esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
In the first phase, a large problem is divided into small-sized clusters. In the conversion phase, the problems are converted into an instance of the TSP. Finally, the clusters are scheduled to available resources in the assignment phase. The proposed technique was validated with comparative experimental results. Another algorithm for efficient resource utilization and energy consumption was proposed in [30]. The proposed algorithm, i.e., ERES, dynamically allocates resources to workflow tasks according to the input tasks. The algorithm dynamically manages the server load, and live migration of the VMs is used to avoid overloaded and underloaded hosts. Simulation results were presented to validate the effectiveness of the proposed algorithm. FIMPSO is another algorithm for load balancing in a cloud computing environment [31]. The hybridization of Firefly and PSO was used in the proposed algorithm. The Firefly algorithm is used to reduce the search space, whereas an improved, modified PSO is used to select the best responses during the resource allocation process. The resource utilization and response time are the targeted parameters of the proposed algorithm. Comparative experimental results were shown to validate the proposed algorithm. Energy-efficient scheduling in the presence of other constraints, such as resource utilization and fault tolerance, is a challenging issue. There is a need to develop solutions that address the problem while considering these parameters. This paper presents a scheduling algorithm in order to address the above-mentioned problem. The proposed algorithm is based on task classification, queueing, and thresholds for allotting VMs to tasks and creating the necessary resources. Queuing is used to hold tasks with different intensities in order to reduce resource allocation time. Task classification is used to find suitable VMs in order to reduce the scheduling time. Thresholds are used to efficiently utilize cloud resources and reduce the failure ratio.
## 6 Discussion
### Motion Estimation  
The motion estimation accuracy of our approach is based on three main goals:  
* Perform a reasonable estimation of the motion scale;
* have the ability to deal with pure rotations; and,
* present real-time performance.  
By analysis of Figure 9 is possible to evaluate the two first goals. It is visible that for sequence A, the original version of Libviso2 presented high inaccuracies estimating the motion scale and had difficulties dealing with the rotational component of the robot motion. Applying the KF to the original version improved the estimation of rotation, but globally, the result was still very far from the ground-truth. On the other hand, using a fisheye camera on our FAST-FUSION approach led to a more stable and accurate estimation (Figure 9b). Without the KF, we saw an initial error estimating the rotation that propagates through all the sequence. However, when applying the KF module, this initial error was corrected, and the estimation got closer to the ground-truth.  
The final solution results for sequences B and C are present in Figure 10. In this we benchmark the raw Libviso2 version without optimizations with our FAST-FUSION approach. In an embedded system, the impact of computational efficiency was high. Figure 10a proves this. We can see a considerable error in the Libviso2 raw estimation in terms of scale and rotation. In addition, the number of frames that it processes was lower than in our approach since it was slower. This contributed to a degeneration of the motion estimation. For FAST-FUSION, we can see in Figure 10b an estimation much more closer to the ground-truth. Sequence C compared the two VO approaches with wheel odometry in a rectilinear path. Although both approaches revealed error, it is visible that FAST-FUSION estimated a scale factor much closer to the ground-truth (Figure 10d). Consequently, it provided a more accurate estimation.  
This being said, we present a version of a state-of-the-art monocular VO method that works with omnidirectional cameras, fused with sensors, and optimized in GPU, providing higher accuracy than the original one. Our system works in an embedded paradigm working in real-time. Besides presenting some estimation errors, we consider the results quite satisfactory, taking into account the conditions: a monocular omnidirectional VO method fused with sensors running in a low-power embedded device.
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 4 The Hybrid Context Reasoning Mechanism
### Principles of the Different Reasoning Methods
#### 4.1.1 Ontological Reasoning  
Ontological reasoning, also referred to as description logic reasoning, uses a set of constructs, such as TransitiveProperty, SymmetricProperty, AsymmetricProperty, ReflexiveProperty, IrreflexiveProperty, subClassOf, subPropertyOf, disjointWith, equivalentTo, and inverseOf, to specify a terminological hierarchy and assert inner restrictions. It is mainly supported by two semantic web representation languages, RDF(S) and OWL. Tableau algorithm is the de facto standard reasoning algorithm that is employed in ontological reasoning. Ontological reasoning enables several kinds of operations, including concept satisfiability, consistency check, class subsumption, and logic inference. Therefore, by means of ontology-based reasoning, some facts that are implicit in the ontology given explicitly stated facts can be deduced. Table 1 presents a list of ontology-based reasoning rules [42].  
