esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### SHTES Systems in Greenhouses
#### 2.2.3 Rock Bed as SHTES System
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Factors**} & \\ \cline{3-8}
**Author** & **Year** & **Method** & & \multicolumn{2}{c}{**Rock Bed Characteristics**} & \multicolumn{1}{c}{**Product**} & \multicolumn{1}{c}{**Suction/Injection Method for Coupling Rock Beds and Greenhouses**} & \multicolumn{1}{c}{**Outcomes**} \\ \hline  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline  & & & & & & & & \\  & & & & & & & & \\ \hline  & & & & & & & & \\ \multirow{4}{*}{[PERSON] et al. [55]} & \multirow{4}{*}{2020} & \multirow{4}{*}{Experimental} & \multirow{4}{*}{2020} & \multirow{4}{*}{2020} & \multirow{4}{*}{NA} & \multirow{4}{*}{\(\surd\)} & \multirow{4}{*}{\(\surd\)} & \multirow{4}{*}{\(\surd\)} \\  & & & & & & &  
Table 2 details the relevant work in applying rock beds in greenhouses. Accordingly, it can be seen that most of the work was experimental, except for one study ([PERSON] et al. [56]) that used numerical methods (CFD). It makes sense to evaluate a new system's efficacy and performance in a specific lab application to identify any potential problems or obstacles. Nevertheless, although [PERSON] et al. [48] presented the recommended method around 28 years ago, the proposed rock bed-greenhouse system with a humid air injection/suction mechanism was not analyzed in detail by numerical techniques. Numerical approaches, on the other hand, are helpful strategies for improving thermal systems' performance. Moreover, according to Table 2, the rock bed's considered porosity range is 0.33-0.49. It is worth mentioning that researchers mostly used a porosity of 0.35. Also, the rock bed's particle diameter range is 0.25-10 cm. A rock bed is a porous region of stone grains arranged randomly and irregularly and has a variety of geometries and shapes. One of the challenging issues in fluid dynamics is the flow passage through a heterogeneous porous region, a subject of ongoing research. If phase change (condensation and evaporation) is added to this problem, it contains more complications. With the passage of moist air inside the porous area, depending on the bed's temperature and the functional state type (charge or discharge), the humid air can condense or evaporate, and its moisture content increases or decreases. Therefore, it can be realized that fluid flow and heat transfer in a rock bed with phase change are complex. This process has been investigated separately in previously published works but has not been studied and analyzed practically for a rock bed coupled with a greenhouse. Moreover, in the proposed system, some injection and suction pipes are embedded in the bed to store and release the solar energy (extra part) in the rock bed. According to the details presented in Table 2, it can be seen that the previous works did not focus on the design of the arrangement and the geometry of these pipes, which are perforated. There are various geometrical factors related to these pipes which affect the performance of the rock bed.
The effect of co-alloying of Ag nanolayers with Ge in Ag/AlN NMLs on the interfacial roughness before and after thermal treatment was investigated by XRD combined with TEM analysis [15]. The addition of Ge to the nano-confined Ag layers was found to induce structural disorder at the Ag[Ge]/AlN interfaces; subsequent annealing led to an improved interface coherency. These studies provide evidence of the possibility to tailor the microstructure, internal stress state and related properties in functional thin films and NMLs by co-alloying [16]. Evidently, the microstructure and stress state of thin films can also be modified by varying the sputter deposition conditions. As demonstrated in the present study and in e.g., Refs. [17; 18; 19; 20; 21], the ion bombardment induced by the substrate bias voltage can greatly affect e.g., the crystallinity, preferential growth direction, stress state and density of deposited thin films and NMLs. Therefore, in this work, Ag[Ge]/AlN NMLs were fabricated with and without RF bias. The effect of the substrate bias on the Ag[Ge]/AlN NML microstructure and stress state are studied before and after annealing in Ar shielding gas or in air using a combined analytical approach by SEM and XRD. A schematic representation of the NML systems studied, also indicating the parameters varied during deposition and post thermal treatment, is shown in Figure 1 (see also Methods). Important microstructural changes were identified and discussed to identify the role of small amounts of Ge at the AlN/ Ag interfaces on the stress, morphology and thermal stability of Ag[Ge]/AlN NMLs, as deposited with and without substrate bias.
