esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Identification of Phenolic Compounds by HPLC/ESI/MS  
Following LC with Amberlite XAD-16, compounds were analyzed by high-performance liquid chromatography (HPLC) on a Varian HPLC system, including an automatic injector (VarianProStar 410, Palo Alto, CA, USA), a ternary pump (VarianProStar 230I, USA), and a photodiode array detector (PDA) (VarianProStar 330, Palo Alto, CA, USA). A liquid chromatography ion-trap mass spectrometer (Varian 500-MS IT Mass Spectrometer, CA, USA) equipped with an electrospray ion source was used. Samples (5 \(\upmu\)L) were injected into a Denali C18 column (150 mm \(\times\) 2.1 mm, 3 \(\upmu\)m, Grace, USA). The oven temperature was kept at 30 \({}^{\circ}\)C. The eluents were formic acid (0.2%, \(v/v\); solvent A) and acetonitrile (solvent B). The following gradient was applied: initial, 3% B; 0-5 min, 9% B linear; 5-15 min, 16% B linear; 15-45 min, 50% B linear. The column was washed and reconditioned. The flow rate was kept at 0.2 mL/min and the elution of phenolic compounds was controlled at 245, 280, 320 and 550 nm. All the effluent (0.2 mL/min) was injected into the source of the mass spectrometer, without division. All mass spectrometer experiments were performed in the [M-H]\({}^{-1}\) negative mode. Nitrogen was used as the nebulizer gas and helium as the buffer gas. The ion source parameters were as follows: spraying voltage 5.0 kV; capillary voltage at 90 V; and temperature at 350 \({}^{\circ}\)C. Data were collected and processed using MS Workstation software (V 6.9).
# Effect of NS-DBD Actuator Parameters on the Aerodynamic Performance of a Flap Lifting Device
## 4 Simulation Results and Analysis
### Discharge Energy
#### 4.3.1 Comparison Results of Aerodynamic Forces and Moments  
Figure 16 shows that the change of discharge energy has no significant influence on the drag reduction caused by the plasma actuator; the drag coefficient curves have the same trend. According to the specific data, enhancing the discharge energy under the angles of attack of 4 and 6\({}^{\circ}\) without flow  
Figure 15: The pressure distribution under single pulse discharges with different pulse widths.  
separation can reduce the drag more effectively to a certain extent. After large area separation of the airflow, this effect is correspondingly weakened. On the whole, the drag reduction of \(10^{14}\) W/m\({}^{2}\) is slightly larger than that of \(10^{13}\) W/m\({}^{2}\).  
It can be seen from Figure 17 that the influence of energy increase on the lift coefficient is not optimistic. The two curves have the same trend, but the curve of the larger discharge energy always lies below the curve of the smaller discharge energy, which can show that the discharge energy increase does not enhance the lift of the airfoil.  
Figure 18 shows the different lift-drag ratios under different discharge energies. When the flow field is not separated, the lift-drag ratio can be increased from 6.04% to 10.75% by increasing the discharge energy. When a flow separation zone of large area occurs above the airfoil, this effect is weakened to some extent, and from the curve, they basically coincide with each other and have little difference in numerical value. Mainly because the discharge with \(10^{14}\) W/m\({}^{2}\) energy density has a better drag reduction effect, it has a slight advantage in the lift-drag ratio.  
Figure 16: Drag coefficient curve of variable energy.  
Figure 17: Lift coefficient curve of variable energy.
## 4 Conclusions  
We present a two-stage framework for note-level polyphonic piano music transcription, which comprises a note recognition stage and a note verification stage. In the note recognition, one CNN is trained for onset detection and another is trained for pitch estimation at each onset. To our knowledge, the combination of two CNNs has not been attempted before for AMT. The note verification for the specific piano is implemented using NMF. The factorization is conducted in the time slice around candidate onset, which only uses attack templates of the candidate pitches. Our experiments are carried out on the MAPS database and the performance of each module is discussed. The experiments demonstrate that CNN performs better than other types of neural networks in the subtasks of onset detection and pitch estimation, and the connection of two CNNs outperforms a single CNN in note recognition. We also observe that the performance of transcription is improved significantly when note verification is applied to the system, and our proposed system performs better than state-of-the-art systems in specific piano transcription.  
