esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Communication Protocol Design
### Evaluation
#### 5.2.2 Results  
The single-node measurement was used to create a baseline with interference coming from the environment only. The results plotted in Figure 10 show that a reliable communication link was established independently of the spreading factor used. The packet delivery ratio was 100%, regardless of the traffic load, as one would expect for a contention-free and clear channel.  
The two-node measurement introduces multiple access interference (MAI) to the channel, as shown in Figure 11, which clearly impacts the packet delivery ratio for the low processing gain case. Under heavy traffic conditions, the average packet delivery ratio dropped to 71% for a spreading factor of 8 and to 99% for that of 16. Examination of the received data showed that packet losses were equally probable due to missed detection of the synchronization header and to the corruption of the payload data.  
Figure 11: Packet reception ratio for the two-node setup under various traffic loads with spreading factors of 8, 16 and 32.  
Figure 10: Packet reception ratio for the single-node setup under various traffic loads with spreading factors of 8, 16 and 32.  
The four-node setup further increased the MAI in the channel, creating a more realistic multi-node scenario. The observed average packet delivery ratios depicted in Figure 12 show that with a spreading factor of 8, more than 16% of the packets were dropped in a low-traffic channel, approximately 50% were lost under moderate traffic and only 2% were delivered in high traffic. Increasing the processing gain to 16 improved the average packet reception ratio to over 90% in moderate network traffic, but it falls down to 41% under heavy loads. Meanwhile, increasing the spreading factor to 32, the protocol delivers over 96% of the packets on average, even in a highly loaded channel.  
In summary, our experiments showed that the designed DSSS communication protocol performs reliably when there is no contention in the channel. A processing gain of 8 already offered reasonable protection against external interferences, such as the ongoing WLAN communication; however, as the number of nodes increased, such a low degree of spreading offered little protection against simultaneous transmission, even under low traffic loads. Therefore, a truly collision-free DS-CDMA communication calls for reasonable spreading factors, which still remains a design parameter, primarily determined by the size of the network and the expected network traffic. A detailed performance analysis with respect to these network parameters is given in [25]. In the presented experimental setup, the desired processing gain was attained by using a fixed chip rate and reducing the effective data rate. Alternatively, the data rate could be fixed, and the chip rate could be increased to achieve the same goal, without increasing the transmitter complexity.
# Coupling Denoising to Detection for SAR Imagery
## 2 Materials and Methods  
In this section, we describe SAR remote sensing datasets that we constructed and the proposed object detection framework which fuses region proposals utilizing denoised SAR image. The remote sensing datasets include not only SAR imagery but also corresponding labeled objects. We develop our object detection framework with the datasets and detail the proposed framework in the rest of this section.
# Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
## 2 Background and Motivation
### Challenges of Characterizing Machine Learning on Edge  
Characterizing machine learning workloads on edge devices involves several significant challenges. A primary issue is the wide variety of edge devices and their distinct hardware configurations, making it difficult to generalize performance metrics and operational timings across different systems. Additionally, energy consumption is closely tied to workload intensity and processing duration, further complicating the characterization process. Additionally, the inherent complexity and variability of machine learning models, coupled with the diverse input and network conditions, complicate predictions about model behavior on various edge devices, even when those devices share identical specifications.  
Moreover, conducting the detailed profiling of machine learning workloads on edge devices necessitates specialized tools and expertise, which may not be readily accessible to developers or end users. Compounding these challenges is the need to protect client data privacy, as profiling often requires monitoring the processing of sensitive information directly on the device. To address these issues, advancements in standardizing benchmarking tools and developing privacy-preserving methods for edge devices are essential as highlighted in surveys like [51].  
Studies such as [52, 53] explore the relationship between memory and layer-wise connections to privacy. These papers demonstrate that through layer-wise encoding, it is feasible to encrypt and process data at the layer level with minimal impact on performance for connected autonomous vehicles (CAVs). Similarly, traditional privacy concerns related to memory usage also warrant further investigation.
