esa-sceva/satcom-chunk-collection huggingface dataset:
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 2 Grid-Forming Inverter Modelling and Overload Mechanisms
### Overload Mechanisms in GFM Inverters
#### 2.2.1 Frequency Regulation Power  
A GFM inverter with primary frequency regulation capability adjusts its steady-state active power in proportion to grid frequency to contribute to overall power balance. The steady-state power of the GFM inverter is only determined by the static gain of its PSL, and not by its dynamics [32]. For this reason, the primary frequency regulation is determined by the droop equation. For the proposed PSL, the frequency regulation depends on \(D\):  
\[\omega_{r}=\omega_{r}^{*}+\frac{1}{D}(P^{*}-P) \tag{1}\]  
Considering that the inverter active power must remain within defined operational limits [\(P_{min}\), \(P_{max}\)], the upper and lower angular frequency limits can be obtained from  
\[\left\{\begin{array}{l}\omega_{min}=\omega_{r}^{*}+\frac{1}{D}(P^{*}-P_{max}) \\ \omega_{max}=\omega_{r}^{*}+\frac{1}{D}(P^{*}-P_{min})\end{array}\right. \tag{2}\]  
Outside these limits, frequency regulation should be removed to prevent overloading, leading to the non-linear droop curve represented in Figure 2a. This behavior could be achieved through virtual power (VP) strategy, whose structure is shown in Figure 2b. Primary frequency regulation is removed by subtracting the non-linear virtual power \(P_{v}\) from \(P^{*}\), expressed in (3). This term represents the active power that would be exchanged with the grid if the frequency range was not limited.  
\[P_{v}=\left\{\begin{array}{l l}D(\omega_{min}-\omega_{r})&\omega_{r}<\omega _{min}\\ 0&\\ D(\omega_{max}-\omega_{r})&\omega_{r}>\omega_{max}\end{array}\right. \tag{3}\]As already mentioned, this approach has proved to be effective to limit active power in GFM inverters with minimum inertia [24,25]. However, overloading could still occur because of the inertial power contribution.
## 5 Simulations and Discussions  
### \(var(\tau_{0})\) Simulations  
In order to evaluate the variance of the new algorithm, combined with the modified CRLB (Equation (22)), MATLAB simulates the variance and CRLB curves for different latency scenarios in different bandwidths. Figure 4a simulates the performance curve for \(BT=0.5,\tau_{0}=\{0.1,0.2,0.3,0.4\}\), which shows that the variance is very close to the CRLB curve with an error of less than 0.5 dB for \(SNR>15\) dB. Additionally, the variance jitter due to \(\partial\tau_{0}/\partial(E(\tau_{0}))\) is within 0.2 dB. Figure 4b shows the performance curve under \(BT=0.25,\tau_{0}=\{0.1,0.2,0.3,0.4\}\), where the variance and CRLB error are less than 0.25 dB when \(SNR>15\) dB.
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 2 Study Area and Database
### Database Description  
#### 2.2.1 Sentinel-1 Data  
In this study, 235 C-band (5.405 Ghz) SAR images acquired by S-1A (launched on 3 April 2014) and S-1B (launched on 25 April 2016) in both ascending (117 images) and descending (118 images) modes were used. These images were acquired between September 2017 and September 2019. The acquisition mode of these images was Interferometric Wide (IW) swath (\(\sim\)250 km \(\times\) -150 km) with VV and VH polarizations. The IW swath is the primary conflict-free acquisition mode used over land. The 235 S1 images are derived from the Level-1 Ground Range Detected (GRD) product with a spatial resolution of 10 m \(\times\) 10 m and a revisit time of 6 days. These images were downloaded from the Copernicus website ([[https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)]([https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)), last access September 2019).  
The Sentinel-1 toolbox (S1 TBX) developed by the European Spatial Agency (ESA) was used to calibrate the S1 images. This calibration converts the digital number of downloaded SAR images to backscattering coefficients in linear unit.
