esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Networked Color Sensors
#### 2.5.2 Partial Color Detection  
Color is a perceived quality. We know that there is a difference between the real color of an object surface, and the color as measured by an imaging device. The surrounding environment influences this partial color, especially the background around an object. The color of the light in which it is perceived, also influences the recorded color. Color temperature [34] of the light source moves more towards blue when the light color temperature is higher, and towards red when the light color temperature is lower. Different light sources like natural light, tungsten filament lamps, halogen lamps, and LED lamps, all have their own light color temperature. These, and the characteristics of imaging devices themselves, influence how we record color.  
Before we attempt to restore the recorded color to the real color, we need to know if the image uses only partial color. Some representative partial color detection methods, including histogram statistics [35], gray balance method [36], white balance method, etc. can detect partial color in images. Histogram statistics show the complete color spectrum of the image. They will give the average brightness of three channel of red, green and blue (RGB) color space. We can judge whether the color of the original image is partial by comparing the average brightness of R, G and B channels. If the brightness of any is higher, the whole image color shifts toward that color. This method can detect that the shift exists, but not the reason for it. The Gray balance method assumes that the mean of the R, G, B values is equal in the whole image, which embodies as neutral \"ash\". The average brightness of every channel is corrected to Lab color space. The difference between recorded values and Lab values shows if a partial color exists. Light strength in the environment influences this method, as well as singular color in the image. White balance method deals with the existing mirror reflection image. It considers that the specular part of the mirror or the white area reflection can reflect the light color of the light source. We count the highest brightness value of every channel, convert it to Lab color space, obtain the relative homogeneous Lab coordinates, calculate the color lengths to the neutral point, and judge whether there is partial color. The absence of white or specular part in the objects distorts the results.  
All three methods are only suitable for some but not all situations. Therefore, average image color and highest brightness value limit partial color detection. We need to develop other methods for partial color detection.
# SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
## 5 Implementation and Evaluation Using an Example
### A Use Case on Chemical Pollution Monitoring
#### 5.2.1 Description of the Scenario  
Chemical pollution inspection is a significant use case defined in the SWARMs project. The effective inspection could be very useful mainly in three situations, including corrective/predictive chemical pollution inspection, after an extreme condition weather, or after an earthquake. For instance, after an earthquake, marine biologists observe some dead fishes on the beach. They suspect that the Hydrogen Sulfide (H\({}_{2}\)S) in a given area of sea (_rgn1_) is the cause. A mission (_mission1_: _inspect the water column rgn1_) is given to the MMT. Based on available vehicles and their capabilities, the MMT generates a mission plan (_mp1_) in order to achieve the goals (e.g., _goal1_: _pollution is detected, goal2_: _emergency level of pollution is estimated_) of the _mission1_. The _mp1_ is a plan which involves two AUVs (_auv1_ and _auv2_) with different capabilities (e.g., _take videos and measurement of the concentration of \(H_{2}S\)_) to accomplish the mission. Equipped with sensors such as \(H_{2}S\)_probe_, _underwater camera_, _lighting system_ and _side-scan sonar_, the two AUVs collaboratively detect H\({}_{2}\)S at two different depths (_p1_ and _p2_). In addition, a set of context information, such as weather (is element in _rgn1_), windspeed (is fast in _rgn1_), and different concentration level (\(H_{2}S\) in _p1_ is thin while \(H_{2}S\) in _p2_ is dense) are also obtained. Based on available context data, marine biologists want to know the emergency level of the polluted region so that they can estimate the biological impact of H\({}_{2}\)S and further take remedial measures accordingly.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method  
### Comprehensive Framework  
The proposed multi-object tracking framework, illustrated in Figure 1 and Algorithm 1, comprises the following four fundamental modules: a multi-scale feature adaptive enhancement (MS-FAE) Module, a cross-frame feature association module (CFAM), a Dynamic Motion Model (DMM), and a bi-Modal dynamic decision method. The architecture is distinguished by two pivotal innovations as follows:  
(1) Efficient dual-modal feature representation: By exploiting the deep semantic features from YOLO [24] for target appearance extraction, the framework circumvents the computational overhead of conventional re-identification (ReID) models, enabling seamless feature sharing between detection and tracking processes.  
