esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Type** & **Authors** & **Cancer Origin** & **Coal** & **Test Set** & **Results** \\ \hline \multirow{6}{*}{Closical Machine Learning} & [PERSON] et al. [19] & brain & hot-spot detection & 4 WSI & Acc = 93.8\% \\  & [PERSON] et al. [18] & digestive system & hot-spot and cell detection & 55 images & Acc = 94.6\%, \\  & [PERSON] et al. [17] & adrenal glands & hot-spot detection & 50 WSI & rho = 0.8 \\  & [PERSON] et al. [20] & gastro-intestinal tract, pancreas & cell detection & 46.4\% & F1 = 90\% \\  & [PERSON] et al. [21] & brain & hot-spot detection and I.1 estimation & 104 WSI & rho = 0.95 \\  & [PERSON] et al. [1] & breast & hot-spot detection & 152 WSI & good agreement \\  & [PERSON] et al. [22] & pancreas & heterogeneity of proliferation & 43 WSI & PPV = 90.2\% \\  & [PERSON] et al. [23] & breast & detecting and tumor nuclear signals & 25 WSI & PPV = 90.2\%, \\  & [PERSON] et al. [24] & breast & hot-spot detection & 50 cases, 3 experts & Acc = 82\% \\  & [PERSON] et al. [20] & breast & I.1 estimation & 30 TMA 1 expert & Acc = 91\% \\  & [PERSON] et al. [19] & breast & I.1 estimation & 80 TMA & Acc = 93\%, \\  & [PERSON] et al. [25] & breast & I.1 estimation & 15 WSI & Acc = 89\% \\ \hline \multirow{6}{*}{Deep Learning} & [PERSON] et al. [29] & breast & hot-spot detection and I.1 estimation & 450 images, 5-fold cross validation & Acc = 97\% \\  & [PERSON] et al. [27] & breast & red detection & 300 samples * & Acc = 98\% \\  & [PERSON] et al. [20] & bladder, breast & I.1 estimation & 300 samples * & Acc = 98\% \\  & [PERSON] et al. [28] & pancreas & nucleus recognition & 20 associated images & bladder F1 = 92\% breast F1 = 74\% \\ \cline{1-1}  & Proposed method & breast & hot-spots detection and I.1 estimation & 50 WSI 2 experts & Acc = 95\%, rho = 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of reference methods, where:Acc-accuracy, F1-F1-score, DL-deep learning * after augmentation, det-detection, class-classification, LI-labeling index, PPV-positive predictive value and rho-Spearman’s rank correlation coefficient.
# Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
## 3 Results
### Knn
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & **Statistics** & \(p\)**-Value** \\ \hline Shapiro–Wilk normality test & 0.7797191 & \(<2\times 10^{-16}\) \\ Anderson–Darling test & 26.2077714 & \(<2\times 10^{-16}\) \\ Cramer–von Mises test & 5.3824693 & \(<2\times 10^{-16}\) \\ Lilliefors (Kolmogorov–Smirnov) test & 0.2067738 & \(<2\times 10^{-16}\) \\ Jarque–Bera test & 1727.1324714 & \(<2\times 10^{-16}\) \\ D’Agostino omnibus test & 155.7050000 & \(<2\times 10^{-16}\) \\ Ljung–Box test & 117.1891801 & \(<2\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of normality tests and the correlation test for residuals of KNN model. that were not taken into account or the reading of the predictors may have occurred with a certain delay). The variability of instantaneous energy consumption and recuperation is explained by KNN in over 90%; however, as the analysis of residuals shows, additional factors influencing energy consumption and recuperation should be investigated, or predictors used in the model with lags due to readings delay should be taken into account.
# PLORC: A Pipelined Lossless Reference-Free Compression Architecture for FASTQ Files
## 2 Proposed Compressor  
FASTQ files consist of fundamental data units called reads. Each read includes four parts [32] (as depicted in Figure 2): ID, sequence, plus, and quality. The sequence part represents the nucleotide bases. The quality part indicates the error probability of the bases. The plus part serves as a delimiter.  
