esa-sceva/satcom-chunk-collection huggingface dataset:
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Vehicle Subsystems
#### 3.2.1 Life Support  
The purpose of Charon's first subsystem, the life support, is to provide all of the necessary elements to keep the crew well and alive during nominal and non-nominal conditions. This includes food and water supplies, as well as atmospheric and thermal control. This can be seen in purple on Figure 4. The nominal mission lasts four days, based on the maximum mission length. The off-nominal mission duration is of seven days, including the time required for rescue.  
Firstly, according to a NASA report [10], each adult crew member needs 1.62 kg of water daily, as well as 1.77 kg of dry food, with an additional 0.8 kg of water to hydrate it. Having six crew members, for a maximum off-nominal duration of seven days, and using a safety margin of 50%, there is a need for a total of 152 kg of water and 111 kg of dry food. However, as to save weight on the capsule, only 20 kg of water will be packed at lift-off. The remainder will be produced by the capsule's hydrogen fuel cells.  
Secondly, the composition of the atmosphere is ensured via a physical filter. Having a mesoporous silica at its centre, the 2 nm pores [11] let the O2, Ar, and N2 particles go through it, while blocking the harmful CO2, CH4 and NH3, creating a breathable atmosphere as close as possible to the one found on Earth, being optimum for the crew. This filter is in principle producible on Mars, and can be used for many flights, in contrast with a conventional chemical filter that has to be regularly renewed. Using a 30% safety margin on the required O2 supply and doubling it for redundancy, three tanks of three litres are required, storing the oxygen at a capsule temperature of 20 \({}^{\circ}\)C and a typical pressure of 13 MPa [12].  
Thirdly, with regard to the thermal control, each crew member will produce between 100-120 W of heat during the flight [13]. Insulating the capsule with a foil similar to the Coolcat 2 Ni from Ruag (Swiss Confederation, Bern, Switzerland) [14] that has a heat flux of 3.4 W/m\({}^{2}\), and with the capsule surface being of 45 m\({}^{2}\), the capsule would continu  
Figure 5: Layout of the vehicle, with cutout.  
ously lose 153 W of heat. Having at least 2 crew members, the capsule will thus need to be actively cooled by the radiator already used to cool the fuel cells.  
Finally, there is no need for a radiation shield. Indeed, according to the Ames Research Center [15], a radiation shield is recommended for a mission of more than 30 days, whereas the maximum off-nominal mission duration of Charon is seven days. Even though the crew travelled from Earth to Mars for longer than 30 months, it is assumed that they are shielded from radiation for that duration. The last off-nominal seven days of their trip to Mars can thus be done without radiation shield.
# Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections
## 3 Model Predictive Intersection Control Design
### Vehicle Control Model  
The acceleration of the autonomous vehicles \(a_{i}\ i\in[1...n]\) calculated by the MPC controller is realized by applying a longitudinal force through the drive system. For this purpose, the following equation is implemented to derive the corresponding forces for the autonomous vehicles:  
\[F_{i,l}=m_{i}a_{i}+F_{i,d} \tag{8}\]  
where \(m_{i}\ i\in[1...n]\) is the mass, \(F_{i,l}\) is the control input, \(F_{i,d}\) is the disturbance acting on the vehicle [27] including aerodynamic drag, rolling resistance and disturbance from the road slope.  
Figure 2: Intersection control procedure.  
The autonomous vehicles lateral trajectory tracking in the intersection is founded on the two-wheeled bicycle model, see [27; 28]. The differential equations of the yaw motion is given as:  
\[\dot{X}= vcos(\psi) \tag{9}\] \[\dot{Y}= vsin(\psi)\] \[\delta\cong tan(\delta)=\frac{L}{R}\]  
where \(v\) is the velocity, \(\psi\) is the yaw angle, \(X\) and \(Y\) are the coordinates of the vehicle in a global coordinate system. Here, a simplified steering model is used with \(\delta\) representing the steer angle, while \(L\) is the wheelbase of the vehicle and \(R\) is the radius of the road curvature.  
