esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Two Improved Algorithms
#### 6.1.2 The Addition of Inter-Satellite Angle Data
In addition, from the above results, we can find that the accuracy of the tesseral \(C_{22}\) term is much better than the zonal \(C_{20}\) term. It is due to the fact that the influence of the tesseral harmonic \(C_{22}\) is much greater than the zonal harmonic \(C_{20}\) in the calculation of gravity-field recovery.
###### Abstract
SIW, which is the realization of waveguides over printed circuit board (PCB) [9] already became the part of mainstream research and widely adopted in engineering work [10; 11; 12]. Variety of SIW cavity-backed planar CP antennas has been reported in the open literature [13; 14; 15; 16; 17; 18]. The antenna reported in [14] is a dual-band CP antenna and in that each band's radiation mechanism is different--here the multilayer substrate-structure has been utilized to realize the \"well impedance-matched\" dual-band CP antenna; however, the feeding method is still based on aperture-coupling. In [16] the SIW based 2 \(\times\) 2 polarization reconfigurable antenna array has been reported over 8.4 GHz, where in CP mode the 3 dB AR bandwidth is 3.25%; nevertheless, the large feeding network of the antenna increases the metallic and dielectric losses. Also, the feeding network arrangement in [16] makes its CP radiating unit less attractive, for the phased-array applications. In this article a single-feed CB CP antenna has been reported, working over the 34 GHz frequency band. To be noted the 34 GHz band is only an exemplary and the same concept can be applied in making the antenna over any desired frequency band as per project specification including the Ku band--which is popular for the satellite communication. The proposed antenna has been designed on a single substrate layer and fed through the another SIW section, forged within the same substrate material. Here the CPatch antenna, which is working as main radiator, has been incorporated within the rectangular cavity (RC). The RC is resonating at its dominant mode TE\({}_{110}\) and the CPatch is resonating at its dominant mode TM\({}_{110}\). The TM\({}_{110}\) mode of the CPatch is a degenerated mode and exploited to excite the circular polarization in antenna structure. To excite the degenerated modes within the patch antenna, the patch has been fed through two district points, separated by quarter-wavelength. The conceptualization of the total antenna working principle has been elaborated in a way that the cavity resonance is only playing the role to inject the energy within radiating structure and do not participate in the total antenna's radiation mechanism The CP wave is only generated due to the excitation of degenerated modes within CPatch--which is fed from two orthogonally oriented junctions. The electric current is coming from the SIW feed-line, when it enters within cavity it first reaches the first feeding point of the CPatch and then it further travels the path length equivalent to quarter-wavelength before reaching the second feeding section connected to CPatch's periphery. Figure 1 illustrate the clear idea and summarize the words into topology. Here the 90\({}^{\circ}\) phase shifter of Figure 1 is equivalents to the quarter-wavelength long electric-path--over the cavity's ground surface. Proposed antenna is simple in design and it can achieve the measured ARbandwidth of 2.3% over the working band's center frequency 33.4 GHz, with the peak gain of 9.6 dBi at 33.7 GHz.
