esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy
## 2 Materials and Methods
### In Situ Data  
In situ reflectance values and water samples were collected using different methodological approaches tailored to the characteristics and monitoring infrastructure of each site. Regarding reflectance data, in Lake Peipsi, data were collected through an ad-hoc field campaign conducted synchronous to the satellite overpass (18 July 2021), thanks to the ability to calculate the orbits of PRISMA in advance using a dedicated tool provided by the Italian Space Agency (ASI) [68]. During the field campaign, above-water reflectance measurements were acquired using the TriOS RAMSES instrument [58], which was mounted on a boat-based platform. The instrument was operated following standardized protocols to minimize sunlight and optimize observation geometry. Measurements were acquired under stable atmospheric conditions, typically between 10:00 and 14:00 local time to minimize variation due to changing solar zenith angle.  
In Lake Vortsjarv, data were acquired using a fixed autonomous monitoring station (HYSTAR), synchronous to the satellite overpasses (20 June 2024, 21 July 2024); the instrument was deployed at the pier near the central part of the lake (58.211 N, 26.108 E). The HYSTAR system is equipped with a hyperspectral radiometer featuring dual optical entrances, configured to measure both upwelling water-leaving radiance and downwelling irradiance, allowing for calculation of the water surface reflectance at high temporal resolution [69]. Measurements were recorded at 20-min intervals under automatic quality control routines to flag suboptimal conditions (e.g., cloud cover or sunlight).  
Water samples were systematically collected during the fieldwork carried out in both lakes within the national monitoring programme, and subsequent laboratory analyses were conducted to determine the concentrations of Chl-a, TSM, and CDOM, as well as microscopic analysis for the characterization of phytoplankton species [70].
# Feasibility Analysis of Industrial High-Temperature Heat Pumps in Greece
## 2 Materials and Methods  
### Description of the System  
The examined HTHP is illustrated in Figure 1. This heat pump produces useful heat in the range of 100 \({}^{\circ}\)C to 180 \({}^{\circ}\)C which is given at a constant temperature. Specifically, the evaporator receives the heat input in the system (process \(4\to 1\)); the compressor consumes electricity to pressurize the working fluid (process \(1\to 2\)); the condenser produces the useful heat (process \(2\to 3\)); and the throttling valve reduces the pressure (process \(3\to 4\)). It is important to note that the heat is transferred with thermal oil from the condenser to the process heat. The selected thermal oil is Therminol VP-1 [20]. The examined capacity values of the heat pump range from 100 kW to 500 kW, with the nominal value being at 300 kW. The HTHP is fed by a waste heat stream of 10 kg/s mass flow rate, which is typical of flue gases, with a temperature level in the range of 50 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. Additionally, the scenario of an ambient-source heat pump is studied. The pinch point in the heat exchangers is set to 5 K [21] in all cases, and the thermal oil temperature difference is 5 K. The heat exchangers in the evaporator and the condenser are assumed to be plate heat exchangers, which are commonly used for this application. The working fluid is selected to be cyclopentane (critical temperature of 238.6 \({}^{\circ}\)C, critical pressure of 45.71 bar), which is a typical choice for operation in HTHPs [22; 23; 24].  
Figure 1: The examined HTHP with low-grade waste heat exploitation.
# Evolution towards Smart and Software-Defined Internet of Things
## 3 Smart or Cognitive IoT Architecture
### Intelligent Hosting Layer  
The intelligent hosting layer provides various functions for the programmer, allowing the hardware details to be masked. This improves smart thing connectivity and makes it simpler to provide various services. The intelligent hosting layer plays an important role in achieving the requirements of a complete IoT architecture which include interoperability, scalability, multiplicity, security and privacy, abstraction provision, spontaneous interaction, and unfixed infrastructure [53].  
Multiple variations in intelligent hosting have been highlighted through time to address the various requirements. A few prominent hosting layer devices are based on RFIDs and Wireless Sensor Networks, and other sensing platforms are Task Computing Framework (TCF), Triple based Distributed Middleware (TDM), Service Oriented Architecture (SOA), UBIWARE, TinyREST, Global Sensor Network (GSN), and Fosstrak, in addition to Robotic Operating System (ROS). Each of these hosting devices brings certain advantages to the table corresponding to specific domains of IoT ranging from simplicity, user-friendliness, and interoperability to mobility, efficiency, and manageability. Even though these platforms show promising capabilities, their outputs are limited and constrained. The challenges they face include resource management and efficiency, scalability, security, and configurability. AI and machine learning play an important role to overcome these challenges by assigning the resources intelligently and efficiently.  
