esa-sceva/satcom-chunk-collection huggingface dataset:
# Technical Performance of an Inflatable Solar Dryer for Drying Amaranth Leaves in Kenya
## 2 Materials and Methods
### Drying Experiments  
The drying experiments were conducted in Thika, Kiambu County, Kenya (1.042957\({}^{\circ}\) S, 37.096790\({}^{\circ}\) E), during December 2016. About 200 kg of fresh amaranth leaves of the variety \"Big Leaf Michicha\" was obtained from the Marikiti market in Nairobi. The leaves were washed in clean water, blanched in water at 90 \({}^{\circ}\)C for about two minutes, and then cooled on an open stand for about five minutes. Subsequently, the leaves were layered 50 mm thick on the trays using a measuring stick. The weight of the fresh leaves was 8.5 kg per tray, corresponding with a loading density of 9.3 kg/m\({}^{2}\). As a control for solar drying, a tray loaded in the same way was placed outside the dryer 900 mm from ground for conventional sun drying. A mesh bag filled with a bunch of leaves was placed on tray 5 in the ISD and another one on the control tray under sun drying. The air velocity at the ISD outlet was regularly measured in the morning and at noon using a digital anemometer (Testo 435, Testo SE & Co. KGaA, Lenzkirch, Germany).  
Drying was started at noon. From the second day on, the fans were turned on at 08:00 and switched off at 18:00. During the drying, mixing was conducted manually every 2 h. To establish drying curves, the mesh bags were weighed at the time of mixing using a digital mass scale (RM/299, Ramtons, Nairobi, Kenya), and about 100 g of leaves were taken from tray 5 in the ISD and from the control tray under sun drying. The samples were kept in airtight aluminum bags for subsequent moisture content analysis according to DIN CEN/TS 14774-3 [25] at the International Center for Tropical Agriculture (CIAT) laboratory.  
Solar radiation (W/m\({}^{2}\)) and voltage (V) of the PV modules, along with temperature (\({}^{\circ}\)C) and relative humidity (%) of ambient air and air inside the dyer (tray 5), were measured using USB data loggers (DL-131, DL-191V, DL-181 THP, Voltcraft, Hirschau, Germany). Solar radiation and voltage were measured in 1 min intervals, whereas temperature and relative humidity were measured in 3 min intervals. Measurements were carried out based on East Africa Time (UTC + 03:00).
# A Multibody Model Calibration Method for Vehicle Misuse Testing
## 4 Nonlinear Parameter Calibration Using the Modified Quarter-Vehicle Model Analysis
### Method Validation via Numerical Simulation
The results for the wheel's center acceleration \(\tilde{z}(t)\) are well coincident during the periods for stages 1 through 3, but the simulation results exhibit smaller amplitudes than those in the tested data. More intense nonlinearity is observed in the test data, and fast vibration components with much higher frequencies than those in the simulated results are present in stages 1 and 2. The fast component present in stage 1 is primarily due to the tire deformation during and after contact with the obstacle. The simulation model does not include tire vibration, which accounts for the amplitude errors. The fast component present in stage 2 primarily results from the contact with the stop-block, which serves to prevent excessive compression of the absorber. This element has not yet been incorporated into the simulation model. The simulation results show sudden changes in the wheel's center acceleration at the specific moments \(t_{2}\) and \(t_{3}\), which are also evident in the corresponding curve for the test data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Description** \\ \hline \(c_{\mathrm{u}}\) & 10.00 & - & \(m_{\mathrm{u}}/m_{w}\) \\ \(c_{\mathrm{\SIUnitSymbolFace}}\) & 0.27 & - & \(z_{\mathrm{u}}/z_{\mathrm{w}}\) in stage 2 \\ \(c_{\mathrm{e}}\) & 11.00 & - & \((k_{\mathrm{e}}+k_{\mathrm{s}})/k_{\mathrm{s}}\) \\ \(c_{\mathrm{t}}\) & 3.86 & - & \((k_{\mathrm{t}}+k_{\mathrm{s}})/k_{\mathrm{s}}\) \\ \(v_{\mathrm{e}}\) & 1.57 & m/s & Equivalent vertical speed \\ \(\omega_{+,\mathrm{\SIUnitSymbolFace}}\) & 46.54 & rad/s & Circular frequency in stage 2 \\ \(\omega_{+,\mathrm{\SIUnitSymbolFace}}\) & 92.40 & rad/s & Circular frequency in stage 3 \\ \(t_{2}\) & 