esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Motion Control for Overactuated Vehicles to Enhance Controllability and Path Tracking
## 3 Linear Time-Varying Model Predictive Path and Speed Tracking
### Linear Time-Varying Model Predictive Control (LTV-MPC)  
The path tracking controller is formulated as a static, convex optimization problem subject to stability constraints, discrete state space dynamics and actuator constraints:  
\[\min_{\delta_{f},F_{u},s_{q},s_{\beta}} \overbrace{\sum_{i=2}^{N+1}\left[q_{V}\left(\frac{V_{i}-V_{\text{ ref}|i}}{V_{\text{ref}|i}}\right)^{2}+q_{q}e_{i}^{2}+q_{\theta}\theta_{i}^{2} \right]}^{\text{path and speed tracking}}+ \tag{8a}\] \[\sum_{i=1}^{N}\left[q_{\hat{\delta}_{f}}\delta_{f|i}^{2}+q_{F_{d} }F_{d|i}^{2}+q_{\psi}\delta_{\psi|i}^{2}+q_{\theta}s_{\beta|i}^{2}\right]\] \[\text{subject to} \Delta\mathbf{x}_{k+1}=\mathbf{A}_{d|k}\,\Delta\mathbf{x}_{k}+\mathbf{B}_{d|k}\, \Delta\mathbf{u}_{k},\quad\mathbf{x}_{1}=\mathbf{x}(t),\] (8b) \[\left|\delta_{f|k+1}\right|\leq\delta_{f,\text{max}},\] (8c) \[\left|\delta_{f|k}\right|\leq\delta_{f,\text{max}},\] (8d) \[F_{d,\text{min}}\leq F_{d|k}\leq F_{d,\text{max}},\] (8e) \[\left|\psi_{k+1}\right|\leq\psi_{\text{max}}+s_{\psi|k},\] (8f) \[\left|\beta_{k+1}-g_{\beta}\psi_{k+1}\right|\leq\beta_{0}+s_{ \beta|k}, \tag{8g}\]  
with index \(k\in\{1,2,\dots,N\}\) and \(N\) prediction steps. \(q_{\star}\) denotes the respective weights. The MPC evaluates the steering command \(\delta_{f}\in\mathbb{R}^{N}\), drive force \(\mathbf{F}_{d}\in\mathbb{R}^{N}\) and slack variables \(\mathbf{s}_{\psi}\in\mathbb{R}^{N}\) and \(\mathbf{s}_{\beta}\in\mathbb{R}^{N}\). These slack variables enable planned SE violations in favor of path tracking. The SE is given by (8f) and (8g). Path and speed tracking is enforced with the first summation term in (8a). Steering and drive force limits are addressed in (8c), (8d) and (8e), respectively. While the steering limits originate from the physical limitations of the actuator, the lower drive force limit aims to represent the tyre-road friction potential at braking and the upper drive force limit aims to map the maximum available motor torque. It is noteworthy that the MPC predicts based on distance, while the actuators update at uniform sampling times. Hence, the controller is evaluated at sampling time \(T_{\text{MPC}}\), allowing equidistant time execution despite distance-based prediction. The static convex optimization problem is formulated using _CasADi_[27] and solved using _qpOASES_[28].
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 4 Rate Adaptation
### Rate Calculation
#### 4.2.3 Moderate Occupancy Stage  
When the queue occupancy ratio is larger than \(O_{th1}\) and less than \(O_{th2}\), network congestion is likely to occur, so the growth rate of the sending rate should be reduced faster than during the light occupancy stage. To do this, we introduce a linear function to linearly reduce \(w\). At the same time, in order to improve throughput, the rate is still not reduced at this stage.  
\[w=\frac{1}{4}\times\frac{O(t)-O_{th2}}{O_{th1}-O_{th2}} \tag{8}\]  
Then \(R(t+1)_{min}\) or \(R(t+1)_{max}\) are adjusted for the next round according to Equations (3) and (4).
# CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
###### Abstract  
In this study, we propose a dual-stream UNet neural network architecture design named CPF-UNet, specifically designed for efficient semantic pixel-level segmentation tasks. This architecture cleverly extends the basic structure of the original UNet, mainly through the addition of a unique attention-guided branch in the encoder part, aiming to enhance the model's ability to comprehensively capture and deeply fuse contextual information. The uniqueness of CPF-UNet lies in its dual-path mechanism, which differs from the dense connectivity strategy adopted in networks such as UNet++. The dual-path structure in this study can effectively integrate deep and shallow features without relying excessively on dense connections, achieving a balanced processing of image details and overall semantic information. Experiments have shown that CPF-UNet not only slightly surpasses the segmentation accuracy of UNet++, but also significantly reduces the number of model parameters, thereby improving inference efficiency. We conducted a detailed comparative analysis, evaluating the performance of CPF-UNet against existing UNet++ and other corresponding methods on the same benchmark. The results indicate that CPF-UNet achieves a more ideal balance between accuracy and parameter quantity, two key performance indicators.  
s +
Footnote †: journal: Medical Image Computing  
emantic segmentation; encoders; decoders; dual-path structures
# Urban Vulnerability Assessment of Sea Level Rise in Singapore through the World Avatar
## 3 Methodology  
### Data Sources  
The study presented in this paper makes use of publicly available data, and details of each of the data sources are reported here, including the quality, credibility, and provenance of the data sources.  
One of the primary sources of data is Singapore's open data portal [29]. Data from the government can be assumed to be highly credible. Datasets sourced from Singapore's open data portal include land plot, cultural sites, and HDB (Singapore's public housing authority) buildings. The land plot data are retrieved from Singapore's Urban Redevelopment Authority (URA) Master Plan 2019 and it is assumed to be up-to-date. The cultural sites include heritage trees, historic sites, tourist attractions, museums, and monuments; they are given as points rather than the area they occupy, which may impact the accuracy of the studies presented later. The HDB dataset, which includes the number of floors in HDB buildings, is likely to be accurate, given that these buildings are government-owned.  
The population distribution data are based on the year 2020 census provided by Facebook's Data For Good [38]. This dataset can be considered credible due to Facebook's collaborations with well-known universities and international agencies like the World Health Organisation. While this study does not account for mobility and migration of population in and out of coastal areas, as well as population growth projection, using the current population still provides a useful sense of scale and distribution of exposure [39]. Since TWA ingests population data as a raster file, the analysis can be easily updated with a more accurate population projection as it becomes available.  
A popular geographic database, OpenStreetMap [28], provides TWA with data such as road networks, building footprints, building usage type, and building address. As this database is crowd-sourced, its accuracy cannot be verified and may not be up-to-date.  
Lastly, the underlying digital elevation model used in this study to model sea level rise vulnerable area is sourced from NASA Shuttle Radar Topography Mission (SRTM) Global 1 arc second elevation model [40]. Although publicly accessible at no cost, the radar used to generate the data does not capture vegetation areas well, such as forests [41]. The resolution of 1 arc second translates to approximately 30 m; this may be insufficient for some more fine-grained studies. For example, it could lead to the incorrect identification of a site as vulnerable to sea level rise when, in reality, it is not. Alternatives to the SRTM's elevation model include LiDAR (light detection and ranging)-based measurements which can provide up to 1 m resolution; however, geographic coverage is extremely limited and data collection can be expensive and time consuming [42].
# Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving
## 4 Results
### During-Take-Over Predictors  
#### 4.2.1 Reaction Time  
The most often studied TO quality indicator is reaction time, also commonly referred to as take-over time (TOT). A shorter reaction time gives the driver more time to better resolve (maneuver) a potentially critical situation.  
