esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Deep Single-Point Estimation of Bathymetry  
Deep neural networks are a family of algorithms that are inspired by and modeled after the human brain. These networks are trained to approximate a mapping between inputs and outputs that minimizes an objective function. Training such networks is done through Stochastic Gradient Descent (SGD); a network's prediction error is calculated according to the objective (loss) function over a batch of training samples and is then propagated backward through the different layers of the network using backpropagation, where an optimizer (SGD) is responsible for updating the different parameters of the network. This process is repeated over multiple iterations of the available data and is stopped according to varying criteria. As part of the optimization process, a learning rate is employed to control the scale of weight updates that are done at each step.  
The Deep Single-Point Estimation of Bathymetry method [42] is a deep learning-based bathymetry inversion method that operates on \(40\times 40\times 4\) px multi-spectral input subtiles (corresponding to the blue (B2), green (B3), red (B4), and near-infrared (B8) bands of the Sentinel-2 satellite constellation [12] at 10 m resolution) to estimate the water depth corresponding to the center of each input subtile. The neural network input is an image of \(40\times 40\times 4\) px input channels conforming to our dataset of \(40\times 40\times 4\) satellite subtiles; and a single output neuron with a Rectified Linear Unit (ReLU) activation, corresponding to the average depth beneath the imaged area. Figure 1 presents the different steps of the DSPEB method.  
The deep learning parameters for DSPEB, including the choice of the model architecture and learning hyperparameters, were studied in a previous work [42]. We found that while networks of varying depth can perform bathymetry estimation, small convolutional neural networks (CNN) are sufficient. The chosen architecture for this work is ResNet20 [44], a small version of a residual architecture that achieves state-of-the-art performance on many computer vision tasks [45]. The network is trained using Adam [46], a standard CNN optimization method based on stochastic gradient descent (SGD). In this work, the hyperparameters of Adam (\(lr\), gradient estimate decay factors \(\beta_{1}\) and \(\beta_{2}\), and \(\epsilon\)) are optimized based on a grid search and findings from our previous work [42].
## 5 Challenges and Limitations  
**Data scarcity.**  
One of the primary challenges in applying video-driven AI methods to AMP predictive modelling is the limited availability of high-quality, labelled data. AMP datasets are often small and imbalanced, which can hinder the training and generalisation of deep learning models. As outlined in the Dataset section, fewer than 100,000 experimentally validated AMPs are currently available for model training. Furthermore, the scarcity of annotated non-AMP sequences compels researchers to rely on ad hoc methods, such as off-the-shelf tools or custom computational pipelines, to generate synthetic non-AMP datasets. These data limitations fundamentally undermine model performance, leading to suboptimal generalisability and feature extraction biases. For example, the elevated misclassification rates of random sequences (a common negative control) reveal inherent biases in learned representations, where models may conflate noise with discriminative patterns.  
**Lack of unified evaluation standards.**  
The absence of a unified evaluation standard in the AMP generation field hampers direct comparisons across studies, making it difficult to objectively assess which computational models perform best. To compensate for this methodological gap, researchers are increasingly compelled to rely on wet-lab experiments for validation--an approach that significantly escalates labour demands, time investments, and operational costs.  
**Translational gaps.**  
Only a few of the AI-generated peptides showed bioactivity, underscoring the need for wet-lab validation.  
**Scalability.**  
While AI-based generation methods reduce computational costs, peptide length exponentially expands the chemical space, making global searches impractical.  
**Computational efficiency.**  
Training and deploying complex video-driven AI models, such as those involving diffusion models or transformers, can be computationally intensive. This can be a barrier to their widespread adoption, especially in resource-constrained environments.  
Model interpretability.  
Many video-driven AI models, such as CNNs and transformers, are known for their black-box nature. This lack of interpretability can be a significant drawback in biological applications where understanding the underlying mechanisms is crucial.  
Adaptation to the biological context.  
While video-driven AI methods have shown great success in computer vision tasks, their direct application to AMP predictive modelling requires careful adaptation. The unique characteristics of biological data, such as sequence variability and functional diversity, necessitate specialised model architectures and training strategies.
