esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
\begin{tabular}{l l l l} P\({}_{\text{spv}}\) & Photovoltaic Array Power (W) & P\({}_{\text{gf}}\) & Grid Power -Fault Condition (pu) \\ \(\Delta\)T & Fault Period (s) & I\({}_{\text{N}}\) & Nominal Current (pu) \\ P\({}_{\text{DC-link}}\) & DC-Link Power (W) & V\({}_{\text{DCL}}\) & DC-Link Voltage (v) \\ C\({}_{\text{DC}}\) & DC-Link Capacitor (F) & K\({}_{\text{p}}\) & Proportional Gain \\ P\({}_{\text{g}}\) & Grid Power (pu) & V\({}_{\text{DCL},\text{f}}\) & DC-Link Voltage-Fault Condition (v) \\ Iq\({}_{\text{l}}\) & Reactive Current(pu) & K\({}_{\text{i}}\) & Integral Gain \\ V\({}_{\text{grms}}\) & Grid Voltage RMS value (pu) & K\({}_{\text{aw}}\) & Anti-Windup Gain \\ I\({}_{\text{grms}}\) & Grid Current in RMS value (pu) & Q & Reactive Power (kVAR) \\ I\({}_{\text{grms}}\) & Grid Current in RMS value (pu) & P & Active Power (kW) \\ I\({}_{\text{q0}}\) & Initial Reactive Current (pu) & S & Apparent Power (w) \\ Ig & Grid Current (pu) & Q\({}_{\text{max}}\) & Maximum Reactive Power (pu) \\ C\({}_{\text{DC}}\) & DC-Link Capacitor (F) & Ts & Sample Time (s) \\ D & Duty Cycle & d\({}_{\text{2}}\) & Duty Cycle for the Proposed Control \\ V\({}_{\text{d\_ref}}\) & d-Axis Reference Voltage (pu) & d\({}_{\text{min}}\) & Lower Limit for the Duty Cycle \\ V\({}_{\text{q\_ref}}\) & q-Axis Reference Voltage (pu) & d\({}_{\text{max}}\) & Maximum Limit for the Duty Cycle \\ I\({}_{\text{q\_ref}}\) & d-Axis Reference Current (pu) & d\({}_{\text{unsta}}\) & Unsaturated Limit for the Duty Cycle \\ I\({}_{\text{q\_ref}}\) & q-Axis Reference Current (pu) & d\({}_{\text{sat}}\) & Saturated Limit for the Duty Cycle \\ \(\Delta\)V\({}_{\text{dc}}\) & DC-Link Voltage Deviation (v) & SG\({}_{\text{speed}}\) & Synchronous Generator Speed (pu) \\ \(\Delta\)P\({}_{\text{pv}}\) & PV-Power Deviation (w) & SG\({}_{\text{angle}}\) & Synchronous Generator Rotor Angle (deg) \\ P\({}_{\text{pv}\text{-rated}}\) & Rated PV Power (w) & \(\omega_{\text{S}\text{G}\text{rated}}\) & Rated Synchronous Generator Speed (pu) \\ I\({}_{\text{d}}\) & d-Axis Current (pu) & \(\delta_{\text{SG},\text{rated}}\) & Rated Synchronous Rotor Angle \\  & & S\({}_{\text{max}}\) & Maximum Apparent Power (w) \\ \end{tabular}
# Leather Defect Detection Based on Improved YOLOv8 Model
## 2 Dataset Preprocessing  
### Image Acquisition  
Due to the lack of a public dataset for leather defect detection, this study was conducted to select and take pictures of defects in a sample from a leather factory. Five common leather defects were collected: scratches, holes, dirt, rotten surfaces and folds. To minimize interference from the acquisition environment and sample variability, we preprocessed the captured images to exclude those unsuitable for training, including those that were blurry or poorly lit. Subsequently, we cropped the images to focus on the defects, creating defect-centered images each measuring 2048 \(\times\) 2048 pixels. Ultimately, we obtained 1065 such defective images. The example pictures of the five defects are shown in Figure 1. The following are descriptions of the various defects: Dirt is an uneven color or foreign substance on the surface of the leather, which may be caused by uneven dyeing, oil penetration or external pollution. Holes are defects formed when a needle or thread passes through the leather leaving small holes during leather processing. Scratch is a line damage on the surface of leather caused by friction, cutting or other external force. Rotten surface is the leather surface being damaged due to moisture, mold or other environmental factors and appearing rotten. Fold is a permanent wrinkle or crack formed in the leather due to folding or compression during processing or use.
