esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 8 Discussion  
We have shown that a multi-stream fusion of application-independent and semantic streams is a prospective approach to image-set similarity evaluation. Our approach gives strong advice on the design of a generalized framework. The design of application-independent streams (e.g., image similarity scoring) should preferably use machine learning-based features instead of hand-crafted features, however, the use of smart distance metrics (e.g., BSS error index) is extremely important. A correct selection of semantic processing streams is also strongly recommended. In the context of using machine learning methodology, the required semantic data sets are much easier to obtain and label than the data needed for universal model creation.  
In future research, we would like to generalize the approach by adopting it to other object categories. Explainable AI (XAI) techniques are also of interest to explain the contribution of particular processing streams and steps to the overall result. XAI techniques allow us to answer an important set of questions: not only whether something is similar, but also why it is similar. This would enable a further generalization of our approach, which could be applied not only in image set comparison, but in other pattern sets as well (e.g., text or multi-modal sets of sources).  
Conceptualization, P.P. and W.K.; methodology, P.P. and W.K.; software, P.P.; validation, P.P.; formal analysis, P.P.; investigation, P.P. and W.K.; resources, P.P. and W.K.; data curation, P.P.; writing--original draft preparation, P.P. and W.K.; writing--review and editing, W.K.; visualization, P.P. and W.K.; supervision, W.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: [[http://ai.stanford.edu/~jkrause/cars/car_dataset](http://ai.stanford.edu/~jkrause/cars/car_dataset)]([http://ai.stanford.edu/~jkrause/cars/car_dataset](http://ai.stanford.edu/~jkrause/cars/car_dataset)) (accessed on 23 June 2021). The authors declare no conflict of interest.
## 3 Research Methods
### TOPSIS Method to Evaluate Fitness Function
Find the positive ideal solution and negative ideal solution: Assuming that the best fitness values \(F_{1}^{\text{min}}\), \(F_{2}^{\text{min}}\), and \(F_{3}^{\text{max}}\) are 11,000, 2000, and 0.82, respectively, and the worst fitness values \(F_{1}^{\text{max}}\), \(F_{2}^{\text{max}}\), and \(F_{3}^{\text{min}}\) are 16,000, 3000, and 0.6, respectively, the positive ideal solution (A\({}^{+}\)) and negative ideal solution (A\({}^{-}\)) are obtained as follows: \[A^{+}=\left\{v_{1}^{*}=0.1284\,\,\,v_{2}^{*}=0.1672\,\,\,v_{3}^{*}=0.1956\right\}\] \[A^{-}=\left\{v_{1}^{-}=0.1868\,\,\,v_{2}^{-}=0.2509\,\,\,v_{3}^{-}=0.2674\right\}\]
5. Calculate the distance between the solutions of each group and the positive ideal solution S\({}^{+}\), and the distance between the solutions of each group and the negative ideal solution S\({}^{-}\) according to the following Equation (32) and Equation (33), respectively. \[S_{i}^{+}=\sqrt{\sum\limits_{j=1}^{n}{{{(v_{\text{ij}}-v_{j}^{*})}^{2}}}}\] (32) \[S_{i}^{-}=\sqrt{\sum\limits_{j=1}^{n}{{{(v_{\text{ij}}-v_{j}^{-})}^{2}}}}\] (33) \[S_{i}^{+}=\left\{S_{1}^{+}=0.0491\,\,\,S_{2}^{+}=0.0583\,\,\,S_{3}^{+}=0.0542\right\}\] \[S_{i}^{-}=\left\{S_{1}^{-}=0.0757\,\,\,S_{2}^{-}=0.0712\,\,\,S_{3}^{-}=0.0859\right\}\]
6. Calculate the relative closeness of solutions of each group to the ideal solution according to the following Equation (34): \[C_{i}=\frac{S_{i}^{-}}{S_{i}^{+}+S_{i}^{-}}\] (34) \[C_{1}=0.6065,\,C_{2}=0.5498,\,\,\text{and}\,\,C_{3}=0.6131\,\,\,\text{are calculated from Equation (34). In TOPSIS, the value of \(C_{i}\) being closer to 1 means the objective is closer to the positive ideal solution (the best solution) and is the farthest away from the negative ideal solution (the worst solution). Therefore, the solution of the third group \(C_{3}\) is the best when selecting the best solution. The use of TOPSIS can help us find a relatively excellent solution among many non-dominated solutions.
