esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Optimization Formulation
### Diverse QoS Constraints of Multiple Requests
#### 3.2.1 Transmission Capacity Requirement  
For real-time services such as video surveillance or continuous data collection, their QoS requirement on data transmission rate can be illustrated as  
\[C_{k}\geqslant C_{req,k} \tag{4}\]  
where \(C_{req,k}\) is the minimum transmission rate requirement of the service \(s_{k}\) of terminal \(u_{k}\).
# Urban CO2 Budget: Spatial and Seasonal Variability of CO2 Emissions in Krakow, Poland
## 2 Methods
### CO\({}_{2}\) Flux Measurements
The anemometer, together with fast-changing valves, have been installed on top of a mast located on a university campus building, with a total measurement height of 39.7 m above the ground level. The remaining components of the system were located in a room on the top floor of the building. Air samples were collected in 5-L redlar bags, enclosed in vacuum containers. Each one-hour measurement cycle consisted of sampling and analyzing two pairs of bags, to obtain two 30-min averaged CO\({}_{2}\) flux values. During the first fifteen minutes of the cycle, the CO\({}_{2}\) concentration in one pair of bags was analyzed, and then, for another fifteen minutes, direct measurements of the ambient air took place. By then, a second set of bags was filled with sampled air and ready for the concentration measurement, which, again, took seven-and-a-half minutes for each bag, and after that, the ambient air was measured directly for the last fifteen minutes. The measurements have been conducted quasi-continuously for two years (2012-2014), with a number of short brakes, due to sampling system maintenance and other reasons. The CO\({}_{2}\) flux was calculated using Equation (2), with a half-hour temporal resolution. The mean values of the updraft and downdraft concentration were calculated using a dedicated software, while the standard deviation of the vertical wind component \(\sigma_{\rm w}\) was calculated with the EddyPro software version 6.0.0 (LiCOR Biosciences, Lincoln, Nebraska, USA). Although the software does not have a module dedicated to the REA method, it makes it possible to make the necessary corrections to the calculated wind components, as well as to obtain wind turbulence characteristics. Momentum and sensible heat flux data have been subject to quality control, based on steady state tests and developed turbulence conditions [26], although their rigorous use for sites located in urban areas may lead to the rejection of more than 50% of the data [31]. According to the three-level quality control system adopted in the CarboEurope-IP and FLUXNET networks [32], 24% of the obtained and analyzed data was at the highest degree of reliability, and 21% at the lowest. The lowest quality data have been rejected. Additionally, some concentration data appeared to be not reliable, due to leakages caused by tedlar bags material fatigue, and had to be rejected as well. In total, 39.6% of the CO\({}_{2}\) flux data was left available for further analysis. In order to define the source area of the REA-AGH site, an analytical footprint function has been calculated for half-hour intervals averaging wind velocity, according to the Kljun model [33]. The results have been averaged over ten-degree angular intervals, and classified according to atmospheric stratification.
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 4 Experiments
### Experimental Results  
#### 4.3.1 Training Model Evaluation  
The training phase is only carried out for scenarios 2 and 3 using deep learning techniques. To evaluate the performance of the network models before detecting drowsiness, we use a confusion matrix and compute metrics of the accuracy, Loss_value, and training time.  
We determine metrics of the precision, recall, and accuracy defined by the confusion matrix, as presented in Section 2.4. Figure 13 shows a confusion matrix for scenarios 2 and 3. In scenario 2, the TP (true positive) and TN (true negative) values of drowsiness and non-drowsiness prediction are 94% and 98%, respectively. In scenario 3, the TP and TN results are 97% and 97% for drowsiness and non-drowsiness, respectively. The metrics of the precision, recall, and accuracy are shown in Table 6. The accuracy of the proposed network models after the training process is 96% for scenario 2 and 97% for scenario 3.  
Figure 14 shows the results of the Loss_value and accuracy of the two scenarios after several training steps (epoch) with the learning_rate of \(1\times 10^{-4}\) and \(1\times 10^{-5}\), respectively. The y-axis represents the accuracy and Loss_value of the two scenarios; the x-axis represents the epochs. The accuracy gradually increases while the epoch increases and the Loss_value decreases. When the epoch reaches 30 and the Loss_value is not improved further, we stop training and move to the testing phase. As observed in Figure 14, Loss_value and Accuracy metrics in scenario 3 tend to be more stable than scenario 2. Loss_value in scenario 2 is higher than scenario 3 and tends to increase at 30 epochs. This means that the level of error (information loss) for training and validation is high in scenario 2. As a result, the accuracy of recognition in scenario 2 is not higher than scenario 3.  
