esa-sceva/satcom-chunk-collection huggingface dataset:
# Dilated Multi-Temporal Modeling for Action Recognition
## 3 Methods
### Module Design
#### 3.2.1 Multiple Dilations  
It is intuitive to utilize different receptive filed blocks to capture the multi-scale temporal context: small ones for short context and large ones for long context. However, directly improving the size of the kernel to capture long context will induce the problem of high computational cost and massive parameters. Based on [11], dilated convolutions effectively increase the receptive field size without added parameters or extra computation. Thus, we argue that time-dilated convolutions can also extend the receptive field in the temporal dimension and can use dilated CNNs to extract the long-range temporal features. Furthermore, this characteristic of dilation motivates us to adopt CNNs with different dilation rates to model different temporal information--small rates for short times and bigger rates for long times.  
As shown in Figure 3, two convolutional operations--depthwise convolutions--with different dilation rates in the temporal dimension aggregate different temporal information. Such operations can be describe as follows:  
\[Y=Conv(X,K,D), \tag{1}\]  
where \(Conv\) denotes the convolutional operation, \(X\) is the input, and \(D\) means the dilation rate of the convolution. \(K\) is a dynamic kernel for each input \(X\) generated by the global branch module [27]:  
\[K=G(X)=softmax(f(W_{2},ReLU(f(W_{1},\Phi(X))))), \tag{2}\]  
where \(\Phi\) denotes the function that aggregates the spatial information by pooling, \(f\) is the fully connected layers, and \(G\) denotes the global module, which generates an adaptive kernel based on the whole temporal information.  
Thus, we can describe it as follows:  
\[Y=Conv(X,G(X),D). \tag{3}\]  
We can set different dilation rates, namely \(D\), to model the different temporal scale information without increasing the size of the kernel. A small dilation rate 1 is used to capture short-range temporal information, while a large dilation rate 2 is used to capture long-range temporal information.  
Multiple scale temporal information is extracted using dilated convolutions with varied dilation. This allows the network to capture information at different scales, with each scale focusing on a different rate of motion. For example, a large-scale receptive field will be sensitive to slow movements, while a small one will be more responsive to rapid changes in the scene. By combining the multi-temporal information from different scales, the network can effectively capture the dynamic motion of the object over time.  
Figure 3: Multiple dilations. The utilization of dilated convolutions allows for an increase in the receptive field size without incurring additional cost. DMT employs different dilation rates for the  
dilated convolutions to capture various temporal information. Specifically, (**a**) a small dilation rate of 1 is used to capture fine-grained temporal information, while (**b**) a larger dilation rate of 2 is utilized to capture more coarse-grained temporal information.
## 3 The Optimization Algorithm Used in the Identification Process
### Description of the HS-JAYA Algorithm
#### 3.2.1 Step 1: Initialization of Optimization Algorithm and Formation of Initial Population  
A population of \(N_{\text{POP}}\) candidate solutions for the inverse problem is randomly generated as follows:  
\[x_{j}^{k}=x_{j}^{L}+{\rho_{j}}^{k}\left(x_{j}^{L}-x_{j}^{L}\right)\begin{cases} j=1,\dots,NDV\\ k=1,\dots,N_{POP}\end{cases} \tag{14}\]  
where \(j^{k}\) is a random number in the (0, 1) interval. The error functional is computed for each of the \(N_{\text{POP}}\) solutions stored in the initial population.  
_HMCR_ and _PAR_ adaptively change in the optimization process, while \(bw\) is not necessary because new trial solutions have to lie on descent directions. Hence, the HS branch of HS-JAYA becomes inherently similar to enhanced JAYA formulations in the sense that it requires only population size to be specified by the user.  
