esa-sceva/satcom-chunk-collection huggingface dataset:
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 3 The Proposed Image Encryption Scheme
### The Encryption Process
#### 3.1.2. Row-by-Row Encryption Processing
In order to describe the scrambling and substitution process of row-by-row encryption more intuitively, a simple example of an image with size of \(M=2\) rows and \(N=4\) columns is given in Figure 7. In Figure 7a, as the index vector \(\mathbf{I}=[3,\,1,\,2]\) of the obtained row scrambling position, the row \(1\) of the image \(\mathbf{P}\) is moved to the row \(I(1)\) of \(\mathbf{C}\), namely the row \(3\). Row \(2\) of \(\mathbf{P}\) is moved to row \(I(2)\) of \(\mathbf{C}\), which is row \(1\); Row \(3\) of \(\mathbf{P}\) is moved to row \(I(3)\) of \(\mathbf{C}\), which is row \(2\).
# Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models
## 4 Results and Discussion
Other recent works [21; 22] achieved between 70 and 80% on different datasets using neural architectures. In contrast, our Model 1 reached 96.5% accuracy using uniform noise augmentation, and Model 2 reached 98.1% accuracy by combining AWGN with an attention mechanism. These results confirm the effectiveness of our architecture and augmentation strategy, \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Class** & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \hline  & **Model 1** & **Model 2** & **Model 1** & **Model 2** & **Model 1** & **Model 2** & \\ \hline Surprise & 1.00 & 1.00 & 1.00 & 0.95 & 1.00 & 0.97 & \\ Neutral & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & \\ Calm & 0.90 & 0.95 & 0.95 & 1.00 & 0.92 & 0.97 & \\ Happy & 0.95 & 1.00 & 0.95 & 1.00 & 0.95 & 1.00 & \\ Sad & 0.95 & 1.00 & 0.95 & 1.00 & 0.95 & 1.00 & \\ Angry & 0.90 & 0.95 & 0.95 & 1.00 & 0.92 & 0.97 & \\ Fear & 1.00 & 0.95 & 1.00 & 1.00 & 1.00 & 0.97 & \\ Disgust & 1.00 & 0.95 & 0.95 & 0.90 & 0.97 & 0.92 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing precision, recall, and F1 score for both models, along with overall accuracy. Figure 6: Training and validation loss curves for both models. (**a**) Model 1 uniform noise; (**b**) Model 2 AWGN. especially when tested on a clean and balanced dataset like RAVDESS. Furthermore, preliminary testing on the SAVEE dataset showed promising cross-dataset generalization without retraining, highlighting the robustness of our models. As seen in several research comparisons in Table 1, many studies have shown positive results. However, the proposed models in this study stand out due to their unique architecture and generalization techniques. While previous studies have achieved good accuracy, this study models consistently perform better. Overall, models not only achieve higher accuracy but also show better generalization and robustness against overfitting, which are not prominently addressed in most of the studies mentioned earlier.
###### Abstract  
Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (_Pica abies_) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3 PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios--SSP1-2.6, SSP2-4.5, and SSP5-8.5--to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management.  
forest biomass modeling; 3-PG model; LSTM; biomass compatibility; forest thinning
## 5 Experiments
### Reconstruction Results
#### 5.3.1 Simulated Dataset Reconstruction  
To validate the efficacy of the proposed method, we conducted simulations comparing the two-stage reconstruction performance of the network under varying intensities of noiseand optical aberrations. Gaussian-distributed noise with a mean of zero and variances of 0.10, 0.05, and 0.01 was introduced. The comparative results are illustrated in Figure 3. As shown in Figure 3, it is evident that even in the presence of noise effects of different intensities, the first stage of the network effectively reconstructs the pupil function with aberrations, while the supervised learning in the second stage significantly enhances the reconstruction outcomes in terms of SSIM and PSNR.  
Further comparative analysis of simulated dataset reconstructions using various methods is presented in Figure 4, where we conducted comparative experiments between the traditional FPM algorithm, the deep learning-based FuNN [26], and SFPT. As shown in Figure 4, our method attains superior performance in PSNR and SSIM metrics for amplitude and phase reconstruction, exhibiting noticeably reduced visual artifacts.
## 3 Remaining Useful Life Prediction Algorithm Based on Double Bidirectional Long Short-Term Memory
### The Principle of Attention Mechanism Network  
The attention mechanism assists the training of data performed by the BiLSTM model. More precisely, it helps the model focus on key information in sequences and increases its sensitivity to important time points. It selects task-relevant information from input sequences and allocates corresponding weights [1; 2; 3].  
