esa-sceva/satcom-chunk-collection huggingface dataset:
# A 3D Printed Membrane-Based Gas Microflow Regulator for On-Chip Cell Culture
## 2 Materials and Methods  
### Design and Fabrication of the Regulator  
Polydimethylosiloxane (PDMS) membranes were formed by molding. Liquid PDMS, (Sylgard 184, Dow Corning, Midland, MI, USA) mixed with its curing agent at a ratio of 10:1 (\(w\)/\(w\)) and degassed, was poured into an ink-jet 3D printed matrix (printer model: Projet 3510, 3D Systems, Rock Hill, SC, USA, photo-curable material: Visijet M3 Crystal) with defined cavities. The polymerization process was enhanced by heating the mold for 1 h at 70 \({}^{\circ}\)C. As a result, 200, 300, and 400 \(\pm\) 5 \(\upmu\)m thick membranes, measured precisely by caliper (Mitutoyo, Japonica), were fabricated and tested.  
Other functional elements of the regulator were also obtained utilizing ink-jet 3D printing techniques; rapid prototyping, simplicity of fabrication, and an increasing resolution are driving the growing popularity of its use in the bio-scientific fields [26,27]. The device contains: bottom part, pressing ring, aforementioned PDMS membrane, sieve, O-ring seal (not printed), top part, and microfluidic connector that joins the regulator with the lab-on-a-chip. The membrane is attached to the sieve by the pressing ring on the top part. The surface of the sieve is structured to properly stiffen the membrane and hold it in place. It is also perforated to enable the gaseous medium flow from the diaphragm. Because of this, the active surface of the membrane is minimized and is 12.5 mm\({}^{2}\). The role of the sieve is important, since it prevents the membrane from any possible breakage during high-pressure measurements. It acts as a reinforcement and protects from any undesired membrane bulge and shape change. If the sieve is not utilized, the gas transport through the membrane is unstable and unrepeatable. The regulator is assembled using a threaded connection between the bottom and top part. Its outer dimensions are 35 mm \(\times\) 24 mm (Figure 2).  
During the operation, the gaseous medium from the pneumatic line reaches the bottom part of the device and comes across the diffusion barrier (PDMS membrane). Depending on the membrane thickness, the flow rate is regulated and then directed to the lab-chip platform containing the cell culture through the microfluidic connector on the top part.  
Figure 2: 3D printed membrane-based gas microflow regulator: (**a**) on the left, exploded schematic view; on the right, cross section with magnified view of the sieve; (**b**) bottom and top part of the regulator prior to assembly.
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
## 2 Multi-Wavelength Path Loss Model with Human Blockage
### Multi-Wavelength Path Loss with Human Blockage in Multi-LED Scenario
Therefore, in the path loss model with mobile human blockage, the value of the path loss for each blockage condition can be characterized by the multi-wavelength path loss model of the free space propagation, and the occurrence probability of each blockage condition can be described by the BP. In the next section, we conduct the multi-wavelength path loss analysis and modeling under free space propagation.
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 6 Conclusions  
Wireless sensor networks require energy efficient schemes to enhance the network lifetime and system throughput, which provides minimum delay and improves the energy-efficiency of the overall system. In this study, an energy-efficient and delay tolerant routing scheme, which not only considers the residual energy of the neighbor node but also minimizes the intra-cluster communication cost of a cluster, was proposed. The proposed scheme divides the whole network into clusters and elects a CH for each cluster by using a weighted sum approach. Further, as soon as an event is triggered, the cluster nodes sense the event and starts the data transmission by using a route with minimum weight via [PERSON]'s routing algorithm. Simulation results indicate that compared with the FML, ESRAD, and SPFL algorithm, the proposed algorithm can prolong the network lifetime and improve the network energy conservation and consumption by reducing the energy consumption of individual nodes. Additionally, it shows delay tolerance owing to the consideration of the distance factor amidst data transmission. It also enhances the network throughput by reducing the packet loss and ensuring successful reception of the packets by the BS. As future work, the proposed scheme can be improved aiming at security routing problems to defend attacks from malicious adversary nodes in the network by establishing a trust evaluation model for each neighboring node to identify the most trust worthy next-hop node. In the data transmission, the links between the nodes and the CH can be made secure by taking into consideration the trust value of each node obtained from the evaluation model to increase the integrity of the received data, and the channel conditions can be considered to make it more suitable for practical applications.  
