esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Classification of Anxiety Based on Physiological Signals
## 3 Materials and Methods
### Data Balancing Techniques  
As previously mentioned, the dataset is imbalanced, with an imbalance ratio of anxiety of 2.75 (165/45). Consequently, we used several data balancing techniques that changed the data distribution. Under-sampling techniques are not an appropriate choice as we already have a small number of observations, and this technique would further reduce our data size. Therefore, four data balancing techniques were applied--Random Oversampling (Over), SMOTE (SMO), ADASYN (ADA), and Borderline SMOTE 2 (Border). All these methods were settled to perform oversampling of the minority class; the parameters were set to guarantee a 1:1 class ratio.  
Random oversampling involves randomly selecting examples from the minority class and adding them to the training dataset [34]. SMOTE (SMO) was proposed to improve random oversampling and works by choosing a random minority class example, founding the \(k\) nearest neighbors for that example, and then creating a new synthetic example by interpolating the features' values of the two examples [35].  
Contrary to Random Oversampling and SMOTE, which are random methods, ADASYN and Borderline SMOTE 2 are informed methods as they act on specific observations. ADASYN (ADA) is a SMOTE version that works similarly but with one important difference: after creating the synthetic samples as described in SMOTE, ADASYN uses a density distribution as a criterion to automatically decide the number of synthetic samples that need to be generated for each minority data example. The resulting dataset will be balanced, and thus it will force the learning algorithm to focus on difficult-to-learn examples [36]. Borderline SMOTE is also a version of SMOTE but differs from other oversampling techniques since it only makes synthetic data along the decision boundary between the two classes. Borderline SMOTE 2 (Border) not only generates synthetic examples using each minority example and its positive nearest neighbors but also does that using each minority example and its nearest negative neighbor [37].
## 3 Results and Discussion
### Optimization of HPLC Analysis Conditions  
Various parameters such as the manufacturer of the column (Phenomenex, Waters, Shiseido, PerkinElmer, and Younglin Biochrom), the column temperature (30 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 45 \({}^{\circ}\)C), and the type of acid (formic acid, TFA, phosphoric acid, and acetic acid) were compared for simultaneous analysis of authentic standards for the eight marker compounds selected (MBRSA, LQRTA, LQRTS, NRG, DDM, PCR, GCRZA, and SZDRA). The HPLC analysis conditions ultimately selected were as follows: Gemini C18 analytical column (internal diameter D. 4.6 mm \(\times\) length 250 mm, particle size 5 \(\upmu\)m, Phenomenex, Torrance, CA, USA), column temperature of 35 \({}^{\circ}\)C, and a DW-ACN system, both components containing 0.1% (\(v/v\)) TFA, as the mobile phase. All authentic standards were detected within 40 min and retention times of the standards, MBRSA, LQRTA, LQRTS, NRG, DDM, PCR, GCRZA, and SZDRA, were 13.45, 19.73, 20.12, 21.79, 26.37, 27.03, 34.33, and 38.81 min, respectively. A typical HPLC chromatogram is presented in Figure 1.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 2 Tracking on the Web
### Web Tracking Methods
#### 2.1.4 IP Address-Based Tracking  
IP address-based tracking is a method of monitoring online activity through the use of a user's Internet Protocol (IP) address. An IP address is a unique numerical identifier assigned to a user each time they access the Internet, and it can be used to approximate the user's location, determine their Internet Service Provider (ISP), and gather network information. Websites and service providers utilize this information to analyze user behavior, track access patterns, and, in some instances, identify specific users or devices. However, since IP addresses are often dynamic and can be shared among multiple users or devices, their effectiveness in accurately identifying individuals is limited.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
###### Abstract  
Compared with text-independent speaker verification (TI-SV) systems, text-dependent speaker verification (TD-SV) counterparts often have better performance for their efficient utilization of speech content information. On this account, some TI-SV methods tried to boost performance by incorporating an extra automatic speech recognition (ASR) component to explore content information, such as c-vector. However, the introduced ASR component requires a large amount of annotated data and consumes high computation resources. In this paper, we propose a pseudo-phoneme label (PPL) loss for the TI-SR task by integrating content cluster loss at the frame level and speaker recognition loss at the segment level in a unified network by multitask learning, without additional data requirement and exhausting computation. By referring to HuBERT, we generate pseudo-phoneme labels to adjust a frame level feature distribution by deep cluster to ensure each cluster corresponds to an implicit pronunciation unit in the feature space. We compare the proposed loss with the softmax loss, center loss, triplet loss, log-likelihood-ratio cost loss, additive margin softmax loss and additive angular margin loss on the VoxCeleb database. Experimental results demonstrate the effectiveness of our proposed method.  
