esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiment  
The experimental study was carry out in an anechoic chamber using a vector network analyzer from Agilent Technologies. The experimental data were compared with the data obtained during the simulation.  
Figure 9 shows the normalized experimental and simulation radiation patterns of the antenna in two planes: azimuth (XY plane) and elevation (XZ plane) for six different frequencies (3, 5, 8, 11, 14, and 16 GHz). The image clearly shows how the shape of the radiation patterns changes with frequency, which reflects the nature of the change in the radiation characteristics of the antenna in the studied range.  
Any observed asymmetry of the radiation pattern is explained by the introduced asymmetry of the antenna design, for example, due to non-ideal geometry of the radiating element or the measurement environment. This asymmetry is especially noticeable at high frequencies (e.g., 14 and 16 GHz), where the radiation patterns acquire a more complex structure, including an increase in the level of side lobes.  
Over the entire frequency range, the azimuth radiation pattern remains generally close to omnidirectional, indicating good matching of the antenna to the specified characteristics. However, it should be noted that the XZ plane diagrams demonstrate greater directivity, which is expected for antennas of this type and is due to their design features.  
As usual, the cross-polarization level increases with increasing frequency. This is due to the amplification of the transverse component of currents on the antenna surface, which are more pronounced in the high-frequency part of the range. At low frequencies, the cross-polarization level is minimal, which confirms good polarization isolation of the antenna.  
The comparison of the experimental graph with the graphs obtained during the simulation is also given in Figure 10. The graphs show the dependences of the reflection coefficient on the frequency for three cases: experimental data (red graph), the original antenna (blue dotted graph), and the modified antenna (green dotted graph). The graphs obtained by simulation (blue and green) assume ideal conditions, such as the absence of parasitic losses, perfectly flat surfaces, and precise dimensions of the structure. In reality, these conditions are unattainable, which explains the discrepancy between the red graph and the calculated ones. The experimental antenna may contain deviations in geometry, which changes the current distribution and, as a result, deviations in antenna matching during simulation and experiment.  
In conclusion, the modifications to the antenna geometry demonstrated a significant improvement in its characteristics. The impedance bandwidth was expanded, which confirms the improvement in antenna matching over a wide frequency range. Experimental and numerical radiation patterns are in agreement with each other, which indicates the correctness of the calculation model. These results confirm the high efficiency of the antenna and its potential for use in broadband applications.  
Figure 9: Normalized radiation patterns in the XZ and YZ planes at six frequencies.
###### Abstract  
Monitoring physical activity, e.g., training load and energy expenditure (EE), is important to optimize the training process in various sports. Especially in team handball, where there is little information about EE in training and competition. The objective of the study was to compare EE in team handball derived from a respiratory gas exchange analysis (spiroergometry) and a local position measurement (LPM) system. Eleven participants completed a validated, team handball game-based performance test and wore a portable spiroergometry system (K5 Cosmed) and an LPM transponder (Catapult ClearSky T6). EE was determined via indirect calorimetry for spiroergometry data and via the metabolic power model for EE for LPM data. EE estimated via the metabolic power model was \(-66\) to \(-63\)\(\pm\) 12% lower than via indirect calorimetry (\(p<0.001\), \(p\)n\({}^{2}\) = 0.97). No correlation was found for the overall test (\(r\) = 0.32, \(p\) = 0.34), nor for every single heat (\(r\)\(\leq\) 0.44, 0.18 \(\leq\)\(p\)\(\leq\) 0.99). Therefore, regression analyses predicting spiroergometry data based on LPM data were not feasible. In line with previous studies, the metabolic power model for EE in team handball (including short-distance movements, great accelerations, and non-locomotive actions) is not suitable.  
