esa-sceva/satcom-chunk-collection huggingface dataset:
# GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
## 6 Conclusions  
In this work, we proposed a Generative Convolutional Vision Transformer (GenConConViT) that extracts visual artifacts and latent data distributions to detect deepfake videos. GenConViT combines the ConvNeXt and Swin Transformer architectures to learn from the local and global image features of a video, as well as an AE and VAE to learn from a video's internal data representations. Our approach provides an effective solution for identifying fake videos while preserving the integrity of media. Through extensive experiments on a diverse range of datasets, including DFDC, FF++, DeepfakeTIMIT, and Celeb-DF (v2), our GenConViT model demonstrated an improved and robust performance with high classification accuracy, F1 scores, and AUC values. Our ablation study reveals challenges in generalizing to unseen or more complex manipulations, highlighting the need for further research to improve its domain adaptability. Overall, our proposed GenConViT model is a promising approach for accurate and reliable deepfake video detection. As our model is open-source, its practical use has already been demonstrated: it was used by TrueMedia.org, a non-profit organization, to detect deepfakes and support fact-checking efforts [88].  
Conceptualization, D.W.D., S.A. and Z.A.; Methodology, D.W.D., H.M., S.A. and Z.A.; Software, D.W.D.; Validation, D.W.D., H.M. and G.V.W.; Formal analysis, D.W.D. and H.M.; Investigation, D.W.D., H.M., P.L. and G.V.W.; Resources, D.W.D., P.L., S.A. and G.V.W.; Data curation, D.W.D. and H.M.; Writing--original draft, D.W.D.; Writing--review and editing, D.W.D., H.M. and G.V.W.; Visualization, D.W.D.; Supervision, H.M., P.L. and G.V.W.; Project administration, H.M., P.L., S.A. and G.V.W.; Funding acquisition, P.L., S.A. and G.V.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Addis Ababa University Research Grant for the Adaptive Problem-Solving Research. Reference number RD/PY-183/2021. Grant number AR/048/2021, and the Research Foundation--Flanders (FWO under project grant G0A2523N), the Flemish government (COM-PRES project, within the relanceplan Valamse VeerKracht), IDLab (Ghent University--imec), Flanders Innovation and Entrepreneurship (VLAIO), and the European Union.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to third party copyrights.  
The authors declare no conflicts of interest.
## 3 Evaluation of the Measured Values  
Experiments were carried out to determine the influence of the samples building orientation and deposition strategy on the coefficient of friction course and wear behavior. Three samples were made with each type of fiber orientation. A total of 30 samples were tested with a tribometer according to ASTM G65-16 (dry sand).  
Figure 5: The series of samples used in the experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Building** \\ **Orientation** \\ \end{tabular} & \begin{tabular}{c} **Sample Type** \\ \end{tabular} & \begin{tabular}{c} **Initial Weight** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **Weight Loss (g)** \\ \end{tabular} & \begin{tabular}{c} **Weight Loss (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} **X** \\ \end{tabular} } & **1** & 13.9903 & 0.0729 & 0.520 \\  & **2** & 14.0028 & 0.0847 & 0.605 \\  & **3** & 13.8603 & 0.134 & 0.967 \\  & **4** & 13.8683 & 0.0641 & 0.462 \\  & **5** & 13.9822 & 0.0631 & 0.451 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Z** \\ \end{tabular} } & **6** & 13.9439 & 0.0933 & 0.669 \\  & **7** & 14.5401 & 0.1143 & 0.786 \\ \cline{1-1}  & **8** & 14.5558 & 0.1177 & 0.809 \\ \cline{1-1}  & **9** & 14.4343 & 0.1074 & 0.744 \\ \cline{1-1}  & **10** & 14.5100 & 0.0988 & 0.681 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of samples’ initial weights and weight losses.  
and different only by individual layers. This loss is expressed numerically in Table 7 and is represented graphically in Figure 7. Comparing the layers, it can be confirmed that the layers parallel to the direction of wear show a greater surface resistance to abrasion.  
