esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Raman Spectroscopy and Hepatitis Diagnosis  
The potential of Raman spectroscopy for hepatitis diagnosis has been under study for almost two decades, beginning with the study by [PERSON] et al., who designed a SERS-based immunoassay containing a three-layered sandwich structure in 2004. On a silicon quartz substrate, Step 1--(3-amino-propyl) trimethoxysilane (APTMS)--and Step 2--glutaraldehyde  
Figure 4: Nanogel-based system for nanotag encapsulation and pH-triggered release for SERS “hotspot”-activated virus immunoassay on a 2D H-BN substrate for HEV diagnosis. Reprinted (adapted) with permission from [76]. Copyright, 2021, the American Chemical Society.  
(GA)--were reacted to generate an APTMS-GA-functionalized surface capable of retaining antibodies. Then, the first layer was formed by attaching a mouse polyclonal antibody against the hepatitis B surface antigen (Step 3), an attached complementary hepatitis B virus surface antigen forming the second layer (Step 4). The third layer (Step 5) was composed of immunogold nanoparticles bearing a monoclonal antibody against the hepatitis B virus surface antigen and labeled using a Raman-active probe, 4-mercaptoethanol acid (MBA). Further enhancement was performed through silver staining by coating the nanoparticles using silver nitrate and initiating a hydroquinone redox reaction (Step 6). The SERS acquisitions were made using a confocal Raman spectrometer with a 514.5 nm laser as the source. The immunoassay showed the labeled compound spectra had a strong peak at 1585 cm\({}^{-1}\), derived from the aromatic ring vibration of the MBA, which was correlated with the antigen concentration. The lower detection limit for the hepatitis B virus surface antigen was 0.5 \(\upmu\)g mL\({}^{-1}\)[77]. The schematic design of this SERS diagnostic model for hepatitis B is shown in Figure 5.  
Rapid progression in applying Raman systems to hepatitis diagnosis has been seen since then, and multiple strategies have been tested. These can be classified into strategies based on the type of sample, the methodology adopted, and the data analysis technique. A detailed discussion about these different strategies is given as follows.
# Study on Shielding Effect of the Pile Group in a Soft-Soil Foundation
## 3 Results
### Displacement Analysis
Furthermore, for the first row of piles, the disturbance induced by the installation of the last three piles was significantly greater than that of the soil mass (point A), which indicates that the pile itself contributed to the shielding effect. Figure 6: Comparison of superimposed accumulative displacement and actual accumulative displacement of single-row piles: (**a**) layout of monitoring points for single row piles; (**b**) layout of monitoring points for three row piles; (**c–e**) displacement curves at monitoring points located at distances of 2.5 m, 5.0 m, and 7.5 m from the single row pile, respectively; (**f**) displacement curves of superimposed displacement and actual displacement. Figure 7: Horizontal displacement trajectories of pile heads and soil points during sequential installation of a 3 \(\times\) 3 pile group. Figure 8 depicts the lateral displacements caused by soil compression in the E and W directions after piling, with the starting points being the model's center (center of P5). As the pile spacing is 2.5 m and the pile radius is 0.25 m, the displayed data starts only from HD (horizontal distance) = 2.75 m, referring to the soil zones immediately adjacent to the outer perimeter of the pile group. The results indicate that disturbance magnitudes on the soil of both sides are distinct, where larger deformation is observed for the soil of the east side. As discussed above, this phenomenon comes from the shielding effect from the first row of piles (P1, P2 and P3). Additionally, the disturbance range is indicated as well; that is, the pile installation no longer affects the foundation soil from HD = 15 m.
# Seismogram Rearrangement as a Perspective Basis for Defining New Attributes
## 1 Introduction
### Article Aims and Findings  
A seismogram is the result of many complex physical processes. It is very difficult to relate every detail in a seismogram with the processes standing behind. Even if it might look like an obvious fact, it is important to remember that the seismogram is the picture of seismic processes at the site. Therefore, the formalization of seismograms is inevitable--one considers what it has at hand. How, then to peer through the fluctuations of the seismogram and try to see something more behind the wiggles?  
