esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Predictive Algorithm of Wear Degree Based on Adaptive Feature Learning
## 2 Establishment of the Model
### Linear Autoregressive Component  
Since various gates in the LSTM unit use non-linear activation functions to map input signals to output signals, these activation functions tend to approach zero derivatives when there is a large-scale change in input signals, resulting in a decrease in the learning rate of the gradient descent algorithm, slow updating of network parameters, difficulty in converging to the optimal solution [28], and a decrease in prediction accuracy.  
To solve the above problem, this paper constructs a composite prediction model using the linear autoregressive moving average model (ARIMA) to correct the non-linear component \(\hat{y}_{T}^{D}\) and optimize the final prediction result. The ARIMA model construction process is shown in Figure 3.  
The time series data used for wear prediction is non-stationary. Firstly, it is necessary to determine the order of differencing d to perform the differencing operation and conduct a stationarity test to obtain a relatively stationary time series. Then, the auto-correlation Function (ACF) and partial auto-correlation function (PACF) of the stationary time series are respectively calculated, and the corresponding order is obtained based on the auto-correlation and partial autocorrelation plots. Finally, the optimal order values p and q are obtained through parameter estimation to complete the construction of the ARIMA model. The prediction component obtained based on this model is represented as \(\hat{y}_{T}^{L}\). The prediction result output by the overall composite model is shown below:  
\[\hat{y}_{T}=\varphi\hat{y}_{T}^{L}+\gamma\hat{y}_{T}^{D} \tag{17}\]  
\(\varphi\) and \(\gamma\) are two hyperparameters. \(\hat{y}_{T}^{L}\) represents the linear prediction component and \(\hat{y}_{T}^{D}\) represents the non-linear prediction component.  
The predicted results of the combination of linear components make up for the inherent deficiency of neural networks' insensitivity to scale, allowing them to output more accurate predicted results at different scales of input sequences, thereby improving the model's generalization ability and stability.
# A High-Resolution MIR Echelle Grating Spectrometer with a Three-Mirror Anastigmatic System
## 4 Results and Discussion
When the Strehl ratio is 0.80 or greater, the image is deemed to be equivalent in quality to a diffraction-limited image. Diffraction encircled energy is the ratio of the energy in the PSF collected by a single circular or square detector to the total amount of energy reaching the image plane from that object point. This is a popular metric for system engineers who commonly want a certain amount of collected energy to fall on a single pixel. This metric is widely used for systems with point images, especially those which need high signal-to-noise ratios. For encircled energy specifications of 50-60%, the criterion is near-linear. However, the specification is more often 80% (or, even worse, 90%) energy within a near-diffraction-limited diameter. At these levels, this criterion is highly nonlinear and strongly dependent on the aberration content of the image, making it a poor criterion, especially for tolerancing. The diffraction energy concentration curve represents the energy concentration of the image spot after a point on the object's surface passes through the optical system. From Figure 11, 80% of the energy was concentrated within 30 \(\upmu\)m, and 70% of the energy of the imaging points of the other two characteristic wavelengths was concentrated within 30 \(\upmu\)m.  
The evaluation of aberration is generally described using a spot diagram, which is a two-dimensional distribution of spots on the image plane. By analyzing the distribution of each intersection point of the image spot, the energy distribution can be estimated to evaluate the imaging quality of the system. The root mean square diameter (root mean square, RMS) is an important parameter, which is the diameter of the diffused spot with 68% energy concentration. The RMS spot sizes for three wavelengths and five configurations in Figure 12 were below 30 \(\upmu\)m, showing that optical aberrations did not significantly degrade the spectral resolution. Figure 11: Plot showing the diffraction encircled energy of the optical system.
## 4 Proposed Protocol  
### The Proposed Network Model  
The proposed network randomly deploys sensor nodes in a cube with a face length of 1000 m. The sink resides at the top middle surface of the network, as shown in Figure 1.  
Nodes are capable of communicating with each other through acoustic waves, as radio waves are more poorly affected by water. The sink uses both acoustic and radio waves. It communicates with sensor nodes through the acoustic waves and with the onshore center through the radio waves. On account of a higher speed of the latter, data that the sink receives is considered as delivered to the onshore data center.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Convergence** & **Range (km)** & **Bandwidth (kHz)** \\ \hline Very long & 100 & Less than 1 \\ Long & 10–100 & 2–5 \\ Medium & 1–10 & Almost 10 \\ Short & 0.1–1 & 20–50 \\ Very short & Less than 0.1 & Greater than 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bandwidth and transmission range relationship for underwater wireless sensor networks (UWSNs).