As all context data are formalized in the SWARMs ontology, it is natural to apply ontology-based reasoning over ontology classes and instances. Several reasoners, including Pellet [43], HermiT [44], and FaCT++ [45], have been developed to support the ontological reasoning in ontologies. However, ontology-based reasoning lacks the ability to express complex relations or rules for reasoning tasks. In addition, it is unable to deal with missing values and reason over uncertainties.  
Figure 4: The hybrid context reasoning method for the context reasoner component.
## 3 Fuzzy-Logic Autonomous Navigation System
### Fuzzification Process of Distance Data  
During the fuzzification process, each input (f\({}_{d}\), r\({}_{d}\), l\({}_{d}\), and \(\theta_{d}\)) to the fuzzy navigation algorithm can be classified as close, med, or far and subsequently assigned weight values between zero and one using the fuzzy membership functions. Figure 6a depicts the membership function used for the fuzzification of inputs f\({}_{d}\), r\({}_{d}\), and l\({}_{d}\). As can be realized, the figure depicts weight values linearly corresponding to the distance data between each grid. Accordingly, the fuzzy navigation system facilitates the mobile robot to perform a linear motion based on its distance from obstacles. Because grid points corresponding to named sub-functions within each membership function are considered as criteria for determining the robot's distance from obstacles as well as its direction of motion and speed via sensor signals, any variations in the grid directly affect the motion of the mobile robot. For example, a reduced range of the \"close function\" in Figure 6a causes the robot to approach the obstacles better. This is due to the narrowing of the recognition range for an input to be classified as \"close\".  
Figure 4: Schematic of fuzzy positioning system [24].  
The sensitivity of the observed variations in assigned weight values is related to the slope of the triangular membership function. In other words, a steep (high) slope implies that the robot would respond more actively to its distance from obstacles. As depicted in Figure 6a, the slope of the triangular function between grid points 10 and 20 is steeper compared with that in other regions, and this makes the robot to avoid obstacles more quickly in that region. Accordingly, the input \(\theta_{d}\) is classified as either L_pos, S_pos, or R_pos, and a weight value between zero and one is assigned to each variable based on the membership function, as described in Figure 6b. In this study, the grid points within input membership functions were adjusted to determine the optimum path to the target.
# Magnetic Nanofluid Droplet Impact on an AAO Surface with a Magnetic Field
## Materials and Methods
### Preparation of Aluminum Sheet Surfaces  
In order to obtain two different wettability types of surface, an anodic oxidation process was adopted. An aluminum sheet (purity: 99.5%; 50 \(\times\) 50 \(\times\) 0.2 mm) was first cleaned in acetone (C\({}_{3}\)H\({}_{6}\)O), ethanol (C\({}_{2}\)H\({}_{5}\)OH), and deionized water by an ultrasonic cleaner for 5 min. Then, the randomly formed anodic aluminum oxide (AAO) structure was performed by the first anodic oxidation process while applying 20 V in 0.3 M phosphoric acid (H\({}_{3}\)PO\({}_{4}\)) solution at 5 \({}^{\circ}\)C for 180 min. The surface wettability was assessed by measuring the contact angle of a magnetic nanofluid sessile droplet (\(\sim\)6 \(\upmu\)L) using a contact angle meter through the conic method (Model 100 SL with an accuracy of 0.01\({}^{\circ}\), Sindatek) at room temperature. The value of each contact angle is the average value obtained by five independent measurements with absolute relative uncertainty of less than 10.98%. As shown in Table 1, the static contact angle of magnetic nanofluids decreased from 66.12 \(\pm\) 1.68\({}^{\circ}\) to 53.01 \(\pm\) 5.82\({}^{\circ}\) after the aluminum sheet was processed at a particular anodizing voltage (20 V). The decrease in the contact angle on the processed surface is attributed to the presence of nanostructures. The nanostructures may lead to the enhancement of the capillary force on the solid surface. As a result, more surface will be wetted by the droplets and thus the area covered will increase. It should be noted that before the experiment in this study, all aluminum sheets had been immersed in ethanol at 25 \({}^{\circ}\)C for 180 min to break the hydrogen bonds and baked using a vacuum oven at 100 \({}^{\circ}\)C for 60 min, and their surface free energies have been calculated by using the contact angles of two test liquids (deionized water and glycerol) through the Owens-Wendt-Rabel-Kaelble (OWRK) method. The calculated surface free energy is 153.94 mN/m (polar part: 133.96 mN/m; dispersive part: 19.99 mN/m) for the processed surface and 53.48 mN/m (polar part: 52.07 mN/m; dispersive part: 1.41 mN/m) for the unprocessed surface.
