esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Pillar Displacements on Geodetic Measurements
## 1 Introduction
The horizontal angle \(\alpha\) is calculated as the difference between the measured horizontal directions [4]:  
\[\alpha=o_{C}-o_{B}. \tag{1}\]  
The bearing between the reference points \(A\) and \(B\) is calculated as follows:  
\[\tan{\
u_{A}^{B}}=\frac{y_{B}-y_{A}}{x_{B}-x_{A}}=\frac{\Delta y_{A}^{B}}{ \Delta x_{A}^{B}}, \tag{2}\]  
while the bearing to the control point \(C\) is calculated as:  
\[\
u_{A}^{C}=\
u_{A}^{B}+\alpha. \tag{3}\]  
The coordinates of control point \(C\) are calculated as follows:  
\[y_{C}=y_{A}+\Delta y_{A}^{C}=y_{A}+d_{AC}\cdot\sin{\
u_{A}^{C}}\ \text{and} \tag{4}\]  
\[x_{C}=x_{A}+\Delta x_{A}^{C}=x_{A}+d_{AC}\cdot\cos{\
u_{A}^{C}}. \tag{5}\]  
Literature (let us name only a few sources: [5; 6; 7; 8; 9; 10]) deals with the influence of the displacement of the signalized points (in our case, the displacement of points \(B\) and \(C\)) and the influence of the centering error (in our example, the displacement of point \(A\)) on the measured horizontal angles; in our paper, we will discuss the influence of these displacements on the position of the control point, i.e., on the coordinates of the control point. The Analysis of the Influence Temperature Displacements of a Pillar on the Position of the Control Point  
First of all, we need to look at the law of propagation of variances and covariances, as we will use this law in what follows. Then, we will discuss the influence of the temperature displacement of the pillar on the determination of the coordinates of the control point on the object, if such a pillar is an orientation point, a survey point, or the control point on the object.
# Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
## 6 Conclusions  
In this paper, we proposed PP-Overgrid, a scalable solution for supporting privacy-preserving DR schemes working in a community of smart buildings, acting as a virtual microgrid regardless of their physical location. Based on a distributed P2P protocol, called Flow Updating, PP-Overgrid is able to estimate of the total power demand of the grid without violating the single users' power profiles. To compute such a power demand in a privacy-preserving manner, we introduced virtual sub-nodes inside each real node of the original Overgrid. Similar to Secure secret sharing algorithms, sub-nodes communicate to the neighboring nodes randomly generated consumption data, guaranteeing that the total sum of aggregated data is equal to the real one.  
Additionally, PP-Overgrid implements two different types of distributed load control to adapt the power consumption of the network to the power constraints communicated by the DSO: a proportional (continuous) reduction of the load and a quantized (probabilistic) activation/de-activation of the loads. We explored the applicability of our scheme using a P2P network simulator and real power  
Figure 11: CDF of the DSO adaptation errors caused by quantized loads and comparison with the binomial distribution.  
Figure 10: Detailed results of the quantized DSO Adaptation, with subnodes \(n=10\) and graph degree \(D=20\).  
consumption traces of residential buildings. Our results show the feasibility of the scheme in matching a desired power constraint, under different network topologies and scenarios.  
[PERSON] conceived the experiments and wrote the paper; [PERSON] performed the implementation and the experiments; [PERSON] analyzed the data; [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript.  
This work has been funded thanks to the National Research Project DEMAND (DistributEd MANagement logics and Devices for electricity savings in active users installations) supported by the Italian Ministry for the Economic Development MISE - identification code: CCSEB_00093.  
The authors would like to thank the anonymous reviewers for their help in improving the quality of the paper.  
The authors declare no conflict of interest.
## 3 Methodology
### Methodological Procedure
This provides robust data support for early warning systems and disaster management efforts. Additionally, the technology's immunity to weather constraints allows for uninterrupted, all-weather, and all-day monitoring, thereby ensuring the consistency and completeness of the collected data. While SBAS-InSAR offers numerous benefits in landslide identification, it is not without its challenges. Specifically, the costs associated with data acquisition and processing are considerable. Moreover, the identification accuracy can be compromised by the interference of complex geological environments and external factors. Simultaneously, this technique exhibits limitations in  
Figure 3: Processing flow for deformation information extraction. precisely delineating landslide boundaries, necessitating the integration of other factors for a comprehensive assessment.
## 2 Materials and Methods
### Interoperability at the System Level  
The concept of creating interoperability at the system level consists in replacing the proprietary parts of the I/O with open standards and their open-source implementations. Despite the application of different strategies for the new network standards 5G and WiFi 7, such as the non contiguous channel bonding, multi-link operation or the cooperative AP, radio based networks cannot guarantee a low latency or deterministic timing behavior [26].  
