esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation  
First, we formulate the problem. At each time step \(t\in\{1,2,\cdots,T\}\), the agent selects each arm (or action) \(k\) once in the set \(A\) of \(K\) possible arms. When the action is at time \(t\), \(a(t)\in[K]=\{1,2,\cdots,K\}\). We assume that a random true reward \(X_{k}(\tau)\) from arm \(k\) is selected in its \(\tau\)th selection. We denote the average experience reward at the current round as \(X_{k-cur}(\tau)\) and return \(0\) at the beginning of each round. \(X_{k-history}(\tau)\) is the historical value of the average experience reward, which changes once during every round and does not include the reward at the current round. Meanwhile \(X_{k}(\tau)\) is pulled according to a probability distribution \(P_{i}(\cdot;\theta^{0})\) with a mean \(\mu_{i}\big{(}\theta^{0}\big{)}\) as \(\mu_{0}^{i}\). We set an array \(A_{k}\) in order to put the arm whose absolute value of the difference between \(X_{k-history}\) and \(X_{k-cur}\) is less than the expected error of the current arm \(mis\) and obtain average expected error \(avg\) in this process. The \(mis\) from the current round denotes that is the threshold between the total number of arm pulls \(S\) and the number of the current arm pulled \(s\). Then, by using this probability of the number in \(A_{k}\), we choose the process of exploring or exploiting. Let \(\hat{X}_{k,s}\) be the empirical mean of arm \(k\) after \(s\) pulls of this arm. When the explore process begins, we control index value \(V_{k,s}\) through \(\hat{X}_{k,s}\), and the problem is parameterized by \(P_{i}(\cdot;\theta^{0})\) on \([0,1]\). The goal of the agent is to select a sequence of actions that maximizes the expected cumulative reward \(\mathbb{E}\Big{[}\sum\limits_{t=1}^{T}u_{a(t)}\big{(}\theta^{0}\big{)}\Big{]}\), where the reward vectors are identically distributed and independent at different times. When the agent does not know the true parameter \(\theta^{0}\), the optimal arm from parameter \(\theta\) is denoted as \(a^{*}(\theta)=argmax_{k\in[K]}u_{k}(\theta)\). Clearly, the optimal choice is to select the best decision or the best arm that with the maximum mean value (\(\mu_{i}\big{(}\theta^{0}\big{)}\)), at this time \(a(t)=a^{*}\big{(}\theta^{0}\big{)}\). We assume that the agent knows the set of possible parameters \(\Theta\) and \(\Theta\) is a finite set. We denote the set \(A=\{a^{*}(\theta|\theta\in\Theta)\}\), which is the collection of optimal arms corresponding to all parameters \(\theta\). The expected cumulative regret of a stochastic bandit algorithm with respect to the best constant decision is defined as  
\[\mathbb{E}[R(T)]=\mathbb{E}\bigg{[}\sum\limits_{t=1}^{T}\Big{(}u_{a^{*}( \theta^{0})}\Big{(}\theta^{0}\Big{)}-u_{a(t)}\Big{(}\theta^{0}\Big{)}\Big{)} \bigg{]} \tag{1}\]  
The minimax rate of the expected cumulative regret is to minimize \(\mathbb{E}[R(T)]\), where the minimal \(\mathbb{E}[R(T)]\) is taken over all cases and the supremum over \(P_{i}(\cdot;\theta)\) for the stochastic case with rewards in \([0,1]\).
## 5 Conclusions  
This paper focuses on the experimental performance evaluation of an advanced sliding mode control scheme, namely the Synchronization Time Delay Estimation Nonlinear Fast Terminal Sliding Mode Control (Syn-TDE-NFTSMC) scheme that was developed in [15]. Overall, by leveraging synchronization errors and integrating the Time Delay Estimation (TDE) method with a Nonsingular Fast Terminal Sliding Mode Controller(NFTSMC), the newly developed control scheme effectively minimized synchronization errors while maintaining computational efficiency. The TDE approach eliminated the need for the explicit computation of complex manipulator dynamics, making the control scheme particularly suitable for real-time applications. Furthermore, the stability of the control system was rigorously proven using the Lyapunov Theorem, ensuring uniform stability throughout its operation. The results of experiments conducted to evaluate the control performance of the newly developed control scheme when it was used to control the motion of a real 2-DOF CKCM showed that the newly developed control scheme outperformed existing control schemes. The obtained results underscore the practicality and reliability of the Syn-TDE-NFTSMC scheme for achieving precise synchronization and robust control in CKCMs. The ability to adapt to different manipulator configurations while reducing computational complexity further enhances its appeal for real-world robotic applications.  
