esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Image Transmission Channel and Quality Evaluation
MAE and MSE can reveal the pixel error between the original and final image. Besides, the PSNR can be defined as the following equation:  
\[PSNR=10 lg\frac{(L-1)^{2}}{MSE}, \tag{5}\]  
Figure 1: Structure of image transmission channel based on VLRC. where \(L\) is the maximum gray level of the image. For 8-bit images of 256 gray level, the value of \(L\) is considered as 256. The larger the PSNR value, the lower the signal loss rate during the signal transmission, the higher the image quality. Besides, as an image quality evaluation model for structural information measurement, the SSIM model can deal with the structure information of the image from three aspects: the brightness, contrast, and structure information of the image, and then synthesizes these three parameters to form the image quality evaluation index. The calculation formula is listed as follows:  
\[SSIM(I,I^{\prime})=\ l(I,I^{\prime})\times c(I,I^{\prime})\times s(I,I^{\prime}), \tag{6}\]  
\[l(I,I^{\prime})=\ \frac{2\mu_{I}\mu_{I^{\prime}}+K_{1}}{\mu_{I}^{2}+\mu_{I^{ \prime}}^{2}+K_{1}}, \tag{7}\]  
\[c(I,I^{\prime})=\ \frac{2\sigma_{I}\sigma_{I^{\prime}}+K_{2}}{\sigma_{I}^{2}+ \sigma_{I^{\prime}}^{2}+K_{2}}, \tag{8}\]  
\[s(I,I^{\prime})=\ \frac{\sigma_{I^{\prime}}+K_{3}}{\sigma_{I}\sigma_{I^{ \prime}}+K_{3}}, \tag{9}\]  
where \(l(I,I^{\prime})\), \(c(I,I^{\prime})\), and \(s(I,I^{\prime})\) refer to the function of brightness, contrast, and structure information respectively, \(\mu_{I}\) and \(\mu_{I^{\prime}}\) denote the average brightness of the original image and the received image, \(\sigma_{I}\), \(\sigma_{I^{\prime}}\) and \(\sigma_{I^{\prime}}\) indicate the brightness standard deviation and covariance of the original and received image, respectively, and \(K_{1},\ K_{2}\) and \(K_{3}\) are the constants. Usually, the larger the SSIM value, the higher the quality of the final image. This also means that the final image is more similar to the original image. In this article, the experiments on image transmission performance and image quality are based on these four evaluation models [25].
# Separation of Iron Oxide from Harbor Waste Using Selective Flocculation
## 2 Materials and Methods
### Methods  
#### 2.2.1 Theoretical Principles of Selective Flocculation  
Selective flocculation is a cornerstone in the field of wastewater treatment, focusing primarily on the aggregation of fine particles into sedimentable masses. This process is particularly important when it comes to separating specific particles from complex suspensions, leveraging the intrinsic properties of the particles and the strategic use of coagulation additives [7,8].  
Flocculation involves the addition of chemical agents, such as coagulants and flocculants, which neutralize the charges on the surfaces of the colloidal particles, facilitating their aggregation into flocs. Coagulants, usually metal salts such as aluminum sulfate and ferric chloride, reduce the electrostatic repulsion between particles. Flocculants, often organic polymers, enhance the formation of bonds between particles, thereby increasing the size and weight of flocs to accelerate sedimentation [9,10].  
Selective flocculation is a chemical process used to separate and remove specific particles from a colloidal suspension, such as minerals or impurities, without affecting other particles present. This process is based on the use of chemical additives (calledflocculants or coagulants) that selectively interact with certain particles, causing them to agglomerate (form flocs) and settle, thus allowing them to separate from the rest of the suspension.  
Selective flocculation uses specific additives designed to interact exclusively with targeted particle types, allowing for their efficient separation from mixed suspensions. The selection of suitable coagulants and flocculants depends on the nature of the particles, the slurry concentration, and the operating conditions such as pH and temperature. The pH adjustments and the use of complexing agents can further optimize the efficiency of selective flocculation [11; 12].  
In the field of industrial and municipal water treatment, selective flocculation is vital for efficient separation of specific particles, improvement of treated water quality, and reduction in operational costs. Control over process parameters, including mixing speed and reaction time, is crucial to achieving efficient and selective flocculation results [13; 14; 15].
