esa-sceva/satcom-chunk-collection huggingface dataset:
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 3 Overview and Operation of IEEE 802.11 and IEEE 802.15.4
### IEEE 802.15.4  
The IEEE 802.15.4 is the MAC and physical layer standard for low rate wireless personal area networks. The IoT devices considered in this paper consist of IEEE 802.15.4-compliant sensor nodes and an infrastructure unit in the form of a network coordinator (sink node). IoT devices have low power and limited processing abilities that often communicate in single and multi-hop fashion. Each IoT sensor node consists of a sensing unit, storage unit, transceiver unit, processing unit, and power unit [33]. IEEE 802.15.4 employs two modes of operation. Beacon Mode, in which slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), as shown in Figure 4, is used for data transmission. Clear Channel Assessment (CCA) is used in the physical layer to determine the channel occupancy [34]. CCA either performs Energy Detection or Carrier Sense, or a combination of both. CCA shall report a busy channel upon detection of any energy above the predefined energy threshold or a signal with known characteristics, such as modulation and spreading features. In IEEE 802.15.4  
Figure 3: WLAN basic media access control scheme; slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) contention-based channel access. DIFS = Distributed Interframe Spacing; SIFS = Short Interframe Spacing.  
WSNs, a channel is sensed only during the CCA period. In _Non-Beacon Mode_, un-slotted CSMA/CA is used for data transmission [35].
## 4 Conclusions  
This study presents a high-performance MIMO antenna design tailored for a 28 GHz band. The compact four-port MIMO structure, with dimensions of 68 \(\times\) 80 mm\({}^{2}\), incorporates a 1 \(\times\) 8 series-fed array for each port, achieving a peak gain of 13 dBi and a bandwidth of 1 GHz at 28.8 GHz. Enhanced performance is achieved through calibrated antenna element spacing and a decoupling strategy utilizing carefully designed MTM unit cells, which successfully minimize cross-talk interaction among antenna elements. The design demonstrates exceptional performance metrics, including mutual coupling below \(-41\) dB, an ECC of 0.00012, a CCL of 0.147 bit/Hz/s and DG approaching 10 dB. Experimental validation confirms these advancements, positioning the proposed design as a strong contender for mmWave communication systems operating in the 28 GHz band.  
This research received no external funding.  
Not applicable.  
All data supporting the findings of this study are already included within the article.  
The researcher would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2025).  
The author declares no conflicts of interest.
## 2 Versatile Video Coding (VVC)  
The VVC codec is based on the same hybrid coding scheme used in previous standards, such as HEVC [2] or AVC [23]. The VVC decoding process scheme is shown in Figure 1 and can be summarized as follows: bitstream is the input, and video is the output of the system. First, the decoding process starts from entropy decoding of the bitstream. Here, context adaptive binary arithmetic coding (CABAC) [24] gives partitioning information of blocksnamed coding tree units using inter-picture prediction, intra-picture prediction, and coded residual data [4]. Therefore, to reconstruct the coded residual data, inverse quantization is applied followed by inverse transformations (primary and secondary). Finally, in-loop filters are used to process accumulative reconstructed residual and predicted data. The in-loop filter contains the DBF, the sample adaptive offset (SAO), and ALF filters. Next, the main modules integrating the decoding process are presented and detailed.
# Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
## 5 Conclusions and Future Work  
Aiming at remote sensing and mapping applications, we have designed a low-cost \(360^{{}^{\circ}}\) image acquisition system through the integration of eight web cameras, which were securely mounted on a circular rig. To solve the mosaicking problems caused by inconstant and inconsistent object-distance information within a scene, a panoramic image generation algorithm was proposed to improve the existing direct mono-cylinder imaging model. The key aspect of the algorithm is the Panoramic Vertical Line Locus matching algorithm called the PVLL algorithm, which is proposed by extending the traditional VLL algorithm. Moreover, the object-distance grids can be generated based on the PVLL algorithm in the overlapping regions of a panorama. Thus, the panorama can be generated by utilizing the multiple projection radiuses from the estimation of object-distance information. The experiments show that the proposed imaging method successfully improves the stitching accuracy and visual effects of virtual panoramas.  