# X-ray Photoelectron Spectroscopy in Mineral Processing Studies
## 3 Spatial Resolution of Photoelectron Spectroscopy  
The previous studies and findings of photoelectron spectroscopy, along with other techniques and mineral processing practice [10; 17; 117], have exposed the issues needed to be answered with XPS. For instance, it is known that about 50% monolayer xanthate coverage of a sulfide surface is enough for flotation recovery of the mineral [5; 10], so the question arises of how the collectors, oxidation products, and other surface species are spatially distributed and how this influences the hydrophobicity  
Figure 2: Cryo-XPS S 2p spectra from fast-frozen sphalerite (1) ground, (2) reacted with 0.1 mM CuSO4, the activated mineral treated with (3) 0.1 mM KBX and (4) 10 mM KBX. Right panels show the bands, normalized by the height and shifted to align the peak position, and the differences between the spectra. For clarity, only S 2p32 components are shown in the S 2p spectra fitting. Reproduced from [102] with permission from Elsevier.  
and flotation. It is also difficult to distinguish mono- and multilayer coatings of flotation reagents [125]. Knowing the distribution of reaction products is necessary in order to understand the origin of retarded reaction kinetics and \"passivation\" of metal sulfide oxidation and leaching. Therefore, researchers need XPS techniques having a high spatial resolution, both lateral and in-depth, which also cause minimize alterations of the mineral/aqueous interfaces upon measurements.
## 3 Results and Discussion
### Field Test Results  
The algorithm was transplanted into the designed temperature monitor, then the fault diagnosis and recovery ability of the algorithm were tested through field experiments. The experimental time was 86,400 s throughout the day. The host computer sent a simulated sensor failure at a random time point. In this way, the algorithm's ability to diagnose and recover data from single-sensor failure and multi-sensor failure were tested. At the same time, it verified that the thermostat would not respond incorrectly due to sensor failure, and recorded the real-time data.  
The field experiment results are shown in Figures 11-13. The algorithm's fault diagnosis function could accurately diagnose different types of single sensor faults or multi-sensor faults. The diagnosis time was less than 1 ms. The fault location function could accurately locate the faulty sensor, isolated the faulty sensor data immediately, and started the predictor to restore the data of the faulty sensor. The data recovery function tracked the temperature changes well, and replaced the faulty sensor data with the output value of the predictor to achieve data recovery. The difference between the predicted value and the actual value was less than or equal to 0.1 \({}^{\circ}\)C, which met the accuracy requirements of the predicted value. The experiments showed that the algorithm was accurate in diagnosis, high in recovery accuracy, and accurate in fault location.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Error** & **Algorithm** & **10 April 800** & **10 December 18:00** \\ \hline \multirow{3}{*}{RMSE/\({}^{\circ}\)C} & BP & 0.2671 & 0.0421 \\ \cline{2-4}  & SVM & 0.6090 & 0.5100 \\ \cline{2-4}  & LSTM & 0.0146 & 0.0221 \\ \hline \multirow{3}{*}{MAE/\({}^{\circ}\)C} & BP & 0.2670 & 0.0418 \\ \cline{2-4}  & SVM & 0.6087 & 0.5088 \\ \cline{2-4}  & LSTM & 0.0109 & 0.0103 \\ \hline \multirow{3}{*}{MRE/\%} & BP & \(\pm\)1.6486 & \(\pm\)0.7402 \\ \cline{2-4}  & SVM & \(\pm\)2.7975 & \(\pm\)6.0617 \\ \cline{1-1} \cline{2-4}  & LSTM & \(\pm\)0.4053 & \(\pm\)1.0337 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The error comparison of the different prediction algorithms.  
Figure 10: A comparison of the relative errors of the winter data forecast values.  
Figure 11: The data recovery for the bias failure of a single sensor.  
Figure 12: The data recovery for the power failure of a single sensor.  
Figure 13: The data recovery for the bias failure of multiple sensors.  
