esa-sceva/satcom-chunk-collection huggingface dataset:
# Images Segmentation Based on Cutting the Graph into Communities
## 3 Method
The element of the matrix \(E_{ij}\) is equal to the edge weight between nodes \(v_{i}\) and \(v_{j}\). The matrix \(E\) is symmetric with respect to the main diagonal. If the image is \(N\times M\), then the matrix size is \(NM\times NM\). The matrix is very large for conventional images. A large number of elements in matrix \(E\) is zero. The number of non-zero elements depends on the correlation radius \(R\). If \(R=1\) then each row has no more than 8 non-zero elements. If \(R=2\) then the number of non-zero elements are not more than 24. The area growing algorithm was used to search for communities on a graph in this work. The task was to find a community \(H\) that includes a node \(v\in V\). This process corresponds to finding a segment that includes this point. The average edge weight \(W\) of subgraph \(H\) is calculated at each step. Nodes from the set \(V\backslash H\) associated with at least one node from \(H\) were considered at each iteration. The association of such a node with the community nodes \(H\) was calculated as the sum of the edge weights. \[w_{i}=\sum_{v_{j}\in H}e_{ij}. \tag{5}\]  
If a node's association with a community exceeds a threshold, the node joins the community \(H\). The threshold value was calculated based on the average community edge weights \(W\). The condition of joining node \(v_{i}\) to the community is written as an inequality. \[w_{i}>hW. \tag{6}\]The parameter \(h\) varies the image segmentation accuracy. This parameter determines the color difference that the algorithm perceives as close colors. The correlation radius \(R\) also has an effect on segmentation accuracy. This parameter determines the number of edges coming from each node and increases the weight of the connection between the vertex and the community. The algorithm is recursive. All points of the already formed community were considered at each step. All out-of-community points were checked for each community point. Asymptotic complexity for this algorithm is quadratic in number of pixels \(O\big{(}M^{2}N^{2}\big{)}\). The average edge weight changes as the community grows. The usual behavior of average community edge weight is presented in Figure 1.  
The average edge weight in the community decreases rapidly at first. Subsequently, the average edge weight comes to an asymptotic value. Increasing the correlation radius increases the size of the community. This fact is explained by the increase in the number of neighbors at each node. An increase in the correlation radius \(R\) results in a slight decrease in the average edge weight in the community. The image segment is easily computed from the set of community subgraph nodes. If the image is segmented completely, then segments formed around different points are calculated.
## 4 Our Proposed Routing Algorithm
### Route Setup Phase
Mobile sink nodes will sojourn at each parking position for enough time to collect monitored data from the sensor nodes in a single-hop or multi-hop communication manner. The sojourn time is determined by the propagation speed and the maximum distance between the source nodes and the mobile sink node. After completing the data transfer, the mobile sink node will move to the next parking position to receive data. While the mobile sink node moves from one parking position \(P_{i}\) to another position \(P_{j}\) it will not receive any monitored data from sensor nodes. When mobile sink node \(MS_{i}\) moves to a parking position \(P_{i}\), it will broadcast a notification message, which includes an ARRIVAL_MSG message and a NEXT_POSITION_MSG message. Each parking position will be kept in a table PP_TABLE for later use. This table will be saved in each mobile sink node, and updated before a new round of data transmission. When multiple mobile sink nodes exist in the rectangle network, we define that the starting point and speed of movement of each mobile sink node is the same. Each mobile sink node will move from the origin of coordinates in succession. The multiple sink mobility strategy is briefly described in Figure 3.  
Figure 2: Parking position selection. In Figure 3, at the point of departure, only one mobile sink node \(MS_{I}\) is necessary to receive monitored data. After leaving the origin of coordinates, another mobile sink node \(MS_{2}\) will participate in the data collection. Sojourn locations \(SL_{i}\) of mobile sink nodes are all chosen from parking positions in the PP_TABLE. Each mobile sink node will chose one of the parking positions from the PP_TABLE as its park position to collect monitored data. Sensor nodes within the transmission range will communicate with mobile sink nodes directly, and will be responsible for other sensor nodes to forward data to each mobile sink node.
