esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile
## 3 Proposed Rational Clustering Method (RCM) Using Information Exchange
### Self-Analysis  
The \(B_{F}\) and \(P_{T}\) were analyzed for the varying information exchange instances in Figure 6.  
For the varying information exchanges, \(B_{F}\) and \(P_{T}\) were analyzed for different slots. The proposed method significantly reduced \(P_{T}\) by providing slots confined to the cluster. The state learning identified \(B_{F}\) precisely over the varying inputs of the network information exchange for leveraging the task completions. Henceforth the successive validations were prevented from reducing \(B_{F}\) for \(DT_{P}\) and \(T_{Alloc}\); this was therefore utilized for maximizing task completion and increasing performance. The \(B_{F}\) was therefore modified for \(DT_{P}\in UAV_{L}\) such that either a cluster or the independent node admitted the task for completion (refer to Figure 6). Figure 7 portrays the analysis of \(\rho(Fl,\,Dl)\) and \(rs\) (%) for the varying state changes. The \(B_{F}\) and \(P_{T}\) provide a visual representation of the different information exchange instances. The \(B_{F}\) (first figure) shows the number of bytes that are being exchanged between two peers, while the \(P_{T}\) (second figure) provides a timeline of the different information exchange instances. By looking at the \(B_{F}\) and \(P_{T}\), one can infer the  
Figure 6: \(B_{F}\) and \(P_{T}\) analysis.  
amount of data that are being transferred and the timing of the data transfer. This helped to identify patterns in the data exchange, which could be used to optimize the performance of the network.  
The analysis for \(\rho(Fl,DI)\) and \(rs\) (%) for the varying \(B_{F}\) is presented in Figure 7. The proposed method achieved less \(\rho(DI,FI)\) by reassigning pending tasks based on \((C_{T}+S_{L})\). In this process, the \(L_{c}\) and \(\rho(DI)\) and \(P_{T}\) and \(\rho(FI)\) were independently analyzed. Considering this feature, the \((s,UAV_{L})\) allocations were aligned across \(P_{T}\) and \(t\) initiated slots. Therefore, the probabilities of delay and failure were less for the information exchanged. In this process, the clusters jointly performed task processing, and the independent nodes identified \(T_{R}\) for processing. Therefore \(rs\) was improved through different sets of nodes for maximizing responses. In Figure 8, the \(T_{Alloc},~{}P_{T}\), and \(T_{R}\) were analyzed.  
The proposed method achieved high \(T_{Alloc}\) with controlled \(P_{T}\) and \(T_{R}\) over the varying state changes. This state changed for \(DT_{P}=1\) or \(2\) and \(\rho(DI,Fl)=0\) leveraged the allocation and reassignments. Therefore \(rs\) increased, and hence \((C_{T}+S_{L})\) was improved. Based on the distinguishable task allocation, the \((C_{T}+S_{L})\) was maximized for \(P_{T}\). Hence, the \(P_{T}\) was reduced with a demanding \(T_{Alloc}\) and \(T_{R}\) improved completion (Figure 8).  
Figure 8: Analysis of \(T_{Alloc},~{}P_{T}\), and \(T_{R}\).
# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Modeling Process
#### 3.3.2 Optimal Model Determination
To address this limitation, we applied the Root Mean Squared Logarithmic Error (RMSLE) as our evaluation metric. By computing the logarithm of both the actual and predicted values and then determining their difference, the RMSLE effectively ensures that smaller and larger errors are treated more uniformly, rendering it robust against outliers. Additionally, we employed the Relative Root Mean Squared Error (RRMSE) as another performance metric. The RRMSE is a nuanced variant of the RMSE designed to appraise the precision of predictive models concerning the span of the target variable. Unlike the RMSE, which is bound by the original measurement scale, the RRMSE affords the flexibility to compare various measurement techniques. Consequently, any inaccuracies in predictions manifest as elevated RRMSE values. \[\text{RMSLE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\log(p_{i}+1)-\log(a_{i}+1))^{2}} \tag{1}\]  
\[\text{RRMSE}=\sqrt{\frac{1}{n}\frac{\sum_{i=1}^{n}(a_{i}-p_{i})^{2}}{\sum_{i= 1}^{n}p_{i}^{2}}} \tag{2}\]  
In the above equations, \(p_{i}\) represents the predicted values, \(a_{i}\) denotes the actual values, and \(n\) is the number of data points. Having established the evaluation metrics crucial for our analysis, we proceeded to evaluate our models under a rigorous validation scheme. Using the TS-Nested CV method, we compared the eight models based on the RMSLE and RRMSE metrics. For this assessment, we primarily used the default hyperparameters for each model. Figure 6 presents the RMSLE and RRMSE values for the eight models after training them to predict the target value, 'SMUP'. As evidenced by the results shown in Figure 6a, the LightGBM model boasts the smallest relative error, or RMSLE, between the actual and predicted values. For instance, when 'SMUP' was set at KRW 10 million, the Linear Regression model approximated it to be around KRW 5.3 million and LightGBM estimated it as KRW 7.6 million. In terms of RRMSE, which evaluates the accuracy of different predictive models relative to the range of 'SMUP' values, LightGBM again showed better performance than the other models. Based on these findings, we identified LightGBM as the most apt predictive model for our study.
