esa-sceva/satcom-chunk-collection huggingface dataset:
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Experimental Results  
In this section, we present the experimental results of our evaluation. We start by presenting the results of the comparison between the two variations (Dynamic and Direct) of the NPA algorithm. Then we present the results of the eNPA algorithm, where we compare different scenarios with the two search methods. Finally, we compare the eNPA algorithm with different energy models.  
The NPA and eNPA algorithms were evaluated using three metrics: the average throughput, the average source to sink delay, and the total energy consumption. The average throughput presents the ratio of packets received (over all packets generated by the sources in the course of the simulation) versus the load of the network and is calculated with the equation below:  
\[Recv\_Pkts\_Ratio=\frac{successfully\_received\_packets}{total\_sent\_packets}. \tag{8}\]  
The total source to sink delay presents the time the packet needs to be transmitted to the sink node and is calculated with the equation below:  
\[Delay_{source\_to\_sink}=t_{arrival\_time}-t_{start\_time}. \tag{9}\]  
The total energy consumption is measured in \(mJ\) and is already presented in Section 5.4. For this part of the experimentation, the total energy consumption of the network is calculated with the listening and moving energy of each node in the network. The moving energy used for this part is the one described in Section 5.4.1. Later we will also experiment using the other moving energy models.
## 4 Discussion
### The Use of Remotely Sensed VIs to Predict R\({}_{s}\)
The NDVI derived from the handheld spectrometer performed better in the model (R2 \(\geq\) 0.67) than the NDVI from the satellite data (R2 \(<\) 0.40). This study showed the importance of considering scale in modeling in order to increase accuracy of predictions [39]. Remotely sensed variables from satellite data may do a better job predicting factors such as GPP, Rs, or biomass on a broader spatial scale where there may be more obvious variation in these factors. Many processes may appear homogenous at a local scale but as the observation scale becomes larger may start to appear heterogenous. Therefore, processes may seem important at one scale but remain trivial in another scale [40]. Further research into the accuracy and the spatial scale of VIs could provide us with the information needed to know whether they will have the ability to predict Rs on the landscape scale in the future.
## 4 Simulation Results  
### Offline Test Campaigns: Tuning of the Controllers  
Before the testing phase in the HiL system, the controllers are first implemented in the co-simulation environment between MATLAB and VI-Grade. In particular, the control solutions are developed in Simulink, while the vehicle model resides in CarRealTime. Thus, a preliminary test campaign aimed at tuning the multiple gains is conducted for both LQR and SMC solutions. In addition, a recursive optimization algorithm is used to identify the tire cornering stiffness \(C_{\mathit{vf}}\) and \(C_{\mathit{yr}}\) of the vehicle model. These are assumed constant and known for the control objectives calculations but appear non-linear for the tire modeling approach adopted in VI-Grade, which relies on Paceika equations [42].  
In this stage, a standardized open-loop cornering maneuver is replicated: the _Step Steer_ test. It is executed in accordance with the ISO7401:2003 standard, also known as the _Lateral Transient Response test method_. However, there is still the risk that the gains are tuned optimally only for these specific test boundary conditions, showing poor performances when the vehicle operates in quite different scenarios with respect to the one considered during the optimization phase. To avoid the tuning process being affected by these problems, multiple functional costs (52) are evaluated, where \(f(t)\) and \(f_{\mathit{des}}(t))\) are real and desired variables, respectively. Investigated vehicle states are lateral displacement \(y\), lateral acceleration \(a_{y}\), yaw rate \(r\), and side-slip angle \(\beta\).  
