esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 4 Instantiation of the Proposed Framework to SoC Estimation and RUL Prediction
### Configuration of Battery State Prediction Instantiated for SoC and RUL
#### 4.1.1 Reference Value Estimation  
In the \"Reference value estimation dimension\", the objective is either SoC or RUL. The battery state of the SoC, by definition, refers to the usable capacity of the battery in its current state in relation to the capacity in its fully charged state [16]. In general, the SoC is a non-linear feature, and its progress depends on temperature and current [17]. There are no direct ways to measure the SoC but there are many ways to obtain SoC estimations. Two well-known methods are Coulomb counting and OCV techniques; however, these methods are known for their limitations, which affect their accuracy and reliability [17]. For this reason, more robust and sophisticated methods are preferred to handle sensor errors and uncertain model knowledge [18].  
We implemented three different methods to determine or estimate the reference values of the SoC:  
* The \"Residual to nominal capacity\" method defines the SoC as the percentage of the battery's current residual capacity compared to the nominal capacity according to the manufacturer's specifications (see Equation (1)) [19]. \[SoC=\frac{C_{res}}{C_{nom}}\times 100\%\] (1)  
Figure 3: The instantiation of the dimensions of the “Configuration of the Battery State Prediction” component for SoC and RUL prediction.  
* The \"Current to maximum capacity\" method defines the SoC as the percentage of the battery's current capacity compared to the maximum battery's capacity in each cycle, representing the fully charged state of the battery in each cycle (see Equation (2)) [16]. \[SoC=\frac{C_{curr}}{C_{max}}\times 100\%\] (2)
* The \"Coulomb counting\" method refers to the estimation of the SoC by applying the Coulomb counting algorithm, which is connected to the SoC's previous state, the measurement of the current, the integration of that current over time, and the battery's capacity (see Equation (3)). \[SoC=SoC(t-1)+\frac{I(t)}{Q_{n}}\times\Delta t\] (3)  
We also implemented two different methods for RUL in order to predict the required amount of charge and discharge cycles from the point of the active profile to the end of the battery's life, given the fact that the end of the battery's life is considered to be the point that the battery's capacity reaches 70% or 80% of its initial nominal value:  
* In the \"Remaining life\" method, reference value of RUL is determined as the remaining battery cycles from the current cycle to the battery's end of life.
* In the \"Total life\" method, the reference value is considered to be the battery's life, and consequently the predictions of the pipeline refer to the battery's lifetime. Thus, RUL is predicted through the subtraction of the current cycle from the predicted lifetime of the battery.
### Canopy Nitrogen Weight Modelling
Support Vector Machines (SVM) are supervised learning algorithms used for classification and regression. SVR uses a decision boundary, known as a hyperplane, to split classes of training data based on data characteristics. The data points closest to either side of the hyperplane are known as support vectors, used as training samples to determine the optimal hyperplane position from the midpoint of the margin. SVR performs modelling in a high-dimensional space. SVR uses a kernel trick (i.e., the Radial Basis Kernel) for nonlinear data, placing the data in a dimensional space to separate into groups based on radial distance between data points. SVR has advantages over simple linear regression models, as its flexibility with nonparametric data has better modelling capabilities [59]. The modelling for this study was written in the R programming language using R Studio (Version 4.0.3), an open-source and free Integrated Development Environment (IDE) [60]. For RF, the \"randomForest\" package was used, and for SVR, the \"e1071\" package was used. Modelling was performed separately for MicaSense-based and PlanetScope-based sensor variables for comparison of UAV and PlanetScope data results. The MicaSense models used all five MicaSense bands, all 14 VIs listed in Table 1, plant height, LAI, soil moisture, and topographic metrics, and the dependent variable was the CNW. The PlanetScope models used all four PlanetScope bands, 11 VIs listed in Table 1 not including the red-edge based indices (CI_RE, NDRE, RVI2), plant height, LAI, soil moisture, and topographic metrics, and the dependent variable was the CNW. Measurements of variables at each sample point were averaged to a 1 m\({}^{2}\) scale. Datasets were randomly divided into 70% training set and 30% validation set. The quality of the models was assessed using the coefficient of determination (R\({}^{2}\)) and the Root Mean Square Error (RMSE), calculated using Equations (2) and (3), respectively [61]:  
\[R^{2}=1-\frac{\sum{\left(y_{i}-\hat{y}_{i}\right)^{2}}}{\sum{\left(y_{i}- \overline{y}_{i}\right)^{2}}} \tag{2}\]  
where \(y_{i}\) is the observed value, \(\hat{y}_{i}\) is the predicted value, and \(\overline{y}\) is the mean of the observed values of the dataset; and:  
\[RMSE=\frac{\sum_{i=1}^{n}{\left(\hat{y}_{i}-y_{i}\right)^{2}}}{n} \tag{3}\]  
where \(\hat{y}_{i}\) is the predicted CNW value (g/m\({}^{2}\)), \(y_{i}\) is the observed CNW value (g/m\({}^{2}\)), \(n\) is the number of observations, and \(i\) is the index of summation in increments of one.
