esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Future Work  
Given the promising results and architectural flexibility, several enhancements are envisioned for future development:  
* **Comprehensive System Level Evaluation.** While the presented evaluation primarily addresses LLM inference accuracy, comprehensive end-to-end system validation--including real-time performance, clinician acceptance, and integrated workflow efficiency--will be the focus of subsequent evaluations. Planned evaluations will incorporate scenario-driven simulations, multi-center deployments, user acceptance tests, and clinical outcome impact studies to provide holistic system validation. In addition, we plan to incorporate comparative benchmarking against established triage frameworks (e.g., ESI, REITS) and conventional ML-based classifiers, once standardized, labeled datasets become available.
* **Integrating Severity Assessment and Prognosis.** Beyond initial triage, we aim to extend the logic of the system to continuously assess patient severity throughout the ED stay. This includes incorporating early warning scores and predictive analytics to forecast patient deterioration. By leveraging time-series data, the system could proactively identify patients whose conditions are worsening, effectively blending triage with short-term prognosis [66].
* **Model Context Protocol (MCP).** MCP is an open standard that facilitates seamless connections between LLMs and external data sources or tools, enabling dynamic, real-time interactions particularly in complex, multi-agent environments [67]. By adopting MCP, we aim to streamline the integration process, reduce the need for custom connectors, and ensure secure, standardized communication across diverse platforms.
* **Expanding Training Data for Rare Cases.** To improve generalizability and robustness, additional datasets from specialty hospitals (e.g., oncology centers) will be integrated. Synthetic case generation and reinforcement learning with human feedback will help address rare or atypical scenarios. These enhancements will allow the system to better handle edge cases while reducing diagnostic uncertainty.
* **Enhancing Explainability and Transparency.** Trust and adoption depend on clinicians understanding how recommendations are generated. Future iterations will add an explanation layer powered by tools such as SHAP (SHapley Additive exPlanations) [68] and LIME (Local Interpretable Model-Agnostic Explanations) [69] to enhance clinician trust.  
The proposed architecture offers a strong foundation for scalable clinical AI. Future development will prioritize robustness, transparency, and measurable clinical impact in real-world healthcare settings.
## 2 The Novel Effective DBA Algorithm for Implementation in NG-PON2 Networks
The NG-PON2 architecture contains simultaneously both time- and wavelength division multiplexing (TWDM) in downstream and upstream directions. For the downstream transmission, four fixed lasers are utilized in the OLT terminal with a wavelength multiplexer. Subsequently, a common light beam is filtered in each ONU unit with active tunable filters that allow a required wavelength for its own receiver. For the upstream transmission, tunable lasers in each ONU unit dynamically assign expected wavelengths. In this way, the utilization of active filters and tunable lasers in the ONU unit is unique. The upstream (1524-1544 nm) and the downstream (1596-1602 nm) wavelength bands are considered for the NG-PON2 system that can provide the 38 dB power budget and can support the 20 km transmission distance with the 1:512 splitting ratio. For increasing bandwidth utilization of the dedicated wavelengths, the main aim of our research work is oriented on the optimization of the decision criterion that defines a minimum bandwidth utilization of the actual wavelength necessary for possibly allocating the next wavelength. The _Rh_ decision criterion is optimized for various numbers of ONU units incorporated into the NG-PON2 network.
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 3 Applications
### Medical and Rehabilitation  
The materials used in medical soft robots can achieve good biocompatibility with human bodies and tissues. In medical and rehabilitation, soft robots can be used in minimally invasive surgery, drug delivery, rehabilitation training, and wearable devices [2]. According to application scenarios, medical robots can be divided into two categories: implantable and wearable [13]. Nevertheless, there exist guarantee conditions, such that the human body will not produce allergic, immune, or rejection reactions to the soft robot, and the robot will not cause secondary damage to human tissues while performing its functions. Implantable soft robots can be used in minimally invasive surgery and drug delivery, and the driving method used is often magnetically driven. For instance, a minimally invasive surgical robot, which is with magnetic-driven steering and navigation capabilities, can be reduced to less than a few hundred microns in diameter and can travel through blood vessels [169]. Another example is a drug delivery robot, which can move and turn in the intestinal model under the action of a magnetic field to demonstrate the virtual drug delivery process. Its average speed can reach 3.1 mm/s [25]. Wearable robots can help our limbs perform rehabilitation training or directly act as prosthetic limbs, which require greater output forces and are suitable for fluid-driven methods. In oral rehabilitation, a pneumatically driven robot can achieve an elongation rate of 800-1000% after driving, which is suitable for sensitive areas of the human body and assists in the treatment of patients with mandibular movement disorders [170]. In hand rehabilitation, a low-cost pneumatic hand rehabilitation robot helps fingers perform flexion/extension training with a maximum output force of 110 kPa [38]. In ankle-foot rehabilitation, a wearable robot driven by pneumatic artificial muscle actuators helps humans with orthopedic ankle and foot or assist walking [171].
