esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Spectral Clustering to Decode Mobility Patterns in Athens, Greece
## 3 Results
The big majority of the commutes in the fourth cluster are made using cars, accounting for 70%. The main purpose of trips going to work, which accounts for 58%. The start times for 75% of the trips range between 6:00 and 17:00. The peak hour for this cluster is at 09:00 in the morning (see Figure 3). The mean trip distance in Cluster 4 is 12.95 km, and the distribution of this variable appears to follow a normal pattern, as illustrated in Figure 4.  
The fifth cluster appears to gather 148 trips (10.99%), which are made using train services, buses, or walking, accounting for a total of 65% of the trips. Reasons for traveling are mainly education and recreation, as revealed in Figure 2. The start times of 75% of these journeys are between 0:00 and 12:00 (see Figure 3). The last comprise a unique characteristic of Cluster 5. The distances of the trips included in the fifth cluster have a wide range of values (see Figure 4). Figure 3: Distribution of departure time (24 h time format) of per cluster: (**a**) cluster 1, (**b**) cluster 2, (**c**) cluster 3, (**d**) cluster 4, (**e**) cluster 5 and (**f**) cluster 6. Last but not least, the sixth cluster includes 62 trips (4.60%) and is dominated by the use of cars (private cars), accounting for around 45% and adding taxi use, the car usage rate touches 50% (see Figure 2). A significant percentage also gathers for public transport such as buses, trains, and walking (49% in total). In this cluster, all trip purposes appear with almost similar percentages. Additionally, the start times of these trips were between 8:00 and 12:00 in the morning. Yet, there is an interesting high peak at 11:00. The distance variable for 75% of the trips seems to take values from 1000 to 5000 m. Table 2 presents all the previously mentioned descriptive statistics. Appendix A presents six maps illustrating the spatial distribution of trip origin locations for each cluster. The reveals some spatial patterns that have to be discussed. In Cluster 1, nearly the one-third of trips originate from the city center. Cluster 2 trips also show a strong presence in the city center and neighbor zones, with lower percentages in the suburbs. In contrast, cluster 3 highlights a high share of trips that start from zones, which are along metro line 3, particularly in the eastern parts of Athens. The opposite trend emerges in cluster 4, where a greater proportion of trips originate from the outskirts. Specifically, north zones concentrate one-quarter of the trips. Cluster 5 spatial patterns exhibit a strong alignment with metro line 1, showing similarities in the spatial distribution observed in cluster 3. Lastly, cluster 6 is similar to cluster 1, with central zones once again displaying the highest concentration of trip origins. Figure 4: Probability density of trip distance included per cluster: (**a**) cluster 1, (**b**) cluster 2, (**c**) cluster 3, (**d**) cluster 4, (**e**) cluster 5 and (**f**) cluster 6.
# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
###### Abstract
We conducted experiments on simulated networks to measure the performance of the proposed approach. We compared it with several other neighbor cooperation approaches, and the experimental results show that ACNR can discover more neighbors and achieve a better name resolution success rate. The rest of this paper is organized as follows. We review related work on NRS architecture and neighbor cooperation approaches in distributed systems in Section 2. Section 3 describes the model of LNMRS, and the fundamental theory used in this paper. In Section 4, the ACNR approach is described in detail; then, we evaluate and discuss the performance of the proposed approach through comparative experiments in Section 5. Finally, Section 6 concludes our work.
