esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Instruments
Finally, \(\sigma(\omega)\) is the absorption cross-section of the sample as a function of the energy \(\omega\), expressed in mm/s, and \(t_{a}\) is the effective thickness of the sample. In the limit of \"thin absorption approximation\" (\(t_{a}<1\)), each contribution to \(\sigma(\omega)\) is expressed as a Voigt doublet, having a Lorentzian component with the natural line width (\(\Gamma_{\rm n}\)) and a Gaussian one with a broadening \(\sigma\), describing a particular distribution of hyperfine parameters [25]. Consequently, the total linewidth of each contribution is approximately given by \(\Gamma_{\rm tot}=\Gamma_{S}+\Gamma_{\rm n}+\sigma\). For both samples, a rather good agreement between experimental and best-fit data was obtained with \(\chi^{2}\)\(\sim\)1200 for the 512 points. Electron paramagnetic resonance (EPR) measurements have been performed at about 9.46 GHz with a Bruker spectrometer (Karlsruhe, Germany). Particular care has been devoted to determining sample mass and position in the resonant cavity for the comparison of signal intensities (areas). The temperature dependence of the spectra has been investigated in the range of 115-370 K.  
The slurries for the electrochemical measurements were prepared by mixing the active materials with carbon (Super C65) and PVdF binder (Soler 5130) at a weight ratio of 80:10:10 in N-methyl-2-pyrrolidone (NMP) (Sigma-Aldrich) and magnetically stirred for about one hour. Afterward, the slurries were coated onto an aluminum foil using a homemade doctor blade, maintained at room temperature until dried, heat-treated at 80 \({}^{\circ}\)C overnight in a vacuum oven, then hot-pressed at 200 psi at 100 \({}^{\circ}\)C for 5 min. The slurries were maintained in a glove box (MBraun, Garching bei Munchen, Germany, O\({}_{2}<\) 1 ppm, H\({}_{2}\)O \(<\) 1 ppm) with an Ar atmosphere to avoid contact with moisture. The electrodes were cut in the form of discs (1 cm in diameter) with a mass loading of about 3 mg/cm\({}^{2}\). Swagelok cells were assembled in the glove box, with the slurries acting as the working electrode, Na metal as the reference and counter electrode, and a Whatman GF/A disc as the separator. The chosen electrolyte was 1 M NaPF\({}_{6}\) in EC:DEC (1:1 wt %). Cyclic voltammetry (CV) was performed by using an Autolab PGSTAT30 (Eco Chemie, Metrohm, Utrecht, The Netherlands) at a scan rate of 0.1 mV/s for five cycles in the potential range of 1.7-4.4 V. For galvanostatic charge-discharge tests, the Swagelok cells were cycled on a Neware (Hong-Kong, China) battery tester in the same potential range for 10 cycles at C-rates of between C/5 and 5C, after a conditioning cycle at C/20 and C/10.
# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 4 Localization  
### Signal Feature Extraction  
As the impulse response function of the PIR sensor is not linear, the output of the PIR sensor \(m_{j}(t)\) is a sine-like waveform, as shown in Figure 6. Because the human target is not rigid and deformable, the \(m_{j}(t)\) is not a smooth curve. Thus, it is not suitable to use the amplitude of the signal directly as the feature for localization.  
Even though the outputs of PIR sensors are not stationary, their energy is stable for a short period of time. In the speech recognition domain, short time energy (STE) is widely used for discriminating voiced and unvoiced segments for speech signals [27]. Inspired by its effective performance, we employ STE as the signal feature to classify whether the PIR sensors are fired. For the \(j\)th PIR sensor, the \(n\)th window of STE is defined as:  
\[p_{j}(n)=\sum_{k=0}^{Z-1}|m_{j}(k)-avSTE_{j}(n)| \tag{3}\] \[\text{with}\;\;avSTE_{j}(n)=\frac{1}{Z}\sum_{k=0}^{Z-1}m_{j}(k) \tag{4}\]  
where \(m_{j}(k)\) is the output voltage (V) of the \(k\)th sampling point and \(avSTE_{j}(n)\) is the average voltage of all sampling points within the \(n\)th window. In each window, there are \(Z\) sampling points. In our system deployment, \(Z=15\), as shown in Figure 7.  
