esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.3 Configuration of Long Term Evolution (LTE) Standard  
We use Cost-Hata (Cost 231) as the path loss model of LTE for all urban, non-urban, and highway scenarios, as it has been extensively used in Europe and North America to model transmission loss in cellular communication [52]. We enable the uplink power control in LTE, which is a technique to mitigate interference at the same time while maximizing the power of the desired received signal [53]. The maximum transmit power is set at 23 dBm following the 3 GPP standard [54]. The Radio Resource Control (RRC) which is the signaling exchanged between the user equipment and the Evolved Node Base station (eNodeB), is enabled, and error models for control and data channels are also enabled. The Bit Error Rate (BER) for \"PiroEW2010\" Adaptive Modulation and Coding (AMC) scheme is set as 0.00005 [55]. Furthermore, we dynamically set the Sounding Reference Signal (\(SRS\)) periodicity based on the number of user equipment (\(N\)) as given by Equation (24).  
\[SRS\ periodicity=\left\{\begin{array}{l}2\ \ \ \ \ \ ;\ \ \ \ \ ;\ \ \ \ ;\ \ \ \ ;\ \ \ ;\ \ \ \ ;\ \ \ \  
In Equation (25), W is the width of the map and \(N_{RSU}\) is the number of RSU nodes, \(N_{row}\) is the number of RSU nodes per row. Figure 6 shows the placement of RSUs in a given map with interconnections between the RSUs.  
Note that as evident from Figure 6, the RSU network is a mesh network having point-to-point links connecting with neighbor RSUs. Furthermore, there is a common bus for communication between a given RSU and SDN controller server, so that it has to use Carrier Sense Multiple Access-Collision Detection (CSMA-CD) for such communication. The data rate of the wired backbone (point-to-point links and CSMA-CD bus) is set to match Gigabit Ethernet parameters. Accordingly, the data rate is set as 1000 Mbps and the propagation delay is set as 10 \(\upmu\)s. Furthermore, all RSUs are equipped with a DSRC interface for I2V and V2I communication. The CSMA-CD channel is connected to the controller in the centralized and hybrid SDVN architectures.
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
## 2 Materials and Methods
### Uv-Vis-NIR Spectroscopy, Stability Studies, and Reconstitution Tests  
AuNRs/LA, AuNRsLA,GG, and AuNRs/GG-LA were analyzed using Shimadzu UV-VIS-NIR 2400 spectrometer. The sample spectra were acquired in water dispersion (0.5 mg\(\cdot\)mL\({}^{-1}\)) at room temperature investigating the wavelength range between 200 and 900 nm. Employing the same conditions, the absorbance spectra of the samples were recorded for 30 days at 1-week intervals, in order to evaluate their stability in aqueous dispersion as a function of time.  
To perform reconstitution tests, 7 mL of sample (AuNRs/LA, AuNRsLA,GG, or AuNRs/GG-LA) was freeze-dried and dispersed again in an equal volume of Milli-Q water, then characterized through UV-VIS-NIR spectroscopy setting the same parameters described for stability tests.
## 2 Fundamental Review on Vibration Isolation Systems
### Base Excitation  
When the force exerts on the base of the system, it will lead to a deformation in the restoring element. This is different from the deformation when the force exerts on the inertial element itself because the deflection of the spring under base excitation will happen at its both ends, i.e., \(F_{spring}=k(x-y)\). Here, \(x\) is the position of the inertial element, and \(y\) is the position of the base, which can be expressed by the following equation of motion:  
\[m\ddot{x}+kx=ky \tag{3}\]  
However, if the base moves in harmonic shape, the term \(y\) can be described as  
\[y(t)=\gamma\sin\omega t \tag{4}\]  
This leads to a similar equation to that of the forced system,  
\[m\ddot{x}+kx=kY\sin\omega t \tag{5}\]  
Therefore, the equation that describes the position of the inertial element will be  
\[x(t)=A\sin\omega_{n}t+B\cos\omega_{n}t+X\sin\omega t \tag{6}\]  
This is the same as Equation (2) except the term \(X\), which now depends on the stiffness of the spring, the amplitude of the base motion, the natural frequency of the system, and the excitation frequency.  
A useful study of vibration isolation is to determine the properties of the system that affect the transmission of vibration the most, which in this case happens to be the ratio between the natural frequency and the excitation frequency. It was observed that the maximum transmission of vibration happens when the ratio \(r=\frac{\omega}{\omega_{n}}=1\), and then the effect of vibration on the inertial element decays with the increment of \(r\).  
