esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sensing Method
### Piezolectric Sensing
#### 2.2.2 Developing Technologies  
In the realm of piezoelectric sensing, a number of novel technologies are currently in the process of development:  
* Novel Forms of Piezoelectric Materials: Piezoelectric materials serve as the core constituents of piezoelectric sensors. The commonly utilized piezoelectric materials are classified into organic and inorganic piezoelectric materials. Organic materials typically exhibit excellent flexibility; however, their piezoelectric properties are generally inferior to those of inorganic piezoelectric materials. Researchers are delving into the manufacturing techniques of piezoelectric materials. By means of additive manufacturing and 3D printing technologies, piezoelectric ceramics, piezoelectric polymers, etc., can be fabricated into sensor components of appropriate geometries. For instance, piezoelectric thin films can be adhered to the surface or embedded within composite structures, departing from the conventional stacked wafer configuration [44].
* Distinct from the piezoelectric effect, which is exclusive to non-centrosymmetric materials, the flexoelectric effect represents an emerging area of research. It characterizes the coupling relationship between mechanical strain gradients and electrode polarization. The flexoelectric effect exhibits storage dependence, and this effect becomes more pronounced as the system size diminishes [45; 46]. At present, investigations into flexoelectricity predominantly concentrate on principle exploration and the observation of the effect in crystals [47]. Researchers have developed curvature and torque sensors leveraging the flexoelectric effect [48; 49]. However, there are no specific instances of its applications in the human body. There remains a vast expanse of research potential for flexoelectric effect sensors in biological tissues. Potential application scenariosmight encompass mass sensing, drug delivery, implantable micro energy storage devices, etc. [50; 51; 52].
* Piezoelectric Power Supply Network: Piezoelectric sensors, serving as an intermediary in the conversion of mechanical energy into electrical energy, hold the potential to power other implanted electronic devices. Nevertheless, currently, piezoelectric nanogenerators (PENG) are still confronted with issues such as low output power, unstable output, and a narrow frequency spectrum range. [PERSON] enhanced the driving capacity of PENG via an improved LC matching network for frequency tracking and power regulation [53]. However, there remains a significant distance to cover before its practical application. Research indicates that resonance-based piezoelectric energy harvesters (PEHs) can amplify the resonance frequency. Nevertheless, this inevitably introduces the drawback of a narrow bandwidth [54], and simultaneously restricts the miniaturization of PEHs [55]. At present, no practical piezoelectric energy harvesting devices are available.
# Phosphine-Free-Synthesized ZnSe/ZnS Core/Shell Quantum Dots for White Light-Emitting Diodes
###### Abstract  
Blue-light-emitting ZnSe core (C) and ZnSe/ZnS core/shell (C/S) quantum dots (QDs) were synthesized with phosphine-free precursors by a thermal decomposition method in paraffin oil solvent and applied to QD-converted light-emitting diodes (LEDs). The optical properties of the synthesized ZnSe and ZnSe/ZnS C/S QDs were characterized by absorption spectroscopy and photoluminescence spectroscopy. Additionally, the quantum efficiency of the QDs was investigated. Their structural properties were studied with X-ray crystallography and transmission electron microscopy. The ZnSe/ZnS C/S QDs showed deep-blue light peaking at 425 nm. The blue-light-emitting ZnSe/ZnS C/S QDs were used as color-converting materials for near-ultraviolet LED-pumped blue LEDs and combined with yellow-light-emitting Zn-Cu-In-S/ZnS C/S QDs to fabricate white LEDs. The white LEDs showed warm white light [(CIE x, CIE y) = (0.4088, 0.3987)], \(T_{\rm c}\) = 3488 K, and R\({}_{\rm a}\) = 61.2]. The results indicate that the ZnSe/ZnS C/S QDs have good potential for white light application after further improvements to their optical properties.  
