esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Workers Exposure to Nanoparticles Studied by NMR Metabolomics
## 2 Materials and Methods
### Statistical Analyses  
All data analyses were performed using the open-source software R [34] and Metabo-analyst 5.0 [35]. Multivariate data analyses were conducted on processed concentration data and binned data separately. As a first step of statistical analysis, principal component analysis (PCA) was used to provide preliminary insight on the data complexity, trends of grouping or identifying outliers. Subsequently, orthogonal partial least squares discriminant analysis (OPLS-DA) was used for sample classification. Multilevel partial least squares analysis (mPLS) was used in the case of comparison of the pre-shift and post-shiftsamples [36]. All reported values of accuracy, sensitivity, and specificity were assessed by means of 100 cycles of a Monte Carlo cross-validation scheme where 90% of the samples were randomly selected at each iteration as a training set to build the model; the remaining 10% were subsequently tested on performance characteristics for the classification.  
In order to identify the most influential and statistically significant compounds, the Wilcoxon rank-sum test and its paired version, the Wilcoxon signed-rank test, were used. Obtained \(p\)-values were adjusted for multiple comparisons using the Benjamini and Hochberg correction [37]. The threshold of adjusted \(p\)-values was set to <0.05 for statistical significance. Fold change was performed following the general formula defined as a logarithm of base 2 of a division of a median concentration of an individual compound in one group by a median concentration of an individual compound in the other group. The result is projected in logarithm to base 2 scale.  
Altered metabolic pathways were detected using Metaboanalyst 5.0 using the metabolite ID taken from the Human Metabolome Database. Metabolic pathway analysis was performed on blood plasma metabolic profiles to reveal the biological impact of NPs inhalation. A plot of affected pathways contained 43 nodes, each representing one pathway, with colour and size coding corresponding to pathway significance and its impact, respectively. The significance was generated from betweenness centrality and out-degree centrality measurements. The pathway impact was generated by the summation of importance measures of matched metabolites to all metabolites present within the pathway.
## 3 Materials and Methods
### Lifespan Estimation  
The battery life prediction model used in this research study accumulates losses of battery based on discharge cycles. This type of module is called a fatigue model, which is one of the cycle counting approaches. Although the fatigue model is a high-level approximation for battery life, it is widely used due to its simplicity and clarity [46; 47; 48; 49]. The fatigue models assume that each discharge of the battery affects battery degradation. For the proposed lifetime estimation, the following scenarios were considered and adopted as described in [37; 50]:  
* From the battery datasheet, the curve showing the number of cycles of a battery as a function of DoD until it reaches the end of its lifetime was utilised.
* The SoC represents the battery capacity in percentage.
* The curve represents battery lifespan in days as a function of its float charging voltage and temperature. Therefore, based on the data obtained from [50], the loss-of-life (\(LoL\)) and maximum expected lifespan in years of batteries are estimated according  
Figure 6: Proposed hybrid energy storage system for ICEVs.  
to [46; 47; 49]. The _LoL_ of the batteries is the sum over all types of operation during the observation period; it was estimated using the following Formula (21) as in [37]:  
\[LoL=\sum_{i=0}^{k}\frac{N_{C}(i)}{N_{C}^{max}} \tag{21}\]  
where, \(i=\{0\dots\dots\dots\dots,k\}=\{0\dots\dots 100\%\}\), \(k\) is the battery DoD and stands for DoD and was used during the discharge operation mode. When DoD is 100%, it means that the battery is fully discharged. \(N_{c(i)}\) represent the number of performed cycles at \(i\)th DoD, \(N_{cmax}\) is the number of cycles to failure, which was attained when 80% of the battery capacity was utilised [49]. Thus, the battery end-of-life (EoL) with several \(i\)th DoD was attained when the _LoL_ was 1 [38; 46; 49; 51]. This can further be formulated as: when \(N_{c}(i)=N_{cmax}\), then \(LoL=1\). Hence, the battery lifespan is represented by the maximum number of services \(N_{(max_{s})}\) of the battery, and represented by Equation (22) as in [37; 47];  
\[N_{max_{s}}=\frac{1}{LoL}; \tag{22}\]  
Additionally, if the vehicle serves several operations (\(N_{op}\)) in a day, then the maximum expected battery lifespan (\(L_{s}\)) is represented by Equation (23) as in [37];  
\[L_{s}=\frac{N_{max_{s}}}{N_{op}}\times\frac{N_{c}^{max}}{366}\left(years\right) \tag{23}\]
###### Abstract  
This study introduces envelope- and machine learning (ML)-based electrical fault type algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software-hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700 GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model's robustness and accuracy under realistic conditions.  