As a result, to isolate vibration, it is advised to reduce the natural frequency of the system, which could be done by increasing the mass of the system or decreasing the stiffness of the system. The first solution is not preferred for vehicles' manufacturers, because they tend to produce lighter vehicles than in the past. Hence, the solution that sounds more feasible is to reduce the stiffness of the system, which could be done in different ways. Oneway to lower the stiffness of the system in order to isolate vibrations is to use the so-called Quasi-Zero- Stiffness system.
# FY3E GNOS II GNSS Reflectometry: Mission Review and First Results
## 2 Instrument Overview
### Non-Uniform Delay-Doppler Mapping  
The fundamental GNSS-R measurement of GNOS II is the delay-Doppler map (DDM) in raw counts. It is computed by a convolution between the received signal and a GNSS signal replica generated by the receiver coherently for 1 ms and an incoherent average for 1 s, giving the sampling frequency of DDM of 1 Hz [19]. The DDM generated by the GNOS II is non-uniform with the dimension of 122 time delays \(\times\) 20 Doppler frequencies. The resolution of Doppler is 500 Hz from \(-\)5000 to 4500 Hz with respect to the specular point. The resolution of delay is 1/8 chip from \(-\)2.875 to 2.875 chips with respect to the specular point and 1/4 chip elsewhere in the range of \(-\)12.25 to 12.125 chips. This non-uniformity characteristic of the DDM is implemented by choosing non-uniform delay intervals (1/4 chip and 1/8 chip) in the 1 ms coherent convolution. This high-resolution DDM can provide more sampling near the specular point and increase the accuracy of computed observables. Figure 4 shows an example of the GNOS II DDM.  
With the eight reflection channels and 1 Hz DDM sampling frequency, the receiver can collect \(\sim\)150,000 DDMs over the ocean, \(\sim\)36,000 DDMs over land and \(\sim\)30,000 DDMs over ice each day. Figure 5 shows the number of tracks for each GNSS satellite from GPS and BDS in one day (25 July 2021), where a track is defined as a sequence of consecutive specular points from a same GNSS satellite. Figure 6 shows the number of tracks for each day in August 2021. Note that one track can contain \(\sim\)520 DDMs on average and \(\sim\)1100 DDMs at most.  
Figure 4: An example of a GNOS II DDM in raw counts.  
Figure 3: Thereflection antenna footprint on the Earth’s surface. The x direction is along-track and the y direction is cross-track. The location with coordinate (0,0) corresponds to the sub-satellite point. The color contour lines stand for the corresponding antenna gain on the Earth’s surface in units of dB.
# Fuzzy Logic-Based Performance Enhancement of FSO Systems Under Adverse Weather Conditions
## 2 Modeling of Rain and Fog Impairments in FSO System
### Rain  
The attenuation caused by rain significantly impacts the transmitted optical power in FSO links. This attenuation is primarily a function of the rainfall rate (R), which is defined as the amount of rain falling per hour, measured in millimeters per hour (mm/h). The specific attenuation caused by rain can be expressed using an empirical relation [4].  
\[\gamma_{rain}=1.076R^{0.67} \tag{1}\]  
In Equation (1), \(\gamma_{rain}\) indicates the attenuation of rain in (dB/km) and R refers to the rain rate in (mm/hr). The value of R differs from one region to another. While the attenuation model is driven by rainfall rate (R, mm/h), it is helpful to relate common rain conditions to their expected impact on FSO performance. Table 1 presents standard qualitative rain intensity levels along with typical attenuation effects for each category [5]. The intensity of rain that affects the FSO link is displayed in Figure 1. Moreover, recent work [21] has explored cloud attenuation modeling that spans from Ka-band to optical frequencies, highlighting the importance of integrated liquid water content in predicting signal loss, a concept relevant when analyzing dense rain or overlapping cloud cover in FSO links.
# Unstructured Data Analysis for Risk Management of Electric Power Transmission Lines +
## 3 Background
### Phases of the Energy Transmission Segment  
The life cycle of transmission projects goes through concession auctions carried out by ANEEL and later through the stages of the environmental licensing process, which will aim to ensure environmental feasibility and mitigation of impacts arising from the installation of these projects. Once these steps have been addressed, the projects can be implemented and executed.  
