esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Generic Context Aware Cross-Layer Framework
### Ontology-Based AmI Context Model
#### 4.3.2 Scenario-Based Customization
A sensor node can use the following first-order logic expression to deduce its data priority context after receiving the event context information generated by the intelligent agents:  
_(eventLocation (close to the sensor) \(\
u\)eventRadius (sensor within the event radius)) \(\land\) (personalAge (high vulnerability state) \(\
u\)personalDisability (high vulnerability state)) \(\land\) activityInput (sensor's generated data) \(\vartriangle\)SensorData (Priority, High)_  
This expression is used by each individual node to determine the priority context of its sensor data according to the attributes of the event (either the sensor node is located close to the occurrence of the event or the node is within the event radius), personal attributes of the user (age- and disability-associated vulnerability), and activity attributes of the user (input data required for inferring the activity). For the scenario presented in Section 3, a piece of sensor data is assigned to high priority when the following three characteristics are met:  
* The sensor generating the data is close to or within the radius of an event;
* The age or disability attribute of the user related to/affected by the event is in high vulnerability state;
* The sensor data is required for inferring the activity of the user related to/affected by the event.
## 4 Discussion  
Road network administrators, particularly those from low- and middle-income countries, are hesitant to develop or implement advanced pavement performance models complemented by basic empirical and probabilistic solutions. Of course, there is no one-size-fits-all or \"one best approach\" for pavement management systems, but considering that these models are generalized for a particular type or group of pavements and do not take into account pavement structure and paving material properties, they are not usable for decision making in terms of pavement comparison within that type or group of pavements. In addition, as shown in this article, false results can be obtained when using generalized empirical pavement performance models.  
Road network administrators need to stop defining themselves by the assets that they manage, but rather by the service that they deliver and the customer's needs and expectations. These needs and expectations center around ride quality and safety. Failing to predict pavement performance during the pavement life prevents them from addressing those needs and expectations and from implementing plans to meet them in the future. As seen in the case study presented in this article, this miscalculation of socio-economic value changed the project payback period by 5 years, the net present value by EUR 67,701.34, and the internal rate of return by 3.337%. This makes a project initially identified as safe and very profitable actually very risky and barely profitable.  
The capability of deriving mechanistic-empirical deterioration models is limited by the fact that simulating seasons in APT is very difficult and expensive. However, the comparison of individual functions can also be biased by the choice of functions. An exponential function can have a much more generalized form than a polynomial one. There may also be a bias in the comparison between the APT model and the LTPPM model concerning the total traffic load. In LTPPM, a specific traffic flow is applied, while in APT, the load is applied at the exact design axle size. Another interesting experiment could be the application of the load and its progression in the pavement design for different tire types or different inflation levels. Load accumulation and load fluctuation in the footprint of heavy vehicles may also have an impact. Combining LTPPM with APT partially addresses this limitation by introducing data influenced by both traffic load and climatic conditions. In relation to LTPPM, it is also possible to outline the direction of further research that should be directed toward relating the course of degradation of the individual parameters monitored to the specific effects of external climatic factors on the individual types of newly applied asphalt-bound mixtures (using self-healing materials or nanomaterials, for example, as presented in studies [43; 44; 45; 46], or secondary raw materials in the production of asphalt-bound mixtures, for example, as presented in [47]).
# A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
## 6 Task Allocation for DRL Based on Greedy Thinking
### Design Details  
In order to solve the task offloading problem by using DRL, this paper modeled the task assignment problem as a Markov decision process (MDP), and the specific description of this process is as follows.  
State space: When scheduling task \(\mathbf{v_{n}}\), the current state of the system depends on the scheduling results of the tasks preceding \(\mathbf{v_{n}}\); therefore, the state space is defined as a combination of the directed acyclic graph (DAG) information and a partial offloading plan, that is, \(\mathbf{S}=\{\mathbf{G},\mathbf{A_{1;i}}\}\), where G is the embedding vector of the DAG (including the index number of task \(\mathbf{v_{n}}\)), the estimated transmission and execution cost of the task, and the task numbers of the direct predecessor and direct successor. Furthermore, in this paper, we set the upper limit of the number of tasks of the direct predecessor and the direct successor to six, and \(\mathbf{A_{1;i}}\) represents the sequence of decisions about task assignment from \(\mathbf{v_{1}}\) to \(\mathbf{v_{i}}\).  
