esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 A Case Study on Reliable Pipeline Monitoring Using Cyber Physical Sensor Networks
### Sensor Layout  
The type of sensors, their placement and their usage in the monitoring environment form the basis for CPSN monitoring systems. Sensors placement for pipeline monitoring can be classified as outside placement and inside placement that may use invasive or pervasive techniques depending on the scenarios given [34]. Monitoring changes in pipelines with visual inspection is a handy technique for monitoring events that occur above ground. Vision sensors allow distinguishing differences or changes in the area around the pipeline. With such sensors, small changes in the physical nature of the pipelines, temperature difference or the event of any fluid or gas leakage can be easily determined. Ground-penetrating radar technique is another widely used method to accurately monitor changes and collect evidence of the existence of any occurrences at the ground level without digging. With the help of acoustic transducers, small fluid or gas leaks are easily identified as they produce frequency oscillations [35,36]. The coverage range of these transducers is very small; hence, a number of transducers are required to cover the desired underground pipeline requiring extensive operational and maintenance efforts. To reduce such efforts, acoustic transducers are only deployed on the pumping stations or near checkpoints. With the mass balancing method [36], the difference in flow of entering and leaving fluid can be monitored though it is not an efficient method for locating major leakages in underground pipelines.
## 4 Interference Monitoring  
The process of the interference monitoring based on TWSVM is shown in Figure 1. Firstly, the training samples are extracted from GNSS signals and then imported into the TWSVM to get TWSVM model; secondly the corresponding characteristic parameters are extracted from GNSS signals to be monitored, and then imported into the trained TWSVM model to get the monitoring results.  
From Figure 1, we can see that it is necessary to extract the features for interference monitoring. Therefore, we firstly study feature extraction from GNSS signals. According to the position of interference monitoring indicators relative to the correlator, the monitoring indicators are divided into pre-correlation interference monitoring indicators and post-correlation interference monitoring indicators. The pre-correlation interference monitoring is mainly implemented through the extraction of the observations of receivers, including antenna, AGC gain, ADC, _etc._ The post-correlation interference monitoring is implemented by observing the carrier to noise ratio, correlator output, _etc._
# Nano-HPLC-HRMS Analysis to Evaluate Leptin Level in Milk Samples: A Pilot Study
## 4 Discussion
During lactation leptin amount in bovine mature milk was 6.14 ug L\({}^{-1}\), but the concentration diminished over time [44; 45; 46] and in addition the milk is processed with a final step of centrifugation (lipid separation). Therefore, leptin, because of its lipophilicity, was not detectable. To quantify leptin in samples of breast human milk, bovine milk, and infant food we used the peptides with _m/z_ ratio of 708.3599, z = 2. As declared in \"Results\" section, we quantified human leptin only in human breast milk samples. The results were in accordance with those found by [PERSON] et al. [47] who quantified a concentration of 5.18 \(\pm\) 4.96 ng mL\({}^{-1}\), by [PERSON] and co-workers [48] who detected with RIA 3.36 \(\pm\) 1.0 ng mL\({}^{-1}\) in whole milk samples and by [PERSON] research group [49] which compared whole and skimmed human milk. Figure 3 shows the detected peptides of leptin in human breast milk sample 1. In liquid infant formula products samples, no leptin was detected (under LLOQ). Once more as for QC samples, we processed the samples also with the immunoaffinity columns bound with polyclonal antibody anti-LEP from rabbit with specificity for bovine. Then we run the raw files with Mascot software and none of bovine leptin digested peptides were found in samples. This finding confirmed the alleged loss of leptin during the defatting process of bovine milk used as principal raw material.
