esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Different Fibers on Performance of Bitumen Binders and Thin-Overlay Bitumen Mixtures
## 3 Results and Discussion
### Performance Analysis of Fiber Bitumen Mixture
#### 3.3.1 Indirect Tensile Testing
The addition of fiber notably enhances the \(T_{\text{FAC}}\) of the bitumen mixture. When a 2% dosage is applied, the \(T_{\text{FAC}}\) of B-2, P-2, and G-2 increased by 2.8%, 2%, and 3.19%, respectively, and the differences among the three were small. When the content increased to 4%, the \(T_{\text{FAC}}\) of B-4, P-4, and G-4 increased by 2.82%, 4.59%, and 1.36%, respectively. The definition of \(T_{\text{FAC}}\) is closely related to the post-peak behavior of the indirect tensile test curve. As illustrated in Figure 8b, the slope of P-4 post-peak curve is the smallest, which signifies that incorporating polypropylene fiber at a 4% concentration leads to the most substantial enhancement in the toughness of the bitumen mixture. The slope of G-4 is the smallest. Incorporating 4% glass fiber into the bitumen mixture enhances its initial crack resistance; it falls short in sustaining resistance against persistent cracking, leading to lackluster overall toughness. Fibers enhance the mixture's resistance to cracking through mechanisms such as lapping, reinforcement, and load transfer. However, fiber agglomeration and uneven dispersion weaken its positive role in the mixture, thereby reducing crack resistance and toughness. The various types and amounts of fiber play an important role in influencing how bitumen mixtures perform during different stages of cracking. Basalt fiber and glass fiber can effectively resist the propagation of small cracks in the early stage of crack resistance due to their high monofilament tensile strength. Polypropylene fiber, with its excellent flexibility and elasticity, is more conducive to delaying the further expansion of cracks, showing the potential to improve toughness in bitumen mixtures [62].
## 4 System Components
### Networking System
#### 4.1.1 Networking Demands  
The data traffic related to the system operation can be classified into two major types, namely command and control traffic, as well as coordination traffic [41]. The former allows the ground control to monitor and influence the behavior of drones, as well as monitor messages with information about the drone's status. The coordination of traffic encompasses information related to cooperation and collision avoidance. In addition to the mentioned system traffic, drone communication can also include payload data related to services implemented on-board such as data generated by observations of the physical environment (e.g., video cameras) and data generated and consumed by passengers.  
In what concerns the air-to-ground communication links, the International Telecommunications Union (ITU), under The 3 rd Generation Partnership Project (3 GPP), classified command and control communication as well as payload communication for drone safe operations in terms of throughput, reliability and latency, as detailed in Table 1. These performance requirements aim to guarantee timely communication, processing and coordinated movement in real time. Although the information provided by 3 GPP refers to air-to-ground links in a centralized scenario (c.f. Figure 1) the same quality requirements should be expected to any air-to-air links between drones.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Data Type** & **Throughput** & **Reliability** & **Latency** \\ \hline DL (Ground station to UAV) & Command and control & 60–100 Kpps & \(10^{-3}\) Packet error rate & 50 ms \\ \hline UL (UAV to Ground station) & Command and control & 60–100 Kpps & \(10^{-3}\) Packet error rate & - \\ \hline UL (UAV to Ground station) & Application Data & Up to 50 Mbps & - & Similar to Terrestrial user \\ \hline \hline \end{tabular}
\end{table}
Table 1: Air-to-ground communication requirements for drone operations specified by 3 GPP [42].  
Communication over air-to-air links is expected to have increased importance in autonomous drone systems, since drones require exchanged information among themselves to be able to take local decisions; Drone swarms require reliable wireless for real-time distributed coordination and processing to achieve system wide goals [43]. Hence, for swarms to attain a wide-area communication in support for coordinated and distributed real-time processing, the networking system needs to have network performance specifications embracing reliability, high throughput and low delays to offer greater cooperation and synchronization for effective control of aerial nodes [12].
# Fully Superconducting Josephson Bolometers for Gigahertz Astronomy
## 1 Introduction
As a consequence, in principle the sensitivity of the nano-TES and the JES can be in situ controlled. Moreover, the nano-TES and JES intrinsic values of NEP were deduced from the transport and thermal data. The nano-TES is predicted to show a thermal fluctuation limited NEP of about \(5\times 10^{-20}\)\(\mathrm{W}/\sqrt{\mathrm{Hz}}\)[33], while the JES is expected to provide an NEP of the order of \(10^{-25}\)\(\mathrm{W}/\sqrt{\mathrm{Hz}}\)[34]. As a consequence, these sensors could be the basis of detectors pointing towards unprecedented sensitivities in the GHz and THz bands. In addition to gigahertz astronomy, these sensors could be employed in medical imaging [35], industrial quality controls [36] and security applications [37]. This review is organized as follows. Section 2 covers the theoretical bases of the nano-TES and JES highlighting their structure and operating principle. Section 3 introduces the thermal and electrical model employed to calculate the performance of the sensors, such as noise equivalent power and response time. Section 4 describes the experimental realization of the nano-TES and the JES. In particular, the nano-fabrication, the thermal,  
Figure 1: Frequency and energy ranges relevant for different GHz astronomical sources, such as cosmic infrared background (CIB), gigahertz-peaked radio sources (GPRS), fast radio bursts (FRB), emission from galaxies and cosmic microwave background (CMB). spectral and electronic transport measurements are shown. Section 5 reports the sensing performance deduced from the experimental data. Finally, Section 6 resumes the results and opens to new applications for the nano-TES and the JES detectors.
