esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical Speciation of Antarctic Atmospheric Depositions
## 2 Materials and Methods
### Sample Collection and Treatment  
From November 2017 to January 2018, 13 atmospheric deposition samples were collected by means of passive samplers (sampling bulks). Bulks were placed in eight coastal and continental sites along the Victoria Land, from 73\({}^{\circ}\)02\({}^{\prime}\) to 74\({}^{\circ}\)54\({}^{\prime}\) S and westward from the Italian research station \"Mario Zucchelli\" (MZS). Figure 1 reports the map of Victoria Land, showing the 8 atmospheric deposition sampling sites (MZS, Faraglione Camp, Inexpressible Island, Edmonson Point, Cape King, Cape Phillips, Sarao Point, Mid-Priestley Glacier).  
Sampling bulks are made of a polyethylene bottle (volume, 10 L) with a polyethylene funnel (volume, \(\sim\)10 L; surface area = 0.034 \(\pm\) 0. 002 m\({}^{2}\)). Each sampling bulk was placed inside a PVC cylindrical container, specifically modified to ensure the positioning of the bulk funnel at 1.5 m above the ground, avoiding the collection of re-suspended soil particles or drifting snow.  
Bulks were removed and replaced after 20-50 days. In addition, sample blanks (hereinafter: field blanks) were also collected in the field, approximately once every month. These blanks were simply placed in the sampling site for 20-30 min and then treated as the deposition samples.  
Bulk atmospheric deposition samples were transported to the clean room in MZS at the end of each sampling period, and left at room temperature for a few days. When no wet deposition was collected, 700-900 mL of ultrapure water were added to collect and re-suspend any particles attached to the inner surfaces of the bulk. Subsequently, samples were shaken and vacuum-filtered through 0.45 \(\upmu\)m membrane filters. All the filtered samples were then frozen at \(-\)20 \({}^{\circ}\)C until analysis. The collected atmospheric deposition samples, the sampling periods, and the filtration volumes are all reported in Table 1.  
Figure 1: Satellite map of the Victoria Land, showing the position of the Italian Antarctic Station (MZS in the text) and the sampling sites.
## 1 Introduction
One limitation of this methodology is the different spatial and temporal scales, which requires the downsampling of the high spatial and temporal resolution of _post_-disturbance UAS imagery [PERSON] et al. (2019), negating the advantages of the UAS. With increasing implementation of planned disturbances and the importance of understanding the economic and ecological impacts of these planned disturbances, there is an excellent opportunity to leverage the rapid, precise, and efficient data collection methods provided by UASs. Moreover, due to the planned nature of prescribed burns and selective timber harvests, as well as advancements to on-board post-survey georeferencing methods (which eliminate the need for time-consuming terrestrial-based surveys) [PERSON] et al. (2019), there is the opportunity to not only repeatedly inventory land cover following a disturbance but also before to develop a comprehensive disturbance analysis which would advance the silvicultural management of local forests [PERSON] (2018); [PERSON] (2018); [PERSON] (2018). The purpose of this research was to investigate the use of UAS technology to support land management, including the inventory of land covers prior to and repeatedly after planned disturbance events, and to confirm whether UAS results produce similar accuracies for both prairie burn and timber harvest treatments.
## 4 Results and Discussion
### Particle Distribution Results for Different RF Plasma Discharge Sequences  
We examined the effects of intermittent or continuous plasma discharge on the particles on the wafer, as shown in Figure 13a. The particle map in Figure 13b presents a comparison of the particle numbers under intermittent and continuous discharge. The particle result shows that the continuous discharge by the RF plasma method reduces the number of particles. Step transitions within the plasma sheath suppress the particles. Intermittent plasma discharges result in higher particle counts than do continuous plasma discharges because particles are attracted to the wafer's positive potential surface when the RF plasma flow is turned off. We observed that the plasma sheath prevented the particles from being attracted to the wafer surface when the particles were negatively charged and suspended in the plasma-on chamber. Therefore, the continuous discharge sequence of RF plasma can reduce particle adhesion to the wafer, as depicted in Figure 13c.  
Figure 11: Particle flow distributions under different gas flow rates and chamber pressures.  
Figure 12: Particle distributions under the following RF settings: no plasma output, top RF (high frequency) output, and BTM RF (low frequency) output.  
Figure 13: Distribution of particles under intermittent and continuous RF plasma discharges. (**a**) Graphs by intermittent and continuous radio-frequency (RF) plasma discharge. (1) The intermittent discharge sequence is a process recipe with no plasma during step transitions. (2) The continuous discharge sequence is a process recipe that keeps the plasma on during step transitions. (**b**) Particle counts under intermittent and continuous plasma discharge. (**c**) (1) Particles are attracted to the wafer surface because of the wafer potential. These particles originate from around related parts when recipe transition without plasma. (2) When the plasma flow is turn on, the plasma sheath prevents particles from being attracted to the wafer.