Figure 9: Performance comparison of routing schemes in terms of network throughput: (**a**) percent packet loss with respect to number of rounds; (**b**) number of packets sent to BS with respect to number of rounds.  
**Author Contributions:** Conceptualization, M.R.; Formal analysis, M.R.; Funding acquisition, S.S.; Investigation, M.R. and S.S.; Methodology, M.R.; Supervision, S.S.; Writing--original draft, M.R.; Writing--review and editing, S.S.  
**Funding:** This research was partly supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education with Grant No. NRF-2018 RID1A1B07048338, and a part of the project titled \"Development of Automatic Identification Monitoring System for Fishing Gears\" was funded by the Ministry of Oceans and Fisheries, Korea with Grant No. 20170388.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Data and Methods
### Factors  
#### 3.1.1 Land Cover (LC)  
To create the land cover maps, we used an SVM algorithm to process color infrared images from September 2021 and September 2019, which were derived from the combination of Sentinel-2 B03, B04, and B08 bands. The resulting color infrared images are presented in Figure 3, in which vegetation appears in shades of red, bare land in cyan or white, and water in black.  
Color infrared images can help distinguish among different plant types, depending on their leaf characteristics [46]. Inside the National Park of Dadia-Lefkimi-Soufli, oak and pine trees account for more than the 70% of vegetation [39]. Oak trees belong to the broad-leaved tree family, and therefore they appear in brighter red in the color infrared image. In Figure 3, oak trees can be seen as clusters of bright red in the northern and southwestern parts of the national park. Pine trees have thinner leaves, and thus they appear to have a darker red color. Finally, shrubs and low grass appear in faint red.  
Figure 2: Flowchart of the applied methodology.  
To classify the images, we considered six classes: pine forest, oak forest, shrubs and low grass, bare land, water bodies, and built-up areas. To train the SVM algorithm, we carefully gathered multiple samples of homogeneous parts from each image, representing one of the six classes. After the classification of the image, we made some adjustments to the product image, mainly to distinguish some parts of bare land from the buildup areas. The accuracy of the land cover classification was estimated using the Kappa coefficient, which was found to be 0.87. The final land cover maps from September 2019 and September 2021 of the National Park of Dadia-Lefkimi-Soufli are presented in Figure 3(a),b, respectively.  
Each type of tree has different flammability properties. Considering that pine trees are more flammable than oak trees, the forest areas were classified accordingly [47]. Finally, since water bodies cannot ignite, they were classified with the fire risk class 'no risk'.  
Figure 4: Final land cover map produced by the SMV algorithm (**a**) from September 2019 and (**b**) from September 2021.  
Figure 3: Color infrared image of the study area produced by the combination of spectral bands B03, B04, and B08 (**a**) from September 2019 and (**b**) from September 2021.  
The classification of land cover, based on the fire risk, is shown in Table 1, and the final, reclassified risk map of the land cover maps is presented in Figure 5.
## 5 Satellite Stabilization Problem
### Example 1. The Joint Estimation and Control Problem  
The proposed observer equation is:  
\[\left\{\begin{array}{c}\frac{d\hat{p}}{dt}=u_{1}(t,\hat{x}(t))/6+K_{1}(t, \hat{x}(t))[z(t)-h(t,\hat{x}(t))],\\ \\ \frac{d\hat{x}}{dt}=u_{2}(t,\hat{x}(t))-0.2\,\hat{r}\,\hat{p}+K_{2}(t,\hat{x}( t))[z(t)-h(t,\hat{x}(t))],\\ \\ \frac{d\hat{r}}{dt}=0.2\,\left(u_{3}(t,\hat{x}(t))+\hat{p}\,\hat{q}\right)+K_{3 }(t,\hat{x}(t))[z(t)-h(t,\hat{x}(t))].\end{array}\right.\]  
Further, we will consider the cases of solving the problem with different models of the measuring system.  
