esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 3 The Notion of Event Detection and Evolution  
There are several important definitions used in this paper. The goal of topic detection is to find, organize and utilize multilingual information from a variety of online data by topic [17]. The concept of \"topic\" is no longer equivalent to the \"topic\" in information retrieval, not a certain \"field\", but a relatively specific \"event\" [18]. In some cases, \"topic\" and \"event\" can be used in general without a strict distinction. Topics can also be thought of as unusual things that happen in a specific time or place. In order to distinguish it  
Figure 2: Statistics about the different kinds of methods in the literature review.  
from linguistic concepts, the TDT evaluation conference defined relevant elements [19] as follows:  
Topic (definition): Topic consists of a seed event or activity, and all the subsequent events or activities directly related to it. A topic starts with an event \(e_{1}\) and followed by other related events \(\{e_{2},e_{3}\cdots\}\). Each event \(e\) contains a set of stories \(\{s_{1},s_{2},\cdots s_{i}\cdots\}\) and a topic consists of a collection of events \(\{e_{1},e_{2},\cdots e_{i}\cdots\}\).  
Event (definition): Event is special cases caused by specific reasons and conditions, occurring at some special times and places, and may be accompanied by specific consequences. An \"event\" is different from the definition of \"topic\". A \"topic\" can be regarded as a set of events. So, in some special situations, \"event\" and \"topic\" can be viewed as the same concept.  
Event evolution (definition): The evolution of topics refers to the existence of identity and correlation among topics at adjacent time, which is evolution relationship of these topics with the change of time. The identity of topic is measured by the similarity of topic phrase. The relation between topic and subsequent topic is defined by the semantic correlation degree of topic.
## 3 Case Study Analysis
### Case Study 6: Drilling Assets  
This case is related to a drilling rig, including hydraulic power units (HPUs), sheaves, and racker systems. The drilling assets are maintained on the basis of a reliability-centered maintenance (RCM) program, which covers comprehensive inspection activities. Therefore, several exploration projects have been initiated to (1) monitor the HPU, (1) predict the evolution of wear in the sheaves, and (1) update the RCM program by replacing, as much as possible, the inspection tasks with condition-based maintenance.  
Several emerging practices have been observed to enable this improvement direction, summarized in Table 8. First, new visualizations of the master data are created to capture critical components, failure modes, failure rate, mean time between failures, and mean time to repair. The second emerging practice is related to a new predictive model to reduce the dimensions of performance and health data. The third emerging practice is related to a new predictive model to estimate sheave wear over time. Sheaves are inspected annually where the depth of the groove is measured. The groove depth changes millimetres from year to year and it is hard to monitor that using sensors. Thus, a predictive maintenance model based on sheave physics and dynamics is proposed to come over condition-based maintenance. The yearly measurements are used to fine-tune the predictive model. The fourth emerging practice is related to a new model to estimate the effectiveness of the RCM program for the racker system and revise the RCM to merge the concept of a CBM for several failure modes. The fifth emerging practice is related to a new guideline for handling and separating maintenance master data. The master failure data have shown  
\begin{table}
\begin{tabular}{l l} \hline \hline
**No.** & **Emerging Practice** \\ \hline
1 & New worksheet to determine maintenance concept and best available technology \\
2 & New worksheet to determine diagnostic coverage of valve monitoring system \\
3 & New predictive model to estimate test passing probability \\
4 & New guideline to explore readiness to build model-based maintenance and test concept \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of emerging practices in Case study 5.  
failure distributions with bi-model shapes, where two groups of failures are indicated, namely minor failures that have a short time to repair and major failures that have a long time to repair.  
The observed impacts of these emerging practices are related to the \"planning\" and \"analysis\" functions. Data-driven predictive maintenance for HPUs and model-based predictive maintenance for sheaves have fed the planning function with new data to prioritize and schedule maintenance tasks. In addition, emerging practices have enriched the \"analysis\" function with several data visualizations that are required to provide insight for planning and recommended actions for improvement. It is also expected that these emerging practices might provide earlier fault detection that enables the utilization of opportunistic maintenance and keeps the level of damage at a minimum.
