esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy Goals for the Data Lifecycle
## 6 Step 3-Aligning the Privacy Goals to GDPR and the Data Lifecycle
### 7-DL-2-Collection  
Collection considers any decisions pertaining to the collection, receipt and/or acceptance of data within the business. For the life logging scenario, at the collection stage, Privacy Goal 1 dictates that there must be justification for collecting data from citizens (a.k.a. the data subject(s)). Here, the legal basis should be consent (Article 6(1)(a)) or, if necessary, in an emergency situation where the citizen's life may be in peril and consent has not been obtained, \"vital interest\" (Article 6(1)(d) can be used. Fairness requires that the citizen understands and has been informed of the purpose of the data collection and how the data will be used within the life logging scenario, who it will be shared with (Privacy Goal 9) and giving the citizen the opportunity to opt out of data collection as part of the contract between the citizen and the project (Privacy Goal 8). Accuracy requires that the collected data is correct which means the person collecting the data must take care to note down the information correctly or, in the case of the life logging scenario, the citizen themselves, need to check that they input the information correctly and update as needed. Purpose limitation and data minimisation dictate that data collected should only be used in accordance with the specified, agreed purpose(s) and any reuse will required consent before the data can be shared. Integrity and confidentiality refer to ensuring the data is collected appropriately, using recognised techniques, maintaining the confidentiality of the citizen (Privacy Goal 1). In this scenario, the citizen themselves will input their own information into the app. This means that they are responsible for checking the accuracy of information input themselves and for only entering data they are happy to share with the app. There will need to be settings available within the app to allow citizens choices around which elements of data can and cannot be shared, stored and used (Privacy Goals 1, 8 and 9).
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Energy-Harvesting Techniques
#### 3.2.1 Solar Energy
By contrast, when the packet transmission period increases, the power consumption of the sensor node decreases. The experimental results show that the proposed packet transmission scheme is superior to the others in terms of data throughput and deadline miss rate. The proposed energy-harvesting method is also predicted more precisely than others by 6.92%. A study [119] used a solar cell to provide energy to a WSN deployed in a greenhouse. The humidity and temperature of the greenhouse were controlled through a management and monitoring system, which was based on a low-power MSP430 (Texas Instruments, Dallas, TX, USA) microcontroller and an ultra-low-power nRF24L01 transceiver (Nordic Semiconductor, Oslo, Norway). The power consumption of the WSN was minimized as much as possible. In another study, a WSN-based ZigBee (CC2420 module) wireless transceiver and microcontroller (low-power ATmega128L) supplied by the solar cell as a battery charger were considered to monitor the humidity, temperature, rain level, and leaf wetness of vineyards [120]. The WSN covered 30 m between ZigBee units with a low power consumption (35 and 38 mW in transmission and reception modes, respectively).
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 2 Material Properties of Graphene
Graphene at this frequency is seldom patterned as it does not support the SP wave with \(n_{sp}\) less than one. In the THz to mid-IR band (Figure 1b), the most important feature is the SP wave with very large \(n_{sp}^{{}^{\prime}}\) and very small loss. The variation of \(E_{F}\) changes \(\sigma^{{}^{\prime\prime}}\), \(\epsilon_{r}^{{}^{\prime}}\), and \(n_{sp}^{{}^{\prime}}\), but only has a small effect on the loss. The metasurface with patterned graphene shows tunable SP resonance frequency together with a large dynamic range of phase variation. Even in the hybrid metasurface where the resonance originates from the metallic antennas, variation of \(E_{F}\) shifts the resonance frequency because graphene modulates the dielectric constant of the environment. When \(\hbar\omega\) is comparable to \(2E_{F}\) around the step in \(\sigma^{{}^{\prime}}\) and \(\epsilon_{r}^{{}^{\prime\prime}}\) (Figure 1c), both intraband and interband transitions contribute to the optical response, such that the metasurface with graphene still has tunability by changing the Fermi level. In the near-IR and visible frequencies where \(\hbar\omega\gg 2E_{F}\), the interband contribution dominates. \(\epsilon_{r}^{{}^{\prime}}\) becomes positive with a constant \(\epsilon_{r}^{{}^{\prime\prime}}\). Graphene behaves as a dielectric layer with universal absorption of 2.3% [44]. The variation of key material properties from sub-THz to near-IR is summarized in Figure 1d. It dictates different modulation mechanisms in the graphene-based metasurfaces, which endows the design with diverse tunability with \(E_{F}\), such as shifting the operation frequency, modulating the efficiency of wavefront shaping, switching among multiple functionalities, and dynamic polarization conversion, as will be detailed in Sections 3 and 4.
