esa-sceva/satcom-chunk-collection huggingface dataset:
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Coordination of Transportation Fleets of Autonomous Drivers
#### Dynamic Reassignment with Compensations
_mediatorEarning_:= _mediatorEarning_ - \(C_{o}\)
6. return \(A_{n}\)
7. else return \(A_{c}\)  
The algorithm is executed by the mediator whenever either a new transportation request (service) is registered, or a driver becomes available (either after terminating a previous mission or because he starts working). In the first step, the system tries to assign pending requests in a rather standard fashion. Then, in Steps 2 and 3, a more efficient global assignment is searched for and the compensation cost of this new assignment is estimated. The new assignment is applied if the accumulated overall mediator earnings, together with the compensation cost, remains positive. This last part assures that the mediator has no extra mediation cost. Regarding Step 2, we use [PERSON] auction algorithm [PERSON] (1999) to calculate an optimal assignment. In particular, we calculate the assignment \(A_{n}\) that minimises \(D(A_{n})+\gamma\cdot C_{o}\), where \(D(A_{n})\) is the sum of the distances of all vehicles in \(A_{n}\) to the corresponding original positions of the assigned service requests. This means we look for assignments that minimise the sum of the distances and also the potential cost of the compensations. \(\gamma\) is a factor for scaling monetary earnings into distance values (meters).
## 2 Related Works
### Networks with Game Theory Based Routing  
Game theory has been used in the past as a model to study different aspects of computer and communication networks. Recently, more and more researchers have focused on developing wireless network algorithms using game theory. The most important application of game theory to wireless sensor networks is routing algorithm design. In [19], the authors used game theory to analyze the outcome of a game in which the deployed sensors belong to different sinks and can receive incentives for cooperative forwarding. The authors in [20] proposed a reliable query routing scheme. In their approach, the candidate nodes are modeled as rational nodes cooperating to discover the optimal network architectures that can maximize the payoff in a game. Here, node payoff means the benefit of a node's action minus its costs. In [21], the authors consider the issue of packet forwarding in multi-class sensor networks using game theory.  
In the incentive mechanism of wireless networks routing protocols, _Ad-Hoc_ VCG [22] is a fundamental and classical method. This algorithm introduced the economic theory and adopted the classical VCG auction model for wireless _ad-hoc_ networks. The main target of _Ad-Hoc_ VCG is how to incite the intermediate nodes to bid their real cost for forwarding packets. _Ad-Hoc_ VCG is based on monetary transfer and has several nice features: it discovers the most energy-efficient path between the source and the destination, and it is truthful and it stimulates the nodes to behave according to the protocol specification. The protocol proposed by the authors of [23] considers both route discovery and packet forwarding on the computed routes. The authors introduce a novel solution concept called cooperation-optimal protocol and prove that it is optimal for a selfish user to fulfill the routing decision in the packet forwarding phase. In [24], the authors introduce a Nash equilibrium-based mechanism named OURS. OURS gives a perfect solution for excessive payoff issues under the VCG mechanism. In the VCG model, the algorithm achieves strategy-proofness for any nodes. In OURS, the protocol releases the restriction of strategy-proofness, and proves the existence of a Nash equilibrium.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 2 Materials and Methods
### Traditional Evaluation Methods for Compaction Quality
#### 2.3.2 Evaluation of Rolling Quality Based on Regression Analysis  
Regression analysis is a statistical method used to explore the relationship between a dependent variable and multiple independent variables, thereby enabling the construction of interpretable regression models to predict future values. Based on the theory of linear regression and incorporating real-time compaction monitoring system data, field test data, and meteorological parameters, this study constructs a multiple linear regression model to assess the dry density of compacted concrete dams. The specific steps are as follows: (a) collect the compaction parameters (such as the number of passes and thickness); meteorological parameters (such as temperature and humidity); and test pit data (such as the uniformity coefficient, P5 content, curvature coefficient, and total material moisture content) to establish a basic database for a compaction quality evaluation; (b) select significant influencing parameters through a correlation analysis and apply five-fold cross-validation to divide the data into training and testing sets; (c) based on the principles of linear regression, fit the dry density prediction model using the training set data; and (d) use the model to predict the test set and analyze the correlation and errors in the predicted results.
