esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Data collection was carried out on the uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) of igneous building stones reported in previous studies. The data were analyzed to introduce a BTS-based classification of these stones. Based on data analyses, igneous stones were categorized into five strength classes: weak (Class I; BTS \(<\) 1.3 MPa), moderately weak (Class II; BTS 1.3-4.2 MPa), strong (Class III; BTS 4.2-10.1 MPa), very strong (Class IV; BTS 10.1-19.3 MPa), and extremely strong (Class V; BTS \(>\) 19.3 MPa). Since tensile strength is one of the critical technical parameters for evaluating the durability of building stones against deterioration processes (e.g., freezing-thawing, salt crystallization, heating-cooling, and wetting-drying), the BTS-based classification developed in the present study can be used as a quick and low-cost tool for the preliminary evaluation of the durability of igneous building stone before its selection as a building material. In this regard, a shift from BTS class I to V will be accompanied by an increase in the durability of the stone against deterioration processes. The validity and applicability of the BTS classification introduced in the present study were verified using the analysis of data published in previous studies on the BTS changes during deterioration processes. As a suggestion, it is essential to develop the BTS classification for other classes of building stones, including sedimentary and metamorphic, and compare the results with the findings of the present study. This could lead to a more comprehensive understanding regarding the evaluation of the various stones' durability against the deterioration processes using BTS.  
Conceptualization, A.J.; methodology, A.J.; validation, A.J. and L.S.; formal analysis, A.J. and L.S.; investigation, A.J. and L.S.; writing--original draft preparation, A.J. writing--review and editing, A.J. and L.S.; visualization, A.J. and L.S.; supervision, L.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Fundacao para a Ciencia e a Tecnologia in the frame of the UIDB/00073/2020 and UIDP/00073/2020 projects of the I & D unit Geosciences Center (CGEO).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 7: The validity and applicability of BTS classification for evaluating the stone durability against deterioration processes ([PERSON] et al. [5], [PERSON] et al. [12], [PERSON] et al. [13], [PERSON] et al. [14]).
## 3 Results and Discussion
### Sensitivity Analysis
Additionally, it does not provide the capability to examine the temporal distribution of wind speeds. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Month**} & \multicolumn{5}{c}{**Number of Stations**} \\ \cline{2-6}  & **110** & **101** & **27** & **40** & **43** \\ \cline{2-6}  & & & **BE \(\geq\) 2 m/s** & **1 m/s \(\leq\) BE \textless{} 2 m/s** & **BE \textless{} 1 m/s** \\ \hline January & 0.44 & 0.61  
The overestimation of wind speed in WRF simulations can be influenced by various factors, including the location and number of vertical levels, grid spacing, and the size and location of the simulation domain. According to [PERSON] et al. (2018), smaller domains tend to have lower wind speeds and less bias, while RMSE increases with domain size and integration time. However, analysis of these topics is beyond the scope of this study. Nonetheless, it is concluded that a suitable configuration was chosen for the simulations, and the resulting data demonstrates reasonable accuracy. This dataset can be utilized in other studies and subsequent processes to develop micro-scale wind atlases for targeted areas in the country, thus helping in wind farm development. Figure 3: (**a**) Topographic map of Iran and locations of the stations classified according to the various categories of BE (as in the legend) of annual 10 m wind speed; (**b**) various categories of BE (as in the legend) and annual mean distribution of the 10 m wind speed (shaded as in the legend) during the period 2004–2020. UE and OE stand for model underestimation and model overestimation, respectively.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 3 Methodology
### The Active Passive Loss (APL)  
To address the model underfitting problem caused by noisy labels, [PERSON] et al. (2017) proposed a framework to construct robust loss functions called APL, which combines the active loss (i.e., CE, normalised CE, focal loss and normalised focal loss) and the passive loss (i.e., MAE, normalised MAE, RCE and normalised RCE). The APL \(l_{APL}\) can be defined as follows.  