The block diagram of our proposed compressor is shown in Figure 3. The input of our compressor is in the format of an 8-bit ASCII (American Standard Code for Information Interchange) code stream. The compressor consists of seven modules: the FASTQ Splitter, the ID Compressor, the Sequence Compressor, the Quality Compressor, the ID Converter,the Sequence Converter, and the Quality Converter. _'ID output'_, _'sequence output'_, and _'quality output'_ stand for the compression results. _'ID bits'_ and _'quality bits'_ represent the number of valid bits in _'ID output'_ and _'quality output'_. The _'code_bits'_ signal represents a stream of the length of Huffman code words. The output is then processed by the three converter modules, respectively, to extract the valid information and form 64-bit output streams.
## 2 MDD/GNSS Installations and Measurement Methods
### Multiple Depth Deflectometer Measuring System  
Four MDD laser sensing modules tied securely with aluminum anchor trunks were installed within a single hole casing which had three long slits along the casing. The aluminum anchor trunks were securely anchored to the surrounding soil through slits at specific depths by mechanically controlled extruding nail-type anchors. The top cap plate placed on the top surface of the track slab was hung by the first anchor trunk with aluminum rods. All anchor trunks were bolted to the laser modules. All anchor trunks with the laser modules were positioned vertically in order at the same interval distance set of 1 m at the initial position (Figure 4). Through the long slits along the casing, the anchor trunks with the laser modules can move freely by the generated vertical deformation of the surrounding soil. All laser sensor modules connected with the spring type cables were connected to an external control module.  
To ensure water resistance, the spring cables and top cap assembly were made waterproof. The anchored MDD casing head facilitated the measurement of net vertical deflections at each depth, enabling localized soil displacement measurements.
## 3 Visual Saliency Estimation
### Evaluation Measures
#### 3.3.2 Measures for Salient Object Regions  
The **Precision/Recall curve** is computed by varying a binarization threshold on the continuous saliency estimation map \(P\), and computing at each level the precision PR and recall RE components:  
\[\text{PR}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
\[\text{RE}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]where TP is the number of True Positive pixels, FP False Positives, and FN False Negatives, obtained by comparing predicted saliency \(P\) with ground truth map \(G\).  
The **F-measure** (\(F_{\beta}\)) [56] corresponds to the weighted harmonic mean between precision PR and recall RE:  
\[F_{\beta}=\frac{\left(1+\beta^{2}\right)\text{PR}\cdot\text{RE}}{\beta^{2}\text{ PR}+\text{RE}} \tag{9}\]  
In this case, the continuous-valued saliency estimation can be binarized with different techniques before effectively computing precision and recall. Furthermore, it is common practice [9] to give more weight to the precision component (considered more important than recall for the saliency estimation task), by setting parameter \(\beta^{2}\) to 0.3.  
The **Mean Absolute Error** (MAE) is computed directly on the prediction, without any threshold, as:  
\[\text{MAE}=\frac{1}{X}\sum_{x=1}^{X}|P(x)-G(x)| \tag{10}\]  
The **Structure measure** (\(S_{\alpha}\)) [57], inspired by the structure similarity (SSIM) from image quality assessment, is the weighted mean between region-aware structural similarity \(S_{r}\) and object-aware structural similarity \(S_{o}\):  
\[S_{\alpha}=\alpha\cdot S_{o}+(1-\alpha)\cdot S_{r} \tag{11}\]  
where \(S_{r}\) covers the object-part similarity with the ground truth, while \(S_{o}\) accounts for the global similarity based on sharp estimation contrast and uniform distribution.  
The **enhanced-alignment measure** (\(Q\)) [58] captures both pixel-level matching and image-level statistics as:  
\[Q=\frac{1}{X}\sum_{x=1}^{X}\frac{1}{4}(1+\xi(x))^{2} \tag{12}\]  
where:  
\[\xi=\frac{2\varphi_{G}\circ\varphi_{P}}{\varphi_{G}\circ\varphi_{G}+\varphi_{P} \circ\varphi_{P}} \tag{13}\]  
Bias matrix \(\varphi_{\{G,P\}}\) is the distance between each value of the binary map (\(G\) or \(P\)) and its global mean, and the two matrices are compared through the Hadamard product (\(\circ\)).
