esa-sceva/satcom-chunk-collection huggingface dataset:
# GDPR and Large Language Models: Technical and Legal Obstacles
## 1 Introduction
### Comparative Analysis: GDPR vs. AI Act Interactions  
The European Union's General Data Protection Regulation (GDPR) and the forthcoming Artificial Intelligence Act (AI Act) are complementary yet distinct frameworks. While the GDPR establishes technology-neutral, principles-based rules to protect personal data, the AI Act adopts a risk-tiered, system-focused approach to ensure AI systems are safe and ethical by design. Importantly, the AI Act is implemented \"without prejudice\" to the GDPR, meaning that organizations must comply with both regimes when processing personal data with AI. The AI Act classifies AI systems based on risk--from banned applications (unacceptable risk) to high-risk systems (e.g., those used in employment, healthcare, or law enforcement) that must meet strict requirements for risk management, transparency, and oversight [58]. In contrast, the GDPR governs all personal data processing based on principles such as lawfulness, fairness, transparency, and data minimization, ensuring robust data subject rights [60]. Together, these regulations aim to safeguard both technological integrity and individual privacy. Sectors such as healthcare and recruitment face dual compliance challenges. Healthcare AI systems must satisfy the AI Act's requirements for risk management and technical documentation while also adhering to the GDPR's strict rules for processing sensitive health data [61]. Similarly, recruitment AI tools must balance the AI Act's bias mitigation and transparency measures with GDPR mandates on lawful processing and non-discrimination [62]. Adopting a \"GDPR-first\" approach--where robust data protection forms the foundation for additional AI-specific controls--is widely recommended as a strategy to achieve an integrated framework for trustworthy AI. In summary, the GDPR and the AI Act together promote a vision of trustworthy AI that ensures both system safety and the protection of fundamental rights. Despite challenges in aligning their differing approaches, integrated compliance strategies can enable organizations to meet both sets of obligations without compromising innovation or privacy.
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 3 Modeling and Parameter Identification
### Parameters Identification  
In previous section, the linear model of stepper motor and load system are obtained, and, in his section, the parameters of these models are estimated through experiments or through calculation. The parameters that can be obtained through experiments are \(t_{mc}\), \(\xi_{v}\), and \(w_{n}\). The parameters that can be obtained through calculation are \(K_{\theta}\), \(\xi_{m}\), and \(w_{n}\). Note that \(w_{n}\), an important parameter in the model, can be obtained both ways.
# Wireless Control Combining Myoelectric Signal and Human Body Communication for Wearable Robots
## 6 Conclusions  
An HBC-based communication module has been developed to wirelessly control the robot hands using myoelectric signals. The use of the 10-60 MHz band has contributed to low transmission loss and high data rate (typically 1.25 Mb/s, up to 20 Mb/s). For transmitting electrode, a technique has been developed to share the detection electrode and transmitting electrode to reduce the Tx module size. Three receiving electrode structures have also been investigated to improve the transmission performance. As a result, up to a communication distance of at least 110 cm along the arm, the PDR achieves 100% and  
\begin{table}
\begin{tabular}{c c} Keep the hand grasping & 100\% \\ Keep the hand opening & 100\% \\ \end{tabular}
\end{table}
Table 4: Success rate of wireless control of robot hand.  
Figure 16: View of experiment using HBC to transmit myoelectric signal for wearable robot hand control.  
Figure 15: (**a**) Measured PDR versus distance. (**b**) Measured BER versus distance.  
the BER is less than \(10^{-6}\) for any of the three electrode structures. This performance is well acceptable at the physical layer of wireless communication. Moreover, the experiment to operate a wearable robot hand has also shown that the developed Tx and Rx modules properly control the robot hand based on the human subject's myoelectric signals.  
The challenge for future research is to extend this technology to wearable robot control using brain waves.  