# GPS Results from Long Time Monitoring of Geodynamic Processes in South-Western Bulgaria
## 5 Discussion and Geodynamic Implications  
For a time period of almost 25 years, the monitoring has covered 28 stations. They have been measured in different years and in a different number of campaigns (Table 2). The number of campaigns varies from two (stations GURM, LOZE2, and MECH); three--fourteen stations; four--six stations; five--three stations; eight--one station (PLA1); and ten--1 station (SOF1). Despite the different number of the measurements, the obtained results are quite homogeneous in the different localities of the studied area and show clear uniform tendencies. In relation to that, the velocities of stations PLA1 and SOF1, with the highest number of measurements, which suggests a very high reliability, do not differ significantly from surrounding stations. This is quite obvious when compared to the very close station LOZ2. The latter, having been measured only twice, shows a slightly slower velocity, but still in the same range and direction. Station MECH (two epochs of measurements) also fits in the local pictures of the surrounding stations (DSEC, BANK, and SLI1). The station GURM shows some slight local deviation, which may be due to the only two epochs of measurement, but may also reflect some specific tectonic setting. To solve this problem, further measurements are needed. A general conclusion that could be made is that the different number of measurements do not affect the significance of the obtained results. However, the greater numbers of measurements provide a higher reliability.  
Some general remarks may be made:  
All velocities are in a southern direction. They are in the range of 1.5 mm/year to slightly over 3 mm/year, almost reaching 4 mm/year (stations DOB1, SATO). The velocities of the stations tend to increase from north (stations around Sofia) to south, passing through an intermediate locality (between Sofia and Kyustendil-Pazardhik), clearly increasing in the southernmost part of the country (around Gotse Delchev). In general, the velocities are oblique to the faults. This is better expressed in the northern part (stations SLIV, MECH, BANK, GURM, BOSN, and VERI), suggesting oblique extension to the structures. Instead,in the SW part (stations ZEME, CARV, FROL, and PADA) the velocities are parallel to some faults, suggesting strike-slip deformation. North of station BOGO extension is probably on the northern located fault (Kustendil fault) and strike-slip deformation to the eastern fault [10].  
This velocity field motivates N-S expressed extension with increasing rates from North to South. The difference in the velocity rates tends to change along geologically suggested active fault zones. This is quite obvious between stations PADA, MALC, and BELM from the south and northern lying stations (DELA, PLA1, MUHO, VETR, etc.). This result points to the significance of the GPS monitoring for the identification and evaluation of active faults.
# Evaluation of Operator and Patient Doses after Irradiation with Handheld X-ray Devices
## 3 Results
According to this, the highest total dose (153.2 \(\upmu\)Sv) was measured at the C irradiation in the Diox 602 device, while the lowest total dose (25.5 \(\upmu\)Sv) was measured at the R irradiation in the NOMAD Pro 2 TM device. When the total dose results for the patient (phantom head) were evaluated in general, the highest values were found in conventional irradiations and the lowest in digital irradiations for all three devices (Table 4). The dose values measured from the cone ends of the devices included in the study are shown in Table 5. According to this, the highest dose (292.6 \(\upmu\)Gy) was measured in the anterior region at the C irradiation in the NOMAD Pro 2 TM device, and the lowest dose (84.3 \(\upmu\)Gy) was measured at the R irradiation in the Diox 602 device. The results of the measurements made in the hands, eyes, thyroid, and gonad regions by taking protective measures are shown in Table 6. A statistically significant difference was found between protected and unprotected irradiation at all dose levels in all regions (\(p<0.05\)). \begin{table}
\begin{tabular}{c c c c} \hline  & **NOMAD Pro 2 TM** & **Rextar X** & **Diox 602** \\ \hline C & 113.4 & 146.4 & 153.2 \\ \hline P & 88.1 & 92.9 & 60.3 \\ \hline R & 25.5 & 58.4 & 42.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Total dose (\(\upmu\)Sv) of organs included in the study for all irradiations (phantom head). \begin{table}
\begin{tabular}{c c c c} \hline  & **NOMAD Pro 2 TM** & **Rextar X** & **Diox 602** \\ \hline C & 292.65 (front) 28.65 (back) & 258.796 & 210.108 \\ \hline P & 133.956 (front) 25.976 (back) & 155.958 & 134.51 \\ \hline R & 113.284 (front) 12.134 (back) & 115.177 & 84.305 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average dose values (\(\upmu\)Gy) measured from device cone tips after all irradiations. In this study, irradiations were made with handheld X-ray devices at the recommended times for C, P, and R systems. The reduction rates of the phosphor plate and RVG systems in the patient's dose compared to the conventional system are shown in Table 7.