## 3 Results and Discussion
### Effect of Bond Strength  
Figure 7 shows the stress-deformation curve for various bond strengths (\(\sigma_{b}\)). The bond Young's modulus (\(E_{b}\)) and the relative particle size (\(h/d\)) were 1.0 GPa and 7.06, respectively. The flexural strength (\(\sigma_{f}\)) and the compressive strength (\(\sigma_{c}\)) increased proportionally with the bond strength. This was due to the breaking condition where the bond was breaking when the maximum stress exceeded the bond strength. However, the deformation of the ice beam according to the constant vertical load showed a constant behavior until the ice  
Figure 6: Effect of bond Young’s modulus: (**a**) stress–deflection curve; (**b**) stress–strain curve; (**c**) ratio of simulated Young’s modulus to bond Young’s modulus.  
was fractured. This indicated that the simulated material stiffness was not affected by the bond strength.  
Figure 8 presents the ratio of the flexural strength to bond strength (\(\sigma_{f}/\sigma_{b}\)). The flexural strengths increased linearly with the bond strength. The slope showed a constant \(\sigma_{f}/\sigma_{b}\) of 1.52. \(\sigma_{f}/\sigma_{b}\) could be defined as the normalized strength. In an ice modeling trial, the normalized strength is a key variable that could characterize the effect of bond strength on target material strength.
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## Sharing Framework of Heterogeneous Battlefield Situation Information  
In this section, we first analyze the consistency of heterogeneous battlefield situation information in content under the situation of multiple combat systems in the battlefield space. Then, we introduce the entity oriented formal description model of the battlefield situation in detail. Last, we introduce the layered framework of realizing heterogeneous information sharing based on a unified battlefield situation information space. Table 1 lists the basic notations and the corresponding definitions.
## 3 Study Areas and Data Process
### Data Process  
#### 3.3.1 Land Subsidence Experiment  
To minimize the process error, such as the Doppler error and vertical baseline error, we set a time baseline threshold of 140 days, a spatial baseline threshold of 200 m, and an average coherence threshold of 0.3. Then, we select 295 pairs of interferograms based on 94 Sentinel-1A images. The spatiotemporal baseline connection diagram of the interference image pair is shown in Figure 2. We establish a linear model under the assumption that the time series deformation of urban areas obeys the linear distribution, and the detailed flowchart is shown in Figure 3.  
Figure 3: SBAS-InSAR data processing process.  
Figure 2: The spatiotemporal baseline map of Sentinel-1A spanning between 2016 and 2022.  
Finally, the result is a line-of-sight (LOS) deformation velocity chart. Since the deformation occurs mainly in the vertical direction, all results are converted from the LOS direction to the vertical deformation.
## 5 Applications Across Diverse Sectors  
### Recent Innovations  
In terms of technology, materials, and applications, additive manufacturing has seen tremendous advancements in the last several years. This section explores the most recent advancements in AM technology that could transform and expand the AM industry (see Figure 2).  