The graphs in Figures 6 and 7 for the X-orientation clearly show that fiber orientation has a significant effect on the abrasive wear of Samples 1-5. If the fibers are laid in the same direction in all layers, this results in a greater loss of material, as shown in the graph in Figure 6. Sample 3, with the fiber deposition strategy under 0\({}^{\circ}\) and, thus, parallel to direction of wear in both the even and the odd layer, with a material loss of 0.134 g, showed the most significant wear. Due to a change in the fiber deposition strategy, a smaller weight loss was recorded in Sample 2 under a 45\({}^{\circ}\) fiber deposition strategy with the same tilt in the even and the odd layer with a material loss of 0.0847 g, and in Sample 1 under a 45\({}^{\circ}\) angle of fiber deposition strategy with opposite slope in the even and the odd layers, with a material loss of 0.0729 g. Sample 5 (with the strategy of fiber deposition parallel to the direction of wear in even layers and perpendicular to the direction of wear in odd layers) with a material loss of 0.0631 g and Sample 4 (with the strategy of fiber deposition perpendicular to direction of the fibers) with a material loss of 0.0641 g showed the highest resistance of the material to abrasive wear in terms of orientation of fibers in even and odd layers.  
The difference in weight between the largest and smallest weight loss, i.e., between Samples 3 and 5, is 0.0709 g.
## Appendix A Model Description
### Phase-Equilibrium Model
The equilibrium constant (\(logK_{p}\)) at standard conditions and the standard heat of reaction (\(\Delta_{r}H^{0}\)), was used in the van't Hoff equation [22] to determine temperature dependence of the equilibrium constant, for the dissolution reactions of phases used in the simulation which are tabulated in Table 1. The name of the phase (defined by dissolution reaction, \(logK_{p}\), and \(\Delta_{r}H^{0}\), as given in Table 1), the specified saturation index (which has a value of zero for equilibrium), and the amount of the phase were the input parameters for the phase-equilibrium module in PHREEQC. The model is described in our previous works [21; 22; 23; 24; 25; 26]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **Reactions** & **log\(K_{p}\)** & \(\Delta_{r}H^{0}\)** & **Ref.** \\ \hline \hline \multirow{2}{*}{Anhydrite} & CaSO\({}_{4+}\) Ca\({}^{2+}\) + SO\({}_{4}\)\({}^{2}\) & \(-\)4.36 & \(-\)1.71 & [23] \\  & Mg(OH)\({}_{2}\) + 2H\({}^{+}\) + Mg\({}^{2+}\) + 2H\({}_{2}\)O & 17.07 & \(-\)115.66 & [27] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermodynamic properties of the phases at 25 \({}^{\circ}\)C used in the simulation.
## 3 Proposed Scheduling Scheme
### Probabilistic Balancing
```
Function ProbabilisticBalancing (\(q_{0},r_{0},S,N.M,r_{n},v_{n}\)) Input : \(q_{0},r_{0},S,N.M,r_{n},v_{n}\) Output: \(\rho\)(probability factor) Step 1: Probabilistic Balancing \(\rho\leftarrow\frac{\mathrm{ap}([S+r_{0}]_{S}(N-M)]}{r_{n}+v_{n}}\) Step 2: Construct a probabilistic balancing tree tree \(\leftarrow\) ConstructProbabilisticTree(\(q_{0}\)) Step 3: Perform probabilistic balancing for each flow in flow_demands do  root \(\leftarrow\) tree.root  while not leaf_node(root) end Step 3: Evaluate congestion on the left and right child nodes  left_congestion \(\leftarrow\) EvaluateCongestion(root_left_child, balanced_flows)  right_congestion \(\leftarrow\) EvaluateCongestion(root_right_child, balanced_flows) Step 4: Compute probabilities based on congestion levels  left_probability \(\leftarrow\) 1 - right_congestion/(left_congestion + right_congestion)  right_probability \(\leftarrow\) 1 - left_congestion/(left_congestion + right_congestion) Step 5: Probabilistically choose the next node to traverse if RandomChoice(left_probability) do  root \(\leftarrow\) root_left_child else  root \(\leftarrow\) root_right_child endif Step 6: Update flow on the leaf node  UpdateFlow(root, flow, balanced_flows)  return balanced_flows endfunction
```  
**Algorithm 2**Probabilistic Balancing for SDN Data Transmission
# Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data
## 3 Local Outlier Factor
### Implementation
#### 3.2.1 Flight Data  
Flight data, a type of multivariate time series used for this analysis, are available in the public domain and are provided by NASA [19]. The entire flight data were disidentified to protect the identities of the airlines as well as the flight crew involved during the actual flights. The flight data used for this study are from three specific aircrafts (identified by the tail number). These aircrafts were flown over different destinations over a given period. In total, there were 186 flight parameters recorded by the various onboard sensors.
