esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Source Layer  
Data collection is one of the most important parts of this research work. Traditionally, the utility data are represented in two-dimensional CAD drawings; however, the problem with the 2D drawings is the lack of containing additional information. Moreover, it lacks the depiction of the depth information in the third dimension. Therefore, a three-dimensional BIM model is suggested in this framework for utility representation. For the development of BIM, which provides the information of underground utilities to the above surface reference, data about the existing terrain information and underground utility location needs to be surveyed. For existing terrain information, data can be collected using surveying techniques such as terrain traditional surveying techniques such as tachometry, total station (TS), and real-time kinematic global navigation satellite system (RTK GNSS), or the latest surveying techniques such as UAV photogrammetry and ranging (LiDAR) or airborne laser scanning (ALS) for the terrain and underground utility location detection. For existing underground utility information, there are several technologies such as infrared thermography, elastic wave method, wave impedance probe (WIF), low-frequency  
Figure 1: Concept overview.  
electromagnetic survey, passive magnetic fields (PMFs), and GPR. Existing information from the local government and consultants, such as CAD drawings, spreadsheets, and design reports, can also be acquired. For this research, existing information of terrain was collected using UAV photogrammetry, and for existing underground utility information, GPR technology was adopted. Moreover, existing information from the local government department and consultants was acquired in 2D CAD drawings.
## 2 Materials and Methods
### Rumex Confertus Volatile Organic Compounds Collection and Analysis  
The _R. confertus_ GLVs, induced by application of the ZJA and DJA, were collected by placing the individual plant leaves into the Nalophan bags (odor- and taste-free cooking plastic bags), which were connected to a pump system (Thermo Fisher Scientific, Waltham, MA, USA). The air containing the GLVs was sucked into collector traps, which consisted of glass tubes (Analytical Research Systems, Inc., Gainesville, FL, USA) with a length of 76 mm and an outer diameter of 6.35 mm, with each tube containing 30 mg of Super-Q adsorbent (Alltech Associates, Inc., Deerfield, IL, USA). GLV collection was carried out for 3 h from four plants simultaneously, always between 10 am and 1 pm. To avoid the collection of any confounding volatiles coming from the outside, purified and humidified air was delivered to the Nalophan bag at a rate of 1.0 L min\({}^{-1}\), while suction to the trap was maintained at a rate of 0.8 L min\({}^{-1}\), i.e., a positive pressure was maintained inside the Nalophan bags throughout. The collection of the VOCs released by the plants were started at 24 h, 48 h, and 72 h after the plants were exposed to ZJA or DJA and lasted for 3 h.  
After collection, the GLVs were eluted from the adsorbent with 225 \(\upmu\)L of hexane, which contained 7 ng of decane (Sigma-Aldrich, Steinheim, Germany) for use as an internal standard. Thereafter, a qualitative and quantitative analysis of the VOCs was performed using a GC AutoSystem XL (PerkinElmer, Shelton, CT, USA) equipped with a 30 m DB-5 MS capillary column (0.25 mm internal diameter, 0.25-\(\upmu\)m film thickness; Restek, Bellefonte, PA, USA). The injection temperature was kept constant at 250 \({}^{\circ}\)C and the GC program was set so that the temperature was raised from 40 to 200 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\). The MS parameters used were as follows: ion source temperature, 250 \({}^{\circ}\)C; interface temperature, 200 \({}^{\circ}\)C; and operating in scanning mode to cover 40-400 \(m\)/z using a scan speed of 1250 amu s\({}^{-1}\).  
The identification of the GLVs was based on a comparison of experimentally obtained mass spectra and linear retention indices (calculated against the C\({}_{8}\)-C\({}_{20}\)\(n\)-alkane mixture, Sigma-Aldrich, Steinheim, Germany) with those available in NIST 17 Mass Spectral and Retention Index Libraries (The National Institute of Standards and Technology, Gaithersburg, MD, USA). The identified GLVs quantification was based on the amount of added internal standard.