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 3 The Proposed POI Recommendation Scheme
### Analysis Phase
#### 3.3.2 Category Similarity Analysis  
To determine the prevalent category based on the time period, the proposed scheme first extracts time period information. Figure 5 shows the partial outcomes of the extraction of the top three categories with the highest frequency of active users. The top categories with the highest frequency for each user can be determined, as abundant data are available to analyze the locations that active users most frequently visit based on the time period.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Label** & **Labeled User ID** \\ \hline
0 & [2, 3, 4, 8, 9, 10, 14, 15, 16, 17,...] \\
1 & [1, 7, 11, 12, 19, 30, 43, 45, 71, 73,...] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Labeled user ID.  
Figure 4: Clustered user groups.  
Figure 5: Extraction of top-category pattern by using highly active users.  
Figure 6 shows the partial outcomes of the extraction of the top three categories with the highest frequency among all users. For instance, bars are locations with a high frequency of visits from 12 a.m. to 9 a.m. and from 9 p.m. to midnight, cafes from 11 a.m. to 3 p.m., and food and drink shops from 3 p.m. to 9 p.m. The proposed scheme employs this information and considers prominent categories with respect to time to suggest nearby POIs for inactive users with limited data available for inference. Additionally, the accuracy of POIs can be enhanced by combining this information with the frequently visited locations of highly active users. In this case, the detailed category similarity is used by adjusting weights.
# Thermophilic Methane Production from Hydrothermally Pretreated Norway Spruce (_Picea abies_)
## 3 Results  
### AD Feed Characteristics  
Hot water extraction of Norway spruce led to higher organic concentrations in the hydrolysate when treated at 170 \({}^{\circ}\)C than at 140 \({}^{\circ}\)C, with total COD (CODt) and soluble COD (CODs) concentrations of 30.7 and 26.9 g/L respectively when treated at 170 \({}^{\circ}\)C compared to 22.3 and 20.0 g/L at 140 \({}^{\circ}\)C (Table 6).  
The sugars found in the hydrolysate were the hemicellulose sugars arabinose, galactose, glucose, xylose, and mannose. Mannose was the major compound in both hydrolysates with 56% and 43% for pretreated at 170 \({}^{\circ}\)C and 140 \({}^{\circ}\)C, respectively. The total sugar concentration was higher (18.6 g/L) when treated at 170 \({}^{\circ}\)C than at 140 \({}^{\circ}\)C (11.9 g/L). All other individual sugar concentrations, except arabinose, was found higher in hydrolysate pretreated at 170 \({}^{\circ}\)C than at 140 \({}^{\circ}\)C.  
Both furfural and HMF concentrations were higher in hydrolysate pretreated at 170 \({}^{\circ}\)C (923 and 451 mg/L respectively) than pretreated at 140 \({}^{\circ}\)C (160 and 168 mg/L respectively) due to higher severity pretreatment.  
The pH of hydrolysate pretreated at 170 \({}^{\circ}\)C (3.7) was lower than for hydrolysate pretreated at 140 \({}^{\circ}\)C (3.8) due to higher acetic acid released from the hydrolysis of acetyl groups contained in the hemicellulose. Acetic acid was the dominant volatile fatty acid in the hydrolysate and is also a substrate for methane production if the concentration is not above the threshold value of inhibition  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **H170** & **H140** \\ \hline Total COD (gCODt/L) & 30.7 \(\pm\) 1.7 (41) & 22.3 \(\pm\) 1.6 (66) \\ Soluble COD (gCODs/L) & 26.9 \(\pm\) 2.6 (41) & 20.0 \(\pm\) 1.9 (66) \\ Acetic acid (g/L) & 1.0 \(\pm\) 0.2 (8) & 0.6 \(\pm\) 0.1 (14) \\ pH & 3.7 \(\pm\) 0.1 (2) & 3.8 \(\pm\) 0 (2) \\ Furfural (g/L) & 0.9 \(\pm\) 0.02 (2) & 0.2 \(\pm\) 0.01 (2) \\
5-Hydroxy methyl furtural (g/L) & 0.5 \(\pm\) 0.01 (2) & 0.2 \(\pm\) 0.01 (2) \\ Arabinese (g/L) & 0.8 \(\pm\) 0.003 (2) & 1.6 \(\pm\) 0.02 (2) \\ Galactose (g/L) & 2.2 \(\pm\) 0.01 (2) & 1.7 \(\pm\) 0.05 (2) \\ Glucose (g/L) & 3.0 \(\pm\) 0.01 (2) & 1.6 \(\pm\) 0.05 (2) \\ Xylose (g/L) & 2.2 \(\pm\) 0.02 (2) & 2 \(\pm\) 0.04 (2) \\ Mannose (g/L) & 10.4 \(\pm\) 0.02 (2) & 5.1 \(\pm\) 0.1 (2) \\ Total sugars (g/L) & 18.6 \(\pm\) 0.01 (2) & 11.9 \(\pm\) 0.3 (2) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Characteristics of hydrolysate pretreated at 140 \({}^{\circ}\)C and 170 \({}^{\circ}\)C (Average values \(\pm\) standard deviation (n, number of samples)).  