## 3 Results and Discussion
### In Silico Target Prediction  
The new dimers **6-9** were analyzed in silico using the Way2 Drug server, which includes the PASS (_Prediction of Activity Spectra for Substances_) application, which allows for the prediction of the potential biological activities of the molecules and the probability of cytotoxicity toward cancer cell lines [28; 29]. The obtained results are summarized in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{2}{c}{**No.**} & \multicolumn{5}{c}{**Probability of Activity Spectrum**} \\ \hline \multirow{3}{*}{6} & (28\%) & (77\%) Glycosphos-phatidylinositol & (47\%) & (33\%) Alzheimer’s disease treatment & (46\%) Antialergic \\  & & stimulant & phospholipase D inhibitor & factor inhibitor & & \\ \hline \multirow{3}{*}{7} & (29\%) & (83\%) Glycosphos-phatidylinositol & (18\%) & (30\%) Cytochrome & (10\%) MAP3K8 \\  & processing peptidase & phospholipase D inhibitor & Antipsychotic & P450 inhibitor & inhibitor \\  & inhibitor & inhibitor & & & \\ \hline \multirow{3}{*}{8} & (75\%) & (84\%) Glycosphos-phatidylinositol & (65\%) & (42\%) Alzheimer’s disease treatment & (41\%) Cytochrome \\  & diseases treatment & phospholipase D inhibitor & diacetylase stimulant & & \\ \hline \multirow{3}{*}{9} & (76\%) Histone & (60\%) Antialergic & (60\%) Neurodegenerative diseases treatment & (58\%) Alzheimer’s disease treatment & (59\%) \\  & deacetylase stimulant & (66\%) Antialergic & (62\%) Antialergic & (65\%) Alzheimer’s disease treatment & Antiashmatic \\ \hline \end{tabular}
\end{table}
Table 1: Probability (%) of activity spectra of dimers (**6–9**) using the PASS program.  
The results show that the new dimers **6-9** have a high probability of biological action aimed at the anticancer potential related to the mechanism of histone deacetylase stimulation, the inhibition of glycosylphosphatidylphosphatidylphosphatidylphosphatidyl D, which may directly lead to the activation of the mitochondrial apoptosis pathway. It is worth noting that the anticancer potentials of dipyridothiazazines **1-4** were also experimentally confirmed in our earlier studies, which was the beginning of the search for new derivatives in the group of dipyridothiazazines with even better activities [21; 24]. Moreover, previous in silico analyses performed for dimers with the lutiidine, \(o\)-xylene, and \(p\)-xylene systems also indicated a high potential for anticancer activity, which was confirmed [25].  
Furthermore, the PASS program indicated the likelihood of dimer activity in neurodegenerative diseases, antipsychotic and antiallergic effects, and the inhibition of cytochrome 450.  
More importantly, in addition to the probable mechanisms of biological action, the PASS program indicated, for the new dimeric derivatives, the likelihood of cytotoxic effects on breast, kidney, colon, pancreatic, and ovarian cancer cell lines, melanoma, and non-small-cell lung cancer. It is worth pointing out that previous studies on dipyridothiazazines **1-4** confirmed this promising anticancer potential [21], which was an inspiration to conduct further cytotoxicity tests of the new dimers **6-9**.
## 3 Results
### Mixing Analysis
The darkest spots (highest Pe) in Figure 3b are in the centre of the U-bend shapes, indicating a constriction in the flow due to the resistance created by the microchannel walls because of the serpentine geometry. The value of Pe also peaks near the outlet because of the sudden release of the flow due to the lack of microchannel wall resistance near the outlet. ) for the serpentine micro-mixer. The experimental mixing efficiencies were calculated using normalised fluorescent light intensities, which directly correlate to the normalised concentration values [46]. Figure 5 compares some examples of experimental and simulation results for two different flow rates. Below a mixer length of approximately 750 \(\upmu\)m, we note that some of the associated errors on the experimental data are comparatively larger than at longer channel lengths. As discussed earlier, the simulations neglect any effects due to micro-fabrication errors. We hypothesize that the larger errors within this regime are due to the fact that minor defects in the fabrication may have a more significant effect when mixing over shorter distances. However, we do find that the overall trend of the experimental mixing efficiency as a function of distance along the serpentine mixer is reproduced in the FEM simulations. At a flow rate of 100 uL/min, both the simulated and experimental results reach a peak mixing efficiency of around 95% at the outlet (mixer length = 1200 um). For the higher flow rate of 200 uL/min, 95% mixing efficiency is already achieved at around 800 um, indicating that at higher flow rates, a shorter serpentine length is required to achieve uniform mixing. Homogeneous mixing over shorter length scales is critical for solution scattering experiments where it is important that any changes in molecular conformation are triggered at the same time, such that the majority of target molecules are in approximately the same conformational state. The similarity of the simulated and experimental results provides us with confidence that the simulations can be used to predict the geometries and mixing times required to achieve homogeneous mixing of the reactants for a range of input parameters.
