esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Particle Swarm Optimization and Experimental Verification
### Parameter Setting  
In order to enhance the speed and convergence accuracy of the algorithm and prevent falling into local optimal solutions, the parameters of the particle swarm algorithm are determined by integrating existing research with continuous experimental adjustments.  
1. The particle swarm is initialized with \(n=50\), the particle dimensionality \(D=3\), and the maximum number of iterations \(T=50\). Set the learning factors \(c_{1}=c_{2}=0.5\), highest weight \(\omega_{\max}=0.9\), least weight \(\omega_{\min}=0.4\), maximum velocity \(V_{\max}=1.5\), and minimum velocity \(V_{\min}=-1.5\).
2. Initialize the velocity \(v\) and position \(x\) for all particles in the population and evaluate the adaptation value. Identify the personal best position \(P\) and its corresponding value \(P_{best}\), as well as the overall best position \(g\) and optimal value \(g_{best}\).  
Calculate the inertia weight \(\omega\) and learning factors \(c_{1}\) and \(c_{2}\), refresh the velocity \(v\) and position \(x\), and perform boundary condition processing. Evaluate whether to update the particle's best location \(P\) and best value \(P_{best}\), as well as the particle swarm's best location \(g\) and best value \(g_{best}\).
4. Check if the stopping criteria are satisfied. If so, end the search process and output the best value. If not, continue the iterative optimization.
# Nondestructive Monitoring Techniques for Crack Detection and Localization in RC Elements
## 7 AE Measurements
### AE Source Location  
Due to the configuration of the experiment, the tensile surface is identified at the bottom of the beam cross sections. Therefore, crack initiation reasonably occurs close to the bottom surface. This is revealed by Figure 9, where the height of localized AE sources is depicted for different loading steps. The fitting line identifies the average height of localized events, exhibiting a slightly ascending trend. At the lowest load level, the average location is close to the bottom surface (\(-0.06\) m). As the cracks propagate towards the top, the AE sources naturally move to higher y-locations, reaching \(0.1\) m.  
The determined locations of AE events concerning different loading stages are depicted together with sensor positions in Figure 10. Location data are only available in 2D, along the beam's lateral axis due to the almost 2D sensor setup, and therefore, information regarding depth position of AE sources cannot be provided. Typically, AE source location is carried out along lines between detecting sensors, i.e., AE0, AE1, AE4 and AE5. Hence, location data do not exactly match the crack initiation from the bottom surface of the beam. One can see localized AE sources starting from the left and accumulating vertically towards the top surface, as the signature of coalescence of uncorrelated tensile cracks and the appearance of diagonal shear cracks. Red circles, representing localized events at the unloading stage, are probably due to friction between aggregates and reinforcing bars during reclosing of the cracks.
## 3 System Framework Overview
### Middleware Layer  
The middleware layer of the proposed system involves a context-aware middleware cloud which acts as a DSS for the entire model providing the required context-aware services and actions for its operation. This component adopts the Infrastructure as a Service (IaaS) properties of cloud computing and it is composed from several modules for acquiring, managing, and storing contextual data. Great importance is also given to the aspects of self-adaptability as well as security and privacy. A detailed description of the modules included into the context-aware middleware cloud, as illustrated earlier in Figure 2, is as follows.
## 3 Related Methods
### Reed-Solomon Error Correcting Code  
[PERSON]'s error correcting code [PERSON] and [PERSON] (1960) was developed by [PERSON] and [PERSON] at MIT Lincoln Laboratory in 1960. By adding \(t\) check symbols to the data, the RS code can detect any combination of up to \(t\) erroneous symbols, and correct up to \(t/2\) symbols or \(t\) known erasures. RS code is often used to correct burst errors on storage media, such as for CD-ROMs. For example, RAID6 extends RAID5 by using two RS parity blocks to recover from the failure of any two hard disk drives in an array of a minimum of four drives.  
RS code is suitable for applying to secret sharing, because the \(t\)-out-of-\(n\) property of a secret sharing scheme is equivalent to the loss of \((n-t)\) shares, with the positions of the missing shares known in advance. In other words, \((n-t)\) check symbols can be introduced at the point when the shares were created, e.g. use two check symbols to ensure that any three out of the five shares can be combined together to reconstruct the original data.  
A secret sharing scheme implemented using the RS code algorithm can be highly efficient, but unfortunately not very secure. A secure secret sharing scheme should distribute shares so that anyone with fewer than \(t\) shares has no extra information about the secret than someone with zero shares. For example consider the secret sharing scheme in which the secret phrase \"password\" is divided into the following shares (plus RS code shares):  
\"pa------\", \"-ss---\", \"----wo--\", \"----rd\"  
A person with 0 of these shares may only perhaps know that the password consists of eight letters. For this they would have to guess the password from 208 billion combinations (\(26^{8}\)). With one share, however, they would only have 308 million combinations (as to guess only the six letters--\(26^{6}\)), and so on as more shares became available. RS code is not a secure secret sharing scheme, because it is possible for a player with fewer than \(t\) secret-shares to partially identify some of the original data.