During the whole experiment, no false response of the monitor due to sensor failure occurred. This algorithm provided a reliable method for ensuring the normal operation of the dry-type transformer temperature monitor.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 2 Magnetic Properties  
As shown in Figure 1b, magnetization curves (B-H curves) are plotted by the values of magnetic flux density (B) against the field strength or magnetizing force (H). As the alternating current (AC) condition is continuously changing the direction of the magnetizing current through the coil in electrical steel applications, this alternating magnetic field can produce a magnetic hysteresis loop in the core material. Residual magnetism is the induction that is left in the material when it has already been magnetized. Coercivity force (or coercivity) is the magnetic field strength that is required to demagnetize the material when it has already been magnetized. When all the magnetic domains within the material have aligned with the external magnetic field, a saturation effect is observed. The slope of the B-H curve at any location is defined as the incremental permeability. Sometimes, permeability is measured from the origin to the target location. This slope is called apparent permeability. The area under the B-H curve is the hysteresis loss in each B-H cycle [3].  
In studies addressing the magnetic properties of NGO electrical steels, core loss, coercive field, permeability, and magnetic induction are the main properties that dictate the magnetic material behavior. The important properties of electrical steel differ for alternating current (AC) and direct current (DC). For AC applications, core loss is of major importance, because the alternating nature of AC causes very rapid domain flipping, and thus hysteresis loss of the core loss becomes a larger component of the energy loss [1]. In contrast, permeability, coercive field, and magnetic induction properties are more important in applications under DC conditions. Hysteresis loss also occurs in applications under DC conditions; however, it is not the main influence on energy loss [1].
# Input Selection Methods for Soft Sensor Design: A Survey
## 5 Feature Selection
### Wrapper Methods  
Such class of methods perform the input variable selection by evaluating the performance of the final model via cross-validation, where each model corresponds to a unique combination of inputs [45]. The assessment is done by using the same criteria that are used to evaluate the predictive performance in the model validation design step, for example, the MSE [164; 165; 166; 167; 168]. In the case of the use of the MSE as an optimality criterion, the drawback is that the best model could not be the optimal one, since models with a large number of inputs tend to suffer overfitting. So other criteria like Akaike's Information Criterion (AIC) [94; 165; 169; 170], or the \(C_{P}\) (Mallows' Coefficient) statistics [1] are adopted since these measures penalize overfitting by determining the optimal number of input variables as a trade-off between the model size and the accuracy.  
With respect to filter methods, these approaches are slower and computationally and time expensive, since a new model is created every time a new subset is picked. Being the evaluation done on the final model, they generally give better results.  
As explained in Section 5, the ideal approach would be evaluating all possible subsets, but as it is infeasible, the use of a search strategy is needed. On the basis of the adopted search algorithm shown in the same section, wrapper methods can be classified as deterministic or randomized [171; 172]. Deterministic wrappers use Sequential Feature Selection greedy algorithms like Forward Selection, Backward Elimination and their variants. They generally present a lower overfitting risk [173; 174; 175]. Randomized wrapper methods are the ones adopting heuristic search and exploit a randomized criterion in the selection of the subset. As already stated, they have more parameters to be tuned [176; 177].
# Market-Oriented Procurement Planning Leading to a Higher Service Level and Cost Optimization
## 4 Results and Discussion
### Simulation Results
Regarding the cost analysis, the MO model had better performance with more than 25% fewer costs compared with the EOQ model and with almost 15% less than the RP model. The MO model opened a new warehouse in week 63, while the EOQ model and the RP model opened a new warehouse later, around week 150. By doing so, these models did not adapt their storage capacities for the required stock, while the MO model anticipated the situation and decided on an investment based on the customer lifecycle of the product. To summarize, for this scenario and lifecycle-demand curve, the MO model presented the best performance for the service level as it equaled the RP model and had better results in inventory costs compared with both other models while securing a higher service level and higher capacity utilization of the employees as it adapted all areas of inventory management to the customer demand.
# Dual-Band Monopole Antenna for RFID Applications
###### Abstract  
Over the past decade, radio-frequency identification (RFID) technology has attracted significant attention and become very popular in different applications, such as identification, management, and monitoring. In this study, a dual-band microstrip-fed monopole antenna has been introduced for RFID applications. The antenna is designed to work at the frequency ranges of 2.2-2.6 GHz and 5.3-6.8 GHz, covering 2.4/5.8 GHz RFID operation bands. The antenna structure is like a modified F-shaped radiator. It is printed on an FR-4 dielectric with an overall size of \(38\times 45\times 1.6\) mm\({}^{3}\). Fundamental characteristics of the antenna in terms of return loss, Smith Chart, phase, radiation pattern, and antenna gain are investigated and good results are obtained. Simulations have been carried out using computer simulation technology (CST) software. A prototype of the antenna was fabricated and its characteristics were measured. The measured results show good agreement with simulations. The structure of the antenna is planar, simple to design and fabricate, easy to integrate with RF circuit, and suitable for use in RFID systems.  
