esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances of MXene-Based Electrochemical Immunosensors
## 3 Electrochemical Immunosensors Based on MXenes
### MXenes as Electrode-Modified Nanomaterials in Electrochemical Immunosensors  
The basis for fabricating a high-performance electrochemical biosensor is to construct a well-defined functional interface. Thus, EMN between the transducer and the target recognition layer is essential for efficient electron transport and immobilization of biomolecules [9; 64]. Therefore, it is of great significance to explore novel EMN with excellent properties. As emerging 2D materials, MXenes have become ideal EMN for their excellent properties, such as high electrical conductivity, large specific surface area, and good biocompatibility. So far, electrochemical immunosensors fabricated with MXenes and their composite have been applied for the detection of various cancer biomarkers (CEA, PSA, CYFRA21-1, Apo-A1 and NMP 22), cardiovascular disease biomarkers (cTnI and cTnT), sweat biomarkers (cortisol), inflammation biomarkers (PCT) and so on. [PERSON]'s group [48] first reported a label-free MXenes-based immunosensor for the detection of CEA (Figure 2), an important broad-spectrum tumor marker in clinical applications. Ti\({}_{3}\)C\({}_{2}\)-MXene nanosheets were treated with APTES to form functionalized Ti\({}_{3}\)C\({}_{2}\)-MXene (_f_-Ti\({}_{3}\)C\({}_{2}\)-MXene) for covalent immobilization of the capture antibodies (anti-CEA). Based on the effects of different redox probes, hexaammineruthenium ([Ru(NH\({}_{3}\))\({}_{6}\)]\({}^{3+}\)) was selected as the preferable redox probe. The as-prepared _f_-Ti\({}_{3}\)C\({}_{2}\)-MXene immunosensor exhibited a linear detection range of 0.0001-2000 ng mL\({}^{-1}\) with a limit of detection (LOD) of 0.018 pg mL\({}^{-1}\). This work opens up a new opportunity for the development of MXene-based biosensors. \begin{table}
## 5 Conclusions
The RF was applied to explore the impacts on the FPAR from the variables including the annual mean air temperature (TAVG), annual total precipitation (PRCP), annual total downward shortwave radiation (SWRad) and annual mean atmospheric CO2 concentration over the terrestrial ecosystems in China and its nine regions from 1982 to 2018; Table S1: The coefficient of determination (\(R^{2}\)), slope and intercept from the linear regression for the product of the MODIS (FPARMCD15A2), the GIMMS (NDVI3g), the ANN-based FPAR developed in this study (FPARANN) and the GLASS (FPARBNU) by comparing to the GPP observations on the eight flux towers of ChinaFLUX; Table S2: The means, trends, coefficient of determination (\(R^{2}\)), significant level (\(p\)-Value) and coefficient of variation (C.V) for the FPAR product of the MODIS (FPARMCD15A2) and the ANN-based FPAR developed in this study (FPARANN) over the terrestrial ecosystems in China and its nine regions during their overlay period from 2000 to 2015; Table S3: The spatiotemporal consistency was compared between the FPAR product of the MODIS (FPARMCD15A2) as a benchmark and the products from GLASS (FPARBNU), GIMMS (NDVI3g) and this study (FPARANN), respectively. The matrices include the root mean square error (RMSE), Kling-Gupta efficiency (KGE), structural similarity index (SSIM) and coefficient of determination (\(R^{2}\)), and are calculated on the pixel scale with data on the day 225 of year 2005, 2010 and 2015; Table S4: The mean and the linear regression-based trends for the FPAR over Chinese terrestrial ecosystems from 1980 to 2018; Table S5: The means and trends of the FPAR over the terrestrial ecosystems in China and its nine regions for the whole study period from 1980 to 2018 and for each decade. **Author Contributions:** Conceptualization, J.W. ; methodology, J.W. and Y.Z. ; software, Y.Z. and J.W. ; formal analysis, Y.Z. and J.W. ; investigation, Y.Z. and J.W. ; resources, Y.Z. and J.W. ; data curation Y.Z. and J.W. ; writing--original draft preparation, Y.Z. ; writing--review and editing, J.W. and A.E.W. ; visualization, Y.Z. ; supervision, J.W. ; project administration, J.W. ; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (31861143015 and 31971507) and the Joint Research Project of the People's Government of Qinghai Province and Chinese Academy of Sciences (LHZX-2020-07). **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. **Acknowledgments:** We would like to thank NASA and ChinaFLUX for data sharing. We are also very grateful to the editor and reviewers for the constructive comments and suggestions, and especially to [PERSON] of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, for his detailed revision of the revision notes and very constructive suggestions, which have greatly improved the quality of this paper. The authors declare no conflict of interest.
