esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Selection of Spectral Range  
According to the standard absorption spectrum in the HITRAN database [36], methane, ethylene, and ethane have absorption signals in 3344-3350 nm, and this wavelength range can be covered by a single ICL laser. Therefore, this wavelength range can be selected for the simultaneous detection of the three gases. Methane has sharp absorption peaks between 3345.56 nm and 3345.83 nm, which are one order of magnitude stronger than those of ethylene and ethane. Ethane has absorption in the whole range of this band, with two absorption peaks at 3344.40 nm and 3348.19 nm. The absorption of ethylene is also dispersed over the whole band, and the maximum absorption at 3346.35 nm has obvious cross-talk with ethane and methane. Thus, appropriate measures need to be taken for the three-component mixture to minimize cross-interference.
## 3 Mathematical Modeling Formulation
### Mathematical Formulation of Technical Objectives
#### 3.4.4 Voltage Sensitivity Index  
The Voltage sensitivity index (VSI) holds significant importance in power distribution networks and microgrids, serving as a crucial indicator of system security and power quality. It assesses the network's capability to uphold stable voltage levels amidst escalating consumer demand, thereby reflecting the system's overall safety. This research endeavors to enhance network security by elevating the VSI to a pivotal objective. The concept of VSI is designed to illustrate a decline as the system load intensifies, ultimately converging towards zero as the network approaches a critical state of collapse and reaches its maximum sustainable load capacity. This study aims to refine the understanding and application of the VSI within energy management strategies to fortify network resilience and reliability under varying operational conditions [26].  
\[\text{Obj}_{6}=\text{VSI}=\sum_{i=1}^{N}\left(\left|V_{i}\right|^{4}-4 \left|V_{i}\right|^{2}\left(R_{\text{eq},i}P_{\text{Lm},i}+X_{\text{eq},i}Q_{ \text{Lm},i}\right)-4\left(X_{\text{eq},i}P_{\text{Lm},i}-R_{\text{eq},i}Q_{ \text{Lm},i}\right)^{2}\right) \tag{26}\]In Equation (26), the variable \(|V_{i}|\) represents the voltage magnitude at bus i, whereas \(R_{\text{eq},i}\) and \(X_{\text{eq},i}\) stand for the total resistance and reactance of the line. Furthermore, the symbols \(P_{\text{LM},i}\) and \(Q_{\text{LM},i}\) correspond to the aggregate active and reactive power of all nodes, respectively. This equation encapsulates the interplay between voltage levels, line characteristics, and power flow distribution within the network, highlighting the intricate relationship between these key parameters in ensuring efficient and stable operation.
## 3 Results
### Icephobic Properties
#### 3.5.1 Ice Adhesion Strength  
The ice adhesion determines the force needed for ice to break away from the surface. The lower the ice adhesion value, the more the surface is icephobic. The reference sample without chemical modification achieved the IA value of 178 \(\pm\) 33 kPa. The samples modified with polysiloxanes obtained IA values in the range from 55 kPa to 118 kPa; so, all chemical modifications caused a decrease in the value of this parameter. The MOD4 sample showed the highest decrease, as the reduction in the IA values was as much as 69% compared to the IA of the reference sample. The MOD3 sample also achieved a very large decrease in IA values similar to the MOD4 sample, as the reduction was equal to 62% compared to the IA of the unmodified coating. The other modified MOD1 and MOD2 samples also recorded positive results, as the IA values for these samples were reduced by 34% and 35%, respectively, compared to the reference value. Another important aspect is that the MOD3 and MOD4 samples recorded IA values below 100 kPa. It is considered that surfaces exhibit low ice adhesion when the detachment force for ice is lower than 100 kPa. From such surfaces, ice is detached under the influence of natural forces such as wind, gravity, or ambient vibration [30]. The effectiveness of improving the anti-icing properties by using the chemical modification of the polymer matrix with double-functionalized polysiloxanes was proven in a work [31], achieving an IA reduction of 51% compared to the IA of the unmodified epoxy resin.
