esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 PSA in a DOF: Dual Pump Configuration
### Analytical Model: Three Wave Approach
Figure 9: False colour plots of maximum PSA signal gain (**a**–**c**,**g**–**i**) and gain extinction ratio (**d**–**f**) as a function of the pump-signal frequency separation \(\frac{\Omega_{ps}}{2\pi}\) and amplitude of dispersion oscillation \(\vec{\beta}_{2}\) (denoted as \(\beta_{2,am}\)). Top and middle row correspond to numerically calculated results, while the bottom row is from the analytical 3-wave model. Three different average second order dispersion \(\overline{\beta}_{2}\) (denoted as \(\beta_{2,am}\)) values are considered: (**a**,**d**,**g**) \(-5\) ps\({}^{2}\)/km, (**b**,**e**,**h**) \(0\) ps\({}^{2}\)/km and (**c**,**f**,**i**) \(5\) ps\({}^{2}\)/km. \(L=1000\) m, \(\gamma=4\) (W km)\({}^{-1}\), \(P=22\) dBm. Since in this model, the high-order waves generated in the DOF, pump depletion, as well as the high-order \(\beta_{m}\)'s were ignored, it is necessary to compare the analytical results with numerical calculation based on the nonlinear Schrodinger equation (NLSE) that is immune to those assumptions.
## 3 LPWAN-Based Agricultural Vehicle Tracking
### IoV through LoRa Connectivity  
LoRa (Long Range) is an LPWAN technology that was first introduced by Cycleo in 2010 and then, in 2012, was acquired and patented by Semtech Corporation. A distinctive feature of LoRa technology is that it allows long-range data transmission for energy-restricted devices at a low data rate. LoRa-compatible devices can demodulate weak signals with their very good receiver sensitivity. LoRa was developed for sub-GHz ISM bands. Two frequency bands are available for application in the European continent that are 863-870 MHz and 433-434 MHz [33]. Conceptually, LoRa describes the PHY layer, in which the chirp spread spectrum (CSS) is used as a modulation technique. On the other hand, the MAC sub-layer, called LoRaWAN (LoRa for Wide Area Networks), enables the end devices to access GWs easily [34].  
LoRaWAN networks are deployed with star-of-stars topology in which GWs perform the bridge functions and relay the messages between the end devices and a network server. All the GWs are linked to the server through an IP connection. It is possible that multiple GWs can receive and forward the data from the same end device. But the network server filters the duplication and authenticates the end devices by selecting the most suitable GW [35]. In LoRa-based IoV network architecture, the agricultural vehicles are equipped with VTU that contains a LoRa transceiver. The GWs have an Internet connection via Ethernet or cellular network. Also, each vehicle/machinery is connected to at least one GW in the farm's borders. Practically, large-scale farmland may be covered with a few LoRa GWs that can be accessed by agricultural vehicles. Figure 2 shows the IoV network architectures with LoRa connectivity to realize the agricultural vehicle tracking application.
## 3 VLC for Underwater Communication
### Key Components and System Design of UVLC
#### 3.3.2 Receiver  
The photodetector is an essential component of the optical receiver. It is used to convert optical signals into electrical signals in UVLC systems [3] There are two main types of photodiodes: PIN photodiodes (PDs) and avalanche photodiodes (APDs). PIN PDs are cost-effective, fast, and operate at low bias voltages ranging from 2 to 5 V. PIN PDs are suitable for high-frequency applications but lacking in internal gain [22; 23]. APDs provide higher gain through avalanche multiplication, but it requires complex circuitry and higher bias voltages, which leads to high noise in communication [22; 23]. There are advanced photodetectors, such as single-photon avalanche diodes (SPADs) and multi-pixel photon counters (MPPCs), which provide high sensitivity and high signal-to-noise ratios. These PDs are ideal for enhancing detection capabilities in challenging environments like UVLC [22; 23]. The work in [23] shows that the use of SPADs can significantly extend transmission ranges. In addition to that, the use of larger photodetector apertures enhances photon collection efficiency, which helps to overcome the channel attenuation in UVLC [23].
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### Fast  
The FAST corner detector was proposed by [42]. Corner detection is accomplished by first picking a collection of training images then running the FAST algorithm on each image to detect points and then using machine learning to find the optimum detection criterion. The detection criteria are judgements regarding whether or not a pixel is a corner. A decision tree is developed that can accurately categorize all of the corners. After that, the decision tree is transformed into C code, which is subsequently employed as a corner detector. The FAST corner detector is ideal for use in real-time applications, but it is not robust to high degrees of noise.
