esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design and Method  
### Description of the Data Aggregation Problem  
In R-WSNs, in the process of data transmission from packets generated by source nodes to these packets accepted by sink, packets will be encoded and aggregated in each sensor with local packets for reducing the data traffic. We let \(\lambda_{i}\) denote the data aggregation rate of node \(i\) and \(\varphi(j,i)\) denotes the aggregated transit traffic rate at node \(i\) from its upstream node \(j\). The aggregated transit traffic of node \(i\) is a superposition of two parts: the transit traffic passed from the upstream nodes, and the raw data  
Figure 4: The process of data aggregation.  
Figure 5: Topology control for data transmission.  
originated from the upstream nodes that is to be encoded using the local information, as shown in Figure 6. That is,  
\[\lambda_{i}=\sum_{i,j\in V,j\in\vec{S}_{i}}(\lambda_{j}\varphi(j,i)+g_{j}(1- \rho(j,i))) \tag{8}\]  
where, \(g_{j}\) and \(\rho(j,i)\) denote the data generation rate of node \(j\) and the data correlation between node \(i\) and \(j\), respectively. For sensor \(j\), the data traffic \(\sum_{i,j\in V,j\in\vec{S}_{i}}\lambda_{j}\varphi(j,i)+g_{j}\) will be transmitted to its downstream sensor \(i\), while these packets will be aggregated by sensor \(i\) firstly and send new data traffic \(\sum_{i,j\in V,j\in\vec{S}_{i}}(\lambda_{j}\varphi(j,i)+g_{j}(1-\rho(j,i)))\) to next-hop sensor along the path subsequently. The next-hop sensor that receives the packet will compress the packets by the Equation (8), so forth, until it reaches the destination.  
In this work, multiple sinks are deployed for balancing the power of the networks, which is recognized and used wildly in WSNs. Generally, these sinks obtain the same function and status in the work. The data from a source node will be transmitted to any sink finally by one or multiple-hops. For each node, the outflow equals or is less than the inflow and generation data from it due to some redundant information removed from data traffic. In the process of packets transmission, a data leak is not considered. Therefore, the data flow traffic for node \(i\) can be expressed as:  
\[\sum_{i,j\in V,j\in\vec{S}_{i}}(\lambda_{j}\varphi(j,i)+g_{j}(1-\rho(j,i)))= \sum_{i,j\in V,j\in\vec{S}_{i}}\lambda_{i}\varphi(i,j) \tag{9}\]  
where, \(\rho(j,i)\in[0,1]\), when a sensor only forward packets for its upstream neighbor to next-hop sensor without any packet generation, data fusion does not occur and \(\rho(j,i)=0\), while the two sensors have the exact same information and \(\rho(j,i)=1\), which indicates that the packets are duplicated from that of another sensor. Let \(w_{i}\) denote the fraction of power consumption for node \(i\) in each time unit. We have  
\[w_{i}= e_{g}\times g_{i}+e_{idle}+e_{l}+e_{r}\sum_{j\in S_{i}}(\lambda_{j} \varphi(j,i)+g_{j})+e_{l}\sum_{j\in S_{i}}(\lambda_{i}\varphi(i,j)+g_{i}) \tag{10}\]
## 4 Simulation and Analysis
### Curvature Radius \(R=90\ m\)  
In this scenario, all other conditions remain unchanged from those described in Section 4.1, except for the curvature radius. The simulation results for the test scenario with an initial speed of \(60\) km/h are shown in Figure 10a-h. All three control systems avoid collision successfully, with relative distances of \(1.05\) m, \(1.02\) m, and \(1.08\) m. In System 0, the lateral offset of the vehicle's center of mass is \(1.15\) m when the vehicle comes to a stop. This represents a significant reduction compared to the scenario with a curvature radius of \(60\) m, although a portion of the vehicle's body still deviates from the lane. In both System 0 and System 0, the triggering of the LKA system results in a noticeable decrease in the lateral offset of the center of mass. For System 0, the lateral offset is \(0.22\) m, and for System 0, it is \(0.31\) m. In both cases, the vehicle's body remains close to the lane centerline when  
Figure 8: Simulation results (\(V=60\) km/h, \(R=60\) m).  