Future research directions include conducting an experimental study of the newly developed control scheme using manipulators with higher degrees of freedom (DOF). While the Syn-TDE-NFTSMC scheme has demonstrated a strong performance in a 2-DOF system, scaling it to higher-DOF manipulators poses new challenges. The new challenges could include increased computational demand, more complex joint interactions, and a higher burden on synchronization error estimation and time delay approximation. To address these challenges, future work could focus on algorithmic optimizations to maintain real-time performance and consider the application of adaptive and intelligent control techniques. These enhancements would aim to preserve the robustness and precision of the control strategy while ensuring scalability to more complex robotic platforms.  
Conceptualization, T.T.C.D., C.C.N. and T.D.T.; methodology, T.T.C.D. and C.C.N.; software, T.T.C.D. and C.C.N.; validation, T.T.C.D. and C.C.N.; results analysis, T.T.C.D. and T.D.T.; investigation, C.C.N., T.T.C.D. and T.D.T.; data curation, T.T.C.D. and T.D.T.; writing--original draft preparation, C.C.N., T.T.C.D. and T.D.T.; writing--review and editing, C.C.N. and T.T.C.D.; visualization, C.C.N. and T.T.C.D.; supervision, C.C.N. and T.T.C.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
All authors announce that they have no conflicts of interest in relation to the publication of this article.
## 3 Datasets  
To evaluate the performance of the proposed summarization model, two text summarization datasets, DUC-2002 [27] and CNN/DailyMail [28], were used in the experimental  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Main Contribution** & **Advantages** & **Disadvantages** \\ \hline
[12] Graph + LLM & Context length reduction & Performance boost & Model heavily dependent \\  & Converts graphs to text for & Effective with suitable &
\begin{tabular}{c} Model heavily dependent \\ on graph and LLM type \\ Large-scale graphs less \\ effective \\ \end{tabular} \\
[15] Benchmark for LLM & Highlights critical factors & Good insight into LLM & LLM lags behind \\ Graph Understanding & affecting LLM & limits & graph-specialized models \\
[16] GoSum & High ROUGE on & Effective summarization & Performance depends on \\  & PubMed/arXiv datasets & & document structure \\
[17] MultiBART-GAT & Abstractive summarization & Controls info accuracy & Computationally \\  & and info accuracy & & expensive \\
[18] HeterMDS & Maintains structure and & High-quality summaries & High integration cost \\
[19] Protein Interaction & High-accuracy interaction & Meaningful explanations & Domain-specific \\ Pred. & predictions & & \\
[20] Discourse, Action & Improves summary & Success in automatic and & Complexity of graphs \\ Graphs & correctness & human evals & Dataset dependency and \\
[21] Document-Level & Significant success on & Handles long documents & \begin{tabular}{c} Dataset dependency and \\ well \\ high cost \\ No context integrity, \\ repetitive sentences \\ \end{tabular} \\ Graphs & \begin{tabular}{c} Task transferability \\ improvements \\ MIS-based structural \\ filtering with LLM \\ integration \\ \end{tabular} & \begin{tabular}{c} Hyperposed node \\ connections \\ Overcomes OOV issue \\ \end{tabular} & \begin{tabular}{c} Ignores semantic similarity \\ across graphs \\ \end{tabular} \\ Proposed framework & \
\begin{tabular}{c} High summarization \\ accuracy, lower cost \\ \end{tabular} &
\begin{tabular}{c} Potential information loss \\ in short summaries \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of the proposed method and state-of-the-art approaches.  