## 6 Results and Discussion  
Using MATLAB, we tested our approach on a \(100\times 100\) pixel picture that represents the distribution of the interest in a given area. The RoI is colored in yellow, green pixels represent no interest. Based on the developed equation given by Equation (9), the algorithm computes the collected interest, and optimizes the objective function f (X). We define two parameters that can confirm if our approach chooses the optimal topology. The first one is the ratio of interest \(\mathrm{Rics}=\mathrm{Calculated\ interest/\,Maximum\ Interest}\), and \(\mathrm{Rips}=\mathrm{Rics/Number\ of\ nodes}\) is the second ratio that shows the average of the interest collected per sensor. Rips is at its minimal value when the area is over-covered, or in other words, the number of deployed nodes is more than sufficient, since the same interest is collected while the number of nodes decreases in time, which makes the Rips ratio increasing in time, meaning that the average of interest calculated per each node increases with time. Otherwise, if the area is less covered, Rips reaches its maximum. Two scenarios are presented where the coverage problem is to be solved while finding the adequate number of nodes. The first one is the case of the area coverage problem. We define three regions RoI.1/2/3 within the area to supervise, at least 97% of the joint interest must be satisfied. The second case is a barrier coverage problem where 100% of the joint interest must be collected. Then a comparison is made between our results and those obtained in [3].
## 2 The Model of CNTRC Beams  
The model of the CNTRC beam resting on a nonlinear elastic foundation is shown in Figure 1a.  
A straight CNTRC beam made of a mixture of SWCNT and an isotropic polymer matrix was simply supported with the length L, thickness h, and width b. The beam was assumed to be immovable at both ends and had different models of reinforcement over the cross-sections, as presented in Figure 1b: uniform distribution (UD), O-distribution, X-distribution, and V-distribution. The material properties of the CNTRC beam were estimated by means of the rule of mixtures. The Young's modulus, shear modulus G, Poisson's ratio \(\upsilon\), and density \(\rho\) of the CNTRC were defined as follows [3; 22; 24]:  
\[E_{11}=\eta_{1}V_{CNT}E_{11}^{CNT}+V_{p}E^{p}, \tag{1}\]  
\[\frac{\eta_{2}}{E_{22}}=\frac{V_{CNT}}{E_{22}^{CNT}}+\frac{V_{p}}{E^{p}}, \tag{2}\]  
\[\frac{\eta_{3}}{G_{12}}=\frac{V_{CNT}}{G_{12}^{CNT}}+\frac{V_{p}}{G_{p}}, \tag{3}\]  
\[V_{CNT}+V_{p}=1, \tag{4}\]  
Figure 1: The model of the CNTRC beam: (a) geometry of the CNTRC beam on an elastic foundation; (b) cross-section for different models of reinforcements.  
\[v = V_{CNT}v^{CNT}+V_{p}v^{p}, \tag{5}\] \[\rho = V_{CNT}\rho^{CNT}+V_{p}\rho^{p}, \tag{6}\]  
where superscripts CNT and p symbolize the related material properties of the carbon nanotube and polymer matrix, respectively; \(\eta_{i}\), \(i\) = 1, 2, 3 indicates the efficiency parameters of CNT; and \(V_{CNT}\), and \(V_{p}\) are the volume fractions for the CNT and polymer matrix, respectively. \(E_{11}^{CNT}\), \(E_{22}^{CNT}\), and \(G_{12}^{CNT}\) are the Young's modulus and shear modulus of SWCNT, respectively; \(E^{p}\) and \(G^{p}\) are the corresponding material properties of the polymer matrix; and \(v^{VCNT}\), \(v^{p}\), \(\rho^{CNT}\), and \(\rho^{p}\) are the Poisson ratios and density of the CNT and polymer matrix, respectively.  