# Risk Assessment of Soil Heavy Metals in the Jiahe River Basin of Yantai City, China
## 3 Results
### Assessment Using Pollution Indices
#### 3.3.1 Contamination Factor (C\({}_{\rm f}\))  
The contamination factor (C\({}_{\rm f}^{\rm i}\)) was originally proposed by [PERSON] [30] and subsequently defined by [PERSON] et al. [31] as follows:  
\[\rm C_{\rm f}^{\rm i}=\frac{\rm C_{\rm i}}{C_{\rm ri}} \tag{2}\]  
where C\({}_{\rm i}\) is the concentration of heavy metal i, while C\({}_{\rm ri}\) denotes the reference value for heavy metal i, which was taken herein as the element abundance of soils in China (Table 1). C\({}_{\rm f}^{\rm i}\) < 1, 1 \(\leq\) C\({}_{\rm f}^{\rm i}\) < 3, 3 \(\leq\) C\({}_{\rm f}^{\rm i}\) < 6, and C\({}_{\rm f}^{\rm i}\)\(\geq\) 6 correspond to low, moderate, considerable, and very high contamination risk, respectively (Table 2). The C\({}_{\rm f}\) values of heavy metals in the 213 soil samples were calculated, and their counts on each risk level are listed in Table 2.  
For Hg, the majority of the soils were at a low risk level, with a small number of samples at a moderate level and only a few at considerable or very high risk levels. Regarding Cd, As, Pb, Cr, Cu, Ni, Zn, Co, V, and Mn, most samples were at a moderate risk level, with a small number at a low level and only a few at a considerable risk level. Additionally, few samples of Cd, As, Cu, and Zn were identified at the very high risk level.
## 2 Material and Methods
### Equivital Physiological Signals Monitoring  
The Equivital (EQV) LifeMonitor (Cambridge, UK) is an advanced wearable sensor system designed for the continuous monitoring of physiological parameters, making it particularly useful for long time intervals of respiratory function evaluation. The device integrated in a smart textile environment is employed in both clinical and research settings to provide real-time data of vital signs such as heart rate (HR), respiratory rate (RR), skin temperature (ST), and electrocardiogram (ECG). The EQV integrates multiple sensors to capture detailed physiological data--a tri-axis accelerometer for movement and position tracking, ECG electrodes for HR monitoring, and respiratory inductive plethysmography (RIP) bands to measure RR and effort--providing a comprehensive picture of an individual's tidal breathing patterns. EQV is a user-friendly and comfortable device for long-term monitoring, making it a suitable candidate for continuous patient monitoring, including remote monitoring.  
This device is mainly used in monitoring vital signal during exercises; its potential for clinical application is yet to be explored. From a clinical point of view, the EQV could be a valuable tool in monitoring of respiratory diseases, e.g., Chronic Obstructive Pulmonary Disease (COPD). A recent study has employed the EQV device to monitor changes in vital signals in patients with COPD [23]. The study highlighted the device's ability to detect significant variations in respiratory rate and heart rate, which are critical indicators and would enable healthcare providers to intervene early potentially improving patient outcomes.  
In this study, the EQV sensor monitor depicted in Figure 2 continuously collects data, including ECG heart rate, respiratory rate, and skin temperature, over a two-hour measurement period for each patient. Simultaneously, the FOT and RESMON devices measure data alternately. Therefore, a synchronization step is essential to align EQV data with FOT measurements for a detailed analysis.