0.035 & s & Time for which the tire disconnects from the obstacle \\ \(t_{3}\) & 0.1023 & s & Time for which the tire reestablishes contact with the ground \\ Damping ratio & 0.002 & N\(\cdot\)s/m & Rayleigh damping proportional to the stiffness \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The systematic parameters of the modified quarter-vehicle model for numerical simulation. Figure 9: A comparison of the results for the misuse test: experimental test data vs. numerical integration results from the modified quarter-vehicle model. In general, the simulation results maintain the fundamental characteristics of the misuse test data. As a result, the effectiveness of the proposed calibration method for vehicle misuse tests is validated. This method is particularly well suited to misuse tests involving high driving speeds, where the tire-ground detachment phase occurs. The absence of tire deformation vibrations and contact with the stop-block may result in inaccuracies in the wheel's center acceleration response. Future work should also consider nonlinear stiffness to enhance the simulation accuracy.
# Numerical Validation of the Radiative Model for the Solar Cadaster Developed for Greater Geneva
## 2 Materials and Methods
### Case Study  
The present study focuses on two fictitious districts composed of three rows and three columns of buildings, i.e., a total of nine buildings. This ability of this kind of fictitious district has already been proven with respect to the study of the solar potential of neighborhoods [26; 27; 28]. In addition, it makes it possible to study the same district at different location, depending on the weather conditions used as input for the simulations.  
These two districts, which are called the homogeneous and the heterogeneous district, have different arrangements, but they share some common urban morphology indicators, which are listed in Table 1.  
Regarding the thermo-radiative properties of the different surfaces, the coefficient of reflection is set to 0.2 for both the ground's and the buildings' envelopes (vertical facades and roofs). These values are taken as a compromise between the typical values for concrete and soil.
## 2 Methodology  
### Study Area and Datasets  
MODIS LST data, with an initial spatial resolution of 1000 m, were downscaled to 90 m using high-resolution land surface thermal properties derived from ASTER and Landsat ETM+ visible/infrared (VNIR) data. In this study, ASTER VNIR data assisted in the decomposition of MODIS LST pixels from 1000 m to 90 m, with ASTER-observed LSTsserving as the reference values to validate the downscaled MODIS LST results [51]. MODIS, ASTER, and Landsat ETM+ images for the Lancaster and York region were acquired on 24 August 2003. MODIS and ASTER data were captured simultaneously at 16:02:52 GMT (11:02:52 local time), while Landsat ETM+ data were acquired at 15:34:26 GMT (10:34:26 local time).  
For the study area in the northeastern United States of America (USA) (Figure 1), we acquired multiple satellite products to support our thermal downscaling approach: (1) MODIS MOD1A1 land surface temperature (LST) and emissivity products as the primary coarse-resolution thermal inputs; (2) ASTER AST_08 LST (90 m) and AST_05 emissivity data for the validation; (3) ASTER AST_11B multispectral data (15-90 m resolution across VNIR-SWIR bands) to derive spectral indices and support image segmentation; and (4) Landsat 7 ETM+ multispectral data (30 m resolution) to enhance the landcover characterization and spatial detail.
## 5 Conclusions  
The usage of FM-OFDM modulation method in underwater acoustic communication channels can be justified by the available possibilities and advantages, for example: non-linear signal processing in transmitting and receiving paths with clipping of strong impulse noises; resilience to Doppler frequency shifts; keeping acceptable data throughput in spite of big time guard intervals, which is unobtainable for other modulation technique. Also, the proposed method is very flexible in managing the spectrum spreading coefficients. That allows for using both narrow-band and wide-band FM-OFDM that are capable of working under conditions of low SNR with acceptable noise-sustainability.  
