esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art Model for Music Object Recognition with Deep Learning
## 5 Experiments and Results
### Comparison with Previous Approaches  
Table 2 shows the current work for OMR. Similar to our work, [PERSON] [24] applied the MuseScore dataset. In terms of recognition accuracy, our method has many advantages, with a mAP of 0.91, a duration accuracy of 0.92, and a pitch accuracy of 0.96.  
Compared to sequential recognition-based approaches, we do not need to crop the image and directly input the entire image. As shown in Figure 11, they can only process one line of music score at a time.  
Compared to object detection-based approaches, our work has many advantages. In References [15; 20], related to music object detection, only symbols are detected, as shown in Figure 12. If one wants to get the duration and pitch of the notes, they need extra steps, such as combining hook, beam, dot, and notehead to parse the duration; if one of the symbols is not detected, it will cause the final result to go wrong. We can directly output the duration and pitch of the note with a pitch accuracy of 0.96 and a duration accuracy of 0.92.  
* Our system generally struggles with rare classes, overlapping symbols, and dense symbols. We have calculated the distribution of symbols, the symbols that the model can recognize account for 99% and it is very challenging to detect these rare symbols (less than 1% of all symbols). For dense symbols (chords), our model will miss some notes.
* As shown in Figures 3 and 4, The duration of the symbols ranges from 0.125 to 4.000, and the pitch label of note ranges from \(-6\) to 14. This means that the duration and pitch of the note can only be predicted within a certain range. If the range is exceeded, the pitch and duration of the symbols cannot be accurately predicted.
## 5 Implementation and User Scenario
### Technical Validation and Experimental Results
#### 5.4.1 Datasets  
The system was tested using two different simulated datasets developed in Python via Google Colab. Both datasets were designed to simulate realistic user interactions in an e-commerce environment with dynamic UI requirements. The first dataset contains diverse user sessions to assess the dynamicity, quality, and adaptability of the system. The second focused on cross-platform performance, ensuring the system's functionality across multiple platforms. It consists of 100,000 simulated user sessions, each representing a sequence of interactions. These sessions are categorized into three key attributes: behavioral (18 features), demographic (14 features), and engagement (14 features). Table 4 shows an overview of the dataset, detailing the number of features and users across different instances.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Instance Name**} & **Behavioral** & **Demographic** & **Engagement** & \multirow{2}{*}{**Number of Users**} \\  & **Features** & **Features** & **Features** & **Users** \\  & **(Count)** & **(Count)** & **(Count)** & **(Count)** & **Users** \\ \hline \hline \end{tabular}
\end{table}
Table 4: User behavior testing dataset.  
The data was generated using modeling behavior patterns based on real-world interaction heuristics. The simulation was conducted using Python libraries, ensuring a balanced distribution across interaction types (e.g., clicks, page views, purchases) and user contexts (e.g., time of day, location, and engagement state). Our approach systematically collects and analyzes user behavior, clustering users into personas using the KMeans++ algorithm. Based on these clusters, the system dynamically generates UI updates using a multi-agent reinforcement learning algorithm, ensuring personalized UX adaptation.  
The system was also validated across different platforms to ensure seamless interactions and UI adaptation across multiple modalities. The dataset captures various user scenarios and platform types (see Table 5). Tests ensured that performance metrics, such as CTR, conversion rate, session duration, user experience, and user satisfaction, remained stable even during transitions between different platform environments.
# An Insightful Overview of the Wiener Filter for System Identification
## 3 Useful Definitions
Let \(\mathbf{h}=\text{vec}(\mathbf{H})\) be the system impulse response and \(\mathbf{\widehat{h}}=\text{vec}\Big{(}\mathbf{\widehat{H}}\Big{)}\) its estimate. A good way to evaluate how close is \(\mathbf{\widehat{h}}=\text{vec}\Big{(}\mathbf{\widehat{H}}\Big{)}\) to \(\mathbf{h}=\text{vec}(\mathbf{H})\) is via the normalized misalignment,  
\[\mathcal{M}\Big{(}\mathbf{\widehat{h}}\Big{)}=\frac{\left\|\mathbf{h}- \mathbf{\widehat{h}}\right\|_{2}}{\left\|\mathbf{h}\right\|_{2}} \tag{16}\]  
or, equivalently,  
\[\mathcal{M}\Big{(}\mathbf{\widehat{H}}\Big{)}=\frac{\left\|\mathbf{H}- \mathbf{\widehat{H}}\right\|_{\text{F}}}{\left\|\mathbf{H}\right\|_{\text{F}}}. \tag{17}\]  
Let \(\mathcal{C}\) be a convex set. The convex envelope of a function \(f:\mathcal{C}\rightarrow\mathbb{R}\) is defined as the largest convex function \(g\) such that \(g(x)\leq f(x)\) for all \(x\in\mathcal{C}\)[23].