\[J_{F}(t)=\frac{1}{t}\sqrt{\int_{0}^{t}(f(t)-f_{\mathit{des}}(t))^{2}\,dt} \tag{52}\]
## 5 NB-IoT Cell Search
### NPSS Detection and Time Domain Synchronization
Based on this analysis, the estimated noise power in Equation (13) can be applied to improve the proposed NF. \[\alpha_{SW}(\tau)=\left|\sum_{r^{\prime}_{f}=1}^{R^{\prime}_{f}}\sum_{v=1}^{V} \sum_{m^{\prime}=3}^{M^{\prime}-v}\sum_{n=1}^{N+N_{g}}c(m^{\prime}-3)r_{p,r^{ \prime}_{f}}^{(m^{\prime})*}(n)c(m^{\prime}-3+v)r_{p,r^{\prime}_{f}}^{(m^{ \prime}+v)}(n)\right|^{2}, \tag{10}\]2  
After the calculation of \(\alpha_{SW}(\tau)\) and \(\beta_{NE}(\tau)\), the proposed timing metric \(\lambda_{prop}(\tau)\) can be obtained with the ratio of the former two parameters. The implementation structure is shown in \[\alpha_{SW}(\tau)=\left|\sum_{r^{\prime}_{f}=1}^{R^{\prime}_{f}}\sum_{v=1}^{V} \sum_{m^{\prime}=3}^{M^{\prime}-v}\sum_{n=1}^{N+N_{g}}c(m^{\prime}-3)r_{p,r^{ \prime}_{f}}^{(m^{\prime})*}(n)c(m^{\prime}-3+v)r_{p,r^{\prime}_{f}}^{(m^{ \prime}+v)}(n)\right|^{2}, \tag{10}\]3  
When the coarse timing synchronization is completed and the location of NPSS is decided, the FFO obtained by the FFO pre-estimator can be used as the estimated value to compensate FFO. Besides, as we have mentioned in Section 4, the NB-IoT multi-path channels have considerable RMS-DS, particularly in ETU channel. In addition, such a large RMS-DS may not only bring the uncertainty of ToA estimation, but also introduce inter-symbol interference (ISI). To counteract these two problems, the fine timing should be completed in time domain joint with IFO estimation. Based on the above considerations, a joint ML fine timing and IFO estimation method following [8] is applied. The cost function of the joint fine timing and IFO estimation can be expressed as  
\[\alpha_{SW}(\tau)=\left|\sum_{r^{\prime}_{f}=1}^{R^{\prime}_{f}}\sum_{v=1}^{V} \sum_{m^{\prime}=3}^{M^{\prime}-v}\sum_{n=1}^{N+N_{g}}c(m^{\prime}-3)r_{p,r^{ \prime}_{f}}^{(m^{\prime})*}(n)c(m^{\prime}-3+v)r_{p,r^{\prime}_{f}}^{(m^{ \prime}+v)}(n)\right|^{2}, \tag{10}\]4  
\[\alpha_{SW}(\tau)=\left|\sum_{r^{\prime}_{f}=1}^{R^{\prime}_{f}}\sum_{v=1}^{V} \sum_{m^{\prime}=3}^{M^{\prime}-v}\sum_{n=1}^{N+N_{g}}c(m^{\prime}-3)r_{p,r^{ \prime}_{f}}^{(m^{\prime})*}(n)c(m^{\prime}-3+v)r_{p,r^{\prime}_{f}}^{(m^{ \prime}+v)}(n)\right|^{2}, \tag{10}\]5 Block diagram of the proposed NPSS detector structure. where \(\tau_{f}\) and \(\tau_{c}\) represent the fine timing offset and coarse timing offset respectively, and \(N_{NPSS}\) denotes the number of samples within one NPSS duration. To counteract the RMS-DS introduced by multi-path channel, whose typical value is 991 ns of ETU, the fine timing gird search range \([-\upsilon,\upsilon]\) must be two times greater than it. Besides, since the NB-IoT frequency offset range is \([-25.5\) KHz, 25.5 KHz] in theory, the gird search range of IFO should be \([-1,1]\). Finally, the correct fine timing and IFO can be obtained by finding the maximum value of absolute value of the two-dimensional grid search of the above cost function.
## 1 Introduction
### Related Work  
The related work in this field has been done by different simulations, environment and performance tools. In various studies, analysts have considered the following factors while evaluating the computer performance: waiting time, load balancing, number of requests, the throughput and rate of transactions. These computer performance factors were evaluated by many approaches. We classified them as fuzzy set theory based and machine learning (prediction) based approaches.
# Optical Boundaries for LED-Based Indoor Positioning System
## 1 Introduction
Investigations of the effect of encoding design and receiver tilts on positioning near the optical boundaries are also carried out and suggestions are given for LED positioning protocol designs based on the results of these investigations. The rest of the paper is organized as follows: in Section 2, the system model showing the problem is described. The derivation of the threshold angle for defining optical boundaries is presented in Section 3. Investigation of the effects of encoding protocol design, and the effects of overlap and receiver tilt in the optical boundaries on positioning are explained in Section 4. The results and discussion are given in Section 5. Finally, in Section 6, the conclusions are presented. Figure 1: Illustration of top view of room showing the overlap of LED beams and tilted receiver tilting away from the transmitter where \(\varphi\) is the angle of incidence and x is the horizontal displacement. (**a**) four similar LEDs, (**b**) four unique LEDs, (**c**) illustration of angle of tilt \(\varphi_{t}\).
