esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 1 Introduction
This paper is important in general for wireless communication systems and the emerging 5G networks and beyond. The paper presents a detailed and up-to-date review of the basic concepts of CE with respect to non-filtered based waveforms and with particular reference to two filter bank multi-carrier based waveforms that are currently being considered as potential candidates for the physical layer design of the emerging next generation of wireless networks. Furthermore, the paper presents a step-wise system description, implementation and performance analysis of relevant CE schemes used in the FBMC-based transceiver. In this case, researchers and practitioners in this field can perceive the different CE schemes in order to make useful decisions based on their application requirements. Finally, this review presents possible research directions in order for FBMC schemes to attain its full potentials over Rayleigh fading channels. Although there are other fading channels such as the Nakagami fading channel and the Rician fading channel, we centre our attention on the Rayleigh fading channel model because it is the most common and realistic channel to model a wireless communication link [21]. The remainder of this paper is structured as follows. Section 2 describes the general structure of FBMC systems which is adopted for designing the FBMC-OQAM waveforms in this paper. The fundamentals of CE and its application to filter bank OFDM-OQAM and GFDM-OQAM is presented in Section 3. A system model description of both FBMC-OQAM waveforms is presented in Section 4 assuming both NPR and Non-PR. In Section 5, the simulation results of the analysed CEschemes are presented. In Section 6, we summarise the findings of this work and suggest possible research directions. Lastly, Section 7 concludes the paper.
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 2 Improved FraSegNet Algorithm
### Model Optimization
#### 2.2.1 ResNet101 Backbone Network  
In the original FraSegNet structure, the backbone network uses shallow convolutional modules for feature extraction. However, recognizing nodules requires a deep analysis of complex textures and multi-scale distributions, which shallow networks cannot capture well, especially deep semantic information. Shallow networks also have limited ability to express features, often missing detailed information, particularly in complex nodule shapes, which can result in key feature loss. ResNet101 is a 101-layer residual network that solves the gradient vanishing issue in deeper networks through residual connections. It also improves feature expression. The main backbone of ResNet101 is shown in Figure 2. The optimization method is as follows:(1) The basic unit of ResNet101 is the residual block, which solves the gradient vanishing problem in deep networks using jump connections. These connections maintain continuity between shallow and deep features. ResNet101 has four stages, each with multiple residual modules. These modules gradually extract features, starting from low to high levels, reducing the feature map resolution while increasing feature dimensionality at each step. To speed up training and improve initial performance, ResNet101 uses ImageNet pre-trained weights as starting parameters, which are then fine-tuned on the rock body image dataset:  
\[\mathrm{y}=\mathrm{F(x,W)} \tag{1}\]  
where x is the input feature.  
F(x, W) is the feature mapping after convolution and nonlinear activation.  
(2) In the nodule identification task, features at different scales are equally important for segmentation. So, the multi-layer feature outputs from ResNet101 are used for feature fusion: (1) Multi-scale feature extraction: Feature maps are extracted at different stages of ResNet101: Stage 1 (224 \(\times\) 224 resolution): Extract low-level texture information. Stage 2 (112 \(\times\) 112 resolution): Capture local structural features. Stage 3 (56 \(\times\) 56 resolution): Extract mid-scale semantic information. Stage 4 (28 \(\times\) 28 resolution): Aggregate global semantic features. (2) Jump connection: The multi-scale features from the encoder are passed to the decoder through jump connections. Combined with the upsampling in the decoder, this restores high-resolution segmentation results step by step.  
(3) Since the feature dimensions and resolutions of ResNet101 are different from the original backbone network, the decoder of FraSegNet needs to be adjusted accordingly: (1) Increase the upsampling module in the decoder to gradually recover features from low to high resolution. (2) Merge the multi-scale features of the encoder using feature  
Figure 2: ResNet101 backbone network (* means multiplication in pixels).  
concatenation or attention mechanisms to ensure that the decoder fully uses the semantic information extracted by ResNet101.
# Dynamic Simulation of a 5 Degrees of Freedom Rotor Dropping on a Protective Bearing
## 4 Conclusions  
Multi-body dynamics simulation is performed on the five-degree-of-freedom rotor-protective bearing system. The contact between the bearing and the rotor is set according to the spring damping model. According to the main influencing factors that affect the rotor drop process, by changing the friction coefficient between the rotor and the bearing, For parameters such as the initial rotation speed of the rotor, multi-body dynamics simulation post-processing was used to study the effects of different parameters on the fluctuation of the rotor rotation speed during the drop process, the fluctuation of the rotation speed of the inner ring of the protective bearing, and the contact force characteristics with the bearing. The following can be concluded:  
(1) Regarding the change in rotor speed after the rotor falls, the friction coefficient between the rotor and the inner ring of the protective bearing and the friction coefficient between the rolling element and the raceway of the protective bearing will affect the change in rotor speed. Increasing the friction coefficient will increase the frequency of rotor fluctuations, but there is little difference between the two friction coefficient changes on the rotor speed.  
