esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bayesian Estimation  
In contrast to maximum likelihood estimation, a parameter such as \(\theta\) is considered not only as an unknown deterministic quantity but also as an unknown random variable. By observing the \(s\)-th sample \(D_{s}\), the probability density function \(P(D_{i}|\theta)\) is transformed into the posterior probability \(P(\theta|D_{i})\), leading to the pursuit of Bayesian estimation. The core objective of Bayesian estimation is to derive the optimal estimation of the parameter \(\theta\) through Bayesian decision theory, thereby minimizing the total expected risk.  
Bayesian statistics diverges from traditional statistical methods by emphasizing the incorporation of subjective prior information regarding the parameters used for the reliability analysis process. The prior distribution is derived from the experimenter's subjective assessment, informed by data collected prior to experimentation. While this subjective approach may appear to conflict with the objective nature of scientific inquiry, it offers significant advantages by leveraging prior knowledge alongside the principles of the likelihood function. This approach effectively addresses the limitations of subjectivity in selecting sufficient statistics for classical estimation, allowing for a more coherent integration of sample randomness and sufficiency.  
We will outline the procedure for Bayesian estimation. We begin by assuming that the parameters \((a,\lambda)\) follow independent gamma distributions, specified by parameters \((a,b)\) and \((p,q)\), respectively. Furthermore, let \(X=(X_{1:m:m:n},\ldots,X_{m:m:m:n})\) denote the progressive Type-II observed samples derived from the generalized Pareto distribution.  
Let \(\widehat{x}=(x_{1},x_{2},\ldots,x_{s},x_{s+1},\ldots,x_{m})\) represent the value of \(X\). The prior distribution is obtained as follows:  
\[\pi(\alpha,\lambda)=\frac{b^{a}q^{p}}{\Gamma(a)\Gamma(p)}a^{a-1}\lambda^{p-1} e^{-ba}e^{-q\lambda},\quad a,b>0,\quad p,q>0. \tag{23}\]  
Based on the previously mentioned joint prior distribution and the likelihood distribution, the joint posterior distribution can be articulated as follows:  
\[\pi(\alpha,\lambda|\hat{x})=\frac{\pi(\alpha,\lambda)l(\alpha, \lambda|\hat{x})}{\int_{0}^{\infty}\int_{0}^{\infty}\pi(\alpha,\lambda)l( \alpha,\lambda|\hat{x})\mathrm{d}\lambda\mathrm{d}\alpha} \tag{24}\] \[\propto a^{m+a-1}\lambda^{m+p-1}e^{-ba-q\lambda}\prod_{i=1}^{m}( \lambda x_{i}+1)^{-(\alpha+1)}\left[(\lambda x_{i}+1)^{-\alpha}\right]^{R_{i}}. \tag{25}\]  
Given that \(\pi(\alpha,\lambda|\hat{x})\) involves the solution of a double integral, it presents significant analytical challenges. Therefore, we will utilize the TK algorithm as discussed by [20] alongside the MH algorithm to derive the approximate posterior distribution expectations. Additionally, we will obtain the approximate Bayesian estimations of \(\alpha\) and \(\lambda\).
# Imaging of Cell Structures Using Optimized Soft X-ray Contact Microscopy
## 3 Results and Discussion  
### Optimization of SXCM Technique for Cell Imaging  
The optimization aimed to select the appropriate absorbed energy of SXR radiation and conditions for developing the photoresist after the SXR exposure. PMMA is a standard, often-used photoresist in SXCM because its particular optical properties provide high resolution. PMMA is also a suitable material for the cell culturing [26; 27; 28]. The photoresist development required the optimized content of the developer mixture (the optimal ratio of MIBK and isopropanol). The thickness of the dissolved photoresist depends on the SXR absorbed energy and the developer concentration. It translates into the height of the cell imprint and, thus, the contrast obtained. To our best knowledge, there is no literature data on the effect of the soft X-ray exposure on the quality of the internal structure imaging of cells of different origin and morphology. Therefore, we adjusted the basic parameters of SXCM imaging to visualize subtle internal structures of adherent DU145, HCC38, and MSC cells.
