esa-sceva/satcom-chunk-collection huggingface dataset:
# A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues +
## 2 Related Works  
The task of serial section registration has made tremendous progress in recent years; smooth and continuous structures of biological tissues can be recovered using the current methods. In biological studies, researchers concentrate on rigid registration techniques because rigid transformation retains the morphological structures of samples as much as possible. A global optimization problem is established, but most methods solve it iteratively and may provide suboptimal solutions. For example, LeastSquare [8] performs registration by aligning one to another sequentially across the whole serial. The local registration is based on SIFT [9] correspondences extracted from an image snapshot taken of each section. A least square solution is computed for this local registration. Based on an initial estimation (i.e., the result of LeastSquare), BUnwarpJ [10] applies nonlinear spatial transformations to achieve subsequent registration. Nonlinear spatial transformation takes the form of nonlinear cubic B-splines; however, because it is based on local solutions, this approach is suitable only for short sequences; long sequences will result in error accumulation and propagation, leading to large nonlinear deformations. CW_R_color [3] extends a 2D image registration method [11] to perform registration of serial sections, and it conducts the 2D image registration method [11] sequentially and bidirectionally to align each pair of neighboring images. The 2D image registration method originates from the bidirectional elastic b-spline model (BUnwarpJ [10]), and, similarly, it is also appropriate only for short sequences. Elastic method [12] establishes an elastic spring mesh on the original image sequence and then connects each pair of correspondences detected by block matching with a spring. All the springs will work in concert to \"drag\" sections towards a global alignment. The global registration problem is solved iteratively to generate a final nonlinear spatial transformation. With a rapid development of deep learning, spatial transformer networks [13] (STN) are adopted to estimate spatial transformation between images for image registration [14; 15; 16]. Meanwhile, some off-the-shelf convolutional neural network models for optical flow estimation such as flownet [17], flownet2 [18], and PWC-Net [19] are utilized for serial-section image registration. Similar to traditional methods, they still choose one image as reference, and align images sequentially across the serial, thus error propagation and accumulation are inevitable.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 3 Thermal Remote Sensing
### Thermal Properties of Crops and Soil
Because \(K\), \(\rho\) and \(c_{\rm P}\) all increase with increasing soil moisture [166], soil with higher moisture content has higher thermal inertia and a smaller diurnal temperature difference, and vice versa. In contrast to LST, thermal inertia is a bulk property and can reflect the change of root-zone soil moisture [165].
# Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges
## 8 Identifying Obfuscation Techniques: The Starting Point
### Static Analysis: Unraveling Code Structure Without Execution  
Static analysis is another cornerstone of the deobfuscation process. This method involves examining the code without executing it, aiming to understand its structure and behavior. By conducting code flow and data flow analyses and recognizing patterns, analysts can begin to map out the program's architecture.
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 4 Performance Evaluation
### Energy Consumption Evaluation  
IoT nodes commonly use batteries; therefore, the energy source of such nodes are the indispensable factor for their persistent survival and activity in network environment. Regarding this issue, the network energy consumption is measured through two approaches named real energy consumption and nominal energy consumption. In fact, these two measurement approaches are the energy consumption values in real world implementation and Cooja emulator environment respectively. The first solution to calculate the energy consumption rate in milli-Joule scales is modeled as [47; 48]:  
\[Energy(\mathrm{mJ})= \frac{(T_{x}\times 19.5\;mA+R_{x}\times 21.8\;mA+CPU\times 1.8 \;mA+LPM\times 0.0545)\times 3V}{4096\times 8} \tag{10}\]  
where \(T_{x}\) and \(R_{x}\) respectively represent the consumed energy in each transmission and reception occurred in a node. In addition, the power consumption level (in milli-watt hour units) in network nodes is calculated through Equation (10) as:  
\[Power(\mathrm{mW})=\frac{Energy(\mathrm{mJ})}{Time(\mathrm{s})} \tag{11}\]  
The energy consumption diagram of the proposed method as well as those of base RPL approach, bounding degree (BD-RPL), modified RPL (M-RPL) and adaptive RPL (A-RPL) version is depicted in Figure 13. It is obvious from Figure 13 that the decrease of exchange rate as well as the increase of available time of network nodes not only have abated network congestion, but have reduced the number of required efforts for data exchange as well. Accordingly, the proposed method consumes less energy as compared to the base approaches; hence, longer network lifetime is provided by this approach.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Part** & **Description** \\ \hline Micro & STM32405-ARM32-bit-Cortex-M4-CPU \\ \hline Flash & Up to 1 Mbyte \\ \hline \multirow{2}{*}{LP Operation} & Sleep, Stop and Standby modes VBAT supply for RTC, 2032 bit backup- registers \\  & +optional 4 KB backup SRAM \\ \hline Radio & TI CC2520 \\ \hline Routing Level & RPL based on border router \\ \hline Network Layer & IPv6 with 6 LoWPAN standards 802.15.4 \\ \hline Application Layer & GLORIOT-Interface + COAP \\ \hline Battery Level & Battery holder for 2 AAA batteries \\ \hline Sensors & Sensors: temperature/humidity(SHT15) \\ \hline Sensor Port & Interfaced with the IRMote-CC2520 \\ \hline Radio Rate & 30 m \(-\)1 dBm in simulation and 2 m in experimental \\ \hline Propagate Model & Unit Disk Graph Model \\ \hline Number of Nodes & 50 randomly-deployed nodes \\ \hline Node Position & Fixed without mobility \\ \hline Sink/Root node Position & X/2, Y0 \\ \hline Warming & 120 s \\ \hline Data Generating & Every 20 s and 30 s UDP packet \\ \hline Simulation time & 2 h \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of sensor nodes Cloriot in Cooja environment.
# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
###### Abstract  
The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95.24-97.61%, and 83.56-85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.  
Keywords:artificial bee colony (ABC); image segmentation; skin melanoma; heuristic method; dermoscopy +
Footnote †: journal: Computer Vision and Image Processing
# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 4 Experiment Results and Analysis
### Ablation Study  
To demonstrate the reliability of the proposed MARCAFNet for various configurations, ablation studies were performed using CSFF, SB, and CS blocks with various dilation rates.
## 5 Discussion
### Influence of Slope Shape on the Stability and Deformation Characteristics of Loess Slopes  
The influence of slope shape on slope stability is still controversial in academia. [PERSON] et al. [56] deeply explored the influence of different slope shapes on the stability of soil slopes through seismic simulation shaking table tests. The results show that the slope shape has a significant effect on the stability and deformation of the slope. The PGA (peak ground acceleration) amplification coefficient of the concave slope at the turning point of the slope is significantly higher than that of the convex slope and the straight slope, which makes the concave slope more prone to damage and deformation. [PERSON] et al. [57] further confirmed the importance of slope shape to the stability and deformation of the loess slope through the combination of a small shaking table test and FLAC\({}^{3\text{D}}\)numerical simulation. Their study found that under dynamic loading, convex slopes are more prone to instability, while concave slopes show stronger stability. In addition, [PERSON] et al. [58] revealed the phenomenon whereby the pore water pressure of the concave slope increased under the action of precipitation through three-dimensional numerical simulation, which led to a significant increase in slope deformation and an increase in landslide risk, once again highlighting the important influence of slope shape on slope stability. On the other hand, [PERSON] et al. [59] found that the concave slope with a medium height and steep slope is more likely to move and deform by using the method of remote sensing interpretation, which provides new evidence for the influence of slope shape on slope stability. This study reveals the close relationship between slope shape and slope under the special dynamic load of coal mining subsidence. Specifically, under the influence of the same coal mining subsidence, when the natural slope does not exceed \(5^{\circ}\), the convex slope shows the largest movement deformation; when the natural slope exceeds \(5^{\circ}\), the deformation of the concave slope becomes the most significant. This finding provides a new perspective for understanding the stability and deformation characteristics of the surface loess slope in the coal mining subsidence area.
###### Abstract
To improve performance under these variable conditions, several researchers have proposed real-time adjustment systems for rotor and separation parameters [15; 16; 17; 18]. However, the focus has mostly been on the separation section, with few studies addressing adaptive control of the threshing zone. [PERSON] et al. [19] suggested that variable-mass threshing conditions require more responsive designs, but practical implementations remain rare. Guide vane inclination is now recognized as a key variable. It has greater influence than feed rate on loss and impurity rates and is second only to rotor speed in its effect on grain damage [20; 21]. Adjusting the vanes dynamically--especially in both the threshing and separation areas--could therefore offer major performance benefits. [PERSON] [22] and others have shown that vane angle changes material movement and system load, which directly affects separation efficiency and power consumption. Most prior research on guide vane systems has focused on simulations or bench-scale testing under controlled conditions. In contrast, this study implements and validates a fully synchronized guide vane control system directly under real-world field conditions. To address this gap, this study investigates an axial-flow combine harvester equipped with eight internal guide vanes. In the original factory configuration, the first two vanes (located in the threshing section) are fixed, while the remaining six (in the separation section) are adjustable. As a result, material movement is only controlled in the latter part of the rotor housing. This configuration is not ideal in high-yielding conditions where crop density is high throughout the system [5; 10]. The main objective of this research was to improve performance by redesigning the system so that all eight guide vanes could be adjusted simultaneously. A mechanical linkage was developed to couple the vane adjustments in both the threshing and separation zones. The integrated system was implemented on a combine harvester and tested under real field conditions. Its performance was compared with an identical combine operating in the same field, under the same crop and environmental conditions. By evaluating the new system, this study aims to determine whether synchronized guide vane control can reduce grain losses, improve separation efficiency, lower fuel consumption, and better adapt to variable biomass in winter wheat harvesting.
