esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 1 Introduction
Figure 1: Clustering in hierarchal routing protocol. In our previous works, we presented modifications of LEACH [12], SEP [13] and TEEN [14] protocols, leading to a better selection of cluster heads by using a K-nearest neighbour algorithm for minimising energy consumption. In this paper, we propose a new clustering protocol that combines the DEC protocol with K-means clustering, which improves the performance of cluster head selection, which is called DEC-KM (deterministic energy-efficient clustering protocol with K-means). K-means stands out as a widely utilised clustering algorithm in machine learning and data mining due to its ability to effectively balance speed and accuracy [15]. K-means is the preferred choice because of its speed, as it is efficient in computation and implementation, making it well-suited for handling large datasets. Additionally, it is accurate and produces reliable, well-defined clusters, particularly for spherical data distributions. In wireless sensor networks (WSNs), K-means clustering is highly beneficial compared with random clusterisation for several reasons. Its capacity to enhance network performance, increase energy efficiency, and simplify data aggregation are key factors contributing to its effectiveness in WSNs. All cluster heads will have a better position in the network because the distance between nodes in each cluster area will be calculated and compared between other nodes to find the shortest using the K-means algorithm. Additionally, the set of heuristic rules is proposed in the last algorithm stage, which ensures choosing the nodes of high energy level close to the centre point in a cluster area and close to the base station, as well as rotating the CHs whose energy has been exhausted due to data transmission.
## 3 Data Preparation
### Data Collection Method
#### 3.3.3 Traffic Condition Data  
For traffic condition, the average value of annual average daily traffic (AADT) was collected over five years from the Traffic Monitoring System (TMS), which is a database maintained by the Korea Institute of Civil Engineering and Building Technology, and the speed limit was identified. The distribution of each variable in these segments is presented in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Average** \\ \hline RORC severity ratio & slight: 41.3\%, serious: 43.9\%, fatal: 14.8\% \\ \hline Point of collision ratio & left: 19.7\%, right: 80.3\% \\ \hline Horizontal alignment ratio & straight: 54.7\%, curved: 45.3\% \\ \hline Radius of curve & 116.14 m \\ \hline Vertical alignment ration & flat: 69.5\%, others: 30.5\% \\ \hline Vertical slope (absolute value) & 0.93\% \\ \hline Reverse curve ratio & yes: 4.5\%, no: 95.5\% \\ \hline Continuous curve ratio & yes: 1.8\%, no: 98.2\% \\ \hline Compound curve ratio & yes: 15.2\%, no: 84.8\% \\ \hline Design speed & 62.1 km/h \\ \hline Number of entrance/exists (no.) & 1.27 \\ \hline Type of fixed objects ration & streetlight: 13.9\%, pier: 9.90\%, traffic light: 10.30\%, utility pole: 23.30\%, road sign: 13.00\%, others: 29.6\% \\ \hline Distance between the roadway and the fixed object of & 6.14 m \\ collision & 1.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of target segments.
## 5 Experimental Results
### Synthetic Data in Point Set Form  
In this section, the performance of NBR-Clust on a set of synthetic datasets generated by [PERSON] et al. (2016) is evaluated. These datasets were generated as multi-dimensional Gaussian mixtures with either 10% uniformly random noise or no noise. The dimension \(D\) (the number of features of the point set) and the number of clusters \(K\) both vary as 2, 4, or 8. There are nine combinations of \(D\) and \(K\), shown as one combination per row in Table 7. [PERSON] et al. use the term density to describe whether or not the clusters of a dataset are equal in size. In the equal density datasets, each cluster contains 100 members. In the unequal density datasets, one cluster has 400 members, and the remaining clusters have 100 members. For example, a D4K8 equal density dataset has 800 members with no noise, and the corresponding dataset with 10% noise has 880 members. The unequal density D4K8 dataset will have 1100 members with no noise and 1210 members with noise. The [PERSON] data contains 10 different generated sets of all 9 combinations of D and K, equal and unequal density, noisy and without noise, for a total of 360 datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **VAT** & **Integrity** & **Toughness** & **Tenacity** & **Scattering** \\ \hline iris & 90 & **90** & 51 & **88** & 51 \\ breast-WI & **81** & **85** & **85** & **85** & **81** \\ _E. coli_ & 55 & **60** & **53** & **53** & 58 \\ wine & 71 & **71** & 71 & **70** & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Percent accuracy for NBR–Clustering on real point set data. Bold text indicates one-step clustering.
