esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### Consistency of Vegetation Phenology Parameters  
The interannual changes of the SOS and EOS under the five methods have great differences above 30\({}^{\circ}\)N (Figure 3). For the SOS (Figure 3A), the variation ranges under Methods 1, 3 and 5 in each region were similar, and the value was mainly concentrated between 83 and 123 days. The variation ranges of the SOS under Methods 2 and 4 were similar, and the value was mainly concentrated between 113 and 148 days. By analysing the SOS under different vegetation types, we also found that it is the same as that in different regions, which shows that the SOS extracted using Methods 2 and 4 is obviously delayed by approximately 30 days compared with Methods 1, 3 and 5. This may be because the threshold limit of method 2 and method 4 on NDVI is obviously higher than that of the other methods, and the regions with low NDVI values are not extracted well, which leads to the lagging of the SOS. However, the changes of the SOS were consistent in different regions, showing that Europe < North America < above 30\({}^{\circ}\)N < Asia. For instance, for MF and WS (Figure 3C), the interannual variation of the SOS extracted using method 3 is obviously higher than that extracted using other methods. In addition, the interannual variation of the SOS under the five extraction methods is small.  
For EOS (Figure 3B), the variation range in each region is larger under each method. The variation ranges of the EOS under Methods 2, 4 and 5 are roughly the same and are mostly concentrated from 260 to 309 days. However, the EOS under Methods 1 and 3 is in most cases delayed from 287 to 332 days. This may also be because the threshold limit of Methods 2 and 4 on the NDVI is obviously higher than that of other methods, and the regions with low NDVI values are not extracted well, which leads to the advancement of the EOS. Except for Method 5, the EOS average change across different regions is the same, showing that North America > above 30\({}^{\circ}\)N > Europe > Asia. However, judging from the EOS under different methods (Figure 3D), the interannual variation range of different vegetation types is large, among which S and G are the most obvious. However, the interannual variation range of the EOS under different vegetation types extracted using Methods 4 and 5 is obviously smaller than that extracted using other methods.  
Figure 3: Vegetation phenological changes above 30\({}^{\circ}\)N from 1982 to 2015. (**A**,**B**) represent the interannual changes of the SOS and EOS in different regions, respectively. (**C**,**D**) represent the interannual changes of the SOS and EOS in different vegetation types, respectively. The length of the box represents the interannual variation range of the SOS and EOS, and the shorter the box, the smaller the fluctuation of the data, and the longer the box, the greater the fluctuation of the data. Among them, the black dot in the middle of the box represents the average value of the data, the top of the whisker in the box represents the maximum value of the data, and the bottom of the whisker in the box represents the minimum value of the data.
# Numerical Analysis of Vibroacoustic Response of Timber Floor Panels Damped with Porous Materials
## 4 Dynamic Analyses and Parametric Study
### Loading  
In this section, the different loading cases are presented. The first one is a walking time history of one person (Figure 5a), and the second one is the time history of a washing machine (Figure 5b).  
* Walking time history The harmonic load function of a person who is walking is presented in Equation (18), where each step is given with a polynomial function which is proposed by [PERSON] et al. [32]:  
\[F(t)=\Big{(}K_{1}t+K_{2}t^{2}+K_{3}t^{3}+K_{4}t^{4}+K_{5}t^{5}+K_{6}t^{6}+K_{7}t ^{7}+K_{8}t^{8}\Big{)}\cdot G \tag{18}\]  
where \(F\) is the load in terms of time \((t)\); \(G\) is the acceleration of gravity; and \(K_{1}\) to \(K_{8}\) are coefficients depending on the walking frequency through previous experimental results [32]. Table 2 presents the values of these coefficients. In our study, the pacing frequency of walking (\(f_{p}=2\) Hz) has been used, while the load duration, \(t_{s}\), is given using Equation (19).  
\[t_{s}=2.6606-1.757\cdot f_{s}+0.3844\cdot f_{s}^{2} \tag{19}\]  
* Washing machine time history The harmonic load function of a washing machine is presented in Equation (20):  
\[F(t)=F_{0}\cdot\sin(\omega\cdot t) \tag{20}\]  
where \(F\) is the load in terms of time \((t)\); \(F_{0}\) is the initial load of the washing machine when it is full (\(F_{0}=638\) N); and \(\omega\) is the angular frequency (\(\omega=10\)\(\pi\) Hz).
