esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Due to the strong decorrelation of the river crossing bridge, the points on the bridge were distributed unevenly and gathered in a decentralized manner, so the characteristic points were selected at the places where the deformation points were dense. Eight characteristic points were selected from the west to the east side of the bridge, as shown in Figure 7. As shown in Table 2, P1 was located near No. 1 pier on the bridge's west side and moves with a mean rate of 1.9 mm/year, P2 shows an annual mean deformation rate of \(-\)1.5 mm/year, P3 shows an annual mean deformation rate of \(-\)6.3 mm/year, P4 shows an annual mean deformation rate of \(-\)10.7 mm/year, P5 shows an annual mean deformation rate of 15.4 mm/year, and P6 shows an annual mean deformation rate of 4.6 mm/year. The annual mean deformation rate of P7 reaches \(-\)1.8 mm/year.  
We divide the deformation characteristic points on the bridge into points on the west side of the bridge, points on the middle span of the bridge, and points on the east side of the bridge according to their spatial distribution. The deformation time series for characteristic points in the three regions during the study period are illustrated in Figure 8. In the figure, P1 exhibits a minor uplift, while the deformation at P2 remains negligible. In contrast, P3 experiences a notable subsidence. The deformations at P4 and P5 are more pronounced, with P4 experiencing a maximum subsidence of 38.5 mm and P5 showing a maximum uplift of 37.3 mm. Points P6 and P7 display minimal deformations.  
While the deformation rate of the bridge structure remains relatively modest (\(-\)15.6 to 10.7 mm/year), it is noteworthy that, during the study period, the maximum uplift and subsidence values recorded were 37.3 mm and \(-\)38.5 mm (Figure 8), respectively. This observation suggests the possibility of partial deformation rebound within the bridge structure which has the ability to dissipate external loads and return to its original state.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Point Number** & **Latitude (Degree)** & **Longitude (Degree)** &
\begin{tabular}{c} **Mean Deformation** \\ **Rate (mm/Year)** \\ \end{tabular} & **Location** \\ \hline P1 & 114.27839 & 30.55468 & \(1.9\) & Near No. 1 pier \\ P2 & 114.27903 & 30.55440 & \(-1.5\) & Between No. 1 pier and No. 2 pier \\ P3 & 114.28010 & 30.55367 & \(-6.3\) & Between No. 2 pier and No. 3 pier \\ P4 & 114.28223 & 30.55258 & \(-10.7\) & Near No. 4 pier \\ P5 & 114.28279 & 30.55241 & \(15.4\) & Between No. 4 pier and No. 5 pier \\ P6 & 114.28645 & 30.55029 & \(4.6\) & Near No. 8 pier \\ P7 & 114.28714 & 30.54988 & \(-1.8\) & Between No. 8 pier and the eastern bridgehead \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the deformation points.  
Figure 7: Distribution of characteristic points along the Wuhan Yangtze River Bridge.
## 5 Discussion  
### Multiscale Model Validation  
As shown in Figure 8 the G* master curves for mortar and mastic have a very similar trend, although shifted.  
This result confirms that the proportion of every single components of the mortar and mastic mix design has been correctly calculated and that the rheological behavior of the mortar (master curve slope) is linked to the rheological behavior of the mastic. The increase in the stiffness from mastic to the mortar is due to the presence of a solid skeleton made of fine aggregates and limestone filler. The same effect is visible in Figure 9 adding the mixture master curve.  
\begin{table}
\begin{tabular}{c c} \hline
**Temperature**[°C] & **TIsf** \\ \hline
60 & 0.00017018 \\
50 & 0.001043079 \\
40 & 0.005653233 \\
30 & 0.074385949 \\
20 & \\
10 & 21.98403888 \\ \hline \end{tabular}
\end{table}
Table 10: TIsf for Mastic-CR master curve at 20 °C.  
Figure 8: MORTAR-CR and MASTIC-CR master curves.  
Figure 7: MASTIC-CR master curve at 20 °C.  
The same overall characteristics of the SMACR mixture are evident both in the MASTIC-CR and MORTAR-CR materials. In particular, the S-shaped master curve function is visible for every scale level and it is further confirmed fitting the well-known sigmoidal model (Figure 10).  
