esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Statistical and Machine Learning Methods
#### 3.2.2 Trend Analysis (Mann-Kendall Test)  
The Mann-Kendall test, a widely used non-parametric statistical method, was applied to identify monotonic trends in air pollutant concentrations (PM\({}_{10}\), NO, NO\({}_{2}\), CO) across the study period (2021-2023). This method is particularly suited for environmental datasets characterized by non-normal distributions and seasonal variations, as it does not require assumptions about data distribution [26].  
To complement the Mann-Kendall test, the Sen's slope estimator was utilized to quantify the magnitude of any detected trends. The null hypothesis (H\({}_{0}\)) assumes no trend, while the alternative hypothesis (H\({}_{a}\)) suggests a monotonic trend. A significance level (\(\alpha\)) of 0.05 was employed to determine statistical significance.  
This approach provides critical insights into the temporal dynamics of air quality, revealing whether pollutant levels are increasing or decreasing over time. Such trends are essential for assessing the effectiveness of emission control strategies and regulatory measures implemented in the study region. For example, a decreasing trend in NO\({}_{2}\) concentrations may indicate the success of vehicular emission standards, while persistent PM\({}_{10}\) levels could signal the need for additional interventions.
## 2 Results
### Distribution of Pore Size in Composites  
The analysis of pore size distribution was conducted on the basis of SEM observation (Figures 2 and 3) of prepared, sterilized composites and composites subjected to an in vitro degradation assay for 12 weeks.  
Figure 2: SEM images of the prepared, sterilized and degraded composites made of Low \(M_{\mathrm{w}}\) PLA. Scale bar, 500 \(\upmu\)m.  
In low \(M_{\rm w}\) PLA composites, the pore size distribution of prepared composites ranged from 20 \(\upmu\)m to 388 \(\upmu\)m in the control (Figure 4). Addition of whiskers, whiskers-Zn and rods led to an increase in proportion of pore size in the range of 0-50 \(\upmu\)m and 50-100 \(\upmu\)m in composite, whereas the inclusion of rods-Sr caused an increase in the proportion of pores between 0-50 \(\upmu\)m but slightly reduced pores between 50-100 \(\upmu\)m, compared to the control. In high \(M_{\rm w}\) PLA materials, the proportion of pore size between 0-50 \(\upmu\)m was slightly lower in whiskers, rods and rods-Sr rich composites, but was higher in whiskers-Zn  
Figure 3: SEM images of the prepared, sterilized and degraded composites made of High \(M_{\rm w}\) PLA. Scale bar, 500 \(\upmu\)m.  
composite, compared to the control. The proportion of 50-100 \(\upmu\)m pores of whiskers-Zn rich composites was reduced by more than half compared to the control, whereas overall this proportion of pore size was maintained about at 40% or above for control, whiskers, rods or rods-Sr composites.  
Figure 4: Distribution of pore size in the composites (%). X-axis indicates the compartment of pore size as such; I, 0–50 \(\upmu\)m; II, 50–100 \(\upmu\)m; III, 10–150 \(\upmu\)m; IV, 150–200 \(\upmu\)m; V, 200–250 \(\upmu\)m; VI, 250–300 \(\upmu\)m; VII, 300–350 \(\upmu\)m; VIII, 350–600 \(\upmu\)m.  
The sterilization changed the pore size distribution in the low and high \(M_{\rm w}\) composites. In low \(M_{\rm w}\) materials, sterilization of the control sample led to an increase in the proportion of pore size between 0-50 \(\upmu\)m and 50-100 \(\upmu\)m. However, the proportion of 0-50 \(\upmu\)m pores reduced in HA rich composites compared to their counterparts at preparation stage. Addition of whiskers and whiskers-Zn, furthermore, led to a slight reduction of the concentration of 50-100 \(\upmu\)m pores, but inclusion of rods and rods-Sr remained this pores concentration at the similar range in the sterilized stage as in their counterparts of preparation products. In high \(M_{\rm w}\) composites, the pore size of 0-50 \(\upmu\)m was reduced after sterilization of control, whiskers and whisker-Zn composites but increased in rods and rods-Sr composites compared to their counterparts in the preparation stage. The addition of whiskers and whiskers-Zn, furthermore, led to an increase in fraction of pore size 50-100 \(\upmu\)m in the sterilized materials.  