The lowest structural level of a seismogram is at the waveform elements, impulses, or wavelets (considered as small waves, not as mathematical tools). In this paper, such chunks of seismogram are defined as elementary or single fluctuations (EF and SF). The next structural level is the short train of SFs/EFs. Here, the authors perceive the next stage of research. The SFs are simpler but easier for manipulation, while the EFs are more complex but also more natural as elementary building blocks of the seismogram, as the simplest shapes still carry alternating change, which actually causes shaking (quaking) in the 'push-pull' pattern.  
Getting to EFs/SFs means prior parsing of the seismogram. Segmentation also provides rearrangement of EFs/SFs to create new waveform structures, which can reveal new information previously invisible. As the simplest regrouping, the overlapping of EFs gave a 'wood', a structure which prevents 'not see the wood for the trees'. To simplify the mess, the EF is approximated by triangles or even by structural axis, making the 'wood' more transparent.  
Sorting the durations of SFs by the length, for both positive and negative ones, led to very interesting results, which peered not only into the seismogram waveform but also into its spectrum.  
More organized, more systematic rearrangement, the ordering of SFs or of EFs by peak amplitudes, yielded the so-called R-envelope, either in natural (R) or in regular (quantized, R\({}_{\text{Q}}\)) forms. It turned out that the R-envelope represents a kind of invariant in the population of seismograms. Moreover, there is a direct correlation between the R-envelope and ordered durations of SFs.  
The difference between R-envelopes, drawn over positive SFs and negative SFs, is expected to produce a rule about seismogram symmetry but enabled, for the present, only individual interpretations.  
Where this approach of seismogram segmentation and rearrangement showed its potential is a new definition of seismogram duration in objective way, which excludes subjective factors but outcomes from the waveform.
###### Abstract
Moreover, the high model complexity and increased number of parameters in DL architectures make them especially prone to overfitting when exposed to noisy, incomplete, or biased data [26]. This susceptibility can lead to unstable or polarized predictions, undermining the model's generalizability and its utility in clinical decision-making. Nonetheless, DL models have demonstrated distinct advantages over traditional ML approaches in learning from high-dimensional and heterogeneous data. Through advanced representation learning capabilities, DL models are better equipped to extract meaningful patterns from complex features, making them more adaptable to real-world clinical data, even in the presence of missingness or noise [27]. This highlights the need for robust model designs that not only handle data scarcity via MTL but also account for data quality issues inherent in healthcare settings. However, existing healthcare MTL studies rarely address two critical challenges: pervasive missingness and heterogeneity across data sources, limiting their effectiveness when applied to real-world clinical datasets with multiple outcomes. In this study, we proposed MTLNFM, a novel multi-task learning (MTL) framework built upon the Neural Factorization Machine (NFM) architecture [28] for patient clinical outcome prediction. The design of MTLNFM incorporated both shared and task-specific components, enabling it to effectively capture commonalities across tasks while preserving differences between individual tasks. To address the pervasive issue of missing data in clinical datasets, we proposed a new preprocessing strategy that explicitly labels missing values, rather than removing or imputing them, which allows the model to learn from the patterns of missingness without introducing additional bias or discarding potential informative cases. We evaluated the performance of MTLNFM through extensive comparative experiments against single-task learning models across three critical clinical outcomes: clinical frailty scale (CFS), length of hospital stay, and mortality. The results consistently demonstrate that MTLNFM outperformed baseline models in both predictive accuracy and generalization. Additionally, through a case analysis, we demonstrated that our model is more robust and unbiased that aligns well with real-world patient outcome distributions. The overall architecture and workflow of the MTLNFM framework is illustrated in Figure 1. The structure of this paper is organized as follows. Section 2 details the materials and methods, covering the workflow of the constructed MTLNFM framework and experimental setup. Section 3 presents the performance results along with statistical comparisons against baseline models. Section 4 discusses the implications of the findings, the benefits and limitations of our proposed framework. Finally, Section 5 summarizes the main conclusions and potential clinical impact of this work.
