esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of New Tunnelling on a Buried Pipeline with Joints and Its Detachment
## 5 Conclusions  
We propose an analytical method to estimate the deflection and bending moment of the jointed pipeline and its detachment. The pipeline is regarded as an elastic beam resting on a Pasternak foundation model. Our proposed method is verified by the centrifuge tests when compared with other methods. Parametric analyses about the main influencing factors are carried out and the findings are as follows:  
* We provide a mathematical model which uses the pulse function to model the joints and uses the iteration method to determine the detachment. The deflections of the jointed pipeline calculated by our method agree with the experimental data which indicates our proposed method's reasonability.
* The maximum value and profile of the pipeline's deflection obtained by our method are close to those calculated by the other methods. Our method can not only predict the deflections of the jointed pipeline, but also determine whether the detachment occurs.
* The deflections of the jointed pipeline increase with the decrease in the stiffness reduction factor \(\beta\), the coefficient of subgrade reaction \(K\), and the Pasternak shear modulus of foundation \(G\), whereas the bending moments decrease. The stiffness reduction factor is of great significance to the discontinuous behavior of the jointed pipeline.
* Detachment beneath the jointed pipeline sometimes happens and sometimes does not, depending on our proposed method's iteration. Thus, more attention should be paid to the detachment between the pipeline and underlying soil.  
Figure 14: Deflection and bending moment of buried pipeline with different Pasternak shear moduli of foundation: (**a**) deflection; (**b**) bending moment.  
**Author Contributions:** Conceptualization, X.L. and Y.D.; methodology, Y.D. and X.L.; software, X.L.; validation, X.L. and R.Z.; formal analysis, Y.D.; investigation, Y.D.; resources, C.Y.; data curation, R.Z.; writing--original draft preparation, Y.D.; writing--review and editing, X.L.; supervision, X.L.; project administration, C.Y.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors gratefully acknowledge the financial support by the National Natural Science Foundation of China under Grant 52108363 and the China Postdoctoral Science Foundation under grant number 2021M700654.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Calculation of the Shadow Area of Human Obstacles under Multiple Light Sources
#### 2.2.1 Full Shadow Calculation of Human Obstacles
According to the geometric optics theory, if \(S_{n}\) is the shadow area generated by the \(n\)-th light source on a single obstacle, then the total shadow area generated by a single human obstacle under multiple light sources is the intersection of the shadow area \(S_{n}\) generated by all light sources on the obstacle, i.e.,  
\[S_{f-s}=S_{1}\cap S_{2}\cap\cdots\cap S_{n} \tag{14}\]  
The above solution method of the shadow area could be converted into its matrix form. If the shadow matrix generated by the \(n\)-th light source for a single obstacle was \(A_{n}\)then the shadow matrix \(\mathbf{E}_{m-s}\) generated by multiple light sources under the occlusion of a human obstacle could be expressed as:  
\[\mathbf{E}_{m-s}(x,y,n)=\left(A_{1}\cdot A_{2}\dots A_{n}\right)_{xy} \tag{15}\]  
where \((x,y)\) is the receiver position and \(\cdot\) represents the dot product, that is, the multiplication of the corresponding elements of the matrix. Moreover, \(\mathbf{A}_{n}(n\in[1,N])\) is the shadow matrix generated by the \(n\)-th light source against human obstacles in the receiving plane, and \(\mathbf{A}_{n}\) could be expressed as:  
\[\mathbf{A}_{n}=\left\{ \begin{array}{c}0,(x,y)\
otin S_{n}\\ 1,(x,y)\in S_{n}\end{array} \right. \tag{16}\]  
We calculated the number of Shadow Grids \(R_{m-s}\) in the shadow matrix \(\mathbf{E}_{m-s}\) and substituted the number of Shadow Grids \(R_{m-s}\) into Formula (6) to obtain the full shadow area \(S_{f-s}\) of an obstacle under multiple light sources, as shown below. \[S_{f-s}=R_{m-s}\times S_{rect} \tag{17}\]  