ZnSe/ZnS quantum dots; core/shell structure; blue LED; warm white LED +
Footnote †: journal: Applied  
11
# Crustal Structure of Hainan Island and Surrounding Seabed Based on High-Resolution Airborne Gravity
## 3 Materials and Methods
### Methodology  
#### Multiscale Wavelet Analysis  
The regional Bouguer gravity contains the density information of the earth. To reveal the crustal structure, we decompose the gravity field data according to the depth of the field source and obtain the gravity anomaly subset of the equivalent layer at a different depth. The multiscale wavelet analysis method was first established in 1985, and it has become more popular in separating the field data. [PERSON] et al. [41] has designed the wavelet base for the multiscale decomposition of the gravity anomaly wavelet and applied this method to geological tectonic research.  
The basis of the multiscale wavelet analysis for regional gravity data processing is that the horizontal scale of gravity anomalies is proportional to the buried depth of the gravity sources [42; 43; 44]. The multiscale wavelet analysis can be expressed as in Equation (1):  
\[\Delta\text{g}(\text{x},\text{y})=\text{A}_{\text{i}}\text{f}(\text{x},\text{y })+\sum_{\text{i}=1}^{\text{i}}\text{D}_{\text{i}}(\text{x},\text{y}) \tag{1}\]  
where \(\Delta\text{g}(\text{x},\text{y})\) is the Bouguer gravity anomaly with coordinates (x,y) on the geoid, _Aif_(x,y) is the wavelet base corresponding to the large-scale regional density structure, _Di_(x,y) is the decomposed Bouguer gravity anomaly that represents the equivalent layer at different depths, and i is the decomposed maximum order.  
In this paper, we use the multiscale wavelet analysis method to decompose the Bouguer gravity anomaly in the study region. The D1-D7 detailed gravity anomaly and 7 th-order wavelet approximation were obtained. (D1+D2+D3) is regarded as the gravity anomaly caused by a shallower crust. In the study region, this layer is related to the uppermost crust. Different wavelet details reflect gravity anomaly sources at various depths.
## 2 Methodology
### Hybrid Particle Swarm Optimization  
The joint optimization model for this study is a typical MINLP problem that contains both 0-1 variables, continuous variables, and nonlinear constraints. Thus, we use the HPSO to solve the optimization model.  
Particle swarm optimization (PSO), proposed by [PERSON] and [PERSON], is advantageous for its rapid conversion speed, few parameters, and easy operation and realization. Therefore, PSO has been widely applied in parameter optimization settings [32], process  
Figure 2: Schematic diagram of the cost matrix for multi-classification tasks.  
parameter design [33], and job-shop scheduling [34]. Its particle speed updating equation and location updating equation are as follows:  
\[V_{ab}^{t+1}=W\cdot V_{ab}^{t}+C_{1}\cdot rand()\cdot\Big{(}P_{\mathit{pbest},ab} -X_{ab}^{t}\Big{)}+C_{2}\cdot rand()\cdot\Big{(}P_{\mathit{gbest},b}-X_{ab}^{t} \Big{)} \tag{12}\]  
\[X_{ab}^{t+1}=X_{ab}^{t}+V_{ab}^{t+1} \tag{13}\]  
where \(W\) is the weight of inertia; \(V_{ab}^{t},X_{ab}^{t}\) are the particle speed and particle location of the \(a\)th particle at the \(b\)th dimension undergoing \(t\)th iteration, respectively; \(C_{1},C_{2}\) are acceleration coefficients; \(rand()\) represents a random number generated between one and zero; and \(P_{\mathit{pbest},ab},P_{\mathit{gbest},b}\) represent the local best and global best positions, respectively.  
