esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Overall Design and System Analysis
### System Implementation
#### 4.3.3 Integrated Hardware and Software Design  
For an underwater system, communication and energy are two primary challenges. Separated hardware and software design will increase energy consumption, cause acoustic and electromagnetic interference, limiting global service quality. To solve this problem, an integrated design is essential, which means that the hardware has to satisfy each demand of CPNT.  
With the integrated signal, the information needed for the CPNT service can be well transmitted. In this part, we discuss the integrated hardware and software design. Since the length of the integrated signal is longer than the positioning signal, the communication hardware can also be used for positioning. That means that common acoustic modem hardware can be reused for communication, positioning, and navigation. Meanwhile, considering the need for timing, a high-precision clock module is added and the entire architecture is shown in Figure 6.  
It is important to conduct an integrated software design to realize the management and coordination of energy and communication resources. The \(U^{2}CPNT\) software typically consists of the following components: user space and kernel space in the embedded Linux system, application modules, and functional modules in the DSP system. And data exchange between ARM and DSP processors is achieved through inter-core communication, as shown in Figure 7. This architecture enables coordinated hardware-software interaction between ARM and DSP while maintaining efficient power utilization and real-time communication, positioning, and networking capabilities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Communication** & **Positioning** \\ \hline Dual-HFM & Signal Detection, Synchronization & Estimation of Signal Arrival Times \\ CP-OFDM & Delivery of Communication Aiding Information & Delivery of Positioning Aiding Information \\ ZP-OFDM & Carrying Communication Data Payloads & Carrying Node Position Information \\ \hline \hline \end{tabular}
\end{table}
Table 2: Function description of each part in signal frame.  
Figure 5: \(U^{2}CPNT\) signal frame structure which consists of Dual-HFM, time slots, CP/ZP and data blocks.
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 3 Materials and Methods  
### Proposed Positioning Method  
In trilateration, the position of the target is in the intersection between geometric forms, such as circles or hyperbolas defined by the distance between the target and the RP [1; 2; 3]. Any ranging information can be used to obtain the final target position, such as time of arrival (ToA), RSSI, or RTT time measurements. A minimum of three sets of reference points and ranges to each one is required for location in 2D. The proposed algorithm is explained in Algorithm 1. First, once the ranging information is received, the MLE weights each source depending on whether the source is new or the system already has information about it. Then, the trilateration algorithm based on the weighted least-square (WLS) algorithm is computed [24]. Once the position is obtained, the algorithm computes the error based on the distance provided by the source and the computed position. Finally, this error is temporarily stored and the weighting factor of the source is updated for the next iteration. In this section, both techniques that will be used in this paper are described: multi-technology fusion and maximum likelihood estimation (MLE).  
```
Input: Distance and position from reference points. foreach Reference Pointdo ifReference Point is newthen  Assign a low initial weight. else  Weight the reference point according to the accuracy of the last measurements. end end  Compute the position of the target by WLS algorithm. Estimate the distance from the target position to each reference point. Calculate the error between the input and the estimated distances. Storage temporarily the error of each reference point. Output: Position of the target.
```  
**Algorithm 1**Positioning algorithm with MLE and fusing technologiesFor a better understanding, we have also provided a flowchart of the proposed positioning method that will help to understand how the whole system works in Figure 1.
## 2 Materials and Methods
### Spectra Processing and Analysis  
Raw NIR spectra of water systems are often difficult to directly extract useful information about the samples, so three different data-processing methods were used to extract information about the water during plasma treatment. Prior to data processing, the \(x\)-axis of all NIR spectra in this study was converted from wavenumber to wavelength by dividing 1 by the wavenumber since it is suitable for aquaphotomics analysis. Three consecutive spectra were averaged and the range from 1300 to 1860 nm (data points: 1202), which contains most water information, was selected for further analysis. The Unscrambler X 10.4 (64-bit) software and MATLAB R2022a software (MathWorks Inc., Natick, MA, USA) were introduced for data analysis.
# Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
## 1 Introduction
### Organization  
The remainder of the paper is organized as follows. Section 2 presents the details of the system model. In Section 3, analytical expression of PEP is provided with derivations. Section 4 provides the details of derivations of ergodic capacity. In Section 5 simulation results and their discussion is given. Finally, Section 6 concludes the work and provides some future research directions.