## 3 Model Structure  
### Variational Mode Decomposition  
Variational mode decomposition (VMD) [15] decomposes time series data f into K intrinsic mode functions (IMFs) of different frequencies, u\({}^{k}\); each u\({}^{k}\) corresponds to a central frequency, \(w^{k}\). To evaluate the broadband of \(u^{k}\), the constrained variational problem follows, as follows:  
\[\begin{array}{c}\min_{\{u^{k}\},\{w^{k}\}}=\bigg{\{}\sum_{k}\
orm{\partial_{t} \Big{[}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}*u_{k}(t)\Big{]}e^{-jw_{k}t}\
orm _{2}^{2}},\\ \text{s.t.}\quad\sum_{k}u_{k}=f.\end{array} \tag{1}\]  
In the above equations,\(\{u_{k}\}:=\{u_{1},u_{2},\ldots u_{K}\}\) and \(\{w_{k}\}:=\{w_{1},w_{2},\ldots w_{K}\}\) denote all modes and their corresponding central frequencies, respectively. \(\partial_{t}\) denotes the differential processing of \(t\), \(\
orm{\cdot}\) denotes the norm processing, \(\delta_{t}\) denotes the Dirac function, \(*\) is the convolution symbol, and \(\sum_{k}:=\sum_{k=1}^{K}\) represents the sum of all modes.  
In order to transform the problem constrained to unconstrained, an augmented Lagrangian function \(L\) is introduced:  
\[\begin{array}{c}L(\{u_{k}\},\{w_{k}\},\lambda)=\alpha\sum_{k}\
orm{\partial_ {t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}*u_{k}(t)\Big{]}e^{-jw_{k}t} \
orm_{2}^{2}+||f(t)-\sum_{k}u_{k}(t)||_{2}^{2}+\\ \langle\lambda(t),\ f(t)-\sum_{k}u_{k}(t)\rangle,\end{array} \tag{2}\]  
where \(\alpha\) is the penalty factor and \(\lambda(t)\) is the Lagrangian multiplier. The IMF component \(u_{k}\) and the central frequency \(w_{k}\) of each mode are updated according to the alternate direction method of multipliers. The complete algorithm flow of the variational mode decomposition is shown in Figure 1.
## 3 Results
### Simulations
#### 3.2.1 Deuterium (D\({}_{2}\))
The diffraction from the classical molecule is shown for three full oscillations, at the same frequency as the quantum D\({}_{2}\) wavepacket (\(T=82\) fs). Comparing the diffraction patterns from the classical and quantum D\({}_{2}\) molecule, it is apparent that the signal in the classical case is stronger and better defined across all values of \(q\), and remains identical for each oscillation. In contrast, the quantum wavepacket disperses with time, see \(t/T>5\) in Figure 3a, an effect which increases the decay of the diffraction signal over time, as seen in Figure 3b. One possible objection to using D\({}_{2}\) as an example, despite the fact that the modified molecular intensity minimizes the effect of the atomic form factors on the scattering, is that D\({}_{2}\) is not an obvious candidate for a scattering experiment and that its small reduced mass exaggerates delocalization. We have therefore performed the same calculation but with a higher reduced mass corresponding to the potassium dimer, K\({}_{2}\). In order to aid comparison, we have not changed the potential, i.e., the potential energy curve remains that of the D\({}_{2}\)\(B\)-state, and all parameters except the reduced mass are left unchanged. We refer to this wavepacket as \(\tilde{\text{K}}_{2}\) to avoid confusion with actual K\({}_{2}\). We first consider the wavepacket, which is plotted in Figure 4a. The greater reduced mass leads to a significant increase in the density of vibrational states, and thus in the number of eigenstates contained in the wavepacket in Equation (2). As a consequence, initial localization is stronger than in D\({}_{2}\), but conversely, the dispersion at longer times (on the normalized \(t/T\) timescale) is also greater. The scattering from the \(\tilde{\text{K}}_{2}\) wavepacket, shown in Figure 4b, is thus more defined at earlier time, but suffers more from the dispersion of the wavepacket at larger multiples of the characteristic period, which leads to a more marked degradation of the diffraction signal at large values of \(q\). Figure 3: Results for D\({}_{2}\) excited to the electronic \(B\)-state by a 14.3 eV pulse with 20 fs duration. (**a**) Contour plot of the D\({}_{2}\) vibrational wavepacket probability density, \(|v(R,t)|^{2}\), with \(R\) the internuclear