esa-sceva/satcom-chunk-collection huggingface dataset:
# CityBuildAR: Enhancing Community Engagement in Placemaking Through Mobile Augmented Reality
## 5 Findings and Discussion
### Limitations  
CityBuildAR app has technical limitations such as limited elements provided, the inability to design without being familiar with the app, and the lack of knowledge shared through the app. The constant addition of new versions and tools to the applications, rewarding participants by recognizing each input, and continuous responsiveness are required to enhance user motivation in using these tools and applications for urban planning [100]. Also, the app does not provide any guidance to the users before the design process, which needs to be addressed before officially launching the app.  
The AR application is presently in the development phase and contains several technological bugs and constraints that affect its performance.  
* Plane-detection problems: The program necessitates the completion of plane detection before object placement, as it is incapable of identifying additional planes after objects have been positioned.
* Restricted design elements: Participants were given a limited array of items for space design, perhaps hindering their creativity and design alternatives.
* Scaling limitations: Users cannot resize items, constraining design flexibility.
* Deletion errors: Specific elements experience faults during deletion, resulting in difficulties in altering the design.  
Notwithstanding these constraints, the program functions as a prototype for participatory placemaking utilizing augmented reality. Insights garnered from the testing sessions will inform subsequent enhancements, rectifying these concerns and augmenting its usability and functionalities.
# NESO-Based Path Following Control for
## 1 Introduction
To improve the maneuverability of underactuated hovercrafts, various control research has been done, such as the backstepping control [18], the fuzzy control [19], the output feedback control [20],  
Figure 1: (**a**) An underactuated hovercraft in the horizontal plan; (**b**) the geometric schematic diagram of line-of-sight (LOS) guidance. the sliding mode control [21; 22], and the robust control [23; 24]. Although there are many studies on the control methods of the underactuated hovercraft and better control performances have been achieved, the safety limit has not been taken into account in the above research. To improve the safety of underactuated hovercrafts, the safety limit constraint (SLC) was considered in controller design by [25; 26]. A safety-guaranteed auxiliary dynamic system was designed to improve the safety and maneuverability of underactuated hovercrafts by [PERSON] in [25], where the yaw controller with the safety limit was designed by control input constraint. However, the turning limit constraint is indirectly realized by restricting the control input. Thus, the safety parameter cannot be precisely limited to the safety limit. Then, a position-constrained line-of-sight (LOS) guidance algorithm and a barrier Lyapunov function-based yaw velocity controller was proposed by [PERSON] in [26] to achieve the safe control for hovercrafts with the SLC, in which the safety limit parameters were directly considered in controller design. However, the nonlinear time-varying safety limit is not taken into account in [26], and it is also too complicated in practice. Inspired by the above-mentioned studies, and to deal with nonlinear time-varying uncertainties and SLC problems, a novel safety control strategy for the path following task of underactuated hovercrafts suffering from the nonlinear time-varying turning SLC and the uncertainties is presented in this paper. The main contributions of the proposed safety control strategy include:  
* A homogenous nonlinear extended state observer is first proposed to approximate the nonlinear time-varying uncertainties of underactuated hovercrafts, which will greatly improve the accuracy and performance of the designed control system. * The nonlinear time-varying turning safety limit constraint of underactuated hovercrafts is first considered in yaw controller design by introducing an auxiliary variable to limit the virtual control input. * The NESO-based backstepping sliding mode control law are initially designed to achieve the dynamic control of path following for underactuated hovercrafts subject to nonlinear time-varying SLC and uncertainties. The rest of this paper is organized as follows: The preliminaries and problems are introduced in Section 2. Section 3 elaborates the control design, including the NESO and the NESO-based disturbances rejection safe control law with SLC, as well as the stability analysis. The illustrated application case study is presented in Section 4, and the conclusions are given in Section 5 finally.
## 3 Network Background
### Fifth-Generation Cellular Networks
Fifth-generation technology will revolutionize industries, shaping the future of mobile broadband (MBB) and the IoT in everyday life, as illustrated in Figure 1.  
