esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 3 Path Planning Design
### Search Path for UAV
#### 3.1.2 \"Square Wave Signal\" Curve Path  
The \"square wave signal\" curve path was introduced in our published work [43]. Here, we introduce this route again. Static targets have two characteristics: a relatively fixed position and insensitivity to time. For these two traits, we need to create logical search plans. The space region is split into the cases of an odd-numbered sequence and a double-numbered sequence based on how the cells are distributed there. When the series is even, the UAV moves from the beginning point \(S\) into the closest cell. Before completing the last row's detection job and returning to the beginning location \(S\), it proceeds first to the far left cell at the space area, then moves in the form of a square wave signal. The steps for odd-numbered columns are the same as those for even-numbered columns, but when it reaches the last column before the last column on the other side of the starting position, the UAV moves in the shape of a square wave signal with a step \(D\) in the direction closest to the unmanned vehicle to the side near the initial position, completes the task of detecting the last row, and then returns to the initial position \(S\), as shown in Figure 6.  
Figure 5: Snake curve route map.  
```
1:A, NearPointxy, \(XMax\), \(YMax\)
2:Path
3:A: Centers of the grid cells within the target region.
4:NearPoint: The point closest to the starting point within the target region.
5:\(XMax\): n column
6:\(YMax\): m row
7:X \(\leftarrow\) The column of NearPoint in A.
8:Y \(\leftarrow\) The row of NearPoint in A.
9:if\(YMax\) is even then
10:for i \(\gets\) 1 to \(YMax\)do
11:if i is odd then
12: Add from C[i][X] to C[i][1] to the Path.
13:else
14: Add from C[i][1] to C[i][X] to the Path.
15:for i \(\leftarrow\)\(YMax\) to 1 do
16:if i is odd then
17: Add from C[i][X\(Max\)] to C[i][X\(+\)1] to the Path.
18:else
19:for i \(\leftarrow\) 1 to \(YMax\)do
20:if i is odd then
21: Add from C[i][X] to C[i][1] to the Path.
22:else
23: Add from C[i][1] to C[i][X] to the Path.
24:for i \(\leftarrow\)\(YMax\) to 1 do
25:if i is odd then
26: Add from C[i][X\(Max\)] to C[i][X\(+\)1] to the Path.
27:else
28: Add from C[i][X\(+\)1] to C[i][X\(Max\)] to the Path.
```  
**Algorithm 1** Make snake curve path  
Figure 6: “Square wave signal” curve path.
## 4 Results  
This section presents a performance evaluation of each system component, including the overall system accuracy and the face detection time calculation results. The tuning of the masked face classifier and sliding window is discussed and presented. Using the acquired data, the energy consumption of one full masked face classification operation is calculated, and the device battery life is estimated.  
All datasets used for training the models were split into three subsets:  
* Training subset consisting of 60% randomly chosen images
* Cross-validation subset consisting of 20% randomly chosen images
* Testing subset consisting of 20% randomly chosen images  
The models were trained using the training subsets and validated using cross-validation subsets. The best-performing model in the validation stage was chosen for testing using the testing subset. As the K-means classifier training is heavily dependent on the initial values, the training was executed 1000 times [2].
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 5 Performance Evaluation
### Energy Consumption in SS-MAC  
The mathematical expression derived in Section 4.3 illustrates that the network size is a vital factor affecting the total energy consumption of the network. Using these expressions and the parameter values in Table 2, we analyze the relationship between the total energy consumption of the network \((E_{tot})\) and the number of member nodes \((N_{m})\) when \(L\) is 3, 4, 5 and 6, respectively, as shown in Figure 10.  
By comparing the four sub-figures, we can reach the conclusion that the energy consumption of the network increases with increase in \(L\) and \(N_{m}\). However, compared with \(N_{m}\), \(N_{t}\) has a more significant impact on energy consumption, because the former mainly affects the energy consumption of the data collection process within a cluster, while increase in \(N_{t}\) means the sharable slot duration of each level increases. Ultimately, the total energy consumption of the network has increased significantly.  
