esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The hybrid model for analyzing distortions of a laser beam passed through a moderately scattering medium with the number of scattering events up to 10 is developed and investigated. The model implemented the Monte Carlo technique to simulate the beam propagation through a scattering layer, a ray-tracing technique to propagate the scattered beam to the measurements plane, and the Shack-Hartmann technique to calculate the scattered laser beam distortions. The results obtained from the developed model were confirmed during the laboratory experiment. Both the numerical model and laboratory experiment showed that with an increase of the concentration value of scattering particles in the range from \(10^{5}\) to \(10^{6}\) mm\({}^{-3}\), the amplitude of distortions of laser beam propagated through the layer of the scattering medium increases exponentially.  
Shack-Hartmann sensor; Monte Carlo simulation; ray-tracing algorithm; geometrical optics; hybrid algorithm; scattering medium; scattered laser beam; adaptive optics Article
## 3 Emotions Dataset  
The current study was an extended work of one of our previous studies [19], where extensive data collection sessions were conducted to record emotional data. A total of 40 healthy subjects participated in the data collection session (male-to-female ratio of 26:14, an average age of 25.2 \(\pm\) 5.9 (years), an average height of 171.6 \(\pm\) 8.4 (cm)). A chest-mounted smartphone with a built-in tri-axial accelerometer and tri-axial gyroscope was used to collect inertial data. Six basic emotions were selected as proposed by [PERSON] and [PERSON] [45] and include \"_happy_\", \"_fear_\", \"_sad_\", \"_disgust_\", \"_anger_\" and \"_surprise_\". For emotional stimuli, an autobiographical memories paradigm was employed, where the subjects were asked to recall a past relevant emotional event before the start of each trial. The subjects walked naturally and no special instructions were given in this regard. The participants recalled a relevant event for each emotion and walked straight for 40 m. The total number of samples in the dataset is 839,800. The class distribution of different emotions is shown in Figure 1a. It can be observed from the class distribution that the dataset is not fully balanced. Figure 2 depicts the data collection process.  
Figure 1: Class distribution for emotions dataset used in this study is shown. (**a**) class distribution of six basic emotions; (**b**) class distribution of hot and cold emotions.  
Hot vs. Cold Emotions Mapping  
A _hot_ emotion is associated with a high degree of interest, passion, or activity along with a high level of arousal, from the biological context, when dopamine--a type of neurotransmitter--is released and blood pumps through the muscles. Hot emotions make us feel hotter and more active, and our skin may get red, exacerbating the feeling and look of a higher temperature. On the contrary, when the arousal level is low, humans experience _cold_ emotions. They could have a more cognitive experience than an emotional experience. While cold, we may remain calm and in control of ourselves. We might also be bored, flat, or sluggish [46; 47; 48].  
We categorized the emotional data into _hot_ and _cold_ emotion classes resulting in a binary classification problem. Table 1 maps all six emotions into two classes. The _hot_ emotions include _angry_, _surprise_, _happy_, whereas the _cold_ emotions include _fear_, _sad_, _disgust_. _Fear_ is an emotion which is associated with both high arousal levels (e.g., a sudden encounter with a harmful object) and low arousal levels (e.g., a concern/worry of being unable to achieve a goal). Since an autobiographical memories paradigm was employed in the data collection, we have placed the _fear_ emotion under the _cold_ category. The class distribution for generated _hot_ and _cold_ classes is presented in Figure 1b (hot: 46.3%, cold: 53.7%).
