esa-sceva/satcom-chunk-collection huggingface dataset:
# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 4 Experiments
### Experiment of Feature Extractor  
As described in Section 3.4, the feature extractor adopts a dual-channel design, comprising two distinct ResNets dedicated to processing RGB and depth data separately. To validate the efficacy of employing two separate channels instead of a shared channel, a comparative experiment was devised. Specifically, an experimental group was established where the two channels of the feature extractor shared one ResNet during the training process. This is equivalent to having a single channel learn and extract both types of data features simultaneously. The experimental outcomes are presented in Table 3, where the suffix '1 CH' represents the experimental group utilizing a shared channel. The results indicate that employing two independent convolutional extraction networks contributes to enhancing model performance. The distinct channels facilitate the network in learning different types of features due to the disparities between RGB and depth features.
# Dynamic Modeling and Altitude Control for Flying Cars Based on Active Disturbance Rejection Control
## 4 Simulation Verification
### Altitude Control  
After the verification of the model, a simulation of the altitude control of the flying car was carried out. In the simulation, the reference altitude was set at 500 m. The initial altitude of the flying car was set at 510 for the first case and 500 m for the second one. In Case 1, a 4 m/s wind gust was set for 100 s and lasted for 20 s. In Case 2, in addition to the wind gust, a 3 m/s constant wind was also set along the \(Z\)-axis of the geodetic coordinate system for 100 s.  
The results are presented in Figures 7 and 8. Firstly, in Figure 7, it can be observed that the proposed controller can realize accurate altitude control. Figure 7a shows the flight altitude, and Figure 7b shows the thrust of the propeller. At 100 s, it is obvious that the flight altitude shows some oscillations under the disturbance of the wind gust. However, with the ESO, it can be clearly seen that the control error converges before 140 s. The average error in the stable stage is less than 1 m.  
The results of the other simulation case are presented in Figure 8. Figure 8a presents the flight altitude, and the thrust is presented in Figure 8b. In this paper, the proposed controller was compared with a PID controller. The parameters of ADRC were set as \(\omega_{o,1}=0.7\), \(\omega_{c,1}=0.25\), \(b_{1}=0.17\), \(\omega_{o,2}=1.2\), \(\omega_{c,1}=0.14\), and \(b_{1}=0.14\). The parameters of the PID controller were set as \(k_{p,1}=0.6\), \(k_{d,1}=0.8\), \(k_{i,1}=0.1\), \(k_{p,2}=0.4\), \(k_{d,1}=0.4\), and \(k_{i,1}=0.1\). It can be seen that the proposed controller can realize accurate trajectory tracking despite wind gusts and constant wind disturbances. The flight altitude of the flying car remains stable and converges to the reference value despite the external wind. From the results, it can be observed that the proposed method has better control effectiveness. Its  
Figure 8: Altitude control of the ADRC and PID controllers. (**a**) Flight altitude. (**b**) Thrust.  
Figure 7: Altitude control with external wind disturbance—Case 1. (**a**) Flight altitude. (**b**) Thrust.  
maximum error is about 20% less than that of the PID controller. Its average error is about 0.6 m, whereas the average error of the PID controller is 0.53 m.  
Furthermore, Figure 9 presents the observation error of the outer-loop controller. It can be seen that the observation error converges to zero by accurately adjusting the controller coefficients. Under this condition, accurate altitude control can be realized, dynamically compensating for external disturbances.  
Then, to verify the control effectiveness under hardware variations, a Monte Carlo simulation was carried out. The results are shown in Figure 10. In the simulation, the aerodynamic coefficients of the propeller were varied by \(\pm\)20%. It can be seen that the proposed controller can realize accurate control under various hardware conditions. The tracking error can converge within a limited time.