# Blockchain Applications in Agriculture: A Scoping Review
## 4 Discussion
### Summary of Evidence
#### 4.1.3 Solutions Maturity  
Another research question that we explored is the maturity level of the solutions (RQ2). One of the main findings was that blockchain applications in the agricultural sector are at a relatively early stage of maturity. More than half of the works (55%) describe the architecture, have conducted some simulations, or have been partially experimental (not in the blockchain). A total of 18% of the papers have conducted experiments to test the functionalities of the blockchain, such as its connection to IoT devices and cost issues, while 13% have made a proof of concept of the proposed solution. According to our results, only 7% are at the level of evaluation, 3% at the level of prototyping, and 4% at the level of piloting the solution.  
The evaluation of the proposed solutions is achieved using datasets from companies and IoT devices [80,88,89], data created artificially [57,90], and real-world data from the agricultural sector [59,70]. There are also applications in our findings where evaluation is limited to laboratory tests or simulations. Based on our results, we only found three prototype applications [91,92,93]. All these prototype applications were published from 2020 onward, showing us that maturity is now growing, and real applications are being created. In addition, we identified four solutions in a pilot phase that have been installed, tested, and used in real conditions. The first application [94] is a pilot, mainly in Nigeria, with the aim of renting tractors for agricultural work. In the works proposed by [PERSON] et al. [79] and [PERSON] et al. [95], the main focus was on the traceability of products, and they have been applied in factories in China. These three applications have been created using Hyperledger Fabric. Finally, another research work [96] uses the IOTA Tangle network to record the data from IoT devices and is in a pilot application in three farms in Greece. Interestingly, no application that uses Ethereum as blockchain technology is yet at this maturity level.  
As the technology matures and more industrial applications emerge, real-world pilot demonstrations, such as the above, will help shape the field of more mature applications and reveal the most appropriate blockchain applications in the agricultural sector.
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### How Do Researchers Combine Audio Features with Other Data Sources? (RQ3)
This integration is not merely additive but complementary: while audio may capture intonation or turn-taking, video can provide facial expressions and body posture, and log data can contextualize student actions and engagement. Machine learning models, particularly those based on deep learning, have enabled the joint modeling of such heterogeneous signals [78]. In our review, we found that nearly one-third of the analyzed studies adopt a multimodal approach, most commonly combining audio with video features. Other studies also incorporate contextual, environmental, biological, or academic performance data. Figure 7 shows the proportion of multimodal studies, while Table 4 summarizes the frequency and function of each data type across the reviewed literature. It is important to note that not every educational task requires multimodal data. In many cases, audio alone suffices to answer the research questions, for example, identifying teacher question types or measuring talk-time distributions. Adding video, physiological signals, or logs could enrich the analysis but also raise the cost and complexity of data collection (e.g., camera setup, synchronization, and classroom consent) and processing. Therefore, although multimodal pipelines are on the rise thanks to modern deep-learning models, a majority of studies remain unimodal because their core goals can be met with audio-only features, which are faster, cheaper, and easier to obtain in real-world school settings.
# A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
## 3 Materials and Methods
### Key Protection Scheme  
The permutation key \(k\) is the essential component to ensure the security of the watermarking process. To protect k, a hybrid asymmetric cryptography scheme based on ECC and AES has been implemented. ECC is an efficient type of public key cryptography. Its security is based on the difficulty of solving discrete logarithms on a field defined by specific equations computed over an elliptic curve. AES is a symmetric block cipher standardized by NIST with a fixed block size of 128 bits and a key size of 128, 192, or 256 bits. AES is the standard algorithm for symmetric encryption because it is very fast and secure. The disadvantage of AES is that the same key is used to cipher and decipher the data, and then the key represents a severe vulnerability. The proposed hybrid asymmetric cryptography scheme has been implemented to ensure a safe AES key exchange.  
ECC generates an ECC public + private key pair for the message recipient and calculates the shared secret key for AES symmetric encryption and decryption. The shared secret key is an Elliptic Curve (EC) point, so it is then transformed into a 256-bit AES secret key. The EC points have the following property:  
\[\left(a\text{ }*\text{ }G\right)\text{ }*\text{ }b=\left(b\text{ }*\text{ }G\right)\text{ }*\text{ }a\]  
where a represents the private key, \(a\text{ }*\text{ }G\) represents the public key, \(b\) represents the cipher private key, and \(b\text{ }*\text{ }G\) represents the cipher public key. During the encryption process, the shared secret key is computed according to Equation (9).  
\[\text{ }sharedKey=\text{cipherPrivateKey }*\text{ }publicKey \tag{9}\]  
Additionally, the cipher public key is encapsulated in the encrypted message and will be used to recover the shared key during the decryption. Finally, the permutation key \(k\) is encrypted by using the shared secret key through the AES algorithm. The shared secret key is not directly sent to the receiver to decipher \(k\) but it is obtained from the private key  
Figure 4: Watermarking process.  
that is owned exclusively by the recipient. In the decryption phase, the shared secret key is computed by the private key and the cipher public key according to Equation (10).  
\[sharedKey=cipherPublicKey\ *\ privateKey \tag{10}\]  
The shared key is used to decrypt the permutation key \(k\). Figure 5 shows the hybrid asymmetric encryption and decryption schemes, respectively.
