esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks
## 3 Results and Analysis
### Results of ANOVA  
ANOVA is a standard statistical tool for determining the effects of a single parameter from all input process parameters. In this analysis, the percentage contribution of each parameter is used to measure its corresponding effect on the output response. In this study, ANOVA was used to analyze the influence of milling parameters, that is, spindle speed, feed rate, cutting depth, and milling spacing will affect the surface roughness. Tables 7-10 list ANOVA results for surface roughness and four input parameters.  
For the cutting depth, spindle speed, feed rate, and milling distance, \(p\)-values are far less than 0.0001, which means that these four processing parameters and surface roughness have a first relation. The main contribution of cutting depth was 51.86%. The contribution of spindle speed was 77.48%. The contribution of feed was 92.3%. The contribution of milling spacing was 71%. ANOVA statistics show that the cutting depth is a parameter with low contribution. From the value of F, it was found that the model is meaningful. In general, surface roughness is measured as a function of feed speed. Therefore, it has the physical significance and the greatest contribution. The contribution of spindle speed and milling spacing is similar.
## 4 Results and Discussion
### The Influence of the Hidden Unit and Sliding Window Lengths on the Experimental Results  
The setting of hidden units and sliding windows has a greater impact on the prediction results of the model. In the choice of the two, the hidden unit is too large, which may lead to an increased complexity of the model and an over-fitting phenomenon. The larger the sliding unit, the more features are perceived, which may slow down the running speed of the model and reduce the prediction accuracy. Therefore, we tested the settings of the hidden unit and the sliding window. The range of the number of hidden units was set from 32 to 128, and the range of the sliding window was set from 3 to 6. It can be seen from Figures 7 and 8 that, in the case of three sampling intervals, when the number of hidden units is 96, the prediction effect of the model is the best. In the case of three sampling intervals, when the sliding window is 9, the prediction effect of the model is the best. The performance comparison under different hidden units is shown in Figure 7, where the \(x\)-axis represents the number of hidden units and the \(y\)-axis represents the values of the MAE and the RMSE. The performance comparison under different sliding window lengths is shown in Figure 8, where the \(x\)-axis represents the length of the sliding window and the \(y\)-axis represents the values of the MAE and the RMSE.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sampling Interval** & \multicolumn{2}{c}{**10 min**} & \multicolumn{2}{c}{**30 min**} & \multicolumn{2}{c}{**60 min**} \\ \hline
**Model Metrics** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline DBSTGNN-Att & 24.23 & 35.22 & 68.01 & 95.46 & 137.11 & 179.57 \\ W/o the attributed KNN graph & 26.02 & 37.98 & 71.24 & 106.43 & 149.81 & 197.13 \\ W/o a dual GLU & 27.66 & 43.83 & 78.45 & 123.14 & 159.35 & 218.70 \\ W/o an attention mechanism & 25.39 & 35.71 & 80.72 & 129.94 & 172.36 & 257.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction performance comparisons of the ablation study of DNSTGNN-Att.  
Figure 7: Performance comparison under different hidden units. (**a**) A 10 min sampling interval; (**b**) a 30 min sampling interval; and (**c**) a 60 min sampling interval.
# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
## 5 Simulation Analysis  
Aiming at the downlink UAV-aided RSMA system designed in this paper, the proposed key algorithm and related systems are simulated and analyzed in MATLAB R2016a.
# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 5 Current Issues and Challenges
According to [12], for example, the most commonly used database (GTSDB database) contains only 600 training images and 300 evaluation images. Of the seven categories classified in the Vienna convention, GTSDB covers only three categories of traffic signs for detection: prohibitory, mandatory and danger. All included images are only German traffic signs, which are substantially different from other parts of the world. To resolve the database scarcity problem, perhaps one of the ideas is to create a unified global database containing a large number of images and videos for road scenes in various countries around the world. These scenes must contain all categories of traffic signs under all possible weather conditions and physical states of the signs. Real-time application: The detection and recognition of traffic signs are caught up with the performance of a system in real-time. Accuracy and speed are surely the two main requirements in practical applications. Achieving these requirements requires a system with efficient algorithms and powerful hardware. A good choice is convolutional neural networks-based learning methods with GPGPU technologies [2]. In brief, although lots of relevant approaches have been presented in the literature, no one can solve the traffic sign recognition problem very well in conditions of different illumination, motion blur, occlusion and so on. Therefore, more effective and more robust approaches need to be developed [12].
