esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Systems Architecture in Industry 4.0: A Systematic Literature Review
## 5 Analysis and Discussion
### Context Modelling and Inference Techniques  
#### 5.2.1 Modelling Techniques  
The context life cycle begins with acquiring data from different sources. In this sense, the architecture of [PERSON] et al. [39] collects data from the physical and virtual space (e.g., ERP, MES) to infer the context of the production environment on the shop floor. It promotes a holistic and dynamic view, considering the different entities, such as tools, machines, parts, products, and personnel, and information related to production orders. [PERSON] et al. [93] add the benefits and importance of integrating business process management (BPM) systems to enrich the knowledge of the context of production processes, providing a structured framework for capturing and analysing related information. Other authors, such as [PERSON] et al. [45], only consider using data from the organisation's legacy systems.  
As shown in Figure 2, 72% of papers use ontologies to represent the context, 12% use object-based methods, and the rest (16%) do not explain the technique used. [PERSON] et al. [77] explain this preference for ontologies because they play a crucial role in interoperability between different actors. [PERSON] et al. [39] consider the high formal expressiveness and the possibility of applying rules to make inferences. In addition, for [PERSON] et al. [76], ontologies facilitate automatic reconfiguration, creating a common vocabulary and enabling the sharing and reuse of knowledge.  
Using ontologies can simplify context modelling, especially in ecosystems with diverse and abundant resources. Ontologies provide a unified framework for the description of domain-specific knowledge, ensuring better interoperability and greater flexibility [82]. Heterogeneous data sources can be integrated through semantic queries in complex environments to improve decision support [87]. This perspective is corroborated by [PERSON] et al. [80], advocating for ontology usage to represent context to aid the decision-making process. Considering the dynamic characteristics, the architecture proposed by [PERSON] et al. [74] increases the adaptability by using ontologies and contextual inference technologies. It is, therefore, essential to develop an architecture based on ontologies that allow the creation of inference engines and automated decision-making applications, enabling them to understand the environment, as described by [PERSON] and [PERSON] [96], cited by [PERSON] et al. [79].  
Finally, the literature discusses different context scales. [PERSON] et al. [90] consider the existence of two spatial levels of context: local and global. Local involves observing the physical state of a workstation, receiving and analysing sensor signals, and notifying local modules and the global module. Meanwhile, the global context awareness module monitors the general state of the environment to ensure safety. The SAHM proposal [78] also considers this perspective and acts locally and globally.  
Figure 2: Comparative list of context representation models.
## 2 Methodology  
### Modeling and Methodology Proposed  
We consider a set of n circles of variable sizes, distributed in a Euclidean plane, see Figure 2. Each circle is defined by the coordinates \((x_{i},y_{i},r_{i})\) where \(x_{i},y_{i}\) are the coordinates of the center of the circle and \(r_{i}\) its radius. The circles can intersect. The objective is to define a tour, having the minimal total distance, passing through all the circles. The trajectory will be composed of segment lines connecting the different circles.  
To address this issue, we propose a methodology based on three successive steps. The first one determines new target locations for the nodes having some communication range intersections. The second one presents an evolutionary algorithm able to find a sub-optimal order to successively visit the target locations. Finally, from geometrical heuristics, the third one reduces significantly the length of the trajectory by passing either inside the areas (without the necessity to go by the center) or at the boundary of the areas.
## 6 Performance Evaluation
### Experimental Set Up
#### 6.1.2 Topology  
The experimental IEEE 802.15.4 network is composed of:  
* One central mote to obtain all the synchronization data from the network, with the goal of storing the information in a file to process it with MATLAB.
* One mote the broadcasts beacon frames based on the BEACON_RATE parameter.
* Thirteen motes flashed with FTSP, AT-FTSP or A2-FTSP. One of them is the root mote, which keeps the global time of the network. Between the final nodes, only three nodes (randomly selected per test) suffer from the variation of temperature, as can be seen in Section 7.