## 5 Numerical Analysis of the Roadbed Heating and Frost Heaving Prevention
### Freezing Depth  
Figure 14 shows the variation in the freezing depth at the center of the roadbed under natural conditions and artificial heating conditions. It is evident that, under natural conditions, the roadbed started to freeze on 24 November, and the freezing depth continued to increase until 17 March of the following year, when it reached a maximum value of 0.89 m, owing to the climatic environment; subsequently, the frozen layer started to thawing from both directions as the temperature rose, and the freezing range was gradually reduced and completely thawed by 4 April, staying frozen for approximately 100 days. Under artificial heating conditions, the onset of freezing occurred on the same day as that of the natural roadbed, since no insulation measures were taken on the roadbed surface. However, the freezing depth was greatly reduced: the maximum freezing depths were 0.15, 0.13, and 0.11 m with heating temperatures of 40, 50, and 60 \({}^{\circ}\)C, respectively. Meanwhile, the freezing depth began to gradually decrease in mid-February without undergoing a bidirectional thawing process, and the layer of the roadbed affected by frost heaving thawed completely once the average temperature rose to above 0 \({}^{\circ}\)C.  
Figure 13: Temperature fields in roadbed cross-section (Y = 20 m) on February 1. (**a**) Natural road base. (**b**) Heating temperature set point of 40 \({}^{\circ}\)C. (**c**) Heating temperature set point of 50 \({}^{\circ}\)C. (**d**) Heating temperature set point of 60 \({}^{\circ}\)C.
# Feasibility Study on Earthquake Prediction Based on Imending Geomagnetic Anomalies
## 2 Methods for Observing and Capturing Geomagnetic Anomalies
### Geomagnetic Anomaly under Intensive Observation  
In the densely arranged magnetic strength sensor network, when the underground geology changes, it can be observed that many stations will have magnetic anomalies at the same time. The situation is the same as shown in Figure 1; the points with red circles have magnetic anomalies. Moreover, the magnetic anomaly with synchronous change characteristics is considered to be the geomagnetic anomaly caused by earthquakes. Figure 2 shows the waveform of a geomagnetic anomaly with synchronous change characteristics.  
\begin{table}
\begin{tabular}{l l} \hline
**Meas. Range** & \(\pm\)**100 \(\mu\)T, Other Ranges on Request** \\ \hline Accuracy at 20 \({}^{\circ}\)C & \(\pm\)2\% \(\pm\) 0.3 \(\mu\)T \\ \hline Operating temperature & \(-\)40 to \(+\)85 \({}^{\circ}\)C \\ \hline Zero drift & \(<\)2 nT/K \\ \hline Output voltage OUT+ ref. to OUT\(-\) & \(\pm\)1 V/50 \(\mu\)T, max. \(\pm\)2.5 V \\ \hline Bandwidth & 0 to 1 kHz (\(-\)3 dB) \\ \hline DC output impedance & \(<\)1 \(\Omega\) \\ \hline Reference output OUT\(-\) & 2.5 V ref. to supply ground (0 V) \\ \hline Max. load between OUT+ and OUT\(-\) & \(>\)1 k\(\Omega\), \(<\)100 pF \\ \hline Noise & \(<\)0.5 nT\({}_{\text{RMS}}\) or 3 nT\({}_{\text{PP}}\) (0.1 to 10 Hz), \\ \(\sim\)150 pT/\(\sqrt{\text{Hz}}\)@ 1 Hz \\ \hline Supply voltage & 5 V \(\pm\) 5\% \\ \hline Supply current & \(\sim\)2 mA \\ \hline Dimensions & 44.5 mm \(\times\) 14 mm \(\times\) 5.5 mm \\ \hline Length of detection coil & 22 mm \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of AM2-1001 sensor.  
Figure 1: Magnetic anomaly in magnetic intensity sensor network (the numbers indicate the station serial numbers).
