esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 3 Approach
### Reference Pressure Extraction
#### 3.3.4 Fusion  
The sensor fusion was made as is described in Table 1. The sensors that have been taken into consideration are the proximity, the light, GPS and the magnetic field sensor. Their decision is fused as follows:  
* If the proximity sensor indication is false, this implies that there is no obstacle blocking the light sensor. As a result, three sensors are available. Hence, the result is determined on the basis of the voting fusion. For example, if the light and GPS sensors identify that the particular data segment is extracted from indoors, then the segment is classified as an indoor data segment.
* On the other side, if the proximity sensor indicates \"true\", then we have only two sensors available. The majority voting can thus not be applied here. Hence, in such a case, the logic  
Figure 8: Magnetometer signal from walking into four consecutive buildings.  
Figure 9: Magnetometer signal from walking into four consecutive buildings and corresponding smoothed moving Standard Deviation (STD) of moving STD with kernel size of 500.  
operation _and_ is applied. For example, if the magnetic sensor indicates disturbances--and as a result, indoors--but the GPS uncertainty is low, which indicates outdoor space, then the segment is classified as outdoors.
## 2 Methodology  
### Acoustic Emission Location  
The arrival time of the acoustic emission signal can be expressed as:  
\[t=t^{\rm(s)}+f\Big{(}x^{\rm(s)},y^{\rm(s)},z^{\rm(s)},\mathbf{s}\Big{)} \tag{1}\]  
where, \(t^{\rm(s)}\) and \(\Big{(}x^{\rm(s)},y^{\rm(s)},z^{\rm(s)}\Big{)}\) represent the original time and location of acoustic emission, respectively. The function \(f\) represents the propagation time of AE, which is related to the source position and the slowness \(\mathbf{s}\) (reciprocal of the velocity \(\mathbf{v}\)). For an isotropic homogeneous medium with velocity \(\mathbf{v}\), it can be written as:  
\[f=\sqrt{\left(x_{i}^{(r)}-x^{(\mathbf{s})}\right)^{2}+\left(y_{i}^{(r)}-y^{( \mathbf{s})}\right)^{2}+\left(z_{i}^{(r)}-z^{(\mathbf{s})}\right)^{2}}/v \tag{2}\]  
where \(\left(x_{i}^{(r)},y_{i}^{(r)},z_{i}^{(r)}\right)\) is the position of the \(i\)th receiver. Substitute Equation (2) into Equation (1) and simplify it; then, the following expression can be obtained:  
\[g_{i1}m_{1}+g_{i2}m_{2}+g_{i3}m_{3}+g_{i4}m_{4}+g_{i5}m_{5}+g_{i6}m_{6}=d_{i} \tag{3}\]  
where  
\[\begin{array}{l}g_{i1}=1,g_{i2}=-2x_{i},g_{i3}=-2y_{i},g_{i4}=-2z_{i},g_{i5 }=2t_{i},g_{i6}=-t_{i}^{2},\\ m_{1}=\left(x^{(s)}\right)^{2}+\left(y^{(s)}\right)^{2}+\left(z^{(s)}\right)^{ 2}-\left(vt^{(s)}\right)^{2}\\ m_{2}=x^{(s)},m_{3}=y^{(s)},m_{4}=z^{(s)},m_{5}=t^{(s)}v^{2},m_{6}=v^{2},d_{i}= -(x_{i}^{2}+y_{i}^{2}+z_{i}^{2})\end{array} \tag{4}\]  
The matrix form of Formula (3) can be expressed as:  
\[Gm=d \tag{5}\]  
Set the solution of the linear equations as \(\widetilde{m}\), then the source parameters and velocity can be expressed as:  
\[x_{s}=\widetilde{m}_{2},y_{s}=\widetilde{m}_{3},z_{s}=\widetilde{m}_{4},t_{s }=\widetilde{m}_{5}/\widetilde{m}_{6},v=\sqrt{\widetilde{m}_{6}} \tag{6}\]
## 3 Results
### Additional Measures
#### 3.3.1 Generated Signal  
A pure sinusoidal wave was generated with a frequency of 250 Hz, sampling frequency of 44,100 Hz, and an amplitude of 10. White noise with an amplitude of 1 was added.  
Table 5 shows that the values obtained by the reference software and by the algorithm are identical.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Reference** & **Algorithm** \\ \hline HNR & 21.79 & 21.78 \\ Autocorrelation & 0.993 & 0.993 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Autocorrelation and HNR values on a generated signal.  