## 5 Implementation
### Hardware Setup
#### 5.1.2 Communication Module  
We connected the ESP8266 Wi-Fi module to the Arduino Mega, which allowed the system to send data to the ThingSpeak IoT platform. ESP8266 was connected to the Arduino Mega via serial communication, thereby ensuring a correct data transfer configuration.  
To upload each track's LDR data to ThingSpeak using ESP8266 and Arduino Mega, the Arduino read the values from the LDR sensors placed on each road and processed them to determine the state of the road. The ESP8266 was connected to a Wi-Fi network and sent an HTTP GET request to ThingSpeak with the processed LDR value. The state of each road was sent as a separate (e.g., road11 dr1, road11 dr2) element of the request, using the ThingSpeak API key to obtain authorization. This allowed road conditions to be monitored through the ThingSpeak dashboard.
## 3 Results and Discussion
### Numerical Simulation of Stress-Relief Groove Utility Analysis
#### 3.1.5 Summary of the Effects of Trenching and Unloading on the Surrounding Rock  
1. Grooving for stress relief exhibits significant stress transfer effects, with a clear trend of stress concentration moving in the direction away from the tunnel. Central grooving proves to be more effective than corner grooving, as corner grooving can cause a certain degree of asymmetric stress distribution.
2. While floor grooving reduces displacement at the tunnel floor, it increases displacement at the tunnel sides, with the impact of the groove's depth being more significant than its width. Changes in the tunnel structure due to grooving may lead to conflicts in surrounding rock control; however, backfilling the central floor post-grooving can effectively reduce side displacements.
3. As the depth and width of the groove increase, the volume of the plastic zone continuously decreases. Under high-ground stress conditions, support measures can slightly reduce the volume of the plastic zone, although this reduction is relatively modest.  
These findings highlight the importance of properly designing the parameters and locations for grooving for stress relief, as well as subsequent backfilling measures, to effectively control tunnel stability and minimize adverse effects of ground stress.
# Development of Ti/Ni Nanolayered Structures to
## 3 Results and Discussion
### Thermal Analyses  
The thermal analyses (DTA, TG, and DTG) of Ni-Ti-4 LDHs have used to study the effect of the reaction time on the complete formation of LDHs structure. TG and DTG curves of Ni-Ti LDHs after reaction times 10 h and 19 h are displayed in Figure 5. The total mass losses of both samples were similar. Both samples have lost 25% through four stages. However, the content of both samples was different, where Ni-Ti LDHs after the reaction time 10 h lost 9.7% of its mass during the first two stages indicating removal of the surface and the intercalated water. In case of the sample after the reaction time 19 h, the amount of both surface and intercalated water were only 5%. The decomposition of the interlayered cyanate anions happened through the third stage indicating that the content of cyanate anions increased by increasing the reaction time from 12.3% to 17%. In the final stage, the dehydroxylation process of the nanolayers was gradually achieved up to 600 \({}^{\circ}\)C and was 3%.  
Figure 4: Infrared spectra of Ni-Ti-4 LDHs after reaction times: (**a**) 10 h and (**b**) 19 h.  
Figure 6 showed DTA curves for Ni-Ti LDHs after reaction times 10 h and 19 h. The DTA results confirmed the TG data, where, TG curve of Ni-Ti LDHs after reaction time 10 h showed one endothermic peak at 302 \({}^{\circ}\)C agreeing with the endothermic and exothermic effects of the removal of the intercalated water and the oxidation of cyanate anions; respectively. The endothermic peak indicates that the heat released from the oxidation reaction is lower than the heat acquired for water removal. It means that the content of cyanate anions is low inside the sample formed after 10 h. In case of the sample formed after 19 h, two exothermic peaks were observed in the DTA curve at 259 \({}^{\circ}\)C and 302 \({}^{\circ}\)C indicating oxidation of cyanate and isocyanate anions. The strong exothermic peak indicates the high content of cyanate anions inside Ni-Ti LDHs after reaction time 19 h.
# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
## Materials and Methods
### SWIR Hyperspectral Imaging Analysis
#### 2.2.2 Principal Component Analysis (PCA)  
Spectral unfolding was performed to convert the 3D data cube to a 2D data matrix. Principal component analysis on mean centered data was performed and unwanted background and edge effects were removed using the interactive score image and score plots. The spectral data and loadings plot were used to identify noisy regions in the data which does not contribute to discrimination, i.e., 920-996 nm. Various spectral preprocessing methods such as multiplicative scatter correction (MSC), standard normal variate (SNV) and Savitsky-Golay smoothing, were tested on the model to enhance chemical differences by eliminating scattering, detector noise, optic effects and rough surfaces. The best model was identified as SNV and was therefore used in the final model. A PCA plot was constructed to display clustering patterns of the different samples in the reference image. A partial least squares discriminant analysis (PLS-DA) model was constructed for classification of raw materials and prediction of raw material constituents in the tea bags. The image matrix was then folded back into three-dimensional forms after classification analysis [13; 28].
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 5 Hybrid SC Converters  
### The Traditional Boost Converters with an Input SC Multiplier  
For achieving a large voltage-gain conversion, a traditional boost converter may work at a relatively high duty cycle, but the efficiency of a traditional boost converter (in their simple topology or interleaving) decreases when their duty cycle approaches one.  
Figure 24: Basic resonant SC circuit simulated, (**a**) schematic with parameters in the simulation, (**b**) evolution of store energy during charge interchange for different values of initial voltage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(v_{\mathbf{C1}}\)**(0)** & \(v_{\mathbf{C2}}\)**(0)** & \(i_{\mathbf{L}}\)**(0)** & **Initial Stored Energy** \\ \hline
9.80581 & 1.96116 & 0 & 10 \\ \hline
9.28477 & 3.71391 & 0 & 10 \\ \hline
8.57493 & 5.14496 & 0 & 10 \\ \hline
7.80869 & 6.24695 & 0 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of simulation in Figure 24b.  
This constrains the voltage gain achievable with a boost converter [46]. Most switched-capacitor converters are mostly voltage multipliers without output voltage regulation, despite regulations that can be achieved with pure SC converters. Combining SC converters with traditional converters results in a good solution to achieve a high voltage gain and regulation. One approach may be connecting an SC converter in a cascaded connection with a boost converter, which means the output of the SC converter is the input of the boost converter. The topology of the _Series-Parallel Converter_ followed by the _Boost Converter_ has been investigated in several configurations [51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101], see Figure 25.  
The switch realization can be done in different options [9]. This helps to synthesize the converter with different configurations. Figure 25a,b have the same equivalent circuits. Those circuits correspond to references [47; 48; 49]; other two similar converters were presented at [50].
# A Review of Ghost Imaging via Sparsity Constraints
## 2 Theoretical Framework of GISC  
According to GI theory [10; 16; 17; 23], we have  
\[\langle\Delta I_{t}(x_{t})\Delta I_{r}(x_{r})\rangle=O(x_{t},x_{r}), \tag{1}\]  
where \(\Delta I_{t}(x_{t})\) and \(\Delta I_{r}(x_{r})\) are intensity fluctuations of a test and a reference arms, respectively, \(O(x_{t},x_{r})\) is a representation of an object's image (such as an image in the real or Fourier-transform space), \(\langle\cdots\rangle\) is the ensemble average. When the intensity in the test arm is detected by a bucket or point detector and the corresponding coordinate \(x_{t}\) is fixed, \(O(x_{t},x_{r})\) can be expressed as \(O(x_{r})\).  
