esa-sceva/satcom-chunk-collection huggingface dataset:
# Affinity Propagation Clustering Using Path Based Similarity
###### Abstract
Next, inverse weighted clustering algorithm [20] is utilized on all local cluster exemplars to select well-suited global exemplars of all the samples. Finally, a sample is assigned to the respective cluster based on its similarity with respect to global exemplars. APC and these aforementioned variants of APC, however, are only suitable for datasets with simple structure (i.e., non-overlap or non-intersecting clusters), since they mainly depend on the Euclidean distance between samples. When dealing with datasets with complex structures, the performance of these approaches downgrades sharply. [PERSON] et al. [21] suggest a path-based similarity for APC (pAPC) to explore the complex structure of samples. Particularly, for pairwise samples, pAPC finds out the longest segment for each path directly and indirectly connecting these two samples. Then it chooses the shortest segment from all the found segments and takes the length of that segment as the path-based similarity between the pairwise samples. Next, pAPC inputs this similarity into APC to cluster samples. [PERSON] et al. [22] proposed APC based on geodesic distances (APCGD). APCGD uses the negative geodesic distance between samples as the similarity between samples, instead of the negative Euclidean distance. These approaches improve the performance of APC on datasets with complex structures. However, their performance is found to be unstable, APCGD involves tuning more than two scale parameters, and both of them are very time consuming. [PERSON] et al. [23] applied \(k\)-path edge centrality [24] on social networks to efficiently compute the similarity between network nodes and then employed APC based on that similarity to discovery communities. To improve the performance of APC on samples distributed with complex structures (as the toy examples in the Section of Experiment shown), we propose an APC based on path-based similarity (APC-PS for short). Different from pPAC, we apply APC based on negative Euclidean distance to find exemplars (or cluster centers) at first. Next, we introduce the path-based similarity to explore the underlining structure between samples and exemplars, and then assign non-exemplar samples to its closest cluster using the path-based similarity. Our study on toy examples shows that, the proposed path-based similarity can better capture the underlying structure of samples than the widely used negative Euclidean distance based similarity. The empirical study on publicly available datasets demonstrate that APC-PS can achieve better results than original APC, pAPC, APCGD and other representative clustering algorithms, and it is more robust to input values of \(p\) than APC. The rest of this paper is organized as follows. APC and the proposed APC-PS is introduced in Section 2. Section 3 provides the experimental results on synthetic datasets and UCI datasets [25]. Conclusions and future work are provided in Section 4.
# Formation of a Colloidal CdSe and ZnSe Quantum Dots via a Gamma Radiolytic Technique
## 4 Results and Discussion  
### Formation Mechanism of CdSe and ZnSe Nanoparticles  
In general, 1.25-MeV \({}^{60}\)Co gamma rays interact with matter predominantly by absorption events (photoelectric absorption and pair production) and scattering events (coherent Rayleigh scattering and incoherent Compton scattering). These interactions result in the formation of secondary electrons in aqueous solution, mainly originating from Compton scattering, which consequently induce among others, hydrated electrons (e\({}_{eq}^{-}\)), hydroxyl radicals (OH\({}^{+}\)), and hydrogen radicals (H\({}^{+}\)) by radiolysis of water according to [25; 26; 27; 28; 29; 30; 31; 32]:  
\[\mathrm{H_{2}O}\ \rightarrow\ \mathrm{e_{eq}^{-}},\ \mathrm{H^{+}},\ \mathrm{H_{3}O^{-}},\ \mathrm{H^{+}},\ \mathrm{OH^{+}},\ \mathrm{H_{2}},\ \mathrm{H_{2}O_{2}}\ \ \ \ \ \ (Radiolysis\ \mathrm{of\ water}) \tag{1}\]  
The hydrated electrons, e\({}_{\mathrm{aq}}^{-}\), are strong reducing agents, and reduce selenium into selenide (Se\({}^{2-}\)):  
