esa-sceva/satcom-chunk-collection huggingface dataset:
# Conditional Dependencies and Position Fixing
## 7 Normalizing the Pool of Data  
The position fixing problem exploits the collection of various quality observations, which need to be processed prior to the final usage. Preparing a pool of data requires their normalization to achieve uniform probability density distributions. Adequate conditional dependency functions need to be created. Moreover, the relative uncertainty vector enables definitions of the basic probability assignments. Fixing aims at the selection of a point that represents the true location of a ship in the optimal way. An evidence fragment supports the choice of each item from the considered set of hypotheses. The degrees of support are expressed as the conditional dependability, which relies on the credibility attributed to the evidence items. The items with low uncertainty should more decisively contribute to the selection. To implement the idea, we need to create a hierarchy (\(\{\Theta_{R\!I}\}\)) for the available evidence. Table 1 contains the selected features for the example pools of the three indications. The recorded instances for each observation enable the creation of structures named _HMi_. The first processing engages histograms with their uncertainty assessments. The evaluation is made with reference to the _HMref_, the attributes of which are presented in the last piece of raw piece in Table 1. The assessments of the _HM1_ and _HM2_ are recalculated with respect to the best third observation. The final ranking list regarding the amount of uncertainty for this poll is \(\{\Theta_{R\!3},\,\Theta_{R\!1},\,\Theta_{R\!2}\}\). The ranges of the membership functions are gathered in the last column labelled \(d_{i}^{M}\). These MFs are important uncertainty-dependent characteristics when obtaining the conditional dependencies, and they are necessary for the verification of hypotheses on the representation of the fixed position. The data presented above enable us to define a membership function as specified by Equation (7). The formula and bin-to-bin additive method lead to the modified diagrams, which can be perceived of as conditional dependency functions:  
\[f_{i}(x)=\frac{\frac{1}{1+e^{-d_{i}(x-x_{j})}}}\quad\text{if }x\leq x_{ic} \tag{7}\]
## 6 Conclusions  
The process of training models for the semantic segmentation of satellite images, especially for tasks like aircraft recognition, heavily relies on manually labeled datasets. Creating such datasets is both time-consuming and expensive, posing a significant challenge to automating the analysis of such images. Additionally, due to the diverse and intricate nature of aircraft images, obtaining an adequate representation for recognition through image segmentation techniques remains a complex endeavor. With the advent of CNNs, new architectures like U-Net have emerged, which are capable of achieving excellent performance even with small training datasets. This research introduced a method for aircraft identification based on the U-Net model, and its efficacy was demonstrated with a modest training dataset (e.g., 100 images used in this study). We adapted the U-Net architecture for aircraft extraction, showcasing its ability to generate high-quality aircraft masks. Our tests demonstrated the efficacy of our method, which yielded a mean IoU score of 95.08% for the overlapped region and a pixel accuracy of 98.24%. Our work shows a promising approach for data-efficient and highly successful aircraft detection in satellite photos, highlighting the promise of CNN-based architectures like U-Net for completing challenging image segmentation tasks with little training data. Future researchmay emphasize refining and optimizing our model, paving the way for even more accurate and robust aircraft image segmentation in remote sensing applications.  
Conceptualization, F.S. and A.Y.; methodology, A.Y. and Y.I.A.; software, F.S. and A.Y.; validation, A.Y. and A.E.T.; data curation, A.Y.; writing--original draft preparation, F.S. and A.E.T.; writing--review and editing, A.Y. and Y.I.A.; supervision, A.Y. and A.E.T. All authors contributed to the study conception and design. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.