Action space: After a user passes over the dependent task to the edge server (ES) \(j\), the edge server cooperates to execute, and this is based on the resources purchased by the user. Therefore, the action space can be defined as \(\mathbf{A}=\{1,2,\ldots,j,\ldots,M\}\).  
Reward function: Since the goal of this paper is to minimize the task completion time, in order to reach this goal, this paper defines the learned reward function as the time increment saved. Therefore, the offloading sub-task \(\mathbf{v_{n}}\) reward function is defined as follows:  
\[r_{n}=\frac{\bar{T}_{j}-\Delta T_{m}^{n}}{\bar{T}_{j}}, \tag{39}\]  
where \(T_{j}\) represents the average execution time of the sub-task on ES \(j\), and \(\Delta T_{m}^{n}=T_{A_{1:m}}^{m}-T_{A_{1:m-1}}^{m}\) represents the actual time spent executing the task.  
Since the purchased resources are limited, a penalty factor is set for situations where the usage exceeds the purchased resources. By taking the dependent task on ES \(j\) as an example, a counting function \(C(j)\) is defined under the final decision on task assignment \(A_{1:N}\). This then provides the count of action j in decision set \(A_{1:N}\). This function can be represented as \(\bar{C}(j)=|\{\mathbf{A_{1:N}}\;in\;x:x=j\}|\). Then, reward R, for executing the current dependent task, can be written as follows:  
\[R=\left\{\begin{array}{ll}\sum\limits_{n=1}^{N}r_{n}&\quad\text{if $\text{C}(j)f_{j}^{n}\leq f_{j}$ and $\text{C}(j)r_{j}^{n}\leq r_{j}$}\\ \sum\limits_{n=1}^{N}(r_{n}-\phi)&\quad\text{otherwise}\end{array}\right., \tag{40}\]  
where \(\phi\) is the penalty factor.
# A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load
###### Abstract
The results of two numerical examples show that for the solution of the statistical model updating equation, the accuracy of the proposed method agrees with that of the Monte Carlo simulation method very well. The static responses obtained by the updated finite element model coincide with the measured results very well. Finally, static load tests of a concrete beam are conducted to testify the effectiveness of the proposed method.
# Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
## 3 Types of Chiral Metasurfaces
### Tunable Chiral Metasurfaces
The CD response and linear to the CPL conversion of the chiral metasurface polarization were dynamically tuned by regulating the Fermi energy level and folding angle of the G-Mori structure. The planar metasurface (0 = 0\({}^{\circ}\)) did not exhibit intrinsic chirality, but when the planar metasurface was folded mechanically into a 3D arrangement in the absence of mirror symmetry, an intrinsic chiroptical response was generated, as demonstrated in Figure 6k. The amplitudes of the CD spectra increase considerably as 0 changes from 0\({}^{\circ}\) to 60\({}^{\circ}\), reaching the maximum at 60\({}^{\circ}\), which is attributed to a steadily improving impedance match among the metasurface and free space, as well as improved geometrical chirality (Figure 6k). The metasurface exhibited a prominent chiroptical response with an almost blocked LCP (t\({}_{\rm LL}\) = 0.014) and transmitted RCP (t\({}_{\rm RR}\) = 0.966), as shown in Figure 6l. The enhanced interproximal coupling effect between neighboring USRRs was responsible for the high intrinsic CD of 0.93, which can be tuned by reverse and continuously regulating the Fermi energy level and folding angle of G-Mori. Because of their capability to control EM waves, reconfigurable chiral metasurfaces are excellent candidates for reconfigurable circular polarizers, dynamic beam shaping, ultrasensitive sensors, reconfigurable chiral sensing system, and polarization modulators.
