esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Rasch Modeling and Item Characterization  
Item Response Theory (IRT) modeling was conducted using a one-parameter logistic (1 PL) Rasch model, implemented in the eRm package in R (v4.3.2). The Rasch model was selected for its desirable psychometric properties, including sample-independent item calibration, interval-scale measurement, and robustness under small-to-moderate sample sizes [20; 21]. The Rasch framework assumes that all items measure the same latent trait--in this case, theoretical competence in anesthesiology--and that they do so with equal discrimination [6]. Item difficulty parameters (\(\beta\)) were estimated from human response data and then fixed, enabling the maximum likelihood estimation (MLE) of latent ability scores (\(\theta\)) for each LLM on the same psychometric scale.  
In addition to the Rasch difficulty estimates, each item was further characterized using the Discrimination Index (DI), defined as the difference in correct response rates between the top and bottom quartiles of human performers. Although a Classical Test Theory (CTT) measure, DI remains widely used for its interpretability and complementary value in assessing item quality [7]. Items were categorized into three difficulty groups (easy: \(\beta<0.3\); moderate: \(0.3\leq\beta<0.8\); hard: \(\beta\geq 0.8\)) and four discrimination groups (poor:DI < 0; marginal: 0 \(\leq\) DI < 0.2; good: 0.2 \(\leq\) DI < 0.4; excellent: DI \(\geq\) 0.4), enabling a stratified analysis of model behavior across psychometric categories [21; 22].  
To evaluate the psychometric validity and Rasch model assumptions, we conducted three core fit diagnostics. [PERSON]'s Likelihood Ratio Test assessed item parameter invariance across examinee subgroups [23], the Martin-Lof Test confirmed the unidimensionality of the item set [24; 25], and the Wald Test [26; 27] verified the item-level model fit. These tests provided rigorous statistical confirmation that the dataset satisfied the Rasch modeling assumptions, allowing for defensible ability estimation and model comparison.
## 4 Comprehensive Performance Evaluation
### Outdoor Environment
#### 4.2.2 Mapping
In outdoor scenes, while LiDAR SLAM provides a more comprehensive environmental structure and outline, it lacks the capability to recognize local objects and features. This limitation, similar to that in an indoor environment, arises from the scanning nature of LiDAR, where point clouds on the same plane exhibit no height differences, making it challenging to distinguish features between each point cloud. Consequently, LiDAR SLAM cannot recognize objects as effectively as Visual SLAM, which utilizes complete color and feature shapes for object identification. The third aspect is about defects and blind spots in local maps. In the mapping analysis of Visual SLAM, we observed the issue of multiple image overlaps caused by vibrations. Additionally, as in indoor mapping, the limited field of view (FOV) results in some local environments that cannot be captured, creating blind spots in the mapping results. However, in LiDAR SLAM, the blind spot issues are also eliminated by the usage of a 360-degree 3D LiDAR for the reconstruction. Moreover, the SC-LeGO-LOAM algorithm used in LiDAR SLAM leverages feature set matching between timestamps for pose estimation and its detection range is broader. Therefore, the impact of vibrations on mapping effectiveness is milder in LiDAR SLAM, with no apparent ghosting phenomena.
# Extracellular Matrix Tunes the Regenerative Potential of Fetal Stem Cells
###### Abstract  
Adult mesenchymal stem cells (MSCs) are a promising cell source for tissue regeneration. However, ex vivo expansion results in cell senescence; cells lose their proliferation and differentiation capacity. Fetal MSCs can offer an alternative due to their robust proliferation and differentiation capacities, as well as their immune privilege properties. Given the rejuvenation effect of the decellularized extracellular matrix (dECM) on adult MSCs, it remains unknown whether dECM influences the regenerative potential of fetal stem cells. In this study, passage five fetal nucleus pulposus cells (fNPCs) and fetal synovium-derived stem cells (fSDSCs) were expanded on dECMs deposited by fNPCs (NECM) and fSDSCs (SECM) for one passage, with expansion on tissue culture plastic (Plastic) as a control. We found that dECM-expanded fNPCs and fSDSCs exhibited both similarities and differences in the expression of stemness genes and surface markers. Expanded fNPCs yielded more differentiated pellets after chondrogenic induction but exhibited no adipogenic differentiation following adipogenic induction in both the Plastic and dECM groups than the corresponding fSDSC group. Despite a significant increase in fNPCs, the dECM-expanded fSDSCs exhibited no increase in chondrogenic potential; however, compared to the Plastic group, dECM-expanded fSDSCs exhibited a small increase in osteogenic potential and a great increase in adipogenic potential. These results suggest that fNPCs are more sensitive to NECM rejuvenation for cartilage tissue engineering and regeneration; in contrast, the dECMs exhibited limited effects on fSDSC rejuvenation in a chondrogenic capacity, except for enhanced adipogenic capacity following expansion on SECM.  
