esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Discussion
### The 3D Point Cloud Reconstruction in Forest Area  
During the 3D point cloud reconstruction process, the lidar data are first projected to generate a sparse point cloud according to Figure 5a, and then the sparse point cloud is densified into a 3D point cloud map, as shown in Figure 5b. When the system returns to the starting point, a loop is formed based on key frame detection. As shown in Figure 5c, the 3D point cloud of the forest area realizes loop correction and reduces the cumulative error. We generate a globally consistent 3D point cloud map, where features such as ground, fences, and trees can be clearly seen.  
Figure 4: The 3D point cloud reconstruction system.  
In order to analyze the robustness and availability of the reconstruction method proposed in this paper with a single-line lidar, this paper collects data from forest areas with undulating terrain and a messy arrangement of trees, and a 3D point cloud map is obtained. The reconstruction map is shown in Figure 6. It can be seen that, in the area with rich environmental features, the 3D point cloud is well preserved and tree reconstruction is more intensive. In the area with sparse environmental features, localization also has good convergence, and the 3D point cloud can still accurately reflect environmental information. Bushes, trees, branches, leaves, and paths all achieve a good reconstruction effect.
# Automated Gluten Detection in Bread Images Using Convolutional Neural Networks
## 4 Results
### Instagram Bread Dataset  
To determine the best approach for utilizing the Instagram dataset, we considered whether training solely on Instagram images could lead to a model capable of generalizing to Pinterest images, or whether a model trained on Pinterest could accurately classify Instagram images.  
We initially trained a model exclusively on the Instagram dataset. The results appeared promising, achieving an accuracy of 83%, precision of 87%, recall of 84%, and F1-score of 83% (see Table 3). However, due to the small number of training samples, the model failed to generalize when tested on Pinterest images, leading to significantly lower performance. While the model demonstrated strong performance on the same dataset it was trained on, its inability to adapt to a different dataset indicated that training on such a limited dataset was insufficient for robust classification.  
Given these findings, we fine-tuned the existing ResNet50V2 model with additional training samples from the Instagram dataset. For this process, 80% of the dataset was used for training, while the remaining 20% was reserved for validation and testing (10% each). Fine-tuning the model on this larger dataset resulted in a modest but meaningful improvement, achieving an accuracy of 78%, precision of 80%, recall of 77%, and F1-score of 77% (see Table 3). While slightly lower than the Instagram-only model in terms of accuracy, the combined training provided a more balanced and generalized model, reducing the risk of overfitting to a small dataset and improving the overall adaptability.  
These results highlight the importance of dataset diversity in deep learning-based classification tasks, especially when dealing with images from different platforms. Training on a limited dataset, even if it achieves high accuracy within its domain, does not necessarily translate into generalization. By integrating multiple data sources, the model effectively learned from diverse distributions, resulting in improved robustness in real-world classification tasks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline VGG19 & 76\% & 80\% & 71\% & 75\% \\ Inception-V3 & 68\% & 65\% & 76\% & 70\% \\ InceptionResNetV2 & 71\% & 77\% & 59\% & 67\% \\ NASNetLarge & 53\% & 52\% & 71\% & 60\% \\
**ResNet50V2** & **77\%** & **79\%** & **77\%** & **77\%** \\ EfficientNetV2L & 50\% & 50\% & 94\% & 65\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testing results on the Pinterest bread dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Training Dataset** & **Records** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline ResNet50V2 & Pinterest & 410 & 77\% & 79\% & 77\% & 77\% \\ ResNet50V2 & Instagram & 66 & 83\% & 87\% & 84\% & 83\% \\ ResNet50V2 & Pinterest + Instagram & 476 & **78\%** & **80\%** & **77\%** & **77\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Instagram bread dataset performance.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 1 Introduction
### Research Contribution  
RPL protocol security has been extensively studied because of the innumerable security threats to resource-constrained devices. The RPL rank and version number attacks, which are two types of RPL attacks, can have critical consequences for RPL networks. A rank attack affects the network performance, low Packet Delivery Ratio (PDR), and the delay and generation of non-optimal paths and loops. A version number attack affects the network performance due to increased overhead control, a low delivery of packet ratio, and a high end-to-end delay. The studies conducted on these attacks have several flaws, such as the following:  
* several security defects and shortcomings regarding network performance and accuracy;
* multiple attacks in RPL networks are not supported;
* multiple RPL network topologies are not supported;
* they do not detect and mitigate the effects of both attacks in the RPL network; and
* they do not isolate the malicious nodes from legitimate nodes in the RPL network.  