(2) Dynamic fusion matching mechanism: A strategy is proposed to dynamically integrate motion matching with feature matching. The mechanism first employs a Transformer architecture to learn global feature correlations between cross-frame objects, and then combines multiple trajectory hypothesis motion matching. This approach effectively addresses the challenges of object disappearance and re-matching caused by occlusion.  
```
Input: Frames \(\{F_{t}\}\), Detector \(D\) Output: Trajectories \(\{T_{k}\}\)  Initialize \(\{T_{k}\}\leftarrow\emptyset\), memory \(\mathcal{M}\leftarrow\emptyset\); foreachframe \(F_{t}\)do \(\text{Det}_{t}\gets D(F_{t})\); /* Stage 1: Feature Enhancement */ foreach\(d_{i}\in\text{Det}_{t}\)do  Extract multi-scale features \(F_{i}\);  Apply attention fusion and deformable conv;  Adaptive pooling to obtain final \(F_{i}\); /* Stage 2: Motion Prediction */ foreach\(T_{k}\)do  Predict \(\$_{k}\) via motion models;  Update noise \(\mathbf{Q}^{t}\), \(\mathbf{R}^{t}\); /* Stage 3: Association */  Encode \(Q_{i}\), \(K_{j}\) with position/motion;  Compute grouped attention \(\Rightarrow S_{app}\);  Recall appearance for occluded tracks; /* Stage 4: Decision and Update */  Compute \(S_{not}\) via IoU, fuse scores \(S_{fuse}\);  Assignments \(\leftarrow\) Hungarian(\(S_{fuse}\)); foreachmatched \((d_{i},T_{k})\)do  Update \(T_{k}\), Kalman state, memory \(\mathcal{M}_{k}\); foreachunmatched \(d_{i}\)do  Initialize new \(T_{k}\), memory; foreachunmatched \(T_{k}\)do if inactive \(>\) 30 then  remove else  predict and update memory return\(\{T_{k}\}\)
```  
**Algorithm 1**Dynamic collaborative multi-object tracking
# Querying on Federated Sensor Networks
## 5 Related Work  
Due to recent technological advances and wide-range application opportunities, in-network querying and indexing on FSN is a promising research area to achieve efficient communication. Recent technological advances enable distant WSNs to be connected under an FSN in various  
Figure 7: Running both Distributed Quad-Tree (DQT) and Geographic Hash Table (GHT) protocols as intra-island querying protocols of the Layered Federated Sensor Network (L-FSN) protocol. A data point label DQT n:m represent the DQT-GHT combination, where DQT protocol is running on islands of size n to m while GHT protocol is running on the rest of the islands. The size of islands in terms of gateways is given in Table 2.  
ways. For example, improvements on smartphone technologies allowed smartphones to be able to establish communication among distant WSN deployment areas [12]. Deployment opportunities of FSNs in various environment from urban areas to underwater allowed diverse applications to be developed including power monitoring, intruder detection, environmental monitoring, localization and tracking [13; 14; 15].  
As an opportunity of studies of more than a decade, gained experience of in-network querying and indexing techniques can be improved to be adapted on FSNs. On a WSN, nodes interact with each other for data processing, aggregation and dissemination purposes [16]. Gathered data is routed to the querying or indexing unit by multi-hop routing techniques. Based on the underlying routing techniques, [PERSON] and [PERSON] categorize in-network querying techniques as geometrical, hierarchical cluster-based, hash-based, and tree-based with a discussion of WSN design metrics, such as energy-efficiency, distance-sensitivity, scalability and fault-tolerance [4].  