## 2 T-S Identification Method
### Fuzzy Clustering  
Initially, a set of n samples \(X=[x_{1},x_{2},\ldots,x_{n}]\) and a number K of clusters is used. In this way, a fuzzy cluster is considered as a fuzzy subset in X, defined as shown below:  
\[\mu_{k}:X\rightarrow[0,1],\ \ \ \ k=1,\ldots,K.\]  
The degree of fuzzy membership for each cluster k is represented as follows:  
\[u_{ik}=\mu_{k}(x_{i}),\ \ \ i=1,\ldots,n\ \ \ \ k=1,\ldots,K. \tag{26}\]  
In this way, \(u_{ik}\) shows the degree of membership of a sample i with respect to a cluster k. In general, \(u_{ik}\) satisfies the conditions provided in (27):  
\[u_{ik}\in[0,1],\ \ \ \forall i,k;\ \ \ \sum_{k=1}^{K}u_{ik}=1,\ \ \ \ \forall i. \tag{27}\]  
The results of applying a fuzzy clustering methodology are collected in a partition matrix U = \((u_{ik})\) that shows all the degrees of membership of the samples with respect to each cluster [36]. To calculate this matrix, the concept of distance to the centers of the clusters is applied, which depends exclusively on the fuzzy clustering method being used.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.2 CoCo-RED  
The default CoAP does not effectively performs group communication and observe resources. Therefore, a congestion control mechanism for observe group communication is presented in [22], named as Congestion Control Random Early Detection (CoCo-RED). The main components of the scheme include:  
1. Determination and calculation of RTO timer
2. Management using Revised Random Early Detection (RevRED) algorithm for avoiding congestion
3. Fibonacci Pre-Increment Backoff (FPB) algorithm for implementing backoff timer  
The CoCo-RED initially sets the RTO value randomly between 2 to 4 s and uses FPB in case of retransmissions to set the RTO. To avoid congestion by Buffer Management Technique (BMT), the proposed mechanism works dynamically and uses RevRED to calculate the network density based on the average queue size (AvgQ). The RevRED algorithm drops the arriving packet before client's buffer queue overflows. The AvgQ size is calculated using the exponential weighted moving average. The algorithm works according to the following principles:  
1. If AvgQ < Min threshold, arriving packet is placed in queue
2. If Min threshold < AvgQ < Max threshold, arriving packet is dropped based on the dropping probability formula presented by the proposed method
3. If AvgQ > Max threshold, arriving packet is dropped based on the exponentially dropping probability formula presented by the proposed method  
In case packets are dropped due to congestion, retransmissions are carried out. For calculating the RTO for packet retransmissions, FPB is employed. For each retransmission, the FPB is multiplied with previous value of RTO for finding new RTO value for next retransmission. The FBP uses Fibonacci numbers to multiply with previous RTO in each retransmission, which achieves lower RTO value compared to Binary Exponential Backoff of default CoAP in each subsequent retransmission.  
CoCo-RED helps to reduce the response time of network in addition to packet loss. However, the backoff values are fixed and do not vary according to the dynamic conditions of network.
# Behavior Analysis of Active and Proactive Headrest during Low-Velocity Rear-End Collisions
## 2 Sled Test
### Headrest Structure  
The headrest provides head support in the seat and is a safety device to prevent momentary backward bending of neck bones during a collision. In this paper, two headrest structures, i.e., active and proactive headtests, were used in the sled experiment.  
The active headrest exhibits a structure, which supports the passenger's head by moving the headrest forward through the mechanism connected from the seatback to the headrest using the upper body's inertia transmitted to the seatback during rear-end collision. Figure 2 shows the components of the active headrest and the conceptual diagram of its operation. It is evident that the movement of the headrest is determined by the force of the upper body acting on the seatback, and, in some cases, the headrest may not work if the upper body fails to impart a force on the headrest deployment structure located in the seatback. The components of the deployment structure of the active headrest include the link and hinge, which connect the headrest with the active panel located in the seatback. The active panel moves backward due to the inertia of the upper body in the collision.  