## 4 Results
### Geodetic Data (GNSS and DInSAR) Analysis
#### 4.5.2 DInSAR Analysis for the Period 1993-2020  
The DInSAR data used for this study covered the period of 1993-2020. In particular, the ERS1/2 and ENVISAT datasets (Figure 10A,D) were first analysed in both acquisition geometries (ascending and descending). As can be seen in Figure 10, the coverage of targets in the study area were well distributed, characterised by a density of about 60 targets/km\({}^{2}\) for the ERS1/2 dataset and about 250 targets/km\({}^{2}\) for the period of 2002-2010 (ENVISAT). In particular, for the periods of both 1993-2000 and 2002-2010, displacement rates in some sectors even higher than 10 mm/yr were recorded. Subsequently, the analysis was extended to the period of 2012-2013 due to the availability of high-resolution data acquired by the Cosmo-SkyMed satellite constellation (Figure 10E). Notably, the density of targets was much higher than previously, with about 2600 targets/km\({}^{2}\).  
Regarding displacement rates, again, values in some sectors were found to be greater than 10 mm/yr, particularly near the Sarno village urban area.  
Moreover, the observation window for displacement rates was extended to 2020 using an additional 138 SAR images from SENTINEL-1, collected in the period of 2016-2020. Figure 10F shows the mean displacement rate for SENTINEL-1 images.  
As with the other periods analyzed, it can be seen that the largest rates of displacement were, once again, concentrated within the faults F1 and F2. Although the SENTINEL-1 images were also in C-band and characterised by medium resolution, it was nevertheless possible to identify a high density of targets, i.e., about 1500 per km\({}^{2}\).
# Intelligent Decision Forest Models for Customer Churn Prediction
## 3 Methodology
### Logistic Model Tree (LMT) Algorithm  
The Logistic Model Tree (LMT) method combines LR with the DT approach. It can generate a model with excellent prediction performance while delivering an explainable structure [38]. Specifically, LMT is essentially a DT with LR functions at the leaves. Every inner node, as in conventional DTs, is coupled with a test on one of the features. The node contains \(k\) child nodes for a nominal feature with \(k\) values, and instances are sorted along one of the \(k\) branches based on the feature value. The node includes two child nodes for numeric features, and the test consists of comparing the feature value to a certain threshold: an instance is sorted down the left or right branch if its value for that feature is less or more than the threshold, respectively [39]. LMT is a tree structure composed of a set of inner or non-terminal nodes \(N\) and a set of leaves or terminal nodes \(T\). Let \(A\) indicate the entire set space, which is covered by all features contained in the data. The resulting tree thus provides a discontinuous partition of \(A\) into sections \(A_{t}\), and each section is depicted by a leaf in the tree:  
\[A=\bigcup_{i\in I}A_{i},\ \ A_{i}\cap A_{i^{\prime}}=\varnothing\ \ for\ i\
eq\ i^{\prime} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
Contrary to conventional DT, the leaves \(i\in I\) are connected to an LR function and not a class label. The LR function \(f_{i}\) considers a subset \(B_{i}\subseteq B\) of all features in the data, and models the class membership probabilities as:  
\[Pr(G=j\mid X=x)=\frac{e^{F_{j}\ (x)}}{\sum_{k=1}^{I}e^{F_{k}\ (x)}} \tag{2}\]  
where  
\[F_{j}(x)=\alpha_{0}^{j}+\sum_{b\in B_{i}}\alpha_{v}^{j}\times v \tag{3}\]
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 6 Performance Evaluation with TEXBAT
In particular, in order to view the performance of the proposed method intuitively, detailed spoofing detection, validation and recovered PVT results of ds2 (Static Over-Powered Time Push) and ds6 (Dynamic Matched-Power Position Push) are provided. Detection results of SQM and RAIM of the two scenarios are also provided for comparison. These two scenarios are chosen because they include different motion states of victim receivers and different power levels of spoofing signals. In addition, the spoofing attacks in the two scenarios falsify the positioning and timing results, respectively.
## 3 Electromagnetic-Thermal Bidirectional Coupling Model Formulation of TPMLG
### Thermal-Fluid Finite Volume Model of the TPMLG
#### 3.2.4 Thermal Conductivity  
Accurate determination of thermal parameters in the LG is a prerequisite for effective simulation before calculating thermal characteristics. Thermal conductivity plays an important role in the heat transfer process, affecting heat transfer rate and temperature distribution. Great differences among material thermal conductivities of the TPMLG result in distinct temperature differences among components of the TPMLG, which is especially evident between metal materials and insulation materials.  