Next, by using the relation \(\dot{\psi}=\frac{v}{R}\), the state-space equations of the vehicle is given as:  
\[\begin{bmatrix}\dot{Y}\\ \dot{\psi}\end{bmatrix}=\begin{bmatrix}\dot{0}&v\\ 0&0\end{bmatrix}+\begin{bmatrix}0\\ \frac{v}{L}\end{bmatrix} \tag{10}\]  
Assuming small velocities and changes in the reference signals (\(\dot{y}_{ref}\cong 0\); \(\dot{\psi}_{ref}\cong 0\)), Equation (10) can be organized as follows:  
\[\dot{x}=Ax+Bu \tag{11}\]  
where \(x=\begin{bmatrix}e_{Y}&e_{\psi}\end{bmatrix}^{T}\) includes the error signals of the lateral position and yaw angle, \(u=\delta\) is the steering input. The performance of the steering control is to minimize the error signals given in the state vector \(x\). Hence, a Linear Quadratic (LQ) controller is applied using the cost function:  
\[J=\int_{0}^{\infty}[x(t)^{T}Q(t)x(t)+u(t)^{T}ru(t)]dt \tag{12}\]  
where \(Q\) and \(r\) are design parameters.  
Solving the Ricatti equation the control gain \(K\) is derived, generating the steering input \(\delta\) for the autonomous vehicles based on the measured error signals.
## 2 System and Channel Models
### System Model  
Consider a single-input/single-output (SISO) UOWC system with an intensity modulation and direct detection (IM/DD) scheme due to the lower complexity and low cost with respect to coherent schemes [26]. The received electrical signal for the proposed UOWC system is given by  
\[y=L\cdot h_{b}\cdot R\cdot x+n, \tag{1}\]  
where \(L\) is the pathloss due to the water turbidity and air bubbles, \(h_{b}\) is the fading coefficient of the UOWC channel due to air bubbles, \(R\) is the detector responsivity, assumed hereinafter to be the unity, \(x\) is the transmitted optical power, and \(n\) is additive white Gaussian noise with zero mean and variance \(N_{0}/2\). Hence, the received average electrical SNR in the absence of fading can be defined as \(\gamma=P_{opt}^{2}T_{b}/\,N_{0}\), where \(P_{opt}\) is the average transmitted optical power, and \(T_{b}\) is the bit period.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 6 Conclusions  
This paper investigates the validation of an impulsive noise-resistant physical layer using GNU Radio and USRP. For this purpose, a new block, namely RC Encoder + WiFi Mapper, is designed and implemented in GNU Radio. It is based on the former implementation of _gr-ieee_ 801.11 made by [PERSON] et al. Under different scenarios in a controlled laboratory, we have done several experiments to validate the proposed approach in the presence of impulsive noise. Using several metrics such as the packet delivery ratio, the average delay and the BER, the performances of the system are evaluated. The obtained results are globally satisfactory and confirm the assertion made in [13]. However, based on the PDR results, we recommend using a transmission power of at least 15 dBm in order to benefit the maximum packet delivery. As perspectives, it may be interesting to do other tests by further increasing the distance between the emitter and the source, as well as the receiver and the source and a voltage above 18 kV if possible. As stated in our Introduction, only a few studies are related to impulsive noise reduction techniques. For this purpose, it would also be interesting to implement in a future work two of the approaches proposed in [6; 7; 8; 9]. This will allow us to compare our experimental results.  
[PERSON] conceived, designed the experiments, and analyzed the data. [PERSON] designed partially the scenarios, supervised the experiments. [PERSON] contributed to the analysis and validated the results with [PERSON]. [PERSON] wrote the manuscript in consultation with [PERSON], [PERSON], [PERSON], and [PERSON] and [PERSON] are the main supervisors of the project.  
The work in this paper is supported partially by grants from the [PERSON] Research chair on Wireless IP technology for Developing Countries, partially by Institut de Recherche d'Hydro-Quebec (IREQ), and partially by Labex \(\Sigma\)-Lim.  
