esa-sceva/satcom-chunk-collection huggingface dataset:
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 2 Materials and Methods
### Denoising Algorithm Description  
The proposed sparse representing (SR) method is based on a theoretical hypothesis that an \(N\)-band hyperspectral \(R_{\rm rs}\) curve \(\mathbf{R}^{N\times 1}\) is a linear combination of seldom atoms (columns) in a K-column redundant dictionary \(\mathbf{D}\). When K \(\geq\) N, the target \(R_{\rm rs}\) spectrum can be expressed as:  
\[\mathbf{R}=\mathbf{D}\alpha \tag{2}\]  
where \(\alpha\) is the sparse coefficient. \(\mathbf{D}\) was generated based on a bio-optical simulated dataset, using the K-singular value decomposition (K-SVD) algorithm. The simulated dataset was generated with wide parameter ranges and contains 10,000 \(R_{\rm rs}\) curves. More details of this dataset can be found in [48]. Equation (1) was established based on the assumption that a \(R_{\rm rs}\) curve is sparse in a high dimensional linear space (\(\mathbf{D}\)). When the \(R_{\rm rs}\) signal is noisy, Equation (2) should be changed, as follows:  
\[\mathbf{R}=\mathbf{D}\alpha+\mathbf{u} \tag{3}\]  
where \(u\) is the random error that is not sparse in the linear space (\(\mathbf{D}\)). As K \(>\) N, the resolution of \(\alpha\) is an underdetermined problem. Thus, for an input noisy signal \(r\), the sparsity of \(\alpha\) can be utilized to solve the problem by:  
\[\hat{\alpha}=argmin\|\alpha\|_{0}\;\text{s.t.}\|r-\mathbf{D}\alpha\|_{2}^{2}=0 \tag{4}\]  
Figure 2: In situ measured hyperspectral data that were categorized in OWTs.  
Once the sparsity coefficients were determined, the denoised signal (\(\hat{\mathbf{R}}\)) can be calculated by:  
\[\hat{\mathbf{R}}=\mathbf{D}\hat{\mathbf{a}} \tag{5}\]  
In addition, we selected three other algorithms as comparisons. They are KRS, GS, and Mean methods.
# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
## 2 Related Work
The proposed approach defines a restricted search space to ensure the timely delivery of time-sensitive data. Thereafter, an algorithm selects a delay-aware energy-balanced path between the source and the sink within the search space to ensure fast communication. The suggested approach attains an improvement in successfully receiving data packets at the sink in large networks. The proposed routing method achieves significant improvementover comparative models for large and densely deployed networks. However, for smaller and sparse networks, the proposed approach will introduce a control packet overhead that may cause the quick cessation of the ring node, thus limiting the application of the proposed approach to large networks only. The authors of [9] addressed the obstruction of energy constraints in wireless sensor networks as a motivating factor for their rigorous research to develop energy-efficient routing protocols. The authors attempted to propose a new protocol, namely the equalized CH election routing protocol (ECHERP), which conserves energy using balanced clustering. The proposed model uses the Gaussian elimination algorithm to evaluate the node combination to select the CH. The comparative evaluation of the proposed model establishes its effectiveness over standard protocols in terms of energy efficiency. Similarly, the authors of [16] also employed an energy-efficient dynamic clustering technique to organize nodes in WSNs. Their model estimates the count of active nodes using signals received from neighboring nodes. In addition, it computes the probability of an active node becoming a CH based on the energy requirement for inter-cluster and intra-cluster communication. The objective of the model is to maximize the network life. A simulation of the proposed approach demonstrated that the clustering method has the ability to scale well for large-scale WSNs also. The authors aimed to extend the work by evaluating the application feasibility of the proposed technique to general WSNs. From the above studies done by various researchers, it is evident that significant research is being conducted on energy-efficient routing in various network models. Among the various approaches, clustering has demonstrated its competence in achieving energy efficiency and, thus, has motivated the authors to pursue research in this direction.
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
## 3 System Architecture  
In this section, we briefly explain the various components of our proposed system, including the OFDM transmitter and receiver, the underwater channel model and the cipher and coding technique used.
