esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Physicochemical Properties
#### 4.1.5 Schmidt Hammer Test  
The Schmidt hammer test is an established technique to determine the mechanical characteristics of rock material because it offers a rapid, inexpensive, and accurate way to quantify the material hardness. The \"Schmidt Hammer Rebound Index\" test was designed for testing concrete. It is also used to measure the strength of rock. This inexpensive, non-destructive test additionally offers information about the compressive properties of rock material [20]. The test value is recorded after the Schmidt hammer is released by springing against the rock surface. The range of the average value is 26.5 to 51.0 MPa, shown in Table 5 and Figure 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Number** & **Cube Sample** & **Schmidt Hammer Values** \\ \hline Pi1 & Pi1 (a) & 26.5 \\ \hline \multirow{4}{*}{Pi2} & Pi2 (a) & 44.7 \\ \cline{2-3}  & Pi2 (b) & 44.0 \\ \cline{2-3}  & Pi2 (c) & 46.3 \\ \hline \multirow{4}{*}{Pi3} & Pi3 (a) & 45.7 \\ \cline{2-3}  & Pi3 (b) & 46.7 \\ \cline{2-3}  & Pi3 (c) & 44.7 \\ \hline \multirow{4}{*}{Pi4} & Pi4 (a) & 41.0 \\ \cline{2-3}  & Pi4 (b) & 49.7 \\ \cline{2-3}  & Pi4 (c) & 48.7 \\ \hline \multirow{4}{*}{Pi5} & Pi5 (a) & 30.0 \\ \cline{2-3}  & Pi5 (b) & 32.3 \\ \cline{2-3}  & Pi5 (c) & 51.0 \\ \hline \multirow{4}{*}{Pi6} & Pi6 (a) & 46.3 \\ \cline{2-3}  & Pi6 (b) & 40.0 \\ \cline{1-1} \cline{2-3}  & Pi6 (c) & 43.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Schmidt hammer test results for the studied rock samples.
## 5 The Implications of AI Approaches in STI Diagnosis and Management  
This study provides practical application of AI technologies in diagnosing, treating, and managing STIs across SSA which are often overlooked. Understanding this has the potential to improve sexual health outcomes, address gaps in STI diagnosis, and surpass the limitations of traditional syndromic management approaches. Traditional diagnostic, prognostic, and therapeutic procedures for addressing STIs in SSA have often lacked accuracy, speed, and accessibility. The integration of AI technologies in STI diagnosis and management offers promising solutions to overcome these challenges. However, SSA's high rates of STI/HIV, limited resources, skilled personnel shortages, and the slower adoption of technology underscore the need for collaboration between SSA governments, developed regions, non-governmental organizations, and the international community. Such partnerships could accelerate the development of the resources and infrastructure required to effectively implement AI technologies in SSA's healthcare sector, while exchange programs and training initiatives would help build local capacity.  
Future research should prioritize the transition from methodological advancements to real-world validation in clinical environments. This shift is critical for assessing AI methods' practical utility, scalability, and impact in improving STI diagnosis and management in SSA. By addressing these gaps, researchers can help ensure AI methods achieve their full potential and contribute meaningfully to public health outcomes. Further research should validate AI and ML applications for STI diagnosis and management in real-world SSA settings, exploring ethical considerations and ensuring the relevance and efficiency of these technologies across diverse communities. Comprehensive datasets capturing relevant population characteristics are essential to drive informed decision-making and guide policy. These findings have implications for STI and HIV management in SSA, affecting both key and general population. Stakeholders, including governments, private organizations, and healthcare providers, must prioritize accurate, timely information to enhance health outcomes. Policymakers and practitioners should review existing policies, supported by monitoring and evaluation frameworks, to improve STI care. Collaboration among healthcare providers, community organizations, and policymers will be vital to overcoming resource constraints, cultural barriers, and social determinants of health, ultimately promoting sexual health across the region.
# Creep Characteristics of Soil in the Sliding Zone of Huangtupo Landslide
## 4 Shear Creep Model and Verification  
### Construction of Burgers Constitutive Model  
The Burgers constitutive model constructed in this section is shown in Figure 12, which was connected by a Maxwell model and a Kelvin model in a series, where \(G_{1}\) and \(G_{2}\) are the shear modulus of the Maxwell model and the Kelvin model, respectively; \(\eta_{1}\) and \(\eta_{2}\) are the viscosity coefficients of the Maxwell model and the Kelvin model, respectively [40].  
