esa-sceva/satcom-chunk-collection huggingface dataset:
# Finding Top-\(k\) Nodes for Temporal Closeness in Large Temporal Graphs
## 1 Introduction
### Our Results  
Our first contribution is the design and analysis of an algorithm for computing the temporal closeness of a node of a temporal graph in a given time interval, whose time complexity is linear in the number \(m\) of temporal edges and whose space complexity is linear in the number \(n\) of nodes. This algorithm, which is an appropriate modification of the one used in [22] and adapted from [29], can be seen as a temporal version of the classical breadth-first search algorithm. Computing the temporal closeness of all nodes in order to compare them and find the nodes with highest temporal  
Figure 2: The evolution of the \(t\)-closeness of [PERSON], in red, and [PERSON], in black, (**left**) and the top-20 nodes in Paris according to the temporal closeness (**right**).  
closeness can, hence, be done in time \(O(nm)\), by applying \(n\) times the algorithm for computing the temporal closeness of a node. This time complexity, however, is much too high in the case of large temporal graphs.  
Our second and more important contribution is showing that the algorithm for computing the temporal closeness of a node can be modified in order to obtain a backward version of the algorithm itself, which allows us to compute the \"contribution\" \(C(u,d)\) of a specific node \(d\) to the temporal closeness of any other node \(u\). By using this algorithm (which is inspired by the earliest arrival profile algorithms of [32]), we can then implement a temporal version of the sampling algorithm introduced in [4] in order to approximate the closeness in static graphs. In particular, for a temporal graph with \(n\) nodes and \(m\) temporal edges, we can compute an estimate of the temporal closeness of all its nodes whose absolute error is bounded by \(\epsilon\) in time \(O\left(\frac{\log n}{\epsilon^{2}}m\right)\), which significantly improves over the time complexity of applying \(n\) times the algorithm for computing the temporal closeness of a node, that is, \(O(nm)\).  
There is a natural way of using this temporal closeness estimation to empirically find the exact top-\(k\) nodes according to our temporal closeness metric. This approach simply consists in running our estimate temporal closeness computation algorithm, in finding the top-\(K\) nodes for the estimated temporal closeness, with \(K>k\), and in computing the exact temporal closeness of these nodes. Our third contribution is an extensive experimental validation of this approach with a dataset of 45 medium/large temporal graphs. Indeed, we show empirically that using this method we can retrieve the actual top-100 nodes of all large graphs we have considered by choosing \(K=1024\), that is (with a little abuse of notation), in time \(O(2048\times m)\), which is between 10 and 100 times faster than computing the temporal closeness of all nodes.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 4 Numerical Simulation
### Channel Model  
UWA channels are characterized by severe multipath interference and time-varying properties. This paper primarily discusses the symbol detection algorithm for CPM based on posterior probabilities in conjunction with FDE; hence, the numerical simulations only consider the multipath interference of the UWA channels. The simulations were performed over a selection of representative channels, including a typical sparse channel, denoted as ChA [15], a measured shallow-sea channel referred to as ChB, and a measured deep-sea channel designated as ChC.  
Table 3 presents the channel impulse response of the sparse channel ChA, with delay values expressed in symbol intervals. The response intervals correspond to integer multiples of the symbol duration. At a transmission rate of 2 kbps, the maximum delay spread is 12.5 ms, and the majority of the channel's energy is concentrated within the first three symbol intervals.  
ChB was obtained through a sea trial conducted over a communication distance of approximately 5.3 km in a trial area with a water depth of around 163 m. The transmitter was deployed at a depth of 80 m, and the receiver was positioned at approximately 97 m. The measured channel response was derived from a transmitted 4-8 kHz chirp signal. Figure 4a illustrates the normalized time-domain response waveform of the measured channel after applying a 4-8 kHz bandpass filter, while Figure 4b shows the corresponding normalized frequency-domain response. The principal energy of ChB was highly concentrated, with a maximum delay spread of approximately 25 ms.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Sampling frequency & 48 kHz \\ Center frequency & 6 kHz \\ Bandwidth & 4 kHz \\ Data rate & 2 kbps \\ Data bits & 1024 bits \\ CP bits & 512 bits \\ \hline \end{tabular}
\end{table}
Table 2: System parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Delay** & 0 & 1 & 2 & 8 & 12 & 25 \\ \hline
**Power** & 0.189 & 0.379 & 0.255 & 0.090 & 0.055 & 0.032 \\ \hline \end{tabular}
\end{table}
Table 3: Delay power profiles of chA [15].  
