esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
###### Abstract  
Visual object trackers based on Siamese networks perform well in visual object tracking (VOT); however, degradation of the tracking accuracy occurs when the target has fast motion, large-scale changes, and occlusion. In this study, in order to solve this problem and enhance the inference speed of the tracker, fast and accurate visual tracking with a group convolution and pixel-level correlation based on a Siamese network is proposed. The algorithm incorporates multi-layer feature information on the basis of Siamese networks. We designed a multi-scale feature aggregated channel attention block (MCA) and a global-to-local-information-fused spatial attention block (GSA), which enhance the feature extraction capability of the network. The use of a pixel-level mutual correlation operation in the network to match the search region with the template region refines the bounding box and reduces background interference. Comparing our work with the latest algorithms, the precision and success rates on the UAV123, OTB100, LaSOT, and GOT10K datasets were improved, and our tracker was able to run at 40 FPS, with a better performance in complex scenes such as those with occlusion, illumination changes, and fast-motion situations.  
feature fusion; pixel-level correlation; Siamese network; attention mechanism +
Footnote †: journal: Applied  
0000-0001-9294-3300]Cited for
# Research Progress in Fluid Energy Collection Based on Friction Nanogenerators
## 3 Optimization of Fluid Energy Harvesting Devices
### Expending Hybrid Energy Harvesting Device  
The power generated by wave energy fluid energy harvesting devices (WEHs) depends on the fluid disturbance in the current working environment, which means there is a strong correlation between the disturbance and the output power. Under weak disturbances, the output power is naturally insufficient, and the volume of a single TENG is limited. The contact area of the frictional layer is limited, which makes it difficult to meet people's demand for electricity for a long time due to the transfer of charges. At present, the output power of a single TENG is limited, and most of the output power is concentrated in the mW order of magnitude. Parallel multi-group TENG is a solution that has been practiced for a long time. After networking, TENG effectively concentrates the energy of multiple individual devices through reasonable design of control circuits, achieving high-capacity output capacity. However, fundamentally, improving individual TENG is the fundamental guarantee for solving the problem of high-capacity TENG networking. A feasible solution to improve the efficiency of a single TENG is to integrate TENG with ENG to form a hybrid energy harvesting system. The working frequency of electromagnetic generator EMG is higher, and compared to TENG, it has the characteristics of high current and low voltage. TENG itself has a higher output voltage, but a lower current. The two work in different working environments and can complement each other's shortcomings [91; 92]. [PERSON] et al. studied a liquid-solid electromagnetic combined water kinetic energy harvesting nanogenerator (TTENG). In addition to the traditional structure mentioned above, an innovative approach is to add a permanent magnet and an induction coil to combine TENG with EMG, resulting in consistent current direction and significantly enhancing the output level of TENG, as shown in Figure 13a,b [93]. [PERSON] et al. designed a composite wave energy harvesting element (WEC) consisting of a mechanical transmission mechanism, a floating ball, TENG, and an electromagnetic induction generator. In this system, the power generation system is designed in a cylindrical shape. The side of the cylinder is equipped with permanent magnets and coils, and it is an electromagnetic induction generator RD-EMG. The cylindrical surface of the cylinder is equipped with MBC-TENG, and the friction layer material is selected as PTFE and nylon brush. The floating ball receives wave disturbance in the water, and its motion is transmitted to the floating ball. The speed is then increased through gear racks and a carefully proportioned variable speed gear mechanism, and the rotational kinetic energy is transmitted to MBC-TENG and RD-EMG, as shown in Figure 13c,d [94]. It has been proven that based on the difference in working frequency, ENGs and TENGs working together can greatly improve the efficiency of energy harvesting equipment.