###### Abstract  
The dead-time effect, as an intrinsic problem of the converters based on the half-bridge unit, leads to distortions in the converter output. Although several dead-time effect compensation or elimination methods have been proposed, they cannot fully remove the dead-time effect of blanking delay error, because the output current polarity is difficult detect accurately. This paper utilizes the zero-voltage-switching (ZVT) technique to eliminate the blanking delay error, which is the main drawback of the hard-switching inverter, although the technique initially aims to improve the efficiency. A typical ZVT inverter--the auxiliary resonant snubber inverter (ARSI) is analyzed. The blanking delay error is completely eliminated in the ARSI. Another error source caused by the finite rise- and fall-times of the voltage is analyzed, which was not considered in the hard-switching inverter. A compensation method based on the voltage error estimation is proposed to compensate the rise- and fall-error. A prototype was developed to verify the effectiveness of the proposed control. Both the simulation and experimental results demonstrate that the qualities of the output current and voltage in the ARSI are better than that in the hard-switching inverter due to the elimination of the blanking delay error. The total harmonic distortion (THD) of the output is further reduced by using the proposed compensation method in the ARSI.  
dead-time effect; zero-voltage-switching (ZVT); auxiliary resonant snubber inverter (ARSI)
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 5 Conclusions  
In order to directly deploy low-power digital communication with LoRa modulation for the wireless transmission of sensor data to a base station, Line-of-Sight propagation is an essential requirement. If this condition is fulfilled, distances up to 30 km are easily covered with the standard \(+\)14 dBm transmit power and an omnidirectional mobile antenna combined with a directive base-stationantenna providing a gain of 9 to 13 dBi. The available link budget suggests that communication is also possible by employing omnidirectional antennas at both sides of the link.  
The measurement campaign indicates that terrain elevation is the dominating factor influencing the radio propagation, as a Line-of-Sight path can be temporarily blocked due to the curvature of the terrain. Tropospheric radio propagation was not observed, as equal positions resulted in reproducible signal levels over longer periods of time.  
Non-Line-of-Sight links are possible, but they require field measurements or ray-tracing simulations to determine the optimal antenna locations. Future research could also focus on the use of other coding gains or on adaptive communication to improve link reliability. However, this will come at the cost of higher power consumption at the energy-constrained sensor node.
# Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks
## 2 Background  
### LEO Satellite Attitude Guidance Profile  
Research on satellite maneuver optimization has been conducted since the 1970s [3; 4; 5]. The satellite's actuator performance limitations, transmission capacity in orbit, and throughput should be considered for the optimal maneuvering of LEO satellites. A depiction of attitude maneuvers required for observation missions of LEO satellites is illustrated in Figure 1. Generally, attitude maneuvering for an LEO satellite can be divided into the target pointing for the duration of the observation mission, maneuvering between target pointings, and sun pointing for maximum power generation. The target-pointing duration is the period in which the attitude is determined according to the user's desired observation target (e.g., Earth, star, or other region in space). Therefore, this duration generally requires minimal or no attitude changes. Furthermore, the attitude required for the target-pointing duration is determined by the satellite's position, velocity, observation target, and observation interval.  
The duration of attitude maneuvers between two target points varies according to the maneuvering constraints and attitude generation method. It is difficult to determine  
Figure 1: Attitude mode of low-Earth orbit (LEO) satellite.  
this duration because of the differences in boundary conditions between the attitudes and angular velocities at the end of the previous target pointing and the beginning of the next pointing [19; 20; 21]. Therefore, because of the different initial and final maneuvering conditions, the available torque and angular rate of the satellite are limited, and it is difficult to obtain a generalized solution [10]. Generally, as the time for attitude maneuvering between target pointings decreases, the number of possible missions increases. In this study, we optimized the attitude maneuvering between target pointings using data-based learning to maximize LEO mission performance.  
Finally, after a LEO satellite completes its observations, a sun-pointing attitude maneuver is required for maximizing the power generation [22].
## 3 Results and Discussion
### Sustainability and Scalability Study  
The implementation of cold plasma technology in value-added liquid products proffers a constellation of boons, primarily predicated upon its capacity to modulate reaction pathways and engender process intensification. Departing from the traditional reliance on syngas derived from fossil fuels, with their attendant environmental encumbrances, non-thermal plasma (NTP) technology facilitates the direct transformation of CO\({}_{2}\) and CH\({}_{4}\) into valuable liquid products. This direct methodology not only streamlines separation protocols but also holds the potential to substantially augment overall process efficiency. Furthermore, NTP enables reactions that are thermodynamically prohibitive under conventional thermal  
Figure 5: OES spectra for plasma-catalytic biogas conversion.  