Both for low and high \(M_{\rm w}\) PLA composites, the period of 12 weeks incubation caused an increase in pore size in control and tested materials; it was worth to notice that the proportion of pore size 0-50 \(\upmu\)m almost disappeared in all composites.
# LEO Precise Orbit Determination with Inter-satellite Links
## 3 Simulation and Method
### ISL Topology Design  
The topology of the ISL shows how the link is connected, which is closely related to the geometric structure of the range-measuring network and has a great impact on the results of orbit determination. Chinese BDS-3 uses such a link topology: as long as a pair of satellites are visible to each other, a link is established [29]. In the following text, this link topology is recorded as \"all-connected\". Obviously, \"all-connected\" link topology can get the maximum numbers of links in a constellation, which helps to enhance real-time communications between satellites thus easily controlling the operation of the entire constellation. On the other hand, this kind of link topology can also achieve the optimal geometry of the inter-satellite ranging network, and it is expected to obtain high-precision orbit determination results. Although \"all-connected\" link topology has the abovementioned advantages, it will result in high construction price and large calculation costs. For the comprehensive consideration of workload and POD effectiveness, some scholars have proposed another so-called \"4-connected\" link topology in which each satellite has two intra-orbit links with neighbors fore and aft in the same orbital plane and two inter-orbit links with satellites in adjacent planes to either side [38]. It is worth noting that the \"4-connected\" scheme has been adopted by the Iridium constellation [39]. Considering that the \"all-connected\" link topology can achieve higher orbit determination accuracy and better demonstrate the effect of ISL POD, we mainly use the \"all-connected\" scheme for simulation experiments and the \"4-connected\" scheme is also adopted for comparison.
# Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System
## 5 Result Analysis
### Energy Loss Analysis of Step Motor  
The stepper motor's voltage and current are 4 V, 1.0 A, respectively, and its power consumption is 4.0 W. As the design uses worm gear, it does not need to maintain the torque of the stepper motor. This results in an energy saving of about 35.2 watt-hours. The rotation of solar panels from 0\({}^{\circ}\) to 180\({}^{\circ}\) takes 30 s for a one-way trip and one minute for a round trip. Therefore, the total energy consumption of the stepper motor of the sun tracking system is 0.073 watt-hours a day. Controlling the stepper motor's elevation angle from 0\({}^{\circ}\) to 46\({}^{\circ}\) takes 1 h 35 min and 31 s. Thus, the stepper motor needs a round-trip for a year, and its total energy consumption is 14.08 watt-hours. The average energy consumption is 0.038 watt-hours a day (Table 7). By using fuzzy logic control to detect the sunshine and solar panel current every two minutes for eight hours of testing, the stepper motor starts 33 times  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Date: 9 May 2015** & **Fixed Solar Elevation Angle: 23.5\({}^{\circ}\)** & **Two-Axis Track Elevation Angle: 5.1\({}^{\circ}\)** \\ \hline Average sunlight & 8 H & 8 H \\ Average curent (A) & 0.58 A & 0.94 A \\ Output voltage (V) & 12 V & 12 V \\ Output power (W) & 6.96 W & 11.28 W \\ Output energy (Wh) & 55.68 Wh & 90.24 Wh \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison analysis table of output power.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Output (A)** & **2.5** & **3** & **3.5** & **4** & **4.5** \\ \hline
0.1 & 0.1892 & 0.7445 & 0.8251 & 0.7915 & 0.8222 \\
0.2 & 0.1892 & 0.7445 & 0.8168 & 0.7915 & 0.8168 \\
0.3 & 0.2022 & 0.7334 & 0.7978 & 0.7915 & 0.7978 \\
0.4 & 0.2085 & 0.5377 & 0.5472 & 0.5472 & 0.5472 \\
0.5 & 0.1892 & 0.4924 & 0.5000 & 0.5000 & 0.5000 \\
0.6 & 0.1892 & 0.4924 & 0.5000 & 0.5000 & 0.5000 \\
0.7 & 0.1892 & 0.4924 & 0.5000 & 0.5000 & 0.5000 \\
0.8 & 0.2085 & 0.4904 & 0.5000 & 0.5000 & 0.5000 \\
0.9 & 0.1918 & 0.1962 & 0.1918 & 0.2085 & 0.1918 \\
1.0 & 0.1892 & 0.1962 & 0.1749 & 0.2085 & 0.1778 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of fuzzy test.  
with equal recovery times of two minutes. The stepper motor starts 240 times. Therefore, using fuzzy logic control can effectively reduce the number of motor starts.
