esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Time-Synchronization Algorithm of Heterogeneous Networks
### Setting of the PTP Message Format  
To synchronize the time between the Ethernet and CAN networks, the PTP messages must be converted into a format suitable for the message format of each network through the gateway. Figure 3 shows a comparison of the formats of the existing PTP and CAN messages and a converted PTP message  
Figure 2: End-to-end mechanism in a CAN–Ethernet network with a gateway.  
format for time synchronization between heterogeneous networks. The PTP message consists of a header and timestamp, and the timestamp consists of a 6-byte-seconds field and 4-byte-nanoseconds field. In contrast, because the total size of the data field of a CAN message is 8 bytes, it is insufficient to store the timestamp of the PTP message. In this study, the upper two bytes of the seconds field in the existing PTP message are removed to store the 8-byte timestamp in the CAN message. These two deleted bytes are information used to mark the accurate year; hence, their exclusion from the timestamp should not significantly impact the system. If the accurate time is required, the master can transmit the 2-byte value deleted during system initialization to the slave and set the complete time.
## 10 Beyond ShinyAnonymizer: Future Directions
### Homomorphic Encryption  
Homomorphic encryption preserves confidentiality while allowing functional analysis, because it will enable computations on encrypted data. For example, ShinyAnonymizer employs homomorphic encryption and anonymization tools to allow businesses to analyze sensitive data without exposing them to the risk of leakage and unauthorized access [36]. Abbreviations for homomorphic encryption can be defined as in (10):  
\[E(f(x))=f(E(x)) \tag{10}\]  
where \(E\) is the encryption function, \(f\) is the calculation function, and \(x\) is the plaintext data. This ensures that calculations can be carried out utilizing encrypted data.  
In conclusion, there is an enormous demand for creativity and development in privacy-preserving ideas for handling data. Future versions of anonymization tools like ShinyAnonymizer might provide more robust promises of privacy protection, while permitting critical data analysis and cooperation in healthcare as well as beyond, by going after methods like artificial intelligence (AI), blockchain, differential privacy, federated learning, and homomorphic encryption.
## 3 Results
### CaS Nanoclusters and Cell Survival  
The percentage of apoptotic and necrotic human carcinoma and normal fibroblasts exposed to the CaS dispersion and vehicle control are summarized in Figure 9. There was a modest, yet statistically significant increase in the concentration of cells that were stained as apoptotic and necrotic at 24 h post-treatment in our normal fibroblast cells. Exposure of the normal cells to CaS nanoclusters does not result in a significant difference in the percentage of apoptotic or necrotic normal fibroblasts as compared to the vehicle control. In contrast, the carcinoma cell line exhibited statistically significant increases in apoptotic cells that were statistically significant at 24, 48, and 72 h. Similarly, the concentrations of necrotic cells increased from 48 up to 72 h. It is apparent that any effects of CaS on normal fibroblasts were not detrimental to the overall population and were short-lived, while in contrast, CaS nanoclusters affected carcinoma cells in a rapid and long-lasting manner.  
Figure 4: The open squares and circles represent the dependence of carcinoma cell density fed with normal media and media mixed with CaS nanoclusters. The total amount of Ca\({}^{2+}\) in the nanocluster dispersion is \(4\times 10^{-8}\) moles. The time \(t=0\) h corresponds to the time of the initial dose. The solid black curve and red line are to guide the eyes through the discussion and do not represent a model fit. The spectrum of the dispersion containing the CaS nanoclusters used for the measurements is indicated in the insert at the lower right-hand side of the figure.  
Figure 5: The density of adenocarcinoma cells following a single dose of CaS NS (S\({}_{1}\), S\({}_{2}\), S\({}_{3}\)) and vehicle controls (C\({}_{1}\) and C\({}_{2}\)). The error bars represent the standard deviation of the measurements. The individual measurements performed are indicated in the insert in the upper right corner of each graph. The amount of Ca\({}^{2+}\) in the NS dispersion corresponds to 4 \(\times\) 10\({}^{-8}\) moles.  