## 2 Related Work
### HF-Based Authentication and Authorization  
[PERSON] et al. [20] proposed a blockchain-based authentication mechanism for IoT in order to eliminate the single point of failure. In their proposed research, they refer to the necessity of device authentication without the use of a central authority, which is used in the traditional Public Key Infrastructure mechanisms (PKI). Blockchain technology is suitable in this architecture and provides the decentralized network structure. The implementation of the blockchain-based authentication mechanism has been conducted with the use of Raspberry Pi devices and the HF platform.  
[PERSON] et al. in [21], proposed a distributed identity-based authentication scheme for IoT devices using permissioned blockchain. The proposed system uses blockchain as a distributed Private Key Generator (PKG), which eliminates the single point of failure and key escrow problem. The proposed authentication scheme has been implemented in HF.  
[PERSON] et al. in [22], suggest a distributed authentication infrastructure that distributes trust points among multiple service providers and connects them through cross-certification. The proposed method creates a unified framework for regulating cross-certification in a unique way. The paper proposes a new decentralized trust model called Meta-PKI that aims to overcome the overconcentration of trust in Certification Authorities (CAs).  
[PERSON] et al. in [23], propose a model for decentralized authorization in constrained IoT environments that uses multiple authorization servers (ASs) and two blockchains, one for authorization and the other for payments. The proposed models aim to reduce transaction costs and delays compared to a single (public) blockchain. The paper investigates interledger mechanisms for securely linking transactions on two blockchains: a private or permissioned chain and a public chain. The evaluation of the proposed models has been carried out on two public Ethereum testnets: Rinkeby and Ropsten and HF. The evaluation considers the execution cost (gas), delay, and reduction in the amount of data that needs to be sent to IoT devices.  
[PERSON] et al. in [24], propose and implement an integrated IoT system that uses HF to secure edge computing devices while providing traceability for the data generated by IoT devices. The proposed model addresses scalability challenges, processing power, and storage issues of IoT edge devices in the blockchain network. The paper also presents a lightweight mutual authentication and authorization model and an HF blockchain middleware module embedded in IoT gateways, ensuring secure data transactions for the IoT-distributed applications.
## 3 Models for Parameters' Identification and UFLS Schemes' Optimization
### Solving the Proposed Optimization Model Using PSO  
From the above description, it can be seen that parameter identification for the simplified SFR model and the robust UFLS scheme design is a standard nonlinear constrained optimization problem. So, a PSO population-based stochastic search algorithm is used to identify the parameters of the simplified SFR model and design the robust UFLS scheme for the small receiving-end power system after islanding in this paper. The tuning process of a UFLS scheme for a small power system utilizing the PSO algorithm is shown in Figure 8.  
The following describes the incorporation of the PSO approach for solving the proposed optimization models described in Equations (6) and (7).  
* Input PSO parameters, system data, active power disturbance set, the maxima imported active power, and the upper and lower boundaries of each variable.
* Time-domain simulation for a period of 50 s is carried out on the full scale model system to determine the system frequency response at COI for the given contingency set.
* Initialize the swarm comprising the four design parameters, \(x=[H\ D\ K\ T]^{T}\), of the simplified SFR model, and relay the parameter \(Z\) of the UFLS scheme, with all parameters satisfying their constraints.
* For each particle in the swarm, the simplified SFR model is used to determine the system frequency response for the given contingency set.
* Evaluate the objective \(g(x)\) of each particle utilizing the objective function in Equation (6).
* Find the best position of each particle, \(p\)best, by comparing the evaluation value of each particle with the one in \(p\)best of the last swarm. If the evaluation value is better, then set \(p\)best as \(g\)best.
* Check the termination condition. If the termination condition is satisfied, go to Step 10.
* The evolved existing population or swarm velocity and position are developed employing Equations (3) and (4).
* Return to Step 4 to repeat the evaluation process with the evolved position.
* Set the latest \(g\)best as the parameters of the simplified SFR model with UFLS.
* Compute the system frequency response using the simplified SFR model with UFLS for each particle in the swarm.