In all tests, the number of initial states is \(N=27\) and \(L_{0}=L_{1}=L_{2}=L_{3}=2\) is the scale of truncation. The initial state estimation vector is \(\hat{x}(0)=\hat{x}_{0}=(24,15,13)^{T}\). Parameters of the mini-batch adaptive method of random search are \(B_{\max}=1000\), \(M=15\) and \(R=8\cdot 10^{-5}\). To synthesize the plant control \(u(t,\hat{x})\) and observer control \(K(t,\hat{x})\), a system of orthonormal Legendre polynomials is used.
## 4 Simulation Experiment Design
### Simulation Parameter Configurations and Analysis Criteria
#### 4.3.2 Research on Properties of Data Dissemination  
We also researched the properties of data dissemination by investigating three key factors influencing the data dissemination effectiveness, i.e., the V2X standard, traffic volume, and radio signal transmission power. The simulation parameters of this research are shown in Table 4.  
This study's evaluation is based on two key metrics: the count of vehicles that successfully receive data packets and the distance over which data are disseminated. Only one RSU is placed in the grid road scenario for the purpose of calculating the data dissemination distance. This distance is calculated as the average distance of vehicles farthest from node 3 in all four directions at the 964-s mark of the simulation, which coincides with the moment that the data packet reaches the end of its lifecycle. To increase the volume of the simulation data and enhance the accuracy of the research without altering the overall scenario architecture and the behavior of the OBUs and RSUs, we generated multiple routing files with the same traffic volume demand but different specific vehicle routes using random numbers for simulation, to create different degrees of data dissemination effectiveness for reference.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Influencing Factor** & **Simulation Value** & **Scenario** & **RSU** & **OBU Broadcast Times (a)** & **Transmission Mode** \\ \hline V2X standard & IEEE 802.11 bd/p & & & BPSK 1/2, QPSK 1/2, \\ Traffic volume & 0.25\(\times\), 0.5\(\times\), 1\(\times\), 2\(\times\) 1 & grid road & node 3 & 3 & QPSK 3/4 and 16-QAM 1/2 \\ Transmission power & 1\(\times\), 2\(\times\), 3\(\times\), 4\(\times\) 2 & & & QPSK 1/2 \\ \hline \hline \end{tabular}  
* In the simulation regarding the traffic volume and transmission power, the V2X standard utilized is IEEE 802.11 bd.
* The traffic volume simulation multiplier is relative to the base traffic flow rate shown in Figure 4(a). 2 The radio signal transmission power simulation multiplier is relative to the base power of 30 mW.  
\end{table}
Table 4: Simulation parameters of the research on properties of data dissemination.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comparison Metric** & **Scenario** & **RSU** & **OBU Broadcast Times (a)** & **Transmission Mode** \\ \hline \multirow{2}{*}{Data throughput} & grid road & node 3 & 3 & QPSK 1/2 \\  & geometric road & Figure 13a in [33] & 5 & \\ \multirow{2}{*}{Transmission reliability} & grid road & nodes 2 and 3 & 0 & BPSK 1/2 and QPSK 1/2 \\  & geometric road & nodes 10 and 22 & 0 & QPSK 3/4 and 16-QAM 1/2 \\ \hline \hline \end{tabular}  
* In each comparison, the data packet length transmitted by IEEE 802.11 bd is 600 bytes, while that of IEEE 802.11p is 300 bytes.  
\end{table}
Table 3: Simulation parameters of V2X performance comparison.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 3 Results
### Datasets and Metrics
#### 3.1.3 Implementation Details  
Our model follows the ByteTrack structure. In the detection phase, we used the YOLOX-x model as the detector, pre-trained on the COCO dataset. For the DanceTrack dataset, we trained this on the DanceTrack training set for eight epochs, with an input image size of 1440 \(\times\) 800. For the MOT20 dataset, we incorporated the CrowdHuman [37] dataset into the training process. The training was conducted using a combination of the training sequences from both the MOT20 and CrowdHuman datasets. The model was trained for 80 epochs with an input image size of 1600 \(\times\) 896. The optimizer used for training was SGD, with an initial learning rate set to 0.001, employing a cosine annealing strategy to adjust the learning rate dynamically. The high-confidence detection box threshold was set to 0.5, the low-confidence detection box threshold was 0.1, and the minimum detection box area was 100 pixels.  
