esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Coupling of a Hybrid Power Plant with a Water Generation System
## 3 Simulations  
### CSP-Biomass Power Plant  
The CSP-biomass HPP is modelled using the commercial software EBSILON(\(\emptyset\)Professional. This tool is designed for power plant and thermodynamic process analysis. The software allows access to a large set of component libraries and input variables to describe in detail the working characteristics of the modelled system.  
The system simulation flow diagram is shown in Figure 1. The solar energy system includes the central receiver, the solar tower and the heliostats field. The solar field uses 110 ha and it is composed of 1458 mirrors made of high reflective glass. The heliostats have a rectangular design of 120 m\({}^{2}\) and an average reflectivity of 0.9 that lead to a total reflective area of 174,960 m\({}^{2}\)[11]. The solar plant requires specific meteorological data, such as the Direct Normal Irradiance (DNI) and the ambient temperature. These data are obtained from previous studies that report an average irradiance in Seville of 850 W/m\({}^{2}\) and an ambient temperature of 20 \({}^{\circ}\)C [52].  
The cylindrical receiver is located at an optical height of 127 m and has a thermal power of 100 MWth at the design point. A mixture of 60% sodium nitrate (NaNO\({}_{3}\)) and 40% potassium nitrate (KNO\({}_{3}\)) molten salts is used as the working fluid to capture and transfer heat from the solar receiver [10]. The salts work as a high-temperature fluid and as a cooling fluid for the receiver. The inlet and outlet temperatures of the molten salts are 290 and 565 \({}^{\circ}\)C, respectively. This results in a mean wall temperature of the receiver of 480 \({}^{\circ}\)C. The thermal storage system consists of two tanks of different temperatures from where the molten salts are pumped either towards the steam generator or the receiver. The thermal storage has a total capacity of 600 MWh, providing an autonomy of up to 4 hours.  
Once heated, the molten salts are led from the hot storage tank to the power block. There, it flows through different heat exchangers to generate steam and passes through the turbine of the plant. The steam turbine turns thermal energy into mechanical energy driving a generator that converts mechanical energy into electricity. The steam leaving the turbine goes through a condenser to be converted to its initial liquid state and complete the cycle.  
The supporting biomass system consists of a biomass boiler fired with olive pomace, which enables plant operation during periods of solar absence or low levels of thermal energy in the storage tanks. In this work, it is assumed that olive pomace is instantly available, already dry with about 10% moisture and is ready to be introduced into the boiler at any point in time. Selected operating parameters for the power block can be seen in Table 1. These values are based on existing reference plants and technical reports concerning similar hybridization projects [51; 52; 53].  
Figure 1: Diagram of the reference HPP.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 3 Methodology
### Overall Algorithm Flow  
As shown above, in a large-scale warehouse environment with numerous obstacles, the multi-mobile robot system has difficulties in quickly and accurately finding the optimal  
Figure 1: Schematic diagram of the Catmull–Rom spline curve.  
trajectory between the starting point and the target point. Moreover, the generated trajectory is prone to falling into a local optimum, leading to the failure of path planning. The LAPGWO algorithm proposed in this paper can effectively realize path planning in a large-scale warehouse environment. The population is evenly distributed during initialization by introducing the logistic chaotic mapping, avoiding omitting the optimal solution. The artificial potential field method is introduced to further accelerate the algorithm's search efficiency. In response to the problem of an unreachable target, a repulsive force correction term function is proposed to adjust the sensitivity of the target guidance to the change of distance and solve the problem of target unreachability. An independent environmental repulsive field and a robot repulsive field are established for each robot. Dynamic obstacle avoidance can be achieved when encountering dynamic obstacles or coming too close to other robots. The overall algorithm flowchart is shown in Figure 2.
