esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### Applications in Fracture Mechanics and Extensions  
As a result of the extensive research conducted in almost two decades, the method has reached a level of maturity that allows its application in a wide range of problems of both academic and industrial interest. Some representative applications would include damage tolerance assessment of aerospace structures [164] and hydraulic fracturing [165]. Significant research effort has also been devoted in implementing the method both in a procedural [161; 166] and object oriented framework [167; 168]. Thus, implementations of the method can be found in several open source libraries and commercial software packages such as Ansys and Abaqus.  
The range of possible applications includes problems far more challenging than two-dimensional linear elastic crack propagation. For instance, the method can be extended to three dimensions in a straightforward way [132; 136; 137], while the treatment of problems involving multiple cracks [163; 166; 169; 170] is also possible. The extension to dynamic crack propagation can be challenging, it is however possible and has been studied in several works, for instance references [171; 172; 173]. The method's flexibility also allows for application to problems involving different types of material models, for instance orthotropic [174], or in the nonlinear domain hyperelastic [175] and elastic-plastic [176]. Finally, other models for fracture, such as the cohesive zone model [115; 177], can also be incorporated with relative ease.
# Weighted Matrix Factorization Recommendation Model Incorporating Social Trust
## 3 User-Based Collaborative Filtering and Trust-Based Social Networks
### Trust-Based Social Networks  
Traditional recommendation models primarily rely on user historical ratings to predict ratings and make recommendations. However, in many cases, available user historical ratings for a user might be insufficient, making prediction difficult. At such times, social networks provide a new dimension, allowing researchers to utilize trust relationships among users within the social network for rating prediction.  
In social networks, trust between users is often represented as binary values (0 or 1), where 0 denotes distrust and 1 denotes trust. For each user \(u\) and each item \(i\), the principle for prediction is as follows. Select other users from the social network whom the predicting user trusts, and based on their ratings for item \(i\), calculate the predicted rating for the predicting user. The basic prediction formula is:  
\[\hat{r}_{ui}=\frac{\sum_{v\in T(u)}r_{vi}}{|T(u)|} \tag{3}\]  
where \(T(u)\) represents the set of other users that user \(u\) trusts, \(|T(u)|\) represents the number of users in the set, and \(r_{vi}\) indicates the rating given by user \(v\) to item \(i\).
## 3 Results
### Establishing the Model for Estimating \(\text{FCO}_{2}\)  
3.1 Optimization of Parameters and Selection of Explanatory Variables for \(\text{FCO}_{2}\) Estimation  
The parameters (Mtry and Ntree) in the RF model that are optimized based on the OOB error using the training dataset are shown in Figure 5. Results clearly indicate that the combination of the two parameters (Ntree and Mtry) influences the performance of the RF model. The small number of Ntree with different Mtry could result in a high error and unstable prediction of the RF model. For example, when the Ntree is 200, the prediction results varied greatly. However, when the number of Ntree is greater or equal to 500 (the default setting for the RF model), the OOB errors among different RF models with different combination of the Ntree and Mtry numbers becomes relatively stable and are similar, indicating that the performance of RF model can not be significantly improved by increasing the number of Ntrees. However, under the different number of Ntrees, increasing the number of Mtrys can improve the RF model's performance (Figure 5). Overall, the results show that the 500 Ntree with 4 Mtry produces the optimal result and the high prediction accuracy with the lowest OOB error of 1.47 g C m\({}^{-2}\) day\({}^{-1}\) (Figure 5). Therefore, the Ntree 500 and Mtry 4 are selected for further analysis.  