Figure 15 shows the training time of the three scenarios. Since scenario 1 has no training process, the training time is 0. For the other two scenarios, the training time is 106 min 3 s for scenario 2, and 197 min 7 s for scenario 3. The training time of scenario 2 is lower than that of scenario 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **Label** & **Precision** & **Recall** & **\(F1\)-Score** & **Support** & **Accuracy** \\ \hline \multirow{2}{*}{2} & Drowsy & 0.93 & 0.97 & 0.95 & 532 & \multirow{2}{*}{0.96} \\ \cline{2-6}  & Non-drowsy & 0.98 & 0.96 & 0.97 & 758 & \\ \hline \multirow{2}{*}{3} & Drowsy & 0.96 & 0.97 & 0.97 & 532 & \multirow{2}{*}{0.97} \\ \cline{2-6}  & Non-drowsy & 0.98 & 0.97 & 0.98 & 758 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of training in scenarios 2 and 3.  
Figure 14: Charts of accuracy and Loss_value for training and validation in scenarios 2 and 3.
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 5 Simulation Results
### Multipath Signal Detection and AOA Estimation  
We focus on the signal transmission and evaluate the AOA estimation between a pair of MSN and AN in this subsection. In the simulation, the number of propagation paths between the MSN and AN is assumed to be \(L=5\). The path parameters are selected randomly and uniformly in the corresponding ranges and listed in Table 2. We clarify that the unit of the propagation delay is chips (with the time duration of one chip in the PN-sequence). The parameter \(|\alpha_{i}|^{2}\) is the received path power. In the simulations, the phase shifts caused by the propagation through paths are ignored (set as zero) because the AOA estimation is performed based on the phase differences of one path on the antenna elements. The received signal is constructed by the multipath superposition according to the signal model given in Equations (4) and (5).  
The power delay profile (PDP) and angular power spectra (APS) of the five MPCs are shown in Figure 3. We can see that the five significant MPCs and the small pulses at other excess delays are caused by the AWGN in constructing the received signal. To estimate the AOA of the LOS path, the updated values of the azimuth and elevation angles in the iterations are plotted in Figure 4. It is shown that \(\phi_{1}\) and \(\theta_{1}\) converge the correct values after three iterations. The spread spectrum gain and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline number of MSNs (\(K\)) & 10\(\sim\)70 & number of paths (\(L\)) & 5 \\ antenna array size (\(M\)) & 2 \(\times\) 2\(\sim\)8 \(\times\) 8 & received SNR & \(-\)30\(\sim\)30 (dB) \\ PN-sequence length (\(N\)) & 1023 chips & chip rate & 20 Mcps \\ convergence threshold (\(\zeta\)) & 10\({}^{-3}\) degree & signal bandwidth & 20 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Propagation path parameters.  
antenna array gain provide the relatively high signal to interference plus noise ratio (SINR), resulting in fast convergence and high accuracy. Considering the complexity of the multipath spatial propagation in radio channels, the spread spectrum scheme in ATML can effectively mitigate the multipath effect by extracting the LOS component for localization.
# Real-Time Seismic Data from the Bottom Sea
###### Abstract  
An anchored marine seismometer, acquiring real-time seismic data, has been built and tested. The system consists of an underwater seismometer, a surface buoy, and a mooring line that connects them. Inductive communication through the mooring line provides an inexpensive, reliable, and flexible solution. Prior to the deployment the dynamics of the system have been simulated numerically in order to find optimal materials, cables, buoys, and connections under critical marine conditions. The seismometer used is a high sensitivity triaxial broadband geophone able to measure low vibrational signals produced by the underwater seismic events. The power to operate the surface buoy is provided by solar panels. Additional batteries are needed for the underwater unit. In this paper we also present the first results and an earthquake detection of a prototype system that demonstrates the feasibility of this concept. The seismometer transmits continuous data at a rate of 1000 bps to a controller equipped with a radio link in the surface buoy. A GPS receiver on the surface buoy has been configured to perform accurate timestamps on the seismic data, which makes it possible to integrate the seismic data from these marine seismometers into the existing seismic network.  
earthquake; marine seismometers; data transmission and management systems; inductive communications 18  
1132
# Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms
## 4 Consensus Strategy
### Parallel Communication Graph  
The parallel communication graph is depicted in Figure 9.  
