esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Mathematical Model of Double-Coupled Duffing System  
### Holmes-Duffing System  
The Duffing system was first proposed by [PERSON] and [PERSON] et al. [23]. As a nonlinear dynamic system. The Duffing system has a simple structure but rich dynamic behavior, which is widely used by scholars in various fields. The physical model of the traditional Holmes-Duffing oscillator (hereinafter referred to as the Duffing system) is shown in Figure 1.  
As shown in Figure 1, the structure of the model is mainly composed of two electromagnets and a cantilever beam, and the electromagnet is fixed on a sliding table. The end of the cantilever beam is recorded as the displacement of the oscillator. When the cantilever beam is not deformed, the horizontal position directly below it is the origin of the displacement. After the electromagnet is energized, the cantilever beam is deformed under force, and there is a stable point on the left and right sides. The displacement of the left stable point is recorded as \(-1\), and the displacement of the right stable point is recorded as \(1\). Assuming that the sliding platform moves in the way of \(F\mathrm{cos}(\omega t)\), the mathematical model of the traditional Holmes-Duffing system shown in Equation (1) below can be established.  
\[\ddot{x}+\xi\dot{x}-x+x^{3}=F\mathrm{cos}(\omega t) \tag{1}\]  
where, \(\ddot{x}\), \(\dot{x}\), \(x\) are the acceleration, velocity and displacement of the vibrator, and \(\xi\) is the damping ratio of the system. In this paper, the value of \(\xi\) is \(0.5\), \(F\mathrm{cos}(\omega t)\) is the elastic restoring force of the system, \(F\) is the amplitude of the built-in driving force, and \(\omega\) is the frequency of the built-in driving force.
###### Abstract  
Edge computing (EC) is a distributed computing approach to processing data at the network edge, either by the device or a local server, instead of centralized data centers or the cloud. EC proximity to the data source can provide faster insights, response time, and bandwidth utilization. However, the distributed architecture of EC makes it vulnerable to data security breaches and diverse attack vectors. The edge paradigm has limited availability of resources like memory and battery power. Also, the heterogeneous nature of the hardware, diverse communication protocols, and difficulty in timely updating security patches exist. A significant number of researchers have presented countermeasures for the detection and mitigation of data security threats in an EC paradigm. However, an approach that differs from traditional data security and privacy-preserving mechanisms already used in cloud computing is required. Artificial Intelligence (AI) greatly improves EC security through advanced threat detection, automated responses, and optimized resource management. When combined with Physical Unclonable Functions (PUFs), AI further strengthens data security by leveraging PUFs' unique and unclonable attributes alongside AI's adaptive and efficient management features. This paper investigates various edge security strategies and cutting-edge solutions. It presents a comparison between existing strategies, highlighting their benefits and limitations. Additionally, the paper offers a detailed discussion of EC security threats, including their characteristics and the classification of different attack types. The paper also provides an overview of the security and privacy needs of the EC, detailing the technological methods employed to address threats. Its goal is to assist future researchers in pinpointing potential research opportunities.  
e _Future Internet_2025, 17, 175
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 5 Experiments and Results
### Datasets  
The datasets used in the experiment are SSDD [88], LS-SSDD [89], and HRSID [90]. These datasets contain SAR ships only. SSDD has 1160 images with 2456 ships. SSDD derives from three satellites: RadarSat-2, TerraSAR-X, and Sentinel-1, which display ships at multiple scales and in multiple scenarios. The HRSID comes from three radar satellites: TerraSAR-X, TanDEMX, and Sentinel-1B, with a total of 5604 images containing 16,951 ships. The data of LS-SSDD are from one satellite, Sentinel-1, with a total of 9000 images. The three datasets are annotated as the format of PASCAL VOC. The size of ships within these three datasets varies widely. According to the MS COCO [91] definition of target size, LS-SSDD consists of no big ships and almost small ships, while SSDD and HRSID contain various sizes of ships. Table 1 demonstrates the details of the three datasets. Because of the scarcity, SAR datasets are smaller than other detection datasets, such as COCO which contains 328,000 images.  