Figure 6: The output voltage of nine PIR sensors in one sensor node for five kinds of activities: (**a**) lying; (**b**) sitting; (**c**) standing; (**d**) walking; and (**e**) transitional activities.  
Certain threshold \(th_{j}\) is set for \(p_{j}(n)\) to indicate whether or not the PIR is triggered:  
\[m_{j}(n)=\left\{\begin{array}{ll}1&\quad p_{j}(n)>th_{j}\\ 0&\quad\text{otherwise}\end{array}\right. \tag{5}\]  
Based on the output of the PIR sensor array \(\mathbf{M}(n)=[m_{1}(n),...,m_{9}(n)]\), we lookup Tables 1 and 2 to figure out which sampling cells are fired and calculate the location estimation of the human target according to Equation (2). Because the output of the PIR sensor is proportional to the surface size and moving speed of the thermal object, the movement of a domestic animal will not trigger the PIR sensors by setting the appropriate threshold for each PIR sensor [28].
## 5 Cross-Referencing Bloom-Filter-Based Data Integrity Framework
### False-Positive Rate Analysis  
#### 5.4.1 False-Positive Rate Analysis of Data Integrity Solution Based on the Standard Bloom Filter  
Simplifying Equation (5), the false positives of the standard Bloom filter-based integrity solution can be represented as  
\[\epsilon=(\frac{1}{e^{(ln2)}})^{\frac{m}{n}} \tag{12}\]  
\[\epsilon\approx(0.61850)^{\frac{m}{n}} \tag{13}\]
# Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks
###### Abstract  
This study introduces a novel method that addresses the challenge of visualizing and quantifying detection coverage areas in wireless sensor networks. The method involves projecting a network of range-free sensors and pre-existing transmitters, located within a predefined area of interest, onto a global coordinate system. Detection areas are defined as those covered by the sensing range of at least three sensors. Pre-existing transmitters located within the detection range of the sensors are assumed to degrade the networks' performance by causing coverage gaps. Interactive satellite maps facilitate the dynamic exploration of coverage via the calculation and visualization of the resulting detection areas. The algorithmic structure of the proposed tool is explained in detail, and four example scenarios demonstrate the tool's capabilities, as well as its flexibility, adaptability, and effectiveness in identifying the triangulated detection areas. Designed primarily as a geometry calculation and visualization tool that allows for the adjustment of sensor parameters such as locations, ranges, and angular ranges of detection, the proposed tool has the potential to enhance decision-making in sensor network configuration, prior to final sensor placement, across a wide range of applications.  
sensor network; transmitters; Python programming; coverage area +
Footnote †: journal:  
0000-0002-4607-3300][PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON], [PERSON] Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks. _Appl. Syst. Innov._**2024**, \(7\), 97. [[https://doi.org/10.3390/asr050097](https://doi.org/10.3390/asr050097)]([https://doi.org/10.3390/asr050097](https://doi.org/10.3390/asr050097))
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 6 New Science Investigations  
Over the past few years, researchers have expanded the scope of GNSS-R beyond its traditional applications and started to explore its sensitivity to and capacity in assessing underlying phenomena and signatures, including mesoscale ocean eddies, targets above Earth's surface, ionospheric plasma depletions, river flow, ocean phytoplankton blooms, presence of ocean microplastics, and studies of deserts.
## 4 Discussion
The difference in pore structure, size and porosity is there to provide the appropriate structure either for MSCs to differentiate to localised specific cells or for those specific cells to be seeded individually and reside within a biomimetic environment. The BM-like structure, as a third layer of the scaffold, is there to restrict the epithelial cells from penetrating into the porous section of the scaffold, where fibroblasts are supposed to reside. While further investigation is required, the present study demonstrates a relatively simple method to fabricate synthetic scaffolds with hierarchical architecture using the same polymer for skin, tendon tissue engineering and possible other tissue engineering applications. Where desirable, different polymers with other physical and biological properties may also be used to fabricate the hierarchical and controlled microstructures demonstrated in this work. With some improvements in the fabrication techniques and adjustment of the cell co-culture methods to suit this study's microstructures, such scaffolds have the potential to be used as a skin or an oral mucosa tissue equivalent to measure toxicity, drug delivery and to model diseases (similar to [33; 34; 35]), as well as for the purpose of regenerating skin or oral mucosa tissue. PGSU, in terms of scalability, can be synthesised and fabricated into large quantities at a low cost; the scaffold is reproducible, and with controlled chemical synthesis and freeze drying, the reproducibility could be high. Furthermore, there are multiple ways to alter both the mechanical properties and degradation rates of the polymer scaffolds (e.g., by changing the glycerol:HDI molar ratio and/or the pre-polymer concentration in the freeze drying solution).