However, the method has several disadvantages, for example: decreased spectral efficiency when compared to regular OFDM by a factor of two; appearance of non-linear distortions in OFDM signal, when received signal contains strong reflected beams. These problems must be addressed with additional research and ways of signal correction, which are not covered by this paper.  
In presented work, the methods of orthogonal frequency division multiplexing in combination with additional frequency modulation (FM-OFDM-QPSK) were presented. The results of marine experiments at the distance of 25 km using low frequency underwater acoustic equipment with central frequency of 400 Hz showed bit error rate of 0.13... 0.001 in conditions of frequency-selective fading in underwater channel with data throughput of 167 bit/s. Similar performance was shown during numerical experiments under AWGN conditions without noise-sustainable coding for multipath responses similar to the ones that were obtained during sea trials. Sensitivity to precision of frame synchronization in FM-OFDM-QPSK method allows utilization only in channels, parameters of which are relatively stable during the time of packet transmission. In cases of active movement of receiver relative to transmitter, resulting non-stationarity of the channel substantially complicates frame synchronization of OFDM symbols.  
Further research will be performed by the team in the field of multi-frequency methods utilization for long-haul underwater acoustic communications under conditions of non-stationary channels and for AUV communication in the aquatic areas with ice coverage.
## 1 Introduction  
Urban underground roads have initiated a new and convenient way for rapid growth of vehicle flows in metropolitan areas. They will play a major role in changing urban traffic conditions, reducing noise and the destruction of the urban three-dimensional space. However, they have a fundamental problem, that is, space is relative airtight, which will cause two problems in both the construction and operation periods [1; 2]. Firstly, heavy emissions accumulate in underground roads and construction air conditions deteriorate. Secondly, in later stages of operation, there will be higher concentrations of automobile exhaust and more danger to the health of the citizens surrounding the air vents [3].  
In order to solve the emission problem in construction, current researchers mostly advocate the use of warm mixing technology. This technology can usually bring the temperature of paving asphalt mixture from 170-190 \({}^{\circ}\)C down to 135-150 \({}^{\circ}\)C [4, 5], which can slightly reduce the emissions in construction.  
In order to solve the vehicle exhaust accumulation problem in the stage of operation, in recent years, researchers in road work have considered vehicle carrier-road pavement materials and developed many new degradable automobile exhaust pavement materials [6, 7, 8]. These photocatalysts are dissolved in a solution and sprayed on the surface of the road to be exposed to automobile exhaust and sunlight. However, the durability of these methods is insufficient because of the thin structure thickness [9]. The photocatalyst cannot stay on the road for a long time, and the road will soon lose the function of degrading the tail gas.  
Hence, a high-performance nano titanium dioxide epoxy emulsified asphalt mixture, instead of a solution, has been introduced as a new pavement material to solve the two problems simultaneously. In this study, it is tentatively applied to a surface wear layer with a thickness of 1-3 cm. The objective of the current research was to investigate the effects of different influencing factors on asphalt pavement degradation exhaust, taking into account the nano material's particle size, dosage in the binder, degradation time, and light intensity [10, 11, 12]. The findings can be seen as important reference indexes in the production of nano titanium dioxide epoxy emulsified asphalt.
## 3 Classification of the Reviewed Literature
### Application Domain and Their Incorporated Contexts
#### 1.1.1 E-commerce Domain
Those results reveal contextual factors, which can be used as marketing tools and in different contexts, require RS's to play different roles. Furthermore, a single algorithm may not meet all the users' needs at the same time, therefore, a hybrid RS, which takes into account different kinds of Web browsing patterns might be better. Another milestone to e-commerce domain is the migration to group commerce. Instead of the classical individual transactions such as C2C and B2C, users now utilize the advantages of social influence and price discount of group commerce to get the lowest price possible as the larger the volume of product bought the lower the unit price. The authors of [60] proposed a group-coupon recommendation mechanism that identifies and analyses three factors to target customers who are interested in a similar product and are geographically close to the retailer to assure successful group transactions. These factors are the individual preferences with respect to the product characteristics, the geographical convenience of the store and the social influence of the product. Their experimental results reveal that presenting a list of group members could significantly increase customer's willingness to purchase due to the effect of social influence. Furthermore, the recommendation mechanism helped customers to discover products of interest to them by reducing the search cost of finding coupon products.
