esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Temporal Trends  
Appendix B summarizes the brief studies analyzed in this survey, while Figure 4 features time trends in the annual number of publications (Figure 4A), their geographic origin (Figure 4B), and distribution between research fields (Figure 4C).  
According to Figure 4A, the number of publications on the topic was four in 2022, compared to two publications in 2021 and zero publications two years earlier, with the overall number of publications slowly, but gradually increasing in recent years. As is also shown in Figure 4, most publications investigating the effect of environmental factors on sleep using low-cost smartphone devices are found in the following five main areas: environmental science; computer science; technology; medicine; and multidisciplinary sciences. The number of publications in the environmental science field appears to be the most frequent, while only one article on the topic has been published in the field of  
Figure 4: Summary of the studies analyzed. (**A**) Annual number of publications, (**B**) geographic origin of the publications, (**C**) research field, and (**D**) environmental risk factors (total number of papers identified and summarized—15).  
multidisciplinary sciences. Geographically, the majority of the articles analyzed originated in China (34%), the US (14%), Poland (14%), Denmark (10%), and Germany (10%).
## Appendix A  
### Proof of Mathematical New Relationships  
In the KF-FFT-SVM model, the KF algorithm is used for predicting \(A_{k}\), which is the acceleration of landslide displacement. \(A_{k}\) represents the high frequency component of the landslide displacement, which may contain more useful feature information. This is why we chose \(A_{k}\) as an analysis object.  
The prediction of \(A_{k}\) is expressed as \(A_{n}\) here. The FFT algorithm is used for analyzing and extracting the feature information in \(A_{n}\). In this study, the feature information was the vibration frequency indicated in \(A_{n}\). If the characteristic frequency is \(f\), then we construct the sequence \(A_{n}^{\prime}=[A_{n},A_{n+1},\ldots,A_{n+m-1},label]\) (\(m\) is an integer here, \(m=1/\!f\)), which represents the one period characteristic waveform. Then, we used \(A_{n}^{\prime}\) to train the SVM model. However, \(1/\!f\) is not always an integer. In this situation, given that the approximation of \(m\) is \(p\), then the sequences should be constructed like this:  
\[\left\{\begin{array}{c}A_{n}^{\prime}(p-q-1)=[A_{n},A_{n+1},\ldots,A_{n+p-q-1 },label]\\ A_{n}^{\prime}(p-q)=[A_{n},A_{n+1},\ldots,A_{n+p-q},label]\\ \vdots\\ A_{n}^{\prime}(p-1)=[A_{n},A_{n+1},\ldots,A_{n+p-1},label]\\ \vdots\\ A_{n}^{\prime}(p+q-2)=[A_{n},A_{n+1},\ldots,A_{n+p+q-2},label]\\ A_{n}^{\prime}(p+q-1)=[A_{n},A_{n+1},\ldots,A_{n+p+q-1},label]\\ \end{array}\right. \tag{1}\]  
Here, \(q\) is an integer, \(0\leq q\leq p-2\). Then, we used the sequence list in Formula (1) one by one to train the SVM and find the best fitting one as the feature'step length'.  
In this study, the characteristic frequency \(f=0.2\) and \(0.9\). Thus, we chose the step length \(m=2,3,4,5,6,7,8\) and constructed sequences like this:  
\[\left\{\begin{array}{c}A_{n}^{\prime}(2)=[A_{n},A_{n+1},label]\\ A_{n}^{\prime}(4)=[A_{n},A_{n+1},A_{n+2},label]\\ A_{n}^{\prime}(4)=[A_{n},A_{n+1},\ldots,A_{n+3},label]\\ A_{n}^{\prime}(5)=[A_{n},A_{n+1},\ldots,A_{n+4},label]\\ A_{n}^{\prime}(7)=[A_{n},A_{n+1},\ldots,A_{n+5},label]\\ A_{n}^{\prime}(7)=[A_{n},A_{n+1},\ldots,A_{n+6},label]\\ A_{n}^{\prime}(8)=[A_{n},A_{n+1},\ldots,A_{n+7},label]\\ \end{array}\right. \tag{2}\]  
We trained the SVM with these eight sequences, respectively, with the method in Section 4.3, and found the best fitting'step length' was six. Then, we used \(m=6\) to build the SVM model and make early warnings.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 5 Performance Analysis
### Simulation Result
#### 5.2.2 Performance Comparison under Different Average Link Loads  
In this part, we fixed the link failure probability as 2.5%, and changed the rate of upstream traffic of each node without changing its proportion, so that the average load of the entire network link was from 0.16 to 0.4. As shown in Figure 8, DTDR still consistently outperforms other algorithms in terms of packet loss rate. In terms of delay, when the average link load is lower than 28% or higher than 36%, the delay of the DRA algorithm is significantly higher than other algorithms, and the reasons were analyzed above. However, the delays of DTDR, OPSPF, and DODR algorithms are not much different, and DODR is slightly better. Compared with the delay changes under different loads, the difference of the performance on delay between algorithms is insignificant. This result shows that the main advantage of the DTDR algorithm lies in its low packet loss rate, which is obvious when the link load is at a medium-low level.  