Figure 13: Simulation of contact force between rotor and bearing inner ring when the initial speed of rotor is 8000 rpm.  
(2) The rotation speed of the inner ring of the protective bearing is affected by the friction coefficient between the rotor and the inner ring of the protective bearing and the friction coefficient between the rolling elements and the raceway of the protective bearing. The friction coefficient between the rolling elements and the raceway has a greater relative influence.  
(3) After the rotor falls, the motion state gradually transitions from cylindrical motion to conical motion. As the friction coefficient increases, conical motion is more likely to occur.  
Therefore, in order to reduce the impact force between the rotor and the inner ring, a coating can be used to protect the inside of the bearing in actual projects to reduce the impact between the rotor and the inner ring, thereby making the system safer and more reliable.  
Conceptualization, X.Y. and J.H.; methodology, X.Y., J.H. and T.M.; software, X.Y.; validation, X.Y., J.H. and T.M.; formal analysis, X.Y.; investigation, X.Y.; resources, J.H. and T.M.; data curation, J.H.; writing--original draft preparation, X.Y.; writing--review and editing, J.H. and T.M.; visualization, J.H.; supervision, J.H.; project administration, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Sequential Recommendation  
Traditional recommender systems focus on identifying static connections between users and items, often neglecting dynamic shifts in user preferences over time. Sequential recommendation approaches posit that the recommendations given to a user at a specific moment should be determined based on the user's prior behaviors prior to that moment. Sequential recommendation captures the dynamic nature of user behaviors, revealing certain relationships between items over a given time span. Sequential recommender systems can capture dynamic changes in user interests by modeling their interaction sequences. The issue of sequential recommendation entails time-based learning to anticipate user interactions with items. The aim of sequential recommendation is to explicitly model users' sequential behaviors. We delve into the intricacies of sequence-based recommendation models. To offer a more intuitive understanding of this concept, Figure 4 presents the architectural diagram of a typical sequence-based recommendation model.  
As illustrated in Figure 4, sequence-based recommendation models are capable of capturing the dynamic changes in user behaviors, thereby generating more personalized and timely recommendations. In its input layer, such a model receives a sequence of user behaviors, which commonly represent the user's historical interactions. These sequential data are subsequently encoded into high-dimensional vectors. Advanced feature extraction is then performed via attention mechanisms or neural networks. Following this step, in the hidden layer, a range of more complex network architectures, such as long short-term memory (LSTM) or transformer models, are employed to capture the temporal dependencies and other latent patterns within the sequence. Finally, the output layer typically comprises a fully connected layer and an activation function, employed to compute scores or probabilities for each potential recommended item.  
In summary, the development of deep learning in recommender systems has witnessed an orchestrated blend of innovations, each building upon its predecessor. These advancements, rooted in complex mechanisms and models, have progressively enhanced the precision and context awareness of recommendations. Sequence recommender systems  
Figure 4: Sequential recommendation procedure.  
(SRS) can be categorized into three groups: standard sequence recommendation, long-short term sequence recommendation, and multi-objective sequence recommender systems.
# Mechanical Properties and Microstructure of Polyvinyl Alcohol (PVA) Modified Cement Mortar
## 3 Results and Analysis
### Compressive Strength  
Figure 6 shows the compressive strength development of cement mortars with 0-2.0% PVA at the early age. Clearly, the growth trend of compressive strength of all mortars was quite similar to that of flexural strength, in which the strength increased at first and then decreased with the increase of PVA content. However, Figure 6 shows that the optimal use of PVA was 0.6% of cement content by mass, and the compressive strength increased by 12.15% as compared to PCM0 after 28 days of curing. Moreover, it is worth noting that the curing age impacted the improvement effect of PVA, and the reinforcement effect was more remarkable at the early curing age. For example, the three-day compressive strength of PCM2 was about 17.6% higher than that of PCM0, while its 14-day strength just increased by 14.9%, as compared to PCM0. Additionally, the excessive adding of PVA might reduce the compressive strength. As shown in Figure 6, the 28-day compressive strengths of PCM4 were about 5.5% lower than those of PCM0.
# Electrochemical Biosensors Based on Conducting Polymers: A Review
## 3 Electroanalytical Methods
### Voltammetry  
In voltamric conducting polymer-based biosensors, a current is produced by sweeping the potential applied between a reference electrode and a polymer-modified electrode over a range that is associated with the redox reaction of the analyte. This redox reaction generates a change in the peak current which can be correlated with the analyte concentration, thus providing specific quantitative analytical information. All the voltamric methods that can be used, such as linear voltammetry, cyclic voltammetry, differential pulse voltammetry, or square wave voltammetry, have the advantage of providing both qualitative information deduced from the potential location of the current peak and quantitative information deduced from the peak current intensity.  
