esa-sceva/satcom-chunk-collection huggingface dataset:
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 5 Performance Evaluations
### Multi-Traffic Scenario
In addition, T\({}_{1}\) and T\({}_{2}\) occurred infrequently, and such degradation was not persistent in the network. However, CCTD preserved improved PDR performance against RPL-OF0 due to the proposed congestion control mechanism, which greatly compensated the degradation caused by the multi-queue model. Finally, we investigated the routing overhead introduced by the threes schemes in terms of average number of transmitted DIO messages. In this context, Figure 18 shows the average DIO messages transmitted per node per hour. As shown in the figure, both CCTD and CoAR incurred additional DIO overhead compared to that in RPL-OF0, especially under heavy traffic load. This was because nodes in CCTD and CoAR transmitted more DIO messages to distribute the congestion  
Figure 16: CDF of the E2E delay of all traffic types at 120 ppm/node: (**a**) CCTD; (**b**) Congestion-Aware Routing (CoAR). Figure 15: Worst-case delay comparison of: (**a**) T\({}_{1}\) traffic; (**b**) T\({}_{2}\) traffic. information based on the corresponding Trickle timer reset strategy in each scheme. The increase in the overhead, however, could be considered a reasonable cost to achieve a fair load distribution in the network and avoid the severe consequences of network congestion. Furthermore, the additional overhead was generally insignificant compared to the total active traffic in the network. For instance, at 90 ppm/node, the overhead in CCTD represented less than 1% of the total traffic.
## 5 Design Aspects  
### Optimality of Equal Allocation  
Throughout the chapter we assumed the trial is designed using equal (1:1) allocation, with \(n\) participants equally randomized between experimental and control groups (\(n/2\) per group). We now provide a formal justification for optimality of equal allocation in this setting.  
Suppose \(n_{E}=n\rho\) subjects are assigned to the experimental group and \(n_{C}=n(1-\rho)\) subjects are assigned to the control group, where \(0<\rho<1\) is the allocation proportion. We are interested in the value of \(\rho\) that minimizes the variance of the treatment contrast \(\Delta_{x}=(\mu_{E}+\gamma x)-\mu_{C}\) for some chosen fixed value of \(x\). This goal is closely related to the maximization of statistical power of testing \(H_{0}:\Delta_{x}=0\). We have \(\widehat{\Delta}_{x}=\overline{Y}_{E}+\widehat{\gamma}(x-\overline{x}_{E})- \overline{Y}_{C}\), and  
\[\text{var}\left(\widehat{\Delta}_{x}\right)=\frac{\sigma^{2}}{n\rho}+\frac{ \sigma^{2}(x-\overline{x}_{E})^{2}}{S_{xx}}+\frac{\sigma^{2}}{n(1-\rho)}, \tag{18}\]  
which is minimized for \(\rho=1/2\), the equal allocation.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Topology Aware Routing Protocols  
The rapid movement of FANET nodes leads to sharp changes in the network topology. The dynamic changes and asymmetric characteristics of topology structure can easily cause network interruption; therefore, many routing protocols based on topology awareness have been proposed to solve this problem. However, many routing protocols aiming at topology changes are proposed based on traditional MANET, such as simply adding GPS positioning capabilities, and then apply to the FANET scenario directly. Obviously there still lack many factors to be considered. Here, we have listed some routing protocols that solving topology problems specially in the FANET scenario. Nodes can obtain information about the changes of surrounding topologies in time, and multiple routing protocols can be combined according to task requirements to adapt to complex scenarios.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 3 Mathematical Formulation of Adaptive Channel Allocation Problem
### The Problem Formalization  
To achieve a higher throughput and meeting minimum cost, our proposal intends to minimize the following functions: \(FNIC\), \(Flink\), and \(Fstop\), where \(FNIC\) is the maximum number of hosts per one NIC, \(Flink\) is the total interference between GW adjacent links, and \(Fstop\) is the number of channel re-allocations.  
\[FNIC=\max_{i}\left\{\frac{\sum\limits_{j\in G_{i}}\left(t_{ij}+t_{ji}\right)} {n_{i}}\right\}, \tag{1}\]  
where \(G_{i}\) represents the set of neighboring APs to \(AP_{i}\).  