For wired networks, Ethernet is by far the most widespread and therefore obsolescence-proof standard. In this respect, Ethernet has no competitors [27] (p. 308).  
Suitable wired standards come from the IoT and automation sectors. Authors in [28; 29] provide methods for the technology selection for the two sectors. Real-time capability is not mandatory for use in most cases in the HiL test benches in avionics. Examples are the domains of the cabin or cockpit. The situation is different for flight controls. Standards for communication under real-time conditions, according to (category 3, according to IEC 61784-2), using Ethernet can only be found in automation technology.  
These requirements are met by the PROFINET IRT, SERCOS III and EtherCAT standards. [16] (p. 145) EtherCAT is the only standard that achieves hard real-time conditions in any topology. In addition, SERCOS III and PROFINET IRT can only operate with additional proprietary hardware, which brings additional obsolescence problems. EtherCAT is a proposed standard for the HighLift scenario. Different systems under test may allow different standards with no timing restrictions. Here, common standards, such as MQTT, are sufficient. In this article EtherCAT is an example of suitable candidates.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.3 PAC R6a Sensor  
This sensor is a nominally 12.7 mm diameter design for low frequency applications and was supplied with a surface-wave velocity calibration by the manufacturer (Physical Acoustics Corp. or PAC, Princeton Junction, NJ, USA). Thus, it is appropriate to compare our bar-wave calibration result to the PAC calibration. Measurement was taken at L = 300 mm; that is, the center of an R6a is placed at this distance. The receiving sensitivity was obtained by subtracting the averaged displacement magnitude spectrum (the red curve in Figure 3b) from the output-voltage magnitude spectrum. Results are shown in Figure 11. The solid blue curve is the bar-wave displacement sensitivity and the dashed blue curve is the normally incident wave displacement sensitivity. Note that the latter exhibits a broadband response from 50 kHz to nearly 900 kHz with the highest peak at 277 kHz. Again, these two are of similar magnitude, but the bar-wave displacement sensitivity has a higher frequency dependence, peaking at 16 dB at 52 kHz. The corresponding velocity sensitivity (red curve) has the maximum  
Figure 10: (**a**) Receiving sensitivity of Valpey-Fisher Pinducer. The displacement receiving sensitivities are shown in blue curves and velocity sensitivities are in red. Solid lines are for the bar-wave data, while dashed curves are for the sensitivities to normally incident waves; (**b**) Receiving sensitivity of DWC B1080-LD. The displacement receiving sensitivities are shown in blue curves and velocity sensitivities are in red. Solid lines are for the bar-wave data, while dashed curves are for the sensitivities to normally incident waves.  
sensitivity of 86 dB, also at 52 kHz. The manufacturer's surface wave data (green curve) was available to 1 MHz and peaked at 77 dB at 54 kHz with a good spectral shape matching to the bar-wave result up to 1 MHz, except over 150-300 kHz. Three additional R6a sensors were similarly tested and their results are given in Appendix C. The similarity of bar-wave and surface-wave velocity sensitivity was also observed. The average peak sensitivity difference of the four tested R6a was 9.0 dB, while the peak frequency difference was 1.9 kHz.  
While details of the surface-wave calibration provided by PAC are not available, it is concluded that the present method provides a practical approximation of the surface-wave calibration data without the need of a large calibration block, but on the basis of verifiable laser interferometer measurements. It should be noted that the NIST AE-sensor calibration service was closed for more than ten years. Thus, the reference standards at various manufacturers are most likely out of date at this time and the present bar-wave calibration method can be substituted in the absence of the NIST standard.
## 1 Introduction
In order to generate plasma in atmospheric air, an electric field higher than 30 kV/cm is required [21]. However, high-voltage devices imply higher costs and constraints in portability due to safety issues. Piezoelectric transformers amplify a low-voltage input to generate breakdown in air through electromechanical resonance [21]. Low-voltage CAP sources have been demonstrated to effectively inactivate bacteria and have shown potential for clinical use due to their low operating cost and low safety risks [22]. Specifically, an air-fed piezoelectric plasma pen showed antimicrobial activity against, _Staphylococcus (S.) aureus_, _S. epidermidis_, _E. coli_, _P. aeruginosa_, and _C. albicans_ on growth media [22]. In this study, the aim was to evaluate for the first time the effectiveness of a piezoelectric CAP device that used ambient air to produce plasma for the inactivation of Gram-positive _L. monocytogenes_ and Gram-negative _Salmonella_ deposited on stainless steel considering the plasma exposure time and distance from the plasma source as variables. Moreover, BSA was used as a soil accumulation model on stainless steel to simulate real-life conditions; thus, the effectiveness of this technology on clean or protein-soiled surfaces was also studied. To the best of our knowledge, this is the first study investigating the inactivation efficiency and inactivation kinetics of a piezoelectric CAP against foodborne pathogens on a food contact surface.