###### Abstract  
Real control systems require robust control performance to deal with unpredictable and altering operating conditions of real-world systems. Improvement of disturbance rejection control performance should be considered as one of the essential control objectives in practical control system design tasks. This study presents a multi-loop Model Reference Adaptive Control (MRAC) scheme that leverages a nonlinear autoregressive neural network with external inputs (NARX) model in as the reference model. Authors observed that the performance of multi-loop MRAC-fractional-order proportional integral derivative (FOPID) control with MIT rule largely depends on the capability of the reference model to represent leading closed-loop dynamics of the experimental ML system. As such, the NARX model is used to represent disturbance-free dynamical behavior of PID control loop. It is remarkable that the obtained reference model is independent of the tuning of other control loops in the control system. The multi-loop MRAC-FOPID control structure detects impacts of disturbance incidents on control performance of the closed-loop FOPID control system and adapts the response of the FOPID control system to reduce the negative effects of the additive input disturbance. This multi-loop control structure deploys two specialized control loops: an inner loop, which is the closed-loop FOPID control system for stability and set-point control, and an outer loop, which involves a NARX reference model and an MIT rule to increase the adaptation ability of the system. Thus, the two-loop MRAC structure allows improvement of disturbance rejection performance without deteriorating precise set-point control and stability characteristics of the FOPID control loop. This is an important benefit of this control structure. To demonstrate disturbance rejection performance improvements of the proposed multi-loop MRAC-FOPID control with NARX model, an experimental study is conducted for disturbance rejection control of magnetic levitation test setup in the laboratory. Simulation and experimental results indicate an improvement of disturbance rejection performance.  
multi-loop model reference control; PID controllers; FOPID retuning control; disturbance rejection control
## 3 Proposed System Model
### NSR Model  
This study made the assumption that each NSR requires the specified number of nodes, CPU capacity, bandwidth, security, and lifetime. Hence, the NSR request was modeled as an undirected graph \(G_{NSR}=(N_{NSR},E_{NSR})\) where \(N_{NSR}\) indicates the set of nodes in NSR and \(E_{NSR}\) denotes the set of linkages in NSR. Each node consists of (\(CPU_{NSR},SPL_{NSR},LT_{NSR}\)), where \(CPU_{NSR}\) is the CPU capacity of the NSR. \(SPL_{NSR}\) is the security power level of the associated NSR. \(LT_{NSR}\)is the lifetime of the NSR. \(BW_{NSR}\) is the set of bandwidth requirements of requested links in NSR [33]. During each interval, the physical infrastructure was used to allocate nodes and establish links for the NSR. Each time a new NSR is received, the physical infrastructure's available resources were updated depending on the preceding request's \(LT_{NSR}\).
# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
## 2 Related Work
### Common Object Detection  
Object detection is a long-standing, primary, and challenging problem in computer vision [10]. The goal of object detection is to determine in the image whether there are any object instances of given classes (such as humans, cars, dogs, or bottles), and if so, to return the spatial position and range of each object instance through a bounding box [11; 12].  
Figure 1: Overview of AIR-YOLOv3: (**a**) UAV system; (**b**) dataset construction; (**c**) fine-tuned YOLOv3 for aerial infrared pedestrian detection; (**d**) channel pruning; (**e**) layer pruning; and (**f**) test results.  
Since deep learning entered the field, there have appeared some milestone approaches, which are divided into two main categories: two-stage and one-stage [13]. Two-stage detectors usually have high localization and object recognition accuracy, while the one-stage detectors can achieve high inference speed [6]. For example, for the same graphics card, the FPS of YOLOv3 is generally four times that of faster R-CNN [14; 15].  
Two-stage detectors split the detection task into two stages, proposal generation and making predictions for these proposals. R-CNN series [16] are the landmark works of the two-stage method. They proposed RPN (region proposal network) to complete the detection task by an end-to-end neural network. A typical algorithm is mask R-CNN [16], which is an extended version of faster R-CNN and is an approach for object detection as well for instance segmentation.  