# Effect of Surfactant on Water Content of Phosphogypsum
###### Abstract  
Phosphogypsum is a kind of solid waste produced in wet process of producing phosphoric acid, which affects the filtration rate and water content of phosphogypsum. The effects of single surfactant sodium dodecyl sulfate (SDS), sodium dodecylbenzene sulfonate (SDBS), stearic acid (SR) and polydimethylsiloxane (PDMS) and coupled surfactants on the water content of phosphogypsum were investigated. The results show that, during the leaching process, surfactant strengthened the interfacial interactions between molecules through hydrophobic and hydrophilic orientation on solid-liquid interface, reduced the surface tension and contact angle to improve the filtration rate and reduced the soluble phosphorus loss, thereby improving the leaching rate of phosphate rock and reducing the water content of phosphogypsum. Among them, the water content of phosphogypsum was better controlled by stearic acid and sodium dodecylbenzene sulfate than the other surface surfactants. Compared with the blank group, the stearic acid and sodium dodecylbenzene sulfonate increased the filtration rate of phosphogypsum by 24.34%, the moisture content decreased by 3%, and the phosphate leaching rate increased by 4.36%.  
Keywords: crystallization; filtration rate; phosphogypsum; surfactant; water content +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion  
### Scanning Electron Microscopy (SEM)  
The microstructure of common buckwheat spent grain was observed by taking SEM images, which can be seen in Figure 2. In the control sample (Figure 2a) the appearance of common buckwheat particles is that of irregular geometric shapes with a rough surface appearance. Moreover, microfibrils and amorphous zones can be seen on the surface [28]. This demonstrates the complex structure of common buckwheat flour, which traps peptides and secondary metabolites [29].  
Enzyme-assisted extraction cleaves the polymeric chains of buckwheat flour and releases metabolites, which can be clearly observed in the SEM images (Figure 2b-d). The crystalline and round structures can be identified as substances of protein origin as well as crystalline sugar molecules. Enzyme-assisted extraction is also used for protein extraction from plants [30]. It has been reported that carbohydrates (_Cellulase_, _Pectinase_, _Viscozyme_ L) increase the extraction yield by releasing the proteins attached to the polysaccharide matrix in the plant materials [31; 32; 33].  
In general, chemical degradation and disruption of plant cell wall matrices using acid or base hydrolysis are both commonly practiced methods that lead to increased extractability and thus, increased bio-functional activity [34; 35].
## 2 Materials and Methods
### Methods
#### 2.3.8 GPP Maps with Composite Images  
Once the vegetation classification map was obtained, the model was applied to the regions having the same land cover of the area within the climatological footprint. By doing so, we produced GPP maps for each Sentinel-2 available image. Then, we aggregated the obtained maps to produce an estimate of the monthly/annual GPP. The latest is indeed a highly important product relevant for further analysis of the ecosystem's status. Annual maps of GPP are useful for trend analysis, the identification of the degradation of the ecosystem functions, and for computing annual NPP if knowledge of the autotrophic respiration processes of the ecosystem is known [35; 36].  
To create an operational monitoring tool easy to be implemented by stakeholders and policymakers that bypasses the limitations of cloudy satellite data, we propose a simplified model for the calculation of GPP for a specified time window. Aggregated products of GPP are computed as (Equation (10)):  
\[\mathrm{GPP}=\sum_{i=1}^{n}\mathrm{GPP_{i}}=\sum_{i=1}^{n}(m\times(VI_{i} \times EV_{i})+b) \tag{10}\]  
where n is the number of days within the time window, and m and b are the model parameters, and therefore, constant. Assuming that the VI is constant within the selected time window (VI\({}_{i}=\) VI), Equation (10) can be written as:  
\[\mathrm{GPP}=m\times VI_{i}\sum_{i=1}^{n}(EV_{i})+b\times n \tag{11}\]  
The model formulation (Equation (11)) is susceptible to high deviation if the assumption cannot be guaranteed. In this research we propose to work with a 30-day time window; however, we also applied a 15-day time window to compare the benefits of increasing the frequency of the map generation.  
The GPP products are finally aggregated to derive a GPP map for the full period of analysis.