Using the selected parameters in the RF model, the importance of all the input variables is measured using the percentage increase in mean square error (% IncMSE) (Figure 6), which represents the contribution of each predictor to the target variable (\(\text{FCO}_{2}\)). The larger the % IncMSE is, the more important \(\text{FCO}_{2}\) estimation is. Some explanatory variables that represent temperature, plant productivity, and moisture availability contribute significantly to the estimation of \(\text{FCO}_{2}\). These variables include NDVI, LSTtd, EVI, and SWCI (Figure 6a). Further, we select the smallest number of variables that have the best prediction ability based on the variable importance ranking (Figure 6a) and the RMSE derived from the CV method (Figure 6b). The RMSE shows that the performance of the RF model first decreases as the number of variables increases. It reaches a minimum of 4 variables and then starts to increase. Therefore, the RF can produce the lowest error (RMSE: 1.055 g C m\({}^{-2}\) day\({}^{-1}\)) when using the top 4 variables. As a result, NDVI, LSTtd, EVI, and SWCI as the input predictors of the RF model are used to predict \(\text{FCO}_{2}\) (Figure 6).
## 2 Methodology
### Customer-Side Inverter Control  
The customer-side inverter (shown in Figure 2) operates in a grid-forming mode, providing a stable 50 Hz voltage to local loads. The control structure is based on cascaded voltage and current controllers implemented in the synchronous \(dq\)-frame.  
The inverter's outer voltage control loop ensures that the output voltage \(V_{L}\) tracks the desired sinusoidal reference.  
The voltage error in the \(dq\)-frame is as follows:  
\[e_{v_{d}}(t) =V_{d}^{*}-V_{d}(t)\] \[e_{v_{q}}(t) =V_{q}^{*}-V_{q}(t) \tag{7}\]  
where  
* \(V_{d}^{*}\) and \(V_{q}^{*}\) are the \(d\)- and \(q\)-axis voltage references;
* \(V_{d}\) and \(V_{q}\) are the measured voltages in the \(dq\)-frame.  
Each error is fed into a Proportional-Integral (PI) controller to produce the reference inverter currents:  
\[\begin{array}{l}i_{cv,d}^{*}(t)=K_{p-v}e_{v_{d}}(t)+K_{i-v}\int e_{v_{d}}( \tau)d\tau\\ i_{cv,d}^{*}(t)=K_{p-v}e_{v_{q}}(t)+K_{i,v}\int e_{v_{q}}(\tau)d\tau\end{array} \tag{8}\]  
where  
* \(K_{p,v}\) and \(K_{i,v}\) are the voltage controller gains.  
The inner current loop tracks the reference currents \(i_{cv,d}^{*}\) and \(i_{cv,q}^{*}\) by controlling the inverter output voltages.  
The current tracking errors are defined as follows:  
\[\begin{array}{l}e_{i_{d}}(t)=i_{cv,d}^{*}-i_{cv,d}\\ e_{i_{q}}(t)=i_{cv,q}^{*}-i_{cv,q}\end{array} \tag{9}\]  
These are fed into separate PI controllers.  
For \(d\)-axis  
\[v_{cv,d}^{*}(t)=K_{p_{i}i}e_{i_{d}}(t)+K_{i,i}\int e_{i_{d}}(\tau)d\tau+v_{d-ff} \tag{10}\]  
For \(q\)-axis  
\[v_{cv,q}^{*}(t)=K_{p-i}e_{i_{q}}(t)+K_{i,i}\int e_{i_{q}}(\tau)d\tau+v_{q-ff} \tag{11}\]  
where  
* \(K_{p,i}\)\(K_{i,i}\) are the PI gains for current control;
* \(v_{d-ff}=\omega L_{f}i_{cv,q}\) and \(v_{q-ff}=-\omega L_{f}i_{cv,d}\) are feedforward decoupling terms to compensate for the \(dq\) cross-coupling.  
The control structure provides exact voltage regulation and effective harmonic isolation, which allows the inverter to produce high-quality 50 Hz sinusoidal output despite upstream disturbances and ensures stable operation of residential loads in the proposed LVDC back-to-back system.
