esa-sceva/satcom-chunk-collection huggingface dataset:
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### Review Methodology  
To ensure transparency, rigor, and reproducibility in the construction of this survey, the methodology for selecting and analyzing relevant studies was designed in accordance with the PRISMA 2020 guidelines [41]. These protocols provide a standardized framework for conducting systematic reviews and reporting search strategies.  
The literature search was carried out across four major academic databases: IEEE Xplore, Scopus, ScienceDirect, and SpringerLink. To capture additional relevant sources and gray literature, Google Scholar was also utilized. The search was limited to peer-reviewed articles published between 2017 and 2024, written in English, and focused on handover (HO) management techniques in the context of 5G, 6G, or ultra-dense wireless networks.  
Search queries were formulated using Boolean operators to combine keywords related to handover and intelligent decision-making techniques. Representative search terms included the following:  
(\"handover\" OR \"mobility management\") AND (\"5G\" OR \"6G\" OR \"ultra-dense networks\") AND (\"AI\" OR \"machine learning\" OR \"fuzzy logic\").  
Search syntax was adapted to the specific requirements of each database. The final searches were completed in May 2025.  
The inclusion criteria required that studies achieved the following:  
1. Focused on handover or mobility management mechanisms;
2. Were relevant to 5G, 6G, or heterogeneous network environments;  
Figure 5: Examples of key handover scenario with ultra-dense heterogeneous networks in smart cities.  
3. Presented sufficient technical detail to allow meaningful comparison. Exclusion criteria included non-English language, lack of technical depth, or focus on unrelated topics such as resource allocation without reference to HO logic. In total, 53 records were initially retrieved. After removing duplicates and screening titles and abstracts, 28 studies were retained for full-text review. Following a detailed eligibility assessment, 17 articles were selected for inclusion in the final analysis. The selected studies were then categorized into three primary methodological groups--AI-based, fuzzy logic-based, and hybrid approaches--for structured discussion in the subsequent sections.
## 3 Thermal Analysis of the WCLL COB Segment
### WCLL COB Thermal Optimization and Determination of Its Thermal Field
#### 3.2.1 DWTs and FW Channels Thermal Optimization  
In order to thermally optimize the WCLL COB segment, three alternative DWTs layouts foreseeing 22, 24 and 26 \"crossed\" DWTs per elementary cell were set-up, foreseeing the crossing of the first row of tubes to remove the temperature hotspot in the central region of the cell (Figure 7). Moreover, the configurations with 24 and 26 DWTs per slice were obtained by moving along the radial and the toroidal directions of the first two rows of tubes and adding a new row of tubes, consisting of 2 or 4 tubes, respectively (Figure 8). Regarding the O6 region, the FW-SW was equipped with 6 channels per cell as well.  
In order to study the thermal behaviour of the three alternative DWTs configurations on each WCLL COB poloidal region to be optimized, a couple of elementary cells equipped with 26, 24 and 22 \"crossed\" DWTs were studied. Three FEM models, with a mesh composed of \(\sim\)3M nodes connected in \(\sim\)4M linear hexahedral and tetrahedral elements, were set-up for each region. Then, loads and boundary conditions already discussed above were considered. Finally, a parametric set of thermal analysis was launched with the aim of achieving the DWTs configuration for each COB poloidal region capable of removing the heat, while keeping the temperature below 550 \({}^{\circ}\)C, with the minimum number of tubes.  
Figure 7: Alternative DWTs layout and detail of the crossing tubes.  
Results are synthetically summarized in Table 4, where the DWTs layout configuration selected, the number of FW-SW channels for each COB region and, moreover, the corresponding maximum temperature reached on the SPs and FW are shown. In particular, in Table 4 the labels \"v06b ref.\", \"alt. 22\" and \"alt. 24\" indicate the reference layout v06b and the two alternative DWTs configurations equipped with 22 and 24 tubes, respectively.
## 3 Results
### Results Analysis  
Table 3 and Figure 7 present the diagnostic performances of the GLCM-based FBLS and CNN models in distinguishing osteoporosis, osteopenia, and non-findings in each cohort. Additionally, we indicated the experimental results on the trained cohort to demonstrate that every model has successfully avoided overfitting. From Table 3, we can see that GLCM-based FBLS achieved a 2.90% increase in AUC, 6.55% increase in accuracy, 11.31% increase in specificity, and 7.38% increase in NPV. The performance results demonstrate that the proposed GLCM-based FBLS can provide athletes with accurate screening of osteopenia and osteoporosis, especially in classifying those with no findings.  