Keywords: fetal stem cell; decellularized extracellular matrix; differentiation; fetal nucleus pulposus cell; fetal synovium-derived stem cell +
Footnote †: journal: Applied Sciences
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 3 Calculation of Hoop Strain Time-History Curve
### Simulation Study  
The above-mentioned MOC is employed in the following pipeline leakage simulation. The parameters of the pipeline are as follow: length of pipeline \(L\) = 55 km, steady mass flow rate \(Q\) = 0.0319 m\({}^{3}\)/s, acceleration of gravity \(g\) = 9.81 m/s\({}^{2}\), NPW velocity \(a\) = 1100 m/s, hydraulic friction coefficient \(f\) = 0.015, inlet pressure  
Figure 4: Difference grid for the method of characteristics (MOC).  
\(P_{m}\) = 1717 kPa, outlet pressure \(P_{out}\) = 1570 kPa, pipe diameter \(D\) = 341 mm, pipe thickness \(t\) = 8 mm, Young's modulus \(E\) = 210 GPa. The pipeline is manually divided into 50 sections; thus, the length of each section \(\Delta x\) = 1.1 km and time step \(\Delta t\) = 1 s.  
All of the grid nodes (except inlet and outlet nodes) are the potential leak points. For each leakage condition, the hoop strain variation of any node along the pipeline can be calculated by the MOC method. Several FBG hoop strain sensors are assumed to be installed along the pipeline; thus, hoop strain variation of any node can be extracted. As an example, three leakages (5.5 km, 27.5 km, 49.5 km away from the inlet, respectively) are simulated to get the hoop strain variations at the outlet node during 1000 s after leak occurrence. As shown in Figure 5, the oscillation of hoop strain due to the impact of high speed flowing fluid with the boundary condition can be observed. A sudden drop occurs in the first few seconds and finally reaches a smaller value than the initial hoop strain. It is noticed that due to the damping of transient events, the hoop strain variation is greater when the leak point is closer to the downstream of the pipeline.  
Generally, a different leak location induces a different hoop strain variation. Therefore, potential relationships can be established between hoop strain variations and leak locations. In other words, the leak point can be located from some crucial hoop strain characteristics. In the following part, the machine learning method is employed to predict leak location based on hoop strain information.
## 3 Results and Discussions  
### Mechanical Property Test Results  
#### 3.1.1 Consolidation Test Results  
The void ratio changes of the clean sand and fibre-reinforced sand under compression and unloading tests are shown in Figure 5. For the same fibre length, the compressibility decreases with the higher fibre content. Notably, the effect of fibre length dominated that of fibre content. With the same fibre content, the compressibility of the sample with 12 mm-long fibres is lower than that of the sample with 6 mm-long fibres. Hence, both fibre length and fibre content affect the compressibility of soil. In contrast, an increase in fibre length and content increases the initial porosity ratio of the samples, resulting in reduced compressibility. Nevertheless, they improve the ability of soil to resist deformation. To further analyse the influence of fibre length and content on the soil quantita  
Figure 4: Unsaturated soil strain control osmometer.  
