esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fully Automated Pipeline for a Robust Conjunctival Hyperenia Estimation
## 3 Results
### Efron Scale Prediction
The standardization processing allows for removing the artifacts (false positive branches) related to the red-like background component. Figure 4: Results of the regression model for the prediction of the Efron scale values, developed starting from the set of features extracted. The correlation between the ground truth and the predicted values is estimated using the [PERSON]’s rank correlation coefficient (ref. plot legend); (**a**) results of a single cross validation of the model. We highlight with the dashed line the axes bisector that corresponds to a perfect prediction. Despite the significance of the correlation found, the model finds many difficulties with low Efron scale values. We note that the predictions are performed on a set of data completely independent to the training set; (**b**) results obtained by the same pipeline on 100 different cross validations. In each iteration, a different 10-fold cross validation was applied for the estimation of the [PERSON]’s rank correlation coefficients. The average [PERSON]’s rank coefficient found is 0.69 with a standard deviation of 0.008. Figure 5: Example of the predictions obtained by the regression model on three samples. For each image, we report the assigned Efron score and our method’s predicted score. We would like to stress that the predicted scores are floating point numbers and therefore they are more descriptive for the redness evaluation, ensuring a finer redness grading scale of values. \begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Spearman’s Correlation** \\ \hline \(score_{1}\) & 0.642 \\ \(\mu_{S}\) & 0.612 \\ \(\mu_{B}\) & \(-0.564\) \\ \(\mu_{G}\) & \(-0.505\) \\ \(\mu_{H}\) & \(-0.480\) \\ \(score_{2}\) & 0.408 \\ pixel-counting & \(-0.395\) \\ \(\mu_{R}\) & 0.314 \\ \(\mu_{V}\) & 0.267 \\ box-counting & \(-0.113\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: [PERSON]’s correlation coefficient scores of single features in relation to the Efron grading scale values, ranked from the highest to the lowest. The analysis of the single feature correlation highlights an unbalanced informative power in the prediction of the Efron scale values. Only three features have a correlation coefficient greater than 0.55 (in absolute values).
## 4 DPI Applied to Honeynet Traffic and Attacks
### Layer 5 to 7 Payload Analysis
#### 4.4.4 Traffic Analysis of Port 80  
For this kind of payload, the inspected fields analyzed were _HTTP request method_. These fields inform the HTTP verb used in the packet and _HTTP request URI_ contains the URI to which the client intends to connect.  
Within this investigation, automated malicious requests sent to the HTTP honeypot trying to gather useful information for a potential further exploitation were detected. Table 9 shows some of these requests and the tools with which they are associated. Most of the data analyzed in this case correspond to bots that also scan for vulnerabilities in port 443 and, therefore, part of the encrypted traffic to this port may correspond to them.  
Besides scan requests, a worm known as _The Moon_ was also caught in the traffic towards the Honeynet. This worm infects some Linksys routers by exploiting an authentication bypass vulnerability.  
_The Moon_ sent a GET /HNAP1 HTTP/1.1 to the honeypot. This is used to identify the router's model and firmware's version. Then, it transmitted _tmUnblock.cgi_ and _hndUnblock.cgi_ scripts that allows execution of commands with no authentication required. After the infection, the worm uses the router to spread itself to other vulnerable devices.  
The HTTP requests associated with the Moon worm and the ones displayed in Table 9 can not correspond to legit traffic, and their presence indicates suspicious activity. Therefore, the Deep Packet Inspection device should block these and similar requests. Addressing other exploitations in this port, DPI can also be implemented, for instance, in application layer firewalls, to detect and respond to malicious packets, such as strings that may evidence code injection.