# DYMatch: Semi-Supervised Learning with Dynamic Pseudo Labeling and Feature Consistency
## 2 Related Works
### Gaussian Mixture Model and Its Application in SSL  
The Gaussian Mixture Models (GMM) are commonly employed in machine learning problems to address clustering problems. This method belongs to mixture models, which assumes that the observed samples with known labels are sampled from a specific multivariate gaussian distribution. Specifically, the GMM assumes that the observed samples in the dataset belong to \(K\) different categories. However, due to the lack of label information of the samples, it is necessary to employ the prior probability \(p_{k}\) to denote the likelihood that the sample belonging to a category and the joint distribution corresponding to this category is modeled as a multivariate normal distribution \(N(\mu_{k},\sum_{k})\). Once obtaining the parameter estimates of the model, the posterior distribution of an observed sample can be calculated, ultimately determining its category. In theory, the GMM is capable of fitting dataset with arbitrary distributions, and it shows better performance when handling dataset modeled as the multivariate Gaussian distribution clusters [30]. In this paper, the model's prediction scores for the samples are continuous variables. Therefore, the GMM model is selected to model and analyze them.  
Within the SSL tasks, there is a crucial task known as noise label learning, also referred to as robust training [31]. In this task, GMM is an important and effective method for estimating noise rates [32; 33]. The estimated noise rate is widely used to reweight samples in robust classifiers [34; 35] or determine the quantity of samples considered as clean training samples [36; 37; 38].  
A novel SSL framework, DivideMix [39], has been proposed to deal with the challenge of training with noisy labels. Firstly, DivideMix proposes the concept of co-division, a process of training two networks simultaneously. The GMM is employed to fit the loss distribution of the training data, which aims to partition the training data into labeled and unlabeled sets. The labeled dataset can be considered as the samples with correct labels, while the unlabeled data can be regarded as the samples with noisy labels. The separated datasets are then used to train an SSL model. By iteratively repeating the previous process, the model gradually overcome the influence of noisy labels. Inspired by DivideMix, DYMatch integrates this dynamic estimation method into semi-supervised classification learning. Specifically, in DYMatch, the dynamic estimation method is employed to yield the local threshold for a specific category, which effectively improves utilization of unlabeled data.
## 2 Materials and Methods
### Research Area  
In the following study, the Yellow River basin of Ningxia, China (104\({}^{\circ}\)36\({}^{\prime}\)-106\({}^{\circ}\)55\({}^{\prime}\) E, 37\({}^{\circ}\)15\({}^{\prime}\)-39\({}^{\circ}\)23\({}^{\prime}\) N), was selected as the research area (Figure 1). Located in northwest China, the 317 km long river ends at the Mahuang Valley in Shizuishan City, a length that accounts for almost one-fourteenth of the entire Yellow River. The river flows through most parts of the Ningxia Hui Autonomous Region, including the cities of Yinchuan, Shizuishan, Wuzhong, and Zhongwei. The Yellow River in Ningxia comprises a long stretch, gentle river, wide riverbed, and abundant water resources. The main land types include cultivated land, forest land, and urban construction land, among which cultivated land and urban construction land are the main land types. The Yellow River basin in Ningxia has a temperate continental climate. Winters in the basin are cold and dry, water in the area may be frozen, and precipitation is rare and unevenly distributed. However, with the development of the economy and the increase in the population, the Yellow River basin in Ningxia region also faces various environmental problems. The effects of overexploitation and pollution of water resources, land degradation, and deterioration of the ecological environment have become increasingly prominent, posing challenges to local sustainable development [24; 25]. Therefore, protecting the ecological environment of the Yellow River basin and promoting the rational utilization and protection of resources have become one of the leading issues in the development of this region.
## 3 Materials and Methods
### Numerical Modeling  
The configurations of the experimentally tested structures were subjected to numerical modeling in ANSYS(r) (version 2024), ensuring the replication of specimen geometries with consistent material properties and characteristics. The three-dimensional models were developed in SolidWorks(r), allowing the creation of a geometry that accurately represents the real structure.  
The models were exported in. STEP format and subsequently imported into ANSYS(r), where the simulations were performed using the Static Structural module. This process enabled a detailed analysis of structural behavior, providing insights into displacements, stresses, and forces under different geometric configurations.  
