esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design Principles
### Consideration of Environment Factors  
The geographical position of \"Dome A\" is 80\({}^{\circ}\)22\({}^{\prime}\) S, 77\({}^{\circ}\)21\({}^{\prime}\) E, 1228 km from the coastline, to which transporting equipment is difficult [12; 13]. At present, only in each summer, the Polar Research Institute of China (PRIC) expedition carries out scientific expeditions for about 2 weeks and the rest of the time, no one is there. It is needed that all the support equipment at \"Dome A\" can cope independently with the severe environment.  
The altitude at \"Dome A\" is about 4093 m and the atmospheric pressure is 570 Mbars [26]. This kind of low pressure has impact on the performance of internal combustion engines and computer hard drives, leading to difficult working conditions for personnel. All electronic devices and modules relying on air cooling are also affected by the geographical factors. Not only the device operating prerequisites but also the reduction in pollution should be considered. The engine needs to be researched specially and the suitable work plan needs designing beforehand. Selection of all equipment must be careful. Priority should be given to the devices with small pollution, low energy consumption, long life, and that are easy to carry. The purchasing cost may be increased owing to these demands but is worth it in the long term.  
The ambient temperature at \"Dome A\" typically ranges from \(-\)25 to \(-\)40 \({}^{\circ}\)C during the summer months and from \(-\)55 to \(-\)85 \({}^{\circ}\)C in midwenter, with the average annual temperature about \(-\)58.5 \({}^{\circ}\)C. Electronic systems must be heated and heavily insulated. Low-temperature materials must be used for all external components as well. NAOISP had customized hull with good heat preservation and controlled the inner temperature specially.  
The average wind speed is less than 2 m/s and the maximum speed is 3.4 m/s [13], so wind power cannot be used due to its scattered distribution [27]. Meanwhile, diesel generators can supply with stable electricity. In 2011 and 2019, diesel power generation facilities were put into operation successfully.
# Automatic Evaluation of Piano Performances for STEAM Education
## 4 Methodology
### Support Vector Machine (SVM) Approach  
Support Vector Machine (SVM) is one of the most powerful classifiers when applied to various classification problems, such as speaker identification and pattern recognition. The concept of SVM is to find the hyperplane to separate the groups of data from different classes, and the best hyperplane is to let the margin between classes be maximized. A hyperplane is defined as Equation (1)  
\[H\ =\ \Big{\{}x\Big{|}w^{T}x+b\ =\ 0\Big{\}} \tag{1}\]  
Figure 3: Overview of the system.  
Figure 2: An example C Major Scale in Staccato.  
where \(w,\ b\) and \(x\) are weight, bias, and data point, respectively. By considering the distance between a point \(x\) and a hyperplane as Equation (2)  
\[distance\ =\ \frac{|w^{T}x+b|}{\|w\|} \tag{2}\]  
an optimal hyperplane can be found by maximizing the distance to the closest data points from both classes.  
In this study, we used the one-against-one method for SVM multi-classification [21]. A number of binary classifiers together with one-against-one techniques can be included to extend SVM for a multi-class classification problem [22].  
As there are three classes, Good, Normal, and Bad, in our task, we need \(\frac{(3\times(3-1))}{2}\) = three SVM classifiers.  
In the SVM classification, the database is split into training-test partitions: training data are used to find the optimal hyperplane. Each testing data are predicted after the predictive model is obtained, as shown in Figure 4. The data samples are first converted from their digital waveform representation to Mel-frequency Cepstral Coefficients (MFCCs).
## 4 Results and Analysis
### Consistency Assessment with MODIS Products  
#### 4.3.1 Comparison with 8-Day MODIS Reflectance Product  
Figure 10 shows partially enlarged details (true color) of 8-day HJ-1A/B TOA reflectance (the upper row) and MODIS MOD09A1 (the lower row) for three different dates, representing the early stage (DOY 113), the vigorous stage (DOY 217) and the end stage (DOY 313) of the growth season respectively [30]. The MODIS images were produced with an RGB color scheme that employs bands B1 (as red color), B4 (as green color) and B3 (as blue color).  