# Simulation and Design of Three 5G Antennas
## 3 Design of 5G Microstrip Array Antennas
### Dielectric Substrate Thickness (h)
\[\epsilon_{\text{e}}=\frac{\epsilon_{\text{r}}+1}{2}+\frac{\epsilon_{\text{r} }-1}{2}\bigg{(}1+12\frac{\text{h}}{\text{W}}\bigg{)}^{-\frac{1}{2}}, \tag{19}\]  
In practical applications, due to the influence of edge effects, the actual patch length 'L' should be determined as per Equation (20). \[\text{L}=\frac{\text{c}}{2\text{f}_{0}\sqrt{\epsilon_{\text{e}}}}=-2\Delta \text{L}, \tag{20}\]  
In this equation, \(\Delta\text{L}\) represents the equivalent radiating aperture length, which can be computed as shown in Equation (21). \[\Delta\text{L}=0.412\text{h}\frac{(\epsilon_{\text{e}}+0.3)\bigg{(}\text{W}/ \text{h}+0.264\bigg{)}}{(\epsilon_{\text{e}}-0.258)\bigg{(}\text{W}/\text{h} +0.8\bigg{)}}, \tag{21}\]  
The calculated value for \(\Delta\text{L}\) is 0.734 mm, and the resulting patch length 'L' is 19.987 mm. As shown in Figure 6, the impact of antenna unit length 'L' on the S\({}_{11}\) parameter was examined by scanning the antenna unit length within the range of 19.3 mm to 20.3 mm. As 'L' increases, the resonant frequency gradually decreases, and the S\({}_{11}\) curve exhibits an  
Figure 5: The impact of antenna element width W on S parameters. overall leftward shift. When 'L' is set to 19.6 mm, the antenna unit resonant frequency is measured at 3.5 GHz. An overview of the various parameters for the microstrip antenna unit is provided in Table 1.
## 2 Literature Review
### Continuous Wavelet Transform (CWT)
This research demonstrated that the more advanced CWT scalogram processing has strong potential for trend detection due to its ability to highlight weak faults. CWT plus scalogram presentation seems to be more suitable for fault trend analysis. However, its computation cost is high. A more powerful computational engine is normally required, which is most likely not feasible to be implemented on the tiny, embedded processor performing real-time control of the machine.
# A Cooperative Transmission Scheme in Radio Frequency Energy-Harvesting WBANs
## 3 Network Model  
As shown in Figure 1, we consider a saturated WBAN that consists of a coordinator and several RF energy-harvesting sensors. All nodes are single-antenna devices that operate in a half-duplex scheme. Instead of batteries, each sensor is equipped with a super-capacitor and an EH circuit that can harvest energy from the radio signal. The coordinator's  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.  
responsibilities include broadcasting RF energy to charge the sensors, managing and allocating wireless channel resources, and receiving health data sent by the sensors.  
Time is divided into periodic superframes, which consist of a download period and an upload period. During the download period, a power splitting scheme is used, and sensors receive command data while simultaneously harvesting energy from the RF signal broadcast by the coordinator. The upload period is divided into time slots, which are assigned to sensors according to the coordinator's schedule. Sensors upload packets in the assigned slots during the upload period. The network is assumed to operate under saturated conditions, with each sensor uploading one data packet per superframe.  
The pass loss model considered in the WBAN is a combined distance and frequency dependency model similar to the classical wireless channel model [28]. According to the IEEE 802.15.6 standard, the path loss model with distance dependency can be described by the equation  
\[PL(d)=PL(d_{0})+10 nlog_{10}(\frac{d}{d_{0}})+X_{\sigma}, \tag{1}\]  
where \(d_{0}\) denotes the reference distance, \(PL(d_{0})\) means the path loss at the reference distance \(d_{0}\), \(n\) represents the path loss exponent, and \(X_{\sigma}\) denotes the shadowing component.  
Sensors need to harvest energy before data transmission, and the harvested energy is limited. It is difficult for sensors to make cooperative decisions independently. The coordinator is chosen as the decider since it has a fixed power supply and enough computing power. During the initialization phase of the WBAN, a sensor estimates whether it can act as a relay node and reports related information to the coordinator. Then, the coordinator determines reasonable assistance plans to enable cooperative relay among sensors.