In summary, this paper discusses the problems of geometric mosaicking errors in multi-camera panoramic systems and conducts research on the combination of accurate calibration, depth estimation, and post-processing techniques to ensure a seamless and accurate panoramic view, despite variations in object distances and eccentric errors between camera optical centers. The method described in this paper is suitable for a multi-camera system with an annular structure, and we plan to extend it to a panoramic system with a 720-degree spherical design in our future work for ground remote sensing applications in both outdoor and indoor environments.  
Methodology, H.C. and Z.Z.; Software, Z.Z. and F.Z.; Writing--original draft, H.C. and Z.Z.; Writing--review and editing, H.C. and Z.Z.; Visualization, Z.Z.; Translation Check, F.Z.; Project administration, H.C.; Funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Liaoning Provincial Science and Technology Mission Plan, Department of Liaoning Province, China (No. 2023020569-JH5/104); the key project of the Department of Education in Liaoning Province, China (No. 2023003); and the National Natural Science Foundation of China (No. 41371425).  
Not applicable.  
Data Availability Statement: Data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy (General Data Protection Regulation possible issues).  
The authors declare no conflict of interest.
# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 2 Materials and Methods
### Classification Models
#### 2.5.1 Principal Component Analysis-Linear Discriminant Analysis  
For each of the three signal types (raw absorbance, first derivative, second derivative), a principal component analysis (PCA) was performed using centered, scaled variables. The first two principal component scores were extracted and plotted with 95% confidence ellipses to visualize group separation, and a scree plot of the percentage variance explained by the first ten components was generated to assess dimensionality.  
A train/test split (70/30) was applied to each signal matrix. On the training portion, linear discriminant analysis (LDA) models were trained under repeated 10-fold cross-validation (five repeats). The number of principal components retained (from 1 to 10) was treated as a tuning parameter; for each candidate dimension, the model's ROC-AUC (Receiver Operating Characteristic curve--Area Under the Curve), sensitivity, and specificity were recorded. The optimal component number was selected by the highest cross-validated ROC-AUC. This best PCA-LDA configuration was then applied to the test set. Test-set predictions yielded a confusion matrix from which sensitivity, specificity, and accuracy were computed, and class-probability scores were used to calculate ROC-AUC.
# Electrospinning-Based Carbon Nanofibers for Energy and Sensor Applications
## 4 Application of Carbon Nanofibers
### Energy Storage Applications
#### 4.1.6 Membrane Electrode Assembly for Fuel Cells  
In the fuel cell system, in addition to the electrode, attention is paid to the development of the membrane electrode assembly (MEA). [PERSON] et al. developed an ion membrane based on multichannel CNFs (Figure 17a) [126]. Using a precursor from PAN mixed with PMMA, they electrospun the material into fibers, stabilized and carbonized at up to 1400 \({}^{\circ}\)C. The higher the carbonizing temperature, the stronger the G peak in the Raman spectra, indicating the presence of the graphitized structure. Furthermore, the multichannel CNFs were decorated with Pt/C catalyst nanoparticles, turning them into an MEA. Not only is the mass transport resistance of the membrane low, but it also facilitates the draining of water from the catalyst. [PERSON] et al. modified the electrospinning collector into parallel electrodes to arrange the electrospun fibers into orthogonally aligned nanofibers (Figure 17b) [127]. The well-aligned fibers formed many small channels which allowed the ions to penetrate easily. After carbonization, the aligned CNFs were decorated with Pt using wet chemistry plating and sputter coating. The membrane structure also helps the plating of the Pt catalyst to occur uniformly.  
[PERSON] et al. used a composite of CNFs from PAN and synthesized a sulfonated poly(etherether ketone) (SPEEK) (Figure 17c) [128]. SPEEK is a low-cost ion transfer material with excellent mechanical and chemical properties. However, since PEEK with high sulfonate content accommodates too much water, it can swell in an aqueous environment. On the other hand, a low concentration of sulfonate causes low conductivity, thus the authors mixed it with CNFs to obtain an MEA layer. The MEA showed good results in the physical strength of the tensile test and ion conductivity. [PERSON] et al. prepared the MEA by mixing the acid-sulfuric-treated CNFs from the PAN carbonization (Figure 17d) [129]. After carbonization, the CNFs emerged in the mixture of sulfuric acid and nitric acid to attach functional groups on the surface of CNFs. The fibers were then mixed with the synthesized SPEEK and cast on a glass substrate. With the hydrogen bond formed between the acid groups on the CNFs and SPEEK, the thickness of the membrane was reported to be around 40 \(\upmu\)m. The composite membrane appears to have high tensile strength with break points at 75 MPa stress and 7% strain. The proton conductivity of the introduced material can be comparable to Nafion.  