###### Abstract  
With the rapid population growth, increasing agricultural productivity is an extreme requirement to meet demands. Early identification of crop diseases is essential to prevent yield loss. Nevertheless, it is a tedious task to manually monitor leaf diseases, as it demands in-depth knowledge of plant pathogens as well as a lot of work, and excessive processing time. For these purposes, various methods based on image processing, deep learning, and machine learning are developed and examined by researchers for crop leaf disease identification and often have obtained significant results. Motivated by this existing work, we conducted an extensive comparative study between traditional machine learning (SVM, LDA, KNN, CART, RF, and NB) and deep transfer learning (VGG16, VGG19, InceptionV3, ResNet50, and CNN) models in terms of precision, accuracy, f1-score, and recall on a dataset taken from the PlantVillage Dataset composed of diseased and healthy crop leaves for binary classification. Moreover, we applied several activation functions and deep learning optimizers to further enhance these CNN architectures' performance. The classification accuracy (CA) of leaf diseases that we obtained by experimentation is quite impressive for all models. Our findings reveal that NB gives the least CA at 60.09%, while the InceptionV3 model yields the best CA, reaching an accuracy of 98.01%.  
t +
Footnote †: journal:  
0000-0001-5000-0000-0001-5000][PERSON] 0000-0001-5000-0000-0001-5000-00001-5000-00001-50001  
Our motivation is to enhance the overall detection model performance. Notably, we aim to strike a compromise between high accuracy and a low misclassification rate. Thus, we compared different competitive machine learning and deep learning algorithms, using several computer vision techniques and various performance evaluation metrics to evaluate our results to find out the most efficient model that can be exploited for real-time leaf disease identification and classification. So, from the findings, we were able to pin down the advantages and inconveniences of each model.  
This present comparative study stands out from other studies in that it is conducted on a massive dataset containing several types of crops and the use of both ML/DL techniques, resulting in weeks of training for the models to be completed. The novelty of this study lies in obtaining the most appropriate combination of the DL optimizer and the CNN model, which delivered greatly better results compared to prior research. The remainder of this article is arranged as follows: the next section provides a review of the existing research on this topic. Then, the third section presents the materials and methodology needed to carry out this study. Afterward, the fourth section reports the experimental results along with their extensive discussion, and finally, the article concludes with the fifth section, which tackles possible future directions for the present work.
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## 3 Bit Error Rate Performance Analysis  
### The Instantaneous SNR  
To derive the BER and channel capacity of the relaying system, we first derive the statistics of the signal-to-noise ratio (SNR). Let \(\gamma_{\ell}\) be the instantaneous SNR pertaining to the \(\ell\)-hop transmission, which is given by  
\[\gamma_{\ell}=\frac{P_{Rx,\ell+1}\cdot h_{\ell}^{2}\cdot E_{b}}{N_{0}}. \tag{14}\]  
As the fading amplitude \(h_{\ell}\) is Nakagami-\(m\) distributed with PDF given in (5), we can obtain the PDF \(f_{\gamma_{\ell}}(\gamma)\) of the instantaneous SNR \(\gamma_{\ell}\) by applying the concept of transformation of random variables ([45], pp. 182-193) and obtain the following expression:  
\[f_{\gamma_{\ell}}(\gamma)=\frac{(mN_{0})^{m}\cdot\gamma^{m-1}}{(\Omega E_{b} \cdot P_{Rx,\ell+1})^{m}\cdot\Gamma(m)}\cdot\exp\biggl{(}-\frac{mN_{0}\gamma} {\Omega E_{b}\cdot P_{Rx,\ell+1}}\biggr{)}\quad\gamma\geq 0. \tag{15}\]  
From (15), we could also obtain the average SNR \(\overline{\gamma}_{\ell}\) of the \(\ell\)-th hop as \(\overline{\gamma}_{\ell}=(\Omega E_{b}\cdot P_{Rx,\ell+1})/N_{0}\). The CDF \(F_{\gamma_{\ell}}(\gamma)\) of the instantaneous SNR \(\gamma_{\ell}\) can be obtained from its relationship with the PDF \(f_{\gamma_{\ell}}(\gamma)\) in (15) as follows:  
\[F_{\gamma_{\ell}}(\gamma) =\frac{(mN_{0})^{m}}{(\Omega E_{b}\cdot P_{Rx,\ell+1})^{m}\cdot \Gamma(m)}\int_{0}^{\gamma}\gamma^{m-1}\cdot\exp\Bigl{(}-\frac{mN_{0}}{\Omega E _{b}\cdot P_{Rx,\ell+1}}\gamma\Bigr{)}\,d\gamma\] \[=\widetilde{\Gamma}\Bigl{(}m,\frac{mN_{0}}{\Omega E_{b}\cdot P_{Rx,\ell+1}}\gamma\Bigr{)}, \tag{16}\]  
where \(\widetilde{\Gamma}(\cdot,\cdot)\) is the normalized lower incomplete Gamma function defined by  
Figure 3: Absolute values of [PERSON]’s Class-A noise samples with \(N_{0}=1\), \(A=0.1\), \(\rho=0.1\) (**top** figure) and with \(N_{0}=1\), \(A=0.01\), \(\rho=0.01\) (**bottom** figure).  
