esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method
## 5 Conclusions  
1. This study introduces the novel application of the SVD method for denoising SO\({}_{2}\) ultraviolet spectral signals. It also proposes an optimized method for selecting the effective order of singular value denoising. This method involves reconstructing each singular value of the original spectral signal into a one-dimensional signal, analyzing it in the frequency domain, and using the frequency value with the highest amplitude as an index to characterize each singular value component.
2. The denoising algorithm's effectiveness is maximized by selecting the singular value order corresponding to the first significantly changed frequency value as the algorithm's effective order.
3. In low SNR conditions, our study reveals that the improved SVD noise reduction algorithm presented in this paper leads to substantial improvements. It boosts the SNR by 18.02% and 16.86%, as compared to the SVDS method and the wavelet transform denoising algorithm, respectively. Additionally, the RMSE diminishes by 15.13% and 14.92%, respectively. Furthermore, the linear relationship between the concentration of SO\({}_{2}\) samples and the characteristic value of the UV spectra achieves a remarkable coefficient of 0.99735. The denoising method proposed in this paper does not require manual setting of various types of parameters, and has a better ability to deal with the noise of UV spectral signals in engineering sites with complex environments.  
These findings highlight the effectiveness of our algorithm, particularly in situations with low SNR. In comparison to wavelet denoising and the SVDS method, our approach offers superior denoising capabilities. Although wavelet denoising and the singular value difference spectral method exhibit good denoising performance, our improved SVD method excels in scenarios with low SNR.  
The focus of this paper is solely on the application of the improved singular value decomposition algorithm to the denoising of SO\({}_{2}\) UV spectra. In future research endeavors, it would be worthwhile to explore whether this method can be extended to the spectra of other characteristic decomposition component gases of SF\({}_{6}\).  
Conceptualization, Z.Q., Z.W. and R.W.; methodology, Z.W.; software, Z.W.; validation, Z.Q., Z.W. and R.W.; formal analysis, Z.Q. and Z.W.; investigation, Z.Q. and Z.W.; resources, Z.Q., Z.W. and R.W.; data curation, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, Z.Q., Z.W. and R.W.; visualization, R.W.; supervision, Z.Q. and R.W.; project administration, Z.Q.; funding acquisition, Z.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Doctoral Research Initiation Fund of Hubei University of Technology Project, FUNDER grant number BSQD2020025.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors thank all reviewers who helped to improve this manuscript.  
The authors declare no conflict of interest.
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 4 Proposed Protocol
### Transmission Phase  
The data transmission phase is divided into rounds. Every round has a time slot of N-nodes, where N-nodes is the number of non-CH nodes in the network. During this period, the Sink/BS node sends a \"Request\" packet to the first CH node. Once the CH node receives this \"Request\" packet, it organizes a programmed transmission with the nodes in its cluster by using TDMA as access method. This process is repeated for all the clusters in the network.  
The \"Death\" packet informs the Sink/BS node that a node has just died. Nodes in a cluster transmit the \"Death\" packet to the Sink/BS node using their respective time slot. If a CH node dies, it transmits its \"Death\" packet to the Sink/BS node when queried by the Sink/BS node. At the end of each round, the Sink/BS node reviews which nodes sent a \"Death\" packet. In case a \"Death\" packet arrives at the end of a round, the Sink/BS node begins the configuration phase, as shown in Figure 8.  
Figure 9 below shows the H-kdtree protocol's algorithm. Algorithm 2 shows the cluster formation process as a complement to the processes described in this section.  
```
0: Matrix obtained in the flooding process, with the following fields: \(id_{node}\), \(x_{cor}\), \(y_{cor}\), \(energy\). The \(dim\) variable is the value of the column to that corresponds to the dimension to be selected, where \(x_{cor}=2,y_{cor}=3\).
0: List with the vectors of the positions selected in each cluster
1:function: cluster_formation \((mat,dim)\)
#Variable vec\({}_{1}\), stores the positional values of cluster\({}_{1}\)
2: vec\({}_{1}=rep(NA,length(mat[,1]))\)
#Variable vec\({}_{2}\), stores the positional values of cluster\({}_{2}\)
3: vec\({}_{2}=rep(NA,length(mat[,1]))\)
#Calculate the median of the selected partition
4:\(v_{m}=median(mat[,dim])\)
#Traverse the data in the selected dimension
5:for (i in 1:length(mat[,1])) do
6: #Subdivide the dimension into two clusters, depending on the median value
7:if\((mat[i,dim]>v_{m})\)then
8: vec\({}_{1}[i]=i\)
9:else
10: vec\({}_{2}[i]=i\)
11:endif
12:endfor
13:return\((list(vec_{1},vec_{2},v_{m}))\)
```  
**Algorithm 2** Cluster formation based on the k-d tree algorithm.  
Figure 8: Protocol header H-kdtree.