# Mechanism and Characterization of
###### Abstract  
The incorporation of reinforcing fillers into natural rubber latex (NR) to achieve superior elasticity and mechanical properties has been widely applied across various fields. However, the tendency of reinforcing fillers to agglomerate within NR limits their potential applications. In this study, multi-walled carbon nanotube (MWCNT)-silica (SiO\({}_{2}\))/NR composites were prepared using a solution blending method, aiming to enhance the performance of NR composites through the synergistic effects of dual-component fillers. The mechanical properties, dispersion behavior, and Payne effect of three types of composites--SiO\({}_{2}\)/NR (SNR), MWCNT/NR (MNR), and MWCNT/SiO\({}_{2}\)/NR (MSNR)--were investigated. In addition, the mean square displacement (MSD), fractional free volume (FFV), and binding energy of the three composites were simulated using molecular dynamics (MD) models. The results showed that the addition of a two-component filler increased the tensile strength, elongation at break, and Young's modulus of NR composites by 56.4%, 72.41%, and 34.44%, respectively. The Payne effect of MSNR was reduced by 4.5% compared to MNR and SNR. In addition, the MD simulation results showed that the MSD and FFV of MSNR were reduced by 21% and 17.44%, respectively, and the binding energy was increased by 69 times, which was in agreement with the experimental results. The underlying mechanisms between the dual-component fillers were elucidated through dynamic mechanical analysis (DMA), a rubber process analyzer (RPA), and field emission scanning electron microscopy (SEM). This study provides an effective reference for broadening the application fields of NR.  
n
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 1 Introduction
The two process above jointly realize an audio retrieval system and the framework includes two main parts: audio fingerprint extraction and audio fingerprint retrieval. The former consists of pre-process and fingerprint computation. The latter contains the establishment of database and fingerprint matching. The novel fingerprint has less data and improves the retrieval efficiency. Figure 1 shows a common audio fingerprinting system including extraction, database establishment, and matching of fingerprints. Figure 1: Audio fingerprinting system framework. The rest of paper is structured as follows: Section 2 explains the proposed audio fingerprint extraction based on LLE. Section 3 describes the process of audio fingerprint matching by bit error rate (BER). Then, in Section 4 we show the experiments that we test the fingerprint and comparisons with other algorithm. Finally, we draw a conclusion in Section 5.
# An Application of Elastic-Net Regularized Linear Inverse Problem in Seismic Data Inversion
## 2 Methods  
### Seismic Inversion for Impedance  
Usually, in seismic inversion process, the seismic data are first inverted to obtain the reflectivity series of underground formation. In seismology, we think the observed seismic data are the convolution result from reflectivity series of underground formation and a source wavelet [6]. For calculation, the convolution relationship is discretized as  
\[\mathbf{d}\mathbf{=Wr+n} \tag{1}\]  
where \(\mathbf{d}\) is the observed seismic data vector, \(\mathbf{W}\) is the wavelet convolution matrix, and \(\mathbf{r}\) is the reflectivity series vector; \(\mathbf{n}\) is the noise in observed seismic data.  
Hence, the reflectivity series inversion is, in fact, a linear inverse problem. Based on least square inverse problem theory, the objective function of seismic inversion is  
\[\min f(\mathbf{r})=||\mathbf{Wr-d}||_{2}^{2} \tag{2}\]  
where\(||\cdot||_{2}\) is L2-norm of a vector.  
However, the solution of seismic inversion is ill posed. To solve the ill-posed inverse problem, the most effective way is regularization constraint [6]. In the early days, Tikhonov regularization was used to stabilize seismic inversion [13; 14]. Hence, the objective function of seismic inversion can be updated as  
\[\min f(\mathbf{r})=||\mathbf{Wr-d}||_{2}^{2}+\lambda_{2}||\mathbf{r}||_{2}^{2} \tag{3}\]  
where \(\lambda_{2}\) is the regularization parameter of Tikhonov regularization. In fact, it is pre-whitening least-square deconvolution [15].  
Next, the final inverted impedance can be obtained from inverted reflectivity series through a simple exponential integration transformation [35], i.e.,  
\[z(i)=z(0)\exp[2\sum_{j=0}^{i}r(j)],i=1,2,\ldots,n \tag{4}\]where, \(z(i)\) and \(r(j)\) are the impedance and reflectivity at samples \(i\) and \(j\), \(n\) is the number of reflectivity series.
