esa-sceva/satcom-chunk-collection huggingface dataset:
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 7 Experiments
### Implementation  
We used the following cryptographic algorithms and settings in our prototype implementation. For the digital signature algorithm, we used ECDSA with SHA-256 whose public key size is 256 bits in both implementations of SENTINEL and \"TLS for IoT.\" To derive a flight session key, we used password-based key derivation function 2 (PBKDF2) [26] with HMAC-SHA256, where the number of iterations was set to 10,000, and the random salt was securely generated with cryptographically secure pseudorandom number generator (CSPRNG). To compute the authenticated code value of each drone's message with a flight session key, we used the HMAC-SHA256. We also used CSPRNG to generate drones' nonces (\(n_{d}\)), ground stations' nonces (\(n_{gs}\)), and the pre-master-secret key (\(pms\)).  
For a prototype of SENTINEL, we implemented a drone program running on a MacBook Pro with Intel Core i7-7920 HQ (3.10 GHz) running on macOS Catalina and used the 5G network interface to connect it to the Internet. We also implemented a ground station program running on a desktop with AMD Ryzen Threadripper 1950X (3.40 GHz) running on Windows 10 Pro and used Ethernet for network connectivity. For further information, the source code of our implementation is available at the public GitHub repository ([[https://github.com/rymuff/drone](https://github.com/rymuff/drone)]([https://github.com/rymuff/drone](https://github.com/rymuff/drone)), see Supplementary Materials). The following sections present our evaluation results.
# Lightning Protection of Photovoltaic Systems: Computation of the Developed Potentials
## 3 System Configuration  
One grid-connected, ground-installed PV plant of 100 kWp nominal power was selected as the case study for the lightning performance investigation. This is a typical small PV application that is found across Europe [25]. Such a PV system is usually connected into the low-voltage distribution system. Additionally, considering the current PV market trend, wafer-based crystalline silicon PV modules were selected for the modelling of the under-study PV application. More precisely, in total 400 poly-crystalline mainstream PV modules of 250 Wp nominal power were selected, forming 20 strings of 20 PVs. Additionally, five multi-string inverters of 20 kW nominal power were selected, while four PV strings were connected to the two DC inputs of each inverter. Considering the electrical characteristics of mainstream poly-Si PV modules, it was concluded that the nominal voltage of each string would be roughly 615 V (assuming that inverter operates under MPPT (maximum power point tracker) control and standard test conditions (STC) e.g., irradiance 1000 W/m2, module temperature 25 \({}^{\circ}\)C and AM = 1.5), while the open circuit string voltage was estimated at approximately 690 V at STC and 835 V at module temperature equal to \(-\)10 \({}^{\circ}\)C. Additionally, the nominal string current and the short-circuited string current (assuming that inverter operates under MPPT control and standard test conditions) would be 8.15 A and 8.65 A, respectively. The dimensions of each PV module were 1640 mm by 992 mm. The metallic frame of PV modules and the metallic support construction were supposed to be connected, while the support construction was installed with steel beams, in a depth of 1.5 m under the ground [26]. The perimeter coppery grounding system was installed approximately 0.5 and 0.7 m away from the support construction [27].
# Maximum Operational Fluence Limits for Temporally Shaped Nanosecond Long Pulses
## 2 Pulse Shape versus Pulse Length  
Safe operational fluences for 1-6 ns top hat pulses have been determined from past damage incidents, represented by the orange dots in Figure 1. To describe this data, we have introduced two numerical fits, as illustrated by the curves given by (1). Throughout our analysis, we will denote the maximum fluence of a square pulse as \(I_{max}\).  
\[\begin{split} J_{max}^{fit1}&=c_{1}\bigg{[}1-\exp \left(-\frac{\tau}{c_{2}}\right)\bigg{]}\\ J_{max}^{fit2}&=\frac{c_{3}\tau}{\sqrt{1+\left( \frac{\tau}{c_{4}}\right)^{2}}}\end{split} \tag{1}\]  
The parameter \(\tau\) represents the pulse duration in our analysis. The first fit corresponds to a dependency akin to that reported by [PERSON] et al. [20], notably diverging from the \(\tau^{\frac{1}{2}}\) relationship found in other literature [23; 26]. We employ two fit functions to showcase  
Figure 1: Maximum fluence as a function of pulse length. Used values versus the two used fit functions.  
the sensitivity of our results to fluctuations. The parameters in the fit functions are as follows: \(c_{1}=3.68\,\mathrm{J/cm^{2}}\); \(c_{2}=0.5\,\mathrm{ns}\); \(c_{3}=6.2\,\mathrm{J/(cm^{2}\cdot ns)}\); \(c_{4}=0.6\,\mathrm{ns}\).
# Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on \(l_{1}\) Norm Sparse Inversion
## 2 Methods
### Pseudo-3D Deghosting Based on Sparse Inversion
Therefore, the pseudo-3D deghosting based on a sparse inversion can be expressed as  
\[\left\{ \begin{array}{ll}\min_{s}\left\|x\right\|_{1}\ \ \ \text{s.t.}\ \ \left\|d- \mathbf{L}\mathbf{S}^{H}x\right\|_{2}\leq\sigma\\ \hat{m}=\mathbf{S}^{H}\hat{x}\end{array} \right. \tag{18}\]  
where \(x\) represents the 3D coefficients in the sparse domain, \(\mathbf{S}\) represents the combined operators of the 2D curvelet transform and the 1D wavelet transform, and \(\mathbf{S}=\mathbf{C}_{2}\otimes\mathbf{W}_{1}\) and \(\mathbf{S}^{H}\) are the inverse transforms of \(\mathbf{S}\). The inversion result is the coefficients in the radon domain, and then we obtain the result in the time-space domain by adopting a conventional inverse radon transform.
# Validation of Wired and Wireless Interconnected Body Sensor Networks
## 3 Proposed Energy Efficient Data Transfer in Wireless DigiAID
This loss will be due to increasing distance and path loss. In order to make transmission, free of loss, adequate level of radio transmission is important to maintain the increasing distance. Figure 4: Path Loss for typical wireless body sensor network (WBSN) environment. The proposed algorithm avoids wastage of the expensive battery resources while maintaining a reliable communication using following steps. Initially, all sending nodes will send data packets at lowest transmission power level \(TX_{low}\). With each data packet, its respective RSSI is also received. The PRR of each sending node is calculated in a proposed algorithm to analyze the link quality. At every reception, previous value of \(PRR_{n}[t-1]\) is overwritten with new \(PRR_{n}[t]\) through taking the average. After every estimated time \(t_{est}\) seconds, PRR is checked. Different PRR threshold will be defined for different health parameters depending on the sensitivity of data. For PRR greater than threshold, TX level is maintained same (trigger bits = 10). And if PRR is consistently greater than threshold then TX level will be decreased (trigger bits = 01) to save energy. For PRR less than threshold, TX level is increased (trigger bits = 00) from \(TX_{low}\) to \(TX_{medium}\). It is important to increase the TX level in order to overcome the loss and send data reliably to the BS. If PRR is good with \(TX_{medium}\) then it remains at same level. Otherwise, again TX level will rise to even greater value (i.e., herewith given experimental setup, it will become \(TX_{high}\)). The trigger for increment and decrement of power levels is given to TX from BS via beacon packets. In the given scenario, three power levels are used. This range of power levels will vary with different experimental systems. The time \(t_{est}\) is different for each node depending upon its priority. The priority level varies depending upon the type of medical data transmitted by that node. Here in our experiment, we have kept \(t_{est}\) constant (i.e., \(t_{est}=10\) s) since the algorithm is analyzed here using dummy data. A 2-bit field (i.e., trigger bits) of beacon message is used in a given algorithm to trigger sending node for choosing appropriate TX power. Algorithms 1 and 2 will run at sending node and BS respectively. ```
Set TX = \(TX_{low}\) %Start transmission with lowest power  After every \(t_{est}\) seconds, check for 2-bit trigger field of beacon message if  trigger bits = 00 then  TX is increased with step size of \(xyz\); exit(); elseif  trigger bits = 01 then  TX is decreased with step size of \(xyz\); exit(); elseif  trigger bits = 10 then  TX is same; exit(); end