## 4 Solution Methodology
### DEMATEL Method  
The DEMATEL method begins with \(n\) experts evaluating the direct impact of each of the \(k\) criteria on the others, using a scale from 0 (no impact) to 4 (very strong impact). This produces \(n\) preliminary matrices:  
\[Z^{(m)}=\left[x_{ij}^{(m)}\right],\ m=1,2,\ \ldots,\ n \tag{1}\]  
Figure 1: Schematic representation of the integrated DEMATEL–PROMETHEE II methodology applied in the evaluation of Industry 4.0 and 5.0 technologies.  
where \(z_{ij}^{(m)}\) denotes expert \(m\)'s assessment of the influence of factor \(i\) on \(j\). Then, the aggregated influence matrix is determined:  
\[A=\frac{1}{n}\sum_{m=1}^{n}Z^{(m)} \tag{2}\]  
After normalizing the matrix \(A\), the matrix \(N\) is obtained:  
\[N=\frac{A}{max\left(\sum_{i}a_{ij},\ \sum_{j}a_{ij}\right)} \tag{3}\]  
On this basis, the total impact matrix is determined:  
\[T=N(I-N)^{-1} \tag{4}\]  
where \(I\) is the identity matrix. From the matrix \(T\), the total influence \(D_{i}\) and received influence \(R_{i}\) are calculated:  
\[D_{i}=\sum_{j=1}^{k}t_{ij}\,\ R_{i}=\sum_{j=1}^{k}t_{ji}\, \tag{5}\]  
We also have centrality \(\left(D_{i}+R_{i}\right)\) and causality indices \(\left(D_{i}-R_{i}\right)\). The obtained values serve as the input data to the PROMETHEE II method. The weights of the criteria are calculated:  
\[W_{i}=\frac{D_{i}+R_{i}}{\sum_{i=1}^{k}(D_{i}+R_{i})} \tag{6}\]
# Characteristics of HEMP Radiation Field on Typical Transport Aircraft with Frequency Sweep Method
## 3 Results
### Electric Field Strength Distribution of Top Layer  
Afterwards, the distribution of the electric field strength \(E(t)\) formed by the HEMP radiation field on the aircraft is analyzed. Numerical results of the top layer on or outside of the aircraft are calculated and analyzed using the frequency sweep method.  
In Figure 8a, the induced electric field strength \(E(t)\) of the top layer of the aircraft is found to be significant at the time \(t=0\) ns. \(E(t)\) is uneven at the same time in Figure 8a,c. In addition, the value of \(E(t)\) in the internal space of the aircraft is relatively small while the peak value of \(E(t)\) exceeds 50 kV/m. These phenomena occur due to two reasons. On the one hand, the incidental HEMP field on the plane, at the same time measurement, is different. On the other hand, the positions of different structures of the aircraft have various reflection properties.  
Figure 6: Electric field strength distribution \(E(t)\) at various values of \(z\) with \(\alpha=0^{\circ}\) outside aircraft with \(E=1\) V/m.  
Figure 7: Cont.  
In Figure 9, tendencies of the electric field strength \(E(t)\) at various positions outside of the aircraft are presented. As shown in Figure 9, the peak value of \(E(t)\) which is around 155 kV/m (three times that of \(E_{0}\)), occurs at the position \(x\) = 0 m, \(y\) = 14.5 m, \(z\) = 6 m. This position is near the back wing of the aircraft, which has a sharp structure. Due to the reflection, the electric field strength near this position \(E(t)\) increases significantly.  
Figure 7: Magnetic field strength distribution \(H\) at different time \(t\). (**a**) \(t\) = 0 ns, (**b**) \(t\) = 10 ns, (**c**) \(t\) = 30 ns, (**d**) \(t\) = 400 ns.
