esa-sceva/satcom-chunk-collection huggingface dataset:
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 2 Related Work  
As it has already been mentioned, the motivation of this work is to ultimately \"discover\" large and somehow \"homogeneous\" AOIs, by merging small geographic tiles, based on sets of tags that have been added spontaneously by Flickr users. We feel that this work is rather novel in its field, however there are a few research works wherein the goal is also to discover AOIs. In addition, since the previously mentioned AOIs are mainly constituted by tourist attractions (since we assume that the majority of tags has been harvested by touristic photos), our work is closely related to research activities aiming to provide recommendations of places and/or to detect trends using information directly from geo-tagged photos collected from Flickr. In this section we present state-of-the-art research activities covering the aforementioned areas. More specifically, in Sections 2.1 and 2.2 we present works focusing on recommendations and AOI extraction, respectively. Moreover, in Section 2.3 we present selected works aiming to extract the semantics of places, by exploiting socially-generated metadata, which are closely related to the proposed approach.
## 2 Materials and Methods
### Machine Learning Methods
#### 2.1.2 K-Nearest Neighbours  
KNN algorithms are widely used for both classification and regression problems. When applied to regression problems, this supervised machine learning method attempts to predict the values of a quantitative variable \(Y\) from the values obtained for the predictor variables \(X_{1},X_{2},\ldots X_{d}\), based on a similarity metric. That is, given a training set \(\{(x^{(1)},y_{1}),(x^{(2)},y_{2}),\ldots,(x^{(m)},y_{m})\}\) with \(x^{(i)}=(x_{i1},\ldots,x_{id}),\ i=1,2,\ldots,m\), the \(K\) shortest distances between the data to be evaluated and the training data are obtained. The predicted value for a test instance \(x\), denoted as \(\hat{y}(x)\), is then obtained as the average of the outputs of its \(K\) nearest neighbours (denoted as \(y_{i}(x),\ i=1,2,\ldots,K\)). That is, \(\hat{y}(x)=\frac{1}{K}\sum_{i=1}^{K}y_{i}(x)\) (see Figure 1). The performance of this method depends on both the value selected for \(K\) and the type of metric chosen for the KNN algorithm [46; 47]. Two well-known metrics used in this method are the Euclidean and Manhattan distances. Although the metric may affect the performance of this method, there seems to be little consensus on which distance is most generally applicable and in which cases [48].
###### Abstract  
A numerical investigation regarding ductility evaluation of steel buildings with moment resisting steel frames is conducted. Bending (\(\mu_{L\phi}\)) and tension (\(\mu_{L\phi}\)) local ductilities as well as story (\(\mu_{S}\)) and global ductilities are studied. Global ductility is calculated as the mean values of story ductilities (\(\mu_{GS}\)) and as the ratio of the maximum inelastic to yielding top displacements (\(\mu_{GI}\)). The ductility capacity is associated to drifts of about 5%. Ductility values significantly may vary with the strong motion, ductility definition, structural element, story number, type of analysis, and model. \(\mu_{L\phi}\) is much larger for beams than for columns. Even though the demands of \(\mu_{L\phi}\) are considered an important issue they are less relevant than \(\mu_{L\phi}\). \(\mu_{S}\) is much smaller than \(\mu_{L\phi}\) for beams. \(\mu_{GS}\) for dynamic analysis give reasonable values, but \(\mu_{GI}\) does not. \(\mu_{L\phi}\), \(\mu_{S}\) and \(\mu_{GS}\) obtained from pushover are larger than those obtained from dynamic analysis and unlike the case of dynamic analysis, \(\mu_{L\phi}\) tend to increase with the story number showing an opposite trend. Considering that: \(\mu_{GI}\) for dynamic analysis results in unreasonable values, pushover analysis does not consider energy dissipation, the strong column-weak beam (SCWB) concept was followed in the model designs, and \(\mu_{L\phi}\) is not relevant in framed steel buildings, the ratio (\(R_{LG}\)) of global to local ductility capacity is calculated as the ratio of \(\mu_{GS}\) to \(\mu_{L\phi}\) of beams, for dynamic analysis. A value of 1/3 is proposed. Thus, if bending local ductility capacity is stated as the basis for the design, the global ductility capacity can be easily estimated.  
