esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 4 Experiments
### Baseline Models and Implementation Details  
#### 4.2.1 Baseline Models  
Since the SP-MIL framework does not alter the baseline model structure, to verify the effectiveness of our proposed general SP-MIL framework, we employed three of the most widely used WS-VAD methods as our baseline models: DeepMIL [7], RTFM [5], and UR-DMU [24].  
DeepMIL model: This model was introduced by [PERSON] et al. [7] to solve the WS-VAD problem. In this approach, the pre-trained features of video segments are first extracted using a pre-trained feature extractor. These pre-trained features are then directly fed into the classifier to generate anomaly scores, and the segment with the highest anomaly score is selected to train the classifier using the video-level label. The objective function is defined as follows:  
\[L_{DeepMIL}=\alpha L_{hinge}+\beta L_{sparse}+\gamma L_{smooth}, \tag{16}\]  
where \(L_{hinge}\), \(L_{sparse}\), and \(L_{smooth}\) correspond to Formulas (2)-(4), respectively.  
RTFM model: This model, introduced by [PERSON] et al. [5], first extracts pre-trained features of video segments through a pre-trained feature extractor. These pre-trained features are then processed by a multi-scale temporal network to derive temporal context features, which are subsequently fed into the classifier to obtain anomaly scores, and the top-\(k\) segments with the highest temporal context feature magnitudes are selected to train the classifier with video-level labels. The objective function is defined as follows:  
\[L_{RTFM}=\alpha L_{hinge}+\beta L_{sparse}+\gamma L_{smooth}+L_{ce} \tag{17}\]  
Here, \(L_{hinge}\), \(L_{sparse}\), \(L_{smooth}\), and \(L_{ce}\) correspond to Formulas (2)-(5), respectively. Notably, the hinge loss in the RTFM model differs slightly from that in Formula (2) as it is applied to the magnitudes of the temporal context features rather than the anomaly scores. UR-DMU model: This model was introduced by [PERSON] et al. [24], it first extracts pre-trained features of video segments through a pre-trained feature extractor. These pre-trained features are subsequently processed by a global and local multi-head self-attention module within the transformer network to obtain more expressive embeddings, which are then fed into dual memory units (DMU) to learn more discriminative features. The embeddings and features output by the DMU are then incorporated into the classifier training process. For further details, please refer to [24].
# Cell Therapy in the Treatment of Coronary Heart Disease
## 3 Cardiac Origin Cells for Cardiac Cell Therapy  
### Cardiac c-Kit+ Cells  
In 2003, [PERSON] and colleagues proposed the presence of regional cardiac stem cells expressing the c-Kit (CD117) stem cell factor receptor in the postnatal myocardium. They based this proposal on c-Kit being a marker for HSCs, which implies a population of progenitor cells exists in the heart [18]. In their study, the researchers demonstrated the lack of expression of HSCs markers (CD34, CD45) in c-Kit+ cardiac cells and their ability to differentiate in vitro towards cardiomyocyte, endothelial, and smooth muscle cell directions [18]. The same group of investigators showed that transplantation of c-Kit+ cells into the ischemic hearts of rats resulted in the recovery of up to 70% of the affected myocardium, probably due to the in vivo generation of new cardiomyocytes from donor cells [18]. Subsequently, similar results were observed in experiments in dogs and minipigs [60; 61].  
The discovery of regional cardiac stem cells initially generated excitement in the scientific community, but subsequent research by independent groups failed to demonstrate the differentiation potential of c-Kit+ cells toward cardiomyocytes, both in vitro and in vivo after transplantation into the heart [62; 63; 64]. Most experimental studies have observed a relative restoration of cardiac contractile function after transplanting c-Kit+ cells into ischemic myocardium. An analysis of 80 preclinical studies evaluating the efficacy of autologous and allogeneic c-Kit+ cell transplantation in acute MI showed a mean increase in left ventricular ejection fraction of almost 11% compared to control groups, with a greater effect observed in small animals than in large animals [65].  
