esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Gradients for Feature Assessment in Point Cloud-Based Data Sets
## 2 Theory
### Dataset SVGEO  
The dataset mainly used in this paper was recorded by Fraunhofer IPM to improve the accuracy of rainwater simulations; therefore, most of it is outdoor and on a very large scale. The data were recorded using a drone flying over the area in a grid and taking vertical pictures. These overlapping images were then used to reconstruct a point cloud using Agisoft Metashape. These point clouds of entire cities and rural areas with a resolution of 3 cm were then subdivided and annotated. Due to the nature of the reconstruction, there are significantly more artifacts and errors than comparative LiDAR-based point clouds. For instance, sharp corners were somewhat smoothed and flat surfaces can have slight ridges making the detection of some small classes such as curbs very challenging.  
Figure 5: Convergence of average pixel gradients (normalized) for Semantic3D and SVGEO, the difference in interpolations is due to the size of the dataset making it more impossible.  
As shown in Figure 6, vertical walls are almost always missing as they are not on any of the photos taken from a bird's eye view. However, when looking at the same section from the top, the point cloud looks almost flawless as seen in Figure 7.  
Figure 8 shows a typical artifact of point clouds generated using photogrammetry, the algorithm has difficulties matching points when lighting suddenly changes such as shadow edges, leading to substantial bumps in flat surfaces such as the roads as shown. This leads to incorrect geometries and causes confusion when relying on geometries as the model would have to gain an implicit understanding of this phenomenon to reach the correct prediction.
# Deep-Learning-Based Approach for IoT Attack and Malware Detection
## 2 Datasets
### CIC-IDS2017 Dataset  
The CIC-IDS2017 dataset, created by the Canadian Institute for Cybersecurity in 2017, is a comprehensive and up-to-date dataset for cybersecurity research [34]. This dataset captures real-world network traffic and common cyberattacks, making it a valuable resource for assessing the performance of security tools like Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). Spanning five days, the traffic includes only normal activity on Monday, with the remaining days featuring attacks such as Brute Force FTP/SSH, DoS, Heartbleed, Web Attacks, Infiltration, Botnet, and DDoS. These events occur during morning and afternoon sessions. The CIC-IDS2017 dataset provides labeled traffic data, including timestamps, IP addresses, ports, protocols, and attack types, in CSV format. Using the B-Profile system, it simulates background traffic from 25 users, involving protocols like HTTP, HTTPS, FTP, SSH, and email. With over 80 network flow features and a comprehensive topology, CIC-IDS2017 is an essential resource for evaluating IDS/IPS systems and advancing network security research (see Table 2).  
The CIC-IDS2017 dataset provides a comprehensive dataset for cybersecurity research. This dataset includes the BENIGN class, representing normal and benign network traffic, as well as various types of attacks. It encompasses multiple attack types, including DDoS attacks, PortScan, Botnet, Infiltration, web-based attacks (Brute Force, SQL Injection, and XSS), FTP brute force attacks (FTP-Patator), and SSH brute force attacks (SSH-Patator). DoS attacks are subdivided into categories such as GoldenEye, Hulk, Slowhttptest, and slowloris. Additionally, attacks exploiting the Heartbleed vulnerability are included in this dataset. This dataset serves as an ideal resource for evaluating and developing IDS and IPS systems, providing a robust and reliable reference point for network traffic analyses and attack detection.  
In processing the datasets, Z-normalization is employed to mitigate issues arising from the different scales of the data. This technique involves transforming the data such that each feature has a mean of 0 and a standard deviation of 1. Specifically, Z-normalization is performed by subtracting the mean of each column from each data point and dividing by the standard deviation of that column. This transformation is crucial as it standardizes the features, allowing the model to evaluate different features on the same scale.  
The equation for Z-normalization is as follows:  
\[\mathrm{z}=\frac{\mathrm{x}-\mathrm{\mu}}{\sigma} \tag{1}\]  
where \(x\) is the data point, \(\mathrm{\mu}\) is the column mean, and \(\sigma\) is the column standard deviation.  
By standardizing the data, Z-normalization enhances the model's ability to generalize across diverse datasets, as it ensures that all features contribute equally to the learning process. This preprocessing step is fundamental in maintaining the robustness and adaptability of the model when applied to new and varied datasets.
# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
###### Abstract  
It is a challenging task for self-driving vehicles in Real-World traffic scenarios to find a trade-off between the real-time performance and the high accuracy of the detection, recognition, and tracking in videos. This issue is addressed in this paper with an improved YOLOv3 (You Only Look Once) and a multi-object tracking algorithm (Deep-Sort). First, data augmentation is employed for small sample traffic signs to address the problem of an extremely unbalanced distribution of different samples in the dataset. Second, a new architecture of YOLOv3 is proposed to make it more suitable for detecting small targets. The detailed method is (1) removing the output feature map corresponding to the 32-times subsampling of the input image in the original YOLOv3 structure to reduce its computational costs and improve its real-time performances; (2) adding an output feature map of 4-times subsampling to improve its detection capability for the small traffic signs; (3) Deep-Sort is integrated into the detection method to improve the precision and robustness of multi-object detection, and the tracking ability in videos. Finally, our method demonstrated better detection capabilities, with respect to state-of-the-art approaches, which precision, recall and mAP is 91%, 90%, and 84.76% respectively.  
object detection; multi-object tracking; improved YOLOv3; deep learning; self-driving vehicles +
Footnote †: journal: Applied  
1
Footnote 1: [[https://doi.org/10.3390/app11073061](https://doi.org/10.3390/app11073061)]([https://doi.org/10.3390/app11073061](https://doi.org/10.3390/app11073061))
## 2 Materials and Methods
### Scenario Development
To facilitate a direct and concise comparison between the two proposed strategies, Table 9 summarizes the main characteristics of Scenario 1 and Scenario 2. The table highlights differences in traffic reduction intensity, green coverage, material treatments, and design approaches, offering a synthetic overview that supports the subsequent impact assessment. Figure 5: **(a,b)** Two different 3D perspectives of Scenario n. 1 simulated with ENVI-met. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Urban** & **Texture** & **Surface** & **Surfaces** & **Albedo** & **Emittance** \\
**Components** & **Texture** & **(m2)** & **Ratio (\%)** & **(\(\rho\))** & **(\(\varepsilon\))** \\ \hline \hline Draining & Green & 2450 & 37.8 & 0.50 & 0.90 \\ pavements & & Light grey & 4030 & 62.2 & 0.20 & 0.97 \\ Total & - & 6480 & 100 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Surface cover (%) treated with draining and photocatalytic pavements and their optical properties. Figure 6: Detailed 3D views of Scenario 2 simulated with ENVI-met: (**a**) draining pavement in a redesigned parking area; (**b**) tree rows along a main road; (**c**) upgraded pedestrian paths with photocatalytic paving. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Variable** & **Scenario 1** & **Scenario 2** \\ \hline Traffic reduction (\%) & 50\% & 30\% \\ Green Cover Ratio (GCR) & 8.8\% (vs. 8.0\% baseline) & 10.2\% (vs. 8.0\% baseline) \\ Vegetation type and & Sparse planting of small & High-density planting, \\ density & trees and shrubs & medium-large canopy trees \\ Material interventions & Minimal changes to paving & 6480 m\({}^{2}\) modified: 2450 m\({}^{2}\) \\  & materials & draining + 4030 m\({}^{2}\) \\  & & photocatalytic pavements \\ Pavement albedo (\(\rho\)) & Mostly unchanged & Up to 0.50 (training) and 0.20 (photocatalytic) \\ Main goals & Maximize air quality and & Compensate lower traffic \\  & thermal comfort via & reduction with material \\  & mobility reduction & and greening interventions \\  & Traffic calming measures: & Comprehensive \\ Intervention strategy & mini-roundabouts, raised & environmental design: \\  & crossings, limited greening & permeable, photocatalytic \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of the main variables and interventions in Scenario 1 and Scenario 2. Overall, the dual-scenario structure allows for a realistic comparison of urban mitigation strategies with varying levels of ambition and feasibility, providing practical insights for environmental planning in university campuses and urban areas located in warm-temperate climates. The results of these simulations form the basis for the impact assessment presented in Section 2.6.