* Evaluate the objective \(F(Z)\) of each particle using the objective function in Equation (7).
* Select the \(g\)best of the relay parameters of the UFLS utilizing the process in Step 6.
* If the termination condition is satisfied, go to Step 17.
* Update the evolved existing swarm velocity and position using Equations (3) and (4).
* Return to Step 11 to repeat the evaluation process with the evolved position.
* The particle that gives the latest \(g\)best has the relay parameters of the optimal UFLS scheme.  
Figure 8: Block diagram of the proposed PSO-based UFLS scheme design.
## 1 Introduction  
Tuberculosis (TB) is one of the most serious health problems in the world, which is a bacterial infection caused primarily by the lung-impaired bacteria Mycobacterium Tuberculosis (MTB). In the past few decades, TB cases have diminished, while in some high-risk populations, TB rates are increasing, especially in metropolitan areas in Western Europe [1].  
An automated method in detecting TB in chest X-rays (CXRs) is based on lung segmentation and feature removal. A computerized process is useful for identifying TB in CXRs based on lung division and extraction. In this examination, we centered around the three classes of anomalies such as textural irregularities, portrayed by diffuse changes by all accounts and construction of an area; central anomalies, which are disengaged outlined changes in thickness; and shape irregularities, where sickness measures have modified the form of typical anatomical designs [2].  
The greater part of the recently evolved frameworks for programmed examination of chest radiographs have zeroed in on a single assignment. For programmed TB identification, there are two primary motivations to consolidate frameworks. First is that it is not conceivable that a solitary framework will do the trick in a large number of settings, so a combination of different frameworks can be applied to the particular setting. For instance, by gauging the yield of a particular anomaly identification framework higher when it is unequivocally connected with TB in a specific populace. The subsequent explanation is an overall useful impact of framework blend on the presentation of directed frameworks [3].  
In several poor countries worldwide, tuberculosis mortality is rising, posing a severe danger in human lives. According to the World Health Organization (WHO), TB has the highest number of deaths, followed by the human immunodeficiency virus (HIV). Southeast Asia and areas of Sub-Saharan Africa are some of the parts of the world that are more affected. According to estimates, since 2012, 8.5 million new cases of TB each year were diagnosed worldwide [4]. TB can be cured by having a legitimate determination in taking the recommended medications in the proper time while maintaining a strategic distance from others to help lessen the transmission of the infection.  
The sputum test is a reliable procedure to diagnose TB, which involves microscopic analysis of the cultured sputum samples. However, the downside of the sputum test is that it relies upon the capacity of the patient to deliver sputum, which is hard to collect in old-aged people and sometimes combined with different sicknesses [5].  
Interferon-gamma release assays (IGRA) are blood tests used to detect tuberculosis but are expensive and difficult to use for screening purposes [6]. CXR is a low-cost method of determining the presence of tuberculosis since any TB symptoms are visible in CXRs. In this research paper, tuberculosis was predicted by analyzing CXRs using a stacked loopy decision tree (SLDT) classifier.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 2 Wi-Fi Basics
### PHY in Wi-Fi
#### 2.1.3 Modulation  
Modulation techniques maximize the number of bits transmitted per subcarrier. Wi-Fi 5 supports up to 256 QAM that modulates 8 bits of information per subcarrier. QAM modulates data in each subcarrier. More bits per OFDM symbol lead to a larger throughput under the condition that the Signal-to-Noise Ratio (SNR) is sufficient. Higher-order modulations are expected to be efficient only under favorable conditions, namely clear environments and short distances.  
The coding rate identifies the portion of bits that is used for the transmission of data and Forward Error Correction (FEC). The supported coding rates are 1/2, 2/3, 3/4, and 5/6. A coding rate of 5/6 means that 83.3% (i.e., 5/6) of the data stream is used to carry data and 16.7% is used to carry FEC.  
The combination of modulations namely, Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), 16-QAM, 64-QAM, and 256-QAM with coding rates, allow the standard to choose the data rate dynamically according to the radio environment [5]. The Modulation and Coding Scheme (MCS) [17] index comprises a given set of parameters to provide a wide variety of data rates for wireless connections. It is part of the rate  
Figure 3: (MU-)MIMO modes.  
adaptation feature in wireless technology and includes the guard interval, coding rate, modulation, channel width, and the number of spatial streams. A discrete list of MCS means that some combinations are impossible (cf. Table 4). For instance, BPSK with a coding rate of 3/4 is not possible.
