esa-sceva/satcom-chunk-collection huggingface dataset:
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 2 Experiments  
### Experimental Apparatus and Samples  
Owing to the difference of wire rope structures and diameters as well as the sophisticated detection environments, steel wire rope inspection signals are often mixed with various interference features and sources. To obtain the weak signals detected from different sensors, liftoff distances, and broken wire sizes, three steel wire rope samples were mainly applied and tested through an MFL testing apparatus, as illustrated in Figure 1. Primarily, there are two types of surface defects in wire rope 1, where a one-wire broken and a three-wire broken defect were made. Similarly, a mixed surface defect with broken and wear characterizations was inspected on the wire rope 2, and an inner defect artificially made in wire rope 3 was also tested, which are indicated in Figure 1a, and the diameters of these steel wire rope samples are 28 mm, 32 mm, and 45 mm, respectively. Further, the MFL based detector is shown in Figure 1b, where the liftoff distance and position of the sensor element could be adjusted according to the feed-through wearing ring, and a position encoder in the detector head can guarantee the exact location of the defect. If any defects were scanned and encountered by the circumferentially installed magnetic sensitive sensors inside the detector, the leaked magnetic flux would be captured by the sensor and transformed to voltage signals. After data acquisition and signal pre-amplification by the circuit modules, different experimental signals are displayed on the computer and further processed in the next step.
###### Abstract  
The purpose of this study was to evaluate the bactericidal effect of 270 nm UV-C light-emitting diode (LED) light delivered through a newly designed prototype device with thin optical fiber against _Enterococcus faecalis_ (_E. faecalis_). The prototype device, developed to integrate UV-C light into a thin optic fiber (diameter 124 \(\mu\)m) connected to a UV-C LED (Luminous Device; Sunnyvale, CA, USA) via a specialized double-lens system that focuses divergent light to achieve a 65 mm working distance and a numerical aperture of 0.22. _E. faecalis_, was cultured at 37 \({}^{\circ}\)C under aerobic conditions for 24 h. The UV-C LED optical fiber was positioned 10 mm above the bacterial culture prepared in the wells of a 96-well plate. The _E. faecalis_ cells were exposed to UV-C irradiation for 0, 10, 30, 60, 90, 120 and 180 s. Following irradiation, the OD600 values were measured after incubation at 37 \({}^{\circ}\)C for an additional 24 h. The data were statistically analyzed using one-way ANOVA, followed by [PERSON]'s honestly significant difference (HSD) test at a significance level of 0.05. UV irradiation at 270 nm significantly reduced _E. faecalis_ growth in a time-dependent manner (_p_ < 0.05). No significant changes were observed at 0 and 10 s, while peak reductions occurred at 120 and 180 s, with effects beginning at 30 s and increasing over time. The 270 nm UV-C wavelength was highly effective in bactericidal action against _E. faecalis_. The custom-designed UV-C delivery system effectively integrated the light source into a thin optical fiber, allowing for efficient UV-C light transmission and demonstrating its potential for application in narrow spaces such as root canals.  
