esa-sceva/satcom-chunk-collection huggingface dataset:
# Reflective Inverse Diffusion
## 2 Background
### Reflective vs. Transmissive Inverse Diffusion
The unoptimized random speckle background can be expressed as a fixed-length random phasor sum,  
\[\left\langle I_{md}\right\rangle=\left\langle\frac{1}{N_{T}}\left|\sum_{n=1}^ {N_{T}}\mathbf{r}\,e^{i\Theta_{mn}}\right|^{2}\right\rangle=\mathbf{r}^{2} \left\langle\left|\frac{1}{\sqrt{N_{T}}}\sum_{n=1}^{N_{T}}\,e^{i\Theta_{mn}} \right|^{2}\right\rangle, \tag{7}\]  
which can be written as a complex number with amplitude \(A\) and phase \(\Theta\),  
\[\left\langle I_{rnd}\right\rangle=\mathbf{r}^{2}\left\langle\left|Ae^{i\Theta }\right|^{2}\right\rangle=\mathbf{r}^{2}\left\langle A^{2}\right\rangle, \tag{8}\]  
where \(A\) and \(\Theta\) are both random variables and \(\left\langle A^{2}\right\rangle\) is the second moment. The probability density function of \(A\) is given by [17],  
\[p_{A}(\alpha)=4\pi^{2}A\int_{0}^{\infty}\rho\int_{0}^{N_{T}}\left(\frac{2\pi \rho}{\sqrt{N_{T}}}\right)J_{0}(2\pi\alpha\rho)d\rho, \tag{9}\]  
where \(\alpha\geq 0\) is the magnitude of the random phasor sum, \(\rho=\frac{\omega}{2\pi}\) from the two-dimensional joint characteristic function of the random phasor sum [17] and \(J_{0}\) is the zero-order Bessel function of the first kind. As the number of SLM segments approaches infinity, (\(N_{T}\rightarrow\infty\)), the probably density function in Equation (9) becomes a Rayleigh distribution [17]. For \(N_{T}=1024\), Mathematica(r) numerically approximated the second moment of Equation (9) as \(\left\langle A^{2}\right\rangle=1\). Substituting the results from Equations (6) and (8) into Equation (3) shows that the ideal enhancement for reflective inverse diffusion is \(N_{T}\),  
\[\eta_{reflective}=\frac{\left\langle I_{max}\right\rangle}{\left\langle I_{rnd }\right\rangle}=N_{T}. \tag{10}\]
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 5 Simulation Performance and Analysis
### Satellite Network Traffic Forecasting
#### 5.3.2 IGWO-BPNN-Based Forecasting for Residuals  
The IGWO-BPNN proposed in Section 4.2 is utilized to forecast residuals. The parameters of the IGWO and BPNN are shown in Table 6. Among the parameters of the BPNN, the number of neurons in the hidden layer directly affects the mapping ability of the BPNN to deal with complex problems. The empirical formula for defining the number of neurons is defined as  
\[n=\sqrt{u+v}+b,\;b\in[1,10] \tag{30}\]  
where \(u\) is the number of neurons in the input layer, \(v\) is the number of neurons in the output layer and \(b\) is the bias parameter.  