In this case, the Laplacian matrix \(\mathcal{L}_{2}\) is  
\[\mathcal{L}_{2}=\begin{bmatrix}-1&1&0&0&0\\ -1&0&1&0&0\\ 0&-1&0&1&0\\ 0&0&-1&0&1\\ 0&0&0&0\end{bmatrix}. \tag{60}\]  
Moreover, it is noted that the graph presented in Figure 9 can be viewed as the combination of two cascade-directed graphs, where \(\mathbf{1}\rightarrow\mathbf{2}\rightarrow\mathbf{4}\) and \(\mathbf{1}\rightarrow\mathbf{3}\rightarrow\mathbf{5}\) and where agent \(\mathbf{1}\) is the leader whose state is known. The desired paths are as follows:  
1. 1 st vehicle: \[\mathbf{p}_{d1}(\gamma_{1})=R_{1}\begin{bmatrix}\cos(\gamma_{1})\\ \sin(\gamma_{1})\end{bmatrix}\;(\mathbf{m})\] (61)
2. 2 nd vehicle: \[\mathbf{p}_{d2}(\gamma_{2})=R_{2}\begin{bmatrix}\cos(\gamma_{2}-\pi/24)\\ \sin(\gamma_{2})-\pi/24\end{bmatrix}\;(\mathbf{m})\] (62)  
Figure 9: A parallel-directed communication graph (PDCG).  
3. 3 rd vehicle: \[\textbf{p}_{d3}(\gamma_{3})=R_{3}\begin{bmatrix}\cos(\gamma_{3}-\pi/12)\\ \sin(\gamma_{3}-\pi/12)\end{bmatrix}\ (\text{m})\] (63) 4. 4 th vehicle: \[\textbf{p}_{d4}(\gamma_{4})=R_{4}\begin{bmatrix}\cos(\gamma_{4}-\pi/24)\\ \sin(\gamma_{4}-\pi/24)\end{bmatrix}\ (\text{m})\] (64) 5. 5 th vehicle: \[\textbf{p}_{d5}(\gamma_{5})=R_{5}\begin{bmatrix}\cos(\gamma_{5}-\pi/12)\\ \sin(\gamma_{5}-\pi/12)\end{bmatrix}\ (\text{m})\] (65)  
where \(R_{1}=3\), \(R_{2}=4\), \(R_{3}=2\), \(R_{4}=5\), and \(R_{5}=1\). Figure 9(a) displays the actual paths of the robots. From Figure 10(a),b, we can obtain that the norm of position errors and linear velocity errors converge to a ball centered at the origin. Moreover, Figure 11(a),b show the performance of the consensus strategy introduced herein.  
Figure 11: Norm of the position and linear velocity errors (PDCG). (**a**) Norm of the position errors. (**b**) Norm of the linear velocity errors.  
Figure 10: Norm of the position errors and performance of unmodeled term approximation (PDCG). (**a**) Norm of the position errors. (**b**) Performance of unmodeled term approximation.  
It is interesting to remark that, by using the proposed consensus strategy--Equations (46) and (47)--a consensus is researched if and only if the directed graph has a directed spanning tree [21]. However, in our case, the root must be the leader whose states are known beforehand.
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 3 Data-Skew-Handling Algorithms
### Hash-Based Partition  
In hash-based partition (HBP) [5], after mapping the input data, the partition processing will redistribute the workload to the nodes based on the join keys. Hash-based partition is not ideal for handling skewed data, since the data skew will go to the same computing nodes. Therefore, hash-based partition is not a good solution for skewed data [21].  
Suppose that we have two datasets \(R(k,v1)\) and \(L(k,v2)\), where the attribute \(k\) is a join key. The data flow of hash-based partition algorithm is presented in Figure 6. The join operation between two datasets \(R\) and \(L\) using a hash-based partition algorithm goes through two phases.  
