esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 3 Local Mapping on Vehicle
### Semantic Element Ranging
#### 3.2.3 Aerial Element Ranging  
The ranging of aerial features is accomplished through target tracking and geo-motion triangulation. Target tracking is based on perceptual semantic detection instance, meaning that only aerial semantic elements detected through perception are tracked. In monocular vision, triangulation introduces scale uncertainty. To address this, the algorithm leverages ego-motion to obtain the relative pose between two tracking frames, which is equivalent to obtaining the baseline of the next two observations (i.e., binocular vision). This approach resolves the scale problem of monocular ranging.  
The derivation formula is:  
\[\mathbf{p}_{i}^{w}=\mathbf{argmin}\sum_{j=1}^{M}\|\mathbf{r}_{ij}\|^{2}, \tag{2}\]  
\[\mathbf{r}_{ij}=z_{ij}-\pi\left(\mathbf{p}_{i}^{w},R_{w}^{e},\mathbf{r}_{e}^{w}\right) \tag{3}\]  
Figure 3: Example of semantic segmentation and detection results of local mapping on a vehicle.  
where \(\left\{\mathbf{R}_{\mathbf{u}\mathbf{v}}^{\ell},\mathbf{t}_{d}^{\mathbf{v}}\right\}_{j=1,...,\mathbf{M}}\) is the pose of the monocular camera at different times, which can be obtained from the ego-motion calculation, and \(\mathbf{p}_{i}^{\mathbf{u}\mathbf{v}}\) denotes the spatial position of the 3D feature points of semantic instance \(i\) in the world coordinate system. Examples here include, but are not limited to, traffic signs, lights, poles, etc. Semantic segmentation and detection are implemented on the same network. \(\mathbf{z}_{ij}\) denotes tracked 2D feature points, and the optical flow method is used to track and determine multi-frame 2D semantic observations belonging to the same aerial landmark, where \(\mathbf{j}\) is used to identify multi-frame observations belonging to the same semantic instance, \(\mathbf{i}\). \(\mathbf{\pi}(\mathbf{\cdot})\) is a reprojection function, and the Gauss-Newton method or the LM method is usually used to minimize the reprojection error to find the optimal \(\mathbf{p}_{i}^{\mathbf{u}\mathbf{v}}\).  
After extracting the 3D semantic features from consecutive multi-frame images, they are transformed into the world coordinate system, with the first frame as the origin using the ego-motion information, \(\mathbf{n}\). Then, the transformed multi-frame semantic elements are merged to generate a local semantic map [33]. An example of the local semantic map is illustrated in Figure 4.
# A Greedy Algorithm for Neighborhood Overlap-Based Community Detection
## 2 Terminology and Examples
### Modularity Score  
The modularity score [2] of a community C of \(n\) vertices \(v_{1}\), \(v_{2}\),..., \(v_{n}\) is defined as:  
\[\sum_{\begin{array}{c}v_{i}\in C,v_{j}\in C\\ i\
eq j\end{array}}A_{i,j}-\frac{k_{i}k_{j}}{2m}\]  
wherein \(A_{i,j}\) = 1 if there is an edge between two vertices \(v_{i}\) and \(v_{j}\) and \(A_{i,j}\) = 0 otherwise. Let \(k_{i}\) and \(k_{j}\) denote the degree of any two vertices \(v_{i}\) and \(v_{j}\), and let \(m\) be the total number of edges in the graph. Essentially, the formula considers all pairs of vertices \(v_{i}\) and \(v_{j}\) among the \(n\) vertices constituting the community and assigns a positive score of \(1-k_{i}k_{j}/(2\times m)\) if there is an edge between the two vertices \(v_{i}\) and \(v_{j}\) and a negative score of \(0-k_{i}k_{j}/(2\times m)\) if there is no edge between the two vertices \(v_{i}\) and \(v_{j}\). Note that the product of the degree of any two vertices in a graph is less than or equal to twice the number of edges in the graph [14]. Hence, for a pair of vertices \(v_{i}\) and \(v_{j}\), the modularity score is in the range (0... 1) if there is an edge between \(v_{i}\) and \(v_{j}\), and the modularity score is in the range (\(-\)1,..., 0) if there is no edge between the pair \(v_{i}\) and \(v_{j}\). The cumulative modularity score [2] of a partition of the network into \(p\) communities is the sum of the modularity scores of the individual communities, defined as:  
\[Q=\sum_{l=1}^{p}\sum_{\begin{array}{c}v_{i}\in C_{l}\\ v_{j}\in C_{l}\\ i\
eq j\end{array}}A_{i,j}-\frac{k_{i}k_{j}}{2m}\]  
Figure 1: Example to illustrate the calculation of the neighborhood overlap score and identification of weak ties.  
