esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Remote Control for Underwater Vehicles
## 3 Requirements and Definition of Working Modes for ROV Control
### Raw Data Streams Analysis
In our design, this information is always transmitted with the longest range acoustic modem installed in the ROV, even when the highest rate acoustic modem is in range: in this way, the latter can be used to transmit more demanding traffic, as presented in Section 3.3. In our design, the longest range acoustic modem installed in the ROV also integrates LBL functionalities, used to let the ROV identify its own position with respect to the control station. Finally, we assume the ROV to be able to capture high-definition 4160 \(\times\) 2336 pixel JPEG images with a size of 2.5 Megabytes in places of high interest (e.g., in the correspondence of valves or fittings). These images will be eventually conveyed to the control station.
## 4 Experimental Results  
The training and testing images used in the experiments are same with [PERSON] et al. [2018], which are preprocessed from the Caltech pedestrian dataset using HOG + CSS + SVM. The original image size is \(108\times 36\). Before being fed into networks, images are cropped to \(84\times 28\). A positive sample is an image including a pedestrian, and a negative sample is an image without a pedestrian.  
During network training, the input samples are randomly selected. A total of 60 samples are used in each batch, including 50 negative samples and 10 positive samples. The ratio of positive and negative samples is 1:5. The reason for choosing this ratio is to make it closer to what happens in real-life scenarios faced by the pedestrian detection system. With the augment method in Section 3.3,  
Figure 7: The examples of positive sample augmentation. (**a**) Origin images; (**b**) images after a mirror flip; (**c**) images with a clockwise rotation of three degrees; (**d**) images with a counterclockwise rotation of three degrees; (**e**) mirrored images with a clockwise rotation of three degrees; (**f**) mirrored images with a counterclockwise rotation of three degrees.  
approximately 60,000 negative samples and 12,000 positive samples are available for the INRIA pedestrian dataset [36], and approximately 60,000 negative samples and 24,000 positive samples are available for the Caltech pedestrian dataset [37].  
The Log-average Miss Rate (LAMR) is used as a performance evaluation for evaluating different pedestrian detection methods, for which lower value means better performance.  
In the process of parameter training, first we fix the learning rate to 0.05. When the LAMR does not decrease, we subtract 0.005 from the learning rate and continue until the learning rate is less than 0.02 and the LAMR no longer decreases. The network reaches an optimal solution.
_Algorithms_**2009**, \(2\), 1087-1104; doi:10.3390/a2031087  
_Algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
Featurered-Based Algorithm for the Automated Registration of Multisensorial / Multitemporal Oceanographic Satellite Imagery  
**[PERSON]\({}^{*}\) and [PERSON]**  
Signal and Communications Department, University of Las Palmas of Gran Canaria, Campus  
Universitario de Tafira, Edificio B de Telecomunicacion, 35017 Las Palmas de Gran Canaria, Spain;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{*}\) Author to whom correspondence should be addressed; E-mail: [EMAIL_ADDRESS];  
Tel.: +34-928-452979; Fax: +34-928-451243.  
_Received: 11 June 2009; in revised form: 31 August 2009 / Accepted: 3 September 2009 / Published: 8 September 2009_  
**Abstract:** Spatial registration of multidate or multisensorial images is required for many applications in remote sensing. Automatic image registration, which has been extensively studied in other areas of image processing, is still a complex problem in the framework of remote sensing. In this work we explore an alternative strategy for a fully automatic and operational registration system capable of registering multitemporal and multisensorial remote sensing satellite images with high accuracy and avoiding the use of ground control points, exploiting the maximum reliable information in both images (coastlines not occluded by clouds), which have been coarsely geometrically corrected only using an orbital prediction model. The automatic feature-based approach is summarized as follows: i) Reference image coastline extraction; ii) Sensed image gradient energy map estimation and iii) Contour matching, mapping function estimation and transformation of the sensed images. Several experimental results for single sensor imagery (AVHRR/3) and multisensorial imagery (AVHRR/3-SeaWiFS-MODIS-ATSR) from different viewpoints and dates have verified the robustness and accuracy of the proposed automatic registration algorithm, demonstrating its capability of registering satellite images of coastal areas within one pixel.  