# Light-Weighting of Planetary Gearbox Based on Multi-Strategy Optimization Sparrow Search Algorithm
## 2 Light-Weighting Problem
### Main Parameters Problem  
#### 2.2.1 Objective Function and Variables  
The minimum volume of the gearbox is taken as the design criterion. Once the ratios have been determined, the size of the gearbox is minimized when the sum of the volumes of the gears is minimized. Therefore, the gearbox volume can be simplified as the sum of the volumes of the individual gears. The external gear can be regarded as a cylinder with the addendum circle diameter as its diameter and face width as its height. The inner gear can be regarded as a hollow cylinder, where the dedendum circle diameter serves as the outer diameter. The addendum circle diameter, on the other hand, constitutes the inner diameter, while the face width corresponds to the height of this cylinder.  
As shown in Equation (14), \(V_{sum}\) is equal to the sum of the volumes \(f\) (\(y\)) of each planetary transmission stage. \(f\) (\(y\)) can be represented by Equation (15).  
\[V_{sum}=\sum_{\xi=1}^{n}f_{\xi}(y) \tag{14}\]  
\[f(\mathrm{y})=\frac{\pi b}{4}\Big{(}{d_{as}}^{2}+3{d_{ap}}^{2}+{d_{fr}}^{2}-{d _{ar}}^{2}\Big{)} \tag{15}\]  
According to Figure 1, \(d_{as}\), \(d_{ap}\), and \(d_{ar}\) are the addendum circle diameters of sun gear, planet gear, and inner gear, respectively. \(d_{fr}\) is the dedendum circle diameter of the inner gear. They can be represented by Equations (16)-(19).  
Designed in accordance with standard gear specifications. The pressure angle of the gear pitch circle is \(20^{\circ}\), the coefficient of addendum is 1.0, and the coefficient of bottom is 0.25.  
\[d_{as}=m(z_{s}+2) \tag{16}\]  
\[d_{ap}=m\big{(}z_{p}+2\big{)} \tag{17}\]  
\[d_{ar}=m(z_{r}-2) \tag{18}\]  
\[d_{fr}=m(z_{r}+2.5) \tag{19}\]  
\(m\) is the modulus. \(z_{s}\), \(z_{p}\), and \(z_{r}\) are the number of teeth of the sun gear, the planet gear, and the inner gear, respectively.  
Considering the requirements of the objective function and constraints, the number of teeth of the sun gear, the planet gear, and the inner gear, \(z_{s}\), \(z_{p}\), \(z_{r}\), modulus \(m\), and face width \(b\) are taken as parameters, as shown in Equation (20). Through the calculation of Equations (14) to (19), the objective function is expressed by Equation (21).  
\[Y=\left(y_{1},y_{2},y_{3},y_{4},y_{5}\right)=\left(z_{s},z_{p},z_{r},\text{m}, \text{b}\right) \tag{20}\]  
\[f(y)=\frac{\pi}{4}y_{4}{}^{2}y_{5}\left(y_{1}{}^{2}+4y_{1}+3y_{2}{}^{2}+12y_{2 }-9y_{3}+\frac{55}{4}\right) \tag{21}\]
# Design of a Novel Linear Shaped Charge and Factors Influencing its Penetration Performance
## 6 Conclusions  
The penetration performance of a novel linear shaped charge, BLSC, is discussed in this paper. Compared with the ordinary LSC with a constant 60\({}^{\circ}\) apex-angle liner, the BLSC, which contains a 40\({}^{\circ}\) small-apex-angle liner and a 60\({}^{\circ}\) large-apex-angle liner, greatly improves the efficiency of the main charge energy. With a 1.0 mm liner thickness, 8701 explosive, and 10 mm standoff distance, the DOP tests show that the penetration performance of the BLSC is 29.72% better than that of an ordinary LSC for a #45 steel target.  
The results from the numerical simulations in a multi-material Eulerian algorithm of penetration performance are all in good agreement with the DOP tests, thus verifying the feasibility and effectiveness of the presented Eulerian model.  
For the BLSC with 0.6 mm thick liner, COMP B as an explosive, small apex angle of 50\({}^{\circ}\) and 100\({}^{\circ}\), respectively, \(n=0.5\), and S = 10 mm is recommended to attain the best penetration performance on a #45 steel target. The greatest penetration depth is about 14.25 mm.  