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 4 Distributed Canonical Correlation Analysis and Its Multiple Formulation
The analysis of the approximated error of Algorithm 3 is similar as that in Section 3. The convergence boils down to that of the power method in [30]. ```
1: In the local servers, calculate local covariance matrix \(M_{i}\) in \(i\)-th local server and broadcast \(M_{i}M_{i}^{*}\) and \(M_{i}^{*}M_{i}\), respectively, to the central server. 2: In the central server, calculate the leading \(k\) eigenvectors of \(\hat{M}_{1}\) and \(\hat{M}_{2}\) as \(\hat{W}_{1}\) and \(\hat{W}_{2}\), respectively, by the power method in parallel. 3: Return \(\hat{W}_{1}\) and \(\hat{W}_{2}\). ```  
**Algorithm 3** One-shot Distributed multiple CCA algorithm.
## 3 Mapping Lithologies in the Kohat Plateau Using SVM and ANN  
A schematic representation of the methodology is presented in Figure 3, starting with processing Sentinel-2 MSI data using the GEE platform. Atmospherically corrected multispectral Sentinel-2 MSI data were taken from the GEE dataset. Date (only images from 2016 to 2020), cloud (pixels < 5% cloud), and vegetation (pixels < 5% vegetation) filters were applied to select pertinent data in the study area. In addition, we used a median filter which sorts all the pixels in ascending order and selects the median reflectance value for each band. Median filtering is a nonlinear technique that can help preserve the sharp features in an image by filtering the noise.  
Figure 3: Block diagram of lithological classification applied in the study.
## 3 Description of System Performance Indicators
### Performance Metrics for Complete Systems
#### 3.2.2 Outage Probability  
In wireless power sensor networks, the probability that the packet transmission time will surpass the timeout threshold even under ideal channel circumstances is defined as outage probability, and the expression is as follows:  
\[\mathscr{O}_{sys}^{(v)}=\Pr\Bigl{\{}T_{\min}^{(v)}\geq t_{out}\Bigr{\}}, \tag{15}\]  
where \(T_{\min}^{(v)}=\min\limits_{k\in\{1,2,\dots,K\}}\Bigl{\{}T_{k}^{(v)}\Bigr{\}}\). Complying with Lemma 1 from the paper [22], we know that  
\[F_{z_{k}^{(\text{HHTC})}}(z) =1-\frac{2}{\Gamma(M)}\biggl{(}\frac{z}{\theta_{k}\omega_{k}} \biggr{)}^{\frac{M}{2}}K_{M}\biggl{(}2\sqrt{\frac{z}{\theta_{k}\omega_{k}}} \biggr{)}, \tag{16}\] \[F_{z_{k}^{(\text{SC})}}(z) =1-2M\sum\limits_{m=0}^{M-1}\biggl{(}\begin{array}{c}M-1\\ m\end{array}\biggr{)}(-1)^{m}\] (17) \[\times\sqrt{\frac{z}{(m+1)\theta_{k}\omega_{k}}}K_{1}\biggl{(}2 \sqrt{\frac{(m+1)z}{\theta_{k}\omega_{k}}}\biggr{)}.\]
## 3 Results and Discussion
### Computational Fluid Dynamics Simulations
#### 3.2.3. Parcel Study  
In unsteady simulations, this parameter (number of parcels) determines the number of parcel streams for each time step. The resolution of the particle size distribution increases with the number of parcel streams. For this purpose, 5-20 parcel streams are typically defined [36]. Therefore, a study on parcel independence is essential to understand how it affects the distribution of droplet diameters. To determine the optimum number of parcel streams for this numerical analysis, parcel stream independence testing was performed. To determine the impact on the distribution of water droplet diameter, three different numbers of parcel streams (9, 10, and 11) were investigated. Figure 8 demonstrates that the number of parcel streams had a reasonable impact on the distributions of droplet diameters, and increasing the number of parcels significantly increased the computing time. Therefore, ten parcel streams were selected for further CFD simulation studies to balance computational time and accuracy.
## 4 Parametric Analysis
### Torsional Force  
According to Figure 8d, the pipeline almost produces no deformation induced by torsional forces because the pipeline is directly above the shield tunnel. The antisymmetric deformation generates on the two sides of the shield tunnel. We select the deformation at \(x=-10\) to illustrate the effect of the torsional forces on the pipeline's deformation shown in Figure 15. The torsional forces are set to be 1000, 2500, and 4000 kN. The pipeline's deformation increases with the increase of the torsional forces.
# Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications
## 4 Applications of Electrospinning in Memory: Storage Electronics
(**g**) I–V characteristics of both pristine PVA-TiO\({}_{2}\) mat and perovskite decorated PVA-TiO\({}_{2}\) mat. The inset box shows the enlarged region of numerous Negative Differential Resistance (NDR). (**h**) The switching performance of perovskite decorated TiO\({}_{2}\) mat during a continuous “write–read–erase” read” sequence. The voltage pulse is depicted in black and the current response is depicted in red. Reprinted with permission from Reference [109]. Copyright 2020 Springer-Verlag GmbH Germany, part of Springer Nature. (**i**) Electron microscopy images of PCL-C\({}_{60}\)/MWCNT fibers showing parallel electrospun fibers onto interdigitated electrodes after the thermal treatment. (**j**) electrical resistance variation of PCL-C\({}_{60}\)/MWCNT fibers-based memory devices under electrical programming and thermal erasing cycles. Reprinted with permission from Reference [110]. Copyright 2020 Elsevier Ltd.  
All in all, memory devices with electrospun nanofibers unlock the potential for electrospinning techniques with low cost and universality. Memory application with a nanofiber-based middle layer provides a directional transmission channel for the carriers and would offer an excellent chance to develop a more stable and larger capacity memory. Moreover, electrospun nanofibers can also be extended to the applications of electrodes of randomness memory (RRAM) [115] or the dielectric layer of field-effect transistors toward nonvolatile memory [116], indicating the versatility for the electrospun one-dimensional fibers.
## 3 Results and Discussion
### Mechanical Properties and Fractography
Due to the stress concentration near the interface caused by interfacial IMC growth, and the thickening of (Cu, Ni)\({}_{6}\)Sn\({}_{5}\) IMC layer, cracks tended to propagate through the (Cu, Ni)\({}_{6}\)Sn\({}_{5}\) IMC layer under external stress. Consequently, the fracture mode of the solder joints changed from within the solder matrix to a mixed fracture mode of the solder matrix and the (Cu, Ni)\({}_{6}\)Sn\({}_{5}\) IMC layer with the increase of thermal shock cycles. The solder joints after 200 cycles and 250 cycles presented similar fracture morphologies with that after 150 cycles. The area fraction of brittle fracture feature on the fracture surface ascended and the area fraction of ductile fracture feature on the fracture surface descended with the increase of thermal shock cycles. The fracture mechanism of solder joints after 250 cycles was predominated by brittle fracture with minor ductile fracture (Figure 9g). The fatigue of the solder matrix, thickening of the interfacial IMCs, and stress concentration near the interface were responsible for the decline in pull strength of the solder joints with extreme temperature thermal shock. In addition to the solder matrix and (Cu, Ni)\({}_{6}\)Sn\({}_{5}\) IMCs, (Ni, Cu)\({}_{3}\)Sn\({}_{4}\) IMCs were detected on the fracture surface of solder joints after 300 cycles; the EDS analysis results of (Ni, Cu)\({}_{3}\)Sn\({}_{4}\) IMCs on the fracture surface is shown in Figure 10b. Figure 8: The variation of the pull strength of Sn-37 Pb solder joints with extreme temperature thermal shock. The percentage in this figure represents the reduction ratio of the pull strength with extreme temperature thermal shock. It reveals that the solder joints after 300 cycles fractured through the solder matrix, (Cu, Ni)\({}_{6}\)Sn\({}_{5}\) IMC layer, and the (Ni, Cu)\({}_{3}\)Sn\({}_{4}\) IMC layer. Figure 9: SEM images of the fracture surfaces of Sn-37 Pb solder joints: (**a**) after reflowing; (**b**) after 50 cycles; (**c**) after 100 cycles; (**d,e**) after 150 cycles; (**f**) after 200 cycles; (**g**) after 250 cycles; and (**h,i**) after 300 cycles.