The authors would like to thank [PERSON] and [PERSON] from Institut de Recherche d'Hydro-Quebec (IREQ) for their help during the experiments.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADC & Analogue to Digital Converter \\ ADMM & Alternating Direction Method of Multipliers \\ BER & Bit Error Rate \\ BPSK & Binary Phase Shift Keying \\ CC & Convolutional Code \\ CS & Compressed Sensing \\ DSAP & Destination Service Access Point \\ DVB-T & Digital Video Broadcasting-Terrestrial \\ FCF & Frame Field Control \\ FFT & Fast Fourier Transform \\ HV & High Voltage \\ IO & Input Output \\ ISI & Inter-Symbol Interference \\ QAM & Quadrature Amplitude Modulation \\ LLC & Logical Link Control \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **LOS** & **NLOS** \\ \hline Simulations & 5 dB & 6.2 dB \\ Measurements & 6.1 dB & 6.9 dB \\ \hline As\({}_{\text{M}}\) & 11 dB & 07 dB \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the performance for a target BER of \(10^{-4}\).
## 1 Introduction
At the middle and lower latitudes, the ozone in the middle and lower stratospheres is also affected by the Brewer-Dobson (BD) circulation [31], which is regulated by the El Nino Southern Oscillation (ENSO), prompting researchers to redirect their focus towards investigating the effects of ENSO on the ozone valley [32]. For instance, [PERSON] et al. [33] studied the combined impacts of ENSO and quasi-biennial oscillation (QBO) on ozone in the TP region. They found that the ENSO signal had a six-month advance effect, and the response of the ozone valley was most pronounced in the subsequent summer following the occurrence of an ENSO signal. The ozone valley responds differently to different combinations of ENSO and QBO phases. ENSO is an important manifestation of the ocean-atmosphere interaction in the tropical Pacific Ocean [34]. ENSO not only affects the circulation of the tropical atmosphere, but the signal can also be transmitted to the middle and high latitudes, and even the polar regions [35; 36]. A large number of observations and simulation results show an abnormal accumulation of total ozone during El Nino (EN) in the Northern Hemisphere mid-latitudes and Arctic regions [37; 38]. Global warming leads to increased occurrence of extreme EN events, and it is expected that such extreme events will occur more frequently in the future [39; 40; 41; 42; 43]. An extremely rare triple-dip La Nina event recently occurred in 2020-2023 [44; 45; 46; 47], and multiple modeling results indicate that the ENSO phase will shift in the second half of 2023, forming a strong EN event ([[http://cmdp.ncc-cma.net/pred/cn_enso.php](http://cmdp.ncc-cma.net/pred/cn_enso.php)]([http://cmdp.ncc-cma.net/pred/cn_enso.php](http://cmdp.ncc-cma.net/pred/cn_enso.php)), accessed on 1 November 2023), introducing a new round of ozone changes. Studying the effects of EN on the ozone valley in the TP is critical and necessary. In previous studies, the influence of variable EN events on the ozone valley of the TP is usually analyzed collectively, without meticulous consideration of the diversity in the development time and the spatial pattern of EN. Different types of EN induce various tropical air-sea interactions, and their transmission of signals to the middle and high latitudes differs, greatly affecting the response of the ozone valley on the TP. This study categorizes EN events in time and space, and explores the diverse influences of different types of EN events on the ozone valley over the TP, in combination with the simulation using the Whole Atmosphere Community Climate Model, version 4 (WACCM4).
## 2 Materials and Methods
### Experimental Procedure  
Before the experiment, retroreflective markers (diameter of 15 mm) were affixed to the surface of the body (Figure 1). According to the Plug-in-Gait Lower Body Marker-set [30], the markers were attached to skeletal landmarks of the posterior/anterior and left/right superior iliac spines, the lower/upper lateral 1/3 surface of the thighs, the lateral femoral epicondyles, the lower/upper lateral 1/3 surface of the shanks, the lateral malleolus, the second metatarsal heads, and the calcaneus for both lower limbs. For amputees, the corresponding markers were attached to the lateral surface with reference to an imaginary line that passed through the rotation axes of the prosthetic knee and ankle.  