* _High-speed sintering_ (_HSS_), a creation of Loughborough University, represents an innovative approach within the powder bed fusion (PBF) category, employing infrared heating to specifically sinter polymer powders. This method allows for quicker production rates and the possibility of mass-producing functional polymer parts. HSS is transforming 3D printing by making it feasible to produce complex, tailored components on a large scale affordably. Because of this, it is now a preferred method in many industries, including aerospace, the automotive industry, consumer goods, healthcare, and medicine [46; 47; 48];
* _Continuous liquid interface production_ (_CLIP_) is a resin-based 3D printing method that expands on the ideas of SLA/DLP procedures. It produces quick printing and smooth surfaces by continually and smoothly converting liquid resin into solid objects by the use of light and oxygen [46; 49];
* _Advancements in laser powder bed fusion_ (_L-PBF_) for additive manufacture of metals place a strong emphasis on material innovation, process control, and monitoring. These developments are intended to improve part quality, decrease porosity, improve mechanical properties, and boost production efficiency. Examples of these developments include monitoring the melt pool, using optical tomography, scanning each layer as it is deposited, applying multi-laser processing, and introducing new materials [46; 50];
* _Directed energy deposition_ (_DED_) methods have developed to use both powder and wire as raw materials, broadening the range of materials to metals, ceramics, and  
Figure 2: Recent inventis in AM processes. Adapted from reference [46].  
composites. This expansion increases the variety of designs that can be created and makes it easier to manufacture bigger parts. Additionally, integrating subtractive manufacturing with DED AM methods further enhances surface quality and dimensional accuracy, particularly for large components [46, 51].  
Improvements in additive manufacturing have led to techniques capable of printing structures with multiple materials or graded properties. These innovations enhance design flexibility and expand the range of applications for printed parts [46, 52].
## 2 Materials and Methods
### Methodology
#### 2.4.4 Ratio Determination  
Taking into account the variability among multiple indicators, it is imperative to systematically ascertain the weights of each indicator and integrate them with specific values to derive a comprehensive evaluative outcome(see Table 3). In the present study, Analytic Hierarchy Process (AHP) hierarchical analysis is employed to ascertain the weights of each indicator factor utilizing robust statistical methodologies. Sensitivity analysis was conducted for this model, with the Road Walkability Index (RWI) selected as the criterion for the analysis due to its significant influence on the results. For both methods, the weight of the selected criterion was varied from 0.099 to 0.154. The sensitivity analysis revealed that the model is highly sensitive to variations in the weight of the selected criterion [49]. Utilizing the consistency matrix method established by [PERSON] et al., [50] each element is systematically compared in pairs, and the significance of the hierarchical factors is ranked through the application of the eigenvector method. Ultimately, the hierarchical importance ranking of each indicator level concerning the overall objective is derived, thereby yielding the weights assigned to each indicator factor [51]. The final measurement of the Street Space Quality Index (SQI) is computed as follows (12):  
\[\begin{array}{l}SQI=0.099 RWI+0.058 RTI+0.062 PCL+0.07 PII+0.069 LDI+0.14 GVI+\\ 0.144 SVI+0.136 VII+0.047 TTI+0.065 II+0.115 CCI,\end{array} \tag{12}\]  
Ultimately, the values of the comprehensive evaluation indices range from 0 to 6. To accurately assess and categorize the current quality of street space in Mingcheng District, this study classifies the quality of street space based on the quantitative results derived from the completed evaluation. Specifically, scores within the interval of 4.8 to 6 denote high quality; 3.6 to 4.8 indicate moderate quality; 2.4 to 3.6 reflect medium quality; 1.2 to 2.4 represent low quality; and 0 to 1.2 are classified as very low quality.
## 4 Wireless Communication Protocol
### Wireless Medium Access Control Layer  
The MAC layer acts as an interface between the physical layer and the interconnection routers to enable chip-to-chip wireless communication. The MAC layer, along with the antenna and transceiver circuitry, enables the NoC routers to use the wireless channel. A MAC mechanism in this layer ensures the collision free communication among the switches equipped with wireless interfaces (WIs). However, adoption of complex MAC mechanisms from macro scale wireless networks is not feasible for multi-chip platforms due to the strict area, power, and buffer overhead constraints. Hence, design of simple, low-overhead, and fair MAC is one of the most challenging design tasks for inter-chip wireless interconnections.  