ChC was obtained through a sea trial conducted over a communication distance of approximately 10 km in a trial area with a water depth of around 3700 m. The transmitter was located at a depth of 50 m, and the receiver was positioned at approximately 3505 m. The channel response was derived from a transmitted 4-8 kHz chirp signal. Figure 5a presents the normalized time-domain response waveform of the measured channel after applying a 4-8 kHz bandpass filter, while Figure 5b depicts the corresponding normalized frequency-domain response. A notable characteristic of ChC is that its primary energy is distributed across four distinct clusters, with a maximum delay spread of approximately 120 ms.
## 2 Results and Discussion
### Preparation of Protein Hydrolysate from Hairtail (T. japonicus) Muscle (HTP)
#### 2.2.2 Anion-Exchange Chromatography of HTP-I  
Peptides contain acidic and/or hydrophobic amino acid residues such as glutamic acid (Glu), tyrosine (Tyr), methionine (Met), and leucine (Leu), and can be sticks to the anion-exchange resins [31; 32]. In addition, acidic and/or hydrophobic amino acid residues in amino acid sequences of peptides will enhance their bioactivities [7; 33]. Therefore, anion exchange resins including DEAE-52 cellulose and Q Sepharose FF are usually applied to purified bioactive peptides from protein hydrolysates [8; 34; 35].  
As shown in Figure 4A, five fractions (DE-1 to DE-5) were separated from HTP-I using a DEAE-52 cellulose column. Amongst them, DE-1 and DE-2 were eluted using deionized water, DE-3 was eluted using 0.1 M NaCl, DE-4 was eluted using 0.5 M NaCl, and DE-5 was eluted using 1.0 M NaCl. DPPH\(\bullet\) and HO\(\bullet\) scavenging activities of HTP-I and five eluted fractions were showed in Figure 4B, and the results indicated that DPPH\(\bullet\) (58.91 \(\pm\) 1.89%) and HO\(\bullet\) (69.74 \(\pm\) 2.61%) scavenging abilities of DE-3 were significantly stronger than those of HTP-I (DPPH\(\bullet\): 40.13 \(\pm\) 1.16%; HO\(\bullet\): 45.6 \(\pm\) 1.35%), DE-1 (DPPH\(\bullet\): 24.51 \(\pm\) 1.06%; HO\(\bullet\): 37.9 \(\pm\) 2.33%), DE-2 (DPPH\(\bullet\): 47.7 \(\pm\) 1.51%; HO\(\bullet\): 35.29 \(\pm\) 1.18%), DE-4 (DPPH\(\bullet\): 30.64 \(\pm\) 1.25%; HO\(\bullet\): 29.39 \(\pm\) 1.63%), and DE-5 (DPPH\(\bullet\): 7.29 \(\pm\) 1.67%; HO\(\bullet\): 13.51 \(\pm\) 0.99%) at the concentration of 5.0 mg protein/mL (\(p<\) 0.05). The results indicated that the highest antioxidant activity of the peptides obtained in DE-3 might be due to the acidic amino acid residues in their peptide sequences. Therefore, DE-3 was selected for the following experiment.  
Figure 3: DPPH\(\bullet\) and HO\(\bullet\) scavenging activity of HTP and its fractions by membrane ultrafiltration at the concentration of 6.0 mg protein/mL. All data are presented as the mean \(\pm\) SD of triplicate results. \(a^{-c}\) or \(A^{-E}\) Column wise values with same superscripts of this type indicate no significant difference (\(p>\) 0.05).
# Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer
## 3 Experiment
### Datasets Setting and Evaluation Index Level  
All experiments in this paper were conducted on two significant publicly available datasets in the vehicle re-identification domain: VeRi776 [28] and VehicleID [29]. For each vehicle in the test datasets, a query image was selected from the perspective of each camera where it was located, the input images in the experiment were modified to 240 \(\times\) 240, and the number of input images in each batch was 32. For training, the input images were preprocessed by random horizontal flipping and random erasing. The evaluation metrics used in the experiments include Rank-1, Rank-5, and mean Average Precision (mAP) computed from the cumulative matching characteristics. Rank-N represents the recognition accuracy of identifying vehicles belonging to the same identity as the query image in the top N sorted results. mAP is another crucial evaluation metric in vehicle re-identification tasks, measuring the comprehensive performance of a model in recognizing objects in images with high accuracy and locating them accurately. A higher mAP score indicates better model performance.
