esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Community Detection Based on Small Cliques
### Preliminaries  
A _weighted graph_\(G=(V,E,w)\) consists of a node set \(V=\{1,\ldots,n\}\) with \(n:=|V|\) nodes, edge set \(E\subseteq V\times V\) with \(m:=|E|\) undirected edges, and _weights_\(w:E\rightarrow\mathbb{R}_{>0}\) assigning a positive real number to every edge. We treat _unweighted graphs_ as weighted graphs where all weights are set to 1 (i.e., \(\forall\{u,v\}\in E:w(\{u,v\})=1\)). In the context of this paper we only consider simple graphs, i.e., graphs without self-loops and multi-edges.  
The _neighbors_\(N(u)=\{v|\{u,v\}\in E\}\) of a node \(u\) are the nodes that are adjacent to it, i.e., that are connected to it by an edge. Subsequently, the _degree_\(\deg(u):=|N(u)|\) of a node \(u\) is its number of neighbors, and the _weighted degree_\(\deg_{w}(u)=\sum_{v\in N(u)}w(\{u,v\})\) is the sum of the edge weights of all edges incident to \(u\). By \(N(S)\) for a set \(S\subseteq V\) we denote the union of all neighborhoods of the nodes in \(S\) without \(S\), i.e., \(N(S):=\cup_{w\in S}N(u)\setminus S\).  
The _volume_\(\operatorname{vol}(S):=\sum_{v\in S}\deg(v)\) of a set of nodes \(S\subseteq V\) is the sum of the degrees of all nodes in \(S\). The _weighted volume_\(\operatorname{vol}_{w}(S):=\sum_{v\in S}\deg_{w}(v)\) of a set of nodes \(S\subseteq V\) is analogously the sum of weighted degrees of all nodes in \(S\).  
A _clique_ is a set of nodes that induce a complete subgraph in \(G\). In other words, \(A\subseteq V\) is a clique, if and only if \(\forall u\in A,\forall v\in A\setminus\{u\}:\{u,v\}\in E\). Then, a _maximal_ clique \(Q\) is a clique that can not be enlarged by adding a node. A clique is called a _triangle_ if it consists of exactly three nodes.  
We define a _community_ as a set of nodes \(C\subseteq V\), with \(|C|=n_{C}\). The _shell_\(S(C)\) is a synonym for the neighbors of the community \(C\), i.e., \(S(C)=N(C)\). The _boundary_\(B(C)\) of a community \(C\) are the nodes in \(C\) that have neighbors in the shell \(S(C)\), i.e., \(B(C)=\{u\in C\,|\,N(u)\cap S(C)\
eq\emptyset\}\). We define the _weighted cut_ of two node sets \(S,T\subseteq V\) as \(\operatorname{cut}_{w}(S,T):=\sum_{u\in S,v\in T}w(\{u,v\})\). As a shorthand, we define the weighted cut of a community \(C\) as \(\operatorname{cut}_{w}(C):=\operatorname{cut}_{w}(C,V\setminus C)\).
###### Abstract  
This study analyzed the effects of changes in the inter-floor noise assessment system in multi-family housing in Korea on heavy-weight impact sound performance assessment. By comparing the existing pre-approval system with the newly introduced post-verification system, we focused on the effects of the evaluation criteria, impact sources, and frequency band considerations on the single numerical evaluation quantities (\(L^{{}^{\prime}}_{i,k,max}\),AW and \(L^{{}^{\prime}}_{i,k,max}\)) and contribution rates by frequency band. For the analysis, impact sounds were measured using a bang machine used in the existing pre-approval system and a rubber ball used in the post-verification system, and the performance of the floor structure was evaluated in the 1/1 and 1/3 octave bands. As a result, the pre-approval system showed a high contribution rate mainly in the 63 Hz band, but the post-verification system expanded the contribution rate to the mid-low frequency band of 63-160 Hz. In particular, the evaluation method using the A-weighted maximum floor impact sound level (\(L^{{}^{\prime}}_{i,k,max}\)) of the post-verification system was found to reflect the performance in the mid- and high-frequency band of 125-250 Hz more effectively. The post-verification system enables a more accurate evaluation of the performance of high-frequency bands that were overlooked in the existing system, thereby enabling a realistic response to the mitigation of inter-floor noise. Accordingly, construction companies must meet stricter performance standards in floor structure design and resilient materials development, in accordance with the new regulations. For example, designs utilizing sound-absorbing ceiling structures and high-performance resilient materials are expected to be effective in reducing heavy-impact noise. This study provides important basic data for tracking the performance of floor structures according to changes in the system, and selecting key frequency bands for reducing heavy-impact noise. In addition, it emphasizes the need to continuously monitor the performance of multi-family housing constructed under the new system and to derive effective design strategies for solving inter-floor noise problems. In the future, it will be necessary to expand the usability of the results of this study through additional studies targeting more diverse floor plans and floor structures.  
