esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Characteristics of a Composition of PSJA Array  
As shown in Figure 12, with the increase in the actuator number, the energy deposited in a single actuator decreases. As a result, the performance of the actuator ought to slow down. The history of jet velocity and gas density at the throat outlet is shown in Figure 13a,b, respectively. Obviously, the jet velocity and duration decreases with the increase in the actuator number. However, the gas density at the throat outlet increases with the increase of actuator number. As the gas kinetic energy is proportional to the gas density, the growth of gas density benefits the gas kinetic energy.  
Figure 11: The total discharge energy and discharge efficiency versus actuator number.  
Figure 10: The discharge characteristics with different actuator numbers. (**a**) is the energy deposition power and (**b**) is the discharge energy in one actuator.  
The jet peak velocity, duration time, and mass ejected with different numbers of PSJA are calculated and results are as shown in Figure 14a. The peak velocity is defined as the maximum velocity at the throat outlet. The definition of the jet duration time can be found in Figure 8a. The mass ejected means the total mass of gas left in the PSJA chamber throughout the ejection process. These parameters decrease with the increase in the actuator number, but the rate at which they decline slows. These parameters are normalized by dividing the value with that of only one actuator, as shown in Figure 14b. This figure shows that the mass ejected is greatly influenced by the actuator. The influence of actuator number on jet duration time is weakest compared with the other two parameters. When the actuator increases to 20, the jet duration time decreases by 33%, while the peak velocity and mass ejected decrease by 54% and 62%, respectively.  
The jet kinetic energy can be calculated as shown in Equation (25), where \(\rho_{\mathrm{o}}(t)\) and \(u(t)\) are the jet density and velocity at the throat outlet face; \(A_{\mathrm{t}}\) indicates the throat outlet area; \(T_{\mathrm{jet}}\) represents the jet duration time. The variation of jet kinetic energy of a single actuator with actuator numbers is shown in Figure 15. As it is proportional to the third power of the velocity, the kinetic energy decreases quickly with the increase in the actuator number. When the actuator number increases to 20, the jet kinetic energy decreases by 92%.  
\[E_{\mathrm{kinetic}} = \int_{0}^{T_{\mathrm{jet}}}0.5\rho_{\mathrm{o}}(t)u^{3}(t)A_{ \mathrm{t}}dt \tag{25}\]  
Figure 14: The gas parameters versus actuator number. (**a**) is the real value and (**b**) is the normalized value.  
Figure 13: The history of gas velocity and gas density at the throat outlet versus actuator number. (**a**) is the gas velocity and (**b**) is the gas density.
# Prediction and Comparative Analysis of Software Reliability Model Based on NHPP and Deep Learning
## 2 Software Reliability Model
### Software Reliability Model Using Deep Learning  
#### 2.2.1 Deep Neural Network  
An artificial neural network is based on the biological structure of the human brain and consists of nonlinear neurons. Based on the information received by the five senses, humans think and judge last, as information moves from one neuron to the next. The neurons (nodes) present in each layer of an artificial neural network are the most basic information-processing units. The network structure consists of three layers: input, output, and hidden layers. The input layer plays the same role as the information obtained by human beings through their five senses or thoughts, whereas the output layer refers to thoughts or judgments based on this. Conversely, the hidden layer learns to think or judge while receiving signals from the previous layer and passes them to the next layer through an activation function. A deep neural network (DNN) refers to a form in which the number of hidden layers is large in an artificial neural network [22; 23]. When data is inputted from the input layer, the next hidden layer moves to the next layer through an activation function after combining the input value, weight, and bias and is transmitted to the output layer. Equation (7) expresses the passing from each layer. It consists of the product of the transformed \(z\) value (input value) in the previous layer, the weight, and the sum of the biases.  
\[u_{ij}=b_{i}+\sum_{k=1}^{j}W_{i,k}z_{i-1,k} \tag{7}\] \[z_{ij}=f\big{(}u_{ij}\big{)}\]  
For each layer, the activation function when moving to the next layer mainly uses the sigmoid, hyperbolic tangent, and ReLU functions. As the activation function used when outputting the result passed to the last output layer is a continuous variable, an identity function was used.  