# Cloudformer: Supplementary Aggregation Feature and Mask-Classification Network for Cloud Detection
## 3 Methodology
### Mask Segmentation  
As shown in the upper right of Figure 1, the \(N\) embeddings output from the Transformer module are inputs into the mask segmentation module. We obtain two sets of information through the Multilayer Perceptron (MLP) structure: (1) \(N\) class predictions, and (2) \(N\) mask embeddings.  
The former set of information is obtained by a linear classifier and SoftMax activation. Per-segment queries in class predictions produce \(K+1\) prediction probabilities \(\{P_{i}\mid P_{i}\in\Delta^{K+1}\}\). It contains \(K\) categories and a \"no object\" category. Therefore, in the cloud detection task, \(\ell_{class}\in\mathbb{R}^{N\times(K+1)}\), where \(K\) = 2 (cloud and background). This classification information is obtained by fusing deep semantic features through a multi-head attention mechanism. Therefore, it combines semantic features and context information more effectively than the pixel-by-pixel-based approach and can help distinguish objects with similar features (such as clouds and snow).  
For the latter set of information, a Multilayer Perceptron (MLP) will convert the input \(N\) queries into \(N\) mask embeddings. The length of pre-segment embeddings is \(C_{g}\). A large amount of spatial information is contained in the mask embeddings. We embed \(N\) mask embeddings \(\ell_{mask}\) at the mask-level into the output of the pixel-level branch through dot  
Figure 5: The Transformer module consists of a standard Transformer decoder and a feature converter. To strengthen the position information between the features, the feature converter uses an asynchronous position code.  
products and then apply sigmoid activation to obtain the binary spatial mask prediction. This process can be defined as:  
\[M=Sigmoid({\ell_{mask}}^{\text{T}}\cdot{\ell_{pixel}})M\in\mathbb{R}^{N\times H \times W} \tag{4}\]  
where T means transpose.  
Finally, class prediction and mask prediction are combined to obtain the standard semantic segmentation task output. We refer to DETR [24]; a focal loss and a dice loss are used as binary spatial mask prediction. For class prediction, cross entropy classification loss is the best choice. The overall loss function can be written as:  
\[\mathcal{L}=\mathcal{L}_{cls}+\mathcal{L}_{mask} \tag{5}\]  
\[\mathcal{L}_{mask}=\lambda_{focal}\mathcal{L}_{focal}+\lambda_{dice}\mathcal{ L}_{dice} \tag{6}\]  
where \(\lambda_{focal}\), and \(\lambda_{dice}\) balance the weight of two losses as hyper-parameters. We set the hyper-parameters to \(\lambda_{focal}\) = 20.0 and \(\lambda_{dice}\) = 1.0.
## 1 Introduction
#### Notation  
In this paper, \(\mathbf{Y}_{10m}\), \(\mathbf{Y}_{20m}\), and \(\mathbf{Y}_{60m}\) will be used to refer to the set of images captured by the S2 constellation at each resolution, respectively, \(\mathbf{Y}_{10m}\in\mathbb{R}^{n_{x}\times n_{y}\times 4}\) is the scene as captured by bands B2, B3, B4, and B8 at 10 m resolution, \(\mathbf{Y}_{20m}\in\mathbb{R}^{\frac{n_{x}}{2}\times\frac{n_{y}}{4}\times 6}\) is the scene as captured by bands B5, B6, B7, B8A, B10, and B11 at 20 m resolution and \(\mathbf{Y}_{60m}\in\mathbb{R}^{\frac{n_{x}}{6}\times\frac{n_{y}}{6}\times 2}\) is the scene as captured by bands B1 and B9 at 60 m resolution. To indicate that a collection of bands has been resampled, either downsampled, upsampled or super-resolved, the new resolution is added as a superscript, i.e., \(\mathbf{Y}_{10m}^{20m}\) would mean \(\mathbf{Y}_{10m}\) at 20 m resolution and \(\mathbf{Y}_{20m}^{10m}\) would mean \(\mathbf{Y}_{20m}\) super-resolved to 10 m. The capital letter D is used in the same way to indicate downsampling in a more general manner.