The CONCERT-HF clinical trial demonstrated improvements in functional and structural characteristics of the left ventricle in patients with heart failure due to ischemic cardiomyopathy following co-transplantation of autologous MSCs and c-Kit+ cardiac cells [66].  
Based on [PERSON]'s 2015 hypothesis, c-Kit+ cells found in the adult heart originate from the epicardium and are considered regional mesenchymal cells based on their immunophenotype, which includes expression of the mesenchymal markers CD90 and CD105 [67]. The beneficial effect of c-Kit+ cell transplantation on myocardial tissue recovery after ischemic injury, as demonstrated in numerous studies, may be due to the release of cytokines (TCA-3, SDF-1), vascular growth factors (VEGF, HGF, erythropoietin, FGFb, osteopontin, SCF), regulatory factors of cardiac differentiation (activin A, Dkk homolog-1, TGF-\(\beta\)), and the promotion of angiogenesis at the injection site [20; 23; 68; 69].
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 6 Protozoa
### Eucocidiorida  
_Eucocidiorida_ is a protozoan order in the phylum Apicomplexa, which contains several important human parasites, such as _Toxoplasma gondii_. _Adelina_ is the most common ento-mopathogenic genera in Eucocidiorida, which infects cooleopteran, dipteran, collembolan, embiopteran, lepidopteran, and orthopteran hosts, including several scarab species. _Adelina melolonthae_ has been found in cockchafer beetle (_M. melolontha_) in France [154; 169; 170; 171; 172; 173] and Turkey [174], _A. seriesthis_ found in prounose scarab (_Sericesthis prounose_), _M. hippocastani_ and _Aphodius howititti_[175; 176], _A. tenebroionis_ in African black beetle (_H. anator_) and the large sand scarab beetle (_Pericoptus truncates_) [177; 178; 179], and an unidentified _Adelina_ species found in European rose chafer (_Cetonia aurata_), Asiatic garden beetle, European chafer, Northern masked chafer (_C. borealis_), and Japanese beetle [70; 158; 180]. _Klossia_ is another genus known to contain entomopathogenic species. _Klossia aphodii_ has been found in _Aphodius fimetarius_ and _A. contaminates_ in Sweden [181]. In all cases, the coccidia infected the fat body and were found to increase larval mortality or reduce adult fecundity in late-instar infections [160].  
Epizootics of _Adelina_ species have been reported in the confused flour beetle (_Tribolium confusum_) and the wax moth (_Galeria mellonella_) [182; 183; 184] but no incidences have been documented in scarab populations. Our limited knowledge about the biology and pathogenicity of these species are an obstacle their development as biocontrol agents. Most work has been focused on stored grain pests, such as _Tribolium_ species [185; 186] but much more work will be needed to determine their potential role as biocontrol agents for scarabs.
## 4 BILSTM-Transformer Based Bems Model
### Offline Training Based on the BiLSTM-Transformer System Architecture
#### 4.3.1 Input and Output  
As mentioned earlier, we chose BiLSTM-Transformer as the core tool to manage the forecasting of load changes in the mall after the charge/discharge dispatch of an electric vehicle connected to a commercial building parking lot. The output of each BiLSTM-Transformer network is the load of the mall at the next time step \(t+1\).  
The forecasting of \(P_{\text{gird}}(t)\) belongs to the time-series-forecasting problem, which is closely related to the previous state. Therefore, the load \(P_{\text{gird}}(t)\) of the mall at the current time step \(t\) is one of the key inputs to the network. In addition to \(P_{\text{gird}}(t)\) itself, the load forecasting also depends on a number of other influencing factors, including the \(P_{\text{demand}}(t)\), the sum of the charging and discharging power of electric vehicles \(P_{\text{Evi}}(t)\), and the current electricity price \(C(t)\) based on the time-characteristic curve. Together, these input variables determine the load \(P_{\text{gird}}(t)\) forecasting at the next time step \(t+1\).  