One-stage detection algorithms can directly generate the class probability and the location of the object through one overall stage [13]. Typical algorithms are YOLO [17], SSD [18], and RetinaNet [19].
# Robotics Applications in the Hospital Domain:
## 3 Robotics in Healthcare: An Overview  
### The Impact of Robotics in Healthcare  
The rapid adoption of robotics in response to the global COVID-19 pandemic has highlighted the potential of this technology to reshape many sectors. Robotics is transforming not only surgery and rehabilitation but also emerging fields like telepresence, remote monitoring [3], and personal care tasks [4]. For instance, robots are being utilized in areas not directly connected to clinical practice, such as transporting medications and medical supplies. These autonomous robots can navigate corridors and deliver critical items like medications, biological samples, and medical equipment quickly and accurately [5].  
The integration of robots into healthcare teams addresses two significant challenges: the need to care for an aging population and the shortage of qualified healthcare professionals. Moreover, it provides personalized care for diverse needs. The integration of research and industry is a crucial aspect of healthcare. The development and implementation of robotic technologies have the potential to significantly transform the delivery of care, improving both its efficiency and quality [6]. In addition to the technical challenges of implementing robotic solutions, their integration in healthcare requires an in-depth study of the ethical, legal, and social implications.  
In the European framework of rights and values, [PERSON] et al., 2017 [7], address the regulation of emerging robotic technologies by highlighting several key legal and ethical issues [7,8]. In response to these major challenges, the RoboLaw project proposed \"Guidelines on Regulating Robotics\", which address the regulation of emerging robotic technologies in Europe with respect to the law and ethics [8]. These Guidelines are the result of a collaborative project at the European Commission level, which aimed to explore the ethical, legal, and social implications of the growing use of robots across various sectors of society. In the context of robots operating in hospitals, a set of EU-based health and safety requirements have been defined. Specific regulations have been developed such as the ISO 10218 [9], is a standard defined for robots and robotic devices, Part 1 specifies the safety requirements for industrial robots, while Part 2 specifies the safety requirements for robot systems and integration. Developed by Technical Committee ISO/TC 184, Automation systems and integration, Subcommittee SC 2, in 2011. Which assesses safety risks based on the training level of users interacting with these robotic systems, ensuring safety through control mechanisms like safety inspections and validations, protective measures such as emergency stop buttons, etc. [7,8].
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 4 Properties of Nonlinear Photoacoustic
### Remote Underwater Acoustic Source
Underwater filamentation is observed with \(P/P_{critical}\approx 2,f\)-number = 80 for nanosecond lasers where filaments propagated up to 25 cm (Rayleigh length 2 cm) [71]. However, both nanosecond and femtosecond pulses cause longitudinally localized laser absorption and generate a single acoustic shock at small \(f\)-number [71]. Underwater optical filaments over 55 cm (Rayleigh length more than 35 cm) generation in water with nanosecond pulse duration laser is reported in [72], where the filament radius (\(50\pm 10\) um) is much larger than the few-micron diameter generated with the femtosecond laser. However, the underwater filament sizes and propagation characteristics of ultra-short and nanosecond pulses may differ due to a variety of factors, including significantly different light intensity in the filament, distinct ionization mechanisms and rates, and effects unique to the large bandwidth of the ultra-short laser pulses. The advantage of using longer laser pulses like nanosecond pulses is that they contain more energy, resulting in high conductivity plasma.
## 3 Security and Privacy Challenges
### Mitigation Strategies Against EC Security Challenges
A signature and anomaly-based secured edge computing intrusion detection system (SEC-IDS) framework is proposed in [73] for improved intrusion detection. A hybrid LDA-LR (Linear Discriminant Analysis-Logistic Regression) edge computing model is proposed in [142], utilizing machine learning and an IDS.
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.2 Impact Strength  
Impact tests were conducted on samples printed horizontally, with results presented in Figure 11A,B for PLA and PET-G composites, respectively. The tests revealed that passing the filament through a MeOH reservoir, resulting in PLA/MeOH and PLA/silanes/siloxanes composites, led to higher impact strength values compared to neat PLA. Specifically, PLA/MeOH had an impact strength of 17.90 kJ/m\({}^{2}\), which is approximately 16% higher than the reference. Short alkyl chain alcohols were more effective at plasticizing the polymer matrix, increasing chain mobility, and resulting in greater impact resistance. For tested alcohols, i.e., i-PrOH, CyOH, and n-BuOH, polylactide was less soluble, thus the plasticizing effect was lower compared to MeOH [16].  
Figure 9: Scheme showing a silane as a crosslinking agent in polymers.  