In the TPMLG, the windings are formed by copper wires which are integrated very closely without gaps among copper wires. The windings are wrapped by insulation paper on the outer surface of approximately 0.5 mm in thickness. Its thickness is too thin to model in the simulation. Therefore, a wall thickness thermal boundary is applied to simulate the thermal resistance effect of the insulation paper which is not displayed in the simplified physical model. Heat transfer is regarded as one-dimensional steady heat conduction inside the thin wall. The thermal conductivities of the materials used for the TPMLG are listed in Table 2[23; 29].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Thermal Conductivity** \\  & **W/(m\(\cdot\)K)** \\ \hline stator core & 42.5/42.5/3.5 \\ PM & 6.4 \\ casing & 13.4 \\ winding & 387.6 \\ sleeve & 202 \\ insulation paper & 0.14 \\ slot wedge & 0.5 \\ air & 0.0272 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal conductivities of different components.
# Boosted Transformer for Image Captioning
## 3 Methodology
### Transformer Background  
Vanilla transformer [6] adopts the encoder-decoder architecture with each part stacking N identical layers, composed of the multi-head attention module and the feed-forward network module. Each module is coupled with the layer normalization and residual connection, which can simplify and steady the optimization process. Given that the transformer contains no recurrence or convolution, the sequential order is not leveraged and can notably affect the quality of generated descriptions. The work in [6] added an extra positional encoding after learning the word embedding to avoid this situation. The positional encoding is defined as:  
\[PE_{(pos,2i)}=\sin(pos/10,000^{2i/d_{model}}) \tag{1}\]  
\[PE_{(pos,2i+1)}=\cos(pos/10,000^{2i/d_{model}}) \tag{2}\]  
where _pos_ is the absolute position of the token in the caption and \(i\) is the dimension. Transformer uses scaled dot-product attention to deduce the correlation between queries \(Q\) and keys \(K\) and then obtains the weighted sum of the values \(V\). Scaled dot-product attention is the basis of multi-head attention, and this computational process is shown as follows:  
\[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{3}\]  
where the attention inputs are composed of queries \(Q\), keys \(K\), and values \(V\) with dimensions \(d_{k}\), \(d_{k}\), and \(d_{v}\). \(\frac{1}{\sqrt{d_{k}}}\) is used to counteract the effect of the large value of \(d_{k}\) pushing the softmax function into the regions of small gradients. Dot-product attention is faster and more space-efficient in practice because it can be implemented by parallel optimization [6].  
Compared with single attention, multi-head attention can learn different representation subspaces at different positions. It contains h identical attention heads, and each head is a scaled dot-product attention, performing the attention function on queries, keys, and values independently. Finally, the h attention outputs are concatenated and projected back to the original dimension, producing the final values.  
\[MultiHead(Q,K,V)=Concat(head_{1},\dots,head_{h})W^{O} \tag{4}\]  
\[head_{i}=Attention(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}) \tag{5}\]  
where \(W^{O}\in\mathbb{R}^{hd_{v}\times d_{model}},W_{i}^{Q}\in\mathbb{R}^{d_{model} \times d_{k}},W_{i}^{K}\in\mathbb{R}^{d_{model}\times d_{k}},W_{i}^{V}\in \mathbb{R}^{d_{model}\times d_{v}}\) are trainable projection matrices. To reduce the total computational cost, the work in [6] projected the original dimension of \(d_{model}\)=512 onto \(d_{k}=d_{v}=d_{model}/h=64\) and \(h=8\).  
The feed-forward network is another basic component. It is a two-layered fully-connected network with a ReLU activation function, which is applied to each position separately and can improve the nonlinear ability of the network.  
\[FFN(x)=\max(0,xW_{1}+b_{1})W_{2}+b_{2} \tag{6}\]  
where \(W_{1}\in\mathbb{R}^{d_{model}\times d_{ff}},W_{2}\in\mathbb{R}^{d_{ff}\times d _{model}}\) are trainable projection matrices and \(b_{1}\), \(b_{2}\) are the biases. In this work, \(d_{ff}\) = 2048 and \(d_{model}\) = 512.
