esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
These conditions are categorized into seven different cases. This is intended for a fundamental understanding of the impact of external forces on solder from the mechanism perspective, as opposed to the emphasis on the application perspective in previous papers. The solder joints have crystal structures, and their inelastic deformation is governed by slip systems that depend on these crystal structures. In crystal plasticity (CP) modeling [29], the slip rate is correlated with the dislocation density. In addition, each grain, distinguished by grain boundaries, operates on slip systems independently, allowing for a closer simulation of the slip system behavior. However, this approach requires the calibration of numerous modeling parameters. An alternative approach is through dislocation dynamics (DD) [30], which also simulates inelastic deformation through the behavior of the dislocations themselves. In the case of DD, it can better predict the interactions such as grain boundary and dislocation. At present, it is feasible to simulate submicron scales such as those of a single crystal or bicrystal structure. Therefore, this paper utilizes continuum-level plasticity theory to study the evolution of inelastic deformation in an entire solder structure under external forces. In this numerical simulation, the study used the associated flow rule based on the J2 flow rule to calculate the inelastic flow and implemented an effective ductile fracture model to determine the fracture limit in the Lagrangian framework. Because the J2 flow rule and ductile fracture criterion have been extensively validated in the previously mentioned papers [17; 18; 19], recently, while effective metal plasticity models based on Eulerian formulation [31; 32] have been developed, this paper employed the Lagrangian theory for more general discussion and simplicity. The models were implemented in the vectorized user-defined material (VUMAT) subroutine of the ABAQUS 2020 program for finite element (FE) analysis. FE simulations were performed for seven different cases, including single loading scenarios for each type, dual loading scenarios, and a complex loading scenario. The results showed that the occurrence of fracture in the solder ball was not solely determined by the magnitude of equivalent plastic strain; rather, it was significantly affected by the evolution of the inelastic deformation modes. Moreover, they illustrate that changing the combination of loading conditions can result in substantial changes in inelastic deformation modes. These discoveries help us to understand how inelastic deformation modes evolve within solder ball joints and hold promise for future research applications. The structure of this paper is organized as follows: Section 2 summarizes the numerical model, and Section 3 describes the FE model. Section 4 presents the results and discussion. Finally, Section 5 provides the conclusions.
## 4 Societal Benefits and Impact
### Contributions to Smart City and Community Initiatives
#### 4.3.4 Extensibility to Broader Urban Challenges  
As the IoPVT ecosystem matures, there are opportunities for its capabilities, beyond safety and health, to be leveraged in areas such as energy management, environmental sustainability, cultural heritage, etc. Data collected for safety applications can help inform energy usage patterns to better balance consumption and production or reduce the carbon footprint of critical infrastructure [99; 100]. For example, lighting systems monitoring pedestrian activity for safety can automatically adjust brightness levels based on actual usage patterns rather than fixed schedules, conserving energy while maintaining safety.  
In coastal communities most affected by climate change, these data can guide adaptation steps, from strengthening sea walls to redesigning green spaces and galvanizing joint climate action within the community. The predictive capabilities of our system can model the compound effects of extreme weather events combined with infrastructure weaknesses, enabling proactive resilience planning. The same infrastructure that monitors safety can be extended to track environmental health indicators such as air quality, noise pollution, or urban heat islands. These factors have significant impacts on public health, and their continuous monitoring provides valuable inputs to healthcare planning and environmental policy.  
Safety monitoring systems in historic districts or cultural sites can be augmented to provide tourism information, crowd management during events, and digital preservation of heritage sites through detailed digital twins. This integration enhances both the safety and experiential value of cultural spaces. This aggregate data mining not only serves as a measure to mitigate risks but also forms a bedrock that allows urbanization to play a role as part of holistic urban resilience and socioeconomic vitality by connecting these dots. The multipurpose nature of the system maximizes return on investment while creating synergies across traditionally siloed urban management domains.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 2 Architecture of Proposed Self-Powered Passive Tag
### High Frequency (HF) Section
#### 2.3.1 RF Differential Rectifier  
The RF to DC conversion is implemented using a three-stage differential rectifier shown in Figure 9. The output DC voltage \(V_{REC}\) is given by Equation (33), where \(P_{av}\) is the RF available power, \(\eta_{A}\) is the antenna radiation efficiency, \(N\) is the number of differential rectifier stages, \(R_{rec}\) is the real part of the input impedance of a one-stage differential rectifier, and \(X_{rec}\) is the imaginary part of the input impedance of a one-stage rectifier cell [57; 58].  