# Truck Platoon Control Considering Heterogeneous Vehicles
## 3 Multi-Vehicle Control
### Collision Avoidance  
This subsection handles the case where a truck is too close to its predecessor truck. In other words, we handle the case where \(D_{k}^{i-1,i}<safeD\). Recall that the distance between \(p^{i}\) and \(p^{i-1}\) at time step \(k\) is \(D_{k}^{i-1,i}=\|\mathbf{P}_{k}^{i}-\mathbf{P}_{k}^{i-1}\|\). In addition, \(safeD>0\) is the minimum distance between neighboring trucks for safety. In simulations, one uses \(safeD=0.5\) in meters.  
If \(D_{k}^{i-1,i}<safeD\) happens, then \(p^{i}\) needs to stop abruptly to avoid collision. Thus, we set the reference speed \(r_{k}^{i}\) as zero in the case where \(D_{k}^{i-1,i}<safeD\) is satisfied.  
Even in the case where we set the reference speed \(r_{k}^{i}\) as zero, it takes some time to decrease the truck's speed from \(s_{k}^{i}\) to zero. The required time to stop is \(\frac{s_{k}^{i}}{a_{D}^{i}}\). Then, the travel distance of the truck while decreasing its speed to zero is \(\frac{(s_{k}^{i})^{2}}{2*a_{D}^{i}}\). To avoid collision, it is desirable to set \(safeD\) so that  
\[safeD>\frac{(s_{k}^{i})^{2}}{2*a_{D}^{i}} \tag{24}\]is satisfied. However, as \(safeD\) increases, the separation between neighboring trucks increases. Thus, the effect of fuel reduction under truck platoon decreases. Thus, there is a trade-off between fuel reduction and vehicle safety.
# Positioning Information Privacy in Intelligent Transportation Systems:
## Attacks Related to Distributed Operation
### Privacy Threats  
Unauthorized use of location data and location-based servicesThis threat could be considered as unauthorized use of tolled highways pretending to have a position of a neighbor vehicle which is not situated on a highway or unauthorized access in a car park [110].  
Disclosure of unwanted informationAs an example, consider a case such as if a person leaving the house empty for a long trip by car or by boat (which may enable house burglaries); how often an employee, supposed to be at the office, is visiting places by car (which may lead to loss of social reputation); how often a spouse is visiting places that he/she has never mentioned to his/her relatives (which may cause family crises), etc.  
Tracking malware applicationsA vehicle-installed software could contain malware to \"steal\" the tracking patterns of the users inside the vehicle, i.e., the user positions and speeds in time; such information, if sold to car dealers, could adversely affect the price of a vehicle, e.g., in function of the types of roads where vehicle and how often it was driven, etc.  
Scrambler attackAn additional measure to achieve a higher level of anonymity in vehicular systems is the utilization of dynamically changing identifiers (from MAC to application layer), so-called pseudonyms [111]. The effectiveness of this approach, however, is clearly reduced if specific characteristics of the physical layer (e.g., in the transmitted signal) reveal the link between two messages with different pseudonyms. In contrast to other physical layer fingerprinting methods, it does not rely on potentially fragile features of the channel or the hardware but exploits the transmitted scrambler state that each receiver has to derive in order to decode a packet, making this attack extremely robust. The most straightforward solution is to employ a cryptographic pseudo-random number generator, possibly seeded by a large number of entropy sources in a vehicle (e.g., engine start time, sensors data, SNR, vehicles nearby, etc.). Another solution is the deployment of constant network-wide scrambler values.  
As compared to security threats, privacy issues always had more involvement by the actual humans. One option to improve privacy in ITS systems is to utilize the solutions with strong anonymity properties [112; 113] or frequently changing pseudonyms [114]. Some researchers foresee that Software-defined networking (SDN) will become an ultimate enabler for overcoming previously listed security and privacy problems of ITS [115; 116] supporting both V2V and V2I scenarios.
