esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 2 Method Development
### Proposed Methodology
#### 2.1.3 Color Tuning Process  
It can be discerned in Figure 3b that the color correction result shows good texture preservation and color recovery. Nevertheless, certain inconsistencies are still found when color-corrected regions are observed and compared to unshadowed regions. For instance, asphalt regions present discrepancies in the color perceived; this is especially noticeable for a yellow color in the regions marked in red rectangles. One further noticeable inconsistency is the color in green areas (marked in blue rectangles); such areas contain oversaturated colors. Therefore, a final tuning process is proposed to be applied in the corrected regions only. In this case, the HSV color space is used to perform color tuning, taking the statistical reference values obtained from the color-corrected image in terms of hue and saturation. This adjustment is executed according to the criterion expressed in Equation (4).  
\[T^{\prime}(x,y)=\left\{\left\{\begin{array}{ll}H_{t}(x,y)=H(x,y)\\ S_{t}(x,y)=\frac{S(x,y)+\mu_{s}}{2}\\ H_{t}(x,y)=\frac{|H(x,y)-\mu_{s}|}{\mu_{s}}H(x,y)\\ S_{t}(x,y)=\frac{|S(x,y)-\mu_{s}|}{\mu_{s}}S(x,y)\\ \end{array}\right\},&\text{if }R=\text{max or }G=\text{max}\\ \\ \left\{\begin{array}{ll}H_{t}(x,y)=\frac{S(x,y)+\mu_{s}}{2}\\ H_{t}(x,y)=\frac{|H(x,y)-\mu_{s}|}{\mu_{s}}H(x,y)\\ S_{t}(x,y)=\frac{|S(x,y)-\mu_{s}|}{\mu_{s}}S(x,y)\\ \end{array}\right\},&\text{otherwise}\end{array}\right. \tag{4}\]  
where \(T^{\prime}\) is the tuned image at the position \((x,y)\) in the HSV color space; \(H\) and \(S\) are the hue and saturation of \(T\), \(H_{t}\), and \(S_{t}\) are the tuned hue and saturation in \(T^{\prime}\); \(\mu_{h}\) and \(\mu_{s}\) are the means of hue and saturation in \(T\), respectively. The resulting image \(T^{\prime}\) is displayed in Figure 4b. When it is compared to Figure 4a, which corresponds to color transfer algorithm output \(T\), it can be noticed that after color tuning, the inconsistencies found in asphalt regions are reduced. It is most noticeable in the regions marked in red rectangles, in which the color consistency between shadowed and unshadowed regions is improved. In green areas, it is shown that color saturation is corrected, and the visual consistency increases  
Figure 3: Input image (**a**) and the proposed color transfer result (**b**). Red rectangles focus on concrete areas and blue in vegetation or grass.  
regarding unshadowed vegetation without losing relevant texture information; this is marked in blue rectangles.  
It can be observed that the results displayed in Figures 3 and 4 still have noticeable shadow boundaries; this is mainly owing to the complexity that shadows in urban aerial imaging present, which complicates the shadow mask creation process. However, the color inconsistency is reduced and improved after final color tuning.
# Deep Pixel-Level Matching via Attention for Video Co-Segmentation
## 4 Experiments
### Ablation Study  
We conducted an ablation study on the ObMiC dataset [15] to evaluate the performance of our framework with or without the attention module. This dataset contains eight videos with 206 frames, so these eight videos are utilized for training, validation and testing.  
In this study, we selected one object in each video sequence in the ObMiC dataset as our framework mainly deals with the single-object segmentation. Due to the fact that the length of the videos in the dataset is short, we selected the first frame in each video to generate the proposals containing the target as the training data and the second frame in each video to generate the proposals containing the target as the validation data; the rest of the frames in each video are considered as the testing data.  
