esa-sceva/satcom-chunk-collection huggingface dataset:
# A UAV Wind Field Perception System Inspired by Biological Perception
## 2 Wind Field Calculation Method and Measurement System Construction
### Three-Dimensional Wind Field Calculation Method Based on UAV  
The relative motion between the airplane and the air allows the aircraft to fly in the air. The lift produced by the aircraft overcomes gravity when the relative speed between the aircraft and the air reaches a specific level, allowing the aircraft to fly through the air. Airspeed \(V_{a}\), or the speed of motion of the aircraft relative to the airflow, and ground speed \(V_{g}\), or the speed of motion of the aircraft relative to the earth's surface, are two ways that people categorize the speed of an aircraft during flight based on different coordinate axes. In the windless state, the ground speed and the airspeed are equal, and when there is wind in the movement process, the ground speed is equal to the vector sum of the airspeed and the wind speed. The vector relationship is as follows:  
\[V_{g}=V_{a}+V_{w} \tag{1}\]  
The measurement principle of three-dimensional wind speed [20] is introduced as follows: the airspeed is calculated based on the the measured static pressure, dynamic pressure, static temperature and other parameters. Then, the three-dimensional wind speed is calculated based on the flight parameters of the UAV, attitude angle, ground speed, and airflow angle. The measurement principle is shown in Figure 2.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### U-Net Ship Detector Training  
We consider full-size training images and their associated ground truth that is a binary pixel mask for each multi-spectral image. In the mask, pixels with a value of one are ship pixels, and are zeroed otherwise. Next, we sample random 64 \(\times\) 64 pixel patches from these images, ensuring, however, that at least five pixels of the patch belong to a ship. Since ships are scarce in the images this step re-balances the distribution of ship vs. non-ship pixels in training.  
During training we further take into account the class imbalance of pixels by using the focal loss [27] to train the U-Net model. For a prediction \(\hat{p}=softmax(\mathbf{p})\in[0,1]^{2}\) and ground truth \(y\):  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}log(p_{t}) \tag{3}\]  
\[p_{t}=\begin{cases}\hat{p}^{i}&\text{if $y=1$}\\ 1-\hat{p}^{j}&\text{otherwise}\end{cases} \tag{4}\]  
We set \(\gamma=2\), \(\alpha_{0}=0.05\) (corresponding to background pixels) and \(\alpha_{1}=0.25\) (corresponding to ship pixels). We train the U-Net detector with the ADAM optimizer while also introducing some data augmentation in training: random vertical and horizontal flips.  
We normalize the training patches in the same way as in the pretext task (see Section 2.3).
## 4 System Design
### Data Collector  
We must define two main parameters in order to fetch the social interactions that we will need to build our dataset: time period (initial and final time) and location (coordinates and radius).  
In order not to fall into a subjective selection, a random search corresponding to a set period is established, with the aim of capturing a first group of potential users to work with. This initial group will serve as the first intuition about the group that we will end up using to subtract social interactions as an object of study.  
Once we have the initial harvest, we define a set of filters in order to have a standardized group upon which we could perform our analysis. The filters through which we ran our initial user list are the following:  
* **User Activity.** Minimum rate of interactions per week.
* **Account Type.** Personal, Organizations, Publicity, etc.
* **Join Date.** To ensure a comfortable and integrated use of the SMN.
* **User Count.** Minimum number of users per location.  
These conditions can help us define a group of steady, regular active users, whose social profile is adequate to infer useful information from it.  
Having built our list of candidates, we proceed to collect a number of interactions from each of the users, attending to the specified parameters for time period and location. A language filter may be used to fetch only certain interactions.  
The data collected by our system are publicly available through the Twitter API. Sensitive and potentially identifiable information, such as profile username or tweet content, is automatically encoded for emotional content upon acquisition. Data are analyzed anonymously and aggregated only. Therefore, the individual consent of the users was not required.
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 3 Multiple Target Tracking
### Track Termination and Validity Testing  
There are three cases of track termination. One is associated but not selected during the track association. Other cases are when the track is continuously updated without measurements over the maximum searching number, or the track speed is below the minimum target speed. After the track is terminated, its validity is tested with the track life length. The track life length is the number of frames between the initial updated frame and the last frame updated by a measurement. If the track life length is shorter than the minimum track life length, the track is removed as a false track.  