In case of the sigmoidal model defined by Equation (9), it can be fitted on the dynamic modulus vs. frequency plane:  
\[\text{Log}(\text{G}^{*})=\delta+\frac{\text{a}}{1+\text{e}^{\beta+\gamma(\text {Log}\omega)}} \tag{9}\]  
where:  
* \(\text{Log}(\omega)\): logarithmic reduced frequency;
* \(\delta\): lower asymptote;
* \(\beta\) e \(\gamma\): shape factors between the asymptotes and the local inflection points (points corresponding to a frequency \(\omega=10-\beta/\gamma\)).  
The fitting issue was solved using the least-squares method implemented in MATLAB that allows the solution of the minimization problem through a sigmoidal function. The mathematical modelling of the complex modulus has allowed the estimation of mastic, mortar and mixture behavior in a wider range of frequencies, providing numerical parameters to compare the mastic combinations (Table 11).  
Figure 10: Sigmoidal model master curves.  
Figure 9: SMACR, MORTAR-CR and MASTIC-CR master curves.  
Another validation of this multiscale approach is shown in Figure 11 and it is related to the existence of a similar TIsf shift factors function for mastic, FAM and AC.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 6 5G Traffic Generation
### 5G Traffic Generation Tool  
For the generation of traffic that can be used in scenarios that use the 5G infrastructure, we have designed and developed a tool that is able to inject signaling and real user traffic in the deployed 5G core, being able to use services located in the virtualized environment or in the Internet through the tool. The main goal of this tool is, consequently, to provide a way of injecting traffic into the 5G core network-virtualized infrastructure without dealing with actual Radio Access Networks and 5G-ready hardware. This tool acts as a signaling NAS traffic generator that is able to communicate with the AMF NF in the 5G core by emulating the operations of User Equipment in a real environment. The tool can perform session management, UE registering and registering, etc. Additionally, it can maintain tunnels that use the GPRS Tunneling Protocol (GTP) with a UPF in the core network and send data through them. The user data to be sent through this tool, which acts as a broker, is captured in a network interface and can have any virtual appliance or hardware machine as a source as long as they are connected to the tool interface.  
As there are no actual radio stations in the infrastructure, the software implements the protocols and functionalities between the stations and the network core, while the communications between User Equipment and gNB are not actually being emulated.  
Figure 3 shows a simplified diagram of the traffic generation architecture. It is based on the deployment of a virtualized 5G core in the cyber range infrastructure, composed of the main Network Functions. The traffic generation software is then connected using virtual network interfaces with the Network Functions that are usually connected to the gNB base stations through the N2 and N3 interfaces (connected to the Access and Management Mobility Function (AMF) and the User Plane Function (UPF)).  
The traffic generation software is deployed in a virtual instance within the virtual infrastructure of the cyber range, and it is equipped with as many virtual network interfaces as needed to connect other virtual instances. These virtual instances would be in charge of running network applications (web browsing, video streaming clients, traffic generation clients, etc.) and, therefore, would act as the users of the 5G network in the emulated environment. The traffic generated by these instances is captured and processed to be sent to the 5G network. This means that, for each emulated User Equipment, a new session is established, a new tunnel ending point is added, and all traffic is encapsulated usinga GTP-User Plane (GTP-U) tunnel. The encapsulated traffic is sent to the UPF Network function from the traffic generation software, and then it is routed to its destination.
## 4 Time-Series Analysis
### Assessment of the Potential of wMPS for Estimating the Oscillation Amplitude
(**a**) Oscillation displacements of 0.5 Hz segment, the sampling rate can reconstruct the displacement signal almost perfectly; (**b**) oscillation displacements of 1 Hz segment, the displacement signals described a nearly ideal sinusoidal oscillation; (**c**) the amplitude of displacement vibration is no longer consistent, but the error is small, indicating that the sampling rate of 2 Hz can still approximately reconstruct the original signal; (**d**) the error of the result obtained is still small, and the sampling rate of 3 Hz also meets the requirements; (**e**) oscillation displacements of 4 Hz segment; (**f**) oscillation displacements of 5 Hz segment; (**g**) oscillation displacements of 6 Hz segment; (**h**) oscillation displacements of 7 Hz segment; (**i**) oscillation displacements of 8 Hz segment; (**j**) oscillation displacements of 9 Hz segment. From (**e**) to (**j**), the error gradually increases with the increase of sampling rate, and the displacement signal can no longer be accurately reconstructed.
