esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Average Contributions by Active OSM Members  
The analyses presented thus far focused on the estimation of the absolute number of OSM members and the determination of active members in each urban area. Another important factor that needs to be considered is the quantity in which an active member contributes to the project. In the following analysis only active members that are part of the aforementioned Senior Mapper group and that did not participate in any data imports for the urban areas have been investigated. The main goal was to retrieve detailed information about the average number of active days a Senior Mapper spends on data contributions to the project and how many objects on average were created in this timeframe in each urban area. The analysis was conducted for the three months prior to the creation date of the history dump file, October 19, 2012. Figure 8 shows the average number of active days of all Senior Mappers and the number of Nodes, Ways and Relations created in this time frame for all tested areas.  
Although the absolute number of members for each tested urban area varies, the distribution of the different mapper groups proved to be alike. A similar result was revealed for the average contribution values and active days of the Senior Mappers. The total average values of all urban areas combined showed that a Senior Mapper is active for about 9 to 10 days and creates around 1466 Nodes, 229 Ways and 4 Relations (Table 2). Additionally, Figure 8 shows that particularly areas with very small communities can generate positive and negative outliers in this analysis such as Cairo and Buenos  
Figure 8: Average Senior Mapper Activity Timeframe and Contributions per Urban Area (Aug.–Oct. 2012).  
Aires. Similar to the result gathered during the temporal dataset quality analysis, Sydney appears to take an outlier role due to the same cause _i.e._, OSM license change. The stronger Nodes and Ways contributions can be accredited to remapping efforts by the OSM community.
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 3 EALC Methodology
### Weight Assignment  
According to the best of our knowledge, all of the previous works statically assign the weight to fitness parameters based on their importance. However, this static weight assignment may bias the fitness function and provide inaccurate results. Figure 3 describes two of such scenarios in which static assignment is not suitable and biases the fitness function.  
Let us suppose all the nodes are in transmission range of each other and one node is to be elected as CH based on its fitness values. We also consider the energy levels of all nodes in percentages:  
_Scenario 1_: If node \"A\" has an energy level at 90% and all other nodes have 50%. Such high differences in energy level may cause the node \"A\" to be elected as a CH. It is obvious from Figure 3 that if node \"A\" is elected as a CH, it will consume more energy of its own as well as of its CMs because of the large distance from other nodes.  
_Scenario 2_: If node \"F\" has the energy level of 30% and all other nodes have 50%. Because of the short distance of form other nodes, node \"F\" may be elected as a CH. Energy consumption rate of CH is much more than the other nodes because it has to relay the network traffic of all member nodes. If node \"F\" become CH, it will consume its energy more rapidly and will die out very soon or result in again calling of the clustering algorithm.  
As we have seen that static assignment may lead towards the selection of inappropriate CHs. One or more parameters may bias the fitness function and provide inaccurate results in many other such scenarios. To overcome this problem, EALC dynamically assigns the weight to each parameter based on its negative impact on the fitness function. First, it normalizes each parameter value and brings it into the range from 0 to 10. Then, the deviation of each parameter from the mean value of all parameters is calculated using Equation (6). This deviation shows the negative impact of a parameter on the fitness function. Higher values of deviation point out the outlier parameter. To avoid this bias, we add some penalty during assignment of weights, based on parameter deviation from the mean value. Equation (7) computes the weight of each parameter:  
\[dev(i)=ABS(mean-parameter(i)) \tag{6}\]  
\[w(i)=\ \frac{1}{dev(i)} \tag{7}\]  
where \"\(w\)\" and \"\(dev\)\" are the weight and deviation of \(i\)th parameter respectively. Sum of all weights must be equal to \"1\". By using these parameters value and their corresponding weights, the fitness value of each node is computed using fitness function (Equation (4)).
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 4 Experiments  
### Datasets  
Our model will be tested on two classical benchmark datasets. One of the datasets is the MoveLens 20M Dataset [21], which has a high user-item interaction frequency and thus can test the model's recommendation accuracy for medium- and high-frequency data. The other dataset is the MovieLens Latest Datasets, which contains more low-frequency user-item interaction data. This dataset can better test the model's recommendation accuracy for low-frequency data. Next, we will introduce these two datasets:  
**MovieLens 20M Dataset (ml-20m) [21]:** A movie rating dataset collected from the personalized movie recommendation website MovieLens4. The dataset contains 20 million 5-star ratings and 465,000 free-text tags applied to 27,278 movies provided by 138,493 users. These users are randomly selected to ensure every selected user has rated at least 20 movies.  