Figure 16: (**a**) Effects of \(\beta\)-CD surfactant on the catalytic activation of porous CNFs in ORR/OER fuel cell [118]. (**b**) CNFs decorated with Co nanoparticles for OER catalyst material [121]. (**c**) NiCoFe metal alloy for catalyst material of both ORR and OER systems [122]. (**d**) CNF fibers from fibroin of silk for catalyst material [125].
## 3 Methodology
### Keypoint Detection with Boundary Removal
#### 3.1.1 Keypoint Detector  
The goal of the keypoint detector is to choose a subset of conspicuous points with good repeatability from the surface of a point cloud. The discovered keypoints should be very robust to noise and highly invariant to rigid transformation.  
Figure 1: The framework of our point cloud registration.  
Given a point cloud \(P=\{p_{1},p_{2},\ldots,p_{N}\}\in R^{3}\) and \(p_{i}=[x_{i},y_{i},z_{i}]^{T}\in P\), the mean \(\overline{p}\) and covariance matrix C of P can be computed as follows:  
\[\overline{p}=\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}p_{i} \tag{1}\]  
\[C=\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}(p_{i}- \overline{p})(p_{i}-\overline{p})^{T} \tag{2}\]  
then, applying eigenvalue decomposition on the covariance matrix C yields the following results:  
\[CV=EV \tag{3}\]  
where V is the matrix of eigenvectors, E is the diagonal eigenvalues matrix of C, and each column in V corresponds to a diagonal eigenvalue in the diagonal matrix E.  
Following that, using Formula (4), point cloud P is aligned with the principal axes established by V, which is known as Principal Component Analysis (PCA).  
\[P^{\prime}=V(P-p) \tag{4}\]  
where \(P^{\prime}\) is a normalized point cloud of point cloud P.  
For each point \(p_{i}^{\prime}\) in point cloud \(P^{\prime}\), a local neighborhood Nbhd (\(p_{i}^{\prime}\)) is used to prune from \(P^{\prime}\) using a sphere with a radius \(r\) (so-called support radius) centered at \(p_{i}^{\prime}\). Let X and Y stand for the x and y components of Nbhd (\(p_{i}^{\prime}\)) as follows:  
\[X=\{x_{1},x_{2},\ldots,x_{l}\} \tag{5}\]  
\[Y=\{y_{1},y_{2},\ldots,y_{l}\} \tag{6}\]  
where \(l\) is the length of Nbhd (\(p_{i}^{\prime}\)). Then, a surface variation index \(\theta\) is defined as follows:  
\[\theta=\frac{max(X)-min(X)}{max(Y)-min(Y)} \tag{7}\]  
where the local neighborhood's geometric variation around the point \(p_{i}^{\prime}\) is reflected by the surface variation index \(\theta\), which is the ratio between the local neighborhood's first two principal axes centered at \(p_{i}^{\prime}\). \(\theta\) is 1 for a symmetric local point set, such as a plane or sphere, and more than 1 for an asymmetric local point set. The point in \(P^{\prime}\) that has a surface variation index higher than \(\varepsilon_{\theta}\) is regarded as a keypoint and is defined as follows:  
\[\theta>\varepsilon_{\theta} \tag{8}\]
## 3 Synoptic and Mesoscale Features  
A cold front advanced to the south over southern China in the afternoon of 15 March 2025. From the weather radars, a squall line appeared over the western coast of southern China in the morning and moved across Hong Kong in the early afternoon. In the mesoscale, moisture was transported to southern China in the lower levels (e.g., 850 hPa and 925 hPa) in association with southwesterly winds at 00 UTC on that day (Hong Kong time = UTC + 8 h). Perturbations could be found in the westerly winds at mid-levels of the troposphere such as 500 hPa and 700 hPa at that time while there was apparent cyclonic shear in the lower levels at 925 hPa and 850 hPa. Veering of winds within the 0-3 km layer was observed with a vertical wind shear of around 15 m/s. Some isolated areas of wind speed divergence could be found in the upper troposphere at 00 UTC, e.g., at 200 hPa, supporting convective developments. The mesoscale setup is typical for the occurrence of intense convection in association with a cold front.  