bactericidal effects; disinfection; _E. faecalis_; fiber optics; root canal therapy +
Footnote †: journal: Applied Sciences
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 2 System Physics Modeling
### Dynamic Heat Transfer Model of System
#### 2.2.2 Dynamic Heat Transfer Model of Tube-Air-ADS
Equation (6) describes the definition of the same parameters related to foam as Equation (7). The heat exchange between the Tube-Air-ADS and the outside world consists of two parts. One is the heat flux A formed by the air at the tube mouth and the room environment, and the other is the inward heat flux B formed by the tube wall and the ethanol bath thermostat:  
\[q_{top}=h_{top}(T_{room}-T), \tag{8}\]  
\[q_{side}=h_{side}(T_{eth}-T), \tag{9}\]  
where \(h_{top}\) is the heat transfer coefficient of air at the top of the glass tube, \(T_{room}\) is the indoor ambient temperature, \(h_{side}\) is the heat transfer coefficient of the side wall of the tube, and \(T_{eth}\) is the ethanol temperature. Since there is a blender in the ethanol bath, the flow of ethanol outside the tube is calculated by forced convection. The heat transfer coefficient can be expressed by \[h_{sidve}=\frac{k_{eth}}{D}\left\{0.3+\frac{0.62R_{e}D^{1/2}P_{r}{}^{1/3}}{\left[1+ \left(\frac{0.4}{P_{r}}\right)^{2/3}\right]^{1/4}}\left[1+\left(\frac{R_{e}D}{2. 82\times 10^{5}}\right)^{5/8}\right]^{4/5}\right\}, \tag{10}\]  
where \(D\) is the diameter of the glass tube, \(R_{e}\) is the Reynolds number, \(P_{r}\) is the Prandtl number, and \(k_{eth}\) is the thermal conductivity of ethanol. During the iteration of the equation, the air in the tube undergoes heat exchange with the tube inner wall, indoor environment, foam, etc., and the temperature field \(\mathbf{T}(z,r,t)\) and velocity field \(\mathbf{u}(z,r,t)\) are continuously updated with parameters. Finally, the temperature field and velocity field reach a stable state. Temperature \(T^{\prime}\) in the thermistor region at \(t_{i}\) is obtained after a given temperature step \(T_{i}(i=1,2,\cdots,n_{u})\) is entered \(t_{i}\). The dynamic heat transfer function of the glass tube air-probe system can be described as \(G_{2}(z)=M(z)\big{/}D(z)\). The number of zeros and poles of \(G_{2}(z)\) will be determined according to the form of the system step response. The time domain function of the system has a complex form; so, it is difficult to obtain the transfer function of the system through the Laplace transform. The system transfer function can be analyzed as the time domain waveform of the system dynamic response, which is a nonlinear least squares problem. Using the Sanathanan-Koerner (S-K) iterative method for least squares solution [44], the solution objective function can be described as  
\[\underset{D,M_{i}}{\text{minimize}}\sum_{k=1}^{n_{f}}\left|W(\omega_{k}) \left[T^{\prime}{}_{i}(\omega_{k})-\sum_{i=1}^{n_{u}}\frac{M_{i}(\omega_{k})}{ D(\omega_{k})}T_{i}(\omega_{k})\right]\right|^{2}, \tag{11}\]  
where \(\omega_{k}\) are the frequency points involved in the process of solving the objective function optimally, \(W\) is the associated weight of the specified frequency, and \(n_{k}\) is the number of frequency points selected.
###### Abstract  
Cyber-physical systems such as Supervisory Control and Data Acquisition (SCADA) have been applied in industrial automation and infrastructure management for decades. They are hybrid tools for administration, monitoring, and continuous control of real physical systems through their computational representation. SCADA systems have evolved along with computing technology, from their beginnings with low-performance computers, monochrome monitors and communication networks with a range of a few hundred meters, to high-performance systems with advanced 3D graphics and wired and wireless computer networks. This article presents a methodology for the design of a SCADA system with a 3D Visualization for Drinking Water Supply, and its implementation in the Lerma Basin System of Mexico City as a case study. The monitoring of water consumption from the wells is presented, as well as the pressure levels throughout the system. The 3D visualization is generated from the GIS information and the communication is carried out using a hybrid radio frequency transmission system, satellite, and telephone network. The pumps that extract water from each well are teleoperated and monitored in real time. The developed system can be scaled to generate a simulator of water behavior of the Lerma Basin System and perform contingency planning.  
cyber-physical systems; SCADA; 3D visualization; water management; real-time monitoring; GIS; Industry 4.0
## 2 Materials and Methods
### Datasets
#### 2.2.4 Imerg7  
Integrated Multi-satellite Retrievals for GPM is a key product of NASA's Global Precipitation Measurement (GPM) mission, which provides multi-satellite high-resolution global precipitation estimates. The first official version of IMERG was introduced in 2014, which built upon the legacy TRMM's 3B42 algorithm with the combination of GPM core Observatory data and a constellation of partner satellites [14]. The algorithm for IMERG was upgraded to version 07 in the summer of 2023. In response to feedback on IMERG V06, IMERG V07 underwent a wide range of changes to all aspects of the algorithm. IMERG V07 is being reprocessed for the entire TRMM-GPM records, which makes it more significant than previous versions, as well as than prior TRMM-based TMPA datasets (3B42, 3B43). IMERG V07 collectively improved the performance in terms of precipitation detection and systematic bias random bias. IMERG V07 provides three precipitation products with different time legacies with a similar high resolution (0.1\({}^{\circ}\)). The Early Run dataset is produced within 4 h after multi-satellite observations and it is useful for monitoring real-time extreme weather events [36]. The Late Run dataset is a more refined version of the Early Run and was produced within 14 h. The Final Run dataset is the most accurate and thoroughly processed version of the IMERG products. It was produced in 3.5 months after extensive validation against rain gauge data and ensuring consistency between the data from other satellites. All three datasets create half-hourly 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) products (3 IMERGHH), while the Final Run additionally offers a monthly satellite-gauge combination dataset (3 IMERGM). In the current study, we obtained the IMERG V07B Final Run dataset from the official website [[https://gpm.nasa.gov/data/directory](https://gpm.nasa.gov/data/directory)]([https://gpm.nasa.gov/data/directory](https://gpm.nasa.gov/data/directory)) (accessed on 21 July 2024), which contained the mean precipitation for each hour and multiplied it by the number of hours per day and the number of days per month to generate the total monthly precipitation.