## 2 Signal Model and Problem Formulation
### ISAC Signal Model with OFDM Waveform
Furthermore, we can compute the range \(r\) and the velocity \(v\) of the reflected object conveniently according to (3) and (4).
## 2 Related Works  
### Encryption-Based Security Methods  
Data encryption techniques have always played a significant role in protecting data security. FHE [15; 32] is a theoretically complete encryption method that allows computations to be performed directly on encrypted data without decryption. This feature makes it particularly important in scenarios involving highly sensitive data processing [33; 34]. The fundamental idea of FHE is to define encryption and decryption functions, \(Enc(\cdot)\) and \(Dec(\cdot)\), in such a way that operations performed on ciphertext map to equivalent operationson the plaintext. Specifically, for plaintexts \(x\) and \(y\), the encryption function satisfies the following relation:  
\[Dec(Enc(x)\oplus Enc(y))=x+y \tag{1}\]  
where \(\oplus\) denotes an operation in the ciphertext domain, allowing addition to be carried out on encrypted data. Similarly, FHE supports multiplication, with the following relation:  
\[Dec(Enc(x)\otimes Enc(y))=x\cdot y \tag{2}\]  
This property allows arbitrary polynomial computations to be performed in the ciphertext domain. However, since each operation requires high computational complexity for encoding and decoding, the computational cost of FHE is extremely high, especially when dealing with large-scale data. As the data size increases, the performance overhead grows exponentially [35]. This makes FHE unsuitable for real-time applications, such as medical image analysis or high-frequency financial trading predictions. In contrast, Secure Multi-party Computation (SMC) [36] divides the data into several parts and distributes them to different participants for joint computation, without revealing the original data of any individual participant. The core idea of SMC is to break down a computation task into multiple sub-tasks, which are executed by different participants, and the results are later integrated to complete the calculation [37]. For example, an addition task \(x+y\), where \(x\) can be split into \(x_{1}\) and \(x_{2}\) such that \(x=x_{1}+x_{2}\), with \(x_{1}\) and \(x_{2}\) being distributed to different participants. Similarly, \(y\) can be split into \(y_{1}\) and \(y_{2}\). This method has the advantage of not requiring centralized data processing, but the drawback lies in the frequent communication needed to ensure the correctness of the computation. The communication cost increases significantly with the number of participants and the complexity of the data, leading to performance bottlenecks in high-dimensional data or multi-node scenarios.
## 4 Experiments and Results
### Experimental Results
#### 4.2.2 The Efficiency of Leading AVs According to Measures of Effectiveness  
The MOEs were applied to verify the efficiency of leading AVs according to their mobility and emissions. The MOEs verification results over different MPRs and traffic volumes are expressed in Figures 8 and 9. Referring to mobility, the average speed improved at a higher MPR and a lower traffic volume. As seen in Figure 8, the fully autonomous condition increased the average speed 1.19 times compared to the 20% MPR. In particular, the fully autonomous condition obtained the highest value of average speed of 10.4 with a traffic volume of 100 veh/h.  
Figure 6: A comparison experiment with go straight movements in the context of a non-signalized intersection: (**a**) mixed-traffic conditions with market penetration rates ranging from 20% to 80%; (**b**) fully autonomous conditions with a 100% market penetration rate.  
Referring to energy, the emissions decreased at a higher MPR and a lower traffic volume. As seen in Figure 9a, the fully autonomous condition increased the NOx by 1.13 times compared to the 20% MPR. The fully autonomous condition obtained the lowest value of NOx of 1.01 with a traffic volume of 100 veh/h. As seen in Figure 9b, the fully autonomous condition increased the HC 1.46 times compared to the 20% MPR. In particular, the fully autonomous condition obtained the lowest value of HC of 0.16 with a traffic volume of 100 veh/h. Hence, the efficiency of leading AVs was clearer at a higher MPR and a lower traffic volume regarding MOEs verification.  
Figure 8: Average speeds with different market penetration rates and real traffic volumes.  
Figure 7: Average rewards with different market penetration rates and real traffic volumes.
