esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Our focus has been on discussing the possible alternatives in the design space. There are a large number of proposed protocols, but there are still multiple mechanisms that have not been fully investigated. There are at least three areas that should be investigated further, namely, heterogeneous networks, connectionless multicast and preemption.  
* In heterogeneous ad hoc networks, nodes have different transmission ranges, channel capacity and receivers have different QoS requirements. This increases the risk of inconsistent routing tables. Also, performing admission control and resource reservation for different QoS requirements becomes challenging.
* Most of the protocols do not use preemption but utilize periodic admission control. In either case, random choice selects the flows, sources and receivers that are allowed or denied. Multicast packets do not have the same \"value\". The value of a packet decreases as it traverses the distribution tree or mesh. Its highest value is at the top, since the loss will affect the most receivers. At the same time, a tree with many receivers might have a different value than one with few receivers. None of such considerations are part of the preemption schemes evaluated.
* Connectionless multicast routing or source routing (source computes the route and includes identification of relay nodes in the packet) has only been included in a few protocols. In particular, when there are only a few receivers, and the groups have a limited lifetime, the overhead of maintaining state at all relay nodes might not be optimal.  
In the previous section we discussed the evaluation of the proposed protocols. As other authors have clearly identified [53], there is need for a stricter documentation and more common evaluation scenarios. These conclusions also are valid for the subgroup of proposals we have analyzed. QoS in multicast is needed in media distribution, and in an ad hoc network this cannot only be associated with streaming. The protocols should therefore also be evaluated for a larger set of groups and sources. The traffic models should also reflect a wider set of types and background traffic. The latter is important, for it is unlikely, that ad hoc networks are dedicated to only multicast.  
A possible lack in most of the proposals is the application viewpoint. For example, few of them discuss the implication of admission control and the application. For some of the proposals, one could risk that participants in video conferences did not have a full mesh view of all the other participants. We believe that future proposals should take an application viewpoint when designing admission control and preemption mechanisms.
## 3 Modified and Simplified T-Type Structure Designed for Low Attenuation Units
### Analysis of the Mechanism Contributing to the Phase Error
In addition, it is worth mentioning that the values of the transistor's parasitic parameters in the above equations are variable in the on/off state, leading to quite  
Figure 3: Typical schematics of the simplified T-type unit: (**a**) small-signal model; (**b**) equivalent circuit for reference mode with series \(R\)Cs; and (**c**) equivalent circuit for attenuation mode with parallel \(R\)Cs. different behavior. From the comparison of Equations (3) and (5), it can be found that there are opposite phase responses in the reference and attenuation modes, as previously mentioned. Moreover, the phase error will worsen with increasing frequency, severely limiting the application in high-frequency broadband circuits. In other words, an important conclusion is revealed that the effect of inductance \(L_{on}\) needs to be minimized or eliminated to optimize the phase error.
# Stability Analysis of Jinchuan Hydropower Station Hydraulic Tunnels during Excavation and Unloading
## 4 Numerical Modeling of the Excavation of Adjacent Tunnels
### Comparison between Numerical Modeling and Site Surveys  
The site surveys show that several instability problems with the surrounding rock occurred in the spillway tunnel and the diversion tunnel during excavation, which agrees well with the deformation zones of the surrounding rock obtained from numerical modeling. For a comparative analysis, two typical zones were selected, as shown in Figures 16 and 17.  
Figure 16 shows the intersection of the #2 edit and the diversion tunnel at the outlet of the diversion tunnel. Stress concentration in many parts of this zone due to excavation and unloading caused large deformation, and tension cracks were observed at the intersection of the upper adit and the main tunnel and the intersection of the lower adit and the main tunnel, as shown in Figure 16a,b. The V-shaped failure shown in Figure 16c was caused by stress concentration at the right angle of the horseshoe-shaped cross-section of the main tunnel. According to site surveys, no regional weak structural planes were discovered, indicating that stress failure brought on by regional stress concentration rather than weak structural planes predominated in this zone. When such macroscopic deformation is discovered, the contractor should act quickly to limit it. For example, they should shorten the gap between steel arches and use more dense rock bolts for bolting. Figure 17 shows the cross-section at stake 0 + 450 m in the spillway tunnel, which is the control area of weak structural planes f31 and f27. The surrounding rock in this zone failed as a result of the penetration of the fault into the spillway tunnel and the diversion tunnel, which was dominated by structural failure due to the fault. Figure 17a shows the shear failure that  
Figure 16: Comparison between numerical modeling results and site failure at the intersection of the diversion tunnel.  
