esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Microstructure  
Figure 2a shows the top-view microstructures with EDS mapping, denoting a well distributed mix of both lanthanide and BCY10 phases with a fairly uniform particle size distribution (\(\sim\)1-2 \(\upmu\)m), and a microstructure of open porosity. According to [PERSON] et al. [37], an enhancement in the electrochemical activity of RP-PCFC cathodes can be obtained for highly porous electrodes. The noted open porosity registered in this work is most likely a result from the relatively low temperature used (1050 \({}^{\circ}\)C). In this respect, [PERSON] et al. [38] found that the best performance for cathodes of the RP-phase La1.5 Pr0.5 Ni0.8C00.2O4+5 was attained when decreasing the sintering temperature from 1150 to 1050 \({}^{\circ}\)C. In addition, no apparent intergranular element diffusion can be identified by the EDS analysis, in agreement with previous studies reporting the good chemical compatibility of lanthanum nickelates with barium cerates [20; 39].  
The cross-sectional features of the composite cathode are shown in Figure 2b, highlighting that the thickness of the composite is close to 20 \(\upmu\)m, which should be sufficient for attaining good performances [38; 39]. Moreover, good adhesion to the electrolyte substrate can be evidenced with no apparent sites of delamination.
## 4 Experiments and Results
### Physical Properties of the Six-Bar Watt Linkage Without Counterweights  
Table 1 presents the physical properties of the six-bar Watt linkage illustrated in Figure 1. The links are assumed to be fabricated from steel with a density of 7800 kg/m\({}^{3}\), while the counterweights are brass, with a density of \(\rho_{cn}=8500\) kg/m\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Link \(n\)** & **1** & **2** & **3** & **4** & **5** \\ \hline Mass \(m_{h_{p}}\) [kg] & 0.6935 & 0.1022 & 0.9636 & 0.1825 & 0.1679 \\ Length \(l_{l_{m}}\) [m] & 0.19 & 0.14 & 0.1341640 & 0.25 & 0.23 \\ Inertia \(l_{xb_{p}}\) [kg \(\cdot\) m\({}^{2}\)] & 0.0011616 & - & 0.0062264 & - & - \\ Inertia \(l_{y_{ph}}\) [kg \(\cdot\) m\({}^{2}\)] & 0.0055653 & - & 0.0065733 & - & - \\ Inertia \(l_{xb_{p}}\) [kg \(\cdot\) m\({}^{2}\)] & - & 0.0006685 & - & 0.0038036 & 0.0029620 \\ Inertia \(l_{xb_{p}}\) [kg \(\cdot\) m\({}^{2}\)] & 0.0016759 & - & 0.0052291 & - & - \\ CoM \(l_{x_{p}}\) [m] & 0.08 & 0.07 & 0.0775170 & 0.125 & 0.115 \\ CoM \(y_{p}\) [m] & 0.0333333 & 0.0 & 0.0655913 & 0.0 & 0.0 \\ \(K_{x}\) [m] & 0.05 & - & 0.0983869 & - & - \\ \(K_{y}\) [m] & 0.1 & - & 0.1966773 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the six-bar Watt linkage. Parameters indicated with a ‘-’ are unnecessary for the numerical analysis.  
The behaviors of the **ShF** (along the x and y axes) and the \(ShM\) before balance optimization are illustrated in Figure 3.
## 2 Aerodynamic Model and Radar Model  
From the above considerations we know the CKF is a good choice for a high-degree nonlinear system, such as the HGV tracking system. Based on CKF theory, the tracking problem of a nonlinear dynamic system can be defined by the following state-space model with additive noise in discrete time [12].  
Process equation:  
\[X_{k}=f(X_{k-1},u_{k-1})+w_{k-1} \tag{1}\]  
Measurement equation:  
\[Y_{k}=h(X_{k},u_{k})+\
u_{R,k} \tag{2}\]  
where \(X_{k}\) is the state variable at discrete time \(k\); \(u_{k}\) is the control input at time \(k\); \(\{w_{k}\}\) and \(\{v_{R,k}\}\) are the independent process and measurement Gaussian noise sequence with zero means and covariance matrixes \(Q_{k}\) and \(R_{k}\), respectively. Furthermore, the process equation will be established based on the HGV aerodynamic model and the measurement equation will be obtained based on the radar principle.  