In this study, the input layer consists of five nodes, corresponding to \(P_{\text{gird}}(t)\), \(P_{\text{demand}}(t)\), \(P_{\text{Evi}}(t)\), electricity price \(C(t)\), and some temporal characteristics (e.g., diurnal temperature curves). After these inputs are processed at the BiLSTM and Transformer layers, the network generates a node at the output layer, which is \(P_{\text{gird}}(t+1)\).  
Due to the powerful bidirectional time correlation capture capability of BiLSTM and the global modeling characteristics of Transformer, the relationship between system input and output can be described as follows: the \(P_{\text{gird}}(t)\) forecasting of time step \(t+1\) not only depends on the current input but also correlates with the state information of the past and future time steps, so as to more comprehensively reflect the change trend in the load of the mall after future charging and discharging scheduling.  
Equation (17): Input and output descriptions:  
\[\hat{P}_{\text{grid}}(t+1)=f[P_{\text{grid}}(t),P_{\text{grid}}(t-1), \ldots,P_{\text{grid}}(t-1l),C(t),C(t-1),\ldots,C(t-l+1)\] \[P_{\text{demand}}(t),P_{\text{demand}}(t-1),\ldots,P_{\text{demand }}(t-l+1)\] \[\sum_{i=1}^{N}P_{\text{EVi}}(t),\sum_{i=1}^{N}P_{\text{EVi}}(t-1), \ldots,\sum_{i=1}^{N}P_{\text{EVi}}(t-l+1)] \tag{17}\]
## Materials and Methods
### Correlation between RMR and \(Q\)
#### 2.7.2 Correlation between \(Rmr\) and \(Q\) Using Their Truncated Versions  
Using truncated versions of \(RMR\) and \(Q\), some attempts have been made for a better correlation [19; 48] of the two. The modified \(RMR\), i.e., \(RMR_{\text{mod}}\), which is a value free from intact rock strength and joint orientation rating, and N (stress free \(Q\), i.e., \(SRF\) = 1) were used for the interrelationship between \(RMR\) and \(Q\) using Equation (20). Through this truncated approach, the correlation between \(N\) and \(RMR_{\text{mod}}\) for the data of Projects 1-2 is also evaluated.  
\[RMR_{\text{mod}}=8\ln N+30 \tag{20}\]  
Secondly, a rock mass fabric index, defined as a function of the rock structure and joint condition, eliminates the groundwater factor as well, resulting in a common fabric chart for a better correlation [19]. In this truncated approach, the rock mass fabric index for the \(RMR\) system (\(F_{\text{RMR}}\)) is the sum of the parameter values related to the rock structure (\(R_{2}\) and \(R_{3}\)) and discontinuity condition (\(R_{4}\)). It can be defined using Equation (21):  
\[F_{RMR}=R_{2}+R_{3}+R_{4}. \tag{21}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Project** & **Site** & \(R_{1}\) & \(F_{\text{RMR}}\) & \(R_{5}\) & \(R_{6}\) & \(RMR_{89}\) & \(F_{Q}\) & \(F_{\text{EMR}}\) & **Predicted** & **Tunnel** & **Span (m)** & **Reference** \\ \hline \multirow{3}{*}{Guledar Dam Turkey} & Limestone & 6.0 & 34.6 & 7 & \(-10\) & 37.60 & 1.88 & 43 & 46.0 & 3.0 & \\  & Sandstone & 6.18 & 22.59 & 7 & 0 & 35.77 & 0.45 & 36 & 49.18 & 3.0 & \\  & Dalabase & 3.62 & 13.29 & 4 & 0 &  
The rock mass fabric index for the \(Q\) system (\(F_{Q}\)) is the product of the relative block size (\(RQD/J_{n}\)) and inter block shear strength (\(J_{r}/J_{a}\)), as expressed in Equation (22):  
\[F_{Q}=\frac{RQD}{J_{n}}\times\frac{J_{r}}{J_{a}}. \tag{22}\]  
A correlation was developed based on the rock mass fabric index chart, as expressed in Equation (23) [19].  