Since the sampling is always limited in practice, the solution of Equation (1) can be approximately presented as  
\[O(x_{r})\approx\frac{1}{N}\sum_{i=1}^{N}\Delta I_{t}^{(i)}(x_{t})\Delta I_{r} ^{(i)}(x_{r}), \tag{2}\]  
where \(N\) is the number of samples. Many algorithms have been proposed to improve the imaging quality of GI [24; 25]. In term of statistical signal processing, \(O(x_{r})\) corresponds to an expectation, and \(\frac{1}{N}\sum_{i=1}^{N}\Delta I_{t}^{(i)}(x_{t})\Delta I_{r}^{(i)}(x_{r})\) denotes an estimator. To further improve the optimization results, the prior information of the object, which can be characterized as some kinds of sparsity constraints in many cases, should be applied in the image retrieval. Equation (2) can be expressed as  
\[\min_{O(x_{r})}\mu_{1}f_{1}\left\{O(x_{r})-\frac{1}{N}\sum_{i=1}^{N}\Delta I_{ t}^{(i)}(x_{t})\Delta I_{r}^{(i)}(x_{r})\right\}+\mu_{2}f_{2}\left\{O(x_{r}) \right\}, \tag{3}\]  
where the first term is the data fidelity term, and the second term is the regularization term corresponding to the prior information of the object, \(\mu_{1}\) and \(\mu_{2}\) are the weight coefficients. Equation (3) is the regularized framework of GISC. At present, the data fidelity term in Equation (3) is usually approximated as [26; 27; 28]  
\[\min_{O}\mu_{1}f_{1}\left\{Y-AO\right\}, \tag{4}\]  
in a way that the existing optimization theoretical achievements such as compressive sensing (CS) [29; 30; 31] can be directly applied, where \(Y\) is a reshaped intensity distributions \(I_{t}(x_{t})\) in the test arm, measurement matrix \(A\) consists of the intensity distributions \(I_{r}(x_{r})\) in the reference arm. Another benefit of this approximation is that the imaging efficiency and imaging fidelity of a GI facility can be discussed in a manner of information theory [32; 33]. It has been demonstrated that speckle patterns can be optimized by maximizing the mutual information between the sampling signals and the probability distribution function of the target's images [34]. Another method to describe an image statistical prior with sparse representation is dictionary learning [35; 36], and the measurement matrix optimization based on dictionary learning and the mutual coherence of the matrix, has also been demonstrated [37].
# Pathophysiologyiology and Treatment of Soriasis: From Clinical Practice to Basic Research
## 5 Challenges and Innovations in Transdermal Drug Delivery System for Poriasis Treatment
### Nanotechnology-Based Transdermal Delivery for Psoriasis Treatment
#### 5.1.3 Lipid Nanocarriers for Psoriasis Treatment  
Lipid-based carriers, typically composed of physiological lipids, are highly biocompatible and are widely employed in pharmaceutical applications. These nanocarriers provide a spectrum of benefits, such as precise drug release, improved stability, biodegradability, targeted drug delivery, enhanced drug payload, and economic efficiency. Particularly, lipid nanocarriers like nanostructured lipid carriers and solid lipid nanoparticles are capable of carrying both hydrophobic and hydrophilic therapeutic agents. A range of lipid nanocarriers, including liposomes, ethosomes, and solid lipid nanoparticles, have shown significant potential in the effective delivery of anti-sporaitic medications. Cationic liposomes consisting of DC-Chol, cholesterol, and anionic liposomes consisting of lecithin, cholesterol, and tetramyristoyl cardiolipin were developed by [PERSON] (144) et al. for topical delivery of psrden (PSR); this enhanced the skin permeability of psr. [PERSON] (145) et al. performed surface modification of glycyrrheinic acid-D-\(\alpha\)-tocopherol acid polyethylene glycol succinate (GA-TPGS) on curcumin-loaded ethanol vesicles to construct curcumin-loaded GA-TPGS-modified multifunctional ethanol vesicles (Cur@GA-TPGS-ES), which exerted synergistic therapeutic effects on psoriasis. [PERSON] (146) et al. embedded calcipotriol in a solid lipid with Compritol 888 ATO as the oil phase and Poroxam 188 (F188) as the emulsifier, forming a new formulation with strong keratin penetration, slow release, and targeted action, thus improving the topical therapeutic effect of the drug and reducing the occurrence of skin irritation.
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 2 Data and Methods
### Error Analysis  
To quantitatively evaluate the _RF_SI_ precipitation detection method, the _SI_ and _CLWP_ values calculated from the AMSU-A observation data are compared with the _RF_SI_ values. The probability of detection (_POD_), false-alarm ratio (_FAR_) and accuracy (_ACC_) are used as evaluation indicators to analyze the precipitation detection effect [24], as shown in formulas (11) to (13).  