Figure 7: (a) Packet loss rate under different link failure probabilities; (b) average end-to-end delay under different link failure probabilities.
## 2 Materials and Methods
### Leaf Spectral Data Collection and Determination of Chlorophyll Content  
Spectral data for the peanut leaves were collected using a spectroradiometer (ASD Field Spec4; Analytical Spectral Devices, Inc., Boulder, CO, Colorado, USA). This device is a full-band geosynchronous spectrometer with a band range of 350-2500 nm and a sampling interval of 1.4 nm at 350-1000 nm and 2 nm at 1000-2500 nm. Sample sites with no pests and diseases were selected in the test area at the flowering (April 29), pod-bearing (May 18) and mature (June 15) stages. Leaf spectral data were collected separately. Two to three samples of peanut plants in each plot were collected and been brought back to the laboratory for leaf hyperspectral data collection. A whiteboard was used for calibration before each measurement was taken. Vegetation radiance measurements were made at five sample sites on each plot, with each result determined from averaging 10 scans at an optimized integration time. The saved spectrum file contained continuous spectral reflectance in 1-nm steps over the 350-2500 nm bandwidth region. The spectral reflectance difference of each sample was determined using ViewSpec Pro software (Analytical Spectral Devices, Inc., Boulder, CO, Colorado, USA) and the average of the 10 data measured at each sample was used as its spectral reflectance.  
Corresponding to the leaf spectrum position, after measuring the spectral reflectance of the selected peanut plants, the chlorophyll content of their leaves was determined using a portable chlorophyll meter (SPAD-502 Plus; Minolta Camera Co. Ltd., Osaka, Japan). Two peanut plants were randomly selected from each density treatment cell. The chlorophyll content of three leaves for each plant was randomly determined and the average of each set of six results was taken to be the chlorophyll content of the related density plot.
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 1 Introduction
### Contribution and Scope  
The main contributions of this research are the following:  
1. The application of the GNDO approach to the problem regarding the optimal placement and sizing of PV sources in monopolar DC distribution networks by improving the existing literature results reported in [22] via the application of the discrete-continuous VSA.
2. The combination of the GNDO approach with the efficient successive approximation power flow method using a master-slave optimization strategy. The main advantage of this combination lies in its reduced processing times (less than 10 min) to solve the studied problem in the DC versions of the IEEE 33- and IEEE 69-bus grids, with excellent numerical results.  
It is worth mentioning that, in this research, optimal renewable generation planning for distribution networks is carried out by considering that the PV sources are designed to track the maximum power point [23]. This allows minimizing the computational complexity of the optimization model, as the decision variables are associated with the nodes where the PV sources will be located and their nominal power rates. In contrast, when the maximum power point is not tracked, it is also necessary to determine the daily dispatch of each generator, which considerably increases the size of the solution space.  
On the other hand, it is essential to highlight that our contribution is based on applying a well-established optimization algorithm based on distribution probabilities in order to solve complex optimization problems, i.e., the GNDO approach. However, this research does not contribute with a new version of this algorithm. Our contribution is indeed the master-slave integration between the GNDO approach and the successive approximation power flow method to locate and size renewable generators based on PV sources for monopolar DC networks. Even though this problem was previously solved for AC distribution networks by [24], the difference lies in the grid structure, as monopolar DC networks do not include frequency and reactive concepts, which makes their mathematical formulation and physical behavior widely different from AC grids.