All the results provide a guide and reference for the structural design of BLSC.  
[PERSON] wrote the article; [PERSON] and [PERSON] contributed to scientific discussions; [PERSON] proposed the original idea. This research was funded by [the National Natural Science Foundation of China] grant number [11772059], and [the National Key Research and Development Program of China] grant number [2017 ycf0822300]. The authors declare no conflict of interest.
## 3 Results and Discussion
### Surface Energy  
Figure 8 shows the change of surface energy in the TMS plasma-modified rayon flock synthetic leather as a function of discharge power. The surface energy of treated samples decreases as the discharge power increases. The surface energy drops dramatically as the discharge power increases from 30 W to 50 W. The reduction of polar surface energy is found to be the most significant at 50 W. A further increase in the discharge power does not affect the surface energy (for example, 60 W). The untreated sample possesses a surface energy of 73.35 mN/m. The surface energy drops to 2.40 mN/m after plasma treatment at a discharge power of 50 W, which is 1/30 th of the original value. This result indicates that TMS plasma treatment can effectively lower the surface energy of rayon flock synthetic leather.  
Figure 7: Photo showing contact angle of plasma-modified sample.
## 3 Research Model and Hypotheses
### Hypotheses
#### 3.2.2 Service Quality (SerQ)  
Service quality is assessed by how closely the provided service level matches user expectations [43]. With regard to online learning, service quality refers to the necessary features and characteristics that ensure efficient support services for students [15]. It includes the assistance provided by IT personnel via helpdesk support, training, and emergency assistance. According to [PERSON] et al. [14], service quality in e-learning encompasses aspects such as responsiveness, empathy, trust, and security provided by the supporting staff in e-learning systems, which are crucial for enhancing the satisfaction level of the users and the overall success of the online learning system. By focusing on service quality, e-learning systems can provide a supportive and reliable environment that meets students' needs and contributes to positive student satisfaction. According to [44], several studies revealed that the quality of distance learning and the level of satisfaction experienced by students are positively related. Therefore, the second hypothesis was developed as follows:  
**H2:**: _Service quality positively influences student satisfaction_.  
Figure 1: The research model.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 1 Introduction
Furthermore, the CSAIM alternately employs channel attention and spatial attention to enhance information exchange between the two branches of the CSLB, thereby expanding the receptive field and improving feature representation. In summary, the main contributions are as follows:  
Figure 1: Model parameters and accuracy tradeoff with other lightweight methods on BSD100 for \(\times\)2 SR. Our proposed DAFEN achieves superior performance, and our DAFEN-S also maintains competitive performance. The Multi-Adds (Multiply-Add Operations) are computed on a 1280 \(\times\) 720 HR image. We propose a lightweight model, DAFEN, with approximately 416K parameters and an ultra-lightweight model, DAFEN-S, with approximately 188K parameters. Compared with other methods, we achieve better performance with fewer model parameters. 2. We design a novel lattice structure, CSLB, which combines GRSB and CSAIM. This structure efficiently extracts features while maintaining the model's lightweight nature. It also enhances information exchange between the two branches through channel and spatial attention, improving feature extraction for remote-sensing images. 3. We design an efficient feature fusion module, FFEM, which consists of a fusion stage and an enhancement stage. In the fusion stage, FFEM effectively integrates multi-level features through forward sequential concatenation. In the enhancement stage, a unique dual-branch design is adopted to perform feature self-calibration and rescaling, which not only refines the features but also enhances the capability of representing complex remote-sensing image characteristics.
# MedChain: Efficient Healthcare Data Sharing via Blockchain
## 5 Evaluations
### Efficiency Analysis  
#### 5.2.1 Theoretical Analysis on Digest Chain  
Given a data stream of \(n\) chunks. Suppose the event of chunk corruption is an independent and identically distributed random variable with probability \(p\). The cost to download and verify the integrity of a chunk is \(b\). If no chunk is corrupted, then the total verification cost is \(b\). On the other hand, if the first \(n-1\) chunks are correct but the last chunk is corrupted, the verification cost is \(2b\). Moreover, if the first \(n-2\) chunks are correct, but the second last is corrupted. The verification cost is \(3b\), no matter whether the last chunk is correct or not. Notably, if the second chunk is corrupted, all the digests need to be downloaded and verified, no matter whether the first chunk is correct.  