###### Abstract
The results indicated that the I or J-shaped monostable shift pattern with PRNDM shift sequence showed the longest task completion time and the lowest subjective ease-of-use, which was explained by the lack of visibility of the gear shifter state and haptic feedback. [PERSON] et al. [10] derived a total of 12 shift button layouts for button-type gearshifts based on four ergonomic layout design principles (consistency, functional similarity, compatibility, and stereotype) and conducted a survey to evaluate them in terms of seven ergonomic evaluation measures (accuracy, convenience, rapidity, learnability, intuitiveness, safety, and preference). As a result, a layout, to which the principles of consistency, functional similarity, and stereotype were applied together, was recommended as the best (most ergonomic) layout design. Despite these previous research efforts, however, there still exists a notable gap in our understanding of how the operation method, in-vehicle location, and their combinations for electronic gearshifts impact driver performance, comfort, and safety. Given that various combinations of operation methods and in-vehicle locations have been applied across various manufacturers/models (Table 1), such a knowledge gap poses a significant obstacle to preventing the abovementioned safety issues and accidents due to driver confusion and errors. As a response to this gap, the primary objective of this study is to identify an ergonomic design for electronic gearshifts that can prevent driver confusion and errors, ultimately enhancing driver safety and comfort. In an effort to achieve this goal, the current study focuses on addressing the following three unexplored research questions:  
**Research Question 1:** What is the best ergonomic operation method for electronic gearshifts? **Research Question 2:** What is the best ergonomic in-vehicle location for electronic gearshifts? **Research Question 3:** What is the best ergonomic combination of operation method and in-vehicle location for electronic gearshifts? To address the three research questions, this study considered three operation methods (lever slide, button push, and dial rotation) and five in-vehicle locations (left wheel spoke, right wheel spoke, upper center fascia, lower center fascia, and center console), resulting in a total of 15 design alternatives based on their combinations. A total of 40 respondents, varying in age and driving experience, assessed each design alternative across nine ergonomic evaluation measures (accuracy, efficiency, rapidity, learnability, intuitiveness, safety, preference, memorability, and satisfaction). The collected data were analyzed to conduct a comparative evaluation, allowing for the identification of an ideal ergonomic operation method, in-vehicle location, and their ideal combination for electronic gearshifts, along with some ergonomic design characteristics that should be considered in gearshift design.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 5 Solving a Pseudo-Static Scenario
### Checking the Fitness of Individuals: Check Function
#### 5.2.3 Endurance Evaluation (Endurance_eval function)  
This function estimates the expected UAV's endurance. As introduced in Section 3, the flight duration has two main components: the power (in Watts) required to hover the drone (\(P_{\rm Hover}\)), and the power consumption at both radio links (i.e., recall from Section 3.2 that it was proportional to \(P_{\rm WiFi}\)).  
\(P_{\rm Hover}\) in rotor-based aircrafts can be easily derived from the Momentum Theory [29] as:  
\[P_{\rm Hover}=\frac{g^{\frac{3}{2}}}{\eta_{ps}r_{p}\sqrt{2N_{R}\rho_{a}\pi}}m ^{\frac{3}{2}} \tag{31}\]  
where \(g\) accounts for gravity acceleration, \(m\) represents the mass of the aircraft, \(\eta_{ps}\) is the rotorcraft propulsion system efficiency, \(N_{R}\) is the number of rotors, \(r_{p}\) represents the radius of each rotor and finally \(\rho_{a}\) represents the density of air. \(N_{R}\), \(r_{p}\) and \(\eta_{ps}\) are UAV-specific. While the first two are fixed and can be readily obtained from manufacturer manuals, the latest is variable (although a constant value is generally adopted) and requires experimental estimation [48].  