Figure 7: Performance comparison of GLCM-based FBLS and three CNN models for testing set and training set.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Model** & **AUC (\%)** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **PPV (\%)** & **PPV (\%)** \\ \hline \multirow{4}{*}{**Train cohort**} & **ResNet-101** & 94.83 & 83.75 & 93.4 & 83.87 & 96.3 & 73.91 \\  & **ResNet-101** & (92.23–07.44) & (79.41–88.10) & (90.48–96.33) & (79.54–88.20) & (94.07–98.52) & (68.74–79.08) \\  & **DenseNet-169** & 93.06 & 78.34 & 97.8 & 78.5 & 98.65 & 68.99 \\  & **DenseNet-169** & (89.39–96.72) & (73.49–83.19) & (96.08–99.53) & (73.66–83.33) & (79.29–10) & (63.55–74.44) \\  & **EfficientNet-B0** & 93.26 & 84.84 & 82.42 & 88.71 & 91.16 & 78.12 \\  & **EfficientNet-B0** & (90.31–96.21) & (80.61–89.06) & (77.94–86.90) & (84.98–92.44) & (87.82–94.50) & (73.26–82.99) \\  & **GLCM+FBLS** & 98.2 & 94.31 & 90.55 & 94.35 & 90.7 & 94.26 \\  & **(96.63–99.77)** & (91.58–97.04) & (87.11–93.99) & (91.63–97.07) & (87.28–94.12) & (91.52–97.00) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._However, the proposed model does not perform well in terms of sensitivity and PPV. After careful study, we believe that there is a large overlap between the samples of osteoporosis and osteopenia, which are different stages of the disease. It limits the model's performance when classifying the different stages of osteoporosis. Nevertheless, our model has shown considerable advantages in terms of AUC, accuracy, specificity, and NPV, demonstrating its potential in accurately determining whether athletes are suffering from osteoporosis. In our future work, we will further focus on improving the ability of the proposed model to screen for different conditions of osteoporosis, hoping to completely improve this problem in the future.
# A Deep Neural Network Model for Speaker Identification
## 2 Materials and Methods
### Experimental Setup  
#### 2.3.1 Speech Dataset  
To investigate the performance of the model in our study, we run speaker identification experiments with Aishell-1 dataset. Aishell-1 is a publicly available speech dataset, which consists of Mandarin speech recorded with a high-fidelity microphone (44.1 kHz, 16-bit). The audio recorded by the high fidelity microphone was down-sampled to 16 kHz and used to produce the Aishell-1 dataset. A total of 400 speakers from different accent regions in China participated in the recording. In our experimental setup, it was divided into the training set, validation set, and test set. The specific usage of the Aishell-1 dataset is shown in Table 1.  
As a public speech data set, the Aishell-1 dataset is often used in related voiceprint recognition experiments. In this paper, we apply it to the speaker identification task. In the traditional way of machine learning, we often divide our data set by 60%, 20%, and 20%. Due to a large amount of data being available, we do not have to compromise for the data number. We used a greater portion to train the model [52]. In our experiments, we divided the data into a training set, validation set, and test set with the proportions of 90%, 5%, and 5%, respectively.
# Model-Based and Model-Free Point Prediction Algorithms for Locally Stationary Random Fields
## 5 Random Fields Cross-Validation  
To choose the bandwidth \(b\) for either Model-Based or Model-Free point prediction, we perform one-step-ahead prediction at several coordinates of the given random field data. To elaborate, consider a random field \(Y_{\mathbb{I}_{k}},Y_{\mathbb{I}_{k}},\ldots,Y_{\mathbb{I}_{k}}\) and suppose only subseries \(Y_{\mathbb{I}_{1}},Y_{\mathbb{I}_{2}},\ldots,Y_{\mathbb{I}_{k}}\) has been observed where \(k<n\). Let \(\hat{Y}_{\mathbb{I}_{k+1}}\) denote the predicted value based on the data \(Y_{\mathbb{I}_{1}},\ldots,Y_{\mathbb{I}_{k}}\); this can be estimated by using either the Model-Based or Model-Free approaches as described in Sections 3 and 4 for some choice of \(b\). However, since \(Y_{\mathbb{I}_{k+1}}\) is known, the quality of the predictor can be assessed. Therefore, for each value of \(b\) over a reasonable range, we calculate the sum of squared errors:  
\[SSE(b)=\sum_{k=k_{o}}^{n-1}(\hat{Y}_{\mathbb{I}_{k+1}}-Y_{\mathbb{I}_{k+1}})^{2} \tag{46}\]  
here \(k_{o}\) should be big enough so that the estimation is accurate, e.g., \(k_{o}\) can be of the order of \(\sqrt{n}\). The cross-validated bandwidth choice would then be the \(b\) that minimizes \(SSE(b)\). For the problem of selecting \(h_{0}\) in the case of Model-Free point predictors, as in [21], our final choice is \(h_{0}=h^{2}\) where \(h=b/n\). Note that an initial choice of \(h_{0}\) (needed to perform cross-validation to determine the optimal bandwidth \(b\)) can be set by any plug-in rule as the effect of choosing an initial value of \(h_{0}\) is minimal.