The participants (both the amputees and the able-bodied control group) were instructed to walk along a 10 m straight path at a self-selected comfortable speed. All the participants wore tight clothing and sports shoes. An eight-camera MOCAP system (Vicon, Oxford Metrics Ltd., Oxford, United Kingdom) was used to obtain the kinematic data of the markers at a sampling frequency of 200 Hz. Camera calibration was conducted with a residual error of <0.3 mm. Three successive trials  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Code**} & **Age** & **BW** & **Height** & **Yr.** & **Yr.** & **Prosthetic Knee** & **Prosthetic Foot** \\  & **(years)** & **(kg)** & **(m)** & **AMP** & **C.P.** & & \\ \hline
1 & 23 & 60.3 & 1.68 & 13 & 4 & Jingbo JB-850 & Jingbo FY-YDJ \\
2 & 30 & 54.4 & 1.74 & 1.5 & 1.5 & Ottobock 3R80 & Ottobock 1A30 \\
3 & 24 & 69.5 & 1.88 & 18 & 10 & Jingbo JB-850 & Jingbo FY-YDJ \\
4 & 27 & 66.3 & 1.69 & 5 & 0.5 & Ottobock 3R80 & Ottobock 1S101 \\
5 & 48 & 82.0 & 1.85 & 17 & 6 & Ottobock 3R80 & Ottobock 1C40 \\
6 & 32 & 80.0 & 1.72 & 7 & 5 & Jingbo JB-951 & Jingbo FY-YDJ \\
7 & 32 & 71.8 & 1.7 & 15 & 1 & Jingbo JB-810 & Jingbo FY-YDJ \\
8 & 27 & 78.1 & 1.75 & 5 & 5 & Nabtseco NK-6 & Jingbo FPJ-4 \\ \hline \hline \end{tabular}  
* BW: body weight; Yr. AMP: years since amputation; Yr. C.P.: years using their current prosthesis.  
\end{table}
Table 1: Participant information for the amputee group.  
Figure 1: Illustration of the gait experiment: (**a**) one of the transfemoral amputee participants, (**b**) the amputee affixed with markers walking under the motion capture system (MOCAP) and on the force platform.  
were performed for each participant. A successful trial was regarded as a complete capture of the gait cycles for both limbs in a continuous sequence. One set of trial data was randomly selected for subsequent analysis. A fourth-order 6 Hz low-pass Butterworth filter [21; 31] was applied to the data using the software bundled with the MOCAP system (Nexus, Vicon, Oxford Metrics Ltd., Oxford, United Kingdom). Based on the position of the markers, pipelines were built, and a skeleton was reconstructed using the Plug-in-Gait Lower Body model, which was available in the Vicon MOCAP system.
## 1 Introduction
### Construction of Parameter Stability Region
However, on the other hand, it reflects the relative conservatism of the proposed method, which is analyzed in Section 2.2. The system state matrix is related to the system operating status (power flow). When the operation mode of the power system is different, the system state matrix varies subsequently, and the PSR need to be re-constructed according to the updated system state matrix in the exact same method. The basis of system dynamic analysis and PSR construction is the system state matrix, whose elements are functions of controller parameters in the system. However, not every controller parameter is strongly related to the system oscillation mode, and the accuracy of these parameters does not have an important influence on the analysis of dynamic characteristics and the construction of PSR. Therefore, it is unnecessary to know the accurate value of each controller in the whole renewable power system; if there are unknown non-dominant controller parameters, the default values can be used instead of accurate actual values. For the important dominant controller parameters, which can be selected to construct the PSR, the scope or feasible areas of the dominant controller parameters are decided by the PSR. The exact value is not required during the PSR construction process, but needed for the PSR-based stability margin evaluation. For unknown parameters that have a great influence on dynamic characteristics, parameters can be obtained by online identification of WAMS in the practical system.
## 3 Results
### Technical Performance Results
#### 3.1.5 SEM Image Analysis  
The calculated DoR values over time are plotted in Figure 9, along with the corresponding standard errors. It was observed that at 7 days, the average DoRs were still quite low and comparable to the error margins. It can thus be concluded that up to 7 days, the slag particles do not react significantly. However, DoR values of 20% to 30% were measured for 91-day samples. This is in alignment with the compressive strength results where no contribution to strength was observed for all three slags at early ages, while the slags showed some strength activity at later ages.  
Figure 7: The clinker degree of reaction in different pastes up to the age of 91 days.