At 06 UTC, the convective available potential energy (CAPE) reached about 630 J/kg. The K-index was about 34 degrees Celsius. The tropospheric was generally unstable, supporting the development of severe convection. Although the CAPE value is not particularly large, it has been recognized that low CAPE/strong shear is common for tropical squall-line environments [28] which is favourable for triggering severe convective systems.  
From the surface wind observations, the westerly winds associated with the squall line first picked up at the western part of Hong Kong, e.g., at the Hong Kong InternationalAirport (HKIA) as in Figure 1a. Figure 1b shows the gusts at the various stations around that time with many at HKIA exceeding 25 m/s. The squall line then moved eastwards across Hong Kong, as shown in the wind observations from Figure 1c,e. From the wind gust observations in the whole event, namely, from Figure 1b to Figure 1d and eventually Figure 1f, the maximum gust was in fact decreasing in the eastward movement of the squall line, suggesting gradual weakening of the squall line in crossing Hong Kong. This may be partly related to the frictional effect of the terrain and urban morphology of Hong Kong.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 3 Analysis of the Influence of Horizontal Parameters of the Mega-Sub Isolated Structure
### Analysis of the Influence of the Horizontal Mass Ratio of the Seismic Isolation Layer
#### 3.2.2 Influence of Mass Ratio on Subframe Response  
The response variation curve of the second isolation sub-frame is shown in Figure 4 below. It can be seen from the acceleration change curve that when the mass ratio is less than 1.0, the acceleration response decreases rapidly with the increase of the mass ratio. When the mass ratio continues to increase, the acceleration response of the sub-frame decreases slowly, reaches the minimum value at 2.0, and then increases slightly, eventually leveling off. The displacement of the isolation layer increases rapidly and then decreases with the mass ratio, and finally tends to be gentle. When the mass ratio is 1.5, the displacement  
Figure 3: Variation of main frame dynamic response: (**a**) Mass ratio influence on main frame displacement; (**b**) Mass ratio influence on main frame absolute acceleration.  
reaches the maximum. When the mass ratio exceeds 3.0, the displacement response tends to be flat.  
When the mass ratio \(\omega\) is around 3.0, the second seismically isolated sub-frame isolation layer's displacement and acceleration response values are smaller, and the damping effect is better at this point. In conjunction with the analysis of the main frame above in this section, it is recommended that the mass ratio \(\omega\) be set at around 3.0 for the design of a mega frame seismic isolated structure, where the total weight of the sub-frame accounts for around 75% of the total weight of the building.
###### Abstract  
Nowadays, several environmental applications take advantage of remote sensing techniques. A considerable volume of this remote sensing data occurs in near real-time. Such data are diverse and are provided with high velocity and variety, their pre-processing requires large computing capacities, and a fast execution time is critical. This paper proposes a new distributed software for remote sensing data pre-processing and ingestion using cloud computing technology, specifically OpenStack. The developed software discarded 86% of the unneeded daily files and removed around 20% of the erroneous and inaccurate datasets. The parallel processing optimized the total execution time by 90%. Finally, the software efficiently processed and integrated data into the Hadoop storage system, notably the HDFS, HBase, and Hive.  
remote sensing big data; data pre-processing; parallel programming; cloud computing
# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
###### Abstract  
The precise ascertainment of stellar ages is pivotal for astrophysical research into stellar characteristics and galactic dynamics. To address the prevalent challenges of suboptimal accuracy in stellar age determination and limited proficiency in apprehending nonlinear dynamics, this study introduces an enhanced model for stellar age determination, amalgamating the Feedforward Neural Network (FFNN) with TabNet (termed FFNN-TabNet). The methodology commences with the acquisition of a stellar dataset via meticulous cross-matching. Subsequent advancements encompass refinements to the activation functions within TabNet, coupled with augmentations to the Attentive transformer module by incorporating an FFNN module. These enhancements substantially boost training efficiency and precision in age estimation while amplifying the model's capability to decode complex nonlinear interactions. Leveraging Bayesian Optimization Algorithm (BOA) for hyperparameter fine-tuning further elevates the model's efficiency. Comprehensive ablation and comparative analyses validate the model's superior performance in stellar age determination, demonstrating marked enhancements in accuracy. The experiment also demonstrates an enhanced ability of the model to capture nonlinear relationships between features.  