# Video Super-Resolution Network with Gated High-Low Resolution Frames
###### Abstract
Specifically, the low-resolution feature information obtained after optical flow estimation is firstly used with the loop-based preserved high-resolution feature information fed into a gated motion compensation network, followed by feature refinement of the aligned features through a fusion network, and, finally, the corresponding high-resolution video frames are generated through a reconstruction network. Compared to existing methods, the advantage of this model is that it can use both high-low resolution neighboring frame information, effectively improving the accuracy  
Figure 1: A motion compensation approach based on deep learning video super-resolution reconstruction. (**a**) The first motion compensation approach uses low-resolution frame information for motion compensation of low-resolution optical streams. (**b**) The second motion compensation approach uses high-resolution frame information for motion compensation of high-resolution optical streams. (**c**) Our approach combines high and low-resolution frame information for a new type of motion compensation. of motion compensation and the reconstruction effect. The introduction of the gating mechanism, on the other hand, enables the adaptive selection of useful information to avoid the effects of motion estimation errors. As a result, this model has better robustness and stability. The main contributions of this paper are as follows:  
1. A new gated motion compensation approach for high-low resolution frames is proposed, adaptively selecting useful information in high- and low-resolution neighboring frames to avoid the effect of motion estimation errors;
2. A pre-initial hidden state network is designed to reduce the impact of video frame imbalance that occurs by adopting a one-way cyclical frame;
3. A local scale hierarchical salient feature fusion network is designed, which can focus on different scales as well as different regional features to obtain locally salient feature information;
4. A plug-and-play hierarchical hybrid attention module is proposed, which can filter useful information from different levels of features and recover better localized high-frequency detail information.
# Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route
## 2 Model and Problem Definitions
### System Model and Threat Model  
As shown in Figure 1, the system consists of four entities: an ORH server, a crypto server, the riders, and the drivers. We use the RS to represent the ORH server and the CS to represent the crypto server in the following paper. Drivers start ride-sharing queries and submit their current status when they are idle or not-full. Riders submit ride requests when they need a ride-sharing service. The RS finds the optimal riders for the driver with minimum average \(ATT\). Besides, we introduce the CS to set up the cryptographic parameters and enable the matching computation. Based on the system model, we make the following assumptions:  
1. In our system, all entities are semi-honest. That means they always honestly follow the scheme but are curious about the information of users. So the goal of privacy is to prevent the RS or the CS from learning about users' sensitive information, which includes precise locations and appointed time.
2. There is no collusion between the RS and the CS because they are managed by different parties. Nor will the RS collude with the drivers because the RS is always a big company and takes much attention to the reputation.
3. The communication channel between two entities is secure, which means no man-in-the-middle attack will be launched. Nobody can utilize channel information to locate users.  
Figure 1: System model.
## 4 SOMS Framework
### The First Stage  
Outage clustering is the main target in the first stage of the proposed structure. The basic and main idea of outage clustering is relating the outage to the nearest depot. However, each outage should be clustered in only one cluster (depot). Additionally, the constraints of the resources and the proficiency of each depot should be considered. The input data of the first stage are as follows:  
* The estimated distance between the outages and each depot.
* The number of resources in each depot.
* The required resources for outage clearing.
* The proficiency of each depot.  
This stage causes a decrease in the processing time for the problem. The objective function of the first stage is defined as Equation (1):  
\[min\sum_{\forall o}\sum_{\forall m}\!d(dep_{\sigma},m)\ s(\sigma,m) \tag{1}\]  
In Equation (1), the variable \(s(\sigma,m)\) is defined to decide which outage \(m\) is assigned to the depot \(\sigma\). Additionally, \(d(dep_{\sigma},m)\) is the distance between the outages and each depot.  
The main target of the objective function is to assign the outages to their closest depots. After solving Stage One, all the outages should be clustered into one depot using Equation (2):  
\[\sum_{\forall\sigma}s(\sigma,m)=1 \tag{2}\]  
It is clear that each damaged component needs some equipment for repair. Regarding the issue, Equation (2) shows that the damaged component (outage) should be clustered into the depot that has adequate equipment for repairing the outage.  
\[Res(\sigma,p)\geq\sum_{\forall m}Res^{N}(m)\ s(\sigma,m) \tag{3}\]  
In Equation (3), the parameter \(Res(\sigma,p)\) and \(Res^{N}(m)\) indicates the number of resources in each depot and required resources for outage clearing, respectively.  