tively, the compression modulus (\(E_{s}\)) and compression index (\(C_{c}\)) are calculated by the following equations:  
\[E_{s}=\frac{1000(p_{i+1}-p_{i})(1+e_{i})}{(S_{i+1}-S_{i})(1+e_{0})} \tag{2}\]  
\[C_{c}=\frac{e_{i}-e_{i+1}}{lgp_{i+1}-lgp_{i}} \tag{3}\]  
where \(e_{i}\) is the porosity ratio of each step, which can be calculated as:  
\[e_{i}=e_{0}-\frac{1+e_{0}}{h_{0}}\Delta h_{i} \tag{4}\]  
where \(e_{0}\) is the initial porosity ratio; \(p_{i}\) is the load value; \(S_{i}\) is the unit settlement; \(h_{0}\) is the initial height; and \(\Delta h_{i}\) is the deformation of the sample under a certain load level. Table 4 summarises the values of \(E_{s}\), \(C_{c}\), and \(C_{s}\). Fibre length and content affect the volumetric compression parameters of soil. Their most considerable influence is observed in PP-12 mm-0.5%, in which \(E_{s}\) increases by 106% and \(C_{c}\) decreases by 51% when compared with those of clean sand. With the increase in fibre content and length, \(E_{s}\) increases while \(C_{c}\) decreases, respectively. The results indicate that fibre addition can improve deformation resistance, increase the initial void ratio, reduce compressibility, enhance integrity, and increase the strength of sand. The friction force and inconsistent deformation of fibres in different directions during the compression process pull the fibres, producing interfacial forces. The magnitude of the interfacial force depends on interfacial friction and adhesion [49]. The force between the fibres and soil particles limits the relative sliding of the soil during the compression process, further reducing soil compressibility.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fibre Length (mm)** & **Fibre Content (\%)** & \(E_{s}\) **(MPa–1)** & \(C_{c}\) & \(C_{s}\) \\ \hline / & / & 8.360 & 0.078 & 0.0071 \\
6 & 0.25 & 12.22 & 0.0534 & 0.0042 \\
6 & 0.50 & 14.59 & 0.0449 & 0.003 \\
12 & 0.25 & 15.06 & 0.0438 & 0.0021 \\
12 & 0.50 & 1
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 1 Introduction
The literature [12] proposes a task allocation mechanism based on the cooperation of edge computing nodes and solves the dual edge node cooperative task allocation problem using an improved particle swarm optimization algorithm to minimize the average task completion latency. The literature [13] establishes a task cloud-side collaboration mechanism based on microservice architecture. The tasks are modeled with directed acyclic graphs, and the scheduling process considers two offloading methods, edge-cloud offloading and edge-side offloading, to minimize the system offloading cost to determine the optimal offloading method. However, the existing scheduling strategies do not consider the influence of power task characteristics and container state on scheduling decisions under microservice architecture, and focus on scheduling tasks in an offline manner, lacking research on multi-task online scheduling. In this paper, we propose an online scheduling strategy for collaborative cloud-side scheduling of distribution station area tasks under a microservice architecture. The tasks are modeled using directed acyclic graphs. The scheduling strategy considers the differentiated characteristics and container states of power tasks, and is divided into two parts, priority strategy and task assignment strategy, and realizes real-time scheduling of multiple tasks in different environments by an improved online algorithm. The structure of the paper is as follows: Section 1 introduces the research background; Section 2 analyzes the application scenario and task characteristics; Section 3 establishes the system model based on container technology and microservice architecture; Section 4 performs the analysis of constraints and algorithm design; and Section 5 performs simulation verification.
## 4 Experiments and Results Analysis
### Performance Comparison of Different Deep Learning Models
#### 4.3.2 Establishment and Optimization of Deep Learning Models  
For LSTM-DNN models, we first determined the search space for three key hyperparameters:  
* LSTM units: [64; 128; 256; 512];
* LSTM layers: [1; 2];
* Fully connected layers: [2; 4].  
For each hyperparameter combination, we trained 5 model instances, conducting 80 experiments in total, then determined the optimal configuration LSTM-DNN (64,1,2) through a three-step screening process. During this optimization process, we discovered the following important phenomena:  
1. As the number of LSTM units increases (especially 256 and 512), models are more prone to non-convergence issues;
2. When the number of fully connected layers increases to 4, model stability decreases;
3. Models with 1 LSTM layer have better stability compared to those with 2 layers;
4. Smaller network configurations (such as 64 LSTM units) often perform better and are more stable than large networks.  
For DNN models, we tested multiple configurations including different numbers of fully connected layers and neurons per layer. For convenience, we used DNN64_2 to represent a DNN model with 2 fully connected layers (64 neurons per layer), and so on. Similar to LSTM-DNN models, we also observed that smaller DNN configurations tend to be more stable than large networks.