\[l_{APL}=\alpha\cdot l_{Active}+\beta\cdot l_{Passive} \tag{1}\]  
where variables \(\alpha\) and \(\beta\) are used to compensate for robust losses and \(\alpha\), \(\beta>0\). The term \(l_{Active}\) represents active losses that explicitly maximise the network's output probability at the class position specified by the label. The term \(l_{Passive}\) represents passive losses, which explicitly minimise the probability at least one other class position. Therefore, for noisy samples, more passive learning can preserve the effective information of the samples and avoid misguiding the model. Conversely, more active learning can speed up the learning of the model and avoid underfitting. However, we found that, for different datasets, the weights of active and passive losses need to be manually adjusted to achieve the best performance, which increases the training cost and limits the generalisation performance of the model. The aim of our method is to automatically adjust the weighting hyperparameters \(\alpha\) and \(\beta\) in remote sensing image retrieval.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 4 Experimental Results
### Experimental Setup and Evaluation Metrics  
We implemented the proposed method based on the new parallel distributed deep learning framework Paddle[39] with version 2.0.0 and developed the code with PaddleDetection [40], an end-to-end object detection development kit based on PaddlePaddle. The experiments were performed in Python 3.7 and the Compute Unified Device Architecture (CUDA) with version 10.0. The ResNet-50 [33] pre-trained on the ImageNet [23] was used as our backbone. We used three parallel GTX Titan X GPUs during training and a single GTX Titan X GPU for testing.  
For the evaluation of the pedestrian detection, we used the standard evaluation metric based on log average miss rate, over false positive per image (FPPI) range of \(10^{-2}\) to \(10^{0}\) (denoted as \(MR^{-2}\)) with the IoU threshold of 0.5. If the overlap ratio between the detected bounding box and the ground truth bounding box was less than 50%, the detected bounding box was determined as false positive. The lower value of the miss rate reflected the better detection performance.
## 3 Results
### SHS in the BE Region and RFA Treatment Response  
Table 3 summarizes the comparison of the SHSs in RFA ablated BE between SHSs in patients with neo-SE and BE at follow-up. For each quantitative metric, we analyzed the mean of all SHSs as well as the maximum or minimum values of individual images, namely the maximum size and depth, highest intensity and STD, maximum orientation and eccentricity, and minimum extent and solidity. Patients having BE with shallower SHSs were more likely to achieve neo-SE on follow up post-ablation (neo-SE mean value: 0.83 \(\pm\) 0.13 mm vs. BE mean value: 0.96 \(\pm\) 0.14 mm, \(p\) = 0.041). Noticeably, the depth difference for the treatment response was mainly driven by the HGD/IMC patients (neo-SE: 0.84 \(\pm\) 0.11 mm, BE: 1.02 \(\pm\) 0.13 mm, \(p\) = 0.050, Table S5) rather than LGD patients (neo-SE: 0.82 \(\pm\) 0.17 mm, BE: 0.90 \(\pm\) 0.14 mm, \(p\) = 0.441, Table S4). Table 3 also shows that the pre-ablation SHS solidity (regularity) in the BE region may be a predictor of post-ablation neo-SE (neo-SE: 0.90 \(\pm\) 0.01, BE: 0.89 \(\pm\) 0.01, \(p\) = 0.053) although it did not achieve statistical significance in this study.