The stages and requirements of environmental licensing depend on several factors, such as size, location, impacts, and environmental agencies' authorization. The following environmental licenses are required:  
Prior License (PL): It is granted in the preliminary planning phase of the project. It assesses the location and environmental feasibility and establishes the essential requirements and conditions to be met in the subsequent phases of the project's implementation.  
Installation License (IL): This authorizes the installation of the project following the specifications contained in the approved plans, programs, and projects, including environmental control measures and other conditions.  
Operating License (OL): This authorizes the operation of a project after verifying the effective fulfillment of what is stated in the previous licenses, with the environmental control measures and conditions determined for operation.  
A project to implement transmission lines follows four distinct phases according to the environmental phases: pre-auction (before PL), viability study (after PL), implementation (after IL), and operation (after OL).
# A Novel Dual-Permanent-Magnet Mechanical Antenna for Pipeline Robot Localization and Communication
## 3 Development of the Novel Mechanical Antenna  
### Dual-Permanent-Magnet Mechanical Antenna  
The structure of the dual-permanent-magnet mechanical antenna is shown in Figure 5a and the physical prototype of the antenna is shown in Figure 5b. It consists of a carrier module, a modulation module, and a control and monitoring module. The carrier module comprises a carrier motor, a rotating shaft, and permanent magnets A and B, in which the carrier motor drives A and B through the rotating shaft to rotate at a constant speed so that the magnetic field is radiated outward steadily. In Figure 5b, the carrier motor is a 12 V RK-370 CA permanent magnet DC motor with a maximum speed of 5400 rpm, and both A and B are N38M-type radially magnetized permanent magnets with a remanence of 1.25 T, \(D\) = 25 mm, \(h\) = 8 mm, and \(d\) = 32.5 mm. The modulation module comprises a modulation motor, limiters, and brushes. The modulation motor is a 6 V geared motor rated at 300 rpm which is used to adjust the angle \(\alpha\) of magnetic moments between A and B, and the limiters can limit the value of \(\alpha\). The modulation motor rotates along with the rotating shaft of the carrier motor, and the brushes are installed to prevent the wires at the outlet of the modulation motor from becoming entangled during the rotation process. The control and monitoring module comprises an Arduino UNO microcontroller, an AQMH2407 ND DC motor driver, and a photoelectric sensor, which are used to control the rotation of the carrier and modulation motors and collect their status information through the photoelectric sensor. The power grid working frequency of 50 Hz should be avoided as the operating frequency of the antenna. The smaller the operating frequency, the deeper the wave will penetrate underground and underwater.  
Figure 4: Comparison of the results of the analytical solution in Equation (4) with those obtained via the finite-element calculation for different ratios of cross-sectional diameter of the permanent magnet to its height (\(D/h\)). Asterisks indicate simulation results and lines indicate analytical results.
## 6 Simulation Result
### Evaluation Results of Modifying C\({}_{th}\)  
In this subsection, we also test the task successful rate and task execution cost of our scheme with diverse C\({}_{th}\) value, which is discussed in Section 3.3. In Figure 10, we see that, when C\({}_{th}\) increases from 80 to 200 parked vehicles, the successful rate increases while the task execution cost decreases. The main reason is that an increase in the number of cluster members results in an increase in the amount of available computational resources in the cluster, which shortens the time consumption for executing the tasks.
## 3 Research Methodology
### Aggregation of Expert Opinions Using the Aczel-Alsina Function  
The criteria for expert selection included the following: (1) participation in aerospace certification programs (AS9100/NADCAP) [53; 54], (2) familiarity with both local and international aerospace supply chains, and (3) direct involvement in SME upgrading projects. This study enhanced the original Aczel-Alsina method [55] to create a grey multi-criteria decision-making matrix. By using interval numbers rather than simple averages, the combined grey number and Aczel-Alsina approach effectively integrates expert opinions while managing the uncertainty and inconsistency inherent in group decision-making processes.  
For example, when expert \(k\) provides an assessment of how factor \(i\) influences factor \(j\), their evaluation is represented by an interval value, \(d^{k}_{ij^{\prime}}\), consisting of a lower and upper bound, i.e., \(d^{k}_{ij}=[\underline{d}^{k}_{ij},\overline{d}^{k}_{ij}]\), where \(\underline{d}^{k}_{ij}\) is the lower bound and \(\overline{d}^{k}_{ij}\) is the upper bound. These bounds are processed separately using the Aczel-Alsina nonlinear function to aggregate the expert opinions. The processing of these lower and upper bounds is detailed in the following section.  