We predicted the reaction times (P2) using Attributes A1-A22 and P1. The histogram of the measured reaction times is plotted in Figure 2a. We achieved the best results with a tree of linear regression models, Algorithm M5' (RSE = 43.57%), which performed significantly better than baseline (\(p<0.001\)), MLR (\(p=0.001\)), or SVM (\(p=0.041\)) model (see Table 4). The model tree is presented in Figure 3. For shorter _TOR-to-first-brake intervals (A21)_, the linear regression model (LM) 1 is used, whereas for longer TOR-to-first-brake intervals, LM2, LM3, and LM4 are considered based on the _TOR-to-first-steer interval (A22)_. LM1 depends mainly (91%) on A21 itself, and similarly, LM2 depends mainly (89%) on A22 itself. In LM3, _attention before reaction (A18)_ is exposed as the most informational parameter, revealing that drivers who directed their gaze to the road first had a delayed reaction (longer reaction time). LM4 shows that for the drivers who took longer to brake or steer, reaction time depended on the type of situation (A11--reactions were faster when the driver had to steer to the left) and visibility conditions (A20--reactions were faster when the obstacle was already visible).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Baseline** & **MLR** & **SVM Reg.** & **M5’** \\ \hline RRSE (\%) & 100 & 58.63 & 61.20 & 43.57 \\ Duration of training (s) & \textless{}0.01 & \textless{}0.01 & 0.07 & 0.02 \\ Duration of application (s) & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 \\ Model size (B) & 485 & 8888 & 59,451 & 20,939 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the TO quality models for predicting reaction time (mean scores).  
Figure 3: Model tree of predicted reaction times, generated with M5’. It consists of two nodes and four leaves—different linear regression models (LMs).  
Figure 2: Histograms of predicted attributes: (**a**) measured reaction times—the solid black bar (at reaction time equal to zero) represents the three occurrences where drivers were already driving manually when a TOR was issued; (**b**) solution suitability—whether the driver reacted according to the TOR stimuli, i.e., steered in the proposed direction or braked when steering was not proposed.
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## 4 Resource Sharing Beamforming Access
### Throughput Analysis
As a solution, we may take as reference the response of the phased shift beamformer. For instance, the minimum angular separation could be taken as the 3-dB beamwidth of the phase shift beamformer. As this criterion could be too optimistic, we may alternatively consider the \(n\)-th null beamwidth (nNB). In the boresight, the nNB is found as \(\theta_{nNB}=2\sin^{-1}(2n/\sqrt{N_{\rm R}})\), for \(N_{x}=N_{y}=\sqrt{N_{\rm R}}\) and \(d=\frac{\lambda}{2}\). The relation between \((u,v)\) and \((\theta,\phi)\) leads to \(\Delta_{uv}=\sin(\theta_{nNB})\). Sticking to the case that \(P_{c}=\frac{\sin^{2}(\theta_{nNB})}{\sin^{2}(\theta_{nMax})}\) and following the steps described in this section, we have represented the throughput in Figure 6. In particular, we consider a massive MIMO architecture and two beamforming techniques, which achieve different angular discrimination values. The angular resolution of low and high angular discrimination beamformers can be approximated by the 2 NB and the 1 NB, respectively, of the phase shift beamformer. As expected, by increasing the number of antennas and improving the angular resolution, we can increase the load of the network without losing further packets. Note that if the load exceeds a given value, the throughput drops drastically.
## 3 Health Score Correction Based on the Reliability Model
### Commonly Used Failure Distribution Curves  
The time to failure of an individual device is a random variable. For a single device in service, its time to failure or mileage to failure is unknown; however, for devices of the same type operating under identical service conditions, the time to failure or mileage to failure follows a determinable distribution pattern, which can be derived by collecting statistical failure data. Suppose \(n\) samples are randomly drawn from the population. Although the sampling results are random, the frequency of samples within different intervals varies. The likelihood of a random variable taking values within specific intervals represents the intuitive meaning of \"distribution\". In practical applications, it is always assumed that the failure time distribution of the population exists and is determinable. Since an individual belongs to this population, in the absence of any prior information, the failure time of the individual is a random variable that follows this distribution [10 ].  
In reliability analysis, commonly used failure distributions include normal distribution, log-normal distribution, exponential distribution, two-parameter Weibull distribution and gamma distribution. The specific type of distribution is often unrelated to the type of product but is instead associated with the type of stress applied, the failure mechanism, and the failure mode. Therefore, the following sections will introduce the probability density \(f(t)\), the cumulative failure probability distribution \(F(t)\), the reliability \(R(t)\), and the failure rate \(\lambda(t)\) for these five failure distributions.