## 3 Results  
Figure 2 shows a comparison between X-ray images and microwave images for two different patients, i.e., patient A (age: 80 years) and patient B (age: 75 years). The blue rectangle in the X-ray images indicates the limited scan area during microwave data acquisition. In both cases, the tumor can be identified on the images of both modalities (red circle) revealed by lower pixel intensities at the tumor location coming from higher signal attenuation of cancerous tissue. In addition, some anatomical features inside the breast next to the tumor can be recognized (see yellow and green ovals in Figure 2a-d).  
Figure 3 depicts two radar signals in the time-domain that were measured at two different positions on the breast as marked in Figure 2b. One radar signal is measured at the location of the tumor and the other is measured outside the tumor region. Given by the higher relative permittivity and conductivity of malignant tissue, a difference in time of arrival and signal amplitude can be observed. Moreover, this figure illustrates the time-domain gating (characterized by a lower and upper limit) used for the RMS computation in Equation (1).  
Figure 3: Comparison of UWB radar signals measured at two different positions on the breast as illustrated in Figure 2b. It can be observed that a difference in time of arrival and signal amplitude occurs. Attenuation and time delay are much higher for the tumor location given by a higher conductivity of the malignant tissue and a higher relative permittivity (i.e., smaller wave speed). The time-domain gatings are illustrated that show the signal regions used for RMS computation.  
Figure 2: Comparison between X-ray image and microwave image for patient A (**a,b**) and patient B (**c,d**). The RMS was normalized by the maximum RMS of the scanned region of the present patient. The lowest intensity values occur in the area of the tumor location given by stronger attenuation of cancerous tissue. The final thickness of the compressed breast during microwave examination is very similar for patients A and B, i.e., 4.4 cm for patient A and 4.3 cm for patient B. During X-ray examination, the breast was slightly more compressed, i.e., 4.1 cm for patient A and 3.7 cm for patient B.
## 4 Results
### Results of Applying FFT to Thermal Image
#### 4.1.3 Thermal Equalization Trend Analysis of Long Pulse Wave  
Figure 11 shows the Line 2 profile graph with the FFT algorithm applied to the images shown in Figures 9 and 10. In Figure 11, the higher the Gaussian weight of the raw image, the higher the influence of thermal equalization. In particular, as the weight increases from 0.5 to 4.0, as shown in Figure 11c, flattening is performed better in the center of the surface, making it possible to clearly detect C2 thinning defects in a uniform state, as shown in Figure 11f. When the FFT algorithm was applied to the raw image, a clear thermal equalization effect occurred when there were no defects. However, it can be confirmed that there is not much difference in detectability when a defect exists.
## 5 Conclusions and Suggestions  
### Conclusions  
From the perspective of temporal and spatial differences, this research innovatively introduces an empirical mode decomposition method to reveal the evolutionary law of public response to release-type communication. Furthermore, this study delves into the differences in public response time evolution law of release-type communication in different regions. The temporal and spatial differences in public response to release-type communication to stop food waste are clarified, which provides important enlightenment for the governance of food waste. The main findings are as follows:  
1. With release-type communication, the public concern about the issue of food waste has gone through five stages: Incubation period, outbreak period, recession period, secondary outbreak period, and fading period. In general, the support intention shows a down-up-down trend, and the implementation intention rises and then descends. Both the support intention and implementation intention appear to have an inflection point and reach the peak during the recession period. Besides, the general effect of public support intention tends to decline while the implementation intention tends to rise by using the EMD method.
2. Public concern and emotional performance toward food waste are different in different regions, which is generally related to the regional economic development level. After release-type communication, the support intention and implementation intention are higher in West China than in East China in general.