\begin{table}
\begin{tabular}{l l l} \hline
**LEILMAC** & \multicolumn{2}{c}{**SSMA**} \\ \hline Packet size (Bytes) & 64 & Packet size (Bytes) & 10 \\ Sending and receiving slot (ms) & 60 & Receiver-initiated mini slot (ms) & 5 \\ Average idle (sleep) duration (ms) & 100 & Sender-initiated mini slot (ms) & 15 \\ The maximum number of nodes a node can send packets & 3 & Duration of one data collection round (s) & 1.6 \\ \hline
**IEEE 802.15.4 MAC** & \multicolumn{2}{c}{**SS-MAC**} \\  & & & \multicolumn{2}{c}{of the four protocols} \\ \hline System size (frames) & 51 & \(\gamma\) & 0.7 & \(R_{c}\) (bps) & 19,200 \\ Minimum value of backoff exponent (macMinBE) & 3 & \(T_{tot}(\mathrm{s})\) & 0.005 & \(P_{tot}\) (bit) & 88 \\ Maximum value of backoff exponent (macMaxBE) & 5 & \(P_{tot}^{\mathrm{SM}}(\mathrm{bit})\) & 48 & \(P_{m}\) (W) & 0.08 \\ Max number of backoffs (macMaxSCMABackoffs) & 4 & \(P_{tot}^{\mathrm{SM}}(\mathrm{bit})\) & 48 & \(P_{m}\) (W) & 0.07 \\ Max number of retries (macMaxFrameBertes) & 3 & \(P_{tot}^{\mathrm{SM}}(\mathrm{bit})\) & 968 & \(P_{tot}\) (W) & 0.07 \\ MAC frame payload (bit) & 968 & \(P_{tot}^{\mathrm{SM}}(\mathrm{bit})\) & 48 & \\ Overhead added in PHY layer (bit) & 48 & \(N_{t}\) & 50 & \\ Translation coefficient from frame to slot (bit/slot) & 80 & & & \\ \hline \end{tabular}
\end{table}
Table 2: Parameters for simulation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \(L\) & \multicolumn{2}{c}{3} & \multicolumn{2}{c}{4} & \multicolumn{2}{c}{5} & \\ \hline \(N_{t}\) & 3 & \multicolumn{2}{c}{5} & \multicolumn{2}{c}{3} & \multicolumn{2}{c}{5} & \multicolumn{2}{c}{3} & \multicolumn{2}{c}{5} \\ \hline \(N_{m}\) & 5 & 10 & 5 & 10 & 5 & 10 & 5 & 10 & 5 & 10 & 5 & 10 \\ \hline \(N_{ch}\) & 13 & 13 & 31 & 31 & 40 & 40 & 156 & 156 & 121 & 121 & 781 & 781 \\ \hline \(N_{node}\) & 60 & 120 & 150 & 300 & 195 & 390 &
## 2 Data Preparation and Methodology
### Model Construction
Python 3.6 and Keras 2.2.4 with TensorFlow 1.8.0 are used to construct the neural networks. One of the key benefits of the PDNetwork lies in its ability to automate the onset time picking and microseismic waveform detection processes. Rather than relying on manual operation, the PDNetwork learns and extracts richer features from semisynthetic noisy waveforms, enabling it to accurately obtain both the onset time and types of microseismic waveforms. The PDNetwork is constructed by integrating two networks. Within this structure, an input can sequentially generate two outputs. Notably, this structure is not intended to simultaneously obtain two outputs from a single input, as the onset time picking output enhances the accuracy of the subsequent detection output. We can observe that the primary output stems from the last layer of the onset time picking section, where it is seamlessly transitioned for feature extraction for the next detection section. Ultimately, the secondary output is derived from the last layer of the detection section. This structure ensures the simultaneous training of the two outputs, thereby enabling the PDNetwork to exhibit a more efficient and accurate performance in the onset time picking and detection of microseismic waveforms after sufficient training. Figure 4: The architecture of the PDNetwork. Two sections are constructed, namely, onset time picking and detection, respectively. The column composed of circles represents the neural network layer, and the arrows with different colors and types represent different operations applied between two adjacent layers. The number represents the dimension of each layer, meaning “features \(\times\) filters”. Batch normalization and concatenation are used to improve convergence during training. Layers #1 and #2 correspond to two outputs of the network: the score map of the onset time picking and the microseismic waveform type. In particular, the input size of the PDNetwork is (30,000, 1), and the sampling frequency of the microseismic monitoring system is 20 kHz. Different microseismic monitoring systems with different sampling frequencies will record the data with different sizes. This means that the PDNetwork will have certain limitations for recorded data with different sizes. Nevertheless, the network can provide a method to process the seismic/microseismic signals recorded by monitoring equipment with the same data acquisition parameters, which will be beneficial for further research in the field of seismic/microseismic signal processing.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 3 Proposed Methodology
### Synthetic Data Generation
#### 3.2.3 DCGAN for Malware Image Synthesis  
To mitigate the limited availability of malware image datasets and improve the robustness of image-based threat classification, this particular study [10] adopts a DCGAN for synthetic data generation. This suggested approach follows our prior work [10], where malware binary samples were transformed into grayscale or RGB images and afterwards used to train a DCGAN model. The generator maps random noise vectors into visually coherent 64 \(\times\) 64-pixel malware images, while the discriminator learns to distinguish between real and synthetic samples. During the training implementations, the adversarial dynamics of the DCGAN gradually improve the visual fidelity, statistical consistency, and accuracy of the generated images. In parallel, the generator employs transposed convolutional layers with batch normalization and ReLU activations, whereas the discriminator is built with striped convolutional layers and Leaky ReLU activations.  