We proposed a single model algorithm and several multiple model algorithms for HGV tracking in our paper [7]. Those algorithms were all developed based on the aerodynamic model (process equation) and the radar measurement model (measurement equation). In this paper we will propose the new tracking algorithm based the same aerodynamic model and the same radar measurement model.
# Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging +
## 5 Designing Methods to Remove False Targets
### Removing False Targets by Nullifying CP Signals
After nullifying CPs, the first \(\mathbf{e}\) on the right-hand side of the approximation in (6) becomes \(\mathbf{e}\odot[\mathbf{W}]_{:,1}\), where \([\mathbf{W}]_{:,1}\) denotes the first column of \(\mathbf{W}\) obtained after performing Step 3 of Algorithm 3. Since \([\mathbf{W}]_{:,1}\) consists of either ones or zeros, \([\mathbf{g}]_{i}\) in (6) is still maximised at \(i=L\), yet with asmaller peak amplitude. This weakened peak can reduce the detecting probability of the true target. Next, we develop another method for handling false targets.
## 6 Conclusions  
1. Integrated analysis demonstrates that the Triassic succession in the Wushi Sag comprises fan-delta, braided-river-delta, and lacustrine depositional systems exhibiting pronounced spatial differentiation. Northern foreland outcrops are characterized by fan-delta to lacustrine assemblages, whereas braided-river delta systems dominate the southern sector. From the Kelamayi to Huangshanjie formations, progressive retreat of northern fan deltas accompanied sustained northward progradation of braided-river deltas, culminating in maximum lacustrine expansion during Huangshanjie deposition, reflecting stage-specific configurations of sedimentary architecture.
2. Spatiotemporal distribution of these depositional systems is governed by multifactorial controls--tectonic activity, sediment supply, and paleoclimate--with tectonics exerting primary influence. Pre-Triassic paleotopography established preferential sediment dispersal conduits that dictated initial depositional patterns, while syndepositional foreland tectonism triggered provenance uplift and basimal subsidence. Thisdynamic regime drove persistent northward migration of depositional centers and expansion of lacustrine domains, ultimately defining the basin-fill framework.
3. Systematic evaluation of source rock potential, reservoir properties, source-reservoir configurations, and migration pathways reveals that hydrocarbon enrichment prospects concentrate within braided-river delta zones (south) and fan-delta belts (north). These intervals exhibit high-magnitude gas generation capacity, well-developed multimodal pore systems in reservoirs, and efficient fault-sandstone carrier networks. They facilitate two principal trap types: stratigraphic and structural-stratigraphic composite accumulations. The spatiotemporal coupling of these critical elements designates this trend as a strategic exploration target in foreland basin settings, with its hydrocarbon accumulation model holding broader implications for resource assessment in complex structural terrains.  
Conceptualization, Q.D.; methodology, Q.D.; software, Y.F.; validation, Y.F.; formal analysis, Y.F.; investigation, Y.F.; resources, M.H.; data curation, Q.C.; writing--original draft preparation, Y.F.; writing--review and editing, Q.D.; visualization, Y.F.; supervision, Y.F.; project administration, M.H.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China, grant number 42202161. The APC was funded by Qingjie Deng.  
Not applicable.  
