esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation Experiments
### Network Topology  
The network topology used to evaluate the proposed SDN-based energy-aware routing approach is shown in Figure 6. The network contains five nodes. Each one of them contains a sensor and access point. The proposed approach will be evaluated based on a transmission of UDP packets from Sensor 1 to Sensor 4 using _iperf_. Two different routing alternatives can be distinguished to transmit the packet. On one hand, the default routing strategy (i.e., the shortest-path routing) will transfer the UDP packet through Node 2. On the other hand, the proposed SDN-based energy-aware routing approach will take into account the energy level in each node to decide the best route. In particular, the energy of each node will be calculated based on the number of bytes sent and received. Then, the SDN controller will alter the routing path based on the energy consumed by Node 2 and route through another path (Node 3 and Node 5) whenever necessary to keep Node 2 alive and balance the energy consumption in all nodes.  
The data traffic and energy consumption of all the nodes in the network resulting from the implementation of the proposed routing approach are analyzed in Section 6. In addition, these results are compared to the ones obtained with the baseline shortest-path routing method.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Emulator & Mininet-Wifi \\ Operating System & Ubuntu 20.04 \\ Memory & 9.8 GiB of RAM \\ CPU & 2.80 GHz \\ Traffic Generator & _iperf_ \\ Link Bandwidth & 10 Mbps \\ SDN Framework & Ryu \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical aspects of emulation.
## 3 Results and Discussion
### Pp-PEG Thin Film Characterization
The increase in the -OH stretching vibrational band (located between 3100 and 3600 cm\({}^{-1}\)) can also be attributed to the prevalence of this plasma-induced monomer fragmentation process since, as a result, more hydroxyl groups are located on the terminal polymer chains (Figure 8b). Within the HPLC studies, the mass spectra of the plasma-polymerized films were recorded by dissolving the obtained polymer in 2 mL of distilled water. By using a mass spectrometer coupled with a liquid-phase chromatograph, the mass spectra of pp-PEG films polymerized at 2, 6 and 10 kHz discharge frequencies were recorded (Figure 9). Our results reveal a relative distance between two adjacent mass intensity peaks of about 24 \(m\)/z, while the literature gives a value for the mass-to-charge ratio corresponding to the EG monomer of 44. Hence, the distribution of mass intensity peaks between 500 and 900 \(m\)/z demonstrates that a minimum number of 21 and a maximum number of 38 repeating units of EG are included in the polymer chain. The spectra also show a distribution maximum located at 748 \(m\)/z, which corresponds to 31 EG units constituting the longest polymer chain. Moreover, comparing the spectra recorded for the sample obtained at different frequencies, it can be clearly noticed that the intensity of the peaks decreases as the rate of polymerization emphasized by the thickness measurements increases (see  
Figure 8: FTIR spectra of pp-PEG films obtained in atmospheric-pressure DBD at different discharge frequencies: (**a**) vibration bands with low oscillation frequency and (**b**) IR-recorded bands with high oscillating frequency; the IR spectrum of ethylene glycol (EG) was added for comparison. Figure 7b). This fall in intensity demonstrates an enhancement at high frequencies of the plasma-induced monomer fragmentation, which was previously found to be responsible for the porosity of pp-PEG films. All these results are consistent with those concerning the global plasma emission or the intensities of associated spectral features and the calculated vibrational or rotational plasma temperatures.
# Experimental Study of Entrainment and Mixing of Renewable Active Particles in Fluidized Beds
## Materials and Methods  
### Biomass Fuels and Bed Material  
The renewable active particles examined in the present study are Solid Recovered Fuel (SRF), bark, sunflower shell, and wheat shell (see Figure 1). The samples were taken from a Hungarian waste treatment plant. The characteristics of the active particles used in the present study are shown in Tables 2 and 3. The examined SRF corresponds to the European standard EN 15359. This standard classifies the SRF based on the net calorific value (NCV), sulfur (S), and chlorine (Cl) data. According to this standard, the examined SRF is classified as 4,2,2. The bark is classified as a non-hazardous waste from the wood processing industry according to the European waste code (EWC code: 030101). Because it is available in a high amount, it can be utilized as renewable biomass fuel in fluidized bed power plants. Sunflower and wheat shells are agricultural wastes, and their availability is seasonal. Because of their high heating values, it is economical to store and transport them. In general, these biomass fuels are secondary or waste biomasses. Thus, the utilization of these fuels does not affect the agricultural food cultivation.  