Figure 9: Illustration of simulation results for different systems.  
stationary. Furthermore, the variation in yaw rate indicates that the lateral stability of System 3, after the LKA system is triggered, is notably superior to that of System 2.  
In addition, simulation tests are conducted with an initial vehicle speed of 50 km/h. Table 4 presents the simulation results for two different vehicle speeds in the scenario involving a 90 m radius curve. It is clear that collision avoidance is successfully achieved for both vehicle speed conditions in the 90 m radius-curve scenario with each of the three different collision avoidance control systems. With the involvement of the LKA system, Systems 2 and 2 demonstrate a significantly reduced lateral offset of the center of mass compared to System 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Curvature** & **Speed of Ego** & \multicolumn{3}{c}{**Collision**} & \multirow{2}{*}{**Final Gap**} & \multirow{2}{*}{**Lateral**} \\
**Radius \(R\)** & **Vehicle** & & & **Avoidance** & \\
**(m)** & **(km/h)** & & & **Successfully** & \\ \hline \hline \multirow{8}{*}{90} & 60 & \(\copyright\) & Yes & 1.05 & 1.15 \\  & 60 & 2 & Yes & 1.08 & 0.31 \\  & 60 & 3 & Yes & 1.12 & 0.22 \\  & 50 & 1 & Yes & 2.80 & 0.54 \\  & 50 & 2 & Yes & 2.83 & 0.29 \\  & 50 & 3 & Yes & 2.82 & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of simulation results for \(R=90\) m.  
Figure 10: _Cont._
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 3 Quantitative Congestion Indicators  
Traffic engineers use different approaches to present and describe the state of traffic flow and to estimate the degree of congestion. The common approach is to describe the state of traffic flow using traffic flow parameters and the fundamental diagram proposed in 1935 by [PERSON] [27]. Another approach relies on the usage of Lighthill- Whitham-Richards (LWR) models, first introduced in 1955 by [PERSON] and [PERSON] [28], and independently in 1956 by [PERSON] [29]. The three-phase traffic theory, developed by [PERSON] between 1996 and 2002 [30; 31; 32], proposes a division of congested traffic into two distinct phases, synchronized flow and wide moving jam.  
Some traffic flow parameters or combination of some parameters can be used as quantitative congestion indicators. Fundamental traffic parameters are flow rate \(q\) (veh/h), density \(\rho\) (veh/km), and speed \(v\) (km/h) according to [33] with basic relationship \(q=\rho v\). Other parameters used to describe the state of traffic flow are given in Table 1. [1,27,34].  
In [35], the authors state that the key performance indicators for urban roads are delay, density, and Level of Service (LOS). LOS is usually measured by speed, density, and volume/capacity ratio [36], and it presents a quality indicator of the road network service.  
There are also hybrid measures, which can be fused by combining two or more measures. The authors in [37] combined flow, measured using loop detectors, and travel time, measured using GNSS probe taxi vehicles, to calculate the link density and determine the shape of the Macroscopic Fundamental Diagram (MFD).  
There are also other indicators regarding the state of traffic flow, which can be provided by GNSS probe data, like Proportion Stopped Time (PST), and Acceleration Noise (AN). The authors in [38; 39; 40] used several indexes such as travel time index, space mean speed index, acceleration noise index, buffer index, and planning time index for estimation of congestion on the link or network. Indexes for a transport link and network congestion estimation are also mentioned in the survey [35]. In Table 2, a short overview of congestion indexes is given per [41]. Hybrid indicators that combine two or more parameters also can be used for congestion description.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Neural (Machine Learning) Methods
#### 5.5.2 ANN Training: Loss Functions and Steepest Gradient Descent  
For training network parameters, initial estimates of node weights and biases may be specified at random, or pre-trained on unlabeled data. The ANN learning procedure is iterative, incrementally updating the parameters, based on minimizing a _loss_ (cost) function. Direct minimization of network accuracy is not feasible, because ANNs need a differentiable loss function, to allow a product chain of derivatives to show which direction to alter parameters, via _steepest gradient descent_, which guides the model towards local loss minima.  
Loss functions approximate a penalty for classification errors. Losses are often chosen empirically and vary greatly among applications [43], but a common one is _cross-entropy_, usually with _softmax_ output units or its variants (Angular Softmax) [44]. Softmax uses a normalized exponential function for interpretation as a probability distribution.  