Figure 2: Cognitive IoT network architecture.
# A Digital Bang-Bang Clock and Data Recovery Circuit Combined with ADC-Based Wireline Receiver
## 2 The Working Principles of the MMPD and DBBPD
### The Working Principles of BBPD  
The BBPD is an oversampled phase detector used in most CDR implementations, which needs to obtain one more edge data between consecutive signal data [17; 18; 19]. Figure 4 shows the judgment principle of a BBPD and the truth table is shown in Table 2. Using three samples taken on three consecutive clock edges (S1, S2 and S3), the bang-bang phase detector can determine whether there is a data transition and whether the clock is leading or lagging the data. In the absence of data conversion, the three sampled data are equal without any changes. If the last edge of the clock is leading, the value of the last sample clock (S3) is opposite to the previous two samples. Conversely, if the clock is lagging, the last two samples (S2 and S3) are equal and opposite to the first sample (S1). Therefore, S1\(\oplus\)S2 and S2\(\oplus\)S3 provide early and late information about sampling clocks:  
1. If S1\(\oplus\)S2 is high and S2\(\oplus\)S3 is low, the clock is late;
2. If S1\(\oplus\)S2 is low and S2\(\oplus\)S3 is high, the clock is early;
3. If S1\(\oplus\)S2 = S2\(\oplus\)S3, the data are not converted.  
When we design our CDR circuit, we need to analyze its performance. A key step is to linearize the nonlinear phase detector [20; 21]. Both the MMPD and BBPD are nonlinear phase detectors, but since the judgment of the MMPD is affected by signal amplitude and the set value of _Vref_, its linearization is more complicated. The BBPD is not affected by signal amplitude, so its linearization model can be easily obtained by analyzing the relationships between its judgment responses and the size of the signal jitter, which was convenient for us to analyze the performance of our CDR circuit [22; 23; 24]. For a 50% transition density, the BBPD gain can be expressed as:  
\[K_{PD}=\frac{1}{\sigma\sqrt{12}}\ \left(Gaussian\ jitter\right) \tag{6}\]  
The BBPD is an efficient and simple two-times oversampling system that doubles the number of clock phases and front-end samplers in CDR circuits. It is precise because double oversampling is too expensive for ADC-based receiver hardware, so the BBPD is not as popular as baud-rate PD in an ADC-based wireline receiver. However, in order not to increase power consumption too much and simultaneously avoid the instability problem of the MMPD, the BBPD could also be very promising in an ADC-based receiver.
## 5 The Random Grouping Selection Algorithm for the JSCC-IA Scheme in the Asymmetric Networks
### The Random Grouping Selection Algorithm
Calculate the SINR of all the data streams according to Equation (33) or Equation (60), and attain the minimal SINR as \(\xi_{i}\). 4. If \(i=1\), set \(\lambda=\xi_{1}\), \(P_{\mathrm{opt}}=P_{1}\) and \(\mathrm{W}_{\mathrm{opt}}=\mathrm{W}_{1}\); otherwise, if \(\xi\!\geq\!\lambda\), set \(\lambda=\xi\), \(P_{\mathrm{opt}}=P_{i}\), and \(\mathrm{W}_{\mathrm{opt}}=\mathrm{W}_{i}\). 5. If \(\lambda>\xi\) or \(i>\Theta\), stop and output \(P_{\mathrm{opt}}\) and \(\mathrm{W}_{\mathrm{opt}}\); otherwise update \(i\gets i+1\) and go to step (2). ```  
**Algorithm 2** Random Grouping Selection Algorithm  
Furthermore, in the case of small \(\xi\), the actual number of iterations is likely to be smaller than \(\Theta\), and the complexity of RGS algorithm is low at the expense of little improvement. On the contrary, when \(\xi\) is large, the RGS algorithm might have to take much more iterations to search the solution, and the improvement is expected to be significant at the expense of larger computational cost. Therefore, the RGS algorithm provides a good tradeoff between the performance improvement and the computational complexity by adjusting the preset parameters. In addition, a large number of low complexity IA algorithms have recently been developed to attain \(\mathbf{V}\) and \(\mathbf{U}\) with a remarkable fast convergence speed [25, 38]. The development of very-large-scale integration (VLSI) also makes it much faster to complete the calculation. Therefore, in the practical situation, \(\Theta\) can be set to be relatively large, and the possibility that \(\xi\) cannot be reached is small.