## 3 Simulation  
We calculated the theoretical magnification of the image formed by the lens L in Figure 1a using Equation (1) for the transversal magnification and Equation (2) for the longitudinal magnification. The simulated focal length was \(f\) = 85 mm and the distance between the lens and the registration plane \((x_{2},y_{2})\) was \(s_{i}\ =\ 347\) mm. The obtained transverse magnification was \(\mathrm{M_{T}}=-3.079\) and the longitudinal magnification calculated was \(\mathrm{M_{L}}=-9.480\). In the simulations, \(x_{o}\) was the width of a small area of the simulated microscopic objects (see Figure 5).  
The selected microscopic objects to be simulated were (a) a resolution test USAF 1951 and (b) a microsphere, both shown in Figure 5. An extra object simulation was also conducted, with a microscope reticle, but corresponding analysis is provided in the  
Figure 4: Magnitude of sparsity criteria (Tamura coefficient, Gini Index, and entropy) as a function of the reconstruction distance (\(\Delta z_{r}\)). The critical points correspond to the best focused axial plane for the different functions (criteria).  
Figure 5: Simulated microscopic objects: (**a**) resolution test USAF 1951 and (**b**) microsphere.  
Supplementary Materials to ensure the manuscript remains concise. In the case of the resolution test chart, the widths of a line in micrometers for the (elements) of group 4 are 31.25(1), 27.84(2), 24.80(3), 22.10(4), 19.69(5), 17.54(6); for group 5, they are 15.63(1), 13.92(2), 12.40(3), 11.05(4), 9.84(5), 8.77(6); for group 6. they are 7.81(1), 6.96(2), 6.20(3), 5.52(4), 4.92(5), 4.38(6); and for group 7, they are: 3.91(1), 3.48(2), 3.10(3), 2.76(4), 2.46(5), 2.19(6). The width of the five vertical bars (three dark bars and two bright bars) corresponding to element 2 of group 4 for the resolution test is \(x_{o}~{}=~{}139.2~{}\upmu\)m, as is shown in Figure 5a. Finally, in Figure 5b, the radius of the microsphere is \(x_{o}~{}=~{}50~{}\upmu\)m.  
In the simulation, the objects are shifted in steps of 1 mm in the axial direction, covering a range from \(\Delta z_{o}~{}=~{}-10\) mm to \(\Delta z_{o}~{}=~{}+10\) mm. In each displacement, the focused images were reconstructed, and the longitudinal and transverse magnifications were calculated according to Equations (7) and (8), respectively. As stated in Equation (19), its validity is ensured by accomplishing the Shannon theorem. In our case, the experimental conditions were \(\delta x~{}=~{}3.45~{}\upmu\)m for the sensor's pixel pitch, \(N_{pix}~{}=\)1500 pixels, and \(\lambda~{}=632.8\) nm wavelength, then ensuring the use of the of the Rayleigh-Sommerfeld theorem in adequate conditions (\(\frac{N_{pix}\delta x^{2}}{\lambda}~{}=~{}28.21\) mm\(>20\) mm\(~{}=~{}\Delta z_{0}\)).
## 4 NMR Relaxation Results Obtained Using the Spectrometer and Peltier Cooled Probe  
Nuclear Magnetic Resonance Relaxation for Determining Mass and Mobility of Hydrocarbons in Porous Materials  
A sample of Barnett shale was measured by NMR Relaxation (NMRR) to obtain an estimate of the quantity and mobility of the hydrocarbons in the pores. Measuring tarry materials in rock pores is not an easy measurement for NMR spectrometers, as the rock material gives a fast decay (extremely broad-line, in spectroscopic terms), due to the dipole-dipole interactions with the rock, and the quantities are small--the total sample is only a couple of 10s of milli-gram, and the organic components resolved are in the 10 s to 100 s of micrograms.  