In Table 1, \"first frame w/o attention\" and \"second frame w/o attention\" indicate that the ResNet was applied as the baseline to receive two input frames and generate the segmentation results without our attention module. On the other hand, \"first frame with attention\" and \"second frame with attention\" indicate that the segmentation results belonging to the two input frames were obtained from our whole framework containing the attention module. After each training epoch, our framework performed the validation for computing the pixel-wise accuracy. The pixel-wise accuracy in each validation is computed by averaging the pixel-wise accuracies of all validation data, and we got the pixel-wise accuracy (Pixel Acc.) in Table 1 by averaging the pixel-wise accuracies obtained in the validations, which were performed after the training loss was converged. When the training step was finished, the testing data in ObMiC dataset were input into our framework for getting the segmentation results, we compared each testing frame's segmentation result with the GroundTruth to gain its own IoU score, and the mIoU accuracy was computed by averaging all testing frames' IoU scores. Both pixel-wise accuracy and mIoU accuracy in Table 1 show that the performance of our framework with the attention module is better than that of our baseline without the attention module. In other words, our attention module is able to increase the accuracy of segmentation results from the deep learning model.
## 3 Machine Learning for IRS-Assisted Communication Systems
### Unsupervised Learning for IRS-Enhanced Communication Systems  
The algorithm helps to find out the error level for every inspection by correctly recognizing the input and output data without any assistance by a supervisor called an UL machine [95]. In brief, the UL technique constructively finds the data connections to form a cluster and obtain an unlabeled input dataset [69]. UL methods do not depend on prior knowledge and this learning technique is not data-hungry. Several UL algorithms have been developed in the recent research community [96], such as clustering of K-means, maximization of expectation, principal component analysis (PCA), and independent component analysis (ICA). These proposed UL algorithms can be deployed in the IRS-enhanced wireless communication to improve the difficulty issue, such as the deployment of BS, UE clustering/association of UE [97], detection of network state [98], aggregation of dataset [99], and cancellation of interference [100]. In [101], authors proposed IRS-assisted UL-CNN, a feedforward neural networks (FNN) algorithm in the MISO communication system with multiple UEs to acquire the maximum sum-rate. To obtain the maximum spectral efficiency, a UL-based learning phase-shift neural network (LPSNet) algorithm was proposed in [90]. The maximization problem of spectral efficiency was formulated and a small number of hidden layers were used in the ML architecture to solve the problem. The proposed algorithm was tested with \(16\times 2\) MIMO configuration.
# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 3 Results
### Experimental Results and Analysis
Notably, the classification rate of the VPD system configured using the SMOTE method demonstrates considerable improvement compared to the results of the non-sampling. Therefore, we conclude that the proposed method, such as the combination of the CNN and the LPCs oversampled by the SMOTE, can efficiently increase the performance for classification between the pathological and normal voices. In summary, our VPD system uses the SMOTE, ADASYN, and Borderline-SMOTE algorithms to generate the binary imbalanced class data in the SVD, and the accuracy of the algorithm is verified using a set of deep learning classifiers such as FNN and CNN. To classify normal and pathological voices, when compared with a VPD system without various oversampling algorithms, all performances of our VPD system with the SMOTE method with the recall, specificity, G value, and F1 value are higher than those of the former. These results confirm that our proposed method is a good strategy for achieving successful classification between pathological and normal voices. It also justifies that our methods for solving class imbalances in a limited pathological speech database can be applied to pathological speech detection in the field of biomedical engineering.
## 5 Policy-based Adaptive QoS Management: CNQF Runtime Validation
### Validation Tests
#### 5.3.2 Runtime Adaptive QoS Control Validation  
In order to validate this aspect, a policy stipulating the reconfiguration of QoS parameters in the routers so as to provide Expedited Forwarding DiffServ treatment to a given CBR test flow was entered into the CNQF GUI tool. The condition stipulated was that bandwidth utilization state of 80% on the ingress edge router outbound interface should be exceeded (indicating high traffic load). Without an adaptive CNQF policy, increasing traffic load from other flows will starve the test flow of resources leading to rapid QoS degradation. In order to alleviate this, the policy enabled preferential QoS treatment to be applied to the test flow at all nodes in the DiffServ domain so that its QoS requirements can be maintained.  