## 6 Results
### Token Usage and Evaluation Costs  
In Table 3, we present the average input and output tokens per prompt technique, model, and temperature setting, along with the associated prices. It is important to note that the number of input tokens does not change depending on the model but varies with the prompt technique. For zero-shot and few-shot prompt techniques, the number of output tokens generated by GPT-4o increases significantly, particularly at higher temperatures. This increase can be attributed to the model's tendency to produce more detailed and verbose responses when given more contextual information, as is the case with few-shot prompting, or when allowed more creativity, as with higher temperatures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Prompt Tech.**} & **Avg. Input** & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Tem.**} & \multicolumn{2}{c}{**Avg. Output Tokens**} & \multirow{2}{*}{**Price**} \\ \cline{4-5} \cline{7-8}  & & & & **Diagnostic** & & **Evaluator** \\ \hline \multirow{8}{*}{Zero-shot} & \multirow{4}{*}{7613} & \multirow{4}{*}{Mixral} & 0 & 1518 & 2520 & 0.11 \\ \cline{3-5} \cline{7-8}  & & & 2 & 1525 & 8249 & 0.17 \\ \cline{3-5} \cline{7-8}  & & &  
Several key insights can be drawn from these results. First, the zero-shot technique consistently results in a lower number of input tokens compared to the few-shot and RAG techniques. This indicates that zero-shot prompting is more efficient in terms of costs and input requirements, making it suitable for straightforward cases requiring quick and concise diagnostics. Second, the RAG technique shows a more balanced approach, with a moderate number of input tokens and consistent output token counts across different temperatures. Third, the significant increase in output tokens for the evaluator (GPT-4o) with zero-shot and few-shot techniques at higher temperatures highlights the diagnostic model's tendency to generate more detailed and exploratory responses.  
Finally, the pricing information indicates that the cost of using these models varies with the prompt technique and temperature setting. Higher temperatures and more context-rich prompts generally result in higher costs due to the increased number of output tokens. This underscores the importance of carefully selecting prompt techniques and temperature settings to balance diagnostic accuracy and computational cost.
###### Abstract  
The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes--such as the formation of ionic and covalent bonds--MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP's quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness-Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57%compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods.  
material generation algorithm; quadratic interpolated learner process; benchmarks; PV source penetration; distribution systems
## 3 Finite Element Analysis
### Finite Element Model  
A finite element model was established using ANSYS (2021R1) software [31]. This study mainly explores the overall mechanical properties of the structure, without considering the local effects of the structure. Therefore, the use of bar-element simulation can meet the calculation accuracy requirements, and related studies have also adopted this simulation method [32; 33]. The deck was modeled using Beam4 element, the bridge tower was modeled using Beam188 element, and the stay cables and longitudinal restraint cables were simulated using Link10 element. The finite element model is shown in Figure 9. The cable is connected to the deck and tower through a rigid arm. Vertical constraints are applied at the beam ends and auxiliary piers. The displacement and rotation of the bottom  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Material** & **Area (m\({}^{2}\))** & **Inertia Moment (m\({}^{4}\))** & **Elastic Modulus (GPa)** \\ \hline Deck & Q345 & 2.16 & 3 & 205 \\ Tower & Concrete C60 & 46.4/112 & 291/3733 & 35 \\ Cable & Parallel steel wire & 0.005/0.007/0.008 & / & 205 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cross-sectional characteristics of components.  
Figure 8: Cable-stayed bridge layout and cross-section (unit: m).  
of the tower are constrained, and the mid tower and deck are connected by longitudinal restraint cables. The equivalent elastic modulus is used to consider the sag effect of the cable. The Newton-Raphson method [34] is used to consider the large deflection effect for geometric nonlinear analysis. The cable forces under dead load are determined using the zero reaction force method. First, vertical supports are installed at the anchorage points of the main girder. The initial cable forces are calculated based on the support reactions, and then iteratively adjusted until the support reactions approach zero.  
The crossing cable is set in an asymmetric arrangement, as shown in Figure 10. The crossing cable extends from the mid tower and intersects with the cable of the side tower as shown by the red line in the figure. The area of the crossing cable for both the side tower and the mid tower is 0.008 m\({}^{2}\), and the stress of the crossing cable under the dead load is 600 MPa.