Additionally, according to the geometric optics theory, if the full shadow area \(D_{m}\) is generated by multiple light sources on the \(m\)-th human obstacle, then the full shadow area \(S_{f-m}\) of multiple obstacles under multiple light sources can be the union of the full shadow area \(D_{m}\) of all obstacles, i.e.,  
\[S_{f-m}=D_{1}\cup D_{2}\cup\dots\cup D_{m} \tag{18}\]  
The above solution method of the shadow area was converted into the matrix form. If the \(M\) shadow matrices \(\mathbf{C}_{\text{m}}(m\in[1,M])\) were generated by multiple light sources for \(M\) obstacles, then the shadow matrices \(\mathbf{E}_{m-m}\) generated by multiple obstacles under multiple light sources could be expressed as the corresponding addition of \(M\) shadow matrices, i.e. :  
\[\mathbf{E}_{m-m}(x,y,m)=\sum_{m=1}^{\text{M}}\mathbf{C}_{m}(x,y) \tag{19}\]  
The number of Shadow Grids \(R_{m-m}\) in the shadow matrix \(\mathbf{E}_{m-m}\) (rectangular grids whose values were not 0 in the shadow matrix) was counted, and the number of Shadow Grids \(R_{m-m}\) was substituted into Formula (6) to obtain the full shadow area \(S_{f-m}\) of multiple obstacles under multiple light sources.
## 3 Nonlinear Mechanical Properties for the PRC with Interfacial Debonding
### Intrinsic Microstructure Parameters  
#### 3.2.1 PVF Influences on the Mechanical Properties of the PRC  
For investigating the PVF influences on uniaxial tensile properties, the mechanical behaviors of composites with 10% SiC particles were also considered and the results are plotted in Figure 2. The SiC/Al composites with and without the interfacial debonding are both considered in the example. It can be concluded that the mechanical properties of the PRC are closely related to the contact area and interfacial properties. Similar to 15% SiC composites, an obvious difference can be seen when the true strain increases to 3.7%. The stiffness properties tend to decrease when the interfacial debonding is considered. In addition, it is interesting to mention that the PVF effects on stiffness behaviors of the PRC with and without interfacial debonding show different variations. In detail, composites with higher PVF will yield more stiffening behaviors only if the interfacial debonding is ignored. Due to the particles restrained on the matrix, the higher stress can be transmitted to the reinforced particles through the matrix, and the stiffness behaviors can be obviously strengthened. However, a completely different phenomenon can be found when the interfacial debonding is considered. The PVF effects on the stiffness properties show evident regional characteristic. In detail, two sublines A-A' and B-B' are defined as shown in Figure 2. The sublines divided the strain region into three regions. On the left side of the subline A-A' is defined as region I. On the right side of the subline B-B' is defined as region III. The region between subline A-A' and subline B-B' is defined as region II. From Figure 2, it can be concluded that the constitutive behaviors become stiffer with the increasing volume fraction in region I. In contrast, an opposite conclusion can be reached in region III. That is, the increasing PVF tends to soften the stiffness behaviors for the particle-reinforced composites. This is due to the variation of interphase between particles and matrix. In other words, a higher PVF leads to an increase in contact region between the particles and matrix. Once interfacial debonding starts, less stress can be transmitted to SiC particles. Therefore, the Al matrix will support more loading which leads to the decrease of the stiffness properties.
## 3 Results and Discussion
#### Analysis of Transportation Impacts  
As discussed previously, transportation can add important impacts to these possible EoL scenarios [7], but it depends entirely on the location of the EoL modules and their final destination. These impacts can be negligible if the sites for the collection of the PV panels, treatment and disposal are assumed to be in the same area, but, in most cases, the data for transport mode and distance are difficult to assess for the general case [2].  