\[V_{REC}=\sqrt{\frac{2P_{av}\eta_{A}N}{R_{rec}}}X_{rec} \tag{33}\]  
The input impedance \(Z_{REC}\) of the three-stage differential rectifier is given by Equation (34), where \(C_{x}=C_{gs_{1}}+C_{sb_{1}}C_{gs_{2}}+C_{sb_{2}}+C_{c_{p}}\), \(C_{c_{p}}\) is the parasitic capacitance of the lower plate in \(C_{c}\), \(R_{DS}\) is the drain-source resistance of \(m_{1,2}\) transistors whose value depends on whether the transistor operates in the subthreshold, linear, or saturation region, and \(R_{o}\) which is the ratio between the output voltage and current of one rectifier cell. The value of the input impedance \(Z_{REC}\) must match the antenna impedance to ensure the maximum power transfer.  
\[Z_{REC}=\frac{1}{3}[R_{rec}-jX_{rec}]=\frac{1}{3}\Bigg{[}\frac{(1+\frac{C_{x}} {C_{c}})(R_{DS}+R_{o})}{\omega C_{x}(R_{DS}+R_{o})^{2}+1}-j\frac{\omega C_{x} (1+\frac{C_{x}}{C_{c}})(R_{DS}+R_{o})^{2}}{\omega C_{x}(R_{DS}+R_{o})^{2}+1} \Bigg{]} \tag{34}\]
# Telepresence Social Robotics towards Co-Presence: A Review
## 2 Co-Presence Taxonomy
### Immersive Qualities
#### 2.1.3 Interactivity  
The definitions of social telepresence, in the context of robot-mediated interaction, rely on the capability to put forward the robot operator's presence to the local person (bystander). Additionally, the extent to which this person is aware that he or she is talking to or interacting with a human being has an impact on social presence [15] and co-presence [63]. Studies on this subject try to understand the effect of the interactivity of the agent on social presence. Such interactivity may refer to a computer agent, a person's avatar, or a telepresence robot, but the focus of this analysis is on the use of a telepresence robot for conversation mediation. Thus, the level of social presence depends on the fidelity of the medium to support the interactivity that characterizes persons' conversations. It includes visual and audio cues, nonvisual sensing (i.e., directional sound and haptics like force feedback and touch) and environmental interactivity (e.g., response rate to user input, reciprocity of the interaction capability between the remote user and local user, and clarity of causal relationships between remote user actions and local user reactions).
## 3 Results and Discussion
### Mechanical Behavior and AE Analysis
In the case of the woven FRP, the load was transferred to longitudinal fibers (0\({}^{\circ}\)) after transverse fiber (90\({}^{\circ}\)) fractures, and the cracks propagated. When the epoxy was photodegraded by UV light, transverse cracks were initiated, and propagated under low tensile loading via surface cracks and their connections. Figure 8a presents the cumulative AE hits. As photodegradation progressed, the time to crack initiation decreased. Note that small microcracks can occur before the first AE hit because a hit is recorded only when a signal exceeds the threshold. The time of the first hit is important because it represents the rate at which the cracks reach a specific size. The trend in cumulative hits was similar after approximately 30% of the lifetime. Figure 8b shows the number of AE signals per second (one black dot corresponds to the number of AE signals that occurred in 1 s). High hit-per-second peaks are labelled #1-4. The maximum  
Figure 6: Photographs of fractured specimens subjected to ultraviolet exposure for (**a**) 0 h and (**b**) 300 h.  
Figure 7: Mean AE amplitudes of GFRP specimens. amplitude changed according to the hits per second (Figure 5a). It is well-known that cracks do not always grow at a constant rate, and there are regions where the cracks grow suddenly depending on the fiber structure. The AE test results indicate that these regions can be detected by amplitude analysis.