The continuous mathematical functions utilized for describing the distributions of material constituents are presented as follows [22,24]:  
\[UD-beam:V_{CNT}=V_{CNT}^{*}, \tag{7}\]  
\[O-beam:V_{CNT}=2\bigg{(}1-2\frac{|z|}{h}\bigg{)}V_{CNT}^{*}, \tag{8}\]  
\[X-beam:V_{CNT}=4\frac{|z|}{h}V_{CNT}^{*}, \tag{9}\]  
\[V-beam:V_{CNT}=\bigg{(}1+\frac{|z|}{h}\bigg{)}V_{CNT}^{*}, \tag{10}\]  
in which \(z\) is the distance of any point on the physical middle surface along the thickness direction and \(z\in[-h/2,h/2]\), and:  
\[V_{CNT}^{*}=\frac{\wedge_{CNT}}{\wedge_{CNT}+(\rho^{CNT}/\rho^{p})(1-\wedge _{CNT})} \tag{11}\]  
where \(\wedge_{CNT}\) is the mass fraction of CNT. The CNT efficiency parameters \(\eta_{i}\) associated with the volume fraction \(V_{CNT}^{*}\) are [3,22]:  
\[\begin{array}{l}\eta_{1}=1.2833,\ \eta_{2}=\eta_{3}=1.0556\ for\ the\ case\ V_{CNT}^{*}=0.12\\ \eta_{1}=1.3414,\ \eta_{2}=\eta_{3}=1.7101\ for\ the\ case\ V_{CNT}^{*}=0.17\\ \eta_{1}=1.3238,\ \eta_{2}=\eta_{3}=1.738\ for\ the\ case\ V_{CNT}^{*}=0.28\\ \end{array} \tag{12}\]
# A Survey on Fault Diagnosis of Rolling Bearings
## 4 Rolling Bearing Fault Type Recognition
### Traditional Fault Type Recognition Methods
[138] input the symbolic entropy of the bearing signals into SVM for fault type identification and obtained good results. ELM is a feedforward neural network that uses random weights between the hidden layer and the input layer, and the output weights of its output layer are calculated through regular processes. With ELM, only the number of hidden layer neurons needs to be set. It has the advantage of rapid processing and good generalization but the disadvantage of overfitting [139]. RF can handle high-dimensional data effectively without a long running time, but the parameter selection of RF often affects the classification accuracy [140]. To avoid setting classifier parameters manually, PSO is used to adaptively determine the optimal parameters of classifiers, e.g., PSO optimized SVM, PSO optimized ELM, and PSO optimized RF, which were proposed to improve the accuracy of fault identification [141; 142; 143].
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 2 IP Mobility Management Protocols
### Macro Mobility Management Solutions  
_Mobile IPv4_: Mobile IPv4 is the most popular protocol used for mobility management. Mobile IPv6 is the successor version that was developed to manage mobility for next generation IPv6 networks. A Mobile IPv4 mobile node has two IP addresses; the home address and the Care of Address (CoA). The home address is static, and it links the mobile node to its home network. A network entity from outside the home network uses this address to communicate with the mobile node. The mobile node registers its static home address with an agent known as the Home Agent (HA). The CoA, on the other hand, is a dynamic address that is given to the mobile node as it moves to another network known as the Foreign Network (FN). It reflects the physical location of the mobile node.  
The process of handoff begins when the mobile node moves to the FN where it registers with a Foreign Agent (FA) similar to its HA, but in the new network. The mobile node notifies its HA in  
Figure 1: Micro versus macro mobility.  
its HN of the obtained CoA by itself directly or through the FA. Any packets destined to the mobile node from a Corresponding Node (CN) will arrive at the HA, which will tunnel the traffic to the CoA (see Figure 2). The FA then detunnels the traffic and sends it to the mobile node. This procedure is repeated whenever the mobile node moves to a new network and has a new CoA.  