## 5 Conclusions  
This study provides the first comprehensive assessment of the utilization of a common directional loudspeaker for impulse response and acoustic parameter measurements. It has enhanced our understanding of how impulse responses can be measured with the use of a single speaker. Different common directional loudspeakers were used for mimicking the sound field created by a dodecahedron speaker for impulse response measurements. Different placements of the loudspeakers were performed (twelve positions similar to the twelve positions of the faces of a dodecahedron speaker, different rotations of the loudspeakers for a total sum of twenty six and fourteen positions). Acoustic parameters obtained with the dodecahedron speaker and the common directional loudspeakers were assessed and compared in order to quantify the results. The evidence from this study points towards the idea that RT can be measured with the proposed method with a mean absolute error less than 0.08 s, as compared with actual measurements with a dodecahedron speaker. Our technique is a clear improvement on current low-cost measurement methods and provides the framework for a new way to perform acoustic measurements with minimum cost. The strength of our work lies in the fact that minimum equipment is necessary to perform acoustic measurements. We believe this solution will assist researchers to measure impulse responses at a low-cost in many fields of interest. Our proposed method clearly has some limitations. The most important limitation lies in the fact that a common 2-way directional loudspeaker has a high directivity in the high frequency range when the tweeter speaker is employed which leads to some variations of the results for certain acoustic parameters compared with measurements with a dodecahedron speaker. However this observation has a promising implication for research. This could hypothetically lead to a method with even better results for omnidirectionality than the current dodecahedron speakers, which could also possibly be utilized for auralization purposes. Our investigations into this area are ongoing and seem likely to confirm our hypothesis. This topic is reserved for future work.  
**Author Contributions:** [PERSON] designed the study, conducted the experiment, analyzed the experimental data and wrote the manuscript; [PERSON] supervised the project, provided suggestions and guidance for the work, reviewed and edited the manuscript.  
**Funding:** This research was partially funded by the Technical University of Crete, School of Production Engineering and Management, grant number 81314.  
**Acknowledgments:** We gratefully acknowledge the help provided by [PERSON] especially in the early stages of this work. We are also indebted to [PERSON] for her help during the final measurement section.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Pharmacokinetics of Marine-Derived Drugs
## 4 Pharmacokinetics Studies in Humans
Astaxanthin was presented in high- and low-density plasma lipoproteins with a prevalence of very low-density lipoproteins containing chylomicrons [172]. The pharmacokineticsof astaxanthin was studied in eight healthy male volunteers after a single dose administration of commercially available capsules filled with the spray-dried cells of microalgae _Haematococcus pluvialis_ (40 mg of astaxanthin per capsule). The maximal concentration in plasma (Cmax = 55.2 \(\pm\) 15.0 \(\mu\)g/L) was achieved in all subjects 8 h after administration, and the AUC was 1347 \(\pm\) 501 \(\mu\)g-h/L. Astaxanthin relatively prolonged time was circulated in the blood (T1/2 = 16.7 \(\pm\) 7.2 h) [173]. Results about the bioavailability and pharmacokinetics of astaxanthin were published by [PERSON] et al. Commercially available _Haematococcus_ algal extract containing 48 mg of astaxanthin was consumed by smoking and non-smoking volunteers. The bioavailability of astaxanthin was higher when administered after a meal than before a meal (AUC = 7526 vs. 2996 \(\mu\)g-h/L). The meal delayed absorption (Tmax after a meal was reached approximately three-fold later than before a meal) and lowered clearance approximately by two-fold. The difference in MRT was not statistically significant. Smoking reduced the half-life elimination time of astaxanthin. The authors suggested that fats in the meal stimulate excretion of bile, which helps the dispersion of this carotenoid and results in more effective absorption [174]. This hypothesis is in line with the previously published data, which demonstrated the significant increase in astaxanthin bioavailability in the formulation of astaxanthin with fats [173]. We have not found the data about chitosan pharmacokinetics in the literature. However, the number of published randomized controlled clinical trials indicates that dietary supplements are useful for weight control due to the ability of chitosan to bind fats in the gastrointestinal tract, followed by its excretion with feces [90].
