esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Sources
#### 2.1.2 Urban Environmental Data Sources  
Urban environmental conditions influence urban heat islands (UHIs) through vegetation cover, water presence, SCS, and LST. The data sources used in this study include:  
* Sentinel-2 BOA Reflectances: Captured by the Multispectral Imager (MSI) sensor aboard Sentinel-2 satellites of the European Satellite Agency (ESA), which have 10 m spatial resolution. These atmospherically corrected measurements are used to derive spectral indices (e.g., the NDVI and NDWI) for monitoring vegetation and water bodies in the city.
* Summer degree-days in base 20: This historical summer temperature data servers to classify cities based on the severity of their summers, the SCS index was used, following the Spanish Building Code (CTE). The index relies on degree-days (DD) at a base temperature of 20 \({}^{\circ}\)C, using data from AEMET (Spanish Meteorological Agency).
* MODIS and ASTER Nighttime LST: To use summer nocturnal LST as reference parameter in the model, we created a synthetic night LST image with a 90 m spatial resolution based on the average summer nighttime LST. This approach addresses the absence of an open product from ESA or NASA that offers both adequate spatial and temporal resolution for this purpose. Thus, we integrated the spatial detail of ASTER with the temporal frequency of MODIS, following methods previously validated in UHI studies (e.g., ref. [33]). Quality control was applied to remove extreme temperature anomalies or cloud interference.
* MODIS with 1 km resolution, daily nighttime LST data.
* ASTER with 90 m resolution, on-demand nighttime LST data.
## 2 State of the Art
### Data Collection
#### 2.1.4 Summary: Retrieval Challenges  
* Despite a large amount of digital and digitized data, a major issue is the findability and missing information about spatial and temporal properties (e.g., the viewport).
* Crowdsourced data collections are particularly well established, but challenging with regard to engaging users on a large scale.
* Teaching digital skills via heritage is frequently used but a consensus is lacking on educational paradigms and methods.
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 2 Centralized MPPT Architecture Based on LoRa
### Power Optimization of PV Systems
#### 2.1.3 Control Techniques
From Equation (10), Equation (13) is achieved as follows:  
\[S=e+\lambda\int e\,\mathrm{d}t=0. \tag{13}\]  
The combination of Equations (9) and (13) and \(S=0\) result in Equation (14):  
\[V_{\text{pv}}+\lambda\int e\,\mathrm{d}t-V_{\text{ref}}=0. \tag{14}\]  
Equation (8) can be rearranged as Equation (15):  
\[V_{\text{pv}}=\left(1-d\right)V_{\text{o}}. \tag{15}\]  
From Equations (14) and (15), the following is obtained:  
\[\left(1-d\right)V_{\text{o}}+\lambda\int e\,\mathrm{d}t-V_{\text{ref}}=0. \tag{16}\]  
Taking into account that, in this system, the control signal is the duty cycle \(d\), then \(u_{\text{eq}}\) can be obtained from Equation (16):  
\[u_{\text{eq}}=\frac{1}{V_{\text{o}}}\bigg{(}V_{\text{o}}+\lambda\int e\, \mathrm{d}t-V_{\text{ref}}\bigg{)}. \tag{17}\]
## 5 Security Analysis
### Resist User Impersonation Attack  
In this proposed scheme, the adversary cannot replay any authentication message without the user \(U\)'s biometric information \(Bio_{U}\), and it also cannot guess the random number \(r_{U}^{\prime\prime}\) successfully to impersonate the server \(V\). Additionally, the adversary does not have the non-negligible probability to forge the patient's signature to the server \(V\). In addition, the server \(V\) also checks the signature \(S_{U}\) to authenticate the patient \(U\)'s identity in the migration registration phase. Thus, the adversary cannot have non-negligible probability to forge \(U\)'s signature \(S_{U}\) under the RSA factoring problem. Therefore, our scheme can resist user impersonation attacks.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 4 Calculation Procedure of Main Circuit Parameters
### Basic Factors
#### 4.1.3 Restriction Conditions  
The restriction conditions mainly include the minimum value of the firing angle and the extinction angle, the tap changer range of the converter transformer and the minimum reactive power consumption constraint for each converter station.