# Key Technologies in the Context of Future Networks: Operational and Management Requirements
## 2 Key-Enabled Technologies for Future Networks
### Network Function Virtualization  
The concept of virtualization has allowed resource sharing (e.g., hardware) between different tenants, each one with its private functionality (e.g., different Operating System). For its part, network virtualization enables the sharing of network resources with different virtual topologies and forwarding logic in the same network infrastructure (e.g., Virtual Local Area Network-VLAN) [15]. However, if a service provider needs a specialized network application (firewalls, Deep Packet Inspection-DPI, etc), it must add a new hardware device. It represents an extra investment (capex/opex) and creates scalability and innovation constraints. In this context, the NFV has gained importance between network operators for its facilities to deploy services or applications as software network functions that can be automatically instantiated in different parts of the infrastructure. In 2012, main telecommunication service providers and the European Telecommunications Standards Institute (ETSI) proposed the NFV approach [11]. This allows the easy deployment of network functions on standard switches, storage or high volume servers as shown in Figure 2.  
The referential architecture defines three main components: the Network Function Virtualization Infrastructure (NFVI); VNFs; and NFV Management & Orchestration (NFV M&O). NFVI controls the hardware resources (computing, storage and network), and uses a virtualization process in order to create software instances (virtual compute, virtual storage and virtual network). VNFs are the network functions that run over the NFVI. NFV M&O orchestrates and manages VNFs and the NFVI. Additionally, this module works with the external Business Support System and Operation Support System (OSS/BSS) in order to bill the services. The main objective of this model is to provide an architecture that allows the easy deployment, orchestration and management of traditional network functions as virtualized instances. These characteristics will enable future infrastructures to provide services regardless of the type of devices or their location and will promote on-demand service modality.
## 1 Introduction
### Satellite  
Satellite application for remote sensing is a consolidated technology which has been used for civil purposes since 1972, when Landsat 1 was launched. Several implementations occurred over the years, increasing satellite capability and accuracy of data provided. Nowadays, several constellations of earth observation satellites have been implemented, which collect data in the radio (\(\sim\)2.5-100 cm), thermal (\(\sim\)8-14 \(\upmu\)m), and visible/near-infrared (\(\sim\)400-900 nm) wavelengths. Earth observation satellites with a spectral resolution ranging from 400 to 900 nm are the most used in agriculture, as they allow for the calculation of tens of different VIs. Satellites with spectral resolution suitable for agriculture uses can be managed either by a governmental agency (usually free of charge) or a private company (usually for sale). Several constellations of satellites acquire data continuously (Sentinel, Landsat, RapidEye, PlanetScope, Spot, etc.). Other constellations acquire data according to an acquisition plan or on demand (Worldview, Quickbird). Both data sources have been used for index-based variable rate nitrogen application [27]. Due to the observation distance (400-800 km, depending on the orbit), satellite-acquired images are affected by distortions and noise, making geometric and radiometric correction necessary. In addition, atmospheric disturbance during satellite acquisition requires specific atmospheric correction, which is impossible in some conditions (e.g., clouds, smog).  
Recently, a new approach to satellite earth observation was proposed, based on the constellation of hundreds of nanosatellites characterized by limited observation width, instead of constellations of 2-5 satellites with a wide observation width [32]. This new approach was made possible by the decrease in low orbit launching costs.  
Figure 1 shows the evolution of data volume provided by several constellations over the years starting from 1972 (Landsat 1 placed in orbit) up to 2020, based on [PERSON] et al. in 2019 [33]. Data volume per hectare per year summarizes information on spatial, spectral, radiometric and temporal resolutions. Over 50 years, data volume per hectare per year showed an exponential increase from 100 B*(ha year)\({}^{-1}\) up to 100 MB*(ha year)\({}^{-1}\). The large amount of data provided rapidly and continuously is a strength of satellite remote sensing in agriculture.