# Evaluation of the GPS Precise Orbit and Clock Corrections from MADOCA Real-Time Products
## 4 Orbit and Clock Evaluation
### Clock Error Statistic  
Similar to the evaluation process of the orbit error, clock-offset products comparison between different Analysis Centers (ACs) typically cannot be directly made because of differences in the choice of the GPS system timescales which influence all satellites of a constellation in the same way and result in a constant systematic deviation [19]. In order to eliminate the deviation, an ensemble clock error should be counted and applied to the clock errors of each satellite. As such, the method of selecting one satellite as a reference (so-called single-satellite method) is typically adopted by most literature [20; 21]. This method requires a reference satellite which has the highest clock-offset precision, otherwise the evaluation results of all the other satellites will be larger than their real values, but such a reference is usually not easy to determine. Therefore, [PERSON] et al. proposed the so-called all-satellite reference method [22] which selects all satellites in the same epoch to participate in the calculation of the system deviation. The solution formula of this method is adopted in this research and it can be expressed as:  
\[\Delta\dot{\epsilon}_{\mathrm{e}}^{\mathrm{i}}=T_{\mathrm{e}}^{\mathrm{i}}- \dot{\epsilon}_{\mathrm{e}}^{\mathrm{i}}-\mu_{\mathrm{e}}, \tag{2}\]  
where  
\[\mu_{\mathrm{e}}=\sum_{\mathrm{i}}^{\mathrm{n}}\frac{\left(T_{\mathrm{e}}^{ \mathrm{i}}-\dot{\epsilon}_{\mathrm{e}}^{\mathrm{i}}\right)}{\mathrm{n}} \tag{3}\]  
denotes the average of precise-minus-MADOCA clock values of all satellites (i.e., timescale difference), which is calculated epoch-wisely. The variation of average does not affect the positioning results,since the average is the same for all satellites and would be absorbed by the estimable receiver-clock offsets of PPP users at each epoch. The number of satellites is n. T\({}_{\text{e}}^{\text{i}}\) and t\({}_{\text{e}}^{\text{i}}\) are the precise clock bias and MADOCA clock bias, respectively. Subsequently, the individual clock systematic deviations are corrected by this ensemble average. It is worth noting that in this comparison the gross error caused by the clock outliers or non-existence of the clock-offset values in the raw data is eliminated.
## 2 Materials and Methods
### Color Analysis (L*a*b*) and Browning Index  
The values of \(L^{*}\), \(a^{*}\) and \(b^{*}\) were extracted from the surface of the apple snacks. Total color differences, \(\Delta E\), between fresh and dried apple were calculated according to the following formula [6]:  
\[\Delta E^{*}=\sqrt{\left(a_{0}^{*}-a_{1}^{*}\right)^{2}+\left(b_{0}^{*}-b_{1} ^{*}\right)^{2}+\left(L_{0}^{*}-L_{1}^{*}\right)^{2}} \tag{1}\]  
where \(a_{0}\)* is the degree of redness/greenness of fresh apple snacks, \(b_{0}\)* is the degree of yellowness/blueness of fresh apple snacks, \(L_{0}\)* is the degree of lightness/darkness of fresh apple snacks, and \(a_{1}\)*, \(b_{1}\)*, and \(L_{1}\)* are the color values of dried apple snacks.  
Another parameter used to determine color changes on the surface of the apple was the browning index (BI) [7,8]:  
\[\text{BI}=\frac{100(x-0.31)}{0.17},\text{ where }x=\frac{a^{*}+1.75L^{*}}{5.645 L^{*}+a^{*}-3.012b^{*}} \tag{2}\]  
The BI is derived from the \(L^{*}\), \(a^{*}\) and \(b^{*}\) values obtained from the color analysis. It quantifies the degree of browning or discoloration of the apple surface.
## 4 Experimental Results and Analysis
### Feasibility Verification of the Feature-Enhanced Point Transformer
These results demonstrate that the feature-enhanced point transformer can substantially improve the accuracy of the point cloud semantic segmentation compared to the original network, and it holds great potential for practical applications. To provide a more intuitive demonstration of the semantic segmentation results, we selected some models where the differences in semantic segmentation performances were more evident in the visualizations, as shown in Figure 11.
## 3 Results and Discussions
It is observed from Figure 9a-c that the convergence occurred on the 34 th, 41 st, and 34 th iterations in the MFO algorithm, whereas, in DFO, it occurred on the 52 nd, 52 nd, and 55 th iterations in response L. It is confirmed from Figure 9d-f. that the convergence occurred on the 40 th, 54 th, and 48 th iterations in the MFO algorithm, whereas, in DFO, it occurred on the 61 st, 43 rd, and 63 rd iterations in response D. It is evident from Figure 9g-i. that the convergence occurred on the 40 th, 41 st and 45 th iterations in the MFO algorithm, whereas, in DFO, it occurred on the 61 st, 57 th, and 67 th iterations in response W. It is confirmed from Figure 9a-c. that the convergence occurred in PSO for response L will be at the 89 th, 63 rd and 87 th iterations for RMSE, MAE, and R\({}^{2}\) values that are higher than the convergence iteration numbers in the MFO algorithm. It is also observed from Figure 9d-i. that the convergence occurred in a higher iteration number in the case of response D and W in the PSO algorithm compared with the MFO. Hence, the MFO algorithm outperformed compared to the DFO algorithm in the convergence of objective values. Figure 5: Comparison of closeness values for (**a**) Response L; (**b**) Response D; (**c**) Response W.  
