esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Datasets
### Cybersecurity  
In the field of cybersecurity, time series anomaly detection datasets are primarily used for monitoring and analyzing network traffic, user behavior, and system logs to promptly identify security threats, intrusion attempts, and abnormal activities. The KDDCUP99 [86] is a classic cybersecurity dataset widely used for research in network anomaly detection. It contains various types of cyber attacks, such as DoS (Denial of Service), Probe, R2L (Remote to Local), U2R (User to Root), as well as traffic data from normal behavior. The Kitsune dataset [82] is a dataset used for network intrusion detection, containing both normal and anomalous network traffic data. This dataset is used to train and test deep learning models to differentiate between normal and malicious traffic. KitNET [117] is a deep learning model that uses a set of autoencoders for online network intrusion detection. This unsupervised method can learn to distinguish the characteristics of normal and anomalous network traffic without the need for expert-labeled traffic data.
## 4 Results and Analysis
### Discussion
#### 4.3.3 Discussion on the Influencing Factors  
It can be seen from the experimental results that the optimization of the ecological environment has a positive impact on the growth of LE, but there are many other factors that also have a great impact on LE. For example, different spatial and temporal distributions of the geographical environment will also affect human health, which will lead to uneven distributions and the relative immobility of changes over time regarding LE [43]; the impact of the random occurrence of natural disasters on social population, economy, and mental health makes LE change [48; 49]; the rapid development of the economy and society makes people pay more attention to their own health and promotes the development of medical and health services. The improvement of public health makes people's LE increase gradually. There are also studies showing that regions with a more equal income distribution and a more developed economy have a more positive impact on LE [50], and the guarantee of national policies, such as pensions [51], medical insurance, and environmental protection, has a beneficial impact on the increase of LE.
## 6 Design Considerations for LPWAN Networks
### Security and Privacy  
Security threats arise, especially owing to the heterogeneity of and physical accessibility to devices and the openness of the systems connected to the Internet through the LPWAN wireless air interface [12]. The risk of vulnerability to cyber-attacks is particularly relevant to IoT LPWAN machine to machine (M2M) environments owing to the massive number of devices involved. To avoid intrusion and hacking, encryption of both the application payload and the network admission request needs to be considered.  
Owing to cost and energy considerations, it becomes necessary for LPWAN networks to settle for simpler communication protocols for authentication, security, and privacy. Over-the-air (OTA), as an alternative to authentication by personalization (ABP), is a key facility to assure that end devices are not exposed to security risks over a prolonged duration [13]. This may be comparatively expensive in proprietary LPWAN networks operating in unlicensed bands. In many networks, strong encryption and authentication schemes such as advanced encryption suite (AES) are used for confidential data transport, Diffie-Hellman (DH) is utilized for key exchange and management, and Rivest-Shamir-Adleman (RSA) is applied to authenticate digital signatures and key transport. These are based on cryptographic suites with robust protocols. Similarly, for 3 GPP cellular-based systems, the subscription identity module (SIM)-based authentication technique provides comparatively robust protection.  
Automating posture management and the use of software updates to patch vulnerabilities may be needed to isolate devices from potential attackers [11]. In order to provide protection and to isolate the device and its application, an overlay network may need to be explored. This provides security as the unique local addresses used in the overlay network do not allow access from outside the overlay.