Regarding \(P_{\rm WiFi}\) at the access point, the power can be expressed as:  
\[P_{\rm WiFi} =\frac{1}{E[T]}\cdot\Big{(}\underbrace{J_{\rm c,s}\,P_{\rm tx,e} }_{\rm idle\ interval}\] \[+\underbrace{J_{\rm tx,s}\,P_{\rm tx,s}+J_{\rm tx,e}\,P_{\rm tx,e}+J_{\rm tx,c}\,P_{\rm tx,c}}_{\rm transmission\ interval}\] \[+\underbrace{J_{\rm rx,s}\,P_{\rm rx,s}+J_{\rm rx,e}\,P_{\rm rx,e}+J_{\rm rx,c}\,P_{\rm rx,c}}_{\rm reception\ interval}\Big{)} \tag{32}\]  
where \(J_{\sigma}\) represents the energy spent during an idle interval; \(J_{\rm tx,s}\), \(J_{\rm tx,e}\), and \(J_{\rm tx,c}\) represent the average energy consumption (in Joules) during a successful, erroneous or collided transmission at the access point respectively; and \(J_{\rm rx,s}\), \(J_{\rm rx,e}\) and \(J_{\rm rx,c}\) represent the expected energy consumption during a successful, erroneous or collided reception respectively. The terms \(P_{i}\), \(P_{\rm tx,s}\), \(P_{\rm tx,e}\), \(P_{\rm tx,c}\), \(P_{\rm rx,s}\), \(P_{\rm rx,e}\) and \(P_{\rm rx,c}\) indicate the probability of such events. A detailed expression of the factors in (32) based on the traffic and our model of the mac-sublayer is deduced in Appendix A.  
Finally, the expected flight duration \(T_{\rm Flight}\) of a particular drone can be calculated by substituting the expressions of \(P_{\rm Flight}\) and \(P_{\rm WiFi}\) in Equation (7).
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 1 Introduction
Therefore, we designed an efficient spatial position transformation module (SPT) to integrate into the GLIM, which can achieve efficient cross-window information interaction with lower computational costs. Meanwhile, a depth-separable convolution was used before the MLP module to enhance the local connection between neighboring windows. Finally, we conducted experiments on four challenging datasets and compared the results with more than ten state-of-the-art models, proving that our model has a better comprehensive performance in the semantic segmentation tasks of urban scene imagery. The main contributions of this paper are summarized as follows:  
1. Aiming at the inability of CNN-based methods to effectively model long-range dependence or global spatial relationships, a global-local spatial information modeling module (GLIM) is proposed, which can simultaneously model global and local spatial information of urban scene distributions. 2. A spatial position transformation (SPT) module is designed to solve the trade-off problem between the model complexity and global information modeling capability in the existing Vision Transformer method. 3. A UNet-like Transformer architecture is designed for segmenting remote sensing urban images in real time, which can obtain high-precision segmentation results while maintaining the model's efficiency.
# Metasurface-Based Polarimeters
## 3 Metasurface-Based Polarimeters
### Metasurface-Only Polarimeters
(**a**) Illustration of the metagrating’s working principle; (**b**) Measured diffraction contrasts (denoted by filled circles) for polarization states along the main axes of the Poincaré sphere (indicated by asterisks) at 800 nm; (**a,b**) Reproduced with permission from [67], Copyright Optical Society of America, 2015; (**c**) Polarization-selective directional scattering of four elliptical polarization states by two pairs of rows superimposed at a 45\({}^{\circ}\) relative angle; (**d**) Measurement of the state of polarization of arbitrarily selected polarizations using the commercial polarimeter (blue) and the metasurface polarimeter (orange) at 1550 nm; (**c,d**) Reproduced with permission from [74], Copyright Optical Society of America, 2016; (**e**) Schematic of ultracompact polarimeters based on dielectric metasurfaces. The inset shows the unit cell; (**f**) Simulated intensity distributions in the focal plane for \(x\), \(y\), \(a\), \(b\), RCP, and LCP incident waves; (**e,f**) Reproduced with permission from [76], Copyright Optical Society of America, 2017.  