For the SSDD, we follow the data split instructed by its owner [92]. Those images with a tail number between 2 and 7 or 0 are divided into the training set, while those with a tail number of 8 are used for validation during the training process and, finally, the remaining images are used for testing. The division of validation, test, and training sets in the HRSID dataset is randomly divided according to 1:1:8. The authors of LS-SSDD gave a fixed division of the dataset into a training set of 6000 and a t  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **The Largest Ship (pixel 2)** & **The Smallest Ship (pixel2)** & **Average (pixel2)** & **Number of Pictures** \\ \hline HRSID & 522,400 & 3 & 1808 & 5604 \\ SSDD & 62,878 & 28 & 1882 & 1160 \\ LS-SSDD & 5822 & 6 & 381 & 9000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of datasets.  
experiments of this paper, we randomly delineated 1000 from the 6000 training sets as the validation set. Table 2 shows the number of images for different purposes after the division of the three datasets.
# Task-Based Motion Planning Using Optimal Redundancy for a Minimally Actuated Robotic Arm
## 3 Workspace Analysis  
The primary workspace of a robotic arm is defined as the set of points that can be reached by its end-effector [11] in a given environment when all of its joints can be actuated. Activating only a subset of joints results in a smaller or equal sub-workspace than in the primary workspace. Therefore, if a target lies within a sub-workspace's boundaries, the existence of a path to reach it, using the appropriate subset of joints, is guaranteed. This premise constitutes the guiding principle for the minimal redundancy motion plan algorithm: instead of applying a direct and exhaustive search to find the least-redundant path (i.e., the path that requires actuating the minimal number of joints), we divide the problem into two steps.  
First, we search through the sub-workspaces to find the arm's minimal redundancy, i.e., the minimal subset of joints that simply ensures the existence of a path to the target. Then, based on the minimal subset of joints, we apply a heuristic search algorithm to find the path itself (Section 4).  
In this section, we introduce an enhanced approach to determine the manipulator's workspace that uses a superposition of sub-regions based on a beta distribution function. This approach makes it possible to depict the manipulator workspace boundaries quickly and accurately. This not only serves to determine the manipulator's minimal redundancy, but also to use it as a search graph, as will be shown in the next section.  
In this example, the manipulator is composed of a 1 m-long planar arm comprised of two to five links (20, 40, 60, or 80 cm long, 4 cm wide, 5 cm high, see Figure 2).
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 6 Results and Discussion
However, there is an increase in the data error rate, being critical when the distance grows. Considering the obtained results and the purpose of the network, it is interesting to choose a high spreading factor value when coverage is capital, albeit the transfer time will be increased. From the LoRaWAN central node, it is possible to send a packet to a node of the Zigbee network or a VLC node with a lamp for data transmission. With LoRaWAN Class A modules, at first it is necessary to send a frame to the LoRaWAN cell gateway, so that the module then opens a reception window to receive data. That is why we would have the same data rate and the same data flow in relation to time, although the reception of data through LoRaWAN and the subsequent sending to another network such as Zigbee or VLC is completed in less time, such as in the case of receiving data through LoRaWAN and sending it through VLC, assuming the use of a bandwidth of 125 kHz in LoRaWAN and a spreading factor of 12. To guarantee a long distance transmission and a frequency of 2 kHz in VLC communication, a delay of approximately 6 s was determined throughout the entire network. Unlike other systems [38; 39], in this case, inter-operability is based on the fragmentation capabilities offered by the HFN, not algorithms or payload encoding. This philosophy allows, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spreading Factor** & **Range (m)** & **Average Time (s)** & **PER** \\ \hline SF12 & 300 & 6.2 & 0.03 \\ SF12 & 600 & 6.4 & 0.19 \\ SF12 & 900 & 6.7 & 0.72 \\ SF9 & 300 & 5.4 & 0.03 \\ SF9 & 600 & 5.5 & 0.26 \\ SF9 & 900 & 5.7 & 0.81 \\ SF7 & 300 & 4.6 & 0.04 \\ SF7 & 600 & 4.7 & 0.4 \\ SF7 & 900 & 4.9 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average time and error rate in the Zigbee–LoRaWAN link. Figure 19: Error rate between sensor nodes and the gateway. in a simple way, the inclusion of other standards without substantially changing the infrastructure or the equipment layout.