## 5 Simulation Results
### Comparison with State-of-the-Art Distribute Algorithms
Similarly, test instances are randomly generated and each algorithm is repeated 10 times. Table 9 shows that the ECNP obtains the best results in the instance with a target density of 1000 km, while the PIDSM achieves the optimal results in all other cases. The variance plot of the comparison results is shown in Figure 8. We can see that the CNP, ECNP, and PIDSM obtain the worst \(RPV\)s in the instances with a target density of 50 km, 5 km, and 10 km, respectively. However, after these worst cases, their \(RPV\)s improve as the average distances among targets increase. Especially in the case with 500 km density, the CNP, ECNP, and PIDSM all achieve the peak of their \(RPV\). Meanwhile, the fluctuations of the PIDSM are significantly smaller than those of the CNP and ECNP, which verifies the stable performance of the PIDSM. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Target Observation**} & \multicolumn{2}{c}{**CNP**} & \multicolumn{2}{c}{**ECNP**} & \multicolumn{2}{c}{**PIDSM**} \\ \cline{2-7}  & \(bRPV\) & \(aRPV\) & \(bRPV\) & \(aRPV\) & \(bRPV\) & \(aRPV\) \\ \hline
[8; 12] & 0.9596 & 0.9388 & 0.9959 & 0.9869 & **1** & **0.9988** \\
[40; 60] & 0.9707 & 0.9381 & 0.9874 & 0.9732 & **1** & **0.9986** \\
[80; 120] & 0.9705 & 0.9374 & 0.9940 & 0.9802 & **1** & **0.9985** \\
[180; 220] & 0.9550 & 0.9177 & 0.9853 & 0.9788 & **1** & **0.9905** \\
[480; 520] & 0.9422 & 0.9094 & 0.9875 & 0.9796 & **1** & **0.9883** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison results of different target observation durations. Figure 7: The impact of the variation of target number on the performance of algorithms. The impact of target observation duration: We consider six combinations with 50 targets, a C-constellation, and low-density target distribution, but their observation duration ranges between [8 s, 12 s], [40 s, 60 s], [80 s, 120 s], [180 s, 220 s], and [480 s, 520 s], respectively. The experimental instances are still randomly generated, and each algorithm is repeated 10 times for each instance. As shown in Table 10, the PIDSM outperforms the CNP and ECNP in all instances. Moreover, the variance plot in Figure 9 illustrates that the PIDSM achieves its best \(RPV\) when the target duration ranges between [8 s, 12 s], [40 s, 60 s], and [80 s, 120 s]. After that, the value of \(RPV\) for the PIDSM decreases slightly when the target observation duration becomes large. By contrast, the \(RPV\) values of the ECNP and CNP are more sensitive to an increase in observation time. Thus, the variation of the target observation duration has the least impact on the performance of the PIDSM.
# Horizontal Loading Performance of Offshore Wind Turbine Pile Foundation Based on DPP-BOTDA
## 3 Results and Discussion
### Distributed Displacement Data
The horizontal displacement data collected at different load levels is shown in Figure 7:2 In the figure, the blue curve is the data collected by the dial indicators, and the red curve is the displacement data calculated by the distributed strain. It can be seen from the figure that the horizontal displacement obtained by the two methods was very close. The ratio coefficient between the displacement of the pile top and load was 0.9136 mm/kN. Figure 7:3 Distributed displacement values at FUT1 direction. Figure 7:4 The simplified model of the steel pipe pile. To further verify the accuracy of distributed displacement calculated by the DPP-BOTDA, the error value was analyzed by the MAPE method. The calculation equation for MAPE is:  
Figure 7:5  
where \(y_{t}\) is the true value as comparison, \(\hat{y}_{t}\) is the value to be compared, and \(n\) is the sample capacity. Finally, the calculated MAPE value was 0.03548. The result shows that the full-scale distributed displacement data of the steel pipe pile calculated by distributed strain data were accurate and credible.
## 5 Results and Discussion
### Aluminosilicate Glasses
The phenomenon is assessed by the use of an attenuation coefficient, which accounts for the anisotropic dispersion of the ultrasonic power of the beam along its path. Nevertheless, the variation in the attenuation due to the microstructure scattering, characteristic of anisotropic structures, is very hard to obtain experimentally. A common methodology is to utilize samples with various orientations of the elongated grains and to evaluate their attenuation by transmission or reflection techniques of waves under normal incidence. Concerning future applications, by applying the procedure described in this work in orthotropic, transversely isotropic, or generally anisotropic materials, one can determine the anisotropic variations of scattering attenuation by describing the microstructure at the grain-scale based on numerical simulation. The simulation method should combine a 2D finite element code and a detailed description of the microstructure that will account for the geometric, elastic, and crystallographic properties of the anisotropic material. This way, the grain orientation, the grains' size, and the anisotropy level are the main input parameters that are implemented for the efficient determination of the acoustic attenuation.
# Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm
## 3 Mathematical Formulation of the Optimization Problem
### Chu-Besley Genetic Algorithm Applied to the Problem of Parameter Estimation
#### 3.1.1 Generation of the Initial Population  
The first step is defining the coding of the estimation problem. In this case, each individual is represented as a row vector (\(Ind\)) and each position in the vector represents each one of the parameters to be estimated.  
\[Ind=\left[Param_{1}\quad Param_{2}\quad\cdots\quad Param_{r}\right] \tag{14}\]  
The initial population is a matrix \((p\times r)\), where \(p\) is the number of individuals in the population and \(r\) is the number of parameters in the problem. The values of each position in the matrix (15) can be randomly generated, but they should fall within the search ranges defined in the set of constraints of the problem, as shown in the Equation (16), where \(rand\) is a random number. Therefore, the population is a different set each time the algorithm is executed. In this way, the neighborhood of the global minimum is reached taking different paths for each solution; those solutions could be different, but all of them exhibit an acceptable accuracy. In addition, the criterion of diversity should be satisfied, i.e., all individuals in the population should be different.  
\[Pop(p\times r)=\begin{bmatrix}Ind_{1}\\ Ind_{2}\\ \vdots\\ Ind_{p}\end{bmatrix} \tag{15}\] \[Param_{1}=Param_{1}^{min}+(Param_{1}^{max}-Param_{1}^{min})\cdot rand \tag{16}\]  
The objective function of each individual of the initial population must be evaluated. Then, the individual with the lowest objective function value (best solution found so far) is selected as the incumbent, because the goal is to obtain the lowest RMSE value between the estimated and the measured data.  
\[O.F.(Pop)=\begin{bmatrix}f(I_{cell_{e}}(Ind_{1}),I_{cell_{m}})\\ f(I_{cell_{e}}(Ind_{2}),I_{cell_{m}})\\ \vdots\\ f(I_{cell_{e}}(Ind_{p}),I_{cell_{m}})\end{bmatrix} \tag{17}\]
## 4 Soil water and drought monitoring for early-warning applications
### Operational Model Setup  
The USAID Famine Early Warning System Network (FWES NET) project at U.S. Geological Survey center for Earth Resources Observation and Science (EROS) had setup an operational crop water balance model since 2000 to monitor crop performance in several regions of the world: Afghanistan, Africa, Central America, and Haiti. Daily and dekadal (10-day) model outputs from the crop water balance are posted in a website (Africa, Central America, Afghanistan). These products along with other satellite-based monitoring products are used to develop weekly weather hazard maps through a consensus among FEWS NET partners such as USGS, NOAA, NASA, USDA and Chemonics International. The products are posted regularly as graphics and image data at the early warning website of USGS at ([[http://earlywarning.usgs.gov/adds/](http://earlywarning.usgs.gov/adds/)]([http://earlywarning.usgs.gov/adds/](http://earlywarning.usgs.gov/adds/))).
###### Abstract  
The purpose of reconstruction of an orbital fracture is restoration of normal structure and volume without visible or functional complications. In a previous study, orbital implants were created using three-dimensional (3D) printing technology to restore orbital fractures. In the present study, the authors compared the efficacy of the conventional manual-bending implant and the 3D-printed standardized implant in order to verify the clinical utility of the fabricated 3D printed orbital implant. In this single-center, retrospective study, the authors evaluated medical records and 3D-CT scans of patients with inferomedial orbital fracture. Selected patients were divided into two groups. Group A underwent surgery with the 3D-printed standardized implant, while group B was treated using a manual technique to mold and trim the implant. A total of 32 patients was included in this study, 16 in each group. The volume of the preoperative lesion side was significantly different from that of the normal side or postoperative lesion side within each group. The volume of the postoperative lesion side was not statistically different from that of the normal side in Group A, but this volume was significantly different from that of the normal side in Group B. The 3D-printed standardized implant provides surgical efficacy to restore inferomedial orbital fracture and has superior surgical outcomes to the manual-bending implant.  
informal orbital fracture; 3D printed implants; manual bending Article
# A New Centralized Clustering Algorithm for Wireless Sensor Networks
###### Abstract  
Clustering is presently one of the main routing techniques employed in randomly deployed wireless sensor networks. This paper describes a novel centralized unequal clustering method for wireless sensor networks. The goals of the algorithm are to prolong the network lifetime and increase the reliability of the network while not compromising the data transmission. In the proposed method, the Base Station decides on the cluster heads according to the best scores obtained from a Type-2 Fuzzy system. The input parameters of the fuzzy system are estimated by the base station or gathered from the network with a careful design that reduces the control message exchange. The whole network is controlled by the base station in a rounds-based schedule that alternates rounds when the base station elects cluster heads, with other rounds in which the cluster heads previously elected, gather data from their contributing nodes and forward them to the base station. The setting of the number of rounds in which the Base Station keeps the same set of cluster heads is another contribution of the present paper. The results show significant improvements achieved by the proposal when compared to other current clustering methods.  