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
###### Abstract  
Unmanned aerial vehicles (UAVs) play a key role in modern surveillance-related missions. A major task for performing these missions is to find the precise location of a moving target in real-time, for which the main challenge is to estimate the target position to high precision using the noisy measurements from the airborne sensors. In this paper, we present a closed-form on-line simultaneous target localization and UAV trajectory optimization method based on the visual platform, which can effectively minimize the localization uncertainty to the target. The proposed method can be elucidated explicitly using two phases, of which, in the target localization phase, the expended information filtering (EIF) is exploited, which can express the predicted Fisher information matrix (FIM) of the target explicitly and iteratively, and in the UAV trajectory optimization phase, the property of the predicted FIM is exploited to establish the UAV waypoint optimization objective by taking account of the UAV motion limit. Compared with existing methods of the same class, the proposed method not only estimates the next target position more correctly, but also takes the error of the target motion into consideration, thus improving the effectiveness of the optimized UAV trajectory. Both simulations and field experiments were conducted, which show that the proposed method outperformed the existing methods.  
unmanned aerial vehicle; target localization; coordinate frame transformation +
Footnote †: journal: Applied Sci. 2021, 11, 114. [[https://dx.doi.org/10.3390/app11010114](https://dx.doi.org/10.3390/app11010114)]([https://dx.doi.org/10.3390/app11010114](https://dx.doi.org/10.3390/app11010114))
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 6 Simulation
### Simulation Results
#### 6.2.2 Impact of TTL  
Figure 4 shows the impact of TTL. Figure 4a shows that the delivery rate of Spray- and-Wait and Bubble Rap is low in the scenario of low TTL, and with the increase TTL, the delivery rate increases rapidly and eventually tends to be high, which reflects the poor adaptability of these two algorithms to different attributes of the service. The delivery rate of the AR-RT algorithm remains stable and always at a high level, and Epidemic and Prophet both increase and then decrease, and the overall delivery rate is low. Figure 4b shows that the average delay of AR-RT increases slightly with the increase of TTL, and the advantage is obvious in the high TTL scenario. The increasing trend of the remaining four algorithms is obvious. Figure 4c shows that the network overhead of Epidemic and Prophet is increasing significantly as TTL increases, the network overhead of AR-RT increases slightly but is within an acceptable range, and the network overhead of Bubble Rap remains basically stable and is always in the lowest state. Figure 4d shows that with the increase of TTL, the average hop count of Epidemic and Prophet increases significantly and is at a higher level, reflecting the inefficiency of forwarding of these two algorithms. The average hop count of the remaining three algorithms remains basically stable, with AR-RT performing better.
## 3 Integrated Disaster Resource Scheduling Modeling
### Pre-Disaster Resource and Facility Optimization  
During the pre-disaster defense planning phase, the core objectives of resource allocation optimization are to achieve efficient resource distribution, minimize dispatch costs, and enhance system stability and emergency response capability. Thus, the proposed objectives include the following targets:  
Figure 3: Ice disaster community network clustering based on fast Newman algorithm.  
* _Minimization of Freshwater and Electricity Procurement Costs_: By implementing rational community partitioning, resource demands in each region are optimally matched before the disaster to reduce procurement redundancy and waste while enhancing the accuracy of resource distribution.
* _Minimization of Cross-Regional Resource Dispatch Costs_: We apply the fast Newman algorithm for community partitioning to optimize scheduling efficiency within each region and reduce economic costs and transportation losses associated with inter-regional resource transfers while simultaneously enhancing the timeliness and accuracy of resource dispatch.
* _Minimization of Deployment Costs for Mobile Energy Storage and Water Supply Vehicles_: By optimizing regional partitioning, mobile energy storage vehicles and water supply vehicles can be strategically deployed to minimize dispatch distances, thereby improving resource allocation accuracy and reducing overall dispatch costs.
* _Enhancement of Disaster Response Capability and System Reliability_: Optimizing the layout of charging stations and reservoirs ensures efficient energy and water supplies during disasters while minimizing supply-demand gaps in high-priority load areas to improve system reliability and resilience [29].  
To achieve the above four objectives, we propose a multi-objective optimization framework and formulate the corresponding objective functions: procurement costs, cross-regional dispatch costs, deployment costs of energy storage equipment and water supply vehicles, and pre-deployment of charging stations and reservoirs. The goal is to maximize pre-disaster resource allocation efficiency to ensure rapid post-disaster response.