Furthermore, Equation (4) shows that each outage should be assigned to a depot that has proficiency in repairing the damaged component:  
\[TD(\sigma,m)\geq s(\sigma,m) \tag{4}\]  
In Equation (4), the parameter \(TD(\sigma,m)\) shows the proficiency of each depot in clearing the outages. In this equation, the binary parameter \(TD(\sigma,m)\) is considered higher than the clustering binary variable \(s(\sigma,m)\). Therefore, if the amount of \(TD(\sigma,m)\) is considered equal to zero, then the amount of \(s(\sigma,m)\) is determined as equal to zero. Therefore, the intended outage will not be clustered in a depot that does not have adequate proficiency.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Bi-Modal Dynamic Decision Method  
The bimodal dynamic decision-making approach serves as the core decision-making mechanism in the tracking system. By dynamically fusing appearance and motion modality information through a carefully designed process, it effectively addresses critical issues such as multi-source information conflicts and matching ambiguity resolution. This method ensures matching accuracy while maintaining computational efficiency. The approach employs a dynamic weighting fusion function to adaptively balance appearance similarity and motion consistency as follows:  
\[S_{\text{fuse}}=\beta(\tau)S_{\text{app}}+(1-\beta(\tau))S_{\text{mot}},\quad \beta(\tau)=\frac{1}{1+e^{0.1(\tau-15)}} \tag{19}\]  
Here, \(\tau\) represents the trajectory duration (in frames), and \(\beta(\tau)\) constitutes a Sigmoid-shaped dynamic weighting function.  
As the trajectory duration increases, the weight of motion information gradually rises from an initial 30% to 70%. This design reflects the increasing reliance on motion consistency for long-term trajectories as follows: new targets are primarily identified based on appearance features, while targets in long-term tracking establish stable motion patterns, leading to progressively higher reliability.
## 2 Temperature Field and Thermal Error Modeling of Nut-Rotating Ball Screw Transmission Mechanism
### Thermal Error Analysis of Nut-Rotating Ball Screw  
Thermal deformation error is an important component of motion error in micro feed systems, and for precision feed control systems, it is necessary to consider research. The nut-rotating ball screw in the micro feed mechanism in this article utilizes the fixed support in one terminal, whereas the free extension is used in the other terminal for installation. Thermal elongation at the ball screw terminal represents the sum of the ball screw thermal error. Ignoring the thread of the ball screw and treating it as a smooth cylinder, the ball screw's thermal error principle can be observed in Figure 10.  
Divide the screw into individual \"micro units\" \(\Delta L_{0}\). For the accurate prediction of thermal error, assume \(\Delta L_{0}\to 0\), and following a period of time when the heat source acts on it, the temperature changes to \(T(x,t)-T_{0}\). Thermal error of the screw's \"micro units\" can be expressed as:  
\[\Delta L(x)-\Delta L_{0}=\alpha_{T}[T(x,t)-T_{0}]\Delta L_{0} \tag{50}\]  
where \(\Delta L(x)-\Delta L_{0}\) stands for the \"micro unit\"'s thermal elongation and \(\alpha_{T}\) indicates the screw's average linear expansion coefficient.  
Thermal error at the screw terminal is acquired by adding all micro unit's thermal elongations, that is, the screw's total thermal error can be expressed as:  
\[E=\int_{0}^{L}\alpha_{T}[T(x,t)-T_{0}]d\delta=\sum_{i=1}^{n}\alpha_{T}[T_{i}( x,t)-T_{0}]\Delta L_{0} \tag{51}\]
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 5 Simulations Verification
### Simulations Summary  
Form the above-mentioned experimental results, five conclusions can be drawn. (a) Due to the consideration of the queuing process and channel errors, our analytical model accurately reflects the performance of platoon communication; (b) Reducing bit error rate is an effective method to improve the performance of intra-vehicle communication; (c) Under the premise of the safety of platoon, system performance can be improved by decreasing packet arrival rate; (d) The rise of platoon size has a negative effect on the performance of platoon communication, so a large platoon size is inappropriate; (e) The IEEE 802.11p broadcast communication satisfies the requirements of timely and reliable transmissions of platoon in practice. It is undeniable that there are some minute differences between the results of simulation experiments and that of real scenarios. This phenomenon is attributed to the factors in two aspects. Firstly, the interference of free vehicles in platoon communication is ignored in our simulation experiments, while the interference is unavoidable in real scenarios. Secondly, platoon communication includes two parts: inter-platoon communication and intra-platoon communication. However, the impact of inter-platoon communication is not taken into account in simulation scenarios. Therefore, simulation results are slightly lower than real results.