Figure 8: Comparative analysis of the Autocorrelation with 25 and 12.5 kHz over 50 kHz sampling frequency.
## 2 Materials and Methods
### Robust Neural Network
#### 2.4.4 Huber Loss Function  
The training process of neural network is to set a loss function and utilize back-propagation (BP) gradient descent algorithm to update the parameters layer by layer. One of the most common-used function is mean squared error (MSE):  
\[\text{MSE}=\frac{1}{n_{s}}\sum_{i=1}^{n_{s}}\left(y_{i}-y_{i}^{*}\right)^{2}, \tag{16}\]  
Figure 9: The principle of dropout during the training step.  
where \(n_{s}\) is the number of training samples; \(y_{i}\) and \(y_{i}^{*}\) are the \(i\)th predicted and the \(i\)th actual outputs, respectively. Besides, mean absolute error (MAE) is another conventional function, which can be defined as follows:  
\[\text{MAE}=\frac{1}{n_{s}}{\sum\
olimits_{i=1}^{n_{s}}}\big{|}y_{i}-y_{i}^{*} \big{|}, \tag{17}\]  
where MSE and MAE can be also called L1 loss and L2 loss, as linear term and quadratic term are used in the MSE and MAE, respectively.  
Comparing MSE and MAE, MSE holds a smoother derivative function, which can benefit the calculation of gradient descent algorithm, whereas a small difference in MAE may cause a huge change in parameter updating. On the contrary, MAE demonstrates a better capability than MSE when fighting against outliers [35]. In this case, a Huber loss function is applied in this study that combines the merits of both MSE and MAE [36], as shown in Figure 10:  
\[\text{Huber}=\frac{1}{n_{s}}{\sum\
olimits_{i=1}^{n_{s}}}\left\{\begin{array}[ ]{ll}\frac{1}{2}\big{(}y_{i}-y_{i}^{*}\big{)}^{2},&\big{|}y_{i}-y_{i}^{*} \big{|}\leq\delta\\ \delta\big{|}y_{i}-y_{i}^{*}\big{|}-\frac{1}{2}\delta^{2},&\big{|}y_{i}-y_{i}^ {*}\big{|}>\delta\end{array}\right., \tag{18}\]  
where \(\delta\) is a hyper-parameter that needs to be set manually. It is set to be 10% in this study.
# Multi-Attention-Guided Cascading Network for End-to-End Person Search
## 4 Experiment
### Experiment Results on Two Datasets  
#### 4.2.1 Evaluation on CUHK-SYSU  
We conducted a comparative study between MGCN and various state-of-the-art methods designed to solve the person search problem on the CUHK-SYSU and PRW datasets, which includes DPM [16], MGTS [17], CLSA [49], RDLR [32], IGPN [50], TCTS [33], OIM [18], IAN [51], NPSM [52], RCAA [35], CTXG [53], QEEPS [19], HOIM [54], APNet [55], BINet [56], NAE [42], NAE+ [42] DMRNet [57], PGS [58], AlignPS [36], AlignPS+ [36], and SeqNet [37] methods.  
Tables 1 and 2 illustrate the experimental results of our proposed MGCN with a gallery size of 100. We compare MGCN with two-step person search methods and one-step algorithms in Tables 1 and 2, respectively. The experimental results show that our model achieved high performance on CUHK-SYSU dataset with mAP and top-1 accuracy of 94.28% and 94.97%, respectively. Compared with the two-stage person search methods in Table 1, we can observe that, with TCTS, MGCN achieves a significant improvement of 0.48% and 0.3%, respectively in mAP and top-1. Among them, the TCTS model adopts random erasure, label smoothing, and triple loss, but still does not show satisfactory performance compared with MGGN. The starting point of this paper is not just to propose a person search method with the best performance. Compared with TCTS, we combine the detection and re-id tasks in a unified framework to eliminate the difficulty of utilizing two-step progress with different tasks. In addition, MGCN aims to collaborate between multiple spaces in a unified framework to learn a consistency graph that is able to uncover the essential structure of multi-view data. Compared to NAE, MGCN shows significant improvement rates of 2.78% and 2.57% for mAP and top-1, respectively. We can see that MGCN seems more robust than network-based PGS, because it takes the fine-grained features in the re-id process into consideration and considers obtaining more discriminative feature embedding. Compared with the end-to-end method, our method is higher than AlignPS by 2.69%, where AlignPS integrates multi-level information and our method focuses more on salient features during the re-identification stage. In addition, MGCN aims to collaborate multiple tasks in a  
unified framework to learn the discriminant pedestrian feature representation that is able to uncover the essential semantic detail features of person search dataset. The results indicate the effectiveness of our multi-attention-guided cascade network.