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 3 Materials and Methods
### Network Architecture
#### 3.3.2 Pooling Layer  
The pooling layer, also known as a down-sampling layer, reduces the dimensionality of feature maps and generates a lower-resolution version of the input signal. It also helps eliminate irrelevant details in the signal. The two most common pooling operations are max pooling and average pooling, which compute the maximum or average within a defined filter region.  
Since the max operator is not directly defined in the complex-valued domain, max pooling in CV-CNN is performed separately on the real and imaginary components. In this work, we adopt magnitude-based max pooling [25], where the complex-valued numbers are projected into the real domain using \(\varphi(d)=|d|\). The magnitude-based max pooling is defined as  
\[\text{arg}\max_{d\in\text{patch}}|d| \tag{6}\]\[\max(d_{1},\ldots,d_{n})=\max(x_{1},\ldots,x_{n})+i\max(y_{1},\ldots,y_{n}) \tag{7}\]  
where patch is a local region in the feature map, and n is the number of elements in the local region.
## 3 Results
### Flexible Properties of PEDOT:PSS-C6 Films  
Films of organic polymers such as PEDOT:PSS have intrinsic flexible properties. In fact, chemical vapor deposition graphene [39] and Ag nanowires [40] were rendered flexible by the overcoating of PEDOT:PSS. However, under extreme bending conditions the conductive network of PEDOT:PSS is known to be unretained. For example, the initial sheet resistance of the PEDOT:PSS film on PET or PI substrates increased by 48.3% or 54.4%, respectively, after 1000 bending cycles in a curvature of 0 to 180 degrees [41].  
Cyclic bending tests were conducted to examine the mechanical reliability of the films. Change in surface resistance as a function of the number of bending cycles at a fixed 3 mm bending curvature was monitored, as described schematically in Figure 1. Basically, the deviation was large in the results of each test, but a meaningful trend was confirmed from the average and standard deviation of the three independent experiments, as shown in Figure 8. In the earlier 'infolding' experiment [18], where PEDOT:PSS layer coated on PET substrate was folded inward, surface resistances were stable until 50,000 cycles of bending. However, in the 'outfolding' bending experiment, PEDOT:PSS displayed a large deviation in surface resistance, even in the initial stage of cycles, and after 100,000 cycles R/Ro values reached 1.2. Bending in an 'outfolding' manner induced direct stretching and a conductive network can be easily disconnected compared to that under 'infolding' bending. On the other hand, PEDOT:PSS-C6 films showed relatively stable resistances, which suggests that the conductive network structure of PEDOT:PSS was clearly stabilized. In general, alkyl chains are considered as a flexible group. In fact, it is reported that alkyl-substituted semiconductors played an important role in the stability of the flexible electronic devices [42]. In the same manner, we speculate that the hexyl chain of PEDOT:PSS-C6 gives the higher flexibility compared to PEDOT:PSS. We tentatively conclude that the introduction of the alkyl group into the free sulfonic acid group of PEDOT:PSS improves the flexibility and water resistance of PEDOT:PSS.  
Figure 8: Surface resistance ratio (R/Ro) of PEDOT:PSS films as a function of bending cycles at a curvature radius of 3 mm.
## 4 Design of Roll Angle Detection Module
### Design of Tracking Loops
In order to enable the FLL to track the input signals with all possible frequencies, the frequency at \(-3\) dB point must be less than 3 Hz when it is used to track the input signals with a minimum frequency, where the bandwidth is set to about 0.3 Hz in that case. The function of FLL in the combined tracking loop is to lock the frequency of input signals as quickly as possible, and help pull the loop into the phase-locking state. When the frequency of input signals increases, the bandwidth of FLL can be appropriately enlarged to shorten the settling time and make it faster to track the frequency of input signals.