## 3 Methodology
### PSS, HRV, and NASA TLX Data Collection and Processing  
In this experiment, the PSS-10 scale was used to assess the subjective stress levels of the navigators. The total scores were categorized into three stress levels, low stress (0-13), moderate stress (14-26), and high stress (27-40), following the classification guidelines of [30; 47]. This categorical approach has been applied in several maritime studies on seafarer stress [20; 21; 22; 23]. The 10 item version was selected for its superior psychometric properties compared to other PSS variants, ensuring both reliability and validity in measuring perceived stress [48].  
In the HRV analysis, the R wave represents the peak change in the ECG, and the RR interval (RRI) in milliseconds is defined as the time between two consecutive R waves. The HRV data characteristics obtained from the measurements include both time-domain and frequency-domain indices. Time-domain indices include the MRR (mean RR interval), SDNN (standard deviation of normal RR intervals), and RMSSD (root mean square of successive RR interval differences). Frequency-domain indices are derived from the power spectrum density of the resampled RRI data, which include low-frequency (LF; power spectrum in the frequency range of 0.04-0.15 Hz) results, high-frequency (HF; power spectrum in the frequency range of 0.15-0.4 Hz) results, and the LF/HF ratio (the ratio of low- to high-frequency results). The HRV indices relate to stress; thus, the indices selected to be utilized in this research were the MRR, SDNN, RMSSD, and LF/HF ratio [32; 49; 50]. The sample RR intervals are depicted in Figure 5.  
For NASA-TLX, there were two types of information for each dimension--weights and scores. The weights reflect the perceived importance of each dimension as a contributor to MW in the specific task, while the scores represent the subjectively experienced magnitude of MW for each dimension. To determine the weights, subjects perform pairwise comparisons for each dimension pair, assigning a score of one to the dimension that contributes more to MW and zero to the other. After completing all 15 pairwise comparisons, the total  
Figure 5: The sample RR intervals, along with activity windows recorded from a participant during the task, are displayed with each example of activities between segments.  
score for each dimension ranges from zero to five. During task engagement, subjects rate each workload dimension based on their experienced MW. The performance dimension is rated on a scale ranging from 0 (good) to 100 (poor), while all other dimensions use a scale ranging from 0 (low) to 100 (high). This research focused specifically on the original version of NASA-TLX, as introduced [39] with the weighted scale.
## 3 Design and Preliminary Verification
### LCT Structure Design
Finally, a simulation analysis is carried out using the electromagnetic field analysis software Maxwell to estimate the self-inductance values of \(L_{1}\), \(L_{\text{P}}\) and \(L_{\text{s}}\), as well as the values of \(M_{\text{P1}}\) (mutual inductance between \(L_{1}\) and \(L_{\text{p}}\)), \(M_{\text{s1}}\) (mutual inductance between \(L_{1}\) and \(L_{\text{s}}\)) and \(M_{\text{ps}}\) (mutual inductance between \(L_{\text{P}}\) and \(L_{\text{s}}\)). The simulation results are listed in Table 2. It can be seen from Table 2 that the values of \(M_{\text{P1}}\) and \(M_{\text{s1}}\) are nearly 0, which is an intuitive proof for the design of the above-mentioned decoupling characteristics.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 3 Methodology
### Self-Supervised Loss Functions
#### 3.5.4. Repulsion Loss (\(L_{repulsion}\))  
To prevent different keypoints predicted on the template from collapsing onto the same spatial location and to encourage them to be spatially distinct, we employ the repulsion loss. Its objective is to penalize pairs of keypoints that are predicted to be too close to each other, thus promoting spatial diversity among the learned keypoint locations across the template structure. The mechanism operates on the soft peak locations derived from the predicted heatmaps via the spatial softmax operation (Section 3.3). It calculates an exponential penalty based on the squared pairwise Euclidean distances between the coordinates \(P=\{(x_{k},y_{k})\}_{k=1}^{K}\) for each batch item.  