* Partitioning phase: The \(k\) attribute in each record of the dataset \(R\) is passed to a hash function by the formula \((R.k\bmod p)\), where \(p\) is the number of partitions. The result of the hash function is also the partition number to which this record was sent. The same thing is done with dataset \(L\).
* Join phase: The partitions will receive a list of data with the same join key \(k\). Here, we can use any join algorithms, such as join, rightOuterJoin, or leftOuterJoin.  
Given an example of two datasets \(R(k,v1)\) and \(L(k,v2)\) with \(R.k=\{1,1,2,2,2,2,4,4\}\), \(L.k=\{1,1,2,2,2,3,4,4\}\), and there are \(p=4\) partitions, in hash-based partition, one partition can receive more records than other partitions. The join computation with hash-based partition is shown in Figure 7. In this example, partition \(P_{2}\) has four records \(\{r3,r4,r5,r6\}\) from dataset \(R\) and three records \(\{l3,l4,l5\}\) from dataset \(L\). Thus, partition \(P_{2}\) produces 12 join results \(\{(r3,l3),(r3,l4),(r3,l5),(r4,l3),(r4,l4),(r4,15),(r5,l3),(r5,l4),(r5,l5),(r6, l3),(r6,l4),(r6,l5)\}\), which is a larger number than in other partitions. Specifically, partition \(P_{3}\) only receives one record \(\{l6\}\) and does not generate any join results. The execution time of a join computation depends on the completion of the last reducer. If the number of identical join keys is too large, then, even if the number of partitions is large, the data with the same join key will only gather on a few certain partitions. This results in some partitions being too big and the others having no data. This easily causes out-of-memory errors or slows down the processing speed. Therefore, processing skewed data is a very important issue for join operations.  
Figure 6: Data flow of a hash-based partition algorithm.
## 3 Results and Discussion
### Sand Properties  
As the porosity (n) of sand decreases, the packing of particles becomes denser, increasing the number of contact points between the particles and the interface continuum surface [22]. This results in greater resistance to shear forces, thereby increasing the \(\upmu_{\text{p}}\). Figure 9 illustrates this relationship across different normal stress levels (25, 50, and 100 kPa), showing a negative correlation between porosity and \(\upmu_{\text{p}}\). Lower porosities (higher dry  
Figure 8: Interface shear strength versus (**a**) normalized roughness and (**b**) hardness.  
Figure 7: Profilometer Initial Roughness Contours for (**a**) Steel, (**b**) PVC, and (**c**) Stone.  
densities) lead to improved interlocking and frictional resistance, enhancing the overall stability and performance of the soil-structure interface.  
In Figure 10, the coarse sand shows a higher \(\mu_{\text{p}}\) with both steel and PVC, similar to the findings of [23]. However, with stone, the medium sand had a higher \(\mu_{\text{p}}\). This difference could be due to the stone's roughness affecting the \(R_{\text{n}}\).  
As shown in Figure 11, the well-graded sand (Sand-C) exhibits a higher \(\mu_{\text{p}}\) compared to the poorly graded sand (Sand-A). These findings align with the study conducted by [24]. This is attributed to the higher density of well-graded sand, resulting in greater \(\mu_{\text{p}}\). This trend is evident with both steel and PVC interfaces. However, a different behavior is observed with the stone interface. The stone's significantly high surface roughness impacts the \(R_{\text{n}}\), thereby affecting the interface friction.  
Figure 10: Peak interface friction coefficient versus continuum surfaces for medium Sand-A and coarse Sand-B.  
Figure 9: Impact of porosity on peak interface fiction coefficient at different normal stress levels.
# Reasoning about Confidence in Goal Satisfaction
## 4 Data Quality Tagging and Propagation Mechanism  
The method presented in this section consists of two complementary sub-methods: _Data Quality Tagging_ and _Propagation Mechanism_. In the former, the modeler tags leaf goals with a qualitative data quality level, which is then converted to initial quantitative confidence levels. In the latter, the confidence levels of leaf goals are propagated to other goals through their links (decompositions, contributions, and dependencies). Both methods are explained in the following subsections.  