distance in Bohr and \(t\) the time; (**b**) Contour plot of the corresponding modified (elastic) scattering intensity \(M(q,t)\) for the wavepacket, with \(q\) the momentum transfer in Å\({}^{-1}\) and \(t\) the time; (**c**) Contour plot of the modified (elastic) scattering intensity \(M(q,t)\) for the _classical_ D\({}_{2}\) molecule, shown over three oscillation periods, \(t\in[0,3T]\). Note that in all three plots the time \(t\) is given in units of the classic oscillation period \(T=82.5\) fs. The modified scattering intensity \(M(q,t)\) is defined in Equation (16), and we use the form factor for H in the denominator.
## 5 Conclusions  
In this article, an automated SIDTLD-AIC technique was established for the proper identification and classification of images into distinct kinds on UAV networks. The presented SIDTLD-AIC model follows a feature extraction module using RetinaNet model in which the hyperparameter optimization process is performed by the use of SSA. Next, the SOA-CLSTM model is applied to classify the aerial images. For assuring the better performance of the SIDTLD-AIC method, a wide range of simulations are executed and the outcomes are investigated in various aspects. The comparative study reported the better performance of the SIDTLD-AIC model over recent approaches with maximum accuracy of 99.50%. Thus, the presented SIDTLD-AIC model can be exploited for aerial image classification in real time environment such as vegetation mapping, crop classification, disaster management, weather prediction, etc. In future, hybrid metaheuristics should be utilized for improving the overall classification performance. Furthermore, the proposed model can be extended to real-time large-scale databases in future. Moreover, the investigation of the performance using statistical analysis can be done in our future work.  
Figure 12: CT analysis of SIDTLD-AIC technique with existing approaches.  
**Author Contributions:** Conceptualization, S.S.A. and N.N.J.; methodology, H.A.M.; software, R.M.; validation, A.M.H., R.M. and M.R.; investigation, A.M.H., M.A.S.; resources, M.R.; data curation, R.M.; writing--original draft preparation, S.S.A., H.A.M., N.N., I.Y.; writing--review and editing, A.M., M.A.S., A.S.Z.; visualization, A.S.Z.; supervision, S.S.A.; project administration, R.M.; funding acquisition, H.A.M. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number (42/43). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R114), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4210118 DSR21).  
Data sharing not applicable to this article as no datasets were generated during the current study.  
The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.
## 3 Results and Discussion
### Verification of the Prediction Model  
Based on the determined calculation model, verification measurements of the set-up of experiments were performed (according to Table 6). A total of 18 experiments were performed, nine for modelling and nine for verification. The individual force components were measured during these experiments as a basis for evaluating the total cutting force of the machining. The resulting values were compared with those from the prediction model (Table 7).  
In Table 9 is the comparison of values from measurements (total machining force \(F_{max}\) was computed from indirect measurements of cutting forces) with predicted values--computed values of \(F_{max}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & \(s\) **(mm)** & \(\alpha\) (\({}^{\circ}\)) & **Predicted \(F_{max}\) (N)** & **Measured \(F_{max}\) (N)** & **Difference** \\ \hline
1 & 0.2 & 10 & 1078 & 1153 & 7\% \\
2 & 0.2 & 30 & 1898 & 1936 & 2\% \\
3 & 0.2 & 60 & 2678 & 2693 & 1\% \\
4 & 1.0 & 10 & 2032 & 2156 & 6\% \\
5 & 1.0 & 30 & 2841 & 2872 & 1\% \\
6 & 1.0 & 60 & 3604 & 3763 & 4\% \\
7 & 0.6 & 10 & 1494 & 1471 & 2\% \\
8 & 0.6 & 30 & 2291 & 2245 & 2\% \\
9 & 0.6 & 60 & 3037 & 3135 & 3\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of statistically predicted magnitudes of the total machining force \(F_{max}\) according to the predictive Equation (2) and measured the total machining \(F_{max}\).  