One of the FSN concepts is connectivity of geographically distant islands. These islands may occur due to partitioning of an existing WSN or prebuilt due to application purposes. A WSN can be partitioned because of node breakdowns caused by some environmental factors such as disasters or caused by device-related factors such as depletion of energy sources. Several related studies focus on on how to restore connectivity among islands when such a damage occurs [17]. Other than network partitioning, there are studies that focus on the island occurrences as part of the application use-case [18; 19]. In these studies connectivity of islands is established by static or mobile relay nodes.  
Mobility on an FSN can be investigated on three units: the event mobility, island and relay node mobility [20]. An event can be static such as temperature, or mobile such as an intruder. Island and relay nodes can be static like cameras or mobile like vehicles. Relay nodes can randomly scattered on the gaps among islands as well as optimally replaced. Islands can be connected with mobile devices which move randomly or deterministically. For instance, people-centric island federation is an example of mobile federation [21].
## 2 Results and Discussion  
### Synthesis of 5a-n  
The requisite spirooxindoles analogous were prepared by a multicomponent reaction (Scheme 1). The advantages of this efficient method were low-cost and readily available synthons for the synthesis of highly divergent compounds with high-importance applications. Fourteen analogues were prepared through the reactions of _bis_-benzylidine **1a-n**, which had been prepared according to our previous publication [9] with thioproline and 5,7-dibromoisatin, to afford the requisite target compounds. The chemical features of the requisite compounds were assigned based on HNMR, CNMR, IR, and CHN analysis.  
According to Scheme 2 and based on our previous study [9], the reaction proceeds _via_ one pot reaction, in which initially 5,7-dibromoisatin **2** reacted with thioproline **3** affording the azomethine ylide after the removal of carbon dioxide from the intermediate. Subsequently, the azomethine ylide reacted with the _bis_-benzylidine **1a-n** to provide the target compounds in a regioselective and diastereoselective manner. The reaction proceeded _via_ path A regio-selectively to afford the regioisomer products **5a-n**, while the second regio-isomers **5a-n**' did not occur (path B). There are possible diastereoselective products that could be formed, but in this case only diastereoselective compounds **5a-n** occurred _via_ the path C not D.
###### Abstract  
This paper presents a cognitive satellite communication based wireless sensor network, which combines the wireless sensor network and the cognitive satellite terrestrial network. To address the conflict between the continuously increasing demand and the spectrum scarcity in the space network, the cognitive satellite terrestrial network becomes a promising candidate for future hybrid wireless networks. With the higher transmit capacity demand in satellite networks, explicit concerns on efficient resource allocation in the cognitive network have gained more attention. In this background, we propose a sensing-based dynamic spectrum sharing scheme for the cognitive satellite user, which is able to maximize the ergodic capacity of the satellite user with the interference of the primary terrestrial user below an acceptable average level. Firstly, the cognitive satellite user monitors the channel allocated to the terrestrial user through the wireless sensor network; then, it adjusts the transmit power based on the sensing results. If a terrestrial user is busy, the satellite user can access the channel with constrained power to avoid deteriorating the communication quality of the terrestrial user. Otherwise, if the terrestrial user is idle, the satellite user allocates the transmit power based on its benefit to enhance the capacity. Since the sensing-based dynamic spectrum sharing optimization problem can be modified into a nonlinear fraction programming problem in perfect/imperfect sensing conditions, respectively, we solve them by the Lagrange duality method. Computer simulations have shown that, compared with the opportunistic spectrum access, the proposed method can increase the channel capacity more than 20% for \(P_{av}\)= 10 dB in a perfect sensing scenario. In an imperfect sensing scenario, \(P_{av}=15\) dB and \(Q_{av}=5\) dB, the optimal sensing time achieving the highest ergodic capacity is about 2.34 ms when the frame duration is 10 ms.  