The input information is denoted by \(X=[x_{1}\), \(x_{2}\), \(\ldots\), \(x_{n}]\), where n is the number of inputs. For a given query \(q\), the attention variable \(m\in R_{n}\) is used to index selected information from input sequence \(x\). The probability of selecting the \(i\)th information, denoted by \(\alpha_{i}\), is expressed as [15]:  
\[\alpha_{i}=p(m=i\mid n,q)=\frac{\exp(s(x_{i},q))}{\sum_{j=1}^{n}\exp(s(x_{i},q ))} \tag{30}\]  
where \(s(x_{i},q)\) represents the scoring function, and \(\alpha_{i}\) represents the attention coefficient.  
\(s(x_{i},q)\) is given by [15]:  
\[s(x_{i},q)=q^{T}x_{i} \tag{31}\]  
The output of the attention layer is obtained by first calculating attention weights for each input vector, then implementing a soft attention mechanism [16] to summarize input information in order to evaluate the bearing degradation function of time. Figure 5 shows the network structure.  
\[y_{att}=\sum_{i=1}^{n}\alpha_{n}x_{n} \tag{32}\]  
The attention mechanism of the DBiLSTM output layer \(H\) is given by [44]:  
\[\alpha=Soft\text{max}(s(H,q)) \tag{33}\]  
\[y_{att}=\tanh(H\alpha^{T}) \tag{34}\]  
The final output \(y_{att}\) represents high-level abstract information of the input, which is used to evaluate the bearing degradation function of time.  
Figure 4: Working principle of double BiLSTM (DBiLSTM).
## 3 Key Technologies of UAV-Assisted SAGINs
### Resource Management  
In SAGINs, UAVs serve as aerial base stations to provide stable network services. Resource management and allocation methods typically involve multiple aspects such as UAV deployment, path planning, data transmission, and spectrum allocation. This section will provide a detailed review of resource management research in UAV-assisted SAGINs, focusing on key areas such as task scheduling, channel allocation, energy management. The survey results are presented in Table 6.
## 3 Performance Analysis of TDOA Method and Beamforming Method Based on the Simulated Signals
### Performance Analysis of TDOA Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AE Source** & **Change in TODA** & **Change \(\Delta t_{2}\)** & **Change \(\Delta t_{3}\)** & **Change \(\Delta t_{4}\)** \\
**Location** & **(\%)** & **Unchange \(\Delta t_{3}\)\(\Delta t_{4}\)** & **Unchange \(\Delta t_{2}\)\(\Delta t_{4}\)** & **Unchange \(\Delta t_{2}\)\(\Delta t_{3}\)** \\ \hline \hline \multirow{8}{*}{(\(350,600\))} & \(-10\) & N.A. & (345, 310) & (351, 383) \\  & \(-8\) & N.A. & (346, 340) & (351, 413) \\ \cline{1-1}  & \(-6\) & N.A. & (347, 377) & (351, 447) \\ \cline{1-1}  & \(-4\) & N.A. & (348, 426) & (350, 488) \\ \cline{1-1}  & \(-2\) & (351, 1020) & (349, 495) & (350, 538) \\ \cline{1-1}  & \(2\) & (349, 456) & (351, 801) & (350, 683) \\ \cline{1-1}  & \(4\) & (348, 376) & (352, 1529) & (350, 800) \\ \cline{1-1}  & \(6\) & (348, 323) & N.A. & (349, 992) \\ \cline{1-1}  & \(8\) & (347, 284) & N.A. & (349, 1400) \\ \cline{1-1}  & \(10\) & (347, 253) & N.A. & (349, 5150) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Localization results of changing TODA.
## 1 Introduction
They performed only an experimentation approach during the design. The brake generates high torque and does not require electricity in off-mode conditions. [PERSON] et al. [30] introduced the two-way SMA spring applied in passenger vehicle suspension to replace the traditional one by improving vehicle comfort and performance. Their experiment results show a significant reduction in the sprung mass displacement from various excitation frequencies. [PERSON] et al. [31] designed and manufactured an SMA-based active aerodynamic for automotive applications. The surface is smooth and self-controlled with a good arrangement compared to the traditional ones. [PERSON] et al. [32] performed both theoretical simulations and experiments for designing an SMA wire-based mechanism for aeronautical applications. They investigated the generated torque used for flap actuation. [PERSON] et al. [33] conducted a research study to morph automotive render skirts that use composite material embedded with SMA wires. The design process includes mathematical modelling and experiments. The findings describe the reduction in the actuation time of 95% with a 62% weight reduction compared to the dome-shaped steel fender skirt. [PERSON] et al. [13] proposed an active thermostat that uses an SMA spring to activate its operations. The design covers mathematical modelling and simulation, which show an engine overheating reduction and low-pressure fluctuation in the cooling system. [PERSON] et al. [34] proposed a novel non-embedded, adjustable, and flexible SMA actuator to be applied in the exhaust nozzle to vary its area. The study includes both theoretical and experimental approaches. This research aims to design the SMA-based Idle Air Control (IAC) actuator for vehicle application. The IAC actuator closes or opens the air path through the bypass for engine idling conditions from its pintle's stroke. The design process includes numerical analysis with COMSOL Multiphysics software and analytical methods with mathematical modelling and simulation using MATLAB software (R2023b). The SMA spring activates the actuator from its phase transformations using electric current. A PID controller and limiters are used for the output responses to reduce the time taken for thermal stimuli. The actuator's operations are evaluated based on the practical spark ignition engine idle-condition input parameters using MATLAB Simulink software (R2023b). The model is simple and weightless, with the rapid opening of the idle air path at a low consumed electric current of only 2 A.
