esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theories
### Multiscale Permutation Entropy [18]  
Permutation Entropy (PE) is a measure method of complexity based on symbolic representation. The main idea is to reconstruct a time series into a symbolic sequence and then compute the entropy of this sequence. Multiscale Permutation Entropy (MPE) extends the concept of PE by incorporating multiscale processes, allowing the analysis of dynamic features of time series across different scales. MPE can be used to evaluate the complexity and randomness of signals, helping to identify which modal components might contain more noise. By quantifying the entropy values of each mode, MPE further enhances the ability to distinguish between noise and signal characteristics. The specific steps of MPE are as follows:  
1. Multiscale Process: Construct multiscale time series by coarse-graining the original time series. Given a time series \(\{x_{i}\}_{i=1}^{N}\) and a scale factor (s), construct the coarse-grained time series as follows: \[y_{j}^{(s)}=\frac{1}{s}\underset{i=(j-1)+1}{\sum}x_{i},j=1,2,\ldots,\left[ \frac{N}{s}\right]\] (10)
2. Calculate the Permutation Entropy at different scales: For each scale (s), calculate the \(PE\)(s) of the coarse-grained time series \(\left\{y_{j}^{(s)}\right\}\), and then follow the standard procedure for Permutation Entropy.
3. _MPE_ is defined as the collection of permutation entropies at different scales: \[MPE=\left\{PE(s)|s=1,2,\ldots,S\right\}\] (11)In the equation, \(S\) is the maximum scale factor.
## 4 Results and Discussion
### Influence of Oil Type and Emulsifier Structure on Emulsion Formation
#### 4.3.2 Effect of Emulsifier Structure on Interfacial Stabilization  
To assess the role of emulsifier design in HIPE formation, results across emulsifiers were evaluated (see Table 6). The structural presence of side chains had a substantial impact. AS2 generated 12 positive HIPE outcomes, followed by AS3 with 9, while AS1--lacking side groups--achieved only 3 successful models.  
The poor performance of AS1 is attributed to its tendency to self-assemble into stacked nanomicelles due to strong \(\pi\)-\(\pi\) interactions, as confirmed by the RDF peak near 3.4 A. These nanomicelles occasionally bridged droplets to form Pickering-type emulsions (Figure 5a) but often failed to maintain contact across the oil phase, which results in phase separation as Figure 5b proves. RDF profiles further confirmed weak dispersion and low interfacial confinement of AS1. In contrast, AS2 and AS3 showed improved dispersion due to their steric hindrance from side chains, enabling more stable monolayer formation (Figure 5c,e). Despite this, some cases still exhibited instability and transition to O/W emulsions (Figure 5d,f), likely due to an imbalance between van der Waals attraction and stacking tendencies. These observations reinforce the significance of both molecular orientation and intermolecular interactions in emulsion engineering.  
Figure 5: Emulsion structures stabilized by AS1, AS2, and AS3 showing differences between stable W/O systems and unstable O/W phase transitions: (**a–f**) morphologies of emulsions formed with each emulsifier; (**a,c,e**) depict successful W/O formation; (**b,d,f**) show failed emulsions transitioning to O/W structures.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 3 Main Experimental Results and Discussion
### Noise Cyclostationary Behavior
#### 3.3.4 Summary of the Results for the Urban Dense 1 Site  
Figure 15 gives the pseudo Wigner-Ville transform analysis for the urban dense 1 site in the time-frequency domain. The periodic impulsive noise is generated in the form of bursts. The time-frequency representation shows an important PSD in the frequency range lower than 20 kHz.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 1 Introduction
When there are abundant critical and emergency data to be transmitted, our scheme can achieve fast and reliable bulk burst data transmission. The rest of the paper is as follows: We discuss related work in Section 2. In Section 3, we discuss the core challenges that need to be addressed to improve the backscatter system efficiency. In Section 4, we describe the CRFID operating procedure optimization by introducing a burst transmission mechanism and erasure coding. The design of the burst data transmission scheme is presented in Section 5, while the performance evaluation results are given in Section 6. Finally, we conclude the paper in Section 7.