Figure 6: The gray bars in the upper and lower panel summarize the effect of a single dose of CaS NS on the density of carcinoma cells and the number of dead carcinoma cells, respectively, as a function of time. The light gray bars in the upper and lower panel represent the density of adenocarcinoma cells and number of dead carcinoma cells in cell cultures fed with media not containing the CaS nanostructures, respectively. The values reported represent the average of 15 measurements performed on different regions of three different flasks. The error bars represent the standard error of the measurements. The dark and light gray bars in the insert represent the dependence of the density of adenocarcinoma cells exposed to a second dose of the CaS NS and exposed to one dose, respectively, on time. The time \(t=0\) h is taken to correspond to the time of the initial dose. The total mol of Ca\({}^{2+}\) in the NS dispersion is \(4\times 10^{-8}\).
# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 5 Results and Discussion
### Results of RQ6  
**Research Question:** What is the distribution of publications in terms of academic and industrial affiliation concerning LLM trustworthiness?  
**Objective:** To assess the level of interest and involvement from academic institutions and industry players in LLM trustworthiness.  
Analysis and Results  
Figure 21 illustrates the distribution of publications based on academic and industrial affiliation concerning LLM trustworthiness. The analysis reveals the following insights:  
* **Academia:** The overwhelming majority of publications come from academic institutions, with 2209 papers (85.9%). This indicates that academic research is the primary driver of studies on LLM trustworthiness.
* **Academia and Industry:** A smaller share of 175 papers (6.8%) involve both academia and industry, reflecting some level of collaboration between the two sectors in this field.
* **Industry:** Industry-only contributions account for 76 papers (3.0%), suggesting that while industry is involved, it is less active in the publication of research focused on LLM trustworthiness.
* **Academia and Government:** Publications from academia and government combined make up 57 papers (2.2%), indicating a smaller involvement from government bodies in this research area.
* **Not Given:** In 39 papers (1.5%), the affiliation is not specified, leaving their academic or industrial origin unclear.
* **Government:** Research contributions from government institutions alone represent 12 papers (0.5%), showing a minimal role for government in this research domain.
* **Industry and Government:** Only three papers (0.1%) involve both industry and government, suggesting a very limited level of collaboration between these sectors in LLM trustworthiness research.  
These findings highlight that academia is the dominant player in the research on LLM trustworthiness, with industry and government institutions having a relatively smaller, though growing, involvement.  
Figure 20: Distribution of research methodologies to investigate LLM trustworthiness.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 4 Statistical Analysis of Channel Propagation Characteristics
### Power-Related Characteristics
#### 4.2.1 Power Decay  
According to S-V channel model, the amplitudes of the MPC decay exponentially due to the multiple reflections in the vicinity of the transmitter and receiver [59]. The propagation delay and the power attenuation (in dBm) of the reflections increase roughly  
Figure 11: Example of a single measured PDP.  
Figure 10: PDF of RMS delay spread values for Op1 and Op2 at measurement location 1. (**a**) Op1 (**b**) Op2.  
linearly with the number of reflections, leading to an exponential decrease in the amplitude of reflections as delay increases [59]. Our purpose is to investigate and show the power decay behavior in case of real deployed network measurements. An exponential decrease was indeed observed in our data, as seen in Figure 12, showing a typical PDP measured at Location 1 and the corresponding exponential fitting, represented as a red dashed line. For the given PDP, the MPC decay follows an exponential relationship with delays, which translates into a linear decay when the received power is in the dB scale. This result is in agreement with the results discussed in the literature [60; 61; 62; 63]. For the fitting, we used MATLAB curve fitting toolbox [64]. This toolbox offers the linear fit with goodness-of-fit (GOF) of 62% for the measured PDP in Figure 12. Similar fittings can be obtained for other measurement locations and both operators.
# Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression
## 4 Case Study  
### Data Sources  
In this paper, SCADA data collected from three wind farms are applied to evaluate the performance of the proposed method. Among them, all wind turbines are horizontal axis wind turbine equipped with an active yaw system and electrical variable-pitch blades. Wind farm 1 (WF1) and wind farm 2 (WF2) are on-shore wind farms located in Hunan  
Figure 4: Results of outlier filtering, where the black points are power points after outlier filtering, and the red points are power outliers.  
Figure 5: The relationship between \(d_{1}/d_{2}\), and the number of iterations, where the red line is the threshold for the end condition of outlier filtering.  
province, China (25\({}^{\circ}\)07\({}^{\prime}\) N, 111\({}^{\circ}\)32\({}^{\prime}\) E) and Yunnan province, China (25\({}^{\circ}\)42\({}^{\prime}\) N, 104\({}^{\circ}\)17\({}^{\prime}\) E). SCADA data in WF1 are collected from 01/01/2017 to 03/31/2017, and SCADA data in WF2 are collected from 03/01/2018 to 05/31/2018. Unlike WF1 and WF2, wind farm 3 (WF3) is an off-shore wind farm built in Jiangsu province, China (32\({}^{\circ}\)31\({}^{\prime}\) N, 121\({}^{\circ}\)11\({}^{\prime}\) E), and the data acquisition time is from 07/01/2018 to 09/30/2018. All raw data are recorded at 1 Hz, and a 10 min average is used in this paper according to [4]. Table 2 lists the detailed information of each wind farms. The first 70% of the measured data are used for training, and the remaining data are used for testing.
## 10 Future and Perspectives
### Quantum Computing for Optimization  
The potential application of quantum computing in the food industry is an exciting frontier. Although still in its developmental stages, quantum computing offers unprecedented computational capabilities for solving complex optimization problems that are currently beyond the reach of classical computing. These systems can process large-scale, multi-variable optimization challenges in record time, providing solutions to problems with numerous conflicting objectives, such as maximizing production yield while minimizing energy usage and environmental impact [89]. Research and development in this area are expected to unlock new possibilities for optimizing complex food production systems.
## 3 Results  
### Specific Growth Rate and Yield of Ulva spp.  
_Ulva_ spp. successfully grew in fish effluent in the lab-scale cultivation system and in the IMTA-pilot system. In experiments LU1 and LU2, higher SGR and yields were attainted in the first two weeks of cultivation (Table 1). In that period, higher SGR were registered in LU2 (_Ulva_ spp. collected in late autumn) in comparison to LU1 (_Ulva_ spp. collected in late spring), although in terms of yield no statistical differences were found.  
In AQUA1, _Ulva_ spp. from _Mondego_ estuary thrived in fish effluent since the beginning of the experiment, whereas for _Ulva_ spp. from coast, positive SGR and yield were only registered after d7 (Table 2). The growth of algae from both sampling sites started to decrease after d14, and negative SGR and yield were registered for d14 and d22. In late autumn (AQUA2), _Ulva_ spp. continuously grew in fish effluent, showing higher SGR and yield in the beginning of the cultivation period, as it was observed for lab-scale experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**SGR**} & \multicolumn{1}{c}{**YIELD**} \\ \hline
**LU1** & & & \\ \hline _d 6_ & 3.82 \(\pm\) 1.61 & 9.98 \(\pm\) 4.59 \\ _d 12_ & 4.94 \(\pm\) 1.32 & 16.85 \(\pm\) 5.88 \\ _d 18_ & 1.24 \(\pm\) 0.40 & 4.83 \(\pm\) 0.84 \\ _d 24_ & 2.94 \(\pm\) 0.67 & 13.28 \(\pm\) 2.58 \\ \hline
**LU2** & & & \\ \hline _d 4_ & 4.92 \(\pm\) 2.45 & 5.20 \(\pm\) 2.72 \\ _d 8_ & 9.98 \(\pm\) 2.56 & 12.34 \(\pm\) 5.22 \\ \hline \end{tabular}
\end{table}
Table 1: Specific growth rate (SGR, % day\({}^{-1}\)) and yield (g wet weight/m\({}^{2}\)/day) of _Ulva_ spp., collected in _Mondego_ estuary, cultivated in fish effluent, for 24 (late spring, LU1) and 9 days (late autumn, LU2), in a lab-scale cultivation system. Values are presented as mean \(\pm\) standard deviation, n = 3.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### Computation Time
#### 4.1.4 Security Impact on Performance  
This assessment measures transmission and reception computation times across all three security approaches. As a result, it provides insights into the impact of MFSPV and DLAPP on the application's performance. Figure 16 presents the total CT results but expresses each time as a relative ratio of the reference task (baseline), which is the non-use of security, making it easier to assess the security impact in performances.  
