esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 3 Design of Data Naming Mechanism  
### Icn  
ICN is an information-centric network. Data objects are independent of location, application, storage, and transportation, allowing for ubiquitous intranet caching and routing. Thus, ICN aims to make the current complex network architecture simpler and  
Figure 8: ISL has the longest communication distance between adjacent orbital planes.  
Figure 7: ISL with the closest communication distance between adjacent orbital planes.  
more versatile, and network elements are no longer servers, routers, terminals, etc. The ICN networking is based on the named content and is a reception-driven model, which is considered to be the future development direction of the network [48]. ICN provides access to named data as a network service, and the network can serve local requests for named data, which means that network nodes can receive requests for named data and perform actions as required (e.g., forward the request to the appropriate next hop). Therefore, each network node on the path can execute forwarding decisions, cache objects, etc., and the network can forward requests along the best path.  
As an implementation of ICN, NDN is based on a request-response model, data naming uses a hierarchical name structure, and naming is based on the content itself. NDN consists of two parts, a requester and a content provider, and communicates through interest packets and data packets. The data retrieval model follows the principle of interest and data flow balance. If the requester requests certain content, an interest packet with the corresponding content name is generated, and it is forwarded through the network to the node where the content is stored. After receiving the interest packet, the node returns a data packet with the corresponding name along the reverse path.  
For IP packets, the core field is the address. For ICN packets, there are two types of packets, including both interest and packets of data, which do not contain an address. The core field of ICN packets is the data name. Therefore, data naming methods have an impact on overall network performance. Naming data content is a key concept. Generally, the ICN name neither represents a network node nor an interface but represents a network node independent of its location, which is suitable for content-oriented principles in IoT applications [49]. This result occurs because the ICN can reduce the data transmission delay and data publisher load through simple data access [13], and its marked advantages in fast and efficient data transmission and reliability make it a promising network mode in IoT applications. Therefore, in the IoT architecture of the mega constellation, the use of ICN ideas and an appropriate content naming mode will improve data transmission efficiency and network performance.
## 4 Experimental Results and Analysis
### Parameter Selection  
The parameter \(q\), which reflects the degree of non-extension in the system, plays a significant role in image segmentation and warrants discussion. Typically, the selection of \(q\) depends on the characteristics of the image being segmented. However, there is no explicit relationship between the value of \(q\) and specific image categories. When \(q>1\), it represents stronger interdependence or collective behavior among the system's components. Consequently, in the following experiments, \(q\) is treated as a manually tuned hyperparameter, with a value of 4 assigned for the BrainWeb dataset and 5 for the MRBrainS13 dataset. After experimental validation Figure 11, these values yielded the best results.
## 3 System Model
The UAV-MEC servers hover over a target area in an elliptical pattern while providing services to the connected MDs. In particular, to prevent trajectories from colliding, a careful deployment plan is essential. We choose each UAV-MEC server's initial center position for the ellipsoidal trajectory from the cluster center. Thus, a specific sub-area within the total coverage region is allocated to each UAV, which helps in providing MEC services to the ground MDs located in that specific sub-area in an efficient manner. Additionally, each MD can establish a connection with one UAV-MEC server at a time, to offload its task request. Then, one can have  
\[A_{ij}\in\{0,1\},\quad\forall i\in\mathbb{M},\,\forall j\in N, \tag{1}\]  
where \(A_{ij}=1\) implies the \(i\)th MD is associated with the \(j\)th UAV; otherwise, \(A_{ij}=0\).
## 3 Graph Neural Networks for Next-Generation Networks
### Link Scheduling  
In access networks, link scheduling involves allocating and managing limited bandwidth resources among multiple users or devices. The goal is to ensure fair and optimal distribution of bandwidth, taking into account individual requirements and the network's capacity. Efficient link scheduling algorithms aim to minimize congestion, reduce latency, maximize throughput, and maintain fairness among users.  