\[\widetilde{\Gamma}(\tau,y)=\frac{1}{\Gamma(\tau)}\int_{0}^{y}t^{\tau-1}\cdot \exp(-t)\,dt. \tag{17}\]
# A Review of Vehicle to Vehicle Communication Protocols for VANETs in the Urban Environment
## 4 Simulation and Analysis
### Results and Discussion
#### 4.2.3 Routing Overhead  
Figure 12 depicts that an increase in traffic density causes an increase in routing overhead for all the three protocols. This is due to the fact that the numbers of control messages depends on traffic density. As the node density increases, the routing overhead also increases. The routing overhead of TFOR and E-GyTARD is lower than DGSR. This is because TFOR and E-GyTARD delivered the larger number of packets to the destination which reduced the routing overhead.
# Design of Time-Synchronized Switch Based on Zynq
## 2 WR Technology
### Synchronous Ethernet (SyncE)  
SyncE is a technology used to achieve frequency synchronization between devices [17]. Its main working principle is to extract clock signals from the received serial data stream using Ethernet physical layer chips. This process typically occurs within the physical layer (PHY) chip, for example, using a digital phase-locked loop (PLL). The extracted clock signal can be output via the BITS interface or can be further transmitted to downstream devices through Ethernet data.  
SyncE employs a hierarchical synchronization network, where the top-level node is referred to as the System Clock Master (STM). It is synchronized with a master reference clock (such as a GPS receiver or atomic clock) and serves as the frequency reference for the entire network. Each node uses the synchronized clock signal received from upstream as the reference for its own clock and subsequently passes it on to downstream nodes, ensuring frequency consistency throughout the entire network [18], as illustrated in Figure 1.  
The advantage of SyncE lies in its relatively simple implementation, as it is not affected by higher-layer protocols. Therefore, it is not impacted by factors such as data network congestion, packet loss, and delay. This allows SyncE to provide clock accuracy similar to that of SDH (Synchronous Digital Hierarchy)/SONET (Synchronous Optical Network) networks, making it suitable for scenarios that do not require high precision in time synchronization. However, it is important to note that SyncE only supports frequency synchronization and does not support time synchronization. Moreover, because SyncE relies on physical layer signal transmission, it tends to have relatively low flexibility in terms of network topology.
# Antihyperglycemic Potential of Mace Water Extract from _Myristica_ fragments [PERSON]
## 2 Materials and Methods
### Test of Acute Effects of Mace Water Extract  
#### Oral Starch Tolerance Test  
The oral starch tolerance test (OSTT) was carried out following Poovita and Parani [30]. Normal male Sprague Dawley rats (BW 191.27 \(\pm\) 16.42 g) were fasted overnight and divided into five groups (n = 3). Each group received distilled water orally (negative control), 10 mg acarbose/kg BW (positive control), 37.5 mg ME/kg BW (dose A), 75 mg ME/kg BW (dose B), and 150 mg ME/kg BW (dose C). Acarbose works by inhibiting the breakdown of polysaccharides and oligosaccharides through the enzymes \(\alpha\)-amylase and \(\alpha\)-glucosidase. Therefore, it is more relevant for use in the OSTT, as the substrate administered to the experimental rats is starch (a polysaccharide), not glucose (a monosaccharide).  
