esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Durability Analysis
#### 3.5.2 Accelerated Loaded Test Results Analysis  
It can be seen in Figure 11 that the dishing depths of the OG mixtures and PADG mixtures were smaller compared to the DG mixtures, when the test was performed in dry condition of 30 \({}^{\circ}\)C. The stronger bonding of the binder and the denser structure of the PADG mixtures could alleviate the deformation of the specimens under the loads. As for the OG mixtures, the high proportion of coarse aggregates can withstand the loads effectively, when in relation to the framework structure. It was also found that the dishing depth of all specimens increased sharply due to immersed treatment, especially the OG mixtures with a greater void content of 13.0%. In water immersion condition, a large number  
Figure 10: Stability of cold mixtures at different cured times at (25 \({}^{\circ}\)C).  
of pores in the mixture will accelerate the water damage of the specimen. Among them, the PADG mixtures had the smallest dishes depth of 9.5 mm, which had decreased by 84.2 and 142.1%, compared to the DG mixtures and OG mixtures, respectively, indicating that the modifier and adhesive can reinforce the strength of the binder in the PADG mixtures. Therefore, it can be concluded that the PADG mixtures have an excellent long-term performance even under traffic loading and adverse weather.
# Privacy and Security in Federated Learning: A Survey
## 3 Security in Federated Learning
### Techniques for Defending against Attacks in FL
#### 3.3.1 Defense against Poisoning Attacks  
The proposed methods are mainly reactive ones which continuously monitor client behaviors. [PERSON] et al. [28] have proposed a method to screen out malicious clients. This method works by using artificial intelligence that detects model changes or nonconforming data distributions. Another method following the same principle but applied to an attack by several malicious users is proposed in [29]. This is called sniper. Another defense against poisoning attacks is proposed in [21]. This technique consists of checking the performance of the global model at each new model update.
## 2 Experiment and Data Preprocessing
### Raman Spectrum Data Preprocessing  
During the aging process of oil-paper insulation, the deepening color of the oil sample causes baseline drift, while the noise interference from environmental rays and vibrations of the spectral detection device may affect the information about aging.  
Figure 3: The original Raman spectrum of the sample.  
Figure 2: Raman spectroscopy detection platform.  
Therefore, noise reduction and baseline correction techniques were further applied for spectral data preprocessing. Specifically, the wavelet transform and cubic spline smoothing methods were employed. The calculation of a wavelet transform threshold was based on the standard deviation of noise, and the number of base points for cubic spline smoothing was set to 8. The results of preprocessing at different aging durations are depicted in Figure 4.  
As seen from Figure 4, the preprocessed signals encompassed the entire information content, covering a wide frequency range from 750 to 3000 cm\({}^{-1}\). However, the high dimensionality of the data was a challenge for their direct use. Various sample categories exhibited a significant overlap across the frequency range, which hampered the discrimination between aging states and increased the complexity of the discrimination model.
## 3 Results
### Analyzing the Shear Wave's Characteristics in the Frequency Domain
#### 3.2.1 Mode(s) Excitation  
For experimental single PWI (first column of Figure 5), we observed that mainly one mode was excited at the shallow tissue depth, whereas two modes were excited for deeper tissue regions. The mode excited on lower frequencies is designated with the term 'primary mode', whereas the other mode is defined as'secondary mode'. This primary mode is the one that will be tracked and fitted to the theoretical A0-mode in the phase velocity analysis to estimate shear stiffness. Applying compounding in the experiment led to one visible excited mode in the spectra of both tissue depths, as can be seen in the second column of Figure 5. For the simulations (third, fourth and fifth columns of Figure 5), we see one excited mode for 15% tissue thickness, and two excited modes for 40% tissue thickness, independent of the application of the compounding technique.  
Figure 5: Fourier energy maps at two paths across the phantom’s thickness—15% and 40%—for the right shear wave in the experimental (single and compounded PWI) and numerical (single PWI, compounded PWI and biomechanics) shear wave acquisitions. Location of the two shear wave paths is indicated in Figures 3 and 4 for experiment and simulation respectively. The primary mode is defined as the mode excited on lower frequencies and the secondary mode is the mode excited on higher frequencies, as indicated in the biomechanics column. Each Fourier energy map was normalized to its maximal energy (displayed in red); amplitudes are given in Table 3. The measured temporal shear wave data for one specific shear wave path across axial depth were cropped in lateral space (12.8 mm) and time (4 ms) such that its spatial and temporal resolution corresponded to the simulated ones.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acquisition** & **15\% Tissue Thickness** & **40\% Tissue Thickness** \\ \hline Experimental & & & \\ \hline Single PWI & 6.52 & 3.42 \\ Compounded PWI & 1.68 & 1.26 \\ \hline Numerical & & & \\ \hline Single PWI & 5.13 & 3.45 \\ Compounded PWI & 0.64 & 0.33 \\ Biomechanics & 33.37 & 31.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tabulation of the magnitude of the maximal Fourier energy amplitude in Figure 5 [mm/s/Hz].