All ANNs are trained to minimize a loss function, which is usually chosen to be related to optimizing the task performance. As direct error minimization is often difficult to use (e.g., not differentiable, as needed for gradient descent training), there are many possible losses, which are often chosen based on empirical performance, rather than elements related to the data.  
SD typically uses two losses: attractor existence loss and diarization loss. The first loss optimizes the estimation of each speaker's presence (for cases with an unknown number of speakers), and the diarization loss optimizes the active probabilities with a _permutation-invariant_ training method [45]. The two losses are summed using a _hyper-parameter_ weighting (hyper-parameters are model elements that are often selected empirically a priori). The number of speakers may be estimated with the maximum \"eigengap\" criterion [46].
## 3 Results and Discussion
### Equilibrium MD Simulations
#### 3.1.3 Local Thermal Equilibrium Near the Solid/Fluid Boundary  
The local thermodynamic equilibrium near the solid/fluid boundary is tested using MD simulation. The slab bins are divided into 0.05 A intervals, which is small enough to capture the full resolution of the structural inhomogeneity. The local thermodynamic equilibrium must be satisfied to use the mathematical relations of thermodynamic properties driven from statistical mechanics [44]. This can be tested by comparing local velocity distributions to the Maxwell-Boltzmann distribution and quantified by the correlation coefficient. The atomic velocity of the slab bean is collected for 200 ns, which is a relatively long time considering the typical data acquisition time of 2 ns. As shown in Figure 5a, the nucleus of solid and fluid atoms can occupy \(0<z<0.1\sigma_{\text{sf}}\) and \(0.73\sigma_{\text{sf}}<z\) as a result of the thermal motions and interactions between solid and fluid molecules. The correlation coefficient, \(R\), represents how well the velocity distribution of MD simulation matches with Maxwell-Boltzmann distribution indicating a perfect match for \(R=1\) and no correlation for \(R=0\). Near the edge of the thermal oscillations, the statistical significance is rapidly broken (see Figure 5b). Statistical Depletion Region (SDR) is defined on the basis of 99% correlations as depicted in Figure 5a. The range of SDR can vary with the thermodynamics states and molecular characteristics as it is closely related to the density distribution. It is speculated that the solid/fluid boundary is located at the SDR where no nucleus occupies. SDR is occupied by electrons and an explicit investigation of electrons in SDR must be carried out by quantum mechanical methods. It is noted that the effect of electrons is implicitly considered by LJ potential in this study.
## 2 Mathematical Model  
Two hollow concentric cylindrical pipes are arranged in a double-pipe geometry with an inner cylinder (the core) located inside an outer cylinder (the shell), see Figure 1. Both cylinders are of equal length, \(L\), in the longitudinal direction, i.e., the \(z\)-direction.  
The inner (core) cylinder has a radius \(R_{c}\) in the radial direction, i.e., the \(r\)-direction, and is symmetrically surrounded by an outer cylinder (the shell) of radius \(R_{s}\), with \(0<R_{c}<R_{s}\). The cylindrical coordinate system \((r,\theta,z)\) is adopted, with \(r\) in the radial direction, \(\theta\) in the angular direction, and \(z\) in the longitudinal direction.  
Axi-symmetric conditions are assumed, and hence, all flow field variables will be considered independent of \(\theta\). For example, the velocity field would therefore be of the form \(\mathbf{V}=(u(r,z),0,w(r,z))\), where \(u\) is the velocity component in the radial (\(r\)) direction and \(w\) is the velocity component in the longitudinal (\(z\)) direction.  
Laminar, incompressible, time-dependent, and non-isothermal conditions are additionally assumed. A heated/hot viscoelastic fluid occupies the inner core region and flows, say in the positive \(z\)-direction. A colder NFBN fills the outer annulus and flows in the opposite direction to the core fluid leading to the counter-flow arrangement.
## 3 Materials and Methods  
The methods we used are briefly described here:  
* Construction of the street network: we selected and exported a map from OpenStreetMap that we imported in NETEDIT (a network editor) to construct the street network. We manually edited the street network (we set the traffic lights times and the right of way at intersections, and we corroborate the number of lanes and the length of links) to be similar to the real network. NETEDIT is a tool included with the Simulation of Urban Mobility (SUMO), which is a package for microscopic and continuous traffic simulation [45] and is the selected software to perform the simulations presented in this work.
