esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Model for Low Aspect Ratio Keyholes or High Welding Speeds  
### Geometry of the KH Front at High Welding Speed  
For a given material, an incident laser power and focusing conditions, when the welding speed is increased, Equation (3) shows that the KH depth decreases. But this scheme of a vertical cylindrical KH is no longer valid at high welding speeds: experimentally, one observes that the KH front is inclined and this inclination increases with the welding speed, so the vertical incident laser beam impinges this part (Figure 2b). Therefore, the model described in Section 2 should no longer be used for determining the KH depth for these regimes.  
In fact, this KH depth mainly results from the KH front inclination that is a function of the operating parameters. The reflected beam on this inclined KH front has a mean inclination that is twice  
Figure 1: For different operating parameters (incident power P and spot diameters d), the plot of P/dR as a function of (V\({}_{w}\)d), corresponding best linear fit using Equation (4) [19] (from data published by [PERSON] and [PERSON] [26]).  
Figure 2: Characteristic longitudinal profile of the keyhole (KH) fronts for different welding speeds. (**a**) At low welding speed, a KH with a high aspect ratio R is obtained due to multi-reflections inside it. (**b**) At high welding speed only the KH front is irradiated; the reflected beam is directed upwards. (**c**) At KH threshold, (R = 1), the reflected beam is horizontal and interacts with the KH rear wall. Corresponding views of the KH aperture (d = 0.6 mm, P = 4 kW; 316L material) [28].  
the inclination angle \(\alpha\) of the KH front. At high welding speeds, the contribution of this reflected beam to the final KH depth is small compared to the first contribution of the vertical incident beam, because of its large inclination 2\(\alpha\) and its decreased intensity resulting from its first reflection [28; 29]. This point is confirmed by the X-ray radiographies of the KH by [PERSON] et al. [21] and [PERSON] et al. [22] where one can see that the KH depth is mainly defined by the impact of the direct beam for a range of aspect ratio of R \(<\) \(\approx\)3. Accordingly, in order to estimate the KH depth for these conditions, only the contribution of the first impact of the vertical incident beam on the KH front is considered in the model that will be discussed in the next Section 3.2.  
With this geometry where \(\alpha\) is the inclination angle of the KH front, it is easy to define that the characteristic aspect ratio R is given by:  
\[\mathrm{R}=\frac{\mathrm{e}}{\mathrm{d}}=\frac{1}{\mathrm{tg}(\alpha)}, \tag{8}\]  
and therefore, this analysis will be restricted to rather low aspect ratios R \(<\) \(\approx\)3.
###### Abstract  
The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point forecasting, the purpose of this study is to quantify the potential uncertainties of wind power and to construct prediction intervals (PIs) and prediction models using wavelet neural network (WNN). Lower upper bound estimation (LUBE) of the PIs is achieved by minimizing a multi-objective function covering both interval width and coverage probabilities. Considering the influence of the points out of the PIs to shorten the width of PIs without compromising coverage probability, a new, improved, multi-objective artificial bee colony (MOABC) algorithm combining multi-objective evolutionary knowledge, called EKMOABC, is proposed for the optimization of the forecasting model. In this paper, some comparative simulations are carried out and the results show that the proposed model and algorithm can achieve higher quality PIs for wind power forecasting. Taking into account the intermittency of renewable energy, such a type of wind power forecast can actually provide a more reliable reference for dispatching of the power system.  
wind power forecasting; wavelet neural network; multi-objective artificial bee colony algorithm; prediction intervals 2018 8 185 10.3390/app8020185
# Study of Precipitation Forecast Based on Deep Belief Networks
## 3 Material and Methods  
### SVM Based on the PSO  
The support vector machine with particle swarm optimization (PSO-SVM) was used to analyze and process the precipitation data in the literature [19]. SVM has many unique advantages in solving small sample, nonlinear, and high dimensional pattern recognition problems. It is given by [PERSON] in 1995 [20]. An artificial neural network model based on hybrid genetic algorithm and particle swarm optimization (HGAPSO) optimization was proposed as an intelligent method for predicting natural depletion of asphaltenes [21]. SVM is a classification method based on statistical learning and Vapnik-Chervonenkis (VC) dimensional theories [22].  