To resolve the problem of discreteness, PSO was optimized, and binary particle swarm optimization (BPSO) was proposed. Its particle speed updating equation is as follows:  
\[S\Big{(}V_{ab}^{t+1}\Big{)}=\frac{1}{1+\exp\Big{(}-V_{ab}^{t+1}\Big{)}} \tag{14}\]  
\(S\Big{(}V_{ab}^{t+1}\Big{)}\) is the probability of particle \(a\) having a \(b\) dimension of one. Finally, Equation (15) is used to update the particle's location in the next iteration.  
\[X_{ab}^{t+1}\Bigg{\{}\begin{array}{l}1,if\ rand()\leq S\Big{(}V_{ab}^{t+1} \Big{)}\\ 0,\mathit{otherwise}\end{array} \tag{15}\]  
The evolutionary algorithm using Equations (12)-(15), namely the HPSO, can solve the MINLP problem.
## 2 Materials and Methods
### Evaluation of Subpleural Perfusion and Lung PBV Score on DE-CT  
We assessed the subpleural perfusion on DE-CT in each 5 mm slice using the methodology previously described by [PERSON] et al. [12]. This methodology is similar to that used for subpleural perfusion assessment in digital subtraction angiography [6; 7]. The subpleural area was defined as \(\leq\)1.5 cm (approximately the width of one rib) from the lateral pleura in the horizontal section.  
The subpleural perfusion level on DE-CT was classified into three types: (1) normal perfusion, (2) wedge-shaped segmental defect: no or poor perfusion spread evenly in a wedge shape due to proximal vessel occlusion, and (3) poor perfusion: no or minimal perfusion (Figure 1a-c).  
CTEPH patients were assigned to either the poorly perfused or normally perfused groups according to the type of their subpleural perfusion. The normally perfused group included patients with normal perfusion of the subpleural space in at least one segment. The poorly perfused group comprised the patients with subpleural spaces that were non- or minimally perfused in all segments [6; 7]. The wedge-shaped segmental defect was considered to be due to proximal vessel occlusion, in which case it was difficult to assess the subpleural perfusion; therefore, the area of a simple wedge-shaped segmental defect was excluded from the subpleural perfusion analysis. In patients with operable CTEPH, the perfusion area was widely defective in a wedge shape since the pulmonary artery was obstructed at either the lobar or the proximal segmental level. In these patients, subpleural perfusion assessment was difficult, and we, therefore, excluded operable CTEPH patients from our study.  
Two cardiologists blinded to the patients' identity performed the subpleural perfusion assessment of DE-CT images. The interobserver agreement was verified by a McNemar test for the first 50 patients (\(p<0.001\)). In cases where the assessment was difficult, a final consensus was reached by consulting an experienced cardiologist and a radiologist (YT and AK). Lung perfusion was quantified using the lung PBV score, calculated automatically by the dual-energy application software _syngo_ CT Workplace, VA44A (Siemens AG, Erlangen, Germany) as the sum of the iodine density scores of 18 lung segments.  
The inclusion criteria were as follows: (a) patients with PAH or non-operable CTEPH who were eligible for BPA; and (b) patients who underwent DE-CT at the time of diagnosis. The exclusion criteria were as follows: (a) patients with PH due to left heart or chronic lung diseases; (b) patients with operable CTEPH, and (c) PAH patients who had previously been treated with PAH-specific drugs or those with CTEPH who had previously undergone pulmonary endarterectomy or BPA.
## 2 Method
### Improvement of FISH Function  
For pores with negative pore water pressure (matrix suction) above the infiltration line, the TSFDM automatically sets their pore water pressure as 0. Then the permeabilitycoefficient is a constant. However, this is unreasonable, as the permeability coefficient of the rock-soil mass in the slope is not a constant, but changes with the saturation degree and porosity. The permeability coefficient can be expressed by the Van Genuchten model that describes the soil water characteristic curve and the permeability of unsaturated slope [11].  
