esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Privacy Policies Multitier Approach
### Hierarching Privacy Policies  
In e-Government environments, data is structured at different abstraction levels, and service providers have different data needs and processing requests when requested to provide an electronic service. These needs and requests may deviate from each other but they are also bound to the restrictions opposed by corresponding ministerial departments and subsequently by legal requirements. Thus, from a modeling prospective, a privacy policy document, for a given electronic service, adheres and complies to the following hierarchy, where each arrow indicates a further level of generalization; Electronic Service \(\rightarrow\) Service Provider \(\rightarrow\) Ministerial Department \(\rightarrow\) Central Government. A similar generalization level for e-Health environments has also been proposed at [10].  
To make the above statement more formal, let us denote \(P_{CG}\) as the privacy policy elements of the central government, \(P_{MD}\) as the privacy policy elements of ministerial department, \(P_{SP}\) as the privacy policy elements of the service provider, and \(P_{ES}\) as the privacy policy elements of electronic service. Therefore, \(P_{ES}\) can be regarded as proper subset of \(P_{SP},P_{SP}\) as a proper subset of \(P_{MD}\), and \(P_{MD}\) as a proper subset of \(P_{CG}\), as depicted in Equation (1), below:\[P_{ES}\subseteq P_{SP}\subseteq P_{MD}\subseteq P_{CG} \tag{1}\]  
The expected inclusion of the aforementioned sets would be that \(P_{ES}\) is a subset of \(P_{SP}\), \(P_{SP}\) a subset of \(P_{MD}\), and \(P_{MD}\) a subset of \(P_{CG}\). However, as we move towards supersets, the specification of elements regards different levels of abstraction. For example, \(P_{CG}\) will contain an element regarding Social Security Number (SSN); based on the underlying legal and regulatory framework SSN falls under the category of personal identifiers (PId) and, consequently, should be treated as confidential data. Thus, \(P_{CG}\) states that SSN should be treated as confidential data during processing and storage. At the next level of abstraction, \(P_{MD}\) assents to the classification of SSN as PId and only specifies that, for the purposes of the specific ministerial department, it can be retained for a specific amount of time (days). Moving along to lower superset levels, \(P_{SP}\) specifies if it will be processed and how by the specific service provider and, finally, \(P_{ES}\) specifies only the purposes for collecting this PId. Depending on the e-Government environment and the structure of the central government (unitary, federal, or con-federal) we consider, the number of sets and subsets could vary; however, the organization and the representation is always viable.
# Reliability of Automated Intracranial Volume Measurements by Synthetic Brain MRI in Children
## 2 Materials and Methods
### Subjective Hydrocephalus Rating  
A total of 5 radiologists with 11 (S.T.), 7 (A.D.), 6 (V.W.), 4 (A.C.), and 2 (N.S.) years of experience in neuroradiology evaluated the examinations subjectively with regard to the presence or absence of hydrocephalus in a binary manner, indicating yes or no. For this purpose, the axial T2-weighted slices of the respective examinations were called up and evaluated in the local PACS environment. The evaluators were blinded to the SyMRI results and the results of the other participants. All MR images were rated in darkened reading rooms on radiology workstations running syngo.plaza (Siemens Healthineers, Erlangen, Germany) with color-calibrated RX440 or RX650 radiology monitors (Eizo, Ishikawa, Japan). The data were transferred to a table and transferred to the statistical software for further analysis. To enable a comparison of the hydrocephalus ratings with the SyMRI results, an increase in the standard deviation of the SyMRI CSF volume value of more than 1.5 times was considered definite hydrocephalus. Borderline hydrocephalus patients with SDs between 1 and 1.5 times were defined as normal, given the potential measurement inaccuracies of the volumetric methods. Cases with parenchymal defects (n = 2) or a  
Figure 3: Sample segmentations derived from 3D slicer depicting masks of cerebrospinal fluid and brain tissue.  
were not considered in this specific analysis (total = 119), because they do demonstrate increased CSF values that are not caused by hydrocephalus.  
The slice positions of all examinations were subjectively evaluated in a binary manner (0 = no error; 1 = error) regarding the presence of segmentation errors in the SyMRI software. The extent of the error was not taken into account. The values obtained were normalised between 0 and 1 as the examinations could have different total numbers of slices. A lower slice position represented a more caudal slice. The 4 quartiles represented the posterior fossa from 0 to 0.25, the base of the skull from 0.25 to 0.5, the cerebrum from 0.5 to 0.75, and the cranial vault from 0.75 to 1. The 4 quartiles were statistically compared with each other.