## 4 Lifetime Improvement Mechanisms in Routing
### Multi-Path Mechanism  
Depending on the methods used for finding the path, routing protocols in WSNs can be classified into three groups as follows: _proactive (table-driven), reactive (on-demand)_ and _hybrid_. According to the on-demand method, the route should be built only when a node decides to send data to a destination, in contrast with the table driven technique in which all of the nodes must exchange route messages periodically to maintain a permanent route table all over the network. Therefore, only the active path, in which a link failure has taken place, should be updated. A hybrid routing protocol is a combination of the both mechanisms [40]. The most popular multi-path routing protocols in wireless sensor and _ad hoc_ networks are based on traditional on-demand single-path routing schemes such as _ad hoc_ On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR) [16].  
There are two techniques for constructing multiple paths in WSNs. In _disjoint multipath_ methods all of the paths are apart from each other so that there is no link/node sharing in link-disjoint or node-disjoint mechanisms, respectively. In this manner, a failure in one path cannot affect the alternate paths. In contrast with the link-disjoint scheme, node-disjoint multi-path protocols avoid sharing any nodes between the multiple routes. This means that these kinds of algorithms try to transmit the data on multiple independent paths simultaneously without any congestion on a specific node along the routes. Another method is _braided (meshed) multipath_ in which the paths are partially disjoint or not disjoint at all [40].
## 2 Description of the Russian Arctic Database  
The presented database contains the results of observations of the geomagnetic field variations with a 1 min sampling period at stations in the Russian Arctic. The stations' geographic and corrected geomagnetic coordinates are given in Table 1, and the map with the stations' locations is shown in Figure 1.  
Figure 1: Stations in the Arctic zone of the Russian Federation monitoring geomagnetic field variations. The data of some stations are also available on the SuperMAG portal, some are part of the INTERMAGNET network, and some are included in the IMAGE and MAGDAS networks. Part of the data were taken from the Common Data Center \"Analytical Center for Geomagnetic Data\". However, data from several stations, for example, on Yamal, are not included in these archives. Naturally, in each of the above archives, data are stored in their own format. The measurements of the absolute values of the geomagnetic field are carried out only at stations included in the INTERMAGNET network. Other stations are variational, so only deviations from a reference level have a physical meaning in their records. The data quality and sensitivity are the same as in the data portals mentioned above. All the collected data have been converted into the standard ASCII format IAGA-2002. The names of the files have the format STAYYYYMMDDvmin.min, where STA is the station code from Table 1, YYYY--year, MM--month, DD--day. Such file names make it easy to select the required intervals for analysis. The data files are sorted by year and stored in a compressed format to speed up the download process. The data are converted into the geomagnetic coordinate system (\(H\), \(E\), \(Z\)): \(H\) axis is directed to the geomagnetic pole, \(E\) axis is directed to the geomagnetic east, and \(Z\) axis is directed vertically downward. The magnetograms of all stations were visually examined and evidently defective files were discarded. However, the remaining files may still contain level jumps, data gaps, and intermittent noise, because their correction requires a special analysis. To quickly browse the data, check for the data availability for specific time intervals, and select events, the quick-look magnetograms are drawn. For a general characterization of conditions in interplanetary space from the OMNI database, the same graph shows variations in the solar wind plasma density \(N\)p, solar wind velocity \(V\), and the vertical component of the interplanetary magnetic field \(B\)z.
## 3 Results
### Reliability  
After carrying out this process, a valid content level instrument was obtained, consisting of 25 items, which represented the second version of the PECID questionnaire. To analyze the reliability of the internal consistency of the instrument, the Cronbach Alpha coefficient was calculated.  
Before the application of the questionnaire to the final sample, and from the data obtained in the pilot test with 134 students, the [PERSON]'s Alpha coefficient of the 25 item scale of perceptions about the potential of cinema as a teaching resource in the Pre-School and Primary Education was preliminarily calculated. The analysis resulted in an Alpha of 0.970, which according to [52], is an excellent reliability value, since it reflects that the answers have a high degree of coherence around the same domain, that is, the perceptions of the potentialities of cinema in the pre-school and primary classrooms.  