## 5 Conclusions  
Tropical forests play a very important role in global carbon cycling and climate change, especially when undergoing tropical forest degradation and deforestation induced by a growing human population and forest resource requirements [90]. Satellite-based estimates of gross primary production (GPP) are a wall-to-wall modelling of vegetation dynamics dependent on NDVI data. As the longest time series of remote sensing data, GIMMS NDVI3g is a critical data source to track vegetation activity and changes for the past 30 years. Based on the GLOPEM-CEVSA model, three different estimates of GPP for Southeast Asia were simulated including GIMMS NDVI3g (GPP\({}_{\text{NDVI3g}}\)), GIMMS NDVI1g (GPP\({}_{\text{NDVI1g}}\)), and MODIS (GPP\({}_{\text{MOD15}}\)), and then validated with ground based GPP. In addition, comparisons were made among the three GPPs as well as against the MODIS GPP product (GPP\({}_{\text{MOD17}}\)). By validation of the intra-annual variability of GPP at the QYZ and PDF flux tower sites and site-year level at an additional eight sites, we found that GPP\({}_{\text{NDVI5g}}\) has a higher accuracy relative to GPP\({}_{\text{NDVI1g}}\) and was comparable with the GPP\({}_{\text{MOD15}}\). However, further validation in other ecosystem types are needed in the future, especially when these products are used for non-forest ecosystems such as grassland and cropland. An independent validation of the intermediate variable FPAR would also help to reduce uncertainty and improve future GPP estimates.  
Ultimately, our results test and validate GIMMS NDVI3g data for long-term quantification and monitoring of vegetation productivity, specifically for our study region of Southeast Asia, which notably includes tropical forests under increasing deforestation pressure. Our findings suggest that these data have the potential to critically advance the current understanding of ecosystem responses to global climate change and human activities at the regional and global scale
## 2 Literature Review
### Environmental Effects on Resin-Printed Part Performance
#### 2.3.2. UV Aging and Degradation in Photopolymers  
Ultraviolet (UV) degradation significantly affects the longevity and efficacy of photopolymer-based materials. The susceptibility of photopolymers to UV radiation poses challenges in outdoor applications, as UV exposure tends to degrade resin properties, resulting in cracks and discolorations over time [44; 45]. Warnings regarding the long-term mechanical behavior of photopolymers under UV exposure have been supported by studies demonstrating a decline in flexural strength and mechanical disintegration resulting from repeated UV cycles [33; 46].  
Laboratory studies confirm that UV exposure leads to a decrease in material toughness and elasticity, ultimately impacting their suitability for load-bearing applications. Thus, current research increasingly emphasizes the need to develop UV-stabilized formulations that can overcome such deficiencies and extend the usability of photopolymers in diverse environments [47; 48].
## 6 Experimental Verification
### Comparison of Experimental Results  
#### 6.3.1 Speed Observation  
In order to compare the speed observation performance of the SAKF and the LPF, make the RV-driving component run in torque mode. Set the reference torque to a sine function with amplitude 0.5 Nm and frequency 1 Hz, and use the ds1104 to sample the speed signal by the SAKF and LPF simultaneously. The bandwidth of the 2-order Butterworth LPFs are set equal to the bandwidth of the SAKF, 25 Hz. The result is shown in Figure 8, where the magenta line is the original speed differentiated by encoder, the red line is the speed filtered by the Butterworth LPF, and the blue line is the speed estimated by the SAKF.  
Figure 6: The method for obtaining the low-speed motion performance of OES.  
Figure 7: The method for obtaining the inertial stability accuracy of the OES.  
As can be seen from Figure 8, the noise from the original speed signal is evidently reduced by both the SAKF and LPF. However, the speed from the LPF has a significant delay, while the delay of the speed estimated by the SAKF is almost negligible. Therefore, comparing with the LPF, the SAKF has a better speed observation performance.
###### Abstract  
The size and number of utility-scale bifacial photovoltaic (PV) installations has proliferated in recent years but concerns over modeling accuracy remain. The aim of this work is to provide the PV community with a validation study of eight tools used to simulate bifacial PV performance. We simulate real 26 kilowatt-peak (\(\mathrm{kW_{P}}\)) bifacial arrays within a 420-\(\mathrm{kW_{P}}\) site located in northern Europe (55.6\({}^{\circ}\) N, 12.1\({}^{\circ}\) E). The substructures investigated include horizontal single-axis trackers (HSATs) and fixed tilt racks that have dimensions analogous to those found in utility-scale PV installations. Each bifacial system has a monofacial reference system with similar front side power. We use on-site solar radiation (global, diffuse, and beam) and albedo measurements from spectrally flat class A sensors as inputs to the simulation tools, and compare the modeled values to field measurements of string level power, rear and front plane of array irradiance, and module temperature. Our results show that state-of-the-art bifacial performance models add \(\sim\)0.5% uncertainty to the PV modeling chain. For the site investigated, 2-D view factor fixed tilt simulations are within \(\pm\)1% of the measured monthly bifacial gain. However, simulations of single-axis tracker systems are less accurate, wherein 2-D view factor and 3-D ray tracing are within approximately 2% and 1% of the measured bifacial gain, respectively.  