# Hot Air and Microwave Combined Drying of Potato Monitored by Infrared Thermography
## 2 Materials and Methods
### Kinetics Experimental Procedure  
Potato sample spheres were dried by hot air drying (HAD) and hot air drying assisted by microwaves (HAD + MW) using a specially designed HAD-MW oven [25], connected to a magnetron to provide energy, with a maximum output of 2000 W at 2450 MHz. The oven was connected to a computer to measure the incident and reflected energy using a directional coupler with a power meter in the waveguide of the magnetron. The drying chamber had a mode stirrer to ensure a homogeneous distribution of microwaves. The selected drying conditions were an air velocity of 1.5 m/s, hot air temperature of 40 \({}^{\circ}\)C and MW energy of 0, 4 or 6 W/g (referring to the initial mass of the sample). All treatments were carried out for 420 min. The oven door is a ventilation grill with a mesh size of 2 mm, where the air and the infrared radiation can cross without limitation, but the microwave radiation, with a longer wavelength, cannot.  
During the HAD treatment (0 W/g), a sample was placed inside the oven to continuously monitor and record the mass using a Mettler Toledo AB304-S balance (precision of \(\pm\)0.01 g). A second sample (or reference sample) was placed next to the previous one and in front of an infrared camera (Optris PI(r) 160 thermal imager; Optris GmbH, Berlin, Germany) to record the infrared emission of the surface of the potato. The surface temperature of this sample was measured using a K-thermocouple connected to an Agilent 34901A multiplexer (Agilent Technologies, Malaysia) and recorded by an Agilent 34972A data logger (Agilent Technologies, Malaysia). Finally, a certified emissivity label (\(\varepsilon\) = 0.95), provided by Optris GmbH, was placed in the same plane as the reference sample, and its temperature was also registered by a K-thermocouple. For HAD-MW treatments (4 and 6 W/g), the experimental procedure was similar, but the surface temperature of the reference samples and the certified emissivity label were monitored only by the infrared camera (Figure 1).  
The environmental conditions (relative humidity and temperature) were also registered.
# Design and Simulation of a Vision-Based Automatic Trout Fish-Processing Robot
## 2 Material and Methods
### Designing the Mechanical Parts
#### 2.3.1. Calculations Related to the Stepper Motors of the Belly-Cutting and Gutting Subsystems  
To determine the required torque to actuate the cutting arm and gutting tubes, the system was simulated. Considering the working conditions in the system, all the necessary parameter and considerations such as static and dynamic friction coefficients, any collisions, and all the constraints, were incorporated and motion simulation was performed.  
Since the linear velocity of the fish varies in different feeding capacities, it is necessary to bring the cutting arm closer to the fish concerning linear transmission speed. It should be mentioned that the belly-cutting time at which the arm is in the cutting position also concerns the fish transfer speed. In addition to determining motor torque, rotational speed, acceleration, and energy consumption were obtained. Finally, the maximum torque required for system functioning at a capacity of 10 fish per minute was selected to determine stepper motor characteristics in both belly-cutting and gutting subsystems.  
A power screw was used to move the cutting arm. Due to the self-locking property of the power screw in the non-working condition, no force will be applied on the stepper motor in idle condition. Since the gutting subset has a different structure, it was necessary to use a stepper motor with a gearbox system due to the inertia of the moving parts so that the suction tubes would stop as soon as the motor stops.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 5 Discussion and Limitation  
This paper proposed a structured approach for incorporating marine casualty data into the S-100 framework. Through schema development, feature encoding, and portrayal testing, it demonstrated how unstructured maritime incident records can be formalized and visualized within navigational systems. While the technical feasibility of the model was confirmed through a case study, several limitations remain. These include challenges in harmonizing heterogeneous data sources, scaling the model for large datasets, integrating user-centered feedback, and extending the current representation approach. The following subsections outline each limitation and discuss potential directions for further development.
## 3 Results and Discussion
### Diffusion Characteristics of the Refrigerant During Vertical Upward Leakage
This leads to greater accumulation near the leakage hole and the floor, consequently increasing the local concentrations. Intermediate-density refrigerants (R32, R290, R744), while denser than air, exhibit stronger turbulent diffusion due to the initial momentum. This results in a more even distribution within the room, leading to lower concentrations above the leakage hole. Steady-state concentration values at 0.5 m were as follows: R717 (6.51%) > R1234 yf (3.87%) > R22 (3.57%) > R32 (2.92%) > R290 (2.83%) > R744 (2.81%). At 0.7 m, the order was as follows: R717 (6.15%) > R1234 yf (3.25%) > R22 (2.72%) > R32 (2.01%) > R290 (1.99%).