Keywords:dual-band antenna; microstrip-fed printed antenna; monopole antenna; RFID; wireless communication +
Footnote †: journal:
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Method
#### 3.2.3 Pre-Processing of a Ship Plume Image  
To improve the quality of the satellite signal, in the data pre-processing step, on each of the analyzed _ship plume images_, we applied the local [PERSON]'s \(I\) spatial auto-correlation statistic [34]. In [12], we showed that the application of this technique substantially improves separability between the ship plume and the background.  
The local [PERSON]'s \(I\) spatial auto-correlation statistic allows the enhancement of the intensity of high-value pixels located in a cluster while suppressing isolated concentration peaks randomly occurring in the background. We characterized a ship plume as a cluster of pixels adjacent to each other with a concentration higher than the background average. This way, calculating the spatial auto-correlation of a _ship plume image_, we combined image denoising with the enhancement of the relevant part of the image.  
An example of a result of an _enhanced ship plume image_ is provided in Figure 5b.  
Formally, [PERSON]'s \(I\) spatial auto-correlation statistic is defined as follows: for each pixel \(i\) of a _ship plume image_, the local Moran's \(I\) is calculated as:  
\[I_{i}=\frac{(x_{i}-\mu)}{\sigma^{2}}\sum_{j=1,j\
eq i}^{N}w_{ij}(x_{j}-\mu), \tag{1}\]  
where \(x_{i}\) is the value of the respective pixel, \(N\) is the number of analyzed pixels of a _ship plume image_ (in our case, \(18\times 18\)), \(\mu\) is the mean value of all \(N\) pixels, \(\sigma^{2}\) their variance, and \(w_{ij}\) is the value of an element in a binary spatial contiguity weight matrix \(W\) at location \(j\) with regard to the analyzed pixel \(i\). The value of an element of the binary spatial contiguity matrix \(w_{ij}\) is 1 for pixels that are considered to be the neighbors of the analyzed pixel \(i\), and  
Figure 4: (**a**) _Ship track_—estimated, based on AIS data records. The ship track is shown for the time period starting from 2 h before the satellite overpass until the moment of the satellite overpass. (**b**) _Wind-shifted ship track_—a ship track shifted in accordance with the speed and direction of the wind. The _wind-shifted ship track_ indicates the expected position of the ship plume. A black arrow indicates the wind direction. For both presented images, the size of the pixel is equal to \(4.2\times 5\) km\({}^{2}\).  
0 otherwise. For the study, the queen spatial contiguity [35], which is the \(3\times 3\) 8-connected neighborhood of the analyzed (central) pixel, was applied. The value \(I_{i}\) becomes the value of the corresponding pixel of the resulting enhanced image.  
From Equation (1), we can imply the weak side of using [PERSON]'s \(I\) statistic for ship plume enhancement: apart from the clusters of high values, the given method enhances clusters of low-value pixels at the same time. The methodology proposed in this study, however, is designed in a way so that this negative impact is minimized.
## 2 Results
### Regulation of _ortT_  
Since cellular growth and metabolism was higher in the absence of _ortT_, we reasoned that _ortT_ is activated during the stringent response. To explore this possibility, we studied whether _ortT_ expression is elevated under several conditions that should elevate ppGpp using quantitative real-time reverse-transcription PCR (qRT-PCR). Four different conditions were used to induce the stringent response: TMP [49], native ReLA production [50], muiprocin (MUP) [51], and serine hydroxamate (SHX) [52]; for all four conditions, _ortT_ expression was induced (Tables 1 and S1). Among these, _ortT_ expression was highest with SHX treatment and lowest with ReLA production. The reason for such low expression of _ortT_ in the case of ReLA production could be that the activity of full-length native ReLA protein is not constitutive and depends on amino-acid starvation condition [50].  