## 1 Introduction
[PERSON] et al. [37] performed topology optimization of robots' upper arm using the SIMP method. [PERSON] et al. [38] proposed a hybrid method for topology optimization by combining the SIMP and GA algorithms, called the SIMP-GA method. If the number and position of fixture elements are not optimal they may result in lower geometric accuracy [39]. Authors combined the Kriging surrogate model and multi-objective bat algorithm (MOBA) to reduce FEA calculations for multi-objective optimization in sheet metal fixtures [40]. Authors \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Author** & **Characteristics and Methods** & **Limitations** \\ \hline [PERSON] et al. [23] & Fixture support positions were identified using Broyden–Fletcher-Goldfarb–Shanno (BFGS) method. & Only applicable on rigid body. \\ \hline [PERSON] et al. [24] & Developed iterative method based on GA to optimize fixture layout. & Only applicable on rigid body. \\ \hline [PERSON] et al. [25] & Find optimal position of fixture elements considering sheet metal using Multi Point Constraint (MPC) feature. & Number of fixture elements cannot be optimized. \\ \hline [PERSON] et al. [26] & Genetic algorithm–ant algorithm (GAAA) was used to reduce assembly variations. & Requires High computational effort. \\ \hline [PERSON] et al. [27] & Fixture layout optimization by non-dominating sorting social radiation algorithm (NSSRA). & It optimizes only positions of clamps; number of clamps were not optimized. \\ \hline [PERSON] et al. [28] & Fixture layout optimization based on N-2-1 locating scheme by combining cuckoo search algorithm. & Objective function is achieved after many evolutionary generations, which increases computational cost. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of currently existing fixture layout optimization methods. combined FEA and the whale optimization algorithm to minimize the overall strain energy of thin-walled parts by optimizing the number and position of locators [41]. The proposed methodology, including problem formulation and stages of fixture layout optimization using topology optimization, is discussed in Section 2. The flat plate and spacer grid case study is solved using the proposed method in Section 3. Linear static analysis is used to solve the problem, because fixture layout optimization problems are solved within the elastic limit range by defining the constraints in the problem formulation or by checking the failure criteria limits. The optimal results of case studies are discussed in Section 4. In Section 5, the experiment is performed to verify and compare the results of the flat plate case study with simulation results. Conclusions are explained in Section 6.
# Intelligent Communication in Wireless Sensor Networks
## 6 Configuration of the Simulation  
Thus, we chose to use the SINALGO simulator (Sinalgo is for free, published under a BSD license), for our simulation platform, it is a simulator that enables creating, simulating and validating distributed algorithms [24]. We have set up a platform to simulate or achieve communication architecture in the midst a set of sensor nodes and a sink in the form of a WSN. It is installed via the method of partitioning the network into groups as sectors and the use of a multi-agent system, which is considered as a method to save the energy of a wireless sensor network. We will, therefore, realize an efficient application in terms of energy consumption, the task duration, and the packet drop ratio. In order to design our simulator, we have adopted the following assumptions:  
* The energy of a sensor is limited;
* The base station has an infinite supply of energy;
* The sink and SNs are stationary;
* At the beginning, each sensor has an initial energy;
* After network activation, each sensor is an agent;
* The manager considers the number of sensors, the radius and the position of the sink;We consider the amount of sensors is limited;
* Sensor positions are known;
* The deployment is random; and
* Two sensors do not occupy the same coordinates.  
All the simulations of our work realized a surface of 1000 m \(\times\) 1000 m with a random diffusion of the sensors for 1000 s. The radio range and the rate of each sensor are limited to 87 m and 1 Mbps, respectively, as proposed in [12]. Transmit and receive power parameters, which directly influence the radio range, were chosen from the platform defined in the sunspot system [17]. The local processing time is set at 40 ms. Table 1 represents the simulation parameters:
## 1 Introduction  
The purpose of reconstruction of an inferomedial orbital fracture is to restore the normal orbital structure and volume by placing the fractured segment in the premorbid position [1, 2]. However, precise restoration of the delicate anatomy of the orbit is challenging [3] and can result in serious and visible enophthalmos, globe dystopia, diplopia, or blindness if sophisticated restoration is not achieved [4, 5, 6, 7, 8].  