To enhance the accuracy of the deformed structural representation, an incremental solution (based on time steps) was adopted, enabling the computation of intermediate results throughout the loading process. This approach accounted for material and geometric nonlinearities, which are crucial for realistically capturing the structural response under load.
# Non-Contact Dermatoscope with Ultra-Bright Light Source and Liquid Lens-Based Autofocus Function
## 3 Results
### Characteristics of the Illumination Unit  
#### 3.2.1 Illumination Intensity and Spectral Data  
The reflector for shaping of the illuminated area, designed with OpticStudio as mentioned before, was milled and polished from aluminum. The light distribution was measured with a goniometer (PM-1200N-1, Radiant Vision Systems, Redmond, WA, USA) and the data exported again to OpticStudio. Here the light intensity at different distances was calculated. As the output of the illumination unit was measured without polarizer, the intensity had to be multiplied by a factor of 0.5 to obtain a good estimate. Furthermore, the illumination unit was measured with an integrating sphere (50 cm diameter, Mountain Photonics GmbH, Landsberg am Lech, Germany; with connected spectrometer: AvaSpec-2048L, Avantes BV, Apeldoorn, The Niederlande) to determine light intensity and spectral data for color correlated temperature (CCT) and CRI. The CCT of the illumination unit is 6350 K with a CRI of 76. In Figure 5, the light distribution at a distance of 45 cm from the illumination unit is shown for an imaged area of 10 cm \(\times\) 10 cm. The overall light flux through this area is 775 km without or 378.5 km with polarizer. The light flux on the area of interest (the area which is captured by the camera) at this distance, i.e., 17 mm \(\times\) 13 mm, is 71.6 lm or 35.8 km after the polarizer. This results in a polarized illuminance of 162,000 lx. To compare this value, the minimal illuminance for office work spaces in Europe according to work safety regulations is 500 lx [22]. The measured illuminance from room lighting on the table in our laboratory was 525 lx. If we consider the illuminance in a doctor's office to be 1000 lx, the illuminance of the area of interest by the presented prototype is 162 times higher. This makes the light situation comparable for every image recorded, as the influence of ambient light on the total illuminance on the area of interest is negligible.
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 3 Experiment Design and Analysis Method
### Analysis Method of Error Distribution Patterns in MLS Point Clouds
#### 3.4.2 Reference Data Generation  
The reference data are mesh-type plane datasets generated from TLS point clouds. The TLS-01 device can acquire point clouds with high accuracy and automatically align the \(z\)-axis with the gravity direction using its built-in IMU sensor. Therefore, the \(z\)-axis values of the TLS point clouds are directly used as height values without additional processing. First, point clouds corresponding to the Columns are extracted from the TLS point clouds. Then, the RANSAC method is applied to generate the initial planes of the Columns. RANSAC, a method for fitting optimal lines or planes while accounting for outliers, has been robustly utilized in Scan-to-BIM studies [24; 25; 26; 27; 28; 38]. Based on the sides of the columns, equations for four planes can be obtained, with each plane defined by the general form in Equation (1):  
\[ax+by+cz+d=0 \tag{1}\]  
The edges of the columns are determined by the intersection lines formed by orthogonal planes. By solving the equations of two intersecting planes and assigning fixed \(z\)-values, the eight vertices that represent each Column are obtained. In this study, the fixed \(z\)-values, in accordance with the design specifications of the Columns, are set to \(z=0\) and \(z=1.5\). Additionally, the coordinate systems of the two Columns are defined separately, with the origin of each set at the centroid of the four vertices derived from the bottom section at \(z=0\). This coordinate adjustment ensures consistency in the directionality of the C2M distance measurement performed later and provides intuitive coordinates for the eight vertices. Finally, these eight vertices are used to redefine four enclosed planes, which then serve as the reference data for subsequent analysis.  
Figure 5: Expected outcomes of ICP registration in this experiment (blue points: MLS point clouds; red lines: reference planes). (**a**) When inner points are more densely distributed than outer points, (**b**) when inner and outer points are similarly distributed, (**c**) when outer points are more densely distributed than inner points.
