esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we presented a novel approach to detect and identify different materials in Raman spectra acquired on 3D structures. Traditional methods to identify materials from Raman spectroscopy are based on the comparison of the detected spectra with predefined spectral libraries. When the sample presents mixtures of different materials, traditional methods tend to fail. In this paper, we propose to use the nonlinear generalization of the spectral unmixing approach that allows us to identify different materials even if they are present in a mixed solution. Nonlinear intimate mixtures of the pure spectra of the single materials mostly characterized the detected Raman spectra. Modeling the mixtures with linear approaches may lead to a non-detection of all the endmembers in the data. Nonlinear approaches in general require extremely complex nonlinear models of the interactions between light and matter that are far from a practical realization.  
The proposed approach takes advantage of an auto-associative neural network (AANN), a deep autoencoder which allows us to project the original Raman spectra into a low-dimensionality linearized feature space. The use of the AANN allows for the solving of nonlinear correlations in the original data. Thus, it is possible to apply the linear unmixing approach directly in the features space.  
The effectiveness of the proposed approach has been tested on two real datasets acquired on 3D printed structures including GO in PCL and PLA matrices, respectively. The obtained results have been compared with those obtained applying the linear approach to the same data and evaluated in terms of Spectral Angle Distance (SAD) and Root Mean Square Error (RMSE). The results demonstrate that the proposed approach overcomes the limitations introduced by linear unmixing and does not require complex modeling of the light-matter interactions.  
The full and straightforward implementation of the method presented here can be suitably and readily considered for the evaluation of practical cases in different sectors. Dealing with a hyperspectral camera can monitor processes and materials by remote control, providing a number of benefits: (i) there is no need to handle the samples, this could be particularly interesting for addressing, e.g., safety issues; (ii) sample size is not a limitation, as occurred in this experimentation as samples had to be located under a microscope, and (iii) the collected data can be directly processed by the unmixing algorithm here proposed.  
Conceptualization, G.L. and C.D.G.; methodology, G.L.; validation, G.L. and C.D.G.; supervision, J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Analysis of the Effect of Temperature on the Sound Transmission Loss of a Curved Plate
## 2 Theoretical Model  
The model of an open-ended curved plate simply supported on all sides and subjected to acoustic excitation in the temperature field is shown in Figure 1. For the sake of computational simplicity, the model was developed using the (\(R\), \(\theta\), \(L\)) columnar coordinate system. The curved plate's radius of curvature is \(R_{0}\), its thickness is \(h\), the circle's angle is \(\theta_{0}\), and its length in the axial direction is \(L_{0}\). The internal part of the plate is concave, the internal temperature is \(T_{2}\), the external side of the plate is convex, and the external temperature is \(T_{1}\). The pitch angle of the acoustic wave is \(\varphi\), and the azimuth angle is \(\xi\).  
The curved plate is assumed to be subjected to no thermal stress at the reference temperature (\(T_{0}\)). The internal temperature of the structure changes evenly throughout the thickness direction when exposed to a thermal environment.
###### Abstract  
The rapid expansion of the Internet of Things (IoT) and industrial Internet of Things (IIoT) ecosystems has introduced new security challenges, particularly the need for robust intrusion detection systems (IDSs) capable of adapting to increasingly sophisticated cyberattacks. In this study, we propose a novel intrusion detection approach based on convolutional neural networks (CNNs), designed to automatically extract spatial patterns from network traffic data. Leveraging the DNN-EdgeIIoT dataset, which includes a wide range of attack types and traffic scenarios, we conduct comprehensive experiments to compare the CNN-based model against traditional machine learning techniques, including decision trees, random forests, support vector machines, and K-nearest neighbors. Our approach consistently outperforms baseline models across multiple performance metrics--such as F1 score, precision, and recall--in both binary (benign vs. attack) and multiclass settings (6-class and 15-class classification). The CNN model achieves F1 scores of 1.00, 0.994, and 0.946, respectively, highlighting its strong generalization ability across diverse attack categories. These results demonstrate the effectiveness of deep-learning-based IDSs in enhancing the security posture of IoT and IIoT infrastructures, paving the way for intelligent, adaptive, and scalable threat detection systems.  