# Characterization and Separation of Traditional and Bio-Plastics by Hyperspectral Devices
## 3 Results and Discussion
### Tools for Determining Spectral Indices Useful for Polymers' Separation
#### 3.1.1 Band Depth  
The Band Depth (BD) is introduced to identify the amplitude of the main absorption peaks of the polymers investigated.  
\[BD(\lambda_{i})=1-CRR(\lambda_{i}) \tag{1}\]  
where \(BD(\lambda_{i})\) is the Band Depth at wavelength \(\lambda_{i}\) and \(CRR(\lambda_{i})\) is the value of reflectance at wavelength \(\lambda_{i}\) after CR.  
Virgin materials present spectral signatures which are considered as the most representative of the polymer and are then employed to compute the band depth.  
Table 2 contains the number of spectral signatures (n), the average reflectance value or Continuum Removal Reflectance (calculated employing the n spectral signatures of the pixels within the ROI) at wavelength \(\lambda\) \(\left(\overline{\text{CRR}}_{\lambda}\right)\), the standard deviation related to the average reflectance (\(\sigma_{\lambda}\)) and the corresponding value of Band Depth (BD\({}_{\lambda}\)). The Band Depth computation is carried out on spectral signatures after the application of the continuum removal procedure.  
Figure 7: Spectral signatures of PLA samples in the NIR region, detected with linear spectrometer, processed with the method Continuum Removal (CR).  
The most evident absorption peaks are located at wavelengths 1660 nm (Band Depth equal to 0.508) for PET, 1170 nm (Band Depth equal to 0.509) for PLA and at 1150 nm (Band Depth equal to 0.522) for PS.
# BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning
## 4 Usage Examples  
The RSS measurements, their labels, the beacon deployment and the environment description are the most significant value of the provided material. However, the Matlab(r) scripts shipped with the data are also relevant, as they not only provide examples on the data usage, but also can be considered an introduction to problems that Indoor Positioning Systems implementers face for BLE-based positioning. The examples presented in the following subsections explore the effects of the measurement device, the environment and the beacon deployment on the detection and strength of the BLE advertisements, and therefore on the positioning accuracy. These examples, although they show issues for which solutions are already proposed [3; 11; 20], are starting points for further refinements to improve the BLE-based positioning techniques.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 2 Materials and Methods
### Proposed FRA6\(\sigma\) Approach--Define Phase
#### 2.1.1 Step 1: Collect FRA Data--Measure Phase  
Step one in the proposed FRA6\(\sigma\) assessment comprises the collection of the FRA data to ascertain a reference serving as a fingerprint or baseline and appraise variations over time. In the beginning, the FRA fingerprint measurement is attained when the transformer is in perfect operating condition typically at the manufacturer's premises during factory acceptance testing. This measurement secures the transformer's frequency response in decibels (dB) spanning from approximately 20 Hz to 2 MHz frequency range, establishing a \"fingerprint\" for comparison. To ascertain the accuracy, the testing equipment (such as the M5500 Sweep Frequency Response Analyzer or Bode 100 Frequency Response Analyzer) is calibrated and ambient factors including temperature and electrical noise are kept to a minimum. Subsequently, the latest FRA (\(X_{latest}\)) measurement is carried out by the same test-bench. This measurement mirrors any kind of mechanical or electrical shifts that may have eventuated owing to transportation, aging, mechanical stress, or insulation degradation. Both record sets are logged methodically for additional statistical analysis.  
Figure 3: Normal distribution curves illustrating Six Sigma assumptions, mean shifts, specification limits (USL, LSL), and defect probabilities with a 1.5\(\sigma\) shift [44].  
Figure 2: Methodology of the proposed FRA6\(\sigma\) approach.  