These generated images augment the training set of the CNN classifier in the image-processing branch, enhancing its ability to generalize to unseen variants of known malware families. Evaluation metrics from our prior work are adopted to demonstrate that DCGAN-augmented datasets yield improved classification accuracy and reduced false positive rates compared to real-related training samples. Additionally, frequency histograms, FID, and classifier-based evaluation results confirm the quality and utility of the synthetic, generated samples. For this study, new malware images were generated using the pretrained DCGAN model from our earlier work, to maintain consistency with the architecture while enabling seamless integration into the proposed multimodal threat detection pipeline. This reuse of a validated model allows for methodological consistency and cost-efficient experimentation in a real-world deployment setting. Table 4 presents the image generation format and key attributes used in the DCGAN-generated dataset.
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 2 Materials and Methods
### Data Processing
#### 2.3.4 Evaluation of the Model  
The present study employed coefficient of determination (R2), root mean square error (RMSE), and relative percentage difference (RPD) as evaluation metrics. R2 is utilized to assess the correlation between the predicted and actual values of a sample. The closer R2 is to 1, the stronger the correlation between these two values. The RMSE value is expressed in the same unit as the original observation. The RMSE can quantify the average magnitude of prediction errors produced by the model, and a smaller value indicates a closer match between the predicted values and actual observations, thus indicating a better fit of the model. The RPD is employed to assess the stability performance of the model. A higher RPD indicates a superior stability performance, typically with an RPD value greater than 3 being suitable for practical applications. The specific calculation is determined by the following formula: xi is the simulated value, yi is the actual value, n is the number of samples available for validation, and SD is the standard deviation.  
\[\mathrm{R}^{2}=1-\frac{\sum_{i=1}^{n}(\mathrm{x}_{i}-\mathrm{y}_{i})^{2}}{ \sum_{i=1}^{n}(\mathrm{y}_{i}-\mathrm{y}_{i})^{2}} \tag{7}\]  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(\mathrm{x}_{i}-\mathrm{y}_{i})^{2}} {n}} \tag{8}\]  
\[\mathrm{RPD}=\mathrm{SD/RMSE} \tag{9}\]
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 3 MUI Cancellation Schemes with Inverted MPSC
### Modified Liu's Scheme with Inverted MPSC
As for [PERSON]'s scheme, however, the spread sequence length \(n\) for the modified [PERSON]'s scheme is one chip longer than that of the conventional [PERSON]'s scheme, while the other parameters are not different between the non-inverted and inverted forms.
# POLIDriving: A Public-Access Driving Dataset for Road Traffic Safety Analysis
## 1 Introduction  
Determining the real cause of a traffic accident is complicated because there is often not enough information at the time. Generally, the cause is attributed to the driver's carelessness or negligence; however, the real cause can be another or a combination of other factors. The Global Status Report of Road Safety [1] from the World Health Organization (WHO) focuses only on five key risk factors, leaving other factors aside. For instance, a real cause of an accident can be a mechanical failure of the vehicle, adverse weather conditions, poor design of the road, or, much worse, a combination of some of them. While more information is available, determining the real cause of an accident will be easier.  