## 2 Principle and Modeling
### Principle of KELM
 \tag{5}\]  
After introducing the regularized item, the following equation is used:  
\[f_{KELM}(\mathbf{X})=[K(\mathbf{X}\cdot\mathbf{x}_{1})\ \cdots\ K(\mathbf{X}\cdot\mathbf{x}_{N})] \times\bigg{(}\dfrac{I}{\mathcal{C}}+\mathbf{\Omega}_{KELM}(\mathbf{X})\bigg{)}^{-1} \cdot\mathbf{Y}. \tag{6}\]  
Then, the network output \(f_{\text{KELM}}(\mathbf{X}_{k})\) of the KELM can be written as  
\[f_{KELM}(\mathbf{X}_{k})=\sum_{k=1}^{n}\Omega_{KELM}(\mathbf{X}\cdot\mathbf{x}_{k})\cdot(( \dfrac{I}{\mathcal{C}})_{k}+\Omega_{KELM}(\mathbf{X}\cdot\mathbf{x}_{k}))^{-1}\cdot \mathbf{Y}_{k} \tag{7}\]  
where \(\mathcal{C}\) is the regular term coefficient. The accuracy of the model increases as the parameter \(\mathcal{C}\) increases, but overfitting is more likely to happen. Conversely, as \(\mathcal{C}\) decreases, the generalization ability increases and error-tolerant rate increases, but underfitting is more likely to happen.
## 3 Results and Discussion  
### Characterization of Graphenes and Membranes  
Graphene (GO and rGO) and membrane characterization was conducted by using XPS and FE-SEM. The XPS C1 spectra of GO and rGO are shown in Figure 2, which also shows the carbon element peaks within them. A non-oxidized carbon peak (C-C, 284.5 eV) was observed in GO and rGO at approximately 22% and 52%, respectively. However, considerably more oxygen-containing functional groups, such as the C-O bond (representing hydroxyl and epoxide groups, 286.7 eV) and the C=O bond (carbonyl, 288.5 eV), were detected in GO compared to rGO [20]. It is considered that GO and rGO have different effects on the hydrophilicity of the membrane. As hydrophilic functional groups (such as hydroxyl, epoxide, and carbonyl groups) are abundant in GO, the presence of GO on the membrane could increase the hydrophilicity of the membrane (compared to one without GO or with rGO).  
FE-SEM images of the GO-blended membrane cross-sectional structure are shown in Figure 3, where it is evident that the addition of GO contributed to the expansion of membrane pores. In particular, the membrane contained a greater number of large and straight pores with a GO content of 0.10 wt%. However, a dense subsurface layer was evident in both the PSf/GO (Figure 3a) and PVDF/GO (Figure 3b) membranes with a GO content of 0.15 wt%, and this phenomenon reduced the membrane flux. These results showed that to improve the morphology, it is necessary to incorporate an appropriate amount of GO nanomaterial into the membrane.  
Figure 2: XPS spectra of (**a**) graphene oxide (GO) and (**b**) reduced graphene oxide (rGO): C1s spectra.
## 4 Scalability  
It is known that cluster based systems are more scalable than other systems. This section shows why our proposal scales better than other proposals. First, we have to take into account that computation is much cheaper than communication in terms of energy dissipation [32]. So, what is desired is architecture with fewer retransmissions. This will imply a saving in energy of the whole system and it will give more scalability to the architecture.  
Let a network of nodes \(G=(V,\,E)\) be, where V is the set of nodes and \(E\) is the set of connections between nodes. Let \(k\) be a finite number of disjoint clusters of \(V\), so \(V=\cup(V_{k})\) and there is no node in two or more subsets (\(\cap V_{k}=0\)), _i.e._, there are not overlapping nodes. Let us suppose \(N=|V|\) (the number of nodes of \(V\)) uniformly distributed in a region. Let us suppose that there is just one cluster head node per cluster, so there are k head clusters in the whole network. Equation 3 gives the number of nodes:  
\[N=\sum_{i=1}^{k}|V_{k}\mid \tag{3}\]  
and the average number of neighbors of a cluster head will be given by Equation 4:  
\[Average=\frac{N}{k}-1 \tag{4}\]  
Four main types of cluster architectures can be distinguished:  
* 1-level cluster (see Figure 4).
* P-level cluster (see Figure 5).
* Planar cluster with 1 hop (see Figure 6).
* Our proposal (see Figure 7). We have considered the worst case where the communication has to be done through a node of an intermediate cluster (it could be done directly if it is reachable).  