Figure 11: Vertical displacement and horizontal displacement curve of the contact surface.  
Figure 12: Burgers model.  
The Burgers model constructed by the Maxwell model and the Kelvin model in a series shows the following:  
\[\gamma=\gamma_{1}+\gamma_{2} \tag{1}\]  
In Equation (1), \(\gamma\) represents the shear strain of the Burgers model; \(\gamma_{1}\) and \(\gamma_{2}\) represents the shear strain of the Maxwell model and the Kelvin model, respectively.  
The Maxwell model is composed of an elastic element \(G_{1}\), a viscous element \(\eta_{1}\) in a series, and its creep equation can be written as  
\[\gamma_{1}=\frac{\tau}{\eta_{1}}+\frac{\tau}{G_{1}} \tag{2}\]  
The Kelvin model is composed of an elastic element \(G_{2}\) and a viscous element \(\eta_{2}\) in parallel, and the creep equation can be written as  
\[\tau=G_{2}\gamma_{2}+\eta_{2}\gamma_{2} \tag{3}\]  
If \(\frac{d}{d_{t}}=B\), insert Equations (2) and (3) into Equation (1):  
\[\gamma=\tau\bigg{(}\frac{1}{B\eta_{1}}+\frac{1}{G_{1}}+\frac{1}{G_{2}+B\eta_{ 2}}\bigg{)} \tag{4}\]  
A differential equation can be obtained by a joint solution:  
\[\tau+\bigg{(}\frac{\eta_{1}}{G_{1}}+\frac{\eta_{1}+\eta_{2}}{G_{2}}\bigg{)} \tau+\frac{\eta_{1}\eta_{2}}{G_{1}G_{2}}\tau=\eta_{1}\gamma+\frac{\eta_{1} \eta_{2}}{G_{2}}\gamma \tag{5}\]  
Solve Equation (5) and obtain its creep equation, such as by Equation (6):  
\[\gamma=\tau\bigg{(}\frac{t}{\eta_{1}}+\frac{1}{G_{1}}+\frac{1}{G_{2}}\bigg{(} 1-e^{-\frac{G_{2}}{\eta_{2}}t}\bigg{)}\bigg{)} \tag{6}\]  
It can be seen from Equation (6) that the creep equation can fit instantaneous strain, initial creep, and stable creep; for creep in the attenuation stage, the viscous element in the Maxwell model needs to be removed in order to fit. In the equation, when \(t\approx 0\), \(\gamma=\frac{\tau}{G_{1}}\), the instantaneous shear modulus \(G_{1}\) can be determined by the instantaneous strain.
# Pan-Cancer Classification of Gene Expression Data Based on Artificial Neural Network Model
## 3 Results
The accuracy was: above 0.85 in colon adenocarcinoma, esophageal carcinoma, kidney renal clear cell carcinoma, rectum adenocarcinoma, stomach adenocarcinoma, and uterine corpus endometrial carcinoma; and below 0.80 in bladder urothelial carcinoma, and prostate adenocarcinoma. We compared ANN with random forest classifier and XGBoost. The results of the three classifiers are similar. T-test demonstrated a statistically significant difference of performance between ANN and XGBoost (\(t\)-test: sensitivity, \(p\)-value 0.001, accuracy, \(p\)-value 0.001, auc, \(p\)-value 0.049), between ANN and random forest (\(t\)-test: auc, \(p\)-value 0.001) and between random forest and XGBoost (\(t\)-test: auc, \(p\)-value 0.0003). The overall accuracy, sensitivity, and specificity of the testing sets for the ANN classifier were 0.83, 0.81 and 0.86, respectively. For the random classifier, we obtained an overall accuracy of 0.86, an overall sensitivity of 0.85 and an overall specificity of 0.87. XGBoost achieved an overall accuracy of 0.90, an overall sensitivity of 0.90 and an overall specificity of 0.89 (Figure 2). The mean AUC values were 0.95 for random forest, 0.92 for XGBoost and 0.89 for ANN. Figure 3A shows the ROC curves obtained with the ANN model in kidney renal papillary cell carcinoma. The best ROC curves for the random forest classifier were obtained in colon adenocarcinoma, kidney renal papillary cell carcinoma and kidney renal clear  
Figure 1: Accuracy with error standard of the classifiers. Artificial neural network (ANN) with blue bars, random forest (RF) with red bars and XGBoost with grey bars. cell and rectum adenocarcinoma (Figure 3B). XGBoost, applied to colon adenocarcinoma, achieved the best ROC curve (Figure 3C). Figure 3: Best ROC curves with: (**A**) the artificial neural network (ANN) model in kidney renal papillary cell carcinoma; (**B**) the random forest (RF) model in colon adenocarcinoma, kidney renal papillary cell carcinoma and kidney renal clear cell and rectum adenocarcinoma; (**C**) the XGBoost model in colon adenocarcinoma. Figure 2: The boxplot shows the accuracy, area under curve (AUC), sensitivity and specificity using the artificial neural network (ANN), random forest (RF), and XGBoost.