## 3 Results and Discussion
### Analysis of LM Canola Monitoring Results over Fifteen Years  
A total of 4326 suspicious samples were initially tested for LMO proteins, such as CP4-EPSPS, PAT/bar, and PAT/pat, using an immunochemical strip kit. Based on these results, an event-specific PCR was conducted to identify the introduced LMO gene, event name, and manufacturer of each confirmed LMO sample. From 2009 to 2023, 441 LM canola samples were confirmed, including 437 GT73 event plants with the introduced CP4-EPSPS gene and 4 Topas 19/2 plants with the introduced bar gene. Over 15 years, LMOs were detected at 65 sites (Figure 2B). For eight years, LM canola was collected from eight sites: one in 2009, five in 2012, one in 2015, and one in 2016. From 2009 to 2016, the number of collected LM canola samples matched the number of collection sites. However, in 2017, the unintentional presence of LM canola seeds in imported non-LM canola led to a significant environmental release of LM canola, particularly in festival areas nationwide. As a result,  
Figure 2: Survey results of LM canola monitoring from 2009 to 2023. (**A**) The number of LMO monitoring sites (circle) and collected suspicious samples (square) by year. (**B**) The number of LM canola collection sites (X-labeled) and collected LM canola samples (triangle) across nationwide LMO monitoring areas over fifteen years.  
the number of collection sites increased to 20, and the number of LM canola samples rose to 93. Following intensive investigation and post-management, the number of LM canola collection sites and LMO samples decreased between 2018 and 2020. In 2021, the number of LMO collection sites increased due to concerns about gene flow from LM canola, which led to the expansion of survey sites (Figure 2B).  
[PERSON] reported that the viability of canola seeds could be maintained for at least five years in disturbed soils and up to 16 years in undisturbed soils at a depth of 20 cm [57]. Similar findings were reported in Korean soil [57]. This research suggests that LM canola may continue to be found at the same survey sites due to the presence of a canola seed bank. To effectively manage LM canola post-release, a re-survey was conducted at least four times a year, leading to a decrease in LM canola re-collection since 2017. Despite a decrease in LM canola samples since 2017 (Figure 2B), concerns about gene flow and the increasing number of monitoring sites led to a rise in collection samples from 42 in 2021 to 155 in 2022. Through post-management and the removal of suspicious LM canola, the total number of suspicious LM canola samples decreased to 74 in 2023. These data indicate that released LM canola may continue to grow at the same sites, underscoring the importance of site analysis and post-management.
# Ultrathin and Electrically Tunable Metamaterial with Nearly Perfect Absorption in Mid-Infrared
###### Abstract  
Metamaterials integrated with graphene exhibit tremendous freedom in tailoring their optical properties, particularly in the infrared region, and are desired for a wide range of applications, such as thermal imaging, cloaking, and biosensing. In this article, we numerically and experimentally demonstrate an ultrathin (total thickness \(<\) \(\lambda_{0}/15\)) and electrically tunable mid-infrared perfect absorber based on metal-insulator-metal (MIM) structured metamaterials. The Q-values of the absorber can be tuned through two rather independent parameters, with geometrical structures of metamaterials tuning radiation loss (Q\({}_{\rm r}\)) of the system and the material loss (tan8) to further change mainly the intrinsic loss (Q\({}_{\rm a}\)). This concise mapping of the structural and material properties to resonant mode loss channels enables a two-stage optimization for real applications: geometrical design before fabrication and then electrical tuning as a post-fabrication and fine adjustment knob. As an example, our device demonstrates an electrical and on-site tuning of \(\sim\)5 dB change in absorption near the perfect absorption region. Our work provides a general guideline for designing and realizing tunable infrared devices and may expand the applications of perfect absorbers for mid-infrared sensors, absorbers, and detectors in extreme spatial-limited circumstances.  
metamaterial; perfect absorber; graphene; coupled-mode theory; tunable device +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
In this paper, based on the ACM lumped parameter model (LPM), nine different control algorithms, including least mean-square (LMS) adaptive feedforward control, recursive least square (RLS) adaptive feedforward control, filtered reference signal LMS (FxLMS) adaptive control, linear quadratic regulator (LQR) optimal control, H\({}_{2}\) control, H\({}_{\infty}\) control, proportional integral derivative (PID) feedback control, fuzzy control and fuzzy PID control, are used to simulate the system and analyse their vibration isolation performance, and an optimal control algorithm can be selected for ACM.