The aggregated values are determined by calculating the lower and upper bounds using Equations (1) and (2), respectively [56]:  
\[\underline{a}_{ij}=\sum\limits_{k=1}^{K}\underline{d}^{k}_{ij}\left(1-e^{( \sum\limits_{k=1}^{K}w_{k}\cdot\ln{(1-f(d^{k}_{ij}))})^{1/\xi}}\right) \tag{1}\]  
\[\overline{a}_{ij}=\sum\limits_{k=1}^{K}\overline{d}^{k}_{ij}\left(1-e^{(\sum \limits_{k=1}^{K}w_{k}\cdot\ln{(1-f(\overline{d}^{k}_{ij}))})^{1/\xi}}\right) \tag{2}\]  
where \(f\left(d^{k}_{ij}\right)\) is defined as follows:  
\[f\left(\underline{d}^{k}_{ij}\right)=\underline{d}^{k}_{ij}/\sum\limits_{k=1} ^{K}\underline{d}^{k}_{ij} \tag{3}\]  
\[f\left(\overline{d}^{k}_{ij}\right)=\overline{d}^{k}_{ij}/\sum\limits_{k=1}^{ K}\overline{d}^{k}_{ij} \tag{4}\]  
\[w_{k}=\frac{a_{k}+p_{k}}{\sum\limits_{k=1}^{K}\left(a_{k}+p_{k}\right)} \tag{5}\]  
and \(\xi\geq 0\).  
The weight (\(w_{k}\)) in Equations (1) and (2) is determined by the expert's academic credentials (\(a_{k}\)) and years of professional experience (\(p_{k}\)).  
Finally, the aggregated impact of expert \(k\) on factor \(i\) and factor \(j\) through the Aczel-Alsina weighted averaging strategy is represented as an interval, as shown in Equation (6):  
\[\overline{A}=[\overline{a}_{ij}]=\left[\underline{a}_{ij},\overline{a}_{ij}\right] \tag{6}\]
## 4 Experimental Results  
### Dataset  
The primary product of Pleiades satellite images were used for performing experimental analysis of the proposed method. The Pleiades program, which was launched by CNES (the French space agency), is the optical Earth imaging component of French-Italian ORFEO (Optical and Radar Federated Earth Observation). The Pleiades constellation consists of two satellites with VHR optical sensors. The Pleiades 1A launched on 17.12.2011 and Pleiades 1B launched on 2.12.2012. Both of the satellites provide 0.5 m spatial resolution for the Pan sensor and 2 m for MS sensor with 20 km swath width and 12 bit radiometric resolution [44].  
The dataset used in this research consists of three Pleiades image scenes, which cover different landscape characteristics (Table 3). The locations of scenes are provided in Figure 3. Ten different sub frames were selected from these image scenes, in order to evaluate the performance of Pan sharpening methods for varying landscape characteristics and seasonal conditions (Table 4).  
Four sub frames cover rural areas that contain forests with different types of trees with varying heights and barren roads between them, and mountains and agricultural fields. Three sub frames cover urban areas that include complex buildings, roads and highways. Finally, three sub frames cover sub-urban areas that include all the complex buildings, factories, vegetation, roads, trees and bare soil parts to perform a precise survey on effects of the proposed method in Pan sharpening of vegetation, impervious and soil surfaces simultaneously.
## 5 State-of-the-Art
### Virtual Machine  
The VM is core to the Wasm environment. To investigate the potential of using VM approaches, ref. [67] used two minimal VMs, extended Berkeley Packet Filters (eBPF) and Wasm, on low-power microcontroller-based IoT devices. The authors designed rBPF, a register-based VM derived from eBPF, and compared it to a stack-based VM based on Wasm adapted for embedded systems. Each VM was implemented in the RIOT IoT operating system, with measurements performed on commercial IoT hardware. As expected, both Wasm and rBPF VMs introduced execution time and memory overhead compared to not using a VM. Interestingly, while the Wasm VM doubled the memory budget for a simple networked IoT application, the rBPF VM only added negligible memory overhead, making it a promising choice for hosting small updatable software modules on low-power networks. In [68], the authors proposed the use of Wasm, a VM standard, to simplify the programming of wearable sensor systems, which currently require different programming languages for different system components. The researchers demonstrated the possibility of implementing a Wasm interpreter for embedded systems such as the Texas Instruments CC2652R system-on-chip, enabling the same code to run across all system parts. Their proof-of-concept implementation used Bluetooth low energy for communication, meaning that smartphones can program the device without special hardware. This suggests that Wasm could potentially streamline development processes for wearable sensor systems.