## 2 Theoretical Analysis
### Obscuration  
In the ASD system, obscuration is one of the most important parameters in supplying the signal for the fire alarm control system. It is a unit of measurement that specifies the smoke detector sensitivity. If there is smoke, the light intensity will decrease. The obscuration is expressed as the percent absorption per unit length. The higher the concentration of smoke, the higher the obscuration level. When a fire occurs, the smoke moves from the fire source through the pipe hole network to the ASD system. If the obscuration in the ASD is over the set value, the fire alarm sounds. In the aspirating smoke detection system, the value of obscuration can be calculated by the following equation [28]:  
\[Obscuration=\left(1-exp\left(-K_{m}\frac{\sum_{i=1}^{N}\rho_{s,i}(t-t_{d,i}) \dot{m_{i}}}{\sum_{i=1}^{N}\dot{m_{i}}}\right)\right)\times 100\%/m \tag{8}\]  
where \(\dot{m_{i}}\) is the mass flow rate at sampling location \(i\), \(\rho_{s,i}\) is the soot density at sampling location \(i\), \(t_{d,i}\) is the prior (delay) at the current time \(t\), and \(K_{m}\) is the mass extinction coefficient associated with visible light.
# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 2 Literature Review Methodology and State-of-the-Art
### State-of-the-Art: A Technology Review from 2005 to 2024
#### 2.3.4 X-Rays  
Similar to image processing, AI can segment X-ray images by identifying objects depicted in them. This is most often used to recognise parts of the human body [109; 110]. The main application of AI in X-ray processing is disease detection. For radiologists, it can be helpful in detecting pneumonia [111], detecting and staging breast cancer [110], or generally helping to detect 14 different diseases [112]. Most of them are concentrated around the chest [109; 110; 111; 112; 113; 114; 115].  
Automated analysis of chest radiograph images can range from detecting whether a given patient is healthy or sick [113; 115] to detecting a specific infection. Due to the recent COVID-19 pandemic, the main focus is on the detection of this disease [113; 114; 115; 116; 117]. Due to its significant similarity with the X-ray image, the second most frequently studied disease for AI recognition is pneumonia [113; 114; 115; 118; 111]. These investigations allow for better differentiation of the pathogens mentioned in a given case [113; 114; 115]. Another disease recognised by X-ray using AI is breast cancer [110]. This method allows for not only its detection but also classification of the stage of advancement.  
When analysing the type of solution, it is worth noting that the predominant number of cases use Convolutional Neural Networks (CNNs) [109; 112; 113; 114; 115; 116; 117]. In one case, only Generative Adversarial Networks (GAN) were used as a complement to the convolutional network [116]. In implementation, the Keras framework was used [112].
# Marine Internet for Internetworking in Oceans: A Tutorial
###### Abstract  
Ever-increasing human activities in oceans require handy, reliable and cost-effective high-speed network access similar to terrestrial Internet services to be available on and under water. However, terrestrial Internet cannot be extended to oceans seamlessly due to huge differences between terrestrial and oceanic environments, while satellite services are still very expensive especially for ordinary users with communication quality susceptible to weather conditions, and cannot cover underwater networks either. Therefore, marine Internet, proposed about six years ago, is the earliest scheme that tries to systematically address the internetworking issue in oceans and still in its infancy stage. This tutorial aims to introduce the principle, architecture and applications of marine Internet, along with discussion on oceanic environments for communication, currently available and under developing communication systems in oceans, as well as challenging issues necessary for further studies to foster the development of marine Internet.  
Internet; marine Internet; internetworking in oceans; Internet of things in oceans and underwater network interconnecting 1
# A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids
## 3 Transformer Health Assessment Techniques
### Health Index Calculation  
Health index (HI) calculation is a useful technique, it is the most basic method that was used to create maintenance strategies for transformers [32]. This method uses the representative indexes of the transformer's operation and statement to convert them into a quantitative index and evaluate the general condition of the transformer. The structure of the health index calculation method is shown in Figure 2.  