Keywords:respiratory gas exchange analysis system; local position measurement system; specific performance testing; oxygen uptake +
Footnote †: journal: _Sciences_
# Estimation of BER from Error Vector Magnitude for Optical Coherent Systems
## 3 BER Estimation from EVM with Carrier Phase Recovery and Differential Decoding  
The estimation of BER from EVM analysis is discussed in this section for QPSK, 16-QAM, and 64-QAM in the presence of laser phase noise and additive white Gaussian noise (AWGN). The carrier phase recovery was implemented using the feedforward blind phase search algorithm [10] which is applicable to arbitrary \(M\)-QAM constellations and suitable for real-time implementation with high linewidth tolerance. The number of test phases was set to 32 for QPSK/16-QAM and 64 for 64-QAM. The filter half width was set to 9 for the different modulation formats and the phase noise was modeled as the Wiener process [10]  
\[\theta_{k}=\sum_{i=-\infty}^{k}{\upsilon_{i}} \tag{3}\]  
where \(\upsilon_{i}\)'s are independent and identically distributed (i.i.d) random Gaussian variables with zero mean and variance  
\[\sigma_{p}^{2}=2\pi(\Delta\
u\cdot T_{s}) \tag{4}\]  
where \(\Delta\
u\) is the combined linewidth of the transmitter and LO lasers and \(T_{s}\) is the symbol period. The simulation was performed over 200,000 symbols.  
Figure 1 shows the sensitivity penalty at the target BER of \(10^{-3}\) for the different modulation formats and also includes the coding penalty due to the differential encoding/decoding process. Figure 2 shows the estimated BER from EVM using Equation (2) for nondata-aided reception following the compensation of the laser phase noise. As it can be seen, the target BER of \(10^{-3}\) is constantly underestimated for the different values of \(\Delta v\cdot T_{s}\). A calibration of the estimated BER is thus necessary to reliably predict the performance of optical coherent systems with EVM analysis.
# Advanced Technology in ATSC3.0: Boosting Data Signal Rates Using Polarization Properties and FSS +
## 2 System Model  
### Channel Model  
We assume a multipath fading environment, modeled as follows.  
\[h(\tau,t)=\sum_{j=0}^{J-1}h_{j}(t)\delta(\tau-\tau_{j}), \tag{1}\]\[h_{j}(t)=\frac{\delta_{j}}{\sqrt{K}}\sum_{k=1}^{K}\exp[j(2\pi f_{D}tcos\alpha_{k}+ \varphi_{k})], \tag{2}\]  
where \(h_{j}\) represents the complex channel coefficient, \(\delta\) denotes the Dirac's delta function, and \(J\) and \(\tau_{j}\) indicate the number of discrete paths and the delay time, respectively. On the receiver side, \(K\) waves arrive at \(\tau_{j}\), with \(g_{j},\alpha_{k}\) and \(\varphi_{k}\) representing the gain of the \(j\)-th path, the angle of arrival (AoA) of the \(k\)-th wave as well as its initial phase, respectively. \(f_{D}\) denotes the Doppler frequency. We assume normalized path gains, meaning \(\sum_{j=0}^{J-1}E[|h_{j}^{2}|]=1\), where \(E[\cdot]\) is the expectation operation (ensemble average). Thus, the frequency response \(H(f,t)\) can be derived by applying Fourier transform to the impulse response, as  
\[H(f,t)=\int_{0}^{\infty}h(\tau,t)\exp(-j2\pi f\tau)d\tau, \tag{3}\]  
\[=\sum_{j=0}^{J-1}h_{j}(t)\exp(-j2\pi f\tau), \tag{4}\]  
where \(f\) represents the carrier frequency. In a mobile wireless communication environment, the frequency response is typically not flat. When \(J\geq 1\), it results in a frequency-selective fading channel, where \(|H(f,t)|\) varies across the transmission bandwidth.
## 3 Science Data Product Performance
### The CrIS/ATMS Instruments and Science Data Products
#### 3.2.2 The MiRS 'Microwave-Only' ATMS EDR Products  
The MIRS processing system is a'microwave-only' retrieval system based on a 1D variational and physical retrieval algorithm using the Community Radiative Transfer Model (CRTM) as the forward and adjoint operators [85]. The MiRS retrieval algorithm is designed as an enterprise processing system and is applied operationally on several microwave sensors from many satellite platforms, including the ATMS aboard the S-NPP/NOAA-20 satellites. The MiRS product suite includes a wide variety of surface and atmosphere products, such as temperature, water vapor, clouds, rain rate, and cryosphere products [97]. Recently, the MiRS team added snow fall rate (SFR, [98]) to the operational list of products, and most of the MiRS products from the S-NPP ATMS have reached the validated maturity stage, while the NOAA-20 MiRS products have passed through Beta and Provisional maturity reviews. Figure 13 shows an example of the S-NPP ATMS-derived MiRS total precipitable water (left panel) compared with the ECMWF analysis fields (middle panel) with a correlation higher than 0.98, as shown in the corresponding scatter plot. Tables 12 and 13 provide the APU validation results for the MiRS product suite.  