Figure 3: Recommendation Layer overview of the TMMNN.  
**MovieLens Latest Datasets (ml-latest) [21]:** A newly released movie rating dataset collected from the same website. The dataset contains 27 million 5-star ratings and 1.1 million tags applied to 58,098 movies from 283,228 users. In this dataset, the users are also randomly selected, and each selected users has rated at least least one movie.  
In order to prove our model's effectiveness, we purposefully divided the above datasets into different sizes, so that the multi-layer perceptron could not learn a linear function due to the lack of training data. Therefore, the model gives full play to the role of matrix factorization to capture the linear relationships to verify the effectiveness of our method.
## 2 Circuit Descriptions
### Proposed Electronically-Tunable Mixed-Mode Biquadratic Filter  
Figure 2 shows that the proposed electronically tunable mixed-mode biquadratic filter employs three CCCCTAs and two grounded capacitors. The multiple current outputs of the CCCCTA are easily implemented by adding output branches to the CCCCTA. The input current of the circuit is applied to the X-terminal of the first CCCCTA, which has low input impedance. The output currents are obtained at high output impedance ports, which simplifies cascading in both CM and TAM operations. The input voltage of the circuit is applied to the Y-terminal of the third CCCCTA, which has high input impedance. Because the circuit has low input impedance and high output impedance for current signals and has high input impedance for voltage signal, it can be used in cascade for realizing higher-order filters [16]. Moreover, grounded capacitors are used in integrated circuits to cancel parasitic impedance effects of active elements [16]. Routine analysis of the circuit in Figure 2 reveals that the following four output voltages and five output currents can be obtained.  
\[\mathrm{V_{o1}}=\frac{-\mathrm{G_{x1}}\mathrm{G_{x2}}(\mathrm{I_{in}}-\mathrm{ G_{x3}}\mathrm{V_{in}})}{\mathrm{D(s)}} \tag{2}\]  
Figure 1: CCCCTA (current controlled current conveyor transconductance amplifier) symbolic representation.  
\[\mathrm{V_{o2}}=\frac{-\mathrm{sC_{1}G_{X1}(I_{in}-G_{X3}V_{in})}}{\mathrm{D(s)}} \tag{3}\]  
\[\mathrm{V_{o3}}=\frac{\mathrm{s^{2}C_{1}C_{2}(I_{in}-G_{X3}V_{in})}}{\mathrm{D(s)}} \tag{4}\]  
\[\mathrm{V_{o4}}=(\frac{1}{g_{m3}})\frac{\mathrm{sC_{1}g_{m1}G_{X1}I_{in}+G_{X3} (\mathrm{s^{2}C_{1}C_{2}G_{X1}+g_{m2}G_{X1}G_{X2})V_{in}}}}{\mathrm{D(s)}} \tag{5}\]  
\[\mathrm{I_{o1}}=\frac{\mathrm{s^{2}C_{1}C_{2}G_{X1}(I_{in}-G_{X3}V_{in})}}{ \mathrm{D(s)}} \tag{6}\]  
\[\mathrm{I_{o2}}=\frac{-\mathrm{sC_{1}g_{m1}G_{X1}(I_{in}-G_{X3}V_{in})}}{ \mathrm{D(s)}} \tag{7}\]  
\[\mathrm{I_{o3}}=\frac{\mathrm{g_{m2}G_{X1}G_{X2}(I_{in}-G_{X3}V_{in})}}{ \mathrm{D(s)}} \tag{8}\]  
\[\mathrm{I_{o4}}=\frac{\mathrm{sC_{1}g_{m1}G_{X1}I_{in}+G_{X3}(\mathrm{s^{2}C_ {1}C_{2}G_{X1}+g_{m2}G_{X1}G_{X2})V_{in}}}}{\mathrm{D(s)}} \tag{9}\]  
\[\mathrm{I_{o5}}=\frac{\mathrm{sC_{1}g_{m1}G_{X1}(I_{in}-G_{X3}V_{in})}}{ \mathrm{D(s)}} \tag{10}\]  
D(s) is given by  
\[\mathrm{D(s)}=\mathrm{s^{2}C_{1}C_{2}G_{X1}+sC_{1}g_{m1}G_{X1}+g_{m2}G_{X1}G_{ X2}} \tag{11}\]  
where \(G_{X1}=\frac{1}{R_{X1}}\), \(G_{X2}=\frac{1}{R_{X2}}\), and \(G_{X3}=\frac{1}{R_{X3}}\).