Figure 2: Example to illustrate the calculation of modularity scores of communities. (**a**) Partition with high cumulative modularity score; (**b**) Partition with low cumulative modularity score.  
Figure 2 illustrates the calculation of the cumulative modularity scores for two different partitions of a graph. The first partition (i) is a more appropriate split of the vertices into two communities (there are more edges among vertices within a community compared to the number of edges crossing the two communities) and hence has a higher modularity score (both the individual community modularity scores and the cumulative modularity score are high) compared to the second partition (ii) for which there are more edges crossing the two communities compared to the number of edges between vertices within the individual communities.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 4 Proposed Scheme
### Data Verification Process
#### 4.4.3 Consistency Verification  
To check the consistency of the query result \(c(ds)_{\omega}\), the user directly uses the hash function \(h_{\upsilon}\) to hash \(C^{*}(C(DS)_{w\in W})\) sent from the fog node and further sends \(h^{u}=h_{\upsilon}(C^{*}(C(DS)_{w\in W}))\) to the blockchain for the comparison between \(h^{ds_{\omega}}\) and \(h^{u}\). Since the data stored on the blockchain are obtained based on the consensus mechanisms, such as PBFT or Raft, if the comparison result shows that the hash value of \(h^{s_{\omega}}\) is equal to the hash value of \(h^{u}\), the user can confirm that the received query result is consistent with the query result sent from the fog node \(g_{\omega}\). Moreover, the fog node \(g_{\omega}\) cannot repudiate the incorrectness and incompleteness of the query result due to network problems. The process of consistency verification is shown in Algorithm 4.  
```
0:\(h^{\delta_{o}}\), \(h^{u}\), ChainGroup \(STORAGE\)
0: The consistency of \(c(ds)_{\omega}\)
1:\(chain\leftarrow ChainFactory.openChain(STORAGE)\);
2:\(table\leftarrow Chain.EQ(chain,num)\);
3:\(list[\:]\leftarrow table.Entry(time)\);
4: Store \(h^{\delta_{o}}\) sent from \(g_{o}\);
5: Store \(h^{u}\) sent from the user;
6:if\(h^{\delta_{o}}\) is equal to \(h^{u}\)then
7: The query result is consistent;
8:else
9: May be something wrong with network problems;
10: return
11:endif
```  
**Algorithm 4** Consistency verification.  
Moreover, the scheme divides the cost the LBSP used to pay for the third-party storage into two parts, one part remains--the original storage cost--and the other part is given by the user incentivized by verifying the hash value of the data published by the fog node on the blockchain. The more times fog nodes return data honestly, the more incentive rewards they receive.
###### Abstract
Alternatively, many modern-day poststocking evaluations incorporate telemetry to examine individual fish behavior and identify biotic or abiotic interactions that may be resulting in recruitment bottlenecks [5; 6; 10; 29; 31; 32]. One of the first evaluations of this type was performed on fall-stocked Muskellunge fingerlings (307-333 mm total length [TL]) in two Wisconsin natural lakes and documented variable survival of tagged fish over 34 d (43-85%) and that fish moved from the stocking location within 14 d [2]. Other radiotelemetry evaluations examining age-0 or age-1 Muskellunge poststocking movement and survival are limited to those conducted in Southern US rivers and Midwestern reservoirs, but these studies documented poor survival despite high poststocking dispersal rates and larger stocking sizes (\(\geq\)255 mm [5; 6; 10]). These studies further document that poststocking survival is highly variable among systems and that poststocking predation risks may not be minimized at stocking size ranges up to at least 335 mm. More recently, age-1 Muskellunge telemetry data collected from Spirit Lake, Iowa, documented that stocked Muskellunge need to be reared to sizes beyond 375 mm for poststocking survival to be high (\(\geq\)90% [29]). Few observations of fish reared, radio-tagged, and stocked at TLs \(\geq\) 375 mm exist; thus, it is relatively unknown if models relating to increased Muskellunge size at stocking accurately represent improvements in Muskellunge poststocking survival in the wild. Furthermore, the emergence of new hatchery production techniques has allowed more consistent production of large Muskellunge in Iowa [31], yet the success of these stockings has not been validated. In lakes where artificial propagation is needed to enhance or maintain Muskellunge populations, identifying cost-effective stocking strategies that augment the adult population may be the most important Muskellunge management tool available to managers. If stocking large yearling Muskellunge results in more predictable stocking success across a gradient of lake types, then more cost-effective stocking programs could be developed, and adult Muskellunge populations would benefit. Thus, the objective of my study was to evaluate poststocking survival, dispersal, movement, proximity to shore, depth, and vegetation usage of large (\(\geq\)375 mm) yearling Muskellunge stocked in two Iowa natural lakes of varying complexity.