In general, link scheduling for device-to-device (D2D) communications is formulated as an NP-hard non-convex combinatorial problem. Traditional methods predominantly rely on mathematical optimization techniques, often requiring accurate CSI, which can be resource-intensive to obtain. A recent alternative involves the application of GNNs to address this issue. In [61], a graph embedding-based method is introduced to achieve link scheduling in D2D communications, eliminating the need for CSI. This method consists of constructing a fully connected directed graph for the D2D network where each D2D pair represents a node and interference links are edges. Input node features and interference links are computed based on spatial distances between devices in each D2D pair and distances between each pair, respectively. This is followed by a deep learning architecture to capture structure-aware node embeddings. Finally, the link scheduling problem is formulated into a binary classification problem to determine whether a D2D link should be deactivated. Therefore, given extracted node embeddings, a multi-layer classifier can generate a link scheduling strategy. Extensive simulation demonstrates that the proposed method performs near-optimally compared to state-of-the-art methods and requires only hundreds of training network layouts. Furthermore, it proves to be competitive in terms of scalability and generalizability to more complex scenarios.  
In wireless multi-hop networks, delay holds significant importance for various applications. However, existing max-weight scheduling algorithms focus on instantaneous optimality and may not perform well in delay-oriented scheduling scenarios. To address this issue, [PERSON] et al. [62] propose a delay-oriented distributed scheduler based on GCNs. The proposed scheduler employs a GCN model to generate node embeddings, capturing both the network topology and multi-step lookahead backlogs. By considering the relationship between current backlogs and the schedule of the previous time slot, the scheduler can make more informed scheduling decisions. In wireless networks that are small to medium-sized and exhibit heterogeneous transmit power, particularly with central links having numerous interfering neighbors, the proposed distributed scheduler surpasses myopic schedulers that rely on greedy and instantaneously optimal maximum weighted independent set solvers. The solution showcases strong generalizability across various graph models while introducing minimal communication complexity overhead.
# Study of the Direct Metal Deposition of AA2024 by ElectroSpark for Coating and Reparation Scopes
## 3 Results and Discussion
### Analysis of Defects  
Figure 13 presents the percentage (%) of defectiveness in the deposits cross-section as a function of spark pulse-energy (_Es_) for three values of frequency. The defectiveness levels induced by depositions are considered acceptable, however at lower energy level all the deposits display a greater percentage of defectiveness. The results indicate that the increasing _Es_ (increasing the voltage and decreasing/keeping constant the capacitance) decreases the percentage defectiveness for all three frequencies sets. It is believed that the percentage defectiveness decreases, since a higher _Es_ will produce a higher volume of the molten splat and a higher thickness splat [2,37]. In other words, by increasing the _Es_ the splat becomes larger [38] and the space between the individual splat reduces, leading to lower level of voids. Moreover, with each successive pass, a higher energy allows the re-melting of a greater amount of the deposited material, leading to elimination of the surface roughness (micro-voids) and undulation (peak and valley) previously created [23].  
Figure 11: Micro-hardness as a function of the distance from substrate/deposit interface for the different parameters adopted: (a) V1, C2, 10 L/min (_Es_ = 0.5 J); (b) V2, C1, 10 L/min (_Es_ = 0.9 J) and (c) V2, C2, 10 L/min (_Es_ = 1.125 J).  
Figure 12: Effect of spark pulse-energy (_Es_) on micro-hardness for three levels of frequency (F1 < F2 < F3).  
Particularly, the voltage value plays a significant role on the percentage defectiveness of the deposit when compared to the other process parameters set. In fact, if the voltage value is increased from V1 to V2 (compare 0.5 J vs. 1.125 J in Figure 13), the level of defectiveness is reduced; while, if the voltage is kept constant (0.9 J vs. 1.125 J in Figure 13), the level of defectiveness does not significantly change. The analysis of the distribution of the defect area observed in the deposits produced at 0.5, 0.9 and 1.125 J is shown in Figure 14. The deposits (Figure 14) are characterized by high percentage of defects having small area. Furthermore, the defects both of small and large areas are reduced when the energy (_Es_) increase due to the voltage increment (Figure 14a,b and Figure 14a,c). The defects areas distributions do not change significantly with capacitance (Figure 14b,c).