\[\mathrm{Se^{0}}\ +\ 2\mathrm{e_{aq}}^{-}\ \ \rightarrow\ \mathrm{Se^{2-}} \tag{2}\]  
In the aqueous solution, cadmium sulfite or zinc sulfite can dissociate into positive Cd\({}^{2+}\) or Zn\({}^{2+}\) cations and negative SO\({}_{3}\)\({}^{2-}\) anions:  
\[\mathrm{CdSO_{3}}\ \rightarrow\ \mathrm{Cd^{2+}}\ +\ \mathrm{SO_{3}}^{2-}\ \ \mathrm{or}\ \ \mathrm{ZnSO_{3}}\ \rightarrow\ \mathrm{Zn^{2+}}\ +\ \mathrm{SO_{3}}^{2-} \tag{3}\]  
The Se\({}^{2-}\) ions can be reacted with Cd\({}^{2+}\) or Zn\({}^{2+}\) ions to generate CdSe or ZnSe nanoparticles throughout the reactant medium via the nucleation process:  
\[\mathrm{Cd^{2+}}\ +\ \mathrm{Se^{2-}}\ \rightarrow\ \mathrm{CdSe^{0}}\ \ \mathrm{or}\ \ \mathrm{Zn^{2+}}\ +\ \mathrm{Se^{2-}}\ \rightarrow\ \mathrm{ZnSe^{0}} \tag{4}\]  
Individual CdSe or ZnSe compounds begin to grow in the following aggregation process:  
\[\mathrm{CdSe^{0}}\ +\ \mathrm{CdSe^{0}}\ \rightarrow\ \mathrm{CdSe^{0}}_{2}\ \ \mathrm{or}\ \ \mathrm{ZnSe^{0}}\ +\ \mathrm{ZnSe^{0}}\ \rightarrow\ \mathrm{ZnSe^{0}}_{2} \tag{5}\]  
\[\mathrm{CdSe^{0}}\ +\ \mathrm{CdSe^{0}}_{n}\ \rightarrow\ \mathrm{CdSe^{0}}_{n+1}\ \ \mathrm{or}\ \ \mathrm{ZnSe^{0}}\ +\ \mathrm{ZnSe^{0}}_{m}\ \rightarrow\ \mathrm{ZnSe^{0}}_{m+1} \tag{6}\]  
where \(n\) and \(m\) can be hundreds to a few thousand depending on the particle size.  
Hydroxyl and hydrogen radicals (OH\({}^{+}\) and H\({}^{+}\)) are also strong oxidizing agents in aqueous colloidal solution. Therefore, to prevent their reactions, isopropanol (IPA) was added to the precursor solutions [27]. All organics in the reactant medium including PVP and IPA, can scavenge OH\({}^{+}\) and H\({}^{+}\) radicals and can simultaneously change into PVP and IPA radicals by the following reaction [33].  
\[\mathrm{PVP+OH^{+}\rightarrowPVP^{+}+H_{2}O;PVP^{+}+H^{+}\to PVP^{+}+H_{2}; IPA^{+}+PVP^{+}\rightarrow crosslinked PVP gel} \tag{7}\]  
\[\mathrm{IPA+OH^{+}\rightarrowIPA^{+}+H_{2}O;IPA^{+}+H^{+}\to IPA^{+}+H_{2}; IPA^{+}+IPA^{-}\rightarrow crosslinked IPA gel} \tag{8}\]
## 3 Background
### IEEE 802.1 Qbv  
To introduce the support for ST, the IEEE 802.1 Qbv, now included in [27], applies a _transmission gate_ mechanism to the egress queues of a switch port (red blocks in Figure 1), which enables time-driven transmissions, i.e., transmissions that follow a predefined time schedule leveraging the common notion of time realized by the IEEE 802.1 AS standard. To this aim, transmission gates are placed at the output of the transmission queues. In particular, there is one transmission gate for each transmission queue and each gate maintains a state (open or closed). When a transmission gate is closed, no frame from the queue is eligible for transmission. Conversely, when the transmission gate is open, the frames can be selected for transmission according to the SP selection. The state of the transmission gates is modified following a _transmission gate control list_ that cyclically repeats, following a transmission gate cycle. Each operation in the transmission gate control list is activated at a precise instant within the transmission gate cycle. Moreover, the IEEE 802.1 Qbv standard implements a mechanism that prevents any lower-priority (i.e., non-ST frame) transmission that would exceed the transmission gate closing time. This way, ST flows do not experience jitter due to the ongoing transmission of a lower priority frame.
# Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach
## 4 Results and Discussions
### Support Vector Classifier  
After the first stage of the simulations with different types of SVCs, models with linear and polynomial kernels were excluded from the experiment due to their low accuracy compared to models with the RBF kernel. The kernel coefficients were varied from 0.012 to 0.018 with the step of 0.002. Results of the experiment obtained through the best SVC are shown in Table 5.  
Results indicate that the use of the SVC described above, with properly fitted parameters, allows obtaining a classification accuracy even higher than the one of ANN models.
