esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Low Cost Communication Technologies for V2I Applications
## 1 Introduction
Furthermore, it has better performance than 802.11p. However, 5G could have some operation failure, which would be a safety problem. That is the reason why it is possible to add reliability to the system, if both technologies are built and made to work in parallel. In addition to this, 5 GAA (5G Automotive Association) examined the coexistence of both technologies for adding more safety and efficiency. Nevertheless, they highlighted that the coexistence will be expensive and really complex, because this means having both system architectures working in parallel simultaneously. They also pointed out that there will be some interference as a result of the coexistence in the same frequency band. The synergy is conceived as a difficult task, which will produce safety problems. To solve it, different technologies with the capability for vehicular communications have been selected, specifically V2I applications. V2I is a priority over V2V because it favors the informative cooperative services. These services will update the road infrastructure and put together roads for new tendencies in connected mobility. After the selection, the technologies were validated by means of a test bed which allowed comparing them. Finally, it took the place a proof of concept with the best technology. We mainly discuss the application of different low-cost technologies for V2I communications, making a final demonstrator of how they could be applied to V2I. The rest of this paper is organized as follows. Section 2 provides a summary of the relevant work which was developed by universities or other centers, which are investigating V2I. Section 3 describes the proposed solution, the selected technologies, and the generated algorithms. Section 4 shows the test bed and its results. Finally, in Section 5 the results highlighting the main conclusions and the future work are discussed.
## 2 Optimal Reactive Power Dispatch Problem
### Objective Functions  
Minimization of total active power losses (TPL) in all branches: Total energy losses in transmission power network are significant because current magnitude flowing through conductors are high and the length of transmission lines can be up to hundreds of kilome  
Figure 1: The IEEE 30-bus power system.  
ters. In order to reduce the cost of energy losses, the minimization of total power losses is a unique solution so far. The total power losses can be found by the following formula:  
\[TPL=\sum\limits_{j=1}^{NB}\sum\limits_{\begin{array}{c}i=1\\ i\
eq j\end{array}}^{NB}G_{branch,ij}\Big{[}V_{j}^{2}-2V_{j}V_{i}\cos\left( \alpha_{i}-\alpha_{j}\right)+V_{i}^{2}\Big{]} \tag{1}\]  
Minimization of total voltage deviation (TVD): In transmission power network, voltage magnitude of load buses and generator buses are the primary concerns for stable operation of the network. The voltage of generators can be controlled by the adjusting exciter, while voltage of loads is dependent on parameters of other components in transmission power network. The good voltage profile is a target of operation of power systems, and it can be achieved by minimizing total voltage deviation. The total voltage deviation is calculated by the model below:  
\[TVD=\sum\limits_{i=1}^{NL}\left|V_{load,i}-V_{expected}\right| \tag{2}\]  
Minimization of voltage stability-L index: In transmission power networks, voltage of load is not stable and fixed at a value due to the change of consumed reactive power and active power. This can lead to different cases of power system status, high voltage, and voltage collapse. Basically, \(L\) index is in a range from 0 to 1 in which 0 is the best case and 1 is the worst status of power system. Therefore, the voltage stability enhancement can be achieved if \(L\) index is very close to 0 [11]. In order to get the target, \(L\) index at each load bus is determined first, and then the highest \(L\) index of a bus is selected for minimization. \(L_{i}\) index of the \(ith\) bus is first calculated by [11]:  
\[L_{i}=\left|\frac{V_{i}-\sum\limits_{j=1}^{NG}V_{j}Y_{ij}}{V_{i}}\right|;i=1, \ 2,\ \ldots,NB \tag{3}\]  
Then, \(L\) index is determined by choosing \(L_{i}\) with the highest value (i.e., \(L\) index = max(\(L_{i}\))).
## 3 Results and Discussion
### Effect of Epoxy Content and Dilution Solution on SO\({}_{x}\) Removal Rate  
As summarized in Table 5, the SO\({}_{x}\) removal rate decreased with the increase in the epoxy content, indicating that the decrease was affected by the solution type. The SO\({}_{x}\) removal rate of the coated textile fabrics using deionized water as the dilution solution decreased by approximately 7% when the epoxy contents increased from 0 to 2%. The SO\({}_{x}\) removal rate of the coated textile fibers using isopropyl alcohol as the dilution solution decreased by approximately 20%, which was approximately 2.8 times the decrease when deionized water was used as the dilution solution. In addition, the SO\({}_{x}\) removal rate of the coated textile fibers using the combination of 90% deionized water and 10% isopropyl alcohol as the dilution solution decreased by approximately 8.9%, when the epoxy contents increased from 1 to 2%. This implies that the use of deionized water for the dilution solution was more effective for alleviating the decrease in the SO\({}_{x}\) removal rate with the increase in epoxy contents than the use of isopropyl alcohol.  