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 2 Materials and Methods  
### Datasets  
According to [24], there is currently no unified bridge crack database published. Therefore, in order to meet the experimental requirements, the bridge crack dataset in [24] is artificially augmented to generate the dataset used in this paper. In order to promote the development of crack detection algorithms, we share our dataset on [[https://github.com/tjdxchy/crack-detection](https://github.com/tjdxchy/crack-detection)]([https://github.com/tjdxchy/crack-detection](https://github.com/tjdxchy/crack-detection)).  
The original dataset is composed of 2068 bridge crack images collected by the Phantom 4 Pro's CMOS surface array camera with a resolution of \(1024\times 1024\). The bridge crack dataset we used was generated by the original dataset through the following operations:  
1. Data preprocessing: Since the pictures in the original dataset all contain cracks, which were not conducive to the differentiation of positive and negative samples during network training, we cropped a \(1024\times 1024\) resolution image into four \(512\times 512\) resolution images. Then, the obtained 8272 pictures were filtered to remove the blurred pictures, and a crack dataset containing 6069 images was obtained. The dataset included 4058 crack images and 2011 background images, with the ratio of crack images to background images being about 2:1. At last, we randomly selected 4856 images as the training set and 1213 images as the testing set.
2. Data augmentation: In order to meet the input requirements of the network, we cropped the picture size to \(224\times 224\) through the random center crop operation. Then, we randomly flipped the training set images to further augment the dataset.  
Some examples of images in our dataset are shown in Figure 1. To create a robust crack classifier, in addition to good images(1(a)), our dataset also contains images of bridge shading(1(b)), water stains, and strong light(1(c)).  
Figure 1: Examples of images contained in our dataset. (**a**) Good images, (**b**) Images with complex shading (noise), and (**c**) Images illuminated by strong light. (All displayed images have a resolution of \(224\times 224\)).  
There are some images containing cracks in edges in our cropped dataset, as shown in Figure 2. Having these images in the dataset has the following advantages: (1) In these images, the cracks only occupy a small proportion of the image. After the CNN operation, the feature map will become smaller. If our model can successfully distinguish the cracks in such images, it is proved that our model can effectively extract the deep characteristics of the cracks. (2) In the actual application, the proportion of cracks in the image is small, so the inclusion of these images can increase the practicality of the proposed model.
# Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles
## 5 Environment
### Drone Environment  
One of the main parts of reinforcement learning is a physical environment, and this environment has been developed for the AR drone. Following the OpenAI decisions, the environment only provides the abstraction, not the agent. This means that the environment is independent of the agent, which resides in the ROS.  
Directed by the OpenAI Gym rule, the environment must contain registration, steps, and rewards. This will make sure that the interaction between the simulation and OpenAI is smooth. The following tasks are executed to achieve this [10].  
1. Registration: registers the training environment in the gym as an available one.
2. Init: in this stage, several parameters such as take-off and landing commands, as well as training parameters such as the value of the speed, desired position, running steps (new command sending time, 0.3 s), the maximum inclination of the drone, and the maximum altitude of the drone, are set. Simulation stabilization is also done at this stage.
3. Reset: this task allows one to reset simulation, pause/resume simulation, reset the robot to initial conditions, and take observation of the state of the drone.
4. Step: for a given action selected by the reinforcement learning algorithm, the quadrotor performs corresponding movements after determining the velocity values as shown in Table 4. The speed value in our simulation is 1 m/s.
5. Observe data: is a function block to obtain data from the drone sensor and also data about position and IMU.
6. Process data: based on the data from the environment and IMU, a reward function is used to compute the progress of the quadrotor to the desired position, such that the quadrotor is given a reward or penalized. Then, the next action will be sent to the drone. Roll, pitch, and altitude movements are also penalized or rewarded.