\[Flink =\sum\limits_{i=1}^{N}\sum\limits_{j=i+1}^{N}\sum\limits_{p=1}^{N }\sum\limits_{q=p+1}^{N}t_{ij}\cdot t_{pq}\cdot f_{ijpq}\cdot c\left(ch_{ij}, ch_{pq}\right), \tag{2}\] \[Fstop =(Number\ of\ channel\ re-allocations). \tag{3}\]
# A Photon Imaging Detector Model with High Resolution and High Counting Rate
## 4 Simulation Results
### Setting of Shaping Parameters
Compared to the shaping strategy with fixed parameters, this adaptive filtering shaping method can not only avoid a high overlapping rate, but also give full play to the shaping performance. The relationship between the shaping error E\({}_{\text{s}}\) of different shaping strategies and the counting rate is shown in Figure 8. As can be seen, the filter with a small t\({}_{\text{s}}\) is less affected by the count rate, but the overall performance is mediocre. The shaper with a large t\({}_{\text{s}}\) performs well at a low count rate, while E\({}_{\text{s}}\) rises sharply due to superposition at a high count rate. Adaptive filtering can maintain the best performance at any count rate.
## 2 Peculiarities of Implemented ML Pipelines for IR and THz Absorption Spectra Analysis
### Data Pre-Processing  
Usually, this step includes noise filtration, normalization, baseline correction, and optimizing the spectral range [49; 50; 51; 52; 53]. In general, higher spectral resolution provides better identification of molecular substances in a studied sample. A quite nonstandard absorption spectroscopy step in the ML pipeline associated with spectral resolution enhancement was implemented in [54; 55]. In our work [55], the multilayer perceptron artificial network and convolution network models were designed and applied for spectral resolution, improving noisy IR absorption spectra of gas mixtures of eight molecules (C\({}_{2}\)H\({}_{2}\), CS, CO, HI, HCl, H\({}_{2}\)O, NH\({}_{3}\), O\({}_{3}\)). The spectral data were generated using the 2020 HITRAN database [56; 57]. As a result, spectral resolution was improved from 5 cm\({}^{-1}\) to 1 cm\({}^{-1}\). The example of decomposition of this gas mixture with concentrations (0.25; 0.00; 0.00; 4.00; 0.00; 0.15; 0.20) \(\cdot\) 10\({}^{5}\) ppm (the order of concentrations corresponds to the order of molecules presented above) is presented in Figure 2. Here, decomposition was carried out for the high-resolution spectrum, noisy low-resolution spectrum, and the last spectrum with restored spectral resolution.
###### Abstract  
In some emerging wireless applications, such as wearable communication and low-power sensor network applications, wireless devices or nodes not only require simple physical implementation approaches but also require certain reliable receiver techniques to overcome the effects of multipath or shadowed fading. Switched diversity combining (SDC) systems could be a simple and promising solution to the above requirements. Recently, a Fisher-Snedecor \(\mathcal{F}\) composited fading model has gained much interest because of its modeling accuracy and calculation tractability. However, the performance of SDC systems over \(\mathcal{F}\) fading channels has not yet been analyzed in the open literature. To this end, this paper presents a systematic analysis of SDC systems over \(\mathcal{F}\) fading channels, including dual-branch switch-and-stay combining (SSC), multibranch switch- and examine combining (SEC), and SEC with post-examining selection (SECps) systems. We first investigate the statistical characteristics of univariate and bivariate \(\mathcal{F}\) distributions. Then, these statistical expressions are introduced into the above SDC systems and the statistical metrics of the output signal-to-noise ratio (SNR) for these systems are deduced in different \(\mathcal{F}\) fading scenarios. Thirdly, certain exact and novel expressions of performance criteria, such as the outage probability, the average bit error probability and average symbol error probability, as well as the average channel capacity for SSC, SEC, and SECps are derived. To find the optimum performance, optimal analysis is performed for the independent and identically distributed cases. Finally, numerical evaluation and simulations are carried out to demonstrate the validity of the theoretical analysis under various \(\mathcal{F}\) fading scenarios. According to the obtained results, the multipath fading parameter has more influence on the performance of SDC systems than the shadowing parameter, the correlation coefficient, or the average SNR. Importantly, the SDC systems can provide switched diversity gains only when the switching threshold is not too large or too small compared to the average SNR.  