All data and materials are available upon request from the corresponding author. The data are not publicly available due to ongoing research using part of the data.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Problem Description
 \tag{3}\]  
The following dimensionless variables are defined for non-dimensionalization of the model Equations (1)-(3):  
\[R=\tfrac{r}{r_{o}},\ \ \ \hat{R}=\tfrac{r_{1}}{r_{o}},\ \ \ R_{1}=\tfrac{r_{1}}{r_{o}},\ \ \ R_{c}=\tfrac{c}{r_{o}},\ \ \ U^{*}=\tfrac{U}{Umax},\ \ \ \tau^{*}(r)=\tfrac{T(r,z)-T_{w}(z)}{Q/ \lambda_{f}} \tag{4}\]  
where \(U_{max}=-\tfrac{1}{4\mu}\tfrac{dp}{dz}r_{0}^{2}\big{\{}1-R_{m}^{2}+2R_{m}^{2} lnR_{m}\big{\}}\) is the maximum velocity, with \(R_{m}=\tfrac{r_{m}}{r_{0}}=\sqrt{\tfrac{1-R^{2}}{2\ln(1/R)}}\). Applying these dimensionless variables in Equations (1)-(3), the resulting dimensionless governing equations with the associated conditions are given by:  
\[\frac{1}{R}\frac{\partial U^{*}}{\partial R}+\frac{\partial^{2}U^{*}}{ \partial R^{2}}+\frac{1}{R^{2}}\frac{\partial^{2}U^{*}}{\partial\varnothing^{ 2}}=-\frac{4}{C} \tag{5}\]  
\[\frac{\partial^{2}\tau^{*}}{\partial R^{2}}+\frac{1}{R}\frac{\partial\tau^{*}} {\partial R}+\frac{1}{R^{2}}\frac{\partial^{2}\tau^{*}}{\partial\varnothing^{ 2}}=\frac{U^{*}}{A^{*}c\overline{U}^{*}} \tag{6}\]  
The corresponding boundary conditions are given by:  
\[ \begin{array}{ll}\text{(i)}&U^{*}=0\text{ and }\tau^{*}=0\text{ at }R=\hat{R}\text{ and }0\leq\varphi\leq\alpha\\ \text{(ii)}&\frac{\partial U^{*}}{\partial\varnothing}=0\text{and }\frac{\partial\tau^{*}}{\partial\varnothing}=0\text{ at }\hat{R}\leq R\leq 1\text{ and }\varnothing=0\\ \text{(iii)}&U^{*}=0\text{ and }\frac{\partial\tau^{*}}{\partial R}=0\text{ at }R=1\text{ and }0\leq\varphi\leq\alpha+\beta\\ \text{(iv)}&\frac{\partial U^{*}}{\partial\tau}=0\text{and }\frac{\partial\tau^{*}}{\partial\varnothing}=0\text{ at }R_{1}\leq R\leq 1\text{ and }\varphi=\alpha+\beta\\ \text{(v)}&U^{*}=0\text{ and }\tau^{*}=0\text{ at }R=\frac{R_{c}R\text{ }\sin(-\theta_{1})}{R\text{ }\sin(\varphi-\alpha)+R_{c}\text{ sin}(\alpha-\theta_{1}-\varphi)}\text{ and }\alpha-\theta_{1}\leq\varphi\leq\alpha\\ \text{(vi)}&U^{*}=0\text{ and }\tau^{*}=0\text{ at }R=\frac{R_{c}R_{1}\text{ sin}(\theta_{1}+\beta)}{R_{1}\text{ sin}(\alpha+\beta-\varphi)+R_{c}\text{ sin}(\varphi-\alpha+\theta_{1})}\text{ and }\\ &\alpha-\theta_{1}\leq\varphi\leq\alpha+\ \beta\end{array}  \tag{7}\]  
where \(U^{*}\), \(\tau^{*}\), and \(\overline{U}^{*}\) are the dimensionless velocity, dimensionless temperature, and average dimensionless velocity, respectively. Moreover, \(C\) is defined by \(\big{(}1-R_{m}^{2}+2R_{m}^{2}lnR_{m}\big{)}\).
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 4 Technical Advantages of Our Solution
### Efficient Signal Fingerprint Database Construction and Dynamic Updating Mechanism  
Traditional methods of signal fingerprint database construction often rely on professional personnel for manual data collection, resulting in long data update cycles and low efficiency. To improve the efficiency of constructing the fingerprint database, this solution introduces a crowdsourcing data collection method that leverages wireless signal features, device information, and environmental sensing data from user equipment. By deploying environmental sensing services on MEC servers, the system can efficiently collect and integrate large amounts of user data in near real-time, enabling high-speed fingerprint database construction and dynamic updating. The system continuously optimizes the fingerprint database based on real-time data from user devices, adapting to changes in network conditions and user behaviors, thus avoiding the lag in updates associated with traditional methods.