In the tracking phase, we first applied the CEF method to filter candidate detection boxes by incorporating predictive information, setting the score update weight to 0.7. Next, we optimized the state space of the Kalman filter and used the AMN method to dynamically adjust the measurement noise covariance during the Kalman filter update steps. Finally, we employed the DHIOU method to calculate the affinity matrix more accurately for trajectory association, with a dilatation coefficient set to 1.2.
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 5 Discussion  
In this paper, we studied, via simulation, the performance of irregular repetition slotted ALOHA under multi-packet detection. Our results suggest that there is a clear difference between frame-synchronous and frame-asynchronous systems, in that with frameless operations both the shape of the packet arrivals tail distribution and the existence of short-range and long-range correlations strongly impact the packet loss ratio and the average delay. To a large extent, these effects emerge only weakly in the case of frame-aligned operations, because this enforces the system to introduce a delay in the newly arrived packets (until the beginning of the next frame), and implies that the backlog of accumulated packets is the key quantity for calculating the performance. These observations seem to suggest that, for massive random access based on coded-slotted ALOHA, those in which the number of active transmitters can be in the order of hundreds or more, frame-synchronous operations are preferable for system stability (i.e., low PLR) over the frameless operations, since the latter is highly sensitive to the presence of correlations and/or heavy tails.  
Both authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grant \"ICARUS - Information Centric Satellite Network for Vehicular Communications\" PID2020-113240 RB-I00, funded by MCIN/AEI/10.13039/501100011033, and by grant \"COMPROMISE - Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data\" PID2020-113795 RB-C33, funded by MCIN/AEI/10.13039/501100011033.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 14: Simulated PLR (**left**) and Mean Delay (**right**) for FA-CSA versus \(\lambda_{a}/k\), with \(\Lambda(x)=0.86x^{3}+0.14x^{8}\), \(n=200\) and \(k=\{1,2,3\}\), and correlated arrivals (\(H=0.75\) and \(r_{1}=0.9\)) with marginal Poissonian distribution.  
Figure 15: Simulated PLR (**left**) and Mean Delay (**right**) for FA-CSA versus \(\lambda_{a}/k\), with \(\Lambda(x)=0.86x^{3}+0.14x^{8}\), \(n=200\) and \(k=\{1,2,3\}\), and correlated arrivals (\(H=0.9\) and \(r_{1}=0.5\)) with marginal Poissonian distribution.
# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 7 Test and Results
### Feature Selection  
Figure 2(a),2(b) report the cumulative feature weights (CFW) obtained applying the NCA and MRMR methods to the training set respectively, cumulating from the most important to the less one.  
As it can be noticed, the CFW curve for NCA algorithm shows a clear linear growth for the top eight features of the ranking and saturation after a knot point. As shown in Figure 4, these features include active power, fundamental active and reactive power for the 3 rd and 4 th time instant after the event; and harmonic active power in the 2 nd and 4 th time instant after the event. Thus, also the dynamic of these parameters supplies useful information, since in some cases the same parameter is selected in different time instants. In order to detect the relevant features, the best regularization parameter that corresponds to the minimum average loss has been obtained using cross-validation. In Figure 5 the average loss values versus the regularization parameter values obtained with this optimization procedure is shown.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training** & **Validation** & **Test** \\ \hline
**\# Events** & 1252 & 160 & 173 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training, Validation and Test sets composition.  
Figure 4: The feature weights obtained with the NCA algorithm. The weights of the irrelevant features are very close to zero. The superscript identifies the observation window after the event.  
Figure 5: The average loss values versus the regularization parameter for the NCA algorithm.  
Figure 3: Cumulative Feature Weights for training data (**a**) NCA algorithm (**b**) MRMR algorithm.  
The feature score proposed by MRMR algorithm does not suggest any cut on the feature ranking since the CFW curve continues to linearly grow until the bottom feature is added. Thus, no useful information is supplied by the algorithm in this context.
