esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Experiment Results
#### 5.4.3 Dynamic Experiment and DEP Model  
In the area covered by the signal, the UWB tag is fixed on an AGV on a production line in an intelligent manufacturing lab in Figure 11. The ID of the tag is DAF3, and the distance of the automatic guided vehicles (AGV) is set at 10 m. The positioning results of the trolley after walking are shown in Figure 17. Interference from various metal obstacles and the walking of stuff or movement of AGV demonstrated a significant impact on positioning accuracy. The maximum deviation was 1.6 m.  
In an independent UWB positioning experiment, errors of \(E_{ME,NLOSI}\), \(E_{AI}\), and the final error of \(e_{DEPM}\) were measured in Figure 18. Then, we repeated this experiment fifty times in the same environment as the lab. Further, we calculated the error weights \(w_{1}\) and \(w_{2}\) in the DEP model using the mean measured errors \(E_{ME,NLOSI}\) and \(E_{AI}\) based on these fifty experiment results. The final data show that the main influencing factors were the multipath effect and non-line-of-sight, with weight a coefficient of 0.975. However, the weight coefficient of atmospheric interference accounted for only 0.00025.  
Figure 16: The relation between 0–4 m distance and refraction error.  
Figure 17: UWB positioning results (horizontal axis unit: cm; vertical axis unit: cm).  
The multipath effect (ME), and non-line-of-sight interference (NLOSI) in Figure 18 were obtained by MATLAB calculation based on the measured data in an experiment. The atmospheric interference (AI) was obtained by MATLAB simulation according to reference [46]. The three errors were accumulated and summed based on variable weights, and the total error is calculated according to Equation (21).
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 4 Application Development with the EMSOnto Approach
### Controller Inconsistencies Identification  
At this stage, all the required knowledge is gathered through EMS-templates. Further analysis enables consistency checking and inference of conflicts. Consistency checking determines if the \(ABox\) is an instance of the \(TBox\). Once this is accomplished, inferences within the \(ABox\) are discovered due to axioms defined in the \(TBox\) and also a set of rules as depicted in Table 4. This section presents the inconsistencies addressed by EMSOnto, conforming to the requirement R3.  
A deep study of conflicts identification and handling is carried out in [37]. A classification of conflicts types (\(C_{I}-C_{VI}\)) is formally defined and evaluated there. The encoding and formulation of conflict detection are defined by DL queries. The ontology defined in [37] is considered in the EMS-ontology design. As a consequence, EMS-ontology is able to respond to conflict identification and their handling. To cover the mentioned features, an extension of EMS-templates targeting knowledge especially important to conflict detection is carried out. An extract of those EMS-templates is presented in Table 8.  
The example in Table 8 states assertions regarding a control goal and its environment in order to investigate the conflict \(C_{I}\). To this end, the following assertions are included: \(\texttt{manipulate}\{(puc1,Cg1)\), \((puc2,Cg2)\}\), \(\texttt{Control}(x)\), \(\texttt{Manipulated}(y)\), \(\texttt{hasFctVar}\{(Cg1,x),(Cg2,y)\}\) and \(\texttt{IsVarLinkVar}(x,y)\). Applying the axioms regarding the role manipulate and \(\texttt{hasFctVar}\) defined in Table 4, the inference \(\texttt{manipulate}(puc1,Cg2)\) is raised. Inferred knowledge is highlighted in Table 8.  
The aforementioned knowledge is enough to investigate the conflict \(C_{I}\) by executing the query \(Q_{CI}\) given by the axiom: \(ControlGoal\cap 2 Manipulated^{-}\)_-_PUC_, which investigates control goals manipulated or affected by at least two different UCs. The execution of that query concludes that ControlGoal[Cg2] and ControlGoal[Cg1] are in conflict with type \(C_{I}\).
## 4 Sensitivity Analysis for Single Factor
### The Effect of Partial TIL Thickness  
One important factor influencing the cooling effect is the thickness of the insulation layer. In this section, the insulation effect was studied when the PTI layer thicknesses were 0.01 m and 0.02 m. According to the similarity criterion, it is equivalent to using insulation layers with 0.1 and 0.2 m thicknesses in engineering practice. The work area of the tunnel is limited during the excavation process. In the case of thermal insulation baffles on both sides, the thickness of 0.2 m is already the limit that can be occupied. The ATWA with a PTI layer with different thicknesses is shown in Figure 9a.  