## 3 Kinematics Analysis
### Forward Kinematics
#### 3.3.2 Verification of the Forward Kinematics Simulation  
The correctness of the forward kinematics solution for the 6-P-RR-R-RR parallel platform is verified via a co-simulation using the ADAMS and MATLAB software. The pose and the motion trajectory of the mobile platform when solved using the MATLAB iterative algorithm are compared with the input pose and trajectory obtained from ADAMS and the pose error and the trajectory tracking error are obtained, thus verifying the correctness and the effectiveness of the forward kinematic algorithm. The correctness of the forward kinematics algorithm is verified by making the platform track a specific three-dimensional trajectory. The tracking curve is illustrated in Figure 10, and the mathematical expression for the curve is:  
\[\left\{\begin{array}{c}x=20\sin\xi\cos\xi\\ y=20\sin\xi\sin\xi\\ z=20\cos\xi\end{array}\right.\text{and}\left\{\begin{array}{c}\xi=\pi t/10 \\ \xi=20\pi t/10\\ 0\leq t\leq 10\end{array}\right. \tag{14}\]In ADAMS, the three attitude angles of the mobile platform are set to zero and the mobile platform is then controlled to move along the three-dimensional curve shown in Figure 10. The displacement corresponding to that of the six oblique driving blocks is extracted and is brought into the MATLAB iterative algorithm and used as the input to the forward kinematics. The pose errors of the mobile platform with respect to the motion time are solved using the iterative algorithm, as shown in Figure 11. Figure 11 shows that when the mobile platform tracks the required spatial curve motion with a specific attitude, the maximum positioning errors generated in the X, Y, and Z directions are \(6.28\) nm, \(6.23\) nm, and \(-45.14\) nm, respectively, while the maximum rotational angle errors of \(\alpha\), \(\beta\), and \(\gamma\) are \(-0.05894\)\(\upmu\)rad, \(0.07117\)\(\upmu\)rad, and \(-0.03976\)\(\upmu\)rad, respectively, as determined by the Matlab numerical iterative algorithm, which verifies the correctness of the forward kinematics numerical iterative algorithm. Because the solution procedure for inverse kinematics is included in the solution procedure for forward kinematics, the verification of forward kinematics also shows the correctness of inverse kinematics.
## 1 Introduction
The modification of transdermal patches of Bup with sustained release properties is one of the challenges of this field. In order to reduce the stress of frequent handling and injection as well as to introduce new sustained release Bup patches and evaluate the feasibility of using such nanofiber mats as a potential TDS, this study aimed to prepare Bup-loaded electrospun nanofiber mats of PVP and PVP/PVA by electrospinning. Electrospun nanofibers were characterized using scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier transform-infrared (FT-IR) spectroscopy and water contact angle measurements. The high-performance liquid chromatography (HPLC) method was employed for the in vitro drug release measurements of Bup from Bup/PVP nanofibers, Bup/PVP/PVA nanofibers and cross-linked Bup/PVP/PVA nanofibers. Even though PVA is known as a low-cost, non-toxic and biocompatible carrier, the management of drug release in PVA-based nanofibers is difficult because of high water solubility and the high hydrophilicity of this polymer. To overcome this demerit, several cross-linking methods have been introduced, among which cross-linking with glutaraldehyde (GTA) is the most common method for modifying \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Carrier** & **Other Ingredient** & **Drug** & **Remarks** & **Ref** \\ \hline PVA & & Ketoprofen & \begin{tabular}{c} A decrease in the rate of release from in the range of \\ 1–24 h after the treatment of PVA mats with methanol \\ \end{tabular} & [40] \\ \hline PVA & Na & Ketoprofen & A significant decrease in the dissolution rate & [41] \\ \hline PVA & poly(p-xylylene) (PPX) & BSA & \begin{tabular}{c} diminishment of burst release and the significant \\ retardation of protein release with applying \\ PPX coating \\ \end{tabular} & [42] \\ \hline PVA & EUDRAGIT1 L-100 & BSA & Modified: pH-dependent release & [43] \\ \hline PVA & CS-EDTA & Lysozyme & Rapid release depending on loading amount & [44] \\ \hline PVA & PEO & Metronidazolezole & \