## 6 Optimization of TMDI and Equivalent TMD
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{3}{c}{**TMD Parameters**} & \multicolumn{3}{c}{**\% Age Reduction**} & \multicolumn{3}{c}{**TMDI Parameters**} & \multicolumn{3}{c}{**\% Age Reduction**} \\ \hline \(\mu\) **(\%)** & \(v_{opt}\) & \(\xi_{opt}\) & **Accel.** & **Displ.** & \(\mu\) **(\%)** & **Beta** & \(v_{opt}\) & \(\xi_{opt}\) & **Accel.** & **Displ.** \\ \hline \multirow{3}{*}{0.75} & 1.006 & 0.036 & 49.80 & – & \multirow{3}{*}{0.5} & 1.047 & 0.040 & 50.09 & – \\  & 1.006 & 0.036 & – & 47.7 & & & 1.037 & 0.040 & – & 47.60 \\  & 1.004 & 0.046 & 54.54 & – & & & 1.042 & 0.045 & 51.58 & – \\  & 0.995 & 0.046 & – & 52.21 & & 1 & 0.5 & 1.034 & 0.045 & – & 48.81 \\  & 1.002 & 0.068 & 59.16 & – & & & 1.042 & 0.053 & 53.94 & – \\  & 0.998 & 0.068 & – & 56.32 & & & 1.027 & 0.053 & – & 50.66 \\  & 0.995 & 0.075 & 61.97 & – & & & 1.034 & 0.060 & 55.90 & – \\  & 0.972 & 0.088 & – & 58.84 & 3 & 0.5 & 1.027 & 0.060 & – & 52.10 \\  & 0.988 & 0.105 & 64.08 & & & & 1.027 & 0.063 & 57.75 & – \\  & 0.957 & 0.105 & – & 60.76 & & & 1.019 & 0.063 & – & 53.19 \\  & 0.980 & 0.120 & 65.78 & – & & & 1.019 & 0.080 & 58.83 & – \\  & 0.949 & 0.105 & – & 62.25 & 5 & 0.5 & 1.011 & 0.080 & – & 54.35 \\ \hline \end{tabular}
\end{table} 
Table 4: Optimal parameters of TMDI and equivalent TMD for the different mass ratios. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multicolumn{3}{c}{**TMD Parameters**} & \multicolumn{3}{c}{**\% Age Reduction**} & \multicolumn{3}{c}{**TMDI Parameters**} & \multicolumn{3}{c}{**\% Age Reduction**} \\ \hline \(\mu\) **(\%)** & \(v_{opt}\) & \(\xi_{opt}\) & **Accel.** & **Displ.** & \(\mu\) **(\%)** & **Beta** & \(v_{opt}\) & \(\xi_{opt}\) & **Accel.** & **Displ.** \\ \hline \multirow{3}{*}{0.75} & 1.004 & 0.044 & 34.46 & – & \multirow{3}{*}{0.5} & 1.034 & 0.046 & 36.05 & – \\  & 0.996 & 0.044 & – & 39.7 & & & 1.027 & 0.046 & – & 40.06 \\  & 1.004 & 0.053 & 39.11 & – & & & 1.034 & 0.053 & 37.56 & – \\  & 0.988 & 0.053 & – & 45.21 & & & 1.027 & 0.053 & – & 41.36 \\  & 0.995 & 0.070 & 44.20 & – & & 1.027 & 0.060 & 40.21 & – \\  & 0.988 & 0.070 & – & 51.32 & 2 & 0.5 & 1.019 & 0.053 & – & 44.30 \\  & 0.995 & 0.090 & 47.32 & – & & & 1.027 & 0.066 & 42.41 & – \\  & 0.980 & 0.090 & – & 54.94 & 3 & 0.5 & 1.011 & 0.066 & – & 46.66 \\  & 0.995 & 0.110 & 49.53 & – & & 1.019 & 0.073 & 44.45 & – \\  & 0.972 & 0.100 & – & 57.46 & 4 & 0.5 & 1.011 & 0.066 & – & 48.68 \\  & 0.9959 & 0.1001 & 51.05 & – & 5 & 0.5 & 1.019 & 0.0867 & 46.19 & – \\  & 0.9651 & 0.1101 & – & 59.37 & 5 & 0.5 & 1.004 & 0.8006 & – & 50.46 \\ \hline \end{tabular}
\end{table} 
Table 3: Optimal parameters of the TMDI and the equivalent TMD, under variable frequency excitation, for different mass ratios.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### Preprocessing
#### 3.2.2 Data Cleaning  
Tasks specific to URL dataset cleaning include handling missing values, removing redundant URLs, removing trailing slashes, removing leading and trailing dots, removing whitespaces, and removing control characters from the URL datasets. By performing these tasks, the dataset's quality and reliability are enhanced, ensuring accurate and functional URLs for classification.