## 4 Application Implementation  
### Development Foundation  
In this section, the fundamentals of the business hierarchy and the developed application modules are described in order to form a comprehensive and clear understanding of the participating entities and components. The current section is brief and it serves mainly for completeness and coherence of the whole paper.  
In this decentralized application, there exist three kinds of users: Super Admins, Admins and Users. Super Admins are staff from the participant CMOs, while Admins and Users are staff from the Members affiliated to those CMOs. In the following schema (Figure 4), a clear diagram demonstrates the user hierarchy inside the application.  
Figure 3: The trust continuum in state-of-the-art networks [25].  
Inside the application, every collective management organization (CMO) can add new Members (e.g., an affiliated music rights company) through its Super Admin profile, while new participants can request to become Admins of an existing Member (to be approved by a Super Admin of its CMO or an Admin of its Member) or regular Users (to be approved by an Admin of its Member). Furthermore, the CMO has full view of Member activities through the Super Admins accounts and is able to act and make significant changes, as explained later in Section 4.3, while a Member has the ability to create, update and delete music asset claims through the appropriate Admin and User profiles. Special focus is devoted to the back-end structure and implementation of the music asset claims. A music asset claim is submitted on-chain through the dedicated smart contract and it essentially consists of the rights data which is defined by the time span, territory, rights industry type and share percentage. Ultimately, the full Solidity structure and its purpose is analyzed in Conflicting Rights Governance, Section 4.3.
# Computational Modeling of Ultrasound C-Scan Imaging Using Transmitted Signal Peak Density
## 5 Conclusions  
Computational modeling of ultrasound C-scan imaging was developed to analyze the feasibility of the peak density parameter of the transmitted frequency spectrum. A previous experimental study was imitated in the computational model for the validation of the analysis. The thin structure was successfully imaged through the computational study. With proper pixel area selection or smaller pixel size, a better boundary detection can be achieved in C-scan imaging while using the ultrasound peak density parameter. Peak density was not effective in giving information regarding the relative position of the structure along with the sample depth. Therefore, the magnitude variation of the signal was analyzed with the MPVD parameter instead of peak density to detect structure position. With the increasing depth of the pepper flake inside the gelatin phantom, the MPVD value changed while following a polynomial function. Therefore, in pitch-catch analysis, magnitude variation of the frequency spectrum is an effective tool for C-scan imaging as well as for extracting additional information about structure position. Further research is ongoing to evaluate the feasibility of this C-scan imaging technique to image various histological features of cancerous tissue. In the future, the feasibility of photoacoustic C-scan imaging will also be explored while using a similar parameter.  
Conceptualization, K.P. and L.L.; methodology, L.L., K.P. and J.S.; software, K.P.; validation, K.P. and J.S.; formal analysis, L.L. and K.P.; investigation, L.L. and K.P.; resources, L.L. and J.R; data curation, K.P.; writing--original draft preparation, K.P.; writing--review and editing, K.P., J.S., S.R., B.A.P., L.L. and J.R.; visualization, S.R., B.A.P. and L.L.; supervision, L.L. and J.R.;project administration, L.L. and J.R.; funding acquisition, L.L. and J.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
## 6 Performance Evaluation
### Impact of the Percentages of Stationary and Mobile End-Devices  
This subsection presents the results obtained in the SHC scenario with varying percentages of stationary and mobile end-devices, respectively. In this evaluation, the number of end-devices that use Pure ALOHA and LBT AFA is the same (i.e., 100).  
Figures 7-9 show the PLR under different traffic conditions (i.e., Case L, M and H).  