# Compensation Method for Polarization Mixing in the Homodyne Interferometer
###### Abstract  
A homodyne interferometer is one of the most important tools in nanometre measurements. However, its nonlinear error seriously affects measurement accuracy at the sub-nanometre level. As one of the dominant factors that cause nonlinear error in a homodyne interferometer with a quadrature detector system, the imperfection of polarizing beam splitters (PBSs) is investigated in this paper. The nonlinear error caused by the imperfection of PBSs in the detection part can be reduced by adjusting the gains of detectors. Nevertheless, eliminating the nonlinear error caused by the polarization mixing of the PBS in the interferometer part is difficult. In this paper, the nonlinear error caused by the polarization mixing of the PBS in the interferometer part is analyzed, and an optical compensation method is proposed to correct this polarization mixing. Theoretical calculation and simulation analysis show that this method can reduce the effect of inherent polarization mixing on nonlinear error significantly. In comparison with using only gain adjustment, the nonlinear error can be reduced by two orders of magnitude when the proposed method is applied. The nonlinear error can be decreased from approximately 4.5 nm to approximately 0.045 nm using the presented method based on the simulation results.  
homodyne interferometer; nonlinearity; polarization mixing; compensation Article
# Enhancing Wind Energy Harvesting Using Passive Turbulence Control Devices
## 5 Conclusions  
In this paper, the galloping piezoelectric energy harvester with two 60\({}^{\circ}\) PTC structures (GEH-PTC) is experimentally and theoretically studied. A theoretical model of the GEH-PTC is established and the accuracy of the model is verified by experiments. The effect of the load resistance on the critical wind speed (\(U_{cr}\)), the response displacement, and the output power of the GEH-PTC under strong and weak coupling conditions are analyzed from the perspective of the electromechanical coupling strength. Results show that \(U_{cr}\) increases firstly and then decreases along with increasing load resistance. Compared with the VIVEH in the studied wind range conditions, the maximum output power and voltage of the GEH-PTC increased by 93% and 66.7%, respectively. When the electromechanical coupling strength shows weak coupling and critical coupling, the output power increases firstly and then decreases with increased load resistance, and it reaches the maximum value at the optimal load, which is 1.1 M\(\Omega\) and 3.0 M\(\Omega\) for the former and the latter, respectively. At 2.646 m/s, the peak output power in the cases of the weak coupling and the critical coupling reached 0.15 mW and 0.5 mW, respectively. With increasing coupling strength and wind speed, the response displacement increases  
Figure 8: Variation of numerical displacement with the load resistance: (**a**) weak coupling \(k^{2}/(4\zeta+4\zeta^{2})=0.029\); (**b**) critical coupling \(k^{2}/(4\zeta+4\zeta^{2})=1\); (**c**) strong coupling \(k^{2}/(4\zeta+4\zeta^{2})=2.9\).  
obviously. Compared with the weak coupling case, the displacement in the strong coupling case increased by 125%. In future work, we will change the coupling strength by changing the piezoelectric materials in the experiment to obtain different coupling strengths. The key factor is the piezoelectric materials, specifically the arrangement mode, numbers, etc.  
[PERSON] and [PERSON] carried out the energy harvesting experiments. [PERSON], [PERSON] and [PERSON] wrote the paper.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 51606171 and 11802237), and the Fundamental Research Funds for the Central Universities of China (Grant No. G2018 KY0306).  
The authors declare no conflict of interest.