Through the above induction, the expected cost of verifying a data stream of length \(n\), denoted by \(C(n)\), can be calculated.  
\[C(n)=E\left(\sum\limits_{i=1}^{n}P(X=i)\cdot i\cdot b\right)\] \[=(1-p)^{n}b+(1-p)^{n-1}p\cdot 2b+\cdots+(1-p)^{2}(n-1)b+p\cdot nb\] \[=(1-p)^{n}b+p^{2}\cdot nb+\sum\limits_{i=1}^{n-1}(1-p)^{n-i}p\cdot (i+1)b\] \[\geq(1-p)^{n}b+p^{2}\cdot nb+pb\cdot(1-p)n\] \[\geq(1-p)^{n}b+p\cdot nb\]  
Let \(C^{\prime}(n)\) be the cost that \(n\) chunks are individually verified. \(C^{\prime}(n)=n\times b\). Then the performance improvement, denoted by \(\Delta C\), can be calculated as follows.  
\[\Delta C=C^{\prime}(n)-C(n)\] \[\leq nb-(1-p)^{n}b-p\cdot nb\] \[\leq b(1-p)[n-(1-p)^{n-1}]\]It can be easily found that, for a given probability of chunk corruption \(p\), the upper bound of \(\Delta\)C increases along with the length of stream \(n\). It implies that the digest chain mechanism can substantially improve system performance when it is applied to longer data streams.
# Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources
## 3 Correlative Interferometer Method
So, the difference between the phase of the received signal \(r\) and the ideal phase \(c\) can be viewed as the effect of additive noise, which is denoted as \(\Delta r\). Since the effect of noise on the phase is related to the SNR, and the effect of \(\Delta r\) on each antenna occurs independently for all antennas, the CI algorithm can accurately estimate the direction if the SNR and the number of antennas are sufficient. In other words, in the case of low SNR and smaller number of antennas, the CI algorithm can significantly degrade direction-finding performance, as shown in Figure 2. Therefore, in these environments, another dimension to suppress the effects of noise might be needed as a solution for the performance improvement. With additional time-domain samples, in this paper, we address these problems in Section 4. ```
0: Directional information dictionary \(\mathcal{A}\), Received signal vector \(\mathbf{y}\), The number of signal sources \(K\)
0: Estimated DOA \(\Lambda\)
1:Initialization : \(\Lambda=\varnothing\), \(Q=\varnothing\)
2:for\(i=1,\cdots,A\)do
3:for\(m=1,\cdots,M\)do
4:\(r_{m}=\angle y_{m}\)
5:\(c_{m}(\hat{\gamma}_{i})=-\frac{2\pi}{\lambda}\mathbf{p}_{m}\cdot\mathbf{s}( \hat{\gamma}_{i})\)
6:endfor
7:\(J_{\text{CI}}(\hat{\gamma}_{i})=\sum\limits_{m=1}^{M}\cos[c_{m}(\hat{\gamma}_{ i})-r_{m}]\). 8:endfor
9:\(Q\leftarrow\) Indices corresponding to \(K\) peaks in \(J_{\text{CI}}(\hat{\gamma}_{i})\), \(\forall i\). 10:\(\Lambda\leftarrow\mathcal{A}(Q)\). 11:Return\(\Lambda\)
```  
**Algorithm 1** Correlative Interferometer for DOA estimation
## 2 Envelope Analysis to Detect Defects in Rotating Machinery
### Signal Modeling for Simulation
Therefore, the peak of the rotation frequency of the envelope spectrum can distinguish the faulty from the normal, and furthermore, it can be regarded as a feature to diagnose faults. In this study, threshold checking using these peak values is used to simply distinguish between normal and defective. Figure 3: Process of envelope analysis: (**a**) band-pass filtered signal models and envelope signals in time domain (top: normal signal model, bottom: faulty signal model); (**b**) envelope spectrum. Figure 2: Signal models for envelope analysis: (**a**) normal signal model (top) and faulty signal model (bottom); (**b**) spectrum of normal signal model (green) and spectrum of faulty signal model (black).
## 3 Results and Discussion
### Performance Characterization of Dust Suppressant
#### 3.2.5 Freezing Point Test  
The freezing point of the dust suppressant formula is \(-5\)\({}^{\circ}\)C according to the third-party detection of those samples, indicating a moderate anti-freezing performance. Therefore, its applicability is limited in northern winters. This dust suppressant formula can be adopted optionally at a temperature below \(-5\)\({}^{\circ}\)C and is therefore suitable for most urban roads in spring, summer, autumn, and early winter.
# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 5 Conclusions and Future Work  
In this work, we have exploited TinyML models for detecting respiratory diseases, particularly asthma, using lung sound recordings. Our custom CNN model achieved an accuracy of 96% while maintaining efficient resource usage. This demonstrates the feasibility of deploying real-time, accurate diagnostic tools on resource-constrained devices, making them suitable for portable medical applications.  
The potential impact of this approach on healthcare is significant. By offering a low-cost, portable solution for respiratory disease detection, our models can enhance access to reliable diagnostics in remote and underserved areas, reducing the reliance on traditional medical facilities and expensive equipment. This advancement is crucial to improving early disease detection and patient outcomes.  
In future work, we will address the challenges encountered with dataset imbalance, which limited the diversity of the training data. To overcome this challenge, we will explore merging multiple publicly available lung sound datasets and applying data augmentation techniques, such as variational autoencoders [30]. Additionally, real-world clinical validation and the inclusion of more respiratory conditions will be key steps toward refining and extending the applicability of our models.  
Conceptualization, Y.A.; Methodology, Y.A., N.B. and H.C.; Validation, Y.A.; Formal analysis, Y.A.; Investigation, Y.A.; Data curation, Y.A.; Writing--original draft, Y.A.; Writing--review & editing, N.B. and M.B.; Supervision, N.B. and H.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The data presented in this study are publicly available at [[https://data.mendeley.com/datasets/jwyy9](https://data.mendeley.com/datasets/jwyy9) np4 gv/3]([https://data.mendeley.com/datasets/jwyy9](https://data.mendeley.com/datasets/jwyy9) np4 gv/3) (accessed on 21 August 2024).  
The authors declare no conflicts of interest.
## 2 Related Work
### Bidirectional Long Short-Term Memory (Bi-LSTM) Network  
A Bi-LSTM neural network can be seen as a combination of forward LSTM and backward LSTM. At a finer granularity of classification, Bi-LSTM can better capture the semantic dependencies in both directions. It controls the stored contents of memory units mainly through three gating devices: the input gate (\(x_{t}\)), the forgetting gate (\(f\)), and the output gate (\(h_{t}\)), as shown in Figure 1.  
The Bi-LSTM neural network is now widely used in many scenarios in NLP and good results have been achieved. Reference [18] proposed a multi-scale deformable CNN to capture the non-consecutive n-gram features by adding an offset to the convolutional kernel for answer selection. Reference [19] used Bi-LSTM for Q&A on marriage law. Reference [20] built a model by LSTM to achieve answer selection for non-factual questions in different scenario contexts. Reference [21] used CNN-LSTM to act as the encoder to predict the most useful pair as the final output from multiple QA pairs by conditional random fields.  
LSTM and gate recurrent unit (GRU) are two variants of RNN, which can solve the problem of gradient disappearance. The performance of GRU and LSTM is similar in many tasks. Compared with GRU, LSTM has more parameters, more powerful functions, and stronger expression ability. However, there is a problem with LSTM: it is unable to encode information from the back to the front. Therefore, Bi-LSTM can better capture bidirectional semantics. In this paper, the QA pair is trained with the Bi-LSTM neural network, which improves the accuracy of the answer selection by enhancing memory function through three gating devices.
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 5 Conclusion
\begin{tabular}{l l} BDS & BeiDou Navigation Satellite System \\ GPS & Global Positioning System \\ SPP & Standard Point Positioning \\ LSTM & Long Short-Term Memory \\ WLS & Weighted Least Square \\ RTK & Real-time Kinematic \\ PPP & Precision Point Positioning \\ WP-TD & Two-dimensional Moving Weighted Average Processing \\ DVL & Doppler Velocity Log \\ RAFSTKF & Robust Adaptive Federated Strong Tracking Kalman Filter \\ RNN & Recurrent Neural Network \\ BDT & Bei Dou Navigation Satellite System Time \\ UTC & Coordinated Universal Time \\ GPST & GPS Time \\ CGCS2000 & China Geodetic Coordinate System 2000 \\ WGS-84 & World Geodetic Coordinate System 1984 \\ ITRF & International Terrestrial Reference Frame \\ IERS & International Earth Rotation Service \\ PDOP & Position Dilution of Precision \\ RMSE & Root Mean Square Errors \\ CDF & Cumulative Distribution Function \\ PDF & Probability Density Function \\ \end{tabular}