## 2 Materials and Methods  
A schematic diagram of the electrochemistry-based process is shown in Figure 2. The gas containing CO\({}_{2}\) and H\({}_{2}\)S is first sent to a classical absorption column. The lean aqueous  
Figure 1: (**A**) A classical thermal process to capture CO\({}_{2}\) from natural gas (NG) when H\({}_{2}\)S is also present. First, both gases are removed in an AGRU (containing both absorption and thermal regeneration). In a Claus unit, the H\({}_{2}\)S is converted into solid sulfur, and the remaining sulfur compounds are removed in a TGTU unit. A second AGRU removes the CO\({}_{2}\) before incineration of the remaining gases. (**B**) The electrochemical-based process proposed in this work involves capturing CO\({}_{2}\) from gas in the presence of H\({}_{2}\)S. Both gases are first absorbed with an aqueous amine solvent. The regeneration is electrochemical, not thermal, with simultaneous conversion of H\({}_{2}\)S in solid S and H\({}_{2}\) and separation of CO\({}_{2}\).  
amine chemically absorbs CO\({}_{2}\) and H\({}_{2}\)S. As a result, two streams leave the column, namely the sweetened gas and the solvent loaded with acid gas. The acid-gas-rich solvent is sent to the electrochemical regeneration cell. During the regeneration stage, the solvent is regenerated, and the acid gases are separated. The lean solvent is then recycled to the absorption column. H\({}_{2}\)S is converted into solid sulfur and gaseous hydrogen via oxidation at the anode, while CO\({}_{2}\) is released due to the preferential complexation of the amine with the metal ions. The detailed mechanisms are described in the next section.
## 4 Description of the Approach
### Prioritization of the Risk Treatment Paths with a Hybrid MCDM Approach
#### 4.1.3 Calculation of the Final Utility Scores and Aggregation into an Overall Utility Score  
The MAUT assumes that every decision is based on maximizing one's own utility [24]. The normalized decision matrix D (Equation (7), Section 4.1.1) forms the basis for calculating the marginal utility score of alternative \(i\) with criteria \(j\)\(u_{ij}\)\(\left(r_{ij}^{*}\right)\). A universal utility function does not exist, but rather the chosen functions highly depend on the decision-maker. In general, the distinction between linear and exponential utility functions is widespread [24, 25, 51]. Figure 3 shows a depiction of the different utility functions. If small changes in the criteria values in the lower third are rated as significant, a concave utility function should be selected. If small changes in the upper third of all values are rated as significant, a convex utility function should be selected.  
Equation (13) depicts the generalized form of the exponential utility function for the marginal utility score \(u_{ij}\), where \(x\) depends on the decision-maker's utility with \(x<1\) for a concave function and \(x>1\) for a convex function [25].  
\[u_{ij}\left(r_{ij}^{*}\right)=\frac{exp\left(\left(r_{ij}^{*}\right)^{x} \right)-1}{exp\left(1\right)-1} \tag{13}\]  
If the distribution is even, a linear utility function should be selected:  
\[u_{ij}\left(r_{ij}^{*}\right)=r_{ij}^{*} \tag{14}\]  
To identify a decision-maker's underlying utility function, direct methods or indirect methods can be applied [24]. With direct methods, the decision-maker answers direct questions about his or her preferences through ratings or preferences on lotteries, etc. Indirect methods can be versions of additive utility methods, which are based on linear programming [52]. If it is not possible to define the decision-maker's risk preference for a trilemma criterion, a neutral utility function should be chosen. As a result, a new decision matrix containing the marginal utility scores is created:  
\[U_{ij}\left(r_{ij}^{*}\right)=\begin{pmatrix}U\left(r_{11}^{*}\right)&\cdots&U \left(r_{1m}^{*}\right)\\ \vdots&\ddots&\vdots\\ U\left(r_{n1}^{*}\right)&\cdots&U\left(r_{nm}^{*}\right)\end{pmatrix} \tag{15}\]  
Eventually the final utility scores \(U_{i}\) are calculated for each risk treatment path, i.e., each alternative, with the marginal utility scores \(U_{ij}\left(r_{ij}^{*}\right)\) and the final weighting \(w_{j}^{final}\):  
\[U_{i}=\sum_{j=1}^{m}U_{ij}\left(r_{ij}^{*}\right)\cdot w_{j}^{final}\,\ \ i=1 \ldots n \tag{16}\]  
Arranging the risk treatment paths descending from \(U_{i,max}\) to \(U_{i,min}\) results in a prioritized list of all paths and thus in the selection of measures that in the next steps are categorized and integrated into the production schedule.