Figure 6 shows a two component fit to hydrocarbons in the porous rock sample. Any residual signal from an empty probe has been subtracted. Both components are exponentials that are not immediately visible to the eye; however, if the longer exponential component is subtracted from the measured data, and the resultant plotted logarithmically, Figure 7, a flat baseline is obtained, with a straight-line residual at shorter times. This appears to be very convincing evidence for a double exponential decay for the FIDS for the hydrocarbons in these pores, which provides important information regarding the mobility and dynamical processes in these samples.  
In the logarithmic plot, the linearity of the baseline and the fitted short-component, following subtraction of the baseline, are also convincing evidence for the linearity of the NMR receiver and the overall quality of the measured time-domain data, including good signal-to-noise.  
Figure 6: A two component exponential fit to hydrocarbons in a Barnett Shale porous rock sample. Measured data: red dots. The longer, more mobile fitted component is the green line, and the shorter, more rigid fitted component is the orange line. Their sum is the blue line.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 4 Modified Identity-Based Encryption Scheme  
In Section 3, we constructed a compact IBE scheme, but this scheme cannot be directly combined with the decomposition method to construct an MIBFHE scheme. The decomposition method requires the identity to be represented as a vector in the security proof, whereas in Section 3, our IBE scheme uses the FRD function to map the identity into a matrix form and incorporate it into the identity public key.  
Therefore, before combining the foundational IBE scheme with the decomposition method, it is necessary to appropriately transform the foundational IBE scheme to ensure the security and correctness of the constructed MIBFHE scheme. Additionally, based on our observations, modifying the identity private key can cleverly utilize the trapdoor error to reduce the overall noise of the scheme.
# Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet +
## 2 Proposed Methods and Materials  
Due to DCNN's disadvantage of having too many training parameters in an FC layer, model testing and training take more time so that overfitting occurs. Additionally, the performance of intelligent defect diagnosis procedures is severely hampered by the continually changing operating load and inevitable noise from the environment. To solve the difficulty of the existing intelligent method, this study proposes a novel technique. This method involves an effective data pre-processing technique, feature selection technique, and best classifier selection technique to detect and classify the bearing signals. In the proposed method we firstly classify the bearing fault by considering the original dataset through GoogleNet, AlexNet, ResNet50, and VGG-16. In the next step, because of the classification's success rate of AlexNet compared to other pre-trained models, we modify the traditional AlexNet model structure, which effectively reduces the parameter quantity, overfitting, and calculation time of the CNN model. In the last step of the experiment, the deep features obtained from the last layer of the proposed modified model are applied separately as the input Softmax, k-NN, and SVM classifier and select the best classifier, which effectively improves the accuracy of the classification results. The flowchart of the proposed method is illustrated in Figure 1.
## 2 Theoretical Modelling
### General Sandwich Beam Specimens
After the material parameters and resonant frequencies are tested, the bending stiffness ratio \(S\) and geometrical parameter \(Y\) can be achieved by Equation (18) and Equation (19) respectively. The structure loss factor \(\eta_{s}\) can be obtained from the frequency response curve of the sandwich specimen. Subsequently, the loss factor and shear modulus of the core material can be calculated by Equation (21) and Equation (22). What needs to be stressed is that the symmetry of specimens does not restrict the application of the aforementioned formulas. When asymmetrical sandwich beam specimens are chosen, the material parameters and resonant frequencies of both the base beams should be measured before the measurement of the sandwich beam. This is the only operation to be added when compared with the traditional test method using symmetrical sandwich specimens.
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### Fluctuation Test  
The ACS system was tested to see how accurately it could respond to large and fast changes in the PWM value. This is because the controller might need to change the power drastically while detumbling due to the oscillating torque values from the Kane damper. It was observed that the flight computer had a typical reaction time of about 3 ms. So, a program was written to randomly pick a PWM value and current direction every 3 milliseconds. The magnetic field was then checked with the gyroscopic magnet. It seemed to change at an interval of a few milliseconds as a preliminary test. More testing was performed later using the IMU.  
Also, longevity was tested again. Longevity tests were performed to see if the fluctuation over time would damage any of the hardware. The magnetic fields were measured every few hours. There was no noticeable change in the magnetic field strength, and the system worked similarly after two days.