esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 5 Application Analysis of Passenger Flow Prediction
### Model Comparison Evaluation  
To better compare the difference in prediction effect between the combined model and the single model, two common evaluation indicators, root mean square error (RMSE) and  
Figure 16: Prediction results of passenger flow at Longtousi (Low passenger flow).  
Figure 14: Prediction results of passenger flow at Guanyinqiao (high passenger flow).  
Figure 15: Prediction results of passenger flow at Jiazhoulu (medium passenger flow).  
mean absolute percentage error (MAPE), were used to quantify the performance of the model according to different categories of stations. Its calculation formula is:  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{\left({{x_{i}}-{p_{i}}}\right)^{2}}}} \tag{10}\]  
\[MAPE=\frac{1}{N}{\sum\limits_{i=1}^{N}}{\left|{\frac{{{x_{i}}-{p_{i}}}}{{{x_{i }}}}}\right|\times 100\%} \tag{11}\]  
where \(N\) is the total samples; \(x_{i}\) is the actual value of passenger flow; and \(p_{i}\) is the predicted value of passenger flow. RMSE and MAPE can represent the gap between the predicted value and real value. Thereby the performance of the model only needs to compare the RMSE and MAPE values of the prediction results. The smaller the value of the two evaluation indicators, the better the prediction result of the model.  
Table 4 shows the RMSE and MAPE values obtained by using the combined model and the single model to predict the passenger flow of different stations. It can be seen that the prediction results of the TCN-LSTM combined model are better than the LSTM single model for high, medium, and low passenger flow stations. However, for the combined model, the prediction accuracy of the low passenger flow is the lowest, and the higher the passenger flow, the better the prediction accuracy of the combined model.
# Enhanced Sensitivity of FeGa Thin-Film Coated SAW Current Sensor
## 3 Technical Realizations
### Preparation of FeGa Film  
Then, the FeGa thin film was deposited onto the acoustic propagation path of the developed SAW chip to build the sensing device by employing the lift-off process. The corresponding details are described in Figure 4. Firstly, the photoresist was deposited on the prepared SAW device. Then, through a photolithography mask, the photoresist was removed in the areas, where the FeGa thin film was to be located; then, the FeGa thin film was sputtered onto the surface of the SAW chip using radio-frequency magnetron sputtering. The sputtering conditions for the base pressure, sputtering power and target-substrate distance were 1.5 \(\times\) 10\({}^{-5}\) Pa, 100 W, and 60 mm, respectively. The Ar gas was used as the sputtering gas, and corresponding pressure was set to 1 Pa. After removed the remaining photoresist, the FeGa thin film with various patterns (dot, grating, and membrane) was formed on the SAW chip. Here, the strip width in each FeGa grating was set to 3\(\lambda\), and the corresponding grating spacing was set to 4\(\lambda\). Similarly, the length width of each FeGa dot was set to be 3\(\lambda\)\(\times\) 3\(\lambda\), and dot spacing in each direction was set to 4\(\lambda\). The developed SAW sensing devices with various FeGa thin-film patterns were pictured in Figure 5a-c.  
Then, the deposited FeGa thin film was characterized by using the scanning electron microscope (SEM) and 2D atomic force microscope (AFM), as shown in Figure 5d,e. It can be seen that the surface of FeGa film was relatively smooth, and the distribution was dense and uniform. The corresponding surface average roughness (Ra) was about 1.56 nm, and the maximum height difference on the surface (P-V) was \(\sim\)15.69 nm.  
Using the network analyzer, we characterized the developed sensing devices as shown in Figure 5f. Among them, the working frequency of the sensing devices with various FeGa patterns (dot, grating, and membranous) were measured as 150.4 MHz, 149.7 MHz, and 149.4 MHz, respectively, and their corresponding insertion losses were both less than 10 dB. The deviation in operation frequency stems from the manufacturing error, and there are almost no unacceptable effects in sensing performance.  
Figure 4: The fabrication process of FeGa film.
## 2 Description of the Problem
### Pipe Curvature  
As it was mentioned in Section 1.3, in this work, the penstock is not considered to be composed of straight segments, as in previous approaches, but able to slightly bend along its layout, as a result of the real deformation that can be observed in long pipes (as shown in Figure 2). Nevertheless, these curvatures are required to be compatible with the material properties (in particular, its stiffness and resistance), and thus a limitation is required to be imposed for the curvature radius \(r\).  