\[F_{RMR}=15\ln F_{Q}+32 \tag{23}\]  
The values of \(F_{Q}\) listed in Table 3 were used for predicting \(F_{RMR}\) values using the rock mass fabric index chart. Thereafter, the ratings for intact rock strength (\(R_{1}\)), groundwater (\(R_{5}\)), and joint orientation (\(R_{6}\)) were applied, defining the predicted \(RMR_{89}\). Through this truncated approach, the correlation between calculated and predicted \(RMR_{89}\) for the data summarized in Table 3 is also evaluated.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 5 Conclusions and Future Research Directions
### Efficient Wireless Connectivity Solutions  
The aim is to provide ubiquitous connectivity to both aerial users and the ground IoT network. Energy, spectrum, and computation resource should be optimized, which have a high impact on the performance of the IoT platform in dynamic conditions. The focus should be given to spectrum allocation using CR technology, which is shared between aerial and ground users. The optimization solution for patrolling UAVs as well as UAVs acting as base stations to provide connectivity in affected areas should also be studied while satisfying QoS requirements of heterogeneous users. Interference mitigation techniques should be investigated including multi-cell cooperation and NOMA. Further, artificial intelligence, machine learning, and deep learning techniques could be considered for design optimization approaches to enhance the performance of a UAV-enabled IoT platform. These can include linear/polynomial models, support vector machines, and complex neural networks.
## 3 Controller Design and Stability Analysis
### Stability Analysis
\begin{array}{ll}\eta_{1}\geq 2\vartheta\lambda_{ \mathrm{max}}(\Gamma_{1}^{-1})\\ \eta_{2}\geq 2\vartheta\lambda_{\mathrm{max}}(\Gamma_{1}^{-1})\\ \eta_{3}\geq 2\vartheta\lambda_{\mathrm{max}}(\Gamma_{3}^{-1})\\ \end{array} \right. \tag{51}\]  
where \(\vartheta\) is design positive constant. Considering (3), (50), design control parameters in (51) and Assumption 2, (50) can be rewritten as follows:  
\[\begin{array}{ll}\dot{V}&\leq-\vartheta(y_{2}^{2}+y_{3}^{2})-\vartheta(\zeta_{ 1}^{2}+\zeta_{2}^{2}+\zeta_{3}^{2})-\vartheta(h_{1}\widetilde{w}_{1}^{T}\Gamma_{1 }^{-1}\widetilde{w}_{1}+\widetilde{w}_{2}^{T}\Gamma_{2}^{-1}\widetilde{w}_{2} +h_{1}\widetilde{w}_{1}^{T}\Gamma_{3}^{-1}\widetilde{w}_{3})\\ &+(\frac{h_{1M}}{2}+\frac{1}{2}+\frac{h_{2M}}{2})\sigma_{M}^{2}+(\frac{h_{1M} \eta_{1}}{2}+\frac{\eta_{2}}{2}+\frac{h_{2M}\eta_{3}}{2})w_{M}{}^{2}+(\frac{ \mathrm{B}_{2}^{2}}{2\varepsilon}-\frac{M_{2}^{2}}{2\varepsilon})y_{2}^{2}+( \frac{\mathrm{B}_{2}^{2}}{2\varepsilon}-\frac{M_{2}^{2}}{2\varepsilon})y_{3}^{2}+ \frac{5\kappa}{2}\\ &=-2\vartheta V+(\frac{h_{1M}}{2}+\frac{1}{2}+\frac{h_{2M}}{2})\sigma_{M}^{2}+( \frac{h_{1M}\eta_{1}}{2}+\frac{\eta_{2}}{2}+\fracConclude from (54) that \(V\leq p\) is an invariant set. The conclusion is expanded is if the initial condition satisfies \(V(0)\leq p\), for \(\forall t>0\), then \(V(t)\leq p\) holds. Considering the preconditions of theorem 1, the following can be obtained:  
\[\dot{V}\leq-2\theta V+\Psi \tag{55}\]  
Furthermore, by solving inequality (55), the range of \(V\) can be obtained as follows:  
\[0\leq V\leq\frac{\Psi}{2\theta}+(V(0)-\frac{\Psi}{2\theta})e^{-2\theta t} \tag{56}\]  
where \(e\) represents the base of natural logarithms. Obviously, analysis indicates that all states of the system are bounded, and the ADSC controller does not have global stability, but semi-global stability. The performance of the designed controller scheme is verified by simulation results in Section 4.