It can be stated that 5G cellular networks represent the fifth generation of mobile communication systems, being designed to address the growing demand for high-speed, low-latency, and reliable wireless connectivity. They introduce advancements such as  
* eMBB for high-speed data transmission;
* URLLC to support mission-critical applications;
* mMTC to enable large-scale IoT deployments. One of the key enablers of 5G is SDN, which enhances flexibility and programmability in network management, allowing dynamic resource allocation and the efficient control of traffic flows.
## 3 Results and Discussion
### Moisture Content  
The moisture content in the raw meat was 72.45%, and as a result of cooking it declined significantly (\(p\) < 0.05) to 62.32-68.91% (Table 1). Meat cooked at 60 \({}^{\circ}\)C/2 h showed the highest moisture retention, and the lowest moisture retention was observed in samples heated at 75 \({}^{\circ}\)C/2 h. The significant effect (\(p\) < 0.001) of both temperature and time of cooking on the moisture content was noted, while the interaction of these parameters was not significant. Similar moisture contents in pork hams sous vide cooked at 61 and 71 \({}^{\circ}\)C and significantly higher moisture contents in meat cooked at lower temperatures were reported by [PERSON] et al. [21]. Limited differences in moisture content with increasing temperature of sous vide cooking and no significant effect of cooking time of lamb joins were noted by [PERSON] et al. [9]. The present results are in accordance with observations of [PERSON] et al. [22], who reported that higher temperatures and longer cooking times lead to increased water losses. The authors suggested that temperatures above 74 \({}^{\circ}\)C and cooking times longer than 240 min do not increase further moisture liberation from meat.  
Loss of water in cooked meat is caused by a leakage of cellular juice under the influence of elevated temperature. Additionally, myofibrillar protein shrinkage, which starts at 40 \({}^{\circ}\)C, leads to a subsequent decline in the interfibrillar volume which in turn reduces myofibril's ability to hold water. Finally, compression of the muscle fiber bundles due to the contraction of the perimysial connective tissue at temperatures 56-62 \({}^{\circ}\)C contributes to water release from meat [3; 36]. Low temperatures applied during sous vide cooking, and consequently low end point internal meat temperatures, apparently favor the meat's ability to retain water in its structures due to lower meat fiber shrinkage.
# Remote Technologies as Common Practice in Industrial Maintenance: What Do Experts Say?
## 4 Results
### Answering the Research Questions  
Based on the core statements in the different categories, the research questions will be answered.  
**RQ1: What are examples of successful remote strategies in industrial maintenance for intelligent manufacturing systems (Industry 4.0)?**  
From the core statements, it can be summarised that remote solutions are not yet primarily used for maintenance in the surveyed companies. Currently, industrial maintenance tasks are mostly performed on-site at the plants and by trained specialist personnel. The instruction of an employee who has not been specially trained by a maintenance expert does not yet take place in practice. Looking at the sub-areas of maintenance, it can be stated that remote technologies are used sporadically in the areas of inspection and servicing, but this does not yet take place in the area of repair. It should be noted here that the distinction between the technical terms was not always clearly delineated by some interview partners. For example, terms such as \"maintenance\" and \"servicing\" were occasionally used as synonyms by the interviewees, as the delineation of the different areas of maintenance cannot always be clearly distinguished from each other in practice. Referring to the stages of human-human communication which have already been introduced, the first two stages of human-human communication via remote are often used in the case of problems with machines. Above all, e-mail and telephone are common methods of contacting manufacturing companies in the event of malfunctions. Although video connections (e.g., via TeamViewer) are also used in addition to e-mail and telephone, the problem is rarely solved in this way. These types of remote support serve to identify faults and to make an initial assessment of the complexity of the problem. Scenarios in which faults are solved remotely are a rarity and are limited to software errors or changes that can be fixed remotely on machines. For the use of data glasses in combination with collaboration software and AR technologies, no explicit use cases and practical experience reports can yet be presented. AR solutions are currently being tested by three of the companies surveyed with regard to their suitability for usability and costs.  