## 4 Wind Retrieval Testing
### Reanalysis Comparisons  
When developing the empirical FDS GMF using the reanalysis data, we only used 80% of the matchup data to tune the model coefficients, while the remaining 20% were used for model testing. The remaining 20% was comprised of 461,932 data points. The aligned ECMWF reanalysis wind speeds and NBRCS derived wind speeds are from 0.6 to 36.7 m/s. As shown in Figure 6, most of the data are located around the 1:1 line. The retrieved wind speeds had a root mean square error (RMSE) of 2.72 m/s and the coefficient of determination was 0.5.  
Figure 5: BF-1B channel 2 examples of empirical geophysical model functions (GMFs) at elevation angles of 35–84\({}^{\circ}\). The fully developed sea (FDS) GMF trained by the advanced scatterometer (ASCAT) observations follow the power function form. The red curves are the best-fit functions of the corresponding elevation angles. The coefficients of the power functions are within the text boxes.  
Owing to the large number of reanalysis data pairs, another test of 10 minus the alignment criteria was conducted. The total number of matchup pairs then became 428,832. After the 80% training and 20% validation, the scatter density plot was obtained, as shown in Figure 6b. Finally, the unbiased RMSE was 2.63 m/s, and the coefficient of determination was 0.55.
#### 2.4.4 Limit of Quantification (LOQ) and Limit of Detection (LOD)  
Limit of quantification is the lowest limit of analyte in a sample which can be detected with valid precision and accuracy [30], whereas the limit of detection is the lowest concentration which can be distinguished from baseline noise [30].  
The limit of quantification was determined by the following equation [32]:  
\[10\times\frac{\text{SD}}{\text{S}} \tag{1}\]  
Limit of detection (LOD) was determined by the following equation [32]:  
\[3.3\times\frac{\text{SD}}{\text{S}} \tag{2}\]  
where SD is the standard deviation error of the regression statistics from the regression line (7 points were used), and S is the slope of the standard curve of the analyte [30,33].
## 3 Experimental  
In this section, the faults that occur in transmission lines and transformers are tested by using the experimental setup. The difference current signals and transients generated from the experimental setup are detected and recorded for analysis with a wavelet. The experimental model is shown in Figure 2.  
Figure 2 shows the fault simulation circuit in the case of transmission lines connected to a transformer. The 40 km 115 kV transmission lines are calculated to be resistor, inductor and capacitor (RLC) parameters that are connected to a nominal pi model and a 15 kVA, 440/220 V transformer. The transformer is designed to accept internal short-circuiting. The design of the transformer permits a tap change every 10% of both the primary and secondary sides to test internal faults in the transformer. Moreover, the faults occurring in the transmission lines are tested by following these conditions:  
* The fault types include a single line to the ground fault, a double line to the ground fault, a line to line fault, and a three-phase fault.
* The fault positions are designated to any phases of the transmission lines at lengths of 30%, 50%, and 70% of the length of the transmission line. The experimental setup is shown in Figure 3.  
Figure 1: Characteristic signals of the mother wavelets.  
Figure 2: Characteristic signal of the mother wavelet.  
After recording the differential current from the transformer, the differential current is obtained through the wavelet transform method. The current signal is transformed using the DWT to extract high-frequency components at scales 1-3 with different mother wavelets, such as the Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif) wavelets. The coefficient from the DWT is varied depending on various factors, and this coefficient is squared to emphasize the change in coefficient. The four mother wavelets are utilized to decompose the differential current signals. The DWT coefficients of the phase faults are larger than those of the phase unfaults.  