There are some limitations of the proposed system. As the biggest dataset for piano AMT, the MAPS has only 270 solo pieces. So, the data may be not enough for training CNNs. Although training data and testing data are from synthesized pianos and a real piano, respectively, they contain overlaps in music pieces. The limited data and piece-dependent scheme led the CNNs to overfit. For the real pieces in the testing dataset, the recording environment was quiet and the distance between the piano and microphones was close. Therefore, one future research direction is to discuss whether the proposed method is robust to noise and reverberation. Additionally, the proposed method cannot estimate note offsets or loudness, which will be another research direction in the future.  
This work is partially supported by the National Natural Science Foundation of China (Nos. 11461141004, 61271426, U1536117, 11504406, 11590770-4), the Strategic Priority Research Program of the Chinese Academy of Sciences (Nos. XDA06030100, XDA06030500, XDA06040603), the National 863 Program (No. 2015 AA016306) and the National 973 Program (No. 2013 CB329302). [PERSON] and [PERSON] conceived of and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 10: Results of specific piano transcription: (**a**) the transcription of our proposed system and (**b**) the transcription of [PERSON]’s system.
## 3 Methodology  
### Modeling of Upgoing and Downgoing Wave Separation Problem  
The goal of signal separation is to recover the unknown upgoing wavefield, \(U\in R^{T\times N}\), and downgoing wavefield, \(D\in R^{T\times N}\), from the observed VSP record, \(X\in R^{T\times N}\). This study employed a U-Net architecture using two independent networks to predict the upward and downward wavefields. When deriving the solution, physical knowledge representations (\(U_{rp}\) and \(D_{rp}\)), morphological knowledge representations, and wavefield directional difference knowledge representations were explicitly introduced as constraints to enhance the physical consistency and accuracy of the wavefield separation. Specifically, our modeling of the upgoing and downgoing wave-separation problems is as follows:  
\[\begin{array}{l}\underset{\theta_{w},\theta_{d}}{argmin}\\ \underset{\theta_{w},\theta_{d}}{argmin}\\ \underset{\widetilde{D}}{\left(\mathcal{G}_{|\theta_{i}}(X)\in R^{T\times N} \right.}\end{array} \tag{11}\]  
where \(X\) represents the original VSP record, \(\widetilde{X}\) represents the predicted reconstructed wavefield, and the remaining terms are the constraints introduced from the knowledge representations, including the constraints in the physical, morphological, and propagation-direction difference. Furthermore, \(||\cdot||\): \(R^{T\times N}\to R^{+}\) is an operator for measuring error, \(\odot\) represents element-wise multiplication, \(\mathcal{G}_{u}\mathcal{G}_{d}\): \(R^{T\times N}\to R^{T\times N}\) represent the two-branch network-mapping functions for predicting upgoing and downgoing wavefields, which recover \(\widetilde{U}\) and \(\widetilde{D}\) from the original wavefield \(X\), and \(\theta_{u}\), \(\theta_{d}\) represent the parameters of \(\mathcal{G}\).  
This modeling process not only needs to measure the reconstruction error between the observed record, \(X\), and the reconstructed wavefield, \(\widetilde{U}+\widetilde{D}\), but also the error between the predicted wavefields, \(\widetilde{U}\) and \(\widetilde{D}\), and their knowledge representations. This constrains the continuity and directional consistency of the wavefield to ensure the physical reasonableness of wavefield separation.
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 4 Hamaliere Landslide Monitoring
### Surface Displacement Monitoring Results
#### 4.3.1 2D Relative Displacement for One Tag  
The 2D displacement of tag A, computed from the radial displacements using multi-lateration and data from the four antennas (see Equation (3)), is shown in Figure 8 against reference tacheometer position measurements. The xOy results are in good agreement with the reference points. Note that, for stable phase periods (for example July 2021), the localization algorithm yields very stable results with a centimeter scale variability, which is in agreement with the theoretical localization error presented in Figure 2. This correspondence between theory and experiment during stable periods is observed for several tags, further validating the measurement error model. Note that Figure 2 does not present any phase bias results, but focuses only on measurement random deviation (dimensions of the localization spot).