## 3 Methodology  
This paper aims to address the prevalent issue of class imbalance in IDS, which can potentially hinder the performance of IDS. In order to tackle this challenge, we propose an IDS that integrates a CNN, BiLSTM, and the focal loss function named CBF-IDS. The primary goal is to enhance detection performance and mitigate the adverse effects of class imbalance on model performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Year** & **Dataset** & **Algorithm** & **Balancing** & **Performance** \\ \hline
[23] & 2019 & KDD99 & CNN & \(\bigstar\) * & Acc = 99.23\% \\
[24] & 2021 & CIC-IDS2017 & BiLSTM & Acc = 97.72\%, F1 = 97.75\% \\
[25] & 2022 & KDD99 & DNN & Acc = 79.26\% \\
[26] & 2022 & BoT-IoT & DNN & Acc = 99.2\% \\
[27] & 2022 & UNSW-NB15 & MGWO + ELM & Acc = 80.93\%, F1 = 78.08\% \\
[28] & 2021 & N-BaloT & LGBA-NN & Acc = 90\% \\
[29] & 2021 & UNSW-NB15, BOUN & ML\_NET & Oversampling & Acc(generic) = 99.8\%, Acc(DDos) = 99.7\% \\
[30] & 2021 & CIC-IDS2017 & Random Forest & ADASYN & F1 = 95.3\% \\
[32] & 2023 & NSL-KDD & MLP, DNN, CNN, CNN-BiLSTM & ADASYN + Tomek & Acc = 99.9\% \\
[33] & 2021 & CIC-IDS2017 & Random Forest & GAN & Acc = 99.83\%, F1 = 95.04\% \\ Our &
\begin{tabular}{c} NSL-KDD, \\ UNSW-NB15, \\ CIC-IDS2017 \\ \end{tabular} & CNN-BiLSTM & Focal loss & Acc(UNSW-NB15) = 82.3\%, Acc(CIC-IDS2017) = 99.53\% \\ \hline \hline \end{tabular}  
* \(\bigstar\) indicates the absence of utilizing class balancing techniques, the same below.  
\end{table}
Table 1: Summary of recent research in machine learning, deep learning, and class balancing techniques for network intrusion detection.  
In order to assess the effectiveness of CBF-IDS, we selected three widely used datasets in intrusion detection research: NSL-KDD, UNSW-NB15, and CIC-IDS2017. We conducted a series of data preprocessing steps on these datasets, including data cleaning, one-hot encoding, normalization, two-dimensional representation, and dataset splitting. These steps are crucial for preparing the data for subsequent model training. Subsequently, we developed a hybrid model based on the CNN-BiLSTM architecture. This model aims to comprehensively extract spatiotemporal features from the datasets and perform the classification task. In the CNN, we employed three convolutional layers to enhance the model's capability to capture spatial features in the data. Furthermore, to better capture temporal features in the data, we introduced a BiLSTM layer, which effectively considers bidirectional temporal dependencies. Additionally, to mitigate the issue of class imbalance in the datasets, we utilized the focal loss function. This loss function increases the weight of minority classes, aiding the model in better learning and focusing on these minority classes, thereby enhancing the model's performance on imbalanced datasets. The subsequent subsections will provide a detailed explanation of the methods described above. The overall workflow of the proposed method is illustrated in Figure 1.
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation
### Generated Interface Instance  
The generator or system responds to the orchestration system request and generates a new instance based on both service contracts (from the provider and the consumer). The new instance is formed in three parts, explained as follows. Figure 3 shows a block description of the generated interface.  
Figure 1: Payload element description.  
* _Consumer Interpreter--Provider._ The new interface provides a service to the consumer using the metadata from the consumer service contract to match the interface of the system. The service is not public to any other system to avoid scalability and security problems; in other words, the service is local to the consumer device and is not registered in the service registry.
* _Core Code Adapter--Translator._ The core part of the interface is the service interface translator. It focuses on several aspects of the service interface of both systems, providing the code necessary to bridge them and transform the payloads.
* _Provider Interpreter--Consumer._ To complete the service interaction, a consumer piece of software is included in the instance, and it can be considered a client to some architectures. It consumes the service from the provider system using the translated message and sends back the response.  
The service instance behaves like a black box between both systems. The service consumer initiates the consumption of the service via the generated interface without knowing of its existence. The instance is in charge of the translation between the system interfaces and proceeds with the consumption of the service using the correct service contract information. The provider answers the request normally, and finally, the interface translates and transmits the response back to the consumer.
## 5 Complexity Analysis  
### Communication Complexity Analysis  
After the sink node completes the 2D convex hull activation process, the communication complexity of the process can be obtained, and it is \(O(n\times h)\) according to the _GiftWrapping_ algorithm [34], where \(h\) denotes the required number of active nodes to achieve the 2D convex hull, and its asymptotic value is \(L*W/\pi*Rs^{2}\). Given that the numbers of elements in  
Figure 8: Flowchart of the depth-adjustment strategy.  