Figure 9: Monthly mean TOA NDVI profiles of six typical land covers (**a–f**) and their standard deviation values at corresponding window locations extracted from the monthly HJ and WELD composites. The six ground photos show the actual ground land cover types for each NDVI temporal profile.  
Overall, a higher visual quality was evident from the upper row images which more clearly illustrated the spatial details of different land covers in Zoige plateau wetland. As MOD09 contains mostly mixed pixels, it cannot reveal the inner differences of the wetland areas. The difference of spectral reflectance for grasslands was regarded as slight in the early and end stages of the growth season [48], and therefore the background information of soil usually dominated in the coarse pixels during these periods. Obviously, the slight difference of grasslands can be clearly distinguished from HJ composites in DOY 113. On the other hand, MOD09 can only delineate the boundaries of some main types of land cover, such as the water body, peat land, and grassland. The complexity of wetland land surfaces was the highest during the vigorous stage in the three stages due to the seasonal inundation and the vigorous growth of vegetation. In this stage, most of the helobious grassland and hygrophilous grassland pixels were mixtures of grass, peat land or water. The characteristics of wetland in the vigorous stage can be clearly indicated in the HJ composition imagery (DOY 217). The MOD09 product has some shortage relative to HJ due to the interaction of coarser pixel size with landscape distribution characteristics leading to loss of spatial information. For example, the lake boundary, which can be clearly recognized in DOY 113 and DOY 313, was mixed with the peat land in DOY 217. At the end stage of the growth season, HJ can still discriminate the spatial characteristics of the wetland landscape.
## 3 Results and Discussion
### Driving Factors of Seasonal CH\({}_{4}\) Fluxes
Because rice paddies are managed ecosystems and the production, oxidation and transport of CH\({}_{4}\) are complex and nonlinear, the seasonal variability in daily CH\({}_{4}\) emissions inevitably varies due to differences in biophysical factors, soil types, regional climates, crop species and field management practices.
# Robotic Manipulation of Cumulus-Oocyte Complexes for Cumulus Cell Removal
## 1 Introduction
(3) It is difficult to accurately calculate the number of the remaining cumulus cells surrounding the oocyte, as the cumulus cells are small, varied in size, and often appear blurred due to out-of-focus imaging conditions. To tackle these challenges, we developed a new physical robotic system with AI-powered technologies. An EPSANet50 network was developed to accurately assess the maturity of oocytes based on the features of the polar body. An adaptive controller was designed to compensate for the positioning errors caused by the varying COC mass and to accurately position the COC at the target position inside the micropipette. An improved Yolov5s network was proposed to accurately detect cumulus cells for the quantitative assessment. The robotic system aims to automate and standardize the removal of cumulus cells from COCs in IVF. Compared with manual operation, the robotic system showed a higher success rate for complete cumulus cell removal and a lower discard rate of immature oocytes. Furthermore, the experimental results demonstrated that the robotic system achieved a higher amplification rate and a lower non-specific rate for genetic testing compared with manual operation, which had the potential to significantly improve clinical practices in IVF.
# Analysis of Channel Potential Recovery According to the Back Pattern in 3D NAND Flash Memory
## 2 Structure and Simulation Set-Up  
Figure 1a shows the program inhibit string of a 3D NAND flash memory structure comprising 16 WLs, a string select line (SSL), a ground select line (GSL) and a common source line (CSL). Table 1 summarizes the device parameters and voltage bias conditions.  
We used three patterns to analyze the recovery of the channel potential caused by the DCP. The threshold voltage (\(V_{1}\)) pattern of WL was divided into E, P1, P2, P3, and E = \(-\)1 V, P1 = 1 V, P2 = 2 V, and P3 = 3 V. Figure 1b shows the approximate pattern used in the simulation. WL 7, 8, and 9 were set as the E pattern, while all the remaining WLs were set to patterns P1, P2, and P3, respectively. In addition, the threshold voltages of the SSL and GSL were set to 1 V for all patterns. Figure 1c shows a pulse diagram of the verify operation and recovery time, where the DCP and recovery time ranged from 0 s to 100 ms. Finally, the inversion layer model from [PERSON] was used for the mobility, and the Shockley-Read-Hall (SRH) recombination model was used for the recombination in the TCAD simulation.
# Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers
## 6 Conclusions  
The genetic algorithm is a powerful tool for solving numerous optimization problems. The UCGA technique presents the possibility of tracking the channel evolution, which has been verified in a number of simulation experiments, reported in the paper. In the majority of the considered simulation setups, the UCGA-AE remarkably outperforms the reference RLS-AE. As a consequence, the UCGA can be considered a robust channel equalizer engine for the future wireless systems, including 6G telecommunications.  
As for any problem solved by means of the GA, it is crucial to properly set the solver parameters and initialize the population. It has been verified experimentally that the single-1 initialization gives faster UCGA convergence than the random assignment of initial values to all chromosomes.  
As a future research task, higher-order modulations, such as QPSK, 16-QAM, 64-QAM, etc., could be taken into account. Another item of particular interest is the chromosome representation--fixed point representation would be welcome as the fixed-point arithmetic can be easily implemented on popular logic structures, such as Field-Programmable Gate Arrays (FPGA), or tiny microprocessors, in contrary to floating-point operations.  
Conceptualization, M.K. and M.J.K.; methodology, M.K. and M.J.K.; software, M.J.K.; validation, M.J.K. and M.K.; formal analysis, M.J.K. and M.K.; investigation, M.J.K.; resources, M.J.K.; data curation, M.J.K.; writing--original draft preparation, M.J.K.; writing--review and editing, M.K.; supervision, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Polish Ministry of Education and Science within the bailout for task \"New methods of signal transmission and reception, design and testing of radio communication systems\" in 2021. The APC was funded by Polish Ministry of Education and Science.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
## 3 The Proposed Method
### Overview  
Given a KG \(\mathcal{G}=\{\mathcal{E},\mathcal{R},\mathcal{T}\}\), \(\mathcal{T}=\{(h,r,t)\}\), \(r\in\mathcal{R}\) is a relation and \(h,t\in\mathcal{E}\) are entities. As discussed in Section 1, on the one hand, embedding learning methods do not make full use of logical rules and suffer from data sparsity. On the other hand, high-quality rules are challenging to obtain efficiently and can only cover some facts in KGs. Our goal is to improve the embedding quality by explicitly modeling the reasoned conclusions of logical rules and discarding incorrect conclusions. Figure 2 shows the overview of Iterlogic-E given a toy knowledge graph. Iterlogic-E is a general framework that can fuse different KGE models.  
The Iterlogic-E approach consists of two iterative steps: rule mining and reasoning, and embedding learning. The first step, rule mining and reasoning, involves two modules: rule mining and rule reasoning. The rule mining module requires input of mining configuration parameters, such as the maximum length of rules and the confidence threshold of rules, in addition to KG triples. Then, it automatically obtains the soft rules from these inputs. The KG triples and the extracted soft rules are input into the rule reasoning module to infer new triples. After that, the new triples are appended to the embedding learning step as candidate conclusions. In the embedding learning step, relations are modeled as a linear mapping operation, and triple plausibility is represented as the correlation between the head and tail entities after the operation. Finally, the incorrect conclusions are filtered out by labeling the conclusions with their scores (In the experiments, we choose the conclusion with a normalized score of more than 0.99 as the true conclusion). The true conclusion will be added back to the original KG triples, and then the rule reasoning module will be performed to start the next cycle of iterative training.  
Figure 2: The framework outlines Iterlogic-E with two iterative stages: (i) rule mining and reasoning and (ii) embedding learning. In stage (i), rules and grounding rules are generated to derive new conclusions. In stage (ii), the framework simultaneously models the conclusions derived from grounding rules and the KG for learning embeddings. After embedding learning, the conclusions are injected into the KG, and then the rule reasoning module is executed to start the next round of iterative training. The weight of a rule is its partial closed-world assumption (PCA) confidence: refer to Equation (1). The facts in red are erroneous conclusions filtered out by our method. The yellow and green spots indicate the values of entity embedding and relation embedding values, respectively.