fault detection; machine learning; power inverters; protective relays; electrical distribution grids; distributed energy resources Article
## 3 Simulation Results
### Case Study Results  
#### 3.4.1 Case Study Data Set  
The dataset used in this work comes from a major manufacturer that specializes in the production of cardboard packaging and cases. It specifies the manufacture of personalized corrugated cardboard packaging as well as the transformation of paper and the manufacture of cardboard boxes. The data represent a series of demands with 775 weekly observations for a cardboard product used for the packaging of bakery goods and on which there is the printing and design of this product as well as the name of the company requesting this product.  
As in the case of the simulated data we used the SES method for the point forecasts. The use of this method in an industrial context is strongly recommended [51; 52].  
Note, according to Table 4 the real demand is not normally distributed and presents a phenomenon of autocorrelation of orders 1 and 2, hence the interest in modeling by an ARMA-GARCH model. Based on the Akaike Information Criterion (AIC) the more adequate model is AR(1) GARCH(1,1). Estimate parameters of this model are given in Table 5.  
Figure 8: Tradeoff curves for AR (1) in the case of gamma distribution for \(\phi\) = 0.70 (**a**,**c**) and \(\phi\) = \(-\)0.70 (**b**,**d**) and lead times of one week (**a**,**b**) and four weeks (**c**,**d**).
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 2 UVSQ-SAT Attitude Determination Considerations  
This section presents a description of the sensors of the UVSQ-SAT CubeSat, reference frames and attitude representation, and the theoretical approach of the method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method (Instruments)** & **Goal** & **Results/Remarks** \\ \hline
[12] & Simulation (Radio Aurora Explorer  satellites (3U CubeSat)) & AD based on gyros, magnetometers, coarse sun sensors, and an EKF & In the sun, the angular uncertainty is between 2\({}^{\circ}\) and 3\({}^{\circ}\), and in eclipse, the uncertainty increases to between 7\({}^{\circ}\) and 8\({}^{\circ}\) \\ \hline
[13] & Simulation & AD using two-step EKF based on a magnetometer only & Attitude accuracies of less than 1\({}^{\circ}\) \\ \hline
[3] & Observation (Radio Aurora  Explorer  satellites (3U CubeSat)) & Photodiodes calibration and AD  from EKF/UKF with albedo model based on the calibrated photodiodes, three-axis magnetometer and gyrometer & Angular improvement of 10\({}^{\circ}\) in sun  vector from the photodiodes, and below 1\({}^{\circ}\) accuracy on the attitude determination \\ \hline
[14] & Simulation & AD via a robust Adaptive Kalman Filter based on magnetometer and gyro measurement & Precision of traditional EKF is about 0.2\({}^{\circ}\), and the maximum estimate error of the robust adaptive filter is 0.1\({}^{\circ}\) \\ \hline
[15] & Simulation and Observation (experimental data with on-ground nano-satellite) & Gain-scheduled EKF (GSEK) to reduce the computational requirement in the nanosatellite attitude determination process & Attitude accuracy below 0.2\({}^{\circ}\) during the entire orbital period. \\ \hline
[16] & Simulation & Magnetometer calibration with  Hyper least square (HyperLS)  estimator for ellipsoid fitting, then utilized for attitude determination & Attitude accuracy below 1\({}^{\circ}\) for simplex UKF \\ \hline
[17] & Observation (images taken from  International Space Station (ISS)) & AD utilizing color earth images taken with visible light camera & Attitude accuracy is about few degrees or less \\ \hline
[18] & Simulation & Heat attitude model for satellite attitude determination & Attitude accuracy between 0.2 \({}^{\circ}\) to 5\({}^{\circ}\) \\ \hline
[19] & Simulation & AD method based on an UKF, using a gyrometer, a magnetometer and solar panels as a sun sensor & The UKF has shown precision in Euler angles of about 1.1\({}^{\circ}\), which is better than for EKF. UKF has a considerably longer processing time compared to EKF \\ \hline
[20] & Simulation and Observation (experimentation on-ground set up) & Thermal imaging sensors to  determine attitude of the Sun and  the horizon by employing a homogeneous array of such  detectors & Angular accuracy below 1\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Security Issues