Once the questionnaire was applied to the total sample of 4659 students, which did not include the participants in the pilot test, the reliability of the scale was again analyzed. The result of the Cronbach's Alpha was again very high, specifically 0.978, with an outstanding reliability value. In addition, in Table 4, the results relating to the item-total correlations and the Cronbach's Alpha coefficient are presented if each of the items is eliminated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Corrected Item–Total Correlation** & **Multiple Squared Correlation** & **Cronbach’s Alpha if the Item Is Removed** \\ \hline
1 & 0.758 & 0.721 & 0.977 \\
2 & 0.748 & 0.711 & 0.977 \\
3 & 0.757 & 0.733 & 0.977 \\
4 & 0.764 & 0.726 & 0.977 \\
5 & 0.795 & 0.776 & 0.977 \\
6 & 0.781 & 0.754 & 0.977 \\
7 & 0.775 & 0.778 & 0.977 \\
8 & 0.807 & 0.834 & 0.977 \\
9 & 0.765 & 0.748 & 0.977 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlations and internal consistency indexes obtained in the final sample.  
As can be seen, the correlations are high and the elimination of any of the items would not result in Cronbach's Alpha exceeding the value of 0.978.
## 3 Analytical Model
### Simulation Results  
In order to validate the effectiveness of the proposed analytical model, a comparison is drawn between its simulation results and those obtained from circuit simulations. The simulations were performed using a 130 nm CMOS process with extracted parasitics  
Figure 6: Clock feedthrough effect on the discharge switch, \(S_{discharge}\).  
included. As illustrated in Figure 7, a close alignment can be observed between the results derived from the analytical model and the circuit simulations across all three larger RC time-constants. A summary of the simulation results for these RC time-constants is provided in Table 1. Notably, the difference between the analytical model and the circuit simulations is maintained below 3.2%, underscoring the high level of accuracy attained by the model.
## 4 Theoretical Background
### Overview of Model Performance Evaluation Metrics  
To properly estimate their capabilities, it is crucial to thoroughly examine the diagnostic abilities of several machine learning algorithms while accounting for variables such as model complexity, computational needs, and parameterization. For this reason, it is necessary to use defined criteria for assessing performance and discriminating between candidates. _Accuracy_, _Precision_, _Sensitivity_, \(F1-score\), and false alarm rates are among these parameters. With these measurements, we can objectively compare and assess the performance of various models, allowing us to make informed decisions based on their individual benefits and drawbacks [27; 28]. These are some of the evaluation metrics that have been used in studies; see Equations (7)-(10).  
\[Accuracy=\frac{TP}{TP+FP+TN+FN} \tag{7}\]  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{9}\]  
\[F1-Score=2\times\frac{Sensitivity\times Precision}{Sensitivity+Precision} \tag{10}\]  
True Positive (\(TP\)) indicates that the model predicted a true outcome, and the observation was accurate. False Positive (\(FP\)) indicates that the model predicted a true result, but the actual result was false. False Negative (\(FN\)) indicates a model that predicted a false result while the actual result was accurate. True Negative (\(TN\)) indicates that the model predicted a false outcome, and the actual outcome was also false. The accuracy of a model is often used to evaluate its performance; however, the accuracy has several drawbacks as well. In unbalanced datasets, one class is more common than the other, and the model labels observations based on this imbalance. In the case of 90% false cases and only 10% true cases, there is a very high possibility that our model would have an accuracy score around 90%. Precision is calculated as the number of true positives over the total number of positives predicted by your model. This metric allows you to calculate the rate at which your positive predictions actually come true. The sensitivity (also known as recall) is the percentage of actual positive outcomes over your true positives. It is possible to evaluate how well our model predicts the true outcome. The \(F1-score\) is the harmonic mean of precision and recall. It can be used as an overall metric that incorporates both precision and recall. We use the harmonic mean instead of the regular mean because the harmonic mean punishes values that are far apart. This paper evaluated classification performance using accuracy, precision, and \(F1-score\) using a confusion matrix. There are deviations in individual values for each classification performance evaluation, but the overall trend is similar. Accuracy has less deviation than other indicators and stable values.