Retaining uniformity between the fingerprint and \(X_{latest}\) is fundamental to tracking down anomalies correctly. Any considerable differences in the \(X_{latest}\) response suggests latent transformer faults necessitating further examination.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Attack Detection in Smart Grid
#### 4.3.1 Largest Normalised Residual (LNR)  
The LNR method uses the normalised residual of each measurement to identify the measurements with the largest deviation from the expected threshold. The measurement with the largest normalised residual is assumed to be bad and will be removed or corrected from the estimation process. The state estimation is then repeated using the remaining measurements, and the process is repeated until no bad measurements are left [80; 81]. The normalised residual \(r_{i}^{N}\) is the deviation measurement of the residual that can be calculated using the following equation:  
\[r_{i}^{N}=\frac{|r_{i}|}{\sqrt{\Omega_{ii}}}=\frac{|r_{i}|}{\sqrt{S_{ii}R_{ii}}} \tag{7}\]  
in which \(\Omega_{ii}\) is the \((i,i)\)-th element of the error covariance matrix, which combines the measurement noise and modeling errors. It represents the uncertainty of the \(i\)-th measurement, which takes into account both the measurement noise \(\epsilon\) and the errors in the model used to estimate the measurement  
\[\Omega=E[e\epsilon^{T}]=SR \tag{8}\]  
in which \(S_{ii}\) is the diagonal element of the inverse of the covariance matrix of the residual vector \(\epsilon\), which represents the variance of the residual between the measured and estimated values; \(R_{ii}\) is the variance of the measurement noise in the i-th measurement. After identifying the measurement, there are two main solutions to process the data: (1) remove the bad data from the measurement dataset; however, the removal of bad data may result in the loss of observability in SE [80]; and (2) correct the bad data using the relation between measurement errors and residuals calculated following the equation below  
\[z_{i}^{corr}=z_{i}^{bad}-\frac{R_{ii}}{\Omega_{ii}}r_{i} \tag{9}\]  
The LNR algorithm's limitations are obvious; the LNR algorithm has to process the bad data sequentially and restart a WLS algorithm for SE. The computational complexity makes it difficult to handle multiple bad data simultaneously, which is not suitable for large-scale power systems [80].
## 4 Calculation of Various Reliability Indices
### Reliability of the WSN
#### 4.1.4 WSN Reliability with Fluctuation in Power Unit Failure  
The power supply plays a crucial role in the performance of the WSN. To optimize the operation of a WSN network, a standby power supply is one alternate option. Here, in the considered WSN, the author has considered the power supply as the main power supply and the standby power supply. The effect of the power supply/standby power supply failure on WSN reliability has been studied by varying it between 0.04, 0.05, 0.06, and 0.07 in the reliability expression obtained in Equation (20). The behavior is shown in Figure 5d and Figure 5e, respectively.
# The Outdoor Field Test and Energy Yield Model of the Four-Terminal on Si Tandem PV Module
## 2 Methods
### Performance Model  
The power output of the module is the product of the short-circuit current, open-circuit voltage, and fill factor (_FF_). First, _FF_ was calculated by the ratio of the spectrum mismatching. The calculation step is as follows. First, generating a correlation chart between calculated _FF_ and the ratio of mismatching at first using random numbers. Then, the trend curve of these two parameters was fitted to the parabolic curve. Finally, the _FF_ was represented as the function of the spectrum-mismatching ratio. The short-circuit current can be calculated as the integral of the product of the spectral irradiance and spectral efficiency of the module, which can be derived from the external quantum efficiency affected by spectrum mismatching. The angular characteristics in the photon absorption were measured in advance. The detailed calculation procedure was identical to our previous work [97].  
We also needed to consider the coupling between spectrum and angles. The atmospheric parameters are dependent on each other. For example, a combination of the incident angle modifiers and the orientations results in a diverse mixture of the direct and the diffused sunlight. The atmospheric parameters were calculated by the spectrum, by a data-fitting calculation called the Bird's model [98; 99] at the University of Miyazaki [100]. The model for this analysis is given in Figure 5[1]. The nonlinear effect from distributed effects often seen in III-V (especially concentrator cells) was not considered [101; 102; 103].  
Figure 4: Seasonal trend of atmospheric parameters in Miyazaki [97]: (**a**) Seasonal trend of aerosol optical depth; (**b**) Seasonal trend of precipitable water.  
Figure 5: The performance model of the tandem solar cells, considering the spectrum and angle interaction [1].
# Trusted Composition of Internet of Medical Things over Imperfect Networks
## 7 Behavior Implementation Using BLESS State-Transition Machine
### Transitions  
Transitions have a label, a source state, a transition condition, a destination state and, optionally, an action. Transitions leaving execution states have boolean expression for transition conditions, while transitions leaving complete states have dispatch conditions. Listing 5 shows transitions implementing the Respiration_Monitor_Thread depicted in Figure 5.  