The proactive headrest uses the deployment signal transmitted from the vehicle's ECU (electronic control unit) in the event of a rear-end collision to activate the headrest's actuator or solenoid and to move a part of the headrest in the forward direction to support the passenger's head. Figure 3 shows the components of the proactive headrest and the conceptual diagram of its operation. When a collision occurs, the time taken to transmit the signal and deploy the headrest is about 30 ms.
## 4 Slp-macgt Model Design
### Relay Node Selection Strategy
```  
**Algorithm 1**Relay Node Selection Strategy  
The selection of relay nodes has an important impact on the safety time. In network communication, safety time is the time required for data to travel from the source node to the destination node, including the data transmission and forwarding time of the relay node. Selecting appropriate relay nodes can optimize the data transmission path, prolong the safety time, and improve the efficiency and reliability of data transmission. Utilizing H-hop-directed routing forwards packets away from the source. Greedy quantitative routing is used to expand the optional range relay node N. It can be seen that there are \(4\pi H^{2}\) forwarding paths generated in these phases. During the relay node-based multi-path forwarding routing phase, the relay node O may be positioned in any direction relative to the sink node. It can be seen that there are \(4\pi l^{2}\), where \(l\in[Hop_{source-\sin k}-H-L,Hop_{source-\sin k}+H-L]\), forwarding paths generated in these phases. Therefore, the number of paths generated by the relay node selection strategy is \(4\pi H^{2}\times 4\pi l^{2}=16\pi^{2}H^{2}l^{2}\) and the probability of path duplication is \(\frac{1}{16\pi^{2}H^{2}l^{2}}\).
## 3 Results and Discussion
### The Effect of NO, iNOS, and nNOS on IQP-Induced Relaxation of the SM Tissues  
Pre-treatment of SMPs with a precursor of NO synthesis--L-arginine (10 uM) and subsequent exposure to IQP (50 uM) did not cause a significant change in the relaxation response of the test substance. The relaxation response of the isoquinoline precursor after the specific blockade of nNOS, eNOS, and inducible nitric oxide synthase (iNOS) by the single or combined administration of L-NNA (10 uM) and aminouanidine (10 uM) was also statistically insignificant (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Tonus of SMPs Caused** & **Background** & **Time of Incubation of** & **Changes in the Tonus** & & \\
**by the Impact Agent** & **Agent, uM** & **Background Agent, Min** & **of SMPs on the** & \(n\) & \(p\) \\
**(Auto-Control), mN** & & & & & \\ \hline IQP (50 uM) & & & & & \\ \(-\)1.08 \(\pm\) 0.14 & & & & & \\ IQP (50 uM) & & & & & \\ \(-\)1.18 \(\pm\) 0.16 & & & & & \\ ACh (1 uM) & & & & & \\
4.91 \(\pm\) 0.96 & & & & & \\
5-HT (1 uM) & & & & & \\
4.84 \(\pm\) 0.52 & & & & & \\ IQP (50 uM) & & & & & \\ IQP (50 uM) & & & & & \\ \(-\)1.44 \(\pm\) 0.16 & & & & & \\ IQP (50 uM) & & & & & \\ IQP (50 uM) & & & & & \\ \(-\)1.10 \(\pm\) 0.04 & & & & & \\ IQP (50 uM) & & & & & \\ \(-\)1.18 \(\pm\) 0.06 & & & & & \\ IQP (50 uM) & & & & & \\ IQP (50 uM) & & & & & \\ \(-\)1.19 \(\pm\) 0.10 & & & & & \\ ACh (1 uM) & & & & & \\
4.91 \(\pm\) 0.96 & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Mutual influence on the CA caused by IQP and exogenous drugs on rat circular gastric SMPs (auto-control). The comparison is between the auto-control and interaction of exogenous reagents; *_p_ < 0.05.  
The application of a spectrophotometric method for measuring the level of NO showed that SMP homogenates incubated for 5 h with L-arginine (100 \(\upmu\)M) or IQP (50 \(\upmu\)M) do not contain measurable amounts of NO (\(n=16\), \(p=12\)).  
The unchanged tonic reaction of IQP in the conditions of prolonged incubation and preliminary NOS blocking, as well as in the lack of measurable amounts in tissue homogenates and the lack of expression in histological tests, gives us the grounds to exclude iNOS participation in relaxation caused by IQP.  