Most characteristic data of the investigated fuels were obtained by generally known or even standard methods, as stated in Table 2. Some further data in Table 3 were measured by specific procedures as described hereafter. The density measurements were carried out by using two different methods, (i) the _gas-pycnometer_ method and the (ii) _bulk volume_ method. These two methods are commonly used for density measurements of irregular granular materials. In the gas-pycnometer method, the sample is placed in a calibrated volume, and the gas (Helium) flow is opened to fill the space around the particles in the chamber. The gas fills all the cavities within the sample, and the measured density is called the _skeleton density_. In the other method, instead of the gas, we immerse  
Figure 1: Photographic view of the active particles used in the present study. (The original horizontal length of each photo is 7 cm).  
the irregular particulate material in a fine powder (fine sand) to fill the relatively large voidage in the sample (among the irregular particles). The calibrated tube containing the powder is mechanically vibrated till the fine powder fills all the gaps. Then, the irregular granular sample volume can be determined by subtracting the fine powder volume. The density measured by this method is called the _apparent density_, i.e., the density of the granular material near the maximum packing limit.  
The bed material used throughout the investigations of the present study is a commercially available sand. Its mean diameter is 0.81 mm, and many further characteristic data of this material were presented elsewhere [16].
# DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework
###### Abstract  
Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.  
5G-/IMT-2020; IMT-2030; framework; mobile edge computing; fog computing +
Footnote †: journal: Future Internet
## 2 Theoretical Research on Indirect Measurement Method Based on Vehicle-Bridge Coupling  
The damage detection method based on vehicle response is based on the vehicle-bridge coupling vibration system model, as shown in Figure 1, which analyzes the interaction mechanism between vehicle and bridge and obtains the bridge damage information. This method is a continuation of the damage detection method of multi-point input single-point output, in the hope that the damage detection can be carried out through the dynamic response of bridge vehicles, which can greatly reduce the number of sensors and does not affect the traffic.
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 3 Materials and Methods
### Positioning Module of RHLS
#### 3.4.1 Input Layer  
The input layer of the proposed system processes Wi-Fi fingerprint and multiple sensor data in one sequence. The input layer is normalized using min-max feature scaling, such that the received wireless signal data and sensor data are between zero and one, to train the middle layer composed of artificial neurons more efficiently. For wireless signals conforming to the institute of electrical and electronics engineers (IEEE) 802.11 standard, the minimum signal strength that can be received is \(-100\) dBm, and the maximum signal strength that can be received is \(-10\) dBm. Based on this, we create a conversion function as in Figure 6a with \(-100\) dBm matched to \(\"0\"\) and \(-10\) dBm matched to \(\"1\"\).  
Figure 4: Masking result.  
Figure 5: Structure of the recurrent neural network (RNN)-based hybrid localization system (RHLS)  
In the case of a signal that is not captured, it is matched with zero. In the case of the geomagnetism value, it has a value of 25 to 65 \(\mu T\), but it may be larger or smaller owing to various factors such as the steel structure in an indoor environment. Based on an analysis of data from various locations in offshore environments, the maximum magnetic value was set to 200 \(\mu T\), and, using this, the geomagnetic value was adjusted to between zero and one, as shown in Figure 6b. Magnetic inclination, as mentioned earlier, is a value representing an angle, and, because of its characteristics, it has only a value between \(-90^{\circ}\) and \(90^{\circ}\). Therefore, by dividing the collected value by 90, the value was adjusted to range from \(-1\) to 1, as shown in Figure 6c. In the case of moving direction, because it is expressed between \(-180^{\circ}\) and \(180^{\circ}\), as shown in Figure 6d, the direction was divided by 180 to give a value between \(-1\) and 1. Equation (3) expresses these functions mathematically. The Wi-Fi signal strength is \(s\), the geomagnetic strength value is \(h\), the magnetic inclination is \(i\), and the direction is represented by \(o\).  