Figure 4 illustrates increasing traffic load generated from the _ntools_ traffic generator which injects new flows into the DiffServ domain with Poisson arrivals and exponentially distributed average lifetime of 120 s. As seen from the corresponding measurement in Figure 5, a 1 Mbit/s CBR test flow traversing the same DiffServ domain is initially unaffected when the traffic load in the network is low, and a slight degradation in bit rate is noticeable as the aggregate traffic increases. When the traffic load causes the bandwidth utilization threshold given in the policy condition to be exceeded, the action part of the adaptive policy is invoked consequently marking the packets to be treated with Expedited  
Forwarding PHB thus restoring the test flow's QoS through priority queuing and scheduling. It can be seen that the reconfiguration takes place at time 70 s because the packet measurements filtered on DSCP == 0x2e (EF DSCP) from this time onwards coincides with the test flow measurement filtered on its IP address. The same test was repeated with the background flows applied but without enabling an adaptive QoS management policy for the test flow. Figure 6 shows the bit rate of the test flow degrading considerably due to the absence of adaptive runtime policy as the background traffic increases due to the absence of adaptive runtime policy control.  
Figure 4: Increasing traffic flow injected into the DiffServ-enabled testbed.  
Figure 5: Effect of adaptive policy QoS reconfiguration on test flow with increasing background traffic load.  
The test results illustrate the effect of the QoS control policies thus validating the operation of the CNQF prototype implementation and the experiments verified the interaction of the various subsystem elements built. Next, we explore _adaptive resource allocation_ for enhanced admission control using implementation of fuzzy rule base system within the CNQF resource broker. As mentioned earlier, the motivation for implementing a fuzzy system within the RB is to enable effective and extensible means of coupling operational policies with rule-based control of network resources in order to achieve more efficient resource utilization as well as flexible tiered service provisioning.
## 2 System Establishment and Modeling
### Modeling
#### 2.2.5 Calculation Flow Chart  
A flowchart is shown in Figure 2 which shows the calculation procedure for the hybrid system developed. The computer model is based on this lumped method developed in the MATLAB/Simulink environment. The calculation process encompasses three primary parts: CPOx fuel processing, SOFC electric power generation, and GT performance evaluation. These sections operate concurrently in every simulation, tightly intertwined to optimize the hybrid system's performance. The analysis begins with determining the aircraft's state (ascending or descending) to adjust engine power and inlet air mass flow based on altitude, BPR, and Mach speed. Subsequently, a compression stage analysis determines the pressure ratio and efficiency.  
The reformer section involves setting the O/C ratio and reactor pressure, using MATLAB R2023a for nonlinear reforming calculations, and adjusting the AS-reformer for optimal fuel utilization. SOFC power generation entails setting current density, ensuring optimal cathode inlet temperature, and achieving 75% fuel utilization while solving electrochemical reactions. Temperature control involves adjusting HPC outlet cathode air fraction (AS-cathode) characteristics and HX effectiveness for the desired performance. The combustor section allocates fuel, determining FAR values to meet temperature demands, influenced by chosen HX effectiveness. The final stages involve turbine calculations, power balance assessment, and engine performance analysis, ensuring optimal SOFC voltage/power, thrust properties, and propulsive efficiencies. Each step requires specific adjustments for compatibility and optimal system performance. The colors of the arrows distinguish different processes associated with a calculation stage.  
Figure 2: Calculation flow chart for the aircraft SOFC/GT system.