bifacial PV; PV performance; model validation; bifacial gain; single-axis trackers +
Footnote †: journal: Applied
# Seismic Responses of GRS Walls with Secondary Reinforcements Subjected to Earthquake Loading
###### Abstract  
Secondary reinforcement has been proven to be effective in increasing the performance of geosynthetic-reinforced soil (GRS) walls under working stress conditions, enabling an eco-friendlier environment. However, the seismic responses of GRS walls with secondary reinforcements are still unclear. In this study, in-depth finite element analyses were used to investigate the seismic responses of GRS walls with secondary reinforcement subjected to earthquake motions. The numerical procedure was first validated using measurements obtained from both a field GRS wall with secondary reinforcement and benchmark large-scale shaking table tests. Then, the validated GRS walls procedure was utilized to explore the effects of secondary reinforcement length and stiffness, the vertical spacing of the primary reinforcement, and wall height on the seismic responses. Based on the study, the following findings can be drawn: (i) the secondary reinforcement length and stiffness under various wall heights and peak ground accelerations (PGAs) have a limited influence on the relative lateral facing displacement and acceleration amplification, however, they can significantly decrease the connection load and the maximum reinforcement load; (ii) increasing the length of the secondary reinforcement is more effective for reducing the connection load and the maximum reinforcement load than increasing the stiffness of the secondary reinforcement; (iii) the effect of secondary reinforcement is more evident for greater wall height, the larger vertical spacing of primary reinforcement, and smaller PGA; and (iv) GRS walls with secondary reinforcement could ease the acceleration amplification. The study has highlighted the salient effect of secondary reinforcement on GRS wall performance under seismic conditions.  
geosynthetics; reinforced soil retaining wall; secondary reinforcement; seismic response; finite element analyses +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Patient Cohort, Image Acquisition Protocol, and Visual Assessment  
All patients with amyloidotic TTR cardiomyopathy that underwent planar scintigraphy with \({}^{99m}\)Tc-DPD at the IRCCS Azienda Ospedaliero-Universitaria di Bologna, Italy, between January 2004 and September 2019 and had images stored on the local Picture Archiving and Communication System (PACS) were retrospectively enrolled.  
Whole body (WB) planar scintigraphy was obtained with a dual-head camera (Discovery NM/CT 670, GE, Healthcare) equipped with low-energy, high-resolution (LEHR) collimators. WB scans were obtained 5 min (early image) and three hours (late image) after an injection of \({}^{99m}\)Tc-DPD with a standard dose of 740 MBq in all patients. Anterior and posterior views were simultaneously acquired. Nuclear medicine (NM) physicians visually assessed myocardial uptake on \({}^{99m}\)Tc-DPD scintigraphy on late images, according to [PERSON] et al. [13], assigning a visual cardiac score ranging from score 0 to 3: score 0, no cardiac uptake with normal bone uptake; score 1, mild cardiac uptake, inferior to bone uptake; score 2, moderate cardiac uptake accompanied by attenuated bone uptake; and score 3, strong cardiac uptake with mild/absent bone uptake.
# The Long and Winding Road to Cardiac Regeneration
## 4 Biomaterials
### Natural Biomaterials
#### 4.2.3 Gelatin  
Gelatin is a natural biomaterial derived from the hydrolysis of collagen; the major protein found in connective tissues.  
Gelatin can be easily processed into various forms, such as films, sponges, and hydrogels. These properties allow for its adaptation to different tissue engineering strategies [237].  
Gelatin provides a supportive environment for cells to grow, migrate, and organize, facilitating tissue regeneration. Gelatin can also be modified to incorporate bioactive molecules and growth factors to further enhance its effects on cells [238].  
In cardiac tissue engineering, gelatin-based materials have shown promise as scaffolds for tissue repair and regeneration. They can be used to create cardiac patches, injectable matrices, and 3D constructs, providing mechanical support, and promoting cell integration and functional tissue formation [239].