# Influence of Degree of Saturation on Soil-Pile Interactions for Piles in Expansive Soils
## 3 Laboratory Testing and Results  
To investigate the effect of the degree of saturation on pile uplift force in expansive soils, the following laboratory tests were conducted.  
1. Index property tests to classify the samples and determine physical characteristics;
2. Oedometer tests including the consolidation-swell (CS) test and the constant volume (CV) test to measure vertical swelling pressure under saturated and partially saturated conditions;
3. Direct shear tests to evaluate the shear strength parameters at varying degrees of saturation;
4. Pile uplift model tests using a custom-designed setup to simulate soil-pile interaction under wetting conditions.  
This sequence was designed following a structured experimental framework similar to those discussed in [PERSON] et al. (2024) [31] and [PERSON] et al. (2025) [32] to provide a comprehensive dataset to support the back-calculation of uplift design parameters.
## 4 Results And Discussion
### Simulation Results of AMC  
For the AMC phase, we have trained another classifier per each received type of signal to specify the modulation type of the combined SoI. As was already mentioned, the SoI is transmitted using four modulation types: QPSK, 8 APSK, 16 APSK, and 32 APSK. The following figures illustrate the AMC results for each type of received signal. As can be seen, the presence of jammers highly degrades the classification accuracy. As Figure 6 indicates, AMC is more efficient using AlexNet in the absence of jamming signals, with a comparative classification precision of 95.00%.  
Figure 5: Comparative elapsed training time results.  
Figure 6: AMC result in the presence of SoI (AMC1) using (**a**) AlexNet (95%), (**b**) VGG16 (90.08%), (**c**) GoogleNet (89.7%), and (**d**) ResNet18 (93.6%).  
Figure 7 shows the AMC results in the presence of CWI, in which the highest accuracy was achieved using ResNet18 with a precision of 92.2%.  
As the AMC results in the presence of MCWI show in Figure 8, the highest accuracy is obtained using VGG16, with a precision of 71.90%.  
Figure 7: AMC results in the presence of CWI (AMC2) using (**a**) AlexNet (90.30%), (**b**) VGG16 (86.1%), (**c**) GoogleNet (88.1%) and (**d**) ResNet18 (92.2%).  
Figure 9 demonstrates the AMC results in the presence of CI. As is clear, the highest precision is 81.90%, using ResNet18.  
Figure 8: AMC results in the presence of MCWI (AMC3) using (**a**) AlexNet (71.4%), (**b**) VGG16 (71.9%), (**c**) GoogleNet (71.10 %), and (**d**) ResNet18 (71.7%).  
According to the AMC results, ResNet18 is more efficient because it shows a higher average accuracy comparatively.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 2 Background  
### Internet of Things Architecture  
The Internet of Things refers to the interconnected network of physical objects embedded with sensors, software, and other technologies to collect and exchange data over the internet. The architecture of the IoT, shown in Figure 1, is typically layered, consisting of the following components:  
* Application Layer: contains the end-user applications that utilize the data and insights generated from IoT devices for various use cases, such as smart homes, healthcare, and industrial automation [5; 14];
* Middleware Layer: provides data storage, processing, and management services, and often includes IoT platforms that facilitate device management and data analytics;
* Network Layer: responsible for connecting devices to the internet and enabling communication between devices and servers;
* Device Layer: this includes sensors, actuators, and other hardware components that collect data from the environment.  
IoT architecture supports a wide range of applications across different domains, including smart cities, agriculture, healthcare, and manufacturing. Each application area leverages the capabilities of the IoT to enhance efficiency, reduce costs, and improve the quality of services [3; 8].  
While the IoT focuses on the connectivity and interaction between physical objects, the Web of Things aims to enhance the IoT by integrating it with the web. The WoT provides a framework for using web technologies to facilitate the interaction between IoT devices, enabling seamless integration and interoperability. In the next section, we will explore the architecture, applications, and specific security challenges of the WoT, highlighting how it builds upon and extends the capabilities of the IoT.
# Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method
###### Abstract  
The lateral line system is an essential mechanosensory organ for organisms such as fish; it perceives the fluid environment in the near-field through the neuromasts on the lateral line system, supporting behaviors (e.g., obstacle avoidance and predation in fish). Inspired by the near-field perception ability of fish, we propose an artificial lateral line system composed of pressure sensors that respond to a target's relative position by measuring the pressure change of the target vibration near the lateral line. Based on the shortcomings of the idealized constrained modeling approach, a multilayer perceptron network was built in this paper to process the pressure signal and predict the coordinates on a two-dimensional plane. Previous studies primarily focused on the localization of a single dipole source and rarely considered the localization of multiple vibration sources. In this paper, we explore the localization of numerous dipole sources of the same and different frequency vibrations based on the prediction of the two-dimensional coordinates of double dipoles. The experimental results show that the mutual interference of two vibration sources causes an increase in the localization error. Compared with multiple sources of vibration at the same frequency, the positioning accuracies of various vibration sources at different frequencies are higher. In addition, we explored the effects of the number of sensors on the localization results.  
artificial lateral line; underwater localization; artificial neural network; multi-source vibration +
Footnote †: journal: Applied Sciences
# Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage
## 3 Framework for a Cloud-Based Video Surveillance System
### Background Processing  
The core of a video surveillance system mainly contains the following modules:  
* Streaming media server module: provides mostly users a unified data access interface, includes real-time video access, historical video playback, download and data transmission protocol.
* Intelligent video analysis module: this module analyses the real-time video streams and detects an object if entering with intelligent video analysis algorithms.
* System storage module: mainly includes data storage interface, and data read interface. Data storage interface can store real-time video in a distributed storage system and establish the index of the video data, then copy multiple redundant replicas. The data read interface obtains historical video data from distributed storage system through searching the index in management module and storage module. The module integrates heterogeneous storage resources and provides a unified excuse for reading and writing. Not only can it reduce the complexity of the system, but also improve the scalability and compatibility of the system [25].
* System management module: mainly includes equipment management, resource allocation. Equipment management can monitor the state of all devices; resource management can integrate all resources in the system, and then allocate resources based on the actual needs of different applications.
# Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
## 5 Experiments
### Performance of CASAE  
To evaluate the performance of the CASAE on metallic defects detection, in this section, we compared the inspection performance with three detection algorithms, including the representative thresholding method [17; 19] and FCN method [36]. Figure 6 shows the detection results, which are marked as a green color under various complex samples. These defective samples consist of defects with ambiguous edges (Figure 6(a2,a4,a5)), different background colors (Figure 6(a1,a6)] and low-contrast scratches (Figure 6(a3,a6)].  
As can be seen from Figure 6, the thresholding methods work well only for obvious defects, e.g., the damage spots in Figure 6(a1), dust in Figure 6(a2), and fibers in Figure 6(a6). They perform poorly on ambiguous defects and low-contrast scratches, e.g., the glue spots in Figure 6(a4), scratches in Figure 6(a6). The Kittler [19] method tends to miss the detection of defects, while the Otsu [17] method easily over-detects, resulting in a large amount of background noise also being segmented, e.g., in Figure 6(c3,c5). For the FCN method [32], it can achieve good detection effects for most of the defects. However, it may be easy to ignore scratches and cannot obtain a fine defect region. In contrast to these phenomena, the proposed CASAE method provides a concise way to distinguish between defects and backgrounds. It shows powerful capabilities in various complex scenarios. The quantitative performance of defects detection results are shown in Table 3. As a typical segmentation network, FCN [36] is directly employed to predict the image for a starting point. As can be seen from Table 3, the AE model outperforms the FCN, which proves that the encoder-decoder structure can learn more semantic information about the defects than repeat convolution operations. Since atrous convolution is very important to produce a robust model that accommodates to the different scales of the defects, we tested its effect on the results by running the same model of a single AE and CASAE with the  
Figure 6: Segmentation results. The six rows are examples of defective images. (**a1–a6**) raw images, (**b1–b6**) results of [PERSON] [19], (**c1–c6**) results of [PERSON] [17], (**d1–d6**) results of a fully convolutional network (FCN) [36], (**e1–e6**) results of the cascaded autoencoder (CASAE) (proposed).  
addition of the atrous convolution. The changes in the atrous convolution and cascaded architecture lead to an enhancement in the IoU of the testing data, as shown in the results.