## 2 Methods
### SiO\({}_{2}\):Air Layer Refractive Index  
The wavelength dependent refractive index of SiO\({}_{2}\) can be described by the following Sellmeier Equation [22]:  
\[n_{SiO_{2}}^{2}(\lambda)-1=\frac{1.9558\lambda^{2}}{\lambda^{2}-0.15494^{2}}+ \frac{1.345\lambda^{2}}{\lambda^{2}-0.0634^{2}}+\frac{10.41\lambda^{2}}{ \lambda^{2}-27.12^{2}} \tag{7}\]  
where \(\lambda\) is the wavelength in micrometers. To determine the effective dielectric function of the SiO\({}_{2}\):air layer (we call it \(\varepsilon_{eff2,\omega}\)) we use the Bruggeman effective medium approximation [23,24]:  
\[f_{SiO_{2}}\frac{\varepsilon_{Air}-\varepsilon_{eff2,\omega}}{\varepsilon_{ eff2,\omega}+L_{m}\Big{(}\varepsilon_{Air}-\varepsilon_{eff2,\omega}\Big{)}}+ \Big{(}1-f_{SiO_{2}}\Big{)}\frac{\varepsilon_{SiO_{2},\omega}-\varepsilon_{ eff2,\omega}}{\varepsilon_{eff2,\omega}+\Big{(}\varepsilon_{SiO_{2}, \omega}-\varepsilon_{eff2,\omega}\Big{)}/3}=0 \tag{8}\]  
where \(f_{SiO_{2}}\) is the filling factor of the SiO\({}_{2}\) nanoparticles (i.e., the fraction of layer volume occupied by the nanoparticles) and in this study we choose \(f_{SiO_{2}}=0.7\). \(L_{m}\) is the depolarization factor, equal to 1/3 if we assume a film of SiO\({}_{2}\) spherical nanoparticles. The photon energy dependent complex refractive index of the SiO\({}_{2}\):air layer was determined considering that \(n_{eff2,\omega}=\sqrt{\varepsilon_{eff2,\omega}}\).  
The ITO:TiO\({}_{2}\):air layers and the SiO\({}_{2}\):air layers can be considered as random networks of nanoparticles. Thus, a filling factor up to 0.7 is a reliable value [25].
# Thermal and Thermomechanical Characterization of Polypropylene-Seed Shell Particles Composites
## 3 Results
### TGA Results  
Figure 3a illustrates the TGA thermograms of pristine PP, JA, and PP-JA composites, it can be seen that the decomposition of the PP is carried out in a single stage, presenting the decomposition at an approximate temperature of 390 \({}^{\circ}\)C, presenting at the end of the analysis a minimum ash content. On the other hand, the JA shows two main stages of decomposition, the first attributed to the decomposition of hemicellulose, while for the second, approximately 350 \({}^{\circ}\)C is attributed to the deposition of lignin and cellulose. These results are similar to other works that report the thermal decomposition of wastes of lignocellulosic origin [31,32]. One way to verify if the decomposition of a material takes place in different stages is by using the derivative of the weight loss with respect to temperature, which can also help identify the decomposition susceptibility of a material with average temperature [33]. Figure 3b shows the DTG curve for PP, JA, and PP-JA composites, and it can be seen that the average decomposition temperature does not show  
Figure 2: DSC thermogram of PP.  
a significant difference, however, the ratio of the curve does show changes, which, as mentioned above, can be associated with the decomposition susceptibility of the material.It is observed that the PP-4-JA and PP-6-JA composites show a lower ratio, which indicates that the speed with which they lose weight is slower compared to the rest of the composites, as well as the pure PP.  
On the other hand, Figure 4a,b shows the TGA thermograms of weight loss and derivative of % weight loss against temperature for pristine PP, PI, and PP-PI composites. The PI shows two main decomposition stages, first attributed to remotion of hemicellulose, and the second one to degradation of lignin cellulose [34]. It can be observed that the behavior is similar to that observed for the composites with JA, in the case of the PI, it also presents a decomposition prior to the decomposition of the pure PP, and as the PI content increases, the composites begin to decompose at a lower temperature. Moreover, the DTG curves allow us to identify that both composites and pure PP have a very similar average decomposition temperature, close to 457 \({}^{\circ}\)C. However, the ratio for the PP-8-PI composite is the one with the lowest ratio, compared to the rest of the composites and the PP. As discussed above, the low value in the ratio indicates that the material is less susceptible to weight loss, thus showing greater stability compared to the rest of the composites.  
Figure 3: TGA thermogram of % weight loss (**a**) and derivative of %wt/\({}^{\circ}\)C (**b**) for PP, JA, and PP-JA composites.