## 3 Results  
### Parameters Retrieved from In Situ Measurements for SCOPE Model  
The leaf traits and canopy structure parameters, including Cab, Cea, Cs, Cw, Cdm, N, LIDFa, LIDFb and LAI, together govern the changes in the canopy reflectance. Based on the inversion procedure as shown in Figure 2, vegetation parameters including Cea, Cdm, Cw, Cs, N, LIDFa and LIDFb were retrieved on the eight fieldwork days (Table 3). Cab and LAI were obtained from field measurements. The results showed that the Cea varied from 2.86 \(\upmu\)g cm\({}^{-2}\) to 13.14 \(\upmu\)g cm\({}^{-2}\) on different days of the growing season, although Cab of the subtropical evergreen forest did not change significantly during the year (Table 1). Cdm and Cw remained almost stable at 0.02 and 0.05, respectively, during the whole growing season. N was also stable at 3, except for a relatively low value in July. Cs varied greatly throughout the year, showing higher values in spring and autumn and lower values in summer. The seasonal variation in LAI ranged from 4.85 m\({}^{2}\) m\({}^{-2}\) to 4.24 m\({}^{2}\) m\({}^{-2}\), with the highest in July and lowest in November (Table 1). The seasonal variation in the pattern of LIDFa and LIDFb showed that the leaf inclination and its distribution both changed during the growing season. The model reproduced the measured canopy reflectance well with sum squared residuals (SSR) between 0.02 and 1.02 (Table 3). The results also showed that the simulated half-hourly \(\text{GPP}_{\text{SCOPE}}\) significantly correlated to the measured \(\text{GPP}_{\text{EC}}\) (R\({}^{2}\) = 0.80, \(p\) < 0.001, Figure 4). The linear relationship between \(\text{GPP}_{\text{SCOPE}}\) and \(\text{GPP}_{\text{EC}}\) was close to the 1:1 line, in spite of some data spread caused by the uncertainty of measured meteorological data and the simplify of the model processes. All these results proved that the SCOPE model could accurately retrieve the characteristics of leaf trait and canopy structure using the measured reflectance as a constraint.  
Figure 3: Schematic overview of SIF simulations and approach comparative analysis.
## 4 Conclusions  
In conclusion, a feedback loop scheme has been proposed for shaping the spectral amplitude in a broad spectral region of HHG. In previous results, it was shown that the same control technique is suitable to reshape the spectral phase in a broad region of the spectrum [20]. The technique is based on the combination of the IR intense driving pulse with the weak signal produced by the HHG process in a selected spectral bandwidth and can be applied in an iterative procedure. The success of the method is determined by the resolution of the scan in the intensity of the XUV pulses and their delay with respect to the IR pulse. For the simulations reported in the present study, the optimization of the spectral amplitude is found to be robust by considering variations of the optimal XUV pulse intensity of 20% and for a resolution of \(\Delta\theta=5^{\circ}\) (_i.e._, for a spatial resolution of \(\approx 11\) nm or a temporal delay resolution of \(\approx 37\) as). Also, the numerical algorithm that has been used in the present study represents the simplest numerical configuration, which has been chosen in order to demonstrate the capabilities of the method in the simplest case. Other possibilities including some genetic or evolutionary-type schemes are being tested. It has been thus demonstrated numerically that the radiation produced by HHG can be successfully used in a feedback loop scheme to weakly perturb the IR intense driving pulse so that optimal shapes of the spectral phase and amplitude can be achieved in a broad region of the spectrum. Using a single iteration (loop), isolated attosecond pulses have been obtained at different central photon energies by optimizing the spectral phase [20] or the spectral amplitude in the present paper, which shows the feasibility of the control technique in relatively simple experimental setups. The use of additional feedback loops allows to further optimize the final solution in some cases. This control tool can be readily applied to obtain other spectral phase and amplitude shapes different from the ones considered in the present study, and also considering higher photon energies, as, e.g., for the coherent control in the X-ray region, with the possibility to shape and achieve much shorter or also longer pulses.  
Support from the Spanish Ministry of Economy and Competitiveness through \"Plan Nacional\" (FIS2011-30465-C02-02) is acknowledged.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 4 Experimentation and Analysis
### Selection of the Main Parameters of the ECA-MRANet Model and Their Effects  
The ECA-MRANet model has three channels, each channel consists of multiple Residual CBAM blocks. In order to obtain an efficient model, in this section, the number of Residual CBAM blocks and batch samples will be investigated using Data Set A. The number of training rounds is 50, utilizing the Adam optimizer and the cross-entropy loss function, with a learning rate of 0.001.