## 4 Flying Base Station Channel Capacity and Positioning
### Base Station Positioning  
#### 4.1.1 Transmission Power Requirement vs. Distance  
We derive the minimum transmission power requirement to support the \(R\) reliable-bit-rate performance from Equation (1) and use linear algebra and \(\text{SNR}=\frac{Pad^{-\gamma}}{W\cdot N_{0}}\), where the numerator above the fraction is the received power after the attenuation/path loss. Because they are the given environmental or system parameters, we introduce a constant \(\beta=\frac{1}{\alpha}WN_{0}(2^{\frac{R}{\alpha}}-1)\). The reliable communication requires the flying base station's power transmission to satisfy the following: \(C=W\cdot\log_{2}(1+\text{SNR})\geq R\). This is equivalent to the following:  
\[C=W\cdot\log_{2}(1+\text{SNR}) \geq R\] \[P \geq \frac{d^{\gamma}}{\alpha}WN_{0}(2^{\frac{R}{\alpha}}-1)\] \[P \geq \beta d^{\gamma} \tag{2}\]  
where \(\beta=\frac{1}{\alpha}WN_{0}(2^{\frac{R}{\alpha}}-1)\) is given from the channel environment.  
Since we prioritize power efficiency, we use the minimum possible transmission power, which is a function of \(d\), as follows:  
\[P(d) = \beta d^{\gamma} \tag{3}\]
## 7 Time Domain Optimization
### Combined Feedback and Feedforward Control
#### 7.1.1 Reduced Wind Turbine Model for Controller Design  
For controller design, a full aero-elastic model is too complex due to the required iterative calculation of the aerodynamics. Here, a SLOW (Simplified Low Order Wind turbine) model based on [58] is used considering only the rotor motion (see Figure 6 (left)). The model consists of a reduced servo-elastic and aerodynamic module (see Figure 6 (center)).  
In the servo-elastic part, the motion of the rotor speed \(\omega\) is described by  
\[J\dot{\omega}=M_{aero}-M_{gen}, \tag{12}\]  
where \(M_{aero}\) is the aerodynamic torque and \(M_{gen}\) is the generator torque. Further, \(J\) is the sum of the moments of inertia about the rotation axis.  
In the aerodynamic part, the aerodynamic torque acting on the rotor with radius \(R\) is  
\[M_{aero}=\frac{1}{2}\rho\pi R^{3}\frac{c_{P}(\lambda,\beta)}{\lambda}\text{ with }\lambda=\frac{\omega R}{u_{\textit{eff}}}, \tag{13}\]  
where \(\rho\) is the air density, \(\lambda\) the tip-speed ratio, and \(c_{P}\) is the power coefficient. A two-dimensional look-up table shown in Figure 6 (right) is used, which is calculated from steady-state simulations with the full simulation model of the NREL 5 MW turbine [37].
# Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models
## 3 Data Description  
Particulate matter (PM) is a particle that is generated naturally or artificially and is contained in the air as an aerosol. The most commonly used PM parameters include PM\({}_{10}\), whose diameter is 10 micrometers or less, and PM\({}_{2.5}\), whose diameter is 2.5 micrometers or less. PM is a fine particle that floats in the air and is a respirable substance that has a significant impact on health. Many countries around the world treat PM as an environmental issue. In October 2013, the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC) classified PM as a Class 1 carcinogen, due to the high toxicity. According to the State of Global Air ([PERSON] et al., 2018) released in 2018, 33.7% of the world was exposed to household air pollution in 2016, and the death toll associated with PM\({}_{2.5}\) reached 4.1 million by 2016.  
PM tends to float in the air and propagate with the flow of the atmosphere. The smaller the PMs, the longer they stay in the air. The diffusion rate varies depending on the particle compositions. The PM forecast is a challenge for climate forecasts, as they show different patterns depending on the climate impact of each country. PM data are the density of the particulate matter, such as PM\({}_{2.5}\) and PM\({}_{10}\) collected from ground stations. In general, it is desirable for the stations to be evenly distributed throughout the country but they usually tend to be concentrated in major cities and towns. The distribution is not even uniform, which makes it challenging to predict such spatiotemporal data.  