## 2 Systems Overview
### Gps/Glonas  
GPS and GLONASS are the main GNSS systems in use today and they are similar in many respects, but with some essential differences. Both systems are able to provide various number of air, marine, and any other type of users with all-weather three-dimensional positioning, velocity and timing, anywhere in the World or near-Earth space. Both navigation systems are based on the concept of \"oneway ranging\", in which the unknown user position is obtained measuring the time of flight of signals broadcasted by satellites at known positions and epochs [21].  
The main difference between the two systems is that GPS and GLONASS operate with different time references and with different coordinate frames [22,23]. Specifically, GPS time is related with UTC (USNO), Coordinated Universal Time (UTC) as maintained at the United States Naval Observatory. In contrast, GLONASS time is related to UTC (SU), UTC as maintained by Russia. The offset between the two time references can be calibrated, but this information is not yet included in the navigation messages broadcasted by the satellites. This causes an increase in the number of unknowns to be estimated from 4 to 5; three coordinates of user position and the biases of the receiver clock relative to the two system time scales (one bias can be replaced by the inter-systems time offset). This problem will eventually be overcome with the new generation of GLONASS satellites (_i.e._, GLONASS-M) that are planned to broadcast the offset between the two time scales. In addition, the GPS and GLONASS datum difference does not require an additional state because WGS84 and PZ90 are known and fixed, and they are linked by a well-defined mathematical transformation (further details are given in [24]). Other differences are related to the signal nature, namely different signal bandwidths and multiple access schemes, which are not relevant to this paper.
## Appendix B Bayesian Inference
\]  
Here, \(\Phi(\cdot)\) denotes the cumulative distribution function of the standard normal distribution, and \(n_{0}\) is the prior citations for each timeline. Substituting Equation (A10) into Equation (A9) gives  
\[ \begin{split} p(\mathbf{D}^{f}\ \Big{|}\ \tilde{\xi}^{f})& =\prod_{i=1}^{I}\Bigg{(}\ \exp\Big{(}-\xi^{f}M_{i}^{f}\Big{)}\prod_{n=1}^{N_{i}^{f}} \tilde{\xi}^{f}\cdot(n_{0}+n-1)\cdot p(t_{n}^{f};\theta_{i}^{f})\Bigg{)}\\ &=\Big{(}\xi^{f}\Big{)}^{\sum_{i=1}^{I}N_{i}^{f}}\exp\Bigg{(}- \xi^{f}\sum_{i=1}^{I}M_{i}^{f}\Bigg{)}\prod_{i=1}^{I}\prod_{n=1}^{N_{i}^{f}}(n_ {0}+n-1)\cdot p(t_{n}^{f};\theta_{i}^{f})\ \.\end{split} \] (A11)  
Thus, the likelihood can be expressed as the form of a gamma distribution with respect to \(\tilde{\xi}^{f}\) as  
\[p\left(\mathbf{D}^{f}\ \Big{|}\ \tilde{\xi}^{f}\right)\propto\Big{(}\tilde{\xi}^{f} \Big{)}^{\sum_{i=1}^{I}N_{i}^{f}}\exp\Bigg{(}-\tilde{\xi}^{f}\sum_{i=1}^{I}M_ {i}^{f}\Bigg{)}\ \.\] (A12)  
**Conjugate Prior. ** Since the conjugate prior of a gamma distribution is also a gamma distribution, we set the prior distribution on \(\tilde{\xi}^{f}\) as  
\[p(\xi^{f};\alpha,\beta)=\frac{\beta^{\alpha}}{\Gamma(\alpha)}\left(\tilde{ \xi}^{f}\right)^{\alpha-1}\exp\big{(}-\beta\tilde{\xi}^{f}\big{)}\ \,\] (A13)  
where \(\alpha>0\) and \(\beta>0\) are the shape and rate parameters, respectively. **Posterior Distribution. ** Based on Equation (A12) and (A13), the posterior distribution of \(\tilde{\xi}^{f}\) is a gamma distribution,  
\[p\left(\tilde{\xi}^{f}\ \Big{|}\ \mathbf{D}^{f}\right)=\frac{\beta^{\alpha^{ \prime}}}{\Gamma(\alpha^{\prime})}\left(\tilde{\xi}^{f}\right)^{\alpha^{\prime }-1}\exp\big{(}-\beta^{\prime}\tilde{\xi}^{f}\big{)}\ \,\] (A14)  