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 2 Link Relationship Graph--The Time Continuity Model of Network Topology
### The Relative Angular Velocity Factor of the Node  
The influence of the relative motion relationship between nodes on the link quality is mainly reflected in the rate of change in the relative angle between nodes, that is, the relative angular velocity between nodes, denoted by \(\dot{\theta}_{S_{i}S_{j}}(t)\). Let \(\dot{\theta}_{\max S_{i}S_{j}}\) and \(\dot{\theta}_{\min S_{i}S_{j}}\) denote the maximum relative angular velocity and the minimum relative angular velocity between the nodes, respectively, the relative angular velocity factor of the node is defined as \(\sigma_{3}(t)=\frac{\dot{\theta}_{\max S_{i}S_{j}}-\dot{\theta}_{S_{i}S_{j}}( t)}{\dot{\theta}_{\max S_{i}S_{j}}-\dot{\theta}_{\min S_{i}S_{j}}}\) when \(\sigma_{1}(t)=1\). In the same way as \(\sigma_{2}(t)\), \(\sigma_{3}(t)\) is a time-varying function either, and \(\sigma_{3}(t)\in[0,1]\). If \(\dot{\theta}_{S_{i}S_{j}}(t)=\dot{\theta}_{\max S_{i}S_{j}}\), then \(\sigma_{3}(t)=0\); if \(\dot{\theta}_{S_{i}S_{j}}(t)=\dot{\theta}_{\min S_{i}S_{j}}\), then \(\sigma_{3}(t)=1\). There is a correlation existing in \(\dot{\theta}_{\max S_{i}S_{j}}\) and \(d_{\min S_{i}S_{j}}\). And it is also important to note that \(\sigma_{1}(t)\!=\!0\) when \(\dot{\theta}_{S_{i}S_{j}}(t)>\dot{\theta}_{\max S_{i}S_{j}}\) or \(d_{S_{i}S_{j}}(t)<d_{\min S_{i}S_{j}}\). \(\sigma_{3}(t)\) indicates the link quality affected by the relative angular velocity between nodes, i.e., the link quality deteriorates as the relative angular velocity increases.  
The relative angular velocity of any two nodes \(S_{i}\) and \(S_{j}\) in a space information system changes periodically. And \(\dot{\theta}_{S_{i}S_{j}}(t)\) can be obtained by the following steps:  
Step 1: analyze the relative angular velocity relationship between the two nodes in a system period.  
Step 2: utilize a Fourier series for fitting the continuous variation relationship.  
Step 3: for the discontinuous point in the angular velocity variation relationship, set a neighborhood of the point with a constant value.  
Step 4: in summary, obtain the piecewise function expression of \(\dot{\theta}_{S_{i}S_{j}}(t)\).  
Figure 1: Schematic diagram of the link relationship between two satellites in the space information system.
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.3 Intensive Learning  
The purpose of intensive learning, Ref. [1] noted, is to arrive at a perfect to the maximum decision. In the scenario discussed and relating to a power grid, according to the prevailing risks, optimal strategy can be achieved using the Monte Carlo tree search, andthe best solution can be derived from it, producing a new state of the power generating grid. Through comparing the two conditions in an intensive learning process, the evaluation function is updated through the completion of the learning process and repeat iteration, which allows the strategy to meet the expected self-healing functionality of the system. The idea behind intensive learning is to use ML algorithms to learn from large amounts of network data to detect anomalies and predict failures. This approach involves collecting a wide range of data from the network, such as performance metrics, configuration data, and log files, and using these data to train ML models. Once models are trained, they can be used to detect anomalies in the network, such as unusual traffic patterns or unusual changes in configuration settings. The models can also be used to predict when failures are likely to occur, allowing the system to take proactive measures to prevent those failures from happening. [PERSON], in [11], suggests that this approach is well-suited for self-healing systems, as it allows the system to learn and adapt to changes in the network environment over time. By continuously collecting and analysing network data, the system can improve its accuracy and effectiveness in detecting and responding to anomalies and failures. The use of intensive learning in developing self-healing systems represents a promising new approach to autonomous network management, which is more data-driven and adaptable than the traditional rule-based systems.