For example, the detection of acetylcholine was successfully achieved using a conductive PEDOT film loaded with Fe\({}_{3}\)O\({}_{4}\) nanoparticles and reduced graphene oxide since the intensity of the oxidation peak present in the cyclic voltammograms was linear to the acetylcholine concentration [85]. Similarly, the detection of serotonin in banana was done by square wave voltammetry using conducting polypyrrole/Fe\({}_{3}\)O\({}_{4}\) nanocomposites [86] and the detection of danazol was performed by differential pulse voltammetry using conducting electrodeposited polyaniline [87].
# Predictors of Speed and Agility in Youth Male Basketball Players
###### Abstract  
Player performance in an intense sport such as basketball is known to be related to attributes such as speed, agility, and power. This study presents a comparative analysis of associations between anthropometric assessment and physical performance in different age groups of elite youth basketball players, while simultaneously identifying the predictors for speed and agility in these players. U14 (\(n\) = 44), U15 (\(n\) = 45), and U16 (\(n\) = 51) players were tested for anthropometry, lower-body power, speed, and agility. U16 players were found to be taller, heavier, more muscular than U14 and U15 players. In addition, the U16 group showed better performance in all performance tests. Age had a significant positive correlation with countermovement (CMJ) and drop jump (DJ) performance in U14 players, and a significant negative correlation with 15m and 20m sprint times in the U15 group. CMJ and DJ emerged as the most significant predictors for sprint and agility variables, respectively. Body fat percentage was found to be a significant predictor for the speed and agility tests in all age groups, but a negative lower-body power predictor. Therefore, besides all sport-specific and fitness tests, it is essential to place emphasis on the percentage of body fat when designing players' individualized training programs, and during team selection.  
team sports; explosive strength; body composition; CMJ Article
## 2 Proposed Methods
### Combined Data Augmentation Scheme  
In ESC tasks, the performance of a model is often constrained by the size of the available dataset. Particularly in cases where the dataset is small, the model is more prone to overfitting, which significantly reduces its performance on unpredictable data [30]. Data augmentation techniques become especially important in this context. By employing reasonable data augmentation schemes tailored to the actual needs of the research scenario, various environments and conditions in the real world can be simulated, providing the model with more diversified training data and enhancing the model's generalization capability [31].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Fusion Scheme** & **Input Feature** & **GPU Capacity (MiB)** & **Inference Time (ms)** & **Accuracy (\%)** \\ \hline - & Fbank & 5866 & 304 & 88.68 \\ Dimensional Merge & Fbank, Noise-Fbank & 17,284 & 1240 & 89.60 \\ Patch-Mix & Fbank, Noise-Fbank & 5868 & 323 & 90.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental analysis of Patch-Mix feature fusion on the UrbanSound8K dataset.  
Figure 2: Acoustic Feature Extraction Process Diagram. \(E_{ij}\) represents the embedding of the \(j\)-th patch of the \(i\)-th spectrogram.  
For the research scenario of this paper, a combined audio signal enhancement technique is proposed. First, an offline data augmentation technique for audio waveforms is designed. Initially, silent parts at the beginning and end of the audio are removed. Then, offline data augmentation is performed on the audio by first applying random scaling to simulate time-stretching and pitch-shifting. Next, Dynamic Range Compression (DRC) is used to generate audio samples with different dynamic range characteristics. Last but not least, to standardize the audio input lengths, we implement random padding, setting the UrbanSound8K audio segments to 176,400 samples for 4 s clips and ESC-50 segments to 220,500 samples for 5 s clips, ensuring consistency across the training process.  
The augmented audio undergoes Log-Mel spectrogram extraction, followed by spectrogram augmentation techniques such as time warping, time masking, and frequency masking. In the data augmentation process for audio signals, to better simulate the variations of urban environmental sounds and to prevent the model from overfitting to a single type of data transformation, a certain degree of randomness is introduced. This ensures that the same audio signal can receive different data augmentation effects in different training batches. Considering the impact of random masking parameter settings on the experiment, the masked segments on the time axis (\(t\)) are set to 1, and the masked segments on the frequency axis (\(f\)) are set to 2, with the maximum segment width \(T=32\) and \(F=32\). The specific data augmentation process is shown in Figure 3.