## 2 Materials and Methods
### Performance Evaluation and Model Criteria  
To assess the accuracy and efficacy of the model predictions, the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used. The reliability of the suggested models and the effect of mix proportions on the concrete compressive strength were investigated using the nonlinear and M5P models, which were evaluated using several common assessment criteria. Their equations are as follows:  
\[R^{2}=\left[\frac{\sum_{p=1}^{p}(y_{i}-\overline{y})(x_{i}-\overline{x})}{ \sqrt{\left[\sum_{n=1}^{n}(y_{i}-\overline{y})^{2}\right]\left[\sum_{p=1}^{p}( x_{i}-\overline{x})^{2}\right]}}\right]^{2} \tag{3}\]  
\[RMSE=\sqrt{\frac{\sum_{n=1}^{n}(y_{i}-x_{i})^{2}}{n}} \tag{4}\]  
\[MAE=\frac{\sum_{p=1}^{p}(y_{i}-x_{i})|}{n} \tag{5}\]  
\(yi=\) laboratory-tested values; \(xi=\) estimated value; \(\overline{y}=\) average of \(yi\); \(\overline{x}=\) average of \(xi\), and \(n\) is the number of datasets.
## 5 Performance Evaluation in Animal Trial
### Ex-Vivo Testing  
In this experiment, the capsule prototype is inserted inside a section of pig's small intestine; the data logger is placed outside. The experimental setup is shown in Figure 10. The capsule then captured images of intestine and sent data to the data logger unit. The data logger stores the image data and also shows reconstructed image on LCD. The image data are then transferred to computer for reconstruction. Several captured lossless WLI and NBI images are shown in Figures 11 and 12 namely with achieved CR. The received images have good quality and detailed features of the mucosa are visible in the images. The CR of the images in the real experiments is similar to the results found during simulation.  
During the experiment, the distance between the capsule and the data logger is varied from 0.3 m to 1 m and images are transmitted successfully. As the data logger is wearable and it is generally worn at one side of the belly, the distance between a swallowed capsule and data logger will be near 0.3 m for human endoscopy.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Design Platform** & **Power (mW)** & **Buffer Memory** & **FPS** & **Mode** & **Compression** & **Validated by** _In-Vivo_ \\  & **AsIC 180 nm** & 14.92 & Yes & - & WLI & Lossy & No \\
[24] & ASIC 180 nm & 0.80 & 2.19 KB & 2 & WLI & Lossy & No \\
[31] & FPGA 65 nm & 10 1 & Yes & 19 & WLI & Lossy & No \\
[15] & FPGA 65 nm & 7 1 & 84 Kb & 24 & WLI & Lossy & No \\ Proposed & FPGA 65 nm & 1.63 & No & 50 2 &
\begin{tabular}{c} WLI \\ and NBI \\ \end{tabular} & Lossless & Yes \\ \hline \hline \end{tabular}  
* Power of the FPGA core, only compressor power not reported; 2 Using 20 Mbps application throughput RF transceiver [32].  
\end{table}
Table 9: Comparison of compressor with other works.
## 2 Method
### System Description
The probability of BER can be represented as [24]:  
\[\Pr(e|S_{1},S_{2})=1-\left\{1-\frac{1}{2}erfc\left[\frac{1}{\sqrt{2}}\left( \sqrt{\frac{E_{s}}{N_{o}}}\sqrt{\frac{\sum\limits_{f=1}^{N_{F}}\sum\limits_{i= 1}^{2}\sum\limits_{j=1}^{2}\alpha_{ij,f}{}^{2}}{2}}\right)\right]\right\}^{2}, \tag{5}\]  
where \(\alpha_{ij,f}\) is the corresponding receiving amplitude for \(Sj\) at receiver antenna \(i\) with rake finger \(f\), and \(erfc\) is the complementary error function. Making the generalization for the case with the ROD of size 8 and rake receiver, the BER formula is as follows:  
\[\Pr(e|S_{1},S_{2},\ldots,S_{7},S_{8})=1-\left\{1-\frac{1}{2}erfc\left[\frac{ 1}{\sqrt{2}}\left(\sqrt{\frac{E_{s}}{N_{o}}}\sqrt{\frac{\sum\limits_{f=1}^{N_{ F}}\sum\limits_{i=1}^{8}\sum\limits_{j=1}^{8}\alpha_{ij,f}{}^{2}}{32}}\right) \right]\right\}^{8}. \tag{6}\]  
Alternatively, the outage probability can also be used for evaluating the system. The bit error rate (BER) is an essential factor when calculating the outage probability in an MIMO-UWB system. The receiver is defined as the \"outage point\" as its BER > \(10^{-6}\). The definition of the outage probability is as follows [25; 34]:  
\[\text{Outage probability}=\frac{\text{Number of outage points}}{\text{Total number of receiving points}} \tag{7}\]
## 2 Materials and Methods
### Statistical Analysis  
Statistical procedures and computations were conducted using SPSS (version 25.0; IBM SPSS Inc, Chicago, IL. USA). Data are presented as mean and standard deviation (SD). The coefficients of variation (CV) for every phase of the BP exercise (mean phase duration) were also calculated. Normality and homogeneity of data were checked by performing the Shapiro-Wilk and Levene tests, respectively. Then, a repeated measure analysis of variance (ANOVA) was conducted to analyse between-interval differences in all dependent variables for every phase of the BP exercise. Partial eta squared (np2) was used as effect size for multiple comparison ANOVA analysis. After that, the Bonferroni post hoc test was applied for pairwise comparisons. Hedges's g effect size was also calculated for pairwise comparisons. Effect size thresholds for [PERSON]'s g were determined as follows: \(\leq\)0.2, trivial; \(>\)0.2, small; \(>\)0.6, moderate; \(>\)1.2, large; \(>\)2.0, very large; and \(>\)4.0, nearly perfect [40]. Significant differences were considered for \(p\leq 0.05\).