## 1 Introduction
Recently, our team developed a smart phone-based motion capture system, termed Mobile Motion Capture (MO\({}^{2}\)CA), with an accuracy comparable to current fixed, lab-based motion capture systems [16]. MO\({}^{2}\)CA offers the advantage of simplicity, in that it utilizes a single smart phone-based camera combined with simple markers, functioning as a system for motion data capture, rather than a complex system with multiple cameras and trained personnel [16]. MO\({}^{2}\)CA also offers the advantage of ready access, in that smart phones are increasingly common affording access to a wide user and patient population, as well as readily integrating with telemedicine. In a recent study, we demonstrated the efficacy of the MO\({}^{2}\)CA system as to its ability to accurately capture variables of gait (stride length, stride time, stride length variability, and stride time variability) from twenty subjects over a range of speeds [16]. Yet, while this approach has been shown to be operative for tracking an individual walking on a treadmill at a short distance from the smart phone, the full distance range of efficacy and related parameters of use remain undefined. Further, as smart phone technology will continue to evolve, it is important to establish a simple meansfor assessment of smart phone-based motion capture that may be employed with future generation devices. In this study, we extend our work and define and delineate the \"operating envelope\" of the MO\({}^{2}\)CA system as to the distance limit of target identification, resolution, and point-to-point discrimination under a range of conditions. We hypothesized that the MO\({}^{2}\)CA system will have a defined operative boundary and function with efficacy and accuracy over: (1) a defined range of distances, (2) a defined inter-target separation, (3) a defined motion speed, (4) and a range of illumination intensities, with an effective resolution to determine its practical utility. As a first step we defined the distance limit of the MO\({}^{2}\)CA system using small targets of varying sizes. We then increased the target number to a target pair to test the distance limit of point-to-point discrimination of the MO\({}^{2}\)CA system with a variety of target sizes and inter-target distances. Next, we determined the distance limit of the MO\({}^{2}\)CA system capturing an object in motion. We then defined the resolution of the MO\({}^{2}\)CA system to capture targets of differing colors under a range of illumination intensities. The definition of these specifications, operating range, and constraints further defines the boundaries of translational utility of this approach for future use in the clinic and the field, and outline a generalized strategy for validation of future motion capture technology for similar use. As a step towards the clinic, two protocols were also conducted to evaluate the efficacy of smart phone-based motion capture, utilizing recognized clinical diagnostic tests and maneuvers to demonstrate the translational applicability and utility of this approach for clinical medicine.
# Power Profile Index: An Adjustable Metric for Load Monitoring in Road Cycling
## 2 Materials and Methods
### Data Analysis  
Potential spikes were checked and removed using specific software (Data Spike ID and FIX chart, WKO5 Build 576; TrainingPeaks LLC, Boulder, CO, USA). Data were extracted using the specialized software WKO5 (Peaksware LLC, Lafayette, CO, USA).  
Values of PO were divided into six intensity zones, according to the TTE associated with each physiological event: Zone 1 (Z1): 2 h to 5 h, exercise intensity around VT1 [44; 53]; Zone 2 (Z2): 30 min to 2 h, efforts near the MLSS [38; 46; 54]; Zone 3 (Z3): 10 min to 30 min, exercise intensity near to RCP [38; 45; 55; 56]; Zone 4 (Z4): 2 min to 10 min; efforts slightly above or below maximal aerobic power (MAP) [38; 42; 43; 45]; Zone 5 (Z5): 30 s to 2 min, efforts slightly above or below the anaerobic capacity [57; 58]; Zone 6 (Z6): 5 s to 30 s, exercise intensity near to maximal anaerobic power [57; 59; 60]. Values of HR were divided into five intensity zones, according to their percentage of the maximum heart rate (HR\({}_{\rm max}\)), as follows: Z1 \(<\) 60% of HR\({}_{\rm max}\); Z2 \(=\) 60% to 70% of HR\({}_{\rm max}\); Z3 \(=\) 70% to 80% of HR\({}_{\rm max}\); Z4 \(=\) 80% to 90% of HR\({}_{\rm max}\); and Z5 \(=\) 90% to 100% of HR\({}_{\rm max}\).  