For example, significant impacts related to the transport of PV waste to the recycling site were calculated for the FRELP process [7]. However, such result applies only to that particular case study; hence, the approach chosen in this study is more general. As the location of the EoL treatment plant was not set in this study, Figures 8-10 show the results of three scenarios: no transport and a distance of 50 km and 100 km to the EoL treatment facility. It is expected that, for the EoL scenarios of landfill and incineration, a maximum distance of 50 km is adequate as these sites are ubiquitous within economic centres. However, a distance of 100 km might be more adequate to represent the travel required to PV recycling centres as these centres are not found regularly. In any case, the information presented below can be scaled to any distance required. For this analysis, only terrestrial transportation (Ecoinvent inventory for \"lorry 16-32 t\") was considered.  
Figure 8: Inclusion of transportation on the final results for the EoL scenarios analysed for Human Health (ReCiPe), considering that the landfill and incineration plants are within 50 km from the collection point in all cases.  
Figure 9: Inclusion of transportation on the final results for the EoL scenarios analysed for Ecosystems (ReCiPe), considering that the landfill and incineration plants are within 50 km from the collection point in all cases.  
The inclusion of transportation increases the overall results for all three ReCiPe environmental categories in an important way, sometimes doubling or tripling the impacts for the recycling EoL scenarios, which is in line with previous study [7]. It is important to highlight that the impacts from the transportation depend not only on the distance from the collection point to the EoL treatment facility, but also on the transportation method.  
The analysis in Figures 8 and 9 shows that a PV recycling plant needs to be at most 80 km further away than the EoL scenarios of landfill and incineration for recycling to have lower impacts, when considering human health and ecosystems.  
Figure 10 shows that the impacts related to ReCiPe resources is strongly influenced by the transportation process in all EoL scenarios analysed. In this case, for the distance of 100 km, the recycling processes are still better environmental options compared with some other choices. However, these results seem to show that it will be important to develop either portable recycling plants or distributed recycling plants that can be located reasonably close to the places where modules reach EoL.
## 3 Results
### Signature Verification Results  
We conducted an additional quantitative evaluation to analyze the impact of generated signatures on a signature verification model. The signature verification model used in the experiment has the same structure as the classifier employed in the auxiliary classification process. We evaluated the model based on the precision, recall, accuracy, and equal error rate (EER). Precision indicates the proportion of predicted original pairs that are actual original pairs, and recall represents the proportion of actual original pairs that were correctly predicted. Accuracy reflects the rate of correctly predicted original and forged pairs. Additionally, we used EER as a key metric to assess the performance of the verification model.  
[PERSON] et al. [11] utilized EER as a metric for detecting forged signatures and evaluating similarity. EER, commonly used in biometric systems, represents the error rate at the point where the false positive rate (FPR) and false negative rate (FNR) are equal. FPR is the rate of incorrectly accepting forged signatures as original, while FNR is the rate of incorrectly rejecting original signatures as forged. The threshold at which FPR equals FNR is denoted as \(\theta_{\text{EER}}\). A lower EER indicates higher accuracy and better distinction between genuine and forged signatures. The calculation of EER is expressed as follows:  
\[\text{EER}=\text{FPR}(\theta_{\text{EER}})=\text{FNR}(\theta_{\text{EER}}) \tag{18}\]  
In our experiments, when the classifier was trained on the same data used for the generation model, it achieved an accuracy of 94.26% and an EER of 0.0570 on the test data. We then retrained the classifier with a larger dataset by adding 12 generated signature images per class, resulting in a total of 21,780 signature pairs with 14,520 new pairs. Including generated signatures in the training data resulted in a classifier accuracy of 94.87% on the test data and an EER of 0.0518, demonstrating a slight improvement in performance (Table 4).  
By adding generated signatures to the original dataset, the accuracy of the model increased from 94.26% to 94.87%, and the EER decreased from 0.0570 to 0.0518. These results indicate that including generated signature data in training helps the verification model in more effectively distinguishing between genuine and forged signatures. The diversity of the training data increased with the inclusion of generated signatures, better allowing the model to generalize to new samples. Additionally, we confirmed that using the signatures generated by the proposed model with an auxiliary classifier resulted in improved verification performance compared to not using the classifier. This suggests that a high-quality signature generation model trained with a classifier can significantly enhance verification performance.