#### 3.1.2 Integrity  
A BC registry is particularly useful in keeping data on a distributed infrastructure that is very resistant to physical damage [77]. However, because of computing, communication, and service capabilities, the quantity of data created in the IoT varies by location and device. IoT sensors are often basic and cannot be successfully monitored for an extended period. This increases security concerns. Furthermore, terminal devices seldom employ security features; as a result, attackers may simply gain passwords and other identifying information, which they can then use to publish and disseminate false information [76]. Moreover, IoT devices often deliver data straight to the control center in a wireless manner after data collection. However, without adequate shielding, this technique is immediately exposed. This will result in the collection of a huge quantity of basic data, as well as the disclosure of personal and private data. It even poses problems with social and public safety [87].  
BCs are notable for their resilience and irreversibility. Because of the system's irreversible and immutable nature, smart contract code bugs are highly dangerous. Moreover, because BC data are immutable, new technology such as quantum computing potentially jeopardizes public BCs with encrypted data [10]. Data or transactions that have been saved on the BC cannot be easily changed. Although BC assures authenticity, it cannot guarantee data correctness. If the saved document, for example, includes erroneous or wrong information, it will waste resources. The BC is a tamper-proof ledger, but if an error occurs, the loss may be irreversible. This differs from a centralized solution, which may roll back the database in the event of an error [155; 156]. This problem appears to be tough to tackle at the moment; it is based on increasing the degree of centralization. This, however, is subject to the underlying application context. Due to the need for monitoring in many circumstances, the criteria should not be entirely decentralized [27].  
As a consequence, the IoT data record and block sequence are synchronized. Inconsistent (forked) BCs can originate from incomplete or inconsistent records [76]. One solution to the inconsistency problem is the hierarchical design of the BC [76]. The data from the whole network are verified and stored in the higher main BC. To accomplish block management, the lower sub-BC controls data for particular physical locations or device components and blocks BC data for distinct levels [76]. Moreover, one of the most often used authentication techniques in IoT is message identification and authentication, which is a protective mechanism for ensuring the integrity and security of data when transferring data [76].
## 2 Piezo-Actuated FSM System  
### Overview of Piezo-Actuated FSM System  
Figure 1a,b shows an exploded and cross-sectional view of the piezo-actuated FSM without the optical mirror. Figure 2 shows an assembled view of the system without and with the optical mirror. To actuate the mover in both rotational directions, the FSM is equipped with two pairs of stack actuators aligned along the system's two axes and  
operated in a push-pull configuration. The optical mirror is directly attachable to the top of the mover, and the mover is suspended from an actuator that provides the prepressing of the piezo stack needed for dynamic behavior. The actuators push against the flexible hinge at the backside of the mover and are mounted on a base plate on the FSM system. The mover is designed to be sufficiently stiff for shifting the structural modes of the moving parts to high frequencies while preventing any deformation of the mirror surface during dynamic operation. This is achieved by attaching a solid \(\phi 60\times 80.5\) mm aluminum cylinder body to the backside of the mover. However, any rotational motion of the mover in the tip or tilt direction would exert shear and tensile forces on the piezo stacks, which must be avoided as they could cause the stacks to crack. Therefore, the piezo stack actuator is built as a housing assembly to avoid cracking in the shear direction.  
A force interface between the rotary mover and each actuator stack, which cannot transmit shear or tensile forces, is established using a flexible hinge bonded to the bottom of the mover and a single contact point at the top of each actuator.  