For the problem of unknown emissivity of the target surface, the current mainstream solution is to use a multi-wavelength temperature measurement algorithm to obtain the true temperature of the target surface with unknown emissivity using a specific emissivity model. [PERSON] et al. [15] proposed a six-wavelength radiation pyrometer to measure the temperature of molten metals. [PERSON] and [PERSON] [16] proposed two three-wavelength radiation thermometry methods to measure the surface temperature of burning carbon particles. [PERSON] et al. [17] proposed an emissivity compensation algorithm for three-wavelength temperature measurement. [PERSON] et al. [18] proposed a fast fiber-optic multi-wavelength pyrometer, which used a preset emissivity model to measure the target temperature. [PERSON] and [PERSON] [19] proposed a passive measurement method of infrared spectral emissivity and temperature of materials, measuring the emissivity and temperature of two different objects. In the radiation temperature measurement of a turbine blade, due to its complex shape, inevitable measurement errors can be induced by a two-dimensional model to analyze the ambient radiation shielded by other blades. Therefore, when correcting for the reflection effect of the target blade and analyzing its reflected radiation, it is necessary to consider its actual shape and to establish a three-dimensional rotating dynamic reflection model of the blade. As the main source of reflection, adjacent blades receive radiation from other adjacent blades and the target blade. Therefore, in the calculation and correction of adjacent blade radiation, the reflected energy on the surface of adjacent blades should be considered. To solve the above problems, this paper proposes a three-wavelength radiation temperature measurement method for turbine blades based on reflected radiation error correction. Firstly, a three-dimensional rotating dynamic reflection model of turbine blades based on discrete surface elements is established. Secondly, a reflection correction method based on measuring the total radiant exitance of adjacent blades is proposed. Thirdly, this paper also proposes a three-wavelength radiation temperature measurement algorithm based on reflection error correction. This algorithm does not require an emissivity model. Combined with the reflection correction method, it can eliminate the influence of reflection and retrieve the true temperature of the target blade without obtaining the emissivity of the adjacent blade and the target blade. Finally, an experiment is designed to verify the method proposed in this paper. The analysis in this paper provides a reference for radiation temperature measurement of turbine blades with unknown emissivity.
# A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4
## 2 LTE V2X Mode 4
### Evaluating RSSI for Released Resources  
In Step c, the UE evaluates the RSSI by the linear average in units of 100 subframes up to 1000 subframes in the past. If other UEs reselect a resource, the RSSI for the released resources may be greatly evaluated since the average value includes the resources that have been used before their reselection. In such a case, they are no longer candidates for the UE despite the resources released by other UEs. As a result, it results in inefficient use of resources.  
Figure 6 shows an example of the RSSI evaluation for the released resources. This figure indicates that UE2 has reselected in subframe 820 and UE1 reselects in subframe 1000. Now, \(R_{1,1020}\) is a candidate resource for UE1 corresponding to the resource released by UE2 in subframe 820. When evaluating the RSSI for \(R_{1,1020}\), UE1 largely evaluates it because it includes the resources before UE2's reselection. \(R_{1,1020}\) may not be able to be included in \(\mathbb{S}_{B}\) since \(\mathbb{S}_{B}\) is composed of resources evaluated to be small RSSI.  
Figure 5: An example of exclusion by Steps a, b, and c.
# Petrophysics Parameter Inversion and Its Application Based on the Transient Electromagnetic Method
## 3 Numerical Simulation
### Response Characteristics of the Geoelectric Model  
To verify the reliability and accuracy of the forward and inversion algorithms for rock physical parameters based on the transient electromagnetic (TEM) method, numerical simulations were conducted using the geoelectric model described previously. The magnetic field response characteristics were obtained, and the predicted responses were compared with the theoretical values. The parameters of the numerical simulation observation system are as follows: the transmitter loop has dimensions of 200 m \(\times\) 200 m; the current source amplitude is 10 A; the effective area of the receiving coil is normalized; the observation point is located at coordinates (0, 0, 0); the sampling delay ranges from 1 \(\times\) 10\({}^{-4}\) ms to 10 ms; and 40 sampling time points are logarithmically spaced within this interval.  
Figure 4 presents the transient electromagnetic (TEM) response curves corresponding to the porosity variations shown in Figure 2. From subfigures (a) to (f), the porosity increases progressively. The red curves represent the theoretical magnetic field attenuation results, and the black curves show the results obtained from inversion. It is evident that the magnetic field amplitude is closely related to the formation porosity, with a larger porosity resulting in greater variation in the magnetic field response. Figure 5 shows the relative errors of the numerical simulation for different porosity values. The inversion results agreed well with the theoretical values, with relative errors below 5%. Figure 6 illustrates the TEM response curves corresponding to the water saturation variations shown in Figure 3. From subfigures (a) to (f), the water saturation increases incrementally. The magnetic field response is sensitive to water saturation, with higher saturation levels producing more pronounced changes in the magnetic field amplitude. Figure 7 displays the relative errors associated with different water saturation values. The theoretical and inverted results are in good agreement, with relative errors of less than 5%. These results demonstrate the reliability and accuracy of the proposed numerical simulation and inversion algorithm.  