Nei(\(f_{j}^{i}\)) - NeigInter\(i^{i+1}\) and NeigInter\(i^{i+1}\) are \(N_{ij}\) and \(N_{c}^{i}\) respectively, node \(f_{j}^{i}\) broadcasts a message to its neighbor child nodes, and all parent nodes compare the distances of all neighbor child nodes and must send a total of \(h*(n-h)\) messages for an overall situation significance. Node \(f_{j}^{i}\) submits the permission to its child node \(f_{min}\) for \(k\) times through an iterative method, sending a total of \(k\times N_{A}\) messages. The number of messages that must be sent by the parent node is twice the total number of nodes in the 2D convex hull activation process, namely, \(2n\). After network deployment is completed, the value of \(N_{A}\) and \(n\) are approximately equal. Therefore, the complexity of network communication is \(\mathrm{O}(h\times(n-h))+\mathrm{O}(k\times N_{A})+\mathrm{O}(2n)\), then the big-O complexity is \(\mathrm{O}(cn)\), where \(c=k+h+2\).
## 3 Results and Discussion
### Fourier Transform Infrared (FT-IR) Spectra of SDexF  
In the spectra of potato starch, bands characteristic of polysaccharides were observed, i.e., those occurring in the fingerprint region (wavenumber range 1500-650 cm\({}^{-1}\)), corresponding mainly to the skeletal vibrations of the glucopyranose ring and occurring at a wavenumber of 1650 cm\({}^{-1}\), attributed to the bending vibrations of the H-O-H bonds absorbed by the polymer of water molecules. The FTIR spectrum of starch also showed a very broad band in the range of 3500-3000 cm\({}^{-1}\), corresponding to the stretching vibrations of inter- and intramolecular hydrogen bonds of hydroxyl groups. When comparing the spectra of starch before and after dextrinization carried out on a semi-industrial scale, apart from changes in the intensity of the bands, no changes in their position, i.e., shifts resulting from stretching or bending vibrations, were observed (Figure 2).  
The possible differences in the intensity of the bands in starch after dextrinization are related to quantitative changes in relation to specific functional groups, mainly hydroxyl, and thus from the reorganization of hydrogen interactions. Similar results were obtained in another paper concerning structural studies of resistant dextrin [60].
## 3 Database Development
### Ground-Motion Selection  
Ground-motion records tend to produce higher uncertainty than the levels associated with capacity and numerical modelling in seismic response analysis. Hence, the uncertainties associated with the seismic demand must be considered by accounting for various ground-motion records to represent the exact seismicity of the considered region [34]. Therefore, ground-motion records associated with previous earthquakes in various parts of the world (Armenia, Chile, Costa Rica, India, Iran, Mexico, Nepal, Peru, and Turkey) were collected, and 54 records were used for the nonlinear response analysis. The ground-motion time histories of the earthquake records were obtained from the Strong Motion Virtual Data Centre, developed at the University of California, Santa Barbara [37]. The variations associated with the moment magnitude and epicentral distance in the selected records are shown in Figure 5. The moment magnitude of the ground-motion records varies from 5.2 to 7.9. The epicentral distance varies from 2.2 to 250 km, considering both near- and far-field earthquakes. This indicates the consideration of a wide range of earthquakes of varying intensity and frequency intervals. Further, the site soil condition of the selected ground-motion records varies from soft soil to rock. The analysis was carried out by scaling the Peak Ground Acceleration (PGA) of the ground-motion records from 0.01 g to 2.0 g at an increment of 0.01 g.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 2 Methods of On-Board Ship Detection
### Sea-Land Segmentation
#### 2.1.1 Judgment on Land and Sea  
In this part, using the latitudes and longitudes of the four corners of the image, the global coastline vector data are superimposed to determine the position of the image. Because every polygon on the vector layer corresponds to a piece of land, the problem of judging whether corner points of the SAR image are sea or land is converted to a problem of whether the points on the vector layer are in the polygons designated as land. The problem of determining whether a point is in a polygon in the vector layer is carried out according to the graphics algorithm [24]; where, the time complexity of the algorithm is O(n) and n is the number of polygonal line segments. Because of the large size of the acquired images and the requirements for efficiency, only the four corner points of the image are used to obtain the approximate position of the image.  