Therefore, there is a requirement for further research to handle the declared security problems for RPL routing protocols in IoT. Accordingly, this research work will extend our published previouswork [12] that investigated in detail the existing research literature and research gaps of RPL attacks, concentrating on the rank attack and the version number attack. In this work, we propose a Secure RPL Routing Protocol (SRPL-RP), and in addition, our research will be based on the work presented in [13; 14] by addressing their existing issues. The main contribution of this research is a combination of the research work in [13; 14] and the addition of new features as follows:  
* the addition of a timestamp threshold to verify the legitimacy of the sender nodes;
* a monitoring table that captures all information of the nodes in the RPL network topology;
* detection of both rank attack and version number attacks based on a comparison of ranks strategy;
* mitigation of the effects of both rank attacks and version number attacks by updating a monitoring table;
* isolation of both rank attacks and version number attacks by using a blacklist table to prevent malicious nodes from joining the RPL network and alerting other nodes to skip them; and
* the provision of multiple types of attacks (rank and version number attacks) in RPL networks, and support for different types of RPL network topologies.
## 2 Materials and Methods
### HPLC Determination of Individual Phenolic Compounds  
The individual polyphenolic constituents were determined by HPLC. Separation was performed on a reversed phase Waters Nova-Pak C18 (150 \(\times\) 3.9 mm, 4 \(\upmu\)m) column with a 20 \(\upmu\)L injection volume at a flow rate of 1 mL/min. Identification was achieved by comparing the retention times of the detected peaks with those of standard compounds, and by UV/VIS spectral data. All analyses were performed in triplicate. The chromatographic conditions are described in [PERSON] et al. [31].  
Determination of Total Phenols (TP), Flavanols (TF), Hydroxycinnamic Contents (HC), Free Sulfhydryl Groups, and Antioxidant Activity (AA)  
Total polyphenol concentration was determined with the Folin-Ciocalteau assay, with the microscale protocol previously reported [32]. Results were expressed as mg/L gallic acid equivalents (GAE). For the determination of the total flavanols, the p-dimethylaminocinnamaldehyde (DMACA) method was employed [33]. The mixture appropriately diluted in methanol wine and DMACA (0.1% in 1N HCl in methanol) was left to react for 10 min, and the absorbance at 640 nm was then measured against a blank without DMACA. The results are expressed as (+)-catechin equivalents (mg/L (+)-catechin). The absorbance at 320 nm was used to estimate the total hydroxycinnamic acid content [33]. The results are expressed as caffeic acid equivalents (mg/L). [PERSON]'s method was adapted to wine samples for the determination of total -SH groups [34]. Free-SH groups were determined after the addition of 5,5/-dithiobis (2-nitrobenzoic acid) (DTNB) which reacted with free thiols to form disulphide and 2-nitro-5-thiobenzoic acid. The results are expressed as glutathione equivalents (mg/L). For the determination of antiradical activity, assays were performed employing a DPPH stable radical [35]. The results are expressed as Trolox equivalents (TE mg/L).
# Effect of Surface Roughness on Aerodynamic Loads of Bluff Body in Vicinity of Smoothed Moving Wall
## 2 Mathematical Formulation  
### Flow Model  
The Discrete Vortex Method in its conventional form (disregarding the turbulence in the flow) is widely employed for solving two-dimensional, incompressible, unsteady flow problems around submerged bodies in a fluid domain [37]. To consider turbulence in the flow, the incompressible Navier-Stokes equations are filtered, resulting in the following transport equations:  
\[\frac{\partial\overline{\mathrm{u}}_{\mathrm{i}}}{\partial\mathrm{x}_{ \mathrm{i}}}=0 \tag{1}\]  
\[\frac{\partial\,\overline{\mathrm{u}}_{\mathrm{i}}}{\partial\mathrm{t}}+ \frac{\partial\left(\,\overline{\mathrm{u}}_{\mathrm{i}}\,\overline{\mathrm{ u}}_{\mathrm{j}}\right)}{\partial\mathrm{x}_{\mathrm{j}}}=-\frac{1}{\rho}\frac{ \partial\overline{\mathrm{p}}^{*}}{\partial\mathrm{x}_{\mathrm{i}}}+\frac{ \partial}{\partial\mathrm{x}_{\mathrm{j}}}\Bigg{[}(\
u+\
u_{\mathrm{t}}) \Bigg{(}\frac{\partial\overline{\mathrm{u}}_{\mathrm{i}}}{\partial\mathrm{x}_ {\mathrm{j}}}+\frac{\partial\overline{\mathrm{u}}_{\mathrm{j}}}{\partial \mathrm{x}_{\mathrm{i}}}\Bigg{)}\Bigg{]} \tag{2}\]  
where \(\overline{\mathrm{p}}^{*}\) is the pressure modified by turbulent kinetic energy, \(\
u\) is the kinematic viscosity, and \(\
u_{\mathrm{t}}\) is the eddy viscosity.  