## 2 Materials and Methods
### Estimation of Power Generation Efficiency  
The voltage \(V_{out}\) measured from the voltage divider circuit was converted to \(V_{in}\), as depicted in Figure 5, and the converted voltage is regarded as the output voltage obtained from the rotary generator (BLDC motor). The generated power \(P_{uvec\_on}\) (shore direction) and \(P_{uvec\_off}\) (offshore direction) were calculated from the relationship, \(P_{uvec\_on\_off}=V_{in}/R^{2}\), using the resistance \(R\) (=\(V_{0}/(I_{satt}-I_{sille})\)) based on the motor specifications, as presented in Table 3.  
The total generated power \(P_{uvec\_both}\) was determined by the summation of \(P_{uvec\_on}\) and \(P_{uvec\_off}\). As \(P_{uvec\_both}\) changed with time owing to periodic wave motions, the average value \(P_{WEC}\) for one wave period was calculated using Equation (3). The averaged value for each wave period was calculated for 15 wave periods of 100 s after the wave generation, and then the upper and lower 20% of \(P_{WEC}\) were excluded to minimize the effects of temporal fluctuations. Consequently, \(P_{WEC}\) was obtained by averaging the remaining six averaged values.  
\[P_{uvec}=\frac{1}{T}\int_{t}^{t+T}P_{uvec\_both}dt, \tag{3}\]  
The depth-integrated and time-averaged wave energy power \(P_{uvec}\) was calculated by Equation (4) through applying the linear wave theory.  
\[P_{uvec}=\frac{1}{16}\rho\text{g}H_{i}^{2}B_{w}\frac{L}{T}\Bigg{[}1+\frac{ \frac{4\pi h}{L}}{\sinh\left(\frac{4\pi h}{L}\right)}\Bigg{]}, \tag{4}\]  
where \(\rho\) is the water density, \(g\) is the gravitational acceleration, \(H_{i}\) is the incident wave height, \(B_{w}\) is the distance between the triangular faces of the flap, \(T\) is the incident wave period, \(L\) is the wavelength, and \(h\) is the water depth.  
The power generation efficiency \(k_{e}\) was calculated using Equation (5) by dividing \(P_{uvec}\) by \(P_{uvec}\).  
\[k_{e}=\frac{P_{WEC}}{P_{uvec}}, \tag{5}\]
# Lateral Dynamic Response of Helical Pile in Viscoelastic Foundation Considering Shear Deformation
## 2 Equivalent Model of Helical Pile  
### Establishment of Equivalent Model  
Under the action of lateral exciting force at the pile top, bending stiffness is a key indicator determining the LDR of the helical pile [43; 44; 45]. Since the helical pile is a variable cross-section structure, it is necessary to establish a reasonable stiffness model for the helical pile body. As seen in Figure 1, \(D\), \(D_{0}\), and \(H\) are the diameter of the straight pile part, diameter of the helix part, and length of the helical pile, respectively. According to the equivalent stiffness method proposed by [PERSON] et al. (2016), the helical pile can be equivalent to a cylindrical pile with the same stiffness, and the equation of equivalent stiffness can be written as  
\[E_{\rm p}I_{\rm p}=E_{\rm p}^{\prime}I_{\rm p}^{\prime}, \tag{1}\]  
where \(E_{\rm p}\) and \(I_{\rm p}\) denote the elastic modulus and cross-section inertia moment of the helical pile, respectively. \(E_{\rm p}^{\prime}\) and \(I_{\rm p}^{\prime}\) represent the elastic modulus and cross-section inertia moment of the equivalent cylindrical pile, respectively.  