\[POD=H/(H+M) \tag{11}\]  
\[FAR=FA/(FA+H) \tag{12}\]  
\[ACC=(H+CN)/(H+CN+M+FA) \tag{13}\]  
where \(H\) is the number of samples that are correctly detected as precipitation; \(M\) is the number of samples that originally have precipitation but are incorrectly detected as nonprecipitation samples, that is, \"misses\"; _FA_ represents samples that are originally nonprecipitation samples but are incorrectly detected as precipitation samples, that is, \"false alarms\"; and _CN_ indicates the number of samples that are correctly detected as nonprecipitation samples.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 2 Related Work
### Backbone Construction
#### 2.2.2 Spanning Trees  
Let a path be a totally ordered set that consists of several nodes. We regard one of these nodes as the beginning of the path and another node as the end of the path. A network can use a spanning tree (ST) to find an efficient path by reducing the number of edges in the graph. The path found could then be used to forward a packet to its destination. A ST is a connected subgraph of an initial network which contains all nodes (vertices) of the network (see Figure 4). A ST contains no circles (loops).  
Figure 3: Maximal independent set. The nodes which form the MIS are colored in green.  
Many ST construction algorithms require that edges have non-negative weights. Some algorithms allow negative weights or do not require any weights at all. The weight of an edge represents the cost of transmitting a packet from one node to another node via this edge representing a communication link. The sum of weights of a path is regarded as the distance. Some ST algorithms also require a \"root\" which marks the beginning of the tree.  
If the distance from the root to any other node of the ST is minimal, then the ST can be regarded as a shortest path tree (SPT). A similar structure is a minimum spanning tree (MST) which is a ST with the minimal sum of weights. However, SPTs and MSTs do not reduce the number of nodes, but the number of edges of all nodes. Because broadcasting still uses each edges of all nodes, flooding by SPTs and MSTs is not optimal [7].  
A maximal leaf spanning tree (MLST) can be used to decrease the number of nodes. We regard nodes that are connected to only one node as a \"leaf\". A MLST is a ST with the maximal number of leaves. In terms of network flooding, non-leaf nodes can be used to distribute a packet through the whole network. A MLST contains the minimal set of so-called \"forwarders\". A forwarder is a node which forwards a received packet in the direction of its destination. The MLST problem is equivalent to the MCDS problem which also produces the minimal set of forwarder nodes. We discuss CDSs in the next section.
# Uni-Planar MIMO Antenna for Sub-6 GHz 5G Mobile Phone Applications
###### Abstract  
This article presents the design of a uni-planar MIMO antenna system for sub-6 GHz 5G-enabled smartphones. The MIMO antenna designed comprises four loop-shaped radiators placed at each corner of the mobile phone board, which follows the principle of pattern diversity. The single-antenna element resonates at 3.5 GHz, its impedance bandwidth is noted to be 1.28 GHz (3-4.28 GHz) for \(S_{11}\leq-6\) dB, and it is equal to 720 MHz (3.18-3.9 GHz) for \(S_{11}\leq-10\) dB. For a single-antenna element, a peak gain of 3.64 dBi is observed with an antenna efficiency of \(>\)90%. The isolation of \(>\)10 dB between antenna elements is achieved for the MIMO configuration. Furthermore, the MIMO antenna designed provides enough radiation coverage to support different sides of the mobile phone board, which is an important feature for future 5G-enabled handsets. In addition, the impacts of human hands and heads on MIMO antenna performance are investigated, and acceptable performance in the data and conversation modes is observed.  
unti-planar; MIMO; sub-6 GHz; pattern diversity Article  
Unti-Planar MIMO Antenna for Sub-6 GHz 5G Mobile Phone Applications. _Appl. Sci._**2022**, _12_, 3746. [[https://doi.org/10.3390/app12083746](https://doi.org/10.3390/app12083746)]([https://doi.org/10.3390/app12083746](https://doi.org/10.3390/app12083746))
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 4 Numerical Results
### Model Reduction  
As Figure 6 suggests, the subset of the 18 most important features leads to the same MAE with that obtained when using the full set of 23 features (for the 30 m case). Thus, the application of models with lower complexity can be attempted because fewer features are required. An investigation of the number of trees, the features used, and the MAE value is shown in Figure 7.  
It is evident that the full set of features requires more trees to obtain the minimum error. By comparison, the reduced subset produces the same result with lower model complexity. Table 3 presents the training and response times of the models with the lowest MAE values. A computer with an Intel i5-5575R processor and 6 GB of RAM memory was used to train and test the models.  
Figure 6: Gradual addition of features with reverse order of importance. The improvement is negligible as features of low importance are added.