## 3 Materials and Methodology
### Preprocessing the Categorized Signals
#### 3.3.6 Features Captured from Implicit Signals  
To understand learner behaviour in terms of time spent, retries to complete the specific module, clicks that the learner has used to complete the module, and interactions, Equations (3)-(6) are used. Finally, the sequence of data will be padded for further processing by the ML model. After capturing these features, the corresponding data will be updated as mentioned in Table 11 and as follows.  
* Time spent: The amount of time spent per task. \[\text{Time Spent per Task}=\frac{\text{Total Time Spent}}{\text{Number of Tasks}}\] (3)
* Retries (normalised): The number of times the test has been tried. \[\text{Retries}=\frac{\text{Retries}}{\text{max}(\text{Retries})}\] (4)
* Engagement rate: The interaction level and their attentiveness can be calculated using \[\text{Engagement Rate}=\frac{\text{Interactions}}{\text{Total Time Spent}}\] (5)
* Clickstream data: The number of clicks used in the specific module. \[\text{Click Rate}=\frac{\text{Total Clicks}}{\text{Total Time Spent}}\] (6)  
Insights from the dataset:  
* Learners 3, 7, and 10 demonstrated strong engagement trends with significant time spent, high click counts, and frequent revisits. These learners are ready for advanced topics and challenges.
* Learners 1, 5, and 8 showed consistent engagement. Providing tailored resources can help them improve their readiness for more complex modules.
* Learners 2, 4, 6, and 9 had minimal interactions, lower revisit counts, and limited time spent. These learners need targeted strategies to boost engagement and improve outcomes.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Step** & **Category** & **Description** \\ \hline Data Cleaning & – & Remove incomplete sequences. Handle missing time steps using interpolation or padding. \\ \hline Transformation & Normalization & Scale sequential data to a fixed range to improve convergence during model training. \\ \cline{2-3}  & Sequence Padding & Ensure all sequences are of the same length by padding shorter sequences or truncating longer ones. \\ \cline{2-3}  & Categorical Conversion & Encode sequential categorical data into numerical format. \\ \hline Feature Engineering & – & Extract features such as time spent, retries, engagement rate, and click rate data to improve the learner’s knowledge domain. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Steps involved in preprocessing implicit signals.
# Practical Model for Metamaterials in Wireless Power Transfer Systems
###### Abstract  
Metamaterials (MTMs) with extraordinary electromagnetic properties are recently applied to wireless power transfer (WPT) systems to improve power transmission efficiency. Although theoretical progress has been made on MTMs in low frequency near field, in the operation frequency of most WPT systems (usually MHz), the design of MTMs still utilizes the model used in high-frequency applications. Therefore, a practical model of MTMs in low MHz band is proposed in this work. The resonance frequency and quality factor are used to describe the characteristics of an MTM slab. The near field WPT systems with MTMs are then modeled as electric circuits, the system efficiency is explicitly deduced, and optimization algorithms are employed to optimize the MTM resonance frequency and maximize the system efficiency. The proposed practical model is validated via a prototype wireless power transfer system operating at 6.78 MHz. Experiments show that the proposed MTM model has good accuracy for low MHz WPT systems compared with the high-frequency model. The proposed practical model of MTMs provides an accurate way to analyze the performance of MTM at low MHz frequencies and greatly benefits the future exploitation of MTM-based low-frequency near field applications.  
metamaterial; near field; power engineering; wireless power transfer; low frequency +
Footnote †: journal: Applied Physics Letters
# Smart Low-Cost Control System for Fish Farm Facilities
## 4 Proposal for Smart Low-Cost Control Systems for Aquaculture
### Proposal for Smart Low-Cost Control Systems for Aquaculture Facilities with Cages in the Sea  
#### 4.1.1 General Aspects  
For the proposal of Smart Low-Cost Control Systems for Aquaculture Facilities with cages in the sea, we propose the combination of two sensing methods to be integrated into a smart Control Portal to generate a Database (DB) for optimal aquaculture management. The deployment of the system is illustrated in Figure 12. The Control Portal will consist of a big DB which includes information from WSN deployed in different parts of aquaculture cages and from remote sensing open access data (such as Sentinel 2).  