## 4 Results
### Experiment Results  
#### 4.2.1 Classification Experiment Results for B1 and Non-B1 Types  
In this study, the linear kernel was selected from among the various types of SVM kernels for simplicity. The SVM learning model was combined with the OAA method which is a multi-class learning technique [19]. The SVM learning was performed by setting one class of data as positive and the other classes as negative. Herein, the B1 type was selected as the positive class, while the B2 and B3 types were the negative classes. The fivefold validation method was adopted to verify the learning data. Accuracy, sensitivity, and specificity were employed as performance indices, as shown in Equation (30), to determine the pattern classification performance for SESCCs. The TP in Equation (30) represents a true positive (i.e., an actual positive was correctly predicted), while TN represents a true negative (i.e., an actual negative was correctly analyzed). Furthermore, FP represents a false negative (i.e., an actual negative was analyzed as positive), while FN represents a false negative (i.e., an actual positive was predicted as negative).  
\[\begin{split}\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP }+\text{TN}+\text{FN}+\text{FP}}\times 100\\ \text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\\ \text{Specificity}=\frac{\text{TN}}{\text{FP}+\text{TN}}\times 100 \end{split} \tag{30}\]  
Table 3 shows the pattern classification results obtained using the proposed method where the ground truth was provided by a domain expert. Substituting the TP, TN, FP, and FN values from Table 3 into Equation (30) resulted in an accuracy of 83.3%, with a sensitivity and specificity of 74.5% and 89.6%, respectively. The graph showing the receiver operating characteristic (ROC) curve in Figure 25 was drawn using the R software.  
Figure 25: Receiver operating characteristic (ROC) curve for the classification results of the B1 and non-B1 types.
## 3 Results and Discussion  
### Surface Potential and Current  
#### 3.1.1 Surface Potential Verification  
The analytical approximation of \(\psi_{\text{S1}}\) is compared against the iterative solution in Figure 3 for different values of the drain voltage and a good agreement between the two is found. The numerical simulation denotes the results with an accuracy of \(10^{-12}\) V obtained through an iterative Halley's algorithm [4] which is adopted in BSIM-IMG model from a 22-nm industrial design kit.  
The maximum absolute errors in the full range of \(V_{ds}\) between the proposed initial solution and the iterative solution for different values of \(V_{fg}\), as plotted in Figure 4. The initial solutions given in [13; 15] are used as references, which are represented by black and blue lines, respectively. The maximum absolute error between the proposed initial solution and the iterative solution is 190.3 nV, which allows for fast convergence. The error under \(10^{-12}\) V for weak, moderate and strong inversion can be obtained after one iteration.  
Figure 4: Absolute error of (**a**) front surface potential (**b**) back surface potential between the iterative solution and the proposed models for different values of \(V_{fg}\).  
Figure 3: (**a**) Front surface potential versus front gate voltage with varying drain voltage; (**b**) Back surface potential versus front gate voltage with varying drain voltage (\(T_{OX1}\) = 1.33 nm, \(T_{OX2}\) = 20 nm, \(T_{Si}\) = 60 nm, \(V_{bg}\) = 0, \(q\Delta\phi_{MS1}\) = 4.45 eV).
## 3 Results
Effect of He-Ne Laser Pre-Sowing Seed Treatment on the Leaf Silybin \(A+B\) Contents of S. marianum Plants  
The silybin (A + B) contents in the fruits of \(S\). _marianum_ were analyzed using high-performance liquid chromatography (HPLC). Our results revealed that the control plants from non-irradiated seeds had an average silybin (A + B) content of 48.98 mg/100 g of dried fruit weight (D.W.). Plants from 2- and 4-min-irradiated seeds had lower silybin (A + B) contents than control plants; on the other hand, plants from 6-, 8-, and 10-min-irradiated seeds had higher silybin (A + B) contents than control plants. The highest silybin (A + B) content (55.66 mg/100 g D.W.) was recorded from plants derived from 6-min-irradiated seeds (Figure 1). \begin{table}
\begin{tabular}{c c c} \hline
**Laser Treatments (Min)** & **Capitula Number (n)** & **Fruit Dry Weight (g)** \\ \hline Control & 3.7 d \({}^{*}\) & 9.4 e \\
2 & 11.5 a & 40.6 ab \\
4 & 9.6 b & 41.6 ab \\
6 & 8.8 b & 26.5 d \\
8 & 5.5 c & 34.9 c \\
10 & 12.0 a & 49.8 a \\ \hline \end{tabular} \({}^{*}\)Means followed by the same letter within a column are not significantly different at the 0.05 level of probability, according to [PERSON]’s test.  