## 3 Phase Characteristics of the Guided Wave Reflected from a Reflector
where \(\theta_{\rm In}\) and \(\theta_{\rm Re}\) are the phases of the incident wave and the reflection wave respectively. From Equations (5) and (6), the phase characteristics of the guided wave reflected from a reflector can be obtained as follows:  
1. If \(\delta_{1}<\delta_{2}\), namely \(\rm RC<0\), so the phase shift \(|\theta|=\left|\arctan(I_{m}/R_{e})\right|=|\theta_{\rm Re}-\theta_{\rm In}|=(2 k-1)\pi\), \(\rm k=1,\,2,\,\ldots,\,n\). 2. If \(\delta_{1}>\delta_{2}\), namely \(\rm RC>0\), so the phase shift \(|\theta|=\left|\arctan(I_{m}/R_{e})\right|=|\theta_{\rm Re}-\theta_{\rm In}|=2 k\pi\), \(\rm k=0,\,1\), \(\ldots,n\). 3. If \(\delta_{1}=\delta_{2}\), \(\rm RC=0\), this indicates there is no reflector in the pipeline. 4. If \(\delta_{1}\gg\delta_{2}\), this means almost all of the incident wave is reflected back. A defect or support reflector can be decomposed into a step down (front-edge of the reflector, namely \(\delta_{1}>\delta_{2}\)) and a step up (back-edge of the reflector, namely \(\delta_{1}<\delta_{2}\)), as shown in Figure 5. Thus, for a defect reflector, the incident wave is in the same phase with the reflection wave generated from the front-edge of the reflector; while the wave reflected from the back-edge of the reflector is in the opposite phase with the incident wave, according to the above conclusions.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 4 Domain Model for Data Sources
### A General View of the Domain  
To represent the relevant data sources in a diagram, we adopt the feature diagram notation [45]. This notation was defined to visually express the features of the domain of interest, and it consists of symbols to represent mandatory features, optional features, group, alternative group, abstract features, and concrete features. The diagram is in fact a visual representation of propositional logic formulas [46].  
In Figure 1, depending on the attachment characteristics of the data sources, the domain is divided into three branches. The first branch represents the data sources which can be attached to geographical areas. The second branch represents the data sources which can be attached to physical objects. The third branch represents the data sources which can be transported to the locations where disasters have been effective.  
The first branch consists of three sub-branches: Airborne, Aerial trackers, and Registration database. The second branch has four sub-branches: Disaster detector, Smart-home detector, Optical detector, and Smart utility meter. The third branch has three sub-branches: Microphone, Carbon dioxide meter, and Microwave radar. The gray-colored rectangles correspond to the concrete features. The white-colored rectangles must be refined into the concrete ones.  
The feature model can be used to create a concrete model by using the following strategy:  
1. Starting from the root node, select the compulsory features, if any.
2. Decide if the optional features must be selected, if any.
3. By obeying the defined semantics, refine the features which are grouped together by the logical operators of the feature model (for example OR, Alternative OR), if any.
4. Continue with this process from the abstract features towards the concrete ones until no optional and/or abstract feature is left unresolved.  
In this strategy, it is assumed that all leaf features are concrete and no cross-tree constraints are specified. In Figure 1, the compulsory features are 'Attached to geographic area' and 'Registration database', since registration of inhabitants is generally enforced by law.  
The model given in Figure 1 can be extended by adding new data sources or refining the existing abstract features. Additionally, there may be many commercially available data sources in the market with varying characteristics. These can be added to the figure as the leaves of the tree structure. We will now elaborate on these data sources in more detail.