Figure 10: Response area chart for the total machining force \(F\).  
Figure 11 shows the processed values of Table 9. As can be seen from the comparison of the predictive model values with those obtained from the verification measurement, the smallest deviation achieved in two cases (Experiments 3 and 5) was only 1%. In two cases, the highest deviation was higher than 5%; in other cases this limit was not exceeded, and it is possible to evaluate the prediction model as sufficiently accurate.
# Communication Transport Protocol Strategies for Rail Applications
## 6 Results  
This section reports the results of the testbed described in the previous section. Two main cases were considered: the transmission of the ERTMS signaling (i.e., PR/MA within the required time for guaranteeing the train operations) in Section 6.1 and the transmission of a critical data in Section 6.2.  
Performance tests were executed creating several tunnel configurations combining 1 or 2 bearers and using either UDP or TCP as tunnel transport protocol or a mix of them but any case can be easily extended. Different types of tests were conducted to measure different aspects in all tunnel configuration scenarios, by using several tools. To measure bandwidth and data transfer performance, Iperf3 was used [20], which is a well-known networking tool capable of producing standard performance measurements for networks, creating data streams to measure the throughput between the two endpoints (i.e., User Agents) for producing as output a time-stamped report of the amount of data transferred and the throughput measured.  
To measure round-trip time (RTT) and message loss rate we used the \"ping\" command, which operates by sending ICMP echo request packets to the target host and waiting for an ICMP echo reply. The program reports errors, packet loss, and a statistical summary of the results, including the minimum, maximum, the mean round-trip times (and standard deviation of the mean). Finally, a traffic generator tool (named udpTrEmu) was developed to simulate the Railway Signaling use-cases related to several traffic profiles typical of railway signaling, whose reference parameters were extrapolated from the requirements on GSM-R networks for ETCS support [21]. Impairments were added through Linux Traffic Control (tc) with Network Emulator (netem) commands directly on the bearer physical interfaces [15]. Netem allows us to add delay, packet loss, duplication and other characteristics to packets outgoing from a selected network interface. Netem is built using the existing QoS and Differentiated Services (diffserv) facilities in the Linux kernel.
## 3 Results and Discussion
### Influence of Illumination  
In order to evaluate the effect of the illumination on the performance of the two scanners, three levels of the illumination (221, 111 and 8 lux) were considered. The light meter LT300 (Extech instruments, Waltham, MA, USA) was used to measure the illumination values. The different levels were selected in order to represent the maximum, intermediate, and minimum illumination in the room. The optimized parameter settings of the scanners were used to acquire the data. The data set acquired by the two scanners in different light intensities showed no significant difference. For example, the deviation obtained for data acquired at different intensities showed minimal spread (standard deviation for laser scanner: 0.00858 mm and standard deviation for white light scanner: 0.0045 mm).  
Figure 18: Scale provided to the users for rating different scanners.
# Deep Learning-Based Digital Image Forgery Detection System
## 1 Introduction
Image tampering usually does not have any visible clues though which one can tell whether the image has been tampered with or not; however, some statistics of the image may be altered. [PERSON] et al. [4] experimented on the copy/move approach and cut/paste-based detection methodology was discussed by [PERSON] et al. [5]; some of these approaches are shown in Figure 1.  