(2400 mg/L) [37; 38]. The concentrations were below for both hydrolyses with acetic acid concentration of hydrolyses pretreated at 170 \({}^{\circ}\)C of 1030 mg/L and at 140 \({}^{\circ}\)C of 590 mg/L.
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 2 Experimental Method
### Sensor Structure  
The Flexiforce A201-1 sensor is constructed from a thin plastic film onto which electrodes of silver ink are printed. Beneath these electrodes lies a layer of piezoresistive material. This assembly is sandwiched between two additional plastic films, creating micro-sized gaps between the opposing piezoresistive surfaces (Figure 1a (top)). Application of force causes these surfaces to contact, altering the current flow and resistance across the electrodes. A sectional view of the disassembled Flexiforce sensor, illustrating the uniform coating of the piezoresistive material on the film, is shown in Figure 1a (bottom). Similarly, the Interlink FSR sensor features piezoresistive material attached to either side of an adhesive layer, positioned along the sensor edges with a gap equivalent to the adhesive's thickness (Figure 1b). Upon force application, the material contacts the electrodes, thereby reducing resistance.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 4 System Design
### Protocol-Abstraction Layer  
The data exchange used between devices of the same network is based on a defined frame format adapted for this communication protocol.  
This is optimal and effective in the case of implementing a network with a single communication protocol. Nonetheless, in the implementation of hybrid networks with different involved protocols, some problems may appear when implementing the data gateway between endpoints. These problems are caused not only due to the different formats used in each protocol, but also due to the use of several parameters needed to properly perform communication, routing, and device identification in the hybrid network.  
For this reason, a solution based on the development of an abstraction layer that will define each of the aforementioned communication parameters in the hybrid network has been developed. The associated frame is defined in the same way regardless of the communication protocol. Thus, this frame will not only work for communication between ZigBee modules, e.g., but it will be used to communicate with any device on the hybrid network, without considering its sub-network or protocol. Figure 3 depicts a diagram associated with the protocol-abstraction layer developed in this work.  
The protocol-abstraction layer is supported by a technology-independent hybrid network frame (HNF) definition. The structure of this frame can be observed in Figure 4.  
The DN field defines the packet's destination network, which is hot-one encoded. In this work, only three bits have been used to indicate each one of the considered technologies (LoRaWAN (bit 0), VLC (bit 1), and ZigBee (bit 2)), and there are still 5 bits left that could be used to further enhance the capabilities of the developed architecture. In addition, part of the P byte (7 least significant bits) describes the frame's purpose, which defines the payload interpretation (Table 1). The most significant bit of the P field indicates the presence of fragmentation. If this flag is active, it indicates that there are still data to be received. Nonetheless, if it is inactive, the frame transmission is over.  
Figure 4: Hybrid network frame (HNF) structure.  
Figure 3: Diagram of the developed architecture.  
The encapsulation procedure of the HNF is the same regardless of the type of network or protocol used. However, it must be considered that not all the associated frames will be the same size in bytes. As an example, if a frame incoming from a ZigBee node were intended to be routed through a LoRaWAN link, this frame should be fragmented according to LoRaWAN's maximum frame size. This size limitation has been integrated into the protocol-abstraction layer by establishing the subframe field's maximum size to 48 bytes, with variable length.