###### Abstract  
Real-time control and monitoring are some of the main goals of Industry 4.0. To meet these requirements, sensors are needed at every step of the production process. Wireless sensors (WS) are better suited due to their flexibility but are limited in energy. In this work, kinetic energy harvesting using piezoelectric technologies are considered to ensure the energy autonomy of a Wireless Sensor Network (WSN). First, unlike most existing works, this paper focuses on WSN rather than a single WS since the control of the entirety of most industrial processes requires several WSs. The solution proposed here is based on deep learning of the harvestable power signals at each sensor deployed on the monitoring system. Specifically, vibration measurements were performed at 12 locations on an ore crushing mill in a mine. From there, a mechanical-electrical conversion model considering the system's dynamics was set up to evaluate the power profile each of the WSs can harvest. Considering that the harvestable power has many peaks due to the different operating states of the engine, we first proposed a Predictor of the Harvestable Power from Vibrations (PHPV). Using a large database, compared to a state-of-the-art predictor, the Predictor of the Harvestable Energy from vibrations (PHEV) allows for significantly reducing the Root Mean Square Error (RMSE). More specifically, the lowest reduction achieved for RSME ranged from 9.4 \(\mu\)W (with PHEV) to 5.9 \(\mu\)W (with PHPV). A decrease in RMSE ranging from 18.45 to 4 \(\mu\)W was obtained for another measurement point. Since harvest rates differ from one location to another, a Hierarchical Energy-Balancing Protocol (HEBP) is proposed to maximize the number of WS capable of transmitting information about the system's state, thus avoiding an interruption of the network coverage. In the HEBP, it is envisaged that some WSs, besides transmitting data, will supply other nodes with an energy deficit to allow them to communicate information about their location. For a minimum packet size of up to 1100 bits, the energy autonomy of all the WSs is ensured, unlike only 66% of the nodes with the previous protocols.  
Industry 4.0; vibration; energy harvesting; energy prediction; WSN; PHPV; PHEV; HEBP
# Improved Recommendations Based on Trust Relationships in Social Networks
## 1 Introduction  
In recent years, social networks have played an important role on the Internet, which focuses on interactions and relationships between people. The Internet can be regarded as a new platform, on which people pursue an increasing amount of activities that they have usually only done in the real world [1]. However, this platform is also troubled by information overload, which makes it difficult to find personalized information from the vast amounts of information on the Internet. To address this problem, recommendations have become increasingly popular. Although there are many studies on recommendations in social networks, most of them still suffer from typical problems such as cold start, sparsity, and unsatisfactory precision.  
According to a Nielsen survey, more than 90% of users believe in what their friends have recommended, and 70% of users believe other users' product scores on commodities on the Internet [2]. The survey reveals that trust relationships between users can determine the effect of recommendations to a great extent. Thus, trust has become a research hotspot in the field of information recommendation. As a result, an increasing amount of studies have been conducted on recommendations based on trust [3; 4; 5]. These methods, however, tend to be vague in defining trust relationships, and they rarely consider indirect trust. To solve these challenges, we investigate here the problem of how trust relationships can benefit recommendations in a social network.  
In this paper, we first define a trust relationship and then propose an improved recommendation method that gives full consideration to the characteristics of recommending and adopts an efficient recommendation algorithm based on formalized trust relationships. In particular, the method differsfrom existing ones mainly in its effective and flexible dynamic trust mechanism; more specifically, it can calculate the comprehensive trust of a candidate user and choose the most credible user to improve the accuracy of prediction. Experimental results show that, compared to similar methods, the proposed method has higher accuracy and can better deal with problems in the current recommendation algorithms, such as cold start and sparsity.  
The rest of the paper is organized as follows. In Section 2, we review related recommendation methods that consider trust and analyze their advantages and disadvantages. The quantifying about the factors of trust in social networks and the formalization of trust relationships are described in Section 3. In Section 4, we elaborate on our proposed algorithm, and evaluate the experiments in Section 5. A conclusion is given in Section 6.
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 3 Results and Discussion
### Microstructure and Morphology of Graphene-Enhanced CuW Composites
The sample without adding graphene mainly shows tungsten and copper diffraction peaks at 20 values of \(\sim\)40.264 (110), \(\sim\)58.274 (200), \(\sim\)73.195 (211), \(\sim\)43.297 (111), and \(\sim\)50.433 (200). While in the XRD patterns of graphene-enhanced CuW composites, minor WC phases can be identified, i.e., the weak peaks emerged at 20 equal to 31.511 (001), 35.641 (100), and 48.296 (101), respectively. This indicates that during the prolonged high-temperature vacuum infiltration sintering process, a very small amount of graphene may react with tungsten to form tungsten-carbon compounds. Carbon atoms of graphene may diffuse onto the surfaces of the W-W skeletons and easily into the octahedral spacings of the tungsten atoms due to the differences of the atom diameters of tungsten and graphene.