# Spot Weld Inspections Using Active Thermography
## 2 Materials and Methods
### Excitation Methods
#### 2.1.2 Induction Heating  
The measurements performed in this manuscript using induction heating are performed using a 1.5 kVA Leon LIH-15 heater. The heating energy is transferred to the sample through an induction coil containing two windings. The induction heating device is mounted using a clamp at the desired location and distance next to the sample to be inspected. The sample is heated using inductive step heating. The pulse duration can be found in Table 3. A visualization of the measurement setup can be seen in Figure 3.  
Figure 3: Schematic overview of the measurement setup used for induction thermography measurements. Both front and back heating are performed. The parameter values can be found in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Parameter Set 1** & **Parameter Set 2** & **Parameter Set 3** \\ \hline
**Induction time front heating [s]** & 5 & 10 & 19 \\
**Induction time back heating [s]** & 4 & 8 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used during inductive thermography of spot welds. Each sample of the A-series is inspected using these parameters.  
Figure 2: Visualization of the measurement setup used for light heating experiments. The **left** image is a schematic representation of the setup, and the **right** image is taken during measurements. The cardboard is placed in front of the thermal camera to prevent the radiation emitted by the camera reflecting on the sample to be inspected.
# A Methodical Approach to 3D Scanning of Heritage Objects Being under Continuous Display
## 4 Materials and Methods  
### Identification of Object Classes in Terms of Posed Scanning Difficulties  
Based on the authors' experience, the heritage sites may contain several types of objects in terms of the possibility of scanning them. To name them:  
* A. It can be moved from an exhibition place to an ad hoc scanning studio. Objects of small dimensions and weight, in condition (shape) that allows them to be moved within the exposure area in accordance with the procedures indicated by a heritage site.
* B. It cannot be moved from an exhibition place. It has to be scanned at the place of exposure, but there is free access to it. The object cannot be moved, for example, because of: its size and weight, its permanent connection with the rest of the exhibition (e.g., base-relief), no permission to move the object due to its condition or even an alarm system.
* C. It cannot be moved from the exhibition place. It has to be scanned at the place of exposure and access to it is difficult. Such objects are B-type objects, although the access to them is additionally hampered by non-removable physical protections in the form of display cases, windows, bars, etc.
* D. It cannot be scanned at all. No access and/or consent from the facility is granted.  
The \"3 DScaMOTO\" methodology, presented in detail in the next subsection, takes into account the presence of all the three types of objects (A, B, C) for which 3D scanning is potentially possible.
# Priority Multi-Server Queueing System with Heterogeneous Customers
As it is pointed out above, although analysis of such multi-server systems is very important for practice, these systems did not get a proper portion of attention of queueing theorists. e.g., the authors of [3] note that \"Despite the large number of systems that can be viewed as instances of a priority queue with multiple servers, the literature devoted to their theoretical analysis appears rather moderate as the inherent complexity of these queues hinders their analysis\". It is also mentioned in [3] that \"To the best of our knowledge, few exact results exist in the case of priority queues with multiple servers even under the simplest assumption of exponentially distributed service times\". In paper [3], the authors analyze the model similar to the considered in our paper with two essential differences: (i) Several priority classes are suggested in [3] while we consider the case of two classes, (ii) arrival flow is assumed be the renewal with phase type distribution of mutually independent inter-arrival times while we assume that the inter-arrival times can be dependent. One more advantages of our paper consists of the fact that we provide exact analysis of the model while only the accurate approximate solution is proposed in [3]. In this paper, we make some step to provide the analysis of a multi-server priority system. We assume that customers of one of the types have the preemptive priority. The service times of customers of both types have phase type distribution (\(PH\)) with different parameters. Exact description of the model is given in Section 2. The most important performance measures of the considered system are probabilities of loss of customers of different types. To calculate these probabilities, we, first, described the process of the system operation by a multi-dimensional Markov chain. This description is given in Section 3. The explicit form of the generator of this Markov is given in that section along with the short explanation of the form of the blocks of this generator. In Section 4, we briefly note how to compute stationary distribution of this Markov chain. We do not go into details of computations and give just an advice how to compute this distribution. It is worth to mention that although the state space of the Markov chain is finite, in situation when the number of servers \(N\) and (or) state spaces of the underlying processes of arrival and service process are large, computation of this distribution is far from the trivial. Using the stationary distribution been calculated, in Section 4 we also derive loss probabilities associated with the system and a number of other characteristics. Numerical results are provided in Section 5.