Figure 14: Total MFSPV operations (per second) in the developed applications for OBU and smartphone X.  
Figure 15: Median security CT results for DLAPP and MFSPV in (**a**) OBU and (**b**) smartphone X (in a total of \(\sim\)400 measurements).  
In transmissions, DLAPP increases the computational time by 35% on the OBU and 44% on the smartphone. In comparison, MFSPV increases it by 21% on the OBU and 4% on the smartphone. DLAPP has a more significant impact on the computing time than MFSPV, as expected according to previous analyses. The same applies to reception times, but greater relative increases can be seen in this case. This difference is understandable since reception times are lower than transmission times (the order of magnitude is smaller). Consequently, even minor increases in reception times result in more pronounced relative changes. Nevertheless, the impact of protocols on reception is still low, increasing it in tenths of a millisecond.
## 1 Introduction
All of the above studies regulate the power between AC and DC sub-microgrids based on fixed droop or proportionality coefficient and do not consider the prioritization of regulation between AC frequency and DC voltage. In recent years, several adaptive or enhanced control strategies have been proposed to improve the dynamic performance and robustness of interlinking converters. For example, an adaptive coordinated optimal control strategy for parallel converters was proposed in [24], which effectively improves power sharing and transient response under unbalanced load conditions. In [25], an improved droop control method was developed by incorporating DC-side power feedback, enabling more accurate regulation under varying operating states. Additionally, ref. [26] introduced a parallel control strategy for bidirectional AC/DC converters that adapts to load changes and enhances coordination between multiple converters. These approaches provide important references and reflect the ongoing evolution of control strategies in hybrid AC-DC microgrids. To solve the above problems, in this paper, an ILC adaptive bidirectional droop control strategy is proposed with the control objective of balancing the AC frequency and DC voltage deviation of the hybrid AC-DC microgrid. The priority of AC frequency control and DC voltage control is adjusted by adding weight coefficient to the bidirectional droop control equations, and in order to reduce the combined deviation of AC frequency and DC voltage, an adaptive method of weight coefficient is proposed to ensure that the ILC prioritizes the control of the quantity with a larger deviation in the process of active dynamic regulation. A hybrid AC-DC microgrid system in islanding mode is established in the Matlab/Simulink simulation platform to verify the effectiveness of the proposed control strategy.
## 4 Field Tests  
### PTDR Application  
The field application tests using the PTDR were carried out at three different positions of the study site at Ny-Alesund, as shown in Figure 3. The PTDR was driven into the ground to a depth ranging from 20 cm to 100 cm. The middle of the electrodes was set as the measurement depth. To place the PTDR module at a certain depth, the hammer was dropped from different falling heights at each blow. The penetration depth was measured by a ruler attached to the connecting rod, as shown in Figure 1. At each depth, the TDR waveforms were recorded within 10 min after the penetration. Figure 7 shows the TDR waveforms recorded along the depth. For all TDR waveforms obtained at the three positions, the travel time increased with an increase in depth. The increasing rate and range of the travel time depend on the location at which the PTDR was driven into the ground. The dielectric constants calculated from the travel time by using Equation (1) ranged from 3.2 to 10.1, which are similar to the typical dielectric constant values of unsaturated soils measured by a conventional TDR probe [11]. At P1, the dielectric constant (\(\mathrm{k_{a}}\approx 3\)) remained constant up to a depth of 60 cm, after which it increased up to 8.2. At P2 and P3, the dielectric constants increased as the depth increased. In the shallow depth, the dielectric constants at P3 were greater than those at P2.  