# Converting MST to TSP Path by Branch Elimination
## 3 MST-to-TSP Algorithm
### All-Pairs Variant  
The second variant optimizes the removal and addition jointly by considering all pairs of links that would perform a valid swap. Figure 9 demonstrates the process. There are 6 removal candidates, and they each have 3-6 possible candidates for the addition. In total, we have 27 candidate pairs. We choose the pair that increases the path length least. Most swaps increase the length of the path but theoretically it is possible that some swap might also decrease the total path; this is rare though.  
The final result is shown in Figure 11. With this example, the variant finds the same solution as the as the greedy variant. However, the _parallel chains_ example in Figure 12 shows situation where the All-pairs variant works better than the greedy variant. Figure 13 summarizes the results of the main MST-based algorithms. Nearest neighbor works poorly because it depends on the arbitrary start point. If considered all possible start points, it would reach the same result as Prim-TSP, Kruskal-TSP and the greedy variant of MST-to-TSP. In this example, the all-pairs variant of MST-to-TSP is the only heuristic that finds the optimal solution, which includes the detour in the middle of the longer route.  
Figure 11: Results of the various algorithms for the toy example.  
Figure 10: Example of the greedy variant of MST-to-TSP. At each step, the longest remaining branch link is removed. The two trees are then merged by selecting the shortest of all pairwise links across the trees.  
Figure 9: All possible removal-addition pairs.  
There are \((2\cdot 3)+(1\cdot 3)+(2\cdot 3)+(2\cdot 3)+(1\cdot 3)+(1\cdot 3)=27\) candidates in total.
## 1 Introduction
### Related Work  
Previous research has explored GPU-based AES encryption, primarily focusing on leveraging GPU architectures to accelerate encryption processes. For instance, NVIDIA's implementation demonstrated that GPU-based encryption could achieve speeds almost 1.7 times faster than traditional CPU-based methods. Similarly, studies have achieved up to 60 Gbps throughput on NVIDIA Tesla GPUs, showcasing significant performance improvements over sequential CPU implementations [19].  
Hybrid computing approaches combining CPUs and GPUs have also been investigated to enhance computational efficiency. [PERSON] and [PERSON] proposed a method to balance workloads between CPUs and GPUs, aiming for efficient processor utilization and acceleration. Additionally, hybrid co-processing has been applied to various computational problems, demonstrating the potential of collaborative CPU-GPU utilization [20, 21].  
However, these studies often focus on specific aspects of CPU-GPU interactions or target particular applications without implementing a fully adaptive workload balancing strategy. Our framework differentiates itself by introducing a comprehensive adaptive hybrid approach that dynamically leverages CPU strengths for tasks such as metadata and small block encryption, while fully exploiting GPU parallelism for bulk data processing. This balanced methodology offers clear performance and efficiency advantages not thoroughly addressed in prior studies.
# Take-Over Requests after Making in Autonomous Vehicles
## 2 Related Works
### Physiological Data Role and Its Aims in Automated Driving  
Higher quality intelligibility services require the AV to be aware of the driver's physical and cognitive states, as user experience depends on the driver's activities and states of affect and cognition. The importance of the driver's state is known as 'opportune moments' to intervene [51], or 'user interruptibility', which determines driver availability of intelligibility services [36].  
Various approaches for detecting a driver's state and distraction have been developed by measuring driving performance, such as braking, steering, and other driving-related measures [52], and by tracking eye and head position [53; 54] and physiological responses. However, to better understand the transition and fluctuation of the driver's state in the event and time series, we adopted a method of measuring physiological responses. Prior research has measured multi-dimensional physiological data to understand driver distractions and opportune moments for when to intervene during a TOR [33; 55]. [PERSON] et al. examined drivers' psychophysiological responses to TORs in different NDRTs, traffic density, and TOR-lead time conditions [55]. [PERSON] et al. proposed an attentive user interface and evaluated whether its features precisely timed TOR notifications at task boundaries (sending a message), and showed that TOR notifications led to better TOR/NDRT performance, less stress, and higher trust/acceptance [33].  
Ultimately, the goal is to build a machine learning model to continuously monitor the driver's state [55; 56; 57; 58], as physiological state is a significant reference for complementary indicators of trust. [PERSON] et al., for instance, collected physiological data to understand the dynamic change of drivers' trust during automation for the purpose of improving the safety and performance and automated driving [59]. Many studies have attempted to accurately verify drivers' physiological states and measure the degree of arousal via electroencephalography (EEG), galvanic skin response (GSR), electrocardiography (ECG), eye-tracking data, and more [57; 60; 61] in both manual and automated driving. Heart rate (HR) is especially useful as an indicator of operator effort [62], and GSR represents the mental workload in a driving scenario [63; 64].  