When the insulation layer thickness is increased from 0.01 m to 0.02 m, the stable temperature of the working area in the tunnel decreases by 0.8 \({}^{\circ}\)C. As shown in Figure 9b, when the insulation layers of different thicknesses are arranged, the cross-section temperature distributions at 0.1 m and 0.2 m above the tunnel floor are drawn, respectively. In the tunnel with a thicker TIL, the low-temperature area covers a larger area, and the temperature of each monitoring location is closer, which means that increasing the thickness of the insulation layer can make the temperature distribution in the tunnel more uniform. By comparing the temperature changes in the same section after thickening the PTI layer, it can be concluded that the main temperature improvement area is in the center of the tunnel. Although the thickness of the insulation is doubled, the temperature reduction is very limited. If it is not necessary for the project, the method of thickening the PTI layer to improve the cooling effect should be used as little as possible.
# Impacts of Heat and Drought on Gross Primary Productivity in China
## 3 Method
### Drought Indices and Levels  
A large number of indices have been developed to quantify and analyze drought [62; 63; 64]. The SPI [65] and SPEI [66] are two commonly used drought indices with multiscale characteristics that are suitable for different periods and regions [67; 68]. The SPI value refers to the number of standard deviations by which the observed precipitation anomaly deviates from the long-term mean precipitation [65]. The raw precipitation data are firstly fitted to a gamma or a Pearson Type III distribution, and then they are transformed to a normal distribution. Therefore, the mean SPI for the location and desired period is zero. Positive SPI values indicate rainfall surplus, whereas negative SPI values represent rainfall deficit. The SPI is based on precipitation alone and does not deal with the evapotranspiration. SPEI is an extension of the SPI and takes both precipitation and potential evapotranspiration into account when determining drought [69; 70]. Thus, SPI can capture the impact of increased temperatures on water demand. In this study, we used both SPI and SPEI. To match the  
Figure 2: Technical flowchart of the study. CRU TS: University of East Anglia Climatic Research Unit (CRU) Time-Series (TS); GPP: gross primary production; SPI: standardized precipitation index; SPI: standardized precipitation evapotranspiration index; T: yearly average of the near-surface daily mean temperature; Tmax: the maximum of the monthly average daily maximum temperature of each year.  
time scales of GPP and temperature series, 12-month time scale SPI and SPEI (hereinafter referred to as the SPI and SPEI) were calculated. A 12-month scale of the SPI/SPEI indicates the meteorological dryness (or wetness) of the previous 12 months as compared to historical observations. The potential evapotranspiration in the SPEI was calculated according to the FAO (Food and Agriculture Organization of the United Nations)-56 Penman-Monteith method [71]. According to the SPI/SPEI drought classification standard, the value ranges of mild, moderate, severe, and extreme drought are \((-1,\,-0.5]\), \((-1.5,\,-1]\), \((-2,\,-1.5]\), and \((-\infty,\,-2]\), respectively.
# Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder
###### Abstract  
Fengyun-3E, which is equipped with MicroWave Humidity Sounder 2 (MWHS-2) for detecting both temperature and humidity, is China's latest polar orbiting meteorological satellite and China's first satellite in early-morning orbit. The observation bias and observation error characteristics of MWHS-2 are evaluated by using ERA-5 as the background field. The results show that the biases range from \(-4\) to \(2\) K, and the observation errors are within \(1.5\) K except for the window channels. A further analysis of the dependence on the scanning angles indicates that observation errors for the window channels and the 118-GHz channels increase with decreasing scanning angles. Observation errors of the window channels and water vapor channels are also latitudinally dependent, and the maximum errors in the high latitudes reach \(2.0\) K, while those in the tropical regions are approximately \(0.8\) K. In addition, the observed brightness temperature of FY-3E MWHS-2 is accompanied by striping noises along the track and high-frequency oscillation noises along the scanlines. The noise mitigation results show that both noises are approximately \(0.2\) K.  