The residual of the satellite network traffic is obtained by removing the linear sequence \(L^{\prime}(t)\) from the original sequence \(D(t)\). In order to verify the forecasting performance of the IGWO-BPNN, the forecasting results \(R^{\prime}(t)\) for the IGWO-BPNN are compared with the results obtained from the BPNN, GWO-BPNN and grey-BPNN [35]. To ensure reasonable results, the parameters of the BPNN and GWO in the comparison algorithms are set to be the same as for the IGWO-BPNN.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameters** & **IMF\({}_{1}\)** & **IMF\({}_{2}\)** & **IMF\({}_{3}\)** & **IMF\({}_{4}\)** & **IMF\({}_{5}\)** & **IMF\({}_{6}\)** & **IMF\({}_{7}\)** & **IMF\({}_{8}\)** & **IMF\  
The root mean square error (\(RMSE\)), mean absolute error (\(MAE\)), determination coefficient (\(R^{2}\)) and mean absolute percentage error (\(MAPE\)) are taken as the evaluation indexes of the forecasting results. The experimental results are shown in Figure 12 (Table 7 for specific values). Among them, the \(MAPE\) and \(RMSE\) are defined in Equations (26) and (27); \(MAE\) and \(R^{2}\) are defined as  
\[MAE=\frac{1}{T}{\sum\limits_{t=1}^{T}}{\left|{R(t)-R^{\prime}(t)}\right|} \tag{31}\]  
\[R^{2}=1-\frac{{\sum\limits_{t=1}^{T}{\left({R(t)-R^{\prime}(t)}\right)^{2}}}}{ {\sum\limits_{t=1}^{T}{\left({R(t)-\overline{R}(t)}\right)^{2}}}} \tag{32}\]  
where \(T\) denotes the length of the traffic data, \(R(t)\) denotes the residual, \(R^{\prime}(t)\) denotes the forecasted residual and \(\overline{R}(t)\) denotes the mean value of forecasted residual.  
As shown in Table 7, the IGWO-BPNN, GWO-BPNN and grey-BPNN represent a BPNN optimized using IGWO, GWO [12] and the grey model [35], respectively. Compared to the BPNN, IGWO-BPNN reduced the RMSE and MAE values by 97.4% and 84%, respectively, and improved \(R^{2}\) by 14.43%. It is indicated that combining IGWO with BPNN is effective in forecasting the residuals of satellite network traffic.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 5 Optimal Dispatching of Micro-Energy-Grid  
### Objective Function  
We use the minimum comprehensive cost \(C_{\text{exp}}\) of the agricultural greenhouse in micro-energy-grid within 24 h as objective function. The comprehensive cost includes the purchase and sale cost \(C_{\text{ee}}\), operation and maintenance cost \(C_{\text{op}}\), environmental cost \(C_{\text{en}}\) and PV incomplete penalty cost \(C_{\text{re}}\). The objective function is [30; 31]:  
\[\min C_{\text{exp}}=\min C_{\text{ee}}+C_{\text{op}}+C_{\text{en}}+C_{\text{re}} \tag{16}\]  
\[\left\{\begin{array}{l}C_{\text{ee}}=\sum\limits_{t=1}^{24}\sum\limits_{s=1 }^{10}p(t,s)\Big{[}C_{\text{buy}}(t)P_{\text{buy}}(t,s)+C_{\text{sell}}(t)P_{ \text{sell}}(t,s)\Big{]}\\ C_{\text{op}}=\sum\limits_{t=1}^{24}\sum\limits_{i=1}^{n}C_{\text{op},i}P_{i}(t )+\sum\limits_{s=1}^{10}p(t,s)C_{\text{op},PV}P_{PV}(t,s)\\ C_{\text{en}}=\sum\limits_{t=1}^{24}\sum\limits_{j=1}^{m}F_{\text{F}}C_{\text{ en},i}P_{\text{CHP}}(t)\\ C_{\text{re}}=\sum\limits_{t=1}^{24}\sum\limits_{s=1}^{10}p(t,s)C_{\text{re},PV}[ P_{PV\max}(t,s)-P_{PV}(t,s)]\end{array}\right. \tag{17}\]  
where \(p(t,s)\) is the probability of scenario \(s\) at moment \(t\); \(C_{buy}(t)\) and \(C_{sell}(t)\) are the purchase and sale price at moment \(t\) respectively; \(P_{buy}(t,s)\) and \(P_{sell}(t,s)\) are the purchase and sell power from or to the grid in scenario \(s\) at moment \(t\), and the latter should be a negative value; \(C_{\text{op},i}\) is the operation cost of type \(i\) equipment at moment \(t\); \(P_{i}(t)\) is the power of type \(i\) equipment at moment \(t\); \(C_{\text{op},PV}\) is the operation cost of PV power generation equipment at moment \(t\); and \(P_{PV}(t,s)\) is the PV actual output power of scenario \(s\) at moment \(t\). \(P_{CHP}(t)\) is the generating power of the CHP set at moment \(t\), \(F_{j}\) is the emission of the \(j\)th pollutant gas from CHP, \(C_{\text{en},j}\) is the sum of the environmental value and penalty of the \(j\)th pollutant gas, \(P_{PV\max}(t,s)\) is the maximum PV output of scenario \(s\) at moment \(t\), and \(C_{\text{re},PV}\) is penalty costs for incomplete PV absorption.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 2 Traditional Methods before Deep Learning