Figure 1: Correlation analysis between perceived fatigue measured with a 0–10 Likert Scale and the Power Profile Index (PPi) for each of the four stage types when applying the weighting stage factor (\(K\)) of 3.9 for high-mountain, flat, and time trials, and 2.3 for prologues. Perceived fatigue data obtained retrospectively from 102 professional cyclists, and PPi computed from 67 WorldTour cyclists.  
For each stage, different intensity scores (internal and external) were calculated as a function of RPE, PO, HR, and duration: (1) sRPE was computed by multiplying the stage duration (min) by the RPE [26]; (2) mean power was shown in absolute (W) and relative (W\(\cdot\)kg\({}^{-1}\)) terms; (3) mechanical energy spent was calculated in absolute terms (kJ), as follows, and in relative terms (kJ\(\cdot\)km\({}^{-1}\)); (4) TSS was calculated as follows, where \(t\) is the time spent (in seconds), NP is the normalized power, IF is an intensity factor (calculated as the ratio between NP of the race stage and the individuals' FTP) [34], and FTP was determined as 95% of the highest 20 min mean power achieved during the last 6 weeks prior to the start of the Tour de France; (5) [PERSON]'s TRIMP (eTRIMP) [9] was calculated, using the accumulated time in the five HR intensity zones (\(tZ_{i}\)), as follows:  
\[Absolute\;mechanical\;energy=\frac{P_{av}(\text{W})\cdot ride\;length\;(\text{s})}{1000 }\;(\text{kJ})\]  
\[\text{TSS}=\frac{t\cdot\text{NP}\cdot\text{IF}}{\text{FTP}\cdot 3600}\cdot 100\]  
\[\text{eTRIMP}=\sum_{i=1}^{5}tZ_{i}\cdot i\]
## 2 Materials and Methods
### Performance Evaluation  
Several statistical metrics were employed to appraise the model's effectiveness [48], including area under the receiver operating characteristic curve (AUC), ACC, MCC, Sn, specificity (Sp), true positive (TP), false positive (FP), true negative (TN), false negative (FN), and precision (P). The computation methods for these metrics are as follows (Equations (8)-(12)):  
\[ACC=\frac{TP+TN}{FP+FN+TP+TN} \tag{8}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{9}\]  
\[Sn=\frac{TP}{TP+FN} \tag{10}\]  
\[Sp=\frac{TN}{TN+FP} \tag{11}\]  
\[P=\frac{TP}{TP+FP} \tag{12}\]  
where _TP_ and _TN_ correspond to the count of accurate IL-10-induced peptide identifications and non-IL-10-induced peptide identifications, respectively; _FP_ and _FN_ correspond to the count of erroneous IL-10-induced peptide identifications and non-IL-10-induced peptide identifications, respectively. Models were compared using AUC values.  
The practice of employing K-fold cross-validation and carrying out independent testing is a customary way to measure the performance of ML models. Split the raw data into K equally sized subsets, selecting one of them as the validation set and using the others as the training set. Execute this operation K times in succession, each time using different training and validation sets, and the results are evaluated and averaged. In this study, we employed 5-fold (K = 5) cross-validation. In independent testing, a distinct dataset is utilized, which is entirely separate from the training set, to assess how well the model performs and generalizes in real-world scenarios.
## 3 Results and Discussion
### Color Parameters  
The color of meats is an important physical parameter that affects consumer choice. It depends on several factors, such as meat composition, nonmeat ingredients, and additives. In particular, the interaction between myoglobin and intrinsic and extrinsic factors regulates the color of ready-to-eat meats [59].  
Statistical analysis of the color parameters of pork meatloaves showed a significant effect of the additive on color parameters L* and a* (_p_ < 0.05). At the same time, the storage time had no significant impact. Turmeric addition contributed to the lower color lightness (L*), and T3 was statistically different from the control (_p_ < 0.05) (Figure 1a). T1 and T3 meatloaves differed significantly in a* parameter and were characterized by considerably lower a* as compared to the control (_p_ < 0.05) (Figure 1b). These findings agree with other authors' reports on meat products formulated with turmeric [21; 23; 60]. Those authors noted that adding turmeric (powder, extract) decreased both L* and a* compared to controls.  