# Learning Motion Constraint-Based Spatio-Temporal Networks for Infrared Dim Target Detections
## 2 Materials and Methods
### Training Phase
#### 2.1.2 Network Details  
The full detection model is exhibited in Figure 5. It consists of two parts: a learning spatio-temporal constraint module (STM) and an extracting deep spatial features module (DFM). The STM is trained for learning the spatio-temporal constraints of multiple frames. The DFM is treated as a feature pyramid structure for learning the characteristics of different spatial dimensions between the target and the background. The input of the model is multiple frames, and the output is the prediction of the frames.  
Specific layer designs are shown in Figure 6. The input layer has four parameters, which are input sequence length t, image resolution, and the number of channels. Given that infrared images are normally dominated by grayscale, we used a single channel as the network input. Then, there are three Conv-LSTM layers. The convolutional kernel sizes are [t, 3, 3, 64], [t, 3, 3, 128], and [t, 3, 3, 64]. The design of the dimension and the channel comply with universal design rules and real experimental effects. To preserve feature maps in the temporal dimension, 3D convolutional kernels are used for the next max pooling operation. The parameter meaning is kept consistent with the preorder layer. Next, the same scale layers would be directly concatenated after up-sampling operations. The output of the network has three patterns: 8, 16, and 32 scales. The choice of scales mainly takes into account the size of the target. Before the calculation of the loss function, we integrated multi-scale feature maps for ease of calculation, which makes use of the list in Python. Notice that we placed batch normalization layers after each feature extraction layer mainly to accelerate the convergence of the model. The batch normalization layers could remove the correlation between features and make all features possess the same mean and variance [25].  
Figure 4: The handing of the time stream. The index i, i - 1..., i - t in the square indicates the image sequence.  
Figure 5: Overview of the proposed model.
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 5 Security Analysis
* Resistance to imitation attacks: Malicious attackers cannot obtain both the TA's master private key and the partial private key generated by the vehicle at the same time, so the attacker cannot successfully imitate a legitimate vehicle to generate a signature, and therefore cannot successfully launch a vehicle simulation attack. Malicious attackers will try to launch RSU imitation attacks to obtain vehicle-related privacy information. However, the attacker cannot generate a legitimate sum, so they cannot successfully simulate a legitimate RSU. * Resistance to single point failure attack: A malicious attacker can launch a single point failure attack on \(RSU_{j}\). However, the registered vehicle information is directly written into the trusted computing tree instead of \(RSU_{j}\)'s memory, so the adversary cannot obtain the registered vehicle information. The exit of a certain \(RSU_{j}\) will not change \(d_{RSU_{j}}\) and \(P_{RSU_{j}}\), so other \(RSU_{i}\) do not need to regenerate \((d_{RSU_{j}},P_{RSU_{j}})\). The multi-registration server structure will not cause the vehicle to fail to register due to a single \(RSU_{j}\), because other servers can also register for \(V_{i}\). The protocol in this chapter takes security and functional characteristics into consideration. Table 7 gives the security and functional characteristics of the protocol and compares them with existing related protocols [28; 29; 30]. The proposed protocol can guarantee all security characteristics. Existing related protocols [28; 29; 30] do not consider or cannot guarantee all security characteristics. The proposed protocol can resist more attacks that may occur in the wireless channel and provide better security than existing protocols. \begin{table}
\begin{tabular}{c c c c} \hline
**Security Attributes** & [28] & [29] & [30] & **Our Protocol** \\ \hline \(P_{1}\) & ✓ & ✓ & ✓ & ✓ \\ \(P_{2}\) & ✓ & ✓ & ✓ & ✓ \\ \(P_{3}\) & ✓ & ✓ & ✗ & ✓ \\ \(P_{4}\) & ✓ & ✓ & ✓ & ✓ \\ \(P_{5}\) & ✓ & ✓ & ✓ & ✓ \\ \(P_{6}\) & ✓ & ✗ & ✓ & ✓ \\ \(P_{7}\) & ✓ & ✓ & ✓ & ✓ \\ \(P_{8}\) & ✗ & ✗ & ✗ & ✓ \\ \hline \end{tabular} \(P_{1}\): “Untracability”; \(P_{2}\): “Anonymity of vehicle identity”; \(P_{3}\): “Perfect forward secrecy”; \(P_{4}\): “Resistance to DDoS attacks”; \(P_{5}\): “Resistance to replay attacks”; \(P_{6}\): “Resistance to man-in-the-middle attacks”; \(P_{7}\): “Resistance to imitation attacks”; \(P_{8}\): “Resistance to single point failure attack”.  
\end{table} 
Table 7: Security feature comparison.