## 4 Results Analysis and Discussion
### Performance Metrics  
In this experiment, the performance of the models was evaluated using the Mean Squared Error (MSE) and the Adjusted R-squared (Adjusted \(R^{2}\)). Unlike the regular \(R^{2}\), the Adjusted \(R^{2}\) takes into account the number of independent variables, penalizing the addition of irrelevant variables. This means that the inclusion of irrelevant features does not increase the Adjusted \(R^{2}\), while it may still increase the \(R^{2}\). The formulas for calculating MSE and Adjusted \(R^{2}\) are provided below. MSE measures the magnitude of the prediction error, while Adjusted \(R^{2}\) assesses the model's explanatory power. Together, these metrics provide a comprehensive evaluation of the model's predictive performance and goodness-of-fit.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{4}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y}_{i})^{2}} \tag{5}\]  
\[AdjustedR^{2}=1-\frac{(1-R^{2})\times(n-1)}{n-p-1} \tag{6}\]  
where \(n\) is the number of samples, \(y_{i}\) is the actual value of the \(i\) sample, \(\hat{y}_{i}\) is the predicted value of the \(i\) sample, \(\hat{y}\) is the mean of the actual values, \(p\) is the number of independent variables.
# The Effect of Non-Transferred Plasma Torch Electrodes on Plasma Jet: A Computational Study
## 3 Results
### Case 1: Study of Cathode Shapes
#### 3.1.2 Arc Formation  
This section examines how cathode shape affects arc formation and the resulting current and voltage behavior at the cathode tip. The electric arc forms between the cathode and anode when the applied current concentrates at the cathode tip. As noted by [PERSON] et al. and [PERSON] et al., the arc naturally aligns along the shortest path between the electrodes [11; 35]. Their studies also showed that the current density on the inner wall of the anode peaks between the cathode tip and the nozzle, allowing the arc length to be estimated from the center of this distribution.  
Figure 5 shows the current density variations plotted on the inner walls of the anode (which is 14 mm from the inlet to the outlet). The plot shows that the arc attaches to the anode at approximately 14 mm from the cathode tip. This attachment location is consistent across all cathode shapes, indicating that arc length does not vary significantly with geometry. However, the arc's intensity differs; cathodes b, e, and f exhibit lower current densities compared to a, c, and d, suggesting shape-dependent arc stability and energy concentration. Further, the plot shows three peaks; the reason might be that once the arc attaches to the anode, it detaches, and further, after a certain distance, it tries to attach again, and that point might indicate the increase in the current density [21].
# Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
## 2 Problem Formulation
### Mathematical Model  
The format and constraints of this research problem are similar to the Capacitated Vehicle Routing Problem (CVRP), which is one type of VRP with limitations on vehicle capacity [30]. This model has the following mathematical formula:  
Variables and Parameters:  
* \(n\): Total number of victims.
* \(m\): Total number of vehicles.
* \(I=\{1,2,\ldots,n\}\): Set of victims.
* \(J=\{0\}\cup I\): Set of all nodes including the depot (depot indexed as 0).
* \(K=\{1,2,\ldots,m\}\): Set of vehicles.
* \(t_{ij}\): Travel time from victim \(i\) to victim \(j\).
* \(Q_{k}\): Capacity of vehicle \(k\).
* \(q_{i}\): Demand of victim \(i\). Decision Variables:
* \(x_{ijk}\): Binary variable indicating whether vehicle \(k\) travels from victim \(i\) to victim \(j\) (1 = yes, 0 = no).