Fengyun-3E, MWHS-2; data evaluation; noise mitigation Article
## 4 MICMAC Analysis for Enabler's Classification  
MICMAC analysis was used to identify and categorize the enablers of PFSC using the driving and dependency values obtained from TISM [40; 41; 42]. The primary aim of this section is to identify the \(\mathrm{D_{R}}\) and \(\mathrm{D_{C}}\) values. The driving values were calculated by adding each row of the enablers using the FRM. Similarly, the dependency values were calculated by adding each column of the enablers. Table 6 shows the results thus obtained. After these values were calculated, they were categorized into four types: autonomous enablers, driving enablers, independent, and enablers linkage enablers [2].  
* Autonomous enablers: These enablers are represented in the first quadrant (bottom left corner). These enablers have low driving and dependence values since they do not have enough relationships with others.
* Driving enablers: These are also called independent enablers. Represented in the second quadrant (top left corner), they have a high driving value and low dependency values. Therefore, they are categorized as the most influential enablers of the PFSC.
* Linkage enablers: These enablers are represented in the third quadrant (top right corner) and have high driving and dependency values. Linkage enablers have a lot of command over the PFSC, but they also rely on other factors.
* Dependent enablers: These enablers are in the fourth quadrant (bottom right corner) and have high dependency and low driving values since other enablers affect them.  
Figure 4 illustrates the enablers classification using the MICMAC analysis. The enablers in the upper half of Figure 4 can be labeled as essential enablers as these play a critical role in the PFSC. From Figure 4, it is evident that most of the enablers are classified as driving and linkage enablers.  
Figure 3: Diagram of perishable food supply chain enablers.
# Stability Analysis in Multi-VSC (Voltage Source Converter) Systems of Wind Turbines
## 6 Conclusions  
In this paper, a modular nonlinear state-space model of a multi-VSC power system was developed, where one VSC corresponded to a wind turbine of a wind farm. The VSCs were connected to the grid through an LC Filter, and state-space submodels in the dq reference frame for the PLL, the CC and the AVC as well as the digital time delay were developed. The novel methodology used for obtaining the equilibrium points was generalized and based on the number of VSCs, as the obtained formulas of the steady-state currents could be used for several cases of different control and system parameters, and their accuracies were proven by the time domain results in the steady state. The eigenvalue analysis with the eigenvalue traces, as well as the participation factor analysis, showed the impact of each VSC's controller in the system's stability in the high- and low-frequency range. The time domain simulations, as well as the FFT analysis when the system started to become unstable, provided results very close to the corresponding eigenvalue-based stability analysis. Therefore, the developed state-space model can be used as a holistic tool that describes quite accurately the small-signal interactions on the grid side of a system with multiple wind turbines and for different control and system parameters.  
Conceptualization, D.D., X.W. and F.B.; methodology, D.D.; software, D.D.; validation, D.D.; formal analysis, D.D.; investigation, D.D., X.W. and F.B.; resources, X.W. and F.B.; data curation, D.D.; writing--original draft preparation, D.D.; writing--review and editing, D.D., X.W. and F.B.; visualization, D.D.; supervision, X.W. and F.B.; project administration, D.D.; funding acquisition, X.W. and F.B. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 861398.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Test Results
### Deformation Capacity  
The maximum deformations of the specimens are listed in Table 5. Overall, the specimens with an outer restrainer of 9 mm thickness exhibit higher strengths and better deformation capacities than those with an outer restrainer of 6 mm thickness. Considering only the completed cycles, no improvement was observed on account of the application of the inner restrainer. However, in the case of the maximum deformation for the entire cycle, an increase was confirmed in specimens R-T6S4 and R-T6S4-G specimens with an outer restrainer of 6 mm thickness, and R-T6S4-G with a partially thickened unbonding material showed additional deformation capacity under compression.  