Figure 8: SEM images and EDS spectra of the uncoated textile fibers. (**a**) Before adsorption of the fine dust particles. (**b**) After adsorption of the fine dust particles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & **Porous Material Type** & **Dilution Solution Type** & **Epoxy Contents (\%)** & **Number of** & **Number of** & **Removal Rate (\%)** \\ \cline{2-6}  & & & & **Washings** & **Freezing-Thaw Cycles** & **SO\({}_{x}\)** & **NO\({}_{x}\)** \\ \hline Z-D-C & & & & 0 & 0 & 83.4 & - \\ (Control) & & & & 1 & 0 & 34.9 & - \\ Z-D-0-1 & & & & 1 & 0 & 74.7 & - \\ Z-D-1-1 & & & 1 & 0 & 66.2 & - \\ Z-D-1-5-0 & Zeolite & Deionized & & 0 & 0 & 68.3 & - \\ Z-D-1-5-1 & & water & 1.5 & 1 & 0 & 60.8 & - \\ Z-D-2-0 & & & & 0 & 0 & 76.7 & - \\ Z-D-2-1 & & & 2 & 1 & 0 & 77.2 & - \\ Z-D-2-10 & & & & 10 & 0 & 77.6 & - \\ Z-D-2-10-F & & & & 10 & 1 & 75.5 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the SO\({}_{x}\) and NO\({}_{x}\) removal rates of textile fabrics coated with zeolite or coconut shell activated carbon.
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Land Cover Data  
The Wood Buffalo National Park Enhanced Wetland Classification (EWC) [68] circa 2017/2018 used in this study has a reported overall accuracy of \(\sim\)75%. Dominant landcovers exceeding 3% of total area across the four lidar surveyed polygons were examined. Given that the current study explicitly examined open water variations through time, the static open water landcover class (including mudflat/salt flats and aquatic bed) was ignored (18.1 km\({}^{2}\) in total). The dominant classes included in the analysis are: (i) marsh (26.7% of the study area), (ii) shrub swamp (36.5%), (iii) treed swamp (11.2%), and (iv) upland forest (12.3%). Here, the \"marsh\" class was an amalgamation of 'emergent' and'meadow' marsh  
Figure 1: **(a)** The Peace–Athabasca Delta (PAD) location in Alberta, Canada (inset); (b) lidar survey areas; (c) landcover classes Ducks Unlimited Canada (DUC) (Ducks Unlimited Canada, 2020) over survey areas [Survey areas A, B and C are in the Peace Delta, and survey area D is located in the Athabasca Delta], (d) area of DUC landcover classes.  
classes, as these are temporally variable and interchangeable due to subtle variations in water level and, therefore, cannot be assumed to be static throughout the period of analysis or even a single year. All upland vegetation (Figure 1) was considered one class, \"upland forest\". However, two swamp classes were maintained, \"shrub swamp\" and all other swamp classes (Figure 1) as \"treed swamp\".
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
## 3 CFP Firmware Design Scheme
### MCU Design  
For the MCU of the CFP, 8051 Silicon Labs chips were selected. These chips are widely available in the industrial sector and provide a reliable library. They incorporate serial peripheral interface (SPI), external memory interface (EMIF), universal asynchronous receiver/transmitter (UART) and system management bus (SMbus) components for communication with external chips and support up to 256 kB of flash memory. There are various models depending on the functions to be used. To design the CFP firmware, the MCU model can be determined by considering the cost and the necessary components. It is also possible to use a different type of MCU that providing the necessary components in this paper. Figure 7 shows the internal functional block diagram and external interface of the proposed MCU.
## 2 Application of BRBS in the Detection of Diabetic Nephropathy Biomarkers
### Ceruloplasmit (Cp)  
Cp is a copper-containing \(\alpha\)-2 globulin with a molecular weight of 132 kDa, mainly synthesized and secreted by the liver, and plays a crucial role in regulating oxidative stress and iron homeostasis [77]. Some researchers have proposed that the glomerular capillary wall excretion rate of Cp in patients with diabetic nephropathy is almost parallel to the urinary albumin excretion rate. Increased excretion of Cp has also been observed in patients with impaired glucose tolerance and diabetes compared to healthy controls [78]. Increased urinary Cp excretion in diabetic patients with normal urinary protein has also been confirmed [79]. Therefore, detecting urinary Cp has a high value for early diagnosis of diabetic nephropathy.  