```
transitions go:start-[]->run{ got_rp:run-[ondispatch]->check_echo { RP_Rate?(rpr) <<rpr=RESPIRATION_RATEandMRP_A()andRA()>> ; if(rpr>=mrp)-> { <<rpr=RESPIRATION_RATEandrpr>=mrpandMRP_A()andRA()>> rma:=false&RM_Alarm!(false) <<RA()andMRP_A()>>) [] (rpr<mrp)-> { <<rpr=RESPIRATION_RATEandrpr<mrpandMRP_A()andRA()>> rma:=true&RM_Alarm!(true) <<RA()andMRP_A()>>) fi }-endofgot_rp got_echo:check_echo-[ondispatchRM_Alarm_Echo]->run { <<RA()andMRP_A()andRM_Alarm_Echo@now >> declareecho_valboolean:=RM_Alarm_Echo@now <<RA()andMRP_A()and(echo_valiffRM_Alarm_Echo)>> {if(echo_valiffrma)->skip-echoassane [] (not(echo_valiffrma))->Lost_Confirmation!-echoassdifferent fi <<RA()andMRP_A()>> } <<RA()andMRP_A()>> }-endofgot_echo late_echo:check_echo-[ondispatchtimeout(RM_AlarmorRM_Alarm_Echo) IoMT_Properties:RM_Echo_Limits]->run{Lost_Confirmation! } **); endRespiration_Monitor_Thread.impl;
```  
Listing 5: Repiration Monitor transitions section.  
* Transition go, with empty execute condition being true, changes state from start to run.
* Transition got_rp, with dispatch condition on dispatch, leaves the run state periodically (every 20 s), performs an action, then changes state to check_echo. Its action reads the current respiration rate, sends an alarm (true) if less than the minimum respiration rate, otherwise sends alarm false.
* Transition got_echo, with dispatch condition on dispatch RM_Alarm_Echo, leaves the check_echo state when an event is received on port RM_Alarm_Echo, performs an action, then changes state to run. Its action compares the boolean value of RM_Alarm_Echo with the alarm value sent during the action of transition got_rp, and sends an event out port Lost_Confirmation if different.
* Transition late_echo also leaves check_echo state, and changes state to run after sending an action out port Lost_Confirmation, but only if its timeout dispatch condition is met. The timeout starts when a value is sent from port RM_Alarm. If RM_Alarm_Echo has not been received within RM_Echo_Limit seconds later, the dispatch condition has been met.  
Thus, Respiration_Monitor_Thread.impl employs three different kinds of dispatch triggers in its dispatch conditions.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 2 Related Work
### Particle Update Strategy in LMOCSO  
The particle search strategy in LMOCSO is proposed based on CSO. The idea of CSO is to randomly divide the population with size \(N\) into \(N/2\) competing particle pairs. The winner and loser individual were selected according to the fitness value in each particle pair, and then the loser learned from the winner. The updated equation for the loser in the CSO is as follows:  
\[v_{l}(t+1)=r_{0}\times v_{l}(t)+r_{1}\times(x_{w}(t)-x_{l}(t)) \tag{6}\]  
\[x_{l}(t+1)=x_{l}(t)+v_{l}(t+1) \tag{7}\]  
where \(r_{0}\) and \(r_{1}\) are random numbers in the range of 0 to 1, \(v_{l}(t)\) is the velocity vector of the \(t\)-generation loser individual, and \(x_{w}(t)\) and \(x_{l}(t)\) are the position vectors of the \(t\)-generation winner and loser individuals, respectively.  
According to Equation (6), the updating trajectory of the loser is shown in Figure 1a. It can be intuitively seen that the loser rotation converges to the winner with a slower convergence speed.  
[PERSON] [42] proposed LMOCSO to accelerate the particle update speed in CSO. The idea of LMOCSO is that the loser first updates based on its impetus, and then the updated loser learns from the winner. It differs from the loser individual update method in CSO, in which the loser first learns from the winner individual and then updates itself according to the impetus. The loser updating trajectory in LMOCSO is shown in Figure 1b.  
According to the idea of LMOCSO and Figure 1b, we can see that the loser at position \(x_{l}(t)\) is first updated based on its impetus to reach the position \(x^{\prime}_{l}(t)\). The updated equation is as follows:  
\[x^{\prime}_{l}(t)=x_{l}(t)+r_{0}v(t) \tag{8}\]  
Then, the updated loser \(x^{\prime}_{l}(t)\) learns from the winner, where \(\Delta^{\prime}(t)\) is the learning vector.  
\[\Delta^{\prime}(t)=x_{w}(t)-x^{\prime}_{l}(t) \tag{9}\]  
Therefore, the updated equation of the loser is as follows:  
\[v_{l}(t+1)=r_{0}v_{t}(t)+r_{1}\Delta^{\prime}(t) \tag{10}\]  
\[x_{l}(t+1)=x_{l}(t)+v(t+1) \tag{11}\]  
For more information about LMOCSO, please refer to [42].  