## 4 Multi-Armed Bandit (MAB) for Optimal MPTCP Scheduling
In Algorithm 2, we define two vectors \(\mathbf{u}(t)\) and \(\mathbf{v}(t)\) representing the number of times that arms/links is scheduled until time \(t\) and the mean observed reward values of arms/links up to the current round \(t\), respectively. The vectors \(\mathbf{u}(t)\) and \(\mathbf{v}(t)\), initialized as \(\mathbf{u}(t)=\mathbf{v}(t)=0\), evolve over time \(t\) (is an ergodic process). The evolution of \(u_{t}(t)\) and \(v_{t}(t)\) for an arm/path \(i\) is as follows:  
\[u_{t}(t+1) = \begin{cases}u_{t}(t)+1&\text{if }t\in\mathbb{S}(t)\\ u_{t}(t)&\text{otherwise}\end{cases}  \tag{18}\]and  
\[v_{i}(t+1) = \begin{cases}\frac{R_{i}(t)+u_{i}(t)v_{i}(t)}{1+u_{i}(t)}&\text{if }t \in\mathbb{S}(t)\\ v_{i}(t)&\text{otherwise}.\end{cases}  \tag{19}\]  
As shown in Algorithm 2 (step 10), the multipath scheduler enters the while loop to choose super arm \(\mathbb{S}(t+1)\), after every transmission round by  
\[\mathbb{S}(t+1)=_{\mathbb{S}\subset\mathcal{I},|\mathbb{S}|=n}\sum_{t\in \mathbb{S}}\Bigg{(}\underbrace{\sqrt{(n+1)\frac{\log(t+1)}{u_{i}(t)}}}_{ \text{exploration}}+\underbrace{v_{i}(t+1)}_{\text{explotation}}\Bigg{)} \tag{20}\]  
**Remark 2** (Upper Bound on Regret). : _We believe that the average regret attained by the proposed multipath MS-UCB scheduling algorithm (Algorithm 1) is upper bounded by a factor, the derivation of which requires further investigations. With relevant insights and modeling ideas from this work, the stability and convergence conditions of the network dynamics with MS-UCB will be studied in the future._
## 2 Preliminary Concepts
### Adhesive joints and Defects  
An adhesive is a substance that has the capacity to join two materials together, independently of their (dis)similarity, giving them the ability to resist any form of separation [20]. When an adhesive connects two separate parts, an adhesive joint is formed. This concept can be further expanded by classifying the parts as substrates, before the bonding process, and adherents, after the creation of the joint [20]. Furthermore, the adhesive layer in contact with the adherents is called an interface, also known as the boundary layer [6].  
When comparing adhesive joints with traditional fastening methods, their benefits include a more uniform distribution of stress along the bonded area, which yields good resistance to dynamic solicitation and excellent load transmission, as well as higher stiffness. Due to not using bulky fasteners, there is also a large reduction of the weight of the structure and, consequently, a minimization of the cost. However, these benefits also come with many disadvantages, which hinder the widespread application of bonded primary structures. Indeed, a major drawback is the absence of effective NDT techniques to detect damage [6; 20]. The NDT methods need to be robust because, even if an adhesive is adequate for an application, many variables can alter the overall quality of the bonded joint and lead to premature mechanical failure. Common problems affecting adhesive joints are the lack of proper surface finishing, an incomplete or altered curing cycle, high humidity, or a degraded chemical composition caused by improper handling or storing of an adhesive [21]. These perturbations, when verified together, can cause a defective part, but there are cases when only one of these issues occurs. This can lead to damage types like those illustrated in Figure 1. These defects can occur in several places in the bond, such as cohesively within the adhesive, inter-facially between the adherent and adhesive,or segmented throughout the bonded joint. Often, these are voids, debonds, porosity, or cracks within the adhesive layer. A few of these defects are detectable by certain NDT methods, but there is one defect in particular that still cannot be effectively detected by NDT, specifically weak adhesion. This defect consists of an inter-facial defect that reduces the maximum strength of an adhesive joint by at least 20% and is extremely hard to detect by conventional methods, as there is no gap or alteration in the structure; just a small reduction in the overall surface adhesion exists.