speaker verification; pseudo-phoneme label loss; deep cluster; multitask learning Article
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## Appendix A  
Let us define \(X_{1}=\left|h_{SR^{\prime}_{M_{L}\pi}}\right|^{2}\) and \(Y_{1}=\underset{1\leq k\leq K}{\max}\left(\left|h_{SE_{k}}\right|^{2}\right)\), according to the order statistic, the CDF of \(\gamma_{EI}^{NJ}\) under the NCE scenario can be expressed as  
Figure 7: Secrecy outage probability vs. the power allocation factor \(\alpha\) for the SCJ-OPA scheme under the NCE and CE scenarios when \(R_{s}=0.5\) (bit/s/Hz), \(M=L=2\), \(K=2\), and \(P_{Total}=12\), \(20\), \(30\) dB.  
\[F_{Y_{E1}}\left(x\right)=\Pr\left(\frac{1+P_{S_{1}}X_{1}}{1+P_{S_{1}}Y_{1}}<x \right)=\int_{0}^{\infty}F_{X_{1}}\left(\frac{x-1}{P_{S_{1}}}+xy\right)f_{Y_{1} }\left(y\right)dy. \tag{11}\]  
According to the relay selection policy and the analysis above, the CDF of \(X_{1}\) and the PDF of \(Y_{1}\) are, respectively, expressed as  
\[F_{X_{1}}\left(x\right)=\left(1-e^{-\lambda_{SE}x}\right)^{M_{1,n}}, \tag{12}\]  
\[f_{Y_{1}}\left(y\right)=K\lambda_{SE}\left(1-e^{-\lambda_{SE}y}\right)^{K-1}e^ {-\lambda_{SE}y}. \tag{13}\]  
Then, substituting Equations (12) and (13) into Equation (11) yields the desired result shown in **Theorem 1** by using the binomial theorem [43].
## 2 Materials and Methods
### Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specification**} & \multirow{2}{*}{**Treatments**} & \multicolumn{3}{c}{**Cold Storage Period (Days)**} & \multirow{2}{*}{**Standard Error**} & \multirow{2}{*}{**ANOVA (Two**} \\ \cline{3-4} \cline{6-7}  & & **1** & **7** & **10** & & **of the Mean** & **Factor: T and Z)** \\ \hline \multirow{3}{*}{pH} & K & 5.47 \({}^{\text{a}}\) & 5.55 \({}^{\text{a}}\) & 5.56 \({}^{\text{a}}\) & 0.08 & \\  & MS & 5.71 \({}^{\text{b}}\) & 5.72 \({}^{\text{b}}\) & 5.77 \({}^{\text{b}}\) & 0.1 & Z \({}^{\text{*}}\) \\  & SEM & 0.09 & 0.11 & 0.08 & & \\ \hline \multirow{3}{*}{Water activity} & K & 0.974 & 0.976 & 0.979 & 0.01 & \\  & MS & 0.970 & 0.978 & 0.971 & 0.01 & - \\  & SEM & 0.01 & 0.01 & 0.02 & & \\ \hline \multirow{3}{*}{Thermal drip (\%)} & K & 24.81 & 24.62 & 21.97 & 2.34 & \\  & MS & 26.09 & 22.94 & 21.31 & 4.25 & - \\  & SEM & 5.66 & 2.13 & 2.10 & & \\ \hline \multirow{3}{*}{Forced drip (cm\({}^{2}\))} & K & 5.41 \({}^{\text{ax}}\) & 4.93 \({}^{\text{ay}}\) & 4.13 \({}^{\text{az}}\) & 1.12 & \\  & MS & 3.76 \({}^{\text{bx}}\) & 3.30 \({}^{\text{by}}\) & 3.02 \({}^{\text{bz}}\) & 0.89 & Z \({}^{\text{*}}\); T \({}^{\text{*}}\) \\  & SEM & 0.87 & 1.11 & 1.05 & & \\ \hline \multirow{3}{*}{TBARS index (mg} & K & 0.60 & 0.63 & 0.68 & 0.05 & \\  & MS & 0.60 & 0.68 & 0.75 & 0.22 & - \\  & SEM & 0.06 & 0.16 & 0.19 & & \\ \hline \multirow{3}{*}{Oxidation-reduction (mV)} & K & 323.19 & 301.17 & 320.21 & 20.09 & \\  & MS & 298.90 & 319.04 & 345.14 & 12.18 & - \\  & SEM & 