## 6 Discussion of the Results
Similarly, this study adjusts the SUI model using statistical techniques such as linear regression, achieving greater precision in predicting signal loss in local environments. This approach reinforces the relevance of tailoring propagation models to the specific characteristics of the terrain and environment, significantly contributing to the optimization of wireless network planning.
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
## 5 Evaluation of UWB Data
### Received Signal Strength (RSS) vs. Ranges  
Another important indicator regarding how the signal is affected by the environment is provided by the received signal strength expressed as power at the receiver tag. According to theoretical formulation, the power increase as the tag approach an anchor while it decreases with increasing distance or in presence of obstacles. This is confirmed from the plots in Figure 8, in which the RSSI is plotted against the tag-anchor distance during the measurement campaign. The plot is repeated for all 8 anchors and considers only the value above \(-80\) dBm which is the threshold below which the system is unable to receive. With the setting defined in the proposed test, \(-103\) dBm is the lowest RSS value at which reception is possible in Pozyx systems.  
In some plots (in particular for anchors 0x617e, 0x672d and 0x6765) it is possible to observe an interruption in the point cloud representing the power with respect to the distance. As the path is continuous, these interruptions probably correspond to obstacles through which the signal has not been able to penetrate, probably in conjunction with wardrobes leaning against the walls or particularly thick walls.  
Figure 8: Received signal strength vs. measured distances for each anchor during the pedestrian test.
# A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory
## 4 Discussion
### Comparison between the IRI Model and HAS Method  
The IRI prediction model has a wide range of spatial scales and can provide better average age prediction according to the change in characteristics of the global ionospheric surface domain. In addition, in the reconstruction process of the HAS method, because the actual data of the stations were sometimes missing, they could not be reconstructed, and the IRI prediction model was used to supplement the missing data. Therefore, the HAS model was first compared with the predicted RRMSEs of the IRI model. Figure 7 compares the results of the IRI model based on the validation data and the HAS method. The results show that the average RRMSE of IRI prediction was larger, and the prediction accuracy of the HAS method improved by 2.06%, which verifies the effectiveness of the HAS method.
## 3 The Asymmetric Hyperchaotic Image Encryption Scheme
### The Diffusion Process  
To reduce the correlation between the neighboring pixels in the image, it is necessary to take the diffusion process. For the diffusion process, there are two major categories: block diffusion and the one based on the pixel-by-pixel mode. For the algorithm proposed in this article, we adopt a one-round operation of the pixel-by-pixel diffusion. The operation is started from the pixel located in the lower right corner of the two-dimensional image, end up with the pixel of the upper left corner of the image. In the whole operation, the spread process is from the bottom to the top, and from the right to the left, which means after the present pixel is relevant to its lower and right neighbors. The details of the operation based on [33] are as follows:  
1. With the generation of the hyperchaotic sequences, the new subsequence for the usage of the diffusion process will be intercepted from one of the obtained sequences. In this  
Figure 6: An instance of division for the plane where the blocks after division are named from A to P.  
Figure 7: The example of the shuffling process based on Figure 6.  
article, the chosen sequence for interception is x(i), and a subsequence was intercepted from the x(i). After that, the subsequence x(i) was reshaped into a two-dimensional sequence x(i, j) with the size \(M\times N\).
2. As the input of the diffusion process, the permutated image PM(i, j) was treated through this subprocess: for each pixel in the permutated image, the relation with the right and lower adjacent pixels was built. The final product after the diffusion is the cipher image C(i, j). The specific algorithm is shown in Algorithm 1.  