Figure 4: Magnetic field response curve when porosity changes. (**a**) porosity 15%, (**b**) porosity 20%, (**c**) porosity 25%, (**d**) porosity 30%, (**e**) porosity 35%, and (**f**) porosity 40%.  
Figure 5: Magnetic field response error curve when the porosity changes.  
Figure 6: Magnetic field response curve when the water saturation changes. (**a**) water saturation 20%, (**b**) water saturation 30%, (**c**) water saturation 40%, (**d**) water saturation 50%, (**e**) water saturation 60%, and (**f**) water saturation 70%.  
Figure 7: Magnetic field response error curve when the water saturation changes.
## 3 Discussion
While our current findings cannot directly establish a relationship between 4-hydroxycinnamic acid and bladder cancer, the higher levels of this compound in healthy volunteers may be evidence of its role in cancer prevention. We have also identified changes in the pentose phosphate pathway demonstrated by the observed decrease in gluconic acid levels. Gluconic acid, a naturally occurring carboxylic acid, as well as its derivatives are used in food, pharmaceuticals, and cosmetics as additives or buffer salts; however, the mechanisms of their biological activity are still not completely understood [20; 37]. Recent research in this area has indicated that gluconic acid and its derivatives may contain antioxidative properties [37]. Lower concentrations of this compound in the BC group as compared to the healthy control might be related to oxidative stress, which has been implicated in the pathogenesis of numerous diseases [38]. Decreased concentration of gluconic acid in the urine of the bladder cancer patients has been reported previously [16; 17]. Interestingly, gluconic acid such as carnosine and \(p\)-coumaric acid has antioxidative properties and was also found to be present in lower quantities in the urine of the BC group. We acknowledge that there are limitations to our study. The analysis was performed with a relatively small number of patients without differentiation of cancer type. However, as emphasized throughout the manuscript, the results do not provide a well-validated panel of biomarkers mainly due to the small number of participants and larger cohort study to confirm or deny this work's results. On the other hand, based on this study, we were able to identify some interesting metabolites not described previously in the view of BC that might contribute to the pathogenesis of bladder cancer or serve as protective compounds in cancer development, and this information can be a good starting point for targeted analysis of these compounds and the pathway they are involved in. Moreover, our work has demonstrated that TFME hyphenated to LC-HRMS can be an alternative platform for untargeted urinary analysis toward biomarker discovery.
## 5 Numerical Investigation on DOP
### Effect of Compression Strength  
As shown in Figure 19, the DOP decreases with increasing compression strengths of UHPC targets when the striking velocity is fixed. Comparing the numerical results of UHPC targets under projectile penetration with the striking velocity at 550 m/s, where DOPs of 100 MPa, 120 MPa, 140 MPa,160 MPa, and 180 MPa targets are 75.2 cm, 65.4 cm, 59.2 cm, 56.1 cm, and 53.2 cm, respectively, it can be observed that the increments between the adjacent compression strength are 9.8 cm, 6.2 cm, 3.1 cm, and 2.9 cm, respectively, showing a decreasing trend. Hence, the enhancement of compression strength contributes to improving the resistance of the UHPC target against projectile penetration. However, it should also be noted that the improvement effect diminishes with increasing compression strengths.
## 4 Discussion
### Influence of External Covering Layer  
In Figure 17, the tensile load-displacement response curves of samples with or without an external covering layer are reported for direct comparison.  
This figure is followed by Table 4, where the features of the compared samples are reported together with the experimental results. For the identical samples, the average values of the parameters were calculated and reported below the two samples.  