The tracking performance is evaluated in terms of the TTL and the MTL [27]. The TTL and MTL are defined, respectively, as:  
\[\text{TTL}=\frac{\text{Sum of lengths of tracks which have the same target ID}}{\text{Target life length}-1}, \tag{37}\]  
\[\text{MTL}=\frac{\text{TTL}}{\text{Number of tracks associated in TTL}}. \tag{38}\]  
A track's target ID is defined as the target with the most measurements on the track. The denominator of the TTL is slightly modified as the target life length minus one because two frames are required for the initialization. The MTL is equal or less than the TTL due to track breakage or overlap. Figure 5 illustrates three tracks originating from one target. The target \(t\) is occluded at frame \(k\). The target \(t\)'s life length is 9. The TTL and MTL of target \(t\) are 0.75 (=6/8) and 0.25 (=0.75/3), respectively. It is noted that if there are multiple tracks on one target in a frame, only one frame is counted for the TTL. Tracks 1-3 in Figure 5 have track life lengths of 3, 5, and 3, respectively. In this illustration, the minimum track life length for the track validity is set to 3 frames.  
Figure 4: Illustration of directional gating process.
## 3 Results and Discussions  
As shown in Figure 13a, the proposed filter is fabricated on a Rogers Duroid 5880 (Rogers Corporation, Chandler, AZ, USA) substrate with a dielectric constant of 2.2, thickness of 0.508 mm and loss-tangent of 0.0009. The frequency response of the prototyped dual-band BPF is measured by the Agilent N5230A (Agilent Technologies, Santa Clara, CA, USA) network analyzer. The proposed filter simulation was performed using the Advanced Design System 2011.10 (ADS) software, and the EM simulated and experimental results are compared in Figure 13b. According to the measurement, the filter has two passbands with the center frequencies of f1 = 4.42 GHz and f2 = 7.2 GHz, and 3 dB bandwidths of 94 MHz and 83 MHz, respectively. Insertion and return losses at f1 and f2 are about 0.5/17.56 and 0.86/17.9 dB, respectively. The filter provides an excellent out-of-band response, showing a good rejection level of 30 dB in the lower stopband, and a 24 dB isolation level with a 25 dB rejection at upper stopband, which extends up to 14.5 GHz. The measured physical size of the filter is 11.4 mm \(\times\) 5.8 mm corresponding to 0.23 \(\lambda_{\mathrm{g}}\times 0.11\)\(\lambda_{\mathrm{g}}\), where \(\lambda_{\mathrm{g}}\) is the guided wavelength at 4.42 GHz. The fabricated filter shows a good selectivity in both bands with sharpness values of 106 dB/GHz and 212 dB/GHz, respectively, calculated using the filter sharpness formula in [61].  
The proposed filter has an acceptable group delay (GD) in both passbands, which is plotted in Figure 14a,b. The maximum variations of GD in the first and second passbands are about 1.33 ns and 1.72 ns, respectively, demonstrating a flat group delay in the two passbands.  
Figure 12: (**a**) Variations of Q. factor versus physical parameters: (**a**) A14, (**b**) A18 and A21.  
In Table 1, a summarized comparison is made between some of the recently reported dual-band BPFs and the proposed one. In this table, the normalized circuit size (NCS) is computed using NCS = (physical size)/\(\lambda_{g}^{2}\)[62; 63]. The properties of the proposed filter and some of the recently reported BPFs are listed in Table 1. As can be seen, the proposed filter has the lowest insertion loss in the two passbands, the highest suppression levels in the upper stopbands, and the widest upper stopband as compared with the reported works in [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. It can also be observed that the return loss in passbands is better than the works reported in [36; 43; 45]. In addition, the presented BPF provides a large tuning range as compared with other works in Table 1. Among the recently reported designs, the proposed BPF offers dual adjustable passbands over the widely used C-band frequency regime for applications such as satellite modems, transceivers and wireless networking technologies based on the IEEE 802.11 family of standards.  
Figure 14: Group delay (GD): (a) first passband, (b) second passband.  
Figure 13: Proposed BPF: (a) A photo of the fabricated filter: (b) Simulation and measurement results.