###### Abstract
Theoretically, atmospheric forcing data, such as air temperature, air humidity and solar radiation, describe the external environment for the carbon cycle process and exhibit strong spatial variation characteristics over complex terrain [13; 14; 15]. Dense field observations are required to determine the spatial patterns of atmospheric forcing data, especially for rugged regions. However, due to insufficient meteorological stations located in mountainous area, the atmospheric forcing data could not be provided accurately at fine resolution [12]. To overcome the limitation of field meteorological observations for running the carbon cycle model, downscaling is a potential method to obtain high resolution carbon flux simulation in topographically complex terrains. Using the relationships between carbon flux estimation and the surrounding landscape, the disaggregation of low spatial resolution carbon flux estimation and the information from ancillary data could be introduced in the downscaling scheme. Spatial scaling is a process of using information available at one scale to derive processes at another scale, including upscaling and downscaling. Downscaling refers to an increase in spatial scale following disaggregation of the coarse-resolution dataset and information from ancillary data at a finer resolution, which restores the variations at a finer scale by assuming the values of the coarser are the mean values at the finer scale. In the last decade, the downscaling of the carbon cycle process has been one of the most challenging issues in environmental science, especially over mountainous areas [16; 17; 18]. In the literature, some studies have been carried out to examine the effects of spatial heterogeneity on the upscaling process of carbon fluxes [8; 19], while little attention has been shown towards the issue of downscaling carbon fluxes over complex terrains. Therefore, it is necessary to downscale carbon fluxes using the subpixel information of topography and vegetation heterogeneity, which is an important issue when evaluating the role of ecosystems in the global carbon cycle. Vegetation heterogeneity and surface topography are important factors that introduce biases into carbon modeling and water fluxes. Vegetation heterogeneity directly influences the photosynthetic capacity per unit of surface area. Topography affects the redistribution of water, radiation and heat, which have significant influences on the process of carbon assimilation. In this paper, the main purposes are (1) to develop a GPP downscaling algorithm using the subpixel information of topography and vegetation heterogeneity and (2) to assess the GPP inconsistency at 1 km and 500 m, to analyze the relationship between spatial heterogeneity and the GPP difference at two scales.
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 4 Experimental Results
### Phoneme Group Model Training  
The baseline model uses MFCC features only. Clustering result showed that voiced and unvoiced phonemes are hard to distinguish. To overcome this problem, autocorrelation feature is added in phoneme group models because it shows very different values for voiced and unvoiced speech signals. Table 7 shows the performance of phoneme group models. This result shows that using autocorrelation is better than the MFCC alone. In addition, the performance of the three models was different for each phoneme group. Since the phoneme group has different characteristics, the optimal model structure is also different. Based on these results, we constructed a hierarchical model using phoneme group models.
# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 3 Descriptive Statistics
### Arithmetic Mean and Geometric Mean  
The arithmetic mean is one of the most famous measures of central tendency of a distribution, and it is calculated by adding a sequence of values and then dividing them by the number of values. Nevertheless, the mean has a small flaw, as it is easily affected by the presence of extreme values or outliers in a distribution. For example, one of the values in the current data set will be changed from 10,000 to 100,000 as shown below. The formula for the arithmetic mean is represented in Equation (1).  
\[\overline{x}=\frac{(\sum_{i=1}^{n}x_{i})}{n}. \tag{1}\]  
where \(\overline{x}\) is the arithmetic mean value and \(n\) is the number of observations.  
\[\overline{x}=\frac{1000+2000+3000+5000+100,000}{\overline{x}=22,200\;\text{ CFU}/\text{g}}\]  
Is this value a good measure of the central tendency for this distribution of values? It is not, and the problem is that the extreme value highly affects the arithmetic mean; therefore, the arithmetic mean is not the most accurate measure of central tendency in data, where its distribution changes multiplicatively and not additively as it is bacterial growth in microbiology.  