The final predicted value was obtained using the above process. The difference between the predicted value \(y\) and the actual value \(t\) is calculated as a loss function. To minimize the difference between these values, the function is updated to have the minimum value based on the differential value for each weight and bias.  
\[Loss=\sum_{i=1}^{n}(y_{i}-t_{i})^{2} \tag{8}\]  
The weight update implies that the loss function is updated by the learning rate (\(\alpha\)) based on the partial derivative value for a specific weight using the backpropagation algorithm. The deep neural network learns through a series of processes and outputs a predicted value that approximates the actual value.  
\[W_{t+1}=W_{t}-\alpha\frac{\partial}{\partial W}Loss(W) \tag{9}\]
## 2 Experimental Details
### Photocatalytic Reduction of Carbon Dioxide  
The photocatalytic reaction to convert carbon dioxide into methane was conducted in a sealed 275 mL Pyrex glass reactor. A total of 100 mg of the as-prepared samples were uniformly dispersed on the rounded base of the Pyrex glass reactor, which had an area of approximately 18 cm\({}^{2}\). The Pyrex glass reactor was then inflated with CO\({}_{2}\) gas (99.999%) at a flux of 300 mL/min for 45 min to exclude air (e.g., O\({}_{2}\) and N\({}_{2}\)). Subsequently, 400 mg of deionized water was injected into the Pyrex glass reactor, which was then sealed. The Pyrex glass reactor was positioned 25 cm directly beneath a 500 W Xe lamp. Every 2 h, 0.4 mL of gas was extracted to determine the yield of CO and CH\({}_{4}\) using a gas chromatograph (GC 2010 plus, Shimadzu, Japan) modified with a FID detector, a packed column, and a converter loaded with Ni catalysts. A certain amount (0.4 mL) of CO and CH\({}_{4}\) gas, with calibrated concentrations, was initially detected using a gas chromatograph to measure the peak area associated with CO and CH\({}_{4}\). Consequently, the concentrations of the resulting CO and CH\({}_{4}\) gas can be determined based on the respective peak areas. The quantity of CO and CH\({}_{4}\) produced can be calculated by multiplying the volume of the reactor.
# Detecting and Mitigating Attacks on GPS Devices
## 5 Detection Techniques and Their Comparison
### GPS jamming Detection
#### 5.1.3 ML-Based Methods for Jamming Detection  
These approaches employ multiple properties of the received signal as features in a supervised ML model. The authors of [72] employed an ML approach to GPS jamming detection. A testbed was developed to generate synthetic GPS signals utilizing GNU Radio with a National Instruments B-210 Universal Software Radio Peripheral (USRP) both with and without the presence of a variety of types of jammer signals that were fed to a u-blox M8 GPS receiver. Four types of jamming signals were considered: barrage noise, single tone, successive pulse, and protocol-aware (P-aware). The authors then performed feature analysis to determine the most salient features in the resulting signal data, such as position accuracy, HDOP, and COP, and used those features for each dataset to train various types of ML models. The performance metrics included detection rate (DR), misdetection rate (MDR), false alarm rate (FAR), and F-score (FS). The authors established that a neural network approach yielded the best performance, with DR, MDR, FAR, and FS values of 98.9%, 1.39%, 0.28%, and 0.989, respectively. K-Nearest Neighbor (KNN) yielded similar performance but suffered from much longer prediction times. Random Forest approaches also performed well but experienced longer prediction times. Other approaches, such as Decision Tree (DT) and Support Vector Machine (SVM), yielded poor performance. This neural network approach is promising and may eventually yield a good, generalized solution to GPS jamming detection; however, an unanswered question is how this approach will perform in complex propagation environments, where the GPS signal and corresponding positioning quality may be rapidly fluctuating. Furthermore, it is unclear if the computational requirements for this type of approach are compatible with embedded applications.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 4 Wideband Antenna for LSA Bands and LTE Mid and High Bands
### Design Procedure and Working Principle
The effective length of the element MNSTS is 41 mm, which is equal to a half-wavelength at 3.65 GHz. The NSTS loop improves the operating bandwidth in the LTE high band and also helps to achieve null-free x-y plane radiation. Now, let us study the reflection coefficient of the L-shaped loop element NSTS excited by the middle element MNO, as shown in Figure 14 (loop STS with MNO curve), in order to further understand the operation of the right-hand element. This antenna resonates at 3.45 GHz, which is close to the half-wavelength mode since the effective length is 41 mm. In this study, the elements on the left side of QOU (Figure 12b) are removed from the design. The predicted current distributions of this antenna at three frequencies, 1.8, 2.6, and 3.6 GHz, are shown in Figure 15. For better presentation, the vertical L-shaped load is unfolded along the +y axis. It can be seen from Figure 15a that the antenna resonates in the quarter-wavelength mode at 1.8 GHz since only one minimum can be observed (in the vertical plate close to the edge point P). Weak currents can be seen in the loop NSTS at this resonant mode but no nulls is observed. Two minima can be seen (in the vertical plate close to the edge QP and in the upper arms of the loop STS), meaning that the antenna resonates in the half-wavelength mode at 2.6 GHz, as shown in Figure 15b. The current distributions in Figure 15c illustrate that the directions of the currents are the same for the upper and lower arms of the loop STS and a null can be seen at point T. Hence, the loop monopole antenna resonates in the half-wavelength mode at 3.6 GHz. In addition, the left element PQUR has a strong current at 3.6 GHz but no null can be observed.