## 5 Results and Discussion
These new inputs were used to test the model, and the accuracy was 29.41%: the accuracy decrease underlines the model's inability to predict the correct class when the equilibrium point was outside the limits. A logical vector was built to monitor the position of the speed-torque equilibrium point and detect changes in air properties. The RMS value was then computed from this vector every second to track the equilibrium point location within the angular speed-torque plane. To demonstrate the monitor technique's validity, Figure 10 reports the results of a final test with a total duration of 60 s and an angular speed range from 1000 to 2500 rpm in different HEPS operating modes: starting from second 26, the equilibrium point moved outside the \(\pm\)5% limits. In Figure 11, it is impossible to detect the equilibrium point leakage: the parameter test did not show any difference between the range from 0 to 26 s and the fault zone from 26 to 60 s.  
Figure 12 shows the angular speed-torque plane and the RMS trend during the final test. Figure 10: Confusion matrix. Figure 12a shows the test propeller torque curve deviation from the \(\pm\)5% limits, as highlighted in the red zone, which was not identified by the parameters reported in Figure 11. Figure 12b reports the RMS trends:  
* When the propeller torque curve was inside the \(\pm\)5% limits (range 0-25 s), the RMS trend was toward 1 with a mean of 0.94;
* When the propeller torque curve was outside the \(\pm\)5% limits (range 26-60 s), the RMS value was toward 0 with a mean of 0.29. The clear difference between the RMS values in the range from 0 to 26 s and the remaining test part demonstrates the statistical index monitor's capability to detect the torque/speed equilibrium point leakage from the \(\pm\)5% limits of a propeller torque curve. Figure 11: Final test parameters trends with the equilibrium point outside the \(\pm\)5% limits highlighted in red: (**a**) propeller torque, (**b**) Electric Machine torque, and (**c**) angular speed. Figure 12: Final test results with the equilibrium point outside the \(\pm\)5% limits highlighted in red: (**a**) propeller torque curve trend in the angular speed–torque curve plane and (**b**) RMS trend.
## 3 Materials and Methods
### Dataset Building
#### 3.1.4 Labeling  
After preprocessing and cleaning the data, the available set had 6,186,572 short clinical statements containing a total of 148,038,397 words. The vocabulary consisted of 49,222 different words. Each statement had in average 23.9 words with a range from 6 to 439 words. The distribution of number of words by statement had a mean = 23.9, variance = 171.3, skewness = 3.13, and kurtosis = 21.05.  
For the ground truth, we automatically labeled the clinical statements--previously processed from the eligibility criteria, study conditions, and interventions--as \"Eligible\" (inclusion criterion) or \"Not Eligible\" (exclusion criterion) on the basis of:  
* Their position in relation to the sentences \"inclusion criteria\" or \"exclusion criteria\", which usually preceded the respective lists. If those phrases were not found, then the statement was labeled \"Eligible\".
* Negation identification and transformation: negated inclusion criteria starting with \"no\" were transformed into positive statements and labeled \"Not Eligible\". All other possible means of negating statements were expected to be handled intrinsically by the classifier.  
The classes were unbalanced, and only 39% of them were labeled as Not Eligible, while 61% were labeled as Eligible. As the dataset was sufficiently large, we used random balanced undersampling [19] to correct it, resulting in a reduced dataset with 4,071,474 labeled samples. The \"eligibility\" variable containing the text for each criterion, as expected in NLP, has a highly sparse distribution and only 450 entries were repeated.