The diameter of a network (d) is defined as the length of the delay-optimal path between the two farthest nodes. Figures 4-7 also show the path between the two farthest nodes.  
Figure 3: \(\lambda\) values as a function of node energy.  
Figure 4: 1-level cluster.  
Figure 5: P-level cluster.  
Figure 6: Planar cluster.  
Figure 7: Our proposal (in the worst case).  
\begin{table}
\begin{tabular}{l l} \hline
**Type of cluster** & **Diameter (\(d\))** \\ \hline
1-level cluster & \(K\) + \(I\) \\ \(P\)-level cluster & 2 \(p\) \\ Planar cluster with one hop & 3 \(k-1\) \\ Our proposal & \(K-1\) \\ \hline \end{tabular}
\end{table}
Table 1: Diameter for each type of cluster (\(k\) is the number of clusters and \(P\) is the number of levels of the hierarchy).  
Figure 8 demonstrates fewer hops are needed in our architecture than for the others, so the total routing overhead of the network is reduced and fewer retransmissions are needed, thus more energy is saved. This implies that our proposal scales better than the others.
# A Cloud-Based Real-Time Mechanism to Protect End Hosts against Malware
## 4 Evaluation
### Scan Result Time  
If a user uses the free VirusTotal service, usually the user needs to wait for a non-predictable time to get the final scan report for an executable. Hence, Skywalker will not stop sending a query to VirusTotal periodically with a scan ID for an executable until VirusTotal sends it the final scan report of the executable. A final scan report uses a positive value to show how many anti-virus engines think that the executable is malicious.  
Similarly, to evaluate the scan result time of various executables with different sizes, we sent each test file individually to VirusTotal 100 times and recorded the average scan result time. As shown in Figure 5, Evernote.exe has the highest average scan result time, 184 s. WinRAR.exe has the lowest average scan result time, 152 s. The average scan report time of the 12 test files is 164 s, which is approximately equivalent to 2.7 min. For each test file, Figure 5 also shows the average number of queries that [PERSON] made to get the final scan report.  
In Figure 5, the test files are listed in ascending order from left to right according to their file sizes. The rightmost file, Evernote.exe, has the largest file size. The average scan result time of all 12 test files is over 140 s. The scan result time of the two smallest files, Rgui.exe and Aero Color Show.exe, is close to the scan result time of the largest file, Evernote.exe. Hence, we think that the average scan result time is uncorrelated with the file size. In fact, the average scan report time highly depends on the scheduling strategy of VirusTotal.  
Figure 5 also shows the average numbers of queries that the 12 test files made to get their final scan reports. For instance, on average, [PERSON] needs to send 11.53 queries to VirusTotal to get the final scan report of Rgui.exe. The average number of queries that all the 12 test files made to get their final scan reports is 10.6 times. In other words, each time when a user clicks an executable, on average, [PERSON] requires querying VirusTotal 10.6 times to get the final scan report of the executable.  
Figure 4: Average scanning time of various executables with different file sizes.
###### Abstract
The solution requires two known lengths on the ground plane and can be applied to fairly straight highways near the surveillance camera. Once the background image is rectified, it is possible to locate the stripes and boundaries of the highway lanes. This paper focuses on estimating one moving vehicle's speed from side view video images obtained with an uncalibrated monocular camera. In our case, the problem of detecting the speed is carried out at the level of individual vehicles, detected using YOLOv3 [17], and we split the road into lanes. We remark that we only add parallel lines to limit the zone of the input and exit of the vehicle's detection and do not consider any other assumption such as ground flat nor prior knowledge about the actual dimensions of some of the objects. We summarize the contributions as follows:  
* We create a set of samples (Dataset) captured with monocular cameras (cellphone cameras) from the side view of the road. The dataset includes different videos with two cell phones to consider the two operating systems on mobile phones on the market, android and iOs. We configured the cameras of the cellphones with the same properties; nevertheless, the cameras are not calibrated with a reference object. * We compare five different statistical methods (Linear, Ridge, Lasso, Bayesian Ridge, Elastic Net Regressions) and three machine learning methods (Random Forest, Support Vector Machine Regressions, Artificial Neural Network) to evaluate their precision of estimation of cars' speeds in real time. * The Linear Regression Model (LRM) yielded the best results obtaining a Mean Absolute Error (MAE) of 1.694 Km/h for the center lane and 0.956 Km/h for the last lane. The results were compared with several state-of-the-art works, having competitive performance. LRM is fast estimating speed in real time and does not require high computational resources allowing a future hardware implementation. Transport engineers could obtain traffic studies in road zones with limited urban infrastructure by using the LMR model inside cellphones. The rest of the paper is organized as follows: Section 2 State of the Art describes some of the works previously developed. Section 3 Premilinars describes the statistical and machine learning methods. Section 4 Materials and Methods describe the equipment used and the process developed in this work. Section 5 Results mention the results obtained. Finally, Conclusion Section 6 enumerates the conclusions obtained from this work.