###### Abstract  
One of the main abiotic factors affecting agricultural productivity in semi-arid regions is salinity. Seed priming is a frequently used method to enhance plant growth under saline environments. The aim of this work was to demonstrate the differences in eight agronomical characteristics of two grass pea varieties under two salinity regimes (80 and 160 mM NaCl) when pre-exposed to seed priming (hydropriming, biopriming with _Bacillus subtilis_ and their combination). The two varieties responded well to the priming treatments, with more beneficial effects monitored for the local variety. Evaluating the root characteristics that are most affected by stress, it was found that, at 80 mM NaCl, the combination of biopriming and hydropriming increased the fresh root weight by 36.8% and root length by 70% in the commercial variety, and by 124% and 47%, in the local variety, respectively. At 160 mM NaCl, biopriming increased the fresh root weight by 40.3% and root length by 50.3% in the commercial variety, while in the local variety, the combination of biopriming and hydropriming increased the fresh root weight by 124% and root length by 47%, respectively. Overall, biopriming and the combination of biopriming and hydropriming significantly enhanced plant growth characteristics of the two grass pea genotypes.  
Lathyrus sativus L.; salt stress; priming; landrace; Bacillus subtilis
## 3 Analysis of the Scattering Phenomenon in Underground Mines and Distribution of Dust Particles
### Joint Distribution of the Arrival Time and the Arrival Angle of the Scattering Downlink Link
#### Derivation of the Arrival Time and Arrival Angle Joint PDF  
By defining \(\tau\) as the arrival time of the signal that travels from the LED to the PD, The total length of the optical communication path from the LED to the dust particle and then to the PD is given by  
\[r+d_{sc}=c\tau, \tag{30}\]  
where \(d_{sc}\) is the distance between scatterer \(S\) and the LED and \(c\) is the speed of light. By using consine law and trigonometric equations, \(r\) can be given as  
\[d_{sc}=\sqrt{D^{2}+r^{2}-2 Dr\sin\theta\cos\Phi}, \tag{31}\]  
where \(D\) is the separation distance between the LED and the PD on the y-axis. Hence, \(r\) can be obtained from (30) and (31) as  
\[  
with dust particles is probabilistic, it is necessary to include in this gain the PDF of the total scattering phase function. Therefore, \(G\) is redefined as  
\[G(\theta_{s})=\frac{\overline{\rho}(\lambda)f_{sca}(\theta_{s})}{N}. \tag{38}\]  
Since the interaction of light with dust particles is considered as a VLC channel component produced by the optical signal reflection until it reaches the receiver and based on the typical Lambertian VLC channel modeling, the mathematical expressions for the Direct Current (DC) gain and the CIR of the UM-VLC channel component produced by dust particles can be written as  
\[H_{sca}=\lim_{N\rightarrow\infty}\sum_{n=1}^{N}\frac{A_{p}(m+1)G(\theta_{s})}{ 2\pi(r+d_{sc})^{2}}\cos^{m}(\phi_{L-S})\cos(\theta)rect\left(\frac{\theta}{ \Theta}\right), \tag{39}\]  
\[h_{sca}(t)=\lim_{N\rightarrow\infty}\sum_{n=1}^{N}\frac{A_{p}(m+1)G(\theta_{s} )}{2\pi(r+d_{sc})^{2}}\cos^{m}(\phi_{L-S})\cos(\theta)rect\left(\frac{\theta} {\Theta}\right)\delta\left(t-\frac{r+d_{sc}}{c}\right), \tag{40}\]  
where \(A_{p}\) is the PD physical active area, \(m=-1/log_{2}[cos(\phi_{1/2})]\) represents the Lambertian mode number of the LED, which is a function of semi-angle at half power (\(\phi_{1/2}\)) of the LED, \(\Theta\) is the PD Field of View (FoV), \(\phi_{L-S}\) is the radiance angle measured between the the normal vector to the LED surface and the vector from the LED to the scatterer \(S\), and \(\delta(\cdot)\) is a unit-area Dirac delta function.  