To investigate the mechanism of _ortT_ activation, we tested whether this toxin gene is expressed via the predicted sigma E and H-dependent promoters (Figure 3A) since sigma E (RpoE) is activated by ppGpp [53] and sigma H (RpoH) is under the regulation of sigma E [54]. We activated sigma E via heat-shock stress [55] and qRT-PCR showed that _ortT_ was again activated (Tables 1 and S1). Therefore, these results demonstrate that OrtT plays a vital role in the adaptive stress response of the cells since _ortT_ is induced under nutrient limiting conditions most likely via transcription factor sigma E and H.  
Figure 5: OrtT reduces cellular growth in the presence of TMP. Growth of the wild-type and \(\Delta\)_ortT_\(\Delta\)_Kan_ strains in response to 0.5 and 1 \(\,\mathrm{\SIUnitSymbolMicro mL}\) TMP or DMSO (negative control). Two independent cultures were evaluated for each experiment. Error bar represents standard deviation.
## 2 Materials and Methods
### Prioritizing Security Fixes
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(S_{i}\) & The \(i\)th security issue \\ \(N\) & The number of security issues detected \\ \(SB\) & The available security budget, expressed in available working hours \\ \(RE_{i}\) & 1 if \(S_{i}\) is remotely exploitable, otherwise 0 \\ \(I_{conf}(S_{i})\) & The impact of \(S_{i}\) on confidentiality \\ \(I_{integ}(S_{i})\) & The impact of \(S_{i}\) on integrity \\ \(I_{avail}(S_{i})\) & The impact of \(S_{i}\) on availability \\ \(I(S_{i})\) & The overall impact of \(S_{i}\) on the IoT platform, considering all security dimensions (confidentiality, integrity, and availability) \\ \(FT_{i}\) & The time needed to fix \(S_{i}\), expressed in working hours \\ \(x_{i}\) & An output variable of the problem; \(x_{i}\) is set to 1 if security budget is \\  & allocated to fixing \(S_{i}\), otherwise \(x_{i}\) is set to 0. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations used in the integer programming problem formulation. vulnerabilities is considered, and (b) by the quantity \((1-x_{i})\), positioning it so that only the impact of security issues that remain unfixed is taken into account. * Subsequently, the cost of implementing fixes to the security issues is calculated using the formula \[Cost=\sum_{i=1}^{N}x_{i}*FT_{i}\] (4) In Equation (4) the cost \(FT_{i}\) of fixing a security issue \(S_{i}\) is multiplied by variable \(x_{i}\) positioning it so that only the cost of fixes that are selected to be applied is considered. * Finally, the security budget constraint is applied, which is formulated as follows: \[Cost\leq SB\] (5) The solution of the integer programming optimization problem is a set of value assignments to variables \(x_{i}\) \[Solution=\{x_{1}=v_{1},x_{2}=v_{2},\ldots,x_{N}=v_{N}\}\] \[\text{where }v_{i}=\left\{ \begin{array}{ll}1&\text{if security issue }S_{i}\text{ is selected to be fixed,}\\ 0&\text{otherwise}\end{array} \right.\] (6)
## 3 On-Site Monitoring Arrangement
### Longitudinal Ground Settlement  
The monitoring results of longitudinal ground settlement during the construction of two-layer pipe jacking are shown in Figure 5, in which the upper and lower layers are counted separately. The dots of the settlement curves represent the ground settlement data of measuring points when the cutterhead is pushed from 50 m to 85 m when the interval is 5 m.  
It can be seen from Figure 5 that: (1) The five settlement curves of the lower and upper pipe in the figure are basically consistent, indicating that the monitoring results collected are reliable. (2) With the approach of the pipe jacking machine ahead, the longitudinal settlement of the ground caused by the lower pipe jacking changes more gently, while the upper pipe jacking changes more rapidly. (3) The cumulative settlement of the pipe jacking machine before reaching the monitoring section accounts for about 60-70%. The main reasons are the discontinuity of pipe jacking and the difficulty in accurately controlling the pressure on the excavation surface, which leads to the larger settlement that has already occurred before the machine reaches the monitoring section. After the machine passes the monitoring section, with the bentonite mud injection, the pipe segment voids are filled, and the settlement tends to stabilize. It can be seen that it is difficult to achieve the ideal balance control of soil pressure in front of the cutterhead, so engineering measures should be strengthened for under-crossing important facilities.