## 4 Methodology
### Digital Image Processing
#### 4.2.4 Principal Component Analysis (PCA)  
Principal component transformation is a statistical technique that selects uncorrelated linear variables called principal components (PC) using orthogonal transformation of a set of possibly correlated variables [12; 48; 69; 80; 81; 82]. It is one of the most effective and commonly used image information extraction methods in remote-sensing geology [12; 48; 61; 69; 80; 81; 82]. It generates very small and easily interpretable independent components through linear transformation and preserves the main information [12; 48; 61; 69; 80; 81; 82]. The first principal component includes most of the information of the original multispectral image, and selecting three transformed principal components for the false color composite can achieve the purposes of data compression and highlighting of target information.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 3 Consideration of Local Scour
### Scour Effect
#### 3.3.2 Mechanical Properties Equivalence  
Scour also changes the mechanical properties of soil, including the coefficient of consolidation degree, lateral earth pressure, void ratio, effective internal friction angle, and effective unit weight. Among them, the change of unit weigh is the largest, and other changes are relatively small [24]. In order to simplify the calculation, facilitate engineering application, and ensure sufficient accuracy, the stress history is taken as the mechanical equivalent criterion to modify the effective unit weight of soil in a certain longitudinal range. According to API, the influence is mainly within 6D, so the soil unit weight after scour \(\gamma\prime_{sc}\) is as follows:  
\[\gamma\prime_{sc}=\frac{6D}{6D-S_{d}}\gamma\prime \tag{12}\]  
Taking pile 1# as an example, the pile length is 40 m, and the soil parameters used for calculation are listed in Table 4, where lateral load F = 400 kN, equivalent scour depth is 1.97 m, and the effective unit weight is corrected; the results are shown in Figure 11a-c. Compared with the traditional method of simply removing a certain depth of soil, the errors of maximum value of shear force, bending moment, and horizontal displacement are 16.2%, 10.6%, and 46.4%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pile** & **Scour** & **Maximum Scour** & **Equivalent Scour** & **Absolute** & **Relative** \\
**Number** & **Volume/m3** & **Depth/m** & **Depth/m** & **Error/m** & **Error/(\%)** \\ \hline
1 & \(-\)1602 & \(-\)4.85 & \(-\)1.97 & 2.88 & 59.4 \\
7 & 113 & \(-\)3.89 & 0.14 & 4.03 & 103.6 \\
19 & \(-\)1163 & \(-\)5.05 & \(-\)1.48 & 3.
# Optimizing Activation Function for Parameter Reduction in CNNs on CIFAR-10 and CINIC-10
## 2 Related Work
### Previous Research  
Since CIFAR-10 was created, it has been of interest for algorithms performing image classification. Different architectures give results that vary a lot on their own (60-99.5% accuracy). The best ones have many parameters (over 50 M), which raises the question of computational cost when scaling the system. This can be a crucial factor in some cases. The following papers present models with marginally better performance (accuracy) but with more headlong parameters. For example, ref. [10] presents a model with six convolutional layers and two FFNNs, with many hidden units. It achieves 87.5% accuracy, which is not a very high percentage for the number of parameters it consists of. Despite the large number of parameters due to the hidden units, the performance of the network is far from the desired result. However, this is not the worst case, because there are such as [11], where there are many more parameters than in [10], yet they achieve similar results. This drives the need for systems with high accuracy and a small number of parameters. Ref. [12] is closer to what this paper aims for, but still there are four times as many parameters compared to our proposed system, at the expense of approximately 4% improvement in accuracy. Obviously, ref. [12] uses fewer parameters than well-known state-of-the-art architectures, but the current paper increases the range of devices on which it can be implemented. As mentioned in [13], when two architectures have similar characteristics, the one with fewer parameters consumes less energy than the other. The idea for this improvement was inspired by SqueezeNet [14], which also reduces the number of parameters significantly, while the performance remains the same. A paper that uses the approach proposed in [14] is reducedSqNet [15], which uses the so-called Fire Module. The approach considered in this paper, however, differs from this one by being based on the basic CNN view with only a modified activation function for feature extraction from the images. However,our proposed system achieves better accuracy while requiring less computational power, memory usage, and computation time.