**RQ2: What are the characteristics of the remote strategies?**  
Based on the literature and data material, the following characteristics for remote maintenance strategies can be stated. As illustrated in Figure 2, they are sorted according to the human-technology-organisation approach:
# Area-Efficient Universal Code Generator for Multi-GNSS Receivers
## 1 Introduction  
A global navigation satellite system (GNSS) is a positioning and time determination system that supports global navigation services using satellite constellations [1]. The position and time information provided by GNSS is widely applied in the fields of national defense, finance, communications, and medicine, and its application has recently spread to various other fields such as autonomous driving and smart devices [2, 3]. Representative GNSS satellite constellations include the U.S. Global Positioning System (GPS), Russia's GLONASS, China's BeiDou Navigation Satellite System (BDS), and the EU's Galileo, and they have been continuously researched and developed with a view to improving the accuracy and use of satellite signals. GNSS signals are transmitted in the frequency bands determined by the satellites. For example, GPS signals, which are the most representative GNSS signals, consist of LLC/A and LLC signals in the L1 (1575.42 MHz) band, L2C signals in the L2 (1227.60 MHz) band, and L5 signals in the L5 (1176.45 MHz) band [4].  
To identify visible satellites among signals received from multiple satellites and to extract navigation messages, GNSS receivers use the pseudo random noise (PRN) code [4]. The PRN code is characterized by having high correlation values for two same PRN codes, and low correlation values for two different PRNs codes. By using this characteristic of the PRN code, GNSS receivers can distinguish signals from different satellites. To maintain the correlation characteristic of the PRN code, it is important to carefully design the code. Note that since most GNSS satellite constellations employ CDMA, a different PRN is assigned to each satellite. Exceptionally, GLONASS L1 OF and L2 OF uses FDMA so that differentsatellites of GLONASS share the same PRN, and the satellites are distinguished using frequency difference.  
Although conventional GNSS receivers have generally been designed for single signals, studies on multi-signal receiver design have recently been actively conducted to achieve high accuracy, precision, and reliability [5; 6; 7]. In general, multi-signal receivers can be divided into multi-GNSS receivers that receive multiple GNSS signals of a single frequency band, multi-frequency receivers that receive single GNSS signals of multiple frequency bands, and multi-GNSS multi-frequency receivers that receive multiple GNSS signals of multiple frequency bands. However, in order for a multi-signal receiver to support various codes, the receiver should support the generation of individual codes. Therefore, the resulting problem of increased complexity must be solved. This paper proposes a structure for a universal code generator that can generate multiple codes by sharing hardware resources. With this foregoing structure, the optimized implementation of multi-signal receivers can be supported.
# Extending Partial Domain Adaptation Algorithms to the Open-Set Setting
## 5 Experiments
### Dataset and Task Description  
Our experiments were conducted for four different DA tasks for images. These were image obstruction, rotation, displacement, and re-scaling. The samples used within these tasks are depicted in Figure 3. Our dataset was comprised of the MNIST [16] and USPS [17] handwritten digit datasets, as in [8; 9]. The former contained \(28\times 28\) pixel gray-scale images, while the latter contained \(16\times 16\) pixel gray-scale images. Both datasets contained 10 classes corresponding to each of the 10 digits. The USPS images were padded to obtain a homogeneous DA problem. That is, we appended zeros on all four edges of each USPS image so that it became a \(28\times 28\) pixel image, where the original image occupied the central pixels. In all of our experiments, MNIST was used as a source dataset and USPS was used to generate a synthetic dataset for each of the tasks.  
For the obstruction task, we set a 14 pixel area starting from the top-left corner in each image in the USPS dataset to have an intensity of 1. Similarly, each image in USPS was rotated counter-clockwise by \(40^{\circ}\) for the rotation task. For the re-scaling task, the image was shrunk by a factor of 0.6 on both the vertical and horizontal axes, and then it was re-centered. Finally, for the displacement task, each image was displaced by five pixels to the left and three pixels upwards. The covariate shift introduced in all four tasks was systematic; the same transformation was applied on all USPS images.  
Figure 3: Sample images from the obstruction, rotation, rescaling, and displacement tasks with different parameters.