## 6 Case Study and Discussion
### Simulation Results  
#### 6.3.1 Case 1  
In case one, the numbers of EVBCDIs and installation capacities of REs are set to a fixed number according to the load demand and neither of them are optimized. The numbers of EVCDIs for RMG1, RMG2 and OBMG are 10, 10, and 20, respectively. WT installation capacities for RMG1, RMG2, and OBMG are 240 kW, 160 kW, and 320 kW. PV installation capacities for RMG1, RMG2, and OBMG are 180 kW, 120 kW, and 240 kW. The two-day operation profiles of RMG1 and OBMG in case one are shown in Figure 6. Profiles of RMG2 are similar to RMG1 and are not demonstrated here.  
Figure 5: Prices of energy exchanging and EV charging and discharging.  
As demonstrated in Figure 6, the EVs tend to charge in RMGs to satisfy their driving demands for the next day, and discharge in the OBMG to participate in the operation of the microgrid and earn some benefit by trading energy with the microgrid operator.  
Figure 6: Dispatching results in Case 1: (**a**) Dispatching profiles of RMG1; (**b**) Dispatching profiles of OBMG.  
For most systems, the electricity prices of the main grid are always related to the load demand. The electricity prices are high in load peak hours, and the prices are low in load valley hours [26]. However, as shown in Figure 6, the microgrids tend to sell energy to the main grid when the electricity prices are high, and purchase energy from the main grid the prices of electricity are low. Therefore, the energy trading among microgrids and the grid can contribute to peak shaving of the grid.  
In Case 1, the total cost of one year is $195,195.80. The costs for RMG1, RMG2, and OBMG are $67,872.80, $38,063.00, and $89,260.00, respectively.
# Electrons and X-rays to Muon Pairs (EXMP)
## 5 PoP Experiment  
We report in this section a possible option for a PoP experiment to be conducted at a facility where a GeV-class high brightness electron beam is operated, which could be possibly used parasitically for electron-photon collisions at \(E_{cm}\), adequate for muon pair production. In order to replicate on a smaller scale the full scale EXMP scheme, a significant reduction in the electron beam energy (necessary to achieve a reasonable small scale PoP) must go along a proportional increase of the X-ray photon beam energy. Small emittance, a reasonable average current, and overall beam quality are other critical parameters to take into account. A natural example to consider is the European X-FEL, based at DESY, operating 17 GeV high brightness electron beams to drive the SASE X-ray FEL. Using an electron energy of 20 GeV, as a reference, the natural scaling of \(E_{cm}\) points to a photon energy of about 1.5 MeV (compared to 150 keV of the 200 GeV scenario described above). Therefore, a PoP experiment conducted at DESY with X-FEL would comprise the use of the spent electron beam emerging from the undulator, after an FEL radiation emission, colliding with an intense ICS gamma-ray beam of 1.5 MeV of photon energy. Such an ICS would be based on an SC-RF small LINAC of about 300 MeV energy, replicating the same time-structure of the X-FEL electron beam (10 Hz macro rep-rate with pulses 0.6 ms long carrying 2700 bunches 1 nC each, for an overall rep rate of 27 kHz). Up to \(5\times 10^{9}\) ICS photons per collision can be taken to the primary collision with the 20 GeV electrons, so to achieve the same \(E_{cm}=346\) MeV as the full-scale EXMP. Nearly 0.3 muon pairs per second can be generated, for \(\sigma_{0}=2\)\(\mu\)m, with a transverse normalised emittance of 0.7 \(\pi\)\(\mu\)m rad (simulated value confirmed by the one predicted by formula 6 of 0.8 \(\pi\)\(\mu\)m rad) at a few GeV of average energy, as shown in Figure 5. Such a PoP would allow the characterization of the full 6D muon beam phase space, together with confirmation of the predicted cross section behavior as a function of \(E_{cm}\) (this can be easily adjusted by varying the ICS photon beam energy, as a LINAC-based ICS is tunable).  
Figure 5: PoP for EXMP: MPP features. Muon beam energy spectra, angular distribution and phase space distribution in x.