regimes. The inherent electric field within the plasma environment engenders the formation of a plethora of reactive species, thereby surmounting thermodynamic barriers. This attribute is particularly salient in the direct synthesis of acetic acid and valuable liquid products from biogas, where thermodynamic constraints impede yields under traditional conditions. Moreover, NTP is capable of potentiating catalytic activity. Plasma elicits photocatalytic activity by diminishing metal oxide cluster size, amplifying the specific active surface area, and prolonging electron-hole pair lifetimes. The synergy between plasma and catalyst can precipitate enhanced conversion rates, energy efficiency, and product ratios. In summation, the adoption of cold plasma in liquid fuel and syngas production presents a propitious avenue towards a more sustainable and potentially efficacious process, achieved through the enablement of direct biogas conversion, circumvention of thermodynamic limitations, and potentiation of catalytic activity.
## 4 Discussion
### Limitations and Possible Future Study  
We used GF1, GF2, GF4, Landsat-8 OLI and MODIS satellite images for our research, but still, it could be better if more satellite images like Sentinel-2, World View-4, IKONAS, SPOT were used for strategic image fusion. Our fusion result was focused on one season, that is, 2017 July 13 (for GF1, GF2, GF4 and MODIS imagery) and 2017 July 17 (for Landsat-8 OLI image) to compare and analyze the result, but it would be much better if we had used different satellite images in different seasons. Similarly, further investigation of FVI quality using more VI like AFRI, GLI, NGRDI, RVI, TVI etc. [9] could give more detail results. We were limited to five (GS, Ehlers, PC, MIHS and HPF) image fusion algorithms but NNDiffuse [59] and other algorithms could be used to investigate the result in a better way. Besides CC and RMSE, there are other quality indices like PSNR [50], entropy [14], and SSIM [60] which can give more quality results and a more robust conclusion. Similarly, in the future, we can use combined bands from different fusion methods (NIR from GS and R, G, B bands from PC or Ehlers and MIHS fusion) depending on their degree of goodness for each band to get a better FVI quality.
# Artificial Intelligence Applications to Smart City and Smart Enterprise
## 2 Contribution
### Driving and Routing Applications  
In reference [9], authors propose a new model for potential pedestrian risky event (PPRE) analysis, using video footage gathered by road security cameras already installed at crossings. The system automatically detects vehicles and pedestrians, calculates trajectories, and extracts frame-level behavioral features. K-means and decision tree algorithms are then used to classify six different classes,which are further investigated to show how they may or may not contribute to pedestrian risk. The system has been tested using video footage from unsignalized crosswalks in Osan city, South Korea.  
[PERSON] et al. [10] implemented two kinds of deep learning techniques to reflect human driving behavior for automated car driving. A deep neural network (DNN) and a recurrent neural network (RNN) were designed by neural architecture search (NAS). NAS is used to automatically design the individual driver's neural network for efficient and effortless design process while ensuring training performance. Sequential trends in the host vehicle's state can be incorporated through RNN. It has been shown from human-centered risk assessment simulations that two successfully designed deep learning driver models can provide conservative and progressive driving behavior similar to a manual human driver in both acceleration and deceleration situations.  
In reference [11], authors propose a solution to the routing problem in vehicular delay tolerant network (VDTN) based on deep learning. The approach adopts an algorithm that leverages the power of neural networks to learn from local and global information to make smart forwarding decisions on the best next hop and best next message. Experimental results show that the proposal is able to gain improvements in network overhead and hop count if compared to other popular routers.  
In reference [12], authors propose a methodology to detect texting and driving behavior of drivers. A ceiling mounted wide angle camera is used to acquire data, and a convolutional neural network (CNN) is adopted for classification aims. The CNN is constructed by the Inception V3 deep neural network, trained and validated on a dataset of 85,401 images achieving valuable results.  
[PERSON] et al. [13] propose a routing system able to continuously monitor traffic flow status providing congestion detection and warning service. The proposed system also considers situations in which information that has not been updated is made available by using a real-time prediction model based on a deep neural network. The results obtained from simulations in various scenarios have shown that the proposal is capable of reducing the average travel time (ATT) by up to 19%, benefiting a maximum of 38% of the vehicles.
