esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Finally, the correlation between expected seismic damage and the seismic vulnerability assessed is expressed in the LM1 method through a semiempirical vulnerability function linked to two parameters: the vulnerability index \(lv_{b}\), and the macroseismic intensity \(I\) in the EMS-98 scale [30]:  
\[\mu_{D}\ =\ 2.5\ [1+\tanh\left(\frac{I\ +\ 6.25\ I_{bb}\ -\ 13.1}{Q}\right)] \tag{1}\]  
where \(Q\) is a ductility factor of value 2.3 for many of the building configurations of BTM [19], and \(\mu_{D}\) is the mean damage degree developed in the EMS-98 scale (Table 2). After the calculation of this index, RISK-UE is used in order to define the distribution of damage probability through a beta type function, more adaptable than the binomial one of the EMS-98 scale [30]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Typologies**} & \multicolumn{2}{c}{**Building Type**} & \(\mathbf{lv^{\min}}\) & \(\mathbf{lv^{-}}\) & \(\mathbf{lv^{*}}\) & \(\mathbf{lv^{+}}\) & \(\mathbf{lv^{max}}\) \\ \hline \multirow{2}{*}{Reinforced} & RC1 & Concrete moment frame & 0.140 & 0.330 & 0.484 & 0.640 & 0.860 \\  & RC2 & Concrete shear walls & 0.140 & 0.210 & 0.384 & 0.510 & 0.700 \\ \multirow{2}{*}{Concrete} & RC3 & Dual system & 0.060 & 0.127 & 0.522 & 0.880 & 1.020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic vulnerability indexes of reinforced concrete building typologies defined in Building Typology Matrix [18; 24]. In this sense, the existence of well-documented damaging earthquakes (such as that in Lorca in 2011) offers an interesting occasion to enhance the empirical methodologies to assess the seismic answer of buildings. This paper is focused on the analysis of the planning and management of ante-earthquake scenarios of emergency contexts by developing methods for the seismic vulnerability assessment of buildings in urban areas. After an introduction section where the seismic vulnerability of buildings has been summarized, a developed model mainly based on the cadastral database is proposed, documenting the method through a field study of the buildings damaged in the Lorca earthquake, 2011 (see Supplementary Materials). In the Results section, the application of the proposal for the planning and management of seismic scenarios is exposed, with the conclusions obtained being provided in the last section. In this sense, this contribution is aimed at the analysis of the seismic performance of reinforced concrete buildings, since this typology is usually the most common in modern urban areas. Nevertheless, the importance of seismic behavior assessment of masonry structures in historic centers must be highlighted for a necessary conservation of the urban heritage. The first studies for assessing the seismic vulnerability of buildings already included the analysis of this typology of structures [14,18]. Recent studies have improved these methodologies in order to give a better estimation of the mechanical performance of this typology of building [31,32].
## 5 Performance Evaluation
### Simulation Study  
#### 5.3.1 Setup  
The experimental study has limited number of nodes and in order to proceed with a larger-scale network performance characterization, simulations are used.  
To show the performance of OSCAR relative to Orchestra in response to the traffic load, we study the performance by varying the number of nodes in the network within the simulation. To achieve this, we use COOJA, the network simulator distributed as part of the Contiki OS. The goal here is to see how OSCAR reacts to a large-scale network. To this end, we have considered a network where the nodes are deployed in a uniform grid  
Figure 8: Comparison between Orchestra and OSCAR according to traffic load: (**a**) average end-to-end delay, (**b**) average radio duty cycle, and (**c**) average packet delivery ratio.  
topology. We have varied the number of nodes from 40 to 100 with a data rate equal to 1 pkts/min, and, as in the experiment, each node sent 2000 packets to the root.
# A Filter Pruning Method of CNN Models Based on Feature Maps Clustering
## 2 Related Work
### Filter Pruning  
Filter pruning refers to the direct deletion of unimportant filters in the neural network and the feature maps associated with it [11]. The process is shown in Figure 3.  
\(c_{i}\) represents the number of input channels in the \(i_{th}\) layer. \(h_{i}\) and \(w_{i}\) represent the height and weight of the input feature map, respectively. \(c_{i}\) filters \(F_{i,j}\in R^{n_{i}\times k\times k}\) can convert the feature maps \(x_{i}\in R^{n_{i}\times h_{i}\times w_{i}}\) into \(x_{i+1}\in R^{n_{i+1}\times h_{i+1}\times w_{i+1}}\) with convolution operations. In this process, the number of convolution operations is \(n_{i+1}n_{i}k^{2}h_{i+1}w_{i+1}\). If one of the filters in \(x_{i}\) is pruned, \(n_{i}k^{2}h_{i+1}w_{i+1}\) will be directly reduced, at the same time the feature maps of the filter output are deleted. \(n_{i}\) refers to the filter size. In other words, the feature maps of the next convolutional layer input are also reduced. Then, \(n_{i+2}k^{2}h_{i+2}w_{i+2}\) convolution operations will also be reduced in the next layer. According to this rule, the subsequent convolutional layers will reduce at a large number of operations.  