Samples 14a and 14b, which were realized without an external covering layer, show slightly worse behavior in terms of strength compared to the control specimen (sample 1). However, their behavior is worse in terms of ultimate elongation and also the type of failure. Hence, it can be concluded that the absence of the covering layer does not improve the adhesion between the layers, but, instead, worsens it.  
Figure 16: Tensile load-displacement response curves of samples 1 and 10, which were sandblasted with standard grit and corundum, respectively.
###### Abstract  
High demand for 6G wireless has made photonics-aided D-band (110-170 GHz) communication a research priority. Photonics-aided technology integrates optical and wireless communications to boost spectral efficiency and transmission distance. This study presents a Radio-over-Fiber (RoF) communication system utilizing photonics-aided technology for 4600 m long-distance D-band transmission. We successfully show the transmission of a 50 Gbps (25 Gbaud) QPSK signal utilizing a 128.75 GHz carrier frequency. Notwithstanding these encouraging outcomes, RoF systems encounter considerable obstacles, including pronounced nonlinear distortions and phase noise related to laser linewidth. Numerous factors can induce nonlinear impairments, including high-power amplifiers (PAs) in wireless channels, the operational mechanisms of optoelectronic devices (such as electrical amplifiers, modulators, and photodiodes), and elevated optical power levels during fiber transmission. Phase noise (PN) is generated by laser linewidth. Despite the notable advantages of classical Volterra series and deep neural network (DNN) methods in alleviating nonlinear distortion, they display considerable performance limitations in adjusting for phase noise. To address these problems, we propose a novel post-processing approach utilizing a two-dimensional convolutional neural network (2D-CNN). This methodology allows for the extraction of intricate features from data preprocessed using traditional Digital Signal Processing (DSP) techniques, enabling concurrent compensation for phase noise and nonlinear distortions. The 4600 m long-distance D-band transmission experiment demonstrated that the proposed 2D-CNN post-processing method achieved a Bit Error Rate (BER) of 5.3 \(\times\) 10\({}^{-3}\) at 8 dBm optical power, satisfying the soft-decision forward error correction (SD-FEC) criterion of 1.56 \(\times\) 10\({}^{-2}\) with a 15% overhead. The 2D-CNN outperformed Volterra series and deep neural network approaches in long-haul D-band RoF systems by compensating for phase noise and nonlinear distortions via spatiotemporal feature integration, hierarchical feature extraction, and nonlinear modelling.  
2025253661 2025253661 2025253661 202525366
# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 3 Processing Capability
### Higher Level: Highly Analysis Ready Data
#### 3.3.2 Clear Sky Observations  
FORCE clear sky observations (FORCE CSO) mines data availability (Figure 7), e.g., to make informed decisions about the parameterization or applicability of a specific method, or to identify areas where commissions errors reduce the amount of usable data [43]. Clear sky observations are defined in response to the quality control settings. For a given time period (in years) and interval (months), the number of clear sky observations are counted, and statistics on the temporal difference between clear sky observations are calculated; currently available statistics are the average, standard deviation, minimum, maximum, range, skewness, kurtosis, median, 25/75% quantiles, and interquartile range. The beginning and end of the intervals act as boundaries for this assessment. This processing scheme reflects the fact, that a single measure of data availability might not yield representative results. As an example (Figure 7), data availability for the first and second half of 2018 is equal in terms of the number of observations and the average time between observations. However, there are large differences in the maximum difference as data are clumped in the first half. This has important implications, e.g., for the detectability of harvesting events.
# Hardware-Aware Mobile Building Block Evaluation for Computer Vision
## 3 Capturing Trade-Offs and Search Space Difficulty  
### Random Sampling versus Best Models  
The aim of hardware-aware network design is to optimally use the available computing budget to find solutions that are as close as possible to the accuracy/complexity pareto front for the targeted hardware platform. Since, in practice, there is a bound on the total number of network variants that can be evaluated within a reasonable time, it is beneficial to start from a family with similar _best_ networks, but a higher density of _good_ networks.  