\[L_{repulsion}=\frac{1}{B}\sum_{b=1}^{B}\left(\frac{1}{\binom{K}{2}}\sum_{i=1} ^{K}\sum_{j=i+1}^{K}\exp\left(-\frac{||P_{b,i}-P_{b,j}||_{2}^{2}}{\sigma^{2}} \right)\right) \tag{7}\]  
where \(P_{b,i}\) and \(P_{b,j}\) are the predicted 2D coordinates for keypoint \(i\) and keypoint \(j\) in batch item \(b\), \(||\cdot||_{2}^{2}\) denotes the squared Euclidean distance, \(\sigma^{2}=5^{2}\) is a hyperparameter controlling the repulsion radius (how close points need to be to incur a significant penalty), and \(\binom{K}{2}\) normalizes by the number of unique pairs of keypoints. The spatial softmax coordinates (\(P_{b,i}\) and \(P_{b,j}\)) are normalized to the range \([-1,1]\) based on the heatmap dimensions before calculating the Euclidean distance to make the scale of \(\sigma^{2}\) consistent. This loss is used in Stage 1 but not in Stage 2.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Comparison of Hydrogen Production Technologies  
For each technology previously addressed, Table 1 presents an overview of the main advantages, disadvantages, hydrogen classification, production price, and an assessment of the technology readiness, as well as an indication of whether this technology falls under the scope of the present work. Based on the review of the current main hydrogen production technologies, it is possible to state that water splitting is one of the most interesting and promising approaches to produce low-carbon hydrogen, since only oxygen and hydrogen result as byproducts of the production process. Electrolysis is the frontrunner to produce green hydrogen at a large scale, since some of the production technologies under this category have already achieved a fully developed and commercially available state, such as AEL and PEMEL. It is seen as a user-friendly technology due to its scalability [53], environmental benefits, and compatibility with renewable energy sources [54]. From a cost perspective, gray hydrogen stands out as the most economical option among current hydrogen production methods. The cost of gray hydrogen ranges from USD 0.7-2.08/kgH\({}_{2}\) for steam methane reforming, and USD 1.24/kgH\({}_{2}\) for partial oxidation and autothermal reforming. In comparison, blue hydrogen produced by methane pyrolysis ranges from USD 1.59-3.20/kgH\({}_{2}\), and green hydrogen produced via electrolysis powered by utility-scale PV in EU and Norway ranges from USD 2.90-14.4/kgH\({}_{2}\) and powered by offshore wind in EU and Norway ranges from USD 2.2-9.4/kgH\({}_{2}\). This means that a trade-off between economic and environmental constraints needs to be made. Green hydrogen prices are influenced by the cost of the renewable energy and of the electrolyzers used in the process [55]. Regulatory frameworks can help reduce the existing gap between green and gray hydrogen. As higher penalties for GHG emissions are imposed, the production cost of gray hydrogen will increase, making low-carbon hydrogen production processes a more competitive technology. Additionally, to ensure a progressive transition from gray to green hydrogen, balancing the economic and environmental dimensions, technologies, like SRM-CCS and methane pyrolysis, that have lower emissions than conventional fossil fuel thermochemical processes, and that are cheaper than green hydrogen processes, can be considered as bridging technologies. It is also important to highlight that, according to the Global Hydrogen Review report released by the IEA in October 2022 [56], in regions with simultaneously good renewable resources and large imports of fossil fuels, green hydrogen production cost is already competitive with gray hydrogen production cost.