Furthermore, the total power received by the PD due to the luminous intensity emitted by the LED can be described as  
\[P_{r}=R_{PD}P_{t}h_{m}+n_{m}, \tag{41}\]  
where \(R_{PD}\) is the PD responsivity, and \(P_{t}\) is the emitted optical power by the LED.  
Finally, The SNR is computed by using the metric \(P_{r}\), whose expression is given as  
\[SNR=\frac{P_{r}^{2}}{\sigma_{s}^{2}+\sigma_{t}^{2}}, \tag{42}\]
## 2 Methods
### Phase Transfer Entropy and Directed Phase-Amplitude Interactions
From the remaining pair, we select the signal oscillating at the highest frequency, \(\mathbf{y}^{\prime\prime}\). Finally, we define \(\mathbf{x}=\mathbf{x}^{\prime}+\mathbf{y}^{\prime\prime}\), and \(\mathbf{y}=\mathbf{x}^{\prime\prime}+\mathbf{y}_{\Delta t}^{\prime}\), so that, by design, \(\mathbf{x}\) and \(\mathbf{y}\) have closely resembling power spectra (see Figure 2D-F). Figure 2: Schematic representation of the simulation model used to obtain data with directed phase-amplitude interactions. (**A**) Sinusoidal signal segments of varying amplitude and period (\(\mathbf{x}_{l}^{\prime}\)). (**B**) A continuous, low-frequency signal (\(\mathbf{x}^{\prime}\)) is obtained after concatenating the sinusoidal signal segments. (**C**) A high frequency signal (\(\mathbf{y}^{\prime}\)) modulated by \(\mathbf{x}^{\prime}\) is computed through Equation (11). Then \(\mathbf{y}^{\prime}\) is time-shifted by \(\Delta t\) seconds (\(\mathbf{y}_{\Delta t}^{\prime}\)). (**D**) \(\mathbf{x}^{\prime}\) and \(\mathbf{y}_{\Delta t}^{\prime}\) are combined with a pair of auxiliary, non-interacting signals \(\mathbf{x}^{\prime\prime}\) and \(\mathbf{y}^{\prime\prime}\) to generate signals \(\mathbf{x}\) and \(\mathbf{y}\). (**E**) Signals \(\mathbf{x}\) and \(\mathbf{y}\) are contaminated with noise and linearly mixed. (**F**) The resulting signals, \(\mathbf{x}_{y}^{\prime\prime}\) and \(\mathbf{y}_{y}^{\prime\prime}\), have very similar power spectra.
## 5 Simulation Method Uncertainties
### Angular Error
#### 5.2.3 Attitude and Position Errors  
From the orientation information provided by the aircraft's IMU, we expect an error of 0.1\({}^{\circ}\) for pitch and roll and 0.2\({}^{\circ}\) for yaw [39]. We also expect position errors of 2 m in eastings and northings, and 3 m in height. All of these factors introduce uncertainty into the calculated incidence and scattering azimuth and elevation angles. If we assume that the errors are normally distributed around the true value and that the error values from the IMU manual correspond to the standard deviations, then, for our particular geometry, the median expected uncertainty of the combination on the incidence and scattering angles in the aircraft's coordinate system are 0.14\({}^{\circ}\) in azimuth and 0.07\({}^{\circ}\) in elevation. In our case, these angular errors arise almost entirely from the attitude uncertainties rather than the position uncertainties, but at very short ranges, they would become significant. These uncertainties are potentially significant, and are difficult to avoid without using a more accurate IMU.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology
### Road Network Search  
The distance between query points and POIs is calculated using the road network distance extracted from the Open Source Routing Machine (OSRM) API. OSRM is an open-source software designed for rapid route calculations, thereby making it well suited for real-time applications and scenarios involving large-scale road networks. It provides accurate and efficient routing results, thereby delivering the shortest route in terms of distance and duration time. The OSRM project is a collaborative effort, and the protocol implemented by the service is version 1 for all OSRM 5.x installations. As of the latest implementation, we are using OSRM version 5.27.1 (Open Source Routing Machine, [PERSON], [[http://www.project-osrm.org](http://www.project-osrm.org)]([http://www.project-osrm.org](http://www.project-osrm.org)), accessed on 7 March 2023).  
In this study, only the distance obtained from OSRM was utilized to derive results. After extracting distances from OSRM, kNN and range queries were performed to obtain results for each method.