switch-and-stay combining; switch-and-examine combining; Fisher-Snedecor \(\mathcal{F}\) distribution; performance optimization; bivariate statistical characteristics +
Footnote †: journal: _Sensors_  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
## 3 Results
### SPME Optimization
#### 3.1.2 Extraction Time  
Different extraction times were tested using the autosampler. These times were 10 s, 30 s, 1 min, 3 min, 5 min, 10 min, 15 min, 20 min, 30 min, and 60 min. Plots of the mass extracted versus the extraction time varied in shape. The profiles were typically linear or logarithmic, with the exception of acetic acid (Figure 2). Equilibrium was reached for most compounds (i.e., the logarithmic curve had started to flatten out), and was not excessively long, with a figure of about 10 min. Additional SPME fiber sorption capacity limitations were noticed after 10 min, for example, ethyl isobutyrate, ethyl lactate, and acetic acid. An extraction time of 10 min was chosen to avoid these possible interactions due to competitive adsorption and apparent analyte displacement after 10 min.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**RT (min)**} & \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**50/30 \(\upmu\)m**} & \multirow{2}{*}{\(100\upmu\)m} & \multirow{2}{*}{\(7\upmu\)m} & \multirow{2}{*}{\(85\upmu\)m} & \multirow{2}{*}{\(65\upmu\)m} & \multirow{2}{*}{\(70\upmu\)m} & \multirow{2}{*}{\(85\upmu\)m} \\  & & **DVB/CAR/PDMS** & & & & & & \\  & & **PDMS** & & & & & & \\ \hline
4.77 & Ethyl isobutyrate & 100 & 58 & 0 & 13 & 75 & 29 & 104 \\
5.53 & Isobutyl alcohol & 100 & 42 & 1 & 95 & 81 & 97 & 83 \\
8.10 & Isoamyl alcohol & 100 & 35 & 0 & 73 & 75 & 66 & 92 \\
11.00 & Ethyl lactate & 100 & 17 & 0 & 71 & 60 & 79 & 132 \\
11.75 & Ethyl caproate & 100 & 24 & 0 & 7 & 53 & 17 & 109 \\
12.92 & Acetic acid & 100 & 5 & 3 & 124 & 38 & 242 & 158 \\
16.38 & Ethyl caprylate & 100 & 67 & 1 & 21 & 98 & 45 & 64 \\
18.44 & Vitispirane & 100 & 61 & 0 & 16 & 90 & 38 & 99 \\
18.34 & Diethyl succinate & 100 & 52 & 0 & 45 & 105 & 56 & 63 \\
20.42 & Ethyl decanoate & 100 & 114 & 12 & 56 & 121 & 72 & 53 \\
21.13 & Benzmethanol & 100 & 23 & 0 & 74 & 86 & 67 & 82 \\
24.03 & Ethyl myristate & 100 & 94 & 32 & 59 & 92 & 66 & 41 \\
30.37 & Ethyl palmitate & 100 & 187 & 132 & 135 & 194 & 155 & 17 \\ \hline \end{tabular}
\end{table}
Table 1: Optimization of solid phase microextraction (SPME) extraction conditions—Fiber selection.  
Figure 2: Extraction time profile of selected volatiles extracted with HS-SPME in an Iowa Mare\(\mathrm{\acute{a}}\)chal Foch. Conditions: 5 min pre-sampling desorption of 50/30 μm DVB/CAR/PDMS SPME fiber; 4 mL wine sample in a 10 mL threaded glass amber vial with PTFE/silicone septa; 10 min incubation at 35 \({}^{\circ}\)C; 10 s, 30 s, 1 min, 3 min, 5 min, 10 min, 20 min, 30 min, and 60 min extraction time at 35 \({}^{\circ}\)C; agitation speed 500 rpm; 5 min desorption time into GC inlet. 10 min extraction time was chosen for the method.