The literature [19] introduced the particle swarm optimization algorithm to search the training parameters in global space to improve the accuracy of the model for precipitation prediction. A combination of the PSO algorithm and SVM model can effectively solve the parameter selection problem of the SVM algorithm. It used the PSO algorithm to optimize the parameters in the literature [19]: Initialize parameters of particle swarm optimization include the population size, maximum iteration number, and the parameters, C and g. Particle swarm optimization is adopted to search the optimal solution of particles in global space by using the cross-validation algorithm. Inputting the training data into the SVM model with the optimal parameters allows the trained PSO-SVM model to be obtained. Figure 1 is the flowchart of the PSO-SVM model.
# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation
### Discussion  
In the analytic model, we made two assumptions: (1) for the network overhead, the dissemination of the messages was maximized and (2) for the freshness, it was minimized. Thus, it was expected that the simulation gives better results than the analytic model for our approach. The results we obtained match the expected ones. So, we have been able to validate our model thanks to simulation. The analytic expression of our approach could be perfected, yet we have been able to confirm that our approach is better than the others in the worst cases. Table 19 presents an overview of the behavior of each approach when it comes to large-scale situations, and it is not based on any threshold. It is based on the results  
Figure 31: Path finding for different numbers of RSUs. (**a**) 2 Random RSUs; (**b**) 8 Random RSUs; (**c**) 11 Random RSUs.  
Figure 30: Path-finding with 1 random RSU.  
obtained with each approach, on both performance indicators that we studied, which are network overhead and data freshness.  
Whether it is for the analytic models or the simulations, we improve the results when we increase the density or the number of sensors. The NDN approach is absolutely not worthy for the vehicular network, as VCDN does better and is already too high compared to the three others. For better freshness and lower cost on bandwidth, the VCDNoCAM is better than the RENE approach, as we can see in the table above. On the other hand, GeoVCDN gives a bit higher network overhead than VCDNoCAM but remains better than RENE. This additional cost compared to VCDNoCAM is widely compensated by the gain in freshness. By comparing VCDN and NDN, we note that the sequence number has a positive effect on the network overhead even if the data are bigger. By comparing VCDNoCAM and RENE, we note that the usage of CAMs to provide ICN exchanges also positively affects the network overhead. With VCDNoCAM, for freshness, it performs as well as NDN and better than RENE, all of that at a lower cost. Our approach is more expansive in terms of bandwidth consumption than RENE and NDN when there are few vehicles or few sensors, but in this case, it is less important to optimize the network overhead. Finally, the values obtained during the simulations are very close to the ones expected from the model and, therefore, validate them. Additionally, with GeoVCDN, we are limited by the CAM frequency, which affects the latency and could make our approach not adapted for some use cases. Yet, using our approach frees some space on the bandwidth for the use cases which need low latency. As we showed with the use case of pathfinding, we have both better quality of service and lower usage of the bandwidth.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 1 Introduction
[21] study, it is shown that one of the crucial features that should be taken into account in onset detection is the transition from a note to another note where there is no intervening silence, i.e., legato singing [21]. The transition means a singer will take a while to reach the target note. If the time for the transiting is not incorporated, the onset detector cannot find the correct times for onset and offset events. These transitions are categorized as a soft onset. This paper aims to introduce a new onset detection algorithm incorporating more knowledge about the singing features for a more accurate onset estimation. Although the result of this study is based on an offline \(F0\) estimator algorithm, the proposed algorithm can work in a real-time environment if fundamental frequencies can be estimated correctly. The following section explains the methodology. After that, in Section 3, the new algorithm will be discussed in detail. Then, the evaluation results for the proposed algorithm will be presented and discussed in Section 4. Finally, the last section concludes the paper and its findings.
## 5 Performance Evaluation  
To analyze and evaluate the performance of the ML-RELM algorithm, we conducted simulation and realistic scenario experiments. The simulation experiments are carried out on a PC (equipped with Windows 7 OS, Intel Core I7 and 8 GB RAM), and all the codes are implemented in MatLab. Realistic scenario experiments are implemented in indoor and outdoor environments respectively.  