\end{table} 
Table 2: Effects of laser treatment on _Silybum marianum_ L. capitula number (n) and fruit dry weight (g). \begin{table}
\begin{tabular}{c c c c} \hline
**Laser Treatments (Min)** & **Chl a** & **Chl b** & **Carotenoids** \\
**(Min)** & **(mg/100 g F.W.)** & **(mg/100 g F.W.)** & **(mg/100 g F.W.)** \\ \hline Control & 41.29 c \({}^{*}\) & 8.91 c & 36.37 d \\
2 & 54.84 a & 10.90 b & 47.98 a \\
4 & 50.07 ab & 10.77 b & 38.38 c \\
6 & 43.11 c & 10.82 b & 40.26 ab \\
8 & 41.59 c & 10.67 b & 37.74 c \\
10 & 55.17 a & 12.28 a & 49.50 a \\ \hline \end{tabular} \({}^{*}\)Means followed by the same letter within a column are not significantly different at the 0.05 level of probability, according to [PERSON]’s test.  
\end{table} 
Table 3: Effects of laser treatment on chlorophyll a, b, and carotenoids contents. The contents of the pigments are enumerated as milligrams per 100 g of fresh _Silybum marianum_ L. leaves. Effect of He-Ne Laser Pre-Sowing Seed Treatment on the Expressions of Chalcone Synthes 1, 2, and 3 Genes in the Petals of S. marianum Plants  
The effects of He-Ne laser seed pretreatment on the expression levels of the _CHS1_, 2_, and 3 genes were investigated using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). All chalcone synthase genes (_CHS1_, _CHS2_, and _CHS3_) were found to be down-regulated in plants derived from 2- and 4-min-irradiated seeds as compared with control plants. On the other hand, we observed a slight up-regulation of these genes (_CHS1_, _CHS2_, and _CHS3_) in plants derived from the 6-, 8-, and 10-min-irradiated seeds, with maxima of 1.1- (for the _CHS1_ and _CHS2_ genes) and 1.2- (for the _CHS3_ gene) fold expressions in the 6-min irradiated groups. In summary, consistent associations of the chalcone synthase gene expressions with the silybin (A + B) contents were observed in all the treatment groups (Figure 1).
## 4 Materials and Methods
### Fatty Acid Profile  
The identification of fatty acids in each sample of oil was performed using a modification of the alkaline transmethylation procedure developed by [PERSON] et al. (2018) (ISO 5508:1990): 50 \(\mu\)L of oil was dissolved in 500 \(\mu\)L of benzene and 500 \(\mu\)L of petroleum ether in a 10-mL volumetric flask. One mL of 0.4 M sodium hydroxide solution in MeOH was added and allowed to stand for 20 min. The volumetric flask was filled up to 10 mL with deionized water and the solution was vigorously shaken and allowed to stand at room temperature for 24 h. A 50 \(\mu\)L aliquot of the resulting benzene:petroleum ether layer was diluted in 950 \(\mu\)L of \(n\)-hexane and used for GC-FID (quantitative) and GC-MS (qualitative) analyses. The GC-FID instrument included a 7890A oven equipped with flame ionization detector (FID) (Agilent, Santa Clara, CA, USA). The separation of fatty acids was performed on an Rt-2560 fused silica column (100 m, 0.25 mm i.d., 0.2 \(\mu\)m; Restek, Lisses, France). The temperature program began with a 2 min hold at 70 \({}^{\circ}\)C followed by an increase to 225 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min. The temperature was held at 225 \({}^{\circ}\)C for 9 min, then raised to 240 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min and held for 6.5 min (total run, 45.5 min). The flows of the FID detector gases were set at the following values: H\({}_{2}\), 30 mL/min; air, 400 mL/min; and make-up flow, 30 mL/min. The heater temperature was set at 260 \({}^{\circ}\)C. The injection volume was 1 \(\mu\)L with a flow rate of 1.2 mL/min (helium was used as carrier gas), an inlet temperature of 225 \({}^{\circ}\)C, and split 1:50. The GC-MS apparatus consisted of a 7890A oven and 5975C mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). The analyses on both instruments were run using the same column, temperature program,injection volume, flow rate, carrier gas, inlet temperature, and split. The temperatures of the ion source and quadrupole were 230 and 150 \({}^{\circ}\)C, respectively. Mass spectrum data were acquired in scan mode (range: 40-400 \(m/z\)) and the spectra were identified using the NIST (National Institute of Standards and Technology) mass spectrum library ver. 2.0f. The retention indices of each analyte were calculated from the retention times of _n_-alkanes by linear interpolation as previously described by [PERSON] (2016). The analyzed fatty acids were identified by comparing their spectra with the spectra of available standards and/or by comparing their retention indices with the NIST database.