# Acoustic Emission-Based Method for IFSS Characterization in Single-Fiber Fragmentation Tests
## 2 Materials and Methods
### IFSS Calculation
#### 2.7.2 AE Method: Fragment Average Length
Once obtained, the data are rescaled so that their sum equals the total fragmented length of the samples, as shown in Equation (8):  
\[l_{total\_frag}=F.\sum_{i=1}^{n_{AE,frag}}X^{\prime}(i), \tag{8}\]  
where \(l_{total\_frag}\) represents the sum of the lengths of all fragments within the sample, \(X^{\prime}\) are the fragment lengths generated by the gamma distribution, and \(F\) is the rescaling factor. Therefore, the rescaled data are calculated according to Equation (9):  
\[X_{scaled}=F\cdot X\prime, \tag{9}\]  
where \(X_{scaled}\) are the rescaled generated data. The process of data generation and rescaling is performed \(n_{gen}\) times. For each data generation, the parameters \(k\) and \(\theta\) of the fitted gamma distribution are obtained, which are then used to calculate the average fragment length obtained in the \(j\)-th data generation, see Equation (10):  
\[l_{AE,avg,j}=k_{j}\cdot\theta_{j}, \tag{10}\]where \(l_{AE,avg,j}\) is the average fragment length for the data generated at the \(j\)-th iteration, which is calculated using the parameters of the fitted gamma distribution, \(k_{j}\) is the shape parameter in the \(j\)-th iteration; \(\theta_{j}\) is the scale parameter in the \(j\)-th iteration. The average fragment length that will be used in the IFSS calculation is obtained by averaging the results from all iterations, see Equation (11):  
\[l_{AE,avg,fitted}=\sum_{j=1}^{n_{gen}}\frac{l_{AE,avg,j}}{n_{gen}}, \tag{11}\]  
where \(l_{AE,avg,fitted}\) is the average fragment length obtained from the fitted gamma distribution in the AE method. Figure 5 shows a graphical summary of the procedure described above. The IFSS is calculated using the results provided by the model \(l_{AE,avg,fitted}\). In parallel, to compare the results, the average fragment length is calculated using the calibrated length of the samples, as shown in Equation (12):  
\[l_{AE,avg,arithmetic}=\frac{l_{cal}}{n_{AE,frag}}, \tag{12}\]  
where \(l_{AE,avg,arithmetic}\) is the average fragment length calculated using the arithmetic mean, \(l_{cal}\) is the calibrated length of the sample, and \(n_{AE,frag}\) is the number of fragments calculated using the AE method.
## 3 Performance Improvement of Multiple Receiver WPT
### Frequency Splitting Elimination  
In the dynamic environment, the movement of the Rx devices causes the changes in the magnetic over the coupling and leads to frequency splitting. It degrades the system efficiency also the fluctuation in the output power of the receiver loop, which can be avoided by maintaining the mutual inductance of Tx and Rx section is almost constant irrespective of the coil separation. The non-identical coil structures can be used to eliminate the frequency splitting by keeping the mutual inductance to be constant irrespective of the distance variation. However, the PTE of the system depends on the coupling between the coil, many reductions in the mutual inductance can minimize the system efficiency. To determine the optimal coil radius, considering the Rx and Tx coils are having the radius of \(r_{R1}\), \(r_{R2}\), \(r_{R3}\), \(r_{R4}\), \(r_{T}\) and the separation distance of \(d_{i}\). The mutual inductance \(M\) can be derived as [35]  
\[M=\mu_{0}\frac{\sqrt{r_{T}r_{i}}}{g}\Big{[}\Big{(}2-g^{2}\Big{)}K\Big{(}g^{2} \Big{)}-2E\Big{(}g^{2}\Big{)}\Big{]}\text{ where }i=R_{x},\ L_{x} \tag{16}\]  
\[g^{2}=\frac{4r_{T}r_{i}}{d_{i}{}^{2}+\left(r_{T}+r_{i}\right)^{2}} \tag{17}\]  
where \(E(*)\) and \(K(*)\) are the second and first order complete elliptic integrals. As the separation between the Tx and Rx approaches to zero, the term \(g^{2}\) becomes one when the radius of the coils are same. At \(g^{2}=1\), the first order and, second order elliptic integrals become infinite and one respectively which makes rapid increase in the mutual inductance when the distance reduces. In order to overcome the above constraint \(g^{2}\) must be kept less than unity also without affecting the coupling and system efficiency for large separation gap between Tx and Rx unit. A different set of mutual inductance are calculated for various \(d_{T}\) and \(d_{R1}\), \(d_{R2}\), \(d_{R3}\), \(d_{R4}\) as illustrated in Figure 8. As the diameter of the Tx coil is increased the mutual inductance values are reduced for the small gap and changes very less for the larger air gap separation. However, when the diameter of the Tx coil is less the variation in the mutual inductance is drastic over the distance. From Figure 8 it's observed that the changes in the mutual inductance for the Tx coil diameter of 12.5 c.m is from 0.67 to 0.59 for the distance 2 c.m to 10 c.m and almost constant from 2 c.m to 8 c.m of distance between the coil. Further increase in the Tx coil diameter of 14.5 c.m, 15.5 c.m result in reduction of the mutual inductance value which can reduce the PTE and the comparison of mutual inductance for selected diameter is presented in Table 2. Hence an optimal value of 12.5 c.m Tx coil diameter is selected for the design to eliminate the frequency splitting by considering the PTE. However, the size of the Tx coil will change depending on the number of Rx, distance, diameter of Rx and number of turns.  