## 1 Introduction
The ML algorithm detected only 5 in 28 BCC lesions, whereas the CNN-based algorithm detected 27 in 28 [18]. [PERSON] et al. use a pre-trained AlexNet convolutional network to extract the features that feed an SVM classifier, in order to classify among four kinds of cancers, including BCC [19]. Recent advances in the field of histopathological and microscopic image analysis, dedicated to the BCC detection, can be found at [20, 21, 22], where the authors use deep learning techniques to detect, classify and identify its patterns. However, our approach covers BCC classification, focusing on distinguishing BCC from Nevus, and employing dermoscopic images. From a clinical point of view, it is very interesting to differentiate BCC from nevus, because both represent the most frequent skin lesions appearing at primary health centers, and a good detection of these types of lesions could lead to a more efficient clinical management, performing a first prioritization of the images that arrive from the Primary Health Center by teledermatology. The main contribution of the paper is that it performs a thorough analysis of deep learning techniques, applied to BCC segmentation and classification. In addition, to the best of our knowledge, there are no previous works that evaluate the influence of a previous segmentation in the classification of skin lesions with a deep neural network.
## 2 Mathematical Model
### Thermal Model  
Transient non-linear thermal analyses of the cooling channels were performed. The thermal problem was nonlinear because of the temperature-dependent material properties that were considered. The associated heat conduction problem is governed by the following differential equation [24; 25]:  
\[\
abla^{2}T=\frac{1}{a}\frac{\partial T}{\partial\theta} \tag{1}\]  
where \(a\) is the thermal diffusivity, \(T\) is the temperature, and \(\theta\) is the time. Continuity of temperature and heat fluxes at the interface between chamber layers, made of CuCrZr alloy, electroformed copper, and electroformed nickel, were enforced through the following equations:  
\[k_{i}\frac{\partial T_{i}}{\partial n}=k_{j}\frac{\partial T_{j}}{\partial n} \tag{2}\]  
\[T_{i}=T_{j} \tag{3}\]  
where \(i\) and \(j\) represent two generic materials in contact, \(k\) is the thermal conductivity, and \(n\) is the direction of the heat flow. Convective boundary conditions were applied to the hot gas side and to surfaces wetted by the coolant flow:  
\[-k\frac{\partial T}{\partial n}=h\cdot(T_{w}-T_{\infty}) \tag{4}\]  
where \(h\) is the convective heat transfer coefficient, \(T_{w}\) is the wall temperature, and \(T_{\infty}\) is the adiabatic wall temperature.
## 3 Results
### Access to Educational Centers
#### 3.2.1 Access to Public Elementary Schools  
Elementary schools are usually meant for children aged 5 to 11 years old. Therefore, most of the pupils arrive at schools with their parents or by school bus. When parents bring their kids to school, they tend to trip chain, as they usually continue their commute to work or elsewhere. As a result, public transport and cars represent attractive transport modes for many people. Nevertheless, if the level of access to elementary schools is high enough, such trips could be easily made by walking and, therefore, not relying on motorized transport.  
Figure 12 shows that the Stanford District has 14 different public elementary schools within its boundaries. Even though the number is considerably high, most of them are in the northeastern and eastern sides of the polygon. As a result, when considering walking as the transport mode, none of the aggregation areas meets the 15 min parameter. The were 58.23% of the block groups having a medium level of accessibility and the remaining 41.77% a low level (see Table 9).  
Distrito Tec has only two elementary schools (see Figure 13), but the surrounding areas have a higher density of destinations. Consequently, 72.72% of the aggregation areas within Distrito Tec have a high level of accessibility and the remaining 27.27% a medium one (see Table 9).  