\begin{tabular}{c} Antimicrobial activity against _Escherichia coli_, \\ _Pseudomonas aeruginosa_, _Apergillus niger_, \\ _Penicillium notatum_ and _Apergillus flavus_ \\ \end{tabular} & [45] \\ \hline PVP & PVA & Ciprofloxacin &
\begin{tabular}{c} Sustained release of the antibiotic protective action \\ against external pathogenic microbes \\ \end{tabular} & [46] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._the water-resistance of nanofibers. Therefore, prepared Bup-loaded nanofiber mats were cross-linked with glutaraldehyde to modify the release profile of the drug.
## 3 Results
### Evaluation of Tracking Reliability
#### 3.3.3 Comparison with the Literature  
The goal of this section is to discuss the presented results in comparison to the approaches discussed in the literature. The main difference between the algorithms described in the literature and our approach is the flexibility of our method through the use of machine learning.  
The papers of [PERSON] et al. [12; 13] describe an energy based train vibration detection. According to our experience, this approach is not feasible for long stretches of monitored fiber with the recording device we used. The paper does not mention the validation data used for drawing the conclusions, nor does it mention a tracking algorithm. The accuracy of the proposed method with respect to positional  
Figure 5: **Top Left**: Subsampled signal through band energy calculation. **Top Right**: Train Detection result for a train on test site 2; the green boxes indicate coupled cable segments; the background without train shows increased noise. **Bottom**: False positive train track due to false positive vibration detections.  
accuracy is reported at 15 m. In comparison, we did not have a ground truth available with such a high accuracy.  
The paper of [PERSON] et al. [14] describes an algorithm which uses a relative variance for vibration detection. The validation data for the method are two trains recorded over 400 s. The paper does not mention any accuracy measurements for the proposed method, nor do the authors mention which tracking algorithm is used.  
The earlier papers of some of the authors of this contribution [15; 16] describe a similar algorithm with some important difference. The earlier versions of the algorithm used a filtering over time which introduced a delay. This has been changed in this contribution to Kalman filtering avoiding a time lag of the method. Furthermore, the edge detection has been refined to be more flexible, which is especially important for rail foot cables.  
We can conclude that this is, to our knowledge, the most extensive study done on railway monitoring using DAS. In comparison to the other literature, we used flexible methods that allow for efficient re-calibration for different track conditions.
# Systematic Literature Review of Challenges in Blockchain Scalability
## 8 RQ3: How Researchers Address the Scalability Issue in Blockchain?
### Other Potential Attempts to Address the Blockchain Scalability Issue  
Some other potential attempts to address blockchain scalability are briefly presented here:  
TrustChain [111] is designed for permissionless/public blockchains and the data structure is entirely tamper-proof and used by agents to store their transactional record. It enables the design of a separate immutable chain of temporally organized interactions with other agents. It is inherently in parallel that every agent creates its own genesis block. Concept Superlight [76] is mainly used in public blockchains and seeks to address the problem of scalability. It is not mandatory for all nodes to store the whole blockchain locally for verification of each transaction. So, the nodes in this framework may validate the transaction using their header. Block Summarization [78] decreases the additional storage for transferable transactions. This approach allows resource-constrained lightweight nodes to store blockchain shaped in such a way that transactions can be independently verified to eventually decrease full node dependency. FRChain [100] consensus model is mostly utilized in permissioned blockchain. It is immune to multiple nodes and blockchain network failures. For block propagation and block validation, FRChain utilizes mutual signing over multicast trees. Fast BFT [64] is a faster and scalable consensus protocol.  