## 3 Results
### Photoreduction of CO\({}_{2}\) over Deposited Photocatalysts
Indeed, examples of titanium dioxide are very frequent in the literature, but few cases of the immobilization of modified nanocatalysts or surface modification have been reported, and they may not be as scalable or flexible as the synthesis discussed here [55; 56; 57]. In addition, the recipes proposed here allow for the identification of an anchorage that lasts even in a demanding environment, such as high temperatures, wide pH variation, or a stirred liquid phase. Multiple reviews can be found in the literature reporting relatively high productivity and selectivity, often using complex and expensive photocatalysts [58; 5]. For example, some authors have reported a very high HCOOH productivity equal to 1100 mmol/kgcat h using a mononuclear Ru complex deposited over C\({}_{3}\)N\({}_{4}\) in the presence of \(N\),\(N\)-dimethylacetamide and triethanolamine that acted as electron donor [59]. This HCOOH productivity, with the same hole scavenger conditions, can be further increased by using a binuclear Ru complex/Ag-loaded C\({}_{3}\)N\({}_{4}\), which allows for the achievement of a productivity equal to 2115 mmol/kgcat h [60]. Despite these interesting results, the main problem with these and many other catalysts reported in the literature relate to their high costs, since they use precious and rare metals such as ruthenium. Furthermore, they show relatively low productivity, and they have only been tested at a lab scale and not on a semi-pilot scale. With the high-pressure setup showed in this and in previous work, after optimizing the immobilization of P25 over glass tiles, and after developing a glass support that was able to withstand the harsh conditions present inside the photoreactor, we were able to achieve an HCOOH productivity of 20 mol/kgcat h, which is unprecedented for immobilized catalysts. The low cost of the P25 and AgNO\({}_{3}\) used for the deposition, together with the high HCOOH productivity obtained and the elimination of the catalyst separation problem, demonstrates the feasibility of continuous photoreduction in the liquid phase.
###### Abstract
* When these approaches were applied to cervical cancer classification, skin lesion classification, and pressure ulcer segmentation and the results were compared to those of state-of-the-art approaches, for cervical cancer classification, they improved the sensitivity by 5.4%; for skin lesion classification, they improved the accuracy by 8.7%, the precision by 28.3%, and the sensitivity by 39.7%; and for pressure ulcer segmentation, they improved the accuracy by 1.2%, the intersection over union (IoU) by 16.9%, and the Dice similarity coefficient (DSC) by 3.5%.