floor impact sound; multi-family housing; heavy-weight impact sound; rubber ball; bang machine; single-number quality contribution; assessment methods; post-verification system +
Footnote †: journal: Applied sciences
To measure these properties, the most commonly used techniques are dynamic shear rheology (DSR), softening point test, penetration test, and Brookfield or rotational viscosity determination [9]. Additionally, a variety of perspectives are utilized through a range of evaluation techniques to explain changes in asphalt materials after ageing. These techniques include Fourier transform infrared spectroscopy (FTIR) [23], spectrophotometry [24], X-ray scattering spectroscopy [25; 26], gel permeation chromatography (GPC) [27], thin-layer chromatography with -flame ionization detection (TLC-FID) [28], atomic force spectroscopy (AFM) [29] and self-diffusion Pulsed Field Gradient Spin-Echo (PGSE) nuclear magnetic resonance (NMR) [30]. NMR spectroscopy is one of the most authentic and efficient techniques used to characterize complex materials i.e., bitumen. It is a conventional tool in the characterization of synthetic and natural products, wherein the structural and conformational behavior of their flexible molecules are investigated using anisotropic media [31]. In dynamic NMR characterization, the determination of self-diffusion coefficients can provide data about self-assembly [32; 33], molecular dynamics, and spatial dimensions of cavities [34] and aggregates [35; 36; 37; 38]. In this study, Fourier transform (FT) NMR self-diffusion coefficient measurements were performed on bitumen samples in order to have a better understanding of their molecular mobility and microstructure modification due to ageing; then, the measurements were compared with the results of rheological testing, AFM, and optical microscopy for validation. Ultimately, obtaining more information on the ageing process will provide consequent advantages in the design of rejuvenating additives and in understanding the structural organization of aged bitumen.
# Bioaugmentation Treatment of a PAH-Polluted Soil in a Slurry Bioreactor
## Materials and Methods  
### Samples and Soil Characteristics  
Soil was collected from a PAH-polluted site in Venta del Gallo (Llanera, Asturias, North Spain; 43\({}^{\circ}\)24\({}^{\prime}\)52\({}^{\prime\prime}\), -5\({}^{\circ}\)48\({}^{\prime}\)57\({}^{\prime\prime}\)) at an altitude of 300 m. This site had previously been used by a chemical factory producing naphthalene, resins, phenols, and other compounds from coal tar processing. A wide variety of contaminants were found in the soils of this area (in the 2.5 Ha original parcel and in the surroundings) as a result of spills and unsuitable waste disposal practices over decades [40].  
To obtain homogeneous material for slurry bioreactor (SB) experiments, ten samples of contaminated soil (approximately 5 kg each) were taken using a hand-auger (Eijkkelkamp), in an area with the average PAH concentrations from within the site, on the basis of data reported by the authors of [27]. All of the samples were then thoroughly mixed to obtain a composite mixture of soil, which was afterwards homogenized and sieved (2-mm mesh size). After a second homogenization, 4-kg representative subsamples of the initial composite sample were taken, labelled, and finally stored at 4 \({}^{\circ}\)C, until being used for the bioslurry experiments.  
The relevant soil characteristics were determined using standard techniques. pH was determined in a suspension of soil and water (1:2.5) with a glass electrode, and electrical conductivity was measured in the same extract (diluted 1:5). Dichronate oxidation was applied to measure the organic matter content and a Bernard calorimeter for the carbonate content. The [PERSON] and Olsen methods were used to determine the nitrogen and phosphorus content, respectively.
## 3 Results and Discussion
### The Selection and Fusion of Data  
#### 3.3.1 Selection of Key Wavelengths  
Regression coefficients (RC) of the established full spectra PLSR models were used to identify key wavelengths related to each WHC trait of the chicken breast fillets. The key wavelengths identified for drip loss, expressible fluid, and salt-induced water gain are shown in Figure 4, and the detailed wavelengths, as well as the comparison are listed in Table 3.  