c 2019 19 5290 27 November 2019; Published: 1 December 2019
###### Abstract  
The traffic queueing problem usually can be solved by two concepts: static and dynamic mode. Usually, the static mode may not reflect the real-world situation. This encourages us to propose the dynamic adjusting model to solve the traffic queuing problem with a time-varying feature. Our model develops two modes and includes eight formulae to calculate the analysis results. In our first calculating mode, the three formulae intend to simulate the maximum traffic flow in the real-world situation. The second mode is to calculate the results based on the simulation data generated by Monte Carlo simulation. To see our model capability, we validate our model by the simulation data and verify the results by the Monte Carlo simulation method. By our proposed performance measurement, the resulting outcomes show that our model outperforms the previous studies. Additionally, our solution procedure is simple and assumes the model following pseudo-randomly, which coincides with the current status. Furthermore, because of the Internet of Things (IoT) trend, one can expect that the data can be automatically collected. This leads to our proposed model can be implemented once the data are obtained.  
dynamic adjusting model; traffic queuing time; Monte Carlo simulation +
Footnote †: journal: Applied Sciences
## 2 Literature Review
### Color Landscape
#### 2.1.4 Color Quantitative Analysis Method  
The color landscape can be categorized in cities and blocks, down to the building materials' details, and there is rich color information. The recording and research of this color information generally fall into qualitative and quantitative categories. [PERSON] (2015) studied the effect of colors, shapes and boundaries of landscapes on emotion perception by in-depth qualitative interviews [6]. The progressive development of contemporary digital technology in software and hardware has assisted landscape research in transforming from qualitative descriptions of color to quantitative instrumental data [4]. [PERSON] (2021) proposed  
Figure 1: The process of color experience.  
an automatic extraction method for color features from landscape images based on image processing through experiments [24]. [PERSON], [PERSON] and [PERSON] (2021) quantitatively evaluated the color harmony and suitability degree of the plant community and external environment according to the Moon-Spencer (M-S) color harmony theory [2]. [PERSON] and [PERSON] (2018) used the quantitative analysis of the color of the Ming Dynasty City Wall in Nanjing as a case study [4]. The study of [PERSON], [PERSON], [PERSON] and et al. (2020) used Xunpu Village in southern Fujian as an example with which to conduct a quantitative analysis of color features and a qualitative evaluation of public perception [20]. [PERSON], [PERSON] et al. (2020) assessed the aesthetic quality of autumn landscape forest using scenic beauty estimation and analyzed the effects of color characteristics on ornamental value of autumn landscape forests based on color composition and color spatial pattern according to human visual characteristics [32]. There are many methods to describe color quantitatively and qualitatively, such as the hue, value, and chroma (HVC) model of the Munsell color system; the red, green and blue (RGB) model of the ratio of the 3-element color light; the hue, saturation, brightness (HSB) model using human eyes as the detector; the cyan, magenta, yellow, key (CMYK) model based on the mixture ratio of inks; and the Commission Internationale de l'Eclairage (CIE) color system described by pure mathematical models, etc., [4].  
How to reasonably quantify and create rich color landscapes to achieve the best visual perception of the color landscape at different visual scales to meet the aesthetic needs of the public better based on culture and regional context has become a challenging issue of the landscape color design and application in ethnic minority areas. There are no studies on color landscape/color-area ratios in an ethnic minority in China. Therefore, the aim of this paper was analyze HSB, color area ratio, primary and secondary colors, and embellishment color landscape modes in ethnic minority areas. The study focuses on the color landscapes of minority regions in China, particularly the landscapes of Xiangcheng County in the Tibetan region.
# Attenuation of Lightning-Induced Effects on Overhead Distribution Systems in Urban Areas
## 2 Lightning Details, FEM Model, and LIGHT-PESTO Code
### EM Fields' Computation  
The EM fields' computation was implemented in a commercial FEM software (COMSOL Multiphysics). For a correct evaluation of the EM fields, it was decided to use the RadioFrequency (RF) module, which enables solving the Maxwell's equations in the time domain for a given geometry.  