## 3 Results and Discussion
### Experimental
#### 3.1.3 The Effects of Raster Angle on Anisotropy
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{6}{l}{**Transverse Build Orientation**} \\ \hline Infill percentages & \(\text{UTS}_{\text{mean}}\) & \(\text{UTS}_{\text{SQRT Error Mean}}\) & \(\Delta_{\text{mean}}\) & \(\Delta_{\text{SQRT Error Mean}}\) & \(\text{E}_{\text{mean}}\) & \(\text{E}_{\text{SQRT Error Mean}}\) \\ \multicolumn{6}{l}{centages} & \(\text{(MPa)}\) & \(\text{(MPa)}\) & (\%) & (\%) & (MPa) & (MPa) \\
40\% & 6 & 0.43 & 0.09 & 0.004 & 1144 & 60.37 \\
60\% & 7.5 & 0.40 & 0.11 & 0.005 & 1271 & 49.15 \\
80\% & 12.8 & 0.40 & 0.16 & 0.024 & 1512 & 48.07 \\
100\% & 20 & 0.61 & 0.22 & 0.018 & 1750 & 51.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._From Figure 19, it can be perceived that at 100% infill, the mechanical anisotropy of samples for both build orientations reached their minimum values. Also, by increasing the infill percentage, the degree of anisotropy decreased noticeably for samples, which emphasises the influence of the air voids and layer adhesion on the final mechanical properties of the 3D-printed parts.
## 1 Introduction
### Organization of the Article and Description of Used Symbols  
The rest of the paper is organized as: Section 2 illustrates literature on this topic. Section 3 illustrates the proposed model (method) including computation of task priority, predicting the time of submission of the next task, and GA-assigned trajectory planning. Simulation experiments, results, and discussion appear in Section 4 whereas Section 5 concludes the paper with leftover a few immediate future scopes.  
The paper presents many symbols and abbreviations that are tried to explain the location where they are used. But Table 1 presents a brief description of each symbol and abbreviations together.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol/Abbreviation** & **Brief Description** \\ \hline \(X\_cen\) & X co-ordinate centroid of the cluster C. \\ \hline \(Y\_cen\) & Y co-ordinate centroid of the cluster. \\ \hline \(num(c)\) & Number of devices in cluster C. \\ \hline \(dev(i)\) & ith device of IoT devices. \\ \hline \(h_{i}\) & Denotes x latitude point of dev(i) location. \\ \hline \(k_{i}\) & Denotes y longitude point of dev(i) location. \\ \hline \(cls\_clg(i)\) & This function denotes Eligibility of the ith device dev(i) as head of its cluster. \\ \hline \((R-E_{i})\) & Residual energy of the ith device. \\ \hline \(\tau(j)\) & Denotes new task. \\ \hline \(Pr(\tau(j),dev(i))\) & Possible priority of a new task \(\tau(j)\) submitted by the device dev(i) of cluster C. \\ \hline \(lG\) & Information Gain. \\ \hline \(Comp\_priority\) & Computation priority. \\ \hline \(Entropy(Parent)\) & The entropy of the parent node. \\ \hline \(Entropy(children)\) & The average of entropies of all its children. \\ \hline \(p_{i}\) & Probability of the random selection of an element in class i. \\ \hline \(Max\_pr\) & The maximum possible priority in the the system. \\ \hline \(Min\_pr(f^{\prime})\) & The minimum priority of a task present in the task queue at current time t. \\ \hline \(multi\_rs\) & Denotes multiple regression function. \\ \hline \(f^{\prime}\) & This function denotes increase the priority value. \\ \hline \(tm\_gen(j)\) & Timestamp of its generation based on current task \(\tau(j)\). \\ \hline \(tm\_exec(j)\) & Timestamp of completing execution of the task \(\tau(j)\). \\ \hline \(len\_tsk(j)\) & Length of the task where priority of the current task \(\tau(j)\). \\ \hline \(ST(i,t^{\prime})\) & A set of task submission sessions of dev(i) till current time \(t^{\prime}\). \\ \hline \(Prty\) & Currently computed priority. \\ \hline \(PTSK\) & Percentage of task that could be completed. \\ \hline \(lqTE\) & Average energy saving in IoT devices. \\ \hline \(UAVE\) & UAV energy saved. \\ \hline \(ADET\) & Average delay in execution of the task. \\ \hline \(UAV\) & Unmanned aerial vehicles. \\ \hline \(ETOGA\) & Energy-Efficient Trajectory Optimization using Genetic Algorithm. \\ \hline \(CEC\) & Comprehensive energy consumption. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols, Abbreviation, and their Descriptions.
