esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The hybrid compensation method demonstrated in this study keeps HFV stable under the concurrent conditions of multi-uncertain factors. The nominal NDI compensates for the modeling error of the longitudinal model using direct subtraction of corresponding terms. Based on multi-adaptive laws, the adaptive NDI controller compensates for the inertia uncertainties, so that the HFV maintains attitude stability under high maneuvering conditions. Furthermore, a disturbance observer helps the improved sliding mode adaptive NDI controller to estimate and actively repair the disturbance. Finally, the hybrid strategy enables the HFV to regain attitude stability with passive and active compensation methods, respectively, in the face of uncertain inertia and disturbance. The proposed method solves the multi-source uncertain robust control problem of the HFV. The subsequent research direction is automatic multi-fault repair.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **HFV without Uncertainty, Disturbance, and Model Error** & **HFV in This Paper** \\ \hline t\({}_{\text{s,max}}\) & 5.15 & 5.28 \\ t\({}_{\text{s,min}}\) & 2.51 & 1.61 \\ e\({}_{\text{ss,max}}\) & 0.0003 & 0.0004 \\ e\({}_{\text{ss,min}}\) & 0.0001 & 0.0001 \\ o\({}_{\text{v,max}}\) & 3.96\% & 7.50\% \\ o\({}_{\text{v,min}}\) & 0.001\% & 0.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis II of performance indicators between ideal and harsh models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **State-of-Art Method** & **Proposed Method** \\ \hline t\({}_{\text{s,max}}\) & 27.27 & 5.28 \\ t\({}_{\text{s,min}}\) & 10.01 & 1.61 \\ e\({}_{\text{ss,max}}\) & 12.29 & 0.0004 \\ e\({}_{\text{ss,min}}\) & 1.3 & 0.0001 \\ o\({}_{\text{v,max}}\) & 166.76\% & 7.50\% \\ o\({}_{\text{v,min}}\) & 38.63\% & 0.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis III of performance between method in [5] and proposed method.  
**Author Contributions:** [PERSON]-[PERSON] provided methodology, validation, and writing--original draft preparation; [PERSON] and [PERSON] provided conceptualization, writing--review and editing, and supervision; and [PERSON] and [PERSON]-[PERSON] provided funding support. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 61773201, the enterprise cooperation project between Nanjing University of Aeronautics and Astronautics and AVIC 601 Research Institute under Grant 1003-KFB18233.  
**Data Availability Statement:** Not applicable.  
The authors of this paper thank the fault diagnosis and fault tolerance control team of Nanjing University of Aeronautics and Astronautics for their technical support.  
The authors declare no conflict of interest.
## 2 Related Work
In Table 1, our work is compared with the main contributions of some of the related studies mentioned above. In a recent study [3], the applications of XAI methods to IDS were classified into black- and white-box methods. White-box models are designed with explainability in mind and do not require an additional layer of analysis to offer an interpretation of the results, such as decision trees or clustering algorithms. However, this intrinsic explainability comes at the cost of higher complexity in the design, and in general, less accuracy [18]. Black-box methods are nontransparent in their decision but post hoc explainable models allow for an explanation of what pushed the model to classify a sample. On a similar note to our proposal, the XAI-IDS framework defines the advantages that an integration of XAI techniques into the detection models could bring to Network IDS [19]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **Datasets** & **XAI Technique** & **XAI Scope** & **XAI Use** & **Accuracy** \\ \hline
[12] & 2022 & IoTID20, NF-BoTIoT-v2, NF-ToN-IoT-v2 & SHAP & Local, Global & Interpretability & - \\  & & NSL-KDD, UNSW-NB15, ToN\_IoT & SHAP, PFI, ICE, PDP & Local, Global & Interpretability & 0.864 \\
[14] & 2022 & NSL-KDD & SHAP & Global & Improving model performance & - \\
[15] & 2023 & NSL-KDD & PFI, SHAP, Lime, CIU & Local, Global & XAI techniques comparison & - \\
[16] & 2022 & NSL-KDD, UNSW-NB15 & SHAP, RuleFit, LIME & Local, Global & Improve IDS trust & 0.99 \\