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
## 4 Carrier Sense Non-Orthogonal Multiple Access (CSNOMA)
### Proposed Protocol
\begin{table}
\begin{tabular}{c l} \hline \hline
**Distance** & **Type of Region** \\ \hline \(r>r_{s}\) & Transmission allowed region (a) \\ \(r_{c}<r<r_{s}\) & Transmission restricted region (b) \\ \(r<r_{c}\) & Transmission allowed region (c) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification of allowed/restricted transmission areas according to the distance from the receiver. to decode the signal originally intended to be received with the reduced aggregate interference. The typical receiver informs the typical transmitter of the result. If the transmission is not successful, the contention window size for the retransmission increases exponentially, i.e., binary exponential backoff is applied. To quantify the network-wide performance of CSNOMA, we analyze ASE of CSNOMA using the stochastic geometry in the next subsection.
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
###### Abstract  
Global navigation satellite system (GNSS) reflectometry (GNSS-R) developed into a promising remote sensing technique. However, few previous related studies considered the potential of its polarization. Owing to lack of sufficient in situ measurement data to support comprehensive investigation of GNSS-R polarization, this study used theoretical models and reference to our previous work to explore this topic. The commonly used microwave scattering models are employed to get the bare soil or vegetation scattering properties of GNSS-R configurations, i.e., the random surface scattering model and the first-order radiative transfer equation were improved and then employed to obtain the scattering properties of both bare soil and vegetation. Since the final output of the space-borne GNSS-R missions is a delay Doppler map (DDM), a spaceborne (DDM) simulator, oriented for the Chinese FengYun-3E (FY-3E) GNSS-R payload, was utilized to obtain the final output at different polarizations. Using the developed models (such as the bare soil and vegetation scattering models), corresponding polarization simulations were performed. That is to say, not only the commonly used LR (left hand circular polarizations (LHCP) received and the right hand circular polarizations (RHCP) received) can be presented, but also the scattering properties at RR, VR, and HR (the transmitted signals are RHCP, while the received polarizations are RHCP, vertical (V) and horizontal (H) polarizations, respectively) can be predicted by our developed models. Results reveal obvious polarization differences for the bistatic scattering and DDM. Therefore, the use of GNSS-R polarization information has potential to provide competitive and fruitful results in the future detection of land surface geophysical parameters.  
GNSS-reflectometry; DDM; polarization; soil; vegetation +
Footnote †: journal: remote sensing  
0000-0002-2300-3300][PERSON]  
0000-0002-2300-3300][PERSON]  
0000-0002-2300-3300][PERSON]  
0000-0002-2300-3300][PERSON]
## 1 Introduction
Many NLP models have performed very impressively with text-related problems, such as LSTM-based models that use hidden layers to capture contextual information and transformer-based models that rely on self-attention mechanisms [16; 17; 18]. Pre-trained language models are also a class of NLP models, which differ from other NLP models in that they are trained on very large datasets in a self-supervised manner. Pre-trained language models have a very strong ability to extract features from text information [19], which has attracted the wide attention of bioinformaticians. As a result, some pre-trained models that treat biological sequences as sentences and use large-scale protein data for training have been developed [20]. In this study, we propose a new method for identifying druggable proteins based on pre-trained models and evolutionary information in the PSSM. The workflow is shown in Figure 1. Initially, the protein sequence is fed into the pre-trained model to generate embeddings, resulting in 1024-dimensional features for each protein. Then, the PSSM information of the protein sequence is captured using the Blast software package [21], and after processing the PSSM, KSB-PSSM (400 dimensions), DPC-PSSM (400 dimensions) and S-FFSSM (400 dimensions) vectors are obtained. Next, feature selection is performed on the three combined vectors and embedding information using a random forest algorithm. Finally, the results are input into a machine learning classifier, and performance testing is performed on an independent test set. This study compares three protein pre-trained models (Prot_T5_Xl_Uniref50(T5), Prot_Bert_BFD(BFD), SeqVec), ten feature selection lengths (from 100 to 2224 dimensions) and five classification algorithms (SVM, RF, NB, XGB, KNN). The best model comes from the combination of T5 and the XGB classifier at 1500 dimensions, with an accuracy of 94.98%.