###### Abstract  
An anomalous increase of the specific heat was experimentally observed in molten salt nanofluids using a differential scanning calorimeter. Binary carbonate molten salt mixtures were used as a base fluid, and the base salts were doped with graphite nanoparticles. Specific heat measurements of the nanofluids were performed to examine the effects of the composition of two salts consisting of the base fluid. In addition, the effect of the nanoparticle concentration was investigated as the concentration of the graphite nanoparticles was varied from 0.025 to 1.0 wt %. Moreover, the dispersion homogeneity of the nanoparticles was explored by increasing amount of surfactant in the synthesis process of the molten salt nanofluids. The results showed that the specific heat of the nanofluid was enhanced by more than 30% in the liquid phase and by more than 36% in the solid phase at a nanoparticle concentration of 1 wt %. It was also observed that the concentration and the dispersion homogeneity of nanoparticles favorably affected the specific heat enhancement of the molten salt nanofluids. The dispersion status of graphite nanoparticles into the salt mixtures was visualized via scanning electron microscopy. The experimental results were explained according to the nanoparticle-induced compressed liquid layer structure of the molten salts.  
Keywords:nanofluids; molten salt; specific heat; compressed liquid layer; nanoparticle dispersion; thermal energy storage +
Footnote †: journal: Applied Sciences
# Check-QZP: A Lightweight Checkpoint Mechanism for Deep Learning Frameworks
## 4 Evaluation
### Analysis of Compression Rate  
Now, we analyze how much storage space is saved when applying compression methods (e.g., compression and quantization) to checkpoint operations. As shown in the previous section, the compression rate is one of the crucial metrics because it determines how much CPU and I/O resources can be saved during checkpoint operations. Figure 4 shows the compression rates of Check-Q, Check-Z, and Check-QZ. As shown in Figure 4, Check-QZ achieved the best compression rate compared to the baseline, Check-Q, and Check-Z; it can reduce the amount of data by up to 99% on average. This outcome can be attributed to the combination of quantization, which represents parameter values with lower bit widths, and gzip, which exhibits the property that the higher the number of repetitive patterns, the greater the compression achieved. Meanwhile, Check-Z, which  
Figure 3: Total checkpoint times for checkpoint methods. (**a**) Total checkpoint time for MOD model; (**b**) Total checkpoint time for MOT model.  
uses only gzip, achieved a low compression rate because it is difficult to find repetitive patterns in FP32. It may incur high overhead due to requiring high CPU utilization to identify and compress the patterns. In contrast, Check-Q and Check-QZ, which utilize quantization technology, demonstrated significant impacts on the compression rate. As shown in Figure 4, Check-Q achieved a compression rate close to 75% by transforming the bit width from 32 bits to 8 bits. It also provides another opportunity to make compression with gzip more effective by reducing the bit range for parameters.  
To clearly understand the checkpoint delay, we also measured the composition of the checkpoint delay. Figure 5 shows the composition of the checkpoint delay by snapshot, quantization, and gzip compression. The gzip component includes the compression performed by the gzip algorithm and the storage of compressed data on the underlying storage device. As shown in Figure 5, gzip compression accounted for the majority of Check-QZ delays. This increase would not compete for bandwidth, since it has no additional write operations beyond the checkpoint operation. The gzip compression delay varied depending on the model structure. As the model became more complex, the checkpoint operation involved more character arrays for mapping parameters. Therefore, the gzip algorithm resulted in an increase in the table size and search time. In contrast, quantization was proportional to the model size since it required only conver  
Figure 4: Compression rates for quantization and gzip. (**a**) Compression rate for MOD model; (**b**) Compression rate for MOT model.  