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 2 Tracking Error Estimation
### Relevant Baseband Signal Model
\begin{array}{rl}E=\sum\limits_{j=1}^{N_{\text{nc}}}E(j)&=N_{\text{nc}}A| \sin c(\delta fT_{\text{coh}})|\sum\limits_{j=1}^{N_{\text{nc}}}R(\delta\tau_ {j}-\tfrac{d}{2})+N_{\text{nc}}n_{\text{E}}\\ P=\sum\limits_{j=1}^{N_{\text{nc}}}P(j)&=N_{\text{nc}}A|\sin\text{c}(\delta fT_{ \text{coh}})|\sum\limits_{j=1}^{N_{\text{nc}}}R(\delta\tau_{j})+N_{\text{nc}}n _{\text{P}}\\ L&=\sum\limits_{j=1}^{N_{\text{nc}}}L(j)&=N_{\text{nc}}A|\sin\text{c}(\delta fT_{ \text{coh}})|\sum\limits_{j=1}^{N_{\text{nc}}}R(\delta\tau_{j}+\tfrac{d}{2})+N _{\text{nc}}n_{\text{L}}\\ \end{array}  \tag{10}\]  
\[ \begin{array}{rl}E^{2}=\sum\limits_{j=1}^{N_{\text{nc}}}E^{2}(n)&=N_{\text{nc}} A^{2}|\sin\text{c}(\delta fT_{\text{coh}})|^{2}\sum\limits_{j=1}^{N_{\text{nc}}}R^{2}( \delta\tau_{j}-\tfrac{d}{2})+N_{\text{nc}}n_{\text{E}^{2}}\\ P^{2}=\sum\limits_{j=1}^{N_{\text{nc}}}P^{2}(n)&=N_{\text{nc}}A^{2}|\sin\text{c}( \delta fT_{\text{coh}})|^{2}\sum\limits_{j=1}^{N_{\text{nc}}}R^{2}(\delta\tau_ {j})+N_{\text{nc}}n_{\text{P}^{2}}\\ L^{2}&=\sum\limits_{j=1}^{N_{\text{nc}}}L^{2}(n)&=N_{\text{nc}}A^{2}|\sin\text{c}( \delta fT_{\text{coh}})|^{2}\sum\limits_{j=1}^{N_{\text{nc}}}R^{2}(\delta\tau_{j} +\tfrac{d}{2})+N_{\text{nc}}n_{\text{L}^{2}}\\ \end{array}  \tag{11}\]  
Equations (4), (10), and (11) are exactly the baseband signal models that can be used for tracking error estimation. These baseband signal models are summarized in Table 1, where they are expressed as the addition of useful signal and noise. The useful signal contains the real tracking error, and the noise effect cannot be cancelled when the tracking error is estimated using these baseband signals. We also make a brief comment about the probability distribution of noise in this table.
# DAB Signal Preprocessing for Passive Coherent Location
## 2 Materials and Methods
### DAB Signal Preprocessing  
A method of DAB signal preprocessing was presented [31] that works in the time domain with the complex baseband DAB signal \(x(n)\). That method employs signal envelope measurements to detect the Null symbol, which occurs at the start of each frame. The envelope is smoothed by processing the amplitude of the \(x(n)\) signal in a moving average filter with \(N=128\) taps  
\[e(n)=\frac{1}{N}\sum_{k=0}^{N-1}|x(n-k)|. \tag{2}\]  
The mean signal level is estimated in a similar way by long-term averaging (e.g., \(M=10^{6}\)) of the amplitude  
\[e_{\rm avg}(n)=\frac{1}{M}\sum_{k=0}^{M-1}|x(n-k)|. \tag{3}\]  
If the signal envelope \(e(n)\) falls below the fixed threshold, i.e., when \(e(n)<0.3\)\(e_{\rm avg}(n)\), the beginning of the Null symbol is detected. When the signal envelope rises above the threshold in this state, the beginning of the PR symbol is detected, and the time instant of this situation (\(n_{0}=n\)) is stored as a reference for the subsequent operations. This method has been shown [31] to perform satisfactorily with the high-SNR signal, for example, when \(x(n)\) is the reference signal from the PCL receiver. However, if synchronization to noisy signals is required, more robust frame synchronization methods (e.g., based on the correlation techniques) may still be implemented [33; 34; 35].