The considered geometry, shown in Figure 2, involves the lightning channel, the building, and the surrounding environment. The electrical infrastructure (i.e., the line conductors) was neglected, since it is commonly assumed that EM fields are computed in absence of the line.  
The lightning channel was a cylinder characterized by a radius of 0.1 m and a height of 1500 m, whose base was located at the center of the geometry. The lightning current distribution (1) was implemented by means of a Lumped Port applied to the surface of the cylinder. Note that the height of 1500 m was lower than a typical assumed value of 8 km, since the traveling wave did not reach the top of the channel in the considered time window.  
The building was a parallelepiped located symmetrically to the lightning channel. The building length was 2400 m in order to simulate an aggregate of houses representing a typical urban area. The height of the building was variable in order to provide a sensitivity analysis on the variation of this parameter. In accordance with [11], the building can be described as a Perfect Electric Conductor (PEC); thus the values of conductivity, permittivity, and permeability inside the building are useless.  
Figure 2: 3D FEM Model.  
The ground was assumed to be a PEC; thus, in order to reduce the mesh discretization, a hemisphere, characterized by a radius of 1500 was implemented. The surrounding environment was assumed to be air, characterized by \(\sigma=0\), \(e_{r}=1\), and \(\mu_{r}=1\).
## 4 Validation and Comparison
### Comparative Testing of Baseline Methods
#### 4.4.6 Path Loss Prediction Method Based on AutoML Tool  
Automated Machine Learning has emerged as a revolutionary approach in the field of machine learning. It automates the often complex processes of model selection and hyperparameter tuning. In this study, we harnessed the power of the TPOTRegressor from the spot library to tackle the path loss prediction problem.  
The parameters of the TPOTRegressor are crucial in determining the effectiveness and efficiency of the automated model search. Table 10 presents the key hyperparameters and their selected values for our path loss prediction task:  
After setting the parameters, the AutoML tool automatically searched for the optimal machine-learning pipeline and found an ensemble model for path loss prediction. This ensemble model combines Stochastic Gradient Descent (SGD) and Random Forest regression. Table 11 presents the key parameter settings of the two regression methods:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Generations & 5 \\ Population Size & 30 \\ Random State & 42 \\ Crossover Rate & 0.8 \\ Mutation Rate & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Parameter and their values for AutoML tool.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Search Space** & **Selected Value** \\ \hline Number of GCN layers & [2; 3; 4] & 3 \\ Units in first hidden layer & [32; 64; 128] & 64 \\ Units in second hidden layer & [32; 64; 128] & 64 \\ Activation function & [“relu”, “tanh”, “sigmoid”] & “relu” \\ K value for KNN & [3; 5; 7] & 5 \\ Learning rate & [0.0001, 0.001, 0.001] & 0.001 \\ Weight decay & \(\{1\times 10^{-4},5\times 10^{-4},1\times 10^{-3}\}\) & \(5\times 10^{-4}\) \\ Loss function & [“MSE”, “SmoothL1 Loss”] & “SmoothL1 Loss” \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hyperparameter search space and selected values for GNN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Search Space** & **Selected Value** \\ \hline Encoder Layers & [1; 2; 3] & 2 \\ Embedding Dimension & [16; 32; 64; 128] & 64 \\ Attention Heads & [2, 4, 8] & 4 \\ Activation Function & [“relu”, “gelu”] & “relu” \\ Learning Rate & \(\{0.00005, 0.0001, 0.001\}\) & 0.001 \\ Weight Decay & \(\{1\times 10^{-4},5\times 10^{-4},5\times 10^{-3}\}\) & \(5\times 10^{-4}\) \\ Loss Function & [“MSE”, “SmoothL1 Loss”] & “SmoothL1 Loss” \\ \hline \hline \end{tabular}
\end{table}
Table 9: Hyperparameter search space and selected values for Transformer.