As expected, the PLR values increase with the percentage of mobile end-devices because in our scenario, they send messages more frequently than stationary ones. Consequently, a higher number of mobile end-devices generates a higher network traffic, and this increases the PLR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MAC Protocol** & \begin{tabular}{c} **Type of** \\ **End-Devices** \\ \end{tabular} & \begin{tabular}{c} **Case L—** \\ **Avg e2 eDelay (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Case M—** \\ **Avg e2 eDelay (s)** \\ \end{tabular} &
\begin{tabular}{c} **Case H—** \\ **Avg e2 eDelay (s)** \\ \end{tabular} \\ \hline Pure ALOHA & Stationary & 0.39 & 0.40 & 0.41 \\
20\% & Mobile & 0.24 & 0.31 & 0.35 \\ LBT AFA & Stationary & 1.23 & 1.78 & 2.13 \\
80\% & Mobile & 1.16 & 1.87 & 2.58 \\ \hline Pure ALOHA & Stationary & 0.39 & 0.40 & 0.41 \\
50\% & Mobile & 0.28 & 0.31 & 0.35 \\ LBT AFA & Stationary & 0.93 & 1.22 & 1.45 \\
50\% & Mobile & 0.85 & 1.16 & 1.60 \\ \hline Pure ALOHA & Stationary & 0.39 & 0.40 & 0.41 \\
80\% & Mobile & 0.25 & 0.33 & 0.34 \\ LBT AFA & Stationary & 0.73 & 0.94 & 1.09 \\
20\% & Mobile & 0.59 & 0.79 & 1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Average e2 eDelay with varying percentages of end-devices that use Pure ALOHA and LBT AFA.  
Figure 6: Case H: PLR with varying percentages of end-devices that use Pure ALOHA and LBT AFA.  
Figure 8: Case M: PLR with varying percentages of stationary and mobile end-devices.  
Figure 7: Case L: PLR with varying percentages of stationary and mobile end-devices.  
Figure 9: Case H: PLR with varying percentages of stationary and mobile end-devices.  
Table 13 presents the average e2 eDelay values obtained with varying percentages of stationary and mobile end-devices under different traffic conditions.  
As can be seen, in this scenario, the percentage of stationary and mobile end-devices does not have a significant impact on the average e2 eDelay.
# Gain-Scheduled Model Predictive Control for
## 3 Controller Design  
To realize the control objectives (1) to (3) listed in the previous section, a gain-scheduled model predictive control method is proposed in this section. First, the dynamic Equation (14) of the CIPS is linearized at multiple different pendulum angles to obtain a series of linearized models, respectively. Second, a series of MPCs are designed based on these linearized models. Finally, one of these designed MPCs is selected to control the CIPS according to the gain schedule algorithm.
# Frequency-Bounded Matching Strategy for Wideband LNA Design Utilising a Relaxed SSNM Approach
## 4 Wideband LNA Design Strategy Using Modified SSNM Conditions
* Use mismatch transfer theory (lossless matching networks preserve reflection coefficients)  
Choose a \(\Gamma_{\text{S2 ref}}\) and \(\Gamma_{\text{L2 ref}}\) value for a trade-off between gain and noise.
## 3 HDELC Algorithm
### Time Complexity Analysis  
We have analyzed the time complexity of the HDELC algorithm. The time complexity \(T_{HDST}\) of the HDELC algorithm is estimated as follows:  
\[T_{HDELC}=T_{FSR}+T_{FM}+T_{CSE} \tag{25}\]  
\(T_{FSR}\), \(T_{FM}\), and \(T_{CSE}\) denote the computational cost of the original feature space reconstruction set, the feature space matching classifier, and the cost of integrating the model for classification prediction, respectively. Of these, \(T_{FSR}\) is related to Algorithm 2 and \(T_{CSE}\) is related to Algorithm 3.  
\(T_{FSR}\) is related to the number of samples \(l\) in the training set, the number of feature attributes \(m\), and the number of feature subspaces \(B\):  
\[T_{FSR}=O(B\cdot l\cdot m\log m) \tag{26}\]The time complexity of \(T_{FM}\) is influenced by the number of training samples \(l\) and the number of feature subspaces \(B\) as follows:  
\[T_{FM}=O\Big{(}B\!\cdot\!l\cdot\sqrt{l+B\!\cdot\log B}\Big{)} \tag{27}\]  
\(T_{CSE}\) is related to the number of classifiers \(G\) in the ensemble system and the number of samples \(l\) in training set using the formula:  
\[T_{CSE}=O(G\!\cdot\!l\cdot\log m) \tag{28}\]  
Since \(G\) and \(B\) are constants, the time complexity of the HDELC algorithm is approximately \(O(l\!\cdot\!m\cdot\log m)\).