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 3 Results
### Comparison with Other Classical Networks  
DANet demonstrated superior performance compared to other conventional semantic segmentation networks in terms of the evaluation metrics, as shown in Table 3. The _F1-score_ metric saw an improvement of at least 0.23% (HRNet-V2) and a maximum of 1.13% (U-Net), while the _Re_ metric improved by at least 0.69% (D-LinkNet) and a maximum of 1.15% (PSPNet). Furthermore, _road lou_ showed an improvement of at least 0.14% (DeepLab V3+) and up to 1.09% (HRNet-V2).  
The effectiveness of the DANet network structure is demonstrated through experimental results. The encoder network in DANet successfully extracts road-related information from remote-sensing images, while the ASPP structure enhances the extracted information by incorporating better contextual, global, and detailed information. Additionally, DANet accurately predicts the challenges associated with road semantic segmentation in remote-sensing images, as discussed in the previous section. The predicted results align with the expected goals, as depicted in Figure 11. In comparison to other classical networks, DANet outperforms in predicting results.
## 2 UAS Platform Types and Characteristics
### Consideration of Payloads, Logistics Requirements  
The pace of technological development and rapid emergence of commercial providers of UAS and their services make estimations of platform and per-hour cost difficult, leaving potential users of UAS technology better served to consider their options based on sensor capabilities of available platforms. Many LASE systems are available with visible-spectrum cameras or, in some cases, near-infrared camera payloads. Often these systems, some of them based on RC model platforms, can be purchased outright for less than the price charged by a UAS services contractor for an individual job. However, numerous investigators have found the inadequacies of such systems which make them unsuitable for generating more than \"snapshot\" views of sites, recalling the pedigree of this class of UAS as situational-awareness tools for the military. When study plans call for spatial analysis of imagery (such as importation into GIS), the simultaneous collection of imagery with positional data is required in order to provide for image georeferencing. The latter capability is found increasingly among the smallest autopilot systems, but continues to require the collection of data from ground control points (GCPs). In order to eliminate GCP data collection, the UAV must add precise attitudinal data to imagery, allowing for _direct_ image georeferencing [14,15]. In general, LALE UAS provide a direct image georeferencing capability, and combining this feature with small size (_i.e._, in LASE UAS) is difficult.  
Payloads that deliver images in the visible and near-infrared spectral ranges tend to be among the lightest and are common to nearly all UAS platforms, due in part to considerable advancements in consumer-grade imaging products during the past decade. Thermal-infrared imaging is found less commonly aboard small UAS, with the exception of the AeroVironment Puma and Raven models whose low-resolution IR sensors are not linked to navigation data. Heavier still among electro-optical sensors are multispectral and LiDAR payloads, which weigh several kg and require power sources beyond the capabilities of small UAS to provide. Such payloads are more commonly found aboard MALE-class or larger UAS; there, high operational costs of the UAS and sensors can be balanced by long flight durations and large coverage areas. Exotic payloads such as atmospheric sampling or gas analysis systems and RADAR currently require the capabilities only large UAS can provide, and are typically deployed in operations funded among multiple publicly-funded agencies.
# Multimodal Natural Human-Computer Interfaces for Computer-Aided Design: A Review Paper
## 6 Devices for Natural HCI  
Although these interfaces are unlikely to completely replace traditional WIMP-based interfaces, the importance of multimodal natural HCIs is growing due to advances in hardware and software, the benefits that they can provide to users, and the natural fit with the increasingly ubiquitous mobile computing environment [125].  