Figure 1: The process of on-board ship detection based on constant false alarm rate (CFAR) and deep learning.
# Correlation Between Co-Design and Psychological Need Satisfaction Based on fNIRs Hyperscanning
## 2 Materials and Methods
### Data Collection  
#### 2.3.1 Blood Oxygen Data  
Data acquisition was performed utilizing two Artinis Brite 24 fNIRS systems to monitor oxyhemoglobin and deoxyhemoglobin concentration variations in the participants' frontal lobes during both traditional and co-design activities. A standardized swimming cap was equipped with two \"3 \(\times\) 3\" measurement arrays, which were securely positioned on each subject's head to enable frontal cortex monitoring. The optode arrays were symmetrically aligned across the forehead region of all the participants, with the precise configuration illustrated in Figure 3.  
The fNIRS acquisition cap was designed based on the 10-20 international standard electrode placement system. The average distance between the optodes and light sources was 30 mm (ranging from 29 to 31 mm). The primary detection region for each channel corresponded to the brain area beneath the midpoint of the emitter-detector pair. We confirmed that the optical distance between the emission light source and the receiving  
Figure 3: Experimental equipment, a pair of Artinis Brite 24-fNIRS devices, demonstrating the experimental channel settings.  
probe was 30 mm, and then the channel optical polarity template was entered with a DPF (Differential Pathlength Factor) value of 6. The central emitters and detectors were precisely aligned along the cranial midline, following the anatomical arc from the nasion through Cz to the inion. To ensure proper vertical alignment, the caps were positioned low on the forehead with their base resting along the eyebrow line, thereby providing adequate forehead coverage and maintaining consistent vertical placement across participants. For each dyad, the cap positioning was carefully examined and adjusted to ensure equivalent placement. Each measurement array contained an alternating configuration of 10 emitters (denoted in red) and 8 detectors (marked in blue), totaling 18 probes that formed 2 \(\times\) 12 measurement channels, as illustrated in Figure 4.  
The sampling frequency was 10 Hz. The spatial positioning of the 24-channel configuration was determined using the Polhemus Patriot system, while the NIRS-SPM toolkit was employed for optical probe registration and spatial normalization. This process enabled the generation of a standardized brain localization map, which was subsequently visualized using the MATLAB 2013b software, as presented in Figure 5.  
Figure 4: Cap configuration. (**a**) Red circles indicate emitters; blue circles indicate detectors. Yellow squares indicate measurement channels between emitters and detectors. (**b**) The positioning of acquisition points in the Oysoft software 3.2.70 x64.  
Figure 5: Right, front, and left optical poles localization brain map.
# Research and Verification of Trajectory Tracking Control of a Quadrotor Carrying a Load
###### Abstract  
This paper assumes that considering the unknown and time-varying nature of different strong and weak wind field disturbances and considering the nonlinear, under-driven, strongly coupled quadrotor carrying, a load is disturbed by the complex and variable wind field and unmodeled parts when flying in the real external environment, which will reduce the control effect of the nonlinear controller and make the vehicle fail to affect the flight effect. In order to ensure that the quadrotor carrying a load can carry supplies in the harsh environment for stable trajectory tracking, a neural network adaptive control algorithm is introduced in the article. The neural network algorithm has the role of online dynamic approximation, the compensation of arbitrary external disturbance and the compensation of external disturbance. Its structure is simple and low computation. In the article, the Lyapunov method is used to design the adaptive weight and estimate the weight of the online neural network, and the stability of the system is proved. Finally, the comparison of three algorithms verified by simulation proves that the above interference problem can be solved effectively by the proposed algorithm.  
Quadrotor; load; neural network; integral backstepping; trajectory tracking +
Footnote †: journal: Applied Sciences