Figure 1: Overview of the piezo-actuated FSM without the optical mirror (which is attached to the mover and operated using two stacked actuators per axis): (**a**) exploded view; (**b**) cross-sectional view.
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 1 Introduction
### Previous Works on Resource Allocation and Detection
The authors of [26] proposed a vehicle density-based two-stage resource management scheme to reduce latency, improve throughput, and reliability for V2V and V2N. In the first stage, the density information for the resource distribution strategy is explored, then, in the second stage, the channel state information and queuing state information are used. The algorithms presented above are summarized in the Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm Group** & **Algorithm Description** & **Reference** \\ \hline \multirow{7}{*}{Sensing based} & Subchannel selection based on RSRP measurement, & \\  & computed using the DMRS. & \\ \cline{1-1}  & Subchannel selection based on RSRP measurement, & \\ \cline{1-1}  & computed using the 3 REs per RB, carrying the first & \\ \cline{1-1}  & stage SCI. & \\ \cline{1-1}  & Semi-persistent subchannel selection to reflect the & \\ \cline{1-1}  & stochastic nature of the aperiodic traffic. & \\ \cline{1-1}  & Partial sensing based on an additional UE to collect & \\ \cline{1-1}  & the sensing results. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithms proposed in the literature.
## 3 Mathematical Approach Using Homomorphic Encryption and Zero-Knowledge Proofs
### Zero-Knowledge Proofs for Training  
For each training iteration, the client, \(C_{i}\) provides a Zero-Knowledge Proof that verifies the model has been trained correctly up to layer \(L_{cut}\) and contains no malicious behavior. Suppose the client wants to prove that they have correctly trained their portion of the network up to a certain point \(t\), without revealing specific details of the training process, Figure 2. The client generates a proof \(\pi_{i}\) that demonstrates the result \(f(X_{i};\theta_{i})\) was correctly computed for the encrypted input data:  
\[\pi_{i}=ZKP.Prove(f(X_{i};\theta_{i}),pk_{s})\]  
where \(pk_{s}\) is the server's public key used by the client to verify the correctness of the computation.  
The server verifies the proof without needing to see the original data:  
\[ZKP.Verify(f(X_{i};\theta_{i}),\pi_{i},\ pk_{s})\ =\ \text{True}\]  
If the proof is correct, the server continues with the training process; otherwise, it halts training and alerts that there is an issue with client \(C_{i}\)'s training.  
Figure 1: Data are encrypted on the client and processed by the server.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## Tuning of P, PD and PDA Controllers for IPDT Plant Considering the Dead-Time Values
 \tag{26}\]  
However, when using higher order controllers based on the plant models (4), two problems appear:  
* Models \(S_{1}(s)\) will preferably be used for P controller design-the use of PD controllers would require to obtain or generate output derivative \(\dot{y}\); for PDA controllers both derivatives \(\dot{y}\) and \(\dot{y}\) have to be acquired, which either requires additional sensors, or filters/observers, and to take into account their parameters;
* Models \(S_{2}(s)\) may directly be combined with the PD controller and models \(S_{3}(s)\) with PDA controller; however, it yet is necessary to take into account impact of the time constants \(T_{i}\). The simplest possibility is to reduce models (4) to an equivalent IPDT system by considering the \"half-rule\" [13] with an equivalent dead-time \(T_{e}=T_{1}/2\). In case of the transfer function \(S_{2}(s)\) it increases the loop deadtime \(T_{m}\) to \[T_{d}=\overline{T}_{d}+\frac{T_{1}}{2}=T_{m}(\tau+\frac{1-\tau}{2})=T_{m}\frac {1+\tau}{2}\] (27) However, experiments show that such equivalence is sufficiently precised just for \(T_{1}<<T_{m}\). For longer \(T_{1}\), approximately for \(T_{1}>T_{m}/2\), it may be more appropriate to work with \"average residence time\" based equivalence [14; 40], when \[T_{d}=\overline{T}_{d}+T_{1}=T_{m}\] (28) Similar estimates of \(T_{d}\) may be derived for \(S_{3}(s)\). Before addressing impact of these simplifications on the controller tuning in details, let us look at the problem of disturbance reconstruction and compensation.