## 3 Results and Discussion  
### TPC and Antioxidant Activity  
In general, the peel of the _C. australasia_ L. showed significantly (\(p\) < 0.05) higher TPC and antioxidant activity than the pulp, as depicted in Figure 3 and Table S2, and as recently described by [PERSON] et al., who compared the TPC in peel and pulp of four Florida-grown finger limes (_Citrus australasia_ [PERSON]) [23]. Considering the content of phenolic compounds in the different extracted fractions, the ethyl acetate fraction of peels showed the highest TPC, followed successively by the butanol and aqueous fractions (265.95 \(\pm\) 0.04 vs. 215.16 \(\pm\) 0.09 and 175.49 \(\pm\) 0.06 mg GAE/100 g d.w., respectively) whereas the hexane fraction presented the lowest content in phenolic compounds (63.43 \(\pm\) 0.02 mg GAE/100 g d.w.). Those differences could be influenced by the fact that the phenolic compounds are moderately polar and they tend to accumulate in the medium-polarity fraction, such as ethyl acetate, as previously described by other authors [24]. Consequently, the high phenolic content was reflected in the antioxidant activity, where the ethyl acetate fraction of _C. australasia_ L. peels also showed the highest FRAP reducing activity, i.e., 176.43 \(\pm\) 0.15 mg TE/100 g d.w., compared to the other extracted fractions, exhibiting also the hexane fraction with the lowest antioxidant activity (0.20 \(\pm\) 0.00 mg TE/100 g d.w.). A higher antioxidant activity in the peel compared to the pulp has been previously reported in different varieties of finger limes by [PERSON] et al. [25], and could be attributed to the fact that peels are directly exposed to biotic and abiotic stresses that trigger the biosynthesis of phenolic compounds [26, 27]. Thus, the ethyl acetate fraction of _C. australasia_ L. peel was chosen from the phenolic compounds for further investigation using mass spectrometry techniques.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 3 Materials and Methods
### Route Geometry  
The description of the route as a list of consecutive rooms is easy to implement. However, it may not be sufficient to convey a clear message to the user as to how the route goes. A person is accustomed to the presentation of a mapped route as a linear trajectory along which they should navigate. In order to obtain such a trajectory, it is necessary to model the geometries of the paths of movement through the building. This problem was the focus of a study [47] in which the authors analysed the positioning of various objects in rooms that represented obstacles during movement and, on that basis, determined the geometries of potential paths. A different approach to automatically determine the geometries of paths was used in another study [48]; the space was divided into logical parts and the geometry determined automatically by triangulating polygons representing the surfaces of the rooms and smoothing the resulting lines.  
Once the route has been calculated, the system should present it to the user, usually as a line on the map that represents the expected trajectory of the user's movement. To create this geometry, the algorithms use a transport network to represent the possible paths of movement of objects. The route is calculated with a graph related to the transport network, and the final route geometry is obtained by connecting the geometries associated with the edges of the graph.  
In car navigation systems, which users are familiar with, there is a road network that gives a good indication of the possible positions of the vehicle. Cars travel along streets and, except in exceptional situations, change direction when they can turn into another street. The road network can be modelled as a graph such that the edges represent road segments and the vertices represent intersections. In the case of indoor navigation, it is not possible to indicate an analogous road network. If the building consisted only of corridors, their axes could be used. However, determining the geometry in a natural transport network is problematic if the route runs through other rooms, open spaces, halls, etc.  
The creation of a path network in a building can be partly automated, but even then, it is good practice to analyse the routing of potential path trajectories and possibly modify the geometry manually. A previous article [49] presented typical cases of path geometry modelling based on the assumption that paths connect characteristic points of a room by straight lines, each with the other. A more comprehensive approach can be found in [50], where the author provides complete guidance on how to model navigation graphs. An analogous approach, supplemented with additional conditions, was used in [51].