occurred along the structural plane at stake 0 + 443 m in the spandrel of the spillway tunnel, and Figure 17b shows the block fall that occurred from the side wall to the spandrel of the diversion tunnel (stake 0 + 590 m in the diversion tunnel), which posed a serious threat to the stability of the surrounding rock and construction safety. The comparison between the numerical modeling and site surveys shown in Figures 16 and 17 indicates that the large deformation zone in the numerical modeling matched the actual failure. Therefore, the numerical modeling method can more accurately reflect the mechanical response of the surrounding rock during the excavation and unloading process. It can also be used to study the interaction mechanisms of adjacent tunnels when multiple tunnels are excavated simultaneously and act as a reference for construction design and subsequent excavation.
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 4 Conclusions  
In this work, we report a fast point cloud registration method incorporating RGB image information to solve the problems of strong dependence on the initial position of the point cloud, poor robustness, and low timeliness of ICP. In the proposed method, the SIFT algorithm is used to extract the feature points of point cloud corresponding to the RGB image and to perform feature point matching with bidirectional KNN. The RANSAC algorithm is used to reject mismatched points and calculate the initial transformation matrix. After applying the pass-through filter for noise reduction and transiting down the voxel grid, the point cloud is subjected to rotation and translation transformation for initial registration. On the basis of the initial registration, the FR-ICP algorithm is utilized for achieving accurate registration. The results, analyzed by registration experiments, showed that our method can solve the problem that point cloud registration easily falls into local optimum, and has advantages in speed and accuracy compared with traditional methods. This work relied on the visible camera of laser equipment to provide some reference for the point cloud acquired by such equipment in the registration work. Nevertheless, it is worth noting that the SIFT algorithm takes some time in extracting feature points. Meanwhile the RANSAC algorithm still suffers from some instability in rejecting incorrect feature point pairs, and only most of the incorrect point pairs could be rejected in our work. Some improved versions of RANSAC, such as some probabilistic versions of RANSACs [31], are available for use in subsequent research work. All these potential problems above will be the direction of further research in the future.  
Conceptualization, Z.Z. and J.J.; project administration, Y.Z.; supervision, D.C., Z.Z. and H.Z.; validation, H.Z.; writing--original draft, H.C.; writing--review and editing, H.C. and D.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (No. 62171327, No. 62171328), the General Project of Hubei Natural Science Foundation (No. 2022 CFB529), the Science and Technology Research Project of Hubei Education Department (No. B2022056), the Science Research Project of Wuhan Institute of Technology (No. K2021083), and the Graduate Innovative Fund of Wuhan Institute of Technology (No. CX2022344).  
Not applicable.  
The authors can provide the raw data of this work, upon reasonable request.  
The authors declare no conflict of interest.
## 7 Discussions  
By identifying and processing effective microseismic signals from a large number of original microseismic signals, we confirmed the good effect of the VMD and GA-SVM-based method for the identification of effective microseismic signals. Compared with traditional identification methods, the algorithm proposed in this paper has better adaptivity and robustness, and can adaptively distinguish between effective microseismic signals and noise signals. Moreover, it is able to suppress the noise in effective microseismic signals, thus helping obtain the microseismic signals with a higher signal-to-noise ratio and better quality. Therefore, the algorithm is practical in judging the effectiveness of microseismic signals, which provides a new idea for the effective discrimination of microseismic signals [21; 22; 23; 24; 25; 26].  
Despite its many advantages over similar methods, the algorithm still has many problems to be solved. It involves machine learning-based result classification and prediction and requires the introduction of abundant effective microseismic signals (including effective signals and noise) into the GA-SVM model for training. This means that a certain preliminary work is needed to obtain accurate prediction results. In addition, a special case was found during the study: IMFs of some effective microseismic signals were recognized as ineffective components after the VMD processing, resulting in the identification of such signals as ineffective signals. But this was a rare case and did not greatly influence the accuracy rate of the algorithm. Hence, no further improvements were made for this case. In the future, we may carry out related research to address this issue.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Data Quantity** & **Number of Effective Signals** & **Number of Noise Signals** & **Accuracy** & **Recall** \\ \hline Actual engineering data & 240 & 135 & 75 & 98.75\% & 97.78\% \\ Algorithmic processing result & 240 & 132 & 78 & 98.75\% & 97.78\% \\ \hline \end{tabular}
\end{table}
Table 2: The accuracy rate and recall rate of effective signal recognition of the algorithm.  