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 2 Related Work
### Probability Based Method  
These methods assume that anomalies occur in low probability regions of the normal data, and the main principle is as follows: (i) Establish the probability density function (PDF) of normal data; (ii) Evaluate the test samples by PDF and low probability density values are most likely to be abnormal. There are various methods depending on the distribution assumptions, such as Gaussian [23], Gaussian Mixture Model (GMM) [24] or Markov random fields (MRF) [25]. For instance, [PERSON] et al. [26] improved on the framework of [23], they applied the CS theory [27] to compress defect-free texture patches into texture features and use GMM to estimate their probability distributions. In the detection stage, a pixel is considered to be defective if the likelihood of the local patch corresponding to it is less than the threshold value. Recently deep learning methods have improved PDF estimation performance. Due to the powerful image generation capability of deep autoregressive (DAR) model, it is also used for anomaly detection.  
[PERSON] et al. [28] applied PixelCNN[29] to circuit board images that predicted pixel likelihood to anomaly detection, but it may not work well in more complex natural images. One advantage of the DAR model is that it can give the likelihood for each pixel, but [PERSON] et al. [30] recently compared several anomaly detection methods, whose anomaly scores are given as softmax output, Euclidean distance, likelihood, etc. The experimental results show that PixelCNN++ using likelihood as the anomaly score has a lower accuracy than the mean. This shows that it is unsuitable to use low likelihood values as the anomaly score directly. Our method combines the advantages of DAR, using likelihood for resampling instead of scoring directly. On the other hand, we use DAR to estimate density for a low-dimensional discrete latent space, which avoids the problem of \"curse of dimensionality\" to improve modeling efficiency.
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 3 Robust Visual Localization
### System Workflow  
The complete system operation comprises all the introduced contributions, as denoted in Figure 9, where the workflow is:  
1. omnidirectional image acquisition, \(I_{1}\).
2. multi-scaled distribution: projection \(p_{1}(u,v)\) of a 3D point, \(P(x,y,z)\), establishes the seed for a multi-scaled distribution, \(\lambda_{i}p_{1}\).
3. EKF prior extracted for motion prediction, hence point appearance prediction on the second image according to \(q_{i}\). Dynamic uncertainty management by propagating \(R\)\(\sim\)N(\(\hat{\beta},\sigma_{\beta}\)) and \(T\)\(\sim\)N(\(\hat{\phi},\sigma_{\phi}\)), thus accounting for the current innovation \(S(t)\), as the uncertainty metric.
4. Bayesian regression computed as per Equations (10)-(13), to produce weighting coefficients for the final matching over a predicted epipolar search area on the next image, \(I_{2}\).
## 3 Proposed System
### Attribute-Based Encryption
#### 3.2.2 Access Structure  
The decryption of the ciphertext is performed when the user's attribute belongs to the attribute set of the secret key.  
For example, a medical college is conducting research on a new strain of COVID-19. If another medical university researcher needs access to the data studied, the private key must be that of a university professor and must be created based on the attributes of the research team, the research team leader, or the department head. The structure (university professor \(\wedge\) research team) \(\vee\) (research team leader \(\vee\) department dean) must be created as shown in Figure 5 ('\(\wedge\)' is an AND gate, and '\(\vee\)' is an OR gate).  
If the researcher is a university professor and dean, the secret key includes the attribute (university professor, dean). When trying to access the data, the researcher did not satisfy the AND gate called the university professor and research team, but the OR gate called the dean so that the researcher could access the data.  
Figure 5: The Structure Access.
# Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis
## 3 Results  
### Evaluation on Transformer-Base Dungan Character-to-Unit Conversion  
The text analysis in the front end affects the quality of speech synthesis in the back end, so we evaluated the Dungan text analyzer, where the character-to-unit conversion module is the most critical factor affecting the quality of synthesized speech. To assess the viability of the transformer-based Dungan character-to-unit conversion module, we utilized a dataset comprising 10,783 sentences in the Dungan language transcribed using Mandarin Pinyin. The dataset's Dungan language and Mandarin Pinyin representations are isomorphic, encapsulating textual attributes like tone and prosodic boundaries inherent to the Dungan language. In our research, we allocated 10% of the total 10,783 sentences to serve as the test set, another 10% as the validation set, and the remaining 80% were designated as the training set. The hyperparameters associated with the Transformer are detailed in Table 2. We employed precision, recall, and F1 measures as evaluation indices, as illustrated in Table 3. The outcomes from the evaluation process affirmed that the proposed Dungan character-to-unit module is suitable for subsequent speech synthesis evaluation.
# Comparison of Two Detector Magnetic Systems for the Future Circular Hadron-Hadron Collider
## 2 Modeling the Magnetic Systems  
### Reference Geometry of the FCC-hh Detector  
A reference geometry for the FCC-hh detector with the baseline magnetic system is shown in Figure 1. The detector is designed to register particles generated in collisions of hadron beams at a center-of-mass energy of 100 TeV and assumed to have a diameter of 20 m and a length of 50 m. This is comparable to the dimensions of the ATLAS [8] and CMS [9] detectors at the Large Hadron Collider [10] at CERN. The central part of the detector houses the tracking system, and electromagnetic and hadron calorimetry inside a 4 T superconducting solenoid with a free bore diameter of 10 m. The forward parts of the detector are displaced from the beam interaction point along the beam axis and two forward solenoid coils with an inner bore of 5 m provide the required bending power for the charged particles escaping the interaction point.  
Figure 1: One quarter of the reference geometry for the FCC-hh detector with the baseline magnetic system. In black, the quarter of the main superconducting coil in the vacuum cryostat and a half of the forward coil in its own cryostat are shown. In brown, the radiation protection conical-cylinder shield made of nonmagnetic material is displayed. In red, the charged particle tracker cylinders are shown. The orange lines represent the locations of the muon detection chambers. Other colours describe electromagnetic and hadronic calorimeters, and nonmagnetic disks of the forward muon system [3]. In this study, regions of these nonmagnetic materials are considered to have free-space permeability.  
Increasing the particle beam colliding energy to 100 TeV leads to an increase in transverse momenta of the charged particles to be detected in the tracking system up to at least 10 TeV/\(c\). The tracker is specified to provide better than 20% momentum resolution for these particles and better than 0.5% momentum resolution at the multiple scattering limit [3]. To achieve this goal, the inner tracker, consisting of three cylinders, has a 1.55 m radius and a length of 32 m.  
The scheme of the FCC-hh detector shown in Figure 1 includes the parts of interest for this study: the main and forward superconducting coils, the charged particle tracker cylinders, and the conical-cylinder nonmagnetic radiation protection shield.
# Real-Time Guitar Amplifier Emulation with Deep Learning +
## 5 Conclusions  
This work considered the use of deep neural networks for modelling of audio distortion effects. A feedforward variant of the WaveNet neural network was tested as a model for three well-known guitar distortion pedals. Different model configurations were examined to find a suitable compromise between modelling accuracy and computational load. Real-time and low-latency implementations of the deep neural networks were developed.  
Models of two popular vacuum tube amplifiers were then created, using the WaveNet-style model, as well as an RNN based model. MUSHRA listening tests were conducted to further validate the neural network models. The results show that both of the neural network types tested can be used to create very perceptually convincing emulations of vacuum tube amplifiers. For the less distorting Mesa 5:50 amplifier, all of the tested models were rated to be Excellent, indicating that even the smaller neural networks tested were sufficiently accurate in this case. For this amplifier, the WaveNet models and the larger RNN were often indistinguishable from the reference. For the more distorting Blackstar HT5M amplifier, only the larger neural network models were generally rated to be Excellent, with the smaller models being judged as somewhere between Good and Excellent on average. In addition to this, all the models included in the listening tests were shown to be capable of running in real-time on a consumer-grade desktop computer, with some of the models only requiring a relatively small amount of the computer's processing power. Possible future work includes investigation and suppression of aliasing that is introduced by the models.  