## 2 Related Works
### Summary  
Works that use different input, model architectures, as well as outputs were reviewed, and the limitations of the works were also highlighted. One of the limitations is that the idea of predicting a path loss exponent based on any of the image input types will result in erroneous path loss prediction. This is because path loss exponents are estimated based on the least squares method. The process of least squares is associated with its error. By further training a CNN that is not 100% accurate in predicting the path loss exponent, the error during path loss exponent computation is compounded with that of the CNN, thereby affecting the final path loss prediction. Furthermore, using an image representing the receiver environment alone, as was performed in many works, is not sufficient to characterize the propagation path. There are works in which predictions of radio maps were performed based on the UNet architecture. Such predictions are for analysis as images representing the distribution of signal strength/path loss are predicted, not the path loss values. Thus, the outputs are not suitable for automated design and optimization.  
On a final note, models valid for multiple frequencies, environments, and antenna heights have not been developed. Radio Frequency Planning Tools are used in wireless network design, which has empirical path loss models available for the designer to choose from. ML path loss models have accuracy better than empirical models, and it is likely that they will replace empirical models in these tools in the future. Hence, it is desirable to have ML models valid for a range of frequencies, antenna heights, and environments. Otherwise, many models have to be developed with varying parameters of frequency, antenna height,and environment, leading to large memory occupation, time wastage in training individual models, and confusion on the particular model to select from the end user viewpoint. Thus, the study fills the gap of non-availability of versatile models by developing a model for different environments, frequencies, and antenna heights. This will make the ML models flexible for use in different environments, frequencies, and antenna heights. The model considered takes as its input some numeric features affecting path loss combined with features extracted from images by algorithms (CNN or hand-crafted). The image is composed of four satellite images tiled together in a 2 by 2 grid and is for four different positions along the path from transmitter to receiver position. This gives more information on the propagation path than using the image at the receiver location alone. The effects of various feature extraction methods and their combinations on accuracy were investigated and reported. Improvements were observed compared to our previous work in [16]. However, the work considered some datasets in which measurements were taken from networks operating within the UHF band in environments that included rural, suburban, urban, and urban highrise at some fixed values of transmitting and receiving antenna heights.
###### Abstract  
The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV's advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV's security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.  
Internet of Vehicles; Internet of Things; machine learning; security; trust +
Footnote †: journal: Sensors  
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## 4 Numerical Results and Discussion
### Evolution of Transient Dynamic Response of Blade off
#### 4.2.4 Impact Effect Due to Sudden Unbalance  
In order to quantitatively evaluate the impact effect, the impact factor [6] is defined as \(\eta=A_{\text{max}}/A_{s}\), where \(A_{\text{max}}\) and \(A_{\text{s}}\) are respectively the transient peak amplitude and corresponding steady-state amplitude. By extracting the instantaneous displacement peak and steady-state vibration amplitude in the time series of vibration response, the impact factors at different locations are obtained, as shown in Figure 15. By comparing the transient peaks, steady-state amplitudes, and the impact factors at different locations, it shows that the vibration response and impact factors at both ends are greater than those at C.G. (node 11), while the values are similar, i.e., for this kind of rigid rotor located in the bearing span, the impact effects on different axial positions are similar with the range from 2.3 to 2.8, which also shows that the impact effect is obvious. Although the difference of the impact effect at different locations is small, the maximum transient responses of both bearings are much higher than that at the C.G., about 1/2 of the bearings. The reason for this is due to the special vibration characteristics when the rotor is subjected to blade off on the first stage gas turbine at a rotational speed of 50,000 r/min. The vibration modes shown in Figure 16 and the critical speed mode shapes shown in Figure 8 are sufficient to explain the reason for these dynamic phenomena appearing, i.e., the vibration amplitude of the bearings at both ends in the pitching vibration mode excited is greater than those near the C.G. of the entire system.  
From the above analyses, it can be seen that the impact has time-dependent characteristics. The large unbalance force as well as the instantaneous impact force caused by the loss of blade are the fundamental reasons for the sharp increase in vibration response. The above-mentioned time-frequency characteristics can provide a better theoretical basis for the fault diagnosis to this type of rotor suffering from blade off.  
Figure 14: Frequency spectrums at different locations: (**a**) 1# bearing; (**b**) node 11 (C.G. position); (**c**) 2# bearing.
## 2 Fundamental Principles
### Signal Noise  
The noise in a detected signal is a result of noise introduced from the measurement hardware, and as result of non-ideal measurement conditions. Noise sources resulting from the measurement hardware can be grouped generally as: readout noise, photon noise, relative intensity noise (RIN) and dark noise [47]. These can be further classified as temporal or spatial noise sources. Temporal sources such as photon and dark noise vary with time and thus can be reduced through averaging of frames. Spatial noise arises from non-uniform outputs in dark current non-uniformity and photo response.  