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.16 HERMES  
HERMES [70] is a lightweight framework for monitoring post-deployment code behavior on WSNs. It has been implemented on the SOS platform. Rather than working at the line-of-code level for developer debugging, such as remote-gdb, HERMES provides a higher level of monitoring, at the level of data-flow interactions using _interposition_ to enable this in a non-intrusive manner. Generated stubs are dynamically linked (or interposed) between _target_ modules and the O/S runtime. This requires no modifications to the original source code. Features supported are: observing in-field execution, and controlling in-field execution. Observation is supported by monitoring the function calls to selected modules, allowing for conditional watchpoints, and synthetic event generation for in-field testing. In-field execution allows dynamic access control (for example, inserting firewall rules dynamically), traffic shaping (for example, by introducing resource allocation policies dynamically), and fixing isolated failures (for example by invoking fail-safe operation when an illegal interaction is observed, or recovery by invoking stateful rollback using interaction traces). The SOS dynamic linker tracks down all provide-subscribe function pairs at load time of a module, a feature which HERMES leverages. HERMES also provides a transparent update feature, to allow a new version of a module to run in parallel with the old one until it has collected enough state to transparently take over operation.  
The key feature for autonomous update support is transparent update, which provides a smooth transition to an updated module. The monitoring feature may also be relevant, though it is possibly too granular for the requirements of update-related software faults. Also it relies extensively on the O/S and network operating correctly, and so is not likely to be useful for autonomous software recovery.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 6 Conclusions  
In this paper, we propose an analytical model to investigate the eavesdropping probability in underwater acoustic sensor networks, which have different channel characteristics from those of terrestrial wireless sensor networks. In particular, we first establish the relationship between the eavesdropping probability and the underwater acoustic channel in both IUSNs and AUSNs considering an isotropic eavesdropper and an array eavesdropper, respectively. We then conduct extensive simulations to validate our model. The simulation results match our analytical results implying that our model is accurate and effective at analyzing the eavesdropping probability in underwater acoustic sensor networks. Besides, we also find that the eavesdropping probability heavily depends on signal frequency. Moreover, the results also show that the eavesdropping probability increases with the increased node density, decreases with the increased wind speed and decreases with the increased spreading factor. Comparing the eavesdropping probability in IUSNs with that in AUSNs, we find that equipping nodes with array line hydrophones can significantly decrease the eavesdropping probability of underwater acoustic sensor networks. One of the future research directions is to evaluate the effectiveness of our analytical model in realistic test-beds though it is challenging to implement underwater acoustic sensor networks (even with isotropic hydrophones) [41].  
**Acknowledgments: The work described in this paper was partially supported by Macao Science and Technology Development Fund under Grant No. 096/2013/A3 and the NSFC-Guangdong Joint Fund under Grant  
Figure 10: Eavesdropping probability in AUSNs with an _array_ eavesdropper with different nodes density \(\rho\) (/km\({}^{2}\)) when \(f=10\) kHz, \(f=30\) kHz and \(f=90\) kHz under different spreading factor \(k\) and wind speed \(w\). (**a**) \(k=1\), \(w=0\) m/s; (**b**) \(k=1\), \(w=10\) m/s; (**c**) \(k=2\), \(w=0\) m/s; (**d**) \(k=2\), \(w=10\) m/s.  
No. U1401251. The authors would like to thank [PERSON] for his constructive comments. The authors would also like to thank the anonymous reviewers for their useful comments.  
**Author Contributions:** [PERSON] proposed the idea, derived the results and wrote the paper. [PERSON] supervised the work and revised versions. [PERSON] derive the initial results and contributed to the initial idea. [PERSON] gave valuable suggestions on the motivation of conducting analysis on eavesdropping attacks in underwater sensor networks and assisted in revising the paper. [PERSON] contributed to revising and proofreading of the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Discrimination Analysis  
#### 3.2.1 Principal Component Analysis  
Since PCA also has a function of revealing the structure of spectral data [11], the spectral matrix was subjected to the PCA process to inspect the separability of different sweet corn cultivars. The 746 samples were plotted in Figure 4 according to their first three PCs scores. It was clear that each cultivar of samples had its own cluster center, and the two clusters were generally separated. However, clear boundaries could not be found as a portion of the samples overlapped. This could be explained by the fact that both cultivars were bred from the same male parent, and the two cultivars were similar in their main compositions. Hence, modeling with more discriminant algorithms was necessary to improve the classification accuracy [45].