## 2 Related Works
### Pedestrian Detection  
Depending on the method of feature extraction, pedestrian detectors can be categorized into the following types: handcrafted feature-based and deep feature-based pedestrian detectors. In early studies, handcrafted feature-based pedestrian detectors used sliding windows to extract features, such as HOG [24], LBP [25], SIFT [26], and Haar [27]. Classifiers such as SVM, AdaBoost, and random forest were trained to filter out the background. Integral channel features [28] combine LUV channels, gradient magnitude, and gradient histograms to effectively capture diverse information from input images. In addition, a deformable part model (DPM) was used in [10] to handle object deformation by partitioning the human body into different parts.  
Recently, a convolutional neural network [29] was proposed. Since then, numerous deep feature-based pedestrian detectors have been designed, which have gradually replaced handcrafted feature-based pedestrian detectors. Among these, the well-known object detector Faster R-CNN [8] is widely utilized for various computer vision tasks, and has become one of the most common pedestrian detectors after fine-tuning. In [30], a scale-aware weight mechanism is proposed to detect large and small pedestrians separately, and then, assign weights to them dynamically. CSP [13] treats pedestrian detection as a high-level semantic feature [31; 32] detection task by predicting the centroids and scales of pedestrians. Following this idea, PP-Net [12] proposes a depth-guided module to capture higher-level information. CSANet [33] uses channel attention and spatial attention together to model context [34; 35; 36] dependencies and enhance pedestrian features. MAPD [19] optimizes a positive sample allocation strategy and utilizes a triplet loss function to learn the high-level ID features of pedestrians, which are then combined with the NMS algorithm. While mainstream detectors can meet the requirements of pedestrian detection in simple scenes, such as sparse scenes, their performance tends to degrade in crowded scenes due to ubiquitous intra-class occlusion.
## 3 Test Results and Analyses
### Influence of the Interface Form on the Load-Bearing Performance of Rehabilitated Pipes
#### 3.3.3 Load-Displacement Curves  
Load-displacement curves of each interface form were obtained, as shown in Figure 14. Observing the load-displacement curves of each specimen, it is clear that the curve trends are basically the same. However, when considering the crack development trend, the pipe sections with a plastic film pasted on it exhibited rapid cracking and interfacial peeling. The naturally bonded pipe sections also showed interfacial peeling during the loading process, while the crack development of the pipe sections with interfacial nailing was relatively slow and there was no interfacial peeling. Additionally, based on the trend of load changes after cracking, the bearing capacity of the pipe sections with a pinned interface no longer increased during the continued loading process due to serious interfacial peeling and remained at a certain level, while the bearing capacity of the pinned and naturally bonded interfacial pipe sections underwent a certain increase after the pipe sections cracked. This illustrates that as the interfacial bonding increased, the longer the pipe in the laminated structure, the more effectively the liner layer functions, resulting in an increased bearing capacity of the after the repair was carried out [23; 24].  
\begin{table}
\begin{tabular}{c c} \hline
**Group** & **Ultimate Load D\({}_{\textbf{u}}\)/kN** \\ \hline S-0 & 6.32 \\ S-1 & 11.70 \\ S-2 & 21.96 \\ S-3 & 10.71 \\ \hline \end{tabular}
\end{table}
Table 10: Ultimate loads of the pipe sections of each boundary form.  
Figure 14: Damage form of pipe sections with a naturally bonded interface: (**a**) Naturally bonded interface; (**b**) interfacial nail implant; (**c**) pasted plastic film.
## 4 Experimental Results
### Experiment 2: Verification of Generated Image  
To verify the plausible generated images, 30% of the original images were reserved as the validation data at the beginning. The remaining 70% of the original images were mixed with generated images as training data. The flowchart of verification process is presented in Figure 13.  
Figure 12: Number of mixed images and accuracy of experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size of Data Set** & **Original** & **Double** & **Triple** & **Quadruple** \\ \hline Total image & 22,489 & 44,978 & 67,467 & 89,956 \\ Benign image & 11,082 & 22,164 & 33,246 & 44,328 \\ Malignant image & 11,407 & 22,814 & 34,221 & 45,628 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number of mixed images.  