12.67 & 23.06 & 12.68 & & \\ \hline \multirow{3}{*}{Total number of microorganisms} & K & \(3.24\times 10^{5\,\text{ax}}\) & \(11.87\times 10^{5\,\text{ay}}\) & \(10.68\times 10^{6\,\text{ay}}\) & \(5.49\times 10^{5}\) & \\  & MS & \(2.35\times 10^{4\,\text{bx}}\) & \(4.06\times 10^{5\,\text{by}}\) & \(7.73\times 10^{6\,\text{bz}}\) & \(4.33\times 10^{5}\) & Z \({}^{\text{*}}\); T \({}^{\text{*}}\) \\  & SEM & 0.92 \(\times\) 10\({}^{4}\) & \(1.35\times 10^{5}\) & \(1.33\times 10^{6}\) & & \\ \hline \hline \end{tabular}  
* K represents the control sample; MS refers to the combined treatments of both methods used; T denotes the cold storage duration; Z indicates the type of treatment;
* \(p<0.05\). No statistically significant differences were observed between the groups (\(p>0.05\)). Statistically significant differences were observed between the groups (\(p<0.05\)): \({}^{\text{a,b}}\) refers to differences within the same column, while \({}^{\text{x,y,z}}\) indicates differences within the same line. The absence of letters or the use of identical letters signifies no statistically significant differences.  
\end{table} 
Table 2: Physicochemical and microbiological properties of refrigerated pork loin following treatment with combined non-thermal processing methods.
## 3 Results
### Evaluation and Comparison of the Models  
Evaluation of model performance is an important step in the spatial modeling process [88]. In this study, we evaluated the performance of the four ensemble models using the area under the ROC curve (AUC), standard error (SE), and 95% confidence interval for the training and testing datasets. The logistic regression (LR) model was used as a benchmark method. ROC curves for the training dataset are shown in Figure 7. The curves show that all tested ensemble models perform well in spatially predicting gully erosion susceptibility. However, the ROC curve for the REPTree model falls below the curves of the other models. Other results of the goodness-of-fit analysis of the training dataset are shown in Table 7. These results indicate that RS-REPTree model has the best performance with the highest AUC (0.874), lowest SE value (0.0191), and narrowest 95% CI (0.834-0.907). Sequentially, the Bag-REPTree, AB-REPTree models have slightly lower performances. Finally, the performances of three ensemble models are better than that of the benchmark LR model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**SE**} & \multicolumn{2}{c}{**95\% CI**} \\ \cline{3-5}  & & & **Lower Bound** & **Upper Bound** \\ \hline
**REPTree** & **0.819** & 0.0238 & 0.774 & 0.859 \\ \hline AB-REPTree & 0.844 & 0.0210 & 0.801 & 0.881 \\ Bag-REPTree & 0.871 & 0.0191 & 0.830 & 0.905 \\ RS-REPTree & 0.874 & 0.0191 & 0.834 & 0.907 \\ LR & 0.825 & 0.0222 & 0.780 & 0.864 \\ \hline \hline \end{tabular}
\end{table}
Table 7: ROC curve using the training dataset.  