Considering the pipe as a simple Euler beam under pure bending [27], a direct relation can be written between the radius or curvature \(r\) and the maximum mechanical stress \(\sigma\), involving the Young modulus of the pipe material (typically mild steel [28]), \(E\), and the diameter, \(D_{p}\):  
\[\sigma_{max}=E\frac{D_{p}}{2r} \tag{10}\]  
The Von-Mises yielding criterion [27] can be used to impose the safety of the pipe as follows:  
\[\frac{\sigma}{S_{y}}\leq 1, \tag{11}\]  
where \(S_{y}\) is the tensile strength of the material. Substituting and reordering, it can be written as:  
\[r(s)\geq\frac{E}{2S_{y}}D_{p} \tag{12}\]  
It is relevant to note that the minimum curvature radius allowable depends on the diameter of the pipe.
# Nutritional Care Process in Hospitalized Patients with Obesity-Related Multimorbidity
## 4 Nutritional Status Assessment
### Anthropometric Measurements  
Anthropometric parameters include the following measurements: height, current weight, calf, arm, and abdominal circumferences. Current weight is unreliable in ORM, as it tends to overestimate energy expenditure. Weight measurement is unrealistic in the hospital environment, and weight estimation formulas tend to overestimate it since they require measuring circumferences, including the abdominal circumference, in patients lying on their back or supine, which makes the measurement inaccurate. Therefore, during hospitalization, it is recommended to use the \"reference body weight\" or \"ideal body weight\" (IBW) for individuals with obesity\", which is calculated using the formula: [(height in meters)\({}^{2}\times 25\)]. Recently, it has been reported that only one-third of excess body weight in individuals with obesity is metabolically active. For this reason, the use of \"adjusted body weight\" (ABW) is recommended, which is calculated with the following formula: [(current weight \(-\) reference body weight) \(\times\) 0.33 + reference body weight] [18; 19]. Estimating BMI as the sole tool for defining and classifying obesity in hospitalized patients is imprecise due to the presence of edema and body positions that interfere with accurate measurements, among other factors. Ideally, BMI should be interpreted alongside other validated indicators of excess fat, such as waist circumference, waist-to-height ratio, or waist-to-hip ratio, or by using body composition methods that determine total body fat percentage or visceral fat. These parameters in patients with ORM contribute to the definition of \"clinical obesity,\" which was recently proposed by The Lancet Commission [32].  
Validated methods for assessing body composition in hospitalized patients include: Dual-energy X-ray Absorptiometry (DEXA), Computed Axial Tomography (CAT), and Magnetic Resonance Imaging (MRI). Multi-frequency Bioelectrical Impedance Analysis (mfBIA) is not recommended for individuals with fluid and electrolyte imbalances or those receiving intravenous solutions, as these reduce the reliability of mfBIA [33; 34]. However, phase angle estimation using mfBIA--related to cell membrane integrity--is useful as a survival prognostic marker (greater than 5.1\({}^{\circ}\)) and as an indicator of muscle mass preservation (greater than 4.8\({}^{\circ}\)). Nevertheless, the lack of availability of this equipment, limited patient mobility, and weight exceeding the usage limits of these devices in hospital settings make their application limited. Thus, waist circumference is one of the most accessible methods for clinical practice in hospitals and should be measured in individuals with a BMI greater than 25 kg/m\({}^{2}\) when insulin resistance or MASLD is suspected. In Mexico, the validated cut-off points as indicators of central obesity are waist circumference greater than or equal to 84 cm in women and greater than or equal to 98 cm in men, or a waist-to-height ratio greater than 0.5 [35].
# SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
## 5 Case Studies  
Our experimental evaluation is a quantitative appraisal of the effectiveness and efficiency of SPOT. To be concrete, we choose Apache Flink [4] as the target DSP platform because of its popularity [43]. We evaluate the effectiveness and efficiency of SPOT through various sets of subject DSP programs, and compare SPOT against two most recent alternative testing methods, i.e., DiffStream [25] and FlinkCheck [26]. We compare them in terms of three aspects: (1) path coverage--JDU path coverage, which works as the metric to measure how good the testing is; (2) efficiency to triggering failure; (3) and stream reordering coverage. The goal of our evaluation is to answer these questions:  
1. To what extent is SPOT applicable to DSP programs?
2. How efficient can SPOT achieve in terms of coverage?
3. How efficiently does SPOT perform in triggering failure?
4. How many reordering situations can be covered with existing methods?  
**Experimental Environment**. We leverage Flink's MiniCluster [[https://ci.apache.org/projects/flink/flink-docs-master/api/java/org/apache/flink/runtime/minicluster/MiniCluster.html](https://ci.apache.org/projects/flink/flink-docs-master/api/java/org/apache/flink/runtime/minicluster/MiniCluster.html)]([https://ci.apache.org/projects/flink/flink-docs-master/api/java/org/apache/flink/runtime/minicluster/MiniCluster.html](https://ci.apache.org/projects/flink/flink-docs-master/api/java/org/apache/flink/runtime/minicluster/MiniCluster.html)), accessed on 28 July 2021 to execute subject programs on a single machine with AMD Ryzen 9 3900X 12-Core Processor and 32 GB of RAM, running 64 Bit Ubuntu 20.04 LTS. Regarding the software, we used jdk-1.8.0. Apache Flink version 1.9.2 works as the DSP platform.  
Our first case study (Section A) is to illustrate how SPOT applies to DSP operators. The second case study (Section B) is used to qualitatively compare the JDU path coverage between SPOT and the recent two PBT-based testing approaches, i.e., DiffStream and FlinkCheck. In the third case study (Section C), we demonstrate that test data generated by SPOT is more efficient in triggering software failure compared with DiffStream and FlinkCheck. The fourth case study (Section D) describes how random data are skewed in reordering situations.
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 3 Results
### Half-Hourly GPP Estimation by Diffuse and Direct APAR (DDA)-Based Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vegetation** & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**Slope**} & \multicolumn{2}{c}{**Variation Coefficient of**} \\ \cline{2-5}
**Types/Metrics** & **APAR\({}_{\text{diffuse}}\)** & **APAR\({}_{\text{direct}}\)** & **APAR\({}_{\text{diffuse}}\)** & **APAR\({}_{\text{direct}}\)** & **Slope (\%)** \\ \hline EBF & 0.275 & 0.121 & 0.097 & 0.032 & 203.125 \\ ENF & 0.667 & 0.445 & 0.262 & 0.094 & 178.723 \\ DBF & 0.585 & 0.272 & 0.266 & 0.120 & 121.667 \\ SAV & 0.593 & 0.489 & 0.230 & 0.221 & 4.072 \\ GRA & 0.422 & 0.384 & 0.182 & 0.098 & 85.714 \\ CRO & 0.495 & 0.290 & 0.217 & 0.129 & 68.217 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The statistical metrics of the diffuse (left side) and direct APAR (right side) towards GPP. significant decline of the RMSE values can be noticed with the increase of diffuse fraction owning averaged R\({}^{2}\) values of 0.659, which indicated that the DDA-based method can well improve the accuracy of GPP estimation due to the influence of diffuse radiation and confirm the necessity of partitioning diffuse APAR on GPP estimations. Figure 4: Comparison of the calculated GPP and observed GPP for different vegetation types: (**a**) EBF; (**b**) ENF; (**c**) DBF; (**d**) SAV; (**e**) GRA, and (**f**) CRO (the label of “GPP_DDA” and “GPP_TA” denote the half-hourly GPP calculated using the DDA method and total APAR-based method; observed GPP are the half-hourly GPP derived from FLUXNET2015). Figure 5: RMSE variations between the DDA-based GPP and observed GPP under different levels of diffuse fraction in different vegetation types: (**a**) EBF; (**b**) ENF; (**c**) DBF; (**d**) SAV; (**e**) GRA, and (**f**) CRO (The level of diffuse fraction denotes the values of diffuse fraction varying from 0 to 1 with 0.1 intervals).