```  
**Algorithm 1**At Sending Node. Figure 5: Distance variation between transmission node and base station while walking.
## 3 Results
### Effect of BHRF1 on Cell Physiology
#### 3.1.2 Controlled Bioreactor Cultures  
In order to confirm the beneficial effect of BHRF1 on cell physiology (i.e., on cell growth and nutrients usage), cultures of both cell lines were performed in a 2 L bioreactor. Bioreactor cultures should not only allow us to disregard the possibility that the differences encountered previously were related to the changing conditions when culturing cells in shake flasks (non pH and dissolved oxygen control), but also to generate enough data to perform the flux balance analysis. In addition, product concentration (IgG\({}_{3}\)) was measured from the moment the cells reached the maximum density. Figure 2 shows the time profile of cell density and viability, as well as the evolution of glucose, lactate and product concentrations. As the experiments were carried out in the same bioreactor in duplicate but not in parallel (different time points), both replicates are plotted for each cell line, showing comparable behavior. A Napierian logarithm of viable cells versus time is plotted for each replicate in Figure 3, to better understand the changes in cell growth. The results show the same behavior encountered in the previous shake-flask experiments, i.e., KB26.5-BHRF1 showed higher final cell density, higher growth rate and less lactate production. The final titer obtained was also higher for KB26.5-BHRF1.  
Finally, before preforming flux balance analysis (FBA), the specific consumption rates for all the metabolites measured in bioreactor experiments were calculated in the exponential growth phase (Table 1). Values were calculated as the mean of the data obtained from duplicate cultures. In general, KB26.5-BHRF1 showed more efficient substrate consumption (especially for glucose and glutamine), and reduced byproduct generation (especially lactate, ammonia and alanine).  
Figure 1: Time profile of viable cell density (\(\blacktriangle\)), cell viability (\(\lozenge\)), glucose (\(\,\approx\,\)) and lactate (\(\,\equiv\,\)) concentration of batch cultures run in shake flasks ((**A1**): parental KB26.5 and (**B1**): engineered KB26.5-BHRF1). Napierian logarithm of viable cell density versus time (\(\bullet\)) ((**A2**): parental KB26.5 and (**B2**): engineered KB26.5-BHRF1). Data obtained from duplicate cultures in shake flasks; the error bar corresponds to the standard deviation.
## 3 Results
### Effect of Thyme EO on A. alternata at Different Exposure Times  
Considering the results obtained in terms of MGI% and growth rate, the effect of exposure time on _A. alternata_ inhibition was tested in the presence of pure EO (10 \(\mu\)L). A MAM test, prolonged for 42 days, was performed removing the paper disk from Petri dishes at different incubation times (6 h-0.25 day, and 1, 2, and 7 days).  
The growth curve of _A. alternata_ exposed to EO for 6 h (0.25 day) showed a similar behavior with that of the control, in both cases a lag-phase was not present. Concerning the maximum growth rate, a lower value (4.3 \(\pm\) 0.16 mm/day) was obtained for the 6 h exposure to EO respect to the control (5.8 \(\pm\) 0.19 mm/day). On the contrary, at the higher exposure times, an EO effect on lag-phase duration was clearly observed: for 1 and 2 days of exposure, the lag-phase lasted 3 days, while prolonging the treatment (7 days), the fungal growth was inhibited for the longest time (14 days) (Figure 3A). The maximum growth rate was in the range between 3.8 \(\pm\) 1.65 and 4.2 \(\pm\) 0.22 and the values, obtained at different EO exposure times (1, 2, and 7 days), were not statistically different: as an example, a \(p\) value equal to 0.16 indicated as \"ns\" was obtained at 21 st day for exposure time of 1 and 2 days (Student's _t_-test).  
Figure 2: MAM in presence of _A. alternata_: mycelial growth rate (at 21 days: \(p\) = 0.18, “ns”).  
Images of _A. alternata_ colonies, at the different EO exposure times, along with the control, are reported in Figure 4. An inhibition of conidiogenesis, for exposure time from 1 to 7 days, was observed at 14, 21 and 42 days of incubation.