## 4 Use Case and Simulation of the Proposed System
### PMIPv6/AAND
#### 4.4.1 MIPv6  
Handover Analysis  
The transfer is analyzed according to the signal flow of MIPv6 [16]. It is calculated on the delay shown in the Equation (2) of the connection layer 2 establishments (TDL2), time delay classification of the MN with the new access router (TD\({}_{\rm{NAR}}\)), time delay detection of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flow ID** & **MN-ID** & **LL-ID** \\ \hline X (WLAN) & MN-1 & XX:XX \\ \hline Y (Wi-max) & MN-1 & XX:YY \\ \hline Z (3G) & MN-1 & YY:ZZ \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulations Flow ID details.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Layer 2 connection (Delay between MN-AP \& Delay between AP-CN) & 0.2 sec \\ \hline Time Delay between MN to NAR (TD\({}_{\rm{RS}}\)) & 0.015 sec \\ \hline Time Delay between NAR to MN (TD\({}_{\rm{RA}}\)) & 0.015 sec \\ \hline TD\({}_{\rm{DAD}}\) & 0.2 sec \\ \hline TD\({}_{\rm{BUH}}\) & 0.2 sec \\ \hline TD\({}_{\rm{HBA}}\) & 0.2 sec \\ \hline TD\({}_{\rm{BUC}}\) & 0.2 sec \\ \hline TD\({}_{\rm{CBA}}\) & 0.2 sec \\ \hline Layer 2 connection (Delay between MN-AP \& Delay between AP-MAG & 0.2 sec \\ \hline Delay between MAG to LMA (t\({}_{\rm{RS}}\)) & 0.015 sec \\ \hline Delay between MN to MAG (t\({}_{\rm{RA}}\)) & 0.015 sec \\ \hline Authentication Delay = (t\({}_{\rm{AAAAreq}}\)+t\({}_{\rm{AAAAres}}\) = (0.2 sec + 0.2 sec) & 0.4 sec \\ \hline Delay of Control Signal = (t\({}_{\rm{PRA}}\)+ t\({}_{\rm{PBU}}\)) = (0.2 sec + 0.2 sec) & 0.4 sec \\ \hline TTD\({}_{\rm{Data}}\) & 0.4 sec \\ \hline PDL & 1024 bytes \\ \hline \(\lambda_{\rm{s}}\) & 100 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 8: Simulation Parameters.  
the duplicate address detection (TD\({}_{\text{DAD}}\)), time delay of the binding update (TDBU) and the time delay of registration (TD\({}_{\text{REG}}\)).  
\[\text{HOD}_{\text{MIPv}}6= \begin{array}{ll}\text{(TD}_{\text{Auth}}+\text{TD}_{\text{RRS}})+ \text{(TD}_{\text{BS}}+\text{TD}_{\text{RA}})+\text{TD}_{\text{NAR}}\\ +\text{(TD}_{\text{BUH}}+\text{TD}_{\text{HBA}})+\text{(TD}_{\text{REG}}+ \text{TD}_{\text{BUC}}+\text{TD}_{\text{CBA}})\end{array} \tag{2}\]  
MIPv6--Handover Simulation Result  
Here, the MN begins with its if_1, i.e., the WLAN. The next interface, if_2 Wi-Max, comes into the simulation at the 11 th second, although the MN transmits its signal to Wi-Max at the 18 th second. 3G comes into the simulation at the 24.5 th second, although the MN hands its signal to if_3 3G after 30 seconds. According to the simulation, the link was lost when MN switches to the if_3 network, i.e., 3G. The subsequent graph represents the MIPv6 simulation performance with multiple interfaces. Figure 13 illustrates the transition of the MIPv6 (here the Y-coordinate describes the packet sequences of the UDP packet sequences from the MN to the CN, which are collected by the CN). At 11.3 seconds, the transfer of the packets from the if_1 to the if_2 is carried out.
## 3 Taylor Series Radial Basis Function (RBF-TS)  
The current authors argue that one of the reasons for the instability of RBF is the strong coupling between the shape parameter (\(\varepsilon\)) and distances (\(r\)). As a first attempt to weaken this coupling, the basis function \(\phi\) and its derivatives are expanded with respect to shape parameter using the Taylor series around a reference shape parameter (\(\varepsilon_{0}\)):  
\[\begin{array}{l}\phi(r,\varepsilon)=\left.\phi(r,\varepsilon_{0})+\left. \frac{\partial\phi(r,\varepsilon)}{\partial\varepsilon}\right|_{\varepsilon= \varepsilon_{0}}(\varepsilon-\varepsilon_{0})+\left.\frac{\partial^{2}\phi( r,\varepsilon)}{\partial\varepsilon^{2}}\right|_{\varepsilon= \varepsilon_{0}}\frac{(\varepsilon-\varepsilon_{0})^{2}}{  
deviates from RBF as \(\varepsilon\) moves away from \(\varepsilon_{0}\). The effect of truncation order on RBF and its derivatives can be deciphered from Figure 2 by comparing sub-figures across columns. It is found that the error decreases with the increasing truncation order or the decreasing order of the derivative. Regardless of the truncation order, the diagonal region of each graph is the region with minimum error; this is expected because \(\varepsilon\) and \(\varepsilon_{0}\) are almost identical in that region. Furthermore, the higher-order terms in the Taylor series become very small and negligible at the diagonal. In contrast, the error increases significantly away from the diagonals and the coupling between \(r\) and \(\varepsilon\) becomes weak. Put succinctly, the coupling between \(r\) and \(\varepsilon\) becomes weaker as \(\varepsilon\) deviates from \(\varepsilon_{0}\). This, in turn, implies that RBF-TS will drift considerably from Gaussian RBF.  