# Interference Avoidance Using TDMA-Beamforming in Location Aware Small Cell Systems
## 2 System Modelling and Problem Formulation
### Problem Formulation  
Channel and interference in dense environments are unpredictable especially in random deployment scenarios where cells are distributed uniformly, which increases the complexity of channel estimation and interference cancellation processes. Omni-directional antennas (_wider beamwidths_) in conventional wireless communication systems have higher interference levels due to the reception from multiple angles. However, antennas in beamforming environments have narrow beamwidths and reception is limited to specific angles from the user point of view. Therefore, if the user has certain knowledge of the arrival angles of transmitted signals, it would curb down huge levels of interference.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Service area & 100 m \(\times\) 100 m \\ \(\mathcal{P}^{-}\)[29] & 23 to 33 dBm \\ Frequency & 3.5 GHz \\ SINR threshold [30] & 2.2 dB \\ Antenna elements & 6 \\ Elements type & Patch \\ Inter-element spacing & \(\frac{\lambda}{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: Simulation settings.  
Figure 1: Polar beam pattern of an array antenna that has 6 elements, inter-element spacing = \(\frac{\lambda}{2}\), and \(\delta=0^{\circ}\).  
Nevertheless, it increases the complexity of the system and addresses more challenges such as the synchronisation issue.  
The ratio of the desired signals in comparison to other channel factors is familiarly described by the SINR metric that is obtained by the following [31]:  
\[SINR_{sn}=\sum_{\xi\in\mathcal{K}}\frac{\mathcal{P}_{i\xi\xi\xi}^{+}\mathcal{g} _{i\xi\xi}}{\sum\limits_{i\in\mathcal{S}/\{s\}}\mathcal{P}_{i\xi\xi}^{+} \mathcal{g}_{i\xi\xi}+\mathcal{N}}\times\alpha_{sn} \tag{3}\]  
whereby \(\alpha\) is the user association matrix of \([s\times u]\) and that \(\alpha_{sn}=1\) if the user \(u\) is associated to the small cell \(s\), otherwise \(\alpha_{sn}=0\).  
Looking at Equation (3), it can be concluded that if the interference power (_denominator_) is reduced, SINR is enhanced. Therefore, the problem is formulated as follows:  
\[\begin{split}\arg\min&\sum_{i\in\mathcal{S}/\{s\}} \sum_{\xi\in\mathcal{K}}\mathcal{P}_{i\xi\xi}^{+}\\ s.t.&\sum_{i\in\mathcal{S}/\{s\}}\alpha_{\bar{y}} \
eq 0\\ &,\forall j\in\mathcal{U}/\{u\}\end{split} \tag{4}\]
# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
## 2 Basic Theory
### Adaptive Activation Function  
[PERSON] et al. [31] proposed a practical activation function, i.e., ACON. The ACON function is a smoothed form of the ReLU function, which decides whether to activate the neurons by the model outputs. And the differential variant of the ACON function can be expressed as follows:  
\[S_{\alpha}(x_{1},\cdots,x_{n})=\frac{\sum_{i=1}^{n}x_{i}e^{\alpha x_{i}}}{\sum _{i=1}^{n}e^{\alpha x_{i}}} \tag{4}\]  
where \(\alpha\) is the smoothing coefficient. When \(\alpha\rightarrow\infty\), then \(S_{\alpha}\rightarrow\max\). If \(\alpha\to 0\), \(S_{\alpha}\rightarrow\max\) mean value. The Formula (4) can be generalized with the model \([f(x),g(x)]\), and the model can be further refined to obtain as follows:  
\[\begin{array}{rl}S_{\alpha}(f(x),g(x))&=f(x)\cdot\frac{e^{\alpha f(x)}}{e^{ \alpha f(x)}+e^{\alpha g(x)}}+g(x)\cdot\frac{e^{\alpha g(x)}}{e^{\alpha f(x)} +e^{\alpha g(x)}}\\ &=f(x)\cdot\frac{1}{1+e^{-\alpha(f(x)-g(x))}}+g(x)\cdot\frac{1}{1+e^{-\alpha (g(x)-f(x))}}\\ &=f(x)\cdot\sigma[\alpha(f(x)-g(x))]+g(x)\cdot\sigma[\alpha(g(x)-f(x))]\\ &=(f(x)-g(x))\cdot\sigma[\alpha(f(x)-g(x))]+g(x)\end{array} \tag{5}\]  
when \(f(x)=x,g(x)=0\), and it is equivalent to ReLU activation function.  