## 3 Performance Evaluation Results and Discussion
### Performance Metrics  
This study assesses the effectiveness of routing protocols by analyzing their performance across four common network parameters, namely network average energy consumption \(E_{average}\), network average end-to-end delay \(Avdelay\), packet delivery rate \(PDR\), and network lifetime. \(E_{average}\) can be calculated as  
\[E_{average}=\frac{\sum\limits_{\begin{subarray}{c}j=1\\ j=1\end{subarray}}^{N_{jit}}E_{j}}{P_{r}} \tag{23}\]  
where \(E_{j}\) is the energy consumption of each participating node and \(N_{pit}\) is the number of nodes involved in the network transmission process.  
_Avdelay_ can be calculated as  
\[Avdelay=\frac{\sum\limits_{\begin{subarray}{c}i=1\\ i=1\end{subarray}}^{P_{r}}\left(r_{time}(i)-s_{time}(i)\right)}{P_{r}} \tag{24}\]  
where \(s_{time}(i)\) and \(r_{time}(i)\) are the times when the i-th data packet is sent from the source node and arrives at the sink node, respectively.  
\(PDR\) is calculated as  
\[PDR=\frac{P_{r}}{P_{s}} \tag{25}\]  
where \(P_{r}\) represents the number of successfully received data packets at the sink node and \(P_{s}\) denotes the number of packets transmitted by the source node.  
Finally, network lifetime is defined as the duration from the start of the simulation until the first node depletes its energy.
# Modeling and Analysis of High-Power Ti:sapphire Laser Amplifiers-A Review
## 3 Phase Distortion Analysis
### Thermal Birefringence
The distortion due to the thermal birefringence is shown in Figure 14, which differs markedly from that in a non-birefringent medium such as YAG. This is owing to the fact that the previously mentioned linear polarization loss is instantly resolved. The loss of linearly polarized light is small spatially, but the amount of loss is dispersed in time, adversely affecting the temporal contrast ratio. \[\lim_{\Delta z\to 0}A_{g}A_{c}A_{t} = \left[I+ \begin{pmatrix}\frac{g_{x}}{2}&0\\ 0&\frac{ky}{2}\end{pmatrix} \Delta z\right]\left[I+ \begin{pmatrix}i\frac{k_{c}} {2}&0\\ 0&-i\frac{k_{c}}{2}\end{pmatrix} \Delta z\right]R^{-1}\left[I+ \begin{pmatrix}i \frac{k_{c}}{2}&0\\ 0&-i\frac{k_{c}}{2}\end{pmatrix} \Delta z\right]R\] \[= I+\frac{1}{2} \begin{pmatrix}gx+ik_{c}+ik_{t}\cos\left(2\beta \right)&-ik_{c}-ik_{t}\sin\left(2\beta\right)\\ -ik_{c}-ik_{t}\sin\left(2\beta\right)&g_{y}-ik_{c}-ik_{t}\cos\left(2\beta \right)\end{pmatrix} \Delta z\]  
\[\frac{dE}{dz}=\frac{1}{2} \begin{pmatrix}g_{x}+ik_{c}+ik_{t}\cos\left(2\beta \right)&-ik_{c}-ik_{t}\sin\left(2\beta\right)\\ -ik_{c}-ik_{t}\sin\left(2\beta\right)&g_{y}-ik_{c}-ik_{t}\cos\left(2\beta \right)\end{pmatrix} E \tag{32}\]  
Figure 13: Forms of the piezo-optical tensors, \(\pi_{mn}\), of the isotropic, the cubic and the trigonal structure. The phase retardation of the medium to be introduced does not form the shape of a single concave lens or a single convex lens [22]. If we express this effect as a Zernike polynomial, we can see that it is convex in the vertical direction and concave in the horizontal direction. Specifically, it is confirmed that the retardation caused by the PE effect induced by the heat birefringence results in an astigmatism aberration of the medium. It is previously known that the thermal distortion induced by Ti:sapphire includes astigmatism [62; 63; 64]. Until quite recently, the cause of astigmatism is attributed to the anisotropy of the thermal conductivity of the medium. Based on our study, the phase retardation effect due to thermal birefringence can also cause astigmatism.
