esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Inertial Macap  
The inertial sensors comprised of a set of \(11\) wearable units (Figure 1b) (Notch Interfaces Inc., Brooklyn, NY, USA) strapped to various body segments of the participant (Figure 3). The units consisted of accelerometers, gyroscopes, and magnetometers. The accuracy of the IMUs was \(2^{\circ}\) for yaw, pitch, and roll rotations, whereas the accuracy of the whole system was optimized by considering several factors such as a correct steady pose, a tight fit of the sensors during the measurements, and avoiding magnetic interference from the environment. They were calibrated to continuously obtain 3D joint angle measurements of knees, hips, shoulders, elbows, wrists, neck, pelvis, and chest (full list in Table 1). Hip and pelvis joint angles were excluded from the analysis since the hip strap moves during cycling due to unavoidable contact with the upper legs and this induced errors in recording angles.  
Figure 1: (**a**) A participant in the time trial position on the stationary bike. The angles obtained from the optical mocap are illustrated. The wearable inertial sensors on the participant can also be seen. (**b**) A single sensor unit.  
Figure 2: Screenshot of projected frontal area camera capture interface. A cyclist is seen in RGB (red green blue) picture format and their projected area is shown as a silhouette. A calibration step is required to eliminate the floor from the projected frontal area calculation. The area in m\({}^{2}\) is also present in the interface and is recorded at 3 Hz.
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
## 6 Conclusions  
The chroma intra-mode decision is interrelated with the luma component, significantly impacting both objective and subjective coding efficiency. Since chroma prediction may utilize reconstructed luma reference blocks, there exists a dependency between components, and intra prediction coding may potentially propagate distortion from luma to subsequent chroma blocks. Therefore, an improved mode decision based on Lagrange optimization for chroma is proposed to address the RD characteristic, where the new Lagrange multiplier can be employed in the LM, LM_T, and LM_L modes. The experiments demonstrate that the proposed method enhances the bitrate performance of chroma intra coding by more than 1.23% under the All-Intra condition compared to the original VVC scheme.  
Conceptualization, W.L. and C.F.; methodology, W.L.; investigation, W.L.; writing--original draft preparation, W.L.; writing--review and editing, C.F.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Basic Research Program of Shaanxi (Program No. 2024 JC-YBQN-0727 and Program No. 2023-ZDLGY-50) and the Research Program of School-enterprise cooperation (No. 441223064).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 WBAN and the Communications Protocol Stack
### Physical Layer
#### 5.2.2 Body Movement and RF Communications  
The movement of the body can have a large impact on the successful transmission of RF based signals with significant loss where limbs cross the path of transmitter and receiver. [PERSON] _et al._[32] observed a stark variance in path loss when comparing an indoor static scenario with an outdoor test where a user was conducting a number of activities (walking, running, cycling, _etc._). Results showed greater losses in the scenarios involving movement. [PERSON] _et al._[14] experimented with the effects that posture can have on RF communications. The researchers used a closed loop transmission and power assignment framework for on-body wireless links. Node adaptive power management used Received Signal Strength Indicator (RSSI) as an additional parameter when measured between sensor and sink. The experiments included subjects being asked to follow a sequence of movements corresponding to four different sitting positions: arms to the side, arms crossed, arms raised, and hand holding one knee. The hand positions represented natural sitting postures with varying degrees of RF attenuation due to signal blockage and sensor node movements.  
Link quality was characterized by observing the RSSI values at the sink node for a given static transmission power from the arm mounted sensor node. The variation of RSSI represents the changes in link quality as a result of body movements and channel condition changes due to unpredictable RF attenuation. This can be caused by subjects clothing, physical stature, and antenna orientation. In the analysis of Packet Delivery Ratio (PDR) and Energy Per Packet (EPP) levels, results showed that varying postural position could have a dramatic effect on RSSI levels. Adapting the power assignment based on received signal strength levels can provide better packet delivery and energy efficiencies in comparison to static energy schemes.