where \(\alpha^{\prime}=\alpha+\sum_{i=1}^{I}N_{i}^{f}\) and \(\beta^{\prime}=\beta+\sum_{i=1}^{I}M_{i}^{f}\). Therefore, the mean of the latent affinity \(\tilde{\xi}^{f}\), denoted by overlines, changes after observing the citation data as  
\[\overline{\tilde{\xi}^{f}_{\text{Prior}}}=\frac{\alpha}{\beta},\quad\overline {\tilde{\xi}^{f}_{\text{Post}}}=\frac{\alpha^{\prime}}{\beta^{\prime}}=\frac{ \alpha+\sum_{i=1}^{I}N_{i}^{f}}{\beta+\sum_{i=1}^{I}M_{i}^{f}}\ \.\] (A15)  
Note that the mode of a gamma distribution, denoted by hats, is not equal to the mean as  
\[\widehat{\tilde{\xi}^{f}_{\text{Prior}}}=\frac{\alpha-1}{\beta},\quad\widehat {\tilde{\xi}^{f}_{\text{Post}}}=\frac{\alpha^{\prime}-1}{\beta^{\prime}}\ \,\] (A16)  
when the shape parameters \(\alpha,\alpha^{\prime}\geq 1\). Unless otherwise noted, the mode of the posterior distribution is used for the estimated value of field affinity \(\tilde{\xi}^{f}\).
## 5 Practical Experience with L2S-M
### Functional Validation and Results  
#### 5.3.1 Throughput Performance  
This first set of validation tests aimed at showcasing the possible impact that L2S-M could have in the available throughput for the virtual functions deployed over a K8s cluster.  
To test this impact, we used the well-known traffic-generation tool iperf3 [54] in order to test the total available bandwidth between the two pods deployed in the scenario, testing both the standard K8s networking (flannel [17] for intra-cluster networking, and K8s NodePort for inter-cluster networking) and L2S-M in each scenario. In particular, the pods were deployed using the following configurations:  
* Two pods deployed in the same node of Campus 2 edge cluster (RPi1).
* Two pods deployed in different nodes of Campus 2 edge cluster (RPi1 and RPi3).
* Two pods deployed in different clusters (RPi1 of Campus 2 edge cluster and Campus 2 cloud cluster).  
For each configuration, an iperf3 flow of 180 s was established between each pod in both directions, using the standard K8s networking and the L2S-M in every iteration of the test. In this regard, each iteration was run 30 times, and then the average of each run was used to calculate the values shown in Table 1.  
As it can be seen in Table 1, the performance of L2S-M does not introduce any significant performance degradation in comparison with the standard K8s networking approaches (Flannel and service abstraction). L2S-M improves the throughput between the pods when they are co-located in the same node since the traffic generated between them does not need to pass through the Flannel agent deployed in the node, which introduces some performance degradation. For the connectivity between pods that are located inside the K8s cluster, but in different nodes, L2S-M and Flannel exhibit quite similar performance, with a slight throughput decrease in the L2S-M case. Overall, the performance between both solutions is very similar, and showcases that L2S-M does not harm the traffic performance over the K8s cluster.  
Regarding the inter-site connectivity scenario, L2S-M provides approximately 50 Mb/s less throughput than its counterpart as expected since K8s services establish the connection directly without the use of IP tunnelling mechanisms, while L2S-M still requires the use of VXLAN tunnels between the infrastructures, which in turn introduces some overhead in the packets exchanged between the pods/VMs. Nevertheless, NodePort communications do not isolate the exchanged traffic between pods, unlike L2S-M, which only distributes traffic to the pods located in the same virtual network.  
These tests showcase that L2S-M does not introduce significant performance degradation in comparison with standard CNI plugin communications in K8s.