[PERSON] et al. [80] designed a sensitive electrochemical aptasensor to quantify Cp using Cp-specific recognition APT. First, the diazonium salt of 4-Aminobenzic acid (ABA) electrochemically reduced was covalently bonded to the MWCNTs/GCE surface [81], and after the carboxyl group was activated by \(N\)-hydroxysuccinimide(NHS) and \(N\)-(3-dimethylaminopropyl)-N\({}^{\prime}\)-ethylcarbodiimide hydrochloride (EDC), the amino APT was immobilized on the electrode surface via amide bond formation. BSA solution was added to prevent non-specific adsorption on the sensor surface. Finally, Cp was detected using DPV and EIS in [Fe(CN)\({}_{6}\)]\({}^{3-/4-}\) solution. Cp-specific binding with APT induces a conformational change in the APT on the electrode surface, and the probe's electron transfer is hindered. The DPV current of the aptasensor decreases with increasing Cp concentration, and the Rct value increases with increasing Cp concentration. Under optimal conditions, Cp concentration shows a linear relationship within the range of 0.02-3.0 ng mL\({}^{-1}\) and 3.0-80.0 ng mL\({}^{-1}\), with a detection limit of 3.7 pg mL\({}^{-1}\). This aptasensor has broad application prospects for detecting Cp in human serum.
# Design of MIMO Antenna with Double L-Shaped Structure for 5G NR
## 3 Results and Discussion
### Comparison with Existing 5G Smartphone Antennas  
To verify the superiority of the proposed antenna array, we compared the proposed design with the several previously reported designs, as shown in Table 1. It can be concluded that the eight-element antenna proposed in this paper provides a wider bandwidth compared to the existing designs. Furthermore, the design presented in this article provides better isolation, ECC, and overall efficiency values, thus overcoming the bandwidth limitations at a lower cost. That is to say, the proposed design can not only be used for multi-band applications of 5G smartphones, but also adapt to the development of future 6G mobile communications.  
Figure 11: Simulated and measured radiation characteristics at 4.4 GHz. (**a**) Ant1. (**b**) Ant2.  
Figure 12: The calculated Envelope Correlation Coefficient of the proposed MIMO antenna system.
## 2 Related Work
### VQ Image Compression  
Vector quantization (VQ) is a simple and efficient image compression technique. It has a high compression ratio with an adjustable bit rate and fidelity. Therefore, it is very commonly applied in real applications.  
Before compression, a codebook should be established. Firstly, three to five typical images are selected. Ideally, both smooth and complex images are included. Then, each image is divided into mutually exclusive blocks of size \(w\times h\). Each of the blocks is treated as a one-dimensional vector with \(K=w\times h\) tuples. According to the desired codebook size \(n\), 256 for example, the overall blocks are clustered into n groups. Finally, the mean vector of each vector group is recorded as a codeword. The collection of \(n\) codewords forms a codebook.  
The compression process is illustrated in Figure 1. The image to be compressed is divided into blocks of size \(w\times h\) in the same way as the codebook training process. Then, each block is rearranged into a vector and compared with the codewords in the codebook. The serial number of the closest codeword is recorded into the index table. As a result, the given image is compressed into an index table.  
A typical setting of parameters is to compress image blocks sized \(4\times 4\) with a codebook of length 256. Under such circumstance, the compression ratio is 16:1. To get a better fidelity of approximation, codebooks with length 512 or 1024 are also applicable. Of course, the compression ratio is reduced at the same time.