## 3 System Model
### Pathloss Model  
The reduction of the power density of radio propagation from the Macro Base Station (MBS) to UE (indoor and outdoor) described in terms of pathloss calculated according to 3 GPP LTE-Advanced pathloss model for urban deployments [32], which is given as.  
\[PL_{M}(dB)=\begin{cases}15.3+37.6 log_{10}(R_{1})+L_{p}&(Indoor)\\ 15.3+37.6 log_{10}(R_{1})&(Outdoor)\end{cases} \tag{1}\]  
The \(PL_{M}\) denoted as pathloss from the macrocell to the User equipment, \(R_{1}\) is the range from the MBS to UE whereas the \(L_{p}\) is the penetration loss through walls.  
Figure 1: Interference scenario in HeNB and nearby macro and femto users.  
Similarly, the path loss from the femtocell to MUE and FUE existing in the close proximity (indoor and outdoor) can be calculated by using the path model as follows:  
\[PL_{F}(dB)=\begin{cases}38.46+20 log_{10}(R_{2})&(Indoor)\\ max(15.3+37.6 log_{10}(R_{2}),38.46+20 log_{10}(R_{2}))+L_{p}&(Outdoor)\end{cases} \tag{2}\]  
The \(PL_{F}\) denoted as pathloss from the femtocell to the User Equipment. Similarly, the \(R_{2}\) is the distance from HeNB to indoor and outdoor users.
## 4 Experimental Procedure and Results
### Partial Experimental Results and Discussion
The trend of the molar ratio of each component at the distillation column top and the HI mass fraction over time are shown in Figure 11. Initially, the HI mass fraction in the distillate at the top of the column was low, as the initial HI solution was dilute, and water, the lighter component, evaporated first. As the concentration of HI in the reboiler gradually increased, the amount of HI distilled out also  
Figure 10: Performance results of HI purification tower. increased, and the separation efficiency of the top and reboiler approached stability, with the I\({}_{2}\)/HI ratio around 0.139 and the H\({}_{2}\)O/HI ratio around 9.54. Additionally, as time progressed, the distillation time became longer, and the concentration of HI in the distillate increased, eventually stabilizing at 38.1%. After adding an enamel-lined H\({}_{2}\)SO\({}_{4}\) evaporator and an HC276 alloy H\({}_{2}\)SO\({}_{4}\) superheater with 70 wt% H\({}_{2}\)SO\({}_{4}\) for the experiment, the H\({}_{2}\)SO\({}_{4}\) solution entered the evaporator at a flow rate of 3.41 L/h, where it was heated and boiled. The gaseous H\({}_{2}\)SO\({}_{4}\) was then further heated in a superheater containing silicon carbide balls to 850 \({}^{\circ}\)C, where it decomposed into SO\({}_{3}\) and H\({}_{2}\)O. Subsequently, the gaseous SO\({}_{3}\) and H\({}_{2}\)O entered the decomposer, which contained an iron oxide-based catalyst, and decomposed into SO\({}_{2}\) and O\({}_{2}\). After the reaction, all gases entered the condenser for cooling. The cooled exhaust gas was absorbed by a two-stage NaOH scrubber, and the remaining O\({}_{2}\) was measured with a gas flow meter. The oxygen production rate and temperatures of each part are shown in Figure 12. During the experiment, as the process continued, the temperatures of the key components gradually increased, and the oxygen production rate also increased. At 300 min, the inlet temperature of the superheater was 150.8 \({}^{\circ}\)C, the reactor inlet temperature was 894.3 \({}^{\circ}\)C, the reactor outlet temperature was 696.7 \({}^{\circ}\)C, and the oxygen production rate was about 356.4 L/h, which corresponded to a sulfuric acid decomposition rate of about 81.5%. Figure 11: Trends in the concentrations of various components and HI mass fractions at the top of the distillation tower.