## 3 Proposed Model
### Model Loss  
The design of our loss model was inspired by the topic attention model [29] and TopicBERT [32]. Three loss functions were employed to train the proposed model. The first of these functions is the topic KL divergence, which computes the difference between the actual topic distribution \(p\) and the learned topic distribution \(q\).  
\[KL(q(R|d)||p(R))=-\frac{1}{2}\sum(1+log(\sigma^{2})-\mu^{2}-\sigma^{2}) \tag{7}\]  
Second, we implemented a data likelihood function that calculates the difference between the actual and reconstructed data. In this equation, \(z_{n}\) represents the latent variable, which is a topic variable related to the input token \(x_{n}\).  
\[logp_{\Theta}(d|\hat{R})\sum_{n=1}^{m}logp\big{(}x_{n}|\hat{R}\big{)}=\sum_{n =1}^{m}\sum_{z_{n}}logp(x_{n}|A_{z_{n}})p\big{(}z_{n}|\hat{R}\big{)}=\sum_{n= 1}^{m}logA_{x_{n}}^{T}\hat{R} \tag{8}\]  
Finally, we utilized cross-entropy to calculate the difference between the model's predictive values \(P_{ic}(i\in\{1,2,\ldots,n\},c\in\{1,2,\ldots,C\})\) and the real data values \(L_{ic}\). Here, \(C\) represents the number of labels and \(n\) indicates the volume of data.  
\[Cross\ entropy=-\frac{1}{n}\sum_{i=1}^{n}\sum_{c=1}^{C}L_{ic}\text{log}(P_{ic}) \tag{9}\]  
Figure 2: Structure of the ALBERT (ELECTRA) neural topic model.  
In summary, our model loss combines topic KL divergence to align topic distributions, data likelihood to ensure the accuracy of data reconstruction, and cross-entropy to optimize classification performance.
# URANS Analysis of a Launch Vehicle Aero-Acoustic Environment
## 3 Results and Discussion
### Incidence of the Pressure Field on the Fairing  
To model the vibroacoustic response of the fairing, it was necessary to know how the pressure field around the fairing was. A diffuse acoustic field is usually used to model the acoustic excitation in vibroacoustic analysis [19]. Taking advantage of the CFD results, this hypothesis can be assessed. A total of 176 sampling points around the fairing were computed to evaluate the pressure field. These points were separated into 11 axial planes with 16 equispaced points.  
Figure 6 shows, in (a), the results obtained for a set of points placed at different heights and, in (b) and (c), the results for two sets at the same height, but different azimuth angles. As seen in Figure 6a, the pressure increased as the exhaust gases' wavefront moved upstream. After reaching the maximum, the pressure decreased as a result of the second wavefront seen in Figure 5. For different height planes, the gauge pressure was delayed in time. Moreover, the maximum peak pressure decreased with height because the magnitude of the pressure wave decreased as it propagated.  
Figure 5: Normalized instantaneous pressure contour of VEGA launch pad environment.  
The discrepancy in the pressure between points at the same height was small, as seen in Figure 6b,c. However, there was an increase in this difference with the increase in height. The maximum pressure variation between points on the same plane was about 1/10 of that for the points at different heights. This was mainly because the fluid front reached each plane at a different time. It should also be noted that the region at the lower part of the fairing supported bigger pressure values than the upper region.  
As the pressure gradient can be used to assess the direction of pressure waves, it was studied from the simulation results. Figure 7a depicts the pressure gradient in each direction, its magnitude, and the pressure gradient streamlines. The pressure gradient at the X and Y coordinates was lower than the axial direction. The streamlines show that the pressure gradient moved axially and converged at the tip of the payload fairing. As seen in Figure 7b, the Z negative component was the main contributor to the pressure magnitude at the tip. This negative component created a gradient pressure force that attracted the flow. This gradient decreased as the pressure wave weakened, with the maximum values occurring around t = 0.1 s. With this analysis, it is clear that the pressure field had a vertical direction due to the movement of the exhaust gases.
# Multirobot Task Planning Method Based on the Energy Penalty Strategy
## 4 GA for Multirobot Task Planning
### Genetic Manipulation  
(1) Selection operation  
The fitness values of all individuals in the current population are calculated and compared. The \(N\) optimal individuals with different breakpoints genes (the size of \(N\) depends on the population size \(M\), usually \(N\in[M/10,M/5]\)) are directly evolved into the next generation, and the remaining \(M\)-\(N\) individuals are selected by roulette [35]. The selection method is based on the differences of gene loci to ensure the optimal maintenance of individuals and the characteristics of population diversity.  