Conceptualization, A.W., E.-P.D., L.J. and V.V.; methodology, A.W., E.-P.D. and L.J.; software, A.W. and E.-P.D.; validation, A.W.; formal analysis, E.-P.D. and L.J.; investigation, A.W. and E.-P.D.; resources, A.W.; data curation, A.W. and E.-P.D.; writing--original draft preparation, A.W. and E.-P.D.; writing--review and editing, L.J. and V.V.; visualization, A.W., E.-P.D. and V.V.; supervision, V.V.; project administration, V.V.; funding acquisition, V.V. All authors have read and agreed to the published version of the manuscript.  
This research is part of the \"Nordic Sound and Music Computing Network--NordicSMC\", NordForsk project number 86892.  
We acknowledge the computational resources provided by the Aalto Science-IT project.  
The authors declare no conflict of interest.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 4 Simulation Environment and Performance Metrics
### Throughput  
In computing, throughput is defined as the amount of data successfully transferred between a source and a sink over a given period (seconds). It can be computed as:  
\[\text{TP}=\frac{\text{Number of successfully received packets}}{\text{StopTime}-\text{StartTime}} \tag{10}\]
## 3 Results and Discussions
### MicroPET Studies in NHP  
The representative summed images from zero to 120 min were co-registered with MRI images to accurately identify the regions of interest (Figure 3). The time-activity curve (TAC) revealed high initial uptake of [11C]**2a** in the brain, which peaked at 3 min post-injection; then, the radioactivity was quickly washed out from all brain regions. The summed image revealed a homogeneous distribution of radioactivity in the brain of the normal cynomolgus macaque. The microPET studies suggested that [11C]**2a** was able to cross the BBB and had a fast washout kinetics from the brain regions. The macaque used in the studies was a healthy young adult, and the distribution of the \(\alpha\)-syn radioligand throughout the brain regions was homogeneous. Higher expression of \(\alpha\)-syn protein in particular regions should not be observed in healthy subjects; thus, homogeneous distribution of the radioactivity in the macaque brain was expected. Nevertheless, PET studies of [11C]**2a** performed on an NHP model  
Figure 2: Regional radioactivity uptake in male Sprague Dawley (SD) rat brain (\(n=4\)). (**A**) Regional brain uptake of [11C]**2a**; (**B**) regional brain uptake of [18F]**2b**. Cb, cerebellum; Ctx, cortex; Str, striatum; Thal, thalamus; Hippo, hippocampus.  
bearing the over-expression of \(\alpha\)-syn aggregation will directly determine the _in vivo_ specificity of the radiotracer.
## 2 Methodology
### Sun Path Condition  
Sun path diagrams effectively illustrate the annual variations in the Sun's trajectory. As shown in Figure 5, the midday sun in Dammam city is at a 40.54\({}^{\circ}\) solar altitude during the winter solstice and 86.12\({}^{\circ}\) during the summer solstice. These angles indicate a 45.5\({}^{\circ}\) deviation between the solstices because of the Earth's declination, varying between \(-23^{\circ}\) and \(+23^{\circ}\) over the year. The solar energy incident on the southern and northern parts of the building is not the same since the southern side has better incident sunlight compared to the northern side. In contrast, considering the eastern and western directions, the incident solar energy exhibits symmetry. As a result, there should be a difference in the light distribution and intensity once daylighting strategy scenarios in the mosque are attached.