In this paper, we compare the performances of deep learning models to predict air pollutant data as spatiotemporal data. We utilize air pollutant data provided by kweather ([PERSON] et al., 2018). Data were collected from 413 discrete stations in Seoul, South Korea. The collected data include PM\({}_{2.5}\), PM\({}_{10}\), noise, temperature, and humidity, and we utilize data that were measured every hour for 75 days from 5 September 2019, to 18 November 2019. We examined the missing data as preprocessing and removed 16 days of data. We also scaled all the data, using min-max scaling. To properly apply deep learning models, the models are trained with the training data, and the model parameters are tuned with the validation data. Then, the model performance is evaluated with the test data, which are unbiased. We randomly separated the data sets into 991, 212, and 213 h for the training data set, validate data set and test data set, respectively, at the ratio of 7:1.5:1.5. In this paper, we design PM prediction models using these data sets and compare the PM prediction performance depending on the data feature selection and temporal and spatial correlations with deep learning models.
## 5 Field Test Configuration and Results  
To validate the proposed credibility evaluation methodology, we conducted a case study on VILS, one of the four main XILS approaches. Specifically, among the various VILS techniques, PG-based VILS was adopted to replicate scenarios that are difficult to implement repeatedly in real-vehicle tests and to capture behavioral characteristics similar to those of actual vehicles. To test the credibility evaluation methodology, we performed PG-based VILS across the six scenarios defined in Section 3.1 (\(K=6\)), which comprised lateral evaluation (A and B), longitudinal evaluation (C and D), and combined longitudinal-lateral evaluation (E and F). For each scenario, both VILS and real-vehicle tests were conducted five times each (\(N=5\)), and the results were measured against the 12 key parameters presented in Table 1 (\(M=12\)). This section describes the vehicle configuration and evaluation environment utilized for the real-world vehicle testing and VILS testing and explains how the credibility of the VILS platform was evaluated based on test results obtained from both real and virtual environments. The procedures to evaluate the verification efficiency and representativeness through geometric similarity calculations are then outlined.
## 2 Materials and Methods
### In Situ Moisture Content Determination, Needle Penetration, and Core Drilling  
This task represents the central objective of this study: the estimation of density of the structural element in situ, which forms the basis for assessing the safety conditions in accordance with Eurocode5 (EN 1995-1-1: 2004) [36]. The task was carried out in two steps: (a) first, using the results of laboratory phase needle penetration and core drilling tests to establish a calibration for estimating density values, and (b) second, applying core drilling tests to measure the real density (measured density).  
An extensive in situ application of the laboratory findings was carried out involving four buildings, courtesy of the company \"Spybuilding\". The buildings of 18 th to the 20 th centuries are located in the historical center and outskirts of Lisbon, as detailed in Table 2. All the buildings were built with a masonry structure and pine woods (_Pinus_ spp.).  
The following tests were performed in situ:  
1. Determination of the wooden elements' moisture contents as well as the relative humidity and temperature values of the building during the days of the inspections (Figure 4);
2. Visual inspection of the geometric dimensions and conservation state of structural elements (Figure 4);
3. Estimation of the density of structural elements with NPR testing in situ (Figure 4);
4. Core drilling of structural elements in situ to obtain the measured density after conditioning (Figure 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Building** & **Original Construction From** & **Location** \\ \hline A & Last quarter of the 18 th century & Historical center of Lisbon \\ B & Middle of the 19 th century & Outskirts of Lisbon \\ C & Last quarter of the 19 th century & Historical center of Lisbon \\ D & The 1940s of the 20 th century & Outskirts of Lisbon \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characterization of the buildings where wood samples were obtained.  
Figure 3: (**a**) Illustration of NPR perpendicular to the grain of _Pinus sylvestris_ beams, and (**b**) specimens after NPR laboratory testing.  
A total of 2 to 5 wood cores with a diameter of 7 mm and lengths up to 120 mm were drilled from each building.