Different from most of the currently available HS and JAYA formulations, it is no longer necessary to define a limit number of iterations or function evaluations for HS-JAYA. In fact, HS-JAYA always forms trial designs trying to improve current best record and the rest of population by moving along descent directions. This is performed by simultaneously perturbing as many variables as possible as well as suboptimal designs.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 3 Efficient Fully Homomorphic Encryption Scheme
### Optimization Based on Multi-Bit Encryption  
In the GSW13 scheme and the NFHE scheme, although the plaintext messages are all \(\mu\in\{0,1\}\), the GSW13 scheme cannot support multi- bit encryption under the condition that the system parameters remain unchanged [17]. In addition, the NFHE scheme adopts the following modifications to realize multi-bit encryption without changing system parameters.  
Encryption Algorithm NFHE.Encrypt(\(pk,\mu\)). For plain- text \(\mu\in\mathbb{Z}_{p}\), uniformly select \(\mathbf{r}_{i}\), \(e_{i,1}\leftarrow\chi^{n}\), \(e_{i,2}\leftarrow\chi\), and \(i=1,\cdots,(n+1)\cdot t\) at random. Calculate \(C_{i,1}=\mathbf{A}^{T}\cdot\mathbf{r}_{i}+e_{i,1}\in\mathbb{Z}_{q}^{n}\) and \(C_{i,2}=\mathbf{b}^{T}\cdot\mathbf{r}_{2}+e_{i,2}\in\mathbb{Z}_{q}\). Let \(\mathbf{C}^{\prime}\) be a matrix formed by arranging \(m=(n+1)\cdot t\) ciphertexts as column vectors, the dimension of which is \((n+1)\times m\). Then output the ciphertext \(\mathbf{C}=\text{mbFlatten}\big{(}\mathbf{\mu}\cdot\mathbf{I}_{N}+\text{mbDpt}\big{(}\mathbf{C }^{\prime}\big{)}\big{)}\in\mathbb{Z}_{p}^{m\times m}\).  
Decryption algorithm NFHE.Decrypt(\(\mathbf{sk},\mathbf{C}\)). For ciphertext \(\mathbf{C}\in\mathbb{Z}_{p}^{m\times m}\) and private key \(\mathbf{sk}=\left(\begin{array}{c}-s\\ 1\end{array}\right)\in\mathbb{Z}_{q}^{n+1}\), let \(\mathbf{s}^{\prime}=\text{pofmb}(\mathbf{sk})\), and calculate and output plain-text \(\mu=\left\lfloor\langle s^{\prime},C_{m-1}\rangle/\left(q/2p\right)+\frac{1}{2 }\right\rfloor\text{mod}p\).  
When not performing homomorphic operation, if the plain-text message is \(\mu^{\prime}\), according to the encryption/decryption process, there will be \(\mu=\left\lfloor\left\langle s^{\prime},C_{m-1}\right\rangle/\left(q/2p\right)+ \frac{1}{2}\right\rfloor\text{mod}p=\left\lfloor n^{\prime}+e/\left(q/2p\right) +\frac{1}{2}\right\rfloor\text{mod}p\) after decryption. When \(\left|e/\left(q/2p\right)<1/2\right|\), there will be \(\mu=\mu^{\prime}\), which can be decrypted correctly. For homomorphic addition, there will be \(\mathbf{C}=\text{mbFlatten}((\mu_{1}+\mu_{2})\cdot\mathbf{I}_{N}+\text{mbDmp}(C_{1}+ C_{2}))\) and \(\text{NFHE.Dec}(\mathbf{sk},\mathbf{C})=(\mu_{1}+\mu_{2})\text{mod}p\). For homomorphic multiplication, there will be \(\mathbf{C}\cdot\mathbf{sk}=\mu_{1}\cdot\mu_{2}\cdot\mathbf{sk}+\mu_{2}\cdot\mathbf{e}_{1}+C_{ 1}\cdot\mathbf{e}_{2}\) and \(\text{NFHE.Dec}(\mathbf{sk},\mathbf{C})=\mu_{1}\cdot\mu_{2}\). Therefore, it can be decrypted correctly.  
After homomorphic multiplication, since the coefficients of \(\mu_{2}\) and \(C_{1}\) are both limited to \(\mathbb{Z}_{p}\), the noise does not exceed \(pN+p\) times of the original ciphertext. Therefore, when performing multi-bit encryption, the noise limit of Theorem 2 becomes \(|\left\langle C_{m-1},s^{\prime}\right\rangle|<q/\left[4p(pN+p)^{L}\right]\). In addition, due to \(pN=pkt\gg p\), the effect of this change on modulus \(q\) can be ignored.
