection and complex phase dislocation can be utilized to seek out EPs. When \(\sigma<\sigma_{\text{EP1}}\) and \(\sigma>\sigma_{\text{EP2}}\), the real parts of the eigenvalues split into two branches. When \(\sigma_{\text{EP1}}<\sigma<\sigma_{\text{EP2}}\), the real parts of eigenvalues degenerated. On the whole, the curves of real parts were anti-crossing and the imaginary part curves were crossing around the EPs for the eigenvalues. The imaginary parts of eigenvalues degenerated at EPs as well. These characteristics are often used to confirm EPs.
## 4 Discussion
### Objective Assessment
However, the resulting accuracy data for our method are highly significant in terms of the high values of PA and SP, and the quite low values of EO and EC, as well as the highest value of OA compared to the other methods for test image A, as shown in Figure 5a, which reveals that our proposed approach performs well in both correctly identifying shadow pixels in the original images into the same class and reducing the probability of misclassifying nonshadow pixels of the original images as shadow pixels.
## 5 Numerical Experiments and Analysis
### Instances and Parameters Setting  
Based on the work of [23], which designed for the deterministic EVRPTW, the test instances are proposed. The node locations are directly from [23] and demands of customers are generated between 50 and 450 kg randomly. According to the geographical distribution of the customer locations, these instances are divided into six groups, i.e., C1, C2, R1, R2, RC1, and RC2. C1 and C2 are in the form of clustered customer distribution; R1 and R2 are random customer distribution; RC1 and RC2 are the mixture of both distributions.  
Figure 1: Neighborhoods for local search.  
C1, R1, and RC1 have a short scheduling horizon, while C2, R2, and RC2 have a long scheduling horizon.  
The EVs are assumed to be equipped with 100 kWh batteries [9]. We adopt the cost parameters of [26; 45]. The maintenance cost is based on the work of [PERSON] and [PERSON] [46]. All EVs travel at a constant speed and the road grade is assumed as zero along all the arcs [9].  
We assume that the expected value of unit energy consumption \(\overline{h}_{ij}\), as well as the maximum deviation \(\hat{h}_{ij}\), are identical for each vehicle with regard to a given arc \((i,j)\), and the maximal deviation \(\hat{h}_{ij}\) is 20% of the expected value \(\overline{h}_{ij}\). The parameters in Formula (18) are adopted from [PERSON] et al. [9]. We set \(s_{1}=20\), \(s_{2}=10\), \(s_{3}=2\)[47]. The penalty factors are \(\delta_{t}^{1}=500\), \(\delta_{t}^{2}=500\), \(\delta_{\epsilon}=1000\), and \(\delta_{q}=500\). The main parameters for the model are shown in Table 2.  
We set initial temperature \(T_{0}=20\) and parameter \(\epsilon=50\)[33]. To obtain the ideal value of cooling rate \(r\), we conducted preliminary tests on several instances. The results are presented in Table 3. Five instances are chosen in column \"Instance\". Column \"GURBOI\" presents the values of each instance. The total cost values (noted as TC, as defined in (1) and (1)) are the best-found solution by NHALNS and are shown in column \"TC\". Three values of cooling rate are chosen to compare, \(r=0.05\), \(r=0.1\) and \(r=0.15\). The maximum computation time for GUROBI is set to 500 s, and for NHALNS, it is 2000 s.  
As can be found in Table 3, small-sized instances (with less than 15 customers) can be optimally solved with different cooling rates. For large-sized instances (with 100 customers), GUROBI cannot find the exact solution. Compared to \(r=0.05\) and \(r=0.15\), NHALNS can obtain a more effective solution when \(r=0.1\). Thus, \(r=0.1\) is the ideal value.  