# Application of Virtual Reality in Learning Quantum Mechanics
## 2 Virtual Teaching Modules
### Oil Drop Experiment  
The oil drop experiment contains a pair of parallel metal plates in the observation chamber and an oil sprayer to produce oil drops between them because water drops are easy to volatilize under the light source. Learners can observe through the microscope that the fine mist of oil is sprayed into the space above the metal plate, and there is light shining between the metal plates for observing the oil drops. When the oil is sprayed, it will rub against the nozzle to generate electric charges. The upper metal plate has a small hole, and the oil drops will pass through it due to gravity (Figure 3).  
The learner can conduct a long-term observation. When the oil drop falls at a constant speed, the gravitational force and the electric field force on the oil drop are equal, i.e.,  
\[m_{\text{drop}}\text{g}=q_{\text{drop}}E \tag{6}\]  
The oil drop is charged by gaining electrons, and its mass can be obtained by measuring the diameter and density. The total electricity of the whole oil drop can be calculated based on the known strength of the electric field as  
\[q_{\text{drop}}=\frac{m_{\text{drop}}\text{g}}{E} \tag{7}\]  
[PERSON] devised an ingenious method to calculate the size of the oil drop, which would fall at a constant speed under air resistance, with the final speed depending on its radius and the viscosity of the air. He used the known viscosity of the air to calculate the radius, mass, and charge of an oil drop. After repeating hundreds of experiments, [PERSON] that the total charge of all oil drops is a multiple of the same number. He used the greatest common factor to obtain the value and determined that this value is the basic charge carried by an electron, i.e., \(1.6\times 10^{-19}\) Coulomb.  
The virtual experiment designed in this study allows learners to adjust the strength of the electric field to make some oil drops remain still and then calculate the charge amount of different oil drops according to the strength of the electric field and the diameter of an oil drop, and finally obtain the greatest common factor of the charge amount for all oil drops, which is equal to the elementary charge of an electron.
# Design and Modelling of an Amphibious Spherical Robot Attached with Assistant Fins
## 3 Dynamic Model of the Robot  
The amphibious spherical robot is modelled as a system consists of three rigid bodies, the spherical shell with its attached assistant fins, the IDU platform and the rotator. Assumptions about these three components are (1) the spherical shell is a uniform hollow spherical shell whose radius is \(R\); the total mass of the spherical shell and its attached assistant fins is \(m_{s}\) while each fin has mass \(m_{f}\); (2) the IDU platform is considered as a uniform rectangular bar with mass \(m_{p}\), length \(2R\), neglecting its structural details; it is mounted along the main axis with its center of mass locating at the center of spherical shell; (3) the rotator is a hollow cube with its mass \(m_{r}\) distributed on the four side faces of the cube; the distance between the center of mass of the rotator \(A\) and the main axis is \(l_{OA}\).
## 2 The Proposed DCLNN Method for Failure Time Prediction
### The DCLNN Strategy for Failure Time Prediction of Escalator  
The accuracy of time series prediction is not only related to the sequence model, but the quality of the time series itself also has a great influence on the prediction result. In this paper, a strategy to combine data quality enhancement with deep neural networks is proposed for time series prediction, which is shown in Figure 1. First, the data sequences are divided into good data and bad data according to a selection indicator; secondly, the bad time series data can be transformed to good data with high quality; finally, the built neural network is used to train and predict the time series.  
Based on the principles of DTW and CNNLSTM, a time series prediction method combining DTW pre-processing and CNNLSTM post-learning is proposed in this paper for escalator failure time prediction, which is called DCLNN.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 6 Effects of Noise and Interference
### Interference  
As previously stated, the L1 C/A GPS frequency is 1575.42 MHz. It is assumed that the receiver system will be stationary. Therefore, the signal will undergo some Doppler shift due to the motion of the satellite. Typically, we can expect this value to be \(\approx\)\(\pm\)5 kHz [43]. Additional Doppler shift due to the orbital motion of the space debris can be \(\approx\)\(\pm\)14 kHz, depending on the altitude. Therefore, received signals can be bandpass filtered over a passband defined by 1575.42 \(\pm\) 0.019 MHz.  
The C/A modulation is a pseudorandom noise (PRN) code that uniquely identifies an L1 signal transmission. Through the signal acquisition process [43], the C/A codes can be replicated and convolved with the received signals to eliminate extraneous transmissions from other GPS satellites. A similar process is applied to other GPS-based systems, providing a range of IOOs from which to select.  