**Keywords:** bending local ductility; tensional local ductility; story and global ductility; moment resisting steel frames; pushover and dynamic analysis; ratio of local to global ductility
## 3 Data Augmentation Method Based on the GAN
### S-GAN Algorithm  
First, the S-GAN algorithm initializes the generator and the discriminator. Next, in each iterative process, the S-GAN algorithm first fixes the generator and the discriminator samples from real data samples and random noise, respectively, and trains and updates the parameters of the discriminator and then fixes the discriminator and generates the model from random. In this process, the generator will continuously adjust its parameters to obtains a higher score of the discriminator. Algorithm 1 describes the details of the S-GAN algorithm.  
For the sample attribute data sequence with a length N, our S-GAN samples capture and learn the distribution of the data in Steps 3 and 4. Their time complexity is O(N). With K updates in Step 5, its time complexity is O(K). Similarly, the time complexity in Steps 7 and 8 is O(N + K). Assuming that the number of training iterations is M, the time complexity of the entire algorithm is O(NMK).  
```
input: (1) \(Z\): random noise, which satisfies Z-N(0,1) (2) X: real samples output: (1) G(Z): fake data generated by learning the distribution of the dataset (2) D(G(z)): a score that represents the probability that the data is fake data
1. for number of training iterations do
2. for \(1-K\) do
3. Sample m examples \(\{x^{1},x^{2},\ldots,x^{m}\}\) from X, which satisfies X-Pg(x).
4. Sample m noise examples \(\{z^{1},z^{2},\ldots,z^{m}\}\) from Z.
5. Train and update the Discriminator by ascending its stochastic gradient: \[\
abla_{0,4}\frac{1}{m}\sum\limits_{i=1}^{m}\left[\rm logD\left(x^{i}\right) +log\left(1-D\left(G(z^{i})\right)\right)\right]\]
6. end for
7. Sample m noise examples \(\{z^{1},z^{2},\ldots,z^{m}\}\) from Z.
8. Train and update the Generator by descending its stochastic gradient: \[\
abla_{0,4}\frac{1}{m}\sum\limits_{i=1}^{m}\log\left(1-D\left(G(z^{i}) \right)\right)\]
9. end for
```  
**Algorithm 1** S-GAN algorithm
## 3 Results
### Characteristics of ISB Estimates
For the daily ISBs between Galileo and GPS, their difference among the stations within the same receiver type is usually confined to 2 ns, and similar situations can also be found for the other three groups of daily ISBs at the stations with the TRIMBLE ALLOY receiver (nine stations) as well as the stations with the SEPT ASTERX4 receiver (only two stations). The difference of daily ISBs between BDS-2 and GPS and between BDS-3 and GPS among the stations within the same receiver type varies within 10 ns for most of the time for all the JAVAD TRE_3, JAVAD TRE_G3 TH, SEPT POLARX4 TR, SEPT POLARX5, and SEPT POLARX5 TR receivers. As for the daily ISBs between GLONASS and GPS, the corresponding varying range of difference is 10 ns for the JAVAD TRE_3, SEPT POLARX4 TR, and SEPT POLARX5 TR receivers, and 20 ns for the JAVAD TRE_G3 TH and SEPT POLARX5 receivers, respectively. The numerical values range from 20 to 45 ns for the daily ISBs between GLONASS and GPS (except for the station CEDU), from 30 to 70 ns for the daily ISBs between BDS-2 and GPS, and from  
Figure 6: Average value of STDs of epochwise ISB estimates over a day for each receiver type based on the data sets from 120 stations spanning a month. 25 to 65 ns for the daily ISBs between BDS-3 and GPS, respectively, while the numerical values of daily ISBs between Galileo and GPS range from \(-3\) to \(-7\) ns for the stations with the TRIMBLE ALLOY receiver, and from \(-5\) to 3 ns for the stations with the other six types of receivers, respectively. In addition, the time series of daily ISBs within the same group exhibit consistent trends, which may be attributed to the jointly employed broadcast ephemeris. To investigate the long-term stability of ISB, Figure 8 presents the STD statistics of daily ISBs (i.e., the average value of the epochwise ISB estimates over a day) over a month (i.e., DOY 41 to 70 in 2020) for each station. The stations with different receiver types are identified by different colors. The two stations encountering the receiver replacement are removed from the analysis here, and the STDs at a station are computed only when at least ten days of daily ISBs at this station are available, to derive reliable statistics. Thus, the STD statistics of daily ISBs are absent for some stations in Figure 8, especially for those between BDS-2 and GPS. As we can see, the effects of receiver types on the long-term stability of ISB are marginal. Different from the short-term stability of ISB, the daily ISBs between GLONASS and GPS achieve the best long-term stability with a STD statistic of 1.258 ns, followed by those between Galileo and GPS with a slightly larger STD statistic of 1.288 ns. The daily ISBs between BDS-2 and GPS and between BDS-3 and GPS have significantly worse long-term stability, and the corresponding STD statistics are approximately twice as large as those between GLONASS and GPS and between Galileo and GPS, which are 2.713 and 2.566 ns, respectively. Figure 7: Time series of daily ISBs for each station (characterized by the receiver types) on DOY 41–70, 2020.