## 3 Results and Discussion  
### Conventional Technical Performances of the Modified Asphalt Binders  
It can be seen in Figure 3a that, after the reinforcement addition, the penetration of the three modified asphalt binders decreased to different levels compared to those of base asphalt. Among them, the penetration of CaCO\({}_{3}\) whiskers-modified asphalt is 53.5, which is the largest reduction. The penetration value of nano-CaCO\({}_{3}\)-modified asphalt is 83, which is the minimum reduction. The aramid fiber-modified asphalt has a penetration value of 80, which is reduced by 10%. In Figure 3b, the bar graph is shown for the softening point of different reinforcement-modified asphalt binders under the same dosage conditions. It can be seen that, after adding the reinforcement, the softening points of the three modified asphalt binders increase to different degrees compared to that of base asphalt. The results of 10 \({}^{\circ}\)C ductility of different reinforcement-modified asphalt binders are shown in Figure 3c. It can be seen that, after adding the reinforcing agent, the ductility of aramid fiber-modified asphalt is slightly increased compared with that of base asphalt, and the ductility of CaCO\({}_{3}\) whiskers-modified asphalt has the largest decrease in ductility.  
Figure 3: Conventional test results of the modified asphalt binders. (**a**) Penetration (0.1 mm), (**b**) softening point (\({}^{\circ}\)C), (**c**) Ductility (mm).
## 5 Knowledge Extraction in SKATEBOARD
### Knowledge Extraction  
Once the data are acquired and the ontology is defined, the next step is the extraction of knowledge from the data themselves. The primary objective of this phase is to extract entities, establish relationships among them, and capture meaningful attributes.  
Entity extraction involves the discovery and detection of entities across a wide range of data. SKATEBOARD utilises NER, focusing on discovering and classifying entities into predefined categories or types. Furthermore, SKATEBOARD performs NEL to connect recognised entities to relevant ontologies, such as DBpedia and GraphBRAIN.  
Relation extraction is an essential step for linking entities together. This phase varies depending on the data's nature, but employs Natural Language Processing (NLP) techniques for unstructured data. The availability of ontologies, as integrated into SKATEBOARD, allows for assigning relationships between the extracted entities based on predefined definitions.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 4 Analysis Dual-Band Antenna Solutions Operating in LTE and 5G Systems at 3.6 GHz  
In the literature, there is a small number of proposed solutions for dual-band microstrip antennas operating only in 5G systems at the frequency of 3.6 GHz and simultaneously in the 5 GHz band [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. The published solutions are characterized by a compact solution, small geometric dimensions, and a wide bandwidth. Many of these solutions are designed on the RT Duroid 5880 laminate with a dielectric constant \(\varepsilon_{r}\) equal to 2.2, and on the FR 4  
Figure 3: LTE-U uplink and downlink.  
Figure 2: LTE-U downlink only.  
laminate with a dielectric constant \(\varepsilon_{r}\) equal to 4.2. In the analysed antennas, the radiating patch in most cases has a rectangular shape and is powered by a microstrip line. The differences in the proposed antennas concern the modification of the radiator shape on the basis of solutions used in fractal antennas. As a result, different bandwidths of the proposed antennas are obtained.  
The antenna proposed in the article, working in LTE-U and 5G systems, is designed for two resonant frequencies, equal to 3.6 GHz and 5.5 GHz, respectively, using the RT Duroid 5880 laminate and a crescent patch. The proposed antenna is designed to achieve a bandwidth covering the frequency ranges from 3.4 GHz to 3.8 GHz (operating frequencies of the 5G system) and from 5.150 GHz to 5.850 GHz (operating frequencies of the LTE-U system).