# Using Virtual Soundwalk Approach for Assessing Sound Art Soundscape Interventions in Public Spaces
## Materials and Methods
### Experiment in the Auralisation Laboratory
#### 2.3.2. Auralization Laboratory Setup  
In order to further compensate for the drawbacks of the soundscape assessment in laboratory conditions following steps were conducted:  
1. the auralization laboratory was equipped with second-order Ambisonics (SOA) system to enable reproduction of three-dimensional spatial relations of both static and dynamic sound sources;
2. panoramic photographs taken at the exact position of the microphone were reproduced on screen placed in front of the listeners simultaneously with the corresponding audio track;
3. an interface was programmed on a tablet to simulate virtual walk and provide intuitive understanding of spatial relations characteristic for urban open spaces in question.  
Digital audio workstation running the application Reaper and the Ambisonics decoder plug-in DecoPro was used for playback, as well as to simultaneously display the panoramic pictures using the VLC plugin. Audio and video playback from the computer were operated by the participant via the tablet, running the DAWOSC application showing a map of the location with measurement points marked and using OSC messages via the wireless computer network, similarly as described in [39]. The auralization room comprised \(16+1\) active speaker system, models Yamaha HS50 and Yamaha SW10 and a 27-inch monitor.  
A common comment was that respondents, when focused on the aural experience, find all sounds in laboratory conditions louder than usually experienced in the city, although the listening level was carefully matched using the sound level meter (SLM) and the SPL values measured in situ. [PERSON] et al. [78] noted good immersion but poor localization when using an Ambisonics system and a FOA microphone with coincident capsules for soundscape assessment. Accordingly, several participants positively commented on the experienced spatial impression.
## 1 Introduction
### Importance of Fourier Phases for Nonlinear Seismic Design  
To demonstrate the importance of the phase spectrum for nonlinear seismic design, we simulated DRSCEMs by taking Fourier phases from different observed ground motions. Since a proper initial Fourier amplitude is needed to simulate DRSCEMs, the Fourier amplitude obtained from the acceleration record during the Northwest China earthquake was assigned as the initial Fourier amplitude. The information of this event is shown in Table 1 record number 1. The Fourier phases are taken from different recorded ground motions shown in Table 1 records number 2 to 7. Phases of different earthquake events with different magnitude and epicenter distance are used to investigate the phase effect on nonlinear response of a structure. Using these simulated DRSCEMs, we calculated the yield seismic coefficient (YSC) demand spectra. The YSC is defined by the yield strength of the structure \(F_{y}\) divided by structural weight \((W)\) as follows:  
\[K_{h_{y}}=\frac{F_{y}}{W}=\frac{A_{y}}{g} \tag{1}\]  
where \(A_{y}\) is the yield acceleration and \(g\) is gravitational acceleration. More details about how to calculate YSC are described at the Section 5.1.  
Calculated results are shows in Figure 1. Figure 1a shows the examples of YSC demand spectra obtained by maintaining the ductility response of a structure equal to 4. It is clearly observed that the YSC demand spectrum is strongly affected by the assigned phase spectrum of earthquake motion. As an example, the value of the YSC demand spectra at the natural period of 0.5 s varies from 0.14 to 0.26, this difference increases the design force by more than 85%. On the other hand, it is commonly recognized that YSC demand spectra are also affected by the initially assigned Fourier amplitude. Several DRSCEMs are, therefore, simulated by different initial Fourier amplitudes taken from Table 1 records number (1, 3 and 7) with the same phase spectrum being fixed to the Kobe earthquake motion phase (record number 8). Figure 1b shows the YSC demand spectrum calculated by assigning different initial Fourier amplitudes but a fixed Fourier phase. From this figure, it is concluded that the effect of assigning different initial Fourier amplitudes is very minor. However, effort to reduce this type of minor effect is still necessary. Therefore, the average Fourier amplitude of several earthquake motions have been taken to reduce this effect. The reason we use the average Fourier amplitude of several earthquake motions as the initial Fourier amplitude is discussed at the beginning of Section 4. Regardless, a modeling the phase spectra of earthquake motion is an important issue for developing seismic design procedures that take into account the nonlinear behavior of structures.  