In the process of CAD modeling, signals that can represent the design intention should first be obtained to identify the user intention. Thus, signal acquisition and identification devices are important for achieving a natural HCI. In this section, the devices used for eye tracking, gesture recognition, speech recognition, and BCI are introduced in detail, as presented in Table 2. In additional, some other devices related to natural HCIs are also briefly described.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Modalities** & **Categories** & **Devices** \\ \hline \multirow{4}{*}{Eye tracking} & Head-mounted & Dikablis Glass 3.0 [126], Tobii Glass 2 [127], Pupil Labs Glasses [128] \\ \cline{2-3}  & Tabletop & Gazepoint GP3 [129], Tobii Pro Spectrum 150 [130], EyeLink 1000 Plus [131] \\ \hline \multirow{4}{*}{Gesture} & Sensor-based & MoCap Pro Glove [132], Cyber Glove [133], Vrtrix Glove [134] \\ \cline{2-3}  & Vision-based & Kinect [135], Leap Motion [44; 136] \\ \hline \multirow{2}{*}{Speech} & DP-100 Connected Speech Recognition System (CSRS) [63], Microsoft Speech API (SAPI) [54], CMUSphinx [137] \\ \hline \multirow{4}{*}{EEG} & Saline electrode & Emotiy Epoc+ [3; 58] \\ \cline{2-3}  & Wet electrode & Neuroscan [56] \\ \cline{2-3}  & Dry electrode & Neurosky MindWave [138], InteraXon Muse [139] \\ \hline \multirow{4}{*}{Others} & Haptic & SensAble PHANTom [110], SPIDAR [113] \\ \cline{2-3}  & VR/AR & HTC Vive [140], Oculus Rift [141], HoloLens 2 [142] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of devices for natural HCIs.
### Atomic and Electronic Structure of [M(Salen)] Complexes and Their Polymers
#### 3.2.2 VB PES  
As noted above in this review, studies of occupied electronic states of the valence band for monomers [M(Salen)] and their polymers using valence photoemission (VB PE) have so far been limited to a few works [45; 46; 53]. These works are devoted to studying the electronic structure of only the H\({}_{2}\)(Salen) molecule and the [Ni(Salen)] complex. These VB PE spectra were recorded with high energy resolution using exciting photon energies of 21.2 eV (He(I) resonance source) and 75, 150 eV (synchrotron radiation) from thin layers of the corresponding substances deposited _in situ_ by thermal evaporation on clean Pt plates under ultrahigh vacuum conditions (about 10\({}^{-9}\) mbar). DFT calculations were additionally used to interpret the VB PE spectra [45; 46]. Important results obtained in these works are that the photoelectron bands observed in the valence-band spectra in the binding energy  
Figure 14: C 1\(s\) (a), N 1\(s\) (b), and O 1\(s\) (c) photoelectron spectra of the H\({}_{2}\)(Salen) molecule and [Ni(Salen)] complex measured using Al K\(\alpha\) radiation (_hv_ = 1486.6 eV) and plotted on the scale of electron binding energies relative to the Fermi level [46].  
range of 8-17 eV are mainly associated with \(\sigma\) molecular orbitals, in which contributions from the C \(2s\) and C \(2p\) AOs predominate. In turn, in the low-energy region of 2-6 eV there are bands associated with molecular orbitals with dominant contributions from Ni \(3d\) AOs. In addition, DFT calculations of total and partial DOS for the valence band of both compounds were found to well describe the shape of their experimental VB PE spectra. In the case of [Ni(Salen)] compared to H\({}_{2}\)(Salen), the center of gravity of partial N \(2p\) and C \(2p\) DOS is shifted toward higher binding energies, while that of O \(2p\) DOS is shifted in the opposite direction (Figure 15). This finding is explained by an increase in the effective charges of carbon and nitrogen atoms and a corresponding decrease in the effective charge of oxygen atoms in the ligand during the formation of the [Ni(Salen)] complex. An equally interesting result was obtained in the work [53], where VB PE spectra of the molecular complex [Ni(Salen)] were measured using ultraviolet, soft X-ray, and resonance photoemission methods using photons with energies from 21.2 to 848 eV. As a result, it was established that electronic states at a binding energy of 3.8 eV are associated specifically with the Ni \(3d\)-derived MOs of the complex (see Figure 8). More detailed information is provided in Section 3.1.2 of the current review.  
Thus, a combined experimental and theoretical approach to studying the occupied electronic states of the valence band can be successfully used for other metal-salen complexes. At the same time, it should be emphasized that for poly-[M(Salen)] polymers, studies of occupied electronic states of the valence band using valence photoemission are currently completely absent.