NHALNS is coded in Python 3.8, and all the experiments are run on a desktop PC with a 3.19 GHz dual processor and 16 GB of RAM.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 4 Proposed Method PDTR
### The Probabilistic Routing Table Set-Up Phase
#### 4.1.2 Transmission Distance Distribution  
The closer the forwarder node is to the sender; the less energy is needed for data transmission. Higher priority is given to forwarders closer to the sender node. From each node to its neighboring nodes, the transmission distance can be calculated by the Euclidian distance, and it is normalized by Equation (6). The furthest node can have a maximum distance of communication radius \(\delta\) value from the sender node.  
Let \(\mathbb{N}_{i}\) be the neighboring nodes of the node \(n_{i}\), such that \(|\mathbb{N}_{i}|=m_{i};d_{i,l}=\Big{(}d_{i,1},d_{i,2},\ldots,d_{i,m_{i}}\Big{)}\), \(m_{i}\)_neighboring node_. The transmission distance distribution function is normalized by Equation (6).  
\[\overline{d}_{i,j}=1+\left(\frac{d_{i,j}}{\delta}\right)\forall n_{j}\in \mathbb{N}_{i} \tag{6}\]  
The probability function of the transmission distance distribution by using Equation (6) is given by Equation (7).  
\[\tilde{d}_{i,j}=\frac{\overline{d}_{i,j}}{\sum_{l=1}^{m_{i}}\overline{d}_{i,l }},\forall n_{j}\in\mathbb{N}_{i} \tag{7}\]  
From Equation (7), we can see that the further nodes have higher probabilities, but we want to give a higher probability to the closer nodes.  
Let \(\overline{d}_{i,j}\) be the mass probability function of the distance distribution (see Equation (8)), such that the higher probability will be given to the closer nodes to the sender node, the smaller distance will have a greater probability. To determine the impact of the transmission distribution on the other two distributions defined in the proposed PDTR, we defined in Table 1, a transmission distance control parameter denoted by \(\beta\) such that: \(\beta\geq 0\), as shown in Equation (8).  
\[\overline{d}_{i,j}=\frac{\Big{(}1-\tilde{d}_{i,j}\Big{)}^{\beta}}{\sum_{l=1}^ {m_{i}}\Big{(}1-\tilde{d}_{i,l}\Big{)}^{\beta}} \tag{8}\]
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 5 Determination of Safe Vibration Thresholds and Development of Mitigation Strategies
### Safe Vibration Thresholds and Mitigation Strategies  
Establishing Safe Vibration Thresholds. Safe vibration thresholds were established by integrating simulation results, field measurements, and regulatory standards. Peak particle velocity (PPV) values for critical components--central pillar (1.763 cm/s), diaphragm wall (1.656 cm/s), and roof (1.537 cm/s)--were obtained through FLAC3D simulations and field validation. These values were compared with the Chinese Blasting Safety Regulations (2.0-2.5 cm/s). Conservative thresholds of 1.8 cm/s (central pillar), 2.0 cm/s (diaphrag wall), and 2.2 cm/s (roof) were defined, ensuring structural safety with an adequate margin. Statistical analysis and stress-displacement correlation confirmed these thresholds minimized damage risks.  
Mitigation Strategies. Mitigation strategies were developed to manage vibration impacts effectively.  
Blasting Optimization: Adjusting blast hole layouts and charge weights, combined with delayed detonation techniques, reduced peak PPV by up to 15%. Structural Reinforcement: Strengthening high-risk components with additional concrete layers or steel bracing enhanced structural resilience. Geotechnical Stabilization: Grouting techniques improved rock mass stability, reducing vibration transmission. Real-Time Monitoring: A network of geophones provided continuous PPV monitoring, enabling dynamic adjustments to blasting parameters.  
These strategies, validated through controlled trials, demonstrated significant reductions in vibration impacts, ensuring compliance with safety standards and providing a robust framework for future projects.
## 2 Time-Frequency Image Feature-Based HI
### Basic Theory of CNN  
CNN is a deep learning model inspired by the visual cortex of the brain. It is widely used in various fields such as computer vision, natural language processing, and speech recognition because it can analyze an original image directly without complicated preprocessing [24]. A typical CNN comprises three layers: convolutional, subsampling, and fully connected. To extract features from image data, the CNN model alternately stacks the convolutional layer and the subsampling layer, and configures the output layer as a fully connected layer.  