# Spatiotemporal Analysis of Web News Archives for Crime Prediction
## 5 Crime Prediction
### Supervised Learning
#### 5.1.1 Prediction Using KNN  
KNN is a model-free algorithm and gives an n training vector in dimensions of feature place. It identifies the k nearest neighbor of the feature vector that was being estimated using Equation (2). KNN works by looking at the history of past crimes and finds a similar crime based on the matched number of neighbors. Its output is class membership, which means the maximum votes identify an object from its neighborhood. Crime prediction can be made most efficiently using KNN because the neighbors of a victim house are more susceptible to the next theft. Therefore, the nearby areas of recent crime locations are considered more vulnerable to the next crime. Considering the similarity between the testing and training data, the distance was calculated to predict the classes of test data. KNN algorithm predicts the test data based on the nearest neighbor method [41]. In this study, [PERSON] is trained using a crime dataset with the help of the Weka tool, and our generated test set was used to predict the event occurring at the specified location. KNN involves the factor of distance, so the distance between the training feature and test feature is computed using the formula in Equation (2):  
\[d_{i}=\sqrt{(x_{i}-x)^{2}+(y_{i}-y)^{2}} \tag{2}\]  
After distance computation, the nearest neighbors are identified using sorting techniques and are assigned the crime type based on the voting of its neighbors. We can implement a KNN model by the following steps [42]:  
1. Input the dataset.
2. The value of k is initialized.
3. Data points are iterated from 1 to the total number of training in order to get the predicted class.
4. Calculate the distance between testing data and each row of training data; considering Euclidean distance as the distance metric because it measures the distance between the pair of samples p and q in an n-dimensional feature space.
5. The values of calculated distances are sorted in ascending order.
6. Top k rows are returned from the sorted array.
7. The most commonly used class of selected rows is returned.
8. Prediction results are returned.
## 4 Results Analysis
### Flight Test Case
GPS, GLONASS and Galileo noise levels are shown in blue, green and red colours, respectively. Figure 15: DDs of L1 carrier phase observations for different receiver combinations with respect to the satellites elevation angles. GPS, GLONASS and Galileo noise levels are shown in blue, green and red colours respectively. Solid black curves represent exponential functions that fit to the estimated DDs. In order to see the noise behaviour of L2 and L5 carrier phase observations alongside L1, DDs are computed and analysed using CDFs. Figure 17a-c shows the CDF for L1, L2 and L5 carrier phase observations, respectively. For both the receiver combinations, 95% of the DD values for GPS, GLONASS and Galileo L1 are less than about 1.4, 4.6 and 1.5 mm, respectively. Similarly, for GPS and GLONASS L2, 95% of the DD values are less than about 2 and 4.7 mm, respectively. Finally, for the GPS and Galileo L5 observations, 95% of the DD estimates are less than about 2.8 and 5.4 mm, respectively. For other receiver combinations, the noise levels are almost similar with regards to L1, L2 and L5 carrier phase observations. Even though the noise level is slightly higher for both L1 and L2 GLONASS DDs, the values are acceptable in the case of kinematic data processing. There is a significant increase in the noise level of Galileo L5 observation when compared to its L1 data-set.
## 3 Results
### Wear Measurement on the Basis of the IOS Compared to WLP
#### 3.1.3 Wear Values at the Wear Step t3  
The mean maximum wear values for the last simulated wear level are shown in Table 3. Whereby, the highest wear value obtained from the IOS was 390 \(\upmu\)m at tooth 27. The WLP data revealed 344 \(\upmu\)m. At t3, there was a range of 415 \(\upmu\)m to 441 \(\upmu\)m for the IOS at tooth 26. The WLP measured 417 \(\upmu\)m. At this point, 3 out of 10 cases showed a maximum deviation of +13%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **FDI 26 (t1)** & **FDI 27 (t1)** \\ \hline WLP (reference) & 227 & 221 \\ OC non-p & 217 (\(\pm\)4) + 4\% & 232 (\(\pm\)4) + 5\% \\ OC p & 239 (\(\pm\)7) + 5\% & 234 (\(\pm\)6) + 6\% \\ Tr3 non-p & 231 (\(\pm\)2) + 2\% & 235 (\(\pm\)6) + 6\% \\ Tr3 p & 223 (\(\pm\)13) \(-\) 2\% & 237 (\(\pm\)6) + 7\% \\ TD & 220 (\(\pm\)2) \(-\) 3\% & 229 (\(\pm\)6) + 4\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wear values at the wear step t1.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **FDI 26 (t2)** & **FDI 27 (t2)** \\ \hline WLP (reference) & 300 & 242 \\ OC non-p & 328 (\(\pm\)9) + 9\% & 267 (\(\pm\)7) + 10\% \\ OC p & 329 (\(\pm\)11) + 10\% & 287 (\(\pm\)11) + 19\% \\ Tr3 non-p & 330(\(\pm\)9) 10\% & 276(\(\pm\)13) + 15\% \\ Tr3 p & 320 (\(\pm\)9) + 7\% & 281 (\(\pm\)8) + 16\% \\ TD & 314 (\(\pm\)7) + 5\% & 273 (\(\pm\)9) + 13\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Wear values at the wear step t2.  