## 3 Implementation Based on Generative Adversarial Network (GAN)
### Experimental Setup
The PSNR [5] is usually expressed in terms of the logarithmic decibel scale (dB) and is calculated as:  
\[PSNR_{dB}=10\ log_{10}\left(\frac{MAX^{2}}{MSE}\right) \tag{23}\] \[=20\ log_{10}(MAX)-10\ log_{10}(MSE)\]  
where  
MAX = Maximum possible pixel value of the image;  
MSE = Mean Square Error. 2. Mean Square Error (MSE): The Mean Square Error (MSE) is the cumulative error between the compressed image and the original image. The lower the MSE, the better the quality of the compressed image. It is calculated as \[MSE=\frac{1}{pq}\sum_{a=0}^{p-1}\sum_{b=0}^{q-1}\ [I(a,b)-K(a,b)]^{2}\] (24) where \(p\ q\): Dimensions of the image. I (a, b): Intensity of pixels (a, b) in the original image. K (a, b): Intensity of pixels (a, b) in denoised images. The root mean squared error (RMSE) is represented as:  
\[RMSE_{x,y}=\sqrt{\frac{1}{m}\sum_{i=1}^{m}\ (x_{i}-y_{i})^{2}} \tag{25}\]  
where \(m\) refers to the total number of pixels in \(x\) and \(y\). 3. Structural Similarity Index Measure (SSIM)  
This measurement is used in the objective quality assessment of the images. It demonstrates image quality in image processing. The SSIM depends on 3 parameters: luminance, contrast, and structural term. The product of these parameters gives the SSIM of the image [16]. The parameter \(L(x_{1},x_{2})\) is the luminance assessment function that determines the quality of having only a small margin between two images in terms of mean luminance (\(ux_{1}\) and \(ux_{2}\)). The parameter \(C\) (\(x_{1},x_{2}\)) is the contrast assessment function that computes the quality of having only a small margin between two images in terms of standard deviations (and \(\sigma_{x2}\)). The parameter \(S\) (\(x_{1},x_{2}\)) is the structure assessment function that determines the correlation coefficient between two images in terms of covariance (\(\sigma_{x1x2}\)). \[SSIM(x_{1},x_{2})=\lfloor L(x_{1},x_{2})\rfloor a_{1}\times\lfloor C(x_{1},x_{2} )\rfloor b_{1}\times\lfloor S(x_{1},x_{2})\rfloor c_{1} \tag{26}\]  
where  
\[L(x_{1},x_{2})=\frac{2\mu_{x_{1}}\mu_{x_{2}}+U_{1}}{\mu_{x_{1}}^{2}+\mu_{x_{2 }}^{2}+U_{1}}\]  
\[C(x_{1},x_{2})=\frac{2\sigma_{x_{1}}\sigma_{x_{2}}+U_{2}}{\sigma_{x_{1}}^{2}+ \sigma_{x_{2}}^{2}+U_{2}}\]  
Figure 4:0  
where \(\mu x_{1}\) and \(\mu x_{2}\) are the local means, \(\sigma x_{1}\) and \(\sigma x_{2}\) are the standard deviations, and (\(\sigma_{x1x2}\)) is the image's cross-covariance of \(x_{1},x_{2}\). Assume \(a_{1}=b_{1}=c_{1}=1,U_{1}=(R\times S_{1})^{2}\) and \(U_{2}=(R\times S_{2})^{2}\), where \(R\) is the size of the image (256 for 8-bit grayscale images), \(U_{1}\) is a small constant value at \(S_{1}\ll 1\), \(U_{2}\) is the positive constant value at \(S_{2}\ll 1\), and \(U_{3}=U_{2}/2\). The simplified version of SSIM is  
Figure 4:1
## 1 Introduction
The suitability of a material is likely correlated with its work function, resulting from the conclusion of the higher work function and the higher resolution of the electrochemical potential [36]. With the help of this non-destructive method, the work function of a metal is mapped to evaluate the relationship between the metal surface defects and the electrochemical potential [37]. [PERSON] et al. [38], employing a small gold sphere bouncing on a planar metal electrode, examined the CE of metals and focused on the charging and discharging process when the metal touched and detached, respectively. The charge transfer was found to be limited to the short contact and no charge was accumulated in the following contacts. It can be seen that the majority of the investigations have been conducted in air or under vacuum. However, the process of the charge transfer between two contacting metals also occurs in underwater conditions. To the best of our knowledge, the only research on metal/metal contact in electrolyte to date is a numerical study of the potential distribution around bimetallic Janus nanoparticles, indicating that the electric field induced by the CE is disrupted beyond the Debye length [39]. Nevertheless, the fundamental mechanism that underlies the charge (electron) transfer and electricity generation via the CE of metal/metal sheets in underwater conditions is still not completely understood. In this study, we propose a novel method using bi-metal CE to generate underwater ionic current signals for self-powered sensing. Focus is given to the charge transfer process during the contact and separation between the two metal sheets in water, where one metal is the working electrode, and the ionic current signals are detected far away by the sensing electrode. The effects of metal type, water temperature, ionic concentration and electrode distance on the sensing capability of the ionic current signals were investigated systematically. As a typical application, underwater information transmission via Morse code was demonstrated using the proposed method.