# Alternative Design of Binary Phase Diffractive Optical Element with Non-\(\pi\) Phase Difference
## 5 Conclusions  
In this paper, a very simple and efficient method for the design of binary phase-only diffractive optical element with non-\(\pi\) phase difference was proposed. Compared with the existing design method based on simulated annealing algorithm, the proposed method needs to optimize the phase distribution of the DOE only once and subsequently with a simple search process of the optimal phase difference. Therefore, the design time is greatly shortened. Simultaneously, the simulation and experimental results show that the designed non 0-\(\pi\) DOEs can generate high-precision target light field. One thing should be pointed out that the proposed method is not general to every binary phase diffractive optical element, but it is limited to the application of creating a symmetric and equal intensity array of spots. Even so, we still believe that the proposed design method has great application prospects in the field of laser beam splitting, pattern generation, and so on.  
Figure 8: Diffraction pattern of the 1\({}^{\#}\) DOE with etching depth of 550 nm. Theoretical depths of the eight patterns are (**a**) 537 nm, (**b**) 537 nm, (**c**) 580 nm, (**d**) 644 nm, (**e**) 633 nm, (**f**) 608 nm, (**g**) 619 nm, and (**h**) 655 nm.  
Figure 9: Diffraction pattern of the 2\({}^{\#}\) DOE with etching depth of 661 nm. Theoretical depths of the eight patterns are (**a**) 537 nm, (**b**) 537 nm, (**c**) 580 nm, (**d**) 644 nm, (**e**) 633 nm, (**f**) 608 nm, (**g**) 619 nm, and (**h**) 655 nm.  
**Author Contributions:** Conceptualization, C.X. and H.P.; methodology, C.X.; software, C.X. and A.C.; validation, C.X.; writing--original draft preparation, C.X.; writing--review and editing, H.P.; supervision, Q.D.; funding acquisition, H.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) (61905251) and Applied Basic Research Programs of Department of Science and Technology of Sichuan Province (2019 YJ0014).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interests.
# Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint
## 3 Experiment and Results  
In this paper, we designed three different experiments for evaluating our proposed GSQF-APD algorithm. The first experiment is designed to demonstrate the disaggregation effectiveness of GSQF-APD. The second experiment is devised to analyze the classification accuracy of the two GSP-based methods, our proposed GSQF-APD, and the other Graph Laplacian Quadratic Form constrained Active Power Disaggregation (GLQF-APD) [40]. The third experiment is carried out to contrast the performance of the proposed algorithm with the state-of-the-art NILM approaches.
## 3 Materials and Methods
### Identity Authentication  
Blockchain-based identity authentication has the characteristics of decentralized authentication, where each node in the network possesses two keys: a private key used to decode the content and enable a node to identify the sender and a public key used to encrypt transactions. Encoding the original communication into one that cannot be deciphered as the original message is known as encryption. Decryption entails modifying the message as it was originally encoded.  
The identity information of the network nodes is stored in the blockchain; therefore, it is complicated to manipulate, and the use of smart contracts to perform addition, deletion modification, and verification operations streamlines the process. In the health sector, in the medical record management process, the recommended identity assignments to the actors are:  
1. User-beneficiary node: The primary function of the user is to check the data and request access. They can add authorized information and authorize other people to check their medical information.
2. User-physician node: The primary function of the physician user is to add, delete, modify, and check data, allowing physicians to add medical records to patients.
3. User administrator node: Manages accounts, passwords, and permissions of users participating in the network, password recovery, and links public and private keys.  
The recommended identity assignment overview in a patient care unit and the medical record management process are shown in Figure 1.