[17] & 2021 & KDDcup’99 & Information Gain, Decision Trees & Local, Global & Enhance trust & \\  & & & Decision Trees & & in IDS & \\  & & & & Improve IDS & \\ Proposed solution & 2024 & UNSW-NB15 & SHAP & Local, Global, Ranged Feature & performance, propose semi-automatic rules & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related works and explainable intrusion detection techniques. F1-score added when same dataset is used. They propose a benchmark of seven black-box models, not including RL. Alongside the benchmark, a comprehensive list of both local and global explanations are defined to be included in an integrated framework. Our work follows the trend of these state-of-the-art XAI methodologies, addressing the implications and design necessary for the use of an RL model, an architecture that has been proposed as useful on explainable IDSs [11], but one where SHAP, or other XAI approaches, have never been directly applied. Furthermore, we propose another level of explanation based on feature value ranges, building on the idea of inferring specific decisions made by a model over a single feature [16; 17], where the common individual and global explanations are presented. These feature-range explanations allow for the proposed creation of fixed detection rules, something akin to the rules that can be discerned from the inherent structure of a DT model [17]. This could help to bring the insight allowed by these prediction models into the security configuration of a system.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 3 Methodology
### Proposed Protocol Design
#### 3.2.1 Certificate Authority (CA) Initialization  
The CA initiates the system by generating certificates for both the server and client nodes. The certificate for client is generated by integrating the client's public key \(k_{pub,C_{i}}\), its identity \(ID_{C_{i}}\), and the signature \(S_{C_{i}}\), which is formulated using the CA's private key \(k_{pr,CA}\). The server certificate is generated by integrating the server's public key \(k_{pub,S_{i}}\), its identity \(ID_{S}\), and the signature \(S_{S}\), which is created using the CA's private key \(k_{pr,CA}\). Additionally, the CA ensures that the certificates are embedded with essential metadata, including expiration dates, to deter reuse or exploitation. These certificates authenticate the server and clients involved in the communication and ensure the integrity of the exchanged data.
# Track-Bridge Interaction of CWR on Chinese Large-Span Bridge of High-Speed Railway
## 3 Track-Bridge Interaction Law of Different Bridge Types
### Suspension Bridge  
#### 3.5.1 Track-Bridge Interaction Model  
The span of the ground-anchored suspension bridge with a double-tower continuous steel truss girder for highway and railway is (84 + 84 + 1092 + 84 + 84) m. The upper layer is equipped with an eight-lane expressway, and the lower layer is equipped with a double-line passenger-dedicated line and a double-line intercity railway, with the line spacing of 4.6 m. The width of the upper deck is 43 m, the width of the lower deck is 30 m, and the beam height is 16 m. The mid-span section of the bridge is shown in Figure 12. The north tower is 203 m high, and the south tower is 191 m high. Both towers have box sections. The side span of the main girder is 350 m (refer to Figure 13). Considering the main cable, suspender, steel truss, orthotropic plate, and the adjacent bridge structure, the model of the interaction between suspension bridge and four tracks is established. Only considering vertical and transverse constraints at the joint of the main truss and the crossbeam of tower, no constraint in the direction of the bridge is considered. The vertical and transverse displacements are constrained at moveable bearings. Six equally stiff springs are used to simulate the pier group foundation at the bottom of the tower. The adjacent bridges will adopt the option of a continuous bridge. The fixed bearing is set at the central pier, and moveable bearings at the other piers. Nonlinear bar elements are used to simulate the ballast track without considering the influences of the track-bridge transverse displacement on the track-bridge longitudinal interaction. By using the linear spring to simulate the vertical stiffness of the fastener, the model of longitudinal force on CWR on a suspension bridge is partly shown in Figure 14.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 10 Splicing and Termination of Hollow-Core Fibers  
### HCF in the SMF World  
Hollow-core fibers in the near future will be connected to active and passive devices: transmitters, receivers, amplifiers, distribution frames, WDM multiplexers, etc., with optical ports designed for solid-core SMF, and two types of fiber splices will be necessary:  
* HCF-HCF splices between cable sections, and for repairs;
* HCF-SMF splices at both ends of optical path, mostly to pigtails with SMFs.  
Making low-loss splices of these two types is considerably different.  
Single-mode HCF with MFD = 17-24 \(\upmu\)m is not compatible with the 50/125 \(\upmu\)m OM2/3/4/5 graded index multimode fibers [66] widely used in data centers, LANs and computer systems, primarily due to the high splice loss in the MMF-HCF direction.  
Low-latency cables with single-mode HC-PBGF and NANF fibers are currently terminated with SMF pigtails at the factory [28; 99] (Figure 20), while HCF-HCF joints between cable sections in long links are fusion splices. Both ends of HCF are sealed by these fusion splices and not accessible--the user only handles connectors with SMFs.