## 2 Materials and Methods
### Dynamic-Mechanical Analysis (DMA) in Shear Mode for Anisotropy Analysis  
Dynamic-mechanical tests were carried out by means of a shear-sandwich configuration (see Figure 1a) using a Q800 dynamic-mechanical analyzer (TA Instruments). The test procedure is described in [31; 32], and is summarized here. Rectangular specimens with nominal dimensions of 2 \(\times\) 2 \(\times\) 6 mm\({}^{3}\) were obtained by cutting 2 mm thick plates of vulcanized rubber (see Figure 1b). Tests were carried out at room temperature, 1 Hz, in strain sweep mode. During each test, first, a conditioning strain sweep from 0.02% to 25% of shear amplitude was carried out, followed by a rest time of 10 min to allow material recovery, and a final strain sweep (0.02-25% of shear amplitude) was carried out. G\({}^{\prime}\) and G\({}^{\prime\prime}\) values of the second sweep are reported and analyzed in this work. In order to investigate the mechanical behavior along different directions, the tests were carried out according to two test configurations, labelled \"through-thickness\" (TT) and \"in-plane\" (IP), and schematically represented in Figure 1c. In the TT configuration, strain was applied to the specimen on the surfaces corresponding to the outer surfaces of the original rubber plate (grey broken figure in Figure 1c), and deformation occurred through the sample thickness. In the IP configuration, strains were applied to the surfaces obtained by cutting the specimens from the original rubber plate, and deformation occurred in planes parallel to the sample surface. At least three repetitions were carried out for each material and test configuration.
## 4 Conditions of Analyses
### Objective Assessment-Contrast Analysis  
For the purposes of evaluating images reconstructed on the basis of registered spurious emissions and subjected to the process of pseudo-coloring, we propose using one of the most frequently used image quality assessment parameters, i.e., image contrast [35]. The assessment of the contrast of colored images was made after re-converting them to a 1-dimensional space of shades of gray. This type of conversion is part of the transition from the RGB color space to the YUV, YCbCr, or CIE spaces. As part of mapping the image in these color spaces, as a result of appropriate mixing of the primary colors R, G, B, a  
Figure 12: Typical color palettes used in commercial rasters: (**a**) “Hot 1”, (**b**) “Radar II”.  
channel representing its brightness is created, to which methods of analytical contrast determination can be applied. This kind of mixing of primary colors is imperfect, but still a representation of the processes described in Section 1, taking place in the human vision apparatus. It seems, therefore, that the evaluation of an image constructed in shades of gray in this way should also take into account the influence of its color on the impressions received by the observer.  
The contrast value of the assessed images was determined in accordance with Equation (6):  
\[K_{I}=\frac{1}{N\cdot M\cdot 255^{2}}\cdot(Q_{1}(m,n)+Q_{2}(m,n)+Q_{3}(m,n)+Q_{4} (m,n)) \tag{6}\]  
where:  
\[Q_{1}(m,n)=\sum_{m=1}^{M-1}\sum_{n=1}^{N}(l(m,n)-l(m+1,n))^{2} \tag{7}\]  
\[Q_{2}(m,n)=\sum_{m=1}^{M}\sum_{n=1}^{N-1}(l(m,n)-l(m,n+1))^{2} \tag{8}\]  
\[Q_{3}(m,n)=\sum_{m=1}^{M-1}\sum_{n=1}^{N-1}(l(m,n)-l(m+1,n+1))^{2} \tag{9}\]  
\[Q_{4}(m,n)=\sum_{m=1}^{M-1}\sum_{b=1}^{N}(l(m,n)-l(m+1,n-1))^{2} \tag{10}\]  
\(M\)--column number of the reconstructed image; \(m\)--number of columns of the reconstructed image (\(m=0\), 1, 2,..., \(M-1\)); \(N\)--row number (lines) of the reconstructed image; \(n\)--number of rows (line) of the reconstructed image (\(n=0\), 1, 2,..., \(N-1\)); \(l\)--value of image pixel amplitude; 255--maximum value of image pixel amplitude.  
The contrast assessment measure used, described by Equation (6), is based on the sum of the values of the differences between adjacent pixels of the image. Its value is therefore influenced by small differences between adjacent pixels. Contrast values were calculated for images reproduced in the color scale of shades of gray for 8-bit color depth and images after performing pseudo-coloring and applying image transformations from the RGB color space to the YUV (color encoding system where Y is a luminance component and U and V are chrominance components, U--blue projection, V--red projection), CIE (color space defined by the Commission Internationale de l'Eclairage), and PAL/SECAM (Phase Alternating Line/Sequentiel Couleur a Memoire) spaces.  
Figure 13: Narrowing the range of color variation for the analyzed color palettes: (**a**) “Hot II”, (**b**) “Hot III”, (**c**) “Radar II”, (**d**) “Radar III”.