Figure 1: Updating trajectories of the loser with two-dimensional decision variables. (**a**) Updating trajectories of CSO. (**b**) Updating trajectories of LOMCSO.
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 5 Conclusions  
This work proposes the Sub-log MPA detector for the SCMA system by using the latest probabilities at the current iteration to update the messages of the remaining nodes, which reduces both the number of iterations to converge and the cost of hardware design. Next, we describe the conventional uniform quantizer for the SCMA system, whose range of the probabilities is limited. To address this problem, we propose a novel quasi-uniform quantizer, which can effectively extend the dynamic range of the quantizer without modifying the RN and the VN message exchange steps. Simulation results confirmed the robustness of the proposed Sub-log MPA detector and the quasi-uniform quantizer for both the AWGN and Rayleigh channels. Future works may focus on more practical issues such as the quantization of multiple-input multiple-output SCMA and low-density parity-check-coded SCMA systems.  
Conceptualization, H.C., M.Z. and R.S.; methodology, H.C.; writing-original draft preparation, H.C.; writing-review and editing, H.C., M.Z. and R.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Grants 62301271 and 62401290, the open research fund of the National Mobile Communications Research Laboratory, Southeast University (No. 2025D13), and the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications under Grants No. NY222083 and No. NY222082.  
Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
The authors declare no conflicts of interest.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 2 GPS/BDS Combined PPP Models  
### PPP Model for Single System  
Taking a GPS system as an example, pseudo-range and phase observation functions of the ionosphere-free combination between a receiver and a satellite G can be written as:  
\[\begin{split} P^{\rm G}&=\rho^{\rm G}+\mathrm{c}\cdot dt _{\rm G}+b_{\rm G}+ZPD^{\rm G}+\mathrm{\zeta}^{\rm G}\\ L^{\rm G}&=\rho^{\rm G}+\mathrm{c}\cdot dt_{\rm G}+B _{\rm G}+N^{\rm G}+ZPD^{\rm G}+\mathrm{\epsilon}^{\rm G}\end{split} \tag{1}\]  
where \(P^{\rm G},L^{\rm G}\) are the pseudo-range and carrier phase observation; \(\rho^{\rm G}\) is geometrical distance; \(\mathrm{c}\) is light speed and \(\mathrm{c}\cdot dt_{\rm G}\) is receiver clock correction; \(b_{\rm G}\) and \(B_{\rm G}\) are receivers pseudo-range and carrier phase hardware delay bias; \(N^{\rm G}\) is ambiguity, and \(ZPD^{\rm G}\) is the slant tropospheric delay, \(\mathrm{\zeta}^{\rm G}\) and \(\mathrm{\epsilon}^{\rm G}\) are noise terms.  
The pseudo-range hardware delay biases \(b_{\rm G}\) is assimilated into clock correction \(\mathrm{c}\cdot dt_{\rm G}\). The carrier phase hardware delay biases \(B_{\rm G}\) is satellite dependent and stable over time, and it will be grouped into ambiguity [27; 28]. Thus, the ionosphere-free combination PPP observation can be rewritten as:  
\[\begin{split} P^{\rm G}&=\rho^{\rm G}+\mathrm{c} \cdot d\overline{t}_{\rm G}+ZPD^{\rm G}+\mathrm{\zeta}^{\rm G}\\ L^{\rm G}&=\rho^{\rm G}+\mathrm{c}\cdot d\overline{t} _{\rm G}+\overline{N}^{\rm G}+ZPD^{\rm G}+\mathrm{\epsilon}^{\rm G}\end{split} \tag{2}\]where \(d\overline{t}_{\rm G}\) and \(\overline{N}^{\rm G}\) are reformed station clock and ambiguity with:  
\[\begin{array}{l}c\cdot d\overline{t}_{\rm G}=c\cdot dt_{\rm G}+b_{\rm G}\\ \overline{N}^{\rm G}=N^{\rm G}+b_{\rm G}-b_{\rm G}\end{array} \tag{3}\]  
For BDS phase and pseudo-range observations the same Equations (2) and (3) also apply.