# Study of Marine Particles Using Submersible Digital Holographic Camera during the Arctic Expedition
## 2 Methods and Equipment
### DHC Engineering
* Weight--23 kg;
* Overall dimensions (D \(\times\) H \(\times\) W)--581 \(\times\) 290.5 \(\times\) 450 mm;
* Variable volume investigated during one exposure--0.2-0.75 L;
* Allowable hydrostatic pressure:
* Without recalibration--50 A;
* With built-in calibration--100 A;
* Mako G-507 CMOS camera (manufactured by Allied Vision [39]);
* Sony DMX264 matrix;
* Matrix size--2464 (H) \(\times\) 2056 (V);
* Pixel size--3.45 \(\upmu\)m \(\times\) 3.45 \(\upmu\)m;
* Wavelength of the laser-diode fiber module;
* For hologram recording--0.66 \(\upmu\)m;
* For illumination and excitation of the phototropic response--0.52 \(\upmu\)m;
* Size of measured particles--from 0.1 to 28 mm;
* Sinking speed during vertical probing--0.1-1.0 ppm;
* Discreteness of counts when forming a depth profile--1 m;
* Hologram Ethernet channel speed--1 Gb/s. Note that the sinking depth of the equipment is determined based on the following points:  
* Water depth;
* Limit constraints associated with design features intended for a hydrostatic pressure of 100 A;
* Need to continuously calibrate the increase at depths greater than 500 m due to a significant change in the refractive index of water. In this case, holography is performed with the set calipers (4) (Figure 2). An information unit of the system represents a set of equipment that performs the functions of communication, calculation, on-board power supply, control, and data switching. The information unit with a connected DHC makes it possible to install and connect hydrophysical sensors accompanying the plankton measurements. Such an aggregation is traditionally called a probe, and in our case, the hydrophiological \"DHC-probe Arctic\" (hereinafter referred to as the \"probe\" (Figure 4)). The design of the DHC probe allows loading it to a depth of 1000 m.  
Technical characteristics of the hydrophiological \"DHC-probe Arctic\":  
* Weight--65 kg;
* Volume studied per one exposure--approximately 0.75 L;
* Permitted hydrostatic pressure--100 A;
* Advantech PCM-9310 CQ-S6A1E on-board computer;
* SSD capacity--250 GB;
* Four channels to connect with hydrophysical sensors (RS485 and RS232)--temperature, pressure, microwave conductivity, and CTD (Valeport Mini);
* Communication channels:
* Wi-Fi backup channel to run and configure the probe for autonomous work;
* 1 Gb Ethernet (to transfer data on plankton and hydrophysics).
# VHF Omnidirectional Range (VOR) Experimental Positioning for Stratospheric Vehicles
## 3 Results
### COTS Receiver Data  
The comparison between the GPS ground track and the generated coordinates is displayed in Figure 1. A plot of the error (i.e., the distance between the GPS data and the COTS radio VOR-based position estimate, in km) is presented in Figure 2, while a visualization of the ground track reconstruction in Google Earth is displayed in Figure 3.  
Figure 1: Comparison between GPS and VOR-based balloon ground track.  
Figure 2: Error on the ground track determination through the COTS receiver data.  
The data originated from the COTS receiver collected radially present a mean error of 5.7 km and a standard deviation of 3.29 km. The relatively low number of position fixes is generated by the data collection processes, as stated in Section 2.3. Although being large  
Figure 3: GPS (blue) and VOR-based (red) ground tracks displayed on a map (plot **A**), location map with balloon trajectory in red (plot **B**).  
and much larger than an average GNSS error, a similar error can be considered compatible with a back-up positioning system. Optimizations can be implemented on the system if considering filters and trajectory and weather prediction models, e.g., an Extended Kalman Filter (EKF).
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### Model Construction  
The tenet of our proposed architecture is to alleviate the problem of tricking the user into malicious activities by incorporating various deep learning architectures, such as CNN, BiGRU, attention mechanisms, and fully connected layers, to effectively learn and classify malicious URLs.  