# Nematic Liquid Crystal Composite Materials for DC and RF Switching
###### Abstract  
Liquid Crystals (LCs) are widely used in display devices, electro-optic modulators, and optical switches. A field-induced electrical conductivity modulation in pure liquid crystals is very low which makes it less preferable for direct current (DC) and radio-frequency (RF) switching applications. According to the literature, a conductivity enhancement is possible by nanoparticle doping. Considering this aspect, we reviewed published works focused on an electric field-induced conductivity modulation in carbon nanotube-doped liquid crystal composites (LC-CNT composites). A two to four order of magnitude switching in electrical conductivity is observed by several groups. Both in-plane and out-of-plane device configurations are used. In plane configurations are preferable for micro-device fabrication. In this review article, we discussed published works reporting the elastic and molecular interaction of a carbon nanotube (CNT) with LC molecules, temperature and CNT concentration effects on electrical conductivity, local heating, and phase transition behavior during switching. Reversibility and switching speed are the two most important performance parameters of a switching device. It was found that dual frequency nematic liquid crystals (DFNLC) show a faster switching with a good reversibility, but the switching ratio is only two order of magnitudes. A better way to ensure reversibility with a large switching magnitude is to use two pairs of in-plane electrodes in a cross configuration. For completeness and comparison purposes, we briefly reviewed other nanoparticle- (i.e., Au and Ag) doped LC composite's conductivity behavior as well. Finally, based on the reported works reviewed in this article on field induced conductivity modulation, we proposed a novel idea of RF switching by LC composite materials. To support the idea, we simulated an LC composite-based RF device considering a simple analytical model. Our RF analysis suggests that a device made with an LC-CNT composite could show an acceptable performance. Several technological challenges needed to be addressed for a physical realization and are also discussed briefly.  
liquid crystal (LC); carbon nanotube (CNT); gold nanoparticle (GNP); electrical conductivity; direct current (DC); radio frequency (RF) switch +
Footnote †: journal: _technologies_
# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 3 Improved Deep Principal Component Analysis Method
### Statistics and Control Limits  
Considering all sub-pivot spaces, \(T_{i,j}^{2}\) for each sub-dataset \(X_{i,j}\) can be expressed as:  
\[T_{i,j}^{2}=X^{T}P_{i+1,2j-1}\ \Lambda_{i,j}^{-1}P_{i+1,2j-1}^{T}X \tag{8}\]  
where \(\Lambda_{i,j}\) is the eigenvalue matrix of the principal component space.  
The Upper Control Limit of \(T_{i,j}^{2}\) (\(TUCL_{i,j}\)) is expressed as:  
\[\int_{0}^{TUCL_{i,j}}\frac{1}{N}\sum_{b=1}^{N}K(\frac{1}{h}(T_{j,k}^{2}-T_{j,k }^{b}))dT_{j,k}^{2}=\alpha \tag{9}\]  
where \(T_{i,j}^{b}\) represents column b of \(T_{i,j}^{2}\); \(K\)() is the kernel function; h is the smoothing parameter; \(\alpha\) is the significance level.  
The Squared Prediction Error (SPE) statistic reflects the degree of deviation in the data and can be expressed as:  
\[SPE_{j,k}=\left\|(I-P_{j+1,2k-1}P_{j+1,2k-1}^{T})X\right\|^{2} \tag{10}\]Similar to the calculation method of the control limit of \(T_{i,j}^{2}\), the Upper Control Limit of \(SPE_{i,j}\) (\(QUCL_{i,j}\)) is expressed as:  
\[\int_{0}^{QUCL_{i,j}}\frac{1}{N}\sum_{b=1}^{N}K(\frac{1}{h}(SPE_{j,k}-SPE_{j,k}^ {b}))dSPE_{j,k}=\alpha \tag{11}\]  
where \(SPE_{i,j}^{b}\) represents column b of \(SPE_{i,j}\).