# Terahertz Raman Measurements Using a Spatial Heterodyne Raman Spectrometer
## 4 Results and Discussion  
### THz Raman Spectrum and SNR of Sulfur  
The measured sulfur powder concentration was 99.9%, and the integration time was 50 s under laser powers of 5 mW, 38 mW, and 70 mW. The obtained spectrum is shown in Figure 4a. The external vibration of S\({}_{8}\) molecules in the low-frequency part shows clear Raman peaks at 27 cm\({}^{-1}\) (0.81 THz), 50 cm\({}^{-1}\) (1.5 THz), and 83 cm\({}^{-1}\) (2.49 THz), and the Raman peak at 151 cm\({}^{-1}\) (4.53 THz) is assigned to the asymmetric S-S bending [21]. With the increase of laser power, the intensity of characteristic peak increases significantly. At 50 cm\({}^{-1}\) in Figure 4a, when the laser power is 5 mW, 38 mw and 70 mW, the intensities are 0.453, 3.739, and 8.571, respectively. There is an approximate linear relationship between intensity and laser power.  
\begin{table}
\begin{tabular}{c c c} \hline
**Components** & **Parameters** & **Performance Index** \\ \hline \multirow{3}{*}{BNF} & Design wavelength & 532 nm \\  & Optical density & OD3 \\  & Transmittance & \(>\)92\% \\ \hline \multirow{2}{*}{Imaging optics} & Diameter & 62 mm \\  & Focal length & 105 mm \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: (**a**) Interferogram of the mercury lamp. (**b**) Spatial frequency profile obtained from the FFT. (**c**) After calibration, the position of the characteristic spectral line of the mercury lamp is 546.07 nm, 573.964 nm, and 579.067 nm.  
To test the system performance, we calculated the SNR of sulfur with different laser powers and integration times. The strongest 50 cm\({}^{-1}\) (1.5 THz) spectral line intensity in the spectrum was divided by the noise value of the system. The result is shown in Figure 4b; the SNR reaches approximately 750 at a low laser power of 60 mW with a short integration time of 20 s. When the integration time is fixed and the laser power is increased to 45 mW, the SNR growth slows down and tends to a fixed value. Similarly, when the laser power is constant and the integration time increases to 5 s, the SNR growth slows down and gradually tends to a fixed value. In the X-Z and Y-Z planes, the SNR curve is approximately a square-root function, which is consistent with the SNR equation [22].
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 3 Rotor Position Measurement for a PMSM
### Analysis of Rotor Motion in a PMSM
#### 3.3.2 Calculation of the Tilting angle\(\_\theta\)  
When the tilting angle of the rotor shaft is \(\theta\), Figure 9 shows the relationship between the camera and the midpoint LED, where R is the distance between the sphere center and the midpoint LED.  
Figure 8: The procedure to calculate three rotation angles for rotor position in a PMSM.  
Figure 9: Calculation of the tilting angle\(\_\theta\).  
When \(\theta=0^{\circ}\), i.e., the rotor shaft is perpendicular to the horizontal plane, the distance between the lens of high-speed camera and the midpoint LED is \(l\). When \(\theta\) varies between \(0^{\circ}\) and \(37.5^{\circ}\), the angle \(\delta\) can be computed by  
\[d=R\times\sin\theta \tag{6}\]  
\[\Delta r=R(1-\cos\theta) \tag{7}\]  
\[\tan\delta=\frac{d}{l+\Delta r}=\frac{Num}{f} \tag{8}\]  
\[Num=\sqrt{\left[\left(u-u_{0}\right)\times d_{x}\right]^{2}+\left[\left(v-v_{ 0}\right)\times d_{y}\right]^{2}} \tag{9}\]  
where \(d\) and \(\Delta r\) are the deflection distances in the horizontal and vertical directions, respectively, \(Num\) is the pixel coordinate distance in the ICF.  
Substituting Equations (6) and (7) into Equation (8) yields  
\[\tan\delta=\frac{R\times\sin\theta}{l+R(1-\cos\theta)}=\frac{Num}{f} \tag{10}\]  
Then, the tilting angle is  
\[\theta=\arcsin\frac{Num(l+R)}{R\sqrt{f^{2}+Num^{2}}}-\arctan\frac{Num}{f} \tag{11}\]