esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1) (GM (1,1)), optimized by moth-flame optimization (MFO) algorithm with rolling mechanism (Rolling-MFO-GM (1,1)), was put forward. The parameters \(a\) and \(b\) of GM (1,1) were optimized by employing moth-flame optimization algorithm (MFO), which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1) for annual electricity consumption prediction when compared with least square regression (LSR), GM (1,1), FOA (fruit fly optimization)-GM (1,1), MFO-GM (1,1), Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1). The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.  
annual electricity consumption; grey model (1,1) (GM (1,1)); Moth-flame optimizer (MFO); rolling mechanism; parameters optimization +
Footnote †: journal: Applied
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 4 Experiments and Result Analysis
### Implementation Details  
The experiments were conducted on a NIVIDA GeForce RTX2070 graphics board, using the Pytorch deep learning framework to build the network and CUDA and CUDNN provided by NVIDIA to train and test the dataset respectively. The experimental input image size is 48 \(\ast\) 48 and the different scale features were obtained through 3D convolution, then entered in the SE module to learn the importance of different channel features and finally obtaining the fatigue state classification results by full binary tree SVM classifier. We trained the model for 100 epochs and the model was trained using stochastic gradient descent (SGD) with an initial learning rate of 0.001 and a momentum of 0.9 during training.
## 3 Results  
### Relation of the Cutting Force and Rock Chips Generation  
The three-axis reaction force occurs when the cutting tool cuts the rock while moving parallel to the rock surface, the cutting force parallel to the cutting direction, normal force perpendicular to the cutting surface, side force perpendicular to the cutting direction and parallel to the cutting surface. Figure 6a shows the cutting, normal, and side forces generated during the experiment. The cutting force, which is related to the generation of rock fragments of various sizes (Figure 6b,c), decreases rapidly when a large rock chip is generated. This can be considered to indicate the generation of rock chips and small particles [6].  
Figure 7 shows the effect of the clearance angle and cutting force generated by cutting the rock with a UCS of 30 MPa. Overall, the maximum cutting force is generated when the clearance angle is \(0^{\circ}\), and decreases as the angle increases, with the minimum value occurring at \(15^{\circ}\).  
Figure 6: Cutting forces and rock chips generated during the experiment: (a) cutting forces during the generation of rock chips, (b) large rock chips, and (c) small rock chips.  
When cutting was performed using C00R20, as shown in Figure 7, the sharp drop point of the cutting force was relatively small compared with the other cases. During cutting by C05R20, the sharp drop point of the cutting force increased compared with that of C00R20, but no significant change occurred with the increase in the clearance angle. Therefore, when the clearance angle is 0\({}^{\circ}\), generating small fragments is the main factor in rock cutting, rather than generating large rock chips.  
This seems to be a phenomenon caused by the friction between the cutting tool and rock. If the clearance angle is 0\({}^{\circ}\), the contact surface between the cutting tool and rock increases significantly, and the friction force increases significantly. This friction makes the cutting surface wider and smoother and produces small and fine rock chips [13].  
The effect of friction can also be confirmed by the sharp decrease in the cutting force while generating rock chips. In the case of the cutting tool with a clearance of 5\({}^{\circ}\) or more, the cutting force decreased by 85% on average after the rock chips were generated. However, when the clearance was 0\({}^{\circ}\), the reduction was only 55%, owing to friction.  
The variation in the cutting force with respect to the rake angle is shown in Figure 8. Overall, the cutting force increases with the rake angle. However, the effect was small compared with that of the clearance angle. The rock chip produced by cutting did not change significantly despite the change in angle.
# Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis
## 3 Uncertainty Flow Framework
### II. Weakly Informative Priors  
The prior, which determines the first and second order statistics of model parameters, is de facto the driving force in bayesian learning. Hence, the proper specification of a model ties closely with the choice of an applicable prior for a given task. Unfortunately, the majority works on Bayesian learning pay overwhelmed attention towards the prior that are informative and conjugate for their analytical convenience, the family of uninformative and weakly informative priors had been largely ignored.  
Argued in [30], differ than conventional implementation on Bayesian neural networks with the common used informative prior, the usage of weakly informative prior, i.e., a semi-flat prior, yielded superior predicative performance in single-label discrimination. Therefore, it is rational to extend this finding in multi-label learning. The formal definitions of informative and weakly informative priors are rendered below, and their corresponding probabilistic density curves are plotted in Figure 2. Their differentiated effects on a simple simulation is shown in Figure 3.  