# Interferometric Wavefront Sensing System Based on Deep Learning
###### Abstract  
At present, most wavefront sensing methods analyze the wavefront aberration from light intensity images taken in dark environments. However, in general conditions, these methods are limited due to the interference of various external light sources. In recent years, deep learning has achieved great success in the field of computer vision, and it has been widely used in the research of image classification and data fitting. Here, we apply deep learning algorithms to the interferometric system to detect wavefront under general conditions. This method can accurately extract the wavefront phase distribution and analyze aberrations, and it is verified by experiments that this method not only has higher measurement accuracy and faster calculation speed but also has good performance in the noisy environments.  
adaptive optics; fringe pattern analysis; wavefront aberration; deep learning +
Footnote †: journal: Applied Sciences
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### Parameter Setting
#### 4.3.3 Exponent Coefficient of Blurriness Comparison Function  
In this section, we evaluate the contribution of \(\lambda\), which is the exponent coefficient of blurriness comparison function \(h(x,y)\). The impact of \(\lambda\) tested on LIVE II [26] dataset is shown in Figure 7 with other parameters the same as SSIM [1]. SROCC, PLCC and RMSE come to the best performance when \(\lambda=1\), and when \(\lambda=0\) our NSSIM degrades to the traditional SSIM [1]. To achieve the best performance and simplify the expression, we set \(\mathbf{\alpha}=\mathbf{\beta}=\gamma=\mathbf{\lambda}=1\), \(C_{1}=0.01\), \(C_{2}=0.03\), \(C_{3}=C_{2}/2\), \(C_{4}=C_{2}\).
# Comparing Approaches for Explaining DNN-Based Facial Expression Classifications
## 2 Background and Related Work
### XAI in Affective Computing and FER  
Like in other AI research areas, there has been an increase in research into explaining models in affective computing and FER as well. This research has been focused on CNNs for the most part, as this is the type of model that is the most prevalent in contemporary research.  
In [10], [PERSON] et al. explained a CNN model that distinguishes pain from other emotions, such as happiness or disgust. For this, they used the XAI method Layer-wise Relevance Propagation (LRP) [26]. They found that while this gives some insights into the model's decisions, it is not distinctive enough.  
A challenge on explainability in computer vision was proposed in 2017 by [PERSON] et al. [12]. The main target of this challenge was to make an explainable model that examines videos of job candidates and gives a first impression in terms of the big five personality traits. An example submission is [27], where class activations and action units are used for explaining the predictions of their CNN model.  
Both [28; 29] use Shapley values to explain their models on sentiment analysis, although this analysis is in a different context than FER. [PERSON] et al. used LRP saliency maps to investigate whether a network has learned concepts (in this case action units), especially in the case where a network originally trained for emotion recognition is used as a base for transfer-learning a model to recognize pain [30].  
[PERSON] and [PERSON] et al. propose a technique for extracting influential landmarks in [11]. They do this in the context of moving faces and use a CNN for the emotion classification. Then, class activation maps are used to find influential regions and from these, landmarks are extracted that are based on action units.
## 4 Discussion
### Leachates and PHREEQ-C Simulations
#### 4.2.3 Sb in Eluates  
Due to the strong bond with Fe-oxy-hydroxides and the electronic configuration similar to that of As, Sb is generally considered to be relatively immobile. According to [71], the transformation of mobile forms of Sb is predominantly controlled by naturally  
Figure 7: Eh–pH diagrams of Hg–S–Cl. White stars represent the seven CO\({}_{2}\)-saturated eluates with concentrations \(>\)0.1 \(\mathrm{\SIUnitSymbolMicro\text{g}}\cdot\mathrm{L}^{-1}\).  