# Human Action Recognition Based on Improved Two-Stream Convolution Network
## 4 Experiments
### Results of Experiments and Analysis
#### 4.3.3 Experimental Overall Analysis
Overall, the neural networks with the BiGRU network reduce the variance of action recognition accuracy, which shows that the BiGRU network plays a positive role in increasing the stability of action recognition. Learning rate is a crucial parameter in the training process of neural networks. A too small learning rate will lead to too slow convergence of the network model, or it will not converge due to the disappearance of the gradient. An excessive learning rate will cause the gradient to vibrate violently near the minimum value and it will not converge. A fixed learning rate can achieve good results in simple tasks, while in more complex tasks such as action recognition, a fixed learning rate is likely to lead to the inability of neural network convergence. Therefore, the dynamic adjustment of the learning rate is very necessary. The experiments in this paper adopt the stagnation descent algorithm to dynamically adjust the learning rate. Figure 12 compares the learning rate and loss of spatial stream network and motion stream network. We can find that the declining trend of learning rate and loss is roughly the same, and the learning rate decreases with the stagnation of loss. The algorithm has achieved satisfactory results. The loss of motion stream is finally stable at about 0.17, and the loss value of motion steam is finally stable at about 0.7. Figure 11: variances of multiple experiment. (**a**) UCF101. (**b**) HMDB51. As can be seen from Figure 8, the spatial stream network ablation experiments improved the accuracy by 2% on the UCF101 data set and 1.5% on the HMDB51 data set. Figure 9 shows that the motion stream network ablation experiments improved the accuracy by 2.1% on the UCF101 data set and 6.2% on the HMDB51 data set. Through the box graph and variance histogram of action recognition accuracy, we find that both BiGRU network and SimAM attention can increase the stability of action recognition. In the comparative experiment, our BS-2 SCN network has the highest accuracy in the framework of a two-stream convolution network. On HMDB51 data set, our BS-2 SCN network has achieved the effect of state-of-the-art. This proves that the BiGRU network with strong mining ability for contextual features helps our neural network to perceive the coherent features of appearance, and the effect of SimAm attention mechanism to improve the accuracy and stability of the neural network.
## 3 Signal Spatial Reconstruction Method for Relay Cooperative Networks
### Orthogonal Optimization of Signal Space
#### 3.2.1 Analysis of Antenna Number Configuration  
For the MIMO-Y channel model of \(K\) users, the requirements for the number of antennas to achieve orthogonal alignment of signal subspaces are analyzed as follows: for example, to meet the signal space alignment conditions of user 1 and user 2,  
\[\left[\begin{matrix}\mathbf{H}^{[r,1]}&-\mathbf{H}^{[r,2]}\end{matrix}\right] \left[\begin{matrix}\mathbf{v}^{[2,1]}\\ \mathbf{v}^{[1,2]}\end{matrix}\right]=0 \tag{22}\]  
and the redundancy of user node antenna is required to realize the spatial alignment of orthogonal signals. According to the matrix null space theory, the left matrix of the formula shall meet the following requirements in dimension: \(column-row\geq\frac{k(k-1)}{2}\). Therefore, the minimum requirements for antenna configuration shall meet the following: \(M=\frac{K(K-1)}{2}\), \(N=\frac{K(K-1)}{2}\).
# Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion
## 5 Experiments Evaluation
### Real-World Experiments
The performance of the algorithms is evaluated based on four indicators: time, FPR, TPR, and deviation mentioned above, as shown in Table 3.  
In the case of approximately the same deviation, our method takes the shortest computation time of 0.01 s while MEWC expenses the longest 0.071 s. For our method, the high-dimensional consistency matrix corresponding to the entire measurement set is decomposed into multiple sub-matrix blocks for solving, improving the real-time performance greatly. Second, MEWC considers consistency and introduces data similarity between frames as one of the weights, so the cardinality of the pairwise internally consistent set is not always the largest, which corresponds to its lower TPR. As PCM assumes that all constraints satisfying the threshold have the same weight, it has to determine the true subset at the level of the entire measurement set and cannot always reject false positive measurements caused by perceptual aliasing. The PCM method accepted a false positive measurement in scene 1, exacerbating the deviation between the positions corresponding to the red squares and blue triangles in the global map. In the absence of prior information, not every inter-robot measurement is meaningful for map fusion. Combined with the simulation results, the experiments in this section  
Figure 10: The fused global map corresponding to scene 2 shown in Figure 8b. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Scene** & **Time (s)** & **TPR** & **FPR** & **Deviation Trans (/m)** & **Deviation Rot (/rad)** \\ \hline \multirow{2}{*}{PCM} & Scene 1 & 19.81 & 0.80 & 0.03 & 0.8332 & 0.1260 \\  & Scene 2 & 53.73 & 0.13 & 0.00 & 4.3790 & 0.0890 \\  & Scene 1 & 19.75 & 0.60 & 0.00 & 0.6331 & 0.0169 \\ \multirow{2}{*}{MEWC} & Scene 2 & 53.90 & 0.33 & 0.00 & 4.1805 & 0.0840 \\  & Scene 1 & 2.61 & 0.80 & 0.00 & 0.6232 & 0.0169 \\ \multirow{2}{*}{Our} & Scene 2 & 3.51 & 0.13 & 0.00 & 4.1110 & 0.0890 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of real-world experiments. again verify the feasibility of our method. Although there is a part of potential inter-robot measurements is discarded following the topologically correct and consistent subset proposed in this paper, our method does not lose any important, meaningful information and achieves high real-time.