# Performance Analysis of Software-Defined Networks to Mitigate Private VLAN Attacks
## 5 Results
### Multiple-LAN Scenario
Finally, the average power consumption for each mitigation strategy is shown in Figure 9. In the ACL-based approach, at the core level, the router consumes slightly more power because the CPU usage is much more intensive than in the SDN-based approach. On the other hand, since the switches are not operating intensively, there is no significant difference at the access level, regardless of the mitigation strategy used.
## 2 Current Issues, System Rationale and Objectives
### Objectives  
Adhering to the above observations, the aim of the proposed AR HUD system is to reduce the driver's distraction by the current attention-commanding infotainment devices to improve driver safety. To achieve this, the researchers have employed various emerging technologies and strategies in the system design and have evaluated a prototype system within a VR simulation environment. The main research objectives for this work are the following:  
1. Design interrupt strategies that could regulate the incoming information from various sources. These strategies will collect, categorise, and prioritiise the incoming data, and release it at safe intervals/points.
2. Provide a minimalistic visual interface (AR) which will be projected in a visible yet non-distractive position within the driver's field of view through a medium-size HUD system. This aims to enable the driver to maintain eyes on the road whilst receiving data or interacting with the HUD interface.
3. Employ gesture recognition for the interaction with the AR visual interface through the minimal movement of the hands, aiming to maintain hands at the steering wheel at all times.
4. Evaluate the system in a VR simulator, collecting data on collision occurrence, speed, lane positioning, distance from neighbouring vehicles, and driver's response times, with a typical HDD versus the prototype HUD interface.
5. The evaluation will further assess the proposed system's acceptability and perceived ease of use and safety.  
The following section presents the implementation of the aforementioned objectives, the development process and challenges encountered during the design and development of the proposed system.
## 3 Case Study: Entire Frame
### Impact of the GACOS Correction  
We investigate the impact of the GACOS correction at Step 0-3. In terms of SB interferograms, the STD of unwrapped phases for each entire interferogram is generally reduced (from 6.7 rad to 4.2 rad on average, from 6.0 rad to 3.9 rad on median), which indicates the GACOS correction significantly mitigated the tropospheric noise (Figure 7 and Figure S1).  
Regarding the result of the LiCSBAS processing, without the GACOS correction, the overall velocity is quite similar, and the STD of the difference in LOS velocity between InSAR and GNSS is 2.4 mm/yr, which is slightly larger than that with the GACOS correction (1.9 mm/yr; Figure 8).  
The time series without GACOS at the distal position 950179 shows clear false seasonal displacements, presumably due to tropospheric noise, which has a seasonal correlation and cannot be removed by the spatiotemporal filter at Step 1-6 (Figure 6c, Figure S5, and Figure S6). The impact of the residual tropospheric noise depends on the distance from the reference point; far points tend to have larger time series STDs (Figure 6d). These results suggest that the GACOS correction plays an important role in more accurately retrieving the time series, especially at distances far from the reference point.  