The geometric mean is the \(n^{\text{th}}\) root of the product of all the values in a data set. The formula of the geometric mean is represented in Equation (2).  
\[\overline{x}_{geom}=\sqrt[n]{\prod_{i=1}^{n}x_{i}} \tag{2}\]  
where \(\overline{x}_{geom}\) is the geometric mean value and \(n\) is the number of observations.  
\[\overline{x}_{geom}=(1000\times 2000\times 3000\times 5000\times 100,000)^{1/5}\]  
\[\overline{x}_{geom}=4959\;\text{CFU}/\text{g}\]  
Alternatively, another way to calculate the geometric mean, which applies perfectly to microbiology, is to log\({}_{10}\) the values of the data set and then follow the steps to calculate the arithmetic mean. The formula of the second way to calculate the geometric mean is represented in Equation (3).  
\[\overline{x}_{geom}=\text{{antilog}}\left[\frac{(\sum_{i=1}^{n}log_{10}x_{i})}{ n}\right] \tag{3}\]  
where \(\overline{x}_{geom}\) is the geometric mean value, \(n\) is the number of observations.  
\[\overline{x}_{geom}=10^{[log_{10}(1000)+log_{10}(2000)+log_{10}(3000)+ log_{10}(5000)+log_{10}(100,000)]/5}\]  
\[\overline{x}_{geom}=4959\;\text{CFU}/\text{g}\]  
Note that the geometric mean value can be presented in Log\({}_{10}\) CFU/g, which in both cases equals 3.6954 Log\({}_{10}\) CFU/g.
## 4 Results  
### The Analysis of Measurement Model  
In this study, structural equations modeling (SEM) and AMOS 24 statistical software were used as the analysis tools. SEM is known as covariance structural analysis which integrates two types of statistic patterns: factor analysis and path analysis. It is mainly used for the purposes of verifying the causal relationship between variables, and to check the interrelated dependencies of two or more variables [49]. In addition, using AMOS software to adapt an SEM approach is very appropriate, as it is specifically designed to help test hypotheses about relationships between variables. Using AMOS can also help researchers identify the fitness of a hypothetical model, the relationship between variables and how significant they are [50]. Based on above, this study applied SEM and AMOS to analyze the relationship between the constructs, to examine the fitness of the model, and to test the hypotheses. With reference to [PERSON] and [PERSON] [49], and [PERSON] et al. [51], two phases of analyses were applied. Firstly, the Cronbach's \(\alpha\) of each context, listed respectively in order of presentation, were evaluated to be 0.919, 0.882, 0.941, and 0.846 which all reached the good reliability level. The confirmatory factor analysis (CFA) was applied to test the discriminant validity and convergent validity, as well as to verify the goodness of fit of the structural model. In this case, the factors loading of one question appeared to offend the estimate (i.e., PE3 did not reach 0.5), it was then excluded and removed from follow-up analysis. Furthermore, the test results of this study showed that both composite reliability and the average variance extracted (AVE) reached the standard (CR value > 0.6, AVE > 0.5) for an appropriate model, as suggested by [PERSON] and [PERSON] [52], and [PERSON] and [PERSON] [53]. Thus, the results indicated that the measurement model is beyond the acceptable level. The test results are illustrated in Tables 3 and 4. Figure 2: Research model.
# Fusion-Based Localization System Integrating UWB, IMU, and Vision
## 2 Related Work
### UWB Positioning  
UWB has become a prominent candidate for high-accuracy indoor localization due to its excellent time resolution, wide bandwidth, and robustness against multipath interference [8]. In indoor localization systems based on UWB, nodes are typically classified into two types: anchor nodes, which are fixed and known in position, and unknown nodes, which are mobile and require positioning. The unknown node's location is calculated using its spatial relationship with multiple anchors [16].  
[PERSON] et al. [17] proposed a Time Difference of Arrival (TDOA) measurement model for UWB, revealing the multimodal statistical distribution of UWB positioning error in space. [PERSON] et al. [18] further characterized the geometric properties of UWB signal propagation, including arrival time delays, angles of arrival and departure, and the variances of these parameters. Common UWB localization techniques rely on Time of Arrival (TOA) [19], TDOA [20], and Two-Way Ranging (TWR) [21] measurement principles, with solution algorithms such as least squares, CHAN, and Taylor series methods [22]. In addition to these traditional approaches, intelligent optimization algorithms have gained popularity in recent years to improve estimation accuracy under non-ideal conditions [23]. For challenging environments with multipath and non-line-of-sight (NLOS) effects, various strategies have been proposed to mitigate signal distortion. For instance, [PERSON] [24] introduced a high-accuracy localization system based on Symmetric Double-Sided Two-Way Ranging (SDS-TWR) combined with trilateration, which reduces errors introduced by timing offsets.  