In [19], [PERSON] et al. demonstrated their paradigm of comparing EDFs on different model families by generating the NDS dataset, which consists of 5000 models from each corresponding design space. In Figure 1, we show 5000 trained models from two of the network families in this dataset, the NASNet [21] and the PNAS [11] model families, and highlight the best of those models on an accuracy/complexity curve.  
Comparing only the best models from these larger sampling spaces, as depicted in Figure 1a, indicates that both families are capable of achieving similar results. However, when we look at all the models in the scatterplots and the corresponding EDF in Figure 1c, we can see that, when randomly sampled, the PNAS family produces more models with good accuracy than the NASNet family. This means that, for a model designer or NASNet, it is easier to find a well-performing model for PNAS than for NASNet.  
Instead of trying to estimate the true pareto front, we propose to use a random sampling accuracy/complexity curve based on a much lower number of samples (only 130). As can be seen in Figure 1b, the quality of this approximation decreases more rapidly for the NASNet family. This means that, like the approach in [19], it implicitly captures the underlying difficulty of finding good solutions, but, as an added value, this approach maintains the trade-off information.  
In essence, our methodology to evaluate model families involves executing the following steps: (1) create a design space for the model family to be evaluated; (2) randomly sample \(\pm\)130 models from this design space and train them in a low epoch regime; (3) collect hardware costs, such as latency from the target hardware platforms; and (4) create a pareto curve based on the optimal accuracy/complexity points from the sampled models.
## 2 Materials and Methods  
### Geological and Hydrological Setting of the Study Area  
DEPZ, located at about 35 km south-east of Dhaka, the capital city of Bangladesh, was selected as the study area in the present research work. This area belongs to Dhamsona Union under Savar Upazila of Dhaka District. The area is situated at the southwestern fringe of a Pleistocene terrace named the Madhupur Tract, an elevated landscape distinct from the surrounding Fluvio-deltaic plains by the Ganges, Brahmaputra and Meghna River. The major geomorphic units of the area are: the high land, the low lands or floodplains, depressions, swamps, marshes and abandoned channels. Soil of the Modhupur Formation, in general reddish brown in color, contains pre-existing paleosol materials. Hydrology of the study area is governed by rainfall intensity and distribution, permanent or ephemeral water bodies and rivers or canals. The average rainfall distribution pattern in various months shows a distinct conformation with the climatic pattern prevailing, with strong Monsoon influence. The average annual rainfall in dry and wet season in the area is about 25 and 380 mm, respectively. The Bansi-Daleshwari and Turag River comprise the drainage network of the area-Bansi on the west and Turag away on the east.
## 5 Case Study
### Bayesian Optimization  
The adopted Bayesian optimization internally manages a Gaussian process model (GP) of the objective function and uses evaluations of the function itself to train the model. Bayesian optimization uses an acquisition function, which the algorithm uses to determine the next point to evaluate. The acquisition function can compensate for sampling at unexplored points or explore areas that have not yet been modeled [35]. A summary of the actual treatment of this technique can be found here. The Bayesian optimization algorithm attempts to minimize an objective function \(f(x)\) in a finite domain. The function can be deterministic or stochastic (as assumed in this research), which means that it can give different results when evaluated at the same point.  
The key elements of the process are:  
1. a Gaussian process of \(f(x)\)[36]
2. a Bayesian updating procedure to change the GP with each new evaluation of \(f(x)\)  
Figure 5: Outline of analysis performed for AI training.  
Figure 6: Stochastic variation in mass.  
an acquisition function \(a(x)\) (based on the GP model of \(f\)) that is maximized to determine the next point of \(x\) for the evaluation of the objective function
4. an evaluation of \(y_{i}=f(x_{i})\) for randomly taken points of \(x_{i}\) within the limits of the variables
5. an update of the GP of \(f(x)\) to obtain a posterior distribution
6. a definition of the new point \(x\) that maximizes the acquisition function on \(a(x)\)  
The interested reader is referred to [37; 38; 39; 40]. The algorithm implements a stop criterion when the maximum number of allowed iterations is reached.