Figure 13: MiRS total precipitable water (**upper left panel**) compared to ECMWF analysis fields (**upper right panel**). The scatter plot (**lower panel**) shows very good agreement with a correlation higher than 0.98 between MiRS retrieved and ECMWF TPW. (Figure courtesy: [PERSON], STAR MiRS team).
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Diffusing Redox Probes
#### 3.3.2 Enzyme-Based Immunosensors  
The previously cited immunosensors were designed to transduce Ab-Ag complexation without amplification, or with a non-enzymatic catalytic amplification process. However, despite their limitations in terms of stability, enzyme-based immunosensors are also widely reported. There are again few examples of immunosensors for antibody detection, and most works report human IgG detection not for practical application but as a model analyte. [PERSON] et al. (2012) reported in 2012 an IgG sensor based on layer-by-layer assembly of MWCNTs and RGO, using poly(diallyldimethylammonium) (PDDA) as binding polymer between layers. In a sandwich-type immunoassay, human IgG was uFsed as the target antigen, HRP-conjugated IgG as the probing antibody and hydroquinone (HQ) as the electron mediator. The LoD was 0.2 ng mL\({}^{-1}\) and the linearity was observed from 1 ng mL\({}^{-1}\) to 500 ng mL\({}^{-1}\). In 2014, [PERSON] et al. (2014) also described an immunoassay for IgG as a model analyte. The working electrode was modified with RGO/AuNPs and capture antibodies, whereas signaling antibodies were coupled to AuNPs-labeled HRP. After performing a sandwich immunoreaction, HRP catalyzed the oxidation of aniline (added in solution along with H\({}_{2}\)O\({}_{2}\)) to produce electroactive polyaniline on the electrode surface (Figure 16A). Polyaniline (PANi) electroactivity was probed by DPV. The LoD was 10 pg mL\({}^{-1}\) and the linear range was reported from 100 pg mL\({}^{-1}\) up to 1 \(\upmu\)g mL\({}^{-1}\). There are much more examples of enzymatic immunosensors for antigen detection. In 2012, [PERSON] et al. (2012) described a PSA sandwich-type immunoassay using graphene nanosheets and HRP-labeled signaling antibody coupled to AuNPs. AuNPs provided a large surface area for the immobilization of HRP-Ab but the authors claimed that they are used also for their participation in the electroreduction of H\({}_{2}\)O\({}_{2}\). HQ was used as redox mediator, and DPV as the electrochemical technique. This immunosensor showed a linear range between 2 pg mL\({}^{-1}\) (the LoD) and a few \(\upmu\)g mL\({}^{-1}\). [PERSON] et al. (2013) described in 2013 an electrochemical immunoassay for CEA based on 3D AuNPs/GR as substrate to immobilize anti-CEA capture antibodies, and nanoporous AgNPs onto which Thi, HRP and signaling antibodies were immobilized (Figure 16C). Using DPV, the authors found a detection range from 1 pg mL\({}^{-1}\) to 10 ng mL\({}^{-1}\) and a LoD of 0.35 pg mL\({}^{-1}\). [PERSON] et al. (2014) also reported a CEA immunosensor, based on a GR macroporous foam as substrate for non-covalent immobilization of the capture antibody. A lectin (concanavalin A) monolayer was immobilized on the GR electrode using
## 3 Digital Signage Systems' Design and IoT Technologies Comparison
### Comparison of IoT Technologies
#### 3.1.1 Personal Area Network (PAN)  
PAN stands for Personal Area Network, and it divides into cabled PANs and Wireless PANs (WPANs). It is a network that we use for communication among computing devices, such as mobiles, laptops, headsets, mouses, printers, etc., and other personal digital devices. We use it for low data rate monitoring and control applications. It requires little time to establish a connection and does not require much power. The most commonly used PANs methods are Bluetooth, Zigbee, and RFID. Their advantages are the low-power consumption and their built-in security method [20]. Bluetooth Low Energy (BLE) is a WPAN designed for applications in healthcare, security, home entertainment industries, etc. For example, most BLE technology in healthcare focuses on patient data monitoring, using medical devices. It is compatible with computers, micro-computers, and mobile phones. The development of BLE-based Real-Time Location Systems (RTLS), especially using wearables, is under research [21]. BLE technology can be used for RTLS in cruise ships and large industrial buildings but requires a reliable infrastructure and maintenance. While RFID has similar characteristics to BLE, and both are suitable for healthcare environments, they both cover short ranges. Moreover, RFID technology transmits low data rates and faces data droppings.