###### Abstract  
Precise drilling and excavation in future Lunar mining sites as well as in building habitats areas will be supported by robotized instrumentation. To ensure accurate positioning of facilities or structures, customized surveying instruments will be used to perform measurements needed for calculating locations of surveyed objects. Precise positioning in unexplored areas is difficult, even on the Earth, with all available support. This issue becomes even more complex on the Moon's surface, considering environmental conditions and the absence of Earth logistics. This paper solves a problem of centimeter-precision positioning on the Moon's surface. The solution is called Lunar Regional Navigation Transceiver System (LRNTS). It is based on a network of transceiver facilities, holding onboard both navigation transmitters and receivers. Transmitting modules of LRNTS act in the same way as the Global Navigation Satellite Systems (GNSS) space segment, sending navigation messages to the receivers. Receiving modules are needed for self-calibration of LRNTS to calculate their coordinates. In this paper, 12 different LRNTS-simulated configuration setups within Shackleton Crater are tested against positioning accuracy and visibility along the crater. The results show that LRNTS of nine transceivers can achieve sub-centimeter horizontal and better than 2 cm vertical accuracy, with consistent visibility of six and more transceivers throughout the Shackleton Crater.  
Lunar navigation; pseudolite; transceiver; control network; Shackleton Crater; Lunar mining 202313371133030371  
1
# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
## 4 Discussion  
### Data Trends  
From the first results presented in this study, it is clear that there are significant trends in the physiological variables of participants who became drowsy during the driving simulation. Moreover, these trends were not observed in participants that did not experience drowsiness. As expected, we observe an increase in both temperature variables in the first period of the driving simulation. This is in accordance with the fact that prior to sleep onset, the body dissipates heat via the extremities to lower the temperature of the core [29]. During this process, blood flow to the extremities is increased (vasodilation), which in turn increases the skin temperature. The result is an increased thermal gradient with the surrounding air to facilitate heat transfer to the environment. The subsequent decrease in skin temperature is also in line with the expectations. To avoid losing too much body heat, blood flow to the skin decreases again (vasoconstriction). Besides, the heart rate of drowsy drivers decreased on average more than that of non-drowsy drivers. We hypothesize that the decreasing trend rate can be interpreted as a secondary mechanism to control T\({}_{\rm C}\), which is in accordance to the statements by [PERSON] and [PERSON] [20]. The human energy balance namely consists of energy producing and energy consuming (or reducing) components. Hence, the heart rate can be seen as a measure for providing energy to the body. Accordingly, lowering the heart rate means lowering the input to the energy balance. Nevertheless, we keep in mind that other physiological processes (e.g., stress) affect the heart rate and its dynamics as well. With respect to the SSS, we can infer that the test subjects evolved to a high sleepiness score in the first protocol, whereas this score did not increase significantly in the second protocol and fluctuated on average between two and three.
## 5 Conclusions  
In this paper, we address the challenge of detecting a weak moving target in the presence of MF-DFM induced by frequency diversity in MF-PBR. To this end, we propose MF-RFT-QCI, which can eliminate the MF-DFM and enables quasi-coherent integration in MF-PBR. It is shown that MF-RFT can be viewed as the extension of the conventional RFT via designing a sequence of MF-based Doppler filter banks. By leveraging the prior knowledge of the carrier frequency, the computational burden of the MF-RFT can be effectively reduced. To further improve the reliability of our approach against residual phase errors, a phase-compensation-based method is also developed by formulating an optimization problem based on the minimum-entropy criterion. The optimization problem is then solved using the ME-PSO algorithm, which can significantly and robustly enhance the output SNR of the fusion range-Doppler mapping of the moving target. Finally, we validate the effectiveness of our proposed method through numerical experiments and field tests, comparing its performance against other existing detectors. The results demonstrate the superiority of the proposed method in terms of detection capability at low-SNR conditions.  
Our future work will further investigate the impact of various types of clutter on the effectiveness of the proposed method. Additionally, we aim to apply our proposed approach to real-world scenarios, including localization and tracking.  
Conceptualization, J.L.; methodology, J.L.; software, J.L.; validation, J.L. and C.S.; formal analysis, J.L. and C.S.; investigation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, C.S.; visualization, J.L.; supervision, C.S. and Z.X.; project administration, C.S.; funding acquisition, C.S. and Z.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (NSFC) under grant 61971379, in part by the Program of Innovation 2030 on Smart Ocean, Zhejiang University under Grant 129000194432301, and in part by the Fundamental Research Funds for the Central Universities under 226-2023-00049.  
Not applicable.  