The inventive idea of this protocol is a message grouping technique that uses hardware-based, protected execution environments (TEEs) and lightweight secret sharing. Satellite chains [96] affect the notion of satellite chains that can run many consensual protocols in private at the same time, thereby significantly increasing the scalability of the system's premises to meet industrial standards.
## 6 Case Study
The slumped construction period is a key attraction of modular buildings relying on a high-efficiency assembly involving vertical lifting of modules and installation of connections (Figure 5). To achieve this goal, adequate equipment and an experienced construction team act as a pivotal part in the assembly of modules. In this project, two tower cranes, 24 t of lifting capacity, were employed for vertical lifting of all 171 modules. Taking into account the C22 \"complexity of connection\", approximately 10 modules were installed per day. Although the contractor pointed out that strong wind and heavy rain impacted on crane operation, which delayed installation works. The total installation took less than five months, which is a substantially shorter construction time than traditional building. Figure 4: Module transport to construction site (photo by [PERSON]). Figure 3: Module manufacture at workshop (photo by [PERSON]). According to the case study, a holistic construction process of modular building was illustrated. Interviewees from all stakeholders scrutinised the identified constraints and highlighted some of them that occurred in this project. In addition, authors asked all stakeholders about potential solutions to these identified constraints that could facilitate widespread application of high-rise modular buildings in the future. Developers were more concerned with C2 \"lack of government support\", and C4 \"higher capital cost\". An interviewee responded, \"Considering the current economic scale (C16), the construction cost of modular building is higher than traditional construction. But, we believe the cost will be decreased dramatically in association with the development and upgrade of supply chain.\", the interviewee added, \"On the other hand, government support like policy, and financial incentive, of course, could lead the direction of modular construction\". Another point that should be noted in the interview is that the significance of BIM application in high-rise modular buildings has been realised. Designers claimed that utilising virtual design construction (VDC), 15 types of prefabricated unit were generated and assembled by BIM software. Various combinations of units can be presented directly to the developer, which eliminates impact of C12, \"poor design flexibility\", as well as C11 \"unable to freeze design early\". At last, seven types of units were selected to form this 18 storey hotel. From the perspectives of manufacturer and contractor, current BIM application in manufacture and assembly stages concentrates on tackling C22 \"complexity of connection\". Implementing BIM software, all details and accurate location can be indicated clearly in 3D and 4D, which enhances off-site production efficiency and reduces in-situ installation period.
# Tire Bubble Defect Detection Using Incremental Learning
###### Abstract  
Digital shearography is a technique that has recently been applied to material inspections that cannot be performed by the naked eyes, including the detection of air bubble defects in tires. Although digital shearography detects bubbles that are not visible to the naked eyes, the process of determining tire defects still relies on field operators, with inconsistent results depending on the experiences of the field operator personnel. New or different types of bubble defects that AI models have not previously recognized are often missed, resulting in an inadequate quality detection model. In this paper, we propose a bubble defect detection method based on an incremental YOLO architecture. The data for this research was provided by the largest tire manufacturer in Taiwan. In our research, we classify the defects into six distinct categories, pre-process the images to allow better detections of less-noticeable defects, increase the amount of training data used, and generate an initial training model with the YOLO framework. We also propose an incremental YOLO method using small-model training for previously unobserved defects to improve the model detection rate. We have observed detection accuracy and sensitivity of 98% and 90% in the experimental results, respectively. The methods proposed in this paper can assist tire manufacturers in achieving semi-automatic quality inspections and labor cost reductions.  
tire bubble defect detection; digital shearography; YOLO; deep learning