_Mobile IPv6_ was developed to provide mobility for IPv6 networks. It considers all the problems experienced in the previous mobile IPv4. Mobile IPv6 eliminates the use of the FA functionality, where a moving mobile node to a FN obtains its CoA using an auto configuration procedure. This is in contrast to use of the periodic broadcast advertisement and registration mechanism used in mobile IPv4. Upon obtaining the CoA, a mobile node sends a binding update (BU) to its HA and the CN. In response to the BU, the HA and the CN reply to the mobile node with an acknowledgement, and thus use the new binding for the following communication with the mobile node. Mobile IPv6 has therefore developed the design of mobile IP to eliminate the use of FA and provide route optimization by establishing a direct communication channel between the CN and the mobile node, as shown in Figure 3. The CN thus sends any future traffic directly to the mobile node. Overall, the MIPv6 design has reduced network load and brought route optimization, but it still depends on access point discovery and HA communication.
## 1 Introduction
Recently, to bypass the problems, the common idea is forming a forbidden area around each hole. As a result, there have been proposed forbidden areas with certain shapes such as circle [29; 30], ellipse [31], hexagon [32]. Although the simplicity of shaped regions can reduce the control overhead, a significant enlargement on the routing path may occur. That is caused by the possibly substantial difference between the forbidden area and the hole. A recent proposal deals with the routing path enlargement problem by describing the forbidden area as the exact polygon whose vertices are the nodes on the hole boundary [33; 34]. However, this approach may incur a significant control overhead since the information needed to represent the forbidden area may be very large when holes become complicated. Moreover, due to the static nature of the forbidden areas and routing paths, all the existing protocols cannot solve the load imbalance problem thoroughly. Novel approaches using the dynamic forbidden area have been proposed in [35; 36; 37]. However, these approaches cannot deal with general cases where there are multiple holes in the networks and the source, and the destinations may stay inside the holes' convex regions. In this paper, we propose a load-Balanced and constant Stretch protocol for bypassing Multiple Holes (i.e., BSMH), which is a new geographic routing protocol for bypassing multiple holes in WSNs [38]. The unique feature of BSMH design is jointly considering three essential factors, which are minimizing the routing path length, minimizing control overhead, and maximizing load balance. We have theoretically proved BSMH's efficiency and its satisfaction following the design patterns. Moreover, we have implemented and evaluated BSMH against state-of-the-art protocols using simulations. The evaluation results show that BSMH outperforms the others in all investigated metrics. The remainder of the paper is organized as follows. Section 2 presents the related works. Section 3 describes our proposed protocol. In Section 4, we theoretically analyze the protocol performance. Section 5 introduces the performance evaluations using simulations. Finally, Section 6 concludes the paper.
## 4 Discussion  
Analysis of the result shows that the SVM algorithms demonstrated a superior performance in terms of both R\({}^{2}\) score and RMSE. This result stems from the fact that the SVM has a robust performance relative to the ANN and LR because the SVM employs the maximal margin hyper plane in predicting the correlation between the features (ICT, curing temp, weight, etc.) and the compressive strength of the AATS. In addition, the SVM only uses a subset of training points which are highly correlated with the dependent variable (strength) in the decision function (named support vectors); consequently, the SVM is capable of eliminating noise which may hamper the prediction result and therefore only the attributes that are highly linked with the AATS strength will be employed for prediction. The ANN performs relatively poorly because with the ANN, different random weight initializations can lead to different validation accuracy. Additionally, the ANN requires tuning a number of hyper parameters such as the number of hidden neurons, layers, and iterations. The linear regression has the lowest performance in terms of both R\({}^{2}\) score and RMSE because the coefficient estimates for LR rely on the interdependence of the features. When features are correlated and the columns of the design matrix X have an approximate linear dependence, the design matrix becomes close to singular and as a result, the least-squares estimate becomes highly sensitive to random errors in the observed target, producing a large variance which could result in a high RMSE and a low coefficient of determination (R\({}^{2}\)).  