To utilize the full potential of the novel wireless interconnect technology, the MAC should be distributed, simple, scalable, and efficient. Following these requirements, many MAC mechanisms has been designed for wireless interconnect fabrics. In [11] a distributed, asynchronous TDMA approach is proposed as it eliminates the need to synchronize transceivers deployed in different chips that have  
Figure 13: Performance evaluation under broadcast traffic.  
no feasible way of sharing a synchronous clock. If the choice of physical layer enables the use of multiple frequency channels, Frequency Division Multiple Access (FDMA) can be used with graphene or Carbon Nanotube (CNT) antennas [31]. Code Division Multiple Access (CDMA) was proposed for inter-chip wireless MAC in [32]. To satisfy the performance and energy requirements of the future multi-chip systems, we advocate the following capabilities of the MAC along with the simplicity, fairness and scalability.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 3 Junction Temperature Optical Sensing Techniques  
The two physical-based techniques, thermistor and thermocouple, shown in Figure 1, have wide temperature measurement ranges and are readily available on the market. However, they suffer from the severe constraint of the mechanical process involved, which includes setting up and disassembling or making dents through the device, to enable probe contact with the chip. Conversely, the electrical-based methods exhibit fast response time and directly indicate junction temperature. Despite the fact that extensive calibration is required for each power circuit during its setup phase, the junction temperature estimation provided by TSEP and TSED is an average measurement [37]. Unlike optical-based techniques that employ light signals for temperature estimation, it is worth noting that both physical- and electrical-based techniques operate on the electrical signal, which is prone to loss due to the self-heating of the measuring devices. As such, both are invasive to the measurement or require additional external circuitry for compensation, increasing the power circuit's complexity. The prominent features, advantages, and limitations of the three temperature sensing techniques are summarized in Table 1.  
Despite several OBS techniques, such as Raman spectroscopy [38; 39], liquid crystal thermography [40; 41], and thermo-reflectance [42; 43], being presented in the literature, only a few have been implemented for junction temperature sensing of power switching semiconductor devices. Although Raman spectroscopy and liquid crystal provide good resolution and are contactless to the targeted surface, they are impractical for large surface temperature measurements, since faster scanning is required [44]. Moreover, they are not commercially attractive due to the set-up complexity and cost of implementation. The three most popular OBS techniques commonly engaged for junction temperature measurement in power switching devices are the TSOP [45], IRC [46], and FBG [47], as highlighted in Figure 1. Unlike electrical and physical sensing techniques that are invasive to the system, OBS techniques are spatially separated from the sensing circuit and the device, since they operate on light signals. As such, they are immune to induced electrical noise and EMI from the surroundings. In the following, the underlying working principle, advancements in performance, and comparison of these three methods are discussed.
# New Methods for the Comprehensive Analysis of Bioactive Compounds in _Camabis sativa_ L. (hemp)
## 2 Results and Discussion
### Identification of Non-Psychaoctive Cannabinoids and Flavonoids in Hemp Extracts
Concerning the MS\({}^{2}\) spectra acquired in the negative ion-mode, CBG generated a product ion at \(m/z\) 247, due to the loss of a C\({}_{5}\)H\({}_{8}\) group; additional fragments were observed at \(m/z\) 297, 271, and 204. The fragmentation of CBD in the negative ion-mode provided almost exclusively one product ion at \(m/z\) 245, attributable to the loss of a C\({}_{5}\)H\({}_{8}\) group, following a retro Diels-Alder reaction, which is a common fragmentation pathway in the field of natural substances when an ESI ion source is employed [9; 34]. Canniprene generated its pseudo-molecular ion [M - H]\({}^{-}\) at \(m/z\) 341 in the negative ion mode; its MS\({}^{2}\) spectrum showed a major product ion at \(m/z\) 326, attributable to the loss of a methyl group. It should be specified that the two peaks that precede each cannflavin belong to different flavonoids, which have not been identified. Even though the UV/Vis spectrum was observed to resemble those of flavones, the molecular mass and the fragmentation pattern was found to be different in the MS and MS\({}^{2}\) experiments.