## 2 Review of Model Construction and Parameter Determination
### Anatomical Constraints
The viscoelastic springs connecting the rigid segments representing the bone and wobbling mass elements were originally represented as linear springs [68], but more recently, damped nonlinear passive springs have been used [69]. As it is not possible to measure the viscoelastic stiffness and damping parameters for these springs, they are often determined using a model driven by experimentally recorded kinematics of the individual. The parameters are varied within set bounds by an optimization algorithm to minimize kinetic and whole-body kinematic differences between simulated and experimental performances [66]. The maximum permissible wobbling mass displacement within shank, thigh and trunk segments during this approach have been 5.0 cm, 7.5 cm and 11.0 cm, respectively [13; 36; 60]. These numbers may be excessive, however, with recent experimental research recording shank and thigh soft tissue displacement during drop landings of up to 1.4 cm relative to the underlying bone [70]. Furthermore, the frequently cited source for trunk wobbling mass displacement limits is a study on viscera displacement during hopping [71]. The viscera represents only one portion of the trunk wobbling mass, with the remaining component (including skeletal muscle and adipose tissue) likely displacing to a lesser degree than the maximum 8 cm reported for the viscera in that study. It is additionally unlikely that peak displacements of the various soft tissue elements within the trunk will occur synchronously. The excessive compliance required within wobbling mass representations likely compensates for lack of compliance elsewhere in these models. One possibility is that it reduces the effect of neglecting joint compression when modeling pin joints between adjacent body segments [60]. Finally, more representative wobbling mass constraints may be achieved via viscoelastic parameters that vary as a function of the local actuator activation levels or force-velocity characteristics [72; 73; 74], and realistic oscillation durations should be ensured.
## 3 Methods
### Winding Angle Detection Method
#### 3.2.3 Wire Skeleton Extraction  
Skeleton extraction is a method that can represent connected multi-pixel regions as single-pixel-width shapes and is often used for feature extraction and target topology representation [31]. The skeleton is extracted by successively using the image open operation and image subtraction, where a portion of the pixels are corrupted in each iteration. When the original image is iterated to no pixel points, the set of corrupted pixels obtained in the previous step is used to perform a bitwise \"or\" logic operation with the original image to obtain the complete wire skeleton.  
As shown in Figure 11a, these white pixel dots obtained after skeleton extraction are the skeleton of the wire. As shown in Figure 11b, using the Hoff straight line detection algorithm [32] for feature straight line identification on the skeleton image, the endpoint coordinates of the feature straight line can be obtained, and with these endpoint coordinates, the slope of the wire segment can be calculated, which can then be converted into a winding angle by trigonometric transformation. Figure 11c shows the comparison between the feature straight line and the original image. It should be noted that if the recognition angle is 90 degrees, the calculated slope will be infinite, so when this occurs, the 90-degree winding angle is output.
# Design and Test of Seeding-Picking Mechanism of Fully Automatic Transplanting Machine
###### Abstract  
The seedling retrieval mechanism is a crucial component of fully automatic transplanting machines, significantly influencing the quality, reliability, and efficiency of the transplanting process. Nonetheless, the existing seedling retrieval mechanisms in current transplanting machines exhibit several deficiencies, including substantial damage to seedlings and inadequate retrieval accuracy. To overcome these challenges, we propose an integrated approach combining pneumatic and mechanical techniques to further improve performance. By employing a lower thimble elevation and clamping mechanism, alongside a mathematical model based on the seedling removal process, this method ensures precise seedling extraction and minimizes damage to the root system and substrate. The novelty of this study lies in its ability to reduce the adhesion between seedlings and the holes of the plug plate, thereby minimizing non-destructive extraction of the seedlings and preserving the integrity of the matrix, which is essential for ensuring healthy seedling growth. Moreover, the optimization of the seedling retrieval trajectory enhances the accuracy of the seedling retrieval mechanism while also meeting the requisite speed requirements. Experimental results indicate that at a rate of 72 seedlings per minute, the extraction success rate reached 94.90%, and the casting success rate was 98.53%. The seedling injury rate was only 1.95%, resulting in an overall success rate of 91.69%. These findings confirm that the device meets operational efficiency requirements and delivers effective performance.  