stellar ages; TabNet; Feedforward Neural Network; Bayesian optimization algorithm +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: School of Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin 644000, China; [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (W.Z.); [EMAIL_ADDRESS] (Y.Z)
## 3 The Proposed Method
### Novel Enhancements for the Detector Module
#### 3.1.3 Tracker Module  
Using label and bounding box information provided by the detector, an IoU-based [26] tracker module is deployed to track the nodes on the camera field of view. By employing a tracker module, the object\" identities can be preserved while they are in the FoV. To track objects in the video, each frame detection is compared with its corresponding previous frame detection, and a metric called IoU [26] is calculated for each object by dividing the overlapping area by the combined area for two consecutive bounding boxes. If the IoU value is greater than a chosen threshold, the object in the current frame is considered the same as in the previous frame.  
Aside from providing object-counting capability, tracking the objects offers further possibilities to develop postprocessing techniques to improve detection results and reduce errors. Within the Tracker Module, to prevent making errors by relying on a single detection score, the network scores for each node are averaged across the sequence of frames until the node exits the field of view of the camera.
## 3 Results
### SAOCOM-1 8-Day Coherence  
As indicated in Section 2.4, three SAOCOM-1B images were acquired over the Corrientes site to form 8-day L-band interferograms and thus evaluate the effect of reducing the temporal baseline on the coherence, with respect to the 16-day nominal revisit time of the SAOCOM-1A images. Table 2 details the interferometric pairs formed and compared to assess \(|\gamma|\), with their corresponding \(B_{\perp}\) values. In this sense, it is important to note that  
Figure 8: Forested area, Corrientes site.  
Figure 7: Area common to the ascending and descending passes used to compare the interferometric coherence (highlighted in yellow). The zoom shows the forested pixels, region C, which is also indicated on Figure 1(a).  
the perpendicular baselines have high variability in these subsets of interferometric pairs, and in some cases its effect might compensate for an expected improvement of \(|\gamma|\) due to using a shorter temporal baseline. Figure 9 reflects this issue, where we have compared 8 and 16-day contemporary pairs whose \(B_{\perp}\) are very different. In all these cases, the effect of the perpendicular spatial baseline is determinant. To limit this dependency, Figure 10 presents the coherence for pairs 3 and 4 in Table 2 with the most similar \(B_{\perp}\) values of \(B_{\perp}\). In this case, decorrelation is mainly governed by temporal decorrelation. The detailed difference is analyzed in Section 4.4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Pair** & **Date 1** & **Date 2** & \(B_{\perp}\)**(m)** & **Btemp.** & \(Z_{2\pi}\) \\ \hline
1 & 12 November & 20 November & 1082 & 8 & 42.0 \\ \hline
2 & 12 November & 28 November & 1816 & 16 & 25.0 \\ \hline
3 & 20 November & 28 November & 734 & 8 & 61.9 \\ \hline
4 & 20 November & 6 December & 476 & 16 & 95.5 \\ \hline
5 & 28 November & 6 December & \(-1211\) & 8 & 37.5 \\ \hline
6 & 28 November & 14 December & \(-1917\) & 16 & 23.7 \\ \hline
7 & 6 December & 14 December & \(-706\) & 8 & 64.4 \\ \hline
8 & 6 December & 22 December & 725 & 16 & 62.7 \\ \hline
9 & 14 December & 22 December & 1431 & 8 & 31.8 \\ \hline
10 & 14 December & 30 December & 1829 & 16 & 24.9 \\ \hline \end{tabular}
\end{table}
Table 2: 8 and 16-day interferometric pairs used to compare \(|\gamma|\) formed with the SAO1-A and SAO1-B ascending images.  
Figure 9: \(|\gamma_{VV}|\) histograms for SAOCOM-1 8-day and 16-day pairs chosen from Table 2.