## 1 Introduction
Fixed observatories have also the capability to fulfill the concept of the Essential Ocean Climate Variables [13] collecting a variety of parameters spanning from the marine physics to bio-geo-chemistry, from meteorology to sea state and water quality that are ancillary to acoustic and radioactivity measurements. In the last decades, the creation, development and management of fixed-point coastal and offshore observatories for marine research was mainly sustained by a great variety of projects funded by the European Commission or the Member States. Several European (i.e., EuroSITES, JERICO, FIXO\({}^{3}\)). In addition, national networks (i.e., IFON, POSEIDON, MOOSE, IEOOS, MARNET) were developed to deepen the knowledge about the trends of ocean physics and biochemistry and a huge effort has been concerted to integrate part of them into worldwide open ocean observatories initiatives such as OceanSITES [14]. More recently, a new form of integration and harmonization was put beside the traditional research-oriented support scheme based on the community legal framework for a European Research Infrastructure Consortium (ERIC). The fixed-point marine observatories are a fundamental component of some initiatives within the ERIC frame, such as the Integrated Carbon Observation System (ICOS) and the European Multidisciplinary Seafloor Observatory (EMSO). In this international frame, the integration of a radioactivity spectrometer (KATERINA II) and an underwater passive aquatic listener (UPAL) into the fixed-point oceanic observatory Western 1-Mediterranean Moored Multisensor Array (W1-M3A) in the North Western Mediterranean Sea was carried out as first attempt to simultaneously use radioactivity and underwater acoustic data for near-real time monitoring rainfall in the open sea. Section 2 describes the area of the experiment and the characteristics of the marine observatory used to host the two sensors. Sections 3 and 4 detail the new features of the sensors and their integration into the W1-M3A monitoring system. Then, preliminary results using the collected data are presented in Section 5, and concluding remarks are laid out in Section 6.
# Real-Time Face Mask Detection to Ensure COVID-19 Precautionary Measures in the Developing Countries
## 3 Methodology  
Face-mask detection using ML-based methods is tedious and time-consuming work due to ML requiring hand-crafted features engineering, which requires domain experts. Particularly, in ML-based methods, early face-mask detection and response generation are also challenging due to low detection accuracy. Considering these challenges, DL-based models are efficiently utilized for face mask detection in surveillance systems. DL provides end to end features extraction mechanism but it requires a huge amount of training data and high computational power. The main motive behind this work is to enhance the performance of the DL-based model and its deployability over edge devices, for which we proposed a new framework as shown in Figure 1. The proposed framework is divided into two major steps such as (1) face cropping and (2) face mask detection.
## 3 Results and Discussion
### Characterization of the Raw Hydrolysates
#### 3.2.2 Content of Hydrolysis Degradation Products  
Raw hydrolysates also comprised degradation products, namely furan derivates (furfurfural and HMF) and aliphatic acids (acetic acid). Furfural was found in the hydrolysates of sawdust, BSG, and corncobs, while HMF was present in all hydrolysates, except grape pomace (Figure 4).  
Nevertheless, the overall concentration of furan derivates was low in all the hydrolysates (<0.5 g/L). These values are in line with the reported values (0.03-0.55 g/L) and is related to the pH adjustment of the raw hydrolysates that probably induced the precipitation and instability of the compounds ([PERSON] et al., 2018; [PERSON] et al., 2018). The highest HMF concentration (0.51 g/L) was found in banana peel hydrolysate, which is probably due to the high glucose content of the material that suffered degradation into furfural during the hydrolysis procedure. On the other hand, sawdust hydrolysate revealed the highest furfural concentration (0.32 g/L), resulting from the degradation of xylose (Figure 3).  