Figure 6: Comparison of actual and predicted values for (**a**) Training Dataset-Response L; (**b**) Training Dataset-Response D; (**c**) Training Dataset-Response W; (**d**) Testing Dataset-Response L; (**e**) Testing Dataset-Response D; (**f**) Testing Dataset-Response W. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Response**} & **Kernel** & \multirow{2}{*}{**Solver**} & **Validation** & **Validation’s Scheme** & **\% of Test** & **No. of Test** \\  & **Function** & & **Scheme** & **Parameter** & **Data** & **Data** \\ \hline L & Linear & SMO & Holdout & 14 & 17.1 & 20 \\ D & Linear & SMO & Holdout & 16 & 21.7 & 25 \\ W & Linear & SMD & Holdout & 15 & 25.2 & 29 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Best Parameters of the SVR model for responses L, D, and W.  
Figure 7: Probability Plot for (**a**) Training Dataset-Response L; (**b**) Training Dataset-Response D; (**c**) Training Dataset-Response W; (**d**) Testing Dataset-Response L; (**e**) Testing Dataset-Response D; (**f**) Testing Dataset-Response W.  
Figure 8: Performance measures of SVR models (**a**) RMSE; (**b**) MAE; (**c**) R\({}^{2}\). Figure 9: _Cont._  
Figure 9: Convergence plot of performance of SVR for (**a**) Response L—RMSE; (**b**) Response L—MAE; (**c**) Response L—R\({}^{2}\); (**d**) Response D—RMSE; (**e**) Response D—MAE; (**f**) Response D—R\({}^{2}\); (**g**) Response W—RMSE; (**h**) Response W—MAE; (**i**) Response W—R\({}^{2}\).
## 5 Evaluation of Elements of a Generation
### The Cost Function
#### 5.2.2 Measuring How Fast the Output Explode in Unstable Systems  
It is common that the proposed EA generates many unstable controllers particularly in the first generations. Instead of discard these controllers is very useful to compare two of them, because the \"less unstable\" could be evolve to a stable one. In present work, XppAut software [30; 35] is used to compute the numerical values. In order to evaluate when a control law causes an unstable response a characteristic of XppAut is used. XppAut has a pre-specified bound, that is the maximum value a variable can reach in magnitude. The program triggers an overflow event and stop the computation. This event truncates the resulting time-series, that is, a shorter time-series is obtained than the will be expected if the simulation had not been stopped. This can be used to measure the divergence speed, in other words, how fast the system 'explode', using the expression  
\[J_{od}(t,e(t))=1-\frac{t_{sim}}{t_{f}-t_{0}} \tag{13}\]  
where \(t_{0}\) is the initial time; \(t_{f}\) is the total simulation time, that is the time that simulation would take if the system does not explode; \(t_{sim}\) is the time at which the system explode, that is the time where any state trespass the acceptable bound.  
From (13) it can be observed that if simulation is completed \(t_{sim}=t_{f}-t_{0}\) then \(J_{od}=0\). On the other hand if simulation is truncated by out-of-bound event, that is \(t_{sim}<t_{f}-t_{0}\) then \(0<J_{od}<1\). The smaller \(t_{sim}\), closer to 1 is \(J_{od}\). Hence \(J_{od}\) is a measure of how unstable a system is.  
In Figure 6 there are compared two output signal caused by two different control-laws, \(u_{a}\) and \(u_{b}\), for the same system. In the figure, \(t_{0}=0\); \(t_{f}=10\); \(e_{a}\) and \(e_{b}\) are error functions obtained with \(u=u_{a}\) and \(u=u_{b}\) respectively; \(e_{max}\) is the bound for \(e\), that is, the simulation stops when \(|e|>e_{max}\); signals \(e_{a}\) and \(e_{b}\) explode at \(t_{sim_{a}}\) and \(t_{sim_{b}}\) respectively. In the figure \(t_{sim_{a}}=3.4\) and \(t_{sim_{b}}=7.9\). According to (13) this result on \(J_{od_{a}}=0.22\) and \(J_{od_{b}}=0.66\), which agree with the fact that \(u_{a}\) explode sooner than \(u_{b}\). Note that, according to (13), \(J_{od}\in[0,1]\), so it is convenient to scale its value, in order to make it more significant.