## 2 Mathematical Preliminaries  
Geometric algebra is a mathematical structure developed over the last 40 years based on Clifford Algebras. A Geometric algebra can be defined simply by specifying appropriate rules for multiplying vectors. Thus, let \(\mathcal{V}^{n}\) an n-dimensional linear space over the real numbers. The geometric product of vectors \(a\otimes b\) if \(a,b\in\mathcal{V}^{n}\), [28], can be decomposed into a symmetric inner product:  
\[a\cdot b=\frac{1}{2}(a\otimes b+b\otimes a) \tag{7}\]  
and an antisymmetric outer product:  
\[a\wedge b=\frac{1}{2}(a\otimes b-b\otimes a) \tag{8}\]  
Therefore, \(ab\) has the canonical decomposition:  
\[a\otimes b=a\cdot b+a\wedge b \tag{9}\]  
The inner product \(a\cdot b\) is a scalar and the outer product \(a\wedge b\) is called bivector (or 2-vector). Geometrically, it represents a directed plane, in much the same way as a vector represents a directed line segment (Figure 1).  
We can regard \(a\wedge b\) as a directed area with a norm \(\|a\wedge b\|\) equal to the usual scalar area of each parallelogram in Figure 1, with the direction of the plane in which the parallelogram lies, and sense that can be assigned to the parallelogram in the plane. Then, just as a vector \(a\) represents (or is represented by) a directed line segment and a bivector \(a\wedge b\) represents  
Figure 1: Vector, bivector and trivector representation.  
a directed plane segment, the trivector (3-vector) \(a\wedge b\wedge c\) represents a directed space segment (the parallelepiped with edges a, b, c).  
However the electrical quantities voltage and current do not have an easy interpretation in classic Clifford algebra. For a clear representation of these waveforms, a new geometric algebra has been constructed--a generalization of the classic Clifford algebra [32] which we have termed \"generalized complex geometric algebra\" (GCGA) and denoted by \(\{CG_{n},\odot\}\). In this framework, \(\mathcal{C}\) is the complex vector space, \(\mathcal{G}_{n}\) is the Clifford algebra or geometric algebra associated to the n-dimensional real space \(\mathcal{V}^{n}\)and:  
\[\odot:(\Re\circ\otimes) \tag{10}\]  
is the new generalized geometric product. A detailed description of this new structure (GCGA), of the geometric product \"\(\odot\)\" and their properties, are given in references [32,33].
## 3 Methodology
### LS-Factor  
In USLE, the L and S factors represent the effects of topography on the erosion rate. Higher slope length and slope steepness factors represent more overland flow and more soil erosion [66]. Moreover, slope variations have a much more significant impact on gross soil loss than slope length variations [67]. Topography is an important factor, especially when the ground slope increases beyond the critical angle. In order to obtain the LS factor for the Mangla watershed, the digital elevation model (DEM) was used in the ArcGIS environment. This is because the DEM provides the slope length and slope steepness of the terrain, which are necessary inputs for calculating the LS factor. It is necessary to account for variables such as flow accumulation and slope steepness while computing LS. The slope steepness and flow accumulation parameters were incorporated through the digital elevation model through the ArcGIS Spatial analyzer extension. From this digital elevation model, runoff accumulation and slope were computed. The LS-factor was computed using Equation (8) as suggested by [PERSON] and [PERSON] [68, 69].  
\[\text{LS}=\frac{\left(\text{Flowaccumulation}\times\text{CellSize}\right)^{0.4}}{ 22.13}\times\frac{\left(\text{SinSlope}\right)^{1.3}}{0.0896} \tag{8}\]  
where, flow accumulation represents the cumulative upslope supporting area for a cell, the LS factor represents the factor of slope length along with slope steepness, Cell Size represents the size of the grid cell, and the slope degree is represented by 'Sin slope' whose value is in sin. The slope classification map and value of the LS factor is illustrated in Figure 5a,b, respectively.