The example of differential current obtained from the transformer (relay 1) in case of internal fault and the coefficient after using different mother wavelets is shown in Figure 4. From the figure, it can be seen that the DWT signals at scale 1 generate much noise compared with those at scales 2-3. The coefficients at scales 2-3 are notable when faults occur, and at scale 3, a lower frequency can be detected compared to that at scale 2. Thus, this research chose the coefficients at scale 2 for analysis of the fault types.  
Figure 3: Experimental setup.  
Figure 4: _Cont._  
Figure 4: Differential current signals from the wavelet transform in the case of internal faults at phases in each mother wavelet: (**a**) Daubechies (db2); (**b**) symlets (sym2); (**c**) biorthogonal (bior3.1); (**d**) Coiflets (coifl).
# Vector Beams with Only Transverse Intensity at Focus
## 2 Methods
### Experimental Measurement  
To obtain vector beams with polarization shown in Figure 1a, a vector wave plate could be used; however, to obtain the polarization state shown in Figure 1b,c, this vector wave plate should be combined with a spatial light modulator. In our experimental measurements (Figure 2), laser light from MGL-F-532-700 (wavelength \(\uplambda\) = 532 nm, output power 700 mW) was collimated by a pinhole PH (diameter \(D\) = 50 \(\upmu\)m) and a spherical lens (L\({}_{1}\)) (\(f_{1}\) = 400 mm). After the lens L\({}_{1}\), the expanded beam propagates through the transmissive spatial light modulator (SLM, Holoeye LC2012, resolution of 1024 \(\times\) 768 pixels, pixel size of 36 \(\upmu\)m, Tokyo, Japan). SLM was placed between two linear polarizers P\({}_{1}\) and P\({}_{2}\) to obtain a linearly polarized beam after phase modulation. Lenses L\({}_{2}\) and L\({}_{3}\) and iris diaphragm D were used to spatially filter the desired beam. Then, the beam was aligned with the center of the vector wave plate (VWP, LBTEK VR1, Shenzhen, China). The resulting beam was focused by a lens with a small numerical aperture L\({}_{4}\) (\(f_{4}\) = 100 mm). Objective lens O\({}_{1}\) (Olympus RMS 20X, Tokyo, Japan) was used to obtain an image at the focus of the lens L\({}_{4}\), and the image was projected onto a CCD camera (UCMOS10000 KPA, resolution of 3584 \(\times\) 2748 pixels, pixel size of 1.67 \(\upmu\)m, Hangzhou, China), in front of which a linear polarizer-analyzer P\({}_{3}\) was placed.  
Figure 1: Direction of polarization of the investigated vector beams: azimuthally polarized (**a**), and sector azimuthally polarized: beams with a V-line singularity (**b**,**c**), and beam with two V-line singularities (**d**).
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
## 1 Introduction
In this study, we focus on using convolutional neural networks to estimate homography. Contrary to the existing CNN-based four-point homography estimation methods, we directly estimate the \(3\times 3\) homography matrix value. The large magnitude difference and large variance in the element values of the homography matrix make it very difficult to directly estimate using neural networks. Our study finds that after the pixel coordinates are normalized, the magnitude difference and variance of the element values of the normalized homography matrix will become very small. On this basis, we extend the affine transformation in the STN [26] to the homography transformation and propose the STN-Homography model to directly estimate the pixel coordinate normalized homography matrix. The contributions of this study are as follows: (1) We prove that the \(3\times 3\) homography matrix can be directly learnt well with the proposed STN-Homography model after pixel coordinate normalization, rather than to estimate the alternative four-point homography. (2) We propose a hierarchical STN-Homography model that yields more accurate results compared with the state-of-the-art. (3) We propose a sequence STN-Homography model that can be trained in an end-to-end manner and yield superior results than those obtained by the hierarchical STN-Homography model and the state-of-the-art.
## 4 Discussion
### Filters  
In deflectometry, noise is an essential factor to consider when measuring power, as power is calculated from the derivatives of the deflection maps measured by a deflectometer. Thus, any noise present in the deflection maps is amplified in the derivative operation. This is why the instrument software has the option to apply average filtering to reduce noise [29].  
[PERSON] et al. studied the noise associated with the combination of mean filtering and phase shifting [29]. They determined that these processes introduce a Gaussian noise in the phase measured (and thus in the lens power). Therefore, the noise in the lens power should also be Gaussian noise and not symmetrical. As already pointed out by other authors, the deflectometry technique has a lower precision for central measurements [19; 30].  