A fractured orbit lacks structural support, and appropriate placement of an implant can be compromised [9, 10]. Traditionally, inorganic orbital implants made of titanium, polyethylene, and poly lactic acid have been used [11, 12, 13, 14, 15]. Those can be manipulated easily but can lead to above-mentioned complications due to inaccuracy [8] and atirogenic injury [3]. Recently, the patient-specific implant (PSI) using three-dimensional (3D)-printing technology has been introduced. This implant can be elaborately shaped to restore normal orbital anatomy [2, 3, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]. However, considering that orbital fracture is a common facial injury, PSI is high-cost, time-consuming, and requiring additional labors for each procedure.  
In a previous study, anatomical restoration of an orbital fracture was attempted by creating a suitable implant using 3D printing technology [26]. The orbital implant reproduces the curvature of the inferomedial wall of the orbit using anatomical data from standardized radiologic images of 100 cadavers.  
In the present study, the authors compared the efficacy of the conventional manual-bending implant and the 3D-printed standardized implant in clinical practice. The use of a 3D-printed standardized implant was hypothesized to allow more precise molding than a conventional implant. The surgical outcomes were evaluated using computed tomographies to assess the accuracy of implant insertion, the degrees of post-operative enophthalmos and diplopia, and limitation of the globe movement.
# The PIV Measurements of Time-Averaged Parameters of the Synthetic Jet for Different Orifice Shapes
## 3 Results and Discussion
### The Velocity of the Synthetic Jet
The centerline dimensionless time-averaged velocity was higher in the case of a square orifice than in the case of a circle orifice at a distance \(x/d<6\). The centerline velocity in the case of the slot orifice was the highest at \(L_{0}=2\) and \(x/d=1.8\), but already at a distance of \(x/d=2\), it decreased below the value obtained for the circle and square orifice. The centerline time-averaged SJ velocity for a dimensionless stroke length equal to 10 has similar values and distributions for all investigated orifics for \(x/d>1\).
# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
###### Abstract  
Cellular vehicle-to-everything (C-V2X) communication has recently gained attention in industry and academia. Different implementation scenarios have been derived by the 3 rd Generation Partnership Project (3 GPP) 5 th Generation (5G) Vehicle-to-Everything (V2X) standard, Release 16. Quality of service (QoS) is important to achieve reliable communication and parameters which have to be considered are reliability, end-to-end latency, data rate, communication range, throughput and vehicle density for an urban area. However, it would be desirable to design a dynamic selecting system (with emphasis on channel coding parameters selection) so that all QoS parameters are satisfied. Having this idea in mind, in this work we examine nine V2X implementation scenarios using Long Term Evolution (LTE) turbo coding with a geometry -based efficient propagation model for vehicle-to-vehicle communication (GEMV), where we consider the above QoS parameters for SOVA, log-MAP and max-log-MAP decoding algorithms. Our study is suitable for 3 GPP cooperative sensing, for the eight scenarios considering medium and large signal-noise-ratio (SNR) values. The proposed model is sustainable despite a doubled data rate, which results in a minimal bit error rate (BER) performance loss up to 1.85 dB. In this case tripling the data rate results in a further 1 dB loss. Moreover, a small loss up to 0.4 dB is seen for a vehicle speed increase from 60 km/h to 100 km/h. Finally, increasing vehicle density has no effect on the implemented 3 GPP scenario considering end-to-end latency, irrespectively from the decoding algorithm.  