### CRFs  
A CRF is mainly applied at the post-processing stage of images to improve the accuracy and continuity of segmentation results. It is a probabilistic graphical model that models the relationships between pixels as an undirected graph and obtains the best segmentation result by minimizing the energy in the graph. In image segmentation, CRFs are usually used to consider the contextual information between pixels; namely, the influence of surrounding pixels on the current pixel and the interaction between pixels. They can also eliminate noise by smoothing adjacent pixels, refine the segmentation results, and improve segmentation quality. Although CRFs have been widely used in image segmentation before the rise of deep learning, they still have some shortcomings such as the need for a large amount of manual feature engineering and parameter tuning as well as a high computational complexity, leading to low efficiency. By comparison, deep learning methods can obtain the best segmentation results through end-to-end training, thus avoiding the tedious process of manual parameter adjustment. In addition, deep learning models have good performance in terms of calculation speed and accuracy and can obtain high segmentation accuracy in a short time. Although a CRF can still be used as a post-processing layer for deep learning models, it is rarely utilized as the main method of image segmentation at present due to the superiority of deep learning.
## 2 Materials and Methods
### Final System Implementation
#### 2.6.1 TI-CC2650 Launchpad MCU + Internal Trimer  
To begin, we propose to connect the BME-680 environmental sensor to the TI-CC2650 Launchpad MCU. The connections between the SCL/SCK and SDA/SDI pins, as well as the DIO4/SCK and DIO5/SDA pins, form the I2C bus. These pins serve as clock and data lines for the I2C connection. To ensure proper operation of the BME-680 sensor, we need pull-up or pull-down resistors on the CSB and SDO pins. Additionally, 43 k\(\Omega\) resistors are connected to the SDA and SCK pins as pull-up resistors. Furthermore, both the sensor and the MCU share a common power supply. Figure 4 provides a visual overview of the circuit connections.  
In this initial scenario, we control the active and sleep times using the MCU's internal timer. Upon power-up, the MCU initializes itself, and the BME680 sensor begins reading environmental data. After processing and transmitting these data in short bursts via the built-in BLE radio, the MCU puts the sensor to sleep, activates the internal ultra-low-power timer, and enters sleep mode itself. Following a specific sleep duration, the timer generates an internal interrupt, awakening the CPU, and the entire process repeats. Figure 4 outlines the operational flowchart of the device with its internal timer. The BLE transmits data packets in the form of bursts, as depicted in Figure 5, which illustrates the current consumption trace of each BLE. These data are acquired using an oscilloscope via a 10 \(\Omega\) shunt, with the system operating at a 2.5 V DC voltage. The trace exhibits various activity sections within the packet. Initial spikes correspond to the processor boot-up, followed by a substantial block representing the BME680 sensor activity. Adjacent to this block, there are five prominent spikes indicating the transmission of BLE data packets. A subsequentminor spike indicates MCU processing activity, while smaller spikes during sleep mode are due to internal regulator activities.  
Through careful experimental investigation of the TI BLE API, we determine that the lowest achievable broadcast duration is approximately 1 s. Throughout our research, we use an Adafruit BLE sniffer for characterization, which reveals that adopting 5-6 packets per broadcast strikes an optimal balance between low-power consumption and reliable operation. These features are reflected in Figure 5.