## 3 Numerical Simulation  
### Numerical Model  
The numerical study is based on the ABAQUS software. Figure 7a shows the FE model of the SCS. The concrete is modeled with C3D8 solid elements. The SSC, the test head, the stay ring and the flange are modeled with S4 shell elements. The tie rods and the inner supporting rods are modeled with T3D2 truss elements. The typical element edge length is 0.2-0.8 m.  
Figure 7: FE model of the SCS: (**a**) global model of the numerical simulation; (**b**) surfaces of contact pairs.  
The role of the inner supporting rods is to help maintain the original shape of the SSC in the construction period, and it is fixed to the internal surface of the SSC. Accordingly, the inner supporting rods are connected to the SSC with shared nodes. The role of the tie rods is to prevent the SSC from uplift in the construction period, and it is fixed to the external surface of the SSC. Therefore, the tie rods are connected to the SSC with shared nodes.  
Figure 7b shows the surfaces of contact pairs. Thirteen sets of contact pairs are defined at the interfaces between the SSC and concrete structures. The surfaces of pedestals are defined as slave surfaces of different surface sets, and the surface of the SSC is defined as a shared master surface of all surface sets. The sliding formulation is defined as small sliding. The classical coulomb friction model is adopted to simulate the interaction between the SSC and concrete structures. The friction coefficient is defined as 0.25 with overall consideration of the authors' previous work [17], as well as the research of [PERSON] and [PERSON] [19], [PERSON] and [PERSON] [20], [PERSON] [21], [PERSON] [22] and [PERSON] [23].
# Multi-Source HRRP Target Fusion Recognition Based on Time-Step Correlation
## 2 Proposed Model  
### Multi-Source Feature Fusion Recognition Network Based on Time-Step Correlation  
A Time-step Correlation-based Feature Fusion (TCFF) model is proposed and combined with the traditional neural network model in this paper. The model uses dual-channel input, and the obtained different HRRP is pre-processed and input into the same feature extraction network to extract deep and easily distinguishable features. According to the correlation between different time steps of the HRRP, the features under different time steps are weighted, and the strong correlation is given higher weights to make better use of the useful information of the target and avoid the adverse effects of redundant information on target recognition. The model structure diagram is shown in Figure 1.
# Injection Molding of Biodegradable Deciduous Teeth Dental Post
## 3 Results and Discussion
### Characterization of PLA/PCL/ACP Material
#### 3.1.4 Biocompatibility Assessment Results  
The cell survival test results, shown in Figure 7, indicate that the cell viability of the group treated with extracts from the soaked deciduous tooth root canal posts (test group) exceeded 70%, confirming that the material does not induce cytotoxic reactions. This finding aligns with previous studies on amorphous calcium phosphate (ACP) particles and ACP-based composites, which have demonstrated excellent biocompatibility in cell viability assays and are widely used in medical applications [36].  
Research has shown that ACP exhibits superior bioactivity and osteoblast adhesion compared to hydroxyapatite (HAp), making it a promising material for biomedical use [20]. [PERSON] et al. [20] investigated ACP-modified PLA scaffolds and demonstrated that ACP enhances mineralization and bone defect repair, further supporting its suitability for dental applications.  
Furthermore, [PERSON] et al. [37] explored the green synthesis of ACP nanoparticles, assessing their cytotoxicity and antimicrobial properties. Their findings revealed that ACP nanoparticles exhibited no cytotoxicity in human epidermoid larynx carcinoma cells (HEp-2) and demonstrated antimicrobial activity against _Streptococcus mutans_ (_S. mutans_) and _Enterococcus faecalis_ (_E. faecalis_), indicating strong biocompatibility and potential for dental applications. These results align with the present study, confirming that PLA/PCL/ACP composites are safe for use in deciduous tooth root canal posts, while [PERSON] et al. [37] confirmed the cytocompatibility and antimicrobial activity of ACP nanoparticles. Furthermore, the absence of cytotoxic effects observed in our composite supports its safe application in pediatric dental post systems. Although a positive control was not included in this assay, the cell viability results align with ISO-defined criteria, supporting the composite's non-cytotoxic classification.  
Figure 7: Cell viability of NIH/3T3 fibroblasts after 24 h exposure to the extract of PLA/PCL/ACP dental posts, prepared by 72 h extraction in DMEM at 37 \({}^{\circ}\)C. The MTT assay showed 98.32% viability compared to the negative control.