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 3 TOSM-Based Autonomous Navigation Framework
### Semantic Modeling Framework
#### 3.1.2 On-Demand Database  
Figure 3 visualizes a TOSM-based environment database of a simple environment containing a multi-floor building. The database includes individuals of defined classes, data properties of the individuals, and object properties between individuals. In the figure, gray squares are individuals from subclasses of _Place_, and blue capsule-shapes are individuals from subclasses of _Object_. Unidirectional black arrows mean _isInsideOf_ property between individuals; two-way red and blue arrows mean _isConnectedTo_ and _isNextTo_ properties, respectively, that have the symmetric characteristic. The table shown on the left side of the figure describes datatype properties for an individual of _automaticDoor_.  
We generate an on-demand database based on the environment database considering the robot's hardware-dependent functionalities when it is assigned a mission. For example, assuming a robot that cannot drive on stairs is given a mission from _room1_ to _sidewalk1_, a database for _building2_ connected to _sidewalk1_ and _staircase1_ is not necessary for the robot; we can create an on-demand database by sorting out the database that the robot needs. Based on the on-demand database, the robot can realize that it is in _room1_ inside _floor2_ inside building1 using _isInsideOf_ properties connected to _room1_. Besides, the paths to _sidewalk1_ from _room1_ can be planned, utilizing the individuals of _Place_ connected to _room1_ by _isConnectedTo_.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datatype Property Hierarchy** & **Domains** & **Ranges** \\ \hline symbolicModel & name ID & Place & string int \\ \hline \multirow{2}{*}{explicitModel} & boundary coordinateFrame & Place & polygon string \\ \hline \multirow{3}{*}{implicitModel} & complexity level & Place & float int \\  & purpose & Room & string \\  & roomNumber & Room & int \\ \hline \hline \end{tabular}
\end{table}
Table 4: Datatype properties for _Place_.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datatype Property Hierarchy** & **Domains** & **Ranges** \\ \hline \multirow{2}{*}{symbolicModel} & name ID & Robot & string \\  & name ID & Robot & int \\ \hline \multirow{3}{*}{explicitModel} & pose & Robot & (x, y, z, quaternion) \\  & coordinateFrame & Robot & string \\  & velocity & Robot & (\(\mu\), v, w, p, q, r) \\  & size & Robot & (l, w, h) \\ \hline \multirow{2}{*}{implicitModel} & capability & Robot & string \\  & purpose & Robot & string \\ \hline \hline \end{tabular}
\end{table}
Table 5: Datatype properties for _Robot_.
# Delamination Effect on the Buckling Behaviour of Carbon-Epoxy Composite Typical Aeronautical Panels
## 2 Theoretical Background
### SMXB Approach  
Delamination growth is assessed via non-linear static analyses using the virtual crack closure technique (VCCT) [26]. The VCCT assumes that the energy released when a crack extends from length \(a\) to \(a+\Delta a\) is the equivalent amount of energy required to close the same crack. The delamination growth can be numerically simulated considering the fail release (FR) approach, together with the VCCT equations. In detail, delamination is built with two models with coincident nodes. The initially unconnected zone is simulated with separated nodes without the possibility of penetration, and the zone in which delamination can propagate is simulated with contact elements that are initially attached and can only disconnect when and where the failure criterion is met (Equation (2)-- linear power law).  
\[E_{d}=\left(\frac{G_{I}}{G_{Ic}}\right)^{\alpha}+\left(\frac{G_{II}}{G_{IIc}} \right)^{\beta}+\left(\frac{G_{III}}{G_{IIIc}}\right)^{\gamma}=1 \tag{2}\]  
In Equation (2), the term \(G_{j}\) is the energy release rate associated with the fracture mode \(j\) and \(G_{jc}\) is the critical value of the energy release rate associated with the fracture mode \(j\). The values for \(\alpha\), \(\beta\) and \(\gamma\) can be found experimentally (the linear criterion assumes \(\alpha=\beta=\gamma=1\)).  