Mathematically, the vorticity is related to the curl of the velocity field. Physically, it is generated from a solid boundary due to fluid movement in the vicinity of the wall linked with the no-slip condition (viscous effects). The well-known vorticity transport equation in its two-dimensional dimensionless form is [11; 15; 37]  
\[\frac{\partial\overline{\omega}}{\partial\mathrm{t}}+(\overline{\mathrm{u}} \cdot\
abla)\overline{\omega}=\bigg{(}\frac{1}{\mathrm{Re}}+\
u_{\mathrm{t}} \bigg{)}\
abla^{2}\overline{\omega} \tag{3}\]  
The equations for, separately, solving the problems of advection and diffusion vorticity are given by the viscous splitting technique [51], which is equivalent to solving Equation (2). The equations are as follows:  
\[\frac{\partial\overline{\omega}}{\partial\mathrm{t}}+(\overline{\mathrm{u}} \cdot\
abla)\overline{\omega}=0 \tag{4}\]  
\[\frac{\partial\overline{\omega}}{\partial\mathrm{t}}=\bigg{(}\frac{1}{ \mathrm{Re}}+\
u_{\mathrm{t}}\bigg{)}\
abla^{2}\overline{\omega} \tag{5}\]
# Biochemical Characterization of Some Plum Cultivars Available on the Romanian Market
## 2 Materials and Methods
### Determination of Phenolic Compounds  
The concentration of total phenolic compounds was determined by reacting 1 mL of each ethanolic extract with 5 mL 10% Folin-Ciocalteu reagent and 4 mL 7.5% sodium bicarbonate for 30 min. The spectrophotometric absorbance was read using an ONDA UV-21 spectrophotometer (ONDA Spectrophotometers Service, Carpi, Italy), at 765 nm, against a control sample and using a calibration curve made for standard gallic acid solutions [16].  
Flavonoids were determined by using the same method after precipitating flavonoid compounds using 32% hydrochloric acid and 37% formaldehyde (extract-hydrochloric acid-formaldehyde ratio of 2:2:1). After this stage, the resulting solution was subjected to the Folin-Ciocalteu reaction, as described above. The concentration was determined by subtracting the resulting amount of non-flavonoid phenolic compounds from the total phenolic inventory, determined as stated above [17].  
Tannins were determined in a similar manner after precipitation in an acidified gelatin and sodium chloride solution (1 mL ethanolic extract in 2 mL distilled water, 2.5 mL 25% gelatin solution, 2.5 mL acidified 1% NaCl solution). The resulting solutions were subjected to the Folin-Ciocalteu reaction, allowing the determination of non-tannin phenolic compounds. These were subtracted from the total phenolic content to determine the total tannin concentration [17].  
Among flavonoids, flavanols (catechins and related compounds) were determined by diluting the fruit ethanolic extracts in 70% ethanol (1 mL extract in 9 mL ethanol) and reading the absorbance at 280 nm, with absorption values converted to concentrations according to equations found in the literature [18]. Anthocyanins were assessed by 10-fold dilution in 70% ethanol and reading the absorbance at 520 and 700 nm; the concentrations were calculated according to the literature [19].  
The phenolic acid content (both hydroxybenzoic and hydroxycinnamic acids) was determined by 10-fold dilution of the fruit extracts in an ethanol-hydrochloric acid mixture (8:2), reading the absorbance at 220, 275, 325, 345 and 380 nm and calculating concentrations according to the literature (values expressed as gallic acid and ferulic acid equivalents, respectively) [20].  
Stilbenes (resveratrol and related compounds) were determined by 10-fold dilution of the fruit extracts in 70% ethanol and reading the absorbance at 304 nm (concentrations expressed as resveratrol equivalents) [21]. Coumarins were determined by dilution in 80% methanol and reading the absorbance at 275 nm [22].