Figure 1: The effect of different concentrations of turmeric and storage time on the color parameters of hermetically packed pork meatloaf: (**a**) L* values; (**b**) a* values; (**c**) b* values; (**d**) C* (saturation); (**e**) h\({}^{\circ}\) (hue angle). Samples: C–control; T1–with 1% turmeric rhizome; T3–with 3% turmeric rhizome. Different A,B,C letters indicate a significant difference on subsequent assessment days at \(p\) < 0.05. Different a,b,c letters indicate significant differences during storage at \(p\) < 0.05.  
The addition of turmeric rhizome at 1% and 3% had a significant effect on an increase in the yellow color intensity (b*) and an increase in the color saturation C* (\(p<0.05\)) (Figure 1c,d). The yellowness (b*) progressively increased as increased the turmeric content, and these values were significantly higher than those observed in control samples. Similarly, a progressive and significant increase of b* was found by [PERSON] et al. [61], [PERSON] et al. [18], [PERSON] et al. [21], [PERSON] et al. [23], [PERSON] et al. [60], and [PERSON] et al. [50]. This result is expected, since turmeric has an intense yellow color. Turmeric's yellow color compounds are three curcuminoids: curcumin (diferuloylmethane), bisdemethoxycurcumin, and demethylox Curcumin [12]; curcumin, the critical coloring agent in turmeric, is extractable and valuable as a natural food colorant [17]. During cold storage of the T1 and T3 metaolaves, a gradual decrease in the b* parameter value and the color saturation (C*) was noted, most likely due to changes in turmeric's natural pigments associated with enzymatic oxidation of phenolic compounds [18; 62].  
The use of h\({}^{\circ}\) is suggested for monitoring the discoloration of meat [63] since potential consumers can better understand color (hue) and lightness (L*) [64]. In the current study, adding turmeric rhizome in T1 and T3 contributed to a shift of the hue angle from orange to yellow, resulting in a significant increase in the \(h^{\circ}\) parameter (Figure 1e). Only in T1 does the h\({}^{\circ}\) decrease over time.
## 4 Experimental Results
### Performance Evaluation and Discussion  
The investigation in this work focused on the effectiveness of individually integrating three distinct regression-loss functions--L2, L1, and Log-cosh--along with their ensemble average combination for predicting facial aesthetic scores. The proposed methodology's validation was achieved via a five-fold cross-validation, employing the SCUT-FBP, UT-FBP5500, and MEBeauty datasets, as elaborated upon in subsequent sections.
## 1 Introduction  
PD is a localized electrical discharge that occurs within the insulator between conductors without causing full insulation failure. It typically arises in specific areas, often correlated with defects or voids. PD is identified by its appearance at weak insulation spots, without forming a complete bridge across the electrodes. It presents as transient pulses that last from nanoseconds to microseconds. Although less intense than full breakages, regular PD can lead to insulation degradation. PD generates electromagnetic, ultrasonic, and occasionally optical signals, enabling detection through various sensing methods. Initiation conditions for PD include the concentration of the local electric field due to voids, cavities, or contaminants. When this field exceeds the breakdown threshold of the medium (like air in a void), ionization occurs. Subsequently, charges accumulate on the cavity surfaces, modifying the electric field and impacting subsequent PD events. This discharge cycle often repeats as the system alternates between ionization, charge redistribution, and stabilization phases. The research addresses the complexities involved in diagnosing partial discharge (PD) in insulation systems with multiple voids, which complicates PD source classification. Traditional diagnostic techniques, which focus on single voids, encounter challenges with signal overlap from multiple sources, impacting fault identification in time and frequency domains. Conventional methods, based on rule-based systems, become unwieldy and struggle to handle complex, overlapping patterns arising from numerous PD sources. Advanced strategies using state-of-the-art signal processing and deep learning (DL) techniques are proposed to address these challenges. Although Phase-Resolved Partial Discharge (PRPD) analysis is effective for diagnosing specific defects by plotting PD events relative to the AC voltage phase angle, it struggles with interpretation in the presence of multiple PD sources, as overlapping signals obscure individual defect characteristics. This highlights the need for improved methods to accurately distinguish between different PD sources and patterns.