# Adsorption of Metolachlor and Its Transformation Products, ESA and OXA, on Activated Carbons
## Materials and Methods
### Physico-Chemical Characterization  
The lignocellulosic composition (cellulose, hemicelluloses and lignin) of the employed precursors was analyzed following a procedure described in detail elsewhere [21]. In brief, the precursors were soxhlet extracted with ethanol for 1 h followed by water for 1 h. For these extracted samples, Klason and acid-soluble lignins were determined by using 72% sulfuric acid solution. Holocellulose was quantified with sodium chlorite treatment and \(\alpha\)-cellulose content was determined by extraction with 17.5% aqueous sodium hydroxide of the holocellulose powder. Cellulose content was evaluated by the sum of \(\alpha\)-cellulose content and ashes, and hemicellulose amount was obtained by the difference between holocellulose and cellulose. The ashes were obtained by weight after the incineration of samples at 600 \({}^{\circ}\)C for 4 h. All of the analyses were made in duplicate.  
The porosity of the materials was characterized by measuring the N\({}_{2}\) adsorption/desorption isotherms at \(-\)196 \({}^{\circ}\)C (ASAP 2020, Micromeritics). The materials were previously outgassed under vacuum (ca. 10\({}^{-4}\) Pa) at 120 \({}^{\circ}\)C for 17 h. The isotherms were used to determine several textural parameters such as specific surface area (\(S_{BET}\)), total pore volume (\(V_{total}\)), micro- and mesopore volumes (\(W_{0}\), \(V_{meso}\)).  
Elemental analysis was carried out using the THERMO SCIENTIFIC FLASH2000 automatic analyzer. Thermogravimetric experiments were conducted using a thermobalance (STA 449 F5 Jupiter, NETZSCH). The superficial pH was determined measuring the pH of an equilibrated aqueous suspension (ca. 1 g/L) of the adsorbent. Surface chemical functionalities were measured by potentiometric titration according to the pKa distribution method based on titration with NaOH (0.1 M). Briefly, a mass of 50 mg of material was dispersed in a volume of 75 mL of NaNO\({}_{3}\) (0.01 M) and degassed by nitrogen bubbling overnight; then, the pH was adjusted to ca. 3 using an HCl solution (0.1 M) and titrated by NaOH using a Titrino Plus system, Methrom. The functionalities were determined using the SAIEUS software applied on the proton affinity versus pH curves [22].
## 4 CFD Simulation of Mixing Processes
### RTD and Mixing Indices
Additional indices such as the Mixing Efficiency Index (MEI) or the Scale of Segregation are used to evaluate the temporal or spatial progression of mixing in both batch and continuous systems [71]. CFD simulations enable the spatial and temporal mapping of these indices throughout the mixing equipment, allowing engineers to identify poorly mixed zones, optimal shear regions, and dynamic changes in non-steady-state operations. These insights are essential for improving mixing performance, especially when physical testing is constrained or impractical [72]. In industrial practice, CFD-based analysis of RTD and mixing indices has been applied to optimize screw configurations in continuous mixers and extruders, redesign impeller geometries and baffles to reduce ingredient segregation, and ensure uniform distribution of critical additives such as enzymes, salts, or micronutrients. For instance, in continuous dough mixing, variability in residence time due to inadequate screw design can result in non-uniform gluten development. In emulsification processes, uneven mixing may promote droplet coalescence, leading to phase separation and reduced product stability. Advanced simulations increasingly integrate RTD with reaction kinetics or rheological evolution models to predict coupled physical-chemical transformations, thereby supporting formulation and equipment decisions [73]. Despite their utility, CFD-based predictions of RTD and mixing quality must be validated with experimental data to ensure credibility. Common validation methods include tracer studies using chemical markers, flow visualization via MRI (Magnetic Resonance Imaging) or positron emission tomography (PET), and post-process statistical analysis of concentration distributions in batch mixing systems. Moreover, achieving reliable results requires ensuring mesh independence, proper solver settings, and realistic boundary conditions tailored to the specific system under investigation (Table 2) [74]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Process** & **Dominant Physical Processes** & **CFD Modeling Approaches** & **Example Industrial Applications** & **Application in Food Industry** & **Reference** \\ \hline \multirow{4}{*}{Mining} & \multirow{4}{*}{Learning or turbulent flow} & Navier-Stokes (RMS, LEs) & \multirow{4}{*}{Emulsion homogenization} & \multirow{4}{*}{Quality control in memory, industry, conditions} \\  & & Traditional motion, shearing & Non-Newtonian fluid-driven (Press, LEs) & & \\  & & International mobility, shearing & Handbook-driven (Press, LEs) & & \\  & & Differential-bility & & & \\  & & Deepening multiple phases & & & & \\  & & CFD-based coupling & & & & \\  & & & & & \\ \hline \multirow{4}{*}{Extinction} & \multirow{4}{*}{Transmission} & Thermo-mechanical coupling & \multirow{4}{*}{Non-Newtonian fluid-driven