* Metrics: We establish the way for performing speed measurements and density measurements. We design the speed metrics (the general speed and the particular speed) and the density metrics (the general density and the particular density) to be used in our procedures.
* Procedures: Our procedures were implemented with codes. The codes behind a simulation present the procedures for estimating the links' travel times, for gradually reducing vehicles (between simulations), and for controlling the simulations. The language of the codes is Python, we write the codes with Python version 3.10.6.
* Tunning parameters: By running simulations and based on the output we were able to tune parameters and modify the codes to improve our procedures.
* Experiments: To verify the method in different traffic scenarios we performed 10 experiments, each unique for its origin-destination table. In the IS of each experiment we consider a constant flow (each 6 s a vehicle enters the network for each entrance), in the subsequent simulations the number of vehicles is being reduced by our method; for an experiment, we run simulations until the FS is reached (all segments and periods present sustainable speed) and with the output of this, we are able to answer our first investigative question.
* Data analysis: With the output data of the FS (and with the output data of the IS), for each link we calculate the person correlation coefficient between DG and VG, then we obtain the percentage of links for which exists a relationship between DG and VG; we did the same with DP and VP. For each link we calculate the difference of AVG (and STD) of VG between the FS and the IS, then we establish the percentage of links for which the difference benefits the FS; we did the same with VP, DG, and DP. With this knowledge, we answered our second and third investigative questions. The data analysis was conducted with MATLAB version R2022a.
# Performance Evaluation of a Quadcopter by an Optimized Proportional-Integral-Derivative Controller
## 5 Conclusions  
In this article, the stability of the quadcopter and the data readings of the sensor have been analyzed using the IMU. The results from the simulation tools not only include the proposed improvement of the PID control way, but the performance evaluation of the developed UAV is also presented. On the other hand, this paper has learned through the development of the flight controller of quadcopter UAV. Through surround sensing and reading the data of roll, pitch, and yaw, numerical computation have been carried out to help UAV communication system integration. The integration of the combination of signal processing and communication is a difficult problem. Even in the era of AI (artificial intelligence) technology breakthroughs, quadcopter UAV autonomous flight technology still needs to be developed further through AI algorithms to implement more accurate hovering, trajectory flight, and obstacle avoidance. The various flight control algorithms of machine vision technology can deeply understand the mystery of drones through this research. Thus, the evaluation of the schemes proposed in the study suggests that much more stable controllability and a reduction in the response time of the UAV system are achievable.  
**Author Contributions:** Formal analysis, J.I.-Z.C. and H.-Y.L.; investigation, J.I.-Z.C. and H.-Y.L.; visualization and resources, H.-Y.L.; project administration, J.I.-Z.C.; supervision, J.I.-Z.C.; writing--original draft, H.-Y.L.; writing--review and editing, J.I.-Z.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Fixed Value** & \multicolumn{2}{c}{**Variated Values**} \\ \hline PID-3 & \(k_{p}=0.1\) & \(k_{d}=0\) \\ \hline PID-4 & \(k_{i}=1/300\) & \(k_{
# Social Recommendation Based on Quantified Trust and User's Primary Preference Space
## 6 Experiments and Validations
### Experimental Results  
#### 6.3.1 Verification Experiment for Quantitative Trust  
We test whether our TQ_RWR method can improve the recommendation results. In this section, we use the social recommendation method SoRec as the baseline method. The initial trust network and the quantitative trust network are used for comparison experiments, and we use _RMSE_ as the metrics of these experiments.  
The results of comparative experiments on the four datasets are presented in Table 3. The SoRec column indicates recommendation without using TQ_RWR to quantify user trust, while the SoRec+TQ_RWR column indicates recommendation using TQ_RWR to quantify user trust.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **FilmTrust** & **Epinions** & **Douban** & **Ciao** \\ \hline SoRec & 0.8934 & 1.2862 & 0.7733 & 1.2164 \\ SoRec+TQ_RWR & 0.8810 & 1.2712 & 0.7670 & 1.2033 \\ Reduce(\%) & 1.38 & 1.16 & 0.82 & 1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of TQ_RWR.  