## 4 Verification and Validation
### Implementation of the Proposed Model in a Construction Example
#### 4.2.2 Discussion of the BNE for Hypothetical Scenarios  
In scenario 1 (Table 6), where the project security level is high, we observe that the BNE with respect to the cost and schedule forecast file is E when the cost of cyberattack \(C_{cyb}\) is 0.33. The equilibrium \(\mathrm{E}=\left(R_{\mathrm{aut}}^{M}\ R_{\mathrm{aut}}^{L}\,D_{\mathrm{accept}}^{ \mathrm{aut}}\ D_{\mathrm{accept}}^{cyb}\ \right)\) (see Figure 4) states that when the cost of the cyberattack is 0.33, a malicious insider whose level is advanced beginner would prefer using authentication to a cyberattack to request the file, and the legitimate insider would also use authentication. The defender observes this, and, in response to that, decides that the best strategy is to accept the access request with the probability \(p_{1}=0.8\) of the malicious insider being detected. On the other hand, for the structural design file, the BNE is D, where \(\mathrm{D}=\left(R_{\mathrm{cyb}}^{M}\ R_{\mathrm{aut}}^{L}\,D_{\mathrm{accept}}^{ \mathrm{aut}}\ D_{\mathrm{accept}}^{cyb}\ \right)\). This states that when the cost of the cyberattack \(C_{cyb}\) is 0.05, a malicious insider would prefer launching a cyberattack to using authentication to request the file. This action is observed by the defender, and the best defense strategy is to accept the request and catch the malicious insider with the probability \(p_{2}=0.7\).  
In scenario 2 (Table 7), where the project security is medium, we see that the BNE corresponding to the cost and schedule forecast file is \(\mathrm{B}=\left(R_{\mathrm{aut}}^{M}\ R_{\mathrm{aut}}^{L}\,D_{\mathrm{ accept}}^{\mathrm{aut}}\ D_{\mathrm{ reject}}^{\mathrm{cyb}}\ \right)\). This states that when the cost of the cyberattack is 0.15 and if the insider is a malicious one whose expertise level is advanced beginner, he/she would prefer using authentication to a cyberattack at this cost to request the file. In this case, the best strategy for the defender is to accept the request with the probability \(p_{1}=0.55\) of the malicious insider being discovered. In addition, it can be seen from this equilibrium that if the insider is legitimate, he/she would request the data through authentication, and this request would be accepted by the defender. On the other hand, in the case of the structural design file, the BNE is \(\mathrm{A}=\left(R_{\mathrm{cyb}}^{M}\ R_{\mathrm{aut}}^{L}\,D_{\mathrm{ accept}}^{\mathrm{aut}}\ D_{\mathrm{ reject}}^{cyb}\ \right)\), with the cost of the cyberattack set to \(C_{cyb}=0\). This shows that if the insider is a malicious advanced beginner, he/she would launch a cyberattack at the cost of zero (i.e., \(C_{cyb}=0\)) rather than using authentication. To counter this act, the best strategy for the defender is to reject the request, given that the probability of the malicious  
insider being detected is \(p_{2}=0.49\), whereas if the insider is legitimate, he/she would request the file through authentication, and this request would be accepted by the defender.
## 4 RoCoMAR Algorithm
### Wireless Link Reinforcement
```
1:\(R_{c}\)\(\leftarrow\)\(\emptyset\);
2:\(R_{b}\)\(\leftarrow\)\(\emptyset\);
3:while connection is ACTIVE do
4:\(R_{c}=\prod\limits_{i=1}^{n}R_{i}\); \(\triangleright\)\(R_{i}\): PDR value of \(t^{th}\) link
5:if\(R_{c}<R_{req}\) && \(R_{c}\geq R_{b}(1+\xi)\)then
6:\(R_{b}=R_{c}\);
7: (\(u\),\(v\)) = Determine poorest link; \(\triangleright\)\(u,v\in G\)
8: Initiate a link reinforcement process (\(v\));
9:elseif\(R_{c}\geq R_{req}\)then
10:\(R_{b}\)\(\leftarrow\)\(\emptyset\);
11: Pause for time(\(t_{rei}\));
12:elseif\(R_{c}\leq R_{b}\)then
13:\(R_{b}\)\(\leftarrow\)\(\emptyset\);
14: Remove newly added robotic relay node;
15: Pause for time(\(t_{rei}\));
16:endif
17:endwhile
```  
**Algorithm 1** Repeated Link Reinforcement Process-Run at destination node.