_Article_  
**Crystal Plasticity Finite Element Method for Slip Systems Evolution Analysis of \(\alpha\)/\(\beta\) Duplex Titanium Alloys during Quasi-Static Tensile Testing**  
**[PERSON] 1,2,3, [PERSON] 1,2,3,*, [PERSON] 1,2, [PERSON] 1,2,3 and [PERSON] 1,2,3**  
1 School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China; [EMAIL_ADDRESS] (Y.Q.); [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (D.W.); [EMAIL_ADDRESS] (Y.Z.)  
2 National Key Laboratory of Science and Technology on Materials under Shock and Impact, Beijing 100081, China  
3 Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, China  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +86-10-68911144-863; Fax: +86-10-68911144-866  
Received: 16 August 2020; Accepted: 29 October 2020; Published: 3 November 2020  
**Abstract:** The crystal plasticity finite element method, modeled on a realistic microstructure image, was developed to investigate the evolution of slip systems in grains of \(\alpha\)/\(\beta\) titanium alloys during quasi-static tensile testing. By analyzing the data of slip evolution of simulation during the overall plastic deformation process, it was found that the prismatic slip systems in the \(\alpha\) phase and the [112] \(<\)111\(>\) slip systems in the \(\beta\) phase played a leading role. By calculating the Schmid factors, it was found that the values calculated from the local stress, which was represented by major principal stress, were larger than the values calculated from the primary uniaxial tensile direction, which was due to the deviation of the local stress direction from the primary uniaxial tensile direction. Furthermore, the deviation of local stress of \(\alpha\) phase was different from that of \(\beta\) phase, which was related to the deformation mechanism. During the deformation, the stress and strain were concentrated in the grains of the \(\alpha\) phase, producing a driving effect on the neighboring grains of the \(\beta\) phase. Subsequently, the incompatible deformation produced the concentration of strain at the grain/interpase boundary, thus strengthening the grain interactions and leading to the deviation.  
**Keywords:** Schmid factors; crystal plasticity finite elements simulation method; \(\alpha\)/\(\beta\) duplex titanium alloys; slip systems
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 3 System Model: Instantiating the vBBU within the Linux Kernel
### Enhancing RT Performance for vBBU Processes with CPU Affinity
By pinning each of these queues to a CPU, the driver registers a Hard-IRQ per queue. An incoming packet is copied to one of these RX-queues according to the traffic flow to which it belongs. Then, the driver raises the corresponding RX-queue's Hard-IRQ or Soft-IRQ to process the packet following the Hard-IRQ or NAPI mechanism. As demonstrated empirically in [54], assigning a specific CPU affinity to NIC's RX/TX queue Hard-IRQs has been shown to effectively reduce packet processing latency in the Linux RT-Kernel. Building on this insight, we implement CPU affinity allocation to the RX/TX queue Hard-IRQs associated with the NIC port utilized by the vBBU as part of the Backhaul in the network scenario illustrated in Figure 1. Let \(Q=\{q_{1},\ldots,q_{7}\}\) represent the set of RX/TX queue Hard-IRQs defined by the NIC's controller, with each of these RX/TX queue Hard-IRQs allocated the CPU affinity \(C_{q_{i}}=C7,\ \forall q_{i}\in Q\). The rationale behind allocating CPU affinity to the NIC's RX/TX queue Hard-IRQs on CPU C7 is twofold. Firstly, this aligns with the CPU affinity allocation to the non-RT thread TASK_GTPV1_U (\(\tau_{5}\)) specified in the TA scenario. This strategic alignment not only reduces packet processing latency in the RT-Kernel by pinning the NIC's RX/TX queue Hard-IRQs to a specific CPU but also optimizes the processing of packets consumed by the user-space process [63]. Essentially, \(\tau_{5}\) represents the vBBU's thread responsible for consuming Backhaul packets. To evaluate the impact of CPU affinity allocation to the NIC on vBBU performance, the IA CPU allocation is integrated with the vBBU CPU affinity allocation in two scenarios: IA-WA, which combines the CPU affinity allocation to the NIC as in IA with the CPU affinity allocation to the vBBU according to WA; and IA-TA, which merges the CPU affinity allocation to the NIC as in IA with the CPU affinity allocation to the vBBU according to TA.