Obviously, the FilmTrust dataset has the highest _RMSE_ reduction rate. Comparing the density of the rating data of these four datasets presented in Table 2, it indicates that the lower the density of the dataset, the more efficient the quantified users' trust by TQ_RWR would be. The experimental results show that the user trust quantification method, based on TQ_RWR, can effectively improve the utilization value of the trust network. Moreover, using a quantitative trust network for social recommendation can effectively improve the recommendation efficiency.
# Effects of Short Fibers on the Long-Term Behavior of RC/FRC Beams Aged under Service Loading
## 3 Experimental Results and Discussion
### Long-Term Behavior  
#### 3.1.1 Mechanical Behavior  
As experienced also for the beams tested in the first part of the program (Set #2), during the final loading stage the beams of Set #3 collapsed by concrete crushing after the yielding of the tensile bars (as expected from the design process). At failure, the PC beams showed the most extensive concrete damage, with concrete spalling at the upper compressive side. The flexural behavior of the reference beams tested under short-term conditions was very similar, showing a typical pseudo bi-linear curve. As also was found on the beams of Set #1 and #2, the influence of the fibers was almost negligible in terms of flexural strength, according to available literature [41; 42; 43; 44]. As for Set #1 and #2, also in Set #3 a slight increment of yielding loads (4-8%) was registered in the FRC beams, due to the tensile contribution of fiber reinforcement.  
For both Set #2 and #3, the flexural stiffness of the FRC beams was greater than that of the PC beams up to the service loads, while for Set #1 they were comparable (Table 3). It should also be noticed that, at the end of the test (17 months for _E17 and 72 months for _E72 beams), exposed beams of Sets #2 and #3 had lower stiffness than the corresponding laboratory beams (up to 50 kN) and the stiffness reduction was more evident in the PC beams (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Beam** & \multicolumn{3}{c}{**Stiffness [kh/m]**} & \multicolumn{3}{c}{**Midspan Deflection [mm]**} & \multicolumn{3}{c}{**Ult. Load**} & \multicolumn{1}{c}{**Steel Yield.**} \\
**ID Tag** & **0-50 kN** & **\(\Delta^{*}\)** & **20 kN** & **30 kN** & **50 kN** & **80 kN** & **Failure** & **d(kN)** & **Load (kN)** & **Ductility** \\ \hline PC\_L & 7027 & - & 1.94 & 3.99 & 7.10 & 12.39 & 45.42 & 107.62 & 84.24 & 3  
Figure 4 shows the load vs. mid-span displacement curves for all of the beams of Set #3, tested after 72 months of environmental and mechanical aging; the higher ductility of these FRC beams with respect to the plain concrete beam can be noticed; a ductility index is here assumed as the ratio between the ultimate and yielding deflection. However, the increase in ductility was limited for beams containing polyester fibers, while it was remarkable (up to 20%) for beams with steel fibers. The measured values of ductility are 3.50, 3.62 and 4.18 for PC, POL and ST beams, respectively (average values for FRC beams).
## 3 Proposed Methodology
### Model Training
#### 3.3.2 Tuned Multi-LSTM-Sentiment  
Hyperparameters such as the number of neurons, batch size, epochs, learning rate, and dropout rate were optimised for the Multi-LSTM-Sentiment model to improve its performance. Initially, the model exhibited slight overfitting, prompting adjustments such as reducing the number of neurons to 30 per LSTM layer and increasing the dropout rate to 30%. These changes helped improve generalisation while mitigating overfitting.  
Reducing the neuron count mitigates the risk of overfitting by simplifying the model's architecture, while a dropout rate of 30% ensured regularisation by randomly disabling neurons during training. This dropout rate was chosen based on prior experiments showing its effectiveness in reducing overfitting without overly compromising model learning. Retaining three LSTM layers ensured that sufficient depth existed to capture intricate temporal dependencies across the multivariate dataset.  
The tuned Multi-LSTM-Sentiment model was then trained using the same input features (price and sentiment) over 40 epochs, with a batch size of 30. The 20% validation split was retained to ensure consistency in model evaluation.  
We visualised the training and validation trends for the MSE, MAE, and MAPE in Figure 3. The validation metrics confirmed that the tuning process improved the model's prediction accuracy while maintaining low error rates across all metrics.