esa-sceva/satcom-chunk-collection huggingface dataset:
# Transformative Technology for FLASH Radiation Therapy
## 2 Beam Conditions for the FLASH Effect
### FLASH with Electron Beams  
Highlights of the observed FLASH effects from the <20 MeV electron beams produced by clinical linacs using pulse repetition rates up to 300 Hz are listed below with references. Beam properties from a compilation of studies observing FLASH electron beam conditions are summarized in Table 1.  
* Study of pulmonary fibrosis from irradiation of the lung [1]: Severe to moderate fibrosis for the conventional average dose rate of 0.03 Gy/s, with a 17 Gy total dose. For an average dose rate of 40-60 Gy/s, the equivalent fibrosis occurred at a 30 Gy total dose.
* Study of neurocognitive impairment in mice from brain irradiation [14]: Severe neurocognitive degeneration at an average dose rate of 0.1 Gy/s, with a 10 Gy total dose. Reduced impairment starts at 30 Gy/s with no neurocognitive decline at 100 Gy/s, with an average dose rate for 10 Gy.
* Skin irradiation (mini-pig) [13]: Fibrosis and necrotic lesions were observed at an average dose rate of 0.08 Gy/s (22-37 Gy total dose), with only mild depigmentation at an average dose rate of 300 Gy/s (22-37 Gy total dose).  
Additionally, there are proposals for very-high-energy electron beams (VHEE) for a more penetrating clinical electron beam. Tumor depths of 30 cm require 200-250 MeV electrons. Treatment models have predicted that delivering 10 Gy/s requires 10\({}^{11}\) e/s for a 200 MeV, \(\sigma\) = 1.5 mm Gaussian beam ([PERSON], personal communication, May 2021). Although not discussed in depth here, there are existing electron accelerator facilities that could be used for FLASH studies. The 5 Hz FAST SRF Linac at Fermilab produces 50 and 300 MeV beams capable of delivering up to 1000 Gy/pulse and a 10\({}^{6}\) Gy/s instantaneous rate based on the above simulation. Clinical scanning capability requires a faster duty cycle of \(\sim\)100 Hz or higher. The CBETA CW recirculating energy recovery linac at Cornell University produces a 150 MeV beam and can scan 10\({}^{6}\) Gy/s at 200 cm/ms (beyond current transverse scanning capability). The size of the ring can be dramatically reduced by replacing the weak permanent magnets in the arcs and a miniaturized version is under conceptual design.
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
###### Abstract  
Trajectory tracking is a crucial aspect of controlling nonlinear systems and is an important area of research. Researchers have proposed several strategies to perform this task in the presence of perturbations, which are the sum of a system's uncertainty, modeling errors, and external disturbances. Nonlinear systems, such as robot manipulators, have complex dynamics, and deriving their exact mathematical models is a tedious task. Therefore, the objective of this research is to design a model-free form of control for such systems. To achieve this goal, a sliding mode control (SMC) with a proportional-integral-derivative (PID) sliding surface was designed and integrated with a saturation-function-based extended-state observer (ESO). In an extended-state observer (ESO), the primary concept is to define the system's perturbation. The ESO estimates the system's states and perturbation, including the known and unknown dynamics, uncertainties, and external disturbances, which are considered as perturbations. The estimated perturbation is used in a closed loop to cancel the actual perturbation. This perturbation-rejection technique improved the controller's performance, resulting in reduced position error, reduced sensitivity to low-frequency elements of perturbation, and a small magnitude of switching gain. The designed control algorithm requires minimal information about the system, specifically position feedback, and, therefore, there is no need to identify the system parameters. A mathematical analysis of the designed algorithm was performed in detail, and the algorithm was compared with the existing ESO-based SMC algorithm. Simulations were conducted using MATLAB/SimMechanics on two different systems, and the comparison results validated the performance of the designed algorithm in comparison to previous research.  
trajectory tracking; sliding mode control; nonlinear systems; perturbation rejection; disturbance observer  
[PERSON], S.