\[\theta=\theta_{r}+\frac{\theta_{s}-\theta_{r}}{\left[1+\left(p/a\right)^{n} \right]^{m}} \tag{6}\]  
\[s=s_{r}+\frac{1-s_{r}}{\left[1+\left(p/a\right)^{n}\right]^{m}} \tag{7}\]  
\[k_{r}=S^{1/2}[1-\left(1-S^{1/m}\right)^{m}]^{2} \tag{8}\]  
\[k=k_{w}k_{r}=k_{w}S^{1/2}[1-\left(1-S^{1/m}\right)^{m}]^{2} \tag{9}\]  
where \(\theta\), \(\theta_{r}\) and \(\theta_{s}\) are the corrected, residual and saturated volumetric water content, respectively; \(s\) represents the corrected saturation degree, \(s_{r}\) is the residual saturation degree, \(a\), \(n\) and \(m\) are the fitting parameters. Formula (8) expresses the relationship between the relative permeability coefficient and saturation degree, from which we can derive Formula (9). \(k_{w}\), \(k_{r}\) and \(k\) are the saturated, relative and corrected permeability coefficients, respectively. The following analysis is based on Formulas (6)-(9).  
The shear strength parameters of saturated and unsaturated soil are quite different, so we improved the FISH function to analyze the shear strength changes in the saturated and unsaturated slope separately. The function can adjust the shear strength parameters automatically according to the pore water pressure [12].  
The specific improvements are as follows:  
(1) Set the fluid tensile strength threshold, which allows the nodal flow outflow and forms a negative pressure zone during the seepage calculation.  
(2) The pore water pressure changes with the hydraulic boundary. Use the built-in variable zone.pp (xcz) to obtain the unit negative pore water pressure, and then calculate the unit saturation degree by Formula (7). Distribute one constitutive model and the initial shear strength parameters to each unit.  
(3) Substitute the obtained unit saturation to Formula (8) to calculate the unit relative to permeability coefficient, and then arrive at the corrected unit permeability coefficient by Formula (9). Meanwhile, adjust the unit shear strength parameters according to the pore water pressure.  
Figure 1 shows the flowchart using the FISH function to calculate the unit permeability coefficient and shear strength parameters of the unsaturated soil.
# Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology
## 4 Design of the IoT Communication Network
For example, in North America, the 915 MHz frequency is used, while in Europe it is 868 MHz, and in Asia the 169 MHz, 433 MHz are used. The bandwidth (\(BW\)) determines the range of frequencies that coded information is spread within. Higher \(BW\) gives a faster data transmission rate however it increases noise sensitivity. In LoRa, the \(BW\) sets are typically 125, 250, and 500 kHz. Spreading Factor (\(SF\)) is the proportion of the symbol rate to the chip rate, and commonly takes a value of between 7 to 12. A higher \(SF\) increases the signal-to-noise ratio, transmission range (to be in the km range), and Time-On-Air (TOA) of a data packet. The coding rate \(CR\) can take the following values: 4/5, 2/3, 4/7, and 1/2. Increasing \(CR\) would increase data transmission rate; however, this can reduce the TOA, and therefore, data transfer becomes less reliable. Based on the above-mentioned parameters, the transmission rate (\(R_{b}\)) in bps for the LoRa network is given by:  
\[R_{b}=SF\frac{BW}{2^{SF}}CR \tag{36}\]  
In the following section, a suitable configuration setting of a LoRa-based communication network that fulfills efficient communication link within the microgrid is to be discussed. Figure 14: Representation of IoT LoRa-based communication network within proposed microgrid design.
## 2 Background  
### Facility Location Problem  
The Facility Location Problem (FLP) has attracted a lot of research focused on problem optimization in recent decades. These studies cover different contexts: the locations of cellular towers [28], the allocation problem considering the COVID-19 context [29; 30], and urban services [31].  
The FLP consists of allocating one or more facilities of different natures in a given location, meeting a set of pre-established criteria. In other words, it seeks to select the best possible alternative to locate a given number of facilities in order to serve a high level of service to customers, minimizing the costs of the operation and maximizing its profits [24; 32].  
