esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data Preprocessing  
Standardization of Spectral Data  
The data of all samples in this experiment were collected at different times, growth periods, geographic areas, and for different wheat varieties. In order to eliminate spectral differences caused by the different backgrounds of data collection, the original data were standardized before analysis [22]. The spectra of all samples in area A were used as the standard spectra. The mean values of the spectra of the healthy samples in area A were divided by the mean values of the spectra of the healthy samples in areas B and C, respectively; thus, two ratio curves were obtained. The ratio curves reflect the spectral difference between the two areas [23]. The ratio at a certain wavelength was calculated using Equation (1):  
\[Ratio_{i}=\frac{\overline{\sum\text{Ref}_{(A)_{i}}}}{\overline{\sum\text{Ref}_ {(B/C)_{i}}}}\text{q} \tag{1}\]  
Figure 3: FHB-infected ear of winter wheat at three measuring angles (front, side, and erect).  
where \(i\) is the wavelength; \(\sum\) represents the sum of reflectance of all healthy samples at wavelength \(i\) in that area; Re\(f\) is the reflectance; and \(A\), \(B\), and \(C\) are the spectra of samples in the areas of Guohe Town, Baihu Town, and Shucheng County, respectively.  
The original spectrum of each sample acquired in area B and area C was multiplied by the corresponding ratio curve to obtain the standardized spectra. The standardized reflectance at a certain wavelength was calculated using Equation (2):  
\[\text{Re}f_{(B/C)i}=\text{Re}f_{(B/C)i}\times Ratio_{i} \tag{2}\]  
where Re\(f_{(B/C)i}\) is the reflectance of the original wavelength \(i\) of area B or area C; Re\(f_{(B/C)i}\) is the standardized reflectance.  
The spectral standardization was used to improve the reliability of the results of spectral identification. In the process, the reflectance of the healthy samples in area A were used as the standard reflectance, and reflectance of the samples in areas B and C were adjusted to the same level of reflectance as the samples in area A. Therefore, the influence of the background difference of the three sets of data on the spectral discrimination were largely eliminated, and the relative difference between the spectra of the healthy samples and the diseased samples were not changed.
## 4 Results
### Comparing Results with MCD64 Products  
Figure 5 presents the two burned area products over the study area in the 2003 period. There were 6082 burned pixels in the MCD64 data, 4022 pixels were burned in the GLASS data, and the difference between the two results was more than 2000 pixels. The differences were mainly concentrated on the left and right sides of the study area.  
Figure 4: Difference between the NBR anomalies for burned (**A**) and unburned (**B**) areas in 2003. The green curve is the value of NBR, and the red dotted line is the horizontal line when NBR is zero.  
In order to compare the detection accuracy, the differences between the two products were selected in the study area. There were 978 pixels that MCD64 identified as unburned, but the TSM algorithm detected them as burned pixels; the LAI and NBR time series are shown in Figure 6A,B. The LAI in 2003 compared to the previous two years had a large degree of reduction; at the same time, the value of NBR in 2003 was generally at a low level compared with that of the previous two years. In addition, there were 2904 unburned pixels in the TSM algorithm detection that were marked as burned pixels in the MCD64, as shown in Figure 6C,D. In 2003, the LAI value did not decrease significantly, and the value of NBR was also at a high level. There were a large number of missed and wrong detection phenomenon in MCD64 product, and the TSM algorithm effectively avoided the phenomenon.
## 2 Starch Modification Processes
### Chemical Modifications
#### 2.4.6. Oxidation  
In oxidation, the hydroxyl groups at C-2, C-3, and C-6 are oxidized to carbonyl groups and then to carboxyl groups. After that, starch is hydrolyzed to amylose and amylopectin molecules by breaking \(\alpha\)-1,4-glycosic linkages, as shown in Figure 5. The main oxidizing agents include periodate, chromic acid, permanganate, nitrogen dioxide, and sodium hypochlorite [97]. However, sodium hypochlorite is widely used as the oxidizing agent for starch modification in the industry [97]. The oxidization process is influenced by the molecular structure, origin, packing of the crystalline lamella, and the size of the amorphous lamella of starch [97].  