## 2 Materials and Methods
### Proposed Methodology for Signal Processing
#### 2.3.1 Noise Reduction and Multiresolution Analysis Using Undecimated Wavelet Transform (UWT)  
The wavelet transforms of any signal \(f(t)\) defined for any moment of time \(t\geq 0\) is defined as  
\[WT_{f}(\sigma,\tau)=\frac{1}{\sqrt{|\sigma|}}\int_{-\infty}^{\infty}f(t)\psi_{ (\sigma,\tau)}(t)dt \tag{1}\]  
where \(\psi_{(\sigma,\tau)}(t)=\frac{1}{\sqrt{|\sigma|}}\psi\big{(}\frac{t-\tau}{ \sigma}\big{)}\) with \(C_{\psi}=\int\frac{|\psi(u)|}{|u|}du<+\infty\). The term \(\sigma\) represents a scaling factor, while \(\tau\) is a translation factor. Common \(\psi_{(\sigma,\tau)}(\cdot)\) wavelet basis functions are the Haar wavelet, Symlets wavelet, Daubechies wavelet and Mexican Hat wavelet [46].  
The process of reducing noise from UWT transformed EEG signals performs multiresolution analysis to detect transient and non-stationary phenomena by decomposing them into sub-bands of analysis. After detection, the transient phenomena related to noise are eliminated and the signal is reconstructed [47].  
Choosing the order of the Daubechies wavelet was conducted based on the same reason, db02 proved to produce the smoother result (Figure 5).  
Figure 4: Flow-chart indicating EEG data-processing pipeline.  
Following the tests, we showed that the sensorimotor rhythms were more obvious on the C3 and CP3 channels for the signals corresponding to the imagery of the right-hand movement and the C4 and CP4 channels for the signals recorded during the imagery of the left-hand movement. Therefore, we applied the multiresolution UWT analysis only for these channels. Considering that the frequency of the recorded EEG signal is between 0-128 Hz, and the spectrum of the mu rhythm is between 8 and 12 Hz and for the beta rhythm between 12 and 30 Hz, it is necessary to decompose the wavelet multiresolution up to level 4. After the first decomposition level the EEG signal is decomposed into the high frequency detail coefficient D1 (64-128 Hz) and the low frequency approximation coefficient A1 (0-64 Hz). At the next level the coefficient A1 is decomposed in turn into the coefficient of detail D2 (32-64 Hz) and the approximation coefficient A2 (0-32 Hz). Following this procedure, D3 (16-32 Hz), A3 (0-16 Hz), D4 (8-16 Hz) and A4 (0-8 Hz) are also obtained. Figure 6 shows the use of the UWT transform, direct and inverse for two levels of decomposition.  
Because the resolution of UWT decompositions decreases simultaneously with the increase in the decomposition level, we chose a maximum of 4 levels of multiresolution analysis. UWT analysis provides very good results for reducing noise and signal artifacts  
Figure 5: Noise reduction for a signal (AC voltage) using UWT wavelet.  
Figure 6: UWT transform, direct and inverse for two levels of decomposition, one-dimensional case.  
allowing more efficient processing. Figure 7 shows the multiresolution analysis mode using UWT for EEG signal.
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 4 Property Analysis of Moving Target in the Complex Image Domain  
Analyzing and understanding the moving targets' characteristics in the SAR image is of great significance for moving target indication. However, it is difficult to derive the analytical expression of the target signal in complex image domain, because there is residual azimuth modulation after azimuth compression. In this section, some properties of the target signal in the image obtained by the proposed imaging method, e.g., the target's range displacement, azimuth displacement, and ATI phase, are quantitatively analyzed.
## 3 High-Speed RoI Extraction through Grid Virtual Division
### OCC Receiver  
As illustrated in the lower part of Figure 3, the OCC receiver consists of six parts: CMOS camera, frame sampling, RoI extraction, pixel sampling, demodulator/decoding and output information. More specifically, the CMOS camera-equipped smartphone first uses the rolling shutter effect to capture images of the modulated light sources, forming bright and dark stripes in the received frame. The received frames then go through the Gaussian blur and image binarization in computer vision techniques to identify the contours of the ROI. Subsequently, the grayscale values of an intermediate row or column are sampled from the recognized RoIs to form a grayscale sequence. This grayscale sequence can be expressed as the following vector:  
\[\vec{g}=\begin{bmatrix}g_{1}&g_{2}&...&g_{i(n-1)}&g_{in}\end{bmatrix}, \tag{2}\]  
Figure 3: The schematic block diagram of an OCC system.  
where \(g_{ij}\) denotes the grayscale value of the \(i\)th row and \(j\)th column pixel. Finally, the grayscale information obtained after sampling is converted into logical data by setting thresholds to distinguish different grayscale value levels of the transmission signal.