process. These datasets were selected to compare the model's performance across different document types and to quantitatively assess its generalization ability. Evaluating the model on diverse contexts is essential. Some information about the datasets is presented in Table 2.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 4 Results and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Device** & **HH** & **HV** & **VH** & **VV** \\ \hline \multirow{4}{*}{Region 1} & CR-1 & 0.8435\(\angle\)2.28\({}^{\circ}\) & 0.0003\(\angle\)125.94\({}^{\circ}\) & 0.0005\(\angle\)74.88\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-2 & 0.9064\(\angle\)0.52\({}^{\circ}\) & 0.0003\(\angle\)–17.10\({}^{\circ}\) & 0.0008\(\angle\)7.46\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-3 & 0.8854\(\angle\)4.08\({}^{\circ}\) & 0.0002\(\angle\)37.09\({}^{\circ}\) & 0.0007\(\angle\)13.82\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-4 & 0.9579\(\angle\)3.44\({}^{\circ}\) & 0.0004\(\angle\)–165.56\({}^{\circ}\) & 0.0002\(\angle\)115.59\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \hline \multirow{4}{*}{Region 2} & CR-5 & 0.9372\(\angle\)1.74\({}^{\circ}\) & 0.0003\(\angle\)–50.99\({}^{\circ}\) & 0.0008\(\angle\)8.84\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-6 & 0.8981\(\angle\) –0.27\({}^{\circ}\) & 0.0003\(\angle\)178.60\({}^{\circ}\) & 0.0004\(\angle\)43.77\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-7 & 0.9820\(\angle\)1.84\({}^{\circ}\) & 0.0001\(\angle\)176.39\({}^{\circ}\) & 0.0006\(\angle\)17.80\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-8 & 0.8343\(\angle\)1.70\({}^{\circ}\) & 0.0005\(\angle\)–44.93\({}^{\circ}\) & 0.0007\(\angle\)0.91\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-9 & 0.8870\(\angle\)4.36\({}^{\circ}\) & 0.0003\(\angle\)158.25\({}^{\circ}\) & 0.0004\(\angle\)61.74\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-10 & 0.8534\(\angle\)0.72\({}^{\circ}\) & 0.0004\(\angle\)176.84\({}^{\circ}\) & 0.0004\(\angle\)152.01\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-11 & 0.9418\(\angle\)4.37\({}^{\circ}\) & 0.0009\(\angle\)70.53\({}^{\circ}\) & 0.0009\(\angle\)59.48\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \cline{2-6}  & CR-12 & 1.0000\(\angle\)0.00\({}^{\circ}\) & 0.0007\(\angle\) –123.76\({}^{\circ}\) & 0.0007\(\angle\) –103.58\({}^{\circ}\) & 0.9605\(\angle\) –1.62\({}^{\circ}\) \\ \cline{2-6}  & CR-13 & 0.9716\(\angle\)13.11\({}^{\circ}\) & 0.0024\(\angle\)–140.14\({}^{\circ}\) & 0.0048\(\angle\) –153.06\({}^{\circ}\) & 1.0000\(\angle\)0.00\({}^{\circ}\) \\ \hline \end{tabular}
\end{table} 
Table 5: Calibration results obtained from the distortion matrix corresponding to Group 1.
## 3 Results
### Emission Characteristics  
#### 3.2.1 Unburned Hydrocarbon Emissions (HC)  
Figure 5 shows HC emissions for _MOB_ and its blends with diesel at different engine speeds. Average HC emission is higher for diesel than that for MOB10 and MOB20 by 6.71% and 8.79%, respectively. Furthermore, the fuel blends containing 20% of biodiesel have higher HC emissions at low speeds compared to those containing 10% of biodiesel.  
Figure 4: Variation in BTE with varying engine speed for biodiesel blends and diesel.  
Figure 5: Unburned hydrocarbon emissions of _Moringa deifera_ biodiesel blends and diesel at varying engine speeds.  
Moreover, it can be observed that each tested fuel had higher HC emissions when the engine was running at lower speeds. Conversely, the amount of HC emissions decreased when the engine's speed was higher. The lean air-fuel mixture is the primary reason for more HC emissions at lower engine speeds as well as poor fuel distribution. The lower temperature and presence of excess air are responsible for lean air-fuel mixtures [42]. Over-rich and over-lean air-fuel mixtures are typical during heterogeneous combustion in diesel engines, which leads to HC emissions. The oxygen content of biodiesel generally leads to lower HC emissions than diesel at high engine speeds due to improved fuel combustion [57].
## 3 Results and Discussion
### Continuous Observation Experiment
#### 3.4.3 The Connection between Wind Fields and VOCs
It is worth noting that there was light rain in the selected area on the night of the 4 th, and the gradual accumulation of humidity caused the VOC concentration to decrease to 17.8 ppb in the early morning of the 5 th. At around eight o'clock in the morning of the 5 th, VOCs accumulated easily and quickly due to vehicle exhaust emissions, low wind speed, and high humidity. As seen from the above observations, the VOCs at IP sites experience noticeable bimodal changes throughout the day. At the IP site, the VOC concentrations are at a low level during the night but gradually rise in the morning, peaking at around 8:00 a.m. At this time, motor vehicle emissions are the primary source of pollution in the area. Incomplete fuel combustion leads to higher pollutant levels when the vehicles slow down, accelerate, and are idle. As solar radiation increased gradually, photochemical reactions increased, atmospheric diffusion conditions improved, and the VOC concentration declined. It then drops and reaches the lowest level. After that, concentrations continue to increase, with another short-lived peak occurring at night. The reasons for this variation are closely related to the emission of pollution sources, meteorological conditions, and chemical reactions. At night, the atmospheric boundary layer is lower, and the wind vector is smaller, leading to a slower dispersion of pollutants. In addition, VOCs accumulate over long periods at night due to industrial emissions. Moreover, stronger solar radiation facilitates the participation of VOCs in photochemical reactions to produce ozone, and the oxidation of atmospheric VOCs with free radicals consumes large amounts of VOCs, thereby leading to a decrease in VOC concentrations.
# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
## 3 Materials and Methods
### Temporal Alignment of the Signals of the Ensemble of WTs  
When extracting the signals from each of the turbines, it is observed that the number of samples differs slightly, so that if, for example, looking at the 10,000 th sample of the same signal from each turbine, their data-times do not match. The first step of our method is therefore to temporarily align the signals of all turbines The turbine with the highest number of records is WT81 with 215,613 and the one with the lowest number of records  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable Name** & **Short Description** \\ \hline _wgen\_avg\_GrTmp\_phsA_ & Average temperature of the generator’s winding, phase A \\ _wgen\_avg\_GrTmp\_phsB_ & Average temperature of the generator’s winding, phase B \\ _wgen\_avg\_GrTmp\_phsC_ & Average temperature of the generator’s winding, phase C \\ _wgen\_avg\_RtrSpd\_IGR_ & Average speed of the rotor at the inductive sensor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Temperature and angular velocity signals corresponding to the generation system used to present the method. The measurements are 5-min averages.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable Name** & **Short Description** \\ \hline _wtrm\_avg\_TrmTmp\_Gbx_ & Gearbox Temperature \\ _wtrm\_avg\_TrmTmp\_GbxOil_ & Gearbox oil temperature \\ _wtrm\_avg\_TrmTmp\_GbxBrg151_ & Gearbox bearing temperature at point 151 \\ _wtrm\_avg\_TrmTmp\_GbxBrg451_ & Gearbox bearing temperature at point 451 \\ _wtrm\_avg\_TrmTmp\_GbxBrg450_ & Gearbox bearing temperature at point 450 \\ _wtrm\_avg\_TrmTmp\_GbxBrg152_ & Gearbox bearing temperature at point 152 \\ _wtrm\_avg\_TrmTmp\_GbxBrg452_ & Gearbox bearing temperature at point 452 \\ _wtrm\_avg\_TrmTmp\_GbxCINiBrk_ & Generator cooling system, cold side (l inlet from radiator) \\ _wtrm\_avg\_TrmTmp\_GbxCINiPrk_ & Generator cooling system, hot side (outlet to radiator) \\ _wtrm\_avg\_TrmTmp\_Brg1_ & Temperature transmission bearing 1 (main bearing system) \\ _wtrm\_avg\_TrmTmp\_Brg2_ & Temperature transmission bearing 2 (main bearing system) \\ _wtrm\_avg\_TrmTmp\_GnBrg2_ & Temperature transmission bearing 2 (main bearing system) \\ _wtrm\_avg\_TrmTmp\_GnBrgDE_ & Generator Bearing Temperature 1 \\ _wtrm\_avg\_TrmTmp\_GnBrgDE_ & Generator Bearing Temperature 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Temperature and pressure measurements collected in the transmission subsystem with a brief explanation. The measurements are 5-min averages.  
is WT84 with 210,689. Therefore, the records whose data-time were not available for all turbines were removed. After completing the alignment process, 201,444 samples on all the turbines were available. This means that 0.0657% of samples from WT1 and 0.0439% from WT4 were removed. This is three orders of magnitude lower than the volume of data lost due to the general failure of the SCADA system.
## 2 Study Area and Methods  
### Investigation  
The construction of Section 3 of the central Yunnan Water Diversion Project is located in Songgui Town, Heqing County, Dali Prefecture. The section spans approximately 26.542 km in length. The depth of the TBM excavation section varies between 450 and 1400 m, and a significant segment, approximately 20.4 km, exceeds 600 m in depth. The excavation profile for the TBM section is circular, with a diameter of 9.8 m. Initial support incorporates steel mesh sprayed concrete and steel arches, while the secondary lining consists of cast-in-place C30 reinforced concrete enveloping the tunnel's entire circumference.  
The TBM excavation predominantly comprises basalt, limestone, dolomitic limestone, and dolomite. In sections with basalt under high in situ stress, rockburst risks emerge. The tunnel traverses weak rock formations, such as mudstone and shale, susceptible to significant deformations due to groundwater's softening impact. There are many bad geological holes involved in this bid section, among which the V category accounts for more than 57.6%. The internal construction of the tunnel is shown in Figure 1.  