## 2 Materials and Methods
### Biome-BGC Model and Its Validation
#### 2.6.2 Indirect Validation of the Biome-BGC Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Indicator** & **Name of Indicator** & **Introduction to the Algorithm** & **Significance of Indicator** & **Reference** \\ \hline \multirow{4}{*}{Degree} & The number of ecological corridors owned by an ecological node & Describes the number of connections between an ecological node and other ecological nodes & [17] \\ \cline{2-5}  & & The ratio of the number of corridors actually existing between the neighboring nodes of a node to the maximum number of corridors that may exist & Indicates the proportion of connectivity between neighboring nodes of an ecological node & [17] \\ \hline \multirow{4}{*}{Clustering coefficient} & The reciprocal of the sum of the shortest distances from a node to all other nodes multiplied by the number of other nodes & Indicates how close the ecological node is to other nodes through the shortest path; at the same time, it also quantifies how much the ecological node is in the geometric center of the network & \\ \cline{2-5}  & & The normalized index of the proportion of all the shortest paths in the network that pass through a node & Indicates the proportion or degree to which an ecological node exists on the shortest path of any two nodes in the network & [17] \\ \hline \multirow{4}{*}{Eigenvector centrality} & Each ecological node is assigned a relative score; connections to nodes with high scores are weighted more than connections to nodes with low scores & The more important the node connected to node A, then the more important node A is; this indicator is used to evaluate the importance of nodes & [43] \\ \cline{2-5}  & & Rank of the importance of ecological nodes, evolved from eigenvector centrality & Similar to eigenvector centrality, it is an indicator used to evaluate the importance of nodes & [44] \\ \hline \multirow{4}{*}{Average degree} & Average of the degrees of all ecological nodes in the network & Evaluates the average connectivity of all nodes in the network & [17] \\ \cline{2-5}  & Average path length & Average of the shortest distance between any two ecological nodes in the network & Indicates the smoothness of energy flow of the whole ecological network & [17] \\ \cline{2-5}  & & & & \\ \cline{2-5} Evaluation of the network & Average clustering coefficient & Average of the clustering coefficients of all nodes in the network & Indicates whether the distribution of ecological nodes in the network tends to be concentrated or decentralized & [17] \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \hline \multirow{4}{*}{Modularity} & Each ecological node in the network is assigned to a different community & Evaluates the effect of network community division & [45] \\ \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the 10 topological indicators.
## Materials and Methods
### Implementation Details
#### 3.6.3 Configurations for Forecasting Models  
The hyperparameters of the various predictive models that we used were mostly determined by trial and error with experiments. For ANNs, we established three hidden layers, each of which has 100, 200, and 200 hidden units. The learning rate was 0.001, the maximum number of iteration was 500, and a rectified linear unit (ReLU) [49] function was used as activation function. Further, for the SVR, the penalty parameter C was 0.25, the kernel coefficient was 0.1, epsilon, which identifies the epsilon-tube distance, was 0.005, and the kernel function was a radial basis function. For GBM, the subsample rate was set to 0.8, the number of sequential trees was 150, and the learning rate was 0.05. Other configurations for models such as RF, ADB, and XGB used the default values specified by the package designer because they worked fine with them.