Keywords:wireless sensor networks; clustering; fuzzy system +
Footnote †: journal:
## 6 Conclusions and Future Work
### Use in Market Surveys and Urban Traffic  
The data received through the 5G base station can not only be used for advertising. Through collating information, we can accurately count the location information of each citizen at a particular time. In addition to advertising, there are two other important uses of such data: market surveys and urban traffic.  
In the past, if a regional city survey was to be done, it was necessary to use city survey personnel to conduct a survey of the passerby in a specific location. It is not only extremely labor-intensive, and the results of the sample survey are not very accurate. At most, it can only be determined that the user has passed through a specific location. Still, it is impossible to determine its true movement purpose or living area, let alone obtain its frequency of occurrence or other background information.  
However, if the data obtained by 5G base stations is used for analysis, all of this will become much simpler. Investigation agencies no longer need to ask investigators to intercept passersby. As long as they obtain time and location information from all base stations, they can determine whether the user is suitable for the subject of investigation.  
After cooperating with other third-party data, they can even take further information about the audience's description, such as the background appearance, proportion statistics, or similar audiences in the area. In other words, the market competition of market research companies will also face brand-new changes and impacts due to the use of 5G base station data.  
In the same way, 5G base station data is also beneficial for planning urban traffic and transportation [20]. In the past, due to the limitations of objective conditions such as mobile devices and positioning technology, when the government wanted to plan urban or national transportation routes it often could only use human resources surveys or refer to some telecom data for evaluation.  
With the popularization of mobile devices and the evolution of 5G technology, it has become possible to obtain citizens' time and location information quickly. For governments of various countries, by directly analyzing such data, they can know how many mobile people there are in each city at each time. And even further, through real-time changes in the number of moving paths, they can dynamically adjust urban traffic flow or accommodate transportation schedules according to the transportation volume.  
Governments also can use traffic movement data as the standard for open business routes, and taxi or self-driving car operators can also plan more effective market pricing strategies or vehicle scheduling arrangements based on such data.
## 3 Results and Discussion
### Polyphenols and Carotenoids Content
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Oat Coltivas** & **Quercetin-3-O-Rutinoside** & **Kaempfer-3-O-Glucoside** & **Luteolin** & **Quercetin** & **Apigenin** & **Kaempfer** & **Sum of Flavonoids** \\ \hline Harná & 3.30 \(\pm\) 0.10 ab  & 1.09 \(\pm\) 0.10 ab  & 2.38 \(\pm\) 0.18 a & 2.97 \(\pm\) 0.04 a & 18.42 \(\pm\) 1.61 c & 9.36 \(\pm\) 1.21 ab & 40.32 \(\pm\) 2.24 d \\ Arden & 3.00 \(\pm\) 0.05 ab & 4.66 \(\pm\) 0.09 e & 4.95 \(\pm\) 0.41 & 2.95 \(\pm\) 0.05 a & 7.94 \(\pm\) 0.18 ab & 10.50 \(\pm\) 0.33 b & 34.00 \(\pm\) 1.11 b \\ Bingo & 2.91 \(\pm\) 0.17 a & 3.04 \(\pm\) 0.02 c & 4.84 \(\pm\) 0.02 c & 2.79 \(\pm\) 0.14 a & 6.75 \(\pm\) 0.14 b & 9.70 \(\pm\) 0.13 b &Despite occurring at low concentrations in cereal grains, carotenoids belong to the nutritionally important compounds, with proven health enhancing properties. The available research results on the carotenoids content and profile in barley and oat are limited. The main carotenoid compound in oat and barley is lutein [42]. Lutein and zeaxanthin presence in retina is associated with reduced risk of cataract development and age-related macular degeneration (AMD) [43]. The barley and oat grain samples analyzed within the present study contained, on average, 800.13 and 757.69 \(\upmu\)g kg\({}^{-1}\) of lutein, respectively. Although identified in the tested samples, \(\beta\)-carotene content was very low compared to lutein (Table 6). [PERSON] et al. (2004) [44], in their study on the extraction and determination of carotenoids in cereals and cereal byproducts, confirmed the presence of lutein, \(\alpha\)-carotene and \(\beta\)-carotene in barley and oat. According to the authors, barley contained on average 860 \(\upmu\)g of lutein per kg d.w., while oat had 230 \(\upmu\)g of lutein per kg d.w. This is in line with our results in the case of barley, but we obtained, on average, higher lutein content in oat grains. This variation may be due to the different cultivars used in the experiment. When comparing individual cultivars in terms of lutein content, again Soldo cultivar of barley was the richest in this carotenoid, and in the case of oat highest contents of this compound were found in the grain of Amant, Pascal and Krezus cultivars.