Figure 11: Main software and hardware elements that comprise the proposed system. Examples are given for sensors and actuators, but the included parameters and the number of elements must be adapted to the monitoring scenario.  
The DB will have a series of algorithms and AI engines to process the received data to ensure that no abnormal values due to network errors are stored. Thus, we ensure the reliability of the received data. Other intelligent algorithms will be applied in the node for fault tolerance and for energy efficiency. Finally, algorithms for control will be applied in the DB using both existing knowledge and AI.  
Similar Smart Control Systems based on data gathered, DB, and AI are already incorporated in smart cities, such as for parking occupancy detection [105], and outdoor lighting infrastructures in ports [106]. Simpler examples can be found for agriculture [107] and farming [108], it is even possible to find some straightforward systems for aquaculture [109].
# A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method +
###### Abstract  
Space-time diversity (STD) has been widely applied in underwater acoustic (UWA) communication due to its exceptional anti-multipath performance. However, underwater noise can seriously affect the processing results of STD. The conventional filtering algorithms cannot deal with the nonlinear components of underwater noise and may not work well for complex-type signals. This study proposes an improved STD method with a joint noise-reduction learning model for the above issues. We construct a noise-reduction learning model dedicated to complex-type UWA signals in the first stage. Complex-type features based on UWA data are extracted for pre-processing data, and a conditional generative adversarial network (CGAN) is used as the backbone network for noise-reduction. Residual learning is used to accomplish noise cancellation and yield noise-reduction estimates. In the second stage, an STD structure based on a weight update strategy is constructed. The STD structure can further constrain the weights of the signals from the main path, enhance the reception of the main path, and suppress the multi-access interference (MAI) caused by the spread spectrum communication. Finally, combining the signals on each path can improve the communication quality of the system based on the principle of the maximum signal-to-interference plus noise ratio (SINR). The simulation and experiments on a lake showed that the proposed method is more robust over the changing signal-to-noise ratio (SNR) and has a lower bit error rate (BER) than conventional methods.  
Article  
A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method  
[PERSON], [PERSON], [PERSON], P. A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method. _Remote Sens._**2022**, _14_, 2430. [[https://doi.org/10.3390/rs14102430](https://doi.org/10.3390/rs14102430)]([https://doi.org/10.3390/rs14102430](https://doi.org/10.3390/rs14102430))
## 6 Experimental Simulation and Analysis of the Results  
To verify the performance of the PEAFC-CL algorithm, a simulation experiment was carried out on the NS2 platform, and the performance was compared with that of algorithms of the same type, with the indoor Shadowing model chosen as the channel model. The simulation parameter settings are shown in Table 2.
## 5 Efficiency Analysis
### Efficiency of the Cryptographic Primitives  
For our simulation model, we first computed the required time for all the cryptographic primitives utilized in our protocol, summarized in Table 3. For all the experiments, the presented times are the average of 1000 executions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Security Properties of Various Schemes**} \\ \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Unframeability**} & \multirow{2}{*}{**Impersonation**} & \multirow{2}{*}{**MVU-MU**} & \
\begin{tabular}{c} **Revocation** \\ **List** \\ \end{tabular} &
\begin{tabular}{c} **Re-Issuing** \\ **of Keys** \\ \end{tabular} \\ \hline
**BPPA**[60] & YES & YES & NO & NO & NO \\
**EMAP**[61] & NO & NO & NO & YES & YES \\
**DKM**[62] & NO & NO & NO & YES & NO \\
**BUA**[63] & NO & NO & NO & YES & NO \\
**PACM**[46] & NO & NO & NO & NO & NO \\
**Our Scheme** & YES & YES & YES & NO & NO \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with existing literature. (MVU = Message Vehicle Untraceability, MU = Message Unlinkability).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Blind Signature** & **Public Key (RSA)** & **AoN-PKEET (EIGamal)** & **Pairings and Other Operations** \\ \hline Blind 0.01 & Encrypt 0.111 & Encrypt 1.0512 & Pairing 10.376 \\ Sign 0.466 & Decrypt 0.615 & Decrypt 0.4735 & Multiply \(4.1\times 10^{-3}\) \\ Unblind 0.01 & & & Inverse 0.151 \\ Verify 0.897 & & & Exponent(wpc) 0.473 \\  & & & Exponent 9.036 \\  & & & Hash \(6.3\times 10^{-3}\) \\  & & & Subtract-Add \(1\times 10^{-3}\) \\  & & & Binary Search 0.011 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cost of the cryptographic primitives (in ms).