# A Set of Tools and Data Management Framework for the IoT-Edge-Cloud Continuum
## 3 Data Management Framework
### Robust Data Flow Analysis and Advanced Visualization for Deployment and Monitoring  
Data flow management and analysis focus on error handling by examining how data move and change to identify potential data integrity issues. The top tools offer visual representations of the data paths for debugging and understanding complex data interactions. Visual analysis also helps optimize data handling, storage, and maintenance. Data flow analysis tools identify vulnerabilities by detecting patterns leading to security breaches, like inadequate data validation and insecure transmission. The tool can be complemented with the following improvements:  
* Apache NiFi: Designed for automating the movement of data between disparate systems, Apache NiFi supports robust data flow management ([[https://nifi.apache.org/](https://nifi.apache.org/)]([https://nifi.apache.org/](https://nifi.apache.org/)) accessed on 17 October 2024). By incorporating Apache NiFi, the proposed system can automate complex data flow operations across IoT, Edge, and Cloud layers, ensuring smooth, secure, and efficient data transmission. It can be effectively used for deploying configuration updates.
* Node RED: Primarily a data flow management tool that can be used for some forms of flow visualization ([[https://nodered.org/](https://nodered.org/)]([https://nodered.org/](https://nodered.org/)) accessed on 17 October 2024). Node RED can extend the utility beyond basic IoT scenarios by providing more complex integration and data flow scenarios tailored for use in industrial applications. It can wire together hardware devices, APIs, and online services in new and interesting ways using browser-based data flow editor.
* Prometheus: While primarily used for monitoring and alerting, Prometheus supports the backend visualization processes and collects and stores metrics, enhancing data flow visibility ([[https://prometheus.io/](https://prometheus.io/)]([https://prometheus.io/](https://prometheus.io/)) accessed on 17 October 2024). This monitoring system and time series database is excellent for recording real-time metrics in a time  
Figure 3: DMF configuration panel.  
series database built using an HTTP pull model. Prometheus integrates Grafana and provides multiple modes for visualizing data. This integration would allow for proactive system management and anomaly detection, further securing the data flow across diverse network environments.  
For the Data Management Configurator, the Docker compose file docker-compose.yml specifies services such as MQTT broker with plugins, plugin manager, and forwarders, binding these services to necessary ports and mounting required volumes for persistent storage.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 2 Snapshot Receivers and Beamforming  
A snapshot receiver operates with a few milliseconds of raw data obtained by the analog-to-digital converter (ADC) output following the RF front-end [15]. This raw data snapshot is sent to a server for processing. As there are limited data available, the server can only achieve signal acquisition: there are not enough data available for a tracking process, nor sufficient data to obtain any information from the navigation message. Since the ephemeris cannot be decoded, this information is obtained from a secondary source. A direct pseudorange cannot be derived from the acquisition results since the transmission time of the satellite is not available. However, given a rough receiver position and time estimate together with the ephemeris data, pseudorange reconstruction can be achieved [23]. Once the PVT has been calculated and verified, the result might be sent back to the receiver, depending on the architecture and needs of the receiver.
## 4 Results and Discussion
Furthermore, in order to predict the beam quality of the new-proposed laser diode, the far-filed radiation profiles are also simulated. The vertical divergence angles are only 40\({}^{\circ}\) as shown in Figure 6, which is better than most of these devices. Figure 4: Radiative recombination rate of various InGaAsSb/AlGaAsSb 3 QWS LDs at 400 mA. Figure 5: Simulated P-I characteristics of various InGaAsSb/AlGaAsSb 3 QW LDs. Figure 6: Simulated vertical angle divergence of 2 \(\upmu\)m InGaAsSb/AlGaAsSb LDs with CBL. Based on our previous experience and research for this work [11; 12], the 2 \(\upmu\)m InGaAsSb/AlGaAsSb samples without and with CBL are fabricated by using solid source molecular beam epitaxy(MBE) technology to further text these predictions. After growth, the Ti/Pt/Au is deposited as the P-electrode, the wafer is thinned to about 90 \(\upmu\)m in thickness, and then deposited with AuGeNi/Au bottom contact and alloyed. All the wafer is cleaved into Fabry-Perot laser chips with cavity length of 800 \(\upmu\)m by conventional steps. The structures are mounted epi-side down onto copper heat sinks, and the facets are kept uncoated. The CW output power and operation voltage characteristics versus current of the various devices are illustrated in Figure 7. It can be seen that the optimized 2 \(\upmu\)m InGaAsSb/AlGaAsSb LD with CBL exhibited the threshold current density is as low as 142 A/cm\({}^{2}\) and \(\eta_{d}\) is 0.158 W/A, while for the conventional devices, the threshold current density of the fabricated is 215 A/cm\({}^{2}\) and \(\eta_{d}\) is 0.122 W/A. The experimental results are basically in agreement with the theoretical results, the small difference comes from the unavoidable technological and processing error.