Additionally, as shown in Figure 5a,b, the snapshot component remained at a low rate. As mentioned in Section 3.2, the latency incurred for maintaining consistency in snapshots was expected to have a minimal impact.
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 8 Future Research Directions
### Interference Mitigation  
For FMCW radar, mutual interference is a challenging task to solve. It occurs when multiple radars operate simultaneously in a direct line of sight [236]. Depending on if the chirp configuration, i.e., slope and chirp duration, is the same between interferer and victim radar, interference can be classified as coherent and incoherent [237]. Coherent interference occurs when the same chirp configuration is used and leads to ghost detections. Incoherent interference is caused by different types of chirps, resulting in a significantly increased noise floor, masked weak target, and thus, reduced probability of detection. In reality, partially coherent interference is more widely seen where the interferer has a slightly different chirp configuration. [PERSON] et al. [238] summarised deep learning methods for interference mitigation. Although these methods achieve better performance than classical zeroing methods, they are generally designed for specific types of disturbances and require significant computational costs. Future research should consider interference mitigation and downstream tasks (e.g., detection) as a whole and build an end-to-end learning framework to optimise them together.
## 3 Results and Discussion
### DFRC Values of ODA and DA Lignin Samples  
To investigate further the lignin structure in terms of their typical qualitative units of both the lignin samples ODA and DA and to subsequently compare it with milled wood lignin a DFRC method was also used to degrade and finally isolate the \(\beta\)-aryl ether structures with the help of the reagents mentioned in the materials section. Finally, these fractions were studied by GC-MS and the results are shown in Table 5. As it is obvious in the table, both the lignin fractions show only G types of units due to a typical softwood biomass nature. The DFRC values for the milled wood lignin is around 450 \(\upmu\)mol/gm and for the case of ODA lignin this value is somehow comparable with a value of 305 slightly lower than the DA lignin value of 332. It can be anticipated here that the ODA Lignin was somewhat in lower value of DFRC than the DA Lignin due to increased amount of HCl addition as it has also been previously reported [24]. Our values stand prominent with the previously investigated articles. One such study revealed that a lignin sample isolated from giant reed biomass had a G units' value of 285.7 \(\upmu\)mol/gm. This lignin was also isolated at rather mild conditions of 49 \({}^{\circ}\)C for 24 h of time using H\({}_{2}\)O\({}_{2}\) and NaOH as reagents [45]. Similarly, maize lignin also showed a G units' value of 342 \(\upmu\)mol/gm while this lignin was isolated via the classical milled wood lignin method. In the similar study, the value was fairly increased to 504 \(\upmu\)mol/gm when the bamboo lignin was isolated and characterized in the same way [52]. In one another study a value of these G units of 310 was obtained when the Eucalyptus woody biomass was subjected to lignin isolation. In this study lignin was extracted via a classical pulping method with Na\({}_{2}\)O while keeping 30% sulfidity at a temperature of 80 \({}^{\circ}\)C for about 120 min of time interval [53]. As it can be anticipated, the harsh lignin isolation conditions may lead to the cleaving of the aryl-ether linkages which are responsible for the higher DFRC values in the lignin samples [54].  
Nonetheless, it can be suggested that our adapted process was quite meaningful in isolating the lignin with reasonable DFCR values proving thus that this modified process without thermal conditions has led to the less degradation of the lignin.  
Figure 2: GPC chromatogram of ODA, DA, and MWL samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **G Units** & **S Units** & **Total** \\ \hline MWL & 450 \(\pm\) 34 & – & 450 \(\pm\) 34 \\ ODA lignin & 305 \(\pm\) 25 & – & 305 \(\pm\) 25 \\ DA lignin & 332 \(\pm\) 30 & – & 332 \(\pm\) 30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: DFRC values of the ODA, DA, and MWL samples.