## 3 Results
### Statistical and Graphical Results  
The main statistical values of the measurements are shown in Figure 3. Although the mean values are similar, there are differences between the ranges of measurements and, to a lesser extent, between the standard deviations of the sample. The mean BBM measurement value was 0.438 V/m (IC95%: 0.328-0.548) and we determined a value of 0.281 V/m for the PEM measurements (IC95%: 0.213-0.350). The BBM and PEM measurements showed a rho Spearman correlation of 0.948 ** (\(p<0.01\)). Taking this value into account, the mean PEM exposure value should be 0.438 V/m (IC95%: 0.340-0.561). We also performed a U-Mann-Whitney test (BBM and corrected PEM measurements) that found no statistically significant differences (\(p>0.05\)).  
Figure 3: Mean value, standard deviation, and measured ranges of the different datasets.  
Figure 2: Ratio of measured BBM values to PEM, PEM-1, and PEM-2 values.  
Figure 6: Satimo PEM and differences in the PEM vs. BBM exposure maps.  
Figure 7: PEM-1 and difference between PEM-1 and BBM exposure maps.  
As shown in Figure 5, the highest exposure values are concentrated near the BTS. If this map is compared with the PEM maps, some differences in the interpolation can be seen that were not revealed by the previous analyses. First, the map obtained using the direct PEM measurements (Figure 6) is able to determine the areas with the highest level of exposure, but not the spatial variation; it therefore offers greater gradients of variation towards the areas with lower levels. The corrections made to the personal exposure meter measurements correct this defect to some extent but still offer differences with respect to the original map. In the PEM-1 case (Figure 7), even higher maximum values can be seen. In this sense, the simple correction of the dataset can lead, after the kriging interpolation process, to an overestimation of the exposure reflected in the map. In the PEM-2 case (Figure 8), the areas with the highest level of error are minimized. There are still some areas (SE of the map) where errors have not been corrected. It is possible that this is due to a defect in the planning of the sampling, which does not correctly establish the perimeter of the area under study.
# Critical Study Quality Management for the Anti-Seepage System in Macau's Landfill Area
## 2 Overview of the Landfill Containment System Project  
Describing the Scale, Objectives, and Key Components of the Macau Landfill Anti-Seepage System Project  
As an urban special administrative region with limited land resources, waste management poses significant challenges in the Macau region. The Macau landfill anti-seepage system project aims to ensure environmental safety and sustainable development during waste burial processes. The scale and objectives of this project hold crucial significance for environmental protection and sustainable development in Macau.  
The project's scale is determined by the amount of waste generated and landfill requirements in the Macau region. The primary objectives of the project include [10] (Figure 5):  
Environmental protection: Ensuring that buried waste does not contaminate soil and groundwater, maintaining ecological balance and the health of ecosystems.  
Sustainable development: Providing sustainable environmental and resource solutions for future generations through effective waste management.  
Key components of this project include:  
Seepage layer design: Designing appropriate seepage layers to prevent harmful substances from waste leaking into soil and groundwater.  
Isolation layer design: Ensuring that post-burial waste does not adversely impact the surrounding environment.  
Drainage system: Designing a drainage system to manage rainwater and leachate, reducing environmental risks.  
Environmental monitoring system: Establishing a real-time monitoring system to track environmental changes during waste burial.
## 4 Comparative Analysis of AI Language Models Based on Scholarly Literature
### Heatmap of AI Chatbot Performance
#### 4.7.2 Insights from the Heatmap  
The heatmap reveals several key insights into the performance of the AI chatbots:  
* **ChatGPT**: Exhibited strong performance in accuracy, domain expertise, and scope of knowledge, thus making it a reliable tool for academic and practical tasks. However, its computational efficiency was only moderate.  
Figure 7: Heatmap: performance scores of AI chatbots across different criteria.  
* **Bard**: Excelled in real-time internet access and computational efficiency but had a lower consistency and scope of knowledge compared to ChatGPT.
* **Llama**: Showed balanced performance with scores ranging from 6 to 8 across all criteria, but it lacked standout strengths.
* **Ernie**: High computational efficiency was a notable strength, but it struggled with consistency and the scope of knowledge, thus limiting its usability outside its primary domain.
* **Grok**: Maintained moderate performance across all criteria, thus indicating versatility but not exceptional capability in any specific area.  
This heatmap provides a comprehensive visual summary, thus aiding in the quick comparison of the AI chatbots' performance and helping to identify the most suitable tool for specific applications. It is important to note that these scores were not based on the experiments we conducted, but rather on the data and insights extracted from the 28 papers reviewed in this study. Assumptions were made to quantify these data points, thus offering general insights rather than precise representations of performance.