## 2 Materials and Methods
### Strength and Power Assessments  
Measurements were taken in January 2023 under standard conditions, that is, during the morning hours (8-9 a.m.), with 72 h of restriction from training and consumption of alcohol and caffeine. The peak torque and power of knee extensors and flexors were tested using an \"isokinetic device\" (HUMAC NORM, Stoughton, MA, USA) [39]. The tests were performed in the presence of the coaching staff who supervised the tests, and the entire procedure was carried out by a technical employee of the university familiar with the device. Particular muscle groups activated under conditions of concentric work (muscle shortening) with isokinetic (constant) loading under laboratory conditions were evaluated. The device was calibrated according to the manufacturer's instructions [39]. Before the test, the participants performed a 10 min warm-up on a stationary cycle (Keiser M3i, Fresno, CA, USA) at 70-80 RPM. To familiarize themselves with the testing procedures, the subjects performed three submaximal and two maximal repetitions (60\({}^{\circ}\)/s) before the main test [40]. A 30 s rest interval was given between repetitions, and a 3 min rest interval was provided between sets [40]. Before the test began, verbal instruction was given to generate as much force and power as possible during the test. In addition, no verbal encouragement was used during the test, but the computer screen was set so that participants could receive real-time feedback. Participants were seated in an extended position, with the backrest at an angle of 85\({}^{\circ}\). The axis of rotation of the knee joint was placed in line with the axis of rotation of the dynamometer. The lever arm pad was fixed at the head of the fibula so that the movement of the ankle joint was not restricted. Tests were performed within a predefined ROM (90-0\({}^{\circ}\)). To minimize compensatory trunk movements during the test, participants were secured using stabilizing straps, according to the manufacturer's instructions [39]. The athletes performed a test of five repetitions of knee flexion and extension at 60\({}^{\circ}\)/s. The training load was selected using Velocity-Based Training (VBT) corresponding to (1-0.75 m/s), which was determined using the Tendo Power Analyzer linear position transducer (Tendo Sport Machines, Trencin, Slovakia) on which the whole training process was based (Table 1). We considered variables most commonly used in practice and scientific research which include mean velocity (MV) (i.e., the average velocity across the entire concentric phase) and peak velocity (PV) (i.e., the maximum instantaneous velocity reached during the concentric phase) [41; 42].
## 3 Optimizing In-Orbit SAR Operational Parameters for Altitude Variation
### Definition of Swath Angle for Image Acquisition
As shown in Figure 13a,c, it is difficult to obtain good performance at the maximum altitude when the incidence angle is used for the reference angle. As shown in Figure 13b,d, however, the RAR performance becomes constant irrespective of the altitude variation when the look angle is used for the reference angle. In this case, the performance curve is generally shifted to the left side for the higher altitude.
# A Ku-Band GaAs Multifunction Transmitter and Receiver Chipset
## 4 Multifunction Transmitter and Receiver
Moreover, the chipset features relatively high OP1 dB and low noise figures over the full Ku-band due to the integration of MPA or LNA.
# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 4 Machine Learning Models for Soil Layer Reconstruction
### Sparse borehole Encoding
Subsequently, both the horizontal (distance1) and vertical distances (distance2) from these interface points to the sample point are computed. For a sample point \(P\), the distance from all boreholes to it is first calculated. This set of distances is represented by an ordered set, \(J=\{j_{1},j_{2},\ldots,j_{n}\}\). In this set, each \(j_{i}\) represents the horizontal distance from the \(i^{th}\) borehole to the sample point \(P\). The elements of set \(J\) are arranged in ascending order and the first \(k\) elements are selected. In Figure 8, boreholes 2, 3, 4, and 6 are the four closest boreholes to the sample point. Next, the coordinates of the points where these boreholes intersect with the soil layers are calculated. According to the Euclidean distance formula, distance\({}_{1}\) and distance\({}_{2}\) can then be calculated. After these three steps, all the feature data of a sample can be generated, including the information on several boreholes, and the information on each borehole contains six sets of data, namely the elevation of the borehole, the coordinates of the borehole plane, the thickness of the soil layer, the coordinates of the soil interface at the borehole location, the horizontal distance from the coincidence point to the sample point, and the vertical distance from the coincidence point to the sample point, as shown in Figure 9. The dimension of a sample is determined by the number of soil layers (\(s_{sum}\)) and the number of selected boreholes (\(k\)). The calculation formula is as follows:  
\[D_{s}=(4S_{num}+6)k, \tag{6}\]  
Figure 9: Coded borehole data structure diagram.