```
1:for\(i=M\);\(i\geq 1;i--\)do
2:for\(j=N;j\geq 1;j--\)do
3:if\(i==M\)&&\(j==N\)then
4: C(i,j) = PM(i,j)\(\bigoplus\)floor(x(i,j)\(\times\)255)
5:elseif\(i==M\)then
6:\(diffu_{1}\) = PM(i,j)\(\bigoplus\)C(i,j+1)
7: C(i,j) = \(diffu_{1}\)\(\bigoplus\)floor(x(i,j)\(\times\)255)
8:elseif\(i==N\)then
9:\(diffu_{1}\) = PM(i,j)\(\bigoplus\)C(i+1,j)
10: C(i,j) = \(diffu_{1}\)\(\bigoplus\)floor(x(i,j)\(\times\)255)
11:else
12:\(diffu_{1}\) = PM(i,j)\(\bigoplus\)C(i,j+1)
13:\(diffu_{2}\) = \(diffu_{1}\)\(\bigoplus\)C(i+1,j)
14: C(i,j) = \(diffu_{2}\)\(\bigoplus\)floor(x(i,j)\(\times\)255)
15:endif
16:endfor
17:endfor
```  
**Algorithm 1** The process of diffusion  
In this part, an example with four pixels was shown to provide the convenience of the comprehension for the method elaborated above. Figure 8 shows the steps of this operation:  
\[C(2,2) = PM(2,2)\bigoplus floor(x(2,2)\times 255))=110\] \[C(2,1) = PM(2,1)\bigoplus C(2,2)\bigoplus floor(x(2,1)\times 255))=59\] \[C(1,2) = PM(1,2)\bigoplus C(2,2)\bigoplus floor(x(1,2)\times 255))=11\] \[C(1,
## 2 Theoretical Foundations
### Optimisation  
Optimisation is a routine procedure for all living beings. It refers to the decisions taken for reaching the best outcome in a given situation. From this point onward, we assume that an optimisation problem is such where an objective function must be minimised within a feasible continuous domain, where such elements can be described as follows.  
**Definition 1** (Minimisation problem).: _Let \(\mathfrak{X}\subseteq\mathfrak{S}\) be a feasible domain, since \(\mathfrak{S}\) is an arbitrary domain, and let \(f(\vec{x})\) be an objective function to be minimised, known as cost function, defined on a nonempty domain \(\mathfrak{X}\
eq\emptyset\) such as \(f(\vec{x}):\mathfrak{X}\mapsto\mathbb{R}\). Thus, a minimisation problem represented with the tuple \((\mathfrak{X},f)\) is stated as_  
\[\vec{x}_{*}=\operatorname*{argmin}_{\vec{x}\in\mathfrak{X}}\left\{f(\vec{x}) \right\}, \tag{1}\]  
_where \(\vec{x}_{*}\in\mathfrak{X}\) is the optimal vector (or solution) that minimises the objective function within the feasible domain, i.e., \(f(\vec{x}_{*})\leq f(\vec{x}),\ \forall\ \vec{x}\in\mathfrak{X}\)._  
**Remark 1** (Particular domain).: _This feasible domain is somehow a general representation of any domain delimited by simple constraints, which can be extended to a more complex one by considering constraints of a different nature._  
**Remark 2** (Maximisation problem).: _In several applications, the objective function models the revenue, profit or utility of a process, so this function needs to be maximised. Let \(\hat{f}(\vec{x})\) be the utility function such that we can state the optimisation problem with (1) by using a simple transformation such as \(f(\vec{x})=-\hat{f}(\vec{x})\)._  
We deal with two kinds of problem domains, continuous and combinatorial, at different levels of abstraction. For the former, \(\mathfrak{S}=\mathbb{R}^{D}\) and \(D\) stand for the number of dimensions that the problem contains. Under some conditions, such as when dealing with engineering applications, the user has prior knowledge about this domain. Moreover, \(D\) is usually fixed and represents the number of design variables. For the combinatorial problem, we say that \(\mathfrak{S}=\mathfrak{H}^{\omega}\) with \(\mathfrak{H}\) as the heuristic space and \(\omega\) as the cardinality of the sequence. Further details about this problem are provided below.
## Abbreviations  
AC Alternative current  
CO2 Carbon dioxide  
DC Direct current  
EV Electric vehicle  
HMI Human-machine interface  
IIREVs Intelligent infrastructure for recharging electric vehicles  
MPPT Maximum power point tracking  
MILP Mixed-integer linear programming  
PV Photovoltaic  
SOC State of charge  
STC Standard test conditions  
**Constraints**  
\(P_{E\_max_{v}}\) Maximum charging power of \(v\) vehicle  
\(P_{E\_aver\_max}\) Maximum average charging power  
\(P_{E\_fast\_max}\) Maximum fast charging power  
\(P_{E\_slow\_max}\) Maximum slow charging power  
\begin{tabular}{l l} \(P_{S\_max}\) & Stationary storage power limit \\ \(P_{G\_I\_max}\) & Maximum grid injection limit \\ \(P_{G\_S\_max}\) & Maximum grid supply limit \\ \(SOC\_V\_max\) & Maximum state of charge of electric vehicle \\ \(SOC\_V\_min\) & Minimum state of charge of electric vehicle \\ \(SOC\_S\_max\) & Maximum state of charge of stationary storage \\ \(SOC\_S\_min\) & Minimum state of charge of stationary storage \\