## 2 Materials
### Satellite Data
#### 2.2.3 Soil Moisture Active Passive (SMAP)  
In 2015, NASA launched the SMAP satellite to monitor the SM content by using L-band SAR (active) and radiometer (passive) instruments. SMAP has a temporal resolution of 2-3 days globally. In this research, we used Level-3 data of SMAP SM, which has 10-km spatial resolution [66].
# Simulation and Design of Three 5G Antennas
###### Abstract  
In the context of 5G networks, this paper investigates microstrip array antennas and mobile terminal MIMO array antennas. It introduces two innovative designs and, based on these, develops and fabricates a mobile terminal antenna. The first of these designs, a 4 \(\times\) 4 microstrip array antenna operating in the LTE band 42 (3.4-3.6 GHz), is researched and fabricated and an innovative approach, combining embedded and coaxial feeding methods, is proposed and employed. Measurement results indicate a bandwidth of 373 MHz (3.321-3.694 GHz), achieving a relative bandwidth of 10.7%. The antenna exhibits a high gain of 12.7 dBi, with an undistorted radiation pattern, demonstrating excellent directional characteristics. The second of these designs, a \"loop-slot\" MIMO antenna designed for 5G mobile devices with metal frames, is investigated. By opening slots in the metal frame and integrating them into the antenna's feeding structure, the decoupling principle is analyzed from the perspective of characteristic mode theory. This design shares resonant modes between the loop and slot antennas, allowing for the overlapping placement of the two antenna units. Experimental results confirm an isolation level exceeding 21 dB, with significantly reduced dimensions. Finally, an eight-unit MIMO antenna is designed and fabricated for 5G mobile devices with metal frames. Continuous optimization of the \"loop-slot\" module layout and unit spacing leads to a compact and miniaturized antenna structure. Measurement results show an isolation level exceeding 17 dB, radiation efficiency ranging from 65.8% to 73.7%, and an envelope correlation coefficient (ECC) below 0.03. Finally, an analysis of specific absorption rate (SAR) demonstrates excellent MIMO performance in terms of human body radiation exposure.  
5G communication; microstrip array antenna; MIMO array antenna; miniaturization; high isolation +
Footnote †: journal: Applied
## Proposed Interpolation Model
### Patch-Based NN Architecture
Each patch based covariance model \(\mathcal{F}^{\mathcal{P}_{s}}_{\Sigma}\) is learnet according to a Maximum Likelihood (ML) criterion. The associated training dataset comprises patch-based EOF decompositions of the forecasted states according to the mean model \(\mathcal{F}^{\mathcal{P}_{s}}\) from states of the training dataset corrupted by an additive Gaussian perturbation with a covariance structure \(\Sigma_{0}\). Here, \(\Sigma_{0}\) is given by the empirical covariance of the EOF patches for the entire training dataset. Overall, for a given patch \(\mathcal{P}_{s}\), we parameterize \(\mathcal{F}^{\mathcal{P}_{s}}_{\Sigma}\) the restriction of covariance \(\mathcal{F}_{\Sigma}\) onto patch \(\mathcal{P}_{s}\) as:  
\[\mathcal{F}^{\mathcal{P}_{s}}_{\Sigma}(x_{\mathcal{P}_{s},t},\Sigma_{\mathcal{ P}_{s},t})=\mathcal{B}^{t}\Psi(\Sigma_{\mathcal{P}_{s},t},\Sigma_{0})\cdot \mathcal{F}^{\mathcal{P}_{s}}_{D}(z_{\mathcal{P}_{s},t},\Sigma_{0})\cdot \mathcal{B} \tag{19}\]with \(\mathcal{F}_{D}^{\mathcal{P}_{s}}(z_{\mathcal{P}_{s}t},\Sigma_{0})\) the diagonal covariance model in the EOF space parametrized by a neural network and \(\Psi(\Sigma_{\mathcal{P}_{s}t},\Sigma_{0})\) a scaling function. Among different parameterizations, a constant scaling function \(\Psi()=1\) led to the best performance in our numerical experiments. Regarding the diagonal covariance model, details on its parametrizations are given in the next section. To illustrate the relevance of the proposed block diagonal covariance matrix parametrization (based on a patch based projection on the EOF space and illustrated for instance by Equation (19)), we also investigate a diagonal covariance matrix model in the patch space.