Keywords: intrusion detection system (IDS); Internet of Things (IoT); industrial IoT (IIoT); deep learning; convolutional neural networks (CNN); network security +
Footnote †: journal:
# High-Dimensional and Multi-Intensity One-Photon-Interference Quantum Secure Direct Communication
## 4 Performance
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **DL04 [14]** & **MDI-QSDC [19]** & **OPI-QSDC [49]** & **Our Protocols** \\ \hline \multirow{2}{*}{Quantum resources} & single photons (ideal) & single photons and & single photons (ideal) & single photons (ideal) \\  & WCSs (practical) & entanglement pairs & WCSs (practical) & WCSs (practical) \\ \hline \multirow{2}{*}{Encode messages in} & polarizations & Bell states & \(0/\pi\) phases & multislice phases \\ \hline \multirow{2}{*}{Resistance to measurement-device attacks?} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} \\ \hline \multirow{2}{*}{Resistance to PNS attacks?} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} \\ \hline \multirow{2}{*}{Quantum memory free?} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} \\ \hline Break PLOB bound? & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} \\ \hline \multirow{2}{*}{Approx. secrecy capacity at 100 km (bit/pulse)} & \multirow{2}{*}{\(1.03\times 10^{-5}\)} & \multirow{2}{*}{\(1.16\times 10^{-8}\)} & \multirow{2}{*}{\(5.72\times 10^{-4}\)} & \(\begin{array}{c}1.05\times 10^{-3}\) (\(M=2\)) \\ \(1.15\times 10^{-4}\) (\(M=20\)) \\ \end{array}\) \\ \hline \multirow{2}{*}{Approx. distance at \(1\times 10^{-10}\) bit/pulse secrecy capacity} & \multirow{2}{*}{156.48 km} & \multirow{2}{*}{151.61 km} & \multirow{2}{*}{434.76 km} & \(\begin{array}{c}485.07\,\mathrm{km}\ (M=2)\\ 493.94 km (\(M=20\)) \\ \end{array}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison with other typical QSDC protocols. Figure 2: Achie secrecy channel capacity \(R\) in \(\log_{10}\) scale in terms of transmission distance. The black solid line is the PLOB bound, the red dotted line represents the MDI-QSDC protocol [19] (with INCUM), and the blue dashed line represents the DL04 protocol [14] (with INCUM). The orange line with “x” markers displays the performance of the OPI-QSDC protocol [49], and the purple, cyan, brown, and green lines with hollow markers are our HDOPI-QSDC protocol with \(M=2\), \(M=5\), \(M=10\), and \(M=20\) bases, respectively. The secrecy capacity of our protocol breaks the PLOB bound at 214.17 km (when \(M=2\)), and its maximum distance is about 494.58 km (when \(M=20\)).
# Construction and Application of an Ecological Quality Evaluation System Based on a PIE-Engine +
## 3 Results and Discussion  
### Spatiotemporal Variation in EFI in the Tailhu Basin  
Between 2001 and 2020, the overall average EFI of the Taihu Basin fluctuated, with an average value of 0.45 (Figure 2a), showing a trend characterized by a decrease followed by an increase. Specifically, the average EFI of the Taihu Basin was 0.464 in 2001, which declined to 0.428 in 2011 and then rose to 0.457 in 2020. The average EFI value of the Taihu Basin excluding impervious surface and water body fluctuated around 0.46 from 2001 to 2011 (Figure 2b), showing a slight downward trend and an obvious upward trend from 2012 to 2022. The result indicated that during the initial decade, urbanization in the Taihu Basin led to an expansion of impervious surfaces, thereby causing a decline in the overall ecological function. During the second decade, the measures of ecological restoration and ecological protection were obviously effective.  