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 3 Trellis Tone Modulation Multiple-Access
### Overview of Trellis Tone Modulation
The design of the trellis matrix is similar to a parity-check matrix of the low-density parity-check (LDPC) code [29], where the number of states and the number of degrees are given. Therefore, the proposed methods for constructing the LDPC code can be used to construct the trellis matrix, for example, a progressive-edge-growth (PEG) algorithm [30]. If the number of states \(M\) is not large, the cycle of length 4 is necessarily included. Therefore, we do not need to put much effort into designing the trellis matrix.
## 3 Materials and Methods
### Data Preparation for Algorithm Validation
#### 3.3.2 Real Students' Data  
Two datasets were used to test the presented algorithms. A newly presented 2021 dataset was from a formative assessment of real students which was carried using Moodle system. The data was collected using a quiz covering 15 topics of a cloud computing course. The course topics varied from common knowledge on computer networks to cloud types, platforms, and cloud services. The size of the item pool was 80 items. Students had 80 min to finish the quiz, and it took them 39 min on average to do it. The quiz was held on 11 February 2021 and had no summative impact on the final grade. In total 106 Vilnius Gediminas Technical University, undergraduate students participated and agreed to share their data for scientific analysis. Each student was served all items from the pool and their answers were recorded in a comma separated file. The file was then used as a proxy for students to test different algorithms. This way each algorithm received the same input data. All 80 items were multiple choice questions with one correct answer, guess factor \(P_{g}=0.25\). Algorithm accuracy was measured in relation to item difficulty and student's topic knowledge as estimated by MLE based IRT with the entire dataset as input. While algorithms had to operate on incomplete data, first asking each student one question, then two, and so on.  
The second dataset explored in this article was already used in publication [45]. This dataset was included to also measure Elo-Bandit algorithm against previously presented upper confidence bound based algorithm using same data and same classical test theory based benchmarks. Data was collected in a quiz held at Vilnius Gediminas Technical University, Lithuania, on 25 April 2019. It had 104 undergraduate student participants, who each answered 60 items covering 15 topics. In the case of CTT based experiment topic knowledge of students was assumed to be the mean of all answers on said topic.
# Truncated Newton Kernel Ridge Regression for Prediction of Porosity in Additive Manufactured SS316L
## 3 Applied Regression Models and Modelling
### Data Preparation and Model Evaluation  
#### 3.4.1 Data Pre-Processing  
The quality of the data is a significant factor in the prediction process. Since multiple data sources were combined into one dataset, data cleansing, including data validation, outliers removal, and preparation for further analysis, should be completed before use [43]. The data cleansing process also modifies the data so that it is best suited to the data analysis task. The cleansing of the dataset highly affects the overall performance and robustness of the prediction model [44].  
For this work, the initial data from the literature contained 232 observations. The cleansing process started with detecting potential outliers by observing the standardized residuals of the least squares regression. An observation with a standardized residual larger than 3 (absolute value) is deemed an outlier [45]. After the outlier removal process, 181 observations from the literature remained in the dataset. These observations from the literature were combined with the experimental results from the 20 specimens fabricated in the current work. Thus, the compiled dataset consisted of 201 observations ready to use for data analysis and modeling. The size of the dataset constructed in this study is almost double that presented in [22] (i.e., 112 points), implying the robustness of this study.  
A clean, standardized, or normalized dataset is usually required before data analysis. This step is especially crucial when the data contains different parameter scales. For example, the layer thickness values in our dataset range from 0.02 mm to 0.25 mm, while the laser power values start at 30 W and increase to 400 W. These ranges in parameter values have to be scaled to a mean of zero and a standard deviation of one. Normalizing the dataset gives the input parameters an equal range of values, thus reducing their bias [46].
# Sim-Real Mapping of an Image-Based Robot Arm Controller Using Deep Reinforcement Learning
## 3 Sim-Real Model Performance  
In this section, we test the performance of the sim-real model that was trained on a simulated scene and review how it performs in the actual world using image processing as a mediator between real and virtual workspaces. In this case, a conventional DRL model is utilized with low-dimensional images, i.e., gray-scaling, binarization, etc., in an actual robot. First, the grasping motion of a four-axis robot arm is optimized in virtual space by adapting DRL and then, the learned model is implemented on the actual machine.