## 5 Results and Discussion
### Mold Model Test Results
#### 5.2.3 Effects of Density on Sand (Case 3 vs Case 4)  
Figure 13 shows the load-settlement curves of loose sand (Case 3) and dense sand (Case 4) to compare the effects of density on bearing capacity. Here, Case 3 showed a constant bearing capacity, while Case 4 showed increasing bearing capacity, obtained a peak value of 212 kPa at 0.08\(B\), and then slowly decayed. As expected, Case 4 obtained about 2.8 times higher bearing capacity than Case 3. This implies that manually compacted or dense sand is stronger than loose or not compacted sand. It can, therefore, be inferred that compaction apparently results in higher density--thus inducing a higher bearing capacity. That is why compaction is very important on the construction site.
###### Abstract  
The spatial topology of large-scale low-Earth-orbit satellite communication networks is dynamically time-variant, and the load on the output ports of network nodes is continuously changing. The lengths and numbers of output port queues at each network node can affect the packet loss rate and end-to-end latency of traffic flows. In order to provide high-quality satellite communication services, it is necessary to schedule and optimize the lengths and numbers of queues used for transmitting time-sensitive traffic flows at each node's output port to achieve the best deterministic transmission performance. This paper introduces a queue scheduling optimization mechanism based on the Particle Swarm Optimization algorithm (PSO-QSO) for large-scale low-Earth-orbit satellite communication networks. This method analyzes the relevant parameters of various traffic flows transmitted through the network and calculates the maximum time-sensitive business load within network nodes. It applies the Particle Swarm Optimization algorithm to calculate the optimal solution for the length and number of queues at each node's output port used for forwarding time-sensitive traffic flows. The mechanism proposed in this paper ensures the deterministic end-to-end transmission of time sensitive traffic in large-scale low-Earth-orbit satellite communication networks and can provide real-time satellite communication services.  
large-scale low-Earth-orbit satellite communication networks; time-sensitive traffic; queue scheduling and optimization; particle swarm optimization +
Footnote †: journal: Physics Letters A  
Article
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 3 Signal Measurement Techniques  
There are different approaches for this phase, one based on time, another on the receiving angle, and a third on the received signal strength [23].
## 3 Results and Discussion  
### Sensory Analysis  
The aroma sensory evaluation of the coconut jam samples from 0 min to post-sterilization are shown in Figure 1. According to the sensory evaluation radar fingerprint chart, all the samples displayed a combination of fruity, honey, caramel and fatty, but presented almost no acid flavor. As the time of concentration increased, the 'odor intensity' of the aroma increased, especially for post-sterilization, which presented higher values significantly than those of other samples (\(p<0.05\)). These results showed that the comprehensive aroma of the coconut jams enhanced greatly after the high temperature sterilization. The coconut jam samples from 0 to 8 min seemed to be characterized by more fruity and honey flavors (induced by the higher contents of ester and alcohol in coconut jam) [23]. According to the results of GC-MS as shown in Table 1, the relative contents of ethyl decanoate and 2-octanol were higher than those of other esters and alcohols in the time range of 0-8 min. It is worth noting that ethyl decanoate had a fruity odor and 2-octanol had aromatic characteristics [24; 25]. Therefore, the odor intensity of fruit and honey in coconut jam was more obvious during this period. As the heating continued from 8 min to the end of the sterilization, the caramel aroma gradually became the dominant aroma. This may be due to the Maillard and caramelization reaction during high temperature because hexanal, furfural and benzecarbonal were detected during this period [26; 27]. It is worth mentioning that the relative contents of hexanal and furfural began to rise significantly after 8 min, and benzecarbonal itself has the characteristics of caramel aroma [28; 29]. Therefore, it is possible that the combined effect of these three compounds ultimately leads to a significant increase in the caramel odor intensity.