# Privacy-Preserving Federated Singular Value Decomposition
## 2 Preliminary
### Differential Privacy  
Besides SA, DP is also exhaustively utilized in the FL literature. DP was introduced by [PERSON] et al. [30], which ensures that the addition, removal, or modification of a single data point does not substantially affect the outcome of the data-based analysis. One of the core strengths of DP comes from its properties, called composition and post-processing, which we also utilize in this paper. The former ensures that the output of two DP mechanisms still satisfies DP but with a parameter change. The latter ensures that a transformation of the results of a DP mechanism does not affect the corresponding privacy guarantees. Typically, DP is enforced by injecting calibrated noise (e.g., Laplacian or Gaussian) into the computation.  
**Definition 1** (\((\varepsilon,\delta)\)-Differential Privacy).: _A randomized mechanism \(\mathcal{M}:\mathcal{X}\to\mathcal{R}\) with domain \(\mathcal{X}\) and range \(\mathcal{R}\) satisfies \(\varepsilon\)-differential privacy if for any two adjacent inputs \(x,x^{\prime}\in\mathcal{X}\) and for any subset of output \(S\subseteq\mathcal{R}\) it holds that_  
\[\Pr(\mathcal{M}(x)\in S)\leq\varepsilon^{\varepsilon}\cdot\Pr(\mathcal{M}(x^{ \prime})\in S) \tag{2}\]  
The variable \(\varepsilon\) is called the privacy budget, which measures the privacy loss. It captures the trade-off between privacy and utility: the lower its value, the more noise is required to satisfy Equation (2), resulting in higher utility loss. Another widely used DP notion is approximate DP, where a small additive term \(\delta\) is added to the right side of Equation (2). Typically, we are interested in values of \(\delta\) that are smaller than the inverse of the database size. Although DP has been adopted to many domains [7] such as recommendation systems [31], we are not aware of any work besides [18] which adopts DP for SVD computation. Thus, as we later show a flaw in that work, we are the first to provide a distributed SVD computation with DP guarantees.
# A Tutorial on Retroreflectors and Arrays Used in Satellite and Lunar Laser Ranging
## 4 Retroreflectors as Ground Calibration Targets  
All SLR stations make use of a calibration scheme to initially determine and then monitor changes in optical and/or electronic system delays that might be caused by changes in hardware or environmental conditions (e.g., temperature). The most common approach is to place a single retroreflector at some carefully surveyed distance from the ground station \"invariant point\", defined as the intersection of the elevation and azimuth axes of the telescope assembly. The retro acts as a point source with a delta function response. This calibration distance is usually measured at the 1 or 2 mm level using accurate ground surveying techniques. Subtracting the known target range from the measured range provides a \"range correction\" which is then applied to all future satellite measurements. For maximum accuracy, calibrations are typically performed hourly.
## 2 Materials and Methods
### Feature Extraction and Classification Algorithms
#### 2.5.3 Filter Bank Combination Characteristics  
Filter banks (FBs) are a commonly used signal processing technique that decomposes the input signal into multiple frequency sub-bands. It has been widely applied in EEG signal analysis and has given rise to algorithms such as filter bank common spatial patterns (FBCSPs) and filter bank canonical correlation analysis (FBCCA). In this approach, the EEG signal is first filtered into different frequency bands using a filter bank. The filter bank analysis decomposes the original EEG signal into five sub-band signals using a set of predefined band-pass filters. Each sub-band covers a specific frequency range and facilitates the effective extraction of frequency-specific neural activity features, while preserving the phase information of the signal. The selected five sub-bands span the frequency range of 2-40 Hz and are defined as follows: [2; 3; 4; 5; 6; 7; 8] Hz, [8; 9; 10; 11; 12; 13; 14] Hz, [15; 16; 17; 18; 19; 20] Hz, [21; 22; 23; 24; 25; 26; 27; 28; 29; 30] Hz, and [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40] Hz. This configuration encompasses commonly observed EEG rhythms, including delta, theta, alpha, beta, and low-gamma bands, thereby providing a comprehensive frequency representation for subsequent feature extraction and classification tasks.  