Figure 1 illustrates the workflow of our proposed system, which incorporates different phases, including a dataset collection phase from the reputation website, the preprocessing of the prepared dataset, a data validation phase, a splitting phase, an embedding (BERT and CharBiGRU) layer, an encoder phase, classifiers, an evaluation phase, and a target prediction phase. By combining the strengths of these components, our proposed system aims to improve the accuracy, efficiency, and adaptability of URL classification. The combination of contextual, sequential, and attention-based information enables the model to capture complex patterns and dependencies, leading to more robust and accurate classifications.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 2 The Proposed Method
### Notations on 3D Line Representation and Triangulation in Plucker Coordinates
#### 2.1.2 Multi-View Line Reconstruction in Plucker Coordinates  
Multiple observation constraints can be simply stacked to solve a Plucker line by linear equations. Given \(n\) observations of the line with \(\{\mathbf{R}_{\text{\tiny{acc}}}^{i},\mathbf{t}_{\text{\tiny{acc}}}^{i}, \mathbf{n}_{\text{\tiny{cc}}}^{i}\}\), \(i\in\{1,2,\ldots,n\}\), the constrain equations can be written as:  
\[\begin{pmatrix}\begin{bmatrix}\mathbf{n}_{\text{\tiny{cc}}}^{1}\\ \mathbf{n}_{\text{\tiny{cc}}}^{2}\end{bmatrix}_{\times}\mathbf{R}_{\text{\tiny{ acc}}}^{1}&-(\mathbf{n}_{\text{\tiny{cc}}}^{1})^{\text{T}}\mathbf{t}_{\text{\tiny{ acc}}}^{1}\mathbf{R}_{\text{\tiny{acc}}}^{1}\\ \mathbf{n}_{\text{\tiny{cc}}}^{2}\end{bmatrix}_{\times}\mathbf{R}_{\text{\tiny{ acc}}}^{2}&-(\mathbf{n}_{\text{\tiny{cc}}}^{2})^{\text{T}}\mathbf{t}_{\text{\tiny{ acc}}}^{2}\mathbf{R}_{\text{\tiny{acc}}}^{2}\\ \vdots&\vdots&\vdots\\ \mathbf{n}_{\text{\tiny{cc}}}^{n}\end{pmatrix}\begin{pmatrix}\mathbf{m}\\ \mathbf{d}\end{pmatrix}=\mathbf{0}_{3n\times 1}. \tag{7}\]  
The least-squares solution of the Plucker line \(\mathbf{L}=(\mathbf{m};\mathbf{d})\) can be found by decomposing the left matrix using Singular Value Decomposition(SVD).
## 4 Numerical Example
### Failure Diagnosis Based on FRPN of CNC Machine Tool  
Based on the main collected data about module failures in Table 1 and expert analysis, the FTA model about the CNC machine tool is available. By using the method in Section 3.2.2, which is described in detail in [33; 34; 35], the corresponding FRPN model can be converted as shown in Figure 6. In this process, the module failures listed in Table 1 that only reoccur at long intervals are removed to simplify the FRPN model of the target CNC machine tool. The relevant data about the FRPN model are collected in Tables 2 and 3 with expert evaluation and historical data analysis about the CNC machine tool.  
Figure 4: Failure correlation graph of the target CNC machine tool.  