Figure 1: YSC demand spectra calculated from DRSCEMs simulated by (**a**) Same initial Fourier amplitude and different phases and (**b**) Same phase and different initial Fourier amplitudes.
# The Life-Cycle Environmental Impact of Recycling of Restaurant Food Waste in Lanzhou, China
## 2 Materials and Methods  
### Life-Cycle Assessment  
Life-cycle assessment (LCA) is used to assess the environmental impact of the entire treatment life cycle of a product, process, or activity [34]. It replaces the traditional abstract assessment of a single component with a specific assessment of the whole process. In this way, it can avoid the limitations of traditional assessments of environmental impact, and can quantify environmental impacts effectively [35]. LCA is the most scientific assessment method of environmental impact of products or environmental management [36], and it can comprehensively and thoroughly capture the energy consumption and environmental impact resulting from the entire process of human activity [37]. It is becoming increasingly valued and favored, and it is widely used in the field of environmental engineering. LCA includes four parts: objective and scope, inventory analysis, impact assessment, and explanation of results.  
In the study of life-cycle assessment, specific objectives and a scope should be determined based on the research area and potential applications. We attempted to assess the life cycle of restaurant food waste in Lanzhou in terms of the resources consumed and the environmental impact of different phases of the anaerobic digestion-aerobic composing technique, and we compared these results to those produced using traditional treatment techniques, specifically, landfills and incineration. The study is expected to provide data regarding the harmless treatment and reclamation of restaurant food waste, as well as also suggestions for the improvement of treatment techniques. The research scope involves the entire process from the production of restaurant food waste to the final disposal. Specifically, the process has eight phases: collection and transportation, pre-processing, solid composting, anaerobic digestion, biogas power generation and heating, biogas slurry composing, and wastewater treatment. The functional unit is here defined as the treatment of one ton of restaurant food waste. Figure 1 shows the system boundaries of the life cycle of restaurant food waste. After food waste is pre-treated, the liquid phase is subject to anaerobic digestion to generate biogas for power generation and the supply of biogas boilers; the solid phase and biogas residue are subject to aerobic composing to produce organic fertilizer.  
Figure 1: System boundaries of life cycle of restaurant food waste.  
Figure 2 and Table S1 (Supplementary Materials) list the life-cycle inventory. There are eight phases of input and output, describing the materials, energy, and pollutant emissions. Finally, the paper analyzes and discusses the results of assessment of the life cycle of restaurant food waste and proposes relevant strategies and suggestions.
## 7 Conclusions  
V2X is a promising new technology, which aims to solve many transportation-related problems, but, first of all, it tries to create accident-free transportation. As we can see, this type of communication has a lot of peculiarities, which the radio access technology has to reflect on. We mainly focused on the direct V2X communication due to its high acceptance among OEMs and the fact that these are typically standardized messages with well-definedservice operations and message formats. In the presented research work, we wanted to highlight the importance of domain-specific optimization and use-case-based evaluation, considering the special requirements of direct V2X communication. The most important requirements, where we found controversies and obstacles, can be found below:  
* The communication range.
* Security flaws, including passive attacks, pilot contamination, MCS saturation, uplink jamming, leaky wave antennas, and the physical layer key.
* Used frequencies.
* Positioning.
* Decentralized network (having multiple MNOs can cause issues).
* A dependency on third-party services (the lack of GNSS signal can result in service degradation).
* Backward compatibility.  
As part of future research, the found controversies could be further evaluated numerically based on the use cases. These controversies can also lay the foundation for further research to eliminate the technology's existing weaknesses. The practical application of the identified problems can differ considerably. Some of the problems have initial solutions, such as local positioning or time-sensitive use cases; others do not. Moreover, there are problems, such as the multiple mobile network operator or the used frequencies problem, where the primary problem is not mainly technological but regulatory or financial.  