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
###### Abstract  
With the proliferation of low-Earth-orbit (LEO) satellites, existing satellite networks need to be enhanced to better handle time-sensitive flows (TSFs). However, the migration of time-sensitive network (TSN) technology to satellite networks is challenged by the large space-time range and limited on-board resources, particularly in providing differentiated quality-of-service guarantees for multilevel TSFs. To address this issue, we propose a multilevel queue-based TSN technique that uses latency requirements as an indicator for traffic scheduling. This approach improves the time-sensitive transmission capability of services with different requirements in LEO satellite networks. We conducted a simulation evaluation under a Walker constellation, and the results demonstrate that our proposed method could significantly improve network throughput, and reduce the packet loss ratio by 90% and the time-out ratio by 14.5%. Additionally, our proposed mechanism could accommodate more TSFs with acceptable latency requirements.  
LEO satellite networks; time-sensitive network; time-sensitive flow; quality of service; traffic scheduling +
Footnote †: journal: Electronics Electronics
## 3 Methodology and Data Description
### Data Description
#### 3.5.1 Summary of Sophisticated Data  
This sophisticated dataset covers both historical and real-time data, allowing for a detailed simulation of the transport network and the optimization of cargo flow as shown in Table 1. By using these data sources, the models can address uncertainties in logistics, optimize network capacity, and simulate traffic flow to identify and mitigate congestion, resulting in more efficient hinterland connectivity for Dar es Salaam Port.  
The parameters explored in Table 1 are supported by previous empirical studies that emphasize their relevance and utility in optimizing hinterland connectivity. For instance, the study in ref. [8] highlights the importance of road and rail infrastructure in facilitating efficient cargo movement, which aligns with the parameters considered in our model. Additionally, the authors of [16; 17] validate the application of automated  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data Type** & **Data Description** & **Source** \\ \hline Port Operation Data & Equipment downtime, cargo processing times & Tanzania Port Authority records \\ \hline Historical Delay Data & Delays due to congestion, equipment failure & Land Transport Regulatory Authority (LATRA) of (Tanzania logs, GPS tracking) \\ \hline Real-Time Traffic Volume & Vehicle counts on major roads & Land Transport Regulatory Authority of Tanzania (Traffic monitoring systems) \\ \hline Road Capacity & Maximum vehicle throughput per hour & Land Transport Regulatory Authority of Tanzania Road management authorities \\ \hline Railway Capacity & Cargo capacity per train, number of daily trips & Tanzania Railways Corporation (railway operators) \\ \hline Cargo Demand & Real-time demand at hinterland destinations & Tanzania Shipping Agencies Corporation (customer logistics databases) \\ \hline Speed and Density Data & Average vehicle speeds, road density & Land Transport Regulatory Authority of Tanzania (Traffic analysis reports, GPS data) \\ \hline Travel Time Data & Travel times between key locations & Land Transport Regulatory Authority of Tanzania (GPS trackers, route monitoring) \\ \hline Environmental Disruptions & Road closures due to weather or construction & Land Transport Regulatory Authority of Tanzania (national weather and transport agencies) \\ \hline Vehicle Types and Behavior & Vehicle size, type, and dynamics (acceleration, lane usage) & Land Transport Regulatory Authority of Tanzania (fleet management systems) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data organization.  
vehicles and multimodal transport systems, underscoring their critical role in improving port-hinterland connectivity and reducing operational costs. Moreover, ref. [14] explores the integration of non-local and multimodal connections, directly linking with our approach of considering diverse transport modes and network dynamics. These references confirm that the parameters we use are not only widely accepted but also crucial for addressing the complex challenges of hinterland connectivity in port optimization.