In this manuscript, the VCCT-FR-based procedure SMXB was considered. This tool is considered to be valuable and robust as it overcomes the limitations of the standard VCCT, implemented in most of the commercial finite element software, related to the mesh and load-step dependency of the results. The SMXB was previously validated for different types of coupons, complex structures and different shapes of delamination in [23; 24; 27; 28], where the theoretical background of the procedure is reported in detail.
# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 6 Discussions
### Restoration Model of Dunhuang Murals  
Previous studies have demonstrated that deep learning techniques can be used to restore damaged murals, such as in restoring their color and content [37; 38]. However, these studies have not considered different types of mural damage during model training, resulting in restoration models with weak robustness. Our study considered both local and global damage restorations and achieved good restoration results.  
First, we constructed a dataset of 26,420 Dunhuang murals by selecting representative images. Subsequently, we reduced the noise in the images by setting different sizes of convolution kernels to reduce the influence of irrelevant contours. Finally, we trained the image restoration model using U- and shift-net architectures and tested the model. We used the model as the technical basis for the proposed Dunhuang mural restoration system.  
The restoration results show that the model performs well in restoring random irregular defects. The restoration of large-area defects can fill color blocks and contents; however, the boundary lines of some images are blurred. This indicates that to restore large-area defects, a better restoration effect may not be achieved based only on the contents of the  
Figure 11: Image selection interface.  
image's undamaged surrounding areas. Future work should consider building information regarding the knowledge contained in murals to improve the accuracy of restoration efforts.  
We employed the method of judging the nearest neighboring pixel blocks in the shift connect layer based on the features of the middle layer. This method allows the deep features of the image to be learned instead of being judged based on only the image's surface. Second, using \(\Phi_{L-I}(I)\) to learn from large-scale data improved the capture of global semantics. Finally, in terms of computation, using \(\Phi_{L-l}(I)\) enables us to directly compute all transformation vectors in parallel instead of solving them sequentially.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 2 Distinctive Characteristics of Tumor Microenvironment and Heterogeneity of EPR
### Cell Communication Within Tumor Microenvironment
#### 2.2.1 Direct Intercellular Communication  
Gap Junctions (GJs) are specialized membrane structures that facilitate direct intercellular communication through the formation of intercellular channels or pores. These pores bridge the 2-4 nm gap between adjacent cells, enabling the direct passage of various molecules and ions (up to 1.2 kDa in size) as well as electrical signals between cells [44,53,54]. These channels consist of two connexons, each formed by hexameric connexin oligomers (CXs) [44,54]. Connexins are synthesized in the endoplasmic reticulum, forming hexameric rings with four transmembrane regions and extracellular loops responsible for cell recognition and adhesion [53,54]. Regulation occurs through the phosphorylation of connexins, determining the assembly or disassembly of gap junctions [44].  
In cancer cells, the loss of functional gap junctions is observed, as various carcinogens, including toxins, organic solvents, metals, and pharmaceuticals, inhibit their function. Furthermore, connexins themselves can inhibit cell proliferation or induce cell migration and invasion outside of the tumor nucleus [44,55].
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 3 Mobile Platforms
### Ground-Based Platforms
#### 3.1.1 Manned Ground Vehicles  
Compared to foot-based surveys, vehicles, such as cars, trucks, or vans, can cover larger distances in less time (hundreds of km in a day) and are able to carry heavier payloads (large detection systems). However, their circulation is restricted to the existing road network and may be limited by the terrain typology (e.g., inaccessible places, like forests, cultivation fields), or other obstacles [17; 43]. Moreover, to keep good spatial resolution, during the survey, the vehicle speed should not exceed 13 m/s [17].  
Since the vehicle occupants may also be exposed to radiation risk, it is not the desirable method to be deployed in response to a scenario where the radiation intensity is high or is unknown (e.g., NPP accident or RN threat) [12; 17].