Since the helical pile and the equivalent cylindrical pile have the same composition materials, it can be obtained that \(I_{\rm p}=I_{\rm p}^{\prime}\) due to \(E_{\rm p}=E_{\rm p}^{\prime}\). Then, the equivalent diameter relationship between the helical pile and equivalent cylindrical pile can be found by comparing the two inertia moments. The inertia moment of the helical pile can be decomposed into two parts:  
\[I_{\rm p}=I_{\rm p1}+I_{\rm p2}, \tag{2}\]  
where \(I_{\rm p1}=\pi D^{4}/64\) denotes the inertia moment of the straight pile part of the helical part, and \(I_{\rm p2}=hb^{3}/12\) represents the inertia moment of the helix part of the helical pile, respectively.  
As shown in Figure 2, when the cutting helix is a lateral section, the vertical projection of its cross-section can be treated as a rectangle, where \(h\) is the length from the outer side of the helix part to the outer side of the straight pile part, i.e., \(h=(D_{0}-D)/2\); the width \(b\) is determined by the thickness \(b_{1}\) and the inclination angle of helixes \(\varphi\), i.e., \(b=b_{1}\csc\varphi\). Then, the inertia moment of the helical pile can be rewritten as  
\[I_{\rm p}=\big{[}3\pi D^{4}+8b^{3}\csc^{3}\varphi(D_{0}-D)\big{]}/192, \tag{3}\]  
The cross-section inertia moment of the equivalent cylindrical pile can be expressed as  
\[I_{\rm p}^{\prime}=\pi D_{d}{}^{4}/64, \tag{4}\]  
where \(D_{d}\) is the diameter of the equivalent cylindrical pile.  
Figure 1: Simplified schematic diagram of helical pile.
## 3 Major Applications of the Smart Grid Served by 6G
### Secured Communication for Operations & Resource Management  
The interconnection, reconfigurability, the wide variety of energy device types, and information networks introduce crucial implications for data security and reliability in smart grid systems [110; 111; 112]. Therefore, a massive amount of data are generated in case of bidirectional smart grid communication, and proper security and privacy are required for the operational and other confidential instances of data [26; 113; 114]. The operational integrity of the smart grid could be compromised by covert cyberattacks or the manipulation of data [115].  
Thus, advanced encryption, authentication, and privacy mechanisms are required to protect the critical data transmitted between smart grid devices and control centers. In traditional artificial intelligence of things (AIoT) frameworks, vast amounts of identical energy-oriented data from the IoT devices of each user are transferred to cloud/edge devices for storage or decentralized processing [116]. However, this approach poses significant risks in terms of privacy infringement and potential data misuse. Federated learning (FL) is an attractive AI paradigm that ensures privacy while preserving data [116; 117]. The energy data owners (EDOs) can collectively train the shared AI model, except in cases of divulging the energy-oriented data by an edge-cloud-assisted FL framework [99; 117]. It facilitates efficient and fortified energy data communication for the respective smart grid users by integrating FL and the blockchain [99]. Furthermore, to address the challenge of limited knowledge regarding confidential multidimensional user data in real-world cases, a bi-layer deep re-enforcement-learning-inspired incentive algorithm is also introduced in [99]. This algorithm incentivizes active participation from energy data owners (EDOs), as well as encourages high-quality contributions to the shared model.  
The software-defined IoT (SDIoT) in 6G makes the CPS more vulnerable to cyberattacks [62]. A novel graphics-processing-unit (GPU)-based adaptive robust state estimator named LSTMKF was introduced in [62]. This state estimator integrates a deep learning algorithm, and is specifically based on long short-term memory (LSTM) with an extended nonlinear Kalman filter. By combining these techniques, the LSTMKF state estimator enhances security measures and effectively manages the load within the system [62]. Edge computing is required for the computational offloading of LSTMKF due to computational complexity. However, the lightweight false state injection (FSI) can poison the state information of the edge devices in the case of deep-learning-based resource allocation [94]. Thus, to enhance security and privacy, an FL with the blockchain can be integrated for edge computing-based distributed computational offloading, as well as in also enhancing energy efficiency [99; 116; 117]. Moreover, the edge/cloud devices can be energized by the NOMA-SWIPT-based technique, which is discussed in an earlier subsection [14].