For instance, [PERSON] et al. [75] prepared biodegradable potato starch films using sodium hypochlorite as the oxidizing agent. According to their results, films made with oxidized starch exhibited lower tensile strength (1.8 MPa) than native starch films (3.8 MPa). However, these films showed lower water solubility with a 52% reduction of WVP compared to native starch films. [PERSON] et al. [97] investigated the effect of zein (the protein found in maize starch) on native and oxidized banana starch along with different film formulations on mechanical and structural properties. The oxidized starch showed a 30% increase in tensile strength and a 53% increase of elongation at break compared to the native starch. However, these results did not agree with the values reported by [PERSON] et al. [75]. Such deviation might be due to the variations in amylose and amylopectin ratios and variable oxidation conditions. The increased tensile strength of films was due to oxidized starch forming hydrogen bridges with the -OH groups of amylose and amylopectin molecules. These linkages provide more structural integrity in the polymeric matrix, thereby increasing tensile strength [78].  
[PERSON] et al. [78] developed potato starch-based films using sodium hypochlorite as the oxidation agent. The films produced from oxidized potato starch showed decreased solubility, elongation at break (0.3%), WVP (21%) values, and increased tensile strength compared to native starch films.
## 2 System Structure and Principle
### Image Acquisition  
The image is captured by 360\({}^{\circ}\) cameras. The 360\({}^{\circ}\) cameras are equipped with four camera components mounted on the front, rear, left, and right sides of the car. When installing the cameras, the front camera is installed on the logo or the grill, and the rear camera is installed near the license plate light or trunk handle, as close as possible to the central axis. Left and right cameras are installed under the left and right rearview mirrors. The number and position of the cameras can also be adjusted according to the actual situation. Wide-angle cameras can be used, which can take complete environmental images around the vehicle at the same time. Images of the surrounding environment are simultaneously collected through the 360\({}^{\circ}\) cameras. After the image correction and image mosaicking, a 360\({}^{\circ}\) image is formed [16].  
The calibration of the cameras mainly involves the transformation of four coordinate systems: world coordinate system, camera coordinate system, image plane coordinate system, and image  
Figure 6: The actual laser grid image.  
Figure 7: Obstacle area testing.  
pixel coordinate system. The calibration methods mainly include the Zhengyou Zhang calibration method [17], the RAC (Radial Alignment Constraint) two-stage method of [PERSON], and the DLT (Direct Linear Transform) method [18; 19; 20].  
Using a given known calibrator, the external parameter rotation matrix of camera \(R\), translation matrix \(T\), principal point coordinates of parametric images \((C_{x},C_{y})\), the height and width of a single-pixel \(S_{x}\), \(S_{y}\), focal length \(f\), and distortion factor \(k\) can be calculated.
## 2 Architecture of Proposed SSPLL  
### Conceptual Block Diagram  
The main objective of this study is to design a dual path type-II SSPLL to reduce the in-band noise with low supply voltage. As mentioned above, the MSSF, in the type-I PLL, can operate well at low voltage without feature limitation. Hence, the MSSF can take charge of the P-path directly. The SSCP is kept to compose the I-path, in order to suppress the noise of the VCO by the type-II structure. Although the performance of the charge pump is degraded, the gain of the I-path is much smaller than the gain of the P-path, therefore, the noise contribution in the PLL system is much smaller than that of the P-path (the theoretical verification will be implemented in the following sections). Moreover, the sub-sampling-based PLL has the probability to lock at an unwanted frequency, so a frequency-locked loop (PLL) is necessary to be added to avoid this frequency uncertainty. We design an all-digital FLL (ADFLL) which consists of a digital divider and an adaptive frequency calibration (AFC) [27]. The all-digital circuit is beneficial for the power saving as it can be fully powered off, after the calibration is done. The overall conceptual block diagram for the proposed system is presented in Figure 1.  
As indicated in [25], the traditional exclusive-OR based type-I PLL always faces the spur noise trouble, because of locking at the non-zero phase offset. Although the ideal MSSF can remove the voltage ripple on the control line of the VCO, current leakage from varactors and clock feedthroughs from the MSSF would introduce large ripples. To mitigate this phenomenon, we propose a differential MSSF to transfer the single path ripples into common-mode ripples.  
Furthermore, we add a voltage-controlled buffer (VCBUF) at the output of the VCO to isolate the signal of the VCO away from the changeable load from the MSSF during the sampling.