## 3 Results
### Challenges Associated with CPs'Services
#### 3.2.5 Provision of Medication Advice  
The staff of CPs in LMICs often provide limited medication advice and counseling to drug purchasers. Studies have shown that only a small percentage, ranging from 2.5% to 70%, of medicine purchasers receive advice on the proper use of medicines. Patients often receive limited information regarding the appropriate dosage, potential drug or food interactions, and possible sideeffects of medications when interacting with pharmacy staff in LMICs [86]. Counseling services related to sexually transmitted diseases (STDs) and contraceptive medicines are often limited in LMICs. Patients seeking information and guidance regarding STD prevention, treatment, and the use of contraceptive methods may not receive the comprehensive counseling they need [126]. However, dietary instructions are provided to only a minority of patients in LMICs. Pharmacies often do not prioritize or provide sufficient guidance on dietary considerations when dispensing medications [127]. Thus, the staff of CPs in LMICs often neglect the well-being of patients and fail to prioritize comprehensive health services. [86]. In some areas, pharmacies in LMICs often fail to provide contact details of consultants or referral materials for diseases that require specialized care. This can be a significant limitation for patients who may need further evaluation or treatment beyond what can be offered at the pharmacy level [124; 128].  
The overall performance of community pharmacies (CPs) in LMICs regarding medication advice and patient counseling falls below the expected standards.  
Many crucial aspects of counseling are still overlooked and not given due consideration by pharmacy staff. The handling of patients, whether with or without a prescription, does not show significant differences. There are six major stages involved in patient handling: requesting medication, filling prescriptions, taking patients'histories, referring to physicians, dispensing medication, and providing advice to patients. In these stages, there are gaps and deficiencies in the level of patient care and counseling provided by CPs.
# MODIS Retrieval of Aerosol Optical Depth over Turboid Coastal Water
###### Abstract  
We present a new approach to retrieve Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently does not conduct AOD retrieval in shallow waters that have visible sediments or sea-floor (i.e., Class 2 waters). Over the global coastal water regions in cloud-free conditions, coastal screening leads to \(\sim\)20% unavailability of AOD retrievals. Here, we refine the MODIS DT algorithm by considering that water-leaving radiance at 2.1 um to be negligible regardless of water turbidity, and therefore the 2.1 um reflectance at the top of the atmosphere is sensitive to both change of fine-mode and coarse-mode AODs. By assuming that the aerosol single scattering properties over coastal turbid water are similar to those over the adjacent open-ocean pixels, the new algorithm can derive AOD over these shallow waters. The test algorithm yields \(\sim\)18% more MODIS-AERONET collocated pairs for six AERONET stations in the coastal water regions. Furthermore, comparison of the new retrieval with these AERONET observations show that the new AOD retrievals have equivalent or better accuracy than those retrieved by the MODIS operational algorithm's over coastal land and non-turboid coastal water product. Combining the new retrievals with the existing MODIS operational retrievals yields an overall improvement of AOD over those coastal water regions. Most importantly, this refinement extends the spatial and temporal coverage of MODIS AOD retrievals over the coastal regions where 60% of human population resides. This expanded coverage is crucial for better understanding of impact of anthropogenic aerosol particles on coastal air quality and climate.  
AOD; coastal water; MODIS; retrieval Article
## 2 Bus Impedance Power Flow Method
### Bus Impedance Matrix  
Figure 1 shows a power system network with \(n\) nodes. The \(n\times n\) impedance matrix (\(Z_{bus}\)) can represent the system as driving-point impedances (diagonal elements) and transfer impedances (off-diagonal elements). This study uses the well-known four rules that build the \(Z_{bus}\) matrix [39].