The comparisons of the wear values within the IOS did not show statistically significant differences between the different intraoral optical impression systems (\(\alpha\) < 0.05, Bonferroni corrected).
## 2 Materials and Methods
### Enhanced Object Detection
#### 2.3.3 Object Detection  
The advancement of deep learning technology has significantly improved image recognition accuracy based on CNNs. A common technique used in CNNs is pooling, which reduces the size of the input feature map, thereby reducing computational load and enhancing model generalizability. However, this process can result in the loss of small features, which can be problematic when fine details are crucial. For example, max pooling selects only the largest value from each region, potentially ignoring small features, whereas average pooling takes the average value of the region, leading to the dilution of detailed information [27]. To address these limitations and achieve fast and accurate object detection, this study used the YOLO-v8 model. YOLO-v8 combines feature pyramid networks and path aggregation networks to merge detailed features and abstract information bi-directionally, thereby enabling more accurate object detection [28].  
YOLO [29] is a single neural network-based model for object detection that divides the input image into A\(\times\)A grids and predicts B predefined bounding boxes for each grid cell. If the predicted bounding box overlaps with the actual bounding box by a certain threshold, the box is considered to contain an object. The probability that the predicted bounding box corresponds to one of the detectable objects is calculated as follows [29]:  
\[\mathrm{P_{CLS}}=\mathrm{P_{obj}}\times\mathrm{IOU^{thr}}\times\mathrm{P_{cls |obj}} \tag{9}\]  
Here, \(\mathrm{P_{obj}}\) denotes the probability that the predicted bounding box contains an object, \(\mathrm{IOU^{thr}}\) denotes the intersection over union (IoU) ratio, which represents the overlapping area between the predicted and actual bounding boxes, and \(\mathrm{P_{cls|obj}}\) denotes the probability that a specific object is classified among the objects of interest. Ultimately, the bounding box with the highest \(\mathrm{P_{CLS}}\) value among the predicted B bounding boxes is selected as the bounding box for the detected object.
## 3 Results and Discussion
### Shear Mechanical Properties
#### 3.2.2 Effect of Cooling Method on Shear Mechanical Properties  
As demonstrated in Figures 11 and 12, the trends in the shear mechanical properties of coarse- and fine-grained granite under different cooling methods are, in general, consistent. However, in comparison with water cooling, liquid nitrogen cooling results in reduced shear strength and cohesion for granite. To illustrate this, let us consider fine-grained granite under a shear angle of 45\({}^{\circ}\). As shown in Tables 2 and 3, the shear strength of granite increased by 10.2% and 3.3%, respectively, after 200 \({}^{\circ}\)C water-cooling and liquid-nitrogen-cooling shocks, while the cohesion increased by 24.5% and 9.1%, respectively. Conversely, when subjected to water-cooling shock or liquid-nitrogen-cooling shock at 600 \({}^{\circ}\)C, the shear strength of granite decreased by 12.2% and 28.2%, respectively, while cohesion declined by 42.4% and 44.8%, respectively. These findings suggest that liquid-nitrogen-cooling shock diminishes the thermal hardening effect and amplifies the thermal damage effect in granite. This phenomenon can be attributed to the greater temperature difference between liquid nitrogen and high-temperature granite compared to that between water and high-temperature granite. When granite is subjected to liquid nitrogen, the thermal stresses generated within the material are greater than those generated by water, resulting in a greater number of thermally induced cracks [30]. This ultimately leads to a reduction in shear strength and cohesion in liquid nitrogen-shock-cooled granite compared to water-shock-cooled granite at the same heat treatment temperature.