After comparing the results obtained with the 20-20 filter and the 5-10 filter to the measurements obtained without a filter, it was observed that both filters significantly reduce the information in the central region of the analyzed optic size, with the effect being more pronounced in the case of the 20-20 filter. This smoothing effect is observed in both ophthalmic lenses and intraocular lenses, with the smoothed zone extending to a central area of 1 mm.  
This smoothing or mitigation of information in the central area, combined with the previously observed lower precision of the device in this region, can lead to the loss of valuable information, particularly when measuring lenses that have distinct characteristics in its central area. Therefore, we believe that to avoid any smoothing effect that may result in measurements deviating further from the actual values, filters should not be used during the process of measuring intraocular lenses with the NIMO TR1504 system. Instead, a better procedure to reduce noise is to take several measurements (ensuring that the lens is properly centered) and compute the average of these measurements. This is equivalent to the temporal averaging procedure widely employed in image processing.
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 2 Proposed ADC Architecture and Pre-Quantization Timing  
### ADC Architecture  
The block diagram of the proposed 1 GS/s 12-bit ADC with pre-quantization timing is presented in Figure 1. An input buffer is placed at the front end in this design. First, the high-input impedance makes the ADC easier to drive, and the low-output impedance enables the sampling channel to obtain a larger bandwidth, especially at high frequencies, which is beneficial for improving the sampling linearity. The second advantage is isolating the kickback noise from the ADC sampling path. The input buffer current is in the mA range, and the performance of the input buffer with low-frequency input is inversely proportional to the current; that is, lowering the current helps improve the linearity of the low-input frequency while also lowering overall power consumption. The SHA follows the input buffer. The performance of SHA is relatively stable under the full input frequency and PVT, although SHA brings extra power consumption and nonlinearity to some extent. Bootstrapped switches, together with SHA, follow the input buffer to improve the sampling linearity and achieve better robustness. Based on PQT timing, the input signal is digitized by five cascading MDACs, where the ten most significant bits are resolved by five cascading 2.5-bit MDACs, and the two least significant bits are determined by a 2-bit flash ADC. Additionally, the clock schematic, combined with the high-speed interface current mode logic (CML), non-overlapping clock, and DLL, provides the needed timing. (\(\phi_{1}\), \(\phi_{1e}\), \(\phi_{2}\), \(\phi_{2e}\), \(\phi_{3}\), \(\phi_{3e}\), \(\phi_{4}\), and \(\phi_{4e}\)).
## 3 Social Relationship Measurement
### (2) Message forwarding  
It is supposed that node \(i\) encounters node \(j\), and node \(i\) has the message to forward. If node \(j\) is the destination of the message, node \(i\) will forward the message to \(j\) and delete the message from its sending queue. Otherwise, there are the following two cases for node \(i\) to forward the message.  
**Case 1: Intra-cluster message forwarding**  
If the destination of the message is in the local cluster of node \(i\), and node \(j\) is also a member of the cluster, node \(i\) will forward a copy of the message to node \(j\). Otherwise, node \(i\) does not forward the message to node \(j\).  
**Case 2: Inter-cluster message forwarding**  
If the destination of the message is not a member of the local cluster of node \(i\), node \(i\) will send a \"Request\" packet to node \(j\) requesting whether the destination is in its local cluster. After node \(j\) receives the \"Request\" packet and checks the cluster table according to the request, node \(j\) will reply to node \(i\) with a \"Response\" packet notifying it about whether or not the destination is a cluster member of node \(j\). If node \(i\) receives the \"Response\" packet and the destination is a cluster member of node \(j\), node \(i\) will forward the message to node \(j\). Otherwise, node \(i\) does not forward the message to node \(j\).  
The message to be sent can stay in the buffer of node \(i\) for a period of time that is dependent on the buffer size of node \(i\). During this period of time, if node \(i\) encounters the forwarding objectives mentioned in the above rules, the message will be scheduled to be sent. Otherwise, the message will be dropped from the buffer of node \(i\) according to the buffer management algorithm. There exists a possibility that the message can not be forwarded if none of the nodes have destinations as local cluster members, and the large buffer size will be helpful for message delivery.