# Effects of Interference and Mitigation Using Notch Filter for the DVB-S2 Standard
## 4 Proposed Solution and Efficiency Analysis
### Effects of Notch Filtering and CWI Removal
First, the White Gaussian noise with SNR = 15 dB is added to the QPSK. In the next step, a CWI is added to the noisy signal, SJNR measurement is used to express the notch filter role in system performance. Here, an AWGN noise with 15 dB SNR and a variable power CWI are added to a QPSK signal and then filtered with a proper notch filter. The output SJNR is calculated and displayed in Figure 26 above. Based on this figure, specifically on the \"without filtering\" curve, the signal degrades dramatically as the JSR increases. Two important parameters affect the quality of the received signal, the amount of spectrum the notch filter removes from the received signal and the diffraction of samples from the original point of the signal constellation due to the CWI. For smaller JSR, when the CWI power is low, the removed bandwidth from the signal (notch filter bandwidth) is the main effect on the system performance, therefore, for low power JSR, the filter with r = 0.9999 helps to improve the SNR. In contrast, when the JSR increases, for a powerful CWI signal, the residual amount of interference after filtering starts to affect the system performance  
Figure 23: Block diagram for the second test analysis. Figure 24: Notch filter performance on JSR. Figure 25: Block diagram for the third test analysis. and decreases the quality of the received signal power, thus, for a larger JSR, a wider notch has the best performance. This figure could be used for adaptive adjustments of the notch filter bandwidth based on the input JSR to obtain maximum system performance.
# On the Effect of Friction on Tibiofemoral Joint Kinematics
## 4 Patient-Specific Musculoskeletal Modeling  
To estimate patient-specific TKA knee joint loads, an already validated, patient-specific musculoskeletal model is applied [35]. This model is based on data for one male subject (age: 86, height: 1.80 m and mass: 75 kg) with an instrumented, posterior cruciate-retaining TKA prosthesis from the 5 th Grand Challenge Competition to Predict In Vivo Knee Loads [78; 79; 80]. For more details of the implant shape and design, the reader is referred to the Grand Challenge knee dataset [78]. Among others, the dataset contains pre- and post-operative Computed Tomography (CT) scans, trajectories of skin markers and ground reactions during standing reference trials and dynamic movement trials, including gait at a self-selected speed as well as measurements from the instrumented knee prosthesis. Additionally, Stereolithography (STL) 3D geometries of the femoral component, tibial tray and insert, patellar button, and segmentation of the post-operative CTs of the partial pelvis, femur, patella, tibia, fibula, partial talus and partial calcaneus are also included in the dataset [81]. For the dynamic analysis, the one gait trial of level walking at self-selected speed (PS_ngait_og_ss1) is utilized [78]. The musculoskeletal model is developed using the AnyBody Modeling System (AMS) v. 7.1 (AnyBody Technology A/S, Aalborg, Denmark) and based on the human model from the AnyBody Managed Model Repository (AMMR) v. 1.6. From this model, we extract all the knee flexion-extension angles as well as all loads associated with muscle, hip joint, gravitational, inertial, etc., which are imposed on the femur. The tibofemoral joint reaction forces and knee ligament loads are included in the joint model described below and, therefore, are not part of that model's boundary conditions.
## 5 Results and Analysis
### Local Strain and Strain Rate Determination at Fracture Locations  
Following the qualitative identification of fracture locations, quantitative analysis was performed to determine the local material response at these critical sites using the virtual strain gauge (with a gauge length of 15 pixels (approximately 5 mm) were defined at fracture locations to extract local strain histories.) approach outlined in Section 4.3. Figure 19 presents a representative example for specimen Z001-29.  