Deep learning has also been explored for direct signal processing. [PERSON] [25] proposed a method to extract accurate ToA from raw Channel Impulse Response (CIR) data using a neural network, thereby enabling accurate positioning in multipath scenarios. Additionally, [PERSON] et al. [26] proposed a hybrid solution that combines the C-Taylor weighting approach with an Adaptive Robust Kalman Filter (ARKF) to optimize the weight assignment of TOA measurements, effectively reducing the positioning error caused by NLOS conditions.  
Overall, UWB provides a strong foundation for high-precision indoor localization. However, signal degradation in complex environments remains a challenge. To improve long-term accuracy and robustness, recent trends are increasingly exploring multi-sensor fusion frameworks, combining UWB with vision, IMU, or other modalities.
## 2 Overview of PSO Schemes and the IPTBK Problem
### Existing IPTBK Techniques for DDoS Attacks  
Typically, IPTBK techniques can be classified into two categories on the basis of when attack traffic flows are traced from the victim to the sources: (i) Ongoing and (ii) post-mortem traceback techniques. Ongoing traceback is a traceback method in which the source of hacking attacks is traced back in real time. In general, attack network flows must be specified from normal network flows immediately. In practice, the real-time tracing of the remote controller is extremely difficult because its true identity is generally disguised by constantly changing spoofed IP addresses. Typical ongoing traceback schemes include input debugging, overlay networks and link testing.  
In post-mortem traceback techniques, the partial path information is filtered and stored for rebuilding the attack paths. This behaviour can assist managers in identifying the attack sources after an attack. Post-mortem traceback techniques include ingress filtering [5], Source Path Isolation Engine (SPIE) [5], PPM [6; 7; 8], iTrace [9] and DPM [10].  
In PPM [6; 7; 8], each router marks packets with some probability \(p\) for information flows. For example, \(p=1/1000\) implies that 1 packet is marked for every 1000 packets received to reduce the consumption of computational resources during the forwarding process. In the traffic information collection process, PPM must record partial path information. Routers are required to modify the header of each packet to store the traffic information in the router, which implies that multiple attackers cannot be traced simultaneously. DPM [10] was proposed to overcome the disadvantages of PPM using an advanced marking mechanism with ingress edge routers and traversing routers. Any spoofed source address can impair DPM. To avoid tracing a counterfeit attack path from spoofed IP addresses, scholars have proposed SPIE, which is a hash-based technique for IPTBK that generates audit trails for traffic within suspicious networks. Researchers must use efficient schemes, such as PSO, ACO and the GA with the SPIE mechanism, to reduce the computational complexity of route reconstruction by filtering partial path information. They must also provide countermeasures against spoofed IP attacks.
###### Abstract  
For electromagnetic (EM) modeling based on the electric-field formulation at low frequencies, the quasi-static approximation (i.e., only the conduction current is considered and the displacement current is ignored) is commonly applied, and a small conductivity value for the air layer is chosen subjectively. Actually, in the air layer, due to the use of the small conductivity value, the quasi-static approximation is ubiquitously violated. However, the effect of the violation of the quasi-static approximation in the air on EM modeling is not well examined in the literature. In this paper, we investigate this issue by comparing the finite-difference modeling results from the calculation with the quasi-static approximation and those considering both the conduction and displacement currents. For the quasi-static approximation, the conductivity in the air is set to be different small values, and zero air conductivity is used for the modeling with both the conduction and displacement currents considered. Several simple models are designed to verify the numerical solution and study how the assigned conductivity for the air affects the modeling accuracy. One flat model and two models with topography are designed to examine the effect of the quasi-static approximation on the EM modeling results. For frequencies used in typical geophysical applications of EM diffusion, using the quasi-static approximation is able to produce accurate modeling results for models with typical earth conductivity. However, if the rough surface topography is considered, the use of the quasi-static approximation can reduce the EM modeling accuracy substantially at much lower frequencies (as low as several hundred Hz), which is probably due to the inaccurate description of EM waves in the air, and poses problems for applications based on direct EM field interpretation.  
electromagnetic method; magnetotellurics; numerical modelling; quasi-static approximation; tomography +
Footnote †: journal: Applied Physics Letters B