Many of these issues are addressed by mid- and long-term solutions for 5G advanced recommendations and 6G visions. This paper provides a brief overview of these activities and some proposals that are highly relevant for C-V2X.  
Abstract, introduction, and conclusions, D.F. and P.V.; requirements, A.W., D.F., and P.V.; enablers, A.K., O.C., and D.F.; projects, D.F. and H.H.; obstacles, W.A., O.C., and D.F.; conceptualization, D.F. and P.V.; methodology, D.F.; investigation, D.F. and P.V.; resources, P.V.; writing--original draft preparation, D.F., P.V., A.W., H.H., O.C., and A.K.; writing--review and editing: D.F., C.H., and P.V.; supervision, P.V.; funding acquisition, D.F. and P.V. All authors have read and agreed to the published version of the manuscript.  
Part of this research was funded by the AIMS5.0 project, which received grant from the European H2020 research and innovation program, ECSEL Joint Undertaking, and National Funding Authorities from 12 involved countries--including Hungary--under grant agreement no. GAP-101112089.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Control Design for Transition Phase
### Guidance Law Design
#### 3.1.1 Altitude--\(\theta\) Channel  
This channel is used for the fixed-wing mode, which controls the altitude by adjusting the pitch angle command to achieve the altitude hold function. Figure 5 shows its geometric principles through a diagram. The guidance law design is introduced as follows.  
The difference between the desired altitude and the current absolute altitude is calculated using Equation (7) to obtain the altitude error; the altitude error is then used in Equation (8) to generate the climb rate command; the climb rate command is converted into the flight path angle command using Equation (9); and the pitch angle command is obtained using Equation (10). The pitch angle command is fed into the outer loop pitch attitude control law to maintain the climb rate. Moreover, the climb rate and the normal load factor are also limited. Therefore, the altitude tracking is realized by transforming the altitude command into the pitch angle command.  
\[\Delta H=h_{c}-h \tag{7}\]  
\[\dot{h}_{c}=k\cdot\Delta H \tag{8}\]  
\[\gamma_{c}=\arctan(\frac{\dot{h}_{c}}{v_{g}}) \tag{9}\]  
\[\theta_{c}=\frac{k_{\gamma}(s+Z_{\pi})}{s}\gamma_{c} \tag{10}\]
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Loss Function Selection  
To assess the performance of the overall framework, an evaluation of different proposed loss functions is needed. In particular, we evaluated 5 different loss functions for the binary backbone, as introduced in Section 3. This first step of the proposed solution tackles the binary segmentation problem, i.e., burned area delineation. Table 1 shows the performance achieved by all the considered architectures with the aforementioned loss functions. We chose the IoU evaluation metric to compare the binary backbones with the considered loss functions. Models with higher IoU scores perform better. The best performance is achieved by the BCE loss in all the models: 0.75 IoU for the DS-UNet (being the best overall), 0.74 for the DS-UNet++, 0.65 for the DS-SegU, and finally 0.72 for the attention-based model.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **BCE** & **Dice** & **B + D** & **B + S** & **sIoU** \\ \hline DS-UNet & **0.75** & 0.43 & 0.55 & 0.12 & 0.20 \\ DS-UNet++ & **0.74** & 0.57 & 0.46 & 0.21 & 0.20 \\ DS-SegU & **0.65** & 0.26 & 0.24 & 0.17 & 0.18 \\ DS-AttU & **0.72** & 0.45 & 0.44 & 0.29 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performances in terms of IoU for the binary-class backbones trained with different loss functions. The best loss configuration for each model is highlighted in bold.  
Given the performance of the binary segmentation model, we now evaluate the quality of the predictions made by the entire framework in the severity estimation problem in terms of rMSE, considering the seven loss configurations presented in Section 3. More specifically, the rMSE metric was evaluated only for the pixels whose severity level was greater than 0, i.e., only damaged areas. The reason behind this decision is twofold: (i) undamaged areas are mainly filtered by the binary backbone, for which we already assessed the performance, and (ii) undamaged areas represent the most frequent class, thus leading to possible incorrect evaluation of the results in terms of rMSE. Table 2 lists the performance achieved by the considered models with different combinations of loss functions. The metric was evaluated by computing the average value of rMSE for the four different classes. Lower rMSE indicates better performances.  