\[s_{normalized}=\left\{\begin{array}{cc}0&,&s<-100,\\ \frac{s-S_{min}}{s_{max}-S_{min}}=\frac{s+100}{90},&-100\leq s<-10\\ 1&,&s\geq-10.\end{array}\right., \tag{3}\]  
\[h_{normalized}=\left\{\begin{array}{cc}\frac{h-H_{min}}{H_{max}-H_{min}}= \frac{h-200}{200},&0\leq h<-200\\ 1&,&h\geq-200.\end{array}\right., \tag{4}\]  
\[i_{normalized}=\frac{i}{180}. \tag{5}\]  
\[o_{normalized}=\frac{o}{360}. \tag{6}\]  
Figure 6: Translate functions of the input layer: (**a**) Wi-Fi signal strength; (**b**) geomagnetic intensity; (**c**) geomagnetic inclination; (**d**) moving direction.
## 4 Implementation Results
The testing platform successfully verified the high-throughput data transmission of uncompressed HD video streams with low latency. The initial synchronization process, i.e., the analog interfaces, as well as the RFIC operations take 0.97 ms, while the compact non-iterative baseband operations take 0.03 ms by using 6040 and 6058 cycles for TX and RX operations, respectively. As a result, the proposed testing platform achieves the transfer latency of only 1 ms, which can be regarded as a zero-delay transmission for VR applications. By not adopting the complex baseband processing, the proposed prototype consumes only 525 mW for handling the uncompressed HD video streams over HDMI protocols. Therefore, the proposed digital baseband architecture is definitely suitable for wireless VR applications, offering error-free, energy-efficient, and even ultra-low-latency solutions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **This Work** & **Baseline BCH** & **[11]** & **[12]** & **[24]** & **[36]** \\ \hline
**Types of ECC** & Interleaved BCH & BCH & LDPC & LDPC & LDPC & LDPC \\
**Code length** (bit) & 1782 & 106,940 & 672 & 672 & 18,900 & 36,408 \\
**Code rate** & 0.89 & 0.89 & 0.5 & 0.5 & 0.9 & 0.9 \\
**Technology** (nm) & 65 & 65 & 28 & 28 & 90 & 65 \\
**Voltage** (V) & 1.0 & 1.0 & 0.8 & 0.9 & 1.2 & 1.0 \\
**Operating speed** (MHz) & 400 & 400 & 400 & 470 & 167 & 500 \\
**Gate count** & 1211 K & 13,388 K & N.A. & N.A. & 520 K & 1200 K \\
**Chip area** (mm\({}^{2}\)) & 1.07 & 10.71 & 0.13 & 0.78 & 63.1 & 4.41 \\
**Normalized area**1 (mm\({}^{2}\)) & 1.07 & 10.71 & 0.30 & 1.81 & 45.57 & 4.41 \\
**Decoding throughput** (Gb/s) & 12.8 & 12.8 & 7.1 & 18.4 & 1.58 & 8.13 \\
**Decoding latency** (ns) & 295 & 16,608 & 95 & N.A. & N.A. & N.A. \\
**Power consumption** (mW) & 70.7 & 471 & 53.6 & 166 & 294 & 397 \\
**Energy efficiency**2 (pJ/bit) & 5.53 & 41.4 & 7.5 & 18 & 186 & 49 \\
**Normalized efficiency**2 (pJ/bit) & 5.53 & 41.4 & 27.2 & 51.59 & 134 & 49 \\ \hline \hline \end{tabular}  
* Normalized area = area \(\times\) (65 nm/technology);
* Energy efficiency = power consumption/decoding
* Normalized efficiency = energy efficiency \(\times\) (65 nm/technology) \(\times\) (1.2/voltage)\({}^{2}\).  
\end{table} 
Table 2: Comparison of ECC solutions for near-field wireless communications. Figure 10: Error-correcting capabilities of different ECC solutions.