# Proposal for a System Model for Offline Seismic Event Detection in Colombia
## 4 Seismic Detection Model Proposal
### Analysis of Seismological Stations
This supports the selection of the stations concerning their geo-referenced position, as it is an indicator of the proximity of the stations to the epicenters (epicentral distance) and the signal attenuation index when arriving at the stations. The stations with the highest identification rate of seismic events must be checked against their epicentral distance. A good relationship for the choice, as the third stage in this process, is to select the stations that have identified the most earthquakes, with a short epicentral distance. It is advisable to include the processing capacity as a third attribute in the selection process, since an additional station can signify the processing of 210,000 additional samples, on average.
## 4 Discussion
It can therefore be deduced that the landslides used for modeling did not capture the variability of geological (thrust and fault proximity) and environmental (lake proximity) conditions. Landslides that are better distributed across the entire study area can better describe the variety of geological and environmental conditions (e.g., thrust, fault and lake proximity) and therefore better predict landslide susceptibility. This outcome answers our research question whether we can perform LSM in an area for which a landslide inventory is not available but where such an inventory is available for the surrounding region. Based on this study, we can infer that the assessment of landslide susceptibility based on surrounding landslide inventories cannot be effectively performed in the areas when the geological and environmental conditions have changed. Generally, based on the results in Table 6, there is a 47%  
Figure 9: Correlation index between raster representing LSI difference between two susceptible models and each landslide-conditioning factor (LCF) (LC: Land Cover, IMI: Integrated Moisture Index, SEI: Side Exposure Index, CTI: Compound Topographic Index, TPI: Topographic Position Index). chance of being correct with the susceptibility zone and a 42% chance of error with one susceptibility zone. Therefore, we can generally perform landslide susceptibility modeling based on the landslide inventory for a neighboring region. However, it should be reiterated that there is 10% chance of our susceptibility map differing from target landslide susceptibility of two zones in some areas. These are generally located in areas with significant changes in the geological or environmental conditions. In this case, when we considered only very low susceptible classes for the decision making, this susceptibility map can be used for decision making and planning. Another aspect which should be discussed here is the variability of landslide type used for LSM. Since the information about the specific landslide type (shallow/deep seated or planar/rotational) is not available within the national inventory, all landslides were used for LSM. A lack of differentiation between landslide types used for LSM is practiced by many researchers [3, 13, 14, 22, 55, 61, 62]. However shallow and deep seated landslides differ in terms of damage influence, size and volume [64]. Deep seated landslides generally appeared due to the relationship between natural dennotation process and long-term rainfall, whereas, shallow landslides are related to short high-intensity rainfall [64]. Therefore, at this moment it is unknown whether high correlation between LSM difference (Figure 8a) and geological factors (faults and thrust proximity) correspond to some specific characteristic and mechanism of deep seated landslides, which are known for their strong connection with geology. Thus, in future works it is worth considering differentiation of landslide type (shallow/deep seated) in susceptibility modeling and its relation with landslide conditioning factors.
## 3 Results and Discussion  
In this study, a theoretical model was developed first, followed by a CFD (Computational Fluid Dynamics) simulation to calculate the radius of the droplet after exiting the nozzle. The results were validated by comparing the simulation values with experimental measurements obtained using a camera. Furthermore, the maximum spreading radius of the droplet was calculated by considering energy conservation and validated from experimental observation under a microscope. Finally, the spreading radii of the droplets required on different conformal surfaces with different curvatures were calculated through the geometric model and validated from experimental measurements. The variables used in the model are shown in Table 1.  
The adjustable parameters in the experiment included the driving waveform of the piezoelectric nozzle, the printing traveling speed of the machine tool, the traveling distance of the droplet, the wettability of the substrate, and the contact angle. By changing these parameters, we were able to adjust the exiting diameter of the droplet \(D_{0}\), the droplet volume \(V\), the traveling speed of the droplet \(U\), the collision direction during falling \(x\), and the contact angle \(\theta_{ink}\). All the parameters associated with the nanosilver ink were set to constant values and were maintained during the experiment.  
Figure 2: Process for printing the conformal antenna based on arrayed nozzle.