## 3 System Overview  
The OPTL inspection tasks include several requirements, such as ensuring that there is no damage to the fittings and power towers, no broken or scattered strands of power lines, and no other attached objects. To meet these requirements, in our study, a 38 kg FPLIR is developed based on a six-rotor system. The overall size of the FPLIR is 2.6 m \(\times\) 2.6 m \(\times\) 1.1 m (length, width, and height). The mechanical structure of the FPLIR consists of two parts, i.e., the flying and walking mechanisms, as shown in Figure 1. The flying mechanism uses the six rotors to generate lift force, land on the power line and fly over obstacles. The propellers are installed under the robot arms, preventing collision with power lines during FPLIR landing. The walking mechanism consists of a walking component and a pressing component. The walking component enables the FPLIR to walk along the power line. The pressing component increases the positive pressure between the driving wheel and the power line to prevent the FPLIR from slipping during the inspection process. The FPLIR needs to use onboard sensors and data processing devices for orientation and navigation to land on the power line. The FPLIR carries an NVIDIA Jetson NX onboard computer, an IMU device, and a Stereolabs ZED2i binocular camera with high resolution and good performance in outdoor environments.  
Figure 2 shows the software architecture of the robot, which consists of perception, positioning, path planning, and control modules. The NVIDIA Jetson NX executes the architecture as a High-Level Flight Controller (HLFC), which includes four steps: (1) process information from the visual system to calculate the camera's pose relative to the power line; (2) run the Visual-Inertial Odometry (VIO) system to achieve localization  
of the FPLIR; (3) plan a safe landing trajectory based on the calculation of the required lift and moment for the FPLIR; (4) transmit the commands to the Low-Level Flight Controller (LLFC) for the robot to land on the power line. The FPLIR uses a cascade control method in its controller, where the LLFC is the inner loop, and the MPC trajectory tracking controller is the outer loop. The critical flight algorithm runs independently on the LLFC, while other computational tasks run on the powerful but less-reliable onboard computer. This approach introduces an isolation layer to ensure the safety of FPLIR flight even if anything goes wrong in the HLFC. The pixhawk4 flight controller is used as the LLFC to keep the FPLIR stable during flight.
## 3 Background  
### Principles of the Classic APF Method  
The APF method makes the object move and reach the target point under the action of a force field, which includes a gravitational field and a repulsive field [19; 20; 21].  
The gravitational field function \(U_{att}(q)\) is as follows:  
\[U_{att}(q)=\frac{1}{2}\epsilon\rho^{2}(q,q_{goal}) \tag{1}\]  
where \(\epsilon\) is the scale factor, \(\rho(q\_{goal})\) represents the Euclidean distance between the target object \(q_{goal}\) and the current position q, and the gravitation \(F_{att}(q)\) of the corresponding target object is the derivative of the gravitational field:  
\[F_{att}(q)=-\
abla(U_{att}(q))=\epsilon(q_{goal}-q) \tag{2}\]  
The repulsive field \(U_{rep}(q)\) function is as follows:  
\[U_{rep}(q)=\left\{\begin{array}{ll}\frac{1}{2}\eta\left(\frac{1}{\rho(q,q_{ obs})}-\frac{1}{\rho_{0}}\right)^{2}&,if\;\rho(q,q_{obs})\leq\rho_{0}\\ 0&,if\;\rho(q,q_{obs})>\rho_{0}\end{array}\right. \tag{3}\]  
where \(\eta\) is the repulsion scale factor, \(\rho(q\_{obs})\) represents the Euclidean distance between obstacle \(q_{obs}\) and current position q, and \(\rho_{0}\) represents the influence radius of each obstacle.  
Then, the repulsion \(F_{rep}(q)\) is the derivative of the repulsive field:  
\[F_{rep}(q)=-\
abla U_{rep}(q)=\left\{\begin{array}{ll}\eta(\frac{1}{\rho(q _{obs})}-\frac{1}{\rho_{0}})\frac{1}{\rho^{2}(q_{obs})}\
abla\rho(q,q_{obs})&, if\;\rho(q,q_{obs})\leq\rho_{0}\\ 0&,if\;\rho(q,q_{obs})>\rho_{0}\end{array}\right. \tag{4}\]where \(\
abla\rho(q_{\text{init}}q_{\text{obs}})\) represents the derivative of \(\rho(q_{\text{,}}q_{\text{obs}})\), the target object generates gravitation, the object is guided to move towards the target object, and the obstacle generates repulsion to avoid the obstacle, as shown in Figure 1. The resultant force field \(U(q)\) (resultant force \(F(q)\)) that an object receives at any point in the field is equal to the vector sum of the target object's gravitational field (gravity) and the total repulsive field (repulsion) of obstacles encountered. The formula is as follows:  
\[U(q)=U_{att}(q)+U_{rep}(q) \tag{5}\]  
\[F(q)=-\
abla U(q) \tag{6}\]