## 2 The Related Work  
### Comparison of Space Domain and Angular Domain in Exclusion Zones  
Existing efforts focuses on releasing new frequency bands to allow 5G deployment. Two methods were proposed in [10] to enable administrations to protect existing users in the 3.6-3.8 GHz band while allowing mobile/fixed communication networks to use them as new entrants. The authors of ref. [11] investigated several BS/ES deployment plans, antenna configurations, and transmitter parameters to explore the millimeter wave frequency spectrum and the coexistence of cellular and satellite services. Techniques for reducing passive interference are often used in contemporary coexistence strategies. Most coexistence studies use the space domain technique. It is further separated into exclusion zones and restriction zones, according to the definition of [11], an exclusion zone is a geographical area within which licensees are not allowed to have active radio transmitters, and restriction zones are geographical areas within which victim receivers will not be subject to harmful interference caused by interferer transmissions. The space domain is based on the interference/noise (I/N) protection threshold technique, involving a maximum permitted interference at the input to the FSS ES [2; 11; 12; 13; 14].  
The restriction zones and BS transmit (Tx) power control schemes were also studied through the distance protection method. The advantages of this scheme are its simple layout, high coexistence efficiency, and adaptability to several environments. In the angular domain with the exclusion zone protection method, the BS can shut off the aligned beams, so that the beams can reach the angular exclusion range (i.e., the beam direction, the BS to ES direction is less than the angular protection threshold). A protection angle can be paired with zone protection to reduce the protection radius in dense and angular areas [2]. This scheme is advantageous in terms of being unrestricted on the space domain. A comparison of the two methods is shown in Table 1.
# CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems
## 3 Designing the Block Interleaver
### Interleaver Structure  
Generally, a transmission data block is mapped to different frequency domain sub-carriers and different time domain Orthogonal Frequency-Division Multiplexing (OFDM) symbols, corresponding to the rows and columns of the data block, respectively.  
In the frequency dimension, in order to combat the frequency selective fading caused by the multipath effect, it is necessary to disperse the data units continuously distributed in the frequency domain to obtain the frequency diversity gain. This can be achieved by sorting the rows in \(\pi_{2}\). As for the time dimension, to resist the time-selective fading of OFDM symbols caused by the mobility of the terminal and avoid the continuous error caused by the deep fading of the channel within a period of time, the method of time interleaving is used to disperse this continuous error. This can be achieved by sorting the columns in \(\pi_{1}\).  
Based on above analysis, we propose a synthesized block interleaver by jointly designing \(\pi_{1}\) and \(\pi_{2}\). The main idea of the proposed block interleaver can be summarized as follows:  
* Each sequence is sorted at an appropriate interval to increase the interleaving distance;
* Adjacent sub-carriers in the same OFDM symbol are spaced as evenly as possible;
* The shift period of the OFDM symbol is extended in a two-dimensional manner to obtain the time diversity;
* Reading order is determined by the result of joint processing.  
Without loss of generality, assume that the \(M\times N\) resource elements to be transmitted are written in columns. The data cells in the \(M\times N\) block matrix will decide which mode is used according to the block size.
## 6 Conclusions  
For bivariate statistical models such as SI, the distribution of weights does not conform to the reality of factors, which require improvement. Moreover, the selection of factors has a non-negligible impact on the performance of LSM models. This study proposes a hybrid optimization method for the SI model, with the aim of addressing these problems and improving the accuracy and reliability of LSM.  
The hybrid approach of GeoDetector and RFE was used for factor screening (the obtained model was named GD-SI). The number of factors decreased from 22 to 12, but the AUC value on the testing dataset increased from 0.931 to 0.936. Results show that the prediction performance of the model was improved, proving the effectiveness and reliability of factor screening. Furthermore, the weights of the GD-SI model were optimized using GPR (the obtained model was named GD-GPR-SI). The GD-GPR-SI (AUC = 0.943) model has a higher AUC value than the GD-SI model (AUC = 0.936) on the testing dataset. Therefore, by optimizing GPR, more reasonable weights were obtained, and the predictive ability of the model was improved.  
The methods proposed in this study improved the predictive ability of the LSM model, which can be used as a general framework for it. The obtained landslide susceptibility mapscan also provide a decision-making basis for landslide prevention and control. Further consideration should be given to the optimization of evaluation units and improvement of the quality of data for modeling.  