Figure 8 shows the profile of the volumetric water content estimated from Equation (2). The volumetric water contents ranged from 9.0 to 38.2 m\({}^{3}\) m\({}^{-3}\), which were similar to the range of volumetric water contents monitored near the study site during summer [45]. The high water content at the bottom of the active layer was considered to have originated from the melting ice lens and from the impermeability of the permafrost. After the PTDR was driven at P3 to the depth of 90 cm and extracted, the depth of the water table was measured directly through the penetrated hole at a depth of 80 cm. Unsurprisingly, the volumetric water content greater than 30 m\({}^{3}\) m\({}^{-3}\) at a depth of 80 cm at P2 and P3 was close to the degree of saturation of 100%. The temperature profile measured by the PTDR is plotted in Figure 9, and compared to the temperature profiles monitored by using  
Figure 7: TDR waveforms measured in the field along the penetration depth: (**a**) P1; (**b**) P2; and (**c**) P3.  
thermoouples embedded in the same site reported by [PERSON] et al. [36]. The subsurface temperature measured by the PTDR decreased with an increase in depth, and the temperature at a depth of 90 cm reached 1 \({}^{\circ}\)C. This implies that the active layer was within a depth range of 20 to 90 cm at the study site.
# Task Migration with Partitioning for Load Balancing in Collaborative Edge Computing
## 2 System Model and Problem Formulation
The transmission delay to offload the task from the vehicle to its serving MECS is  
\[T_{n}^{trans}(t)=\frac{\lambda_{n}}{R_{n,m}}\, \tag{2}\]  
The load imposed on the MECS by the task is the computational workload required to complete the task within the delay constraint. As the vehicle has already offloaded its task  
Figure 2: The operation of a MEC network. to the serving MECS and the task is placed in its waiting queue, the task must be completed within a time, excluding the transmission delay under the delay constraint. Therefore, the CPU cycles required to complete the tasks in the waiting queue are  
\[l_{n}=\frac{\eta_{n}}{T_{n}^{max}-T_{n}^{trans}}, \tag{3}\]  
The load of each MECS is equal to the sum of the workloads of tasks in the waiting queue \(Q_{m}^{m}(t)\) of MECS \(m\) at the end of time slot \(t\). \[\zeta_{m}(t)=\sum_{n\in\overline{Q_{m}^{m}(t)}}l_{n}\, \tag{4}\]  
To measure whether the load of each MECS is evenly distributed among the MECSs in the MEC system, the MECS controller calculates the average load difference among the MECSs in the MEC system. \[\partial=\frac{1}{M}\sum_{m=1}^{M}\sum_{m^{\prime}\
eq m}^{M}|\zeta_{m}-\zeta _{m^{\prime}}|\, \tag{5}\]  
As the number of vehicles associated with each MECS is not evenly distributed, the number of tasks offloaded to the MECSs is not even. Therefore, because the workloads imposed on MECSs differ, the loads of some MECSs are high and those of others are low. If tasks are migrated to an overloaded MECS or a particular MECS simultaneously, the delay requirements of tasks may be violated because of the increased time spent in the waiting queue of the MECS. Thus, to increase the satisfaction of QoS requirements and system throughput, we propose a task migration with partitioning method to balance the loads among MECSs in the MEC system, as follows:  
\[min.\ \partial\, \tag{6}\]