Water is the most important component of chicken meat (about 75%). As Table 3 shows, the bands at 1414 and 1896 nm were likely due to water present in the muscle samples [10; 20; 42]. The bands at 1414 nm and 1896 nm were related to the O-H second overtone and O-H combination bonds, respectively. Chicken meat is famous for its high protein content. The bands at 2110 and 2180 nm likely correspond to protein present in the muscle samples [2; 7]. The bands at 1230-1440 nm likely correspond to C-H bonds and appeared many times in the previous reports [7; 43].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Model** & **No.** & **LV** & **Rc** & **RMSEc** & **Rcv** & **RMSEcv** & **Rp** & **RMSEp** \\ \hline \multirow{4}{*}{Drip loss} & Full spectra & 198 & 10 & 0.81 & 0.80 & 0.75 & 0.90 & 0.73 & 0.93 \\  & Key wavelength & 5 & 4 & 0.79 & 0.82 & 0.75 & 0.89 & 0.73 & 0.91 \\  & texture & 30 & 5 & 0.65 & 1.03 & 0.53 & 1.16 & 0.50 & 1.18 \\  & **Fusion** & **35** & **8** & **0.89** & **0.61** & **0.82** & **0.76** & **0.80** & **0.80** \\ \hline \multirow{4}{*}{Expressible fluid} & Full spectra & 198 & 10 & 0.60 & 2.02 & 0.49 & 2.21 & 0.52 & 2.19 \\  & Key wavelength & 5 & 3 & 0.53 & 2.11 & 0.49 & 2.21 & 0.47 & 2.25 \\  & texture & 30 & 4 & 0.24 & 2.46 & 0.20 & 2.48 & 0.15 & 2.50 \\  & **Fusion** & **35** & **6** & **0.62** & **2.01** & **0.53** & **2.16** & **0.56** & **2.10** \\ \hline \multirow{4}{*}{Salt-induced water gain} & Full spectra & 198 & 9 & 0.72 & 17.06 & 0.69 & 18.21 & 0.70 & 17.64 \\  & **Key wavelength** & **4** & **3** & **0.71** & **17.20** & **0.69** & **18.14** & **0.69** & **18.04** \\ \cline{1-1}  & texture & 24 & 4 & 0.07 & 24.36 & 0.00 & 24.99 & 0.07 & 24.30 \\ \cline{1-1}  & Fusion & 28 & 6 & 0.69 & 18.20 & 0.67 & 18.36 & 0.68 & 18.16 \\ \hline \hline \end{tabular}  
* LV: latent variables; Rc: coefficient of calibration; RMSEc: root-mean-square error of calibration; Rcv: coefficient of cross-validation; RMSEcv: root-mean-square error of cross-validation; Rp: coefficient of independent prediction; RMSEp: root-mean-square error of independent prediction.  
\end{table}
Table 2: Prediction of the WHC traits of the chicken breast file with different input variables.  
Figure 3: The raw spectral curves of all 90 fillets.
## 3 Application of Induction Motor Fault Diagnosis to The Problem of Unbalanced Training Data
### Signal Processing Method
#### 3.1.2 Fast Fourier Transform Two-Dimensional (FFT)  
The Fast Fourier Transform is a linear integral transform that is often used when transforming the signal between the time domain and the frequency domain. The conversion formula is shown in Equation (1), and \(f\) represents the frequency. This research used the same processing method as the time domain with two-dimensional matrix to convert the frequency domain signal into an M \(\times\) M image matrix, which is used as the input for subsequent induction motor fault diagnosis and analysis.  
\[X(f)\int\limits_{-\infty}^{\infty}x(t)e^{-j2\pi ft}dt \tag{1}\]
## 3 Optimizing the UCSP Using POGA-DP
### Time Slot Scheduling Based on Genetic Algorithms
#### 3.2.2 The Swap Operation with the Judgment Mechanism  
The crossover operation [40] involves exchanging segments of genetic information between two individuals during the genetic process, thereby generating new individuals. In this way, new advantageous genes may be produced, significantly enhancing the search  
Figure 1: Descriptive chromosome representation.  
capability of the algorithm. Consider a UCSP consisting of two classes, \(e_{1}\) and \(e_{2}\), with multiple courses scheduled across 25 time slots. Figure 2 shows two different solutions, A and B, for the UCSP. Compared to solution A, in solution B, the time slots for courses \(c_{4}\) and \(c_{5}\) have been swapped.  
It is important to note that when swapping courses for \(e_{1}\), courses \(c_{5}\) and \(c_{7}\) cannot have their time slots exchanged. This is because \(c_{5}\) is a joint class for both \(e_{1}\) and \(e_{2}\), and if \(c_{5}\) is moved to the \(c_{7}\) time slot, this would result in \(e_{2}\) needing to schedule both \(c_{5}\) and \(c_{6}\) in the same time slot, which clearly violates the hard constraints. Specifically, if a course needs to be moved to a new time slot, all related entities (such as classes or teachers) must be available at that time. Therefore, the swap operation involves a validation mechanism to ensure that invalid solutions are not generated during the swap process. This mechanism works by checking whether the related entities of a randomly selected course are free in the new time slot.