## 3 Microwave NDT Techniques
### Resonator Method
The proposed design enables the precise detection of tiny cracks on metal surfaces with a minimum detectable size of 0.5 mm. Furthermore, in this study, the discrepancy between the simulated and actual experimental outcomes was remarkably low, at only 9% [85]. The findings reveal that measurement variations correlate closely and quantitatively with the crack's width and depth, demonstrating the probe's effectiveness in pinpointing and assessing the dimensions of surface cracks with millimeter-scale depths and widths. Further, researchers have developed planar resonant probes to overcome the limitations of CSRR probes. In [86], the authors reported the imaging results of an ultra-high frequency (UHF) probe implemented using a spiral resonator (SR) with eight turns for inspecting hidden defects and corrosion metal. The results show that the UHF resonant probe rendered high-resolution images at a much lower frequency in comparison to the Ka-band near-field probe frequency [86]. The effectiveness of UHF probes can be further enhanced by developing techniques for characterizing cracks and optimizing the selection of modes. In another study, [PERSON] et al. [70] developed a planar resonator probe for high-resolution polarization-independent microwave near-field imaging, particularly aimed at detecting small defects in materials. A demonstration of the system and some results are shown in Figure 8. The probe operates at a frequency below 1 GHz and offers a resolution of approximately 3 mm. Experimental comparisons with various other probes demonstrated that this planar probe provided a considerable enhancement in imaging for low signal-to-noise ratio (SNR) scenarios, indicating its higher sensitivity and better imaging capability for both metallic and dielectric samples, including polarized targets like narrow cuts [70]. An expanded study of [70] in [87] presented the use of a UHF microwave planar resonator probe for NDT of surface-breaking defects in metallic materials. This paper demonstrated the probe's effectiveness in detecting and characterizing micro-cracks through simulation analysis and experimental results, highlighting its high sensitivity and imaging capability for detecting cracks as fine as 0.07 mm. These findings emphasize the probe's potential as a reliable crack inspection and characterization tool in various industries. Despite their success, the challenging aspect of the resonant method is optimizing the circuitry utilized with the resonance probe. The circuitry is essential to ensure the probes work at the design frequency, with slight frequency variations being detectable.
## 2 Channel Propagation Characteristics
### Characteristics of Mid-Band and High-Band Frequency Spectra
#### 2.1.2 Abundant Bandwidth  
Abundant bandwidth in the mid-band and high-band frequency spectrums for 5G communication refers to the availability of large portions of the radio frequency spectrum within these frequency ranges. The mid-band spectrum for 5G typically covers frequencies between 1 GHz and 6 GHz. It offers a good balance between coverage and capacity. The specific frequency bands that fall within the mid-band spectrum can vary from country to country, but common examples include the 2.5 GHz, 3.5 GHz, and 4.9 GHz bands [15], as presented in Table 1. The high-band spectrum, also known as the mm-wave spectrum, provides extremely high data rates and low latency but has limited coverage compared to the mid-band spectrum [16].
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## Appendix B Fitting Stretched Exponential Path Loss Model to the Measurement Data  
In this subsection, the SEPLM is verified and validated for mmwave NLOS links with actual path loss measurement data. For finding the best fit of the parameters \(\kappa\) and \(\zeta\), linear mean-square estimate is employed [47]. The path loss expression in (3) is transformed into a linear expression in the following steps:  
\[L(r)=\exp(-\kappa r^{\zeta})\] (A37)  
Taking natural logarithm of both sides of above expression, the following equation is obtained,  
\[ln(L)=\ln(\exp(-\kappa r^{\zeta}))\] (A38)  
\[\ln(L)=(-\kappa r^{\zeta})\] (A39)  
Again taking natural logarithm of both sides of above expression, the following equation is obtained,  
\[\ln(\ln(L))=\ln(-\kappa r^{\zeta})\] (A40)  
\[\ln(\ln(L))=\ln(-\kappa)+\zeta\ln(r)\] (A41)  
The above expression can be written as a linear function of the data as follow,  
\[Y=AX+b\] (A42)  
Here, \(Y=\ln(\ln(L))\), \(b=\ln(-\kappa)\), \(A=\zeta\) and \(X=\ln(r)\). \(A\) and \(b\) are the parameters that minimize the mean square estimation error and are computed according to the following expressions,  
\[b=\frac{E(XY)-E(X)E(Y)}{E[X^{2}]-E^{2}[X]}\] (A43)  
\[A=E[Y]-bE[X]\] (A44)  
Here \(E(.)\) represents the mean value of the measured data. The SEPLM parameters \(\kappa\) and \(\zeta\) can be obtained using the relation, \(\zeta=b\) and \(\ln(-\kappa)=A\).