**Parameters** & \\ \(\Delta t\) & Time interval between two samples \\ \(\gamma\) & Power temperature coefficient \\ \(c_{EV\_p}\) & EV penalty tariff \\ \(c_{G}\) & Grid energy tariff \\ \(c_{G\_NH}\) & Grid energy tariff in normal hours \\ \(c_{G\_PH}\) & Grid energy tariff in peak hours \\ \(c_{S}\) & Storage energy tariff \\ \(c_{PVS}\) & PV shedding energy tariff \\ \(C_{P}\) & Controller proportional gain \\ \(E_{Bat}\) & Energy capacity of the stationary storage (kWh) \\ \(E_{v}\) & Energy capacity of the \(v\) vehicle (kWh) \\ \(G_{test}\) & Fixed solar irradiation for testing \\ \(M_{y}\) & Charging mode of vehicle \(v\) \\ \(N_{PV}\) & Number of PV panels \\ \(N_{p}\) & EVs total number \\ \(NOCT\) & Nominal Operating Cell Temperature \\ \(P_{PV\_STC}\) & PV power under STC \\ \(SOC_{EV\_artr_{v}}\) & SOC of vehicle \(v\) at arrival \\ \(SOC_{EV\_dep_{v}}\) & State of charge of electric vehicle \(v\) at departure \\ \(SOC_{EV\_des_{v}}\) & SOC of vehicle \(v\) at departure \\ \(SOC_{S_{0}}\) & Initial SOC of stationary storage \\ \(t_{0}\) & Initial time instant \\ \(T_{air\_test}\) & Fixed air temperature \\ \(t_{arr_{v}}\) & Arrival time of \(v\) vehicle \\ \(t_{ch_{v}}\) & Estimated charging time of \(v\) vehicle set by the user \\ \(t_{dep_{v}}\) & Departure time of \(v\) vehicle \\ \(t_{est\_ch_{v}}\) & Estimated charging time of vehicle \(v\) \\ \(t_{F}\) & Time instant at the end of time operation \\ \(V_{ref}\) & Reference voltage of the DC bus \\
**Indices** & \\ \(i\) & Index of time \\ \(v\) & Index of EV number \\
**Variables** & \\ \(C_{EV\_penalty}\) & EV penalty energy cost \\ \(C_{G}\) & Grid energy cost \\ \(C_{S}\) & Storage energy cost \\ \(C_{PVS}\) & PV shedding energy cost \\ \(g\) & Solar irradiation \\ \(k_{D}\) & Power distribution coefficient \\ \(p_{EV_{e}}\) & EV charging power of \(v\) vehicle \\ \(p_{G}\) & Grid power \\ \(p_{G\_I}\) & Grid injection power \\ \(p_{G\_S}\) & Grid supply power \\ \(p_{G\_ref}\) & Grid power reference \\ \(P_{IIREV\_s}\) & IIREVs total demand power \\ \(P_{IIREV\_s}\) & IIREVs total power \\ \(P_{IIREV\_s}\) & IIREVs shed power \\ \end{tabular}
# A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach
## 4 Experiments and Results
### Evaluation of YOLO V5 Training  
#### 4.2.1 Computer Hardware Configuration  
Deep-learning networks require robust hardware support. In our case, we utilized a Linux-based system with CUDA 11.1, Python 3.8, and Pytorch 1.8.0. The hardware setup included an Nvidia A40 GPU with 48 GB of memory. This powerful configuration enabled us to efficiently perform all the training and generation operations.
## 5 Experiments and Results
### Human-Based Evaluation  
Five experts completed the annotation work to ensure the quality and accuracy of the resource annotations. They collaboratively established a standard logical reasoning chain through discussion and negotiation. This chain includes key steps such as intent recognition, bias detection, controversy point labeling, cause analysis, and consistency checks. The aim was to comprehensively capture the key elements of doctor-patient communication, thereby providing data support for optimizing communication strategies and improving healthcare service quality. We compared the performance of seven models across four methods from the perspectives of ACC, logicality, and clarity: Native, Chain-of-Thought (CoT), Logic-LM, and DIKWP-AC.  
For ACC, we used the BLEU (Bilingual Evaluation Understudy) metric to detect the degree of consistency between the model's reasoning results and the real-world situation or standard answers. Table 14 shows the BLEU scores of each model under different methods.  
The evaluation results based on the BLEU metric show that the DIKWP-AC method performs exceptionally well in enhancing the logical reasoning ability of large models, particularly in Claude-34B and MedGPT, where the BLEU scores are significantly higher than those of other methods. This provides essential reference points for selecting and optimizing the application of large models in logical reasoning tasks. We used GPT-4 to evaluate the different models in terms of logic and clarity. The evaluation criterion was that if GPT-4 judged Model A to have stronger logical reasoning than Model B, it was marked as 1. Similarly, if GPT-4 judged Model A to have stronger accuracy than Model B, it was also marked as 1. Table 15 shows the comparison of logical consistency and clarity across models under different methods, with a focus on DIKWP-AC.  