Conceptualization, J.W.; Formal analysis, J.W.; Investigation, T.I. and I.K.; Data curation, T.I. and I.K.; Project administration, J.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the JSPS KAKENHI Grant Number 19H02139.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Results and Discussions
### System 3: The IEEE 118-Bus
#### 5.3.3 Case 3: Simultaneous Minimization of APL, VD, and VSI\({}^{-1}\)
  
* Case 3.3: DGs with lagging power factor (PF = 0.82)
* 20 & 1.9294/1.3467 & \multirow{10}{*}{1.1372} & \multirow{10}{*}{0.8794} & \multirow{10}{*}{-} \\  & 39 & 3.1467/2.1964 & \multirow{10}{*}{1.1372} & \multirow{10}{*}{0.8794} & \multirow{10}{*}{-} \\  & 46 & 2.3361/1.6306 & \multirow{10}{*}{1.1009} & \multirow{10}{*}{0.9083} & \multirow{10}{*}{89.60} & \multirow{10}{*}{**1.84E-02**} \\ \cline{1-1}  & 74 & 2.3503/1.5174 & \multirow{10}{*}{134.978} & \multirow{10}{*}{0.00600} & \multirow{1}{*}{**1.1009**} & \multirow{10}{*}{0.9083} & \multirow{10}{*}{**89.60} & \multirow{10}{*}{**1.84E-02**} \\ \cline{1-1}  
1. Two main mechanisms are combined to construct the proposed QODELFA; the former finds the global optimum solution using DE, whereas, the latter implements a local permutation using LF. Furthermore, the initial population of the combined DELF is generated by applying the QOBL concept, which consequently increases the diversity and exploration of the initial solutions. As a result, the implementation of the proposed QODELFA ensures the convergence towards the optimum rapidly and reliably. In addition, the elite solutions are elected in each generation, which guarantees the efficient and flexible flow of solutions to the optimal region inside the search space. 2. The obtained results presented in this paper show that the SDs are quite small. Hence, the proposed algorithm's performance stability and robustness are validated. 3. The effectiveness of the QODELFA is also verified by demonstrating the convergence characteristics of the proposed algorithm as given in this paper. Apparently, the optimal solutions are obtained with a small number of iterations. Additionally, the disadvantages are summarized:
1. The computational time for QODELFA is slightly more than some original algorithms discussed in the literature and the paper. This is mainly because of the combined framework of three powerful search mechanisms together; namely, QOBL, DE, and LF, in addition to the implementation of crossover and selection many times during the execution of the proposed algorithm. Nevertheless, using a relatively more powerful computer can overcome this problem. Also, a slight additional computational time can be neglected when much better solutions are obtained. 2. The proposed algorithm has been tested using well-known benchmark functions, including, many minima, bowl-shaped, valley-shaped, and other difficult objective functions besides the OPDG problem solved in this paper. Hence, it is recommended here, to further investigate the performance of the algorithm in other engineering applications.
## 3 Results and Discussion
### Effect of Axial Loading on Elastic Supports
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Approaches**} & \multirow{2}{*}{\(\omega_{i}\)} & \multicolumn{6}{c}{**Nondimensional Axial Loading** (\(\overset{-}{F_{0}}\)**=\(F_{0}\)L\({}^{2}\)/\(EI_{0}\)**)} \\ \cline{3-10}  & & \(-\)**3** & \(-\)**2** & \(-\)**1** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Li et al. & & & & & & & & & & & & \\
[4] & **BE** & 1 & \(-\) & \(-\) & \(3.5160\) & \(5.4082\) & \(6.3830\) & \(8.2497\) \\  & & & 2 & \(17.9631\) & \(19.6928\) & \(22.0345\) & \(24.7759\) & \(26.6100\) & \(30.8390\) \\ Present & \(n=1\) & \(r^{2}=0\) & 1 & \(-\) & \(-\) & \(3.5160\) & \(5.4208\) & \(6.3830\) & \(8.2497\) \\  & & & 2 & \(17.9629\) & \(19.6925\) & \(22.0343\) & \(24.7757\) & \(26.6098\) & \(30.8388\) \\  & & & 3 & \(58.4812\) & \(59.7873\) & \(61.6961\) & \(64.1515\) & \(65.9298\) & \(70.4305\) \\  & & \(r^{2}\