The spatial differences of the EFI were significant (Figure 3), with the western and southwestern regions of the Taihu Basin dominated by forest and grassland, exhibiting the highest EFI value and exceeding 0.7 in most areas. The northwestern and southeastern regions dominated by cropland also displayed higher EFI values, with most areas recording EFI values above 0.5. Conversely, the densely urbanized northeastern region along the lake showed lower EFI levels, despite some cropland areas maintaining higher EFI levels. In addition, the narrow wetland area south of Taihu Lake also had lower EFI.  
Figure 3: Distribution of the mean EFI in Taihu Basin from 2001 to 2020.  
Figure 2: Average annual EFI change for (**a**) the entire Taihu Basin and (**b**) the Taihu Basin excluding impervious surface and water body.  
From 2001 to 2011, the EFI decreased in the majority of areas (Figure 4a), except for the forest region in the west and southwest. The decreased areas constituted approximately 73% of the total area. Between 2011 and 2020, only about one-fourth of the entire Taihu Basin experienced a decrease in the EFI (Figure 4b), primarily concentrated in the south-eastern part of Taihu and certain cropland areas in the west. Conversely, the EFI increased throughout the rest of the Taihu Basin, with the increment mainly within 0.1.
## 1 Introduction
[PERSON] et al. [20] tried to control the flow separation on the suction side of the low-pressure turbine blade using DBD plasma. [PERSON] et al. [21] and [PERSON] et al. [22] applied DBD plasma near the tip of a squaeler tip blade to reduce the tip leakage flow. Researchers have also tried to enhance the DBD plasma induced flow. [PERSON] et al. [23] experimentally investigated the effects of the input waveform shape on the DBD plasma actuator. They utilized various multi-frequency waveform types and showed that the velocity profile of the induced flow was strongly affected by the input waveform. [PERSON] et al. [24] showed that the velocity of the induced flow increased as the width of the insulated electrode increased for the case with triangular waveform input power. [PERSON] et al. [10] experimentally investigated the effects of the magnitude and frequency of the input power, as well as the thickness of the dielectric barrier on the DBD plasma induced flow. Their results showed that the velocity of the induced flow was higher for the case with a higher voltage, higher frequency, and thinner dielectric barrier. Some researchers have attempted to improve film cooling effectiveness using DBD plasma. [PERSON] et al. [25] numerically studied the effects of the mainstream velocity, blowing ratio, number of actuators, electrode position, and power input on the film cooling effectiveness of a 35-degree cylindrical film cooling hole. [PERSON] et al. [26] also conducted a numerical study on three film cooling hole shapes and showed that the DBD plasma suppressed the counter-rotating vortex pair and improved the film cooling effectiveness. [PERSON] et al. [27] experimentally investigated the effects of DBD plasma on slot film cooling using IR thermography. They showed that the film cooling effectiveness increased by up to 15% after applying DBD plasma. Previous studies have shown that DBD plasma can improve film cooling effectiveness by inducing the coolant toward the endwall. Most of these were numerical studies. The aforementioned experimental study [27] utilized IR thermography, the results of which were sensitive to the temperature increase caused by the DBD plasma. In this study, DBD plasma was applied to a 30-degree film slot, and the effects of blowing ratio, input voltage, and the location of the electrode on the film cooling effectiveness were experimentally investigated. For the film cooling measurements, the PSP technique was utilized in order to avoid the effects of the temperature increase from the DBD plasma.
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 3 Results  
### Experimental Setup  
In this section, the Tensorflow framework [45] is used to train the decoding network. The parameter settings are shown in Table 2.  
The parity-check matrix H of the LDPC code with a codeword length of 576 is selected according to the IEEE 802.16e standard [46]. The network is trained using mini-batch gradient descent, where each mini-batch consists of 240 data blocks generated at different SNR levels in the same proportion. The Adam optimizer is employed with a learning rate of 0.001 for optimizing the network parameters. The training data are generated based on [31] at multiple signal-to-noise ratios (SNRs), which ensures a more diverse training dataset and improves the BER performance of the network.
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
## 6 Framework for Dynamic Logical Channel Prioritization  
The 5 GS in the role of a TSN bridge according to the introduced integration architecture can be depicted as shown in Figure 3. UE1 and the UPF correspond to ports that are connected to the TSN domain, and which are therefore configured via the CNC, each with a GCL.  