Photon noise also known as shot noise is a statistical variation in the arrival rate of photons on the camera detector governed by Poisson statistics and as such the magnitude is linked to the square root of the signal optical intensity as shown in Equation (12). Dark noise results from a charge accumulation in each pixel due to thermal fluctuations in the silicon which can release electrons. Readout noise arises from converting the pixel charge carriers into a voltage signal and processing from analog-to-digital conversion with the largest contributor being the amplifier electronics. RIN describes the instability in the power level of the light source and is typically independent of laser power.  
Spectrometer CCD's commonly have cooling which results in contributions to noise from dark current being insignificant. Noise contributions from photon variance reduces as photon flux increases due to the square root relationship with signal. When the measured signal has a low optical intensity, the read noise will be greater than the photon noise and the signal is termed read-noise limited. Increasing the optical power input into the spectrometer or increasing the integration time will allow the pixels to collect more photons, which in turn increases the SNR, there will then come a crossover point where photon noise exceeds read and dark noise, and the signal is termed photon or shot-noise limited:  
\[\sigma_{photon}=\sqrt{\frac{\rho\eta\tau}{hv_{0}}\frac{P_{0}}{N}\left(\gamma_{ S}R_{S}+\gamma_{R}R_{R}\right)}, \tag{12}\]  
where \(\eta\) is the quantum efficiency of the detector, \(\tau\) is the exposure time, \(h\) is the Planck constant, \(v_{0}\) is the centre frequency of the light source spectrum, \(P_{0}\) is the optical power, and \(\gamma_{S}\) and \(\gamma_{R}\) denote the quantity of input power that will exit the interferometer from each arm, assuming \(R_{S}=R_{R}=1\).
## 3 Results and Discussion
### Ductility of Specimens  
The behavior of the tested specimens in terms of ductility/brittleness was evaluated by calculating the ductility of the specimens, considering a displacement ductility ratio or index \(\mu=\frac{\Delta\text{ss}}{\Delta y}\) using Equation (1), as presented in Figure 4[48; 49; 50]. The ductility index \(\mu\) of all the columns from the test results are presented in Figure 15. It can be observed that ductility index of the tested columns was influenced by the amount of eccentricity (i.e., e = 0 to 15, 35, and 50 mm). The eccentricity has a negative effect on the ductility of columns, i.e., changing the eccentricity of the axial load from 0 to 15, 35, and 50 mm resulted in significant reduction in the ductility of the columns. For example, GC specimens, i.e., GC-0F-15E, GC-0F-35E, and GC-0F-50E tested under 15, 35, and 50 mm eccentricities, respectively, experienced an 18%, 26%, and 35% reduction in ductility in comparison to the concentrically (e = 0 mm) tested columns (GC-0F-0E). The drop in the load-carrying capacity and ductility of the tested specimens showed a similar trend for various eccentricities in GCD group columns.  
It was observed that plain (without fibers) specimens of GC and GCD groups had a lower ductility index than their counterparts in the CC group. The ductility index of concentrically loaded plain columns in GC and GCD groups was 13% and 22% lower, respectively, than their counterparts in CC group columns. The ductility index of eccentrically loaded columns in the GC group, GC-0F-15E, GC-0F-35E, and GC-0F-50E, was decreased by 11%, 9%, and 8%, respectively, than CC-0F -15E, CC-0F-35E, and CC-0F-50E. The specimens of group GCD, i.e., GCD-0F-15E, GCD-0F-35E, and GCD-0F-50E, had 20%, 18%, and 12% lower ductility index, respectively, than their counterparts in CC group, viz., CC-0F -15E, CC-0F -35E, and CC-0F -50E. It was also observed that group GC specimens had higher ductility than group GCD specimens. The ductility index of group GCD specimens, GCD-0F-0E, GCD-0F-15E, GCD-0F-35E, and GCD-0F-50E, was 10%, 9%, 8%, and 5% lower, respectively, than their counterparts in group GC.  
The addition of fibers in GC specimens, i.e., GC-0.75F-0E, GC-0.75F-15E, GC-0.75F-35E, and GC-0.75F-50E, resulted in improved ductility by 55%, 40%, 35%, and 25%, respectively, than the corresponding specimens with no fibers in the same group. The ductility index of GCD specimens, i.e., GCD-0.75F-0E, GCD-0.75F-15E, GCD-0.75F-35E, and GCD-0.75F-50E, has been increased by 47%, 35%, 25%, and 20%, respectively, than their corresponding  
Figure 14: The axial load–lateral displacement behavior of GCD columns.  