Two design standards, KDS 14 31 60 and ANSI/AISC 341-16, stipulate cyclic tests to qualify BRBs [1; 19]. Both standards require that the cumulative inelastic deformation of a BRB achieve a minimum of 200 times the yield deformation, during which fracture, instability, and joint failure do not occur. The yield deformation, cumulative deformation, and their ratios for each specimen are listed in Table 6; all specimens met the cumulative inelastic deformation requirement. In particular, the specimens using an outer restrainer of 6 mm thickness show an improvement of approximately 24% in the cumulative inelastic deformation capacity upon application of the inner restrainer.
## 3 Results and Discussion
### Physical Properties
#### 3.1.2 Ultrasonic Pulse Velocity  
The ultrasonic pulse velocity (UPV) test was conducted to assess the porosity of the CEB and its capacity to absorb sound. Table 4 shows the effect of fiber incorporation  
Figure 10: Apparent density of composite specimens at different fiber replacement levels.  
on the UPV. Figure 11 also presents the variation of UPV as a function of JF content for different percentages of cement stabilization. It was clearly observed that UPV decreased significantly when the JF content in the composite increased. It is important to note that the lowest value was observed for the sample with a content of 0.2% JF and 4% cement stabilization, which exhibits a reduction of about 36% compared to the non-filled sample with 12% cement stabilization. The variation between the highest and lowest values of UPV is approximately 40%, 28% and 20% for cement contents of 4%, 8% and 12%, respectively. These findings indicate that the composite filled with JF has the ability to decrease the speed of sonic waves' propagation. Therefore, the addition of Juncus fibers improved the capacity of CEB to absorb ultrasonic waves and promote the damping vibration, which allows us to obtain a high level of sound insulation. These results could be attributed to the increase in the composite's porosity by adding vegetable fibers, since the speed of wave propagation in solids is higher than in voids, and also the low UPV of Juncus plants. Similar findings have been highlighted in the literature [27, 49, 50, 51]. These studies confirmed that building materials including vegetable fibers have good sound absorption characteristics. Figure 11 also shows the influence of cement content on UPV. It can be noticed from the results that the increase in the cement content from 4% to 8% and from 8% to 12% increased the average value of UPV by about 10%. Based on these results, the increase in UPV could be explained by the increasing amount of calcium silicate (C2S and C3S) created by the increasing cement content. The increasing quantity of these hydrates, which originate from cement hydration, reduces the void network in the matrix and improves its compactness by assuring good bonds between fibers and soil particles, in turn reducing the porosity of the earth block, providing the highest value of UPV.
# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
## 5 Experimental Section
### Evaluation Metrics  
For traditional balanced datasets, accuracy is the most commonly used evaluation metric. However, for unbalanced datasets, it would be biased to use accuracy alone to evaluate. This paper uses multiple evaluation metrics to comprehensively evaluate various algorithms. Let true positive (TP) represent the number of correctly identified phishing emails, true negative (TN) represent the number of correctly identified benign emails, false positive (FP) indicate the number of benign emails misclassified as phishing emails, and false negative (FN) indicate the number of phishing emails misclassified as benign emails.  
* F1-Score: A more robust evaluation metric as it considers both precision and recall and is particularly useful when dealing with imbalanced datasets. \[\text{F1-Score}=\frac{2\times Precision\times Recall}{Precision+Recall},\] (2) where \(Recall=\frac{TP}{TP+FN}\) is used to measure the proportion of phishing emails that are correctly identified among all the phishing emails, and \(Precision=\frac{TP}{TP+FP}\) indicates the proportion of emails correctly classified as phishing among all emails classified as phishing emails.
* Accuracy: The proportion of correctly classified emails with respect to the total number of emails. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}.\] (3)
* G-mean: An important metric based on the geometric mean of the majority class accuracy and minority class accuracy. A higher G-mean value indicates a better decision boundary. \[\text{G-mean}=\sqrt{\frac{TP}{TP+FN}\times\frac{TN}{TN+FP}}.\] (4)
* Matthews Correlation Coefficient (\(MCC\)): A metric for evaluating binary classification performance. It combines the information from \(TP\), \(TN\), \(FP\), and \(FN\), making it suitable for evaluating the ability of classifiers to handle imbalanced datasets. The \(MCC\) score ranges from \(-1\) to \(1\), where a score of \(1\) represents a perfect classifier, \(0\) represents a random classifier, and \(-1\) represents a classifier whose predictions are completely opposite to the true classes.  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}. \tag{5}\]
* Area Under the ROC Curve (AUC): This term refers to the region of the ROC curve that is under the curve and is frequently used to assess the effectiveness and quality of binary classification models.