The results show that DIKWP-AC performs exceptionally well in logic, especially compared to Native, CoT, and Logic-LM. DIKWP-AC significantly enhances the model's reasoning ability. While its win rate is lower than that of experts, it still provides practical improvements, indicating that it has strong potential to enhance logical reasoning. Regarding clarity, DIKWP-AC offers significant improvements compared to the Native and CoT methods. DIKWP-AC achieved win rates of 70.2% and 60.3%, respectively, demonstrating its strong ability in language generation and expression clarity. However, unlike human experts, DIKWP-AC lacks clarity, suggesting that experts still hold an advantage in domain-specific reasoning and expression.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Native (\%)** & **CoT (\%)** & **Logic-LM (\%)** & **Expert (\%)** \\ \hline Logicality & 94.2 & 89.5 & 74.2 & 50.3 \\ Clarity & 70.2 & 60.3 & 53.2 & 33.1 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison of logicality and clarity based on human evaluation.
## 4 Results
### Drill Rate Test  
The drill rate test (DRT) goal, detailed in Section 2, is to ensure an WOB and RPM that result in a safe and efficient drilling. The DRT is a pre-operational test that should always be carried out due to the changes that occur in the drilling of the actual formation, i.e., the lithology and static drilling parameters, such as the drill bit and diameter of the well. For this reason, a DRT must be carried out in an agile and fast way.  
The dataset was initially separated by different RPM values (110, 120, 130, 140, and 150 [rev/min]) and afterward, the means of the WOB, ROP, and MSE values were determined for the WOB groups of 3, 5, and 7 [klb]. Figures 4-6 show the relation between the traditional ROP vs. WOB plot and the MSE vs. WOB plot for WOB groups of 3, 5, and 7 [klb], respectively.  
Through analyzing Figures 4a, 5a, and 6a, it can be observed that only the curves for 110 and 120 [rev/min] present region II and the inflection point. The inflection point is the maximum point where an increase in the WOB value causes an increase in the ROP value; from this point, a decrease in the ROP occurs (inefficiency, as per Figure 1). The other uncited curves only show region III, indicating an inefficient drilling process, where the increased WOB value causes a decrease in the ROP value.  
Figure 4: WOB groups of 3 [klb]: (**a**) ROP vs. WOB plot; (**b**) MSE vs. WOB plot.  
Figure 5: WOB groups of 5 [klb]: (**a**) ROP vs. WOB plot; (**b**) MSE vs. WOB plot.  
By analyzing Figures 4b, 5b, and 6b, a similar behavior can be observed for all curves, where a lower WOB results in a lower MSE response. Among all analyzed graphical responses, the one linked to the lowest achievable MSE is the RPM of 130 [rev/min] (minimum MSE of 6.72 [kpsi]). However, as can be seen, this curve only presents the third region, i.e., the inefficient drilling process. The 110 and 120 [rev/min] curves present minimum values of 59.52 and 118.87 [kpsi], respectively.
## 2 Materials and Methods
### EC Data Processing and Gap Filling  
Raw data from the EC systems was processed offline using the improved EdiRe software (V1.5.0.32) [37]. The output of half-hourly LE and H\({}_{\rm s}\) was calculated from the mean covariance of water and temperature fluctuations with fluctuating vertical velocity; half-hourly F\({}_{\rm c}\) was computed by the eddy covariance method as the mean covariance between fluctuations in vertical wind velocity (w\({}^{\prime}\), m s\({}^{-1}\)) and the CO\({}_{2}\) density (\(\rho_{\rm c}\)\({}^{\prime}\), mg m\({}^{-3}\)). For data quality control, spectral loss correction [38], 3D coordinate rotation [39], sonic virtual temperature conversion [40], Webb-Pearman-Leuning (WPL) correction [41], and spike detection [42] were included in the program. In the spike detection, the values that exceeded the mean value by \(\pm\)5.5 times the standard deviations in a window of 10 values were labelled as spike. The unfavorable data were deleted and gap-filled to calculate the daily mean values. Short gaps (\(<\)2 h) were filled with linear interpolation, and larger data gaps were filled using methods based on the empirical relationships between LE, H\({}_{\rm s}\), F\({}_{\rm c}\), and meteorological data such as air temperature, PAR, etc. [43]. Where meteorological data were missing, the mean diurnal variation method was applied using a 7-day data window [12].