Figure 7: dB of the coil with cross-coupling compensation (**a**) dB(S) when Rx1-on; (**b**) dB(S) when Rx2-on (**c**) dB(S) when Rx3-on; (**d**) dB(S) when Rx4-on.
# A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations
## 2 System Description
### Embedded Software  
The microcontroller MSP430F5359 is responsible for receiving and processing data from the sensors installed in the ULD. It also acquires the battery monitoring modules' data to update the HMI with the battery packs' percentages. After initializing the application, calibrating the piezoelectric signals, and initializing the smoke detector, the communication between the microcontroller and the wireless communication module is implemented.  
The wireless communication management microcontroller, ESP32, initially establishes a connection with the MSP430F5359 microcontroller and then scans for nearby Wi-Fi networks with a predefined SSID. After a successful Wi-Fi connection to the HMI wireless network, the ESP32 connects to the MQTT server hosted by the INTELLICONT HMI.  
The INTELLICONT embedded software can set the ULD and the RMC unit in three different states: (a) \"Idle\", during which the system does not send any report to the HMI. (b) \"Lock\", during which the ULD sends one lock report to the robotic platform every 100 msecs. The lock report contains two variables that correspond to the locking sensors' state. When the robotic platform receives the appropriate values of the variables, the load or unload mode of the ULD is enabled. (c) \"Normal\", during which the ULD sends periodic reports of the values of all the sensors of the ULD to the HMI. The reporting period is configurable via the HMI ULD dashboard.
# Research on Two-Dimensional Digital Map Modeling Method Based on UAV Aerial Images
## 4 Experimental Validation  
### Data Collection Platform Setup  
In the process of image acquisition, to ensure that the captured images are clear, the flight altitude of the drone is typically kept below 200 m. Wind speed can significantly impact the stability of drone photography. Given the presence of tall buildings in urban environments, low-altitude winds are generally classified as level 4 or lower, with speeds under 7.9 m/s. Consequently, when selecting a drone model, its wind resistance capability should be at least 10 m/s. During image capture, it is essential to control the drone's flight speed to ensure that its endurance time is sufficient to cover the entire scene intended for detection. To meet this requirement, the drone's flight endurance must be no less than 30 min. The sequence of images captured by the drone is intended for creating panoramic images through image processing techniques; therefore, the resolution of the drone's onboard detection system must be at least 10 million pixels.  
Taking all the aforementioned factors into account, the experimental platform utilized in this study is the DJI Mavic Air 2 drone equipped with a wide-angle camera. This platform boasts a maximum flight range of 5 km, and the wide-angle lens has a maximum tilt angle of 84\({}^{\circ}\), which fully satisfies the requirements for the experiments conducted in this paper. Furthermore, the lens captures images with a resolution of up to 48 million pixels, and even during continuous shooting, each image retains a resolution of 12 million pixels. The lens has an equivalent focal length of 24 mm, and the available shooting modes include spherical, wide-angle, and vertical. During the actual imaging process, the flight altitude of the drone significantly influences the size of the captured image area. To obtain sequential images conducive to target extraction, the final flight altitude was determined to be between 140 and 145 m after several test flights. All image acquisitions for this experiment were performed under normal lighting conditions in the morning. To facilitate the storage of captured data, the platform is equipped with 8 GB of onboard memory. Using the experimental platform for aerial photography, a total of 30 images were captured for this scene, and some of the obtained orthophoto source images are shown in Figure 4. In  
Figure 3: Flowchart of the two-dimensional digital map modeling method based on UAV aerial images.  
addition, the computer used for image processing in this article has an Intel (R) Core (TM) i5-6300 HQ @ 2.30 GHz CPU with 8 GB of RAM.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 2 Knowledge-Based Approaches
### Categories of Knowledge Representation and Reasoning Approaches
#### 2.1.5 Tree-Based Formalisms  
Tree-based formalisms use hierarchical structures to represent knowledge in a sequential decision-making fashion. They allow for the classification or regression of input data by partitioning it into smaller subsets based on specific attributes or conditions. A conclusion is made by traversing a path through the tree nodes until the path reaches a conclusion represented by a unique leaf node. Examples of tree-based formalisms are decision trees [16] and random forests [17].