# Effect of Wetting Conditions on the In Situ Density of Soil Using the Sand-Cone Method
## 2 Density Tests Using Sand-Cone Method  
### Considerations in Sand-Cone Method  
The calculations involved in the sand-cone method are described in ASTM D 1556-07 [3], BS 1377-9 [4], and KS F 2311 [5]. The volume of the excavated hole, \(V\), can be determined using the following expression:  
\[V=\frac{M_{1}}{\rho_{d(sand)}} \tag{1}\]  
where \(M_{1}\) is the mass dry sand used for filling the hole and \(\rho_{d(sand)}\) is the dry density of the sand used for the calibration. A calibration container has a known volume and a dry and smooth inner surface.  
The extent to which sand is packed or the test hole is filled can be influenced by the wetness of the test hole and filling sand, decreasing the reliability of the determined in situ density. Depending on the wetness of the inner surface of the hole, sand grains may stick to the surface and to each other, resulting in the formation of voids and the underestimation of the hole volume, as shown in Figures 1 and 2.  
Figure 1 presents a schematic of the sand-filling process in test holes with dry and wet surfaces, and Figure 2 shows photographs of real sand-filled dry- and wet-compacted grounds and hole surfaces after the removal of the sand. The soils had water contents of 2% and 16%, respectively, and a dry unit weight (density) of 16.59 kN/m\({}^{3}\) (1.69 g/cm\({}^{3}\)). Detailed soil properties are presented in the test-procedure section, and fill sand was painted in red to show the color difference from the ground. Thus, the difference in sand-filling processes between dry and wet test-hole surfaces was examined. In addition, the use of moist sand to fill a test hole may also cause sand grains to stick to the hole surface and each other. Therefore, these two factors (moist compacted ground and moist filling sand) were investigated in this study.  
Figure 1: Sand-filling process in dry (**a**) and wet (**b**) ground holes.
# Does Organic Farming Increase Raspberry Quality, Aroma and Beneficial Bacterial Biodiversity?
## 4 Discussion
### Quality Parameters of Organic and IPM Raspberry Fruits
Similarly, application of _Pseudomonas fluorescens_ on strawberry plantlets altered fruit acidity [81]. In our work, canonical correspondence analysis reveals possible interaction between tittatable acidity of fruits and _Methylobacterium_ and _Sphingomonas_ genera (Figure 4), more abundant in IPM fruits. _Methylobacterium_ spp. is mainly found on the phyllosphere where it is able to use methanol as the sole carbon source, whereas several _Sphingomonas_ spp. strains have recently gained attention for their ability to produce gibberellins [56; 82; 83], together with ACC deaminase-mediated reduction of ethylene in the host [84]. The particular combination of hormones may delay late ripening processes, such as degradation of organic acids. Additionally, the direct contribution of _Methylobacterium_ or _Rosenbergiella_ spp. [85; 86] to fruit organic acid content cannot be ruled out. For raspberries, the increase of fruit weight is an important factor to reduce production costs, especially labour [87]. Although plant nutrition is one of the main factors directly influencing berry weight [21], application of plant growth promoting bacteria on fruits also contributed to the increase of berry weight and size [81]. In our study, fruit weight was significantly higher in IPM fruits and correlated to _Brevbacillus_ and _Rosenbergiella_ abundance (Figure 4). _Brevbacillus_ spp. has been recorded in very diverse environmental habitats and several strains successfully promoted plant growth [88]. For instance, application of _Brevbacillus_ spp. on eggplant and pepper led to an increased number of fruits per plant as well as fruit size, weight, and yield [89]. _Rosenbergiella_ is a novel genus initially identified in floral nectar of almond [90], but it has been found in the flowers of several plant species [86], pollen and insects associated with the flower [91]. This bacterium has been isolated also from fruit pericarp [92], suggesting that its population on the flower could be transmitted also on fruit during development and possibly to seeds [93]. In seeds, _Rosenbergiella_ seems to promote germination by the production of IAA [94]. Although the _Rosenbergiella_ spp. strain, isolated in this work from fully ripe fruit, did not show IAA production, this genus is known to include IAA-releasing species which may promote plant and fruit growth [95]. IAA has also an important role in fruit development which was demonstrated by the overexpression of the auxin synthesis-related gene (DefH9-iaaM) in transgenic plants, that showed increased fruit size and number [96]. Altogether, the observed differences in fruit quality, size and yield may originate indirectly as a result of bacterial influence on plant growth and fitness at different phenological stages or on different organs.