Figure 3: Coding representation of \(m=14\), \(n=3\).  
Figure 2: Two-segment chromosome coding format.  
(2) Cross operation  
The order cross (OX) and the circular cross (CX) are used for the cross operation and for performing two kinds of cross operations with a certain cross probability Pm during the operation. Respectively, the two crossover operators change the two segments of gene information encoded by individuals in the operation process; that is, the robot task content and task number. The OX is used to copy the continuous exchange gene fragments in a randomly selected pair of parents to the offspring at the same location. Then, the remaining gene information of the parents is placed in the offspring to complete the crossover operation, as shown in Figure 4.  
CX is used to copy part of the gene information to the offspring through multiple cyclic operations in two parents. Only one gene can be selected for each crossover in order to realize the crossover and exchange of discontinuous gene information. Then, the remaining gene information of the parent is placed in the offspring to form a new individual to complete the cyclic crossover operation, as shown in Figure 5.  
Figure 4: Order cross operation (\(m=14\)).  
(3) Mutation operation  
According to the mutation probability Pm, the mutation operation of the number and sequence of tasks is realized by using the inversion, exchange, and insertion mutations.  
The inversion mutation reverses the continuous gene segments (task point execution sequence) of a segment, as shown in Figure 6.  
The exchange mutation randomly selects two genes in a chromosome to exchange positions, as shown in Figure 7.  
Figure 5: Circular cross operation (\(m=14\)).  
Figure 6: Inversion mutation operation (\(m=14\)).  
The insertion mutation randomly selects a gene or part of a gene in a chromosome to insert into a random site, as shown in Figure 8.
## 4 Conclusions  
For the indoor environment, the LOS VLC system could not always have been guaranteed due to many effects, but it can greatly influence the communication quality. To address this unavoidable problem and have high-speed communication simultaneously, a dual-hop VLC system based on a blue micro-LED was proposed and experimentally demonstrated. The deployment of relay nodes can ensure high-speed communication indoors and solve the NLOS problem. To the best of our knowledge, this was the first time that a dual-hop VLC link was based on a micro-LED. In addition, in order to better analyze the cost and benefit of the relay node, a comparison between the single-hop and the dual-hop link was presented. On the premise of increasing the communication distance from 2 m to 4 m, there was an admissible decrease in modulation bandwidth between 880 MHz and 715 MHz. Meanwhile, the data rate corresponding to the FEC limit remained in the order of Gbps. The result demonstrated a dual-hop VLC system based on blue micro-LEDs with a data rate up to 1.1 Gbps through simple OOK modulation over a 4 m NLOS free-space link, with the help of a low-cost AF relay. In addition, when the overall link distance increases from 4 m to 8 m, the dual-hop system is still capable of high-speed communication. The data rate within the FEC limit at 8 m is 1 Gbps. The available 8 m transmission distance exceeded the LOS distance for the indoor LOS link, which can be applied to more indoor scenarios. Experimental results confirmed that the dual-hop VLC system based on micro-LEDs can tackle critical NLOS problems in VLC systems and is sufficient for high-speed indoor application in the future.  
Conceptualization, Z.W. ([PERSON]) and Y.Z.; methodology, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]) and Y.Z.; formal analysis, Y.Z.; investigation, Y.Z.; resources, [PERSON] ([PERSON]) and [PERSON], Y; data curation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--original draft preparation, Y.Z., [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--review and editing, H.Y.F.; visualization, Y.Z.; supervision, H.Y.F.; project administration, H.Y.F.; funding acquisition, H.Y.F. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Shenzhen Technology and Innovation Council (WDZC202008 20160650001).  
**Institutional Review Board Statement**: Not applicable.  
**Acknowledgments:** The authors thank their colleagues [PERSON], and [PERSON] from Department of Electronic Engineering, Tsinghua University, for their kind help toward research work.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
Figure 10: Measurement of maximum data rate within FEC limit at different lengths (The blue font on the histogram represents the BER for the corresponding case).
# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
## 4 Experiments
### Dataset and Experimental Setup  
We adopt the public FLIR dataset [23] and self-build IAT dataset [15] for our experimental studies.  
FLIR [23] collects 9214 infrared images with annotations, where the labeled objects contain a person, car, and bicycle. It is acquired via a thermal camera mounted on a vehicle, and all images are taken on the streets and highways, as illustrated in Figure 5. To evaluate the capability of our method with limited data, we perform experiments with full, half, and one-quarter of training examples in FLIR. The statistics of the training datasets are shown in Table 1. Although the numbers of training images are different in the three datasets, their test sets are the same as those provided in the FLIR benchmark.  
The IAT [15] consists of 2750 infrared images with aerial targets, including five categories: airline, bird, fighter, helicopter, and trainer. All images are captured by ground-to-air infrared cameras, and some samples on IAT are shown in Figure 6. Different from the images with target occlusions in FLIR, IAT contains small targets in complex aerial backgrounds, and the main challenge of it is background interference. We split IAT with the ratio of 7:3 as the training set and test set, respectively. Similar to FLIR, we use all and half of the training images to implement experiments, as presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Person** & **Car** & **Bicycle** \\ \hline FLIR & 22,372 & 41,260 & 3986 \\ FLIR-1/2 & 10,997 & 20,700 & 1979 \\ FLIR-1/4 & 5574 & 10,286 & 928 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numbers of instances on FLIR datasets.  
Figure 4: The framework of SMG-Y.  
All experiments are implemented on a PC with an i7-8700K CPU and a signal GTX1080 Ti GPU. For SMG-C, we adopt CenterNet with ResNet-18 [19] as the source model, because it is light-weight and enough to provide the guidance. The FEN of the target network in SMG-C is the fully convolutional upsampling version of Deep Layer Aggregation (DLA-34) [38]. For SMG-Y, YOLOv3 with DarkNet-53 [18] is used as the source model and the backbone of the target network is DarkNet-53. The source models of two frameworks are RGB detection models trained on COCO [13].  
The input resolution is set to \(512\times 512\) in SMG-C and \(416\times 416\) in SMG-Y. During the training process of two frameworks, we follow their original papers [18; 19] separately for training setting and hyper-parameters, unless specified otherwise. In the inference, we  
Figure 5: Samples on FLIR dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Airline** & **Bird** & **Fighter** & **Helicopter** & **Trainer** \\ \hline IAT & 121 & 535 & 667 & 310 & 469 \\ IAT-1/2 & 64 & 277 & 321 & 152 & 242 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numbers of instances on IAT datasets.  
Figure 6: Samples on IAT dataset.  
evaluate the performance with the mean Average Precision (mAP) at IoU of 0.5, which is a common metric for object detection tasks.
###### Abstract  
Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the lifetime of a network. We believe that balancing the energy consumption in per unit area rather than on each single sensor can provide better-balanced power usage throughout the network. Our former work--Balanced Energy-Efficiency (BEE) and its Multihop version BEEM can not only extend the network longevity, but also maintain the network coverage. Following WSNs, Internet of Things (IoT) technology has been proposed with higher degree of diversities in terms of communication abilities and user scenarios, supporting a large range of real world applications. The IoT devices are embedded with multiple communication interfaces, normally referred as Multiple-In and Multiple-Out (MIMO) in 5G networks. The applications running on those devices can generate various types of data. Every interface has its own characteristics, which may be preferred and beneficial in some specific user scenarios. With MIMO becoming more available on the IoT devices, an advanced clustering solution for highly dynamic IoT systems is missing and also pressingly demanded in order to cater for differing user applications. In this paper, we present a smart clustering algorithm (Smart-BEEM) based on our former work BEE(M) to accomplish energy efficient and Quality of user Experience (QoE) supported communication in cluster based IoT networks. It is a user behaviour and context aware approach, aiming to facilitate IoT devices to choose beneficial communication interfaces and cluster headers for data transmission. Experimental results have proved that Smart-BEEM can further improve the performance of BEE and BEEM for coverage sensitive longevity.  