## 1 Introduction
Another disadvantage of lithography is that if standard lithography is used, in order to create a few nanometer gap features, extreme ultraviolet (EUV) will be required, which is very expensive and disadvantageous for low-cost applications. OAD technology is another method of making SERS active substrates. A number of studies have been conducted to fabricate nanostructured SERS substrates by OAD technology [16; 17]. It is a simple method of fabricating SERS substrates, and these fabricated substrates exhibit good sensitivity and uniformity. However, to fabricate SERS substrates by the OAD method, one will need a custom designed electron-beam/sputtering evaporation system. In addition, the vapor incident angle to the substrate normal is around 86\({}^{\circ}\), which means that only a small portion of the material is deposited on the substrate, which results in increased manufacturing costs. Lastly, the anisotropic character of the optical properties, as well as the SERS responses of the substrates [4], lead to the need of special angle coupling of the excitation laser which makes it not convenient to use. In this study, we used a hot embossing technique to create a uniform and closely packed nanocone arrays on a substrate. Furthermore, by utilizing the self-aggregating nature of nanoscale metal materials, we were able to sputter uniformly distributed silver nanoparticles at the tip of the nanocone array structure. Using this method, we can create a uniform silver nanosphere array with very small gaps between the nanoparticles to create hot spots evenly. We used this structure as our SERS active substrate for rapid detection of low concentrations of analytes. The analytes tested in this study included Rhodamine 6G (R6G) and DEHP. R6G is a highly fluorescent rhodamine family dye, and it is commonly used in SERS experiments as a probe molecule. DEHP is a PVC plasticizer commonly used in food-related containers, such as plastic packaging (PVC cling film), plastic bags, plastic bowls, and plastic cups. According to the Taiwan Ministry of Health, DEHP is defined as an environmental hormone and is defined as a Class 2B carcinogen. The ability to quickly detect low concentrations of DEHP helps prevent people from eating food contaminated with DEHP, which will benefit people's health. The developed method can be used to fabricate disposable, low cost and highly sensitive SERS substrates. The results show a strong and uniform SERS enhancement effect. It not only opens up possibilities for using SERS in routine food safety analytics but also helps SERS to be widespread in many other applications as well.
# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 5 Discussion  
To assess the effectiveness of the proposed method, each module was isolated for a series of evaluation, yielding the mAP results for 7-shot small-sample object detection after incorporating different modules, as listed in Table 8.  
The results indicate that each employed module contributed significantly to enhancing the precision of the base model. Considering the small-scale nature of defects like pits and cracks in steel materials, as well as the significant variation in pixel scales across the four defect categories, the incorporation of the feature pyramid network (FPN) yielded an increase of 0.89% in comparison to the base model. The integration of FPN in conjunction with DMSANet further improved the model's performance by 2.34%. In light of certain constraints within FPN, an enhanced version was implemented, leading to a substantial enhancement of 2.65% as compared to the base model. The subsequent incorporation of SMSANet into this configuration resulted in a remarkable performance boost of 4.3%. These outcomes provide compelling evidence for the effectiveness of the refined modules in our framework.  
As depicted in Figure 7, the comparative experiments substantiated that the network developed in this study was highly effective, which successfully identified new classes of defects with a constraint of no more than 10-shot samples. The model's proficiency in defect detection remained robust regardless of whether the images contained only one type of defect or were composed of two or three different variations. In Figure 8, we present a visual depiction of the detection results, using a range of colors to distinguish different defects. The visualized steel defect detection results demonstrate that the proposed few-shot object detection method exhibits excellent generalization capabilities.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Module Integration** & \multicolumn{5}{c}{**Adoption Status**} \\ \hline Backbones & ✓ & ✓ & ✓ & ✓ & ✓ \\ FPN & & ✓ & ✓ & & \\ DMSANet & & & ✓ & ✓ & \\ MS-SPAN & & & & ✓ & ✓ \\ SMSANet & & & & & ✓ \\ mAP (\%) & 72.28 & 73.17 & 74.62 & 74.93 & 76.58 \\ \hline \hline \end{tabular}
\end{table}
Table 8: mAP results with different modules added.  
As performance metrics are highly valued in industrial production, this paper conducted FPS and mAP evaluation on the four original models and the proposed model. The specific results are detailed in Table 9. The results demonstrate that the proposed model's accuracy outperformed that of Faster R-CNNs with three different backbones (VGG16, ResNet50, ResNet101), and YOLOv4, by 20.86%, 9.66%, 4.3%, and 21.76%, respectively. YOLOv4 achieved the optimal real-time performance, while our model, with superior accuracy, attained a frame rate of 63.8 FPS. With its ideal balance, the proposed approach emerges as a suitable choice for deployment in the domain of industrial production, particularly in scenarios where precision is of paramount importance.