In traffic accident prevention, specifically in the research area of traffic accident prediction, it is essential to count with a driving dataset that correlates and integrates information from heterogeneous sources. According to [PERSON] et al. [2], the data sources commonly used in this area are driver's data, vehicle data, weather conditions, traffic accidents, traffic flow, traffic events, light conditions, and road infrastructure. Thus, the key to a new driving dataset targeted to this area is the inclusion of information from most of these sources.  
There are great driving datasets such as A2D2 [3], KITTI [4], BDD100K [5], or Apollo Scape [6]; however, most of them target autonomous driving applications. There are alsodatasets such as comma.ai [7] or comma2k19 [8] that target autonomous driving and road safety applications, but these datasets include information from one or at most two sources. Vehicular traffic flow and accident prevention applications require driving datasets that include as much information as possible from various sources. Considering that our work focuses on traffic accident prevention, specifically in predicting risk levels of suffering a traffic accident, generating a driving dataset that fulfills the requirements of this type of application is essential.  
Our main contribution is to provide the research community with a driving dataset that correlates and integrates information from heterogeneous sources. Through POLIDriving, we provide 15 h of driving data from five real drivers and three extra hours of synthetic driving data from a fake driver with risky driving behavior. We also describe the process of data labeling through semi-supervised and ensemble learning. Finally, we provide two supervised learning models to predict risk levels of suffering a traffic accident.  
The rest of this article is organized as follows: Section 2 presents related driving datasets, Section 3 describes the process of generating the POLIDriving dataset, Section 4 presents the results of this work, Section 5 discusses the most relevant implications of the dataset, and Section 6 presents the conclusions of this work.
## 4 Conclusions  
US imaging based on the pulse-echo method assumes that the sound velocity has a constant value in the imaging media. In general, a conventional B-scan imaging system uses a sound velocity of 1540 m/s as the mean value for biological soft tissues. Since the sound velocity for biological tissues ranges from 1400 m/s to 1600 m/s, differences in the sound velocity affect beamforming and cause image degradation. In particular, the sound velocity for fat tissue is 10% slower than the mean value for biological soft tissues, so a thick layer of subcutaneous fat degrades image quality in terms of the spatial resolution and SNR. Thus, this study proposed a method of compensating for image degradation due to fluctuations in sound velocity. This method uses simultaneous multimodality imaging with US and magnetic resonance to estimate the distribution of the sound velocity in order to correct beamforming. An experiment was conducted with a phantom and imaging of a human neck was performed to evaluate the feasibility of the proposed method. A cross section of an MRI image and the corresponding cross section of an ultrasound image were compared to accurately measure the sound velocity. An MR-visible fiducial marker was developed and attached to an ultrasound probe for use in an MRI gantry. The experiment with a phantom indicated that the spatial resolution and SNR improved. During imaging of the human neck, an acoustic standoff pad was placed between the ultrasound probe and the surface of the skin of the neck to mimic a layer of subcutaneous fat. The sound velocity in the pad was estimated using simultaneous US imaging and MRI. Imaging of the neck indicated that the correction of the beamforming using the estimated sound velocity resulted in improved image quality for the thyroid region. The current results indicated that the proposed method is a feasible form of beamform correction in terms of improvements in the spatial resolution and SNR.  
Conceptualization, I.A.; methodology, K.I., I.A.; software, K.I; validation, K.I., I.A.; formal analysis, K.I., I.A.; investigation, K.I., S.A., K.N.; resources, I.A.; data curation, K.I., S.A., K.N.; writing--original draft preparation, K.I.; writing--review and editing, K.I., I.A.; visualization, K.I., S.A.; supervision, I.A.; project administration, I.A.; funding acquisition, I.A.  
This study was supported by the MEXT-Support Program to Strategic Research Foundation at Private Universities, 2013-2017. The authors wish to thank [PERSON] of Ricoh Co., Ltd. for his help with fiducial processing.  
The authors declare no conflict of interest.