## 5 Guidance and Control Algorithm
### Rendezvous
#### 5.1.1 Fuel-Optimal Control Algorithm  
According to [39; 40], the analytic solution of the fuel optimization problem is derived using the calculus of variations and is described in the HCW equation. This analytical solution reduces the computational burden and explicitly obtains optimal solutions without solving the problem onboard. Consequently, the controlled reference orbital states and optimal thrust vector are derived as functions of the state vector and fundamental matrix. The general state-space equation is expressed as  
\[\dot{\mathbf{X}}(t)=\mathbf{\mathrm{A}}(t)\mathbf{X}(t)+\mathbf{\mathrm{B}}(t)\mathbf{u}(t), \tag{1}\]  
where \(\mathbf{X}(t)=\begin{bmatrix}\mathbf{r}(t)&\dot{\mathbf{r}}(t)\end{bmatrix}^{T}\) is the state vector, \(\mathbf{u}(t)\) is the control input, \(\mathbf{\mathrm{A}}(t)\) is the state matrix, and \(\mathbf{\mathrm{B}}(t)\) is the input matrix. The state matrix \(\mathbf{\mathrm{A}}=\begin{bmatrix}\mathbf{0}_{3\times 3}&\mathbf{\mathrm{I}}_{3\times 3}\\ \mathbf{\mathrm{A}}_{1}&\mathbf{\mathrm{A}}_{2}\end{bmatrix}\), \(\mathbf{\mathrm{A}}_{1}=\begin{bmatrix}3n^{2}&0&0\\ 0&0&0\\ 0&0&-n^{2}\end{bmatrix}\), and \(\mathbf{\mathrm{A}}_{2}=\begin{bmatrix}0&2n&0\\ -2n&0&0\\ 0&0&0\end{bmatrix}\). The fundamental matrix associated with the HCW equation is well known from [41]:\[\mathbf{\Phi}=\begin{bmatrix}\mathbf{\Phi}_{A}\\ \mathbf{\Phi}_{A}\end{bmatrix}=\begin{bmatrix}4-3\cos nt&0&0&\frac{\sin nt}{n}&\frac {2(1-\cos nt)}{n}&0\\ 6(\sin nt-nt)&1&0&\frac{2(\cos nt-1)}{n}&\frac{4\sin nt}{n}-3t&0\\ 0&0&\cos nt&0&0&\frac{\sin nt}{n}\\ 3n\sin nt&0&0&\cos nt&2\sin nt&0\\ 6n(\cos nt-1)&0&0&-2\sin nt&4\cos nt-3&0\\ 0&0&-n\sin nt&0&0&\cos nt\end{bmatrix}, \tag{2}\]  
where \(n\) denotes the mean motion.  
For the fuel-optimal reconfiguration problem in circular orbits, the independent variable is time, where \(b(t)=1,k=1\), and \(f(t)=1\). Therefore, the cost function and analytical solution are derived as follows:  
\[J=\frac{1}{2}\int_{t_{0}}^{t_{f}}u_{opt}^{T}u_{opt}d\tau. \tag{3}\]  
Consequently, the explicit analytic solution is described as  
\[\mathbf{S} =\int_{t_{0}}^{t_{f}}\mathbf{\Phi}_{A}^{T}\mathbf{\Phi}_{A}d\tau \tag{4}\] \[K =\mathbf{\Phi}_{f}^{-1}\mathbf{\xi}_{f}-\mathbf{\Phi}_{0}^{-1}\mathbf{\xi}_{0}\] \[u_{opt} =\mathbf{\Phi}_{A}\mathbf{S}_{f}^{-1}\text{CK}\] \[J =\frac{1}{2}K^{T}\mathbf{C}^{T}\mathbf{S}_{f}^{-1}\mathbf{C}K\] \[\mathbf{\xi}=\begin{bmatrix}\mathbf{r}\\ \mathbf{r}\end{bmatrix} =\mathbf{\Phi}(\mathbf{\Phi}_{0}^{-1}\mathbf{\xi}_{0}+\mathbf{C}^{-1}\mathbf{S}\mathbf{S }_{f}^{-1}).\]
# Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
## 5 Lightweight Identification Model for Shoveling Position
### Network Model and Improvements
#### 5.2.2 Introduction of RFB Module  
With the progression of deep learning, efforts are being made to optimize performance, spatial awareness, and computing efficiency to boost the extraction of target feature information and improve outcomes in visual tasks. Presently, some widely utilized techniques for target feature extraction encompass SPP [20], SPPF [21], ASPP [22], SSPCSPC [23], and RFB [24]. SPP fuses features at different levels of an image by pooling feature maps of different scales so that the network can capture characteristics that are important at different scales of the input image. SPPF enhances SPP by incorporating a feature fusion step with multi-scale pooling; hence, it augments the richness and diversity of feature representation. ASPP uses dilated convolutions to improve resolution while keeping the original receptive field. This makes it better at adapting to the edges of objects. The SSPCSPC model acquires spatial information at several scales while employing CSP modules to augment feature variety. RFB strengthens the interaction between features through recursive feature fusion and improves the ability to understand complex scenes. Table 6 displays the pertinent parameters for the aforementioned feature extraction methods.  