# Compact 15 mJ Fiber-Solid Hybrid Hundred-Picosecond Laser Source for Laser Ablation on Copper
## 2 Experimental Setup
### All-Fiber Master Oscillator  
The self-made all-fiber master oscillator is composed of a mode-locked laser oscillator and a two-stage fiber preamplifier. The oscillator is an all-fiber SESAM passively mode-locked laser, which provides seed pulses with average power of 11 mW and repetition rate of 28.49 MHz. It is operated at a central wavelength of 1064.1 nm with a 3 dB spectral bandwidth of \(\sim\)0.12 nm. Then, the average power is amplified to 50 mW by a single-mode Yb-doped fiber preamplifier (preamplifier 1); the pulse width is measured to be 40 ps, and corresponding peak power is 44 W. A 1350 m long single-mode passive fiber is selected as a pulse stretcher to broaden the pulse width to hundreds of picoseconds. The fiber stretcher is proposed to accurately match the gain spectrum of Yb-doped and Nd-doped lasers. The last stage of the all-fiber master oscillator is a 10/130 \(\upmu\)m double-clad pumped fiber preamplifier (preamplifier 2), which further increases the average power of stretched pulses to provide sufficient power for the follow-up Nd:YAG solid state amplifiers. When the pump power of the LD3 is 1.5 W, the output power is further amplified to 580 mW. The collimator is adopted to align and expand the seed laser beam output from all-fiber master oscillator to achieve cavity mode matching with the regenerative amplifier. In particular, to improve the stability of the front-end fiber seed source, we designed engineering prototypes for the fiber oscillator and the fiber amplifier, respectively. We realized the productized design of the front-end fiber seed source through fixing, packaging, and anti-vibration optimization of all fiber components. Among them, the prototype size of the fiber oscillator was 180 \(\times\) 140 \(\times\) 28 mm\({}^{3}\), and the prototype size of the fiber amplifier was 380 \(\times\) 170 \(\times\) 80 mm\({}^{3}\).
## 6 Discussion
Finally, it is observed that the size of the platform is smaller than other models analysed, which could enhance its performance in adverse environments where the space is reduced or where the terrain is wet and has low cohesion. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & **Ibericadron XAG** & **Milodón** & **Robotnik** & **Hygrobot** \\  & **R150 V2** & **M50** & **RB-VOGUI** & **Author** \\  & [11] & [12] & [13] & **Proposal** \\ \hline Weight & N/A & 160 kg & 165 kg & 65 kg \\ Dimensions X-Y-Z (mm) & 1515 \(\times\) 1090 \(\times\) 965 & 1000 \(\times\) 800 \(\times\) 2300 & 1044 \(\times\) 776 \(\times\) 742 & 870 \(\times\) 650 \(\times\) 500 \\ Power supply system & Li-Po Battery & Battery 1 & LiPo Battery & PEM fuel cell + LiPo Battery & Battery \\ Charging capacity & 0.865 & ND 1 & 1.44 & FC: 2.37 \\  & & & & Bat: 0.22 \\ Energy density (Wh/kg) & 144 Wh/kg & ND 1 & ND 1 & FC: 118 \\  & & & & Bat: 110 \\ Energy distribution (\%) & Battery 100 & Battery 100 & Battery 100 & FC: 90 \\  & 4 & 2 & 6 & Bat: 10 \\  & 20 & 30 & 60 & 5 \\  & 0.107 2 & 0.107 2 & 0.107 2 & FC: 0.109–0.160 3 \\  & 4.3 & 4 & 9 & Bat: 0.107 2 \\  & 19.88 & ND 1 & 37.5 & 37.06 \\ Max. torque/slope & 1000 Nm & 45\({}^{\circ}\) slope & 47\({}^{\circ}\) slope & 14.80 Nm \\ Load capacity (kg) & 150 & 100 & 150 & 50 \\ Motor power (W) & 3000 & 1000 & 1500 & 324 \\ Movement control & RTK & RTK + RC control & SLAM & Remote control \\ Protection rating & IP67 & - & IP53 & IP50 \\ \hline \hline \end{tabular}  
* Not disclosed by the manufacturer. 2 Source: Spain Electrical Grid (04/04/2025). 3 International Renewable Energy Agency [22].  
\end{table} 
Table 10: Comparison between the commercial models of ground robotic platforms and Hygrobot.