Figure 4 illustrates an example for the translation of a GCL's requirements into discrete sets of logical channel priorities for the well suited case in which \(T_{GCL}\) matches the time grid of the TTI. Assuming that Figure 4 depicts the GCL controlling the egress port of UE1 (Figure 3), the derived logical channel priorities are applied in the downlink. Here, \(x\) stands for the blocking priority. The LCP vector \(\tilde{c}_{5,1}\) corresponds to the Default Radio Bearer carrying Best Effort traffic with non-TSC priority. The GCL contains four gate control entries for four traffic classes each. The time interval values \(\tau_{k}\) depict the duration for every gate state, which carries the values \(c\) (closed) or \(O\) (open) for the four traffic classes. Every traffic class contained in the GCL is mapped to its own Radio Bearer, resulting in four logical channel instances for TSC traffic. Together with the Default Bearer, five DTCH instances are configured for this UE in the downlink.  
The representation of the scheduling conditions in the form of LCP vectors makes the traffic shaping applicable to the MAC scheduler, as the only differences with the former scheduling procedure are cyclic iteration over the LCP vectors at every TTI and the interpretation of the blocking priority. The temporal variation of the logical channel priorities ensures guaranteed channel access for every TSN stream over the NG-RAN air interface, analogous to the functionality of the 802.1 Qbv TAS. However, the proposed mechanism does not increase the reliability of the communication link provided by the 5 GS to TSN, and therefore needs to be combined with further uRLLC mechanisms for high reliability, such as the use of a robust MCS, Packet Duplication via Packet Data Convergence Protocol (PDCP) layer, or redundant N3 paths.
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
###### Abstract  
Underwater object detection plays a significant role in marine ecosystem research and marine species conservation. The improvement of related technologies holds practical significance. Although existing object-detection algorithms have achieved an excellent performance on land, they are not satisfactory in underwater scenarios due to two limitations: the underwater objects are often small, densely distributed, and prone to occlusion characteristics, and underwater embedded devices have limited storage and computational capabilities. In this paper, we propose a high-precision, lightweight underwater detector specifically optimizing for underwater scenarios based on the You Only Look Once Version 8 (YOLOv8) model. Firstly, we replace the Darknet-53 backbone of YOLOv8 with FasterNet-T0, reducing model parameters by 22.52%, FLOPS by 23.59%, and model size by 22.73%, achieving model lightweighting. Secondly, we add a Prediction Head for Small Objects, increase the number of channels for high-resolution feature map detection heads, and decrease the number of channels for low-resolution feature map detection heads. This results in a 1.2% improvement in small-object detection accuracy, while the remaining model parameters and memory consumption are nearly unchanged. Thirdly, we use Deformable ConvNets and Coordinate Attention in the neck part to enhance the accuracy in the detection of irregularly shaped and densely occluded small targets. This is achieved by learning convolution offsets from feature maps and emphasizing the regions of interest (RoIs). Our method achieves 52.12% AP on the underwater dataset UTDAC2020, with only 8.5 M parameters, 25.5 B FLOPS, and 17 MB model size. It surpasses the performance of large model YOLOv8l, at 51.69% AP, with 43.6 M parameters, 164.8 B FLOPS, and 84 MB model size. Furthermore, by increasing the input image resolution to 1280 \(\times\) 1280 pixels, our model achieves 53.18% AP, making it the state-of-the-art (SOTA) model for the UTDAC2020 underwater dataset. Additionally, we achieve 84.4% mAP on the Pascal VOC dataset, with a substantial reduction in model parameters compared to previous, well-established detectors. The experimental results demonstrate that our proposed lightweight method retains effectiveness on underwater datasets and can be generalized to common datasets.  