automatic transplanting; seedling retrieval organization; motion simulation +
Footnote †: journal: Applied Sciences
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
## 2 Materials and Methods
### Multi-Channel Electronics
#### Main Board
The main board provides both input and output triggers. The input trigger allows the ultrasound system to be controlled by an external device. Thus, the ultrasound system starts a send/receive event or sequence only if a trigger signal is present. The input trigger is sampled with a frequency of 1 MHz. The output trigger works the other way around. Here, the ultrasound system generates a trigger signal (resolution of 120 MHz) for each send/receive event or sequence and thus controls an external device. Both input and output are decoupled via optocouplers and operate at +5 V TTL levels. Since the required pulse width may vary from application to application, it is possible to define the pulse width of the trigger by the firmware and software. The logic contents of the FPGA are stored in a flash device, which is programmable via a JTAG interface. At the power-on moment, an initialization routine configures the main board FPGA. During this time, the FPGA has no function yet and an additional PLD was installed, which has its logic content directly after the supply voltage is switched on and already serves basic functionalities. In order to be prepared for future tasks, the main board has two card edge connectors, which can be used to connect PCBs to the system that can perform additional functions. The add-on cards can be controlled by the main board FPGA via a 19-bit address and 16-bit data bus. In addition, a large number of other control signals as well as some connection lines to the PLD are connected to the interface.
###### Abstract  
This paper proposes a technological framework designed to mitigate the inherent risks associated with the deployment of artificial intelligence (AI) in decision-making and task execution within the management processes. The Agreement Validation Interface (AVI) functions as a modular Application Programming Interface (API) Gateway positioned between user applications and LLMs. This gateway architecture is designed to be LLM-agnostic, meaning it can operate with various underlying LLMs without requiring specific modifications for each model. This universality is achieved by standardizing the interface for requests and responses and applying a consistent set of validation and enhancement processes irrespective of the chosen LLM provider, thus offering a consistent governance layer across a diverse LLM ecosystem. AVI facilitates the orchestration of multiple AI sub-components for input-output validation, response evaluation, and contextual reasoning, thereby enabling real-time, bidirectional filtering of user interactions. A proof-of-concept (PoC) implementation of AVI was developed and rigorously evaluated using industry-standard benchmarks. The system was tested for its effectiveness in mitigating adversarial prompts, reducing toxic outputs, detecting personally identifiable information (PII), and enhancing factual consistency. The results demonstrated that AVI reduced successful fast injection attacks by 82%, decreased toxic content generation by 75%, and achieved high PII detection performance (F1-score \(\approx\) 0.95). Furthermore, the contextual reasoning module significantly improved the neutrality and factual validity of model outputs. Although the integration of AVI introduced a moderate increase in latency, the overall framework effectively enhanced the reliability, safety, and interpretability of LLM-driven applications. AVI provides a scalable and adaptable architectural template for the responsible deployment of generative AI in high-stakes domains such as finance, healthcare, and education, promoting safer and more ethical use of AI technologies.  
**Keywords:** generative AI; large language models (LLMs); AI safety; content filtering; AI ethics; responsible AI; Compound AI; prompt injection; API gateway; information security
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 3 Experiments and Results  
In this section, several experiments are designed to verify the methods mentioned in the above sections. As shown in Figure 10, they are the experimental area scene. The test path connects area A and B and the total length is approximately 130 m. There is a square area which is about 54 square meters between the two areas and the total experimental area is about 360 square meters. We use a Xiaomi 6 mobile phone that has installed our developed data acquisition software to collect data with a sampling rate of 50 HZ. In order to test the performance of the MCF algorithm, the actual azimuth angles from A\({}_{1}\) to A\({}_{2}\), A\({}_{2}\) to B\({}_{1}\), and B\({}_{1}\) to B\({}_{2}\) are measured beforehand, which are 88.1\({}^{\circ}\), 357.17\({}^{\circ}\), and 268.1\({}^{\circ}\), respectively. The size of the particle set is 100 in the genetic-particle filter and all the data processing and simulation work are done on the computers.