**Keywords:** image registration; feature detection; contour matching; optimization approach; multitemporal and multisensorial images
#### 2.1.4 Data Processing
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{\text{N}}(y_{i}-\hat{y}_{i})^{2}}{\text{N}}} \tag{1}\]  
\[\text{R}^{2}=1-\frac{\text{Residual sum of squares}}{\text{Total sum of squares}}=1-\frac{\sum_{i=1}^{\text{N}}(y_{i}-\hat{y}_{i})}{\sum_{i=1}^{\text{N}}(y_{i}- \overline{y}_{i})} \tag{2}\]  
\[\text{LOD}=\frac{3\cdot\sigma}{s}=\frac{\text{RMSE}_{\text{cal}}\cdot\sqrt{ \text{N}_{\text{cal}}}}{s\cdot\sqrt{\text{N}_{\text{cal}}-1}} \tag{3}\]  
\[\text{LOQ}=3.3\cdot\text{LOD} \tag{4}\]  
where \(y_{i}\), \(\hat{y}_{i}\), and \(\overline{y}_{i}\) are the ith experimental, calculated from the calibration curve and average concentration, respectively; \(s\) is the slope of the predicted vs. measured concentrations values in the calibration phase; and N\({}_{\text{cal}}\) is the amount of data in the calibration set. The RMSE and bias are expressed in the same unit as the concentration (ppm, in this case), whereas the correlation coefficient is a pure number. All data pre-processing, variable selection, and regression analysis steps were performed in a Matlab environment version 2023b (The MathWorks Inc., Natick, MA, USA) using PLS toolbox version 8.7.1 (Eigenvector Research, Inc., Manson, WA, USA).
###### Abstract  
This study aimed to identify equivalent processing conditions using ultrasound (US), pulsed electric fields (PEF), and high-pressure processing (HPP) for shelf-life extension and to ensure the safety and quality of Reineta Parda apple puree (AP) in comparison to conventional thermal pasteurization (72 \({}^{\circ}\)C/15 s, CTP). The processing conditions were selected to achieve at least 5 log CFU/g inactivation of _Escherichia coli_. HPP (400 MPa/1 min), US (60 \({}^{\circ}\)C, 20 kHz/12 min), and PEF (57.2 \({}^{\circ}\)C, 10 kV/cm, 70 bipolar pulses of 8 \(\upmu\)s each) reduced _E. coli_ counts by 6.6, 6.1, and 5.8 log CFU/g, respectively, thus achieving the pasteurization status. After processing, HPP samples showed higher levels of total antioxidant activity (DPPH and ABTS) compared to the other samples. HPP and PEF samples showed lower browning degrees than the CTP samples. All treatments ensured indigenous microbial stability (below 1 log CFU/g) for at least 30 days under refrigeration. Principal component analysis showed that the HPP samples increased their similarity to the untreated fresh AP during storage based on the pH, total soluble content and water activity, retaining its fresh-like qualities. HPP and PEF were found to be potential alternatives to the CTP of AP, resulting in a safe, minimally processed product with improved antioxidant activity.  
n onthermal pasteurization; ultrasound; pulsed electric fields; high-pressure processing; _Escherichia coli_; apple puree
# Characterization of a Modified Clinical Linear Accelerator for Ultra-High Dose Rate Beam Delivery
## 1 Introduction  
Research on Ultra-High Dose Rate Radiotherapy (UHDR RT) is experiencing significant growth, prompting the scientific community to investigate the reasons behind the normal tissue sparing effect, known as the FLASH effect. The FLASH effect refers to the phenomenon where a high dose is delivered in a very short time, resulting in less damage to normal tissues compared to conventional dose rates while maintaining the same tumor control. The challenges associated include understanding the underlying radiobiological mechanisms, the generation of high-quality beams, ensuring accurate dosimetry and closely monitoring the radiation beam during treatment [1; 2; 3; 4; 5; 6; 7].  
Although the beam characteristics that induce the FLASH effect are not yet fully understood, there is a consensus on the relevance of certain irradiation parameters' thresholds. UHDR RT requires instantaneous dose rates around \(10^{5}\) Gy/s and at least 40 Gy/s as average dose rates [1; 2], with single irradiation fractions lasting less than 200 ms [7].  
The availability of machines capable of delivering UHDR beams is crucial for every aspect of the research and to take the necessary steps toward potential clinical applications, from radiobiological studies [2; 8] to the development of dedicated Treatment Planning Systems (TPS) [8; 9]. For these reasons, this work addresses the need for UHDR RT research facilities focusing on the growing interest in modifying clinical linear accelerators (LINACs), a method that gathered successful results over the last decade [10; 11; 12; 13; 14].  