###### Abstract  
This paper describes a new method for the detection of moving objects from moving camera image sequences using an inertial measurement unit (IMU) sensor. Motion detection systems with vision sensors have become a global research subject recently. However, detecting moving objects from a moving camera is a difficult task because of egomotion. In the proposed method, the interesting points are extracted by a Harris detector, and the background and foreground are classified by epipolar geometry. In this procedure, an IMU sensor is used to calculate the initial fundamental matrix. After the feature point classification, a transformation matrix is obtained from matching background feature points. Image registration is then applied to the consecutive images, and a difference map is extracted to find the foreground region. Finally, a minimum bounding box is applied to mark the detected moving object. The proposed method is implemented and tested with numerous real-world driving videos, which show that it outperforms the previous work.  
moving camera; motion estimation; moving object detection; image processing Article Moving Object Detection from Moving Camera Image Sequences Using an Inertial Measurement Unit Sensor
# Services Sector Export in Europe
## 2 Literature Review
### Impact of the Innovation Economy and Modern Technologies on Service Export Growth  
Trade in intermediate services has been a lot more dynamic than trade in goods, as growth in trade in intermediate services is closely connected with the development of the knowledge-based economy [29]([PERSON] et al., 2018). Technological innovations increasingly make services exportable at low costs. The globalization of services provides new opportunities for the development of service exports for countries with different levels of economic development [5]. Furthermore, the export of services stimulates innovation due to the increase in return on investment [9].  
Notably, Information and Communication Technologies (ICT) has become one of the main driving factors in the development of the service sector, transforming traditional services, generating a wide range of new services, and changing the channels of their distribution [30]. Increasingly, services can be traded between sellers and buyers on the global level almost immediately via satellite networks [5]. The Internet and other network technology systems, such as mobile phones, big data and artificial intelligence, ensure technical changes in production methods and business processes, which promotes the export of technological services.  
According to the World Trade Organization [23], some groups of services, such as ICT and financial services, often surpass the average growth rates of service exports. Furthermore, [PERSON] et al. [5] note that services, such as telecommunications, computer and information services now constitute one of the most dynamically developing sectors ensuring growth in service exports.  
Service export share growth was promoted by the international outsourcing of services, which grew significantly as a result of the dramatic decrease in telecommunication costs, wider access to the Internet and ubiquitous use of computers [25]. Additionally, geographic distance causes considerably less trade costs online than offline [31].  
Summarizing the literature review in this subsection, we can notice that researchers agree that the growth in trade in intermediate services is closely connected with the development of the knowledge-based economy. In addition, technological innovations can reduce the prices of services and make a number of services exportable. The growth rate of exports of some services based on innovation exceeds the growth rates of exports of other services.
# Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU
## 4 Results
### The Running Time and Speedup of Form A in Standard Laplacian smoothing
#### 5.1.4 Analysis of the Improvement of Iterative Convergence Speed  
The iteration procedure dominates most of the running time. Therefore, accelerating the speed of iteration convergence is the core task to improve the efficiency of the algorithm. Data dependence is the main problem that needs to be solved. Using thread synchronization is an effective solution to this problem. However, because it is limited by the current GPU architecture, a block contains a maximum of 1024 threads; that is to say, the old solution only allows 1024 threads to participate in the calculation at the same time, which is less than the usual number of simultaneous calls by the GPU.  
As mentioned in Section 3.2.3, a new kernel with multiple blocks is designed to compute the smoothing process of the same set of nodes to make the iteration more efficient. Even though the data used in the iteration may be duplicate, the interleaving operations make each thread converge faster and precisely as well. First, the iteration times for multiple blocks are a great deal less than for a single one; see Figure 10. Moreover, the final result is close to or even equal to the true value (i.e., the error is less than 0.0001). In any case, as shown in Figure 11, this version runs 2\(\sim\)6 times faster.  
Figure 11: Speedup of multiple blocks over single block in different data layouts.  
Figure 10: Iteration times of single block and multiple blocks.
# Computing Idle Times in Fuzzy Flexible Job Shop Scheduling
## 3 Idle Times with Fuzzy Durations  
In a deterministic setting, given a schedule, \(\phi\), it is trivial to compute core idle time before an operation, \(o\): it suffices to subtract the completion time of its immediate predecessor in the resource \(RP_{\phi}(o)\) from the starting time of \(o\). Then, the core idle time for a resource \(r\), \(\text{CIT}_{\phi}(r)\), is obtained by adding the core idle time for all the operations assigned to the resource. Equivalently, \(\text{CIT}_{\phi}(r)\) can be obtained as the difference between the time span since the resource starts processing the first operation until the last operation is finished and the total time spent by the resource executing operations. Finally, the total core idle time, \(\text{TCT}_{\phi}\), is the sum of core idle times across all resources \(r\in\mathbf{R}\).  