In order to reduce the one-sidedness of the experimental results in the simulation experiments, each simulation experiment is carried out 50 times. Each node will be randomly re-distributed in the experimental area, taking 50 times average of the Root Mean Square (RMS) [26] as the basis for evaluation:  
\[\text{RMS}=\sqrt{\frac{1}{N_{t}}\sum_{i=1}^{N_{t}}\Bigl{(}\left(\hat{x}_{i}-x _{i}\right)^{2}+\left(\hat{y}_{i}-y_{i}\right)^{2}\Bigr{)}} \tag{14}\]  
where \(\left(\hat{x}_{i},\hat{y}_{i}\right)\) is the estimated coordinate of the node. \(\left(x_{i},y_{i}\right)\) is the actual coordinate of the \(i\) node, and \(N_{t}\) is the number of nodes that can be located. In addition, our performance comparisons are implemented among those schemes, including the DV-hop, PDM, LSVR in this section. Since the PDM method needs to set the threshold of TSVD to discard the eigenvalue; the LSVR method needs to set the kernel parameters, the penalty coefficient \(C\) and the width of insensitive loss function \(\varepsilon\). For the sake of fairness, in the experiment, the TSVD method abandons the eigenvectors whose eigenvalues are less than or equal to 2. The \(C\) and \(\varepsilon\) of the LSVR method follows the settings in reference [8], and the kernel function is chosen to be the Gaussian kernel function. The kernel function is related to the distance of the training samples, so the kernel parameter is set to 40 times the mean value of the training samples.
# Evaluation of Keratin/Bacterial Cellulose Based Scaffolds as Potential Burned Wound Dressing
###### Abstract  
The study presents the preparation and characterization of new scaffolds based on bacterial cellulose and keratin hydrogel which were seeded with adipose stem cells. The bacterial cellulose was obtained by developing an _Acetobacter xylimum_ culture and was visualized using SEM (scanning electron microscopy) and elementally determined through EDAX (dispersive X-ray analysis) tests. Keratin species (\(\beta\)-keratose and \(\gamma\)-keratose) was extracted by hydrolytic degradation from non-dyed human hair. SEM, EDAX and conductometric titration tests were performed for physical-chemical and morphological evaluation. Cytocompatibility tests performed in vitro confirmed the material non-toxic effect on cells. The scaffolds, with and without stem cells, were grafted on the burned wounds on the rabbit's dorsal region and the grafts were monitored for 21 days after the application on the wounds. The clinical monitoring of the grafts and the histopathological examination demonstrated the regenerative potential of the bacterial cellulose-keratin scaffolds, under the test conditions.  
bacterial cellulose; keratin; stem cells; wound dressing; in vitro and in vivo tests +
Footnote †: journal: Medical Bioengineering, \"[PERSON]\" University of Medicine and Pharmacy, 11-16, M. Kosgalineucn Street, No. 9-13, 700454 Iasi, Romania 3
## 4 Blockchain
### Characteristics of Blockchain  
A blockchain can be characterized as a distributed ledger of any type of transactions, where a transaction is the exchange of data (medical data, consumer details, product data, etc.). The blockchain offers many benefits and advantages over traditional centuries-old centralized systems. The advantages range from security to efficiency and from immutability to non-alterability [27]. Its main characteristics are as follows:  
* P2P: the blockchain network is peer-to-peer, which means that all members of the network interact with each other and resolve network-related issues in a collective manner.
* Cryptographically Secure: blockchains rely on public-private key cryptography for security and secrecy. Each user has a public key through which they receive cryptocurrencies and information, whereas the private key is for sending information. Each user has an address, which is derived through the hash of a public key of the user, to receive funds through. Each blockchain may use a different version of a hash function, which essentially encrypts information to add security to information transfer processes. For the Bitcoin blockchain, the hash function is SHA256, whereas for Ethereum it is ETH HASH.