The mechanical parameters of the rock mass derive from those of Class V surrounding rock in the numerical simulations. Steel arches are represented as cable elements, spaced at 1 m intervals. The reinforcement and sprayed concrete layer are abstracted as shell elements and paired with the steel arches to shape the support structure. Table 1 lists specific parameters.
## 3 Results
### Impact of CNCORS on BDS/GPS Joint POD
#### 3.1.2 Comparison with GBM Orbit  
BeiDou orbits from strategies 1 and 2 are compared with the GBM products. The results in Table 5 show that the precision of the overlapping arcs is not the same as the comparison with GBM. The results in the cross-track direction vary significantly. The average difference of the GEO satellites exceeds 3 m. The difference of strategy 1 from the GBM products in all three directions is also larger compared to strategy 2. These results occurred because, similar to strategy 2, the GBM POD adopts few stations in China; therefore, the difference between the orbit results and strategy 2 is relatively small. Given the use of more stations, the precision of the POD in strategy 1 should be improved.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Satellite Type**} & \multirow{2}{*}{**PRN**} & \multicolumn{2}{c}{**Along-Track**} & \multicolumn{2}{c}{**Cross-Track**} & \multicolumn{2}{c}{**Radial**} & \multicolumn{2}{c}{**3D**} \\ \cline{3-10}  & & **S1** & **S2** & **S1** & **S2** & **S1** & **S2** & **S1** & **S2** \\ \hline \multirow{6}{*}{GEO} & C01 & 217.5 & 227 & 287.3 & 287.8 & 11.5 & 10.8 & 369.6 & 376.8 \\  & C02 & 256.7 & 169.3 & 367.4 & 366.1 & 13.8 & 13.5 & 455.8 & 410.1 \\  & C03 & 61.7 & 108.1 & 430.3 & 430.7 & 22.0 & 22.1 & 443.8 & 456.1 \\  & C04 & 182.7 & 122.4 & 67.6 & 89.7 & 34.8 & 37.1 & 200.3 & 172.4 \\  & C05 & 144.1 & 123.1 & 351.5 & 348.9 & 13.7 & 14.1 & 397.6 & 385.6 \\  & **mean** & **172.5** & **150.0** & **300.8** & **304.6** & **19.2** & **19.5** & **373.4** & **360.2** \\ \hline \multirow{6}{*}{IGSO} & C06 & 11.7 & 8.9 & 20.4 & 17.1 & 6.4 & 7.1 & 24.5 & 20.0 \\  & C07 & 10.7 & 12.4 & 21.7 & 18.1 & 10.7 & 11.2 & 26.6 & 24.7 \\ \cline{1-1}  & C08 & 14.3 & 12.2 & 19.6 & 18.6 & 7.6 & 9.8 & 25.6 & 23.9 \\ \cline{1-1}  & C09 & 13.5 & 15.5 & 19.3 & 17.9 & 5.9 & 7.8 & 24.5 & 25.3 \\ \cline{1-1}  & C10 & 17.2 & 20.4 & 21.0 & 19.8 & 8.8 & 9.2 & 28.8 & 32.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with GBM orbit from strategies 1 and 2 (unit: cm).  
Figure 2: Comparison of overlapping arcs precision between strategies 1 and 2. (**a**) 3 DRMS; (**b**) Along-track RMS; (**c**) Cross-track RMS; (**d**) Radial RMS.
## 1 Introduction
In [16], the aforementioned challenges are analyzed in detail, including the review of several research projects that have been conducted in an effort to overcome these challenges. This paper differs fundamentally from [16] in that it reviews existing techniques that have been proposed in the literature for HCWSNs with the goal of identifying the gaps between their capability and desired HCWSN performance, and then motivate further research to close the gaps. The techniques that were reviewed in this paper span multiple layers including data link (MAC), network (routing), and transport (end to end reliability). The treatment presented in [16] does not encompass multiple layers. The organization of this paper is as follows: Section 2 provides a survey of major currently existing HCWSNs. Noting that the MAC and routing protocols strongly influence the reliability, energy efficiency and timely data delivery in HCWSNs, the next two sections are devoted to these protocols. Specifically in Section 3, we review the existing MAC protocols and their suitability in a HCWSN. Section 4 details the characteristics and operation of routing protocols that have been implemented in the HCWSNs reviewed in Section 2. In Section 5, several transport layer protocols and a network coding based protocol are reviewed to determine their ability to achieve and maintain end to end reliable communication in HCWSNs. Section 6 offers a discussion of several open issues that require further research. The conclusions are presented in Section 7.