Figure 5: Subsystem division of the target CNC machine tool.  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline \(p_{i}\) & **Failure Denoted by \(p_{i}\)** & \(\theta_{i}\) & \(p_{i}\) & **Failure Denoted by \(p_{i}\)** & \(\theta_{i}\) \\ \hline \(p_{1}\) & Crush of the protective cover & 0.46 & \(p_{17}\) & The rotation of the main shaft & 0.69 \\ \(p_{2}\) & Large vibration amplitude & 0.51 & \(p_{18}\) & The hydraulic pressure chuck sounds & - \\ \(p_{3}\) & The tool holder is not located accurately & 0.62 & \(p_{19}\) & The hydraulic tool holder is not flexible & - \\ \(p_{4}\) & Crumbs cannot be ruled out quickly & 0.65 & \(p_{20}\) & Knives cannot be replaced at will & 0.90 \\ \(p_{5}\) & Bad lubrication & - & \(p_{21}\) & Too much noise of the main motor & - \\ \(p_{6}\) & The external voltage is unstable & 0.68 & \(p_{22}\) & The sleeve does not stretch & - \\ \(p_{7}\) & NC hardware damage & - & \(p_{23}\) & The hydraulic chuck is automatically & 0.68 \\ \(p_{8}\) & The feed speed is stable & 0.82 & \(p_{24}\) & There is no electric current can flow & 0.76 \\ \(p_{9}\) & Lack of driving force & 0.61 & \(p_{25}\) & Bad cooling of the air-conditioning & 0.80 \\ \(p_{10}\) & Low detection sensitivity & - & \(p_{26}\) & The bed saddle does not move with the & 0.55 \\ \(p_{11}\) & Low accuracy of the & 0.58 & \(p_{27}\) & Failure of the \(S_{1}\) & - \\ \(p_{12}\) & Tool wear overmuch & 0.70 & \(p_{28}\) & Failure of the \(S_{2}\) & - \\ \(p_{13}\) & Liquid coolant leakage & 0.74 & \(p_{29}\) & Failure of the \(S_{3}\) & - \\ \(p_{14}\) & The pressure of the hydraulic chuck & 0.63 & \(p_{30}\) & Failure of the \(S_{4}\) & - \\ \(p_{14}\) & cannot be raised & 0.63 & \(p_{30}\) & Failure of the \(S_{4}\) & - \\ \(p_{15}\) & A blank screen of the computer’s & 0.82 & \(p_{31}\) & Failure of the \(S_{5}\) & - \\ \(p_{16}\) & X-axis can not move to the zero point & 0.75 & \(p_{32}\) & The CNC machine tool can not work & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(p_{i}\) and \(\theta_{i}\) of the fuzzy reasoning Petri Net (FRPN) model for the CNC machine tool.  
Figure 6: Fuzzy reasoning Petri Net (FRPN) model of the CNC machine tool.  
Based on the methods in Section 3.2, the subsystem weight vector considering the failure diagnosis based on FRPN is obtained as \(w^{f}\) = [0.165, 0.257, 0.235, 0.137, 0.206].
## 3 Principles of Automotive HMI Design
### ISO Standards  
The International Organization for Standardization (ISO) provides key guidelines to ensure that automotive Human-Machine Interfaces prioritize usability, safety, and user satisfaction. Two significant standards, ISO 15007 and ISO 9241-110, play a crucial role in shaping the design and evaluation of HMIs in the automotive industry [18].  
ISO 15007 focuses on visual clarity, particularly in driver information systems. It provides a framework for quantifying and describing driver visual behavior, including parameters such as glance duration and frequency. By setting these measures, ISO 15007 helps ensure that displayed information--such as speed, navigation prompts, or warning signals--is easy to understand without overloading the driver with excessive visual demands. This standard is particularly useful for evaluating Transport Information and Control Systems (TICSs) and ensures that they align with the driver's natural visual behavior.  
ISO 9241-110 emphasizes ergonomic principles in human-system interaction. It provides guidelines on creating user-friendly interfaces by ensuring attributes like consistency, task compatibility, and user control. The standard emphasizes that interfaces should align with user expectations and minimize unnecessary complexity, ensuring drivers can operate vehicle systems without distraction. This principle is vital for ergonomic placement of controls and displays, ensuring critical information remains within the driver's line of sight. Using eye-tracking methods in automotive HMIs underlines the importance of adherence to standards in reducing driver workload and improving situational awareness [19; 28]. Usability-focused guidelines, such as those in ISO 9241, can lead to more effective in  
Figure 4: Principles of UX design in automotive HMI.  
vehicle navigation system designs. These insights reinforce the relevance of ISO standards in shaping HMIs that are both safe and efficient.  
By adhering to these standards, automotive HMIs ensure uniformity and safety across the industry. This not only facilitates better usability for drivers but also ensures that automotive systems meet global benchmarks for ergonomic and visual interaction. These guidelines help reduce driver workload by enabling intuitive control mechanisms and clear information presentation, enhancing situational awareness and decision-making [29]. While these principles are well established, the rapid evolution of automotive technologies introduces new challenges for their effective application, particularly in the context of AV and connected ecosystems.  
In general, ISO standards act as a foundational framework, enabling manufacturers to develop HMIs that prioritize user experience while adhering to critical safety and usability metrics.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 5 Numerical Simulation  
In this section, to verify the performance of our proposed strategy, we conducted a series of evaluation experiments. First, we simulated and compared the LEO constellation cloud network offloading strategy with the access satellite offloading strategy and neighbor satellite offloading strategy proposed in the existing papers. Then, we evaluated and compared our joint optimization strategy and benchmark strategy in the whole LEO constellation offloading scenario.