The black curve shows the local axial strain history, \(\varepsilon(t)\), measured at the primary spall crack location. The history captures an initial compressive phase from the incident wave, followed by tensile loading as the reflected wave creates tension. Fracture occurs at approximately \(t=240\) us, with a peak tensile strain of approximately \(980\,\upmu\varepsilon\).  
The red curve shows the corresponding strain rate history, \(\dot{\varepsilon}(t)\), calculated using the numerical differentiation procedure with Tikhonov regularization described in Section 4.3. To determine a single, representative strain rate for correlation analysis, we implemented a quantitative procedure. The representative strain rate was calculated as the average of the strain rate data (\(\dot{\varepsilon}(t)\)) over the time interval corresponding to the final tensile loading phase, specifically where the local tensile strain (\(\varepsilon(t)\)) increased from 0 to its measured peak value. For the example shown in Figure 19, this procedure yields a representative strain rate of approximately \(26\,\mathrm{s}^{-1}\). This method provides an objective and reproducible measure of the loading rate immediately preceding fracture and forms the basis for the rate-dependent strength relationships analyzed in the following section.  
Figure 18: Experimental comparison of post-spall failure modes from post-test photographs. (**a**) Three large, localized fractures in a plain concrete specimen, indicating imminent and complete separation. (**b**) Multiple (at least four) cracks in a deformed UDRC specimen which is held together by the central reinforcement, demonstrating the crack-bridging effect and maintenance of structural integrity.
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 3 System Design  
In this section, the design of a wrist-worn 915 MHz SpO2 sensor is presented. First of all, an overview of SpO2 will be given. The system architecture is then explained jointly with a description of the 3D system model used to design the 915 MHz antenna. Finally, the antenna topology will be described in detail.
## 3 Explosion Test Program
### Damaged HFPC Panels
#### 3.2.3 PVA-Steel HFRC Specim  
Similarly, PVA-Steel HFRC panel damage is recorded and summarized in Table 6 and Figure 10. The detailed information for S9 to S12 panels was as follows.  
Figure 10: Damage mode of PVA-Steel HFRC panels.  
Figure 9: Post-blast front and rear surface craters of PP-Steel HFRC panels.  
For 0.5% Steel + 1.5% PVA HFRC, 5 (front) and 27 (rear) radial cracks were found for S9-1. S9-2 showed 14 and 23 radial cracks for front and rear surfaces.  
Panels of 1.0% Steel + 1.0% PVA HFRC had constant damage phenomenon whereby about 8 and 20 radial cracks were recorded on the front and rear surfaces.  
Also, panels of 1.33% Steel + 0.67% PVA HFRC were featured with 16 (front) and about 35 (rear) radial cracks.  
For 1.5% Steel + 0.5% PVA HFRC, 12 (front) and 19 (rear) radial cracks were found for S12-1 panel. S12-2 panel showed 7 and 28 radial cracks on the front and rear surfaces.  
Figure 10(a) gave the front crater diameter and depth information, and the rear crater diameter is shown in Figure 10(b). Also, the least damage of crater diameter and depth does not happen to the same fiber hybrid, e.g., 1.33% Steel + 0.67% PVA HFRC had the smallest front crater diameter while 0.5% Steel + 1.5% PVA HFRC panel had the least front crate depth. In general, it is hard to determine which hybrid fiber mix performed best in terms of blast resistance.
## 1 Introduction  
Fractal geometry was first put forward to describe the self-similarity of natural phenomena by [PERSON] in the 1960s, and gradually became an important approach to the quantitative characterize spatial distribution and anomalies in geoscience over the past several decades [1, 2, 3, 4, 5, 6, 7]. Various calculated approaches for fractal dimensions--including the Hausdorff dimension [8], the box-counting dimension [9], the information dimension [10], and the correlation dimension [11], etc.--have been proposed and applied to describe the self-similarity [12] and self-affinity [13] of fractals for irregular shapes or complex objects in geology [14]. Multi-fractal theory, including spectrum-area (S-A) models and singularity analysis, is used to detect the true anomalies caused by mineralization [15, 16] in geochemical [17, 18, 19, 20], geophysical exploration [21, 22] and signal detectors in seismology [23].  