integration as it is operating in transmission-mode, while maintaining high efficiency due to the relatively high refractive index and negligible absorption [22]. One example of a dielectric metasurface integrated polarimeter is depicted in Figure 3e, which contains a silicon metasurface and array detector with a distance of 4.2 \(\upmu\)m corresponding to the focal plane of the metasurface. The metasurface consists of multiple pixels, where each pixel has four different areas containing periodically arranged elliptical silicon pillars resting on a silica substrate. Within the pixel, each area functions as a polarization-sensitive focusing lens, separating four polarization components from the incident light and focusing them to the detector's surface. Upon the excitation of a normally incident beam with arbitrary SOP, four focal spots are generated in the focal plane, which show different intensity distributions for different polarizations, as shown in Figure 3f. By detecting the intensities of the focused four polarization components in the focal plane (\(I_{x}\), \(I_{y}\), \(I_{a}\), and \(I_{l}\), which refer to the intensities of horizontal (H), vertical (V), \(+45^{\circ}\), and LCP components, respectively) and carefully calibrating the system, the Stokes parameters can be retrieved to fully describe the SOP of the incident light. Since each part of the incident light is fully used to determine the SOP, the detection efficiency only depends on the focusing efficiency of each flat focusing lens, which is theoretically estimated to be above 60%.
## 4 Multicast Routing Approaches in DTN
### Social-Aware Multicast Routing in DTN
#### 4.1.2 Interest Profile  
Q-learning-based Gain-aware Routing: The Q-learning-based Gain-aware Routing (QGR) protocol [38] is designed to maximize the commercial gain of the content provider through a composite routing index, combining revenue from delivery and the forwarding cost when disseminating commercial contents (e.g., coupons). Any node that runs this protocol can identify the encountered node as a content destination or as a relay, based on the interest level on the content and interaction patterns. The state-action functions of Q-learning enable the nodes to learn a set of forwarding actions to identify the right path with maximized gain. Figure 8 shows the interaction and routing scenario in the QGR protocol. Each N-i node is indicated with the redemption probability (i.e., interest level). Higher total gain can be achieved when N-0 chooses N-1 as a relay, even though N-2 can also contribute to higher revenue but lower dissemination potential. QGR is applicable and beneficial to marketing applications because it can maximize commercial gain. However, it has no mechanism to detect and handle node selfishness in terms of social context for D2D content dissemination.  
Multicast Polymorphic Epidemic Routing: [PERSON] et al. [39] proposed a multicast polymorphic epidemic routing scheme that utilizes the interest similarity of nodes to aid multicast data dissemination in DTN. Each node stores its interests into an interest profile using a \(Z\)-dimensional array, where \(Z\) is the total number of interests in the network. A similar structure is stored in each packet header as a packet relevance profile. This scheme computes a cosine similarity metric based on the interest profile of two nodes during each contact. A packet will be relayed if the cosine similarity value is high, which indicates both nodes belongs to the same group. In the case of different group node contact, the cosine similarity metric will be computed from the packet relevance profile and interest profile of the contacted node. The authors introduce adaptive recovery schemes along with this scheme to facilitate multicast routing.  
Community and Social Feature-Based Multicast: Community and social feature-based multicast [40] is a compare-split scheme based on community detection that includes two algorithms: Multi-CSDO involves destination nodes only in community detection, and Multi-CSDR involves both the destination nodes and the relay candidates in community detection. In this scheme, each message contains a set of destinations. When the message holder encounters a destination node, it will forward the message to the encountered node. The destination set in the message holder will undergo hierarchical clustering and will be divided into two communities. The nodes that have higher social  
Figure 8: QGR interaction and routing scenario [38].  
similarity to each of the community will carry the message for that community. Social similarity is based on dynamic social features that make up an interest profile in each node.