Our goal will be to find a schedule minimising the total core idle time. However, extending these definitions to the fuzzy context is not trivial. The cause is the assumption of independence underlying the definition of the fuzzy subtraction, together with the fact that it is not the inverse of the sum (thus losing the equivalence between the two ways of defining the core idle time for a resource).  
In the sequel, we propose three different ways of computing the core idle time of a resource, \(r\in\mathbf{R}\), given a fuzzy schedule, \(\phi\), \(\widehat{\Gamma\Gamma}_{\phi}(r)\). In the first two cases, we start by proposing a means to compute the core idle time, \(\hat{l}_{\phi}(o)\), of every operation, \(o\), scheduled in the resource, \(r\), and we then obtain \(\widehat{\Gamma\Gamma}_{\phi}(r)\) as the sum of core idle times across all operations executed in \(r\). In the last case, we adopt a less granular approach and define \(\widehat{\Gamma\Gamma}_{\phi}(r)\) directly as the time span between the starting of the first operation in the resource and the completion of the last operation in the same resource, minus the time spent by the resource processing its operations.
## 4 Experimental Study
### Experimental Results and Analysis
#### 4.2.2 Analysis of the Repeatability of Test Data  
Repeatability reflects the degree of inconsistency in the characteristic curves obtained during multiple full-range tests by the sensor in the same direction. The closer the characteristic curves overlap under the same input conditions, the better the repeatability and the smaller the data error. As shown in Figure 10, the magnetic induction density \(B_{\mathrm{z}}\) corresponding to each force value during four loading/unloading cycles under different design loads was recorded, with the average and standard deviation calculated.  
From Figure 10, it can be observed that the standard deviation of the magnetic induction density \(B_{\mathrm{z}}\) collected under each design load was less than 1.5%, indicating a low degree of data dispersion and good stability of the monitoring technique. However, the larger standard deviation in certain working conditions may be attributed to improper instrument operation during the experiment. A comparison revealed that, in the loading range of 0-20 kN, the variation range of the magnetic induction density \(B_{\mathrm{z}}\) is larger than in the other loading ranges. This may be due to the nonlinear strain occurring in the steel strand during the tensile process. At the beginning of tensioning, the steel strand experiences linear strain, which then tends toward nonlinearity as the tensile force increases. Additionally, the probe and the permanent magnet may not have been perfectly aligned during the sensor's fabrication, contributing to the larger variation in the magnetic induction density \(B_{\mathrm{z}}\) observed during the initial stage of tensioning.  
Combining the detailed descriptions of various sensors in the Introduction with the research results of the sensor proposed in this paper, Table 5 compares them in terms of accuracy, reliability, and manufacturing cost.  
Figure 10: Distribution of magnetic induction density \(B_{\mathrm{z}}\) under different design loads.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 2 Related Work  
### Background on OMA LWM2M  
OMA LWM2M ([4, 5]) is an application layer communication protocol between a Server and a Client. It defines a set of REST-based interfaces and resources where each information made available by the Client is a Resource. This object model is easily extensible, and the Object and Resource registry is open to the industry.  
Resources are organized into Objects. Objects and Resources can have multiple instances, and attributes, i.e., metadata, can be attached to them. A Resource which is instantiated within an Object Instance is a resource which can either: (i) contain a value, or (ii) be used by the Server to trigger an action in the Client. The Resource can be identified by its path, expressed in the following format: ObjectID/ObjectInstanceID/ResourceID. A Client must support three LWM2M mandatory Objects: (i) Security Object (ID: 0), that contains keying material enabling access to a specified Server; (ii) Server (ID: 1), that containsinformation relating to the connection of a Server; (iii) Device (ID: 3), that contains device related information.  
Four interfaces are designed between the Client and the Server: (i) Bootstrap and (ii) Client Registration that are used to make the Client accessible from the Server, (iii) Device Management and Service Enablement that is used to read/write a Resource or an Object, or to execute an action on the Client, (iv) Information Reporting that enables asynchronous information delivery. The main operations defined by these interfaces are illustrated in Table 1.  
We refer the reader to [4; 5] for further details on LWM2M.
## 2 Proposed Method
### Pre-Processing
#### 2.1.2 Objective Analysis  
The above subjective observations are affected by environmental and psychological factors. In order to explain the reliability of the image enhancement method proposed in this paper more clearly, Equivalent Number of Looks (ENL) and Contrast Improvement Index (CII) [28] are used to assess enhanced images.  