## 3 Cluster Formation  
Next step after CH selection stage is the cluster formation phase, which starts by broadcasting the advertisement messages by CHs to announce their selection to other nodes, and ends by sending back a join-message to the optimum CH by each node. We group cluster formation schemes into optimal clustering, event-driven clustering and failure management schemes. In optimal clustering schemes, the focus is either to manipulate the size of the clusters according to the type of the application and data transmission, or to minimize and balance the energy expenditure in the network by considering the factors such as data correlation, relay traffic and residual energy. On the other hand, the event-driven clustering schemes are proposed to prolong the network lifetime by eliminating dispensable clustering throughout the network and trigger the cluster formation stage only when and where it is needed, and failure management techniques are discussed to detect faults and recover from failing situations. The taxonomy of cluster formation phase is given in Figure 7.
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 6 Experimental Results
### Occlusion and Gradient Estimation  
The results of the training procedures introduced in Section 5 are shown in Table 3. The best loss values of the training are presented, i.e., the dice-loss (denoted with Loss) and the MSE loss of the multi-task occlusion and gradient estimator heads. In addition, the pixel-wise accuracy and the Intersection over Union (IoU) metrics are also given for the occlusion predictions. The results of the four previously introduced network variants are compared in the table. Moreover, during one of our experiments, the smooth L1 loss was used (with \(\beta=0.01\)) to train the gradient direction estimator head.  
Based on the results shown in Table 3, we concluded that the SAR-UNet-L1 architecture slightly outperformed the other 4 network variants on the metrics related to the occlusion mask prediction task, and strongly surpassed them on the gradient direction estimation task. We strongly suspected that this increased accuracy was caused by slowly vanishing the smooth L1 loss around zero that allowed the persistence of useful gradients around small  
Figure 3: Results of training the union-normalized (**top**) and the intersection-normalized (**bottom**) agents. The figure shows the rolling average rewards (**left**) and the total number of timesteps taken (**right**) during occlusion-avoidance.  
loss values. We found that separable convolutions caused the generalization capabilities of the network to improve, while only slightly deterring the performance of the network. As a result we decided to keep these modifications as implemented for the later experiments. Although a 60% IoU measure is generally not considered great, we emphasize that, in this task, the agent often had to predict the occlusion mask for fully occluded objects--a completely impossible task. For this reason, we accepted the training results, since the the neural network often had little to no clue about the environment and, therefore, was met with a task incorporating increased complexity as a result of fully occluded objects.
## 5 Performance Evaluation
### Simulation Setup
We set the time slot length \(\tau=30\) min, the idle power ratio \(\alpha=0.5\), \(P=20\) W, and the service delay constraint \(\delta_{th}=0.5\) s. Since we use the GCT dataset, we set \(z_{th}=0.1\) by following the configuration used in prior work [39], which used the same GCT dataset for their performance evaluation [37]. We configure an STGCN model as follows. The first ST-Conv block implements a unit configuration of (64, 32, 64), while the second ST-Conv block utilizes a unit structure of (64, 32, 128). The output layer is configured with (256, 128) units. The DQN model is configured as follows. The first convolutional layer uses one input channel and 32 output channels, while the second convolutional layer employs 64 output channels. Following a flattening process, three fully connected layers are applied. The first layer reduces the size from 64 times the size of a state to 48, the second layer maintains this dimension, and the final layer outputs the action size. The ReLU function is used as the activation function. All simulation experiments are conducted on a computer equipped with the 12 th generation Intel Core i5-12400F 2.50 GHz, 32 GB RAM, an NVIDIA GeForce GPU with 12 GB memory, CUDA version 11.4, and driver version 472.88. All deep-learning models are implemented and executed using PyTorch 2.2.2 and Python 3.11.5. We compare the performance of the proposed method with those of four alternatives under the same condition. Specifically, we selected \(ES_{R},ES_{P},GA_{A}\), and \(GA_{T}\) as the comparison targets (Table 2). The method \(ES_{R}\) is an ideal method that uses the real workload distribution (i.e., \(\tilde{\lambda}(t)\)) during a time slot \(t\), which cannot be known at the beginning of the time slot. In other words, \(ES_{R}\) employs the ES to determine \(\eta(t)\) by using \(\tilde{\lambda}(t)\). The results obtained by \(ES_{R}\) are the performance baseline in each simulation scenario. On the contrary, \(ES_{P}\) determines \(\eta(t)\) by using ES, which takes the predicted workload distribution (i.e., \(\tilde{\lambda}(t)\)) at the start of a time slot as input. Since GA is an iterative process, it may terminate without finding a valid \(\eta(t)\) if GA does not iterate enough. Thus, as a comparison  
Figure 5: Simulation topology. target, we select \(GA_{A}\), which represents the GA configured for the shortest search time in scenarios where the GA is able to find a valid solution for all the input states. \(GA_{T}\) is the GA-based method whose solution search time is the same as that of our method.