Figure 7: Correlation diagram of the STD of unwrapped phases in the 306 interferograms before and after the GACOS correction. The grey line denotes a 1:1. The STD decreased from 6.7 rad to 4.2 rad on average and from 6.0 rad to 3.9 rad on median by the GACOS correction.
## 4 Simulation Results  
### OFDM Communication System  
This section deals with a simulation model of the OFDM communication system, as described in Figure 9, regarding RF and NB-PLC links. As depicted in Figure 9, the data are processed using the IFFT algorithm, which converts the time-domain signals into frequency-domain signals. This operation splits the signal into multiple subcarriers which are assigned to specific data symbols. According to the selected standards, pilot symbols are added for channel estimation and equalization purposes. Furthermore, when transmitting data between the SM and DC, an appropriate modulation scheme is crucial. The choice of modulation directly impacts the efficiency and reliability of data transmission. At the receiver side, a zero forcing (ZF) equalizer is used in order to mitigate the effects of channel distortion and interference. The performances of simulated OFDM communication chains are evaluated by computing the BER for each value of the signal-to-noise ratio (SNR), reflecting the accuracy of data transmission and representing the ratio of erroneous bits to the total transmitted bits. All simulations are implemented on Matlab tools.
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 10 Solar Sails  
### Operating Principle  
A solar sail is a form of propellant-less spacecraft propulsion system that generates thrust by means of momentum exchange with the incoming solar radiation [128]. Solar sails have a flat surface  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Company/Institution with Location** & **Engine** & **Thrust (mN)** & **Ly (o)** & **Power (W)** & **Propellant** & **Heritage** & **Remarks** & **Ref.** \\ \hline Mass Space Ltd. & & & & & & & & & \\ Southampton, UK & & & & & & & & & \\ \hline Primes Aerospace Company & & & & & & & & & \\ Redmond, WA, USA & & & & & & & & & \\ \hline Bausk, Natick, MA, USA & MPACS & 0.144 & 830 & \textless{}10 & PTFE (Teflon) & - & & & \\  & & & & & & & & & \\ Bausk, Natick, MA, USA & BmP-220 & 0.14 & 536 & 7.5 & Teflon & - & & & [9-30] \\ \hline GWU, Washington DC, USA & & & & & & & & & \\ \hline University of Illinois, & & & & & & & & & \\ Champaign, IL, USA & & & & & & & & & \\ \hline Würzburg University, & & UWER Acc & & & & & & & \\ Würzburg Germany & & Thruster & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of Pulsed Plasma and Vacuum Arc Thrusters.  
and are usually made of thin reflective material supported by a lightweight deployable structure. As they do not use a propellant, solar sails by definition, possess infinite specific impulse [48]. However, the main drawback of a solar sail is very low thrust levels resulting in a long time to gain appreciable momentum change. The generated force vector (\(\mathbf{\tilde{f}_{srp}}\)) on a solar sail is a function of the solar radiation pressure (\(P\)), surface area of the sail (\(A\)) and angle of attack (\(\alpha\)) [129]. Accordingly, the acceleration is obtained from [PERSON]'s Second Law as described below:  
\[\mathbf{\tilde{a}_{srp}}=\frac{\mathbf{\tilde{f}_{srp}}}{m_{s}}=\frac{2 PA\cos^ {2}(\alpha)\mathbf{\tilde{n}}}{m_{s}}, \tag{20}\]  
where \(\mathbf{\tilde{n}}\) is the sail normal vector and \(m_{s}\) is the mass of spacecraft.
## 3 Measurement Results
### Measurement Results Using the Implemented Sensor on a Printed Circuit Board (PCB)
voltage to the input capacitance. The gain of the capacitive-sensing mode is expressed by the following equation:  
Figure 8.0  
The gain could be adjusted from 250 mV/pF to 1250 mV/fF. The measured capacitance noise floor at 1 Hz was 0.142 aF/\(\surd\)Hz. The RMS capacitance noise floor was 0.313 aF\({}_{\text{rms}}\) over the bandwidth from 1 Hz to 200 Hz. Figure 8.1 (a) Linearity expressed as the ratio of the digital output code to the input capacitance. (b) Capacitance noise floor. Figure 8.2 (a) Output in the current-sensing mode. (b) Transfer function of the current-sensing mode. Figure 8.3 Measured input referred current noise in the current-sensing mode. In the capacitive-sensing-mode test, the capacitive DAC inside the chip was tuned to reduce the offset due to the parasitic capacitance. For the capacitive inputs, the difference due to the input parasitic capacitance had a value of 2.67 pF caused by the PCB and the capacitive sensor. After adjustment, the difference due to the input parasitic capacitance was 0.09 pF. The measured offset voltage of the capacitive-sensing mode after adjustment is shown in Figure 8.4  
The performance summary of proposed multimodal readout circuit is shown in Figure 8.5
# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 5 Results and Discussion
### Results of RQ5  
**Research Question:** Which research methodologies are employed in the studies on LLM trustworthiness?  
**Objective:** To understand the research approaches and methodologies that are commonly used to investigate LLM trustworthiness.  
Analysis and Results  
Figure 20 illustrates the distribution of research methodologies employed in studies on LLM trustworthiness. The analysis reveals the following key insights:  
* **Solution Proposal:** The largest share of research methodologies is in the form of solution proposals, with 1520 papers (42.3%). This indicates a predominant focus on presenting novel solutions or frameworks aimed at enhancing the trustworthiness of LLMs.
* **Evaluation Research:** Evaluation research accounts for 810 papers (22.5%), highlighting the importance of assessing the effectiveness and trustworthiness of proposed models, tools, or methods.
* **Validation Research:** With 649 papers (18.0%), validation research plays a crucial role in confirming the reliability and robustness of LLMs within specific application contexts.
* **Conceptual Proposal:** 400 papers (11.1%) are categorized as conceptual proposals, which involve theoretical contributions aimed at advancing the understanding of LLM trustworthiness.
* **Opinion Paper:** Opinion papers make up 127 contributions (3.5%), representing subjective perspectives or expert views on LLM trustworthiness.
* **Experience Paper:** Experience papers, accounting for 91 contributions (2.5%), focus on practical experiences, lessons learned, or case studies related to LLM trustworthiness.  
These findings suggest that most of the research on the trustworthiness of LLMs revolves around proposing solutions and evaluating their effectiveness. Validation research also plays a significant role, while conceptual contributions and experience papers are relatively less frequent.  
Figure 19: Distribution of research contributions in LLM trustworthiness studies.
# Comparison of Topologies Generated by Evolutionary Neural Architecture Search
## 2 Neural Architecture Search
### Evolutionary Architecture Search  
Neuroevolution, also known as Evolutionary Architecture Search, is a technique that combines deep neural networks (DNN) with Evolutionary Strategy (ES) [16]. The goal of ES is to optimize the topology of the neural network architecture.  
In recent neuro-evolutionary approaches, the Genetic Algorithm (GA) has been widely used to design DNN architecture [7; 8; 17; 18]. The EA method evolves a population of DNN architectures, and in each generation, the automatically designed DNN architectures are trained and tested. The offspring inherit information about layers and links from their parents, which are then subject to mutations.  
One of the most well-known techniques for Neuroevolution using GA is Neuroevolution of Augmenting Topologies (NEAT) [19]. NEAT has demonstrated its success in solving the XOR problem by evolving a simple neural network architecture with a minimum number of layers and nodes. Additionally, coDeepNEAT, which is based on the NEAT methodology, can even automatically design Convolutional Neural Network (CNN) architecture through evolution [7]. Unlike other approaches, coDeepNEAT uses a node as a layer in a DNN, which simplifies the network architecture. However, NEAT is a constructive algorithm that must start from a minimum architecture, so it cannot begin from a middle architecture state.  
To address this limitation, researchers have attempted to introduce destructive operations, such as deleting nodes. However, this approach alone is insufficient to enable starting from a middle point in the architecture search process.  
A potential solution to the problem of starting the architecture search from a middle state is the use of a variable chromosome GA with a genetic operation called chromosome non-disjunction [8]. This operation involves changing the number of chromosomes in the population, which in turn alters the volume of genetic information and consequently affects the architecture of the artificial neural network. This unique feature of the operation allows the search process to start from a middle state in the architecture evolution process. However, it should be noted that previous implementations of this technique using nodes and links have had limitations in terms of designing complex artificial neural network architectures.