This work presents the reversible modification of a LINAC Elekta SL 18 MV toward the delivery of a 10 MeV electron UHDR beam and its dosimetry characteristics in different irradiation settings, i.e., varying the source-to-surface distance (SSD) and with the optional use of a cylindrical polymethyl methacrylate (PMMA) applicator.  
Dosimetry measurements in UHDR mode were performed with GafChromic films EBT XD and compared to the results obtained for the conventional regime with both films and an Advanced Markus (AM) ionization chamber. Thin silicon sensors, placed at the exit of the accelerator head, were used to monitor the output beam pulses and evaluate their temporal uniformity within the pulse, as well as the pulse-by-pulse stability.  
This upgrade of the LINAC Elekta SL 18 MV facility provides the researchers a valuable tool for UHDR studies, allowing the testing of different beam monitoring technologies and perform radiobiological experiments. Additionally, the positioning of perforated templates at the applicator's end could allow future studies on the characteristics of Spatially Fractionated Radiation Therapy (SFRT) [15; 16].
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 4 Performance Evaluation of Concept Drift
### The Evaluation Metrics  
For algorithms based on discriminative learning and hybrid learning, accuracy recall, precision, \(F_{1}\)-score, Matthews' correlation coefficient (MCC), and Cohen's kappa \(k\) are mainly used for classification problems, and mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) are mainly used for regression problems. For algorithms based on generative learning and others, the number of hidden nodes per time step (HN), the number of hidden layers per time step (HL), parameter count (PC), and execution time (ET) are mainly used. In particular, they are unique evaluation metrics under the framework of deep learning. It is worth noting that the MCC and [PERSON]'s kappa \(k\) evaluation metrics are mainly used for unbalanced data. The definition of MCC is shown in Equation (7).  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{7}\]  
where \(TP\) represents true positives, \(TN\) represents true negatives, \(FP\) represents false positives, and \(FN\) represents false negatives. The definition of [PERSON]'s kappa is shown in Equation (8).  
\[k=\frac{P_{0}-P_{e}}{1-P_{e}} \tag{8}\]  
where \(P_{0}\) and \(Pe\) are the success rate of the actual and random predictors.  
In addition to the above basic evaluation metrics, some researchers also use some evaluation metrics for the application field of algorithms. For example, the DeepPocket algorithm, which is mainly used in the field of financial investment, mainly uses maximum drawdown (MDD), Sharpe ratio (Sr), and conditional value at risk (CVaR) to evaluate its performance.
## 3 Results
### Comparison and Validation by Factor Analysis  
The best way to validate the effectiveness of our OGE algorithm is to observe how many ore spots can fall into the anomalous image objects, but this is not enough because most of the anomalous patches in Figure 13 are barren. Comparing Figure 13 to Figure 1, we also discover that the presence of ore spots and anomalies is not completely controlled by the spatial distribution of the outcropped bedrocks and known geological structure. For example, over a third of the known ore spots occur within the N\({}_{2}\)b and Quaternary-covered areas with less geological exposure. Inasmuch, it is difficult to directly validate the anomalies from the perspective of regional geology. In this Section, the factor analysis is used to do so. As we know, factor analysis is a well-validated technique that is widely used to reduce a large number of variables into fewer numbers of factors [32]. Although irrespective of the spatial structure of geochemical patterns, it facilitates the identification of multivariate geochemical anomalies. This is consistent with the major aim of OGE. So, supposing the results of factor analysis are tenable and close to truth, we can cross-validate the correctness and robustness of our model's outputs by observing if the factor-score anomalies overlap considerably with the anomalies delineated in Figure 13.  
We can make the following observations from Figure 14: (1) Nearly all the factor-score anomalies, no matter what the elemental association each factor represents, reside within the OGE-derived anomalous areas, and occupy most of the interior space. This result strongly supports the basic correctness of our model's outputs. (2) Our OGE model is advantageous since quite a few ore spots that cannot be identified by the factor-score anomalies are well-identified by OGE. (3) The I-series elemental association can be further divided into five factors: Factor 1: As-Pb-Zn, Factor 2: Ag-B-Cu, Factor 3: Au-Hg, Factor 4: Mo-U, and Factor 5: Fe\({}_{2}\)O\({}_{3}\). Likewise, the S-series elements can be divided into: Factor 1: Bi-W, Factor 2: Pb-Sb-Sn, Factor 3: Mo-U, Factor 4: Nb-Fe\({}_{2}\)O\({}_{3}\), and Factor 5: Be-F. These divisions improve the interpretability of the extracted anomalies and facilitate the mineral species-specific geochemical exploration.  