## 4 Experiments  
### Datasets  
We assess the performance of SLRBC using the publicly available electronic medical record datasets from CCKS2018 ([[https://www.sigkg.cn/ccks2018/page_id=16](https://www.sigkg.cn/ccks2018/page_id=16)]([https://www.sigkg.cn/ccks2018/page_id=16](https://www.sigkg.cn/ccks2018/page_id=16)) (accessed on 10 November 2023)) and CCKS2019 ([[https://www.sigkg.cn/ccks2019/page_id=62](https://www.sigkg.cn/ccks2019/page_id=62)]([https://www.sigkg.cn/ccks2019/page_id=62](https://www.sigkg.cn/ccks2019/page_id=62)) (accessed on 10 November 2023)). The entity distribution statistics of the datasets are shown in Tables 2 and 3. CCKS2018 consists of 5 entity types and comprises 3251 sentences for training, 358 for validating, and 432 for testing. CCKS2019, on the other hand, defines 6 entity types and includes 5708 sentences for training, 755 for validation, and 743 for testing.  
The datasets are labeled with the BIO annotation method, in which the first character constituting the entity is labeled as \"B-Type\"; the rest characters constituting the entity are labeled as \"I-Type\", and the other non-entity characters are labeled as \"O\". As a result, there are 11 different labels in CCKS2018 and 13 in CCKS2019.  
Since the word frequency dictionary needs to be constructed in the representation layer of SLRBC, we extract words from the datasets and count the frequency of each word. In this regard, the CCKS2018 word frequency dictionary encompasses 7619 words, while  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**CCKS2018** & **Anatomical** & **Symptom** & **Independent** & **Medicine** & **Surgery** \\
**Location** & **Description** & **Symptom** & **Medicine** & **Surgery** \\ \hline Number & 7838 & 2066 & 3055 & 1005 & 1125 \\ Proportion & 51.95\% & 13.69\% & 20.25\% & 6.66\% & 7.46\% \\ \hline \end{tabular}
\end{table}
Table 2: CCKS2018 dataset entity distribution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**CCKS2019** & **Disease** & **Image** & **Laboratory** & **Surgery** & **Medicine** & **Anatomical** \\
**Diagnosis** & **Inspection** & **Test** & **Surgery** & **Medicine** & **Location** \\ \hline Number & 4212 & 969 & 1195 & 1029 & 1768 & 8426 \\ Proportion & 23.93\% & 5.51\% & 6.79\% & 5.85\% & 10.05\% & 47.88\% \\ \hline \end{tabular}
\end{table}
Table 3: CCKS2019 dataset entity distribution.  
the CCKS2019 word frequency dictionary comprises 10,566 words. Exemplary words and their respective frequencies from the word frequency dictionary are depicted in Figure 5.
## 5 Experimental Results
### Security Analysis
#### 5.6.1 Cryptographic Strength of Parameters  
As detailed in Section 4.4, the proposed protocol employs carefully selected parameters: the prime modulus \(p\) = 2,147,483,647 and the generator \(G\) = 37. These choices achieve an optimal balance between computational efficiency and cryptographic strength, making the protocol well-suited for resource-constrained IoT devices. The selected parameters provide a robust cryptographic foundation, ensuring security without compromising performance.
## 4 Experiment Setup
### Compared Models  
Our empirical evaluation contextualises the performance of the NSTD model by benchmarking it against a selection of advanced deep-learning models and traditional financial strategies. The models in this comparative analysis include the following:  
1. **Deep Learning Approaches:** * **Transformer:**[19] Employs self-attention mechanisms for long-range dependencies in time-series data. * **LSTM (Long Short-Term Memory):**[32] Excels in learning from sequences with long temporal dependencies. * **GRU (Gated Recurrent Unit):**[33] Designed to capture dependencies of varying time scales in sequential data. * **RNN (Recurrent Neural Network):**[34] Processes temporal sequences through a directed graph architecture. * **EIIE (Ensemble of Identical Independent Evaluators):**[6] Processes time series for each asset independently while maintaining shared network parameters. * **EI3 (Ensemble of Identical Independent Inception):**[35] Extracts and aggregates multiscale price movement information, integrated with a recurrent reinforcement learning framework.
2. **Traditional Financial Strategies:** * **OLMAR (Online Moving Average Reversion):**[36] Utilises mean reversion effect based on moving average. * **UBAH (Uniform Buy and Hold):**[37] Allocates funds uniformly across assets, maintaining them without further trading actions.