* Append-Only: blockchains are append-only record-keeping decentralized technologies. This means that data can only be added to the blockchain and cannot be changed or modified once added to it. Hence, transactions and information in the blockchain remain forever.
* Time-Stamped: transactions are stored in chronological order.
* Decentralized: the blockchain is decentralized, meaning no single authority controls, influences, or manipulates the data records that exists in a blockchain.
* Consensus Mechanism: The validation mechanisms, that is, how transactions are added to the blockchain, are defined through a consensus mechanism that differs from blockchain to blockchain. For instance, for the Bitcoin network, \"Proof of Work\" is used, which corresponds to the computational power each user/node has. The higher the computational power, the greater the likelihood of producing a block to add to the blockchain [28].
## 5 Federated Learning with Homomorphic Encryption
### Related Works
[PERSON] et al. [68] noticed that previous works do not protect the dataset size of each client. This information can inadvertently reveal sensitive data, such as the number of patients in the local hospital, rare diseases among the regions, etc. They considered a healthcare scenario and proposed a protocol for private federated averaging for the cross-silo setting using partial homomorphic encryption based on the Paillier scheme. In their protocol, each client interacts with a randomly selected neighbor to send the encrypted calculation result, instead of sending them to the server. The final result is then sent to the server for decryption. In this way, the local results of each client remain hidden from other clients and from the server. [PERSON] et al. in 2023 [PERSON] et al. (2023) proposed an HE-FL optimization scheme, named FedML-HE, that minimizes the size of model updates for encrypted computation while preserving privacy guarantees. The work addresses challenges related to communication and computation overhead (e.g., 10\(\times\) reduction for HE-federated training of ResNet-50 and 40\(\times\) reduction for BERT). In their approach, an honest-but-curious server aggregates the encrypted gradients from clients before decrypting them. Two techniques are introduced: parameter efficiency and parameter selection. In parameter efficiency, the goal is to reduce the model size through techniques such as model compression and parameter efficient tuning like in FLZip (2018). In parameter selection, the idea is to hide parts of the model instead of encrypting the whole model. The proposed solution was implemented using PALISADE for HE. The experimentation shows that the communication and computation overheads are reduced using the optimization techniques. The effectiveness of the parameter selection defense was also tested against gradient inversion, and the results show that encrypting 42% of the parameters is effective when using random selection mechanism, but using a more robust selection mechanism by selecting more important parameters is more efficient, and it is necessary to just encrypt 10% of the parameters to counter the DLG attack.
# Improvement of the Transglycosylation Efficiency of a
## 3 Results and Discussion
### Thermal Stability of Best Variants  
All variants had significantly lower melting temperature (\(T_{\text{m}}\)) values than the WT enzyme (64.4 \({}^{\circ}\)C; Table 4). For the W465 variants, the decrease in \(T_{\text{m}}\) was not dramatic (63 \({}^{\circ}\)C). For Y419N, the \(T_{\text{m}}\) decreased by approximately 2 \({}^{\circ}\)C, whereas the mutation of W394 had the largest effect on \(T_{\text{m}}\). (58.0-59.4 \({}^{\circ}\)C). It appears that the decrease in \(T_{\text{m}}\) could be linked to solvent exposure: W465 is placed deep in the active site 'below' the ligand, whereas Y419 and especially W394 are on the outside, more exposed to the solvent. The \(T_{\text{m}}\) values are in the same range as previously reported for recombinantly produced LnbB WT and variants (61-62.5 \({}^{\circ}\)C) [9] yet with slightly more variation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Enzyme** & \(T_{\text{m}}\) [\({}^{\circ}\)C] \\ \hline WT & 64.4 \(\pm\) 0.01 \({}^{\text{a}}\) \\ W394F & 58.0 \(\pm\) 0.06 \({}^{\text{e}}\) \\ W394H & 59.4 \(\pm\) 0.12 \({}^{\text{d}}\) \\ Y419N & 60.6 \(\pm\) 0.03 \({}^{\text{c}}\) \\ W465F & 63.0 \(\pm\) 0.04 \({}^{\text{b}}\) \\ W465H & 62.9 \(\pm\) 0.01 \({}^{\text{b}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermal stability of LnbB WT and selected variants determined as the thermal unfolding transition midpoint, \(T_{\text{m}}\) in \({}^{\circ}\)C, at which half of the protein population is unfolded—often referred to as the melting temperature. Superscript letters (\({}^{\text{a--e}}\)) indicate significant difference in the \(T_{\text{m}}\) value between enzyme variants (\(p<0.05\)).  