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 8 Conclusions and Future Research
 \tag{13}\]  
Corrupted receptions (to any destination) can be calculated by summing external corruption transmission events:  
\[P_{\text{rx},e}^{(j)}=\sum_{i=1,\ i\
eq j}^{S}P_{\text{tx},e}^{(i)} \tag{14}\] \[J_{\text{rx},e}^{(j)}=\rho_{\text{rx}}^{(j)}\cdot\overline{T}_{ \text{data}|c}+\rho_{\text{idle}}^{(j)}\cdot\left(EIFS+\delta\right) \tag{15}\]  
Finally, the event on hearing collisions (to any destination) can be calculated as follows:  
\[P_{\text{rx},e}^{(j)}=1-P_{i}-P_{\text{tx},e}^{(j)}-\sum_{i=1}^{S}P_{s}^{(i)} \tag{16}\] \[J_{\text{rx},e}^{(j)}=\rho_{\text{rx}}^{(j)}\cdot\overline{T}_{ \text{data}|e}+\rho_{\text{idle}}^{(j)}\cdot\left(EIFS+\delta\right) \tag{17}\]  
The previous expressions rely on transmission and reception times, which depend on the data bit-rate and the operation mode (individual VoIP channels or broadcast):  
* One individual VoIP bi-directional channel per station: \[\begin{cases}T_{\text{tx},\text{data}}^{(j)}=\\ \text  
In the previous equations \(T^{(j)}_{\text{data}}\) and \(T^{(j)}_{\text{ack}}\) represent the duration of the reception/transmission of a data packet or ACK at station \(j\); \(\overline{T}_{\text{data}|c}\) and \(\overline{T}_{\text{data}|e}\) and \(\overline{T}_{\text{data}|s}\) account for the average duration of a collided, erroneous or successful transmission, respectively.  
\[\overline{T}_{\text{data}|s} =\frac{1}{\sum_{j=1}^{S}P^{(j)}_{\text{tx},s}}\cdot\sum_{j=1}^{S} P^{(j)}_{\text{tx},s}\ T^{(j)}_{\text{tx},\text{data}} \tag{11}\] \[\overline{T}_{\text{data}|e} =\frac{1}{\sum_{j=1}^{S}P^{(j)}_{\text{tx},e}}\cdot\sum_{j=1}^{S} P^{(j)}_{\text{tx},e}\ T^{(j)}_{\text{tx},\text{data}} \tag{12}\]  
The expression of \(\overline{T}_{\text{data}|c}\) is more complex as, if a collision between two stations ocurrs, it must preserve the longest duration collision time (the slowest stations negatively affect to the fastest ones). To capture this effect we take the terminology from [61], where \(d\in\{1,\ldots,N_{c}\}\) represents different traffic groups ordered from greater to lower occupation times, in the set of stations considered. \[\overline{T}_{\text{data}|c}=\frac{\sum_{d=1}^{N_{c}}\left(P^{N(d)}_{c}+P^{ \mathcal{H}(d)}_{c}\right)T^{(d)}_{\text{tx},\text{data}}}{\sum_{d=1}^{N_{c}} \left(P^{N(d)}_{c}+P^{\mathcal{H}(d)}_{c}\right)} \tag{13}\]  
where \(P^{N(d)}_{c}\) and \(P^{\mathcal{H}(d)}_{c}\) represent the probability that a station collides with any station from the same or higher (faster) class, respectively, and can be calculated as indicated in [12].
## 2 Materials and Methods
### Data Analysis and Parameters  
The static measures of head rotation and inclination, taken with DG and the eyeTilt application, respectively, were automatically displayed during the assessment and recorded by the operator. Data collected with MoCap were first processed using the SMARTTracker (version 1.10, BTSBioengineering SpA, Milano, Italy) software. In this step, each marker was manually assigned to its corresponding anatomical landmark. Reconstructed data were then imported into the SMARTAnalyzer software (version 1.10, BTSBioengineering SpA, Milano, Italy) for further analysis. Custom routines within the software were employed to compute quantitative measurements. For the static trials, head inclination and rotation angles were calculated. For the dynamic trials, the computed outputs included angles during head flexion/extension, lateral bending, and rotation. The dynamic ROM was subsequently calculated as the difference between the maximum angular displacement achieved during each movement and the neutral head position.  
Figure 3: Schematic representation of head movements. Flexion/extension (**a**); lateral bending (**b**); and rotation (**c**).
# Incremental Formula-Based Fix Localization
## 2 Background
### Formula Satisfiability and Solvers  
_The formula satisfiability_ (SAT or SMT) is the problem of determining if there exists an assignment to variables in a given logic formula such that the formula evaluates to true. If such an assignment (called _model_) exists, the formula is called satisfiable (SAT); otherwise, it is called unsatisfiable (UNSAT).  