# Experimental Study on Shear Mechanical Properties of Pile-Soil Interface Under Freezing Conditions
## 2 Equipment and Methods  
### Experimental Instruments  
A pile-pressing test system for freezing strength under low-temperature environments is developed, and the structure diagram of the test system is illustrated in Figure 1. The system mainly included four parts: a test bench, temperature control system, loading system, and a data acquisition system. The test bench included a soil container and the test pile. A transparent organic glass barrel with a height of 270 mm and an inner diameter of 640 mm was utilized to contain the soil, and a circular hole (geometric diameter: 44 mm) was drilled at the center of the bottom surface. The test pile fixed in the center of the container was threaded through the hole, with a diameter of 40 mm and a length of 250 mm. A series of micro soil pressure sensors and strain gauges were installed along the pile. Additionally, some soil-temperature sensors were installed in the soil at 5 cm, 10 cm, and 15 cm depth, respectively. At each depth, three soil temperature sensors were positioned at 0 cm, 8 cm, and 16 cm away from the pile side.  
The pile-pressing test was conducted in the low-temperature laboratory where the temperatures were controlled by a constant temperature refrigeration system. The temperatures of the soil around the pile were monitored in real time using thermocouples and a data logger. A reasonable design of a lever-loading system was utilized to apply axial stress. The shear stress on the contact surface was calculated from the strain measured by the strain gauge, and then the shear strength was determined as well.
## 2 Project Overview  
The Shenzhen Metro Line 13 is a north-south urban rail transit line starting at the Shenzhen Bay Port and traversing the Nanshan District and Bao'an District, totaling 22,434 km. The line is between the central development axis and the western development axis of the city, connecting the Hohai Central Urban Area and the Western High-Tech Industrial Park. It serves as a fast connection between these two regions. This study is based on the left-line project from Liuxiandong Station to Baimang Station, focusing on the section from 0 to 650 m in the tunnel, with a total length of approximately 2036 m. This section represents a typical complex geological formation. The Liuxiandong Station's elevation to Baimang Station's section ranges from 24.26 to 42.85 m above ground, with a slightly undulating terrain. A mid-tableland landscape, with localized gully landscapes between tabletlands, characterizes the initial topography. The predominant geological layers in this section include the fourth series of artificial fill (Q4 ml) and fluvial deposits (Q4 al + pl). The fluvial deposits mainly consist of silty clay, plastic fine-grained clay, and sand layers. The existing geological layers exhibit distinct stratified structures above the fill layer. The underlying bedrock consists mainly of mixed granites from the Jixian Formation to the Qingbaikou Formation (Jx-Qby) and biotite granite from the Yanshan period (\(\gamma\beta\)5K1). The tunnel body passes through moderately to slightly weathered rocks as the primary geological formation, followed by residual layers and highly weathered rocks. The tunnel also intersects sporadically with intensely weathered and moderately weathered schists.  
The shield tunneling machine selected for the Liubai section of Shenzhen Metro Line 13 is the \"Xinhe 15\" EPB and single-shield TBM dual-mode shield tunneling machine, designed and manufactured by China Railway Construction Equipment Group. The main configuration parameters are detailed in Table 1. The design concept of this shield tunneling machine integrates the functionalities of both EPB and single-shield TBM, allowing for in-tunnel conversion. The EPB mode is suitable for weak geological conditions, employing the EPB tunneling method to ensure the face support's stability and prevent uneven ground settlement. On the other hand, the single-shield TBM mode applies to complex rock formations, enhancing the tunneling speed in such conditions and avoiding risks associated with slow progress and severe tool wear when using the EPB mode in complex rock formations.