# Dual-Boost Inverter for PV Microinverter Application--An Assessment of Control Strategies
###### Abstract
Additionally, the grid connection of this inverter is another big challenge because finding a direct relationship between the output current and control variables is difficult. In [10, 11, 18, 20, 21, 22], the experimental validation of the DBI with grid connection was performed. A linear control strategy to regulate the output voltage of each DC-DC converter was used in [11, 20]. Furthermore, an active and reactive power control was incorporated in the cascaded scheme, which presents five control loops in total to incorporate the inverter with the grid, generating a complicated design of the control strategy. In [22], three types of voltage modulators were presented and implemented; symmetric (control of each DC-DC converter), asymmetric (control of the whole system), and passivity-based (based on the control of each DC-DC converter) modulators. According to these results, the control based on passivity-based modulation presented the best output current tracking performance, because the control strategy considers the output current, the DC component of the output current, and the output voltage of each DC-DC converter. This control scheme is composed of three loops: a DC component compensator to reduce or eliminate the DC-bias presented in the output voltage of each DC-DC converter, a PR controller with a voltage drop compensator to regulate the output voltage, and the passivity-based modulation strategy. A summary of the aforementioned control strategies is shown in Table 1.  
In this context, this work proposes an analysis and comparison of four control strategies for grid-connection of the DBI topology. In particular, the classical linear approach [11, 23] and three non-linear strategies are considered: finite control set-model predictive control (FCS-MPC) [15], flatness-based control [14], and sliding mode control [18]. The remainder of the paper is organized as follows. A detailed description of the DBI topology and its modeling are presented in Section 2. The details of the control strategies selected in the comparison are introduced in Section 3. The comparison of the results is presented in Section 4, and Section 5 presents the main conclusions and accomplishments of this paper.
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Metal or Metal Oxide Nanoparticles
#### 3.4.1 Enzyme-Based Immunosensors
Figure 20: (**A**) PNIPAAm-based immunosensor for CA125, CEA and PSA detection; (**a–c**) Electrochemical response of this sensor to CA125, CEA, and PSA, respectively. Chronoamperometric results were obtained at \(-\)0.2 V. After incubation with the target proteins, HRP/antibody-tagged PPy NPs were bound to the primary antibody on the PNIPAAm-Au, and 0.1 M hydroquinone and 0.5 mM H\({}_{2}\)O\({}_{2}\) were addede in PBS buffer. Reprinted from [107], Copyright 2016, with permission from Elsevier; (**B**) GOx-modified gold nanorods for TNF-\(\alpha\) detection. Reprinted from [111], Copyright 2013, with permission from Elsevier. with the use of HRP-conjugated anti-EpCAM. With 4-terbutylcatechol and H\({}_{2}\)O\({}_{2}\) as substrate and cosubstrate added in solution, they obtained a LoD of 0.8 pg L\({}^{-1}\). At last, another way to use metals in electrochemical immunosensors is to generated production of M\({}^{0}\) at the electrode vicinity, afterwards electrochemically dissolved using ASV or SWASV, as it has been reviewed in the first section of this article. [PERSON] et al. [115] described AuNPs-modified carbon SPE for Ara-h1 (a peanut allergen) detection, based on AlkP-labelled signaling antibodies, able to produce Ag\({}^{0}\) form Ag\({}^{+}\) added in solution. Capture antibodies were grafted on AuNPs immobilized on the working electrode. The produced Ag\({}^{0}\) was detected by ASV around 0.2 V. Ara-h1 was quantified between 13 and 2000 ng mL\({}^{-1}\), with a LoD of 4 ng mL\({}^{-1}\), which could be considered as a relatively high detection limit considering other reported works featuring ASV. Figures of merit discussed in this section are summarized in Table 7.
## 2 System Model  
In a cell-free massive MIMO system in a finite-size network region, there are \(M\) APs with a single antenna distributed randomly in the region, and the APs serve \(K\) terminals equipped with a single antenna, \(M\gg K\). We assume that all APs are connected to a CPU via an unspecified backhaul network, as shown in Figure 1. The wireless channel changes slowly, and the channel model is the fading channel model in which fading coefficients are constant over time-frequency blocks. For the sake of simplicity, we omit the indexes of different time-frequency blocks. The channel gain \(g_{mk}=\sqrt{\beta_{mk}}h_{mk}\) between the \(m\)th AP and the \(k\)th terminal remains constant in a coherence time block \(\tau_{c}\) and a coherence bandwidth block \(B_{c}\), where \(\beta_{mk}\) and \(h_{mk}\) are the large-scale and small-scale fading coefficients, respectively. The channel vector of the \(k\)th terminal is  
\[g_{k}=[g_{1k},g_{2k},\ldots,g_{Mk}]^{T}=\Big{[}\sqrt{\beta_{1k}}h_{1k},\sqrt{ \beta_{2k}}h_{2k},\ldots,\sqrt{\beta_{Mk}}h_{Mk}\Big{]}^{T}, \tag{1}\]  
and  
\[G=[g_{1},g_{2},\ldots,g_{K}]. \tag{2}\]  
The large-scale fading \(\beta_{mk}\) is affected by path loss and shadow fading between the AP and terminal and the small-scale fading coefficients \(h_{mk}\) are independent and identically distributed (i.i.d.) and satisfy \(h_{mk}\sim\mathcal{CN}(0,1)\). Furthermore, the large-scale fading coefficients are assumed to be known as needed.  