# Semantic Segmentation of Packaged and Unpackaged Fresh-Cut Apples Using Deep Learning
## 2 Materials and Methods
### Experimental Samples  
The apples used in our experiments belonged to four different varieties: Granny Smith, Golden, Fuji, and Pink Lady. The last two have a mostly reddish skin, while the skin of Golden apples is yellowish and the one of Granny Smith apples is greenish. Therefore, three main peel colours are present in the data set: red, yellow, and green. Each piece of fresh-cut apple can present to the camera the skin, the pulp, or a combination of them. The visual evaluation of digital images has made evident that the separation of skin from pulp could be attempted using the simple colour information only for the reddish and greenish varieties. To achieve a satisfactory separation of these two parts in yellowish apples is challenging, if not impossible, on the basis of the simple colour information: skin and pulp are almost equal visually, especially when the product is fresh.  
The data set of RGB images for the experiment contained, for all the considered varieties, three different kinds of arrangement of apple pieces:  
1. Randomly disposed unpackaged pieces (each making visible both peel and pulp in variable proportions) (Figure 1).
2. Unpackaged pieces orderly disposed with the pulp oriented upward (making the peel invisible) (Figure 2).
3. Randomly disposed pieces inside a plastic bag (each making visible both skin and pulp in variable proportions) (Figure 3).  
Figure 1: Randomly disposed unpackaged pieces (each making visible both peel and pulp in variable proportions).
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 4 Experiments
### Parameter Selection  
This method involves four main parameters: \(\tau\), \(\gamma\), \(\delta\), and \(\Sigma\), each influencing the matching process and the final accuracy statistics differently. Below, I explain the selection rationale for each parameter.  
**Selection of \(\tau\) and \(\gamma\):** These two parameters are thresholds set to determine whether template matching is successful. Although they do not directly affect the point set matching process, they determine the accuracy statistics of the matching result.  
\(\tau\) is determined based on the maximum allowable error under scale distortions in the images. Considering the actual detection environment of the train images and the real target detection results, we set \(\tau\) = 200 to account for potential scale variations.  
\(\gamma\) represents the proportion of successfully matched points in the template point set. We set \(\gamma\) to 0.75, meaning that if the proportion exceeds this value, the match is considered successful. A higher \(\gamma\) can reduce false positives but may increase the risk of missing matches, while a lower \(\gamma\) may lead to more false positives. Based on experimental results, we selected this value as an optimal balance.  
**Selection of \(\delta\):** We chose \(\delta=1\) for the iterative optimization process of point set alignment. The point set coordinates are normalized along the height direction of the image during alignment, but there is local nonlinear distortion in the width direction. The train's speed feedback mechanism somewhat limits this distortion, and the horizontal offset usually does not exceed the height of the image. Therefore, \(\delta=1\) ensures that each point's Gaussian distribution in the template aligns with the height direction of the image, causing most matching points to fall within the high-response region of the Gaussian distribution. This enhances the robustness of the matching process and avoids excessive variance, which could lead to too much overlap of template points and local optima.  
**Selection of \(\Sigma\):**\(\Sigma\) is a key parameter in the template point set matching process. We tested the impact of \(\Sigma\) values ranging from 0.1 to 1 on the matching success rate. As shown in Table 1, the experimental results demonstrate the following:  
When \(\Sigma\) is less than 0.9, the image recognition accuracy remains close to 100%. It is only when \(\Sigma\) exceeds 0.9 that the recognition accuracy starts to decline. This indicates that the method has good adaptability to parameter selection.  
For matching success rate, \(\Sigma=0.5\) produces the best results, showing that this value yields the most stable matching outcome, as more successfully matched points enhance the robustness of the matching process.  
Based on the above analysis, we selected the following final parameters for this study: \(\delta=1\), \(\Sigma=0.5\), \(\tau=200\), and \(\gamma=0.75\). This combination demonstrated the best matching performance in our experiments.
# Thermal Mapping of Power Semiconductors in H-Bridge Circuit +
## 4 Thermal Stresses of Power Devices at Various Loading Conditions
### Simulation and Experimental Verification
The detail thermal stress comparison between the simulation and experiment is listed in Table 3. It can be seen that, even though the inverter and rectifier modes can alter loss dissipation between the IGBT and the diode, the simulated thermal stress of both the IGBT and the diode is higher than in the experiment. This is because the loss curves of the power device are inherited from the datasheet, but are not accurately characterized by using the experimental setup. Moreover, the thermal impedance of the power module itself and cooling solutions also needs better characterization. Figure 10: Experimental setup of H-bridge circuit. (**a**) Control block diagram. (**b**) Photo of experimental setup.