# Fish Body Pattern Style Transfer Based on Wavelet Transformation and Gated Attention
## 3 Proposed System
### Wavelet-Gated Styling Network
#### 3.2.2 Wavelet-Based Downsampling  
Traditional convolutional downsampling methods utilize stride-based operations to compress feature maps spatially, often causing high-frequency detail loss and local feature confusion [25]. To mitigate this issue, we integrate a multi-resolution feature extraction module based on discrete wavelet transform (DWT). It consists of a lossless feature encoding block and a feature representation learning block. In the network encoder, two parallel branches are used to process low-frequency and high-frequency information, respectively, and then channel-level splicing or weighted summation is performed in the fusion module. WGSNet employs DWT in its high-level semantic layers, replacing traditional downsampling while removing redundant instance normalization layers. In standard TransformerNet architectures, the conv2 and conv3 layers use a stride of 2 to reduce the size of feature maps. Instead, in the conv1 layer, reflection padding is applied to maintain the image size, followed by a 9 \(\times\) 9 convolution with stride 1, yielding a 32 \(\times\) H \(\times\) W feature tensor. We replace the conv2 and conv3 downsampling operations with Haar wavelet downsampling [26], ensuring that spatial dimension information is encoded into channel dimensions to reduce information loss.  
In the first wavelet module (DWT1), the input feature map \(P\) is decomposed into a low-frequency approximation component (LL) and high-frequency components (LH, HL, HH), as shown in Figure 6. The output of this module consists of 64 channels. Similarly, the second wavelet module (DWT2) transforms 64 input channels into 128 output channels, effectively preserving both local and global details.  
The low-frequency component (LL) is generated by the LDF through progressive decomposition along the row and column directions of the image, preserving the global structure and smooth information. The high-frequency components (LH, HL, and HH) are extracted by cross-combinations of HDF and LDF, corresponding to horizontal edges (LH), vertical edges (HL), and diagonal details (HH), respectively.  
The decomposed four-channel features are fused into \(Y\) via a channel concatenation operation, followed by a \(1\times 1\) convolution layer for channel dimension compression. This process is described in the following Equation (3), where \(Y\in\mathbb{R}^{B\times\mathbb{C}\times\frac{H}{2}\times\frac{W}{2}}\):  
\[\begin{split}& Y=Concat(Y_{LL},Y_{HL},Y_{LH},Y_{HH}),\\ & Y_{out}=Conv_{1\times 1}(Y)\end{split} \tag{3}\]  
Since low-frequency components contain global statistical information, applying direct channel normalization can reduce style leakage [23]. Thus, we integrate batch normalization and activation functions within the DWT process, eliminating instance normalization layers previously used in conv2 and conv3.  
Figure 6: Illustration of the wavelet transform method. HDF represents a high-pass decomposition filter; LDF represents a low-pass decomposition filter.
## 3 Proposed System
### Loop Closure Detection  
After the feature regions have been extracted from a new image and used to update the vocabulary CF tree, the loop closure detection phase can begin. First, a set of matching candidates for the new image can be obtained from the inverse index. Then, these images are compared and grouped for similarity based on the weight computed from the vocabulary CF tree. Finally, the best loop closure candidate is selected based on the similarity score.
## 1 Introduction
However, they require several seconds or even several minutes to acquire an image [25]: hence, they cannot capture the dynamic displacements. They can be only used for long-term static displacement monitoring of, e.g., glaciers [28], landslides [29; 30], dams [14; 31; 32], etc. In this paper, the commercial real-aperture radar (RAR) interferometric sensor IBIS-S is used for measuring dynamic response of a multitrack steel truss railway bridge in both longitudinal and lateral directions. The dynamic responses under different loading conditions, including wind, low-speed passing metro vehicles, and high-speed passing trains (up to 200 km/h) are investigated. Radar measurements are carefully analyzed with the help of lidar data, as well as considering the bridge structure properties. The research reveals displacement details of the multitrack bridge that were never highlighted before.