While iNOS is induced and synthesized in certain prolonged (5 to 8 h) external influences on the cells, and its activity is unrelated to Ca2+ concentration, the other two isoforms (nNOS and eNOS) are expressed in mammal cells incessantly and synthesize nitrogen oxide as a response to increased intracellular Ca2+ concentration and, in some cases, as a response to other stimuli [37].  
Our interest was caused by multiple increases in nNOS expression following the joint application of 5-HT and IQP which was established through an immunohistochemical measurement.
# A Review on Decentralized Finance Ecosystems
## 4 The Taxonomy
### Risks
#### 4.4.1 Technical Risks  
The DeFi realm grapples with inherent technical risks. Although Ethereum, as the leading public blockchain, has not encountered major breaches, other sectors in DeFi have faced targeting. Technical risks and breaches have targeted blockchain-based wallets, centralized exchanges, and DApps [84]. Concerns in DeFi revolve around fraudulent activities, money laundering, and illegal financing, which are all persistent issues in the cryptocurrency sphere [68]. The reliability of DeFi hinges on the security of underlying smart contracts and blockchain protocols. Errors or vulnerabilities in code can lead to substantial hacks and losses for DApp users. Yet, achieving error-free code is highly challenging, given the ongoing evolution of blockchain protocols. Additionally, identifying bugs in smart contracts is tough due to technological novelty and the lack of standardized procedures [77]. These technical risks pose significant challenges in the DeFi landscape.  
Smart Contracts Risk  
Smart contracts are critical in the DeFi ecosystem and carry significant risks. This is because malicious actors can exploit any bugs or errors in the code to achieve their goals. A notable example is the _Qubit Finance_ incident, where an attacker exploited a bug in the Qubit smart contract, converting an Ethereum-based token into almost USD 80 million worth of Binance Coin [80]. Although the deterministic and decentralized execution of smart contracts has its advantages, there is a potential for coding errors to create vulnerabilities that enable an attacker to drain the smart contract's funds, cause disruptions, or render the protocol inoperable [42]. The proactive involvement of the community in identifying and reporting bugs plays a pivotal role in upholding the integrity of the DeFi ecosystem.  
Transaction risks refer to the shortcomings or breakdowns in the underlying blockchain network. If the network for base-layer settlement is successfully attacked, permits double-spending becomes overly costly for transactions, or lacks the necessary capacity, these failures will affect the application layer. Therefore, the planned Ethereum 2 upgrade aims to improve performance significantly, representing a crucial development for DeFi. Thisupgrade will also transition Ethereum to proof-of-stake consensus, eliminating energy-intensive proof-of-work mining [79].
###### Abstract  
The operational reliability of power systems is threatened by the random failure of components and uncertain power output of renewable energies, such as photovoltaics. Under such circumstances, reliability evaluation is necessary for maintaining a continuous and stable energy supply. However, traditional reliability evaluation methods are usually extremely time-consuming, considering the numerous system states that need to be analysed. Hence, the reliability evaluation process cannot follow up the dynamic changes in PV output, which makes the timeline of the evaluation disappointing. This paper proposes an efficient reliability evaluation method for power systems with PV integration. The method reveals the analytical relationship between the reliability levels of the power system and the uncertainty factors that influence the reliability, such as the PV output. In this way, the dynamic reliability evaluation is achieved, and the evaluation results can be updated timely when the output of PV changes. First, a Gaussian mixture-hidden Markov model (GMM-HMM) is used to model the distribution characteristics of PV output. Then, the state enumeration and the hyperbolic truncated polynomial chaos expansion method are used to determine the analytical relationship between the reliability indices and PV output. Lastly, based on the analytical function, the operational reliability of the power systems is dynamically evaluated considering the real-time PV output. The effectiveness of the proposed method is verified using the modified IEEE 30 system as an example.  