## 2 Materials and Methods
### Image Analysis  
The histological images were analyzed for the cell density in each of the three zones, as well as for the neo-tissue formation (sGAG and collagen deposition) within the hydrogel for all three zones. The analysis of the data was performed using ImageJ J v2.0.0 software. The images were spatially calibrated using their integrated scale bar. For the analysis of H&E staining, the microscopic images were first split into the three-color channels RGB. Then, the green channel was selected as the optimum channel for the identification of the nuclei. Next, the threshold was adjusted before using the \"analyze particles\" plugin. The minimum size was set between five and 35 pixels depending on the image, while the maximum size was set at 300 pixels. The circularity range was set between 0.45 and 1.00 for all the images. This analysis of the H&E images was performed using the ROI (region of interest) manager to select three regions of interest corresponding to the three different zones and to analyze them separately using a different threshold and size spectrum depending on their specific data. For the samples stained with Alcian Blue and Picrosirius Red, the image analysis was performed with the use of the Trainable Weka Segmentation plugin [23]. This plugin utilizes a segmentation algorithm that combines a collection of machine learning algorithms with a set of selected image features to produce pixel-based segmentations. Three classes were used for the training of the algorithm and the classification of the results: (a) a class for the negative background staining of the alginate, (b) a class for the stained cell nuclei, and (c) a class for the positively stained areas (areas with darker blue or red color for AB and PR, respectively). The training of the algorithm was performed by manually selecting multiple areas belonging to each of the classes using the Image J's selection tool and categorizing them according to their corresponding class. After that, the features of the input image were extracted and converted to a set of vectors of float values (format for Weka classifiers). Finally, the plugin created and displayed the resulting image. This image was equivalent to the current overlay (8 bit color with each color corresponding to a specific class). The training features used for the training of the algorithm were Gaussian blur, Sobel filter, Hessian matrix, and the difference of the Gaussians. After training, the same classifier was applied to all the images of the same staining. The plugin performed the image segmentation on the basis of the current classifier and the selected features and created a stack of images, each one highlighting one of the selected features. By selecting the desired 8 bit color image from the stack and setting an appropriate threshold, we performed area measurements on the objects within the images to evaluate the sGAG content of the areas positively stained for collagen (for all the zones and timepoints). The ratio between positive area and total area for the samples analyzed is presented as a percentage.
# CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification
## 4 Experiment
### Result
#### 4.4.2 Out-Of-Domain  
We evaluate the robustness of models sampled through each acquisition function by measuring their Out-Of-Domain (OOD) performance. This experiment is conducted with models trained on 15% of the data after the last iteration. As presented in Table 3, we train the models on IMDB, SST-2, and QQP and then evaluate them on SST-2, IMDB, and TWITERPPDB. In this experiment, CFP-AL consistently records the highest performance, demonstrating its robustness. This indicates that sampling without relying on the classifier in a single domain and reflecting the model's own representations into the scoring method can build a more robust model. CFP-AL demonstrated robustness by consistently improving performance in-domain with each iteration and showing the least variance and best Out-Of-Domain performance. These results indicate that CFP-AL is less affected by various factors such as random seeds, initial data, or classifier performance, and overall, it achieves superior and stable results.  
Figure 5: Our main result. The x-axis is the ratio of the total data used for learning, and the y-axis is accuracy. In-domain (ID) accuracy of different acquisition functions. 1% is the initial labeled data, and performance is evaluated by sampling an additional 2% cumulatively up to 15%. The chart presents the experimental results including the best performance, and all results are in Table 1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**TRAIN (IN)** & **SST-2** & **IMDB** & **QQP** \\
**TEST (OOD)** & **IMDB** & **SST-2** & **TWITERPPDB** \\ \hline Random & 82.55\({}_{(\pm 2.11)}\) & 79.77\({}_{(\pm 1.27)}\) & 85.82\({}_{(\pm 0.46)}\) \\ BERT-KM & 84.46\({}_{(\pm 1.22)}\) & 80.99\({}_{(\pm 1.01)}\) & - \\ Entropy & 82.93\({}_{(\pm 4.92)}\) & 83.21\({}_{(\pm 0.23)}\) & 85.68\({}_{(\pm 1.26)}\) \\ ALPS & 84.65\({}_{(\pm 2.24)}\) & 81.06\({}_{(\pm 0.78)}\) & 85.23\({}_{(\pm 0.56)}\) \\ BADGE & 85.33\({}_{(\pm 1.42)}\) & 82.41\({}_{(\pm 0.92)}\) & - \\ CAL & 84.35\({}_{(\pm 1.44)}\) & 83.58\({}_{(\pm 1.43)}\) & 86.32\({}_{(\pm 0.42)}\) \\ CFP-AL & **86.62\({}_{(\pm 1.09)}\)** & **85.19\({}_{(\pm 0.47)}\)** & **86.77\({}_{(\pm 0.47)}\)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: OOD (Out-Of-Domain) accuracy is evaluated by a model trained with 15% of labeled data obtained by each acquisition function. Bold is the best.