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 2 Application of VSL and RM Control in HDV Traffic Flows
### Classical VSL Approaches
#### 2.1.3 VSL Based on Negative Feedback Loop  
The strategy of feedback VSL controllers is also based on collecting current traffic data on the congested motorway segment as shown in Figure 1, but with the objective of maintaining the controlled motorway segment at a defined set point such as the critical density \(\rho_{c}\). Such strategies do not require predictive traffic flow models. In [42], a simple VSL with integration action (so-called I-controller) for VSL control was proposed. It controls the flow of vehicles on the motorway segment before the problematic bottleneck occurence area. The working principle of the controller is based on the ALINEA algorithm [32]. Instead of a traffic light at the on-ramp controlling the flow of vehicles entering the urban motorway, VSL reduces the mainline speed limit. Similar to RM, VSL also controls the flow of vehicles by reducing the mainline speed. This achieves the effect of a \"virtual ramp\" on the motorway segment under VSL, which controls the output flow of the controlled segment or the inflow to the next adjacent problematic segment of the urban motorway. In [43], a distributed VSL controller based on control feedback adapted to eliminate shock waves was tested. The VSL on each controlled segment is operated by a separate controller (cumulatively 10 controllers). Such a VSL control system has proven to be successful in preventing the generation of shock waves, which cause minor disruptions to traffic flow. By applying VSL, \(TTS\) was reduced by 20% compared to the uncontrolled case in [43]. In [44], a comprehensive feedback control strategy was proposed for mainline traffic flow control enabled via VSLs, considering multiple bottleneck locations. The feedback control results were compared with the optimal control results to evaluate the performance of the proposed strategy, and it was able to come close to the optimal control results.
# Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index
## 1 Introduction
The inputs that were used to develop these models were the unit weight, uniaxial compressive strength (_UCS_) and Brazilian tensile strength (_BTS_) of the rock. They concluded that the _FIS_ model is an applicable technique in order to be used in the same field for further studies. [PERSON] et al. [16] proposed predictive equations for calculation of rock _BI_ as a function of intact rock properties including rock density, Schmidt hammer rebound number and p-wave velocity. They used a hybrid approach and combined the firefly algorithm and ANN models to develop the equitation. [PERSON] et al. [17] examined the feasibility of genetic programming model for predicting the brittleness of intact rocks. Their study used multiple input variables including _UCS_, _BTS_ and unit weight to forecast the _BI_ of the rock mass. While several previous studies acknowledged the suitability of ML techniques for solving the engineering problems, several ML techniques remained unused or barely applied to predict the rock _BI_. To the authors best of knowledge, no study is available which examined the feasibility of well-known ML techniques such as chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) for predicting the _BI_. Thus, in this study the abovementioned ML techniques plus ANN technique (as a benchmark one in field of ML) were employed for _BI_ prediction purpose. The performance of each model was evaluated through five performance indices and a gain chart. Additionally, three best models of this study are discussed in more details.
# Design and Experiment of High-Resolution Multispectral Polarization Imaging System
## 4 Research on Multi-Dimensional Information Fusion and Enhancement Processing Algorithm
### Experimental Simulation  
The simulation is aimed at a green grass field and green-coated board with low contrast. They are put together to distinguish and identify the green grass field and green-coated board, and the polarization fusion method is used to improve the contrast of the green-coated board. The image fusion process based on 2D wavelet transformation is shown in Figure 3. The simulation results are shown in Figure 4, where the S0 image is the image at the total light intensity of the target, and the DOP image is the polarization degree image.  
Figure 2: Filter bank representation of two-dimensional Mallat fast algorithm. (**a**) Wavelet decomposition. (**b**) Wavelet reconstruction.
# Rain Streak Removal for Single Images Using Conditional Generative Adversarial Networks
## 3 Proposed Model  
The proposed approach uses image-to-image translation for the image de-raining task. In a GAN, the generator produces the output based on the latent variable or the noise variable (\(z\)). However, in the proposed approach, it is necessary for a correlation to exist between the source image and the generator output image. We have applied the conditional GAN [16], which is a variant of the traditional GAN that takes additional information, y, as the input. In this case, we provide a source image with rain streaks as  
Figure 1: High-level architecture of the proposed model (CGANet).  
additional information for both the generator and discriminator. \(x\) represents the target image.  
The objective of a conditional GAN is as follows:  
\[L_{cGAN}\ (G.\ D)=E_{x-pdata(X)}[log(D(x.\ y))]+E_{z-pz(Z)}[log(1-D(G(z.\ y).\ y)) \tag{1}\]  
where \(pdata(X)\) denotes the real data probability distribution defined in the data space \(X\), and \(pz(Z)\) denotes the probability distribution of the latent variable \(z\) defined on the latent space \(Z\). \(E_{x-pdata(X)}\) and \(E_{z-pz(Z)}\) represent the expectations over the data spaces \(X\) and \(Z\) respectively. \(G(.)\) and \(D(.)\) represent the non-linear mappings of the generator and discriminator networks respectively.  