[PERSON] et al. [33] state that it is possible to group the location problems in a Euclidean plane or in a network. The authors describe that the location models in the plan commonly involve problems in which the possibilities for locating installations are endless. Network problems are continuous or discrete. They are continuous if the location of the facilities is at the vertices or anywhere on the arches. They are discrete if the location of the facilities can only be set at the vertices.  
According to [PERSON] and [PERSON] [34], the most common model to solve location problems are the discrete ones, which consist of structuring pre-selected datasets of the possible locations that should be used to install the units. [PERSON] and [PERSON] [22] corroborate this statement, defining that the possible allocation points of an installation do not exist anywhere in the space but are generally considered through previous studies and analyses.  
Today, facility-location models are well-structured and widespread in the most diverse areas of engineering, with wide applications and more than a century of history in the scientific literature [33]. However, this scenario was only reached due to the complexity imposed by the FLP, which has grown in recent years, leading to the need to develop mathematical models that can generate optimal solutions [5; 25; 26].  
As location models seek to find the best places to allocate facilities according to locations of customer demand [35], researchers created vast volumes of literature to grapple with this issue. One of the problems that were addressed is maximum coverage, which suits the need to locate police facilities.
## 4 Methodology
### Back Propagation Neural (BP) Network  
The topology of the BP neural network model includes an input layer, hidden layers, and an output layer. Increasing the number of hidden layers or the number of nodes in each hidden layer can improve training quality and reduce training errors. The BP neural network model can be represented by the following equations:  
Objective Function:  
\[\text{min}E(w,b,v,c)=\frac{1}{2q}\sum_{p=1}^{q}\sum_{k=1}^{l}\left[\mathcal{O }_{k}(p)-\hat{G_{k}}(p)\right]^{2} \tag{5}\]  
Constraints:  
\[\mathcal{O}_{k}=f(\sum_{j=1}^{m}v_{j,k}y_{i}+c_{k})\quad k=1,2,\ldots,l;j=1,2, \ldots m \tag{6}\]  
\[y_{i}=f(\sum_{i=1}^{n}w_{i,j}x_{i}+b_{j})\quad j=1,2,\ldots,m;i=1,2,\ldots n \tag{7}\]  
\[f(x)=\frac{1}{1+e^{-x}} \tag{8}\]  
where \(w\) and \(v\) represent the weight matrices to be determined, while \(b\) and \(c\) represent the weight matrices to be determined. \(\mathcal{O}_{k}(p)\) denotes the actual output of the \(k\)-th neuron in the output layer for the \(p\)-th test point, and \(l\) is the total number of neurons in the output layer. \(v_{j,k}\) represents the weight of the \(j\)-th neuron in the hidden layer to the \(k\)-th neuron in the output layer; \(w_{i,j}\) is the weight of the \(i\)-th neuron in the input layer to the \(j\)-th neuron in the hidden layer; \(y_{i}\) denotes the output of the \(j\)-th neuron in the hidden layer; \(x_{i}\) is the value of the \(i\)-th neuron in the input layer; \(c_{k}\) and \(b_{j}\) are the thresholds of the \(k\)-th neuron in the output layer and the \(j\)-th neuron in the hidden layer, respectively; \(m\) and \(n\) represent the number of neurons in the hidden layer and the input layer, respectively; \(q\) is the number of samples in the training set.
# Millimeter-Wave High-Gain Dual-Polarized Flat Luneburg Lens Antenna with Reflection Cancellation
## 4 Implementation, Measurements and Discussions
### Measurement of FLLA
As demonstrated in [20], the focal plane of the FLL is not a planar one, but a curvature one for which a shorter focal length is required for a larger shift in the feeding source.