Given the above, by calculating the ore-spot recognition rate and conducting factor analysis, the OGE-derived anomalies in Figure 13 get validated.  
Figure 14: The geochemical anomalous areas (white) delineated in Figure 13 with relevant factor-score anomalies (colored polygons) overlaid on them. (Left): the I-series elements. (Right) the S-series elements. Note that: the factor-score anomalies were obtained by setting the binarization threshold as mean + 1 \(\times\) standard deviation. Other legends in this figure are consistent with those in Figures 9 and 10.
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 2 Dual-Carrier NFC System  
### System Overview  
The system block diagram of our implantable sensor system is shown in Figure 2. The WPT and the NFC share the same coil pair through dual carriers.  
In the power link (the dark red blocks), the out-body coil (\(L_{\mathrm{o}}\)) is driven by a Class E power amplifier (PA) to generate the power carrier [14; 15]. The Class E topology is chosen for its high efficiency, which is theoretically 100%. A high-speed power MOSFET is turned on and off at 2-MHz frequency, with its switched-on period controlled dedicatedly to ensure the Class E to operate at its optimal point for high power efficiency. A rectifier and a regulator qualify the harvested power carrier from the in-body coil (\(L_{\mathrm{i}}\)) to supply the in-body circuit. The pulse-width modulation (PWM) on the power carrier is used for the adaptive power adjustment of the power link. The adaptive power adjustment adjusts the transmitted power of the Class E PA to keep the PDL constant over different coupling coefficients in a wide operating range of coil separation. That is, when the coupling coefficient increases, the transmitted power of the Class E PA will be reduced adaptively.  
The downlink modulator (the golden yellow block) performs the BPSK modulation on the power carrier. The in-body receiver (the purple grey blocks) acquires the modulated power carrier from \(L_{\mathrm{i}}\). In the in-body receiver, a comparator shapes the modulated power carrier to a square-wave signal and feeds it to the clock generator and the downlink demodulator. The clock generator offers high quality clocks by tracking the power carrier frequency during the period when the power carrier is not modulated [16]. The downlink demodulator is an all-digital BPSK demodulator with ultra-low power [17].  
The in-body transmitter (the green blocks) generates the BPSK-modulated uplink carrier and drives \(L_{\mathrm{i}}\) by the uplink PA. The uplink modulator is simply implemented by a multiplexer to select the carrier or the inverted carrier. The out-body receiver (the cyan blocks) acquires the uplink signal from \(L_{\mathrm{o}}\). Because the power carrier brings strong interference to the out-body receiver, the high-order active filter (uplink filter) is installed before the uplink demodulator [18].  
The uplink and the downlink work in half-duplex to avoid their mutual interference. The data rate of the uplink and the downlink are both 10 kbps in our system, as it is a sufficient data rate to send intraocular pressure data, in-body system power statuses and control commands.  
Figure 2: Block diagram of our implantable biomedical sensor system utilizing the proposed inductive link. The WPT and the NFC share the same coil pair by using two carriers. This paper focuses on analyzing and optimizing the inductive link circuit, which is enclosed in the dashed box.
## 3 Analysis of Results for Optimal Model
### BEMF Analysis  
The back electromotive force (BEMF) is voltage induced when the magnetic flux generated in the permanent magnet is linked to the coil of the stator when the motor rotates under no-load conditions, and can be expressed as the product of the flux linkage and the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Before Optimization** & \multicolumn{2}{c}{**After Optimization**} \\ \hline Speed [r/min] & 1800 & 4500 & 1800 & 4500 \\ Rated current [Arms] & 10 & 10 & 10 & 10 \\ Line to Line voltage [Vrms] & 135.6 & 128.9 & 120.0 & 116.8 \\ Torque [Nm] & 17.7 & 9.1 & 17.5 & 8.1 \\ Ripple factor [\%] & 5.1 & 12.1 & 6.3 & 4.9 \\ Power factor & 0.690 & 1.0 & 0.848 & 0.960 \\ Efficiency [\%] & 93.4 & 94.2 & 95.3 & 95.1 \\ Material cost [p.u] & 506.1 & 506.1 & 501.2 & 501.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of designs before and after optimizing the process with LPM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **LPM (Optimal Point)** & **FEM (Optimal Point)** & **FEM (Design Point)** & **FEM (Test Point)** \\ \hline Winding method & Concentrated & Concentrated & Concentrated & Concentrated \\ Number of series turns per phase & 80 & 80 & 80 & 80 \\ Resistance of phase [Ohm] & 0.2087 & 0.2087 & 0.2087 & 0.2087 \\ Base speed [r/min] & 1800 & 1800 & 1800 & 480 \\ Rated current [Arms] & 5.0 & 5.0 & 10.0 & 5.0 \\ Load angle [deg] & 14.43 & 14.43 & 23 & 23 \\ No-load linkage flux [Wbrms] & 0.0512 & 0.0516 & 0.0516 & 0.0516 \\ Output torque [Nm] & 8.0 & 8.1 & 17.5 & 7.9 \\ Copper loss [W] & 31.2 & 31.2 & 124.7 & 31.2 \\ Iron loss [W] & 24.9 & 22.5 & 39.1 & 4.1 \\ Efficiency [\%] & 96.3 & 96.6 & 95.3 & 91.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of analysis using LPM and FEM for a six-phase PMa-SynRM.  