Figure 11: The algorithm’s processing result of actual engineering data.
## 2 Materials and Methods
### Model Development
#### 2.5.1 Modeling Strategy and Algorithm Selection  
To address the challenge of data scarcity, the modeling strategy focused on interpretable and structurally simple regression algorithms capable of generalizing from small datasets. More complex models, such as ensemble methods or neural networks, were deliberately excluded for two main reasons. First, given the limited size of the dataset, their use would increase the risk of overfitting and reduce the model's ability to generalize. Second, since the proposed pipeline is intended for early-stage adoption by maintenance teams with limited expertise in Machine Learning, it is essential to prioritize models that are inherently interpretable, avoiding the opacity and complexity of black-box approaches [35; 36; 37; 38]. To implement the selected models, the training pipeline was developed using Scikit-learn, an open-source Python library widely used for machine learning tasks. Its transparency and extensive documentation make it particularly suitable for building robust and reproducible predictive workflows in real-world applications. For each model, a grid search was performed to select the best-performing hyperparameters based on the validation criteria established for this study.  
The following four algorithms were selected based on their alignment with the project's constraints and goals:  
* Linear Regression: A baseline model that assumes a linear relationship between input variables and the target. It is simple to interpret and computationally efficient but lacks flexibility for capturing non-linear dependencies.  
* Ridge Regression: An extension of Linear Regression that includes L2 regularization to mitigate overfitting. It performs better than standard Linear Regression when multicollinearity is present but still struggles with non-linear patterns.
* Decision Tree: A non-parametric model that splits data based on feature thresholds. It captures non-linear relationships well and is easy to interpret but is prone to overfitting.
* K-Nearest Neighbors (KNN): A simple, instance-based learner that makes predictions based on the average of the closest training samples. While it lacks traditional interpretability (e.g., coefficients or rules), its conceptual simplicity and local behavior make it useful for benchmarking in low-data contexts. However, it is sensitive to feature scaling and may become computationally inefficient when applied to large datasets.  
To ensure that all input features were on a comparable scale, particularly for models sensitive to feature magnitude, such as KNN, Linear Regression, and Ridge Regression, the RobustScaler method was applied during preprocessing. This approach also mitigates the influence of outliers while preserving consistency across all algorithms.
## 4 Understanding Centralised Identity Systems
### LDAP: The Directory-Based Approach  
Lightweight Directory Access Protocol (LDAP), initially created by the University of Michigan, is a simplified network protocol designed to access and manage directory information. LDAP's operational foundation is based on a client-server model, where the client requests resources and the server serves those requests. The protocol achieves efficiency by transmitting only necessary data, bypassing many of the complex mechanisms inherent in the X.500 Directory Access Protocol (DAP). As a result, it has become essential for managing user data and network resources, particularly in environments where directory services must operate effectively across a range of devices, including those with limited processing capabilities [21; 22].  
LDAP is a directory-based identification service used in systems such as Microsoft Active Directory (AD), owing to its responsiveness and capacity to manage network resources. Its hierarchical structure allows quick data retrieval, authentication, and user authorisation [23]. Consistency in LDAP is achieved by using LDAP commands to manage AD objects, such as creating and changing user and computer records. Furthermore, LDAP directories are designed to store user email addresses and distribution lists in corporate email systems, simplifying email routing and improving organizational collaboration [24].
# On the Optimal Calculation of the Rice Coding Parameter
## 1 Introduction
### How the Rice Parameter Affects the Compression Ratio  
In Wireless Sensor Networks, transmitted frames do not encode a single measurement value. Instead, in order to minimise the overhead caused by headers of telecommunication protocols, a sequence of measurements is stored, coded, and finally transmitted by the device in a single frame. We initially assume in this article that such a sequence is encoded using a single Rice parameter.  
The data sequence presented in Table 1 is used to illustrate the effects of choosing the Rice parameter, \(r\), on the total amount of bits encoded. Figure 1 shows the the total amount of bits encoded for this sequence for different values of the Rice parameter. It can be observed that the total encoding length equals 76 bits when \(r=2\). If \(r=3\), the total encoding length decreases to 64 bits. If \(r=4\), the total encoding length increases again to 65 bits. Therefore, the optimal parameter \(r\) for this dataset is \(r^{*}=3\).  