eq 0\) & 1 & \(-\) & \(-\) & \(3.5159\) & \(5.4207\) & \(6.3829\) & \(8.2495\) \\  & & & 2 & \(17.9591\) & \(19.6884\) & \(22.0296\) & \(24.7706\) & \(26.6044\) & \(30.8328\) \\  & & & 3 & \(58.4518\) & \(59.7573\) & \(61.6651\) & \(64.1193\) & \(65.8967\) & \(70.3955\) \\ \(n=2\) & \(r^{2}=0\) & 1 & \(-\) & \(-\) & \(3.5160\) & \(5.4208\) & \(6.3830\) & \(8.2497\) \\  & & & 2 & \(17.9629\) & \(19.6926\) & \(22.0343\) & \(24.7758\) & \(26.6098\) & \(30.8389\) \\  & & & 3 & \(58.4812\) & \(59.7873\) & \(61.6961\) & \(64.1515\) & \(65.9298\) & \(70.4306\) \\  & & & 1 & \(-\) & \(-\) & \(3.5159\) & \(5.4207\) & \(6.3829\) & \(8.2495\) \\  & & & 2 & \(17.9590\) & \(19.6884\) & \(22.0296\) & \(24.7707\) & \(26.6044\) & \(30.8329\) \\  & & & 3 & \(58.4518\) & \(59.7573\) & \(61.6651\) & \(64.1193\) & \(65.8968\) & \(70.3956\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: First three nondimensional natural frequencies of uniform cantilever beams with the axial loading. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(n\)} & \multirow{2}{*}{\(c\)} & \multirow{2}{*}{\(\omega_{i}\)} & \multicolumn{6}{c}{**Nondimensional Axial Loading**} \\ \cline{3-14}  & & & \(-\)**3** & \(-\)**2** & \(-\)**1** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
1 & 0 & 1 & - & 1.2733 & 2.3386 & 2.8588 & 3.1813 & 3.4016 & 3.5618 & 3.6837 & 3.7798 & 3.8577 & 3.9223 \\  & & 2 & 6.  
numerous parameter studies are required, it is assumed that the translational and rotational springs vary by the same values. As illustrated in Figure 15, the compressive load at which the first natural frequency vanished varied significantly depending on the taper ratio. At values below \(\exp(6)\), it exhibited similar compressive load values, regardless of the taper ratio. For elastic springs with a value of \(\exp(6)\) or less, the compressive loading at which the first natural frequency vanished was almost identical, regardless of the taper ratio. It converges at values greater than \(\exp(14)\), and the effect is greater at \(n=2\) than at \(n=1\).
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 3 Materials and Methods
### Gathering Wave Data  
The artificial neural network has been trained to identify waves entering the OWC using measured data. The data were collected by AZTI-Tecnalia at the breakwater of Mutriku in the northern coast of Spain using an Acoustic Doppler Current Profiler (ADCP) depicted in Figure 12.  
ADCP is a hydro-acoustic measurement tool for measuring water current speeds. This device works with the Doppler effect of sound waves returned from particles. ADCPs can send and receive sound signals using a piezoelectric transducer. The distance is estimated based on the time sound waves travel back. The frequency shift of the echo is proportional to the water velocity within the acoustic path. Measuring 3D speed needs at least three beams [36; 37].  
Recently further features were included to ADCPs to measure wave and turbulence data. The specific ADCP instrument for wave characteristic measurements is called the Acoustic Wave and Current (AWAC) profiler. It offers the possibility of measuring the wave's height and direction [38; 39]. The AWAC system uses four transducers to transmit four beams; one middle transducer points vertically upward, and three transducers positioned symmetrically at 120 degrees from each other and are angled 25 degrees from the vertical axis [40; 41].  
The device installed in Mutriku is of Workhorse 600 kHz from Teledyne RDI and it is mounted upside down at the seafloor 1 m in front of the openings of the OWC's capture chamber. This distance is 5 times smaller than the wavelength \(\lambda\) (see Figure 1) which allows the registered wave trains to enter the chambers without the risk of changing. The considered data in this paper are from 12 May 2014 where measurements of the first 20 min were recorded every 2 h and with a sampling period of 0.5 s. The data were gathered and treated by AZTI-Tecnalia and a spectral analysis has been performed. The wave spectrum of the site of Mutriku is illustrated in Figure 13.  
Figure 12: Acoustic Doppler Current Profiler in Mutriku.  
Figure 13: Representative spectral analysis of the waves at the site Mutriku on 12 May 2014 [20].