# Non-Thermal Technologies in Food Processing: Implications for Food Quality and Rheology
## 4 Ionising Radiation (IOR)
Additionally, Co-60 gamma irradiation applied to an industrial powder white, yolk, and whole egg at doses between 0 and 25 kGy, revealed that irradiation with doses up to 5 kGy inactivate microbes to non-detectable levels and no significant variation in the viscosity of irradiated egg white samples was observed [138], whereas the whole egg or egg yolk samples exhibited some changes in rheological properties depending on the dose level. This suggests that irradiation can induce the structural degradation of proteins.
###### Abstract  
This study presents a radiofrequency (RF)-energy-harvesting integrated circuit (IC) for powering wireless sensor networks with a wireless transmitter with an industrial, scientific, and medical (ISM) of 915 MHz. The proposed IC comprises an RF-direct current (DC) rectifier, an over-voltage protection circuit, a low-power low-dropout (LDO) voltage regulator, and a charger control circuit. In the RF-DC rectifier circuit, a six-stage Dickson voltage multiplier circuit is used to improve the received RF signal to a DC voltage by using native MOS with a small threshold voltage. The over-voltage protection circuit is used to prevent a high-voltage breakdown phenomenon from the RF front-end circuit, particularly for near-field communication. A low-power LDO regulator is designed to provide stable voltage by using zero frequency compensation and a voltage-trimming feedback. Charging current is amplified N times by using a current mirror to rapidly and stably charge a battery in the proposed charger control circuit. The obtained results revealed that the maximum power conversion efficiency of the proposed RF-energy-harvesting IC was 40.56% at an input power of \(-6\) dBm, an output voltage of 1.5 V, and a load of 30 k\(\Omega\). A chip area of the RF-energy-harvesting IC was \(0.58\times 0.49\) mm\({}^{2}\), including input/output pads, and power consumption was 42 uW.  
Keywords:radiofrequency; ISM 915 MHz; energy-harvesting IC; native MOS; Dickson voltage multiplier; over-voltage protection circuit; low-dropout regulator; charger control circuit +
Footnote †: journal: Sensors
## 4 Discussion  
In the present study, an average of 42 laryngeal articulations were recorded for each participant during performance. Comparing the participants' EGG signals with the EGG signals recorded during the execution of laryngeal movements equivalent to those performed during a Valsalva manoeuvre, the waveform of the EGG signal was identical. However, the mean values (calculated over the interval that corresponds to a laryngeal articulation) of all participants for SpO2 (97%) and HR (105 bpm) indicated that it was not a Valsalva manoeuvre.  
Regarding the perception tests, the mean value attributed by the two listeners on a scale of 0 to 100 for all participants was 61%. Considering the extremes of the VAS used, we can sustain that the laryngeal effort of the participants is moderate. When calculating the correlations between the scores of the two listeners the value was 0.079, indicating that there is no correlation between the answers of the two listeners.  
Even with no evident relation between the scores of the listeners, the correlations between all the measurements and the listeners' scores (total of 250 pairs of observations by each listener) were calculated. In this case, the conditions of applicability of the Pearson correlation test are corrupted, since the observations have dependencies (by groups regarding each trumpet player). Only the variable LA1 dur had a significant correlation (\(p=-0.296\)) with listener JS scores, but, considering the above, we could infer that there are in fact no significant correlations between any of the variables and listeners' evaluations.  
Air emission has been shown to be an important factor in the control of laryngeal stress. When exhaling the air to the trumpet, one must consider the narrow space of the tube of the mouthpiece and of the lead pipe through which the air passes. If the amount of air during the emission is too great, it will not be fully utilised, which will cause excess air to be wasted at the ends of the mouthpiece.  
This study contributes to a deeper understanding of laryngeal activation in trumpet performance. Having control over the exact amount of air needed to perform a musical passage is essential, for the larynx to be relaxed during performance.