# Development of Anti-OSCAR Antibodies for the Treatment of Osteoarthritis
## 3 Results  
### Generation of Anti-OSCAR Antibodies by Phage Display  
Synthetic human single chain variable fragment (scFv) libraries [24; 25] were panned in alternating rounds against human and mouse OSCAR extracellular domains that were fused to human IgG1 Fc (designated hOSCAR-Fc and mOSCAR-Fc). ELISA screening and sequencing of the third- and fourth-round output clones revealed seven unique scFv sequences that demonstrated species cross-reactivity. These clones were converted into hIgG1 by cloning the variable domains into pclW3.3 vector (a pclDNA3.3-based mammalian expression vector that was constructed in-house by adding unique restriction sites, an artificial intron, a Kozak consensus sequence, a human serum albumin signal sequence, and the woodchuck hepatitis virus post-transcriptional response element). The ability of the converted IgGs to bind to both human and mouse OSCAR was then assessed by ELISA. Five of the seven clones retained their cross-species OSCAR-binding activity. However, while the IgGs of the remaining two clones, namely, D2 and D11, bound strongly to mOSCAR-Fc, they could no longer bind significantly to hOSCAR (Figure 1A). Nonetheless, they were included in subsequent in vitro functional assays with murine cells, with the assumption that their hOSCAR-binding activity could be rescued by further engineering.
## 2 Materials and Methods
### Analysis of Volatile Organic Compounds  
#### 2.3.1 Sample Preparation and SPME Extraction  
The analysis aimed to compare the two species, _T. mesentercium_ and _T. borchii_, and, within each species, samples were divided into distinct groups. The 107 _T. mesentercium_ and 60 _T. borchii_ samples were homogenised by grating, creating two separate batches for each species. Further homogenisation groups taking collection sites into account were then created. For _T. mesentercium_, the truffles were homogenised to create four subgroups in relation to the sites in which the collection areas fall: (1) Partenio-Taburno; (2) Alburni; (3) Eremita-Marzano; (4) Picentini. Similarly, for _T. borchii_, four subgroups were formed from the Caserta and Salerno provinces based on the collection altitude, named (1) Caserta hill, (2) Salerno hill, (3) Caserta coast, and (4) Salerno coast (see Table 1 for further details).  
Truffle sample volatile compounds were extracted using the solid-phase microextraction (SPME) technique. A quantity of 2 g of each sample gleba was placed in a 20 mL glass vial, sealed with a screw cap equipped with a Teflon/silicone septum, and conditioned at 53 \({}^{\circ}\)C for 5 min. After this equilibration period, the SPME fibre (Supelco Co., Bellefonte, PA, USA), coated with a 2 cm long 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) stationary phase, was exposed to the sample headspace for 13.6 min [17].
## 2 Materials and Methods
### EEG Data Collection and Analysis
#### 2.3.1 Pre-Processing  
The EEG data processing was performed using the EEGLAB Toolbox [38] in three key steps: (i) pre-processing, (ii) removing unwanted noise and artifacts, and (iii) segmenting the data into epochs. The raw EEG signals were filtered with 1 Hz and 40 Hz cut-off frequency high- and low-pass zero-phase FIR filters (EEGLAB eegfiltnew filter), respectively. Next, the data were down-sampled to a 512 Hz sampling rate. The filtered EEG datasets were inspected for bad channels (having extremely high or low amplitudes or excessive noise) and were interpolated in EEGLAB using spherical spline interpolation (16 subjects, average number of bad channels: 3). After interpolation, the dataset was re-referenced to the average reference, and Independent Component Analysis (ICA) [39] was performed to separate eye, muscle and channel noise artifacts from the neural signals. The resulting independent components (ICs) were labeled as potential artifacts using the IC Label EEGLAB plugin [40]. Eye blinks, muscle and channel noise artifact components with a confidence level above 80% were removed from the component set to reconstruct the artifact-free signals. Datasets still showing artifacts after cleaning were further inspected and the remaining artifactual independent components with a sub-80% confidence level were removed manually.
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 6 Internet-of-Things Architectures for Modeling Trust
### Hybrid Architectures  
The hybrid scheme is a mix of centralized and distributed approaches to overcome the limitations of these approaches. A 3-tier (cloud-cloudlet-device) architecture is proposed in [44] to propagate trust values to a central cloud. This architecture facilitates the report/query of trustworthiness of IoT devices from the local cloudlets. Although this approach used a hybrid method, it uses a central cloud for propagating the trust values assembled from one cloudlet to the other cloudlets.