In addition, [PERSON] et al. [31] further proposed Meta-ACON, which can adaptively adjust activation or not according to the input. Meta-ACON is an application of ACON-C in neural networks. ACON-C can be described as follows:  
\[f(x)=\lambda x,g(x)=\mu x(\lambda\
eq\mu) \tag{6}\]  
\[f_{\text{ACON-C}}(x)=S_{\alpha}(\lambda x,\mu x)=(\lambda-\mu)x\cdot\sigma[ \alpha(\lambda-\mu)x]+\mu x. \tag{7}\]  
\(\lambda\) and \(\mu\) are channel constraints. The first-order derivative of the ACON-C is as follows:  
\[\frac{d}{dx}[f_{\text{ACON-C}}(x)]=\frac{(\lambda-\mu)(1+e^{-\alpha(\lambda x -\mu x)})+\alpha(\lambda-\mu)^{2}(1+e^{-\alpha(\lambda x-\mu x)})x}{(1+e^{- \alpha(\lambda x-\mu x)})^{2}}+\mu \tag{8}\]  
\[\lim_{x\rightarrow\infty}\frac{d}{dx}[f_{\text{ACON-C}}(x)]=\lambda,\,\lim_{x \rightarrow-\infty}\frac{d}{dx}[f_{\text{ACON-C}}(x)]=\mu(\alpha\text{>}0) \tag{9}\]  
The second-order derivatives are as follows:  
\[\begin{array}{rl}\frac{d^{2}}{dx^{2}}[f_{\text{ACON-C}}(x)]&=\frac{\alpha( \mu-\lambda)^{2}e^{\alpha(\lambda-\mu)x}}{(1+e^{\alpha(\mu-\mu)x})^{3}}\\ &\cdot\frac{(\alpha(\mu-\lambda)x+2)e^{\alpha(\lambda-\mu)x}+\alpha(\lambda- \mu)x+2}{(1+e^{\alpha(\lambda-\mu)x})^{3}}\end{array} \tag{10}\]  
Making its second-order derivative equal to 0, and the first-order derivative upper and lower bounds are obtained as follows:  
\[\text{maxim}(\frac{df_{\text{ACON-C}}(x)}{dx})\approx 1.0998\lambda-0.0998\mu \tag{11}\]\[\text{minima}(\frac{df_{\text{ACON-C}}(x)}{dx})\approx 1.0998\mu-0.0998\lambda \tag{12}\]  
ACON-C has a threshold range of (\(-\)0.0998, 1.0998) in the first-order derivative. These bounds are learnable and are determined by \(\lambda\) and \(\mu\). Based on ACON-C, Meta-ACON averages the channels and then passes two convolution layers so that all pixels in each channel share a weight.
# An Evaluation Framework and Algorithms for Train Rescheduling
## 4 Framework Part I: Classification of Algorithm Capabilities and Characteristics
Examples are (i) the rolling horizon approach (decomposition in time), (ii) partitioning trains into groups and sequentially solving the problem associated with each group, and (iii) approaches where entire administrative areas are considered as single entities to carry out inter-area coordination among trains [25]. _Solution space exploration:_ Based on the country, IMs have specific rules for resolving conflicts during a disturbance. For example, in Sweden, the general dispatching strategy gives priority to on-time trains over the trains that deviate from the originally planned schedule. The reason behind this rule is to prevent a delay from propagating to trains that run according to schedule [28]. The dispatchers can, however, make exceptions to this rule when it is well-motivated. An algorithm that abides by such specific rules cannot fully explore the solution space for all possible rescheduling solutions. _Control loop:_ A _control loop_ gives the interaction between the rescheduling tool and traffic operations [26]. In _open loop_ rescheduling, the rescheduling decisions are computed and implemented only once at the beginning of a selected time window (e.g., two hours from the time when disturbance occurs). In _multiple open loop_ rescheduling, the algorithm can be applied at successive times over the time window. Whenever additional information regarding traffic conditions is available, the calculations can be reconsidered [11]. However, the algorithm does not consider the actions computed and implemented during its previous runs in the selected time window. A closed loop rescheduling is defined as a multiple open loop with memory [26]. In this type of control loop, dispatching actions are immediately computed and adjusted every time updated information is available, on the basis of the current traffic state and the previously computed rescheduling decisions. In a closed loop, information updates are taken into account whenever available [11]. _Evaluation context:_ The context in which the algorithm designer evaluates his/her proposed algorithm can be classified as: a station or a terminal area, a line, or a network. _Applicable scenarios:_ An algorithm could be designed such that it is applicable only to a subset of the possible disturbance scenarios. In contrast, an algorithm may be able to solve any type of disturbance scenario. An algorithm with the latter functionality could be more relevant in a practical context, where any type of disturbance scenario could arise. The applicable scenarios include the types of disturbances that an algorithm has been demonstrated to be able to solve.