Figure 6: Gully erosion susceptibility maps based on: (**a**) AB-REPTree, (**b**) Bag-REPTree, (**c**) RS-REPTree, and (**d**) REPTree.  
Model performances for the testing dataset based on the ROC curve, AUC, SE, and 95% CI values are shown in Figure 8 and summarized in Table 8. All models performed well, but the proposed new ensemble model, RS-REPTree, has the highest prediction capability based on its AUC (0.860), SE (0.0315), and 95% CI (0.793-0.912). It is followed by the Bag-REPTree (AUC = 0.841), LR (AUC = 0.824), AB-REPTree (AUC = 0.805), and REPTree (AUC = 0.800) models. Overall, our results show that the new ensemble models of REPTree outperform and outclass the standard REPTree model in gully erosion susceptibility mapping.  
Figure 8: ROC curve and AUC of the models: (**a**) training dataset, and (**b**) validation dataset.  
Figure 7: ROC curves related to the susceptibility models used in this study.
# Numerical Analysis of an Upstream Tailings Dam Subjected to Pond Filling Rates
###### Abstract  
One of the challenges in upstream tailings dam projects is to ensure the allowable rate of deposition of tailings in the pond (i.e., pond filling rate) while maintaining the stability of the dam. This is due to the fact that an upstream tailings dam is constructed by placing dikes on top of previously deposited soft tailings, which could lead to a decrease in dam stability because of the build-up of excess pore water pressure. The main purpose of this work is to investigate the effects of pond filling rates on excess pore water pressure and the stability of an upstream tailings dam by a numerical study. A finite element software was used to simulate the time-dependent pond filling process and staged dam construction under various pond filling rates. As a result, excess pore water pressure increased in each raising phase and decreased in the subsequent consolidation phase. However, some of the excess pore water pressure remained after every consolidation phase (i.e., the build-up of excess pore water pressure), which could lead to a potentially critical situation in the stability of the dam. In addition, the remaining excess pore water pressure varied depending on the pond filling rates, being larger for high filling rates and smaller for low filling rates. It is believed that the approach used in this study could be a guide for dam owners to keep a sufficiently high pond filling rate but still ensure the desirable stability of an upstream tailings dam.  
Keywords:excess pore water pressure; pond filling rates; upstream tailings dam; numerical analysis +
Footnote †: journal: Applied Sciences
## 5 Survivability Analysis
\begin{split} P_{1}&=\frac{\lambda_{1}}{\mu_{1}}P_{0}\\ P_{2}&=\frac{\lambda_{1}+\xi_{1}}{\gamma_{1}}P_{0} \\ P_{3}&=\frac{\lambda_{1}\lambda_{2}}{\mu_{1}\mu_{2}} P_{0}\\ P_{4}&=\frac{\lambda_{1}(\lambda_{2}+\xi_{2})}{\mu_{1} \gamma_{2}}P_{0}\\ P_{5}&=\frac{\lambda_{2}(\lambda_{1}+\xi_{1})}{\mu_{2} \gamma_{1}}P_{0}\\ P_{6}&=\frac{(\lambda_{1}+\xi_{1})(\lambda_{2}+\xi_{2} )}{\gamma_{1}\gamma_{2}}P_{0}\\ [1+\frac{\lambda_{1}}{\mu_{1}}+\frac{\lambda_{1}+\xi_{1}}{ \gamma_{1}}+\frac{\lambda_{1}\lambda_{2}}{\mu_{1}\mu_{2}}+\frac{\lambda_{1}( \lambda_{2}+\xi_{2})}{\mu_{1}\gamma_{2}}+\frac{\lambda_{2}(\lambda_{1}+\xi_{1 })}{\mu_{2}\gamma_{1}}+\frac{(\lambda_{1}+\xi_{1})(\lambda_{2}+\xi_{2})}{ \gamma_{1}\gamma_{2}}]P_{0}=1\end{split}  \tag{17}\]  