In [32; 33], a health index calculation method is applied to assess the distribution transformer conditions comprehensively. The statement of the transformer is classified in a range from \"perfect health\" to \"very poor condition\". The overall health index is presented in the following equation:  
\[\mathrm{HI}=\frac{\sum\limits_{\mathrm{ci}=1}^{n}\left(S_{\mathrm{PI}}.W_{ \mathrm{PI}}\right)}{S_{\mathrm{max}}\sum\limits_{\mathrm{ci}=1}^{n}\left(W_{ \mathrm{PI}}\right)} \tag{1}\]  
where:* HI is the health index metric;
* \(\text{S}_{\text{Pi}}\) is the score of each assessment condition that is identified based on the measured data
* \(\text{S}_{\text{max}}\) is the maximum score of assessment condition
* \(\text{W}_{\text{Pi}}\) is the weight of each assessment condition;
* n is the number of the assessment condition.  
The method was tested on a 3-phase 15 kVA distribution transformer. The monitored components are dissolved gases, oil, and furan. The results showed that this method could only diagnose the general conditions of transformers. It cannot indicate exactly the type of possible faults. To identify the failure components, additional analysis is required.  
In [34], the data collection criterion was improved to estimate the transformer's health. This paper pointed out that the conventional health index calculation usually requires many investigation parameters that could raise the testing and operating costs. The paper proposed an improved health index table that requires only 15 testing parameters instead of 24 testing parameters in the conventional health index calculation. The new health index table was carried out on thirteen testing transformers. The achieved results were compared with the conventional health index calculation and only have 7% different. This will help the transformer evaluation process to be implemented faster and easier to apply in practical works. The drawback of the health index calculation method is that it requires many parameters to evaluate the transformer's conditions, therefore, the monitoring may be costly. The diagnostic system only reflects the preferences of the human-expert in interpreting results because the set of rules derives from heuristic experience. In this way, it is difficult to translate them into mathematical formulas.
# A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
## 4 Challenges Still Exist in the Field of Clustering Algorithms
### Data Object Representation  
It is also difficult for clustering algorithms to accurately describe data objects. Inappropriate data object representation is a problem. Additionally, there is a disparity in the representation of data items between domains. Data objects may be represented in a number of different ways, with some being represented as feature vectors and others as graphs with an associated concept of object similarity [9]. Distinctions in data object representation between domains of use provide a fruitful field for study. Finding an effective data representation in clustering operations is important since it improves the efficiency of clustering algorithms. By highlighting these pockets of concentrated data, the clustering technique may be made more robust and used on a larger scale. Locating sections of data that can be compressed, regions that can remain in main memory without swapping, and regions that can be ignored due to noise or lack of relevance to the results of a clustering procedure are all helpful.
# In Search of the Densest Subgraph
## 3 Measures of Graph Density and Associated Results
### Polynomial-Time Solvable Cases
#### 3.1.7. Dense Subgraph with Sparse Cut  
[PERSON] and [PERSON] [15] defined a density measure, which aims at finding a subgraph \(S\), such that \(S\) is dense internally, but it is sparsely connected to the rest of the graph. In this sense, it kind of \"bumps out\" of the graph. Therefore, it may better capture a community in a social network, than an approach that focuses purely on the internal density.  
To implement the objective, they take two types of edges into account: internal and cut edges. The internal edges are the ones within the subgraph \(S\), while the cut edges are those that have precisely one endpoint in \(S\). With non-negative edge weights, let \(w_{in}(S)\) be the total weight of internal edges, and \(w_{cut}(S)\) be the total weight of cut edges. With an arbitrary non-negative parameter \(\alpha\), they defined the following density measure:  
\[f_{\alpha}(S)=\frac{w_{in}(S)-\alpha w_{cut}(S)}{|V(S)|}\,.\]  
The role of the parameter \(\alpha\) is to allow controlling the relative importance of the two aspects: internal density vs. sparse cut to the rest of the graph.  