reliability evaluation; power systems; photovoltaic; GMM-HMM; polynomial chaos expansion
## 1 Introduction
[6] used both numerically simulated structure and laboratory test of a small-scale five-story frame where damage patterns were designed by replacing some components. In addition, there exist few studies that used the data from full-scale structures, e.g., [13; 17] used full-scale instrumented bridges under normal operational and environmental conditions with minor damage levels. In summary, past applications are limited, and more realistic damage patterns of full-scale structures due to real or simulated hazards, e.g., using shaking table tests, are needed for further development of SHM using ML. The main contribution of this study is to develop a systematic two-stage framework, namely ARIMA-ML, to combine TS modeling techniques and ML approaches for detecting structural damage. The first stage focuses on the TS modeling, and the second stage performs the recognition tasks. Specifically, ARIMA-ML consists of four main modules: (1) pre-processing, (2) model parameter determination, (3) feature extraction, and (4) classification. The performance of the framework was validated using data from full-scale shaking table tests of a three-story steel frame making use of the average segment accuracy and confusion matrix. In this study, the feature importance (\(FI\)) score was analyzed to examine the most important features for damage detection and pattern recognition, illustrating the need for higher order coefficients and validating the superiority of the proposed framework. The specific contributions are summarized below:  
* To relax the stationarity requirement, the ARIMA model was introduced accompanied with the model parameter selection criteria and a candidate model mechanism. * Instead of using intuition to determine a suitable function of the ARIMA coefficients as damage features, all the coefficients were used as damage features to build a \((p+q)\)-dimensional vector space where \(p\) and \(q\) are, respectively, the number of coefficients in the AR and MA parts of the ARIMA model. * In the classification module, an ensemble voting classifier (EVC) [22] was applied to improve both the accuracy and robustness, where multiple ML classifiers, e.g.,support vector machine (SVM), neural network (NN), random forest (RF), and logistic regression (LR), were engaged. * The framework was validated using realistic full-scale shaking table tests of a three-story steel frame under different earthquake hazard levels.
# Link Budget Analysis with Laser Energy for Time Transfer Using the Ajisai Satellite
## 2 TLTT Link Budget
### Minimum Required Laser Energy for TLTT  
The Geiger-mode avalanche photodiode (GmAPD) is considered for TLTT weak signal detection due to its single-photon detection capability, which follows Poisson statistics in the photon detection model. Background noise coming from both the sky and the Sun interrupts the signal photon detection because the GmAPD detector is triggered by the first few photons arriving at the detector, regardless of whether these are signal photons or background noise. However, background noise is made negligible by not only employing three types of filters in the receiver (i.e., special spatial, spectral and temporal filters), but also by executing the TLTT link during the night, when the intensity of background noise, at 532 nm of laser wavelength, is about five orders of magnitude smaller than in the daytime [12].  
The probability that the received photons will create primary photoelectrons on the GmAPD is expressed as:  
\[P\big{(}m,n_{p}\big{)}=\frac{n_{p}^{m}}{m!}e^{-n_{p}} \tag{9}\]  
where \(m\) is the number of primary photoelectrons that trigger the avalanche process. To allow single-photon detection, the GmAPD is biased above the breakdown voltage with a much higher gain than that used in the linear-mode APD and triggered when at least a single photoelectron is generated. According to Equation (9), the probability of generating no primary photoelectrons is \(\exp\left(-n_{p}\right)\). Therefore, the probability of triggering the detector (i.e., detection probability) is given by:  
\[P_{D}(m\geq 1)=1-P\big{(}0,\ n_{p}\big{)}=1-e^{-n_{p}} \tag{10}\]  
Considering that the number of laser pulses (\(N\)) is fired from the ground station during the observation time interval \((\Delta t)\), the expected number of events (\(C_{N}\)) triggering the detector (in other words, successful counts of TLTT link) is expressed statistically as:  
\[E(C_{N})=P_{D}N=P_{D}(f_{L}\Delta t), \tag{11}\]  
where \(f_{L}\) is the repetition rate of laser pulses. The threshold of detection probability in which the count of the TLTT link occurs once during the observation time interval (i.e., \(E(C_{N})=1\)), can be calculated by:  
\[P_{TH}=\frac{1}{f_{L}\Delta t}=1-e^{-n_{th}} \tag{12}\]  
where \(n_{th}\) is the threshold number of received photons to make one successful TLTT link, which corresponds to the minimum energy level (\(E_{min}\)) needed to create more than one photoelectron in the given time interval. It is worth noting that the average number of received photons is linearly proportional to the transmitting laser energy. UsingEquations (8), (10) and (12), \(n_{th}\) and \(E_{min}\) can be written, in terms of average number of received photons and the transmitting laser energy, by:  
\[n_{th}=\frac{ln(1-P_{TH})}{\ln(1-P_{D})}n_{p} \tag{13}\]  
\[E_{min}=\frac{\ln(1-P_{TH})}{\ln(1-P_{D})}E_{t} \tag{14}\]