## 3 Results
### Color Measurement (L, a, and b Values)
#### 3.1.3 b Values  
The results of the statistical analysis of the yellow-blue color component, or b values, are shown in Figure 6. The three crown materials, E-max, Lisi, and Celtra, had positive b values, demonstrating yellow components. The b value was significantly low in Celtra (\(p<0.05\)) compared to E-max and Lisi, as shown in Figure 6A.  
Regarding the b values after immersion in saliva and coffee and after brushing, all the materials showed a positive yellow component. The changes in the yellow component values were significant only for Celtra (\(p<0.05\)), as shown in Figure 6A,B.  
Figure 5: Mean \(\pm\) SD of a values (**A**) at the starting point, following immersion in artificial saliva and following brushing; (**B**) at the starting point, following immersion in coffee and following brushing. Different letters denote statistically significant differences. The comparison of different crown materials at the same measurement time point is indicated by upper-case letters. The comparison of the same crown material at different measurement time points is indicated by lower-case letters.
# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
## 4 Experiment and Results
### Performance Criteria
#### Triplet Semi-Hard Loss  
Triplet Loss trains a neural network to maximize the distance between embeddings of different classes while ensuring that embeddings of the same class are closely grouped together. This is achieved through selecting an anchor sample, along with a positive and negative sample [44]. This triplet loss aims to guarantee that a picture \(x_{i}^{d}(\textit{anchor})\) of a certain entity is closer to all other images \(x_{i}^{p}(\textit{positive})\) of that entity than it is to any image \(x_{i}^{n}(\textit{negative})\) of any other entity. Figure 8 illustrates this.  
The number of triplets that can readily satisfy the requirement in Equation (3) would increase if all potential triplets were generated. Since they would still be propagated over the network, these triplets would not support training and would slow down the computation.  
\[\|x_{i}^{a}-x_{i}^{p}\|_{2}^{2}+\alpha<\|x_{i}^{a}-x_{i}^{n}\|_{2}^{2},\forall( x_{i}^{a},x_{i}^{p},x_{i}^{n})\in\tau. \tag{3}\]  
where the boundary amid positive and negative pairings is defined by \(\alpha\). \(\tau\), which has a cardinality of \(N\) represents the complete set of all possible triplets in the training set.  
Therefore, \(L\) is equal to the loss that is being reduced in the below equation:  
\[\sum_{i}^{N}[\|f(x_{i}^{a})-f(x_{i}^{p})\|_{2}^{2}+\alpha<\|f(x_{i}^{a})-f(x_{ i}^{n})\|_{2}^{2}|+\alpha]_{+}. \tag{4}\]  
Using a smaller batch size while choosing the hardest negatives, on the other hand, can result in collapsed models (i.e., \(f(x)=0\)) and inadequate local minima early in the training process. To counteract this, choose \(x_{i}^{n}\) such that:  
\[\|f(x_{i}^{a})-f(x_{i}^{p})\|_{2}^{2}<\|f(x_{i}^{a})-f(x_{i}^{n})\|_{2}^{2}. \tag{5}\]  
where the negatives are contained within the margin \(\alpha\). The function \(f(x)\) explains the embedding function, which receives an image \(x\) into a d-dimensional Euclidean space. The image \(x_{i}^{a}\) is anchor, image \(x_{p}^{a}\) is positive, and \(x_{p}^{n}\) is negative subsequently. The part \(\|f(x_{i}^{a})-f(x_{i}^{p})\|_{2}^{2}\) from the equation subtracts the anchor image with the positive image between the embeddings to determine Euclidean distance. Meanwhile the second part, \(\|f(x_{i}^{a})-f(x_{i}^{n})\|_{2}^{2}\), calculates the Euclidean distance between the embeddings by subtracting the anchor image from the negative image. The negative quintessences from Equation (5) are classified as _semi-hard_ because, while being farther from the anchor than the positive exemplar, they are challenging because the squared distance is close to the anchor-positive span. In other words, these are called triplets when the negative creates a positive loss, despite being further away from the anchor than the positive.  