## 5 Evaluation
### Results and Discussion
#### 5.2.5 Number of Hops  
In this experiment we study the impact of grid size on the number of hops required for data to reach the CCS. Figure 9 shows the comparison of the impact of different grid size ranges on number of hops. The graph is plotted as length of grid versus number of hops. As the grid size increases the transmission distance is increased, hence number of hops between sensory nodes and CCS increases. Note that the number of hops in CMIMO and traditional SISO remain the same (The two curves are overlapping). HMIMO has an edge over the previously proposed mechanisms in terms of reduction in number of hops.  
Figure 10 shows the percentage comparison of number of hops for our scheme and in CMIMO. For a grid size of 300 \(\times\) 300, data is reaching CCS 80% of the time in two hops. For large networks, the number of hops in CMIMO are increasing, whereas there is no node that requires more than three hops to send data to CCS for this particular network setup and parameters.  
Figure 10: Histogram of number of hops to Central Coordination Station.  
Figure 9: Mean number of hops to Central Coordination Station.
## 3 Materials and Methods
### Facilities
As is illustrated in Figure 2, pressure transducers (model: BD/SENSORS DMP334, BD SENSORS GmbH, Thierstein, Germany) which had been calibrated by the manufacturer with an accuracy of 0.175% FS (full scale) were positioned immediately close to the nozzle inlet and in the chamber. Simultaneously, the temperature near the nozzle inlet was measured using Omega T thermocouple whose total accuracy was \(\pm\)1 K. The real-time data were obtained by a QuantumXMX840, (HBM, Darmstadt, Germany) data acquisition and monitoring system. During each test, the impingement on the samples by the jet would not begin until the pressures and temperatures obtained by the transducers had stabilized at the desired values. In this way, the practical application conditions for the SC-CO\({}_{2}\) jet in the bottom hole were simulated. The aggressive intensity of the SC-CO\({}_{2}\) jet rock erosion was evaluated by measuring the volume, \(V\), of the erosion hole. The erosion volume \(V\) was obtained by three steps, as shown in Figure 4. First, a certain amount of salt of density of \(\rho_{\mathrm{s}}=2.165\) g/cm\({}^{3}\) was weighted using an electronic balance with a resolution of 0.001 g, and the mass was \(m_{1}\). Then, the erosion hole was filled up with the salt. Finally, the remaining salt was weighted by the balance to get the mass, \(m_{2}\), and the erosion volume was calculated using Equation (6). To reduce the influence of accidental factors and improve the reliability of this research, each rock erosion experiment was repeated three times and then the measured erosion volumes were averaged and used as final data to analyze the effects of nozzle configuration. In addition, the erosion depth was measured using a vernier caliper with an accuracy of 0.02 mm. \[V=\frac{m_{1}-m_{2}}{\rho_{\mathrm{s}}} \tag{6}\]  
where \(V\) is erosion volume (cm\({}^{3}\)); \(m_{1}\) is mass of salt before filling (g); \(m_{2}\) is mass of salt after filling (g); and \(\rho_{\mathrm{s}}\) is density of the salt (g/cm\({}^{3}\)). Figure 3: Detailed structural parameters of the nozzles: (**a**) Convergent nozzle; (**b**) Laval-1 nozzle; (**c**) Laval-2 nozzle; (**d**) Pictures of the nozzles.
# Experimental Study on a Prediction Model of the Shrinkage and Creep of Recycled Aggregate Concrete
###### Abstract  
The significant difference between recycled aggregate and natural aggregate is the content of the attached mortar layer. With the increase of the replacement rate of recycled aggregate, the shrinkage and creep of recycled aggregate concrete is significantly increased. In this paper, 180-day shrinkage and creep tests of recycled aggregate concrete with different water-cement ratios were designed in order to analyze the effect of the substitution rate and water-cement ratio on shrinkage and creep properties. The results show that the shrinkage strain of recycled aggregate concrete with a substitution rate of 50% and 100% at 180 days is 26% and 48% higher than that of ordinary concrete, respectively, and the growth of group II is 22% and 47%, respectively. When the load was 180 days old, the creep coefficient of recycled aggregate concrete with a substitution rate of 50% and 100% in group I increased by 19.6% and 39.6%, respectively compared with ordinary concrete, and group II increased by 23.6% and 44.3%, respectively. Based on the difference of adhering mortar content, the creeping increase coefficient and shrinkage increase coefficient of the attached mortar were proposed, and a shrinkage and creep model of recycled aggregate concrete was established. When compared with the experimental results, the model calculation results met the accuracy requirements.  