## 3 Results and Discussion
### Simulation Results of FCNT  
The potential applications of CNT are as nanocarriers, and CNT acts as an artificial channel connecting the inner and outer cell membranes to deliver drug molecules. However, PCNT preferred to stay in the hydrophobic region of the membrane with arbitrary orientations. We, therefore, resorted to FCNT. Initially, we embedded the FCNT vertically and horizontally in the membrane center and then released it. As shown in Figure 4A, the FCNT was kept almost perpendicular during the whole simulation, as the tilt angle was always more than 75\({}^{\circ}\) (Figure 4C). Interestingly, it was found that the FCNT rotated from horizontal to vertical in Figure 4B, as the tilt angle increased gradually from 0\({}^{\circ}\) to 70\({}^{\circ}\). This rotation was driven by electrostatic interactions. Therefore, we counted the number of hydrogen bonds formed between hydroxyl groups on the ends of FCNT and lipid headgroups (see Figure 4D). In both systems, there was an average of about 5 hydrogen bonds formed between FCNT and lipid bilayer, which made them interlocked with each other, and FCNT was prone to vertical orientation. Compared with PCNT, FCNT is more suited to serve as an artificial channel to transport drug molecules.  
Figure 3: Trajectories of PCNTs with different lengths entering the membrane. (**A**–**F**) The initial and final snapshots of each system. Water is not shown for clarity. (**G**) Time evolutions of the COM distance in the \(z\)-direction and the tilt angles of each system.
## 2 Probabilistic Modeling of Uncertainty Sources
### Marginal Distribution Modeling of Uncertainty Sources  
The calculation formula for the power output forecasting error of uncertainty sources is as follows:  
\[\Delta P_{\text{U},t}=P_{\text{U},t}^{F}-P_{\text{U},t} \tag{1}\]  
where \(\Delta P_{\text{U},t}\) is the forecasting error of uncertainty sources at time \(t\); \(P_{\text{U},t}^{F}\) and \(P_{\text{U},t}\) are the power forecasting value and measured value of uncertainty sources, respectively.  
Let \(f(\chi)\) be the probability density function of the random variable \(\chi\), its kernel density estimation formula is as follows [22]:  
\[f(\chi)=\frac{1}{nw}\sum_{i=1}^{n}K(\frac{\chi-\chi_{i}}{w}) \tag{2}\]  
where \(w\) is bandwidth; \(K(\cdot)\) is kernel function; \(n\) is sample size; \(\chi_{i}\) is the \(i\)-th random sample.
## 2 Calibration Inaccuracies
This will make sure that the light rays captured by the camera become more parallel to each other, and thus the position at which they intersect on the micromirrors becomes more similar. Even though these adjustments reduce the impact, they introduce several difficulties in handling the display and also do not completely remove the issue. These shifts and distortions, after calibration, may also be observed when taking a closer look at the DDHOE while projecting a 2-color integral image (presented in Section 4). As we could see in the experiments from the previous paper, none of these issues are directly visible when one just wants to show a 3D object. However, they can become an issue for augmented reality applications, where an accurate alignment between the projected scene (virtual content) and the real-world environment is required. It is also possible to take a mathematical approach to solve the issue by calculating the amount of shift. By measuring the direction of the incoming and outgoing light rays, one may determine the shift based on the normal around which the light ray reflects. However, this would require a highly accurate calibration of the internal parameters of both camera and projector, as well as the relative positioning of the camera w.r.t. the projector. A small error in these parameters can cause a large error in the calculated shift, especially when working on this scale. Different materials for the DDHOE may also have different properties, and could impact this calculation even further. A short overview of such an approach is given in Appendix A. In conclusion, to overcome all the difficulties mentioned above, we resorted to an optical solution, which would reveal the absolute position of each elemental micromirrors irrespective of the positioning of projector, calibration-camera, and DDHOE. The idea is to introduce small diffuse markers on the DDHOE screen that will serve as a ground-truth anchor to determine the on-screen location of the brightest points.
## 4 Discussion
For slow and micro-movements, the control structure is not optimized, and the accuracy could be improved by using an internal PID instead of a PD controller to correct static errors. However, a PID would be less accurate for fast trajectory tracking [27]. Future works could also explore more complex NN structures, for example, architectures specialized in time-series prediction as RNN or LSTM.