WSN; MIMO; Energy Efficiency; QoE; Clustering; IoT; Future Network; 5G +
Footnote †: _Sensors_**2017**, 17, 1574; doi:10.3390/s17071574
# EVM Loss: A Loss Function for Training Neural Networks in Communication Systems
## 4 Conclusions  
This work has demonstrated the shortcomings of two of the typical approaches used in the implementation of neural networks in communication systems, namely; the split complex approach, whereby transmitted signals are split into their real and imaginary constituents and used to train two separate neural network models, and the use of mean square error as an objective function. It was shown that by splitting the signals into their real and imaginary components, the system was being deprived of valuable information that can be exploited and used to more accurately recover the signals. Extending this, it was also shown that there is a disconnect between training a neural network using the mean square error objective function when the communication system will be deployed and evaluated via its BER performance. This was rectified via the use of the proposed EVM loss method which seeks to reconnect the disparity between the evaluation performed when training a neural network and the evaluation of the overall communication system. The results validated the capabilities of our method, which consistently obtained the lowest EVM when deployed in a real experimental setup using OFDM and SEFDM with varying bandwidth  
Figure 6: Error Vector Magnitudes across the subcarriers for the 3 neural network receiver approaches for SEFDM with \(\alpha=0.8\).  
Figure 7: Error vector magnitudes across the subcarriers for the 3 neural network receiver approaches for SEFDM with \(\alpha=0.6\).  
compression factors, resulting in a 5 dB to 10 dB improvement in the received symbols measured EVM compared to the baseline implementation, with bandwidth compressions down to 40% compared to OFDM, resulting in a spectral efficiency gain of 67%.  
Conceptualization, S.S.; funding acquisition, M.J. and S.D.; investigation, S.S. and P.A.H.; supervision, M.J., S.D. and P.A.H.; writing--original draft, S.S.; writing--review and editing, M.J., S.D. and P.A.H. All authors have read and agreed to the published version of the manuscript.  
[PERSON] acknowledges an EPSRC studentship for his PhD studies. This project was supported by EP/T032170/1.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 5 Numerical Results
### The Influence of Service Rate  
Figures 4 and 5a,b show \(E(T_{total})\), \(P_{r}\) and \(P_{b}\) as a function of \(B_{2}\) for different computing capabilities of a first-tier MEC server \(u_{1}\), respectively. Equations (16) and (17) predict service rate inversely correlates with delay. Doubling \(u_{1}\) from 6 to 12 packet/slot decreases \(E(T_{total})\), consistent with M/M/c/K queuing model. It is obvious that increasing \(u_{1}\) contributes to a reduction in both \(E(T_{total})\) and \(P_{b}\), while concurrently enhancing \(P_{r}\). We can see that \(E(T_{total})\) and \(P_{b}\) are large, and \(P_{r}\) is small when \(B_{2}\) is small. As the value of \(B_{2}\) escalates, \(E(T_{total})\) and \(P_{b}\) exhibit a declining trend, whereas \(P_{r}\) experiences a noticeable upsurge. This trend signifies an inclination towards a greater number of tasks being offloaded to first-tier MEC servers with a larger computing capability. However, after certain points of \(B_{2}\), there is a reversal in this trend. At this point, \(E(T_{total})\) and \(P_{b}\) begin to ascend while \(P_{r}\) starts to dwindle, primarily due to the substantially heightened load on second-tier servers. We can also see that the larger the \(u_{1}\), the smaller the optimal bias factor \(B_{2}\).  
\begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Values** \\ \hline \(p_{m,k}\) & \(\{0.2,\,0.8\}\) \\ \(\lambda_{u}\) [nodes/m\({}^{2}\)] & \(1.2\times 10^{-4}\) \\ \(\lambda_{m}\) [nodes/m\({}^{2}\)] & \(3\times 10^{-4}\) \\ \(u_{1}\) [packet/slot] & 9 \\ \(u_{2}\) [packet/slot] & 4 \\ \(c_{2}\) & 1 \\ \(r_{2}\) & 4 \\ \(P_{m,1}\) [dBm] & 45 \\ \(P_{m,2}\) [dBm] & 30 \\ \(P_{u}\) [dBm] & 20 \\ \hline \end{tabular}
\end{table}
Table 3: Parameter Values.