In this study, we prefer the \"semi-hard\" category among the \"easy\" and \"hard\" triplet categories because it allows the model to learn valuable features without overfitting, and it generalizes well to the test data [40].
## 3 Numerical Analysis Results  
Before closing the investigation on stress concentration factors, the convergence of 3D finite element analysis depending on the mesh size was checked. The coarse mesh model with seed size of 2.0 mm showed just 0.537% deviation compared to the fine mesh model with seed size of 0.2 mm, and stress concentration factors were highest in the first engaged thread and decreased in each successive thread moving toward the end of the bolt as listed in Table 4. Due to the time-consuming and costly nature of computation, a seed size of 0.2 mm was used for the thread and transition zone and a size of 2.0 mm was used for the body of the bolt and nut as shown in Figure 8. CA4X elements were used for the two-dimensional (2D) FE model, and C4D8 elements were used for the 3D FE model.  
In this section, various bolted joint conditions with geometric design modifications were numerically analyzed to determine the extent to which they reduce stress concentrations. The design modifications included nut type (standard vs. round), nut height (number of engaged threads), shank diameter, and relief cone angle.
# Sloping Farmlands Conversion to Mixed Forest Improves Soil Carbon Pool on the Loess Plateau
## 2 Materials and Methods
### Experimental Design and Soil Sampling  
Five land use types, sloping farmland (SL), grassland (GL, natural restoration), shrubland (SL, _Caragana korshitskii_), forestland (FL, _Robinia pseudoacacia_), and mixed forests (ML, _Robinia pseudoacacia + Caragana korshitskii_, in the watershed were selected for study. Three forest types were planted on sloped farmlands, and the grasslands developed from abandoned sloped farmlands (control). Between 1988 and 1990, all forests, shrubs and grasslands were planted or naturally restored by the local farmers. In 2018, our project team established 12 plots in these affected systems. Management histories for the 30 years of plant growth were obtained by interviews with local farmers and village elders and by reviewing rental contracts between farmers and the government.  
In each vegetation restoration type, three 20 m \(\times\) 20 m plots were established in August 2018 when the plant biomass peaked. Five quadrants (1 m \(\times\) 1 m) were separately chosen in each of the four corners and center of the plots. Litter horizons were removed before soil sampling. Soil sampling, using a soil drilling sampler (9 cm inner diameter), was performed in the 0-20 cm soil layers. We then mixed the same layers together to form one sample. All samples were sieved through a 2 mm screen, and roots and other debris were removed in the field. Each sample was air-dried and stored at room temperature for the determination of soil physical and chemical properties. The soil bulk density (g cm\({}^{-3}\)) of the different soil layers was measured using a soil bulk sampler with a 5 cm diameter and 5 cm high stainless steel cutting ring (3 replicates) at points adjacent to the soil sampling quadrats. The original volume of each soil core and its dry mass after oven-drying at 105 \({}^{\circ}\)C over 48 h were measured for bulk density determination. The morphological traits of the herbage in each age group are listed in Table 1. The plots were all located near the top of the loess mounds. All plots were located in the hill-slope, loess-derived soil and north-faced slope. And there was little difference among the sites in regard to gradient, altitude, or previous farming practices.  
Figure 1: Location of the Zhifanggou watershed on the Loess Plateau, China.