The most relevant degradation product was the acetic acid that was present in all raw hydrolysates at concentrations ranging from 0.47 to 2.90 g/L (Figure 4). Corncobs and sawdust hydrolysates revealed the highest concentrations of acetic acid (2.90 and 2.75 g/L, respectively), which is consistent with the high xylans fraction of these raw materials (Table 2). In fact, acetic acid is derived from acetyl groups of hemicellulose, and increased concentrations of this degradation product are found in hardwoods [47,51].
# Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method
## 2 Related Work
### Design of Artificial Lateral Line Array  
Artificial cilia sensor arrays designed by simulating the structure of the superficial neuromast (SN) are more representative designs [26; 27; 28; 29; 30; 31; 32]. For example, [PERSON] et al. developed a cross-cross-shaped artificial lateral array by designing a sensor that mimics a cilia bundle to successfully localize a dipole source, verifying the sensing capability of artificial laterals [28; 29]. [PERSON] et al. created a novel sensor by mounting polymer cilia with electrodes at the bottom, fabricated by stereolithography, on a thin film, and a one-dimensional array of 10 such sensors. A one-dimensional array composed of such sensors responded well to dipole stimulation [32].  
Another part of the lateral line system is the canal neuromast (CN), which is a primary morphology (one of them) of the fish lateral line system and is the most visual representation of the lateral line system of the fish trunk [33]. The design and fabrication of bionic sensors that mimic the CNs are also hot spots explored by related scholars [34; 35; 36; 37; 38]. For example, [PERSON] et al. developed an artificial pipeline array by integrating a cantilevered sensing unit in polydimethylsiloxane (PDMS) with a high-pass filtering capability to attenuate low-frequency stimuli [34]. [PERSON] et al. developed an artificial lateral array, mimicking pipeline literals by encapsulating an artificial cilia sensor inside an open-hole pipeline and simulating the structure of a lateral pipeline neural mound; compared to the sensor, directly exposed to the fluid, the construction of the mimic pipeline lateral line can effectively suppress the noise [38].  
Artificial sideline arrays developed using commercial pressure sensors for dipole source detection have also received extensive attention and intensive research [39; 40; 41]. For example, [PERSON] et al. developed a sideline array integrating IPMC velocity modal and pressure sensors to achieve multimodal sensing fusion and reduce localization errors [39].  
In summary, researchers have conducted much work in simulating lateral line systems for structural mimicry and the rapid development of lateral line arrays using commercial sensors. Various sensors and artificial lateral line arrays have been designed and developed based on different sensing principles, which are becoming closer to biological lateral line systems in structure, and their performances are gradually improving. However, there is still a significant gap in the sensing capability of the existing physical lateral line system. Most of the research focuses on the lateral line arrays themselves, and the actual sensing capability of the lateral line arrays needs to be further investigated.
# Motion Compensation for Long Integration Times and DoA Processing in Passive Radars
## 5 IDEPAR Demonstrator for DVB-S
Tracking results consist of declared target trajectories that can be used to estimate target parameter dynamics or to provide the DoA estimation stage input. * The DoA estimation is carried out in a new beam-space with better accuracy and better resolution, using beamforming weights calculated for maximizing the directivity. Figure 4: Characterization of commercial axially corrugated conical horn [31]. Figure 5: Surveillance planar array [31]. The resulting 3D (range-Doppler-azimuth) estimations are processed by a Cartesian tracker to obtain the target's location in the (x-y) plane. The coordinate transformation is performed by non-linear functions that depend on the definition of the PR scenario. Due to the non-linearities of the measurement model, the optimal filter is also non-linear, and suboptimal solutions can be considered: coordinate transformation in combination with a linear Kalman filter [40], an extended Kalman filter [41], or unscented Kalman filter [42]-based solutions. Detection to track the association process for confirmed targets is performed directly from the Cartesian tracks' estimations.