## 3 Results
To assess the coherence among the different models, we computed the correlations of their probabilities of being mown parcels, depicted in Figure 4. The observed positive correlations suggested a similar signal across the various models, even though they were all executed using a reference dataset created based on the UAC outputs. However, these correlations did not reach 1, indicating some discrepancies between the labels assigned to parcels by the different algorithms. The highest similarity was noted between the PLS-DA models developed from the week_std k-means and week_fft k-means datasets. As shown in Figure 4, PLS-DA models created from datasets generated through the IF and k-means techniques did not exhibit strong convergence; their correlation was generally below 0.80 in most cases. This contrast is further highlighted when comparing the reference datasets used to execute the PLS-DA. Only 1858 parcels/year were consistently labeled as mown by these two methods, representing 11.56% in the k-means dataset and 6.76% in the IF dataset. To evaluate the performance of the models, external validation was required as the reference values were established through unsupervised learning. According to the validation performance detailed in Table 8, PLS-DA demonstrated superior performance, particularly when utilizing datasets derived from k-means, with a validation accuracy  
Figure 4: Correlogram of the probability of being a mown parcel following the 6 partial least squares discriminant analyses performed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Dataset** & **N** & **Calibration** & **Validation Accuracy** \\  & & **Components** & **Accuracy** & **(N = 68)** \\ \hline K-means & week\_std & 4 & 0.99 \(\pm\) 0.0004 & 0.48 \\  & week\_fft & 7 & (N = 42,784) & (N = 33) \\  & yearstats & 6 & 0.99 \(\pm\) 0.0004 & 0.66 \\  & yearstats & 6 & (N = 42,784) & (N = 37) \\ \hline Isolation forest & week\_std & 7 & 0.95 \(\pm\) 0.0006 & 0.18 \\  & week\_fft & 9 & (N = 277,547) & (N = 12) \\  & yearstats & 6 & (N = 277,547) & (N = 21) \\  & yearstats & 6 & 0.94 \(\pm\) 0.0006 & 0.40 \\  & & & (N = 277,547) & (N = 27) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Prediction performance of the partial least squares discriminant analysis to predict seemingly mowing events. reaching up to 0.66, a criterion for discriminating the best model. An indirect validation approach involves applying the developed PLS-DA models to the entire dataset to observe the number of detected mown parcels. Table 9 presents the count of parcels labeled as mown by the best PLS-DA algorithm for each studied year. The probability threshold set to label a parcel as mown was defaulted to 0.5. However, testing alternative threshold values resulted in noticeable variations in the number of parcels tagged as mown, as shown in Table 9. Consequently, fixing this threshold would be necessary. Unfortunately, the available validation set did not provide the number of mowing events associated with each parcel.
## 2 System Characteristics
### The Theoretical Analysis of M-PPM Photon-Counting Communication System
\tag{13}\]  
where  
\[\psi\left(i,\lambda_{\rho}\right)=\frac{\lambda^{i}(t-\rho\tau)^{i}}{i!}e^{- \lambda(t-\rho\tau)} \tag{14}\]  
In particular, the mean and variance of the photocount distribution are [40]:  
\[\mu_{K}=(\rho_{\max}-1)-\sum\limits_{\rho=0}^{\rho_{\max}-2}\sum\limits_{i=0}^ {\rho}\psi\left(i,\lambda_{\rho+1}\right) \tag{15}\]  
\[\sigma_{K}^{2}=\sum\limits_{\rho=0}^{\rho_{\max}-2}\sum\limits_{i=0}^{\rho} \left(2\rho_{\max}-2\rho-3\right)\psi\left(i,\lambda_{\rho+1}\right)-\left( \sum\limits_{\rho=0}^{\rho_{\max}-2}\sum\limits_{i=0}^{\rho}\psi\left(i, \lambda_{\rho+1}\right)\right)^{2} \tag{16}\]  
The symbol error probability (SER) of the M-PPM system is therefore [42]:  
\[P_{s}=\frac{1}{M}\sum\limits_{\rho=0}^{\rho_{\max}}L(\rho)\Big{[}Mf_{Y|X}(\rho |0)-F_{Y|X}(\rho|0)^{M}+F_{Y|X}(\rho-1|0)^{M}\Big{]} \tag{17}\]  
Combined with Equation (14), where  
\[f_{Y|X}(\rho|1)=f\left(\rho,\lambda_{\rho}^{s}+\lambda_{\rho}^{n}\right) \tag{18}\]  
\[f_{Y|X}(\rho|0)=f\left(\rho,\lambda_{\rho}^{n}\right) \tag{19}\]  
\[F_{Y|X}(\rho|0)=\sum\limits_{i=0}^{\rho}f_{Y|X}(\rho|0) \tag{20}\]  
\[L(\rho)=\frac{f_{Y|X}(\rho|1)}{f_{Y|X}(\rho|0)} \tag{21}\]  
Once the M-PPM symbol is detected, the symbol information will be demapped into log\({}_{2}\)(M) binary bits, and the resulting bit error ratio (BER) is [43]:  