## 4 Conclusions  
In this study, the Taguchi method and ANOVA were applied to optimize MQL conditions for surface roughness. The best cutting parameters of hard milling of AISI H13 determined as cutting speed, feed rate, depth of cut, and hardness of workpiece were obtained in order to get improve the surface roughness under MQL conditions' optimization. A regression model for surface roughness was established. The results of the study are as follows:  
1. In MQL conditions, the water-soluble oil lubricant, the 50 mL/h fluid flow and the 3 kg/cm\({}^{2}\) pressures provided the best results for surface roughness in hard-milling of SKD 61.
2. Lubricant and pressure are two factors most affecting the surface roughness. The pressure factor contributed 68.127% and the lubricant factor contributed 30.189% of the total effect. The effect of them carried statistical significance. The three parameters of MQL conditions explained 99.76% of variability in surface roughness.
3. Under MQL conditions, the optimum cutting parameters are 70 m/min for the cutting speed, 0.01 mm/tooth for the feed rate, 0.2 mm in depth of cut, and 40 HRC for workpiece hardness. Generally, higher cutting speed, lower feed rate, lower depth of cut, and lower workpiece hardness lead to good surface roughness under the MQL condition.
4. Feed rate and depth of cut are the most influential factors related to surface roughness in the linear regression model. The effect of these factors (_i.e._, feed rate, depth-of-cut, and workpiece hardness) have statistical significance.
5. The linear regression model is considered to be statistically significant. The four parameters of cutting conditions explained 85.09% of variability in surface roughness.  
The author would like to thank [PERSON], [PERSON] and [PERSON] in Precision and Nano Engineering Laboratory, Department of Mechanical Engineering (ME) at the National Kaohsiung University of Applied Sciences (KUAS) for their important helping and useful discussion in the experiments of the study.  
[PERSON] conducted the theoretical analysis and experiments and prepared the manuscript. [PERSON] provided the guidance and improved the quality of the work.  
The authors declare no conflict of interest.
## 2 Fundamental Models and Literature Search
### Literature Search
Moreover, we also noted that a substantial portion of the highly cited papers originates from the research group headed by Professor [PERSON] and Professor [PERSON] at the University of Southern California. This underscores the far-reaching impact of their pioneering contributions to the field of marine data collection during its early stages. \begin{table}
\begin{tabular}{c c c c} \hline
**Rank** & **References** & **Pub. Year** & **No. of Citations** & **Journal Title** \\ \hline
1 & [46] & 2010 & 160 & _International Journal of Robotics Research_ \\
2 & [47] & 2011 & 158 & _Journal of Field Robotics_ \\
3 & [48] & 2012 & 156 & _IEEE Journal on Selected Areas in Communications_ \\
4 & [49] & 2013 & 115 & _Journal of Field Robotics_ \\
5 & [41] & 2018 & 100 & _IEEE Systems Journal_ \\
6 & [32] & 2020 & 76 & _IEEE Internet of Things Journal_ \\
7 & [50] & 2010 & 58 & _2010 IEEE International Conference on Robotics and Automation (ICRA)_ \\
8 & [24] & 2019 & 42 & _Journal of Network and Computer Applications_ \\
9 & [51] & 2020 & 34 & _IEEE Internet of Things Journal_ \\
10 & [35] & 2018 & 34 & _IEEE Sensors Journal_ \\ \hline \end{tabular}
\end{table} 
Table 1: Identification of highly cited articles. Figure 7: Summary of the articles by publication year and titles. Since there are too many journals/conferences involved, we grouped them into a small number of categories. Figure 8 shows the national distribution of the research institutions to which these references belong. From the figure, it can be observed that research teams from China constitute the majority (37.8%), followed by the United States at 25.2%. In third place is Canada, accounting for 5.4%. This indicates that China and the United States are leading in research and investment in maritime data collection compared to other countries worldwide. However, it is worth noting that a larger number of published papers does not necessarily indicate stronger research capabilities. The above is just the author's interpretation of the graphical data.