## 2 Open Challenges When SS Meets ISAC
### Security and Privacy Concerns  
Spectrum sensing in ISAC raises potential eavesdropping and spoofing threats, where attackers can exploit sensing data to manipulate the system. Secure spectrum- sensing techniques, such as privacy-preserving FL, need further exploration.
# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
The proposed approach involves three FDI strategies: a basic FDI scheme, an advanced FDI scheme, and an integrated FDI scheme. Basic schemes accurately detect and isolate faults in PPT actuators but have low precision and high error rates. The advanced scheme analyzes relative attitude data from formation flying, resulting in excellent detection but lacking isolation capability. With the integrated scheme, accuracy, precision, and minimal misclassification rates can be achieved, as well as insights into thrust production levels during faults can be obtained. Compared with traditional attitude control actuators, fault diagnosis for PPTs has been less explored because of challenges with force measurement and mathematical modeling.
## 2 Theoretical Framework
### Propagation through Gases  
Key components of the atmospheric make-ups are the distribution of various gases at various altitudes, loftiness, and breadths, which also cause some levels of multipath attenuation. The electromagnetic wave attenuation due to atmospheric gases can be estimated for water vapour and dry air using the expressions in Equations (19) and (20) [58]:  
\[\gamma_{wp}=\left[\begin{array}{c}3.27x10^{-2}r_{t}+1.67x10^{-3}\frac{ \rho_{t}^{\prime 2}}{r_{p}}+7.7x10^{-4}f_{c}^{0.5}+\frac{3.79}{\left(f-22.235\right)^{2}+9.8 \  
where \(\gamma_{att}(f),\gamma_{att}(r),\gamma_{att}(c),\gamma_{att}(g)\) are the respective individual attenuation losses due to free space, rain, gases, and cloud, all of which are defined in the expressions in Equations (5), (6), (12) and (21).
## 5 Conclusions  
As demonstrated in Section 3.1 and visually presented in Figure 5, the proposed reinforcement parameter adaptation successfully converges to a detector with both high sensitivity and specificity performance. This is achieved from a variety of initial settings, some very \"liberal\", allowing for many false positive detections, and others very \"conservative\", skipping some real events. In all cases, here, we use the same event-generation parameters. The later correspond to a relatively good separation between normal and seizure epochs with a confusion factor of 0.02. From Figure 7, it is clear that for all initial values, the detector parameters reach approximately the same optimal levels due to the reinforcement algorithm. As the process is continuously active, certain fluctuations are present, but the system recovers its target parameters and shows asymptotic stability.  
To answer the question of how far one can trust the method with respect to the separation between seizures and normal periods, the experiments reported in Section 3.2 are performed. From the results of the first test, shown in Figures 8 and 9, it is clear that, independently of the initial detection parameters, the reinforcement adaptation provides stable sensitivity for generation parameters corresponding to confusion factors of less than 0.2. For the specificity, the adaptive process fails already for confusion factors greater than 0.1. The conclusion is that for systems with marginal or poor distinction between normal and seizure states, the detector can still adapt for proper detection of the events but will also produce false positive detections.  
The final test explores the limits of the seizure event durations so that the approach will still be useful. The results shown in Figures 10 and 11 show that, again, the sensitivity reaches high values independently of the initial detector settings for all maximal event durations ranging from 10 up to 60 simulation steps. The specificity trace, however, is not stable for short seizure events. For short seizures the algorithm will manage to restore an optimal detection sensitivity but at the expense of letting through false positive detections.  
This research was partially funded by \"De Christelijke Vereniging voor de Verpleging van Lijders aan Epilepsie\", Program 35401, Remote Detection of Motor Paroxysms (REDEMP).  
All data used in this work were synthetically generated by the algorithms described and, upon request, can be made available by the corresponding author.  
The authors declare no conflicts of interest, The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.