3. The results of spatio-temporal differences analysis show the changes in the support intention and implementation intention after the release-style communication. Specifically, 11 regions, including Gansu, Guangxi, Guizhou, and Qinghai, increase the support intention. However, the support intention in six regions, including Beijing, Guangdong, and Hainan, goes negative. Moreover, the support intention in 17 regions, including Anhui, Jiangsu, Henan, and Shandong, and elsewhere, is lower at different levels. It is remarkable that the support intention has changed significantly in three regions of Beijing, Guangdong, and Fujian. In terms of the implementation intention, 14 regions (such as Gansu, Tibet, Xinjiang, and Hebei) increase, while 18 regions decrease in varying degrees (such as Beijing, Jiangsu, Zhejiang, and Shandong). Among them, the implementation intention of Shanghai significantly increases, while Shandong significantly decreases.
# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 2 Data and Methods
### Search Coil Magnetometer  
The Search Coil Magnetometer (SCM) is a three-axis search coil magnetometer that measures varying magnetic fluxes across the multiple frequency bands detailed in Table 1[33].  
Since frequency range of whistlers rarely exceeds 20 kHz [22; 34], we focused on the Extreme-Low-Frequency (ELF) band. In such band, SCM provides (1) magnetic field waveform data sampled at 10.24 kHz and expressed in nT(2) Power Spectral Density (PSD) data expressed in nT\({}^{2}\)Hz\({}^{-1}\). PSD data are obtained with a STFT in 400 ms length windows. The frequency range extends from \(f_{MIN}\sim 200\) Hz to \(f_{MAX}\sim 2200\) Hz, with a frequency resolution of \(\sim\)2.5 Hz, resulting in a total of \(\sim\)801 frequency bins.
# Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks
## 2 Proposed Methodology
### B. Fusion Layer  
In a multi-label setting, we run multiple CSVMs depending on the number of objects. Each CSVM will apply a set of convolutions on the image under analysis as shown in Figure 1. Then each convolution layer is followed by a spatial reduction layer. This reduction layer is similar to the spatial pooling layer in standard CNNs. It is commonly used to reduce the spatial size of the feature maps by selecting the most useful features for the next layers. It takes small blocks from the resulting features maps and sub-samples them to produce a single output from each block. Here, we use the average pooling operator for carrying out reduction. Then, to exploit the correlation between objects present in the image, we propose to fuse the feature maps provided by each branch. In particular, we opt for the max-pooling strategy in order to highlight the different detected objects by each branch. Figure 4 shows an example of fusion process for two branches. The input image contains two objects, Laboratories (object1) and Bins (object2). The first CSVM tries to highlight the first object, while the second one is devoted for the second object. The output maps provided by the pooling operation are fused using the max-rule in order to get a new feature-map where the two concerned objects are highlighted as can be seen in Figure 4. We recall that the feature maps obtained by this operation will be used as input to the next convolution layer for each branch.  
Figure 4: Example of fusion of output maps of two convolutional neural networks (CNNs).
## 3 Results and Discussion
### Comparison of the Adsorption in Microsphere with the Adsorption on Flat Surface  
The reasons for high enzyme activity and specific activity of lipase in gigaporous microspheres are explained by the difference of the curvature and the monolayer/multilayer adsorption. The adsorption on a flat surface was further compared to find what happened when there was no limit of pores. Quartz crystal microbalance with dissipation monitoring (QCM-D) technology was adopted to investigate the adsorption behavior of lipase on PST chips and the influence on enzyme activity. Figure 6 showed the adsorption parameters of lipase on PST chips.  
Figure 6: Adsorption of lipase on PST chip determined by QCM-D ((**a**) Adsorption mass; (**b**) \(|\Delta D/\Delta f|\) ratio; (**c**) Lipase activity; (**d**) Specific activity).  
From Figure 6a, we could see that the increase of adsorption mass was a rough linear rise on the chip when excluding the influence of diffusion transfer in pores. The change of the \(\mid\Delta D/\Delta f\mid\) ratio in Figure 6b reflected the tightness degree of lipase adsorbed on the chip. At the beginning, lipase molecules mainly interacted with the surface of the chip, and the interaction was strong. Therefore, the \(\mid\Delta D/\Delta f\mid\) ratio was very low. With the increase of the adsorption mass, the enzyme layers got thicker, and the \(\mid\Delta D/\Delta f\mid\) ratio increased rapidly. After the time point of 10 min, more multilayer adsorption of enzyme molecules enhanced the tightness of the layers, and the \(\mid\Delta D/\Delta f\mid\) ratio then became stable.  