# On the Estimation of the Moving Mass of a TMD Installed on a Lively Structure
## 5 Conclusions  
As a continuation of [16], in which the authors presented a methodology based on the transmissibility function to estimate the natural frequency and damping ratio of an SDOF system installed on a lively structure, this work has been devoted to presenting a novel methodology to estimate its moving mass. It is also based on estimating the transmissibility function between the moving mass of the SDOF system and the point of the structure to which it is attached; however a second step is required in which a couple of accelerometers are estimated by applying a force on the moving mass. The three FRFs take part in a rapid computation that leads to a curve that can be fitted to the analytical expression of an isolated SDOF accelerance to estimate its properties. In addition, and for comparison purposes, the mass modification technique has been presented. As shown, the presented methodology constitutes a better choice with relation to other procedures such as the mass modification technique, which, in its most robust version, requires more than two response functions to be estimated and processed, thus involving more experimental time and computational effort to provide similar or worse estimates.  
As can be seen in Table 1, both methodologies provide similar estimations for the natural frequencies with very low relative errors. The damping ratio value, which is always the magnitude with the highest uncertainty, estimated by means of the mass modification technique is only slightly better, but the novel methodology provides a clearly better estimation of the moving mass with lower effort. Although, in this work, 13.10% of the original moving has been added to it, the estimation made by means of the mass modification technique could improve if more scenarios, in which more mass is added, were considered, leading to an even longer process.  
Conceptualization and methodology, A.M.; methodology and software, C.P. and A.I.-P.; validation, A.M., C.P. and A.I.-P.; formal analysis and investigation, A.M.; resources, C.P.; data curation, C.P. and A.I.-P.; writing--original draft preparation and editing, A.M.; writing--review, C.P., A.I.-P. and A.L.; visualization, A.M.; supervision, project administration and funding acquisition, A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministerio de Economia y Competitividad, Spanish Government, through the research project number RTI2018-098425 and the Ministerio de Educacion, Cultura y Deporte, Spanish Government, through the predoctoral grant number FPU16/01339.  
Not applicable.  
Figure 7: Comparison between the accelerate computed with the isolated SDOF system (gray line), the left hand side of Equation (9) (continuous black) and its fitting to the analytical expression (dotted).  
Data Availability Statement: The data presented in this study are openly available in Mendeley Data at 10.17632/xx46 xthc5z.1, reference number [17] (accessed date: 3 April 2021).  
The authors declare no conflict of interest.
## 4 Limitations
### Resolution Analysis
To maintain correct correspondence, this relation has to be satisfied also when the WAI is wavy, \(\forall_{k}\):  
\[\theta_{k}^{\mathrm{w}}=\arccos\ (\hat{\mathbf{v}}_{k}\cdot\ -\hat{\mathbf{z}})= \theta_{k}^{\mathrm{flat}}+\psi_{k}=\arccos\ \left(\hat{\mathbf{v}}_{k}^{\prime}\cdot\ -\hat{\mathbf{z}}\right)+\psi_{k}, \tag{25}\]0  
Equation (33) includes the small margin \(\Delta p/2\), which guarantees that the adjacent projected spots \(s_{k}\), \(s_{k+1}\) do not merge into a single spot. From Equations (25), (26), (32) and (33),  
\[ \begin{array}{ll}D+\frac{h\,_{0}-z_{h}}{h_{0}}D-\frac{\Delta p}{2}&>(h\,_{0} -z_{h})(\tan\theta_{k}^{\mathrm{w}}-\tan\theta_{k+1}^{\mathrm{w}})\\ &\approx\frac{h\,_{0}-z_{h}}{\cos^{2}\theta_{k}^{\mathrm{flat}}}(\eta_{k}- \eta_{k+1})\\ &\approx\frac{h\,_{0}-z_{h}}{\cos^{2}\theta_{k}^{\mathrm{flat}}}\frac{d\psi}{d \theta}(\theta_{k}-\theta_{k+1})\end{array} . \tag{34}\]  
Therefore, when \(h\,_{0}-z_{h}\) is too large or too small, \(D\) limits the dynamic range of the WAI slope changes \([\theta_{k}-\theta_{k+1}]\); beyond this range, it will cause a spot detection error. Assume that the WAI has spatial period \(\lambda\), in which the WAI inclination varies as \(\theta(x)=\Phi\cos(2\pi x/\lambda)\), and \(\Phi>0\). In the worst case, at which \(s_{k+1}-s_{k}\) is minimal, the WAI angle changes maximally between samples: \(|\theta_{k}-\theta_{k+1}|=2\Phi\). To avoid correspondence errors, the sampled interval \(D\) must satisfy  