In an image de-raining task, the higher-order color and texture information has to be preserved during the image translation. This has a significant impact on the visual performance of the output. Adversarial loss alone is not sufficient for this task. The loss function should be optimized so that it penalizes the perceptual differences between the output image and the target image.  
Our implementation architecture is based on the work of Isola's [15] Pix2 Pix framework. It learns a mapping from an input image to an output image along with the objective function to train the model. In Pix2 Pix, it suggests \(L_{1}\) (mean absolute error) loss instead of \(L_{2}\) (mean squared error) loss for the GAN objective function, since it encourages less blurring in the generator output. \(L_{1}\) loss averages the pixel level absolute difference between the target image and the generated image \(G(z.\ y)\) over the image space \(x.y.z\).  
\[L_{1}(G)=Ex.y.z[\parallel x-G(z.\ y)\parallel] \tag{2}\]  
Finally, the loss function for this work is as follows:  
\[L(G.\ D)=L_{cGAN}\ (G.\ D)+\lambda\ L_{1}(G) \tag{3}\]  
Lambda (\(\lambda\)) is a hyperparameter that controls the weights of the terms. In this case, we kept lambda = 100 [15]. When training the model, lambda was increased to train a discriminator and minimized to train a generator. The final objective was to identify the generator \(G^{*}\) by solving the following optimization problem:  
\[G^{*}\ =\arg min_{G}\ max_{D}\ (L_{cGAN}(G,D)+\lambda L_{1}(G)) \tag{4}\]
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 3 Results
### Variables to Estimate Emissions
#### 3.1.3 Burned Area  
The improvements in MCD64 Collection 6 identified 52% more burned areas than version 5.1 did. The validation using Landsat imagery indicated an underestimation in the identification of the burned areas from the MCD64 product, especially when the burned areas were both few and small in extent, because of the coarse spatial resolution of the MODIS data. For instance, for the wet year 2014, an omission error of 80% was observed. The accuracy can be improved in years with more forest fires  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **PF** & **OF** & **POF** & **OPF** & **DTF** & **TF** & **G** & **CF** \\ \hline Estimated & & & & & & & & \\ Minimum & 0.50 & 0.47 & 0.42 & 0.50 & 0.37 & 0.41 & 0.39 & 0.46 \\ Mean & 0.51 & 0.57 & 0.48 & 0.54 & 0.47 & 0.50 & 0.45 & 0.49 \\ Maximum & 0.52 & 0.61 & 0.52 & 0.60 & 0.55 & 0.59 & 0.53 & 0.52 \\ CONAFOR & 0.75 & 0.71 & 0.74 & 0.74 & 0.96 & 0.5 & s/d & 0.81 \\ \hline \hline \end{tabular}  
* PF: pine forest, OF: oak forest, POP: pine-oak forest, OPF: oak-pine forest, DTF: dry tropical forest, TF: tropical forest, G: grassland, CF: cloud forest. CONAFOR: Comisión Nacional Forestal.  
\end{table}
Table 2: Estimated burning efficiency and literature data.  
Figure 4: Variable importance.  
and large burned areas, such as in 2017, which has a 70% accuracy, with less than 30% in omission and commission errors.  
Figure 5 shows all of the burned areas identified by the MCD64 product in the period of 2011-2014. Spatially, zones with a concentration of burned areas were identified, as in the Guerrero and Oaxaca States into Southern Sierra Madre and Southern Pacific Coastal Plain and Hills ecoregions, or in the Yucatan and Campeche States into Plain and Hills of the Yucatan Peninsula ecoregion. Temporarily, there were large differences in the period; 2014 reported less than 1000 km\({}^{2}\), in this case we expect fewer emissions. Meanwhile, other years reported more than 2000 km\({}^{2}\) of burned areas. The extreme case was in 2013, when were reported more than 5000 km\({}^{2}\) of burned areas, which could be translated into eight times more emissions that in 2014.