## 2 Basic Concepts and Work Scope
### Positioning Data Aggregation Domains for Fingerprinting-Based Positioning
#### 2.4.3 Position Estimates from Aggregated Fingerprints in the Fingerprint Domain  
Case 3 (in Table 2), in Figure 7c, consists of aggregating the fingerprints in the fingerprint domain before performing the mapping of the fingerprints to an estimated position through a pattern-matching method. In this case, a collection of several, say \(n\), sets or vectors of fingerprint samples is presented as the input. All of the \(n\) fingerprint vectors are aggregated in the fingerprint domain. This aggregation is carried out using a suitable function or operation, built into the pattern-matching method, or in a separate module prior to the pattern matching. In the case of combining the aggregation with the pattern matching, the pattern-matching method is applied simultaneously to the \(n\) input fingerprint vectors to produce as the output a position estimate for all of the input vectors. In the case of a pattern-matching method based on an NN, it can be left to the training phase to find a weighted average for the input fingerprints, thus integrating the aggregation as part of the NN model. In contrast, performing the positioning data aggregation in a separate module, may destroy information that otherwise could be used by the NN from the input to the output to produce a better position estimate. Nevertheless, the aggregation can be carried out in a module independent of the pattern-matching method if desired. For example, a common approach applied in field measurements consists of averaging the fingerprints collected in the time domain for a fixed position and then inputting these into the pattern-matching method.  
In case number 3 in Table 2 and in Figure 7c, the actual aggregation of fingerprints is assumed to be part of the pattern-matching method, and therefore, it is not explicitly shown.  
In the context of the work presented in this article, the \(n\) input vectors of fingerprints will be collected simultaneously at adjacent positions by means of the 2D sensor array when the 2D sensor array is at a fixed position in space.  
In the literature, some authors consider a collection of fingerprint vectors collected by moving a positioning device along a path. In contrast to the approach discussed in the previous subsection of aggregating the position estimates in the position domain, some authors aggregate the fingerprints collected by moving along a path in the fingerprint domain by considering a time-series of spatially distributed fingerprints. These research works are discussed in Section 3.2.
## 4 Energy Detection Model
### Receiver Operating Characteristic Curves  
In order to evaluate the performance of SS techniques, several metrics are used. The most common are detection probability and false alarm probability. The probability that the SU correctly declares that a PU is present when the PU is really present is defined as the detection probability (\(P_{d_{i}}\)). The probability that SU incorrectly declares that the PU is present when the PU is actually absent is the false alarm probability (\(P_{f_{d_{i}}}\)) [11].  
The interdependence between the probability of signal detection and the false alarm probability has been mostly expressed through receiver operating characteristic (ROC) curves (Figure 4). In Figure 4, the different spaces above and below the diagonal (also known as the line of no-discrimination) identify the quality of ED. If the ED process can be expressed as the line of no discrimination, this means that the quality of the ED process corresponds to a random guess. Generally, the ROC space above the diagonal represents good detection results (better than random) while the space below the diagonal line represents poor ED (worse than random). Hence, the ED will be less accurate if the curve is closer to the 45-degree diagonal of the ROC space (Figure 4). The closer the curve follows the left-hand border and the top border of the ROC space, the more accurate the ED process will be (Figure 4). The area under the curve is also a measure of the detection accuracy. A larger area under the curve means that there is better ED accuracy and vice versa (Figure 4). The ROC concept, as a frequently used concept for evaluating the PU signal detection efficiency, is further used in the results section of this work.