* Informative Prior
* Normal Prior \[\theta_{N}\sim N(\mu,\sigma^{2})\] (7) \[p(\theta_{N})=\frac{1}{\sqrt{2\pi\sigma^{2}}}exp(-\frac{1}{2 \sigma^{2}}(\theta-\mu)^{2})\] (8)
* Weakly Informative Prior
* Uniform Prior \[\theta_{U}\sim U(\alpha,\beta)\] (9) \[p(\theta_{U})=\frac{1}{\beta-\alpha}\] (10)
* Cauchy Prior \[\theta_{C} \sim Cauchy(\alpha,\beta)\] (11) \[p(\theta_{C}) =\frac{1}{\pi\beta(1+(\frac{x-\alpha}{\beta})^{2})}\] (12)Figure 3: Effects of Different Priors on Posterior in a Simulated Binary Classification. Note, here, the simulated posterior inferences were based on a simple three layer Bayesian neural network with five units in the hidden layer on a binary classification problem. Left panel of Figure 3 reflects how prior, i.e., denoted as red and blue dotted lines, transformed to the weight posterior, i.e., identified as green and light blue lines. The right panel of Figure 3 demonstrates the sampling value of yielded weight posterior, based on 500 posterior samples. Reflected by the separateness of yielded weight posterior, it is clear that Cauchy prior achieved the most discriminability compare to other two priors, e.g., Normal and Uniform priors.  
Figure 2: The Probabilistic Density Curves of Normal, Uniform, and Cauchy Priors. This graph demonstrates the probabilistic density curve for each of the prior applied in this research, i.e., Normal, Uniform and Cauchy priors. Here, the further specification of input-to-hidden, hidden-to-hidden priors are defined as the hierarchical shrinkage in variance of the corresponding priors.
# Method to Characterize Potential UAS Encounters Using Open Source Data
## 1 Introduction
### Motivation  
Mitigations for airborne collision risk and optimization of airspace operations are strongly dependent on the distribution of geometries and behavior of aircraft encounters. For example, collision avoidance systems are designed to determine, communicate, and coordinate avoidance maneuvers when they determine that a maneuver is needed to avoid a collision. These systems are the last and third layer for airspace conflict management and are employed after separation provision and strategic mitigation have failed. Collision avoidance systems may leverage vehicle-to-everything (V2X) communication technologies to improve performance and safety. How an aircraft behaves will influence the development of V2X routing protocols, link budgets, and energy requirements [8].  
Fast-time Monte Carlo simulations are often utilized to evaluate the performance of aviation safety systems, such as detect and avoid (DAA) for UAS, for close encounters between aircrafts [2; 3; 4]. The design and effectiveness of these simulations are dependent on how close encounters are defined. The relative geometries and separation between aircraft are important criteria when defining these encounters. Simulated encounters are generated based on what safety function is being evaluated and the performance or behavior of the aircraft involved. For example, encounters meant to promptcollision avoidance maneuvers may not be suitable to evaluate a system attempting to maintain well clear.  
Encounters and manned aircraft behavior have historically been based on an abundance of radar observations, with these models refined for decades [1; 2; 3]. However, as UAS are not routinely operating beyond visual line of sight, we cannot characterize UAS encounters using observed aircraft behavior. While independent trajectories of representative UAS behavior can be generated [2; 3], an alternative approach to characterize the geometry and feasibility of potential UAS vs. UAS encounters is required for the development and evaluation of DAA safety systems.
## 5 Conclusions  
The paper describes the possibilities of detection and classification of speed sensor faults in the PMSM control system based on MRAS, SMO, and neural networks. One of the estimators is responsible for the detection, while the neural structure is responsible for the classification. Detection is fast enough for the system to switch to sensorless mode even when the signal is lost. This made it possible to conduct experimental research. This is considered to be a significant advantage of the work, as most of the articles in the literature show only simulation results. Damage classification is an additional element of the monitoring system. It allows for obtaining practical information about the damage: the need to replace and change the scaling or calibration parameters. It can also indicate that the measured signal is disturbed. In advanced machine condition monitoring systems, such solutions are sought. Research shows a clear advantage of the SMO-based system. The estimation of the speed using the SMO system is more accurate. The obtained results also show the importance of conducting further research in the field of speed sensor faults classification with the use of neural networks. The next step in the authors' research will be the use of deep learning methods.  
Conceptualization, M.D. and K.J.; methodology, M.D. and K.J.; software, K.J. and V.P.; validation, M.D. and K.J.; formal analysis, M.D., K.J., V.P. and K.K.; investigation, M.D. and K.J.; resources, M.D. and K.J.; data curation, K.J.; writing--original draft preparation, K.J.; writing--review and editing, M.D., V.P. and K.K.; visualization, K.J.; supervision, M.D.; project administration, M.D.; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic under the project VEGA 1/0363/23 and from the statutory funds of the Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 17: Failure classification times for signal loss based on SMO (**a**) and MRAS (**b**) and signal interference based on SMO (**c**) and MRAS (**d**).