# Coaxial Fibres Incorporated with Phase Change Materials for Thermoregulation Applications
###### Abstract  
Nowadays, the growing concern about improving thermal comfort in different structures (textlies, buildings, and pavements, among others) has stimulated research into phase change materials (PCMs). The direct incorporation of PCMs into composite materials can cause mechanical impacts. Therefore, this study focuses on the design of phase change coaxial fibres (PCFs), using commercial cellulose acetate (CA) or recycled CA obtained from cotton fabrics (CAt) as the sheath and polyethylene glycol (PEG) 2000 as the core, via the wet spinning method; the fibres vary in molecular weight, concentration and ejection velocity. The fibres were assessed for their optical, chemical, thermal, and mechanical properties. The presence of PEG2000 is confirmed in the core of the fibres. Thermal analyses revealed a mass loss at high temperatures, attributable to the presence of PEG2000. Notably, the fibres with CA (Mn 30,000) showed superior thermal and mechanical performance. The melting point of PEG2000 incorporated into these PCFs coincided with the melting point of pure PEG2000 (about 55 \({}^{\circ}\)C), with a slight deviation, indicating that PCFs were obtained. Finally, the results point to the application of the fibres in civil engineering materials requiring a phase change between 50 and 60 \({}^{\circ}\)C, providing promising prospects for their use in applications requiring thermoregulatory properties.  
phase change fibres; core-shell fibres; cellulose acetate; polyethylene glycol 2000; recycled cellulose acetate; cooling technologies; wet spinning Article
# Automatic Modulation Recognition Using Compressive Cyclic Features
## 2 Statistical Characterization of Signal of Interest
Along with this formulation of feature vector, a hierarchical scheme is widely adopted by the AMR [8], with which lower-order CCs are used to make initial classification, followed by higher-order CCs to further refine the decision. The hierarchical scheme not only leads to a more perfect performance but also saves the computational cost of CC-based AMR.
# Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology
## 2 Medical Data Management Platform
### Blockchain and Medical Data Storage  
Ref. [28] first proposed the application of blockchain technology to the trusted management and sharing of medical data. They designed a blockchain-based medical information sharing scheme called MedRec based on Ethereum [29]. This scheme achieved decentralized integration of medical data across different medical organizations, allowing third-party users to access the data with permission. However, the PoW (proof of work) protocol used in MedRec has issues such as low throughput and high energy consumption. To overcome the limitations of block size and PoW consensus cost on large-scale medical data storage management, [PERSON]. [30] designed the MedBlock system to address the problem of patient medical records being scattered and difficult to integrate in different hospital databases. This system uses an improved consensus mechanism to achieve efficient medical record access and retrieval. MediChain [31] is based on Hyperledger Fabric and implements patient-centered medical data management. It uses off-chain storage for data assets to reduce blockchain storage pressure and allows users to access data through standard browsers and mobile applications. The approach of storing raw data off-chain has also been used in [32; 33]. [PERSON]. [34] developed a medical data sharing model called MedChain to enable flexible sharing of real-time data generated by sensors and monitoring devices, as well as efficient support for metadata updates. Ref. [35] proposed a distributed multi-level management solution for EMR (Electronic Medical Record) data storage security based on blockchain, utilizing smart contracts to support automatic management and sharing of EMRs, as well as patient control over data access permissions. Ref. [36] analyzed security, privacy, and efficiency issues in medical data management based on public and permissioned blockchains, and proposed a medical data sharing and privacy protection electronic health system called SPCChain. SPCChain uses special key-blocks and micro-blocks to store patient EMRs, achieved improved retrieval speed, and used a re-proxy encryption scheme to enable patient medical data sharing while protecting privacy.
# What's in Your Fruit Juice?--Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy
## 2 Materials and Methods  
### Materials and Reagents  
In this study, a total of 68 FJ samples were used, comprising 28 samples from single fruit types, including apple, orange, lemon, grapefruit, pomegranate, and cranberry, and 40 samples representing a blend of various fruit juices (Table A1). Samples were collected from grocery stores in Columbus, OH, USA. Both spectral collection and acid analysis (ascorbic and citric acids) were performed on the same day of the purchase to prevent possible spectral changes and ascorbic acid (vitamin C) loss. Subsequent analyses for soluble solids, sugars, and titratable acidity were carried out within the next two days from the purchase; until then, samples were stored at 4 \({}^{\circ}\)C to prevent compositional changes.  
The chemicals and reagents, including water, NaOH, H\({}_{2}\)SO\({}_{4}\), and tris(2-carboxyethyl) phosphine (TCEP), utilized in the experiments were sourced from Fisher Scientific (Fair Lawn, NJ, USA) and Sigma Aldrich (St. Louis, MO, USA). They were of either ACS or HPLC grade quality. Additionally, for the individual sugar analysis, glucose, fructose, and sucrose were in >99% purity, purchased from Fisher Scientific (Fair Lawn, NJ, USA). Ascorbic and citric acid standards were \(\geq\)99% pure (Sigma Aldrich, St Louis, MO, USA).