## 2 Research Methodology
### Model Evaluation Metrics  
In assessing the practical application value of predictive models, single indicators often fail to comprehensively reflect their strengths and weaknesses. Therefore, this study introduces four typical regression performance evaluation metrics [31; 32]: mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R\({}^{2}\)), and overall index (OI). Each of these metrics possesses distinct characteristics and can measure model accuracy and stability in unconfined compressive strength prediction tasks from different dimensions.  
Among them, MAE quantifies the average deviation degree of prediction results and demonstrates lower sensitivity to extreme outliers. RMSE places greater emphasis on the impact of larger errors on overall model performance, making it suitable for tasks requiring high precision. R\({}^{2}\) serves as a standard for measuring the variance explanation capability of predicted values relative to actual observed values, with values closer to 1 indicating better fitting performance. OI introduces error normalisation and efficiency correction terms based on traditional error evaluation, providing an overall assessment of model generalisation capability and stability. The calculation expressions for these four metrics are presented as follows:  
\[\text{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}{\left|{{y_{i}}-{{\hat{y}}_{i}}} \right|} \tag{7}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{\left({{y_{i}}-{{\hat{y}} _{i}}}\right)^{2}}} \tag{8}\]  
\[\text{R}^{2}=1-\frac{\sum\limits_{i=1}^{n}{\left({{y_{i}}-{{\hat{y}}_{i}}} \right)^{2}}}{\sum\limits_{i=1}^{n}{\left({{y_{i}}-{{\overline{y}}}}\right)^{2}}} \tag{9}\]  
\[\text{OI}=\frac{1}{2}\bigg{[}1-\frac{\text{RMSE}}{{{y_{max}}-{y_{min}}}}+EC \bigg{]} \tag{10}\]  
where \(y_{i}\) represents the actual sand strength value, \({\hat{y}}\) represents the average of actual strength values, \({\hat{y}}_{i}\) represents the model-predicted sand strength value, \(EC\) denotes the efficiency coefficient, and \(n\) represents the corresponding sample size. Through the combined application of these metrics, a more comprehensive and objective evaluation of model performance in addressing MICP-modified soil strength prediction problems can be achieved, providing reliable foundations for model optimisation and selection. While R\({}^{2}\) is commonly used to evaluate regression model performance, its interpretation can be less intuitive for nonlinear models. In this study, R\({}^{2}\) is used solely as a performance indicator to quantify the proportion of output variance explained by the model relative to the mean baseline. It is interpreted in conjunction with RMSE, MAE, and OI to provide a more comprehensive and model-agnostic assessment of prediction accuracy.
## 4 Results
### Within-Case Analysis
#### 4.1.7 Case 7  
The discussion focused on the warehouse located in Buccinasco (MI) in the north of Italy (for further information on the warehouse features, see Table 3). The main takeaways of the interview are discussed as follows:  
1. Location. The manager considered the location to be the most important warehouse feature. In detail, accessibility was rated as the key aspect when assessing the quality of a warehouse. The interviewee considered this category attractive (rated \"A\") when it came to delivery lead time and on-time delivery. Especially regarding direct deliveries to the final customers, called last-mile deliveries, the warehouse location significantly affects the lead time provided; a strategic location can speed up the deliveries. According to the interviewee, these characteristics of the location also impact responsiveness (\"O\"); a strategic location can increase the 3 PL's ability to respond quickly and proactively to the customer's needs.
2. External Areas. Importance is given to the fundamental role of the loading bays; this feature is considered a \"must-be\" in relation to all the indicators (\"M\"). This equipment is a requisite to ensure flexibility in the logistics process. In addition, the correct density of loading bays helps to meet customers' emergencies.
3. Building. Layout and size are the most critical elements since the layout can have an impact on productivity (\"O\") if it is not adequate for the logistics functions carried out. For costs, the building is rated as a \"must-be\" feature.
4. Internal Areas. A particular mention was given to the fire-fighting systems, which are crucial and essential. As for the other interviewees, internal areas can help to provide a wide range of logistics services; the layout of the internal structure, as well as the technical features that characterise the warehouse, may or may not allow some specific activities to be carried out (rated \"O\"). In this regard, it was stated that, for instance, the possibility of inserting mezzanines could make it possible to have more space and thus integrate part of the customers' production process (e.g., packaging or labelling activities) or even have cold rooms to manage products that must be kept at controlled temperatures.