## 4 Results and Discussion
### ML Classification
#### 4.3.2 Comparing between Meta-Heuristic Ensembles and Traditional Classifiers
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **CV** & **Set** & \multirow{2}{*}{**No. of**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**Precision**} \\ \cline{3-10}  & & & & **Overall** & **Weighted** & **Micro** & **Macro** & **Weighted** & **Micro** & **Macro** & **Weighted** \\ \hline \multirow{4}{*}{SE} & TVS & 20 & - & 0.833 & 0.938 & 0.833 & 0.563 & 0.833 & 0.833 & 0.575 & 0.775 \\  & TVS & 10 & - & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\  & KFCV & - & 5 & **0.924** & **0.972** & **0.924** & **0.758** & **0.924** & **0.924** & **0.787** & **0.908** \\  & KFCV & - & 10 & **0.933** & **0.964** & **0.933** & **0.856** & **0.933** & **0.933** & **0.855** & **0.923** \\  & MCCV & 10 & 5 & 0.817 & 0.856 & 0.817 & 0.756 & 0.817 & 0.817 & 0.734 & 0.776 \\  & MCCV & 20 & 5 & 0.858 & 0.911 & 0.858 & 0.746 & 0.858 & 0.858 & 0.745 & 0.809 \\  & MCCV & 10 & 10 & 0.858 & 0.893 & 0.858 & 0.805 & 0.858 & 0.858 & 0.765 & 0.820 \\  & MCCV & 20 & 10 & **0.908** & **0.946** & **0.908** & **0.806** & **0.908** & **0.908** & **0.803** & **0.875** \\  & TVS & 20 & - & 0.875 & 0.957 & 0.875 & 0.625 & 0.875 & 0.875 & 0.683 & 0.850 \\  & TVS & 10 & - & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ VE & KFCV & - & 5 & **0.958** & **0.982** & **0.958** & **0.856** & **0.958** & **0.958** & **0.865** & **0.946** \\  & KFCV & - & 10 & **0.941** & **0.975** & **0.941** & **0.861** & **0.941** & **0.941** & **0.859** & **0.928** \\  & MCCV & 10 & 5 & 0.917 & 0.936 & 0.917 & 0.865 & 0.917 & 0.917 & 0.883 & 0.922 \\  & MCCV & 20 & 5 & **0.925** & **0.969** & **0.925** & **0.796** & **0.925** & **0.925** & **0.803** & **0.901** \\  & MCCV & 10 & 10 & 0.900 & 0.935 & 0.900 & 0.824 & 0.900 & 0.900 & 0.816 & 0.877 \\  & MCCV & 20 & 10 & **0.921** & **0.964** & **0.921** & **0.781** & **0.921** & **0.921** & **0.778** & **0.897** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: ML multinomial classification performance metrics.
## 4 Computational Fluid Dynamics (CFD) Simulations  
### Mesh Distribution  
Considering the complexity of the aerodynamic interference for multi rotors, CFD simulations with ANSYS are applied to obtain the flow field distribution of multi-rotor system and provide visual figures to explain the experimental results. Transient flow solver, combining two algorithms: Pressure-Implicit with Splitting of Operators (PISO); and Semi-Implicit Method for Pressure-Linked Equations (SIMPLE); is used in simulations. The adjustable time step based on the dimensionless, and the second order discretization schemes are utilized for the time discretization and also for convection and diffusion terms. The meshing distribution is shown in the Figure 8. The total number of cells in meshing is about 2.38 million, which is divided into 5 regions, including a cylinder stationary region and 4 cylinder rotating regions.  
Figure 7: Variation of Figure of Merit (FM) with different RPM.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 2 Research Methods and Data
### Data
#### 2.2.1 Underground Facilities  
The attribute information for six types of underground facilities in the target area, namely the water supply, sewer, gas, electricity, communication, and heating pipes, was collected. The length and number of pipelines in the target area are shown in Table 2. The density of underground pipelines was the highest for sewer pipes, followed by water pipes and gas pipelines. The number of water pipelines was the largest.  
The attribute information for these underground facilities includes the pipeline length, pipe diameter, years of use, and burial depth. Based on the results of correlation analyses conducted in previous studies, the pipeline length, diameter, and years of use were selected as the input data [13]. The six types of underground facilities were merged into a single type of underground facility to construct the dataset. The basic unit of the underground facilities was determined by the length of the pipeline, with the pipe diameter categorized as either 50 mm or 100 mm based on the specific conditions. The years of use were classified into sets of 5- or 10-year increments, and the dataset was compiled by aggregating the basic units of pipelines within these ranges. Table 3 shows the datasets established based on the data classification criteria. A total of 24 datasets were constructed after the application of SMOTE.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Case** & **Grid** & **Accident** & **Density** & **Permeability** & **Thickness** & **Height** & **Year** & **Diameter (mm)** & **Risk Level (Level 2’s Range)** \\ \hline
1 & & & & & & & & 3 (1) \\
2 & & & & & & & 50 & 3 (1–2) \\
3 & & & & & & & & 3 (1–3) \\
4 & & & & & & & & 3 (1) \\
5 & & & & & & & 100 & 3 (1–2) \\
6 & & & & & & & & 3 (1–3) \\
7 & 500 m & Accident & Density Permeability & Thickness & Height & & & 3 (1) \\
8 & & & & & & & 50 & 3 (1–2) \\
9 & & & & & & & & 3 (1–3) \\
10 & & & & & & & & 3 (1) \\
11 & & & & & & & 100 & 3 (1–2) \\
12 & & & & & & & & 3 (1–3) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Condition of the datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pipe** & **Length (km)** & **Number of Pipelines** \\ \hline Water & 10,283.35 & 1,048,566 \\ \hline Sewer & 10,827.97 & 394,958 \\ \hline Communication & 9431.65 & 162,735 \\ \hline Power & 3125.24 & 168,431 \\ \hline Gas & 10,584.73 & 666,820 \\ \hline Heating & 2434.56 & 61,939 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data characteristics of underground pipelines.
# Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems
### Related works
Hence, the remaining portion, the so-called residual self-interference (RSI), is still present at the relay input. The distribution of the RSI is investigated in [23; 24]. This RSI is mainly due to the channel estimation uncertainties and transmitter noise. Therefore, the quality of channel estimation plays an important role for limiting RSI if the conventional modulation techniques are utilized. The authors in [25] employ a superimposed signaling procedure (asymmetric modulation constellation) in the basic point-to-point FD communication for cancelling the SI and further retrieving the desired information contents without requiring channel estimates. They show that for the same average energy per transmission block, the bit error rate of their proposed method is better than that of conventional ones. The RSI evidently degrades the performance of the communication quality. To this end, the authors in [26] study the degrees-of-freedom (DoF), i.e., the slope of the rate curve at asymptotically high SNR and its relation to the performance of an FD cellular network in the presence of RSI. Moreover, the authors in [27; 28] investigate the joint rate-energy and delivery time optimization of FD communication, respectively, when RSI is still present. Furthermore, the authors in [29] study the sum rate capacity of the FD channel with and without such degradation. In the presence of RSI, the authors in [30] study the capacity of a Gaussian two-hop FD relay. Robust transceiver design against the worst-case RSI channel helps find the threshold for switching between HD and FD operating modes. This setup is commonly known as a hybrid relay system [31]. The authors in [32] investigate a robust design for multi-user full-duplex relaying with multi-antenna DF relay. In that work, the sources and destinations are equipped with single antennas. Moreover, the authors in [33] investigate a robust transceiver design for FD multi-user MIMO systems for maximizing the weighted sum-rate of the network. The robust design against worst-case RSI is investigated by authors in [34].
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 4 Towards More Targeted Negative Sampling Strategies for Dynamic Link Prediction
### Background on Dynamic Link Prediction
However, computing the scores for all possible edges scales quadratically with the number of nodes. Even for reasonably sized networks with a few thousand nodes, this renders the exhaustive comparison intractable. Consequently, as is the case in Static Link Prediction, it is common to strongly subsample the set of possible negatives. We now formally define this crucial step of the evaluation. **Definition 8** (Negative Sampling). : _A **negative sampling** strategy is a mapping that takes as input a positive event \((u,v,t)\in\mathcal{H}\) and returns a set of K-associated negative events \(\{(u^{(k)},v^{(k)},t)\}_{k=1,\ldots,K}\) occurring at the same timestamp \(t\)._  
**Problems with Naive Negative Sampling Strategies**. A straightforward NS strategy is to swap the source \(u\) and/or destination node \(v\) of the positive event \((u,v,t)\) with other nodes \(u^{\prime}\in\mathcal{U}\) and/or \(v^{\prime}\in\mathcal{U}\) uniformly at random. Although common, this strategy is naive. The vast majority of possible negatives at time \(t\) tends to be unrealistic for various intuitive reasons. For instance, many edges never occur at all in the graph, and many nodes only interact long before or after time \(t\) (i.e., they are probably _inactive_ at this time). Such an unrealistic NS strategy may give an unbiased estimate of the exhaustive performance (obtained through a comparison of the positive with all the possible edges). However, including trivial negatives into the comparison will steer the accuracy to a value close to 1, rendering such accuracy rather uninformative. To obtain accuracy estimates which align better with the actual task at hand, it is therefore important to only consider more challenging negatives. In what follows, we will investigate how to do so.