## Theoretical Considerations
### The Scattering Coefficient as Measured by In Situ Transmissometers  
In field measurements, \(b_{p}\) is derived from the total absorption and beam attenuation coefficients (\(a\) and \(c\), respectively) as measured by instruments such as WETLabs ac9 and its later variants. Any detector has a finite field of view (FOV), therefore beam transmissometers are defined by their acceptance angle \(\theta_{acceptance}\), which differs from \(0^{o}\). If we want to compare, in a future study, our theoretical results to available in situ measurements, \(b_{p}\) must be derived from the scattering cross section, rebuilt from the normalized phase function integrated between \(\theta_{acceptance}\) and \(\pi\) instead of 0 and \(\pi\)[30]. To make a distinction, when \(C_{sca}\) is calculated by integrating the scattering function between \(\theta_{acceptance}\) and \(\pi\), the symbols \(C_{sca}^{\theta_{a}}\), \(b_{p}^{\theta_{a}}\) and \(\widetilde{b_{bp}^{\theta_{a}}}\) (\(=b_{bp}/b_{p}^{\theta_{a}}\)) will be used. As in [PERSON] et al. [5], we set the acceptance angle to \(1^{o}\), which is consistent with acceptance angles of commercially available beam transmissometers such as the WETLabs C-Star (\(1.2^{o}\)) or WETLabs ac9 (\(0.93^{o}\)) ([30] and references therein).
## 2 Related Work  
### Depth Completion  
As image-guided depth completion techniques become increasingly recognized for their advantages over non-image-guided approaches, significant research efforts have focused on enhancing multimodal data integration. Common methodologies utilize deep learning models that take sparse depth maps alongside RGB images as inputs [6; 12] or employ distinct initial convolutional layers for feature extraction [9; 13; 14]. Another common approach involves two-stage networks that progressively refine predictions from a coarse resolution to a fine resolution [15; 16]. However, these approaches often oversimplify the problem, failing to capture the complex spatial relationships that exist between RGB images and their corresponding depth maps.  
To improve the precision of data fusion, advanced network architectures have been designed to combine intermediate features extracted from various levels. For instance, the models proposed in [17; 18] employed dual-branch encoder architectures that independently process RGB images and raw depth data, linking relevant features before passing them to the decoder. The approach in [19] refined the network design by incorporating multiscale skip connections, improving heterogeneous data sources. While dual-encoder networks primarily emphasize data fusion at the encoding phase, they often overlook the decoding stage. In response, some studies [20; 21] introduced dual-encoder-decoder networks to optimize depth completion tasks.  
Concurrently, the evolution of SPN-based networks progressed steadily, including CSPN [22], CSPN++ [23], and DySPN [24]. These approaches leverage affinity matrices derived from RGB data to effectively enhance the upsampling of raw sparse depth maps.
## 4 Simulation Results and Attack Test
### Statistical Analysis  
#### 4.2.1 Histogram Analysis  
The distribution of gray values from a digital image can be effectively and visually revealed through the histogram of the image. In order to satisfy the aim of the encryption for an image, the histogram of the encrypted image should almost be or close uniform and, nevertheless, it should show its prominent difference from the one derived from the plaintext image, because when distribution of the gray value is more even, it will be harder for the attacker to obtain information from the encrypted image through statistical attacks. The histogram of the four images mentioned above and their encrypted versions are shown in Figure 11. The obvious characteristics of the plaintext image are clearly displayed through the histogram. Meanwhile, the distribution of the histogram derived from the cipher image is reasonably uniform, which will precisely reduce the correlation of the adjacent pixels and there will be no information provided for the attackers.  
Figure 11: _Cont_.  
Figure 10: The simulation test for the images. (**a–c**) are the original image, encrypted image and decrypted image of the Lena image, respectively. (**d–f**) are the original image, encrypted image and decrypted image of the Cameraman image, respectively. (**g–i**) are the original image, encrypted image and decrypted image of the Pepper image, respectively. (**j–l**) are the original image, encrypted image and decrypted image of the Baboon image, respectively.