# Development of a Social DTN for Message Communication between SNS Group Members
## 3 How to Build a Social DTN
### DTN Routing Protocol
#### 3.4.1 Routing Message Type and Creation of a Routing Table  
Route establishment operation starts when the source node sends a route establishment request (rreQ_m) to the destination node. A relay node that has received this request creates a routing table entry with the destination node address (DA) in the message as the terminal node address (TNA), and the address of the adjacent node that forwarded this message as the relay node address (RNA). It then forwards the message. The destination node that has received the route establishment request (rreQ_m) creates a route establishment response (rreP_m) with the source node (SA) as its destination and sends it back to the adjacent node from which it has received the route establishment request. A node that has received this message completes the routing table with the source node address as the terminal node address, and the address of the adjacent node that has sent the message as the relay node address. Relay nodes repeat this routing table creation until the route is established up to the source node. After that, the source node sends data messages (dsnD_m), which contain text, image, etc., to the destination node. A relay node selects a relay node with an RNA whose TNA is identical to the DA in the received message (dsnD_m) as the relay node to which it will forward the message. This is repeated until the message reaches the destination node.  
The message types and message elements described above are shown in Table 4. The route establishment request ID is a message used to indicate which route establishment request is new. Its initial value is \"1\". It is incremented by one each time the source node sends a route establishment request. The MAC address of each terminal is used as the DA and SA for that terminal. The structure of a routing table is shown in Table 5.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Element** & **Type** & **rEQ_m** & **rreP_m** & **dsnD_m** \\ \hline Message type m\_type (1 byte) & Yes & Yes & Yes \\ Route establishment request ID (8 bytes) & Yes & Yes & Yes \\ Destination node address DA (17 bytes) & Yes & Yes & Yes \\ Source node address SA (17 bytes) & Yes & Yes & Yes \\ Transmitted data (n bytes) & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Message types and elements route establishment request (rreQ_m); route establishment response (rreP_m); data messages (dsnD_m).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Element** & **Remarks** \\ \hline Terminal node address (TNA) & DA and SA, which are elements in each message \\ \hline Relay node address (RNA) & The address of the adjacent node that has received \\ \hline \hline \end{tabular}
\end{table}
Table 5: Structure of a routing table.
## 2 Related Work
### NIR Colorization  
Recently, scholars have proposed some deep learning methods for NIR image colorization. [PERSON] et al. [5] used deep multi-scale convolutional neural networks combined with joint bilateral filter to transfer RGB color spectrum to NIR images. This approach fails to colorize objects correctly, where object appearance and color do not correlate. [PERSON] et al. [27] used a generative adversarial network (GAN) architecture model to colorize NIR images and later added triple loss to optimize colorization performance [28]. Both methods proposed by [PERSON] lost a lot of texture information and details in the generated results. Moreover, they are trained and tested via image patches, which is not suitable for large-scale images.  
U-Net is widely used in the field of NIR image colorization due to its ability to effectively preserve low-level and high-resolution features. UNet-based colorization networks mainly include: CNN-based colorization network and CycleGAN-based colorization network. CNN-based colorization network uses UNet as the core of its network structure. [PERSON] et al. [6] colorized NIR images using an end-to-end network S-Net based on UNet followed with a shallow codec. [PERSON] et al. [29] added a variational auto-encoder (VAE) to UNet. CycleGAN-based colorization network used the UNet as a generator to colorize NIR images. [PERSON] et al. [30] used UNet as the generator of CycleGAN [31] that requires less computation time and converges faster. [PERSON] et al. [32] used UNet with cross-scale dense connections in both generators to increase the learning capacity for high-level semantic information. However, [PERSON] et al. [10] thought that the colorization according to context is a high-level semantic work and the standard UNet is not enough to model this problem, so they added a ResNet block in the UNet architecture to enlarge its capacity. In addition, [PERSON] et al. [8] used ResNet as the generator to increase their performances at a lower computational cost. These studies reveal that UNet combined with ResNet is feasible for NIR image colorization and ResNet can improve the performances at a lower computational cost.