## 5 Characteristics of the Antenna Directional Patterns  
The directional patterns were calculated directly in the program CST Microwave Studio Suite. Figure 6 shows the Cartesian and spherical systems of coordinates. The antenna center was located in the origin of coordinates (not to clutter the figure, the antenna in it is displaced from the center by a parallel image transfer). Our calculations was performed in two stages. The normalized antenna directional patterns in the E- and H-planes (the solid and dotted curves in Figures 7a-7f, respectively) were first calculated for a series of frequencies from the range 3... 16 GHz. Then, the 3D directional patterns (Figure 8a-f) were calculated at the same frequencies.  
As expected, the widths of the directional patterns in the H-plane appear larger than in the E-plane. The difference at a frequency of 3 GHz (Figure 7a) is especially pronounced. At low frequencies (3-5 GHz), a sufficiently high level of side and backward radiation is observed (Figure 7a,b). At high frequencies, the directional patterns are considerably narrowed, and the side and backward radiation levels significantly decrease. Owing to the specific asymmetric antenna geometry in the E-plane, the maximum of the directional patterns at all frequencies in this plane are shifted by 20... 30\({}^{\circ}\) from the intuitively expected direction \(\Theta=0^{\circ}\) normal to the antenna aperture. Thus, the positions of antenna radiation maximum in the E- and H-planes are sufficiently stable in the entire frequency range.  
Figure 5: Frequency dependence of the parameter \(S_{11}\) of the modified antenna.  
Figure 6: Systems of coordinates used to calculate the directional patterns.  
Figure 7: Antenna directional patterns in the E- and H-planes.  
The 3D antenna directional patterns (Figure 8a-f) reflect the specificity of their behavior in the orthogonal planes described above. The most important property of the patterns is the stable direction of the radiation maximum. They are mostly smooth, and only at high frequencies does their irregular behavior become noticeable. As a whole, we can state that the antenna bandwidth appears practically the same as the matching band. Thus, the stability of the total directional pattern is provided with the constructive combination of the modal directional patterns. By the set of parameters--the occupied volume and the working frequency band--the antenna is a good candidate for the element of the antenna  
Figure 8: 3D antenna directional patterns.  
arrays. Moreover, as shown by the experimental investigations, the antenna array can generate effective radiation under conditions of short-pulse excitation [23]. In general, it is accepted to characterize the antenna quality as the element of the antenna array by the size utilization factor (SUF) [15]. It is equal to the ratio of the Chu sphere diameter for the corresponding matching band to the diameter of the sphere circumscribed around the antenna and expresses the proximity of the lower matching frequency to the fundamental Chu limit. In our case, SUF = 0.93.
# mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
## 4 Experiment and Evaluation
### Experimental Setup  
#### 4.2.1 Chirp Parameters  
Millimeter-wave radar uses the principle of FMCW, which requires the setting of FMCW parameters, as shown in Table 2. the starting frequency of the Chirp signal is set according to the operating band of the radar used, and the operating band supported by IWR1642 is 77-81 GHz. the theoretical resolution [11] of imaging is related to the wavelength, and the wavelength is related to the frequency, and their relationship is \(\lambda=c/f\), where \(c\) is the speed of light. Therefore, the higher the frequency, the smaller the wavelength, and accordingly, the smaller the resolution value, the stronger the resolution ability. In addition, according to the description of bandwidth in the literature [30]: \(B=ST_{c}\), the slope of the Chirp signal, and the number of samples, together determine the bandwidth that can be used. From the formula introduced in the literature [30]: \(d_{res}=c/2B\), it is known that the larger the bandwidth, the higher the distance resolution obtained. Since the operating mode used by the radar supports a maximum sampling rate of 6250 ksps, 6200 ksps was chosen, and according to the signal processing knowledge, it is known that the higher the sampling rate, the closer the digitized signal is to the original analog signal.
# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 1 Introduction
This approach is able to capture dependencies at a lower computational cost than wrappers [35] but is highly affected by the biases of a given learning algorithm [36]. Hence, in this study, a Relieff-RF combination model was used in feature selection. Firstly, Relieff was employed to perform feature screening, eliminate low-correlation features and reduce the dimension of the feature space quickly and efficiently. Then, the optimal dataset was selected on the basis of the importance ranking of RF. The method applied in this study combined the advantages of the filtering and embedding approaches. Above all, the aim of this research is to deep mine the spectral and spatial characteristics of Sentinel-2 and determine its optimal feature datasets for lithological mapping by mean of GIS and remote sensing techniques. Lithological mapping was performed in an area in Xitieshan, which is located in the central segment of North Qaidam; this area is also an ultrahigh pressure (UHP) metamorphic belt of Northwest China. Three moderate-resolution satellite datasets, namely, Landsat-8 OLI, ASTER and Sentinel-2, were used to assess the capability of Sentinel-2 for the lithological mapping of the study area. Furthermore, PCA, MNF, ICA and GLCM were adopted to improve the lithological mapping accuracy of different rock units. During feature selection processing, the Relieff-RF model was applied to select the optimal features for lithological mapping.
## 3 Results and Discussion
### Optimization of DES-TF-SPME Conditions
#### 3.3.2 The pH of Samples  
The extraction efficiency of the formaldehyde derivative may be influenced by the pH of the samples. In the pH optimization stage, changes in sample pH ranging from 3 to 9 were done to test the extraction performance of the DES-based sorbent. The obtained results (Figure 7a) indicate that the optimal pH of the samples is 7. The obtained result is confirmed by the research of [PERSON] and [PERSON] [37], who found out that 3,5-diacetyl-dihydrotidine is stable at a neutral pH. At acid conditions, the formation of formaldehyde derivative is reduced while at alkaline pH the derivative is unstable. Furthermore, at alkaline pH, the hydroxyl ions may induce changes in the sorbent material lowering the affinity of the formaldehyde derivative. Based on the obtained results (Figure 6a), in further testing, the samples were brought to pH 7.
## 2 Materials and Methods
### Preparation of carboxymethylcellulose Microspheres Loaded with BTA  
For the preparation of carboxymethylcellulose microspheres and encapsulation of BTA, CMC-Na was dissolved in water, at room temperature, and left under magnetic stirring overnight to give a 5.0% \(w/w\) solution. Then, PVA was dissolved in water at 90 \({}^{\circ}\)C, under magnetic stirring, until complete dissolution and homogenization to give a 5.0% \(w/w\) solution. A BTA alcoholic solution of 5.0% \(w/w\) was also prepared. The feed solution was prepared by mixing, under magnetic stirring until complete homogenization, 25 mL of CMC-Na (5.0% \(w/w\)), 5 mL of PVA (5.0% \(w/w\)) and 20 mL of BTA (5.0%\(w/w\)), resulting in a solid content of 5% (\(w/w\)). The polymer:inhibitor ratio in the feeding solution was kept at 1.25:1.  
The resulting solution was sprayed in a mini spray dryer (Buchi mini spray dryer B-290) using a two-fluid pressurized nozzle (Figure 1). Experiments were carried out at the following operating conditions (Table 1): aspirator setting, 100%; pump rate, 2.5 mL/min; spray flow rate, 440 and 600 L/h; and inlet temperature, 170, 180 and 190 \({}^{\circ}\)C. The atomized powder was blown into the cyclone separator system and collected in the collection vessel at the bottom of the cyclone. Exhaust air was extracted out of the cyclone by a vacuum pump and filtered through a polyester textile filter. For comparison purposes, empty microspheres (CMC-MS) were prepared by the same procedure, but in the absence of BTA in the feed/sprayed solution (results not shown). Microspheres containing encapsulated BTA were named CMC.BTA-MS.  
Figure 1: Schematic illustration of the preparation of CMC.BTA microspheres by a spray drying procedure.