Differently from the binary segmentation problem, no loss function demonstrated to be the best performing one across the different models. Instead, among the four considered models, sIoU-based loss functions always performed the worst and second-to-worst (except for DS-UNet architecture trained with sIoU-MSE losses). Such low performance is motivated by the performance achieved in terms of IoU in the delineation task: in these cases, the binary backbone is not able to correctly identify burned areas thus the quality of the severity estimation task is negatively affected.
# Reconstruction of Land and Marine Features by Seismic and Surface Geomorphology Techniques
## 1 Introduction
These techniques that are part of seismic geomorphology are grouped into the following main themes: seismic attributes, seismic sedimentology (including slicing techniques), volume rendering and geo-body extraction, and machine learning. In addition, the integration of seismic geomorphology and surface geomorphology techniques is proposed through an integrated surface and subsurface workflow toward a reliable 3D model of the earth and its geomorphology. It is important to note that this research was conducted in the framework of Recovery Assistance for Cohesion and the Territories of Europe (REACT-EU) for Italian National Operational Program\" PON Research and Innovation 2014-2020\" projects on innovation and green issues (DM 1062/2021) granted in December 2021. The project was financed for green research project aiming at studying quantitative geomorphology from images. This research review intends to answer some questions such as where and which seismic and surface geomorphology techniques are used more often in identifying marine and land surface features? which approaches and technologies give better results? information in-dicating the possible combinations of technologies to obtain the best quality results and the ratios of different technologies were also analyzed. In addition, more details about the use of software for data processing are provided. In the future, the results of this research review will be based on during marine deep sediments identification for a new strategy of coastal protection in Sicily and for Italian coastal protection in general.
## 3 System Model and Problem Statement
### Fairness Model  
Our characterization of fairness consists of two important indicators. First, the overall situation of UAV coverage for each user is measured using the average coverage score. In any time slot \(t\), if a user falls within the coverage area of a UAV, the user's device is considered to be covered in the current time slot. Therefore, we obtain the coverage score for a single user as follows:  
\[c_{D}(k)=\frac{s_{D}(k)}{D},k\in\mathcal{K} \tag{1}\]  
where \(s_{D}(k)\) is the number of time slots in which user \(k\) is covered during the flight cycle \(D\), and \(c_{D}(k)\in(0,1)\). From this, we obtain the average coverage score for all users as:  
\[c_{D}=\frac{\sum_{k=1}^{K}c_{D}(k)}{K} \tag{2}\]  
In addition, in order to ensure fair service for each user, it is necessary to consider the issue of geographical fairness. If the UAVs are less inclined serve certain users (who may be in a remote location, for example) due to an increased consumption of flight resources, then even if the overall coverage score is high, it cannot be guaranteed that every user will receive relatively fair service, and some users may never receive service.  
In order to ensure that every user is able to communicate, we refer to the Jain fairness index to characterize geographical fairness [29]. The Jain fairness index is a standard for measuring fairness in a network. It considers all users in the system, not just those who are assigned the least resources [30]. The value of the Jain fairness index is always between 0 and 1, where 1 represents absolute fairness. In this paper, the fairness index \(f_{D}\) is defined as:  
\[f_{D}=\frac{\left(\sum_{k=1}^{K}c_{D}(k)\right)^{2}}{K\sum_{k=1}^{K}c_{D}(k)^{ 2}} \tag{3}\]
## 2 Passive Wake-Up Radio
### Discussion of the Passive Wake-Up Radio Parameters  
A range of hardware design parameters affects the overall performance as well as the power consumption of the passive wake-up radio. In this section, we discussed how these various parameters affect the different reviewed prototypes in Table 1.