Figure 11: Location of supermarkets and convenience stores in Stanford District merging OpenStreetMap and Google Maps databases.  
The lack of accessibility in the Stanford District stems from the area being much larger than that of Distrito Tec. It also relates to the methodology for the measurement considering the three closest destinations and not the shortest one. Before arriving at a specific conclusion or intervention to solve such an issue, more research should be conducted, such as evaluating the unit capacity of each elementary school and the demand for them from the local population.  
It is also important to mention that, in order to achieve a modal shift from cars to walking, safety plays an important role. Parents greatly value their children's safety, so if they perceive that walking may be unsafe (because of theft, narrow sidewalks, etc.), they will avoid walking. Therefore, making walkable cities and streets is crucial. Additionally, it is important to mention that complementing commutes with sustainable motorized transport modes such as school buses represents a strong alternative to encourage sustainable mobility for educational commutes.
# Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
## 2 Introduction of Relevant Basic Models
### Attentional Mechanism  
It has been widely pointed out in the literature that different segments in the spectrum contribute differently to fault characteristics. This inspired the use of attentional mechanisms. In this study, the segmented spectral sequence signals are used as input, and an attention mechanism is employed to generate an attention coefficient for each segment sequence, which enhances or weakens the characteristics of a segment signal by weighting the product. The attention weights are also visualized to locate the frequency domain interval segment where the fault information is located, which is consistent with the mechanism of increasing the amplitude of the rotational frequency and its multiples in bearing fault diagnosis.  
The computational process of the attentional mechanism can be described as follows:  
\[\alpha_{i}=softmax(s(x_{i},q))=\frac{exp(s(x_{i},q))}{\sum_{j=1}^{N}\bigl{(}s( x_{j},q)\bigr{)}} \tag{13}\]  
Among them, \(\alpha_{i}\) represents the degree of correlation between the ith input sequence and the ith query sequence, that is, the ith attention coefficient. \(s(x_{i},q)\) represents the attention scoring function, which is used to calculate the correlation between the input sequence and the query sequence. \(x=[x_{1},x_{2},x_{3},\ldots,x_{N}]\in R^{D*N}\) represents the input vector, and q represents the query vector.  
Here, the query vector, key vector, and value vector are all hidden states of the input vector x. The query vector and key vector generate attention weight coefficients by the scoring function and then assign the coefficients to the corresponding value vector segments to enhance the signal representation by weighted fusion.  
Different scoring functions can produce different attention mechanisms. The scoring function used in this paper is obtained from the following formula:  
\[s(x_{i},q)=q^{T}tanh(Wx_{i}+b) \tag{14}\]  
where \(W\) and \(b\) are the trainable weight matrices and bias terms respectively.  
Finally, the enhanced representation of the input data \(y_{att}\) is obtained by the following formula:  
\[y_{att}=\sum_{i=1}^{N}\alpha_{i}x_{i} \tag{15}\]  
Then, the enhanced signal \(y_{att}\) is used as the input for further diagnosis. The softmax classifier was used to complete the final bearing status classification.  
\[y=softmax\left(W_{f}y_{att}+b_{f}\right) \tag{16}\]  
where \(W_{f}\) and \(b_{f}\) are the weight matrix and offset term, respectively. The network output is interpreted as the probability of various classes [35], and the final fault classification diagnosis is carried out.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 2 Machine Learning Tools and Signal Processing
### Machine Learning Tools
#### 2.1.1 Multilayer Perceptron (MLP) Network  
MLP is the type of feed-forward ANN most frequently used. MLP consists of three groups of layers: the input layer, hidden layer, and output layer. The weights and biases are initialized with pseudo-random values and are changed through supervised learning methods. The gradient descent-based back propagation methods are used to tune the parameters. The input layer receives a dataset and passes it into the hidden layer. The hidden layer process the data with the help of activation function and finally, the targeted output is passed to the output layer [18].