The authors would like to thank the editors and the reviewers for their valuable suggestions.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Detector** & **Processing Time (M = 3)** & **Processing Time (M = 5)** \\ \hline MF-NCI-EL & 12.56 s & 23.25 s \\ MF-NCI-UEL & 14.21 s & 24.55 s \\ MF-RFT-NCI & 13.67 s & 23.57 s \\ MF-RFT-QCI & 35.86 s & 49.98 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the processing times of the different detectors.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 5 Experiments
### Experimental Settings  
#### 5.1.1 Datasets  
To evaluate the proposed approach, we follow the convention of [24; 25; 29] to conduct experiments on both synthetically generated datasets and real-world benchmarks. For synthetic datasets, we generate instances with 20, 50, 100, and 150 nodes, respectively, where customer node coordinates are uniformly sampled from the unit square \([0,1]\times[0,1]\), with the depot located at the origin. Delivery node demands are drawn from the integer set \(\{1,\cdots,9\}\), while pickup node demands are assigned inversely to maintain a 1:1 ratio between pickup and delivery nodes. Regarding real-world benchmarks, we employ datasets from [30] to benchmark conventional VRP-PD solutions and utilize instances from [29] to evaluate the improved VRP-PD. These real-world datasets exhibit significantly different node distributions and demand structures compared to the synthetic instances.
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 4 Results and Discussion
### Evaluation Methods
#### 4.1.1 Simulation Method  
To evaluate the performance of the proposed adaptive noise cancellation algorithm by simulation, the proposed algorithm is implemented on the experimental board. As shown in Figure 7, the experimental board includes one main board and one DA/AD data conversion card. The 14-bit D/A data conversion card is used to generate two signals. One signal is the communication signal which is mixed with S\(\alpha\)S noise. Another signal is the S\(\alpha\)S noise with variable time delay. Then, the two signals are processed by the proposed algorithm after 14-bit A/D conversion.  
To evaluate the performance of the proposed algorithm against impulsive noise, the noise is presented as the \(S\alpha\)S process when \(\alpha\) = 1.5, \(\beta\) = 0, where the location parameter \(\delta\) is 0 and the dispersion \(\gamma\) is 1. In the non-Gaussian noise environment, the mixed SNR is defined in [33]  
\[SNR_{m}=10\log_{10}\left(\frac{\sigma_{\mathrm{s}}^{2}}{\gamma}\right) \tag{19}\]  
where \(\sigma_{\mathrm{s}}^{2}\) is the variance of the signal and \(\gamma\) is the dispersion of the impulsive noise. Firstly, the time delay estimation performance and noise cancellation performance are evaluated in different mixed SNR environments respectively. Secondly, the noise cancellation performance of proposed algorithm is evaluated when the time delay between the primary input and reference input is changing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **ALUTs** & **Block Memory (Kbits)** & **DSP Block** \\ \hline MCC-ETDE & 16,478 & 2048 & 309 \\ Control Module & 867 & 0 & 0 \\ ANC & 1620 & 0 & 32 \\ Total Utilization & 18,969 & 2048 & 341 \\ Utilization Rate & 9\% & 14\% & 44\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware Resource Consumption.  
Figure 7: Experimental board.
## 4 Methodology
### Historical Trajectory Embedding
#### 4.2.1 Historical Trajectory Aggregation  
For a given pair of adjacent collection points \((C_{A},C_{B})\) along a determined path, the set of historical trajectories can be represented as \(T_{H}=T_{1},T_{2},\ldots,T_{TN}\). Considering that the density of the spatiotemporal positions within one trajectory \(T_{i}\) is much lower than that of the path space points, the trajectory aggregation method is used to aggregate all historical trajectories into a complete spatiotemporal trajectory.  
A historical aggregation module was proposed in [13], which separated the spatiotemporal positions in the trajectory into time slices and then took the position with the highest probability in each time slice as the position of the aggregated trajectory in that time slice. Our approach is similar, except that we divide the spatial positions within the path intospatial slices rather than time slices; then, for a given spatial slice, we find all of the time points that are on this slice and use the average time of these trajectory points as the time at which the aggregated trajectory is on this spatial slice. Therefore, the aggregated trajectory \(T_{H}^{g}\) of the historical trajectory set \(T_{H}\) can be represented as follows:  
\[T_{H}^{g}=Tr_{1}\oplus Tr_{2}...\oplus Tr_{N} \tag{9}\]  
where \(Tr_{i}\) represents the i-th trajectory in the historical trajectory set \(T_{H}\), and N is the number of trajectories in the set. The symbol \(\oplus\) denotes the trajectory aggregation operation. Specifically, for a fixed position point, we find all of the times that pass through this position and use the average value of these times as the time of the trajectory point at this position. After aggregating the time for each position point, the trajectory is sorted according to the position order to obtain the aggregated trajectory.