# Internet of Things in Marine Environment Monitoring: A Review
## 3 A Review of Existing Marine Environment Monitoring Projects and Systems
Even though many researchers discussed different options for energy harvesting including solar, wind, waves, and ocean currents, only solar energy has been used in about a third of these systems and applications. While most systems use GPRS and/or ZigBee for wireless communications, a few systems use underwater acoustic communication [38; 47; 53; 66]. Offshore and deep-sea fish farming has been emerging, which provides more opportunities and also challenges, not only for marine environment monitoring but also for operation controls and marine environment protection.
# An Improved Cohesive Zone Model for Interface Mixed-Mode Fractures of Railway Slab Tracks
## 3 Simplified PPR Traction-Separation Law
### Path Dependence of Work-of-Separation
#### 3.2.1 Proportional Separation  
The proportional separation path is shown in Figure 5. The variable \(\theta\) in Figure 5 is the separation angle between the path direction and tangent, and \(\Delta_{r}\) is the separation for the proportional path. With the increase in \(\Delta_{r}\), the interface gradually debonds. The work-of-separation is calculated with the following expression [32].  
\[W_{sep}=\int_{0}^{\delta_{r}}T_{n}(\Delta_{r}sin\theta,\Delta_{r}cos\theta)sin \theta d\Delta_{r}+\int_{0}^{\delta_{r}}T_{t}(\Delta_{r}sin\theta,\Delta_{r} cos\theta)cos\theta d\Delta_{r} \tag{17}\]  
where \(\delta_{r}=\sqrt{\delta_{n}^{2}+\delta_{t}^{2}}\). The first term in the work-of-separation expression is the work conducted by the normal traction (\(W_{n}\)), and the second term in the expression is the work conducted by the tangential traction (\(W_{t}\)). \(W_{sep}=W_{n}=\phi_{n}\) when the separation angle \(\theta\) is 90\({}^{\circ}\). When \(\theta=0^{\circ}\), the work-of-separation \(W_{sep}\) and \(W_{t}\) are the same as the mode II fracture energy \(\phi_{t}\).  
Figure 6 illustrates the variation of \(W_{sep}\), \(W_{n}\), and \(W_{t}\) with respect to the separation angles. The results for the PPR model are on the left and for the SPPR model are on the right. The changing laws of \(W_{sep}\), \(W_{n}\), and \(W_{t}\), with respect to the separation angles for different models, are the same. Especially, when the separation angle is 0\({}^{\circ}\) or 90\({}^{\circ}\), the curves for the SPPR model are exactly the same as the PPR model.  
If \(\theta\) is equal to 0\({}^{\circ}\), \(W_{sep}\) and \(W_{t}\) increase from 0 to the mode II fracture energy (200 N/m) with the increase in \(\Delta_{r}\), while \(W_{n}\) remains zero. When \(\theta\) is equal to 90\({}^{\circ}\), \(W_{sep}\) and \(W_{n}\) reach the mode I fracture energy (100 N/m), and \(W_{t}\) stays at zero. For the intermediate angles (0\({}^{\circ}<\theta<\) 90\({}^{\circ}\)), the \(W_{sep}\), \(W_{n}\), and \(W_{t}\) of both models change monotonically with respect to the increase in the separation angle \(\theta\). These verify that the PPR model and SPPR models both guarantee the consistency of the cohesive constitutive model.  
There is a difference between the PPR model and the SPPR model. When 0\({}^{\circ}<\theta<\) 90\({}^{\circ}\), the work conducted by the normal traction \(W_{n}\) for the PPR model only has a small change with increases in the separation angle. In contrast, the SPPR model has a more obvious and uniform change within the whole separation angles. This is due to the fact that the cohesive interaction region for normal traction of the PPR model is smaller than the SPPR model here (\(\phi_{n}<\phi_{t}\)), leading to a smaller \(W_{n}\) for the PPR model under mixed-mode fracture conditions.  
Figure 5: Proportional separation path (\(\Delta_{r}\)) with the separation angle (\(\theta\)). Before separation (**a**); after separation (**b**).