# Improved Road Segment-Based Geographical Routing Protocol for Vehicular Ad-hoc Networks
## 1 Introduction
In such a scenario, the vehicle takes a longer time for data delivery or, sometimes, the packets are dropped in-between communications in the networks. In geographical routing-centric information dissemination, nodes always find neighbor nodes that are closer to the destination and transmit the data packet towards the same. Many geographical routing protocols have been suggested based on greedy forwarding, where the source node selects the relay node near the destination within its communication range to forward the data [11; 12]. The limitation of greedy geographical forwarding is that it does not always work, particularly in sparse vehicular network environments [13]. There might be a situation where a node may not find a closer node than itself to the destination to forward the data. Due to the dynamic nature of VANETs, packets can be sent in the wrong direction due to the existence of a routing loop in the network. This loop is induced by a right-hand rule that is used to overcome the problem of void network scenarios [14]. To address these issues, a robust geographical routing protocol must be aware of more appropriate routing information to address network disconnection issues with lower network overheads for routing information gathering [15]. In this context, the main aim of this paper was to design and develop an improved road segment-based geographical routing protocol (ISR). The contributions in the paper can be summarized as follows:* Firstly, the design of ISR is presented considering segment formation, head node selection, and score calculation. * Secondly, algorithms were developed for the complete process execution of head node-based information dissemination in a vehicular traffic environment. * Thirdly, simulations are performed to assess the performance benefits of the proposed information dissemination-centric vehicular routing framework. The remainder of the paper is structured as follows. Section 2 discusses the literature review of existing geographical routing protocols. Section 3 presents the detail of the proposed ISR framework. Section 4 discusses the simulation setup and results. Section 5 presents the conclusion and future work.
## 2 Audio Features and Pre-Trained Neural Networks
### Audio Signal Features
#### 2.1.1 Cepstral Features  
Cepstral features represent the cepstrum, a depiction of acoustic signals that is commonly utilized in homomorphic signal processing, and is often characterized by the conversion of signals combined through convolution, into the sums of their specific cepstra [14]. Cepstral coefficients were found to be one of the most commonly utilized features for classification of acoustic scene and events.  
The mel-frequency cepstral coefficients (MFCC) were the most widely apparent, and are based on a filter that models the behaviour of the human auditory system [14], making it advantageous in terms of sound identification. The MFCCs can be acquired through taking the log of the mel spectrum. Following this, the discrete cosine transform (DCT) of the log spectrum are obtained, with the MFCCs being the result of the DCT's amplitudes [15].  
Calculation of the MFCC coefficient starts by dividing the time-aligned four-channel averaged audio signal \(y_{arg}(t)\) into multiple segments. Windowing is then applied to each of these segments prior to being subject to the discrete Fourier transform (DFT), resulting in the short-term power spectrum _P(f)_[16].  
The power spectrum _P(f)_ is then warped along the frequency axis \(f\), and into the mel-frequency axis \(M\), resulting in a warped power spectrum _P(M)_. The warped power spectrum is then discretely convolved with a triangular bandpass filter with \(K\) filters, resulting in \(\theta(M_{k})\)[16]. The MFCC coefficients are calculated according to Equation (2) [16].  
\[MFCC(d)=\sum_{k=1}^{K}X_{k}\cos\Bigl{[}d(k-0.5)\frac{\pi}{K}\Bigr{]},\ d=1\dots D \tag{2}\]  
where \(X_{k}=\ln(\theta(M_{k}))\), and \(D<<K\) due to the compression ability of the MFCC [16]. Nonetheless, these were also found to be prone to loss of substantial information due to its sensitivity to noise [17]. Similarly, its performance can be affected by the shape and spacing of the filters and the warping of the power spectrum [16]. Nevertheless, the MFCC approach has several advantages due to its simple computation, and flexibility with regards to integration with several other features [16].