Table 6 shows that SPP, SPPF, and RFB have low numbers of parameters and computational demands. Nevertheless, ASPP and SSPCSPC have higher numbers of parameters and computational demands, which makes the models more complicated and memory intensive. SPP pooling typically immobilizes and may not adjust to variations in input size, thereby overlooking useful details. SPPF utilizes multi-scale pooling, executing several downsampling operations on the input feature maps, in contrast to SPP. Nonetheless, SPPF generally employs fixed pooling window sizes, which are inflexible and may not facilitate efficient feature extraction in every context. Therefore, it is not the best choice for tasks such as detecting shovel point positions. RFB focuses on capturing the details of local features and, compared to SPPF's global average pooling, better keeps spatial information and picture details, which makes it easier to find edge features at shovel point positions. The structure is seen in Figure 21.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters** & **GFlops** \\ \hline SPP & 7,225,885 & 16.5 \\ SPPF & 7,235,389 & 16.5 \\ RFB & 7,895,421 & 17.1 \\ SSPCSPC & 13,663,549 & 21.7 \\ ASPP & 15,485,725 & 23.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of parameters and GFlops of five feature extraction models.  
Figure 20: The bottleneck structure of ghost module, where: (**a**) step size is 1; (**b**) step size is 2.
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 5 Discussion
### Advantages and Drawbacks of the ARIBC  
When comparing the ARIBC against alternatives reviewed in Section 2, the following can be noted:  
* In terms of functionality, the ARIBC supports both eponymous and anonymous reporters. Moreover, it allows an initially anonymous reporter to later revoke her/his anonymity, should s/he so chooses, so as to allow her/his participation in a reward scheme.
* Contrary to simple, web-based applications with no supporting identification infrastructure, the ARIBC allows secure two-way communication between authorities and reporters.
* In terms of implementation, the ARIBC is simpler to implement than schemes based on traditional PKI. A reporter's public key is derived from her/his identity, hence no pre-enrollment is required, unless the reporter chooses to become eponymous at the registration phase.
* Being based on IBC, the ARIBC requires neither certificate management nor a key revocation mechanism, in contrast to traditional PKI-based schemes.
* Being based on IBC, the ARIBC has an inherent key escrow mechanism. Whether this characteristic of IBC-based schemes constitutes a drawback or not depends on the context of the particular application scenarios. In the case of anonymous reporting, the conjecture (to be confirmed experientially by means of user acceptance studies) is that it is not. For eponymous reporters, solutions to the key-escrow problem such as the one in [53] can be considered.
# Automated Design of the Deep Neural Network Pipeline
## 2 Background
### Deep Neural Network Pipeline  
This section describes the deep neural network pipeline. At each point in the pipeline, design decisions have to be made to choose the appropriate technique to use for the particular process. These decisions are usually made manually by researchers. The research presented in this paper aims to automate the decision making using optimization. Based on a survey of the literature [9; 10; 11], we summarize the processes comprising the deep neural network pipeline to include the following:  
* Preprocessing (PP)--Involves processing the data, images or text, to be in a format required for application of the neural network with the aim of cleaning and altering the input data with the goal of bettering the eventual performance of the neural network. Studies have shown that in the preprocessing stage, not only are the techniques themselves important; the order in which they are applied will affect the performance of the stages that follow [12]. Different preprocessing techniques are used for image processing and text processing.
* Data Augmentation (DA)--Data augmentation techniques are used in image processing to increase the set of images available for training and testing of the neural network. Data augmentation has been shown to result in better performance across the many problem instances that image processing is comprised of [13].
* Feature Engineering (FE)--Feature engineering techniques are used in text processing to extract relevant features from data. When applying a well chosen feature engineering technique, it has been shown to provide a significant increase in the accuracy of the deep neural networks that use the produced feature representation, both in the case of sentiment analysis [14] as well as spam detection [15; 16].
* Neural Network Architecture Selection (NNAS)--From previous work, it is evident that different deep neural networks are suitable for different image processing [17; 18; 19] and text processing [20; 21]. This step in the pipeline involves determining which neural network architecture should be used to solve the problem at hand.
* Hyper-parameter Tuning (HT)--Deep neural networks requiring various parameters to to be tuned, e.g., learning rate [2].  
When considering the No Free Lunch Theorem [22], it is clear that the task of designing a deep neural network pipeline is one that will have to be repeated for each new problem domain, and potentially for each dataset. A detailed description of the various options for the processes making up the deep learning pipeline for image processing and text processing is given in Appendix A.