## 6 Conclusions  
In this paper a classification model was trained that is able to predict AEs at least 1 min in advance. It was shown that time series features/characteristics can improve the model's performance  
Figure 8: Lead time of prediction.  
Figure 7: Distribution of false positives dependent on potnumber.  
significantly. Since AEs occur rarely and a time base of 1 min was chosen, the dataset was extremely imbalanced. A combination of undersampling and cost-sensitive algorithms was proposed to solve this problem. The F1 score of the best model, namely a Random Forest Classifier is about 29.3%. A post analysis showed that disabling removing false positives if the prediction is up to 30 min in advance and disabling the model during events can increase the F1 score to 32.4%. Nevertheless in comparison to Reference [16] the F1 is low and needs to be improved. Including a low frequency and high frequency filtered noise signal, which will be available in future may improve the score. In future also live tests should be performed to prove if the lead time is sufficient and in general which actions like alumina feeding or beam downward movements are promising to prevent anode effects. In addition it needs to be examined if the amount of false positive are detrimental for the pot's condition. For example a pot might be in an underfeed phase, right before turning into overfeed phase. An additional feeding to prevent the anode effect might not create any disadvantages, for example, due to sludge accumulation. But still there are open questions. Like [PERSON] et al. [5] said \"a cell having AEs is synonymous to having a problem\", this would mean that preventing AEs only does not fix the root cause. However the symptoms of harmful PFC gases and reduced energy and current efficiency may be avoided for the most part.  
Conceptualization, R.K. and N.G.; methodology, R.K.; software, R.K.; validation, R.K.; formal analysis, R.K.; investigation, R.K. and A.M.; resources, R.K. and A.M.; data curation, R.K.; writing--original draft preparation, R.K.; writing--review and editing, R.K., N.G., R.D., A.M. and D.T.; visualization, R.K.; supervision, D.T.; project administration, D.T.; funding acquisition, R.K., N.G. and D.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Bundesministerium fur Bildung und Forschung (03 SFK3B1-2).  
Authors would like to thank their industrial partner, TRIMET Aluminium SE for collaboration on this project.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TAE & TRIMET Aluminium SE Essen \\ AE & anode effect \\ PFC & perfluorocarbon \\ ACM & anode current measurement \\ LVP-AE & low voltage propagating anode effects \\ NP-AE & non-propagating anode effects \\ IR & imbalance ratio \\ KEEL & knowledge extraction based on evolutionary learning \\ \end{tabular}
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Adaptive  
This section presents two adaptive routing schemes for FANETs.
# A Micro-Segmentation Method Based on VLAN-VxLAN Mapping Technology
###### Abstract
In 2010, [PERSON], an analyst at Forrester, formally proposed the concept of \"zero trust\". In 2016, Kindervag proposed the Zero Trust Architecture (ZTA) as a means of addressing the overlinace on trust in traditional cybersecurity architectures [6]. However, one of the major challenges with the ZTA has been security within cloud data centres, particularly in relation to the east--west traffic, which refers to the communication between servers within the cloud. To overcome this challenge, our paper proposes a micro-segmentation approach based on VLAN and VxLAN mapping. By implementing our approach, boundaries can be established for each cloud host, effectively eliminating blind spots in the ZTA. Our approach is hardware-brand agnostic, easy to implement, and has minimal service interruptions, making it suitable for scenarios with high real-time service demands. We demonstrate the effectiveness of our approach through a comprehensive evaluation of the degree of network aggregation and the share of visible traffic. Moreover, we propose an enhanced asset behavioural profiling algorithm that considers the stability and periodicity of intranet asset behaviour. The algorithm incorporates a time dimension into the traditional trust assessment model, and experimental results demonstrate its superior performance. In summary, the contributions of our paper are as follows:  
* We propose a hardware-brand agnostic micro-segmentation approach based on VLAN and VxLAN mapping that effectively removes blind spots in the \"zero trust\" architecture with minimal service interruptions and high suitability for real-time service demands. * We demonstrate the effectiveness of our approach through comprehensive evaluation of network aggregation and the share of visible traffic. * We design an enhanced asset behavioural profiling algorithm with a time dimension that significantly improves traditional trust assessment models. This paper is structured as follows. In Section 2, we review the related literature. Section 3 outlines our proposed method, while Section 4 provides an analysis of the experimental results. We conclude with a summary of our findings and future research directions in Section 5.