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 4 Discussion
The quantitative model between the influencing factors and the assessment results show that human activities have a more substantial impact on water provisioning output than natural climate factors, which reminds us that protecting the total water storage and developing the economy is fundamentally an organic unity and complement each other. The climate factors, especially temperature and precipitation, have more substantive impact on water provisioning efficiency and trend. It confirms that combat climate change measures are essential for improving water provisioning efficiency and intervening in its trends. We conducted this comprehensive study based on remote sensing products and lake bathymetric data, analyzed the characteristics of water provisioning development of 70 counties in Minnesota from temporal and spatial perspectives, interpreted ecosystem services from multiple dimensions and quantitatively explored their influencing factors. Nevertheless, the study has some limitations: (1) Due to the limited availability of lake bathymetric data, some lakes without lake bathymetric data were not included in the analysis, which might result in some deviations between the county-level analysis and the actual situation. (2) We only selected five time periods (i.e., 1998, 2003, 2008, 2013, and 2018) as the representative years to explore the spatial characteristics of water provisioning ecosystem services at the county level. It should be noted that each time period is just one snapshot in time, which might not capture the changes between the representative years. (3) The natural ecosystems are dynamic complexes of plants, animals, micro-organism communities, and non-living environments interacting as functional units [84]. Their mechanisms and functional processes are complicated and correlated. We adopted a linear model to simulate the various influencing factors, which can reflect the function intensity of influencing factors, but it cannot simulate or express the specific process and its mechanism of influencing factors. (4) Our proposed framework highlights water storage as the main factor affecting lake ecosystem services. However, there are other factors (e.g., water quality, air quality, local climate regulation, aesthetics) that may also significantly affect lake ecosystem services [85; 86]. Due to the lack of relevant data at the individual lake level, we did not integrate these factors into our framework. Future research can focus on applying the multidimensional ecosystem service assessment framework to other ecosystem services and exploring models that simulate real processes of how influencing factors work.
# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
## 2 MIMO System Design with CoI Cancellation
### Relationship between CE-DFE and DA-DFE  
The DA-DFE used for underwater acoustic communication can be found in Ref. [9]. Comparing the DA-DFE and the proposed CE-DFE, the structures are almost the same, consisting of a feedforward filter, feedback filters, and IC filters. There are some differences between CE-DFE and DA-DFE.  
1. The biggest difference between the DA-DFE and the proposed CE-DFE is that the DA-DFE uses some criteria, such as least mean square or recursive least square, to train the filters, while the CE-DFE uses channel estimates to measure the filters directly. As the underwater acoustic channel is usually typically sparse, channel estimation can be performed using the compressed sensing method, which needs fewer training sequences, while the DA-DFE needs long sequences to achieve convergence.
2. The CE-DFE always performs block by block. It is assumed that, within a data block, the channel is time-invariant, but varied in the next data block. The filters of CE-DFE will not change within one data block. In the decision-directed mode, some periodical training sequences are essential to prevent error propagation, because the decoding symbols are used for Doppler estimation and channel estimation, which may appear as erroneous symbols. Meanwhile, the DA-DFE iterates symbol by symbol, and the DA-DFE filters are updated symbol by symbol; thus, the filters can track the underwater acoustic channel. As a result, the computational complexity of DA-DFE is typically higher than that of CE-DFE, if the filter length relatively large.
3. The Doppler estimation and compensation can be performed block by block for CE-DFE, while the DA-DFE utilizes the phase lock loop to track channel variance.
4. The CE-DFE has fewer controlling parameters than DA-DFE, and DA-DFE is very sensitive to parameters such as the length of filters, forgotten factor and training length. Therefore, the CE-DFE is more robust to underwater acoustic channel.
## 4 Performance Analysis  
To simply the performance analysis, the mobile sensors are supposed to be uniformly distributed within the restricted region and the sensor velocity distribution is the same across the region (i.e.,  
Figure 6: Example of distributed gateway reselection during the cluster merge procedure. The connection between a pair of distributed gateways is illustrated by a dashed line: (a) before gateway reselection and (b) after gateway reselection.  
the random walk mobility model). By analyzing the proposed clustering scheme in different aspects qualitatively or quantitatively such as neighboring node properties, node density change, and simplified models for describing the process of cluster formation, its robustness and scalability can be clearly specified.