5G; V2V; turbo codes; GEMV model; QoS parameters; implementation scenarios +
Footnote †: journal:  
Article
## 6 Experiments
### Experiments with 2 Robots  
First, we perform the experiments in a setting with two robots. The starting positions and poses of the robots are visualized in Figure 7, although only two robots are initialized. We collect trajectories and metrics for all three compared methods, presented in Table 4. A representative sample of trajectories is visualized in Figure 8. Observing the experiments, it becomes evident that the proposed YANA method effectively avoids and anticipates the motion of another agent in the scene. While other approaches (YA(n)A and base TD3) succeed in most cases, collisions occur in specific scenarios. For YA(n)A in Experiments 1 and 5, the robots occasionally arrive at a single location simultaneously, lacking a learned exchange strategy that results in collisions. In the TD3 case, an issue arises in Experiment 2 due to an approaching frontal obstacle. The training environment featured solely static obstacles, rendering frontal dynamic obstacle avoidance challenging, as the obstacle advances earlier than anticipated. In scenarios in which arriving at the goal was always successful, distance data indicate that YA(n)A and TD3 select more optimal routes with less consideration for other agents in the scene. Nevertheless, YANA's distance measurement remains comparable to other approaches. However, comparing the average number of steps taken, YANA exhibits a longer time to reach the goal in certain scenarios. For instance, in Experiment 4, for the robot displayed in red, the YANA approach waits for the green robot to navigate to a safe distance before initiating its own movement. In contrast, YA(n)A and TD3 start navigating towards the goal immediately. From the results, we can see that while YANA outperforms other approaches in terms of safety, YA(n)A and basic TD3 could still be used for robot navigation in this setting, provided that collision strategies are improved.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Av.** & **Std.** & \multirow{2}{*}{**Av. Steps**} & **Std. Steps** & **Av.** & **Std.** \\  & **Distance** & **Distance** & & & **Collisions** & **Collisions** \\ \hline
**Exp. 1**: & & & & & & \\ YANA & 14.914 & 0.122 & 192.15 & 2.433 & 0 & 0 \\ YA(n)A & 8.778 & 3.987 & 123.6 & 66.637 & 1.35 & 0.875 \\ TD3 & 11.992 & 0.06 & 143.85 & 2.433 & 0 & 0 \\ \hline
**Exp. 2**: & & & & & & \\ YANA & 7.803 & 0.238 & 92.45 & 17.074 & 0 & 0 \\ YA(n)A & 8.199 & 0.161 & 94.1 & 3.447 & 0 & 0 \\ TD3 & 4.696 & 1.606 & 54.3 & 17.496 & 1.55 & 0.825 \\ \hline
**Exp. 3**: & & & & & & \\ YANA & 7.737 & 0.103 & 107.35 & 7.271 & 0 & 0 \\ YA(n)A & 8.742 & 0.444 & 103.8 & 8.338 & 0 & 0 \\ TD3 & 7.448 & 0.051 & 85.7 & 1.128 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiments with 2 robots.  
Figure 7: Simulated experiment setup. Figures (**a–f**) visualize each robot’s starting position and heading for each respective experiment. Large circles represent the robot’s starting position and the arrows represent their heading. Smaller circles represent goal points for robots of the same color. The grid is represented with 1 by 1 meter-sized squares.
## 3 Results and Analysis  
### Training Details  
To increase efficiency and save resources, we set the batch size to 16 and cropped images in the dataset. The reason is that high-resolution images can increase processing time and resource consumption. We employed a uniform cropping method for both high-resolution (HR) images with 2K resolution and low-resolution (LR) images with 512 resolution. Specifically, each HR and LR image was uniformly cropped into 64 smaller segments. This processing method facilitates our model training.  
We divided the training process into two main stages to optimize the performance of the super-resolution network model. In the initial stage, we use the L1 loss function to measure pixel-level differences, known as pixel loss, with the goal of cultivating a model that highly values the peak signal-to-noise ratio (PSNR). The learning rate for the initial model weights was set to \(1\times 10^{-4}\), employing an exponential decay strategy, where the learning rate is halved after every \(2\times 10^{-5}\) mini-batch updates. This strategy helps in the gradual and fine-tuning of model parameters during the training process.  
Subsequently, the model which is already well trained towards PSNR is used as the initial state of the generator within the GAN framework during the second stage. At this stage, the training of the generator adopts the Charbonnier composite loss function which replaces the L1 loss, with parameters \(\lambda\) = 5 \(\times\)\(10^{-3}\) and \(\eta\) = 1 \(\times\)\(10^{-2}\). Specifically, the learning rate starts at 1 \(\times\)\(10^{-4}\) and is halved after reaching preset iteration thresholds (50 K, 100 K, 150 K, and 200 K). The purpose of this strategy is to balance efficiency with sufficient detail exploration in the learning process, avoiding too rapid a convergence to suboptimal solutions. For the optimizer, we use the Adam optimizer with \(\beta_{1}\)=0.9 and \(\beta_{2}\)=0.999. The generator and discriminator are trained alternately until the model converges, with the generator containing 23 RRCAB blocks.