Figure 10: Transglycosylation reactions using pNP-LNB as donor and lactose as acceptor at pH 4.5 and 30 \({}^{\circ}\)C for LnbB W394H and varying substrate initial concentrations. **(A)**: 2.5 mM pNP-LNB, 200 mM lactose; **(B)**: 2.5 mM pNP-LNB, 400 mM lactose; **(C)**: 5 mM pNP-LNB, 200 mM lactose; **(D)**: 5 mM pNP-LNB, 400 mM lactose; **(E)**: 10 mM pNP-LNB, 200 mM lactose; and **(F)**: 10 mM pNP-LNB, 400 mM lactose. The formation of transglycosylation products was followed over 26 h and quantified as LNT equivalents. Purple crosses represent the total formation of all LNT isomers, while pink triangles represent the formation of LNT (LNB-81,3-Lac), orange squares LNB-81,2-Lac, green circles LNB-1,1-Lac, and blue triangles LNB-81,6-Lac.
# Evaluation of Anti-Wear Properties of Metalworking Fluids Enhanced with Halloysite Nanotubes
###### Abstract  
The study of nanoparticles as additives for metalworking fluids (MWFs) with applications in the metal removal processes, or machining, has received increasing attention due to the possible enhancements on tribological properties. In this study, low-cost and environmentally friendly nanoparticle additives of halloysite clay nanotubes (HNTs) were dispersed in metalworking fluids utilized for milling processes. Concentrations of 0.01, 0.05, 0.10 wt. % were incorporated into a mineral oil (MO) and a semi-synthetic fluid (SF) by ultrasonication. The anti-wear properties of metalworking nanofluids were characterized with a T-05 block-on-ring tribotester at a contact pressure of 0.5 GPa. Surface roughness of worn block materials was obtained with an optical 3D surface measurement system. Results showed that at a concentration of 0.10 wt. % HNTs block mass loss was lowered by 24% for the MO + HNTs nanofluids. For the SF + HNTs, a reduction of 63% and 32% in wear mass loss and coefficient of friction (COF), respectively, were found at the same concentration. The tribological enhancing mechanism for the applied contact pressure was proposed to be due to a reduction of the area of contact and nanoparticle sliding between surfaces with no HNT deposition, evidenced by energy dispersive spectrometry (EDS). Furthermore, surface roughness studies of worn blocks showed smoother surfaces with lower groove density with the addition of nanoparticle additives. The results of this study demonstrate that HNTs can improve the lubricity of metalworking cutting fluids used for machining processes, enhancing tool life and providing better surface finish of products.  
nanofluids; metalworking fluids; halloysite nanotubes; wear; coefficient of friction; surface roughness +
Footnote †: journal: Applied Physics Letters
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 3 Design of the Protocol Stack
### Design of Control Plane
#### 3.1.3 Signal Protocol
In order to reduce unnecessary label distributions and table query operations, the signal protocol mechanism inspired by PHP (penultimate hop popping) is used. In the mechanism, the destination node assigns the same specific label(0 xff), which is used in the AOS frames sent by the penultimate hop and tells the downstream node that it is the  
Figure 6: Processing of Building an label switching path (LSP). destination of the path, to the upstream nodes of all LSPs. By doing this, in each LSP, the destination node does not have to distribute a normal label and query the table. Based on the design discussed above, all protocol massages need to be transmitted in control channels established by the link management protocol. When routing protocol collects network resources, the local resources needed must be collected by link management protocol. In addition, the signal protocol can build LSPs according to the route path calculated by the routing protocol. It can be seen that the three protocols work closely with each other to realize the functions of control plane together.