# A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
## 3 System Model and Network Controller Design
### Network Model
UAV \(u_{i}\)'s eight relocation spatial grids at time slot \(k\) are denoted by \(\mathbf{L}^{u_{i}(k)}=L_{st}^{u_{i}(k)}\), where \(s,t\in\{-1,0,1\}\) represents the coordinates of the spatial grids. Specifically, \(s\) represents the left and right direction, where \(-1\) means leftward, \(0\) means no motion, and \(1\) means rightward; and \(t\) represents the front-rear directions, where \(-1\) means forward, \(0\) means no motion, and \(1\) means backward. For example, \(L_{-1,-1}^{u_{i}(k)}\) represents the spatial grid of UAV \(u_{i}\) in the front left at time slot \(k\). When receiving a message, the UAV evaluates the signal-to-interference-plus-noise ratio (SINR) of the channel based on the bit error rate (BER) of the message. For simplicity, the value of SINR is quantized to \(\xi\) levels. Each UAV broadcasts its quantized value of the SINR and the index of transmit frequency chosen according to \(C_{\psi}\) at time slot \(k\) to its neighbor nodes. Let the vector \(\mathbf{h}_{u}^{(k)}=\left[h_{u,i}^{(k)}\right]_{1\leq i\leq N}\) denote the channel power gains of UAV \(u\)'s \(N\) channels, \(C_{h}\) the cost of frequency hopping, \(C_{p}\) the cost of data transmission, and \(C_{m}\) the cost of track replanning. Figure 4: Schematic diagram of the relocation of a UAV. The black dotted line in the figure represents the wireless channel between the UAVs, and the red grid represents the grid world of UAVs, and the red arrows represent the directions in which the UAV may move after the jammer adjusts the jamming strategy.
## 2 Materials and Methods
### Soil Analysis  
Soil factor analysis was conducted to elucidate the impact of soil properties on the wine-associated microbiota and metabolite profiles. The samples were air-dried and ground through a 20-mesh sieve for pH determination. The samples were extracted with 2.5 times their weight of distilled water, and the pH was measured using a pH meter (CyberScan pH 510, Thermo Fisher Scientific, Waltham, MA, USA). The air-dried samples were ground through a 60-mesh sieve to determine total carbon (TC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), and organic matter (OM) contents. A 200 mg soil sample was weighed, placed in a tin capsule, and crimped using a sample press. Total carbon (TC), nitrogen (TN), phosphorus (TP), and potassium (TK) were quantified via high-temperaturecombustion (1150 \({}^{\circ}\)C) using a Vario MACRO cube elemental analyzer (Elementar, Germany) with thermal conductivity detection (TCD). Calibration was performed using certified reference materials, and the analytical precision was maintained within a 1.5% relative standard deviation (RSD) [15]. Soil organic matter (OM) content was calculated as total carbon (TC) multiplied by 1.724, following the Walkley-Black correction, which accounts for incomplete oxidation during wet digestion [16]. The soil C/N ratio was calculated based on the contents of total carbon and total nitrogen.
## 5 Experiment and Result Analysis
### Weather Clustering and Data Decomposition  
Clustering the data using the K-means algorithm yielded 94, 49, and 41 days of sunny, cloudy, and rainy weather, respectively. Taking sunny weather as an example, Figure 7 shows the power components obtained by the MISAO-ICEEMDAN algorithm for a sunny day. From the figure, it is clear that 12 IMF components and one RES component are obtained from the ICEEMDAN decomposition, and each sequence is arranged according to the frequency from high to low. Among them, IMF1, as the dominant component, has a smooth and relatively stable curve, which can characterize the trend of the original PV power signal well. In contrast, the remaining components accurately show the local characteristics of the power data. Through in-depth analysis, it is found that the fluctuations of different frequency series have a certain regularity. The high-frequency IMF componentsusually reflect short-term rapid fluctuations, which are likely to be related to transient changes in local meteorology (e.g., short-term cloud cover) or transient responses of the equipment. In contrast, the low-frequency IMF and RES components are more indicative of long-term trends and overall energy levels, which are closely related to the cyclical changes in solar radiation as well as the inherent characteristics of the system. Each mode is independent of the other, effectively avoiding the problem of mode mixing. At the same time, the residuals of the RES component are gradually stabilized, which means that after the ICEEMDAN decomposition, the remaining unexplained part of the IMF component is close to the stable random noise, which strongly verifies the effectiveness of the decomposition. In the prediction model, each power component is set as an output series, and its corresponding meteorological data are used as an input series, which is finally superimposed to obtain the prediction results.