Figure 13: Flowchart of generated images verification process.  
Figure 14 displays the number of images and the accuracy of verification. The number of images after augmentation is presented in Tables 9 and 10 displays the accuracy, specificity, and sensitivity of verification. The experimental results reveal that the accuracy and sensitivity improved after the original data set was augmented. The highest accuracy rate reached 99.60%, and the highest sensitivity was 99.80% in other words, the generated images are able to be trusted to solve the problem of sparse medical images.  
From Figures 12 and 14, it can be noticed that increasing the size of data into quadruple does not show significant accuracy improvement. The reason might be that the images diversity is sufficient for the network to learn the features of lung tumors when increasing the size of data into triple. Moreover, the accuracy in experiment 2 reached 99.6% which is higher than that of in experiment 1 might be the reason that the goal of conducing experiment 2 was to verify the generated images which contain noises in order to train the network more diversity. Therefore, the generated images are more appropriate to help network extracting different features but not for testing. Moreover, the sensitivity is another important index evaluation for the network, especially in medical applications. From those experiments, after data augmentation, the highest sensitivity achieved is 99.8%.
# Temporomandibularibular Disorders and Bruxism in Patients Attending a Tinnitus Clinic
## 3 Results
### Timititus/Depression/Anxiety Scores and TMD/Bruxism  
THI, BDI and SAI scores were compared between three groups of the patients classified according to their diagnosis with tinnitus only, tinnitus and either TMD or bruxism, tinnitus and both TMD and bruxism. Significant difference was found in SAI scores between the groups (\(p\) = 0.045) (Table 2). Patients diagnosed with tinnitus and both TMD and bruxism had significantly higher SAI score (mean = 49.17) compared with patients having only tinnitus (mean = 38.75) (\(p\) = 0.037, Post Hoc analysis). No statistically significant difference was found in THI and BDI scores between the three of the groups (\(p\) = 0.260) (Table 2). Yet, there was a tendency for all of the studied scores to increase with increased number of TMD/bruxism diagnoses, namely, the tinnitus only group scored the lowest, tinnitus combined with both TMD and bruxism scored the highest (Figure 4). This was especially evident regarding the THI score and the SAI score.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Severity Scores** & **TMD/Bruxism Diagnosis \({}^{\star}\)** & \(n\) & **Mean** & \(\pm\)**SD** & \(p\)**-Value \({}^{\star}\)** \\ \hline \multirow{4}{*}{THI (tinnitus)} & Single & 23 & 49.22 & 25.504 & \\  & Both & 12 & 54.83 & 30.361 & 0.829 \\ \cline{1-1}  & None & 16 & 49.13 & 29.285 & \\ \cline{1-1}  & Single & 23 & 8.48 & 6.251 & \\ \cline{1-1}  & Both & 12 & 13.17 & 13.790 & 0.260 \\ \cline{1-1}  & None & 16 & 8.13 & 7.210 & \\ \cline{1-1}  & Single & 23 & 42.00 & 10.353 & \\ \cline{1-1}  & Both & 12 & 49.17 & 11.731 & **0.045** \\ \cline{1-1}  & None & 16 & 38.75 & 10.459 & \\ \hline \end{tabular}  
* THI: Timitus Handicap Inventory; BDI: Beck’s Depression Inventory; SAI: State Anxiety Inventory; SD: Standard Deviation.
* Three diagnosis groups: “Single”—tinnitus combined with single TMD or bruxism; “Both”—tinnitus combined with both TMD and bruxism; “None”—tinnitus only. \({}^{\star}\) Significant values are denoted in bold.  
\end{table}
Table 2: Descriptive statistics for the THI, BDI and SAI scores in tinnitus only, tinnitus combined TMD or bruxism, and tinnitus combined TMD and bruxism groups.  
Figure 3: Prevalence of TMD/bruxism among studied sample.