## 2 Experimental
### Characterization  
X-ray diffraction (XRD) spectra were recorded on a powder diffractometer (Rigaku DX-2000, Rigaku Co., Tokyo, Japan), with Cu K\(\alpha\) as the X-ray source (\(\lambda\) = 1.5418 A); the 2-theta range was 20\({}^{\circ}\) to 80\({}^{\circ}\), with a step width of 0.01 and count time of 4 s/step. The morphology of samples was characterized by a scanning electron microscope (SEM, JEOL-5900 LV, Peabody, MA, USA). The Brunauer-Emmett-Teller (BET) surface area was measured on a Micromeritics ASAP 2020 (Norcross, GA, USA) adsorption apparatus, using N\({}_{2}\) adsorption, at \(-\)196 \({}^{\circ}\)C. The chemical states of the ZnO samples were characterized by X-ray photoelectron spectroscopy (XPS, PHI 5000 VersaProbe, ULVAC-PHI, Chigasaki, Japan). The electron spin state and the structure of the surface of the ZnO NPs were observed by an electron paramagnetic resonance (EPR) spectrometer (Bruker EMX-10/12, Karlsruhe, Germany), operating in the X-band frequency (_f_\(\approx\) 9.774 GHz). Photoluminescence (PL) measurements were conducted in ambient conditions, with a RF-5310 pc fluorescence spectrophotometer (Shimadzu, Kyoto, Japan), with an excitation wavelength of 325 nm.
# Effects of Adding Alkali Metals and Organic Cations to Cu-Based Perovskite Solar Cells
###### Abstract  
First-principles electronic band calculations were used to investigate the effects of alkali metals and organic cations added to Cu-based perovskite solar cells. The copper d-orbital band was slightly above the valence-band maximum and functioned as an acceptor level for carrier generation. Excitation from iodine p-orbitals and copper d-orbitals to alkali metal s-orbitals could suppress carrier recombination and promote carrier transport. Experimental solar conversion efficiencies increased after adding both Cu and Na, in agreement with the calculations. Total-energy calculations indicated that the perovskite crystal stability increased with the addition of ethyl ammonium, although the total energy decreased with the addition of Cu and Na.  
copper; sodium; ethylammonium; first-principles calculations; perovskite; solar cell +
Footnote †: journal: Applied sciences
## 2 Dynamic Lognormal Shadowing Framework
### Fade Durations and Level Crossings
For any practical application of interest in wireless communications as in diversity schemes, multi-hop networks etc., a domain can be identified inside of which the values of the vector process permit the network operation, whereas out-crossings of this domain correspond to transitions to outage periods. For example for the case of selection combining (SC) in a dual diversity scheme the shaded area in Figure 1 corresponds to outage, whereas inside the domain \(D\) operation is assured. The values \(X_{th1},X_{th2}\) correspond to threshold values below which the receiver cannot decode reliably. In Figure 2 the corresponding outage and operation domains are shown for the case of a regenerative dual-hop link. The AOD is expressed in terms of the out-crossing rate as:  
\[d=\frac{P(\mathbf{Z}_{t}\in D^{c})}{v^{+}} \tag{35}\]  
where \(D^{c}\) is the complement of \(D\). The study of LCR through an appropriately filtered process also gives insight to another aspect of the proposed model in relation to other approaches of the shadowing modeling by human shadowing. In [18; 19] the shadowing effect is modeled by a finite-state Markov chain, where transition probabilities are derived. The shadowing event length is modeled as exponentially distributed and the appearance of obstacles as Poisson distributed. The model presented here may be considered also consistent with this approach, as it is well known that the level crossing of a high level of a Gaussian process is approximated by the Poisson distribution. The calculation of multi-dimensional LCR can be found in Appendix A. Another application of the multi-dimensional LCR proposed here is the calculation of the average times per meter traveled that a moving mobile must switch base stations in order to always be served by the base station with the least path loss. As stated in [10] this is in fact, the basis for any good hand-off algorithm and can serve as a benchmark for comparison and evaluation of practical algorithms. The essential difference here with [16] is the novel application of multidimensional level crossings and the flexibility of dealing with any kind of filtering of the initial Markov process, instead only considering moving averages. Figure 1: Shaded area of outage for a dual-diversity scheme for selection combining (SC) method. Figure 2: Shaded area of outage for a regenerative dual-hop link.