## 2 Materials and Methods
### Measurements  
X-ray diffraction (XRD) was characterized using a D/MAX 2200 diffractometer (Rigaku, Tokyo, Japan) with \(\lambda\) = 1.5406 A. Data were collected at the rate of 4\({}^{\circ}\)/min and step 0.02\({}^{\circ}\) with Cu K\(\alpha\) irradiation operated at 40 kV and 45 mA. Scanning electron microscope (SEM) (FEI, Eindhoven, The Netherlands) observations were carried out on a FEI-Sirion with a field emission of 20 kV. The X-ray energy dispersive spectroscopy (EDS) of LDH was obtained by SEM equipped with the energy-dispersive X-ray spectroscopy (EDAX) Genesis (EDAX Inc., Mahwah, NJ, USA) through a spectrum imaging technique. The specimens were examined in a HITACHI 1H-7650 transmission electron microscope (TEM) (Hitachi, Tokyo, Japan) operated at the accelerating voltage of 100 kV. Thermal analysis was carried out with a thermogravimetric (TG) analyzer (TGA pyris 1 from Perkin Elmer, Perkin Elmer Co., Walther M, Boston, MA, USA) using a constant heating rate of 10 \({}^{\circ}\)C/min under the pure nitrogen atmosphere from 50 to 800 \({}^{\circ}\)C. The weight of the samples was kept within 3-4 mg. All cone calorimeter tests were carried out in a cone calorimeter (FTT0007, Hongkong Universal Technology Co., Hong Kong, China) at an incident heat flux of 50 kW/m\({}^{2}\) according to the ISO 5660-1 standard. The polymer sample (100 \(\times\) 100 \(\times\) 5 mm\({}^{3}\)) is placed horizontally on a balance holder. The elongation at break and tensile strength of all samples was performed by a RGD-20A material test machine (produced by Shenzhen Regear Instrument Cooperation, Shenzhen, China), according to the national standard GB/T 16421-1996.
## 4 Experiments and Results
### Effects of LCCDNN and FCCDNN Parameters on Neuron Classification
#### 4.3.2 Accuracy Analysis of Different Numbers of Intermediate Layers  
Figure 13 shows the classification results with different numbers of intermediate layers by using the LCCDNN learning model. The number of intermediate layers increased gradually, while the other parameter settings remained the same. Figure 13a shows the neuron classification accuracy after 100 learning epochs in the C. elegans dataset. When the number of intermediate layers was 5, 10, and 15, the accuracy of the training set was 98.24%, 98.04%, and 96.27%, and the accuracy of the test set was 85.13%, 87.44%, and 74.10%, respectively. Therefore, when the intermediate layer is 10, the LCCDNN model can learn with higher accuracy in the C. elegans dataset. Figure 13b shows the learning results in the zebrafish dataset with the intermediate layers of 1, 5, and 10, where the accuracy of the training set was 88.34%, 98.17%, and 91.72% the accuracy of the test set was 72.09%, 81.16%, and 64.65%, respectively. From Figure 13b, we can see that the classification accuracy increased at first, then decreased when the intermediate layer number of LCCDNNs increased, so the suitable intermediate layer number for the zebrafish dataset was five.  
Furthermore, we investigated the effect of the number of intermediate layers on the neuron classification for the FCCDNN learning model. Figure 14 shows the change in accuracy with the different numbers of intermediate layers, while the number of building blocks and the mini-batch size remained the same. As shown in Figure 14a, the classification accuracy of the C. elegans dataset decreased when the number of intermediate layers increased gradually. When the number of intermediate layers was 1, 5, and 10, the accuracy of the training set was 97.45%, 96.34% and 95.16% and the accuracy of the test set was 83.33%, 81.03%, and 77.95%, respectively. From Figure 14b, we can see that the zebrafish dataset had the highest classification accuracy when the intermediate layer number was 10, the accuracy was 94.02% in the training set, and the accuracy was 72.33% in the test set. It can be seen that LCCDNNs have a higher classification accuracy of neurons than FCCDNNs.  
Figure 12: Comparison of accuracy with different mini-batch sizes by FCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.
## 3 Results and Discussion
### Structural Comparison of P3 HBHrk with Different Nucleating Agents
#### 3.2.2 Degradation of Nucleated Samples During Processing  
The potential degradation of P3 HBHrk during the extrusion process in the presence of nucleating agents was evaluated by analyzing the molecular weights of the samples using gel permeation chromatography (GPC). As shown in Figure 7 and summarized in Table S2, both the weight-average (\(M_{w}\)) and number-average (\(M_{n}\)) molecular weights of most nucleated samples remained consistent with those of the neat P3 HBHrk copolymer (\(M_{w}\) = 245,280 g/mol; \(M_{n}\) = 122,440 g/mol). This consistency indicates that the incorporation of nucleating agents did not lead to polymer degradation during extrusion.  