# Studies to Overcome Brain-Computer Interface Challenges
## 2 Studies to Overcome BCI Limitations  
### Arm Movement Prediction  
If BCI users can control the robot arm like their own, they can do various things daily. Therefore, many research groups have attempted to predict arm movement and control the robot arm. It is necessary to identify how brain activities change according to arm movement to predict it. In 1982, [PERSON] discovered that the firing rate of neurons in the primary motor cortex differed according to the direction of arm movement [16]. It means that neurons have a directional preference for action. [PERSON] also found that arm movements could be predicted based on firing patterns of neurons [17]. These research results of [PERSON] became the basis of research to control a robotic arm by predicting arm movements from brain signals. In 2008, a research team at the University of Pittsburgh succeeded in eating marshmallows by controlling a robotic arm in real-time in monkey experiments [18]. A research team from the Brown University and the University of Pittsburgh succeeded in drinking beverages by controlling a robotic arm in real-time with quadriplegic patients in 2012 and 2013, respectively [19; 20]. However, existing BCI methods for predicting arm movement measure brain signals by inserting needle-shaped electrodes into the brain. This invasive method requires surgery and causes damage to brain cells. In addition, the invasive method makes it difficult to measure the signals over time [21].  
To solve these problems, we developed a technology to predict three-dimensional arm movement using magnetoencephalography (MEG) signals without surgery in 2013 [4]. Movements could be estimated with statistically significant and considerably high accuracy (\(p<0.001\), mean \(r>0.7\)) from all nine subjects. We analyzed MEG signals based on time-frequency analysis and extracted features for movement prediction using channel selecting, band-pass filtering (0.5-8 Hz), and downsampling (50 Hz). Current movements were predicted using 200 ms intervals (11 time points) of downsampled MEG signals. Then, x, y, and z velocities of movements were estimated using a multiple linear regression method. MEG signals from \(-60\) to \(-140\) ms were critical and 200-300 ms intervals were sufficient to predict current movements. To our knowledge, there was only one paper predicting three -dimensional arm movements using non-invasive neural signals before our study. However, the study's accuracy was quite low (mean \(r\) = 0.19-0.38) and the prediction results were unreliable because of the experiment paradigm [4, 22]. In our recent study, the prediction accuracy was highly improved using LSTM instead of multiple linear regression [23].
# Unilateral Condylar Hyperplasia in Surgeons' Perspective--A Narrative Review
## 2 Mandibular Asymmetries Differentiation  
An important topic is the differentiation between UCH and any other forms of mandibular asymmetries (MA), which can be related to different scopes of etiological factors, including: trauma, inflammation, TMJ abnormalities, condyle/head anatomical variations, benign/malignant TMJ tumors, cases with syndromic/non-syndromic genetic syndromes, or others. Mandibular asymmetries can be related to various factors. Anatomically, asymmetry can be described as the under- or over-development of one side of the mandible (ramus, angle, body, symphysis, or condyles) from various factors. [PERSON] et al. underline some key factors that might divide MA into developmental (hemi-mandibular hyperplasia (HH), hemimandibular elongation (HE), hemifacial microsomia, Parry-Romberg), pathological (cysts, tumors, idiopathic condylar resorption, infections), traumatic (condylar fracture), and functional once (mandibular displacements, luxations, and others) [3]. Other developmental or genetic-related factors co-existing with mandibular asymmetry might include changes in the eye socket, external/internal ear area, temporomandibular area joint structural morphology, zygomatic and maxillary bones, or facial nerve functioning [4; 5; 6; 7; 8; 9; 10; 17; 18; 19].  
Other classifications of MA with recognition of dental, skeletal, and occlusal asymmetries features include [PERSON] et al.'s studies [7]. The mentioned asymmetry has been divided into dental, skeletal, muscular, functional, and a combination of all. On the other hand, a study by [PERSON] et al. divided MA into congenital, developmental, post-traumatic, and pathology-related categories [8]. Other classifications are also known [9; 10].  
Concerning the temporomandibular joint (TMJ), one of its components is the mandibular condy (MC), along with its head and neck. Its position in the glenoid fossa of the temporal bone is limited anteriorly by the articular eminence. During growth and maturation, appropriate condy growth ensures its adequate shape, size, and position. When some growth disturbances or abnormalities occur, various forms of condylar disfigurement might be present. Growth disturbances in the first trimester might result in condy aplasia, hypoplasia, or related disorders. An understanding of the anatomy of the MC within the glenoid fossa can help in any surgery planning in UCH cases [15; 16; 17; 18; 19; 20].