Target Masking  
It is possible that two targets may orbit with small separation distances between them. In a situation where one target is slightly higher in altitude than the other, the BCE at a single receiver may not be distinguishable from the BCE of one target. The resulting magnitude reduction and zero Doppler readings will look identical to that of Figures 2 and 8a. Therefore, one target essentially masks the other since only one target is detected instead of two. This necessitates a receiver array, as when two targets are crossing the baseline over one receiver, the other receivers would still detect unique Doppler shifts. This suggests that a multiple-receiver setup would achieve higher detection rates. However, a formal analysis will be conducted in a later study.
## 4 Laser Methods for the Activation of the Irrigation Solution
### Er: YAG and PIPS  
Erbium: yttrium-aluminum-garnet laser (Er: YAG) (2940 or 2780 nm) uses a fiber tip when it is in contact with the solution and produces a small explosive boil forming cavitation bubbles due to the laser being highly absorbent in water, causing an approximately 1 to 3 \(\upmu\)m in penetration depth [60], that effectively removes the smear layer, creating a rough and porous surface in root dentin [36; 62]. In recent years, a new technique called photon-induced photoacoustic streaming (PIPS) was invented, in which Er: YAG laser is used with sub-ablative energy (20 mJ, 15 Hz) and ultra-short pulses (50 \(\upmu\)s), which leads to intracanal cavitation and shockwaves as a result of photoacoustic and photomechanical effects [63; 64]. In this technique, PIPS-specific laser tips remain at the root canal entrance [64], and then short low-energy laser bursts are directed into the canal to propel irrigants throughout the canal system [60]. The photoacoustic shock waves in the PIPS technique allows the irrigant to flow in three-dimensional (3D) directions, resulting in a deeper cleaning of the entire root canal system [63]. The abovementioned has been confirmed by SEM, since activated 17% EDTA as well as MTAD using PIPS promoted SSL whole removal, resulting in the opening of dentinal tubules [36; 48]. Additionally, the use of Er: YAG laser activation using a wavelength of 2940 employing 1000 \(\upmu\)s pulse and 50 mJ at 10 Hz (average power, 0.5 W) does not show high bond strength values of the post in the cervical and apical positions, however, PIPS laser-activated irrigation showed higher efficiency, avoiding failures [17; 48].
# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
## 4 Experiments
### SMG-Y Results  
Similar to SMG-C, we conduct experiments on both FLIR and IAT datasets to evaluate the performance of SMG-Y. SMG-Y is compared with the baseline detector, YOLOv3 [18].  
Table 5 presents the results of SMG-Y on the FLIR benchmark. The mAP of SMG-Y exceeds the baseline method nearly 10% on the same dataset, and the gap of them increases with the decrease of training examples. On FLIR-1/4, SMG-Y achieves 62.5% AP in bicycle detection in comparison with 29.1% for the baseline. We also observe that the accuracy of SMG-Y on FLIR-1/4 (74.5% mAP) outperforms the baseline method trained with full FLIR (69.4% mAP), which demonstrates SMG-Y maintains remarkable accuracy with limited training data. When the dataset is reduced to 1/4 of the original, the mAP of SMG-Y decreases by 2.5% (from 77.0% to 74.5%). However, the mAP of the baseline method drops by 13.2% (from 69.4% to 56.2%). The low reduction of SMG-Y indicates that it can take full advantage of the knowledge from the source model and decrease the data dependency of the network.  
Figure 7: Visualization results on IAT-1/2.  
We visualize some results of SMG-Y and its baseline YOLOv3 when both of them are trained on FLIR-1/4, as shown in Figure 8. We find that the baseline method hardly predicts the position of the bicycle because it is always obscured by people. Furthermore, due to insufficient training data, YOLOv3 is difficult to recognize objects with special gestures, such as the sitting woman in the last row of Figure 8 (note that most people in the training dataset are walking or riding). However, SMG-Y overcomes those problems and detects precisely under the circumstances of severe occlusion and appearance change even if the training examples are limited.  
Experiments are also conducted on the IAT benchmark, and the results are shown in Table 6. We witness a sharp fall in the baseline accuracy as the number of training instances decreases. In contrast, SMG-Y trained on IAT-1/2 keeps competitive accuracy with 96.2% mAP, which is slightly lower than that trained on the full IAT dataset.