## 3 Design Consideration for g-C\({}_{3}\)N\({}_{4}\)/CQDs-Based Nanocomposites
### g-C\({}_{3}\)N\({}_{4}\)/CQDs-Based Nanocomposites for Different Antibiotic Degradation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Composite** & **Target Pollutant** & **Source of Illumination Light** & **Degradation Efficiency** & **Ref.** \\ \hline Carbon-dots/g-C\({}_{3}\)N\({}_{4}\) & Rhodamine B (RhB) dye & 3 W LED lamp (365 \(\pm\) 5 nm) & RhB degradation: 0.013 mol L \({}^{-1}\) & [60] \\  & & & min\({}^{-1}\) within 60 min & \\ \hline g-C\({}_{3}\)N\({}_{4}\)/Carbon dots (0.5 wt\%) & Phenol & 300 W Xe lamp (\(\lambda\)! 400 nm) & Phenol degradation: 100\% within 200 min & [61] \\ \hline Carbon-dots/g-C\({}_{3}\)N\({}_{4}\) & 2,4-dichlorophenol & Solar light & 2,4-DCP degradation: 71.53\% within 2 h & [62] \\ \hline N-doped & RhB dye & 300 W Xe lamp (\(\lambda\)! 420 nm) & RhB degradation: 100\% within 4 h & [63] \\ Carbon-dots/defect-rich g-C\({}_{3}\)N\({}_{4}\) & & & & \\ \hline g-C\({}_{3}\)N\({}_{4}\)/S-doped carbon nitride & & & & \\ quantum dots (S-CNQDs) & Methyl Orange (MO) & 300 W Xe lamp (\(\lambda\)! 350 nm) & MO degradation: 100\% within 2 h & [64] \\ \hline Zn\({}_{2}\)V\({}_{2}\)O\({}_{7}\)/Carbon dots/g-C\({}_{3}\)N\({}_{4}\) & RhB dye & 300 W Xe lamp (\(\lambda\)! 420 nm) & RhB degradation: 97\% within 30 min & [65] \\ \hline BiVO\({}_{4}\)/Carbon dots/g-C\({}_{3}\)N\({}_{4}\) & Minocycline & & Mn-HCL degradation: 65.3 within 140 min & \\  & hydrochloride (Mn-HCL) & 350 W LED lamp (460 nm) & 140 min & [66] \\  & and RhB dye & & RhB degradation: 85\% within 30 min & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Carbon quantum dots/g-C\({}_{3}\)N\({}_{4}\)-based photocatalysts for pollutant degradation under various conditions.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Comparison Experiment
#### 4.5.2 Comparative Analysis of Different Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **P** & **R** & **mAP** & **FPS** \\ \hline Faster RCNN & 0.75 & 0.65 & 0.71 & 25.0 \\ Cascade RCNN & 0.79 & 0.72 & 0.78 & 35.1 \\ TPH-YOLOv5 & 0.738 & 0.68 & 0.725 & 108.5 \\ YOLOv5 & 0.722 & 0.604 & 0.686 & 118.0 \\ YOLOv8 & 0.743 & 0.75 & 0.756 & 103.5 \\ ours & 0.824 & 0.744 & 0.82 & 110.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative test results of different models. paper not only achieves superior precision and recall, with mAP reaching 0.448, but also inherits the advantage of the light weight of the YOLO series, and the model's detection speed of 110.2 FPS is sufficient to meet real-time detection demands. This shows that the algorithm in this paper is still able to maintain high robustness and efficiency in the face of complex and variable defect detection tasks and is suitable for different datasets and application scenarios. Figure 9 presents a comparison of detection results between the proposed model and YOLOv8 on the Tianchi dataset. In both sample (a) and sample (d), although YOLOv8 correctly recognizes the defect categories, its confidence level is slightly lower than that of this paper's model. In sample (b), YOLOv8 has a missed detection situation and does not recognize the defects of the three-line type. And YOLOv8 in sample (c) has a misdetection situation, incorrectly recognizing the stain type as the hole type. The proposed model significantly improves the classification and detection accuracy of fabric defects.