From Figure 6c,d, we could see that the enzyme activity increased with the increasing adsorption amount, but the specific activity had little change, which is dramatically different from that of gigaporous particles. For the adsorption on the chip, the effect of pores was completely ruled out. However, for gigaporous particles, though most pores distributed above 100 nm, there were still so many small pores in which the diffusion resistance and the structure change of lipase existed, which could reduce the specific activity of lipase.
## 2 Materials and Methods
### Air Pollution Monitoring at National, Regional and Local Networks at Novi Sad  
The monitoring of outdoor ambient air quality is usually done via networks at the national and local levels. However, despite having a high quality of instrumentation, these kinds of networks are usually very sparse, may not monitor all of the needed parameters, and give little insight into personal exposure. In specific cases, when rapid deployment and increased temporal resolution may be of more interest, IoT (Internet of Things) enabled low-cost sensors may provide interesting complementary data.  
In the city of Novi Sad, there are three main groups of monitoring stations: stations that belong to national networks, stations in the regional network, and stations in the local monitoring networks.  
National Network, (website [[http://www.amskv.sepa.gov.rs/pregledstanica.php](http://www.amskv.sepa.gov.rs/pregledstanica.php)]([http://www.amskv.sepa.gov.rs/pregledstanica.php](http://www.amskv.sepa.gov.rs/pregledstanica.php)))  
* NN1, Novi Sad-Rumenacka (air quality variables that are monitored: SO\({}_{2}\), NO\({}_{2}\), CO, PM\({}_{10}\), PM\({}_{2.5}\), wind direction, wind speed) type of station: traffic (Lat/Long: 45.26263\({}^{\circ}\), 19.81902\({}^{\circ}\))
* NN2, Novi Sad--Liman (air quality variables that are monitored: SO\({}_{2}\), O\({}_{3}\), NO\({}_{2}\), CO) type of station: background (Lat/Long: 45.23864\({}^{\circ}\), 19.83570\({}^{\circ}\)) Regional Network, (website [[http://www.amskv.sepa.gov.rs/pregledstanica.php](http://www.amskv.sepa.gov.rs/pregledstanica.php)]([http://www.amskv.sepa.gov.rs/pregledstanica.php](http://www.amskv.sepa.gov.rs/pregledstanica.php)))
* RN1, Novi Sad--Sangaj (air quality variables that are monitored: BTEX, H\({}_{2}\)S, SO\({}_{2}\), t, RH, wind direction, wind speed), type of station: industrial, (Lat/Long: 45.27237\({}^{\circ}\), 19.87333\({}^{\circ}\)) Local network, (website [[https://environovisad.rs/air_points/mm3](https://environovisad.rs/air_points/mm3)]([https://environovisad.rs/air_points/mm3](https://environovisad.rs/air_points/mm3)))
* LN1, Novi Sad--Intersection of Rumenacka and Bulevar Jase Tomica, type of station: urban/traffic, (Lat/Long: 45.26348\({}^{\circ}\), 19.81903\({}^{\circ}\))
* LN2, Kac--Kralj Petar 1, type of station: suburban/traffic, (Lat/Long: 45.29980\({}^{\circ}\), 19.93926\({}^{\circ}\))
* LN3, Novi Sad- Suncani kej 41, type of station: urban/background, (Lat/Long: 45.24000\({}^{\circ}\), 19.85139\({}^{\circ}\))
* LN4, Sremska Kamenica, Kamenicki park 1-14, type of station: suburban/background, (Lat/Long: 45.22931\({}^{\circ}\), 19.84898\({}^{\circ}\))
# The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches
## 4 Applications
### Scenario 3: Entrance Ramp  
Figure 10 represents a scenario corresponding to the (d) region in Figure 7. This scenario is designed to simulate the reduction in land count at merging points, such as highway entrances, and the case in Figure 10 represents a prominent example where all stages of aggressive driving are detected. Upon closer examination of this simulation, in Stage 1, the vehicle underwent a rapid lane change just before the merging point, and another lane change occurred after entering the merging zone. In Stage 2, an aggressive land change was executed prior to the entrance zone, and no further lane changes were made after navigating through the curved section. In Stage 3, multiple lane changes were made to secure a larger turning radius before entering the merging zone. In Stage 4, just before entering the merging zone, the vehicle swerved across all lanes, creating a situation where it aggressively squeezed into the merging area without executing lane changes in advance. In practical terms, all of these cases successfully detected the trajectory as lane changes during the curve without any confusion.