### Contribution of the Paper and Road Map  
To solve the problem of misalignment of coils caused by the drone's imperfect landing, a battery charging system based on wireless power transmission was developed in this work. The system is composed of a ground charging station and a receiving coil with the load. The charging station is equipped with multiple transmitting coils, whereas, on the receiver side, just one receiving coil is used. Multiple transmitting coils are placed on a movable bed which can move in four directions (positive \(X\)-direction, negative \(X\)-direction, positive \(Y\)-direction, and negative \(Y\)-direction). A control technique based on the hill climbing algorithm was implemented to control the alignment between the transmitting and receiving coil. The system was developed in a way allowing the drone to land freely on the charging station without the worry of alignment between the receiving and transmitting coil. The drone can land freely on charging station and the coils will adjust automatically in proper alignment to start the power transmission. Previously, the misalignment issues were solved using different methods, such as the design of a charging station with overlapped coils [35], using a target detection technique based on image processing [36], and using a positioning system based on physical sensors [37]. The proposed system is based on a novel method which uses the hill climbing algorithm on backscattered voltage signals of the transmitting coils. It improves the accuracy of the system and eliminates the flaws found in previous techniques such as the image-processing technique, where there are chances of missing the target. Also, it gives the freedom for the drone to land freely on a charging station and makes the system more adoptable by avoiding the use of physical sensors. The configuration of the proposed system is discussed in detail in Section 2.
## 3 Results and Discussion
### Specimen Experiment
#### 3.1.5 Equilibrium Moisture Content  
As it can be seen from the isothermal equilibrium moisture content test results in Figure 8, as the temperature increases, the adsorption capacity of the CHM-WSE increases; as the relative humidity increases, the adsorption capacity of the CHM-WSE also increases. The adsorption increases significantly when the relative humidity is around 80%. The experimental results show that the fitted curve of adsorption content is related to temperature, relative humidity and the specific surface area of the composite hygroscopic mortar. The isothermal adsorption curve of composite hygroscopic mortar is written as follows [31]:  
\[\phi\leq 60\%:u=6.93T^{0.48}\phi^{1.01}s^{0.3} \tag{6}\]  
\[\phi>60\%:u=2.52T^{0.34}\phi^{5.31}s^{1.39} \tag{7}\]  
where \(u\) is equilibrium moisture content per unit area (in kg/m\({}^{2}\)); T is environment temperature (in K); \(\phi\) is the relative humidity (in %); \(s\) is specific surface area (in m\({}^{2}\)/g).  
According to the equilibrium moisture content equation, the adsorption/desorption moisture content of hygroscopic material is related to the distribution of material aperture. When the specific surface area rises, the pore volume increases and the adsorption/desorption capacity grows accordingly.
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
###### Abstract  
This article presents the design of a novel, compact, 4 \(\times\) 2 planar-array antenna that provides quad-beam radiation in the broadside direction, and it enhances coverage and serviceability for millimeter-wave applications. The antenna utilizes a corporate (parallel) feed network to deliver equal power and phase to all elements. Non-uniform element spacing in the two orthogonal planes, exceeding 0.5\(\lambda_{1}\) (\(\lambda_{1}\) being the wavelength at 30 GHz), results in a quad-beam radiation pattern. Two beams are formed in the xz-plane and two in the yz-plane, oriented at angles of \(\theta=\pm 54^{\circ}\). However, this spacing leads to null radiation at the center and splits the radiation energy, reducing the overall gain. The measured half-power beamwidth (HPBW) is 30\({}^{\circ}\) in the xz-plane and 35\({}^{\circ}\) in the yz-plane, with X-polarization levels of \(-20.5\) dB and \(-26\) dB, respectively. The antenna achieves a bandwidth of 28.5-31.1 GHz and a peak gain of 10.6 dBi. Furthermore, increasing the aperture size enhances the gain and narrows the beamwidth by replicating the structure and tuning the feed network. These features make the proposed antenna suitable for 5G wireless communication systems.  
5G; array antenna; millimeter wave (mmWave); parallel feed; quad-beam +
Footnote †: journal: Electronics Letters  
1
Footnote 1: [[https://doi.org/10.3390/electronics14051056](https://doi.org/10.3390/electronics14051056)]([https://doi.org/10.3390/electronics14051056](https://doi.org/10.3390/electronics14051056))
# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 3 Results
### Experimental Setup  
#### 3.2.1 Experiment 1  
As KNN, BP-NN, RF, Naive Bayes, and Linear-SVM have been widely used, these methods were implemented for evaluating the performance of the extracted features. To convincingly estimate the proposed method, two of the five ECG signals from the subject were combinatorially selected as the training set, and the remaining three were utilized as the testing set. As a result, each experiment was repeated ten (\(C_{5}^{2}\)) times.