Additionally, the statistical values displayed by each model corroborate that the prediction of the AATS compressive strength with the SVM model is singularly accurate. This is inconsistent with results obtained from the prediction of volcanic scoria-based concrete [33]. However, the results from the present study align with the results obtained using the SVM in the prediction of light weight aggregate concrete (LWAC) [35]. Nonetheless, due to the inexistence of the literature on the prediction of the AATS compressive strength, the results cannot be compared appropriately because of the difference in the materials used. Thus, the comparison was made with similar materials such as the scoria used in the previous reference. The resemblance of the scoria with the termite soil is that both materials are classified as natural pozzolanas. Furthermore, it is worth recalling that the various statistical measures and results obtained peaked the importance and effect of the input parameters on the compressive strength.
## 6 Conclusions  
In the prediction of rare earth component content, the single color feature of the extracted image often fails to encapsulate all the image's information, resulting in suboptimal prediction outcomes from soft sensor models. Therefore, in this paper we propose a one-dimensional convolution method for \(\Pr/\Nd\) component content prediction based on multiple residual attention blocks. Initially, machine vision technology is employed to extract the color space information of the image. Subsequently, the gray level co-occurrence matrix is utilized to extract the spatial texture feature information of the image. Simultaneously, a simulation of the actual field situation is conducted and the corresponding extraction order information is added to the component content of the rare earth element solution that requires prediction. The RF-RFECV algorithm is then used to filter the  
Figure 10: Relative error comparison diagram of the predicted value of the model.  
aforementioned features to obtain mixed features. Finally, the BP, SVM, GBDT, and ELM models were validated on different feature sets. Our experimental results indicate that the multi-feature extraction method can provide more information and enhance the prediction accuracy of the model.  
In response to the above mixed features, this paper introduces a one-dimensional convolutional network (MRAB-CNN) based on residual attention blocks. The model integrates the residual network with the attention mechanism, enabling it to extract deeper data information while suppressing the weight ratio of noise through the attention mechanism. When compared with GA-ELM, LSSVM, Xgboost, and RAB-CNN models, the root mean square error of the model in predicting rare earth element component content is the best. This model exhibits exceptional performance in the prediction task of rare earth component content and can meet the real-time and rapid detection requirements of rare earth component content at the extraction site.  
Conceptualization, F.X. and J.C.; methodology, F.X.; software, J.C.,validation, J.Z.; formal analysis, F.X.; resources, J.C.; writing--original draft preparation, F.X.; writing--review and editing, F.X., J.C. and J.C.; supervision, J.C.; funding acquisition, F.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Key Research and Development Program of China under Grant 2020 YFB173700, in part by the Regional Project of NSFC under Grant 61963015 and 62363010, and funded by Science and Technology Research Project of Education Department of Jiangxi Province (GJJ190314), and the Graduate Innovation Fund Project (YC2021-B144, YC2022-S526).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials
### Dataset  
High spatial-resolution Ziyuan-3 (ZY-3) images in 2013 and 2018 were used as experimental data, the acquisition dates and spatial resolutions are listed in Table 1. Image preprocessing included radiometric correction, image registration, and image cropping using ENVI 5.6 software.
## 4 Experimental Results
These models allowed us to obtain energy-QoS costs (Equation 15) for different values of \(\alpha\) as shown in Figure 6. The choice of parameters \(a=0.1\) and \(b=1.0\) was made to approximately normalize (to 10) the varying range of the response time and energy per job. The former gets around 100 s close to full system utilization, whereas the latter was around \(10\) K  
Figure 4: Job response time for the local (\(\alpha=0\)) and remote cluster (\(\alpha=1\)). Figure 5: Experimental and theoretical values for the energy per job. It is interesting to observe the values of \(\alpha\) that minimize the overall system cost. These are graphically illustrated in Figure 7 for the cases \(a=0.1\), \(b=1.0\) (left), and \(a=b=1.0\) (right). The choice of \(a\) and \(b\) would in the end depend on the importance that we would like to give to both Energy and QoS. However, these two sets of values will serve us to illustrate the behaviour of the Energy-QoS cost. The horizontal axis depicts values of \(\alpha\) in the range 0 to 1. As previously explained, when \(\alpha=0\), all the load is sent to the local cluster. At \(\alpha=1\) all load is sent to the remote cluster. The load is shared between the two clusters for values of \(\alpha\) between these two extremes. The figures show that the choice of \(a\) and \(b\) can affect the cost of the load sharing, as well as the operating point (_i.e._, the value of \(\lambda\)).