## 3 Results and Discussion
### Inlet Temperature  
Figure 7 shows the thermal and electrical performance of the considered photovoltaics-thermal panels with the variation in the inlet flow temperature. The thermal efficiency of both systems decreases with the increment in the inlet temperature. Additionally, electrical efficiency also displays a reduction. Thermal production of conventional PVT panel is on average 0.2% superior to PVT with integrated TEG modules. The electrical efficiency of TEG modules varies from 0.31% to 0.11%. The highest TEG's efficiency is achieved when the inlet flow temperature is 40 \({}^{\circ}\)C. In contrast, the higher thermal efficiency is achieve in both panels at the inlet flow temperature of 10 \({}^{\circ}\)C. With the integration of the TEGs in the PVT panel, electrical production increases on average 1.37%.  
As it has been commented, as lower is inlet flow temperature of the working fluid of the photovoltaics-thermal panel with TEG modules, the higher is electrical efficiency of the thermoelectric modules. In this optimal situation, the global efficiency of the panel is also better than that obtained with higher inlet temperatures. Global efficiency of 86.5% and 86.2% is achieved in PVT + TEG panel and conventional hybrid panels, respectively, with an inlet flow temperature of 10 \({}^{\circ}\)C. In comparison, when the flow inlet temperature is 25 \({}^{\circ}\)C global thermal efficiency is 81.2% and 81.1%.
# Co-Production of Hydrogen and Methanol Using Fuel Mix Systems: Technical and Economic Assessment
## 3 Results and Discussion
### Process Performance Analysis
#### 3.2.3 Carbon Conversion and Emissions  
The carbon conversion from feedstock to fuel (methanol in this study) and net CO\({}_{2}\) emissions are some of the performance indicators that are used to analyse the conversion of carbon from feedstock to product. The comparative analysis between two cases on carbon efficiency (\(\eta_{\rm c}\)) in case 2 takes a lead of almost 4% over the case 1 design, where the actual \(\eta_{\rm c}\) of case 1 and case 2 is calculated as 29% and 33%, respectively. Moreover, the CO\({}_{2}\)-specific  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Units** & **Case 1** & **Case 2** \\ \hline CH\({}_{3}\)OH Production & kg/s & 29.97 & 37.91 \\ \hline H\({}_{2}\) Production & kg/s & 4.22 & 3.87 \\ \hline Total Fuel Produced (CH\({}_{3}\)OH+H\({}_{2}\)) & kg/s & 34.19 & 41.78 \\ \hline Energy of the Produced Fuel (H\({}_{2}\) and CH\({}_{3}\)OH) & kg/GJ & 8.893 & 11.501 \\ \hline Electricity Produced & MWe & 709 & 620 \\ \hline Heating Duty Required & MWt & 1666.07 & 1680.49 \\ \hline Cooling Duty Reuired & MWt & 280.17 & 303.00 \\ \hline Energy Integration & MWt & 956.22 & 1142.96 \\ \hline Auxiliary & MW & 413.42 & 543.60 \\ \hline Efficiency (CH\({}_{3}\)OH+H\({}_{2}\)+Electricity) & & 47.1\% & 50.6\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of case 1 and case 2 in terms of process performance.  
Figure 5: Process efficiency and energy requirements.  
emissions for case 1 and case 2 during the production of methanol and hydrogen on a mass basis are estimated to be 2.12 and 1.62, respectively. Figure 6 represents the comparison between CO\({}_{2}\)-specific emissions and the carbon conversion efficiency of both cases.
###### Abstract  
Daeshiho-tang (DSHT) is a traditional herbal formula consisting of six herbal medicines: _Bupleurum filactum_. _Scutellaria haciaensis_ [PERSON], _Paonia taciflora_ [PERSON], _Pneum palmatum_ [PERSON], _Poncirus trifoliata_ ([PERSON]) [PERSON], and _Pinellia termate_ ([PERSON].) [PERSON]. In this study, we developed a simultaneous analysis method based on high-performance liquid chromatography for the quality control of DSHT. Chromatographic separation of 10 marker components (gallic acid, albiforin, paeoniforin, naringin, benzoic acid, bacialin, porcinir, wogonoside, bacialenin, and wogonin) was achieved using a water-acetonitrile system as the mobile phase with a SunFire C18 reversed-phase column. The developed analytical method was validated with respect to linearity, limit of detection, limit of quantitation, recovery, and precision. Among the 10 markers of DSHT in the established assay, bacialin, the main compound of _Scutellaria bacialensis_ [PERSON], was present in the highest concentration (36.86-46.17 mg/g). The validated assay will be useful for the quality control of DSHT.  