# Advantage of Steerable Catheter and Haptic Feedback for a 5-DOF Vascular Intervention Robot System
## 3 Experiments  
We compared the performance of the existing curved catheter and the developed steerable catheter (Figure 10) for the vascular interventional robotic system. The effectiveness of the haptic function was experimentally tested. The phantom employed in this experiment was manufactured by United Biologics [23]. It was designed to include vascular lines of the abdominal region. The experiment comprised three scenarios, as shown in Table 1. Each scenario targeted three points of the vascular phantom. Figure 11 presents details regarding the three targets and their paths. The path to the first target was smooth compared with those to the other targets. The third target was the most difficult to reach, as the path was highly curved. The operator controlled the slave robot by using the master device with visual feedback from top-view and side-view cameras. For each scenario, the time taken to reach the target and the repulsive force measured during the insertion of the catheters were measured. The experiment was repeated 10 times for each target to obtain average data.
## 3 Finite Element Simulation of the CRCP Segment
### Type of Cracking Model Used in FE Simulation  
The smeared cracking approach has been used to simulate the cracking behavior of concrete. In this approach, the cracked material is treated as continuous, and the discontinuity of the displacement field caused by the crack is spread across the element by changing the concrete constitutive equation following crack development in the element. Cracking in concrete occurs when the tensile stress exceeds the tensile strength of concrete.
# Image Mosaicing Applied on UAVs Survey
## 4 Aerial Panorama Applications
### Feature-Based: Global Single Transformation
#### 4.1.5. SURF  
SURF-based aerial panoramas are attractive for their accuracy, comparable to SIFT in a shorter period of time. An example is the implementation of the process on workflow technology and a geoprocessing workflow tool called GeoJModelBuilder as a four-step process. First is detecting and registering; second is KNN for matching points [99]. RANSAC is used for transforming estimation and finally warping all images to the same coordinate system. The workflow approach is proposed to provide users a flexible way to create a workflow to fulfill their needs. The workflows could be bound to different algorithms for better results or less time consumption. Tests of the SURF algorithm with fast approximate nearest neighbor search (FANN) feature matching [100] were carried out through ROS using Google Maps for the simulation of the panoramic images. In the tracking object case, the methodology can be used with a Kanade-Lucas-Tomasi tracker (KLT) to track a region of interest [101]. The stitching process can be used for position estimation, as presented in [102], where position estimation methodology is applied for path planning and distance calculation by the triangle similarity principle and fusion images. In Table 6 these implementations are presented.
## 3 Test Methods
### Selection and Calibration of Measurement Equipment  
For data collection, the Donghua DH5922D Dynamic Signal Testing and Analysis System (Jiangsu Donghua Test Technology Co., Ltd., Jingjiang, China) (Figure 3a) was employed. This system supports high sampling rates, ensuring that even subtle variations in vibration signals are accurately recorded. It is equipped with real-time signal analysis capabilities, allowing for continuous monitoring of the quality of the vibration data. Before the measurements, the DH5922D system was calibrated using a calibration signal source to ensure the accuracy of data acquisition. Additionally, synchronization between all sensors and the data acquisition system was verified to avoid any timing errors in the data.  
The study utilized Donghua 2D001 accelerometers (Figure 3b), with a sensitivity of 0.3 V\(\cdot\)s\({}^{2}\)/m, a maximum range of 20 m/s\({}^{2}\), a frequency response range of 0.25-100 Hz, and a maximum sampling frequency of 500 Hz. In the laboratory environment, these sensors were initially calibrated using a known vibration standard source to ensure that their sensitivity and range met the required specifications. Prior to field measurements, the sensors were further calibrated on-site using known vibration sources (e.g., impulse loads) to verify their response accuracy in the actual measurement environment. During the testing process, regular recalibration was conducted to maintain the stability of sensor performance over the extended measurement period.  
Figure 3: Data acquisition instrument. (**a**) DH5922D dynamic signal test and analysis system; (**b**) 2D001 acceleration sensor [24].
## 4 Results and Discussions
### Physicochemical Properties
#### 4.1.1 Unconfined Compressive Strength or Uniaxial Strength
Figure 9: Results of the unconfined compressive strength (UCS) tests for the Inzari Formation rock samples. This figure displays the compressive strength values for each sample tested. The results are critical in determining the suitability of the rocks for construction use, as compressive strength directly correlates with the rock’s ability to withstand loads. finer-grain textures tend to be stronger than those with coarser textures. Since the majority of the tested rock samples have fine grains, they provide high strength values. \[\text{UCS}=P/A \tag{1}\]  
where  
\(P\) = load of rock failure (kN). \(A\) = rock sample cube's cross-sectional area (m\({}^{2}\)).