## 1 Introduction
### Definition of Problems
#### 1.2.1 Availability  
**Encryption Key Generation Problem**  
In some cases, dynamic encryption key technology uses a synchronous communication method to regenerate the key [17; 18]. The newly generated encryption key's generation information or generation request is generated through the request and response processes between both ends of communication. However, asynchronous communication (For example, ACL Asynchronous Connection-Less) between devices is used in the low-power wireless communication environment of IoT [19]. A sensor device sends or propagate the environment sensor data periodically to other sensor devices or gateway device; the devices that receive them do not send the response. Thus, the conventional dynamic encryption key technology that uses synchronous communication is difficult to apply to low-power wireless communication of IoT.  
**Encryption Key Synchronization Problem**  
Encryption key synchronization problem refers to a case in which synchronization to a same encryption key fails when the encryption key is changed to the new subsequent key at both ends of communication because the synchronization of same key is not performed properly [17]. In the case of low-power Bluetooth communication, about 2% packet loss occurs depending on the transmission interference between devices or the network structure [20; 21]. Encryption key synchronization problem occurs if packet loss occurs when both ends request the key generation information or notification to generate a dynamic encryption key. If the encryption key synchronization problem occurs due to packet loss, the request and response information should be sent and received based on the synchronization method, but this is not suitable for asynchronous communication method.  
**Performance Problem**  
In the case of IoT devices that perform low-power wireless communication, data are often transmitted in real-time from a low-performance device. Therefore, the performance of dynamic encryption key technology is another factor that should be taken into consideration. It is not suitable to have the encryption key generation require a high computational complexity. Furthermore, it is difficult to apply if too much time is consumed to generate encryption keys and encrypt data in the IoT environment that requires real-time operation.
# Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU
## 3 GPU-Accelerated Parallel Algorithm for 3D Laplacian Smoothing
### Details and Key Issues of the Parallel Algorithm  
#### 3.2.1 Finding Neighbors  
Since the new positions of smooth nodes depend on the coordinates of their first-order neighbors, they must be found first. Generally, the first-order neighbors of a node are determined by the spatial topological relationship of the mesh in which the node is located. For a tetrahedron, any node is the neighbor of the other three nodes.  
Figure 3: The whole working process of 3D Laplacian smoothing.  
Figure 2: Different data layouts. (**a**) Array of structures without coalescing; (**b**) Array of structures using alignment specifiers; (**c**) Structure of arrays.  
A simple and clear method is to allocate an array to store the neighbors in the node structure, and then search each tetrahedron in the mesh in turn [21]. However, since the number of neighbors for each node is unknown, we can only allocate the memory of the neighbor array step by step in the kernel function. This potentially creates data conflicts for the writing operations; it is likely that more than one thread will write a neighbor of a node to the same element of the array at the same time (i.e., the same memory address). In addition, due to the existence of variable-size arrays in such data structures, sorting and other operations will fail in subsequent implementations.  
A feasible method is to change the storage structure of neighbors [27]. We use edges as basic units to store neighbor information. The process of finding the first-order neighbors for each node is as follows.  
First, all edge sets are copied in reverse order and sorted according to the IDs of node A and then node B so that all the edges with the same node A are listed in succession. These edge sets constitute the neighbors of a particular node. In this way, the index of each neighbor can be determined by the head pointer or length of each segment. The head pointer can be obtained by performing a unique operation. The length can be easily obtained by calculating the distance between each pointer. The essential ideas are illustrated in Figure 4.  
With the help of the CUDA _thrust_ library [28], these unique operations can be easily parallelized; then, the head positions of all segments can be obtained, and another parallel primitive is invoked to calculate the lengths of all segments in parallel. Finally, a CUDA kernel is designed to write the header pointers and the number of the neighbor into memory.