simultaneous analysis; quality control; traditional herbal formula; daeshiho-tang +
Footnote †: journal: Applied Sciences  
time (90 min) required to separate the eight components and were primarily focused on establishing the conditions for the separation of isomers and profiling.  
The constituent herbal medicines of DSHT contain many phytochemical components, namely: triterpenoids (saikosaponin A) from _B. falciparum_[12], flavonoids (baicalin, baicalein, and wogonin) from _S. bialensis_[13], phenolic compounds (gallic acid and benzoic acid) and monoterpneoids (albiflorin and paenofilm) from _P. lactiflora_[14], anthraquinones (physcion and chrysophanol) from _P. paltmut_[15], flavonoids (naringin and poncirin) from _P. trifoliata_[16], and phenolic compounds (homogentisic acid and 3,4-dihydroxybenzaldehyde) from _P. termate_[17].  
In this study, a chromatographic analysis method based on HPLC separation coupled with diode array detection (DAD) was developed to target the 10 marker compounds (gallic acid, abiflorin, paenofilm, and benzoic acid from _P. lactiflora_, naringin and poncirin from _P. trifoliata_; and baicalin, wogonoside, baicalein, and wogonin from _S. bialensis_) within a relatively short time for the consistent quality control of DSHT.
## 3 The Materials and Methods
### Backbone Architecture
STDA efficiency is realized in the form of specific orderly steps: input tokens are split into nonoverlapping patches to allow local processing, then the spatial-to-channel transposing of these patches is performed to allow cross-spectral mixing, and finally, global context extraction is performed using sub-ordinary depthwise separable attention. Incorporating the attention mechanism in this manner helps the network reason globally and identically across space and the spectrum, which amplifies identity recognition ability even when the visuals are hard or stripped. Each block of the transformer carries out the following in the given order:  
\[F^{(l)}=MLP\Big{(}STDA\Big{(}LN\Big{(}F^{(l-1)}\Big{)}\Big{)}+F^{(l-1)}\Big{)} \tag{5}\]  
where \(LN\) refers to layer normalization, _MLP_ is a two-stage feed-forward network, and residual connections are kept to allow for non-disruptive gradient flow attenuation. This captures high-order spatial and spectral interactions to further improve model performance when identifying subjects despite extreme occlusion or distortion. In order to decrease the computational burden, Stage 4's output of the high-dimensional feature tensor is filtered through a LoRaLin (low-rank linear) transformation. Instead of using a standard fully connected (FC) layer with a weight matrix \(W\in R^{d_{in}\times d_{out}}\), we decompose it into two smaller matrices:  
\[W\approx AB,\ A\in R^{d_{in}\times r},\ B\in R^{r\times d_{out}},\ r\ll min \big{(}d_{in}\times d_{out}\big{)} \tag{6}\]  
This approach balances the number of parameters and matrix multiplications while preserving adequate expressive power. Based on our observations, an \(r\) value of 32 offers the best balance of efficiency with accuracy. The final output of the backbone is an \(l_{2}\)-normalized identity embedding vector \(z\in R^{128}\), computed as  
\[z=\frac{h}{\left\|h\right\|_{2}},\ h=LoRaLin\Big{(}F^{(4)}\Big{)} \tag{7}\]  
The model is trained with an additive angular margin softmax loss (ArcFace or ElasticFace variants) to maintain strong separation for different classes and compaction for similar classes, conditioning them with this normalized vector. The full backbone is around 6.4 million parameters and consumes about 180 MFLOPs for every forward pass with a 224 \(\times\) 224 input. Relative to traditional backbones like ResNet-50 or ViT-B, this is a 10-15-fold reduction in memory and computational expense, making the model extremely efficient for embedded inference.