We assume that the terminal does not move quickly in the considered scenario. When the correlation time is greater than the channel feedback delay, which is typically sub-millisecond, the channel is considered to be slow-varying. In other words, we assume that the terminal has sufficient time to feedback the CSI to the AP in FDD system. Consider the frequency \(f_{c}=2.0\) GHz, the system bandwidth \(B=10\) MHz, and the orthogonal frequency division multiplexing (OFDM) size \(N=2048\). The maximum delay spread is \(\tau_{max}=5\) us. The velocity of users is \(v=2\) m/s. The coherence time is \(31.7\) ms. There are \(6184\) samples in a coherence time-frequency block [19; 21]. Section 2.1 notes that the channel is less hardened in cell-free massive MIMO systems. Therefore, both the transmitter and the receiver obtain the instantaneous CSI, as shown in Figure 2. Although the overhead  
Figure 1: In a cell-free massive MIMO, APs and UEs are equipped with one antenna.  
of feedback of the CSI increases, the simulation results show that the spectral efficiency is substantially increased.
# Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System
## 2 Orbital Optoelectric Tracking System Design
### Orbital Optoelectric Tracking System Based on Rotating Reflector
During the target movement imaged into the field-of-view of the optical camera through the reflector, the effective distance \(|MN|\) in the field-of-view can be given by the following relationship:  
\[|MN|=2x+\left(\frac{\sin\alpha h}{\sin 2\beta}+l\sin\alpha\right)\cdot\frac{ \sin(2\beta+\alpha)-\sin(2\beta-\alpha)}{\sin^{2}(2\beta)-\sin^{2}\alpha} \tag{7}\]  
Based on the geometric relationship of the system and the target, it is known that  
\[\theta=\frac{1}{2}(\frac{\pi}{2}-\arctan\frac{l-x}{h}) \tag{8}\]  
Based on the target velocity, the relationship between the angular velocity of the reflector and the target velocity can be derived. \[m=x-AM=x-\frac{l\sin\alpha}{\sin(2\beta+\alpha)}\cdot\frac{h/\sin(2\beta)+l}{l} \tag{1}\]0
## 1 Introduction  
The aim of this work was twofold. First, to evaluate the suitability of Z-cut single-crystal quartz and glassy silica as reference materials for terahertz (THz) and microwave (MW) measurements. The current lack of reference materials suitable for calibrating and testing measurements of complex permittivity at THz and MW frequencies is an acknowledged and pressing issue that requires being addressed [1, 2]. Quartz and silica appear to be good candidate materials due to their wide availability and affordability, standard fabrication techniques, and high chemical purity. A reference material for spectroscopy should have highly reproducible properties that vary less than typical measurement uncertainty, and that are constant among all reputable manufacturers and production batches. To verify this, we examined five samples of Z-cut quartz and seven samples of vitreous silica. We report our findings in detail, confirming that Z-cut quartz is a good reference material. However, silica glass of nominally the same grade exhibits significant variations in both refractive index and absorption at THz frequencies, although the refractive indices at 36 GHz show negligible variation.  
The second aim of this work, which is directly related to establishing reference materials, was to obtain accurate data on the complex permittivity of quartz and silica. Although there is extensive literature on both quartz [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] and vitreous silica [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], the reported data vary significantly. If quartz and/or silica are to serve as reference materials, their complex permittivities must be accurately known. We report values of the refractive index and absorption coefficient for the o-ray in quartz between 0.2 and 6 THz, for the e-ray in quartz between 0.2 and 5 THz, and for vitreous silica between 0.2 and 4.5 THz. We also report measurements at 36 GHz for the o-ray in quartz and for all silica glass samples. In addition, we have examined variations in the Boson peak in silica glass, and report for the first time a negative relationship between refractive index and Boson peak frequency.