The development of deep learning has led to improving methodologies where state-of-the-art methods, such as CNN, Mobile Net, and ResNet50v2, automatically extract the potential features, having been trained on large datasets. Some of the examples of CNN-based feature extractions are deep features utilized for image quality assessment [6], skin lesion classification [7], or person re-identification [8]. These extracted features are adapted into the inherent structural patterns of the data. This is the main reason behind their non-discriminative and robust architecture compared to the hand-engineered features. In this paper, motivated by the deep learning technique, we propose a transfer learning-based approach. It is an effective architecture with which we incorporated the weights of a model previously trained on a large database, and hence, it benefitted from using the meaningful weights without having to train the model from scratch. We present an architecture based on the ResNet50v2 architecture that employs the use of transfer learning for the detection of tampered images, specifically, spliced images. We used the pre-trained weights of a YOLO CNN model to detect images that were specifically tampered with using the image splicing technique. Furthermore, this study makes the following contributions to this field of research:  
* Detailed analysis between deep learning and hand-engineered techniques;
* Proposed ResNet50v2-based architecture for the authentication of original and forged images;
* Utilization of the transfer learning technique to effectively train our proposed model on benchmark datasets CASIA_v1 and CASIA_v2 [9];
* Discussion of the limitations and future directions of this research that can be carried forward. The rest of the paper is organized as follows. Section 2 discusses the literature review and Section 3 explains the proposed system architecture. In Section 4, we present the dataset details. Section 5 presents the experimental results, discussion, and future work. Section 6 presents the conclusion.
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 1 Introduction
A tabu table and grid-weight table were used in their study to determine the trafficability of off-road regions. Simulations based on a tracked vehicle M1A1 in off-road environments showed that their improved ant colony path-planning algorithm considerably improved computation efficiency. In correspondence to two kinds of obstacles and terrains in the path planning of unmanned tracked vehicles, [PERSON] et al. [25] introduced different types of potential energy function models and designed a multi-stage optimization channel path-planning method with the potential field gradient as a condition of optimization. [PERSON] et al. [26], based on 3D terrain map information, employed a random-sampling-based scheme so that vehicles can adaptively generate a path in complex environments within a short time. To address the problem of unknown environments in local path planning, [PERSON] et al. [27] proposed a target point extraction algorithm to extract targets in free spaces and found in their experiments that their method performed well in long-distance path planning. [PERSON] et al. [28] defined two metrics for the path planning of off-road autonomous ground vehicles under uncertain terrain environments, namely state mobility reliability and mission mobility reliability. They also developed a path-smoothing algorithm to address the suboptimality of their method. [PERSON] et al. [29] accounted for LIDAR-vegetation interaction and developed a LIDAR processing algorithm for off-road autonomous navigation, which can distinguish navigable vegetation from non-navigable solid obstacles and has practical value for the development of autonomous off-road navigation algorithms. To address the limited capacity of conventional methods in long-distance off-road path planning, [PERSON] et al. [30] put forward an improved A-Star algorithm based on terrain data; their improved method achieved a path-planning efficiency 4.6 times higher and a maximum acceleration 550 times higher than the conventional A-Star algorithm. Based on previous works on off-road path planning, a multilevel adaptive path-planning model based on improved ACA was proposed in the present study. The influence of terrain and surface features on road trafficability was used as the key information to adjust the algorithm to fit areas of different scales so that the model could be effectively applied to large areas and complex environments.
# Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking
###### Abstract  
By aiding GPS receiver tracking loops with INS estimates of signal dynamics, GPS/INS ultra-tight coupling can improve the navigation performance in challenging environments. Traditionally the INS data are injected into the loops once every loop update interval, which limits the levels of dynamics accommodated. This paper presents a sample-wise aiding method, which interpolates the aiding Doppler into each digital sample of the local signal to further eliminate the dynamic errors. The relationship between the tracking error and the aiding rate is derived analytically. Moreover, the effects of sample-wise aiding using linear and spline interpolations are simulated and compared with traditional aiding under different INS data update rates. Finally, extensive tests based on a digital IF (intermediate frequency) signal simulator and a software receiver validate the theoretical equations and demonstrate that the dynamic stress error can be significantly reduced by sample-wise aiding.  
ultra-tight coupling; GPS/INS integration; Doppler aiding; high dynamics; tracking loop Article