###### Abstract  
The trend of the next generation of the internet has already been scrutinized by top analytics enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the global population will have their personal data covered under privacy regulations. This alarming statistic necessitates the orchestration of several security components to address the enormous challenges posed by federated and distributed learning environments. Federated learning (FL) is a promising technique that allows multiple parties to collaboratively train a model without sharing their data. However, even though FL is seen as a privacy-preserving distributed machine learning method, recent works have demonstrated that FL is vulnerable to some privacy attacks. Homomorphic encryption (HE) and differential privacy (DP) are two promising techniques that can be used to address these privacy concerns. HE allows secure computations on encrypted data, while DP provides strong privacy guarantees by adding noise to the data. This paper first presents consistent attacks on privacy in federated learning and then provides an overview of HE and DP techniques for secure federated learning in next-generation internet applications. It discusses the strengths and weaknesses of these techniques in different settings as described in the literature, with a particular focus on the trade-off between privacy and convergence, as well as the computation overheads involved. The objective of this paper is to analyze the challenges associated with each technique and identify potential opportunities and solutions for designing a more robust, privacy-preserving federated learning framework.  
federated learning; differential privacy; homomorphic encryption; privacy; accuracy +
Footnote †: journal:  
0000-0001-4000-0001-4000][PERSON]
## 3 Results and Discussion
### Thermal Conductivity Test
#### 3.2.4 Freeze-Thaw Cycles  
Figure 13 illustrates the relationship between the number of freeze-thaw cycles and soil thermal conductivity, showing a gradual decline in thermal conductivity as the number of freeze-thaw cycles was increased. The thermal conductivity of untreated loess decreased by 9.2%, while that of carbonated solidified loess decreased by 5%. One-way ANOVA was used to test the thermal conductivity, in order to investigate whether there were significant differences between the different numbers of freeze-thaw cycles. Statistical calculations were performed for each dosage, and the \(p\)-value obtained for all dosages was less than 0.05, indicating that there were statistically significant differences between the mean thermal conductivity values for the different numbers of freeze-thaw cycles. This reduction was primarily attributed to structural changes in the soil induced by repeated freeze-thaw cycles, which progressively disrupt heat transfer pathways. During the freezing process, free water in the soil undergoes a liquid-to-solid phase transformation, forming ice. This phase change causes an approximately 9% volumetric expansion, exerting stress on the surrounding soil particles. Such stress may lead to structural damage, including loosening of the soil matrix and the enlargement of existing pores. Additionally, the pressure generated by the expanding ice may create new cracks, further compromising soil stability [48]. These structural alterations reduce connectivity between soil particles, thereby diminishing their ability to conduct heat efficiently. During the thawing process, the melting of ice and the redistribution of water exacerbate structural damage. The movement of water within soil pores weakens the bonding between particles, preventing the soil from fully restoring its original structure. Over repeated freeze-thaw cycles, these damages accumulate, leading to a gradual increase in pore size, expansion of pore volume, and an increase in the number of cracks. These changes significantly raise the air content within the soil, and since air has an extremely low thermal conductivity (0.026 W/(m\(\cdot\)K)), this adversely affects the soil's overall thermal conductivity. In carbonated solidified loess, CL plays a cementing role by effectively binding soil particles and carbonation products while the carbonation products fill the pores, collectively reducing the impact of freeze-thaw cycles on soil structure. From a macroscopic perspective, the cumulative effects of freeze-thaw cycles over time lead to significant structural degradation. The progressive loosening of the soil matrix, increased porosity, and reduced integrity of the solid phase collectively weaken the heat transfer efficiency. However, carbonized stabilized loess mitigates structural degradation, reducing the destructive impact of freeze-thaw cycles on soil thermal conductivity.  
Figure 12: Thermal conductivity of different dry densities.