## 3 Results and Discussion
### HS-SPME GC-MS Analysis of Volatile Organic Compounds  
Volatile compounds of Romanian paprika were analyzed using SPME followed by GC-MS, as shown in Table 1. This study identified 32 volatile compounds divided into seven classes in the samples, including 16 hydrocarbons, 5 aldehydes, 4 ketones, 3 alcohols, 2 esters, 1 sulfur compound and 1 heterocyclic compound (Table 1). Hydrocarbons had the highest proportion (50.6%, Figure 3a) in the samples, inducing fruity, camphorous, sweet, lemony, pine-like, minty, woody, resinous, balsamic, plastic, roasted, citrus, floral, aromatic, green, chemical, herbaceous, clove, pepper or spicy odors [6; 25]. The limonene was the most abundant in the paprika samples, although its amount varied between samples as follows: P7 (100%) > P8 (42.8%) > P6 (41.6%) > P3 (30.2%). M-cymene may be formed from oxidation of monoter hydrocarbons by isomerization and oxidation [26; 27]. Figure 3: Classification of volatile compounds identified in paprika by chemical class (**a**) and aroma profile (**b**).
## 3 Results
### Prevalence of ARGs in Swine Wastewater Treatment Process  
In the raw wastewater, the highest concentrations of antibiotic resistance genes were _tet_M at 5.39 \(\times\) 10\({}^{8}\) copies/L and _suI_M at 5.80 \(\times\) 10\({}^{8}\) copies/L, followed by _suI_2 at 1.92 \(\times\) 10\({}^{8}\) copies/L and _emr_B at 1.09 \(\times\) 10\({}^{8}\) copies/L (Figure 4). By contrast, _tet_B was not initially detected, _qnr_A and _qnr_S were absent, and _flo_R was observed at 4.24 \(\times\) 10\({}^{6}\) copies/L. After flocculation, _tet_M declined sharply to 1.10 \(\times\) 10\({}^{6}\) copies/L and _suI_2 decreased to 1.10 \(\times\) 10\({}^{6}\) copies/L, while _emr_B remained at 4.42 \(\times\) 10\({}^{6}\) copies/L. In this same step, _flo_R dropped below the detection limit. During sedimentation, _tet_B appeared at 2.69 \(\times\) 10\({}^{5}\) copies/L, whereas _tet_M surged again to 5.19 \(\times\) 10\({}^{8}\) copies/L. Subsequent sand filtration caused _tet_M to rise further to 1.05 \(\times\) 10\({}^{9}\) copies/L and _emr_B to increase to 1.45 \(\times\) 10\({}^{8}\) copies/L. Ozone oxidation then significantly reduced these genes. After the first ozone reactor, _tet_B decreased to 2.80 \(\times\) 10\({}^{4}\) copies/L, and _tet_M dropped to 3.75 \(\times\) 10\({}^{7}\) copies/L in the second. Similarly, _suI_1 and _suI_2 were lowered to the 10\({}^{7}\) copies/L range, and _flo_R fell to 2.49 \(\times\) 10\({}^{6}\) copies/L. Throughout these steps, _qnr_A and _qnr_S remained undetected. Overall, the ozone treatment achieved an approximate 96.8% reduction in all target genes compared to their initial levels. Activated carbon filtration further reduced _tet_M to 2.85 \(\times\) 10\({}^{6}\) copies/L and _suI_2 to 2.74 \(\times\) 10\({}^{6}\) copies/L. Despite these reductions, the final effluent from the deaeration tank still contained 2.75 \(\times\) 10\({}^{4}\) copies/L of _tet_B, 1.24 \(\times\) 10\({}^{6}\) copies/L of _tet_M, and about 1.66 \(\times\) 10\({}^{6}\) copies/L of _suI_.  
Figure 4: Absolute abundance of ARGs at each treatment process.