## 2 Methodology  
The aim of this study is to develop a fuzzy logic-based framework, describing all the necessary steps, and an integrated and quantitative tool for the evaluation of SCs through the usage and integration of LARG paradigm, implemented with ExcelTM software package. Figure 1 summarizes the main steps to be followed to develop the fuzzy logic-based framework and implement the tool in ExcelTM. To facilitate its practical implementation, the following step-by-step process summarizes how the framework is implemented in ExcelTM, in alignment with the phases illustrated in Figure 1 and described in the following paragraphs:  
* Data collection: Gather company-specific values for all selected KPIs, as listed in Table 1, and define the corresponding benchmark values. * KPI scoring: Calculate the normalized score for each KPI, based on the company value and benchmark. * Fuzzification: Convert the normalized KPI scores into linguistic terms (Very Low, Low, High, Very High) using trapezoidal membership functions. * Hierarchical aggregation: Combine KPIs progressively according to the structure in Figure 2, using fuzzy if-then inference rules to obtain intermediate and final evaluations. Figure 1: Methodological framework. * Truth value calculation: Determine the degree of truth for each rule-based output, reflecting the contribution of each KPI. * Defuzzification and normalization: Aggregate the fuzzy outputs into a crisp score using the fuzzy mean method and normalization. * Performance interpretation: Use the final values to interpret performance across the LARG dimensions and SC areas, identifying areas requiring improvement.
## 1 Introduction
### Polymer Gears  
By integrating sensors, it could be possible to monitor even small changes in gears due to wear. This monitoring is even more important with the increasing use of polymer gears. One of the commonly used polymer materials for gears is polyoxymethylene (POM). POM gears have many advantages over steel gears: low cost, low weight and the ability to run without lubrication [5; 17]. However, they have a higher wear rate, which depends on the surface temperature of the gear flank [18; 19]. For the reliable operation of POM gears over long lifetimes and potentially critical operating conditions, condition monitoring could help to assess gear performance and predict potential failures. For the overall wear process of POM gears, three phases were found. First, there is a running-in phase, then a linear wear phase and finally a rapid wear phase [18; 19]. At higher loads, the first two phases become shorter until they are absent at a certain load. It was shown that POM gears may experience this phenomenon when subjected to loads exceeding 10 Nm at an ambient temperature of approximately 25 \({}^{\circ}\)C [18; 19]. Apart from abrasive wear, thermal wear is the main wear component for a POM/steel pairing [20]. The wear rate is similar to that of a POM/POM combination [18]. However, the running-in phase tends to be shorter for a POM/steel combination than for a POM/POM combination [21].  
In conclusion, POM gears can have an advantage over steel gears and the wear mechanisms can be similar. However, due to a higher wear rate and more complex interdependencies, the disadvantages outweigh the advantages and the use of POM gears remains limited.
## 3 Results
### Extraction of gDNA from CDBT-F-G1 Isolate and PCR Amplification of 18s RNA Gene  
The genomic DNA extracted from CDBT-F-G1 was subjected to PCR amplification using 18s rRNA primer (Figure 3). The size of PCR product was found to be 658 bp. The PCR product was sequenced (Xcelris Labs Ltd., Ahmedabad, India) and the sequences were used in BLAST analysis to determine possible similarities and find the most probable genus of the CDBT-F-G1 fungal isolate.  
Molecular Evolutionary Genetics Analysis (Mega 7) software was used to conduct sequence alignment. The aligned sequences with top-scores were used to develop phylogenetic tree. The analyses used to construct a phylogenetic tree used the following specifications: nucleotides were used as substitution types and maximum likelihood was used as the statistical method. Sequence similarity searches were performed for the fungal sequences against the non-redundant database maintained by the National Center for Biotechnology Information (NCBI) using the BLAST tool to infer functional  
Figure 3: Agarose gel-electrophoresis of PCR amplified 18s rRNA gene product. PCR amplification and agarose gel electrophoresis were as described in the Section 2. Lane 1: DNA markers (100 bp ladder from NEB Inc., Boston, MA, USA), Lane-2: PCR amplified 18s rRNA gene product.  
Figure 2: Morphological appearance of CDBT-F-G1 fungal isolate. Staining of CDBT-F-G1 isolate by lactophenol cotton blue was as described in the Section 2. (**a**) CDBT-F-G1 growing on PDB containing 1% Kraft lignin, and (**b**) appearance of CDBT-F-G1 conidia when stained with lactophenol cotton blue (magnification: 100\(\times\)).  
and evolutionary relationships between sequences, and to help identify members of gene families. This analysis confirmed that CDBT-F-G1 isolate has maximum similarity with _Pestalotiopsis_ spp. (Figure 4).  
Continuing with our quest to identify species expressing lacases to be used in commercial process for the pretreatment of lignocellulosic biomass, we have attempted to optimize and partially characterize the enzyme produced by _Pestalotiopsis_ spp. CDBT-F-G1 fungal isolate and compared its properties to other lacases previously reported.