occurring precipitation and adsorption processes (e.g., [72]). Sixteen samples out of 35 (Table 6) showed concentrations above the legal limit of Sb (5 ug\(\cdot\)L\({}^{-1}\)) for drinking water. PHREEQC simulations revealed that Sb in solutions is mostly dominated by Sb\({}^{5+}\) and the main three species were SbO\({}_{3}\); Sb(OH)\({}_{6}\)\({}^{-}\) e CaSb(OH)\({}_{6}\)\({}^{+}\). In the Eh-pH diagram of Sb-S-Ca-Fe (Figure 8), all CO\({}_{2}\)-saturated leachates are included in the stability range of SbO\({}_{2}\) (usually known as cervantite- Sb\({}_{2}\)O\({}_{4}\)). Despite the fact that cervantite was not found is the ASS mine, it was recognized as the primary mineral in several Sb-mines from Tuscany (e.g., the Selva mine and the Le Cetine mine ([73; 74; 75] and reference therein)). However, all Sb-compounds are also found close to the stability range of tripubyte (FeSO\({}_{4}\)) and romeite (Ca\({}_{2}\)Sb\({}_{2}\)O\({}_{7}\)). As well as for As, Sb(OH)\({}_{6}\)\({}^{-}\) and SbO\({}_{3}\) (two of the predominant water-soluble Sb compounds at neutral pH), can be co-precipitating with Ca\({}^{2+}\) ions to produce insoluble and stable calcium antimonate Ca[Sb(OH)\({}_{6}\)]\({}_{2}\) ([76], and references therein) or romeite that may form over calcite crystals.  
As previously evidenced, PHREEQC simulations indicate that tripubyte was always occurring, with the exception of sample LM31. Tripubyte is not found as the main mineral because it is likely that part of hosted-Fe\({}^{2+}\) tends to precipitate as Fe-oxy-hydroxide. Nevertheless, its presence was confirmed by various PHREEQC simulations, which also showed that SI always clustered around 1.5 (Table S1). The thermodynamic analyses (log K, Table 2) of tripubyte demonstrated that this mineral phase is stable and is recognized as crucial sink of Sb in the natural environment [42]. Therefore, it is clear that the formation of such stable secondary Sb minerals (romeite and tripubyte) via precipitation plays a key role in the sequestration of mobile species of Sb in the natural environment [42; 71; 77]. To confirm the presence of tripubyte in soils, although its value is expected to be low, a detailed mineralogical study of the toposils is required.  
Figure 8: Eh–pH diagrams of Sb–S–Ca–Fe. Red circles represent the CO\({}_{2}\)-saturated eluates.
## 3 Results and Discussion
### Results on Different Value of \(\alpha\)  
As mentioned before, changing the value of \(\alpha\) may have an impact on the results of the proposed method. Setting a greater value of \(\alpha\) makes the constraints (7) and (8) more relaxed. Consequently, the likelihood of achieving a better objective function is increasing.  
Whenever Case 1 is solved with a bigger value of \(\alpha\), i.e., \(\alpha=0.1\), the result is different from the result presented in Table 1. It results in a smaller objective function, which is 45. However, the weekly production load is widened to the range 1149 to 1371. The results using various values of \(\alpha\) are presented in Table 6.  
Similar results are obtained whenever Case 2, Case 3, and Case 4 are solved using various values of \(\alpha\). A summary of the results is displayed in Tables 7-9, respectively. As predicted, there is a tendency that the larger the \(\alpha\), the smaller the value of the optimum objective function. However, the larger the \(\alpha\), the wider the range of weekly production loads obtained. Naturally, these results must be communicated to the solution's users, for example, the company's production manager. Finally, the manager can determine which value of \(\alpha\) is being used. For sure, not only the objective function value but also the acceptable range of weekly production load is becoming the criteria for determining the \(\alpha\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\alpha\) & **Objective Function Value** & **Weekly Production Load Range** \\ \hline
0.0025 & 49 & 1257–1262 \\
0.0050 & 48 & 1255–1265 \\
0.0100 & 47 & 1249–1268 \\
0.0500 & 46 & 1229–1299 \\
0.1000 & 45 & 1149–1371 \\
0.4000 & 44 & 925–1670 \\
0.5000 & 43 & 744–1814 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of Case 1 at various values of \(\alpha\).  
Figure 8: Results of Case 4 in the form of Excel spreadsheet.
# A 5G NR FR2 Beamforming System with Integrated Transceiver Module
## 2 A 28 GHz RF Beamforming System and Transceiver Module
### Development of 28 GHz RF Transceiver Module
In order to supply stable power to the transceiver module and reduce electrical signal interference between the block, the power supply and ground were separated by the functional block. Additionally, to simplify the digital control of the RF chipsets, the use of SPI was employed.