Ten minutes after drug/sample administration, blood glucose levels were measured (minute 0), and then starch (3 g/kg BW) was given orally. Blood glucose levels were measured at 30, 60, 90, and 120 min after starch administration. Peak blood glucose (PBG) was determined by observing blood glucose levels during the test time interval. The area under the curve (AUC) value was calculated using the formula:  
\[\text{AUC (mg/dL\_hour)}=(\text{BG}_{0}+\text{BG}_{30}\times 0.5)/2+(\text{BG}_{3 0}+\text{BG}_{60}\times 0.5)/2+(\text{BG}_{60}+\text{BG}_{90}\times 0.5)/2+(\text{BG}_{90}+ \text{BG}_{120}\times 0.5)/2\]  
where \(\text{BG}_{0}\) is the blood glucose level before starch administration and \(\text{BG}_{30}\), \(\text{BG}_{60}\), \(\text{BG}_{90}\), and \(\text{BG}_{120}\) are blood glucose levels 30, 60, 90, and 120 min after starch administration. Dose B (75 mg ME/kg BW), which was the lowest dose with a lower AUC value than the negative control in the oral starch tolerance test, was chosen for use in the oral glucose tolerance test and antihyperglycemic test for 28 days.  
The results of our previous research [28] showed that the total phenolic content of the mace water extract used in this study was 24.53 \(\pm\) 2.86 mg GAE/g ME, and thus 1 mg of extract contains 0.02453 mg GAE. Therefore, the doses of 37.5 (dose A), 75 (dose B), and 150 (dose C) mg ME/kg BW correspond to 0.92 mg total phenolics from ME/kg BW (dose A), 1.84 mg total phenolics from ME/kg BW (dose B), and 3.68 mg total phenolics from ME/kg BW (dose C), respectively (i.e., 75 mg ME\(\times\)0.02453 mg GAE/mg ME = 1.84 mg GAE).
## 1 Introduction
* The southern shores of Attica, in an area of approximately WNW-ESE direction, between Piraeus and Glyfada. * The area along the Leuces islands, north of Aegina. * The northern suburbs, near the Olympic facilities, near the ML = 3.2 event that occurred on 16 January 2015. * The region between Mt Penteli and Marathon Bay [28]. * The Thriassion plain and its extension in the Athens basin. * A notable spatiotemporal earthquake cluster was located along the southern shores of Attica. The most notable events in this region occurred on 7 and 13 October 2023, with ML = 3.9 and 3.8, respectively, in the area east of Keratea (SE Attica). While most earthquakes occur on faults with surface expression, some have been identified in areas where this does not apply, such as the cluster of earthquakes in the northern suburbs of the city. Blind faults in major cities are a type of fault that can be difficult to detect and monitor. Unlike surface faults, which can be identified by visible features such as cracks or ridges on the ground, blind faults do not have any surface expression. The utilization of earthquake tomography as a technique to identify this kind of  
Figure 2: Distribution of focal mechanisms of the significant earthquakes that occurred between 1981 and 2019 (M > 5.0) [26]. The color of the focal mechanism represents the depth (km) of each event. The details of the focal mechanism solutions are shown in the Supplementary Material, Table S1. Abbreviations—ECG: Eastern Corinth Gulf; SEvG: Southern Evia Gulf; TNF: Thriassion Normal Fault; FyNF: Fyli Normal Fault; PNF: Palaiokoudoura Normal Fault. Fault traces as in Figure 1.  
structure in metropolitan areas can be extremely beneficial for city planners and emergency management officials. The study is focused on the investigation of the crustal structure in Attica and nearby areas near the Eastern Corinth Gulf, utilizing manually located events. To gain a comprehensive understanding of the local fault systems, we used P- and S-wave travel-time data from over 3000 seismic events recorded by the Hellenic Unified Seismological Network (HUSN) [29]. These seismic events were recorded between 2018 and the present day, providing us with an extensive and rich dataset. With this dataset, we were able to meticulously examine the intricate details of the local fault systems. These findings form the backbone of our research, enabling us to gain valuable insights into the local faults and geology.