## 3 Methodology
### Machine Learning Methods
#### 3.4.1 Support Vector Regression (SVR)  
Support Vector Regression (SVR) is an extension of Support Vector Machines (SVM) designed to address regression tasks. SVM is a powerful ML tool used for both classification and regression tasks [52]. It operates based on the principle of maximizing the separating hyperplane between different classes in a multidimensional space. The formulation of this hyperplane is achieved through the following process:  
\[minimize\frac{1}{2}\|w\|^{2}+C{\sum_{i=1}^{n}\xi_{i}} \tag{12}\]  
\[subject\text{ to }y_{i}(w\cdot x_{i}+b)\geq 1-\xi_{i},\ \ \forall x_{i}\ \ \xi_{i}\geq 0 \tag{13}\]  
where w is the field width vector, C is the relation between field width and misclassifications, \(\xi_{i}\) is a weak variable, a \(y_{i}\) is an equivalent label of \(x_{i}\).  
The SVR method is employed to determine the location of mobile nodes in indoor environments based on RSSI fingerprints. SVR analyzes the relationships between trained grid points and their corresponding fingerprints, modeling each point and performing regression to accurately estimate the nodes' locations. Listed below are the hyperparameters employed for Grid Search optimization:  
* Number of neighbors (n_neighbors): {1, 3, 5, 7, 9, 11};
* Weighting scheme (weights): {'uniform', distance'};
* Search algorithm (algorithm): {'auto', 'ball_tree', 'kd_tree', 'brute'};  
Figure 3: Division of the experimental area into 256 (**a**) and 1024 (**b**) sectors.  
* Distance metric (p): {1, 2} (1 for Manhattan distance, 2 for Euclidean distance).
## 3 AHB Research and Development, 2012-2017
Initially, biomass yields are projected for each region's climatic and edaphic condition using a modified 3 PG (Physiological Principles in Predicting Growth) growth model tailored to hybrid poplar coppice production [29]. Land that could produce more income from poplar than that of the current crop(s) is then identified with an improved agricultural economic model. Generally, these are marginal croplands or pastureland. Optimum biorefinery locations and capacities are next modeled using the Geospatial Bioenergy Systems Model (GBSM) [30] based on feedstock and fuel conversion costs. The IMPLAN model at that point evaluates the economic and social impact of biorefinery operation. Finally, a hybrid poplar submodule, developed in the Environmental Policy Integrated Climate (EPIC) model, simulates soil biogeochemical processes and soil and water quality impacts from poplar biomass cultivation. All modeled outputs are then combined to quantify the available land and production capacity across the four AHB regions. In aggregate, there are approximately 6.54 and 32.46 million respective hectares of croplands and rangelands in the region, of which 6.03 (92.2%) and 12.86 (39.6%) million hectares are potentially suitable for poplar cultivation under the right economic and market conditions. Thus, AHB has concluded that there is an adequate land base to support a poplar biofuels industry. For instance, marginal lands without irrigation alone could annually supply 120 tera grams of feedstock for conversion into approximately 39,730 million liters (I) of aviation fuel assuming a conversion rate of \(330\,1\,\mathrm{M}\mathrm{g}^{-1}\)[31]. The focus on marginal lands is rooted in a potentially large base assuming the breeding of adaptable crop varieties [32]. Weaknesses involve economic viability and the uncertainty of environmental impacts and benefits. The significant influence of land costs within bioenergy financial models drives the need for less expensive but lower quality land, the tradeoff between soil quality and yield notwithstanding [21].
## 4 Experimental Results
### General Performance
As the number of image frames increases, the update frequency of the vocabulary tree accelerates, and the number of potential loop detection matching images increases. Particularly in scenarios where numerous images exhibit similar environments, a larger number of images necessitates calculations and comparisons, which affects the efficiency of loop detection. Nevertheless, even in such cases, the response time per image can still be maintained within 70 ms.