Table 1 will be used in further sections to provide examples of the proposed heuristics, algorithms, and bounds throughout this article.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(i\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \(n_{i}\) & 5 & 7 & 4 & 4 & 12 & 15 & 11 & 45 & 54 & 1 \\ \(L(n_{i})\) & 3 & 3 & 3 & 3 & 4 & 4 & 4 & 6 & 6 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example input dataset (\(n\)) with \(N=10\) elements. \(L(n_{i})\) represents the minimum number of bits needed to encode \(n_{i}\), i.e., \(L(n_{i})=\lfloor\log_{2}n_{i}\rfloor+1\) using standard binary representation (also known as beta codes).  
Figure 1: Number of bits used to encode randomly generated data by using different Rice parameters \(r\in[0,6]\) for the input data sequence presented in Table 1. The total bit-length function \(f(r)\) is only defined for integer values of \(r\) (marked with dots and squares in the figure). A continuous line was added between the points to improve visibility.
# AFM Characterization of Track-Etched Membranes:
## 2 Samples and Experimental Technique  
The objects of our study were TMs obtained with the following procedure. Polymer films \"Torayfan\", T2372 (Toray, Japan) with a thickness of 10 \(\upmu\)m were irradiated with accelerated Xe ions with energy of 125 MeV using the U-300 accelerator at the Flerov Laboratory of Nuclear Reactions of the Joint Institute for Nuclear Research (JINR, Dubna, Russia). Next, the films were treated with a solution of chromic anhydride (250 g/L) in 40% sulfuric acid at a temperature of 80 \({}^{\circ}\)C to form pores of the required diameter. The following samples were taken for the study: polycarbonate (PC) with pores of 400 nm in diameter, polyethylene terephthalate (PETP) with pores of 1 \(\upmu\)m, and polypropylene (PP) with pores of 240 nm (pore sizes were indicated by the manufacturer). These samples were synthesized by Prof. [PERSON] (JINR) and kindly presented in our joint work previously [15].  
The atomic force microscope NTEGRA PRIMA (NT-MDT) was used to obtain topography maps of the samples. This microscope makes it possible to obtain images at different scales depending on which auxiliary equipment is used. It is equipped with an optical viewing system, as well as with the isolated table TableStable TS-150 which provides active vibration protection (within the range from 0.7 to 1000 Hz). For accurate measurements, we used the SMENA SFC050 PRO measuring head, which allows obtaining topography maps with an extremely low noise level in \(Z\) coordinate (RMS = 0.05 nm) within the area of 100 \(\times\) 100 \(\upmu\)m. Before measuring each sample, a calibration was performed using the TGZ-2 periodic structure.  
Silicon cantilevers NSG-10 (NT-MDT) with a typical radius of curvature of 6-10 nm were used as a probe. To obtain a topography map of the samples, all measurements were carried out in the semicontact (tapping) mode, in which the cantilever tip slightly touches the sample surface at the lowest point of its oscillations. This mode was used to minimize the deformation of the sample due to the friction of the cantilever on the sample surface during scanning, since the needle only \"tapped\" the sample.
## 1 Introduction
### Outline of the Paper  
This paper is structured to methodically explore the integration of artificial intelligence into cross-cultural language learning, culminating in the development and evaluation of the CILS. The layout of the paper is as follows:  
Section 1: Introduction--this section sets the academic and practical context for the research, outlining the necessity and urgency of integrating AI into language learning for cross-cultural communication.  
Section 2: Literature Review--a systematic review of existing research in language learning, AI in education, and cross-cultural communication, situating the current study within the broader field of educational technology.  
Section 3: Theoretical Framework--this section discusses the foundational theories underpinning the CILS, detailing its design and functionality based on established language learning theories and AI technologies.  
Section 4: CILS Framework--this section describes the proposed system's technical specifications and functionalities, highlighting its adaptive capabilities and cultural sensitivity.  
Section 5: Case Studies--this section presents empirical research validating the effectiveness of the CILS through various case studies, illustrating its application and impact on real-world educational settings.  
Section 6: Discussion--this section synthesizes the findings, discussing the implications for future research and the potential and limitations of AI in education.  
Section 7: Conclusions--this section recapitulates this study's major findings and contributions, offering recommendations for future work and summarizing the significance of AI in enhancing cross-cultural communication through language learning.  
This structured approach ensures the research is presented clearly and logically, facilitating a deep understanding of how AI can transform language learning in culturally diverse contexts.