Building on this prior work, we collected driver's physiological responses as a measure of various psychophysiological states, including arousal, cognitive load, and trust.  
In this study, HR, GSR, and EEG were collected to observe patterns between the modalities and feedforward timings of TOR conditions and to better understand and interpret the subject's situation. Unlike simple observation, sensor data confirms the subject's in-situ condition, information with which the AV system can provide optimal information for the driving context. GSR and HR were analyzed to support our results and for triangulation.
## 2 Methodology of Research
### Solution Method Under Limited Available Data
#### 2.2.1 The Developed XGBoost Algorithm for Power Consumption Prediction  
The main objective of this algorithm is to establish the true relationship between the input features and the target output by using an ensemble model composed of multiple regression trees. This model is formulated as follows:  
\[\hat{y}_{i}=\sum_{k=1}^{K}f_{k}(\textbf{X}_{i}) \tag{9}\]  
where **X** refers to the vector of the input features, \(y\) stands for the target output (in our case, the power consumption of a typical cooling plant), and \(f\) demonstrates the \(k^{\text{th}}\) regression  
```
Input: Data and initial prediction (e.g., mean of \(y\)) Output: Optimal solution \(\hat{y}\) procedure OptimalSolution Initialization: Set initial prediction for\(k=1\) to\(K\)do  Compute gradients \(g_{i}\) and Hessians \(h_{i}\) based on current predictions  Fit regression tree \(f_{k}\) to these gradients  Determine optimal splits maximizing gain (with regularization)  Assign a weight to each leaf (using the closed-form solution)  Update model: \(\hat{y}_{i}^{(k)}\leftarrow\hat{y}_{i}^{(k-1)}+f_{k}(\mathbf{X}_{i})\) endfor Final Prediction:\(\hat{y}_{i}\leftarrow\sum_{k=1}^{K}f_{k}(\mathbf{X}_{i})\) endprocedure
```  
**Algorithm 1** Steps of the Proposed Solution Technique  
Time-Based Features:  
\(\diamond\)   Hour of Day: Captures diurnal consumption patterns.  
\(\diamond\)   Season: Accounts for seasonal variations.  
\(\diamond\)   Temperature-Based Features:  
\(\diamond\)  \(T_{t}^{in}\): Represents the water intake temperature, a critical factor that significantly affects power consumption. During the training process, this parameter is modeled using two input features: Temp_in_low and Temp_in_high. The former captures how much the water temperature falls below the threshold, while the latter reflects the amount by which it exceeds the threshold.  
\(\diamond\)  \(P_{t}^{CO2}\): Previous Hour Power Consumption: Incorporates the previous hour's power consumption to capture temporal dependencies and inertia in power usage.  
As can be seen, each feature set captures distinct consumption patterns, and combining them with the gradient boosting framework of the XGBoost algorithm enhances predictive performance.
## 4 Results  
### Validation of Hygro-Chemo-Thermo Transport Model  
#### 4.1.1 Experimental Validation of Chloride Ingress Under Temperature Gradient  
To validate the derived numerical model, the experimental test data produced by [PERSON] et al. were adopted [35]. The experiments were conducted using a chloride ponding method under varying initial temperature conditions to investigate the effects of temperature variation on chloride penetration. The key test variables were the initial temperature conditions, the water-cement ratio, and the relative humidity differences between the top surface and the interior of the concrete. Concrete specimens were prepared with two different water-cement ratios, 0.55 and 0.65, and the concrete mix design was as shown in Table 1. All specimens were stored in the air for 24 days to create varying humidity conditions, considering the effect of the moisture gradient. To enhance the chloride ingress into concrete, a ponding reservoir was located on the top surface of the concrete specimen with a plastic sleeve and was filled with 5% sodium chloride (NaCl) solution. A steel clamp and silicone were used to secure the plastic sleeve. For the initial temperature condition, the top chloride solution was heated using a heating stick to 35 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, establishing temperature gradients as driving forces. The numerical and experimental results were compared for specimens exposed to 5% NaCl under these temperature gradients over 3, 6, 12, and 24 days. Figure 2 illustrates the experimental setup, and Table 2 presents the total chloride profile based on varying water-cement ratios, temperatures, and exposure durations across different depths. Each specimen was embedded with two SHT75 Sensirion humidity and temperature sensors. The sensors were positioned at depths of approximately 20 mm and 40 mm from the top surface, to measure the internal temperature of the concrete. Data were recorded at 15 min intervals using an EK-H3 Sensirion data logger.