## 1 Introduction
(iv) The proposed MCETT approach based on PMS fits well with the tracking of spawned and newborn targets in the clutter and missed detection environments. This paper is organized as follows. Section 2 first elaborates and analyzes the existing problems of the typical measurement partitioning algorithms in multiple extended target tracking. Section 3 proposes a fast and robust prediction-driven algorithm (i.e., PMS) to partition the measurements set accurately for improving the target tracking performance. In Section 4, a multiple extended target tracking approach based on PMS is proposed for tracking closely spaced extended targets. Section 5 presents simulation results and demonstrates the effectiveness of what we proposed. Section 6 further discusses and compares the proposed algorithm with existing measurement partitioning algorithms. The last section concludes this paper.
## 7 Security Analysis
### Informal Security Analysis
(9) Malicious Message Tracing: In the protocol proposed in this paper, the pseudonym \(PDID_{i}^{X}\) of a legitimate device is assigned by the edge gateway, where \(PDID_{i}^{X}=h(DID_{i}^{X}||SK_{EQ_{i}^{X}})\), with \(DID_{i}^{X}\) being the real identity of the device and \(SK_{EQ_{i}^{X}}\) being the private key of Edge Gateway \(EQ_{i}^{X}\). Therefore, \(PDID_{i}^{X}\) is unforgeable. When a legitimate user sends malicious messages, the edge gateway can trace back to the user's real identity \(DID_{i}^{X}\) by decrypting \(PDID_{i}^{X}\).
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 2 Accelerator Neutrino Beams
### Decay Volume  
Every neutrino beam includes a _decay volume_ to allow for the charged mesons to decay (see Figure 1). The decay volume is usually a cylindrical hollow tunnel, whose radius and length determine the number of neutrinos that reach the detector. The beam of secondary hadrons must have a relatively small divergence so that the particles do not impinge on the cylinder's wall before decaying. The cylinder ends up with a _hadron dump_ that stops all charged hadrons from reaching the experiment.  
The decay volume is generally not instrumented except for monitored neutrino beams, where the number of leptons produced by the kaon decays is recorded. Muons from \(\pi^{+}\to\mu^{+}\
u_{\mu}\) impinge on the hadron dump and may be detected by positioning radiation-hard devices just after the dump (see Section 9).
## 5 Result and Discussion
### Simulation Results
#### 5.2.2 Actual Power System
Similar to the result for the IEEE test system previously, the operation cost of the DSO in Case 2 was lower than Case 1, while the MGO profit in Case 2 was lower than Case 1. It can be seen that the DSO operational cost was the lowest, even though the MGO profit was little reduced in Case 2. Therefore, it should be noted that the proposed approach provides the optimal balance for reliable and economical operation between the DSO and MGO.
# V-SOC4 AS: A Vehicle-SOC for Improving Automotive Security
## 3 Controller Area Network (CAN)  
The CAN protocol is a broadcast protocol that connects several electronic control units (ECUs) in-vehicle network to exchange messages among them. The messages exchanged are called CAN frames. A CAN frame is composed of the following fields:  
* Arbitration field (11 bits CAN 2.0A) establishes the priority of the message, 0-bit is dominant while 1-bit is recessive. When two or more nodes transmit a data frame at the same time, if node A has a dominant bit and node B has a recessive bit, node A wins the arbitration and can send the message on the bus.
* Control field (4 bits): consists of data length control (DLC) and contains the length of the data payload.
* Data field (64 bits): contains the payload.
* The main issue of the CAN protocol is that it does not implement any security mechanism against cyber-attacks [2].
# Efficient Optimization-Based Trajectory Planning for Truck-Trailer Systems
### Finding Trajectories Without a Known Path  
Finding trajectories without an a priori known path implies the use of global optimization. This global optimization problem has the advantage of not needing an initial guess; however, a major disadvantage is that it relies on the use of stochastic algorithms. These are mainly a group of rapidly expanding biologically-inspired optimization algorithms [15; 16; 17]. However, there are a variety of other stochastic algorithms [18; 19]. The use of these algorithms can lead to very interesting solutions where solving a single optimization problem ensures that suitable trajectories are found.  