counterparts with no fibers in the same group. The inclusion of steel fibers helped to arrest the microcracks and acted as a bridge across the cracks to prevent expansion; hence, they significantly enhanced the ductility of GC and GCD specimens [31; 33; 39; 40; 52]. The trend of improved ductility in GPC with the addition of fibers has also been reported in previous studies [39; 40]
# Fuzzy Logic-Based Controller for Bipedal Robot
## 5 The Fuzzy Controller for Bipedal Robot
### Fuzzification  
#### 5.1.1 The Input, Output Signals, and Physical Value Domains  
The input signals were the position error \(e(t)\) and velocity error \(\dot{e}(t)\) of the joints generated from comparing quantities that define the robot's motion trajectory represented by the joint coordinates \(q_{d}\) and their derivative \(\dot{q}_{d}\), with response values of the corresponding quantities, \(q\), \(\dot{q}\). These quantities were determined according to Equation (13). The output signal of the controller was the value of driving moments \(U(t)\) at the driven joints, which ensured the motion trajectory of the robot--i.e., with the errors \(e(t)\), \(\dot{e}(t)\) as small as possible, relative to the desired value--and ensures the relative equilibrium.  
The driving moments \(U(t)\) depended on the motion requirements of the robot as well as the geometrical, kinematical, and dynamical parameters of the robot. For crisp controllers in general, and IDPD and IDPID controllers, the computation of these moments is based on a dynamical model, which is a system of differential equations of motion, and can have kinematic constraints by errors of position \(e(t)\) and velocity \(\dot{e}(t)\). With the fuzzy controller applied in this paper, the calculation of \(U(t)\) did not use the dynamical model but only relied on a fuzzy rule system built based on expert knowledge and on the errors \(e(t)\), \(\dot{e}(t)\). To accomplish that, one of the first requirements was to determine the physical value domain of the control moments \(U(t)\) as well as the physical value domain where the errors \(e(t)\) and \(\dot{e}(t)\) could occur. Using the minimum and maximum limit values, the physical value domains of errors \(e(t)\), \(\dot{e}(t)\), and driving torque \(U(t)\) are denoted as follows:  
\[\left\{\begin{array}{ll}e_{i}=[e_{imin},e_{imax}](rad),&i=1,\ldots,7\\ \dot{e}_{i}=\left[\dot{e}_{imin},\dot{e}_{imax}\right](rad.s^{-1}),&i=1,\ldots,7\\ U_{i}=[U_{imin},U_{imax}](Nm),&i=1,\ldots,7\end{array}\right. \tag{20}\]
## 5 Hydrogen Only Combustion with Direct Injection  
A promising approach to improve hydrogen engine performance is to inject hydrogen directly into the cylinder during the compression stroke [65]. By doing so, the backfiring problem of PFI configuration can be avoided, since fuel injection occurs when the intake valves are already completely closed. Pre-ignition issue can also be avoided to a certain extent by reducing the exposure time of hydrogen mixture to hot-spots. The volumetric efficiency loss for PFI due to the displacement of air by hydrogen, as discussed above, is no longer an issue, if injection occurs after the inlet valves are closed. When injecting fuel late during the compression stroke, high injection pressure (i.e., \(\geq\)100 bar) is required to overcome the elevated in-cylinder pressure. Concurrently, higher injection pressure can increase fuel mass flow rate compared to typical low pressure PFI, which can provide higher energy input for the same injection duration, to drive high load operation. Therefore, a number of studies [38; 65; 66] demonstrated that high load hydrogen high pressure direct injection (HPDI) operation, under optimal operating conditions, can achieve similar efficiency as traditional diesel engines. Hydrogen HPDI also enables very flexible engine operation due to the many tuning parameters, for instance injection pressure, injection duration, ignition timing and injector orientation, which can be tuned to optimize the engine performance.  
However, the high auto-ignition temperature of hydrogen still has to be overcome. [PERSON] and [PERSON] [43] investigated hydrogen DI HCCI mode in an optical engine with a compression ratio of 7.5 and it was reported that air intake preheating and high non-cooled internal EGR level are needed to auto-ignite hydrogen in this mode. It should be noted the mixture homogeneity may be affected by the late hydrogen DI after the intake valve is closed. Single-kernel flame propagation was also observed from OH laser induced fluorescence results for all equivalence ratios studied from 0.40 to 0.59, which is atypical to the multi-kernel fast combustion for hydrocarbon fuels in HCCI [43]. Authors proposed that coupling SI after the start of auto-ignition can establish a second flame front expanding towards the first one, similar to twin-spark engines, for better controlled auto-ignition. Glow plug and diesel pilot are also common ignition sources reported in literature. It is noted that hydrogen HPDI has been applied in modified SI as well as CI engines with a range of compression ratios. Typically, the conventional fuel injection system was replaced by a high pressure injection system for hydrogen DI. Additional engine modifications are required to accommodate the ignition assistance of choice.