The ENL and the CII represent the enhancement effect of image contrast, the definition is as follows:  
\[ENL=\frac{\mu}{\delta} \tag{3}\]  
\[CII=\frac{C_{1}}{C_{0}} \tag{4}\]  
Here, \(\mu\) is the statistical mean of the input image, \(\delta\) is the standard deviation of the input image, \(C_{1}\) is the contrast of the enhanced image, and \(C_{0}\) is the contrast of the original image. The ENL becomes smaller, the image contrast gets larger, while the overall image displays clearer. When \(CII>1\), it indicates that the image contrast is enhanced, which is larger, the enhancement of the image comes better. The comparison results are shown in Table 1.  
In Table 1, the pre-processing method combines the top-bottom hat transformation with the Gamma transformation improves the image contrast greatly.
## 2 Background
Radiance-based approaches are the most frequently used for the proximal sensing of F and include the Fraunhofer line discriminator (FLD) approach using two spectral bands [48; 49], FLD approaches based on three or four bands (i.e., 3 FLD, 4 FLD) [11; 50]), the improved FLD (iFLD) method [51], and the spectral fitting method (SFM) [52; 53]. A common aspect among all of these methods is that measurements of E and L inside and outside the atmospheric absorption band are combined to decouple F from L. The decoupling relies on assumptions of F and reflectance (R) inside the exploited absorption feature, while the assumptions applied to estimate F and R inside the absorption band basically allow for categorizing using retrieval approaches. The FLD and 3 FLD methods require 1 and 2 spectral measurements outside the absorption bands to estimate F and R inside the band. The iFLD approach uses a polynomial fit and some correction factors to estimate F and R inside the band using several spectral bands that are outside. The SFM method uses the full spectrum's information around the absorption band to decouple F and L by spectral curve fitting. The method relies on general mathematical functions that represent canopy R and F around and within spectral windows centered at oxygen absorption bands (for further discussion on the retrieval methods, see [39] (in review, this issue)). In vivo passive fluorescence detection from remote sensing measurements was initiated in the 1980s [54] and further developed in the 1990s. It has made significant progress since then, with currently more than 50 papers per year on the subject (according to the Web of Science database). Reliable F retrievals rely on precise and spectrally detailed L measurements in very narrow parts of the spectrum, while the F signal is small. This requires very accurate measurements (spectrally and radiometrically) of L (and E) and, in consequence, necessitates a common approach/protocol to be established for such measurements to enable reliable and consistent F retrievals.
# Effectiveness and Design of PBL-Based Project Approach for Non-Major University Computing Courses
## 2 Theoretical Background: Problem-Based Learning and Computing Education  
### Problem-Based Learning (PBL)  
Problem-Based Learning (PBL) was developed by [PERSON] and [PERSON] at McMaster University's medical school in Canada during the 1960s [10]. It was designed to address the limitations of traditional lecture-based education, which merely transmits knowledge without fostering meaningful application in real-world contexts. PBL emphasizes student-led exploration of problem situations, enhancing students' problem-solving abilities [11; 12].  
PBL also promotes collaboration and communication among learners, encouraging active participation and fostering creativity, critical thinking, and problem-solving skills. [PERSON] emphasized that instructors should act as facilitators rather than mere knowledge transmitters [13]. In this role, instructors support students in independently exploring problems and seeking solutions while providing guidance when necessary [4]. Thus, in PBL-based project courses, instructors must create an environment where students can collaborate effectively with peers.  
The learning process of PBL, as outlined by [PERSON] and [PERSON] [14], can be summarized as follows: 1. Learners assume roles relevant to the presented problem situation. 2. They encounter unstructured problems. 3. Learners identify what they already know and determine what additional knowledge is required to solve the problem. 4. They explore further and define the issues that need to be addressed. 5. Learners evaluate possible solutions and select the most appropriate one.  
The essence of PBL lies in discovering and defining unstructured problems and extending the relevant solutions. This study employs the PBL process outlined by [PERSON] and [PERSON] [14] to design computing classes, enabling students to collaborate with peers and deepen their understanding of code analysis and algorithm development.  
In addition, several studies have demonstrated the benefits of applying PBL in computing education. For instance, [PERSON] [6] developed a PBL-based computer instruction model focused on broadening learners' thinking processes. Similarly, [PERSON] [9] explored the effectiveness of PBL in programming education, emphasizing how PBL enables learners to structure and solve unstructured problems independently, highlighting its educational value. Furthermore, [PERSON] [15] conducted research on the practical application of PBL in introductory computer engineering courses, confirming that PBL-supported instruction positively impacts the development of creativity and problem-solving skills required for computing professionals. This body of research collectively underscores the educational significance of PBL in fostering creativity, collaboration, and problem-solving capabilities, which are critical competencies for learners in computing disciplines and align with the demands of interdisciplinary and real-world problem-solving contexts.