Silane-modified PLA samples showed that the PLA + BRB176/MeOH composite had the highest value. The amino groups present in the modifier could interact with the polylactide matrix (esterification, self-condensation), and hydrogen bonding between PLA and the amino group was also possible, which increased the durability and effectiveness of the modifier compared to other systems [17]. Tensile strength tests (Section 3.3.1) showed a similar effect. In the publication titled \"Enhanced Performance of Polylactide Film via Simultaneous Biaxial Stretching and Silane Coupling Agent as a Thermal Shrinkable Film\" by [PERSON], [PERSON], and [PERSON], a crosslinking reaction of amino silane was proposed (Figure 12) [17].  
Figure 11: Impact strength of (**A**) PLA/alcohols/silanes/siloxanes; (**B**) PET-G/alcohols/silanes/siloxanes.  
Figure 10: Mechanical properties: (**A**) tensile strength for PET-G/alcohols/silanes/siloxanes; (**B**) elongation at break for PET-G/alcohols/silanes/siloxanes.  
In contrast, PET-G composites showed decreased impact strength in each case compared to the reference sample, with alcohol or alcohol/silanes/siloxane modification leading to increased brittleness. Poor solubility of the polymer in methanol hindered the diffusion of the solvent, resulting in a lack of observed plasticizing effects on the matrix.  
Table 2 summarizes the physical and chemical properties of the alcohols used in the study, as well as the thermal and strength test results of L-FDM prints. L-FDM prints of PLA with alcohols having the lowest pKa values (MeOH and BnOH) exhibit the highest impact resistance, with values of 17.9 and 17.2 kJ/m2, respectively.
## 6 Conclusions and Outlook  
We have established a formal (algebraic) system of the semantic integration of observation-based information and showed a successful approach to the challenge of integrating them in a forest fire scenario. This illustrates the functionality that we envision for a future Observation Web and introduces a solution for semantic interoperability. It provides an alternative to common logic-based attempts. Compared to these works, the algebraic approach directly reflects that (1) sensing is a function from the world to data; and (2) any form of information integration is a function, too. Such (dynamic) behavior cannot be directly represented by logic-based approaches, which have to rely on system. However it should be noted that the algebraic approach has drawbacks beyond its current rare use in the Semantic Web community. Most importantly, it cannot be used to define disjoint concepts, and domain level semantics might be overwritten during instantiations.  
The proposed approach adds a next level of maturity to our previous work [12]. If we now, for example, had all satellite and VGI data available for the 2010 forest fire season in France, we could find out how many forest fire events were detected by both sources and how many were detected by only one. Such additional information, which can be derived from using the two sensor sources, satellites and VGI in this case, can be used to further calibrate the involved sensors and thus to improve measurement quality [50].  
In order to further validate and extend the presented formal theory, we plan to experiment with a more detailed workflow for processing forest fire information, as for example presented in [20] and to implement also other cases, such as the above mentioned snow map, oil spill monitoring and urban planning systems. The latter activity will also show if the assumptions that we derived from DUL and SSN hold true.  
As the generalization of processing operators from multiple concrete cases to common functions on observation results would add considerable value in terms of re-usability and can be achieved quickly, we intend to focus on these parts of the implementation first. Our intermediate goals are: (1) the examination of an integrative quality model (including propagation) for classical sensor based information, results of environmental simulations and VGI; and (2) experimentations on scale transitions.  
We are confident that, in the end, we will contribute to the establishment of a digital nervous system for our planet and to the development of further value added processing. We believe that both are essential pillars for integrating environmental and geospatial matters into future internet applications.
## 3 Study Area and Dataset
### Ancillary Data  
Given the difficulties involved in extracting roads from remotely sensed images, linear vector data from the BDCarto(r) database were also collected for roads in the study area. BD CARTO(r) is a mapping reference database produced by IGN and uses a departmental scale (1:50,000). These linear vector data were converted into polygons by building buffers. The buffer widths depended on the type of road: 10 m for two lane roads and 20 m for multi-lane highways.
# A New Ice Accretion Model for Aircraft Icing Based on Phase-Field Method
## 2 Materials and Methods  
The numerical procedure of aircraft icing includes grid generation, flow field calculation, droplet trajectory calculation, ice accretion calculation, etc. The structured meshes generated by solving elliptic partial differential equations are taken as background grids, based on the given geometry. The Navier-Stokes equations based on the density pattern are solved to obtain the flow field. In the solver, the finite volume method based on a second-order central scheme with artificial viscosity by [PERSON] is applied for spacial discretization, and an explicit fourth-order Runge-Kutta algorithm is used for the time discretization. The S-A model is chosen for the turbulence model. In order to accelerate the convergence, the local time step and residual smoothing methods are introduced. The droplet motion equations based on Eulerian two-phase flow are solved to obtain the droplet trajectories. The spatial and temporal discretization methods are the same as the flow field equations. On this basis, the impingement characteristics of droplets can be calculated. Then, according to the droplet impingement characteristics, the ice shape can be predicted by the ice accretion model.