# Research on Driving Scenario Knowledge Graphs
## 3 Methodology: DSKG Construction
### Construction of Ontology for Driving Scenarios
#### 3.2.1 Definition of Classes and Their Hierarchical Structure
These static objects can be further decomposed into Urban Infrastructure, Traffic Infrastructure, Traffic Control Facilities, and Traffic Information Facilities. Urban Infrastructure includes buildings, vegetation, streetlights, fire hydrants, etc. ; Traffic Infrastructure includes fences, tunnels, bridges, etc. ; Traffic Control Facilities include traffic lights and roadblocks; Traffic Information facilities include traffic signs and information display screens. (3) Temporary Change: This describes the non-persistent temporary changes of entities within the Base Road and Roadside Facility layers in specific scenarios. This layer does not introduce any new entity classes defined in the preceding layers but consists of temporary modifications of elements from Layers 1 and 2. This layer categorizes temporary road events into Road Condition Changes and Road Surface Changes. For each type of temporary event, information such as the lane position, starting point, and end point of the event is specified. Based on the description of the first layer, changes in road conditions can be divided into changes in road curvature, slope, coverage, lane width, number, centerline, etc. In addition to these temporary events closely related to the first layer of road structure, it also includes road surface changes caused by changes in weather conditions, such as dryness, moisture, icing, or reflective road surfaces. (4) Dynamic Objects: This describes dynamic objects in the scenario that affect the occurrence of events. Dynamic objects can be classified from the perspective of vehicles, people, animals, and other objects. The movement of these objects evolves over time and can be described through trajectories. (5) Environmental Conditions: This describes the natural environment in which the traffic scene is located. Weather Conditions include weather conditions, temperature, humidity, wind speed, wind direction, visibility, etc. Lighting Conditions include the position of light sources, the type of light sources, the intensity of light, the direction of light, and whether there is reflected light, etc. (6) Digital Information: This describes all information based on digital data between vehicles, infrastructure, or both, including digital signals from information devices such as roadside units and edge computing units. The description of roadside units includes the change status of traffic signs and traffic lights. It is noteworthy that this layer describes the change status of traffic signs and traffic lights but is limited to the description of changeable information, while the respective objects themselves have already been placed in Layer 1. The description of edge computing units includes the change information of perception and control.
## 2 System Model and Problem Formulation
### Problem Formulation
Inspired by [2; 3; 4], considering the transmitting cost, we give the user's utility function based on the received SINR \(\gamma^{\prime}\) in the following:  
\[U=\gamma^{\prime}-C_{u}P. \tag{5}\]  
For jammer, it eavesdrops the feedback SINR \(\gamma^{\prime}\) from BS to the user at the end of each time slot and obtains eavesdropping result \(\gamma^{\prime\prime}\). Similarly, considering that it is hard for a jammer to eavesdrop \(\gamma^{\prime}\) accurately, we introduce eavesdropping error \(\varepsilon_{2}=|\gamma-\gamma^{\prime\prime}|/\gamma\), which means the deviation degree of eavesdropped SINR \(\gamma^{\prime\prime}\) with the actual SINR \(\gamma\). Similarly, considering the jamming cost, we give the jammer's utility function based on the eavesdropped result \(\gamma^{\prime\prime}\) in the following:  
\[V=-\gamma^{\prime\prime}-C_{j}J. \tag{6}\]