electric angular velocity, as shown in Equation (22). Therefore, magnetic torque, which is the first term in Equation (1), can be calculated with constant calculated flux linkage. In addition, since the BEMF is proportional to the speed, the ratio of the harmonic components analyzed through FFT did not change, and the relative torque pulsation level could be estimated through the size of the harmonic components. Figure 7a is a BEMF waveform analyzed at 480 r/min, and Table 4 shows the magnitude of the harmonic components of the phase voltage and the line voltage. The magnitude of the eleventh and thirteenth harmonic components affecting the torque pulsation during six-phase operation were analyzed to be 4.0% and 2.9% of the magnitude of the fundamental, respectively, and the magnitude of the fifth and seventh harmonic components affecting the three-phase operation were analyzed to be 7.0% and 3.1%, respectively.  
\[E=\omega_{e}\lambda_{PM} \tag{22}\]
# Event-Based Path-Planning and Path-Following
## 4 Simulation Results
The left-hand part of Figure 15 shows that the AUV freely passes between obstacles at the bottom part of the scene because they are located far enough away from each other (the distance between obstacles is more than 70 m). In turn, the AUV does not try to squeeze between the tightly placed obstacles at the top of the scene and detours them by turning to the right. Such behavior allows for the vehicle not to be caught in a potential trap and to use only standard path planning algorithms. Figure 13: Evolution of the path curvature in Scenario 1.
# AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
## 4 AWMC Description
### Implementation
#### 4.2.4 Visualization  
First, we used the Google API to fetch Google Maps from around the world. Then, each city was marked on the map by its latitude and longitude. People could click on these markers to see the weather conditions in each city. In the left part of the web, we produced a line chart to visualize the changes of anomalies for each city in a certain time interval. We also produced the line charts to visualize weather data changes in the same time interval. People could find the relationship between the anomaly scores and the weather data via changes in these lines and understand the causes of weather anomalies. In addition, we produced box plots to visualize the dispersion of weather data. A box plot is a statistical chart used to show the dispersion of a set of data. In a box plot, there is a rectangular box with the upper and lower ends of the box corresponding to the upper and lower quartiles of the dataset. The upper and lower quartiles are defined as \(Q3\) and \(Q1\), and the distance between \(Q3\) and \(Q1\) is called the interquartile distance (\(IQR\)). In addition, we drew a line in the rectangular box, which is the median of the dataset, and two lines at \(Q3+1.5 IQR\) and \(Q1-1.5 IQR\) that are outlier truncation points. The outlier truncation points were split into upper and lower. If the data are larger than the upper or smaller than the lower, then they are marked as an outlier in the box plot. People can also analyze the causes of weather anomalies by using the box plots and line charts of anomaly scores. Lastly, we added a click function to the anomaly-score line chart. We clicked on each point in the anomaly-score line chart. Then, the map visualized the extent of anomalies for each city by circles. The size of the circle is based on the anomaly score. If the anomaly score is larger, then the circle is larger. People can use this visualization to compare the extent of anomalies in their city with that in other cities to further understand their weather conditions.  
We used Echart [36] from the Apache open-source project to draw these line charts and box plots. In addition, we used the AntV-L7 API to visualize the anomalies of each city on the map. AntV-L7 API is an open-source WebGL-based visual analysis engine for large-scale geospatial data launched by the AntV data visualization team of Ant Group.