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## Appendix A Two-Stage Detection Algorithm
The algorithm is divided into three main steps: (i) elimination (i.e., also referred as censoring) of samples that are not regarded as being noise-only (i.e., pure noise samples) observations, (ii) computation of the noise-power threshold level, and finally, (iii) the hypothesis test used to detect the synchronization sequence. These steps make it possible to decide reliably whether the synchronization sequence is present or not in a given received buffer. Figure 10 summarizes the three steps composing the Synch sequence detection algorithm employed in the second stage. Next, we describe each one of the three steps composing the second stage Synch detection algorithm. The procedure followed in the second-stage of the synchronization sequence detection algorithm essentially involved a hypothesis test that followed the Neyman and Pearson lemma described in [29]. The Neyman and Pearson lemma defines that the hypothesis testing used to detect the presence of synchronization sequence is based on the inequality shown next. \[\frac{\text{PDP}_{H_{1}}}{\text{PDP}_{H_{0}}}>\alpha, \tag{11}\]  
where the hypothesis \(H_{0}\) is discarded (i.e., rejected) in favor of the hypothesis \(H_{1}\) when the PDP set contains the desired signal, i.e., the synchronization sequence. Therefore, the hypothesis \(H_{0}\) is the one representing the case where only noise is present. The just described hypothesis test is said to be optimum when and only when the Cumulative Distribution Function (CDF) of the ratio in (11) gives that the hypothesis \(H_{0}\) is known, so that it is possible to compute a threshold level that fulfil the following equality:  
\[P\left\{\frac{\text{PDP}_{H_{1}}}{\text{PDP}_{H_{0}}}>\alpha\mid H_{0}\right\} =P_{FR}, \tag{12}\]  
for a pre-defined (i.e., given) probability of false-rejection, \(P_{FR}\). Therefore, the calculation of the detection threshold level considers that the Probability Distribution Function (PDF) of both random variables, \(\text{PDP}_{H_{1}}\) and \(\text{PDP}_{H_{0}}\), is known.
# Physical Hybrid of Nanographene/Carbon Nanotubes as Reinforcing Agents of NR-Based Rubber Foam
## 3 Results and Discussion
### Thermal Degradation  
The thermo-oxidative stability results of NR/CNT/GNS foams obtained from the TGA and its derivative differential thermogravimetry (DTG) are shown in Figure 8. It can be seen that all the samples have a two-step thermal degradation in TGA curves (Figure 8a). Additionally, the DTG plots in Figure 8b showed two distinct peaks that correspond to the local maximum rates of weight loss in all nanofillers' contents. The first step around 400 \({}^{\circ}\)C was related to the decomposition of small molecules and additives such as oil, blowing agent, etc. [34]. The second degradation step occurred in the region of 450\(-\)500 \({}^{\circ}\)C and was related to the NR degradation. In Table 2, the thermal properties of the prepared foams are directly compared to highlight the effect of CNT/GNS hybrid nanofiller on the degradation of the NR foams. It was observed that the degradation temperatures of nanocomposites at 5% and 50% weight loss are higher than that of raw rubber and with increasing hybrid nanofiller content. The percentage of remained ash rose from 5.7% to 12.23% by weight. A significant temperature rise was also observed at 50% and 5% weight loss, which increased from 407 \({}^{\circ}\)C to 414 \({}^{\circ}\)C and from 339 \({}^{\circ}\)C to 346 \({}^{\circ}\)C, respectively. This increase could be related to the interaction between NR and the nanomaterials, limiting the release of gases from the heat degradation as it causes them to cross a more tortuous path. The increase in the thermal stability of polymer composites having well-dispersed nanofiller has been reported in the literature [35].  
The maximum DTG peaks in the samples were elevated by increasing the nanofillers' content in both weight loss steps. The addition of inorganic fillers such as the GNS/CNT hybrid causes higher thermal resistance in the rubber due to their higher inherent thermal resistance compared to the NR. Therefore, the thermal stability of the nanocomposites increases when the CNT and GNS are incorporated, and these nanomaterials can act as a delaying agent in the degradation of rubber matrices. This can be ascribed to the ability of the carbon-based fillers that contribute to thermally protect the polymer matrix and hinder the transport of degradation volatiles through the matrix by the formation of a tortuous path [36]. The weight of the sample at the first T\({}_{\text{Peak}}\) decreased slightly from 50.54% to 50.18%; however, a significant rise in the weight of the samples was observed at the second Tpeak, which increased dramatically from 6.77% to 14.28%.