Various studies of fractals for the calculation of magnetism and gravity have been proposed; for example, the fractal dimension of the fractional Brownian surface model was used to describe the statistical self-similarity in topographic surfaces [24]. Bouguer and free air gravity anomalies were analyzed under fractal geometry and fractal dimension analysis for the optimal design of a 2-D gravity survey network, which was conducted to obtain an optimum scale for gridding intervals for the generation of bouguer anomaly maps [25]. The matched filtering method from a fractal model was performed to identify volcanic magnetic anomalies from an oilfield [26]. The fractal-based wavelet filtering technique was developed to separate satellite gravity anomalies from the background in the Nanjing district of China [27].  
The edge detection method from the gravity anomalies showed an important role in the delineation of multi-scale concealed faults or different lithological contacts, and various methods from fractal geometry were used. However, few methods of fractal dimensions were applied to detect the edge of potential gravity anomalies, and many techniques--including kinds of horizontal and vertical derivatives [28], total horizontal derivatives [29], gravity gradient tensor eigenvalues [15], tilt angles and Theta maps [30]--were widely used to locate anomalous source boundaries in tectonic identification and division.  
This study attempts to use the surface variance statistics of the fractal dimension to delineate the boundaries of geological bodies and structures. The gravity anomaly of a theoretical density model is established, and a comparison with the conventional methods is made. The method was applied to the Robusha chromite deposit [31], and the results of the maximum, minimum, average and four directions of the fractal dimension of the gravity data in this area were obtained. Combined with magnetic anomalies and geological conditions, the boundaries of chromite deposits are delineated.
## 1 Introduction
In the first case, voltage measurements are taken on the scalp through electrodes following different arrangements (10-20 being the most-used). Up to a thousand measurements per second are recorded on each electrode, which implies applying the source identification algorithm the same number of times. In the case of electroencephalography, for which the maximum frequency is 120 Hz, it is feasible to use both architectures. However, the Mode 2 architecture consumes fewer hardware resources, which can contribute to the development of portable electroencephalographs. The problem that we are addressing involves developing and implementing two efficient loop unrolling architectures for a stable source identification algorithm in FPGAs. To achieve this, we set the following objectives:Develop loop unrolling architectures for implementing a stable source identification algorithm. Analyze the stable algorithm to determine the regularization parameters numerically. Validate the FPGA implementations. We make the following contributions:  
1. Development of two efficient architectures based on a loop unrolling design for source identification problems. Architecture Mode 1 is simple and allows for different operating speeds. 2. Architecture Mode 2 uses a control system based on multiplexors, reducing resource consumption and complexity and allowing multichannel problem usage. 3. Implementation of the source identification algorithm in four FPGAs. 4. Design of a control system based on multiplexors for both architectures. 5. Analysis and comparison of resource consumption. 6. Creation of ad hoc synthetic examples: particularly, the jump function, which is commonly used in electronic applications. 7. Implementation of synthetic examples in MATLAB 2013a. 8. Numerical analysis to choose the regularization parameters to handle the numerical instability of the algorithm. 9. Validation of the performance of the hardware architectures compared with software results in terms of error. This work is organized as follows. In Section 2, we present the foundations for this work. Section 3 presents the mathematical model that relates the sources with measurements. Furthermore, we solve the forward and inverse problems. The solution to the inverse problem provides the stable source identification algorithm; Section 4 presents numerical examples associated with forward and inverse problems. These examples are developed using MATLAB software to validate the source identification algorithm given in Section 3. In Section 5, we implement the source identification algorithm in FPGAs using the two proposed architectures. In Section 6, we validate the hardware implementations using the same examples developed in Section 4. In Section 7, we discuss the obtained results. Finally, we present our conclusions in Section 8.