## 3 The Dynamic Reliability Evaluation and Critical Component Identification Method for Power System
### The Dynamic Reliability Evaluation Model  
The algorithm for dynamically evaluating the operation reliability of a new energy power system proposed in this paper consists primarily of offline modelling and an online calculation model.  
The process of offline modelling is as follows:  
1. The potential distribution function of the PV is derived using GMM-HMM.
2. The S system states with the greatest probability are chosen to compose the critical states set.
3. For each possible distribution function, all critical states are enumerated, and the analytical function between PV output and reliability levels for each state is determined.  
After offline modelling is complete, the system's reliability levels for each critical state and the analytical function between PVs can be determined. It is not necessary to recalculate the OPF model if the output of PV in the power system varies.  
The process of online calculation of power system operation reliability proposed in this paper is as follows:  
1. For the current PV output, the Viterbi algorithm is utilised to calculate the corresponding hidden state and GMM-HMM parameters.
2. The reliability levels of the time are calculated according to the calculated polynomial coefficient.  
The mean value of the period's reliability levels can be determined by sequentially calculating. The dynamic evaluation algorithm of the power system operation reliability is shown as Algorithm 1.
# Effect of Ozonized Water against Pathogenic Bacteria and Filamentous Fungi on Stainless Steel
## 3 Results and Discussion
Within each treatment group, different letters indicate significant differences between means at \(p\) < 0.05 (ANOVA and Tukey HSD post hoc test). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Inoculum**} & \multirow{2}{*}{**Strain**} & \multicolumn{4}{c}{**Inactivation Curve**} & \multirow{2}{*}{**Inactivation Model**} \\ \cline{3-3} \cline{5-6}  & & & **RMSE** & **R\({}^{2}\)** & **1D-Value** & **Model** \\ \cline{3-6}  & & & **(c)** & **(m)** & **(m)** & **(m)** \\ \hline \multirow{3}{*}{**multi-layer**} & Ab & nd & nd & nd & nd & - \\  & Hb1 & \(\delta\) & 0.5613 & 0.96 & 0.48 & [43] \\  & Hb2 & \(\delta\) & 0.3724 & 0.97 & 3.86 & [43] \\  & Pn1 & nd & nd & nd & nd & - \\  & Pn2 & nd & nd & nd & - \\ \hline \multirow{3}{*}{**single-layer**} & Ab & \(\delta\) & 0.1842 & 0.92 & 5.00 & [41] \\  & Hb1 & K\({}_{\text{max}}\) & 0.0949 & 1.00 & 0.18 & [45] \\ \cline{1-1}  & Hb2 & \(\delta\) & 0.4658 & 0.91 & 0.09 & [41] \\ \cline{1-1}  & Pn1 & \(\delta\) & 0.5224 & 0.89 & 1.62 & [41] \\ \cline{1-1}  & Pn2 & \(\delta\) & 0.3875 & 0.94 & 4.89 & [41] \\ \hline \hline \end{tabular} Note. Nd: not determined since the minimum value of 1.0 LCR was not reached within the time considered.  
\end{table} 
Table 3: Inactivation parameters of the fungal strains inoculated as a multi-layer or a single-layer on SS tiles and treated with 8.5 mg L\({}^{-1}\) ozone in acidified ultrapure water (Ab = _Apergillus brasiliensis_ ATCC 16404; Hb1 = _Hymphipichia buttonii_ SSICA 175717; Hb2 = _Hymphipichia buttonii_ SSICA 251105; Pn1 = _Penicillium nonticum_ SSICA 1169; Pn2 = _Penicillium nonticum_ SSICA B4798).