In [20], the authors presented a method for finding trajectories using a stochastic algorithm (SPS, Stochastic Fractal Search) within a single optimization problem. The authors themselves pointed out that such trajectories are not necessarily optimal, but they are very close to optimal.  
Another disadvantage, probably the biggest from the applied point of view, is the time needed to find such a solution; in the case of finding a global minimum without an initial estimate, it is not currently possible to talk about real-time deployment.
# Lightweight and Privacy-Preserving Multi-Keyword Search over Outsourced Data
## 1 Introduction  
With cloud computing technology, users with limited local resources do not need to purchase expensive hardware to support massive data storage [1]. Thus, for economic savings, individuals and enterprises can engage cloud servers to maintain big data, for example, videos, animations, and images. However, some sensitive information of outsourced data may be leaked, since the data owner would lose control of these data [2, 3]. Therefore, to protect the privacy of user data, they should be properly processed before being outsourced, so that only the data in ciphertext format are maintained at the cloud server side.  
When retrieving the interested data, users can request the cloud server to search over outsourced dataset with some specific keywords [4, 5, 6, 7]. However, there are two issues to be addressed in the keyword search scenarios for outsourced data: data privacy and efficiency. For example, in the Internet of Things (IoT) scenario, the keywords of the collected data may contain sensitive information of outsourced data, which means the search keywords cannot be submitted to the cloud server in plaintext format. Otherwise, the users' privateinformation could be deduced by the cloud server from outsourced documents. To support privacy-preserving data search, and many single/multiple keywords search schemes have been proposed [8; 9; 10; 11].  
However, existing keyword search solutions in private/public key settings require heavy computations in both data processing and search trapdoor generation phases at the user side. For example, in [12], users need to perform high-dimensional matrix operations, such as multiplication and inversion operations, where the matrix dimension is determined by the cardinality of the keyword set. Furthermore, in [13; 14], users have to compute heavy exponentiation operations for generating searchable indexes and search trapdoor. However, these resource-intensive computation operations cannot be afforded by resource-constrained devices, thus, those existing keyword search constructions are not applicable to IoT-related application scenarios.
# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 1 Introduction
We use four standard RBMs of different sizes to form the encoder and decoder of the Stacked RBM-AE model. The Stacked RBM-AE model compresses the sensing data by using its mathematical characteristics. In our experiments, we explore the performance of the model and compare it with other compression methods, and we propose an energy optimization method of the model to reduce the energy consumption of calculation. The contributions of this paper are summarized as follows:  
* We developed a hybrid model named Stacked RBM-AE that combines four standard RBMs with the feature of auto-encoder to compress and reconstruct the sensing data. * We proposed a new method of data compression, which has better reconstruction accuracy than the traditional algorithm under the same compression ratio. * We proposed an energy optimization method by pruning the model parameters, and we analyzed the efficiency of pruning different proportion model parameters on the reconstruction accuracy of the Stacked RBM-AE model under the same compression ratio. The remainder of this paper is organized as follows: Section 2 introduces the architecture of the Stacked RBM-AE, and the details of Stacked RBM-AE training. Section 3 discusses the results of the experiments. Specially, we conduct several tricks on energy optimization. In Section 4, we sum up our algorithm, and discus further work. The code of the Stacked RBM-AE model is available online ([[https://github.com/LJianlin/Stacked-RBM-AE](https://github.com/LJianlin/Stacked-RBM-AE)]([https://github.com/LJianlin/Stacked-RBM-AE](https://github.com/LJianlin/Stacked-RBM-AE))).