\[\begin{split}& P_{b}=\frac{M}{2(M-1)}P_{s}\\ &=\frac{1}{2(M-1)}\sum\limits_{\rho=0}^{\rho_{\max}}L(\rho)\left[ Mf\left(\rho,\lambda_{\rho}^{s}\right)-\left[\sum\limits_{i=0}^{\rho}f \left(\rho,\lambda_{\rho}^{n}\right)\right]^{M}+\left[\sum\limits_{i=0}^{ \rho}f\left(\rho-1,\lambda_{\rho-1}^{n}\right)\right]^{M}\right]\end
###### Abstract  
The regions in China that intersect the 400 mm annual precipitation line are especially ecologically sensitive and extremely vulnerable to anthropogenic activities. However, in the context of climate change, the response of vegetation Net Primary Production (NPP) in this region has not been scientifically studied in depth. NPP suffers from the comprehensive effect of multiple climatic factors, and how to eliminate the effect of interfering variables in the correlation analysis of NPP and target variables (temperature or precipitation) is the major challenge in the study of NPP influencing factors. The correlation coefficient between NPP and target variable was calculated by ignoring other variables that also had a large impact on NPP. This increased the uncertainty of research results. Therefore, in this study, the second-order partial correlation analysis method was used to analyze the correlation between NPP and target variables by controlling other variables. This can effectively decrease the uncertainty of analysis results. In this paper, the univariate linear regression, coefficient of variation, and Hurst index estimation were used to study the spatial and temporal variations in NPP and analyze whether the NPP seasonal and annual variability will persist into the future. The results show the following: (i) The spatial distribution of NPP correlated with precipitation and had a gradually decreasing trend from southeast to northwest. From 2000 to 2015, the NPP in the study area had a general upward trend, with a small variation in its range. (ii) Areas with negative partial correlation coefficients between NPP and precipitation are consistent with the areas with more abundant water resources. The partial correlation coefficient between the NPP and the Land Surface Temperature (LST) was positive for 52.64% of the total study area. Finally, the prediction of the persistence of NPP variation into the future showed significant differences on varying time scales. On an annual scale, NPP was predicted to persist for 46% of the study area. On a seasonal scale, NPP in autumn was predicted to account for 49.92%, followed by spring (25.67%), summer (13.40%), and winter (6.75%).  
climate change; 400 mm annual precipitation; net primary production; spatial and temporal variation; second-order partial correlation; Hurst index +
Footnote †: journal: Journal of Environmental Research and Public Health
# Planar Array Failed Element(s) Radiation Pattern Correction: A Comparison
## 2 Design Methodology  
The design scheme presented in this paper is divided into two parts; the first part is developing a planar array model with an 8 \(\times\) 8 element array. Secondly, four different optimization processes were applied to the planar array model, described in Section 2.2. A flowchart of the whole process is shown in Figure 1.  
This paper illustrates a suitable optimization method for failed planar array radiation patterns where the optimization is done in MATLAB through simulation. All the simulation was done with a 64-bit operating system that contains an Intel(R) Core (TM) i7-4790 CPU processor and 16 GB installed memory. This simulation is for research purposes only. To be implemented in real-time, the optimization can be programmed in C language and processed in real-time on Digital Signal Processor (DSP) embedded processor platform, which will make the process faster. This simulation has the limitation of computer capability and software limitations.
## 6 Proposed Method
### System Model
#### 6.1.2 Energy Consumption Model  
The energy consumption model is explained in the Figure 8 below [56].  
The consumed energy of the transmitting node of \(l\) bits data to cluster head is mathematically expressed in Equation (13).  
\[\text{E}_{\text{non-CH}}=l\ ^{*}\ \text{E}_{\text{energy}}+l\ ^{*}\ \text{E}_{\text{tm}}\text{d}^{2}_{\text{cn-CH}} \tag{13}\]  
where d\({}_{\text{cn-CH}}\) = Child node to CH distance  
Now the consumed energy of the cluster head CH is expressed in (14).  
\[E_{CH} =\ \left(l\Big{(}\frac{n}{c}-1\Big{)}*E_{energy}+\ \frac{n}{c}*\ E_{con} \right)+\ E_{RX}(l,d)+\ E_{TX}(l,d_{CH-BS}) \tag{14}\]  
where n is the number of alive nodes in the network, c the number of clusters in the network, E\({}_{\text{RX}}\) is the energy consumption of the cluster head, and d\({}_{\text{CH-BS}}\) is the base station to cluster head distance.