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 4 SINBAD-ARES Accelerator Design
### Photo-Injector Design
It is not trivial to predict which one of the two beam distributions will provide a better result after compression in the magnetic chicane, therefore the importance of being able to compare the working points experimentally is evident. \begin{table}
\begin{tabular}{c c c c c} \hline
**WP** & \(E_{gun}\) **[MV/m]** & **Laser RMS Spot [mm]** & **Laser RMS Length [ps]** & **Laser Trans/Long Shape** \\ \hline Transv. Flat-Top & 110 & 0.05 & 0.1 & FT/Gauss \\ Transv. Gaussian & 110 & 0.05 & 0.1 & Gauss/Gauss \\ Blow out & 70 & 0.04 & 0.076 & FT/Gauss \\ \hline \end{tabular}
\end{table} 
Table 3: Laser and RF gun parameters used for obtaining the beam distributions shown in Figures 8 and 9. All the working points presented are obtained for a bunch charge of 0.5 pC. In the last column, the transverse and longitudinal shaping of the laser profile are specified. The following abbreviations are used: FT = Flat-Top, Gauss = Gaussian. Figure 8: Examples of electron bunches (Q = 0.5 pC) simulated in the ARES RF gun by using different settings of the photo-cathode laser. The plots showing the longitudinal phase space include the values of the coefficients for a fit of the beam distribution: \(F=A\cdot z_{\text{b}}^{3}+B\cdot z_{\text{p}}^{2}+C\cdot z_{b}+D\), where \(z_{b}\) is the longitudinal coordinate related to the beam center of charge position. The flexibility of the laser shaping allows for producing electron bunches with very different shapes of the longitudinal phase space and different non-linear components. The possibility of realizing a transversely flat-top profile allows for improving the non-linearity of the longitudinal phase space and the beam transverse emittance. Working closer to the so-called blow-out regime allows for further reducing the non-linear terms both in the transverse and in the longitudinal phase-space. The simulations have been performed using the code ASTRA [32]. Figure 9: Evolution of transverse emittance, longitudinal emittance and bunch length from the cathode to the linac entrance for the working-points represented in Figure 8.  
The possibility of changing the cathode and the laser shape will allow the experimental study of the limitations in bunch compression linked to these input parameters.
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Data from PCA Parameters (DPCAPs)  
Table 4 presents the performance evaluation of three machine learning algorithms (Gradient Boosting, XGBoost, and Random Forest) on data from PCA (principal component analysis) parameters. The results show that:  
Random Forest obtains the best performance with an accuracy of 41.98%, an F1 score of 41.45%, and an MCC of 28.23% while displaying a relatively short execution time (3.14 ms).  
Figure 8 illustrates the ROC curve and the confusion matrix of the model that achieved the highest accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & **Time (ms)** & **Accuracy (\%)** & **Precision (\%)** & **MSE** & **F-Score (\%)** & **Recall (\%)** & **MCC (\%)** \\ \hline Gradient Boosting & 12.13 & 39.93 & 39.29 & 5.74 & 39.29 & 39.93 & 25.42 \\ XGBoost & 4.91 & 37.20 & 36.97 & 6.25 & 36.97 & 37.20 & 22.23 \\ Random Forest & 3.14 & 41.98 & 41.45 & 5.97 & 41.45 & 41.98 & 28.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of algorithm performance on data from PCA Parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & **Time (ms)** & **Accuracy (\%)** & **Precision (\%)** & **MSE** & **F-Score (\%)** & **Recall (\%)** & **MCC (\%)** \\ \hline Gradient Boosting & 8.26 & 26.96 & 26.87 & 7.11 & 26.87 & 27.99 & 09.52 \\ XGBoost & 4.50 & 27.99 & 28.06 & 6.95 & 28.06 & 27.99 & 11.48 \\ Random Forest & 2.34 & 30.72 & 30.54 & 6.59 & 30.54 & 30.72 & 14.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of algorithm performance on Data from Ratio Index Parameters.  
Figure 7: (**A**) Confusion matrix and (**B**) ROC curve for the Random Forest model on Data from Ratio Index Parameters.
# A Real Model of a Micro-Grid to Improve Network Stability
## 4 Microgrid Voltage Stability Control  
This section chapter characterizes the microgrid control strategy, which is designed to improve the voltage stability. The voltage in the 24 V\({}_{\text{DC}}\) grid is intended to be stable, namely, to exhibit the lowest possible fluctuation rate. An exact voltage value, however, cannot be obtained if several energy sources and multiple appliances are connected; importantly, the proposed strategy will maintain the voltage within the range of (22-26) V.