## 5 Datasets and Model Performance Evaluation Metrics
### Average Delay  
The average delay can effectively reflect whether the video stream can be transmitted successfully in a limited time. The average delay includes mainly the average transmission delay, the average processing delay and the average propagation delay. When the video file is large, the article will consider primarily the average transmission delay and the average propagation delay. When the number of files is large, the article will consider the average queuing delay and the average propagation delay. The total latency or response time of the deep learning-based fog cloud computing framework proposed by [PERSON] et al. [50] is reduced by 79.7% compared to cloud platforms. Compared with the greedy power conservation algorithm [96] (GPC) and the greedy priority departure algorithm (GPDV), the DQN-based scheduling algorithm proposed by [PERSON] et al. [65] reduces the average delay by 10.2% and 21.1%, respectively.  
[PERSON] et al. [91] compared the average processing latency of all schemes. In the DDPG and offload-only schemes, as the computing power of the fog server increased, the delay caused by task processing decreased. Our proposed scheme reduces latency by approximately 18.8% compared to the offload-only scheme. This is because the offload-only processing model does not take into account the impact of service caching on network performance.
## 4 Machine Learning Analysis Based on Actual Measurement Data  
### Overview of Neutron Transmission Spectroscopic Imaging Experiments of LBE Solidification  
The NTSI experiments during the solidification process of LBE were performed using the energy-resolved neutron imaging system RADEN in the Materials and Life Science Experimental Facility (MLF) of the Japan Proton Accelerator Research Complex (J-PARC) [25] with the neutron wavelength resolution of = 0.25%. The details of these experiments are already reported [26; 27]. The LBE sample had a solidified volume 80 mm in vertical length, 80 mm in width, and 10 mm in thickness, and it was contained in a 304 stainless steel container with 1-mm-thick walls. The entire container was covered with glass fiber for heat insulation. A heater block at the top of the container and a heat sink at the bottom were installed. The sample temperature was checked using thermocouples inserted in seven locations around the container. A gas electron multiplier (GEM)-type detector (nGEM, Bee Beans Technologies [28]) was used as the neutron time-of-flight (TOF) imaging detector, covering a 100 mm \(\times\) 100 mm detection size. The pixel resolution was 0.8 mm in length and there were 128 \(\times\) 128 pixels in total. In the experiment, the sample was heated for 60 min at first until about half of the top of the LBE sample reached a temperature higher than 124.7 \({}^{\circ}\)C, which is the melting point of LBE. Then, while holding the initial state, the first neutron transmission TOF imaging measurement was performed for 60 min. After that, the heating was stopped and the gradual solidification process from the downward direction of the sample was measured over 270 min as time-accumulated measurements every 30 min.
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 3 Strategies for Analyzing Image DNA Techniques
### Image DNA DB  
The DNA of target images stored in the DNA database consists of real-valued vector data, making exact matching infeasible and incompatible with traditional indexing techniques. Filtering decisions based on metrics such as Euclidean distance or cosine similarity often requires computing the similarity between a query DNA and every DNA stored in the database. While these calculations are independent and can be parallelized to reduce processing time, the computational cost scales linearly with the size of the DNA database.  
Recent advances in vector database (VDB) technologies [28] provide a way to significantly reduce DNA similarity computation time. Approximate indexing techniques, commonly used in VDBs, enable probabilistic similarity calculations that address the inefficiencies of brute-force methods. In this study, we utilize FAISS [29], a VDB supporting incremental updates, with the HNSW (Hierarchical Navigable Small World) [30] indexing technique to construct the DNA database.  
Image processing-based DNA techniques generate a large and variable number of 128-dimensional feature vectors per image, often reaching hundreds, depending on image complexity. Consequently, their DNA databases are significantly larger than those of other methods and require additional overhead for managing the mapping information for individual vectors. Moreover, the similarity between the DNA must be calculated based on  
Figure 4: True and false detection rates on image DNA similarity.  
the ratio of matched vectors between the query DNA and stored DNA, rather than simple pairwise comparisons, further complicating the computation process. This ratio is referred to as the Matched Vector Ratio (MVR).  
For our experiments with Nabla DNA, we configured different resolutions and depths for storage DNA and indexing DNA to evaluate their performance. Specifically, the notation x8d4x16 indicates that vector indexing is performed with a resolution of 8 and a depth of 4, while storage DNA is created with a resolution of 16 and a default depth of 8. Such configurations allow us to analyze the impact of varying DNA parameters on indexing and retrieval performance.