## 4 Imaging of Bragg-Dip Profiles and Evaluation of Detailed Inner-Grain Information
### Measured Neutron Transmission Spectra of Three Evaluated Grains
Hence, such small changes in the crystal orientation were not observed in the Bragg-dip neutron IPF map presented in the previous study [19]. Here, the difference in the crystal orientation between Regions (1) and (2), where the difference in the interval of Bragg-dips is relatively  
Figure 5: (**a**) Imaging of (101) Bragg-dip wavelength of Grain 1. (**b**) Imaging of (110) Bragg-dip wavelength of Grain 1. (**c**) Imaging of (101) Bragg-dip wavelength of Grain 2. (**d**) Imaging of (110) Bragg-dip wavelength of Grain 2. (**e**) Imaging of (101) Bragg-dip wavelength of Grain 3. (**f**) Imaging of (110) Bragg-dip wavelength of Grain 3.  
large, was calculated as an example. The crystal orientation at each region was evaluated with the database of Bragg-dip wavelength pattern used in the previous study [19]. As a result, the angular difference between Regions (1) and (2) was evaluated as 0.4\({}^{\circ}\). Thus, such a small change in the crystal orientation in each crystalline grain, which had not been observed by the previous method, could be newly observed by the single Bragg-dip profile-fitting analysis method. Imaging of Width (FWHM) of Bragg-Dip and Identification of Grains with Small Orientation Differences  
The changes in the crystal defect, such as the micro-strain and the mosaic spread angle, can be observed by analyzing the width of Bragg-dip. However, in this study, the crystal defect was observed as a mosaic block and another crystalline grain whose crystal orientation was slightly different. Figure 6: (**a**) Imaging of the interval between (110) Bragg-dip wavelength and (101) Bragg-dip wavelength of Grain 1. (**b**) Neutron transmission spectra of Regions (3) and (4). (**c**) Imaging of the interval between (110) Bragg-dip wavelength and (101) Bragg-dip wavelength of Grain 2. (**d**) Neutron transmission spectra of Regions (5) and (6). (**e**) Imaging of the interval between (110) Bragg-dip wavelength and (101) Bragg-dip wavelength of Grain 3. (**f**) Neutron transmission spectra of Regions (1) and (2). Figure 7a,b shows the results of imaging the width (FWHM) of (101) Bragg-dip and (110) Bragg-dip of Grain 3. We found that the FWHM of each Bragg-dip was broader at Region (7), indicated by white circles in Figure 7a,b. In particular, (101) Bragg-dip width drastically changed in Region (7). Figure 7c-e show the comparison of three neutron transmission spectra of Mini Regions A, B in Region (7) and entire Grain 3. Mini Regions A and B consist of 3 \(\times\) 3 pixels of the detector. We found that the Bragg-dip widths/profiles evaluated at Mini Regions A and B in Region (7) were different. It is discussed in the following sections.
# Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
## 7 Experimental Test Environment  
### Hardware and Infrastructure Setup  
A test system was established to measure and analyze video streaming performance in the MPT-GRE multipath network, as illustrated in Figure 4. The experiment utilized two Dell PowerEdge R620 servers were sourced from Dell Technologies, based in Round Rock, United States with the following specifications:  
* Memory: 4 \(\times\) 8 GB 1333 MHz DDR3 SDRAM
* Processors: Two 6-core Intel Xeon E5-2620 processors clocked at 2.00 GHz
* Network Interface Card (NIC): Intel quad-port Gigabit Ethernet controller, with two ports engaged for testing purposes.  
The two interfaces were connected through a Cisco Catalyst 3550 switch manufactured by Cisco Systems, Inc., Thailand, with bandwidths limited to 5 Mbps and 20 Mbps. Two independent physical paths (eth1, eth2) were established between the interfaces, with corresponding IP addresses assigned within the experimental network.  
The experiment's bandwidth settings were designed for real-world network conditions, incorporating various path capacities, adaptive bandwidth fluctuations, and constraints specific to 4K video streaming. We configured paths with 5 Mbps and 20 Mbps transmission speeds with the proper weights (weight_out) for these paths.  
The MPT-GRE software created the tunnel (tun0) between the two servers, which operated on the Ubuntu 22.04.4 LTS (Jammy Jellyfish) Linux operating system. This experimental setup aimed to assess the effectiveness of the MPT-GRE tunnel in real-time 4K video streaming. Performance was evaluated using videos from the Waterloo Streaming QoE Database, with quality metrics SSIM, MSE, and PSNR employed to quantify improvements in streaming quality.