# Bone Conduction Auditory Navigation Device for Blind People
## 5 Evaluation of the Proposed Navigation System
### Method of Obstacle Detection  
A high-resolution depth sensor (RealSense 435i [24]) and the \"you only look once\" (YOLO) object detection algorithm, which detects objects by CNN, were adopted. To reduce the computational cost of the CPU of Lattepandaalpha864, the Tiny-YOLOv2 was used instead of YOLOv2. The frame rate of the video monitored by the camera was set to 4 FPS. This is because the walking speed and stride length of a visually impaired person with a white cane are approximately 1 m/s and 0.55 m, respectively. The frame is then refreshed twice for each step at this frame rate.  
This system can detect what the object is, as well as the distance to the object. The experiment was conducted in the campus courtyard, so that the target of the detection was limited to a walking person or a moving bicycle. When a person or bicycle is recognized as an obstacle in the walking path, the object name, and the distance from the subject to the detected object are announced, as shown in Table 10.  
In this system, the digital image is acquired at every frame at 640 \(\times\) 480 pixels. When the object in the image is detected by a CNN as a person or a bicycle, the object is surrounded by a bounding box in the image. If the center coordinate of the bounding box is in the left-side rectangular area with a size of 210 \(\times\) 480 pixels, then the binaural warning sound in the direction of 330\({}^{\circ}\) was reproduced. If the center coordinate is in the center rectangular area with a size of 210 \(\times\) 480 pixels, the sound for 0\({}^{\circ}\) was reproduced. In cases other than these two conditions, the sound for 30\({}^{\circ}\) was reproduced.
## 3 Results and Discussion  
### Dispersion of Silica NPs in Aqueous Solutions  
We measured the sizes and electric potentials of silica NPs (7 nm CSNP, 12 nm CSNP, 12 nm FSNP, and 22 nm CSNP) in aqueous solutions. TEM images confirmed that the sizes were as expected; aggregation was lacking (Figure 2). When NPs were suspended in brine, the mean DLS diameters were approximately 10, 15, and 25 nm, respectively (Figure 3a). The sizes in dispersions with surfactant and polymer were similar to those of bare NPs (Figure 3b). Thus, aggregation was absent; the dispersions were thermodynamically stable and homogeneous in the presence of surfactant and polymer, attributable to the negatively charged surfaces of silica NPs [35]. The zeta potentials showed that stability was attributable to the constant distance between NPs at the oil-water interface; particle-particle repulsion was in effect (Figure 3c). However, the bare FSNPs were approximately 12 nm in diameter, but formed undispersed (aggregated) clusters attributable to incomplete dispersion or physical agglomeration during drying, although the \(\zeta\) potential was less than \(-\)38 mV (Figure 3c). Thus, regardless of dispersion in water, the FSNPs remained agglomerated, unlike the CSNPs.  
Figure 1: The diagram of the core flooding system.  
Figure 3: (**a**) DLS analysis of differently sized silica NPs, (**b**) DLS analysis of 7 nm with anionic polymer and zwitterionic surfactant, and (**c**) zeta potential analysis of differently sized silica NPs in colloidal dispersions.  
Figure 2: TEM images of differently sized silica particles. (**a**) 7 nm CSNPs; (**b**) 12 nm CSNPs; (**c**) 22 nm CSNPs; (**d**) 12 nm FSNPs