The RBF-TS approach is tested for interpolation and approximation of derivatives (Section 5.1). It is shown that RBF-TS does not improve the accuracy where its accuracy depends on \(\varepsilon_{0}\). That is because of the nature of the Taylor series, which produces a reasonable approximation at and near \(\varepsilon_{0}\) and a bad approximation as \(\varepsilon\) moves away from \(\varepsilon_{0}\). Therefore, it is necessary to find a way to relatively strengthen the coupling when \(\varepsilon\) is away from \(\varepsilon_{0}\).
## 2 Mathematical Description  
### Electrochemical Model  
A comprehensive 1D model is employed to depict the water transport in the through-plane direction of a single fuel cell MEA. The Nernst equation employed to describe the reversible cell potential is given as [22]:  
\[\Delta E_{0}=-\frac{\Delta G}{2F}+\frac{RT}{2F}\ln\left[\left(\frac{p_{H_{2}}} {P_{ref}}\right)\left(\frac{p_{O_{2}}}{P_{ref}}\right)^{1/2}\right] \tag{1}\]  
Figure 2 shows the schematic diagram of the 1D model domains consisting of anode and cathode GDLs, CLs, and ionomer membrane [23; 24]. The central part of the MEA is the PEM as a semipermeable membrane separating gases and allowing ions to pass through it. The model accounts for mass transport in the gas and polymer phases coupled to the current distribution in the electrolyte and gas-diffusion electrodes. The domains of this schematic model are described in Figure 2. The overall redox reaction can be divided into two single steps in anode catalyst layer (ACL) and cathode catalyst layer (CCL) separately by Butler-Volmer kinetics given by [22].  
\[i=i_{0}a\left(\exp\left[\frac{\beta\cdot 2F}{RT}\eta\right]-\exp\left[-\frac{ (1-\beta)\cdot 2F}{RT}\eta\right]\right) \tag{2}\]  
where \(\beta=0.5\) is the half-reaction symmetry factor, \(\eta\) is the activation overpotential defined as [25]:  
\[\eta=\left\{\begin{array}{ll}\Delta E-\Delta E_{0}&\quad\text{in }\,\,\,\,\text{Anode catalyst layer}\\ \Delta E_{0}-\Delta E&\quad\text{in }\,\,\,\text{Cathode catalyst layer}\end{array}\right. \tag{3}\]  
The inner potential difference between two points in the bulk of two conducting phases of electron and proton can be defined as:  
\[\Delta E=E_{elec}-E_{prot} \tag{4}\]  
Figure 1: Structure of open-cathode PEM fuel cell: **(a)** anode channel and cathode channel diagram; **(b)** the through-plane configuration of Open-cathode PEM figure.  
The reversible potential difference is split into two parts based on the different catalyst layers:  
\[\Delta E_{0}=\left\{\begin{array}{ll}\left(-\frac{T\Delta S_{H\Omega R}}{2F}- \frac{RT}{2F}\ln\left[\frac{P_{H_{2}}}{P_{ref}}\right]\right)&in\ \ ACL\\ \left(-\frac{\Delta H-T\Delta S_{G\Omega R}}{2F}-\frac{RT}{4F}\ln\left[\frac{P_ {\Omega_{2}}}{P_{ref}}\right]\right)&in\ \ CCL\end{array}\right. \tag{5}\]  
The direction from the anode side to the cathode side is defined as a positive direction. The proposed model is depicted as steady state, isobaric, and laminar flow in all channels. Detailed governing equations contain the transport of gas species, water, heat, and energy. Related partial differential equations of the steady state are summarized in Table 1.