According to the ability of cluster heads to provide services for the system, the system survivability state can be divided into three conditions. * System available state, which includes states \(S_{0}\), \(S_{1}\), and \(S_{2}\). In this state, at least one of the pCH and the bCH are in the C state and can provide data gathering, receiving, processing, and transmitting services. In addition, when the pCH is in the C state and bCH is in the S or F state, the bCH should be reselected in time to guarantee the availability of the system. The corresponding probability \(P_{av}\) is expressed as \[P_{av}=P_{0}+P_{1}+P_{2}\] (18)
* System weak state, which includes states \(S_{3}\), \(S_{4}\), and \(S_{5}\). In this state, both the pCH and the bCH are not in the C state, but can still receive data. This state can be regarded as an emergency state of the system. In order to ensure that the system is restored to the available state, the cluster head node needs to be charged or maintained as soon as possible after the system enters the weak state to ensure normal service. When the bCH is in S state, the bCH will enter the F state as soon as possible and fly to the charging pile to charge, so as to restore the system to available as soon as possible. The corresponding probability \(P_{wk}\) is expressed as \[P_{wk}=P_{3}+P_{4}+P_{5}\] (19)
* System unavailable state, which includes state \(S_{6}\). In this state, pCH and bCH are all in state F and cannot provide service. The corresponding probability \(P_{unav}\) is expressed as \[P_{unav}=P_{6}\] (20)
# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
## 2 Data Introduction and Processing Methods
### Data Introduction  
The CSES-01 satellite is equipped with eight types of payloads, including the Search-Coil Magnetometer (SCM), Electric Field Detector (EFD), High-Precision Magnetometer (HPM), Plasma Analyzer Package (PAP), Langmuir Probe (LAP), GNSS Occultation Receiver (GOR), Tri-Band Beacon (TBB), and High-Energy Particle Package (HEPP)/Detector (HEPD), to measure the electromagnetic field, plasma parameters, energy particles, etc. [20]. Figure 1 shows the layout of the payload configuration onboard CSES-01. The details of the payloads can be found in their respective publications [25; 26; 27; 28; 29; 30]. According to the results of the in-flight commission test, data validation, and data quality assessment through comparison with multisource observations, we confirmed that most of the payloads are working stably in-orbit, providing high-accuracy and reliable data products, except for PAP and TBB [22]. The PAP was contaminated after launch, leading to lower absolute values of _Ni_ and larger _Vi_ than expected at this altitude of the ionosphere. Fortunately, after comprehensive analysis through comparison with other missions, we found that the relative values of _Ni_ and _Vi_ are stable and have the capability of describing the ionospheric variation features similar to those observed from other missions. It is recommended that the relative values of PAP be carefully used for scientific applications [31]. The middle band (400 MHz) of TBB malfunction  
Figure 1: The layout of the payloads onboard CSES-01. data products of TBB have not yet been completely assessed [32]; thus, it was not included in this study. The status of each scientific payload onboard CSES-01 is summarized in Table 1.  
The standard data products of CSES-01 are classified into five levels, see [PERSON] et al. [21] for details. Among these, the Level-2 standard products are the calibrated physical parameters with the necessary information, including geographic and geomagnetic coordinates, time, altitude, etc., which can be directly applied to scientific applications and shared with the international scientific community through the website ([[https://www.leos.ac.cn/](https://www.leos.ac.cn/)]([https://www.leos.ac.cn/](https://www.leos.ac.cn/)), accessed on 1 August 2024).