## 5 Experimental Results
### Comparison with Standard Compressors. **LCA-online:(I)** is compared with other standard compressors in terms of the compression ratio, the consumption of memory space, and the compression time. Table 2(a) shows the result of compression ratio. Table 2(b) gives the result of main memory usage. Space usage is represented by the ratio to input text size. For general texts, the compression ratio of **LCA-online:(I)** is worse compared with other compressors. The repetitive substrings in typical texts are generally short, for example, single words in English texts, short tags in XML documents. Our algorithm seems to be weak to capture such short repetitive substrings. On the other hand, it achieves a higher compression ratio for the repetitive texts because our algorithm can replace long common substrings by the same nonterminals from the analysis of Section 3.3. **LZW** does not work well for the repetitive texts in spite of maintaining the dictionary. This is because LZW(LZ78) parsing does not guarantee to capture long and frequent substrings. **gzip** and **bzip2** also do not work well because they compress the input text in limited segments, not using the whole text. **Re-Pair** and **LZMA** use the whole text to powerful compression and thus they have a better compression ratio than ours. Therefore, as seen in Table 2(b), they require more memory space than the input text. On the other hand, the space requirement of ours and that of **LZW** depend on the output size. Especially, our compression ratios for repetitive texts are very high than that of LZW. Thus, the space usage becomes very small when the input text is sufficiently compressed. Table 2(c) shows the average time per 1 MiB for the compression processing. **LCA-online:(I)** can achieve fast compression independent of the kind of texts. But it is a little slower in the case of general texts than that of repetitive texts. This is because our implementation of the reverse dictionary seems to cause a little slowdown with increasing of the size of the dictionary. The other compressors, especially in **gzip**, **Re-Pair** and **LZMA**, are quite slow depending on the kind of text, especially in biological data with small alphabet. _Algorithms_**2012**, \(5\)
# Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM
## 4 System Performance Evaluation Method
TPR is the ratio of true positive to true positive + false negative:  
\[TPR=\frac{TP}{TP+FN} \tag{8}\]  
Figure 4: ROI extraction result. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Sensitivity** & **Specificity** & **Classification Accuracy** \\ \hline SVM & 0.8360 & 0.9110 & 0.9000 \\ RF & 0.9240 & 0.8600 & 0.9150 \\ AB & 0.7930 & 0.8700 & 0.8750 \\ radiologists 1 & 0. 5000 & 0.8000 & 0.8000 \\ radiologists 2 & 0.5200 & 0.8889 & 0.8500 \\ ASVM & **0.8540** & **0.9383** & **0.9410** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of calcification test results. The ideal breast calcification algorithm should have high sensitivity and specificity. In order to fully evaluate the performance of computer-aided detection and diagnostic systems, a receiver operating characteristic (ROC) curve was proposed. With the sensitivity as the abscissa and the specificity as the ordinate axis, these coordinate points are connected to form the ROC curve. Figure 5 shows ROC distributions obtained by four different methods. The distribution of the sensitivity against the specificity for each method is illustrated with different colors. It is observed that the ASVM algorithm has a higher true positive rate and a lower false positive rate, compared to the AB and RF methods. It is found that as the area under the ROC curve nears to 1, the diagnosis becomes more reliable [25]. Otherwise, the diagnosis is not reliable. Moreover, Figure 4 indicates that the ASVM algorithm has the largest area under the ROC curve. [PERSON] [30] is based on using local features extracted from a bank of filters to obtain a local description of the microcalcifications morphology. They achieved an AUC value of AZ = 0.918 by using 280 mammograms. [PERSON] [31] used a novel methodology based on quantum signal processing and cellular automata to detect microcalcifications in mammograms. They achieved an AUC value of AZ = 0.8297 by using 121 mammograms. [PERSON] [32] integrated the possibilistic fuzzy c-means clustering algorithm and weighted support vector machine for the detection of MC clusters in full-field digital mammograms. The proposed method is evaluated using a database of 410 clinical mammograms and achieved an AUC value of AZ = 0.8676. Our work compared to other similar works that used the DDSM. Our mammograms are enhanced by Contourlet transformation and morphology. The rough ROI is obtained by the improved K-means algorithm, and finally, the adaptive support vector machine (ASVM) is used to obtain an accurate ROI. They achieved an AUC value of AZ = 0.9572 by using 400 mammograms. The difference in the results we get last may be the reason for the amount of data.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 5 Results and Analysis
### The Impact of the Packet Length on the Packet Reception Performance of LoRa  
Noise interference makes data packets more susceptible to individual bit errors. However, the link or transport layer protocol relies on checksums to detect errors in the packets and discard any packets that contain one or more error bits. Therefore, bit errors may cause a decrease in the communication quality, increase the packet loss rate, or increase the transmission delay. However, different packet lengths have different responses to noise interference. Long packets are more susceptible to interference, but short packets have to pay more for the header. Therefore, optimization of the packet length is an important research topic in wireless communication. In this section, we study the impact of the packet length on the packet reception performance of LoRa. A theoretical analysis on the impact of the length of the packet was conducted, and related experiments were then designed for evaluation.  