The main result of [PERSON] and [PERSON] (1998) is that a subgraph \(S\) that maximizes \(f_{\alpha}(S)\) can be found in polynomial time for any \(\alpha\). It is particularly interesting that some closely related problems are **NP**-hard. An example is the _sparsest cut_ problem, which aims at minimizing the value of \(w_{cut}(S)/|V(S)|\) for \(|V(S)|\leq|V(G)|/2\). Note that, if \(\alpha\) is so large that \(w_{in}(S)\ll\alpha w_{cut}(S)\), then, with the maximizing \(S\), the value of \(-f_{\alpha}(S)/\alpha\) is close to the sparsest cut value.  
The authors also presented a nearly linear time approximation algorithm for the problem.
## 4 Experiment
### Location Estimation by Existing Methods
#### 4.2.3 Location Estimation by Proposed Method  
The proposed system first estimates the position using a k-NN-based position fingerprinting method and then corrects the estimated position using a particle filter. Figure 10 shows the particle distribution and the variation in the coordinate positions estimated using k-NN for each minute.  
The proposed A and B use the likelihood functions A and B, respectively. Figure 11 shows the error distribution in the room. It shows that errors are higher near the walls and obstacles because the RSSI values are affected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SVM (RBF)** & **SVM (Polynomial)** \\ \hline Minimum error (m) & 0.77 & 0.32 \\ Maximum error (m) & 3.38 & 4.56 \\ Average error (m) & 2.18 & 2.56 \\ Std Dev (m) & 0.31 & 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimation errors of SVM-based method.  
Figure 9: Errors vary with the value of \(k\).
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 3 Results
### U-Net Classification  
The model was applied to the Tully River catchment section of the aerial imagery that was excluded from the training. It took approximately two hours to create the output rasters and an additional 2.5 h to threshold, filter, and merge the classification tiles to a single polygon feature class. The result was the model-inferred extent of banana plantations, with a probability of 90% or greater.  
Figure 6 shows examples of the U-Net classification of banana plantations in the Tully River catchment. In all examples the U-Net classification matches the validation data except in Figure 6(4c) where the U-Net classification was identified a new banana plantation that was missed in the validation dataset because of the young age of the plants and low canopy cover resulting in this area not being identified as a banana plantation by the human operator.  
Figure 6: Examples (1–4) show correctly classified areas of banana plantations for the U-Net model. The left column (**a**) shows the 3-band true-color aerial photography. The middle column (**b**) shows the validation data and the right column (**c**) shows the output U-Net model classification. Note in Figure 6(4c), the U-Net classification identified a new plantation that was missed in the validation dataset.  
Figure 7 shows areas where the U-Net classification did not perform well. Figure 7(1,2) show a papaya plantation and sugarcane crop respectively which the U-Net has incorrectly classified both as banana plantations. Figure 7(3) shows an area of misclassification possibly related to the effects of shadow on the banana plantation, and Figure 7(4) shows the U-Net classification not correctly classifying blurred areas of the aerial photography.  
Figure 7: Examples (1–4) show incorrectly classified areas of banana plantations for the U-Net classification. The left column (**a**) shows the three-band true-color aerial photography. The middle column (**b**) shows the validation data and the right column (**c**) shows the output U-Net classification.
## 4 System Design
### Overview  
Figure 6 shows the overall structure of applying the DQN-based network routing algorithm to the erasure coding network topology environment proposed in this paper. The algorithm utilizes SDN controllers, a network architecture approach that can intelligently and centrally control or program networks using software applications. Therefore, it can collect relevant data from the erasure coding network topology environment.  
Data required when configuring network routing paths using dynamically changed network bandwidth and failed node and switch information are collected and stored in the database at regular time intervals. The stored data correspond to the input layer parameters of the neural network model in a DQN-based network routing algorithm.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 5 Performance Evaluation
### Comparison with Existing Algorithms  
This section presents the comparison of our proposal to the existing ones. Following the previous observation, we set the values of \(\alpha\) and \(\gamma\) to 0.5 and 0.4, respectively.