## 3 Experimental Results
### Analysis of the Degree of River Blockage  
The degree of river blockage can be divided into two cases: partial blockage and complete blockage. There were 60% partial blockages and 40% complete blockages in these tests. The influences of slide angle, landslide volume, and particle size on the degree of river blockage are summarized from the test data, as shown in Figure 17.  
The degree of river blockage increases significantly with increasing slide angle, landslide volume, and particle size. As shown in Figure 17, the degrees of river blockage for the conditions of slide angles of 35\({}^{\circ}\) and 40\({}^{\circ}\) are 10% and 20% higher, respectively, than those of 30\({}^{\circ}\). The increase in slide angle transforms more gravity-potential energy from landslides into kinetic energy when the landslide materials enter the river. When the landslide materials move slowly in the chute, it is easier for a large portion of the landslide materials to stay on the chute. Figure 17 shows that when the landslide volume or particle size increases, the degree of river blockage also increases significantly. The increase in landslide volume could promote the formation of landslide dams. When the material source is sufficient,  
Figure 16: The variation in velocity entering the river.  
Figure 17: Variations in river blockage degree with slide volume under different conditions of slide angle and particle size.  
the landslide is more likely to form a landslide dam, completely blocking the river. As the particle size increases, the landslide will enter the river at a higher speed without staying in the chute, and the degree of river blockage will increase.
## 5 Discussion and Practical Tests  
### Volatility spillover Effect: Intensity and Direction  
In addition to research modeling, this study uses the volatility-correlation relationships to re-detect the intensity and direction of the volatility spillover effect in carbon-energy markets (see Section 2.2). Figure 2 graphs the estimated probabilities of various volatility states derived by the bivariate SWARCH model. To identify the specific state for each point in time, we use a maximum value criterion. Specifically, if the HV-HV state's estimated probability is higher than that of the other three states, we define this point in time as an HV-HV state. Next, the observation percentage for each volatility state is calculated and presented in Table 6. First, the state of EUA = LV and WTI = LV (LV-LV) is the most common (64.54%), whereas the state of EUA = HV and WTI = HV (HV-HV) is the most uncommon (4.50%). Last, 19.98% of the observations are identified as the state of EUA = HV and WTI = LV (HV-LV) and the state of EUA = LV and WTI = HV (LV-HV) is identified in 11.00% of our observations. Next, we conduct two comparative analyses to examine the intensity and direction of the volatility spillover effect. The first comparative analysis is based on LV-LV versus HV-HV. Under the LV-LV state, the volatility spillover effect becomes minimal because both EUA (carbon market) and WTI (energy market) face a low-volatility regime. Thus, the carbon-energy correlation is mainly driven by the fundamental channel. On the other hand, the volatility spillover effect becomes maximal under the HV-HV state. Accordingly, the carbon-energy correlation under the HV-HV state reflects both the fundamental channel and the volatility spillover effect. Returning to Table 5, \(\rho_{1,1}\) is 0.1751 (or 17.51%) and \(\rho_{22}\) is 0.3609 (or 36.09%). The rise in the correlation from the LV-LV state to the HV-HV state reflects the intensity of the volatility spillover effect in carbon-energy markets.
## 2 Theoretical Model
### Friction Coupling Model  
According to fluid flow conditions, friction models can be classified into the steady state friction model and the unsteady state friction model. The steady state friction model is commonly used to one-dimensional and steady flow state, on the contrary, the unsteady state friction model is adopted when the fluid is under turbulent flow state. The unsteady state friction models can be divided into two types: the quasi-steady state friction model based on the quasi-steady assumption and the uncconstant friction model.  