## 5 Low-Power Cellular Technologies for IoT
### NB-IoT
#### 5.2.1 NB-IoT Standardisations  
The initial standardisation of NB-IoT was established in the 3 GPP standard release 13 to utilise 180 kHz bandwidth LTE resource blocks for the uplink and downlink channel transmissions. The 3 GPP release 13 standard outlines the LTE and GSM support capabilities for machine-type communications and the design targets of achieving a long battery life, massive connectivity, greater coverage, deeper penetration, and positioning enhancement. It was originally designed with the LTE modulation schemes, channel coding techniques, and numerology features in the communication type with a low data rate, restricted mobility, and latency requirements. To reduce the IoT module cost and complexity, the LTE-connected mode mobility was removed in NB-IoT [64].  
To provide an enhanced user experience in NB-IoT applications and extend the applications to other use cases, 3 GPP LTE release 14 was introduced. This allowed service offerings such as coverage enhancement, positioning accuracy, improved data rates, multicast capability, lower power class features, non-anchor carrier support, and the opportunity to support more IoT devices in one eNB [65]. Release 15 optimises early data transmission (EDT) support during the radio access (RA) connection procedure. It is used to enable transmission in the uplink channel within the RA connection procedure thereby reducing the connection setup time and signalling overhead, as shown in Figure 4. The process in EDT is that the IoT module initially indicates the intention to transmit data during the RA procedure by making use of the special (N) narrowband physical random-access channel (PRACH) preamble dedicated to EDT by the evolved base station in the system information blocks (SIBs). Data transmission in releases 13 and 14 is only possible after the RA procedure is completed. This standard has the advantage of reducing latency and improving the battery life of IoT devices deployed in limited network reception areas. The eNB transmits the maximum transport block size (TBS) for the data in EDT mode. Release 16 covers the enhancement in the 3 GPP standardisation release 15 5G new radio (5G-NR) [30]. The NB-IoT enhancement in the standard includes the self-organising ability, able to co-exist with NR, mobility enhancement, improved multi-carrier operation, scheduling enhancement, improved transmission efficiency, and improved user equipment power consumption. The support for non-terrestrial access in 5G NR enhancement will enable the support for NB-IoT and eMTC deployment using satellites and high-altitude platforms (HAPs). The deployments standard is based on MNOs and UE capabilities but 3 GPP release 14 and higher is recommended. With new IoT network roaming agreements in place, IoT devices will work out of the box in developing countries when they become available.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 3 Optimization
### Ga  
Since traditional optimization algorithms deal with local properties of the iteration points, they may stall at a local extremum. In contrast, GAs use a random search in addition to a systematic search, which prevents these methods from hitting a local minimum or maximum. In fact, it was proved that under certain conditions, Gas converge to the global optimum [147]. Gas are structured to solve real-world problems by simulating processes that occur during natural evolution. The coding mechanism maps each solution as a unique binary string. The set of strings constitutes a population, and it evolves from generation to generation through the use of genetic operations, the most common of which are reproduction, crossover, and mutation [148]. The number of function estimates in one generation depends on the generation base used for optimization. For conventional Gas, determining the population size depends on the specific task and is highly dependent on chromosome length and power [149]. When applying Gas, most of the steps can be performed in different ways, and there are many parameters that require precise tuning. Consequently, it may be necessary to test different combinations of possible parameter choices and possible ways of applying genetic operators in order to achieve the maximum benefit from GA for each task. The concept of the genetic algorithm, first formalized in [150] and extended to functional optimization in [151], involves the use of optimization search strategies modeled after Darwinian notions of natural selection and evolution. Many extensions of and improvements to the simple GA optimizer have been developed and used. Among them are the concepts of elitism [152], the use of steady-state algorithms [153], the use of real coded parameters [154], the concepts of community-based genetic algorithms [155], and approaches applicable to the traveling salesman problem.
# Improving Non-Autoregressive Machine Translation Using Sentence-Level Semantic Agreement
## 3 Method  
Previous work [8; 9] has pointed out the improvements in word alignment, especially how it can improve the performance of the NAT model. However, the previous work is mainly based on the word-level semantic alignment. Meanwhile, the effect of self-attention [3] is to find the nested words in the source sentence for the word in the target sentence. From the model to the training objective, there is no mechanism to ensure the semantic alignment between the source and target sentence. In this paper, we attempt to explore the sentence-level semantic relationship between the source and target sentences. We propose a novel method, Sentence-Level Semantic Agreement (SLSA), to ensure the representation of the source and generated sentences is similar.  