# An Image Processing Method for Measuring the Surface Area of Rapeseed Pods
## 2 Materials and Methods
### Measurement Principle and Operation
#### 2.3.2 High-Precision 3-D Laser Scanner Measurement Method  
The instrument used to measure the actual area of the pods was an LD-SurveyorZS ultra-high precision 3-D laser scanning system (LDI; Logistic Dynamics, Inc., Seattle, WA, USA) (Figure 7). The system configuration included a 3-D laser probe (sensor) system, a probe movement and positioning system, a 3-D laser scanning auxiliary fixture, scanning control software, and data processing software. The laser scanning probe used in this measurement was LDI-SLP250, which is accurate at scanning up to 0.01 mm. It can be used to detect the vast majority of industrial product sizes. The 3-D laser probe was installed on the probe movement and positioning system (mainframe) and calibrated so that the data detected could be converted to the coordinate system of the measuring machine. The 3-D laser probe system was the sensing device used for acquiring 3-D data. It works with a triangular measurement method comprising a high-precision laser transmitter located in the middle of the scanning probe and a CMOS image sensor with a certain angle with the laser projection installed on both sides of the probe. Upon projecting the laser onto the surface of an object, the laser line is misaligned by the different 3-D surface structures. The misalignments are then read and analyzed by the CMOS image sensor. This process, combined with the movement of the probe and the function of its positioning system, enables the 3-D structural data of the measured object surface to be obtained.  
No pretreatment was required when the 3-D laser scanning system (described in Figure 7) was used to scan and measure the pods as long as the pods were arranged and fixed on the scanning fixture in sequence (Figure 8). Two fixtures can be used for clamping to improve the efficiency of laser scanning, and a remarkable scanning fixture can hold 20-30 pods at a time. The scanned data are encapsulated by Geomagic Quality (ControlX; 3-D Systems, Cary, NC, USA). The geometric attributes, such as volume and surface area, can be calculated directly after encapsulating the point cloud data (Figure 8).  
Figure 7: LDI-Surveyor ZS/SLP ultra-high accuracy high-speed 3-D laser scanning system and the structure of scanning probe.
# BIM-Based Management System for Off-Site Construction Projects
## 4 System Implementation
### System Functions and Implementation Detail
#### 4.3.5 Installation Management and Progress Monitoring  
The BIM-based management system can monitor onsite installation work and OSC project progress. The site manager can manage the status of products from their entry to the site to their assembly. The proposed system can manage onsite construction by integrating multidimensional attribute information for each component using BIM and sensor-based real-time location tracking technologies such as RFID, RTLS, and GPS. The product delivery status is recorded by receiving the recognized product location and time data through a sensor on the gate. The arriving products are moved to the installation location and assembled in order. Moreover, the proposed system can track the installation status of products by receiving data from sensors attached to drones and equipment for moving and lifting products. If the position of the detected component matches the position in the BIM model, the installation process is complete, and the construction is completed through the detection procedure.  
The management system integrates all the project statuses, including production, logistics, and onsite installation to enable monitoring according to the set conditions. To this end, the data collected from the various channels are integrated and managed in the system based on BIM. The management system can enable the monitoring of progress from a microlevel, such as a component, to the macrolevel (the entire project). For example, a manager can verify the lifecycle stage of a particular component (production, logistics, or installation). A component is recognized as a model object with a unique ID in the system, and the CDM interconnects the information of all the components through BIM. In this manner, the proposed system can implement the calculation result of the numerical data through the BIM-based interworking. For example, the percentage of components with the same shape and size in production, transport, and installation can be identified. Moreover, the achievements against goals at present or at a given point in a project or phase can be evaluated. This system has a progress monitoring dashboard to effectively visualize the status of all or individual phases. The screen displays important project and stage management indicators (e.g., numerical and 3D models for schedule, cost, and component status). Furthermore, the key indicators are arranged on one screen to allow users to rapidly recognize abnormalities and prevent significant problems.