The uncconstant friction model includes one-dimensional and two-dimensional uncconstant friction model. Depending on the mechanism, one-dimensional uncconstant friction model mainly includesthree types: the empirical equation model represented by [PERSON] model based on instantaneous acceleration [23], the weighted function model represented by the Zielke model when considering the influence of historical speed and historical acceleration on the flow state [15] and the irreversible thermodynamic model represented by Axworthy model [27].
# Cybersecurity Comparison of Brain-Based Automotive Electrical and Electronic Architectures
## 4 Architectures
### One-Brain (Centralized) E/E Architecture
#### Disadvantages of One-Brain Architecture:  
Figure 11: Use Case Representation of One-Brain Architecture.  
Figure 12: Realization of One-Brain Architecture in Medini Ansys Medini Analyze.  
* Earlier handling of the hardware/software architecture is required. The hardware and software architecture should be defined during the concept phase of the vehicle. For example, the performance requirement of the HPC (Memory Capacity, Number of Cores, Number of Boards, etc.) needs to be calculated considering the number of functions to be hosted on the HPC in the future, which is sometimes difficult to predict.
* Since this is new type of architecture, all of the stakeholders (OEM, Tier1 Supplier, Tier2 Supplier) who are involved in the development of the architecture need to understand the architecture in detail. This requires a huge communication overhead.
* The failure of the centralized Brain will lead to the whole vehicle to be non-functional.
# A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform
## 3 Materials and Methods  
The workflow of the study is shown in Figure 2. The stages involved collecting retinal images from public data, preprocessing the images, designing the model, training, and validating the model with fine-tuning, and testing the model with an external dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Model** & **Dataset** & **Advantage** & **Disadvantage** \\ \hline
[19] & DenseNet121 & APTOS, EyePACS, ODIR & Able to classify based on different datasets. & The stage of DR is not detected. \\ \hline
[51] & CNN & Kaggle & The model achieves an accuracy of 89\%. & The model needs to be tested on different datasets. \\ \hline
[52] & VGG16 & IDRiD & The model could detect different stages of DR. & The model was tested on very few images. \\ \hline
[53] & Vision Transformer & DDR, IDRiD & The model could detect different stages of DR. & The model has a class imbalance problem and tests on a few images. \\ \hline
[54] & ResNext, DenseNet & APTOS & The model classifies the DR with high performance. & The model has a class imbalance problem and tests on a single dataset. \\ \hline
[55] & Capsule network & Messidor & The model detects the stages of DR. & Only four stages are detected and tested only on a single dataset. \\ \hline
[56] & InceptionV3, Resnet50, CNN & Messidor, IDRiD & The model classifies the DR with high performance. & The features are extracted using only two pretrained models. \\ \hline
[35] & U-Net & APTOS, Messidor & The model segments and detects the stages of DR. & Only four stages are detected, and parameter tuning can be performed. \\ \hline
[57] & EfficientNet, VGG16, InceptionV3 & APTOS & The model classifies DR after CLAHE preprocessing. & The stage of DR is not detected, and only a single dataset is evaluated. \\ \hline
[58] & InceptionV3 & APTOS & The model classifies DR stages after CLAHE preprocessing. & The model classifies DR \\ \hline
[59] & Swin Transformer & APTOS & The model classifies DR with high performance. & The stage of DR is not detected, and the model can be tuned with more datasets. \\ \hline
[43] & VGG16 & APTOS, Mauritius & The model detects the stages of DR. & The model needs to be tuned for moderate and proliferative DR. \\ \hline
[45] & Wavelt with Swin Transformer & APTOS & The classification accuracy was improved. & The study utilized only a single image set for testing the model. \\ \hline
[48] & DWT with KNN, SVM & Messidor & The model classifies the normal and DR images perfectly. & The stage of DR is not detected, and the dataset contains fewer samples. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inferences from previous studies related to the classification of DR stages.