recycled aggregate concrete; shrinkage and creep; attached mortar; prediction model +
Footnote †: journal: Applied Sciences
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 3 Data Preparation and Data Preprocessing
### Data Preprocessing
#### 3.2.5 OD Map-Matching  
Preprocessing of the trajectory data can effectively eliminate data such as missing values, outliers, and drift points, but it cannot solve the problem of data accuracy due to GPS devices. This results in taxi trajectories not being accurately displayed on the road but floating on the roadway. Therefore, it is necessary to match the taxi trajectory data with the road network map before processing and analyzing it. The purpose of OD map-matching is to take the accuracy errors brought by GPS devices and accurately locate them on the correct road sections.  
In this paper, a weight-based OD map-matching algorithm is used. The specific OD map-matching process is mainly divided into the following five steps:  
Step 1: Connect the start and end points of the taxi track segments to the road network. In this process, accurate map data needs to be acquired to match road segments.  
Step 2: Update the weights of the road sections within the radius of the taxi trajectory point. The distance between the trajectory point and the candidate road section, the angle between the coordinate direction of the trajectory point and the direction of the road section, and the topological relationship between the candidate road section at the current point and the road section where the previous point is located are regarded as the weighting factors.  
Step 3: Search for the shortest path with weights between the start and end points of taxis. Assign the appropriate weight coefficients of the above factors, calculate the total weights, and use the total weights as the best matching path.  
Step 4: Obtain the correspondence between taxi track points and road sections and the corresponding points.  
Figure 3: Characterization of passenger-carrying fields.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 4 Case Study of Transmission Line Insulator Defect Detection
### Implementation Process of Defect Detection  
The implementation process is shown in Figure 9. The model is firstly pre-trained using ImageNet [41], a public image dataset. The pre-trained model is transferred to the transmission line insulator image dataset by the stage-wise training method, while the training process is divided into two stages. Moreover, before training the model, random combinations of geometric and optical transformations (including flip, scale, hue, etc.) will be used to augment the samples, thereby further increasing the robustness and generalization capabilities of the model. At the same time, three training techniques including Mosaic data augmentation, cosine annealing, and label smoothing were applied to improve the accuracy and generalization ability of the model.  
In Figure 9, the optimal anchor size obtained based on the K-means clustering algorithm in the training set is (15,14 21,135 21,15; 39,18 81,21 107,36; 252,52 265,80 266,126), including the width and height of nine groups of anchors. The first three groups of anchors correspond to the 16 \(\times\) 16 feature map, the middle three groups of anchors correspond to the 32 \(\times\) 32 feature map, and the last three groups of anchors correspond to the 64 \(\times\) 64  
Figure 9: Model training and test process.  
feature map, which are responsible for detecting targets of different sizes. The parameter settings during training and test are shown in Table 1.
## 4 Experimental Results and Discussions
### Dynamic Elasticity Test  
Dynamic modulus of elasticity tests were performed on eco-friendly stabilized soil bricks with different stabilizer contents at each wet-dry cycle. The results, presented in Figure 9, demonstrate that the relative dynamic modulus of elasticity decreased by up to 45% at the test termination point of the 12 th cycle. The experiment revealed that no significant stabilizer content-dependent changes were observed in the relative dynamic modulus of elasticity during the first three cycles. However, a steeper decrease in the relative dynamic modulus of elasticity was observed as the stabilizer content decreased after three cycles. At the 12 th cycle, the relative dynamic modulus of elasticity was 1.15-fold higher at a stabilizer content of 15% compared to 5%, indicating a lower rate of decrease in the dynamic modulus of elasticity with increasing stabilizer content. These observations underscore the importance of stabilizer content in determining the dynamic behavior of eco-friendly stabilized soil bricks and provide valuable insights for their design and optimization.  
Figure 8: Effect of varying wet–dry cycles on unconfined compressive strength (UCS) (**left**) and normalized UCS containing 1.5% wood chip (**right**).