* \(w_{i}\): Continuous variable representing the waiting time for victim \(i\) to receive deliveries. Objective Function: \[\min\sum_{i\in I}w_{i}\] (1) Constraints:
* Service Constraint. Each victim is visited exactly once by exactly one vehicle: \[\sum_{j\in J,j\
eq i}\sum_{k\in K}x_{ijk}=1,\quad\forall i\in I\] (2)
* Capacity Constraint. The demand of all victims served by a vehicle does not exceed its capacity: \[\sum_{i\in I}q_{i}x_{ijk}\leq Q_{k},\quad\forall k\in K\] (3)
* Flow Conservation. Ensuring vehicles leave a victim after arriving: \[\sum_{i\in J,i\
eq j}x_{ijk}=\sum_{h\in J,h\
eq j}x_{jhk},\quad\forall j\in J,\forall k\in K\] (4)
* Waiting Time Calculation. The waiting time for each victim takes into account the travel times from the previous victim: \[w_{i}\geq t_{ji}+w_{j}-M(1-x_{ijk}),\quad\forall i,j\in J,i\
eq j,\forall k\in K\] (5) where \(M\) is a large number.
* Probability Constraint. Ensure that travel is only scheduled on paths with acceptable feasibility under uncertain conditions: \[p_{ij}\geq\theta x_{ijk},\quad\forall i,j\in J,i\
eq j,\forall k\in K\] (6) where \(\theta\) is the minimum acceptable probability for a path to be considered feasible. \(p_{ij}\) represents the probability that the road condition between nodes \(i\) and \(j\) is passable during the planning period.
* Non-Negativity and Integrality. \[x_{ijk}\in\{0,1\},\quad\forall i,j\in J,\forall k\in K\] (7) \[w_{i}\geq 0,\quad\forall i\in I\] (8)
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 4 Numerical Evaluation of the Optimal Performance
### Channel BER Deviations from the Contact Plan  
While the current practice (i.e., ION-DTN CGR implementation) does not consider per contact BER values, in the possible case that BER values become associated with contacts for computing accurate bundle service times, underestimations are still possible, given that the expected BER values may be lower than the ones to be encountered when transmitting the bundle.  
The impact of channel BER deviations from the plan is depicted in Figure 5 for two instances of 0.1 s and 1 s one-way propagation delay. The rest of the parameters were kept identical to the previous cases. To remove extra complexity in the presentation of the results, it was assumed that only one channel BER deviation may occur with a probability of \(\epsilon\). When that event happens, the effective BER for the bundle transmission increases by a factor of \(M_{e}>1\). The case depicted in Figure 5 assumes that \(M_{e}=4\). The numerical solution of the optimization problem shows that for small values for the propagation delay, the differences between methods 2-4 were minimal. Methods 2 and 3 performed almost  
Figure 4: Average network response time as a function of the packet loss ratio of (all) the links. With a higher chance of packet drops, the expected response time of the flow increases due to the additional transmission rounds needed for reliability. The differences from the ideal performance also become stronger. Cases with a one-way propagation delay of \(D=0.01\) (**a**) or \(D=0.1\) (**b**) are shown.  
identically for the same reasons given above. Larger propagation delays led to significant performance drops from the ideal performance.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 3 Methodology
### Swish-ELU EfficientNet-B4
#### 3.3.4 Prediction Module  
In the prediction module, we introduce three dense layers with ReLU activation, combined with the dropout layers. Incorporating dropout after each rampant layer aids with computing the competent features within the input data while minimizing the potential for overfitting. Then, a dense layer with \(L_{2}\) regularization is introduced to prevent overfitting by penalizing large weights. The model learns more minor consequences, leading to a reduction in the complexity of the dense layer, and facilitating better generalization. The \(L_{2}\) regularization added to the loss function of the dense layer can be represented as follows:  
\[L_{2}=\lambda\times\left\|M\right\|^{2} \tag{3}\]  
where \(\lambda\) represents the regularization parameter for controlling the regularization strength, and \(\left\|M\right\|^{2}\) denotes the squared \(L_{2}\) norm of the weight matrix \(M\) associated with the dense layer.  
Finally, the extracted deep key features are passed to the last softmax layer to accomplish the classification task. The last layer of the proposed framework is the fully connected  
Figure 4: The Architecture of Proposed ELU-EfficientNet-B4 Framework.  
layer with the softmax activation function that estimates the probabilities associated with each output unit, as follows:  
\[\mathcal{S}(I_{x})=\frac{e^{I_{x}}}{\sum_{n=1}^{K}e^{I_{K}}} \tag{4}\]  
Here, \(K\) represents the number of classes, and \(I_{x}\) and \(I_{K}\) represent the input and output vectors, respectively.