An exception was observed in the sample containing ultrafine cellulose (PHH_05 UFC), which exhibited a significant increase in \(M_{w}\) to 300,520 g/mol, approximately 50,000 g/mol higher than that of the neat polymer. A slight increase in \(M_{n}\) was also observed (\(M_{n}\) = 124,650 g/mol). This increase was not attributed to chemical modification but was likely due to physical interactions between the hydroxyl groups on the cellulose surface and the polymer matrix, which may have promoted chain entanglement during processing [39]. This phenomenon appears to be specific to PHH_05 UFC, as it was not observed in samplescontaining other nucleating agents that lack surface functional groups capable of inducing such interactions.  
Overall, these results demonstrate that most nucleating agents, when used at low concentrations, do not cause degradation of P3 HBHHx during extrusion. This thermal and molecular stability is critical for industrial applications, as it ensures that the crystallization performance can be enhanced without compromising the polymer's structural integrity. As a result, the mechanical properties of the material are likely to be preserved, rendering these formulations suitable for a wide range of practical applications.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 2 Materials and Methods
### Optical Propagation for Photoacoustic Computed Tomography (PACT)  
A full description of optical propagation would use the radiative transport equation in its full generality. This equation can be modeled by the use of Monte Carlo methods, with significant computational cost [30; 31]. Since the the photoacoustic effect depends on the optical fluence rate, and the wavelengths used correspond to the near infrared (NIR) regime, a diffusion approximation to the radiative transport equation is often used [32]. The diffusion equation for the fluence rate is:  
\[-\
abla\cdot\kappa(x)\
abla\Phi(x,\lambda)+\mu_{a}(x)\Phi(x)=q(x)\ \ x\in\Omega \tag{1}\]  
\[\Phi(x)=0\ \ x\in\partial\Omega. \tag{2}\]  
In this equation, \(\Phi\) is the optical fluence rate, or intensity of the light. \(\mu_{a}\) is the absorption coefficient at each point in space, while \(\kappa=\frac{1}{3(\mu_{a}+\mu_{s}^{\prime})}\) is the diffusion coefficient, calculated using the absorption coefficient and the reduced scattering coefficient \(\mu_{s}^{\prime}\). \(q\) is the source function, the initial laser pulse that irradiates the tissue medium. \(\Omega\) is the tissue domain of interest, usually a subset of \(\mathbb{R}^{n}\), with \(n\) corresponding to the dimension of the problem (2 or 3). Equation (2) corresponds to the Dirichlet boundary condition used in this study. The amount of pressure generated by the optical energy is given by a constant of proportionality, the Gruneisen parameter \(\Gamma=\frac{\alpha v_{s}^{2}}{C_{p}}\), where \(\alpha\) is the volume thermal expansion coefficient, \(v_{s}\) is the speed of sound, and \(C_{p}\) is the heat capacity at constant pressure [33]. Therefore, for a given fluence rate \(\Phi\), the initial pressure is given by:  
\[p_{0}(x)=\Gamma(x)\Phi(x)\mu_{a}(x). \tag{3}\]  
While \(\Gamma\) will physically vary over space, this small deviation is ignored in this study, and the parameter is assumed constant, with a value of 0.1, which approximates standard tissue.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 2 Previous Work
The unit normal vector derived from the first fundamental form of the color image is introduced in [28], thus preserving local and global features simultaneously. Deep learning facilitates the creation of local-global decolorization method hybrids [29,30], but also combines local, global and exposure features [7], and produces reversible conversion [2]. Deep learning brings the advantage of automatic feature learning, handling complex patterns, and the possibility of dealing with diverse data. Nevertheless, deep learning methods are prone to overfitting, require substantial computational resources, and are extremely dependable on the training data quality.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.12 Multimetric Next-Hop Vehicle Selection for Geocasting in Vehicular Ad-Hoc Networks  
Multimetric next hop vehicle selection for geocasting in VANETs was presented in [41]. The authors proposed to choose an optimal next-hop vehicle (ONHV) based on various metrics divided into link-based and node-based metrics. The link-based metrics were link delay, link jitter, and link lifetime; the node-based metrics were velocity and degree. The metrics were individually evaluated, and objective function \(OF_{i}\) was then calculated for every \(i^{th}\) (node) in the routing table. The \(OF\) was calculated as shown in Equation (13).  
\[OF_{i}=Max(w_{1}*link_{lifetime}+w_{2}*link_{delay}+w_{3}*link_{ jitter}) \tag{13}\]  
where \(w_{1}\), \(w_{2}\) and \(w_{3}\) are weighing factors for corresponding link metrics that act as tuneable parameters for application and \(\sum_{i=1}^{3}w_{i}\)=1, the node with highest \(OF\) as ONHV.