## 2 Idealized Parallel Plate MEMS Actuator: Introduction of the Pull-In Effect  
### Model and Differential Equation  
First, the 2D schematic of an idealized parallel plate actuator is shown in Figure 1. This is a spring-mass-damper system and represents a typical second-order system. The initial gap distance between two electrodes is described by \(d\).  
The static and dynamic properties of this idealized oscillator structure can be characterized by a differential equation describing that the sum of the inertia force, the friction force and the elastic restoring force is equal to the external force (here the electrostatic actuation force):  
\[m\ddot{w}_{z}(t)+c\dot{w}_{z}(t)+kw_{z}(t)=F_{e}(t) \tag{1}\]  
where \(m\), \(wz(t)\), \(\dot{w}z(t)\) and \(\ddot{w}z(t)\) represent the mass, position, velocity and acceleration, respectively. The spring constant \(k\) is an inherent material property of the spring which represents the tendency to return to the initial state when the cantilever is subjected for a moment to an external excitation force \(F_{\rm e}(t)\). The damping coefficient \(c\) represents the characteristic that the vibration amplitude gradually decreases due to inherent or external friction. If an external voltage \(V\) is applied, the movable electrode will be pulled down by the electrostatic force \(F_{\rm e}(t)\)[36]:  
\[F_{e}(t)=\frac{\varepsilon AV^{2}(t)}{2(d-w_{z}(t))^{2}} \tag{2}\]  
where \(\varepsilon\) is the dielectric constant of the surrounding medium, \(A\) is the area of the plate and \(d\) is the initial distance between the two electrodes. Dynamic variables are the displacement \(w_{z}(t)\) of the movable plate and the applied voltage \(V(t)\). Equation (2) shows a nonlinear relation between \(F_{\rm e}(t)\) and \(w_{z}(t)\). Here, the elastic force \(F_{\rm elast}(t)=kw_{z}(t)\) reveals a linear dependence on the displacement \(w_{z}(t)\). A schematic of the elastic force and the electrostatic actuation force is shown in Figure 2.  
Figure 1: A 2D schematic of a parallel plate MEMS capacitor. A static situation is depicted here, where the elastic restoring force is in equilibrium with the external electrostatic force.
# Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies
## 2 Related Work
### Diagnostic Approaches for Students' Knowledge States
#### 2.2.3 Deep Learning-Based Knowledge State Diagnosis  
The advancement of deep learning technology has enabled its introduction into diverse fields, knowledge state diagnosis being a prime example. Common models in this space include deep knowledge tracing (DKT), which leverages recurrent neural networks (RNNs) and long short-term memory (LSTM) to model learners' knowledge states. The adoption of deep neural networks has significantly improved the predictive accuracy of such models [4]. However, the DKT model faces challenges in diagnosing students' knowledge mastery because it has difficulty outputting students' mastery levels for each knowledge point. Conversely, the DKVMN model uses a key matrix to store all the knowledge points across exercises and a value matrix to record students' mastery states for each knowledge point. This enables the model to output mastery states for each individual point [23]. While the DKVMN model combines the strengths of both the BKT and DKT models, however, the DKVMN model models simulated knowledge points rather than real knowledge points and ignores the effect of the student's current knowledge level on knowledge growth. The Self-Attentive Knowledge Tracing (SAKT) model utilizes a self-attentive mechanism to assign varying weights to input sequences. This enables the model to give different degrees of importance to a student's historical practice while making the current predictions [24]. In response to the limitations of these approaches, numerous studies have proposed targeted improvement models. Through continuous research in the field of deep knowledge tracing, these models have undergone significant enhancements in both prediction accuracy and interpretability.  
Collaborative Learning Grouping Approach Incorporating Deep Knowledge Tracing Optimization Strategies  
Our collaborative learning grouping approach, which integrates deep knowledge tracing optimization strategies, comprises three main modules. Firstly, the data preprocessing module encompasses the standardized processing of two parts of data: the students' answer information and the knowledge point information of the exercise. Secondly, the knowledge state diagnosis module, utilizing our innovative DKVMN-EKC model, accurately assesses students' masterly levels of various knowledge points. Thirdly, the student grouping module, in conjunction with the K-means algorithm, clusters students based on their knowledge mastery levels as features to obtain similar student clusters and assigns students to different learning groups. This process is schematically represented in Figure 1.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 6 Applications and Outcomes from Spaceborne LiDAR Data  
Data from spaceborne LiDAR systems have facilitated numerous studies and practical applications in Earth observation, environmental monitoring, and planetary science. This section categorizes key applications, summarizing representative studies and findings.