## 3 Results
### Segmentation
Two datasets were used for evaluation: the Chronic Wound Dataset [52] and the FUSeg Challenge 2021 Dataset [53]. The x-FUSegNet achieved acceptable results in the FUSeg Challenge 2021, placing it at the top of the leaderboard. [PERSON] et al. [57] introduced a method for diabetic foot ulcer segmentation by combining self-training with mixup augmentation. Researchers trained the neural network to generate weak labels using the self-training process. The model design utilizes an Attention UNet architecture, trained with dice and cross-entropy loss functions, and employs five-fold cross-validation for robustness. Researchers evaluated the method on three datasets: DFUC2022 [58], FUSeg [53], and RMIT [59], and demonstrated improvement in dice scores. Table 3 summarizes recent studies, highlighting AI models, data types, and performance metrics used for DFU segmentation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref. Year** & **Study Aim** & **Data Type** & **Model Used** & **Code Availability** & **Hyperparameter** & **Training Protocols** & **Evaluation Metrics** \\ \hline \hline \multirow{8}{*}{[49]} & \multirow{8}{*}{2021} & Evaluate & \multirow{8}{*}{Image} & \multirow{8}{*}{Net} & \multirow{8}{*}{Net} & \multirow{8}{*}{Net} & \multirow{8}{*}{Net} & \multirow{8}{*}{Net} \\  & segmentation & & & & & & DICE: 95.35 \(\pm\) 0.40\% \\  & methods for DF & & & & & IoU: 91.11 \(\pm\) 0.72\% \\  & & & & & & 2. SPD: & 2. SPD: \\  & monitoring & & & & & & 2. Spe5.24 \(\pm\) 0.52\% \\  & & & & & & & 1.00: 90.3 \(\pm\) 0.95\% \\  & & & & & & & 3. SegNet: & \\  & & & & & & & & DICE: 93.30 \(\pm\) 2.91\% \\  & & & & & & & IoU: 87.57 \(\pm\) 5.01\% \\ \hline \multirow{8}{*}{[21]} & \multirow{8}{*}{2022} & Segment diabetic & \multirow{8}{*}{Image} & \multirow{8}{*}{Net} & \multirow{8}{*}{Net} & Learning rate: 0.01, & \multirow{8}{*}{Net} & \multirow{8}{*}{Net} \\  & foot images that & & & & & & 2. SpeNet: 95\(\pm\) 0.005, & 2. SpeNet: 95\(\pm\) 0.005, & 3. UNet: 95\% \\ \cline{1-1}  & & & & & & 2. SpeNet: 95\(\pm\) 0.005, & 3. UNet: 95\% \\ \cline{1-1}  & & & & & & 2. SpeNet: 95\(\pm\) 0.005, & 3. UNet: 95\% \\ \cline{1-1}  & & & & & & 2. SpeNet: 95\(\pm\) 0.005, & 3. UNet: 95\% \\ \hline \multirow{8}{*}{[50]} & \multirow{8}{*}{2022} & Image & \multirow{8}{*}{Fast R-CNN} & \multirow{8}{*}{Net} & \multirow{8}{*}{Net} & \multirow{8}{*}{Net} & \multirow{8}{*}{Net} \\  & & & & & & & 2. SpeNet: 95\(\pm\) 0.
## 2 Principle
### Polarization-Related Dynamic Equalization  
Polarization characterizes a fundamental multiplexing technique, which allows doubling the spectral efficiency of fiber optical transmission systems [34]. To accommodate the usage of two polarization tributaries, polarization de-multiplexing (PolDemux) algorithms represent a crucial part of digital coherent receivers, which makes it conceivable to efficiently compensate for time-varying state of polarization (SOP). The PolDemux approaches include MIMO algorithms such as LMS and RLS, and the constant-modulus algorithms (CMA).  
In particular, Stokes space-based DSP techniques improve the polarization de-multiplexing, polarization dependent loss (PDL) compensation, and other polarization-related propagation impairments in coherent receivers, particularly in terms of convergence speed and transparency to higher-level M-ary modulated signals. The block diagram of the DSP subsystems of a coherent transceiver for adaptive equalization of both polarization and phase diversity is presented in Figure 9, which includes the front-end compensation, static equalization, adaptive equalization, carrier phase recovery, and the symbol decision [35].  
Furthermore, the [PERSON] space-based PolDemux method has specific advantages including the higher convergence ratio, the improved robustness against phase noise, and the transparency to higher-level M-ary modulated signals.