Figure 8: Experimental evaluation of the impact of CR on the packet reception performance of LoRa.
# ARMONIA: A Unified Access and Metro Network Architecture
## 4 Simulation Results  
We evaluated the performance of the proposed ARMONIA-related algorithms through simulation experiments. Simulations were performed over the metro network topology of Figure 6 that consists of 15 nodes, organized into two hierarchical layers. Layer 1 consists of nine metro-edge nodes, while layer 2 consists of six metro-core nodes. The physical link distances between the network nodes are drawn from a uniform distribution on the interval [10-200] km. The span length was taken to be equal to 80 km. For links shorter than the span length only a pre-boost amplifier was used. Each link supports up to 320 spectrum slots of 12.5 GHz width. For the transmission we assumed coherent BVTs that support different modulation formats (DP-BPSK, DP-QPSK) and baud rates that vary between 30-50 Gbaud. The launch power of the BVTs was taken to be equal to 0 dBm and 13% overhead was assumed for FEC.
## 5 Results and Discussion
### Comparison Results between Different Simulation Tools  
In the next phase, the link budget is calculated using the proposed tool and the same parameters are also inputted in the Airlink, LinkCalc, AfarCommunications, AlcomaLink, and Fairview microwave simulations. Figures 8 and 9 show the variation in the received signal level at the maximum distance. As rule, as distance increases, the strength of the signal received weakens. The parameters of the five simulation tools have little difference, so the proposed scenario is verified by all five simulation tools. For demonstration reasons only we have also submitted in the same diagram the real-experimental curve of the assembly mentioned earlier, though in real life there are certain legal constraints, mainly because in practice the transmitted power is adjusted to around 6 dBm to be in compliance with the ETSI standard [16] (the E.I.R.P value limit is set below 1 W in Europe for frequencies at 5 and 11 GHz). As shown in Figures 8 and 9, all seven curves have the same inclination and degradation degree, but in different levels of received signal strength.
## 3 Results and Discussion
### Effects of Bearing Lengths  
Figure 21 shows the effects of the bearing lengths (\(B_{L}\)) on the curvature of single-grain conditions (\(G_{R}=0.75\)). The ECI values decreased when the bearing lengths increased. In other words, if the bearing length was extended further, it could help strengthen the micro-extruded pins. If the \(B_{L}\) value was increased up to 1.26 mm, which was slightly larger than the exit diameter (1.14 mm), it could almost eliminate the curvature in the micro-extrusion. As a result, the extension of the bearing lengths  
Figure 20: The results comparison among different grain sizes.  
(\(B_{L}\)) from the high strain and opening area could help reduce the curvature effects influenced by the grain sizes, grain boundaries, and grain orientations.  
Based on the results, the following observations and analysis could be made:  
* For single-grain conditions (\(G_{R}\) = 0.75), if the grain sizes and grain boundaries were symmetric along the extrusion direction, the grain orientation was the dominating factor for the curvature. If the grain sizes and grain boundaries were not symmetric along the extrusion direction, the grain boundary at the high strain and die opening area was the dominating factor for the curvature. The interactive influences of the grain boundary and grain orientation also affected the curvature.
* For poly-grain conditions (\(G_{R}\) < 0.75), if the number of grains across the specimen increased up to 2.7, the curvature effects were dramatically reduced. The influences of the grain boundaries and grain orientations were not significant.
* For all conditions, if the bearing lengths (\(B_{L}\)) were extended beyond the exit diameter (\(D_{E}\)), the micro-extrusion pins were straightened.  
Although this work was carried out by using the numerical simulation method, the findings of this research work fundamentally helped explain how the micro-extrusion parts were curved. Future work within this research would apply the FE techniques to the designed grain sizes and orientations under controlled micro-extrusion process parameters. Higher-grade aluminum alloys and multi-material extrusion would also be considered by using this same technique. The results of all the studies could be integrated in order to design and develop single-crystal parts by using the grain selector. The insights gained from this series of ongoing studies could be applied to the control and optimization of lightweight multi-material micro-parts.