Conceptualization, Y.Y.; Data curation, Y.Z.; Formal analysis, F.Z.; Investigation, C.S.; Methodology, C.C.; Software, C.C.; Supervision, C.S.; Validation, F.Z.; Visualization, C.S.; Writing--original draft, C.C.; Writing--review & editing, Y.Y. and C.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42071379, 41701448; Technical development project (Potential pipeline threat event identification from the perspective of unmanned aerial vehicle) of East Crude Oil Storage and Transportation of National pipe network group Co., Ltd., grant number GWHT20220021074.  
Not applicable.  
The data used in this study are available on request from the corresponding author.  
We would like to express our gratitude to the reviewers and editors, whose comments and suggestions have greatly helped to improve the quality of the manuscript. We are also grateful to [PERSON] (Southwest Petroleum University, China) for help in revising the manuscript.  
The authors declare no conflict of interest.
# Optimizing Aircraft Routes in Dynamic Conditions Utilizing Multi-Criteria Parameters
###### Abstract  
The growth of air transportation volume and increasing requirements for efficiency require the improvement of algorithms for planning optimal aircraft flight routes. Traditional methods, such as the A*, B*, D* and Dijkstra algorithms, are widely used in navigation systems, but they have a number of limitations when applied in a dynamically changing environment, in particular due to the need to take into account weather conditions, air traffic, economic factors, and aircraft characteristics. This article provides a comprehensive analysis of existing approaches to optimizing airline routes, the advantages and disadvantages of each, and possible areas for their improvement. Particular attention is paid to multi-criteria parameters that affect routing efficiency, such as fuel consumption, safety aspects, forecasting accuracy, and adaptation to changing flight conditions. A methodological solution is proposed to improve route construction algorithms, which involves taking into account variable parameters in real time and integrating them into modern navigation systems. In addition, optimal flight paths were modeled using the improved algorithms, which allow for increasing the efficiency of decision-making in the field of air traffic control. The results of the study can be useful for airline companies, airspace authorities, and navigation software developers.  
air traffic; Dijkstra's algorithm; flight trajectory; weather in-air traffic management; optimal aircraft routes; resource planning; aviation logistics Article  
* 2025  
15 May 2025  
26 May 2025  
27 May 2025
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 4 Implemented Attacks and Relative Mitigations
### Energy Drain Attack  
As said before, WSNs, in the IoT field, despite the huge benefits they can provide, raise not negligible security issues due to the inherent security vulnerabilities which could be found inside the communication protocol or the IoT devices which are involved in these particular networks. Leveraging these vulnerabilities, an attacker can focus its malicious intent on the most resource- and power-constrained IoT devices: sensors and actuators which are part of a WSN. The idea, related to the energy drain attack, also named energy depletion attack [40] is the following: the attacker aims to overwhelm the target device, in order to force it to execute energy-hungry operations such as receiving a large number of ping messages, for example. This attack can lead to the complete draining of the devices' batteries very rapidly. Extending this kind of attack to all the sensors and actuators composing the WSN, it is clear that to successfully target each device and complete this attack will lead to the shutdown of the whole WSN. Most of the existing energy drain attacks target the MAC layer. The traditional strategy, adopted by an attacker, is: the attacker makes a broadcast of forged packets including in them simply garbage data which have, however, to be verified and processed by the receiver device, consuming its energy. This kind of attack is also named as garbage data verification, since the receiver device, in behalf of the attacker, has to verify data which are essentially forged garbage. Another name which can be used to refer to this kind of attack is denial of sleep or sleep deprivation torture [41]. The denial of sleep, applied to powered devices, reflects in the energy depletion. This is an attack that could be made using several exploits such as executing collision attack, repeating handshaking operations, flooding the node with useless ping messages and so on, with the common aim of preventing the node from switching into its sleep phase.