First, we need to obtain the sentence-level representation of the source and generated sentences. As pointed out in previous work [10; 11], the average of the word-level representations is a effective way to represent a sentence. Similarly, in machine translation, it is also a good representation for a sentence [12; 13]. So in this work, we also utilize this method to represent a sentence.  
Given a source sentence, we use \(H_{E}=\{h_{E}^{1},h_{E}^{2},\dots,h_{E}^{m}\}\) as the word-level representations output by the top layer of an encoder. Similarly, \(H_{D}=\{h_{D}^{1},h_{D}^{2},\dots,h_{D}^{n}\}\) are the word-level representations output by the top layer of a decoder. The source sentence-level representation \(\overline{H}_{E}\) is computed as:  
\[\overline{H}_{E}=\frac{1}{m}\sum_{t=1}^{m}h_{E}^{t}. \tag{6}\]  
The generated sentence-level representation \(\overline{H}_{D}\) is computed by the same method:  
\[\overline{H}_{D}=\frac{1}{n}\sum_{t=1}^{n}h_{D}^{t}. \tag{7}\]
## 2 Materials and Methods
### Procedures
#### 2.3.3 sEMG Instrumentation  
The sEMG response of the six core muscles analysed was collected across all experimental plank variations. The sEMG signals were recorded for a 10-s period by telemetry through the Mega WBA(r) wireless system (Mega Electronics LTD, Kuopio, Finland), using pre-gelled disposable bipolar Ag-AgCl surface electrodes (Arbo Infant Electrodes, Tyco Healthcare, Germany). Electrodes were placed parallel to the muscle fibre orientations with a centre-to-centre spacing of 20 mm [54]. Previously to the electrode placement, the testing skin area was prepared shaving and cleaning with alcohol wipes the overlying skin region for each examined core muscle. Electrodes were then placed on the right side of the trunk for the RA and LES, assuming their symmetrical activation during isometric abdominal exercises [55], and on both sides for the IO and EO muscles, looking for possible differences in muscle activation. Electrode positions were chosen according to the procedures of Surface EMG for Non-Invasive Assessment of Muscles (SENIAM) project [54], and if not obtainable, following related studies [33; 56; 57]. For the location of the anatomical landmarks, the manual palpation technique was used with a skin marker to correctly point and place everyelectrode [58]. Lastly, the quality of the sEMG signal was visually inspected when asking participants to contract the involved musculature by performing basic movements.
## 5 Results and Discussion
### Discussion  
Combined with the above experimental results, it can be concluded that the effect of the Euclidean clustering algorithm is highly dependent on the design of the distance threshold, the distribution of the MLS point cloud data is uneven and unstructured, and the problem of under-segmentation often exists in the application of road surfaces extraction. The MLS point cloud data are numerous, and the spatial distribution is extremely irregular, which makes it impossible to segment the road surface extraction using RANSAC. The accuracy of the traditional regional growth algorithm depends on the growth standard and location of the seeds, which is computationally expensive and easily causes over-segmentation. The method proposed in this paper enables the road surface to be extracted completely, which greatly shortens the processing time and improves the efficiency. It can be observed from Table 7 that the proposed method achieved over 95% in all three quality evaluation metrics for the road surface extraction in both scenarios. This indicates that the vast majority of the roads were correctly extracted, and the results closely resemble the manually collected road surface data.  
In conclusion, the experimental analysis of MLS point cloud data in two highway scenarios demonstrated that the combination of supervoxel segmentation and improved regional growth segmentation algorithm performs well in highway road surface extraction. Not only does it exhibit high accuracy, but it also outperforms other automated extraction algorithms in terms of efficiency. In addition, the proposed method successfully extracted precise road surface point cloud data in both flat and undulating road scenarios, proving its robustness across different structural data applications. Therefore, the proposed approach has advantages in terms of road surface extraction accuracy and algorithm efficiency.
# Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
## 3 Experimental Setup of A-RoF Based DAS  
To show the experimental realization of the A-RoF-based DAS methodology, Figure 3 shows the proposed system that has been explored utilizing an industrial use-case scenario. The experimental setup is divided into the following steps:  
Figure 3: RoF OFH integration system with DAS for IF carriers. A Mach-Zehnder as an external modulator is used with a single mode fibre of 10 km deployed.  
Figure 2: Block diagram explaining the operation principle of RoF-based optical transport system of DAS network capable of supporting 5G and 6G services.