\[D>\frac{h\,_{0}}{2h\,_{0}-z_{h}}(\frac{\Delta p}{2}+\frac{h\,_{0}-z_{h}}{\cos ^{2}\theta_{k}^{\mathrm{flat}}}\frac{d\psi}{d\theta}2\Phi)\approx\frac{h\,_{0}} {2h\,_{0}-z_{h}}(\frac{\Delta p}{2}+2\Phi\frac{\Delta p}{\Delta\theta}). \tag{35}\]  
As described in Section 3.2, the WAI sampling is quasi-periodic when the WAI is wavy, while the sampling tends to be periodic whose period is \(D\) around when the perturbation amplitude of the water surface becomes smaller. According to the Nyquist sampling criterion \((\lambda/2)>D\), combining it with Equation (35) yields  
\[\frac{2h\,_{0}-z_{h}}{h\,_{0}}\lambda>\frac{\Delta p}{2}\frac{\Phi}{\Delta \theta}(8+\frac{2\Delta\theta}{\Phi}), \tag{36}\]  
where \(\Delta\theta/\Phi\) represents the relative angular resolution in which the WAI can be recovered: the ratio of uncertainty to dynamic range. Since \(2\Delta\theta/\Phi\ll 8\), we obtain an uncertainty principle  
\[\frac{2h\,_{0}-z_{h}}{h\,_{0}}\lambda>\frac{\Delta p}{2}\frac{\Phi}{\Delta \theta}(8+\frac{2\Delta\theta}{\Phi}). \tag{37}\]  
As Equation (37) shows, the relative WAI slope angular resolution can be traded off for the spatial resolution \((\lambda)\) of the WAI, before errors stemming from aliasing and correspondence take effect.
## 2 Applications and Background of Uwsn
### Applications of Uwsn
#### 2.1.5 Military Operations  
[PERSON] et al. present an underwater wireless network for Mine Countermeasure (MCM) operation. It is one of the earliest efforts of MCM operation with Underwater Wireless Network. Authors use Autonomous Underwater Vehicle (AUV) with side scan sonar for mapping, identifying and classifying the target area [21]. Multiple number of AUVs can be used at the same time and they exchange information with themselves. Gateway buoys are located at the water surface and equipped with both acoustic modem and RF transceiver. These buoys allow the AUVs to be monitored from a distant operating station.
## 3 Results and Discussion
### Analysis of Surface Temperature  
As the most directly affected factor of volcanic activity, the characteristic change trend of temperature is also one of the research contents in the lower atmospheric region. In this paper, we used USGS Landsat 8 data to obtain the surface temperature of the small island of Tonga between April 2019 and April 2022, as shown in Figure 7, where the solid black line indicates the profile before the 15 January 2022 eruption and P1 to P4 are the selected feature points.  
As shown in Figure 7, the surface temperature around Tonga varies cyclically, with cooler temperatures from April to August (Figure 7a-d,h-j,p-s) and warmer temperatures from November to February (Figure 7f-g,k-n,u-w). However, in November and December 2022, the temperature on Ahapai Island was unusually high (Figure 7v,w), and the surface temperature of the crater was 10 \({}^{\circ}\)C Celsius higher than that in previous years (Figure 7). The surface temperature of Ahapai Island decreased in February 2022. In this paper, four feature points were selected for time series analysis in Ahapai Island (Figure 7x). Points P1 and P2 were located near the crater, and P3 and P4 were located on the east and west ridges. The time series results of the feature points are shown in Figure 8. Until November 2022, the surface temperature at point P3 was always higher than that at points P1 and P2 in the crater, and the surface temperature at point P4 was the lowest. However, starting from October 2022, the surface temperatures at points P1 and P2 suddenly increased, and their values were also more significant than those at point P1. The temperatures were P1, P2, P3, and P4 in descending order, and this order became clearer in December 2022.  
Overall, the HTHH volcano had an apparent change in surface temperature before and after the eruption, with the HTHH volcano having an anomalously high surface temperature before the eruption and a decrease in surface temperature after the HTHH eruption [40; 41]). The decrease in the surface temperature of Ahapai Island may have come from gases released by the eruption [42], such as sulfur dioxide, water vapor, and other volcanic gases.