## 4 Traffic Filtering-Based Techniques
### Traffic Control (TC)
#### 4.2.3 Advantages and Disadvantages  
Like QR, the TC is a simple approach to reduce redundant traffic in HSR networks. The TC can be applied for any traffic such as unicast, multicast and broadcast in ring topologies.  
However, the TC approach has some disadvantages. Like QR, the TC cannot prune traffic for unused rings. The TC allows network traffic to be forwarded into all rings of the networks. Unlike QR that removes circulated traffic from both DANH and QuadBox rings, the TC approach is applied for only DANH rings. In other words, unicast traffic is still duplicated and circulated in QuadBox rings. Additionally, the TC approach adds more delay to the traffic transmission.  
Figure 5: Forwarding frames in the failure-free case under the TC.  
Figure 6: Forwarding frames in the failure case under the TC.
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 4 Simulation Results and Discussions
### Convergency of The Proposed Algorithm  
Finally, we examine the convergency of the proposed algorithm through tracking the iteration times during optimization process, as shown in Figure 8. The BER target is set to be \(10^{-6}\) for all RNs, and we consider the cases when the number of RNs \(K\) equals to 4, 8, and 16, respectively. The simulations are performed for 1000 times of channel realizations and the results are obtained by taking the expectation of iteration times over all channel realizations. It can be found that the iteration time keeps below 5 for any case with arbitrary \(E_{c}\) and \(K\). It confoirms the convergency of the proposed algorithm, which is invariant of the number of RNs, \(E_{c}\) and also \(\Gamma_{k}\). And the complexity of the proposed algorithm is relatively low, with the final solution obtained with quite limited time of iterations.  
Figure 7: Block error rate (BLER) performance of SWIPT aided OFDM-IDMA system with the proposed power allocation algorithm and equal power allocation.The Cyclic Redundancy Check (CRC) sequence of length-16 is tailed at the end of each frame. \(K=16\).
## 4 Experimental Evaluation and Results
### Limitations and Examples  
To evaluate the method edge cases, five common movements evaluating changes in body pose and related to human mobility, motion performance, and posture (walking, climbing the stairs, balance, self-care, or work activities) were selected, where the test subjects are leaning left, leaning right, leaning forward, leaning backward and finally the subject touching their face (seen in Figure 6). As we can see, in most cases where the subject is holding their hand in front of the face, our solution successfully predicted the joint orientation, except for where the subject was instructed to fully touch their face. This is due to the model being unable to accurately predict the user's hand depth relative to the face, and not the IK method itself, suggesting further research could be expanded by a more accurate depth estimation method or depth sensor. The disparity between object joint orientation and IK prediction can be seen in Figure 7; as we can see, some misalignment can occur due to the fact that IK must ensure that the joints create a valid human pose without scaling the human body, as our solution does not, at the moment, account for the person's height.  
Figure 6: _Cont._
# Mesh Stiffness and Dynamic Modeling and Analysis of Modified Straight Bevel Gears
## 4 Dynamic Model of Straight Bevel Gears
### Overall Assembly Matrix  
The generalized displacement vector \(\mathbf{q}\) for the entire gear-rotor system can be expressed as:  
\[\mathbf{q}=\left\{q^{1},q^{2},q^{3}\cdots\cdots q^{n+1}\right\}^{T} \tag{46}\]  
Assembling all the elements, you can obtain the system's overall damping matrix \(\mathbf{C}\), gyroscopic torque matrix \(\mathbf{G}\), and stiffness matrix \(\mathbf{K}\). The equation for the overall system of the straight bevel gear transmission system can be written as [2]:  
\[\mathbf{M\ddot{q}}+\left(\mathbf{C}+\mathbf{\Omega}\mathbf{G}\right)\mathbf{ \dot{q}}+\mathbf{K}\mathbf{q}=\mathbf{Q} \tag{47}\]  
The damping in various parts of the system is implemented using proportional damping:  
\[\mathbf{C}=\lambda\mathbf{M}+\eta\mathbf{K} \tag{48}\]  
Here, \(\lambda,\ \eta\) refer to the coefficient of mass matrix and stiffness matrix (\(\alpha=0\), \(\beta=10^{-7}\)), respectively.