YOLO; lightweight detector; small-object detection; deep learning +
Footnote †: journal: Applied
## 2 Formulation  
Boundary-layer flow of non-Newtonian fluids is classically modeled by the Cauchy equations, which constitute a coupled system of PDEs. Upon simplification, the'scaled' Cauchy equations are obtained as follows:  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y} = 0 \tag{1}\] \[u\frac{\partial u}{\partial x}+v\frac{\partial u}{\partial y} = u_{e}\frac{\mathrm{d}u_{e}}{\mathrm{d}x}+n\bigg{|}\frac{\partial^ {2}u}{\partial y^{2}}\bigg{|}^{(n-1)}\left(\frac{\partial^{2}u}{\partial y^{2 }}\right) \tag{2}\]  
The reader is referred to [4; 6] and the references therein for details of the derivation process as we aim to be brief and concise. The variables \(x\) and \(y\) represent the (scaled) spatial variables, while \(u\) and \(v\) represent the corresponding scaled orthogonal velocities of the moving fluid, in the \(x\) and \(y\) directions, respectively. A stream-wise velocity outside the boundary layer is assumed, where it is represented by \(u_{e}(x)\) as in Equation (2). We shall assume \(u_{e}(x)=x^{m}\) as was used in [6]. (We remark that in [10], the condition at infinity was given as \(u=y^{m}\), see (3) below; however, the governing system of equations did not have the term involving \(u_{e}\). Simpler boundary conditions at infinity are also found in the literature, cf. [1; 25].) The boundary conditions of interest to us are (as in [2; 6]):  
\[u=v=0\ \ \text{on}\ \ y=0,\ \ \ u\to u_{e}(x)\ \ \text{as}\ \ y\to\infty \tag{3}\]  
We remark that different sets of boundary conditions, in addition to the one above, are found in the literature. The Equations (1) and (2) admit self-similar solutions via the transformation:  
\[u=x^{m}f^{\prime}(\eta),\ \ \ v=\alpha x^{s+m-1}\big{(}s\eta f^{\prime}(\eta)-(s+ m)f(\eta)\big{)} \tag{4}\]where \(s=(1+m(n-2))/(n+1)\), \(\alpha=n^{1/(n+1)}\), and where \(\eta=y/(ax^{s})\) is a similarity variable. This reduces the system (1) and (2) into the following ODE:  
\[\frac{1}{n}(|f^{\prime\prime}|^{n-1}f^{\prime\prime})^{\prime}=m((f^{\prime})^{ 2}-1)-\frac{2 mn-m+1}{n+1}ff^{\prime\prime} \tag{5}\]  
**Remark 1**.: _The function \(f\) may be referred to as the shape function or the auto-similarity function._  
The boundary conditions (3) are transformed into:  
\[f(0)=f^{\prime}(0)=0,\quad f^{\prime}(\infty)=1 \tag{6}\]  
We shall assume positive curvature solutions where \(f^{\prime\prime}>0\) on the entire solution domain. This is very typical in the literature and applications, c.f. [10; 12; 25]. Thus, we have the following boundary value problem:  
\[(f^{\prime\prime})^{n-1}f^{\prime\prime\prime}=m((f^{\prime})^{2}-1)-kff^{ \prime\prime} \tag{7}\]  
subject to  
\[f(0)=0,\quad f^{\prime}(0)=0,\quad f^{\prime}(\infty)=1, \tag{8}\]  
where \(k=\frac{2 mn-m+1}{n+1}\).  
Numerical solutions of this particular problem (7) and (8) can be found in [6]. Analytical investigation of this problem was conducted in [26], where the focus was mostly on existence and uniqueness in addition to the asymptotic behavior of solutions.
# A Study on DNN-Based Practical Model for Predicting Spot Color
## 3 Results
### Hyperparameter Optimization  
Before evaluating the model, hyperparameter optimization was conducted to optimize its performance. The effectiveness of a DNN depends heavily on the number of hidden layers and the number of neurons within each layer [45]. Therefore, we experimented with different combinations of hyperparameters to determine the ideal set. Individual model tests for each combination used the K-fold cross-validation method to evaluate performance on unseen data.  
Table 5 shows that the model with 5 hidden layers and 512 neurons in each layer achieved the best performance on the validation dataset, with K set to 9. In this configuration, the model predicted colors with an average difference of 3.6280. Therefore, the model will be further evaluated based on these findings.