# Effective Receiver Design for MIMO Visible Light Communication with Quadrichromatic LEDs
## 3 Proposed Receiver Design
### Complexity Analysis  
In this section, we provide a discussion on the computational complexity of the proposed algorithm that depends on the optimization-solving process at each iteration and the total number of iterations. During the first stage, only three iterations are necessary for each initial solution while \(L_{\text{refine}}\) iterations are required in the second stage. Assuming that the interior point method is used by the solver, the order of operations [13] required for solving the convex problem is given by  
\[\begin{split}&\mathcal{O}\left(M^{2}N_{\text{r}}\right)+\left[ \mathcal{O}\left(M^{3}\right)+\mathcal{O}\left(M^{2}\left(\begin{array}{c}M \\ 2\end{array}\right)\right)\right]+\mathcal{O}\left(M^{2}\right)\\ &+\mathcal{O}\left(N_{\text{r}}\right)=\mathcal{O}\left(M^{2}N_{\text{r}} \right)+\mathcal{O}\left(M^{4}\right)\,,\end{split} \tag{21}\]  
where \(\mathcal{O}\left(M^{2}N_{\text{r}}\right)\) denotes the constructing the convex optimization problem, \(\mathcal{O}\left(M^{3}\right)+\mathcal{O}\left(M^{2}\left(\begin{array}{c}M \\ 2\end{array}\right)\right)\) denotes the order of operations required during the convex solving process, \(\mathcal{O}\left(M^{2}\right)\) denotes the operations required for computing the first and second derivatives of the objective and constraint functions, and \(\mathcal{O}\left(N_{\text{r}}\right)\) denotes the normalization operation, respectively. Consequently, the total complexity cost of the algorithm can be estimated as  
\[\begin{split}&\mathcal{O}\left(\left(3N_{\text{initial}}+L_{ \text{refine}}\right)M^{2}N_{\text{r}}\right)+\mathcal{O}\left(\left(3N_{ \text{initial}}+L_{\text{refine}}\right)M^{4}\right)\\ &=\mathcal{O}\left(N_{\text{initial}}M^{2}N_{\text{r}}\right)+ \mathcal{O}\left(N_{\text{initial}}M^{4}\right),\end{split} \tag{22}\]where \(N_{\text{initial}}\gg L_{\text{refine}}\) in general. It can be seen that the worst case complexity of the algorithm depends on the number of initial solutions, the modulation order, and the number of receivers. In specific scenarios with fixed values of \(M\) and \(N_{r}\), the only variable in (22) is the number of initial solutions \(N_{\text{initial}}\). Moreover, as \(N_{\text{initial}}\) increases, the optimality of the solution in the proposed algorithm also increases. Therefore, \(N_{\text{initial}}\) is the dominant factor that affects both the complexity and the performance of the system. The trade-off between the computational complexity and the performance improvement through \(N_{\text{initial}}\) should be carefully considered in order to ensure the specific overall requirement of the receiver.
## 2 Materials and Methods
### Methods
#### 2.3.2 Correlation Analysis  
To depict the main climatic factors which control the vegetation dynamics and in the Horn of Africa during the period 2000-2019, correlation analysis between NDVI and climate variables such as precipitation and LST was computed. Similarly, the Pearson correlation analysis between annual eWUE and SPEI was performed at the pixel level to incorporate other climatic factors and observe the difference in vegetation production changes due to drought. We used an upscaled 16-day MODIS Terra NDVI and MODIS LST at a 1 km spatial resolution from the Climate Engine, and resampled the CHIRPS daily gridded precipitation at a 5 km resolution using bilinear resampling method to match the NDVI and LST grids. Further, we resampled the SPEI with a 0.05\({}^{\circ}\) resolution, LST and precipitation data to the spatial resolution of the MODIS GPP and ET at a 0.0083\({}^{\circ}\) (\(\sim\)921 m) resolution. The \(r\) was computed in Equation 2 following the approach [40; 41]:  
\[r_{xy}=\frac{\sum_{i=1}^{n}[\left(x_{i}\ -X\right)\left(y_{i}-Y\right)]}{\sqrt{ \sum(x_{i}-X)^{2}\ \sum_{i=1}^{n}\left(y_{i}-Y\right)^{2}}} \tag{2}\]  
where \(r_{xy}\) is the correlation between \(X\) and \(Y\) variables, \(yi\) refers to NDVI in the \(i\)th year, and \(xi\) represents the climate (precipitation or LST) of the \(i\)th year; Y is the total mean NDVI value for the 20 years, and X is the total average climate value for the study period. The r was tested using F-Test (CORRCOEF function) in MATLAB R2016a (The MathWorks, Inc., Natick, Massachusetts, United States) for each pixel [33], and it was tested at significance level threshold of P value = 0.05 for this study.
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 6 Proposed Methodology  
The proposed methodology employs a hybrid DL model integrating RNNs, LSTMs, and CNNs to enhance brain stroke prediction [52, 53]. This technique detects temporal connections in sequential health data as well as spatial links between static health measures. RNNs assess sequential trends, LSTMs deal with long-term dependencies, and CNNs extract features from structured numerical data [54, 55]. The following subsections describe each model component's involvement in the overall system process.