# Thermo-Responsive Shape-Memory Effect and Surface Features in Polycarbonate (PC)
## 1 Introduction  
The ability to return the original shape of a material, but only at the presence of the right stimulus, such as heat, light and chemicals, is called the shape memory effect (SME) [1; 2; 3]. Consequently, the materials with SME are known as shape memory materials (SMM) at present. The family of SMM has expanded dramatically in recent years, in particular, to include many types of polymeric materials. According to, for instance [4], most polymers are naturally heating/chemo-responsive SMM. In addition to continuous efforts to develop new SMMs [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16], the SME in many engineering polymers has been systematically characterized and gradually utilized in some engineering applications [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27].  
Polycarbonate (PC) is a widely used thermoplastic engineering polymer particularly due to its high toughness, good flame resistance and durability. PC is a typical linear co-polymer with carbonate as a repeating unit. Well-documented experimental results of its standard properties are now available in the literature. Its heating-responsive SME as revealed in Figure 1 inspires us to further explore its shape memory behavior for potential engineering applications in the future.  
Figure 1: Cont.  
In this paper, the influence of programming temperature and programming strain via uni-axial tension on the heating-responsive SME in a commercial PC is systematically investigated. In addition, since PC is a commonly used optical polymer, the surface features of programmed samples are examined at both macro and micro scales.  
The shape recovery in this PC is not due to relaxation/creeping, since it is able to maintain the temporary shape unless heat, which is used in this study as the stimulus, is applied. Thus, it is important to characterize its shape fixation ratio and shape recovery ratio under different processing parameters.
## 2 Materials and Methods
### Methodology Framework  
The modeling procedure for the prediction of IRI was carried out in four main steps: (I) data collection, (II) data preparation, (III) training models, and (IV) validation of models. A detailed description of each is briefly recalled below (Figure 4):  
1. Data collection: in this step, the data generated from laboratory experiments were collected and summarized into two groups whereas the first groups contains all input parameters such as: road length, analysis area, summed cracks, maximum depth of rut, average depth of rut and the second one encloses the output parameter (IRI).
2. Data pre-processing: in this step, PCA and the Savitzky-Golay filter were used to reduce the dimensions data and reduce extreme values in the distribution of data.
3. Data preparation: in this study, the holdout validation method was used for training and validating the models as it is a popular and effective method for generating the datasets for training and testing the models [44; 45; 46; 47; 48; 49; 50; 51], and thus the collected data were divided into two parts. The first part included 70% data which was used to train the models, whereas the second  
Figure 3: Typical artificial neural network.  
part contained 30%, the remaining data and this was used to validate the models as the ratio 70/30 for dividing the training and testing dataset was a common ratio used in applying the ML models [29; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71].
4. Training the models: the models were created using a 70% training dataset. PSOANFIS was created by combining PSO and ANFIS, GANFIS was created by combining GA and ANFIS, FAANFIS was created by combining FA and ANFIS. Out of these, PSO, GA, FA were used to optimize the consequence, antecedent parameters for giving the best ANFIS. An artificial neural network (ANN) was created using sigmoid algorithm.
5. Validation the models: after optimization of consequence, antecedent parameters, as well as training models, validation of the models were carried out using testing dataset via two methods namely RMSE and R.