## 2 Background  
Generally, in a single-stage LNA design, a simultaneous signal and noise match (SSNM) at the input and a conjugate signal match at the output section of the active device cannot be achieved [15]. The designer is responsible for balancing input and output mismatch levels _IM_ and _OM_, respectively. In either situation, the resulting output or input impedance may have a high reflection coefficient, which is unfavorable to system integration. Impedance matching at the input port will not provide the best noise performance or the lowest input reflection. Therefore, source inductance feedback is introduced to get \(S_{11}^{*}\) closer to \(\Gamma_{opt}\)[17]. By doing this, the impedance matching networks become simpler when \(S_{11}^{*}\) and \(\Gamma_{S}\) are brought closer to the center of [PERSON]'s chart, as shown in Figure 1.  
Previously, in [16], the authors introduced the deterministic design technique for the two-stage LNA using the feedback approach, with a linear gain of 18 dB. To obtain a gain of 20-30 dB at microwave frequencies, different LNAs are applied in a three-stage architecture, depending on the technology and the application [18].  
In our previous publication [9], we presented a simple synthesis methodology for 3-stage LNA, where only the small signal parameters were presented. This extended and comprehensive contribution will show:  
* Comprehensive design details for each stage.
* Noise Figure measurements.
* The LNA's large-signal performance simulation.
* The LNA robustness under input overdrive for both the continuous-wave (CW) and pulse-wave.
* Comparison with state-of-the-art amplifiers of similar technology.
# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
## 4 Experiment
### Visualization  
In order to demonstrate the efficacy of MAFE regarding the OFER problem, some occluded samples were subjected to attentional visualization. Figure 9 illustrates the attentional visualization of some FER networks on OFER samples.  
The first row of Figure 9 shows some OFER sample pictures, followed by the attention maps of FDRL [52] and ARM [53] on OFER samples in the next two rows, and then the attention of PTIR-50. The results show that these three networks focus significantly on the occluded part of the OFER samples, leading to poor recognition accuracy. In contrast, the last row of images for MAFE demonstrates that the occluded region receives minimal attention, highlighting MAFE's advantage in handling OFER samples.  
We selected representative samples with mouth-occluded and eye-occluded regions from the dataset and visualized their feature activation maps in Figure 10. These findings demonstrate that the model exhibits reasonable robustness to occlusion and is not overly sensitive to the position or nature of the occluded region.  
Specifically, MAFE introduces a simplified global feature extraction network, PTIR-50, which efficiently captures global features while maintaining a lightweight structure. The fine-grained branch, SWIN-E, extracts detailed features from the image, improving the model's ability to recognize subtle differences in facial expressions. Additionally, the con-feature loss function facilitates mutual guidance between global and fused features,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **RAN [15]** & **SCAN-CCI [41]** & **MPA [43]** & **MAFE** \\ \hline RAF-DB & 82.72 & 89.02 & 89.54 & 92.11 \\ FERPlus & 88.55 & 89.42 & 89.13 & 90.15 \\ Occlusion-RAFDB & 82.72 & 85.03 & 87.47 & 89.42 \\ Occlusion-FERPlus & 83.63 & 86.12 & 86.28 & 86.94 \\ \hline \hline \(p\)-value & 0.057 & 0.086 & 0.038 & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(p\)-values of methods evaluated on four datasets.  
Figure 9: Attention visualization. The visualization of the attention of some FER networks on OFER samples demonstrates the superiority of MAFE when considering OFER samples.  
allowing MAFE to learn more discriminative features, thereby enhancing both recognition accuracy and robustness.