_Maximum satisfiability (MaxSAT or MaxSMT)_ is an optimization version of the SAT problem where the goal is to find a model for a given formula such that maximizes the number of clauses satisfied together. Such a maximized subset of clauses is called a _maximal satisfiable subset (MSS)_. The complement of MSS is called a _minimal correction subset (MCS)_, which is a minimal subset of clauses that, if removed, can make the remainder formula satisfiable again. _Partial MaxSAT_ (\(p\)_MaxSAT_) is an extension of the MaxSAT, in which clauses are marked as either \"soft\" or \"hard\". The goal in pMaxSAT is to find a model that satisfies all \"hard\" clauses and maximizes the number of satisfied \"soft\" clauses.  
Although the SAT problem was known to be NP-complete, recent automatic solver algorithms have shown that they can solve large SAT formulas encoding practical industrial problems. _SAT solvers_ are software programs that accept a logic formula in conjunction normal form (CNF) and decide if the formula is satisfiable. If the formula is satisfiable, the solver returns \"SAT\" and provides a satisfiable model for the formula. Otherwise, the solver returns \"UNSAT\" and may produce an unsatisfiable core, which is a subset of clauses in the formula that cannot be satisfiable together, as an explanation for the unsatisfiability. Some recent solvers support _incremental SAT solving_, which facilitates solving a series of closely related formulas. Incremental SAT solvers remember already learned information after checking the satisfiability of one input formula and utilize this information to avoid repeating redundant work in further satisfiability checks of additional formulas.  
Generally, MaxSAT solver algorithms perform a succession of SAT solver calls; after each call, they add additional cardinality constraints to reach an optimal solution. The state-of-the-art MaxSAT solvers are based on the Core-guided MaxSAT algorithm, which leverages the unsatisfiable core produced by the SAT solver. Specifically, after each call to the SAT solver, they relax clauses in the unsatisfiable core by associating a relaxation variable with each such clause. To reach optimal solutions, they add cardinality constraints to constrain the number of relaxed clauses.
# A New Approach to Border Irregularity Assessment with Application in Skin Pathology
## 1 Introduction
### Related Works  
There are relatively few works in the literature which cover skin histopathological images processing of skin whole slide biopsy images stained with hematoxylin and eosin (H&E), the standard stain in histopathology. We list some of the notable examples and briefly detail methods for automatic diagnosis of skin lesions.  
Skin epidermis segmentation problem is addressed in [19; 20; 21; 22] using a variety of techniques, such as [PERSON]'s thresholding, shape features, statistical analysis, stain unmixing, morphological processing, and thickness measurement. The problem of melanocytic tumor depth measurement was first addressed in [23] and then improved in [24]. Based on the work in [25] for melanocyte detection and the approach in [22] for epidermis segmentation, [PERSON] et al. [26] proposed a computer-aided technique for automated analysis and classification of melanocytic tumor. They first segment both epidermis and dermis regions, then compute a set of features reflecting nuclear morphology and spatial distribution, and finally classify the lesion image into different categories such as melanoma, nevus or normal tissue by using a multi-class support vector machine (mSVM) with extracted epidermis and dermis features. [PERSON] et al. [27] used a deep learning approach to diagnose three common skin conditions--basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses. [PERSON] and [PERSON] [28] proposed an automated method for differential diagnosis of squamous cell carcinoma (SCC) in situ from actinic keratosis. They first segment epidermis and remove cornified layer, then specify epidermis axis using the paths in its skeleton and remove the granular layer via connected components analysis, and finally perform classification based on intensity profiles extracted from lines perpendicular to the epidermis axis.  
None of the above-mentioned image classification methods considers morphometric features of rete ridges, whereas uniformity and symmetry of both elongation and thickening of rete ridges are among most important diagnostic criteria related to the morphometry of epidermal component [7; 8].  
There are many works in the field of medical image processing regarding automatic diagnosis of melanocytic nevi using clinical and dermoscopy images (for example, see [29; 30; 31]) and in recent years the deep learning approach helped to significantly increase the accuracy of methods for automatic classification of melanocytic lesions [32; 33; 34]. However, to the best of our knowledge, automatic diagnosis using histopathological images has not yet been sufficiently addressed.  
Since histopathological image analysis is the gold standard for diagnosing and grading skin tissue malignancies, our method will provide a valuable contribution towards automating this procedure.