Uncertainty has been explored for modeling and analysis at design time [21] and also at run-time [22]. Our focus here is on supporting decision making based on goal models (mainly at design time, but also with data that reflects run-time snapshots) in the presence of uncertainty. In terms of the tridimensional conceptual basis introduced by [PERSON] et al. [23] for uncertainty in goal modeling, our work focuses on: (1) _location_: uncertainty in inputs and outputs represented by the satisfaction levels of the model elements, (2) _level_: scenario uncertainty (e.g., without statistical evidence), and (3) _mature_: epistemic uncertainty (i.e., that could be reduced empirically, but likely with costly efforts).
## 3 Method
### AE-GRU Model  
To achieve this objective, a novel deep learning model for predicting a tropical cyclone track was proposed based on the application of an AE neural network and a GRU neural network. The model is comprised of three parts, shown in Figure 1. Given the tropical cyclone tracks and the meteorological factors related to tropical cyclones, the first part preprocesses a series of two-dimensional tropical cyclone data with multiple meteorological factors. The preprocessed data are then input to the AE layer, which can extract features and generate feature vectors. The third part uses the feature vectors generated from the AE layer as the data input to train the GRU prediction layer.  
An AE is a neural network that aims to achieve a reduction in the number of dimensions and data denoising. The application of an AE in pattern recognition [32], fault diagnosis [33], and feature learning [34] has recently achieved good results. The AE layer can learn to compress the original data from the input dataset and generate a representation (encoding) of the original data, then decompress (decoding) the representation into something that closely matches the original data. In this study, we propose the utilization of an AE to extract implicit data features from the spatio-temporal matrices \(tc_{i}\in X\). These extracted features from the AE layer will be used as input to the GRU layer.  
The GRU is a variant model of an RNN, which differs from general neural networks that only establish connections between layers. As the biggest characteristic of an RNN, it can establish connections between neurons within layers. Hence, an RNN has the ability to process sequential information. However, the disadvantage of an RNN is its inability to deal with long-term dependencies. Therefore, the use of a GRU is proposed to alleviate this problem by adding gate structures and memory cells, which have been widely applied in machine translation [35], automatic speech recognition  
Figure 1: Structure of the AE-GRU model.  
(ASR) [36], and regression forecasting [27]. In this research, we propose the use of a GRU layer to model the temporal features of tropical cyclones generated from the AE layer. The landing position of the target tropical cyclone at the next moment can then be predicted using this model.
# New Security Improvements in Next-Generation Passive Optical Networks Stage 2 +
## 6 Conclusions
OLT sends a request to ONU for key exchange phase, but OLT waits with a quite window interval. An ONU receives the request and answer with new PLOAM message and sends PLOAM message(s) in next BWmap allocation. Once the key is established, the ONU uses this key in bidirectional communication. The direction of future work will be the implementation of the proposed security improvements in the real NG-PON2 network and a proposal of a solution for nonrepudiation of the data origin and delivery between ONUs in the NG-PON2. Conceptualization, V.C., T.H., and P.M. Methodology, V.C. and P.M. Validation, T.H. and V.O. Formal analysis, T.H. Investigation, V.C., T.H., P.M., V.O. Resources, T.H. and P.M. Writing, original draft preparation, V.C., T.H., P.M., and V.O. Writing, review and editing, V.C. and T.H. Visualization, V.O. Project administration, P.M. and T.H. Funding acquisition, P.M. and T.H. The presented research has been supported by projects of the Ministry of the Interior of the Czech Republic under Grant No. VI20172019072 registration, E-infrastructure CESNET--modernization, registration number CZ.02.1.01/0.0/0.0/16_013/0001797; the National Sustainability Program under Grant No. LO1401; and partly by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development, and Innovations project reg. no LM2015070. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
AES-CMAC Advanced Encryption Standard cipher-based message authentication code  
AES-CTR Advanced Encryption Standard cipher operated in the counter mode  
AES-ECB Advanced Encryption Standard cipher with electronic codebook mode  
C-IBS Complementary index-based syndrome  
CC Correction code  
CRPs Challenge-response pairs  
DRAM Dynamic random access memory  
EER Equal error rate  
FAR False acceptance rate  
FRR False rejection rate