## 7 Conclusions  
Poor accuracy is obtained by DNN-HMM -based standard ASR systems in recognition of children's speech in noisy conditions due to two mismatches between the system training and testing. The study indicated that an effective way to tackle the effects of these mismatches is to combine data augmentation (adding different types of noise to adults' speech in the training phase) to the modification of both the \(F_{0}\) and speaking rate structure of children's test speech to make the children's speech in testing become closer to the adults' speech in training. The study showed that compared to the baseline ASR system, the recognizer, which combined the data augmentation and time-scale modification, yielded substantial improvements in WER in all the noise conditions studied. For example, for the most severe noise type (factory noise), the WER values obtained using the baseline system were very poor in all SNR categories (the average WER = 41.29%), but the WER values obtained by the combined system in the \"all\" scenario dropped to clearly lower levels (the average WER = 14.88%). For the least severe noise type (volvo noise), the corresponding improvement in the averaged WER given by the combined system was from 19.93% to 9.14%. Despite the fact that the study showed promising results in recognition of noisy children's speech, new research is needed to understand, for example, how the reported results are affected when the amount of adult speech is increased in training. Moreover, studying how the proposed ASR system functions in the presence of adversarial attacks is another topic of future investigations.  
Conceptualization, H.K.K. and S.R.K.; methodology, H.K.K. and S.R.K.; software, H.K.K.; validation, H.K.K., S.R.K., P.A. and M.K.; formal analysis, H.K.K. and S.R.K.; investigation, H.K.K. and S.R.K.; resources, H.K.K.; data curation, H.K.K.; writing--original draft preparation, H.K.K. and S.R.K.; writing--review and editing, H.K.K., S.R.K., P.A. and M.K.; visualization, H.K.K.; supervision, P.A. and M.K.; project administration, P.A. and M.K.; funding acquisition, P.A. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Academy of Finland grant numbers 329267 and 330139.  
Not applicable.  
Publicly available datasets were analyzed in this study. These data can be found here: [[http://www.thespeechark.com/pf-star-page.html](http://www.thespeechark.com/pf-star-page.html)]([http://www.thespeechark.com/pf-star-page.html](http://www.thespeechark.com/pf-star-page.html)) and [[https://catalog.ldc.upenn.edu/LDC95S24](https://catalog.ldc.upenn.edu/LDC95S24)]([https://catalog.ldc.upenn.edu/LDC95S24](https://catalog.ldc.upenn.edu/LDC95S24)) (accessed on 10 June 2021).  
The computational resources were provided by Aalto ScienceIT.  
The authors declare no conflict of interest.
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 3 System Model
### Task Model  
For \(n\)-th industrial terminal, its computation task is denoted as \(D_{n}\). The data arriving at the data queue of \(n\)-th industrial terminal is denoted as \(A_{n}\) and it is assumed that \(\mathbb{E}[(A_{n})^{2}]=\eta_{n}<\infty\), \(n=1,\ldots,N\).  
When \(d_{n,m}=1\), \(n\)-th industrial terminal offloads all task \(D_{n}\) to \(m\)-th industrial base station. When \(d_{n,m}=0\), \(n\)-th industrial terminal does not offload any task to \(m\)-th industrial base station. According to the execution location of the computation task and the size of the offloading data, the offloading decision is binary with two cases: end computing and edge computing.  
Correspondingly, each industrial terminal has a task buffer, the queue length of the buffer in \(t\)-th time slot is denoted as \(Q_{n}(t)\). Thus, the queue length can be updated as  
\[Q_{n}(t+1)=\max\{Q_{n}(t)-D_{n},0\}+A_{n}, \tag{1}\]