# Improvement of the Sensor Capability of the NAO Robot by the Integration of a Laser Rangefinder
## 3 Results  
The dynamic control of the robot's movements is based on the knowledge of the mass distribution and moments of inertia of its different solids. These parameters, which must be considered in the calculation of the robot's direct dynamics, are contained in three terms named mass (\(M\)), center of mass (\(CoM\)), and inertial matrix (\(l_{0}\)) [53]. The addition of the helmet, the rangefinder laser, and relative accessories on the robot's head (an additive weight of about 250 g) has modified the mass distribution concerning its original configuration. Since this modification could reduce the robot's stability, balance, and mobility, the effects of the new weight distribution on the inertia matrix of the robot's head have been studied. Thus, these inertial parameters have been recomputed, considering the actual distribution of the weights and the dimensions of each part that belong to the helmet concerning the coordinate system shown in Figure 3a. The new inertial matrix is shown in (1).  
\[CoM(S)=\begin{bmatrix}-0.00155\\ 0\\ 0.0734\end{bmatrix}[I_{0}(S)]=\begin{bmatrix}0.00645&9.327\;10^{-6}&4.632\;10^ {-5}\\ 9.327\;10^{-6}&0.00621&-3.265\;10^{-5}\\ 4.632\;10^{-5}&-3.265\;10^{-5}&0.000161\end{bmatrix} \tag{1}\]
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 6 Simulation Results
### Performance Comparison
#### 6.3.3 PDR  
Figure 9 shows the PDR of the existing scheme WDFAD-DBR and our proposed schemes. In all schemes, PDR is increasing with the increase in node density. The reason for the increase in PDR is that void hole probability decreases with the increase in the density of nodes. The reason for less PDR of WDFAD-DBR is that this scheme only considers data transmission up to two hops, which does not eliminate the void hole occurrence. In WDFAD-DBR after two hops, a void hole may occur that will result in packet drop, which decreases the PDR. The PDR of WDFAD-DBR is slightly better than the RE-PBR because of better neighbor selection and holding time to avoid redundant transmissions, which lead to a better network lifetime and improved PDR than RE-PBR; whereas, RE-PBR is effective when the node density is 300-400; after that, its PDR again decreased because of high collision and higher packet drop rate.  
In the proposed scheme A-DBR, if a void hole occurs after a two-hop transmission, then it adaptively adjusts its transmission range to find the forwarding neighbors and forward the packet towards the sink. In the second proposed scheme C-DBR, the network is divided into to clusters to further enhance the PDR and increase the network lifetime. Each cluster head is selected on the basis of residual energy, and cluster heads then communicate with one another to transmit the packets and adaptively adjust the transmission range, as well. In the proposed scheme CA-DBR, there are less packet drops, which results in increased PDR. In the proposed scheme B-DBR, instead of dropping the packet in the case of the void region, it uses backward transmission and forwards the data packet towards the base station. In WDFAD-DBR, it considers the current depth of the node and its expected next neighbor node depth; however, considering depth up to two hops is not the solution for void hole avoidance; thus, the PDR of WDFAD-DBR decreases.
###### Abstract  
This study investigates the optimization of ball burnishing parameters for enhancing the surface quality of pure Titanium (Ti) grade 2 titanium alloy under dry and Minimum Quantity Lubrication (MQL) conditions. Using a Taguchi L18 experimental design, the research systematically examines the effects of three critical parameters: burnishing force (50-200 N), feed rate (0.5-2 mm/min), and number of passes (1-4). Surface quality was evaluated through roughness measurements (Ra and Rz values), with Analysis of Variance (ANOVA) employed to determine the statistical significance of each parameter. The results demonstrate that MQL conditions consistently outperform dry burnishing, contributing 50.93% to the total variance in surface quality. The optimal surface finish (Ra = 0.306 \(\upmu\)m) was achieved under MQL conditions with a burnishing force of 200 N, feed rate of 0.5 mm/min, and four passes. Statistical analysis revealed that the burnishing environment was the most influential factor, followed by the number of passes (23.87%) and burnishing force (9.97%). A regression model with an R-squared value of 87.66% was developed to predict surface roughness under various parameter combinations. These investigations will be helpful in the development of sustainable and efficient methods for the surface engineering of Ti-based materials for the aerospace and biomedical industries.  
ball burnishing; surface roughness; titanium grade 2; minimum quantity lubrication; surface quality optimization; sustainable manufacturing Article