## 2 Related Work
The authors in [76] develop a PSO-based super twisting sliding mode controller to balance the consumer demand according to the generation using dynamic pricing. An intelligent framework based on genetic and binary particle swarm optimization algorithm is developed in [77] for DSM in SPG. Similarly, an intelligent framework based on GACO is proposed in [78] for the energy optimization of renewable integrated SPG. A two-stage framework is developed in [79], where the electric load is forecasted for efficient EM in SPG. In [80], RESs are integrated with SPG to solve the DSM problem. The authors in [81] introduce the DR program in SPG to solve the DSM problem. Refs. [77; 78; 79; 80; 81], solve the DSM problem with the aim to minimize energy cost, carbon emission, user comfort, and PADR. Likewise, the authors develop control approaches in [82; 83] to solve energy and load balancing problems in SPG and data centers, respectively, with the aim to minimize energy costs. The proposed model is compared with existing models in aspects of techniques, objectives, scenarios, and pricing schemes in Table 1. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref.** & **Algorithms** & **Objectives** & **Scenarios and DR** & **Remarks** \\ \hline \multirow{4}{*}{[73]} & DE, BPSO, WDO, GA, and EDE & Utility bill, PADR, and user discomfort & Scenarios: interruptible, noninterruptible, and hybrid loads, DR: RTP & Only intrinsic algorithms are considered, and pollution emission is ignored. \\  & & & Case studies: with grid only, and with RES and ESS, DR: RTP & Additionally, RES, electric vehicles (EVs), and battery charging/discharging scheduling are not catered to. \\ \hline \multirow{4}{*}{[74]} & GA, BPSO, WDO, DE, EDE, and EDGE & Energy cost, PADR, and carbon emission & Case studies: with grid only, and with RES and ESS, DR: RTP & User comfort is ignored, and objectives are achieved at the expense of a slow convergence rate. RES are considered; however, EVs and battery charging/discharging scheduling are not catered to. Comparison is only made with intrinsic algorithms. \\ \cline{1-1}  & MILP, ACO, and ANN-EDE & Energy cost, PADR, and pollution emission and pollution emission & Scenarios: with microgrid and without microgrid, DR: flat pricing scheme (FPS), load and generation prediction & Load and microgrid generation are stochastic and intermittent, which makes its accurate prediction difficult. User discomfort is ignored. Only an intrinsic model is used, and achievement of conflicting objectives is difficult. \\ \hline \end{tabular}
\end{table} 
Table 1: Summarized related work.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Network Layer
#### 5.3.3 Routing  
HARE uses its own distance vector routing protocol inspired by RPL to build a destination oriented directed acyclic graph (DODAG) [23]. This protocol is only executed as part of the _Network association mechanism_ and the _STA association mechanism_, so that there is no continuous routing packet exchange among neighbors. Thus, according to the responses to the discovery message coming from other nodes, each STA determines the best candidate to become its parent in its path to the GW; i.e., the node with the minimum \(S\) value from:  
\[S=a_{1}\cdot(P_{\text{TX}_{max}}-\text{RSSI}_{\text{TX}})+a_{2}\cdot(P_{\text{TX }_{max}}-\text{RSSI}_{\text{RX}})+a_{3}\cdot r+a_{4}\cdot c, \tag{1}\]  
where \(P_{\text{TX}_{max}}\) is the maximum transmission power of the transceiver (in dBm), \(\text{RSSI}_{\text{TX}}\) is the RSSI received at the candidate (in dBm), \(\text{RSSI}_{\text{RX}}\) is the RSSI received at the STA itself (in dBm), \(r\) is the ring to which the candidate belongs, and \(c\) is the current number of candidate's children. The \(a\) weights are attached to every _primary beacon_, and can be tuned by the GW according to environmental requirements (A study on selection of optimal routing configuration parameters in multi-hop LPWANs like HARE can be found in [38].).  
Once the best parent is computed, the STA sends it a unicast request. This request will be forwarded by the parent through its own path until reaching the GW, which will send a packet via broadcast confirming the association and providing the requesting STA with its new address. This way, both the newly associated STA and its parent are informed of the establishment of the new path.  
When the association process is finished, the STA exactly knows which is its ring, and the next hop its messages must follow to reach the GW. As long as the STA is associated to the network, it uses the same routing path, which is only recomputed after an internal or external (i.e., from its parent) failure. Indeed, no new routing process is initiated unless it is part of a new _network association_ instance.