## 2 Preliminaries and Models
### System Model  
In our system model, we consider a typical SG communication architecture [15; 16; 17; 8; 9; 11], as shown in Figure 1. It is based on the SG network model presented from the National Institute of Standards and Technology (NIST) and consists of six domains, i.e., the power plant, the transmission domain, the distribution domain and a CC, a residential GW, and the user domain. We mainly focus on how to report and aggregate the users' privacy-preserving data into the CC. Hence, the system model divides especially the BAN into numbers of Household area network (HAN) equipped with a SG and every BAN includes a GW and numbers of users.  
CC: It acts as the SBS and EMS in reality. It needs to monitor the actual data on how much power is consumed at which timestamp in one BAN (neighborhood), how much power should be reserved for the next time period, and cumulative consumption for individual billing on a monthly basis, and how much power is being distributed to a specified neighborhood. In the paper, it is curious about the individual fine-grained data and may attempt to it as far as possible by all available resources, so it is assumed a semi-trusted entity.  
GW: A powerful entity, acting as the local MDMS, represents a locality (e.g., a region within a building) is responsible for aggregating real-time spatial data in a neighborhood and individual temporal data in a series of timestamps, and then transmitting the aggregation to the CC. The employment of GW relieves CC of aggregation and reducing largely the communication latency. However, the cost that potentially malicious attacks done to users or power suppliers is unignorable, as discussed earlier. We assume it is a malicious entity here. A BAN GW represents a locality (e.g., a region within a neighborhood). For facilitating the communication between BAN GW and CC, WiMax and other broadband wireless technologies can be adopted. We consider a scenario that one BAN neighborhood covers a hundred or more HANs, so the longest distance from the BAN GW to a HAN is more than a hundred miles, so WiMax maybe more suitable for this kind of distance communication. Household Smart Meter (HSM): A bidirectional communication entity deployed at householders' premises. The modern SM is given a certain level of autonomy via trusted elements and the ability to collect, store, aggregate, and encrypt the usage data. Hence it has two interfaces--one interface is for reading power of householders and the other one acts as a communication GW. Even if we assume SM is tamper-resistant, it is not powerful as a GW, so it may be vulnerable to be compromised by the GW to infer the object users' data.
# Designing an Interactive Visual Analytics System for
## 6 Case Studies
### Case Study: Performing a Comparative Analysis on Multiple Regions
#### 6.2.1 Trend Analysis  
Annual precipitation trend analysis was performed to identify trends across the six weather stations' precipitation data. Figure 15 shows performed trend analyses. We conducted a comparative analysis with three regression methods (linear, polynomial, and exponential) to examine their similarities and differences. Figure 15A shows the performed analysis with linear regression. We identified increasing precipitation patterns in most regions except Los Angeles (\(R^{2}=0.004\), \(p=0.9309\), \(\tau=0.001\)) and Seattle (\(R^{2}=0.006\), \(p=0.1808\), \(\tau=0.009\)). The trends in Chicago, Kansas City, and Houston showed statistically significant increases (\(p\leq 0.0001\)). With the polynomial regression method (degree = 2), we found similar results with slightly increased trends (Figure 15B). Interestingly, all weather stations showed curved patterns, indicating that precipitation trends decreased around the 1980s before increasing again in the 1990s. Exponential regression produced results similar to linear regression. These statistically validated increasing patterns align with our initial observations in the temporal precipitation visualization (Figure 14). [PERSON] et al. [9] emphasized the presence of heavy precipitation events in most parts of the U.S. in 2017. Our trend analysis visualization confirms a similar phenomenon (an increase in precipitation) occurring throughout the U.S.
## 2 Background  
### Federated Learning Applicability  
The origins of Federated Learning date back to a set of seminal papers in 2015-2016 [22; 23] with Google's GBoard (Android keyboard) one of the early and prominent production systems that FL was tested and still in use today [24]. There are several real-world cases where FL is an appealing setting. First, as the premises of FL is to avoid overwhelming, and often sensitive, volumes of data moving from the edge to the cloud for training, organisations such as hospitals and financial institutes can take advantage of distributed learning without sharing patient (i.e., bio-signals, imaging) and customer data (i.e., credit scores) with other branches and third-party organisations [9]. Second, model training is feasible on data collected from geo-distributed devices (i.e., smart phones, IoTs, wearable, voice assistants) where the generation rate and high communication cost makes it impractical to send data to centralized data centers for processing [16]. Third, it allows for the training of models to be achieved on larger datasets (the \"big data multiplier\"), since the data from multiple parties are aggregated and used to achieve intelligence far greater than what a single entity could achieve [25]. Fourth, the central server does not exercise control over the clients and hence, clients can go offline willingly or due to unreliable network links, and participate in the model training process only if they desire with the server selecting clients based on availability and resource capacity criteria.