## 3 Results and Discussion
### Performance of Enhancement Ephemeris Monitoring  
Section 2.2 shows that the satellite autonomous integrity monitoring method was used to compare the ephemeris broadcast by the satellite with the message generated by the satellite's autonomous orbit determination system and the adjacent previous set of broadcast ephemerides. The inter-satellite link ranging residual was used to evaluate the broadcast satellite ephemeris forecast errors. In the following section, the feasibility of three autonomous ephemeris integrity monitoring methods was analyzed.  
Figure 12: QQ analysis chart of the frequency hopping monitoring data (**a**) and the phase hopping monitoring data (**b**) of the C30 satellite’s clock.  
Figure 13: QQ analysis chart of the frequency hopping monitoring data (**a**) and the phase hopping monitoring data (**b**) of the C34 satellite’s clock.
# FPGA Applied to Latency Reduction for the Tactile Internet
## 6 Implementation Description  
The FK-HMD and KFF-HMD hardware modules associated with the master device (HMD) and the IK-HSD, FK-HSD, and FBF-HSD hardware modules associated with the slave device (HSD) (Figure 4) were designed using a parallel implementation in order to prioritize the processing speed. The implementations were designed in FPGA using a hybrid scheme with fixed point and floating point representation in distinct parts of the proposed architecture. In the portions that adopt the fixed point format, the variables follow a notation expressed as \([sV.N]\) indicating that the variable is formed by \(V\) bits of which \(N\) bits are intended for the fractional part and the \(s\) symbol indicates that the variable is signed. In this case, the number of bits intended for the integer part is \(V-N-1\). For the representation of floating point variables, the notation [F32] is adopted. Most of the implemented circuits were designed using a 32-bit single precision (IEEE754) floating point format representation. The fixed point format was used only on the circuit that implements the trigonometric function block (TFB) module, as illustrated in Figure 5. TFB is the module responsible for performing trigonometric operations through the hardware implementation of CORDIC (COordinate Rotation DIgital Computer) [55]. For that, a Xilinx CORDIC IP Core was used. This implementation uses data representation in a fixed-point format using the [s16.13] representation.  
As illustrated in Figure 5, the TFB module receives data from external circuits in the 32-bits floating point standard. A conversion to the fixed point numeric representation type represented by the \([s16.13]\) notation is performed through the Float to Fixed-point (F2 FP) module that has been implemented in hardware. After the CORDIC hardware operations are performed, the data in the fixed point format is transformed back to the 32-bit floating point through the Fixed-point to Float (FP2F) module which was also implemented in hardware.  
Several of the proposed methods to be presented use the constants \(L_{1}\), \(L_{2}\), \(L_{3}\) and \(L_{4}\). They represent physical characteristics of the PHANToM Omni device as illustrated in Figure 2. These constants use the 32-bit floating point numeric representation.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 3 LoRaWAN v1.1 Structure Analysis
In the 1.0 version, there is only one device root-key. However, there are two device root keys in the LoRaWAN v1.1: A Network Key (NwkKey) and an Application Key (AppKey). While the Network Key (NwkKey) in the 1.1 version is equivalent to the Application Key (AppKey) in the 1.0 version, the extra key (AppKey) in the 1.1 version is to ensure that the network operator would not be able to see the application data. Hence, the confidentiality of the message is secured. In addition, to strengthen the security, the four session-keys (FNwkSIntKey, SNwkSIntKey, NwkSEncKey, and AppSKey) are derived from the two device root-keys. The first two Network session keys (FNwkSIntKey and SNwkSIntKey) are used to ensure the data integrity for both uplink and downlink. The Network Session Encryption Key (NwkSEncKey) is used to encrypt and decrypt the uplink and downlink MAC payloads. The Application Session Key (AppSKey) is used to encrypt and decrypt the application payloads. Thus, the confidentiality of the data is secured. Figure 2: LoRaWAN v1.1 Key Distribution Structure.