Features are then extracted from the filtered signals using CSP and CCA methods. Finally, the CSP and CCA features are combined through a cross-feature fusion strategy. The detailed procedure is as follows:  
First, the input data \(X\) are processed using a set of predefined filters, resulting in a filter bank denoted as \(W_{z}\).  
\[W_{z}=(w_{1},w_{2},\ldots,w_{n}) \tag{23}\]  
In this equation, \(w_{i}\) denotes the \(i\)-th filter, and \(n\) represents the total number of filters in the filter bank.  
The input data are then transformed using the filter bank \(W_{z}\), resulting in the filtered data \(X^{\prime}\), as expressed by the following equation:  
\[X^{\prime}=W_{z}X \tag{24}\]  
Feature extraction is performed on the filter bank outputs using the CCA algorithm (Equation (21)) and the CSP algorithm (Equation (19)), resulting in the following expression:  
\[\rho(Xt,Y) = (\rho_{(w_{1}X,Y)},\ldots,\rho_{(w_{n}X,Y)}) \tag{25}\] \[f_{(X^{\prime})} = (f_{(w_{1}X)},\ldots,f_{(w_{n}X)}) \tag{26}\]The combined feature \(T_{f,\varphi}\) is obtained by cross-fusing the features extracted using the CSP and CCA algorithms.  
\[T_{f,\varphi}=Concat(f_{(w_{1}X)},\rho_{(w_{1}X,Y)},\ldots,f_{(w_{n}X)},\rho_{( w_{n}X,Y)}) \tag{27}\]
## 3 Technical Preliminaries  
In this work, a system of anonymous processes communicates through multiple channels with beeps. A beeping channel represents a limited form of communication in which a process can only sense the presence/absence of a signal on the channel. Therefore, when a process hears a beep on the channel, it represents a signal on the channel. Otherwise, when no beep is detected, it represents silence. The processes communicate through beeping on any of the \(k\) channels, and each channel is identified by its unique name from the range \((1,k)\). The number of channels \(k\) is significantly less than \(n\), where \(n\) is the number of processes for the largest group. All processes know \(k\), the number of channels, and they can beep on any specific channel using the channel's name.  
A distributed system is considered that consists of \(N\) anonymous processes that are partitioned into \(k\) channels, see Figure 1. Each channel \(j\) has \(n_{j}\) processes, where \(j\in(1,k)\) such that \(\sum_{j=1}^{k}n_{j}=N\). The \(N\) processes are arbitrarily assigned to \(k\) channels. A process that is assigned to channel \(j\) is said to be a member of group \(j\). The number of processes for group \(j\) is denoted by \(n_{j}\), where \(n_{j}\geq 1\).  
The model of communication is synchronous and the time is partitioned into equal time slots, also known as rounds. The clocks of all processes are synchronized such that each process knows the start and the end of each round. All processes begin execution simultaneously with synchronized clocks and identical initial states.  
A strong communication model is assumed in which a process can only beep on a single channel in any given round. In addition, the processes can only listen for beeps on a single channel in a single round. Although a process is assigned to a specific channel, it can choose to listen to any other channel during an execution. During a round, when two or more processes keep on the same channel, each process listening to the channel cannot identify the source of the beep. Otherwise, when no beep is transmitted on a channel, the feedback is silence.  
Figure 1: Distributed system overview with anonymous processes assigned to \(k\) channels.
## 4 Role of URLLC in Operating IoT  
Although mMTC is specifically categorized and designed to meet the IoT requirements, URLLC holds the key ingredients for effective IoT operations. When multiple operators control time-critical devices remotely, as shown in Figure 8, the latency and reliability play a vital role in the smooth operation of the IoT devices. It is quite challenging to operate mission-critical and real-time IoT devices over a wireless connection [33]. A massive multiple-input multiple-output (MIMO) technology has recently become quite applicable to manage a massive number of devices. However, the struggle to meet latency and reliability requirements remains problematic.  
Many potential real-time IoT operational issues can be overcome with the integration of the tactile Internet [34], URLLC, and MIMO radio access technologies. In the following subsections, some of the basic URLLC disputes are explained when operating IoT devices.