###### Abstract  
Deforested and converted tropical peat swamp forests are susceptible to fires and are a major source of greenhouse gas (GHG) emissions. However, information on the influence of land-use change (LUC) on the carbon dynamics in these disturbed peat forests is limited. This study aimed to quantify soil respiration (heterotrophic and autotrophic), net primary production (NPP), and net ecosystem production (NEP) in peat swamp forests, partially logged forests, early serial grasslands (deforested peat), and smallholder-oil palm estates (converted peat). Peat swamp forests (PSF) showed similar soil respiration with logged forests (LPSF) and oil palm (OP) estates (37.7 Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\), 40.7 Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\), and 38.7 Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\), respectively), but higher than early serial (ES) grassland sites (30.7 Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\)). NPP of intact peat forests (13.2 Mg C ha\({}^{-1}\) yr\({}^{-1}\)) was significantly greater than LPSF (11.1 Mg C ha\({}^{-1}\) yr\({}^{-1}\)), ES (10.8 Mg C ha\({}^{-1}\) yr\({}^{-1}\)), and OP (3.7 Mg C ha\({}^{-1}\) yr\({}^{-1}\)). Peat swamp forests and serial grasslands were net carbon sinks (10.8 Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\) and 9.1 CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\), respectively). In contrast, logged forests and oil palm estates were net carbon sources; they had negative mean Net Ecosystem Production (NEP) values (\(-0.1\) Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\) and \(-25.1\) Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\), respectively). The shift from carbon sinks to sources associated with land-use change was principally due to a decreased Net Primary Production (NPP) rather than increased soil respiration. Conservation of the remaining peat swamp forests and rehabilitation of deforested peatlands are crucial in GHG emission reduction programs.  
deforestation; forest logging; tropical climate; peat swamp forests; oil palm estate; NEP Article
## 4 Discussion
### Carbon Storage and Mineralization in Soil Aggregates
Overtime, as the readily available organic carbon was depleted, the rate of mineralization gradually decreased, showing a relatively stable trend. In this study, SOC mineralization in soil aggregates showed a highly significant positive correlation with precipitation (\(p<0.001\)). Organic matter in soil aggregates, such as plant residues and dead microbial matter, were the primary materials for mineralization [34]. Precipitation provided the necessary moisture for the decomposition of this organic matter, thereby increasing the mineralization rate. Increased precipitation also alters soil oxygen exchange conditions. Appropriate moisture content facilitates oxygen penetration, providing the oxygen needed for the aerobic decomposition of organic carbon by soil microbes. Soil moisture conditions also indirectly affect soil temperature and chemical properties, which collectively influence the mineralization process of organic carbon. Furthermore, compared to other particle sizes, the 1-2 mm aggregates showed more excellent cumulative mineralization and are more closely related to MAP, SOC, and SWOC. It could be due to the size of 1-2 mm aggregates being more conducive to microbial activity and organic matter accumulation. The particle size provided a larger surface area for microbial contact with organic matter, while maintaining suitable moisture and gas exchange conditions, crucial for microbial metabolic activities. Precipitation, as a critical climatic factor, directly affects soil moisture conditions and microbial activity. The 1-2 mm aggregates under different precipitation conditions exhibited a more robust moisture retention capacity, thereby affecting the rate of mineralization and organic matter transformation. The organic matter content within aggregates, especially SOC and SWOC, directly influenced the mineralization process. The 1-2 mm aggregates, due to their specific physical structure, may more effectively retain and transform SOC and SWOC, thereby impacting the mineralization process.
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 2 Preliminaries
### n-Round AES  
AES-128 has 10 rounds of operation, which usually takes 10 clock cycles to calculate in hardware implementation. Using AES-128 in side-channel authentication requires multiple traces to ensure authentication accuracy. Each execution requires a fresh plaintext. Furthermore, only the middle round of each trace is used in the authentication to prevent the security threat from conventional side-channel key recovery attacks.  
An easy alternative option is to use a modified AES that has more than 10 round operations, which is called a \(n\)-round AES. Here, \(n\) should be larger than 10 and big enough, e.g., \(n=1000\), so that a single side-channel trace is enough to perform the authentication. A \(n\)-round AES could simplify the system and the modification of the AES hardware could be minimized as well. To prevent security threats from conventional side-channel key recovery attacks, several rounds, e.g., 4 rounds, near the public data are not used in the \(r\)-round AES authentication.  
An illustration of 10-round AES and \(n\)-round AES is shown in Figure 1.