# Cyclic Detectors in the Fraction-of-Time Probability Framework
## 2 Materials and Methods
### Monte Carlo \(P_{\text{fa}}\) versus Nominal or Design \(P_{\text{fa}}\)  
For detectors whose threshold is determined using Monte Carlo simulations (Section 3.2) we have, as expected, that the Monte Carlo (effective) \(P_{\text{fa}}\) is practically equal to the nominal or design \(P_{\text{fa}}\).  
For detectors whose threshold is derived analytically, it make sense to check how far the effective Monte Carlo \(P_{\text{fa}}\) is from the nominal or design \(P_{\text{fa}}\).  
In this experiment, the Monte Carlo \(P_{\text{fa}}\) versus the nominal \(P_{\text{fa}}\) is computed for the QF detectors. The results are reported in Figure 4.  
Figure 3: ROC: Monte Carlo \(P_{\text{d}}\) versus Monte Carlo \(P_{\text{fa}}\). (**Left**) \(T=2^{15}T_{s}\); (**Right**) \(T=2^{19}T_{s}\). Quadratic form detectors (blue): (\(\bigtriangledown\)) cyclic pdf (kernel-based estimator); (\(\square\)) cyclic CDF (Fourier coefficient of the indicator); (\(\bigcirc\)) cyclic autocorrelation (cyclic correlogram); (\(\lozenge\)) cyclic CDF (kernel-based estimator); (\(\star\)) cyclic 4 th-order moment.  
According to the results of Section 3.6, when the data-record length increases from \(T=2^{15}\) to \(T=2^{19}\), the MC-\(P_{\text{fa}}\) approaches the nominal-\(P_{\text{fa}}\) for the QF detectors based on estimates of second- and 4 th-order cyclic statistics or based on estimates of the cyclic CDF (Fourier coefficient of the indicator). Moreover, for the QF detectors based on estimates of the cyclic CDF (kernel-based estimator) and the cyclic pdf (kernel-based estimator), the effective Monte Carlo \(P_{\text{fa}}\) is far from the nominal or design \(P_{\text{fa}}\). Therefore, according to the results of Section 3.6, this fact could suggest that, for these detectors, the normalized estimation errors are not asymptotically complex normal. At least if the smoothing parameter \(b\) is chosen according to [PERSON]'s rule (30).
# Recent Progress on Solution-Processed CdTe Nanocrystals Solar Cells
## 1 Introduction  
Thin film solar cells based on solution-processed semiconductor nanocrystals (NCs) offer the potential of high light absorption, low-cost, and easy large-area production with good stability [1; 2; 3]. To date, solar cells based on sintering NCs, such as Cu(In,Ga)(Se,\(S\))\({}_{2}\) (CIGS) [4], Cu(Zn,Sn)(Se,\(S\))\({}_{2}\) (CZTS) [5], and CdTe [6; 7; 8; 9; 10; 11] with good device performances have been investigated. Among all kinds of NCs solar cells, CdTe NCs solar cells have many advantages, such as a less complex compound and high optical absorption coefficient (\(>\)10\({}^{4}\) cm\({}^{-1}\)), suitable bulk band gap of 1.5 eV [12; 13] (Figure 1) and large exciton Bohr radius [14]. CdTe NCs are synthesized in a solution phase [15; 16; 17] and can be dispersed in many organic solvents [18]. The CdTe NCs solution can be coated on substrates using drop-casting, spin-coating, or ink-jet printing to fabricate all kinds of electronic devices. In this research, we focus in particular on CdTe NCs for photovoltaic applications.  
In this review, we begin with CdTe NCs fabricated by using different methods. We then introduce the fundamentals of CdTe NCs solar cells. We follow with a detailed review of CdTe NC solar cells with different device structures (Schottky, all NCs p-n junction, and organic-inorganic hybrid structure). Finally, we will discuss the challenges and future prospects of CdTe NCs solar cells.