# Upper Extremity Kinematics and Electromyographic Activity in Uninjured Tennis Players
## 2 Materials and Methods
### Data Analysis  
Joint kinematics and EMG signals were analyzed from three trials using a commercial software program (Visual3D, C-Motion, Inc., Germantown, MD, USA). Both fatigue and physiological variability were assumed to be negligible; subjects were given a consistent 10 s rest between groundstrokes, which is more than the timing between subsequent hits in competitive match play. Position data were filtered using a GCVSPL filter [23]. Segment coordinate systems were defined in accordance with International Society of Biomechanics (ISB) recommendations [24]. The upper-body kinematic chain was constrained to have six degrees of freedom at the pelvis, trunk, and upper arm, and two DOF at the forearm and hand. Each subject's stroke mechanics were divided into three phases: (1) racket preparation, (2) acceleration, and (3) follow-through, which were defined by four time points: (1) initiation of backswing, (2) termination of backswing, (3) ball impact, and (4) end of follow-through. Ball impact was determined based on the acceleration of the four racket markers. Wrist and elbow kinematics were calculated per ISB standards [24].  
Biomechanical variables of interest included elbow (flexion-extension) and wrist (pronation-supination, flexion-extension, radial-ulnar deviation) range of motion, and angular velocities. To create a normative database, the mean and \(\pm\)1 standard deviation were created for each variable. Data were additionally grouped based on age (12-13 and 15-17), sex, and USTA ranking (<500 and >500, taken at the time of testing). Statistical parametric mapping (SPM) was used to investigate if there were any significant differences in the kinematic data of these groupings [25]. This analysis was performed using open-source spm1d code implemented in MATLAB (MathWorks, Natick, MA, USA). Joint range of motion and angular velocities were compared between groupings for both swing types using an SPM two-tailed \(t\)-test (\(\alpha\) = 0.05). A scalar output statistic, SPM1, was calculated for each time point. When the SPM1 value exceeds the critical threshold value of \(\alpha\) = 0.05, there are significant differences between groups at this time point. Time points of significant difference were indicated by solid bars under each kinematic graph.  
Raw EMG signals were bandpass-filtered at 10 Hz and 500 Hz. EMG data were then filtered with a fourth-order low-pass Butterworth filter using a 10 Hz cutoff, and the root-mean-square values were calculated. The threshold was determined from each subject's quiescent trial. For each groundstroke trial, EMG activation was taken to occur when the amplitude was 3 standard deviations above the threshold value for a 50 ms window, and EMG termination occurred when the amplitude dropped 3 standard deviations below the threshold value [26].
# Bio-Based Polyester Fiber Substitutes: From GWP to a More Comprehensive Environmental Analysis
## 1 Introduction
When only climate factors are accounted for, there is a great chance to miss adverse effects or trade-offs in other categories such as water consumption, eutrophication, acidification, ecotoxicity, land use, etc., which may stem from the agricultural practices or the modified supply chains. Furthermore, an LCA study [25] that compared recycled PET and PLA polymers against the conventional PET in Europe found that recycled PET outperforms both counterparts. By examining 11 non-climate impacts in addition to climate change, the study found that the bio-based representative does not show clear environmental advantages and that current European waste management practices strongly question its sustainability [25]. Given that melt spinning is the only step between polymers and synthetic fibers in question, very close outcomes can be expected on the fiber level. Last but not least, in the absence of comprehensive LCA studies on the substitution of fossil-based fibers by the bio-based alternatives, the political or corporate decisions of prioritizing bio-based fibers and textiles over conventional ones stay without a scientific foundation. The present paper acts in this information vacuum and aims to shed light on the implications of bio-sourcing for polyester by performing a cradle-to-gate LCA on polyester fiber and its bio-based substitutes with varying levels of bio-content. By assessing a wide range of environmental impacts, the study revolves around two main hypotheses that: (i) bio-sourcing decreases the climate impacts of bio-based fibers relative to fossil-based polyester as a consequence of omitted consumption of fossil-based feedstock; (ii) bio-sourcing simultaneously causes adverse effects in non-climate impacts of bio-based fibers relative to the conventional polyester. The impact assessment was performed with the Environmental Footprint method and three adaptations on carbon crediting and distance-to-target weights (see Methods and Materials). This method was chosen as the future reference method of the European Union (EU) for the product's environmental footprint while the EU's bio-based policy is expected to drive the innovation for these and products alike. The results, therefore, enable institutional decision-makers, sustainability managers as well as the general public to grasp the environmental consequences of this well-intended strategy. To do so, the on-hand manuscript is structured in the following way: the first part defines the goal and scope of this study and gives an overview of the methodology. The prevailing production pathways on the market for each of the fibers and their modeling are elaborated in the subsequent section. Finally, we present the most relevant results for each examined fiber and summarize the discussion of these results.