The convolutional layers convolve the input from the previous layer with multiple kernels and send it to the activation function to construct a feature map. The output feature map is the convolutional results of the input maps and can be calculated as follows:  
\[x_{j}^{l}=f\left(\sum_{i\in M_{j}}x_{i}^{l-1}*k_{ij}^{l}+b_{j}^{l}\right), \tag{3}\]  
where \(*\) is a convolutional operator, \(x_{i}\) is \(i\)th input map, \(k\) is a \(S\times S\) convolutional kernel, \(b_{j}\) is an additive bias, \(M_{j}\) is a feature map of the convolutional layer, and \(l\) is the \(l\)th layer in the network. Finally, the convolutional results are passed to the activation function, \(f\)[33]. The two most commonly used activation functions (i.e., rectified linear unit (ReLU) and sigmoid function) are defined as follows.  
\[ReLU(x)\ =\ max(0,\ x) \tag{4}\]  
\[sigmoid(x)=\frac{1}{1+e^{-x}} \tag{5}\]  
The subsampling layer, located after the convolutional layer, produces low-resolution maps by extracting the most significant local features. The most commonly used max-pooling layer extracts the maximum value from each region, as shown in Figure 2.  
All feature maps are combined into a 1-D vector to construct a flatten layer. Then, in the fully connected layer, all neurons of both layers are connected, like a traditional multilayer neural network. The output of the fully connected layer can be expressed by  
\[O=f\left(\sum_{j=1}^{d}x_{j}^{F}w_{j}+b\right), \tag{6}\]  
where \(O\) is the output value, \(x_{j}^{F}\) is the \(j\)th neuron in the fully connected layer, \(w_{j}\) is the weight corresponding to \(O\) and \(x_{j}^{F}\), \(b_{i}\) is the bias corresponding to \(O\), and \(f\) is a sigmoid function [28].  
Figure 2: Max pooling transformation.
## 3 Routing Protocols for UWSN
### Protocols Featuring Energy Efficiency
#### 3.4.1 Focused Beam Routing (FBR)  
To avoid unnecessary flooding of the data, [PERSON] et al. proposed a noble routing scheme called FBR [59]. FBR is suitable with both the static nodes and the moving nodes of the network. Moreover, the network with FBR needs not to be synchronized with global clock. Just by knowing the location of the source and destination a successful transmission is possible irrespective of the location information of the other nodes. FBR performs flooding for routing data packets and this flooding is restricted by the transmission power. To minimize the energy consumption of the network, this protocol engages different power levels for transmission. Figure 10 explains how a data packet is being forwarded in FBR. Node A is the source and node B is the destination where node A sends a multi-cast request to its neighboring nodes. This is called an RTS packet which holds the positioning data of node A and B. The lowest power level has the minimum range and range area increases when we switch it to a higher power level. But switching of the power level only occurs when it can not find any node within the designated rage for the next hop. But receiving nodes do not decide which power level to be used instead open loop power control and power level is decided by the transmitting node. It is also considered that there are definite power levels ranging from P1 to PN which can be increased from one level to the upper level when needed [72]. The nodes have to be within the perimeter of the defined power level to receive any detectable signal. Now the locations of the nodes which receive the multi-cast RTS earlier from node A is determined.  
This location is relative to the line AB. The nodes that lie within the angle \(\pm\theta/2\) at the source node A are selected as the eligible nodes for forwarding. If a node is eligible, this will respond to the received RTS. But in real life FBR may face some difficulties. First, due to transient state of the underwater condition and due to heavy current it may happen that, no node is within the cone of \(\pm\theta/2\). Sometimes it may happen that some of the eligible nodes remain outside of the forwarding cone angle. In this case it can not find the eligible node for the next hope within the forwarding zone, source node needs to rebroadcast the RTS. Thus the overhead will increase which eventually disturbs the transmission. The sink is considered to be fixed here which makes the network more restricted.