## 2 Results and Discussion
### GRD Is Not the Prominent Binding Domain for High IQGAP-CDC42 Affinity
#### 2.1.2 Endogenous IQGAP1 also Binds CDC42 GDP  
Serum-stimulated HEK293 cells, endogenously expressing IQGAP1 full-length (FL), were now used to carry out a pull-down assay with purified GST-fusion proteins of CDC42 and RAC1 in either GDP-bound or GppNHp-bound forms. IQGAP1 FL bound to these GTPases, regardless of their nucleotide status even though the binding to GDP-bound proteins was observed to be much weaker than the GppNHp-bound proteins (Figure 1C). This pattern corresponds to the binding behavior of C794 and not with the binding of GRD1 alone. Densitometric evaluation of three independent pull-down experiments showed that IQGAP1 FL binding to CDC42 GDP is much stronger than to RAC1 GDP (Figure 1C).  
Altogether, our data suggest that IQGAP1 forms a complex with CDC42 through different sites in both nucleotide-dependent and nucleotide-independent manner.
## 4 Congestion Mitigation Algorithms for WSNs
### Previous Work on WSN Congestion Mitigation Schemes
#### 4.1.1 Conventional Algorithms for Congestion Mitigation in WSN
In this technique, chunks of data are defined according to different priorities. SoNCF, a framework for self-organizing network coordination, was proposed in [57] In this algorithm, a pulse-coupled oscillator model is used for dynamic time partitioning. The problems with exposed and hidden nodes are solved using the proposed algorithm. Moreover, the relief from traffic congestion is to be distributed. Priorities for data packets were not considered in the study. WSNs play an important role in the IoT [58]. The Internet integrates 6 LoWPANs and WSNs. However, an Internet Protocol (IP) stack designed for wired networks is used to implement the sensor nodes [59]. However, the use of the Transmission Control Protocol IP (TCP/IP) standard in 6 LoWPAN networks and WSNs presents a number of issues and challenges, including buffer and energy resources and bandwidth limitations. In addition, User Datagram Protocol (UDP) lacks a congestion control mechanism at the end of data transmission, while TCP requires additional resources for connection establishment and termination.
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 3 Observation Model of Multi-Frame Blind Super-Resolution  
Classical MFSR methods assume that these low-resolution images are obtained from an unknown high-resolution image after warping, blurring, down sampling and noise. Therefore, the observation model of multi-frame blind super-resolution can be expounded as  
\[\mathbf{y_{n}}=\mathbf{D}\mathbf{K_{n}}\mathbf{F_{n}}\mathbf{x}+\mathbf{\varepsilon_{n}}, \tag{1}\]  
where \(\mathbf{y_{n}}\) represents the \(nth\) LR image, \(n=1,2,\ldots,N\). \(N\) is the number of LR images. \(\mathbf{D}\) represents the down sampling matrix. \(\mathbf{K_{n}}\) represents the blur matrix of the \(nth\) LR image. \(\mathbf{F_{n}}\) represents the sub-pixel displacement motion matrix of the \(nth\) LR image. \(\mathbf{x}\) is the HR image. \(\mathbf{\varepsilon_{n}}\) is the noise of the \(nth\) LR image. The observation model of MFSR is shown in Figure 2. Mathematically, \(\mathbf{x}\) needs to be calculated, \(\mathbf{y_{n}}\) and \(\mathbf{D}\) are known, \(\mathbf{K_{n}}\), \(\mathbf{F_{n}}\) and \(\mathbf{\varepsilon_{n}}\) are unknown in Equation (1). The solution is unstable and not unique. Therefore, MFSR is an ill-posed inverse problem. It is unlikely that the calculated HR image is completely consistent with the real HR image, due to various unknown factors. Super resolution reconstruction can only make the reconstructed HR image quality as good as possible.  
\(\mathbf{K}_{n}\), \(\mathbf{F}_{n}\), \(\mathbf{x}\) can be estimated in the Bayesian MAP framework as  
\[\begin{split}\hat{\{\mathbf{K},\hat{F},\hat{x}\}}=\operatorname*{ argmax}_{\mathbf{K},\mathbf{F},\mathbf{x}}p(\mathbf{K},\mathbf{F},\mathbf{x}|\mathbf{y_{1}},\mathbf{y_{2}},\dots,\mathbf{y_{N}}) \\ =\operatorname*{argmax}_{\mathbf{K},\mathbf{F},\mathbf{x}}p(\mathbf{y_{1}},\mathbf{y_ {2}},\dots,\mathbf{y_{N}}|\mathbf{K},\mathbf{F},\mathbf{x})p(\mathbf{x})p(\mathbf{K})p(\mathbf{F}),\end{split} \tag{2}\]  
where \(\mathbf{p}(\mathbf{x})\) represents the image prior, \(\mathbf{p}(\mathbf{K})\) represents the blur prior, \(\mathbf{p}(\mathbf{F})\) represents the motion prior.
# Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization
## 4 Proposed Distributed Extended Kalman Filter Based 3D Jammer's Localization Schemes
### Distributed Extended Kalman Filter (DEKF) for 3D Localization
Thus, the first derivative of jamming power with respect to jammer's position at time \(k\) becomes as follows:  
\[\frac{\partial P_{IB}}{\partial x_{I}}\Big{|}_{\tilde{x}_{I,k-1}} =C\frac{x_{B}\widehat{x}_{I,k-1}}{(x_{B}+\widehat{x}_{I,k-1})^{2} +(y_{B}+\widehat{y}_{I,k-1})^{2}+(z_{B}+\widehat{z}_{I,k-1})^{2}} \tag{27}\] \[\frac{\partial P_{IB}}{\partial y_{I}}\Big{|}_{\tilde{y}_{I,k-1}} =C\frac{y_{B}\widehat{y}_{I,k-1}}{(x_{B}+\widehat{x}_{I,k-1})^{2} +(y_{B}+\widehat{y}_{I,k-1})^{2}+(z_{B}+\widehat{z}_{I,k-1})^{2}}\] (28) \[\frac{\partial P_{IB}}{\partial z_{I}}\Big{|}_{z_{I,k-1}} =C\frac{z_{B}\widehat{z}_{I,k-1}}{(x_{B}+\widehat{x}_{I,k-1})^{2} +(y_{B}+\widehat{y}_{I,k-1})^{2}+(z_{B}+\widehat{z}_{I,k-1})^{2}} \tag{29}\]  
where \(C\) is a constant given by:  
\[C=\frac{10n}{ln10} \tag{30}\]  
Hence, the proposed DEKF for the three-dimensional (3D) localization can be implemented while using the Equations (13)-(17) with the aid of Equations (20), (25)-(30).
## 4 Security Attacks
### Consensus and Incentive Layer
#### 4.3.4 Finney Attack  
A Finney attack [27; 28; 29; 31; 42] is a variant of the race attack that requires the attacker to control significant mining power. In this scenario, the attacker pre-mines a block containing a double-spending transaction but withholds the block from immediate broadcast. Instead, the attacker first uses the funds from the unbroadcast transaction to make a purchase from a merchant. After the goods or services have been received, the attacker then releases the pre-mined block, which includes the conflicting transaction, thereby effectively reversing the original payment. This method not only relies on network propagation delays but also on the attacker's ability to manipulate block creation and confirmation timing. As with race attacks, increasing the waiting time for multiple confirmations before finalizing transactions can help mitigate the risk of Finney attacks.
# Security and Independence of Process Safety and Control Systems in the Petroleum Industry
## 5 Measures to Resist Cyberattacks
### Zero Trust Versus Shell Protection  
In the early days of the computer age, computers were monolithic colossuses that filled entire rooms, and the only form of communication was done in the form of stacks of punch cards that were carried in and out. However, the world moved on, and modern computer networks saw the light of day.  
At one point, computers from different organizations began to be connected, and when the Internet became public in the late 1990s, anyone could connect to any computer system. This also left the way open to attackers, and many organizations found that securing each machine was difficult. Thus, shell protection of local area networks was implemented in the form of a firewall; to quote [PERSON]: \"a hard shell around a soft, chewy center\" [33].  
In recent years, shell securing with firewalls in IT networks has proved challenging, partly because a number of services now require the creation of \"holes\" in the firewall for them to work, partly because today there are so many mobile devices that are brought  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Field** & **Explanation** \\ \hline DataSetWriterId & Identifies the DataSetWriter and in the publishedDataSet. \\  & A number that is increased for each \\ Sequence number & DataSetMessage. Can be used to verify the \\  & ordering and to detect missing messages \\  & A timestamp describing when the data in this \\ Timestamp & DataSetMessage was obtained. \\  & Version information about the configuration of \\ Version & the DataSetMetaData. \\  & Status information about the data in this \\ Status & DataSetMessage. \\  & When no DataSetMessages are sent for a \\  & configured time period, a keep alive \\ Keep alive & DataSetMessage is sent to signal the \\  & Subscribers that the Publisher is still alive. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data Fields in DataSetMessage.  
Figure 3: PubSub without active broker with data diodes.  
into corporate networks, and partly because the use of cloud computing has increased to a great extent. Thus, a new trend that almost brings us back to the starting point has become popular: zero trust computing [34, 35].  
The concept is that security should no longer be based on shell protection where all devices inside the firewall are trusted but instead require all devices to authenticate themselves to any other device with which they mean to interact. Furthermore, all devices must then also be authorized in order for them to be allowed to interact with a given device.  
Zero Trust computing requires mechanisms for key distribution and key management in place; it often involves a PKI, although there are also other ways to do it. This also means that one must rely on the use of cryptographic mechanisms in OT networks that would use this.