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 2 Materials and Methods  
### Overview of Study  
In this study, the ship occupancy time in each cell was calculated by density analysis using AIS data and a density map was created from the results. Figure 1 shows a flowchart of the study process. First, a large volume of AIS data was preprocessed with Python, and the extracted data were divided into data period of season, ship type, length, and speed. Then, in the geographical information system (GIS), the spatial extent of the area of interest (AOI) was defined and the grid of a certain size was created. The densities were calculated using structured query language (SQL) by importing the preprocessed AIS data and grid cells into database. The density values were subjected to descriptive statistical analysis and the data were scaled via log transformation. Finally, a density map was created in GIS environment using a standard-deviation-based stretch symbolization method. After visualizing the traffic patterns for each type of ship, the high-density areas were quantitatively extracted by adjusting the color ramp ratio of the stretch with the density values of all ships.
# Underlying Factors and Strategies for Organizational BIM Capabilities: The Case of Iran
## 4 Analysis and Results
### Structural Model Assessment  
Bootstrapping is a technique for estimating the distribution of any statistic for any distribution. Thus, it was used to assess the importance of path coefficients and to test the proposed hypotheses. The number of bootstrap samples used in this study was 5000 ([PERSON] et al., 2011 [106]). The critical t-values for a two-tailed test were 2.58 (significance level = 1%), 1.96 (significance level = 5%), and 1.65 (significance level = 10%) [108]. All four hypotheses were supported (Table 8). Hypotheses 1 and 4 were found positive and significant at the 1% level, whereas Hypothesis 2 was found positive and significant at the 5% level. In addition, Hypothesis 3 was found positive and significant at the 10% level.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Constructs** & **Indicators** & **Loadings** & **AVE** & **CR** & **CA** \\ \hline \multirow{8}{*}{BIMCR} & SBIM5 & 0.799 & & & \\ \cline{2-6}  & SBIM6 & 0.720 & & & \\ \cline{2-6}  & SBIM7 & 0.773 & & & \\ \cline{2-6}  & SBIM8 & 0.728 & 0.596 & 0.911 & 0.887 \\ \cline{2-6}  & SBIM9 & 0.774 & & & \\ \cline{2-6}  & SBIM10 & 0.841 & & & \\ \cline{2-6}  & SBIM14 & 0.763 & & & \\ \hline \multirow{4}{*}{OCU} & SBIM2 & 0.799 & & & \\ \cline{2-6}  & SBIM1 & 0.852 & 0.705 & 0.878 & 0.791 \\ \cline{2-6}  & SBIM3 & 0.866 & & & \\ \hline \multirow{4}{*}{BIMCR} & CBIM4 & 0.866 & & & \\ \cline{2-6}  & CBIM9 & 0.882 & & & \\ \cline{2-6}  & CBIM8 & 0.845 & & & \\ \cline{2-6}  & CBIM17 & 0.892 & & & \\ \cline{2-6}  & CBIM13 & 0.801 & & & \\ \cline{2-6}  & CBIM5 & 0.774 & & & \\ \cline{2-6}  & CBIM18 & 0.810 & & & \\ \cline{2-6}  & CBIM15 & 0.760 & & & \\ \hline \multirow{4}{*}{OCA} & CBIM16 & 0.823 & & & \\ \cline{2-6}  & CBIM11 & 0.739 & & & \\ \cline{1-1} \cline{2-6}  & CBIM12 & 0.806 & & & \\ \cline{1-1} \cline{2-6}  & CBIM14 & 0.788 & 0.606 & 0.902 & 0.870 \\ \cline{1-1} \cline{2-6}  & CBIM19 & 0.778 & & & \\ \cline{1-1} \cline{2-6}  & CBIM10 & 0.733 & & & \\ \hline \end{tabular} Note: AVE = Average variance extracted; CR = Composite reliability; CA = Cronbach’s alpha.  
\end{table}
Table 6: Measurement model assessment.  
Figure 1: Measurement model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constructs/Indicators** & **BIMCR** & **OCU** & **OBIMC** & **OCA** \\ \hline SBIM5 & 0.799 & 0.509 & 0.327 & 0.385 \\ \hline SBIM6 & 0.720 & 0.504 & 0.266 & 0.226 \\ \hline SBIM7 & 0.773 & 0.536 & 0.204 & 0.275 \\ \hline SBIM8 & 0.728 & 0.362 & 0.325 & 0.274 \\ \hline SBIM9 & 0.774 & 0.327 & 0.370 & 0.306 \\ \hline SBIM10 & 0.841 & 0.489 & 0.388 & 0.398 \\ \hline SBIM14 & 0.763 & 0.553 & 0.311 & 0.345 \\ \hline SBIM1 & 0.569 & 0.799 & 0.275 & 0.362 \\ \hline SBIM2 & 0.4