Compared with the weight pruning method of the neural network, filter pruning is a structured pruning method, which does not introduce additional sparse operations, so there is no need to use sparse libraries or any specific hardware devices. The number of pruned filters will directly affect the number of convolution operations and the large reduction in the number of convolution operations will greatly increase the operational efficiency of the neural network. Most of the existing filter pruning methods are based on the importance of filters. The importance of filter is based on the L1 norm. The greater the L1 norm value of the filter, the higher the importance of the filter is.
## 2 Methodology
### Multipath Transmission Algorithm
#### 2.3.1 The First Type of Simplification Operation  
The core idea of the first type of simplification operation is to ensure the independence of the new paths by fine-grained management and deletion of the heterogeneous subnetworks where the source and destination nodes are located. As illustrated in Figure 4, new links are obtained by deleting different subnetworks and links. The red, yellow, blue, and purple clouds on the left represent different heterogeneous networks. The source and destination nodes are node S and node D, respectively. The first calculated path is S-E-I-K-N-D. Through the first type of graph deletion operation, we deleted the nodes and links that the purple heterogeneous network and the first path in the red and yellow heterogeneous networks passed through. Therefore, the second calculated path is S-A-B-G-L-D. The specific steps are as follows:  
Subnetwork lookup: Identify the subnetworks where the source and destination nodes are located separately. This step ensures that the network locations of the source and destination nodes are identified.  
Figure 4: Schematic diagram of the first type of simplification operation.  
Path node subnetwork identification: Identify the subnetwork in which each node (other than the source and destination nodes) on the first path that satisfies the multi-constraint requirement is located. This step provides the basis for subsequent simplification operations by identifying the network locations of the intermediate nodes on the path.  
Subnetwork compare and delete: Determine if the subnetwork where each node on the path is located is the same as the subnetwork where the source or destination node is located. If it is the same, delete the node and its associated links. Otherwise, delete all nodes within the subnetwork where the node is located and the links associated with each node in the subnetwork.  
These operations result in a new network topology graph. At this point, the NSGA-II-based multi-constraint optimal path algorithm is used again to find a second path that satisfies the multi-constraint requirement. If the second path exists, the search is successful; otherwise, the second type of simplification operation is used.
## 5 Experimental Results Analysis
### Evaluation of Language Model  
The encoder-decoder LSTM model was trained for 30 epochs. Figure 11 illustrates the accuracy curve and the loss curve. The proposed language model achieved 99.12% accuracy with a loss of less than 0.02. Here, the blue line illustrates the training score, while the orange line illustrates the test result. This graph demonstrates that the training score converges after the first epoch and then remains constant during the remaining epochs. By contrast, after substantial fluctuations the test score converges within one epoch. Moreover, after convergence, the training and test scores become nearly identical. Despite the fact that the test score is just slightly lower than the training score, we can conclude that the proposed model is highly generalized.  
The proposed language model's performance is measured by the BLEU score. BLEU is an acronym for BiLingual Evaluation Understudy. The BLEU score is a statistic for measuring the similarity between a generated sentence and a reference sentence. Perfect matches get a BLEU score of 1, while perfect mismatches receive a BLEU score of 0. The cumulative BLEU score is calculated using Equation (11), where \(L_{p}\) denotes the predicted sentence's length, \(L_{o}\) denotes the length of the original sentence, and P denotes precision. The BLEU score represents the most-reliable metric for measuring a language model's performance. The proposed language model achieved a cumulative BLEU score of 0.93, which is standard.  
\[BLEU=MIN(1-\frac{L_{o}}{L_{p}},0)+\sum_{n=1}^{4}\frac{logP_{n}}{4} \tag{11}\]
## 3 Results
### Analysis of Quality of Service  
Following the Quality of Service (QoS) performance of our close-to-ground scenario, the 2.4 GHz case is analyzed. Bit Error Rate (BER) is a key parameter used for determining the QoS of wireless communication systems. BER expression for QPSK, \(M\)-PSK and \(M\)-QAM modulations can be calculated by (11)-(13):  
\[BER_{QPSK}=Q\left(\sqrt{2\frac{E_{b}}{N_{0}}}\right) \tag{11}\]  
\[BER_{MPSK}\cong\frac{2}{\log_{2}M}Q\left(\sqrt{2\frac{E_{b}}{N_{0}}\log_{2}M \cdot\sin\left(\frac{\pi}{M}\right)}\right) \tag{12}\]  
\[BER_{MQAM}\cong\frac{4}{\log_{2}M}Q\left(\sqrt{3\frac{E_{b}}{N_{0}}\cdot\frac {\log_{2}M}{M-1}}\right) \tag{13}\]  
where \(E_{b}=P_{Rx}/R_{b}\), with \(P_{Rx}\) the received power obtained for each spatial location along the pathway in the considered scenarios as RSSI measurements. The noise power can be estimated as \(N_{0}=K\cdot T\cdot B\), with \(K\) the Boltzman's contant, \(T\) the temperature (290 K), and \(B\) the frequency bandwidth of the 2.4 GHz and 868 MHz bands  
Figure 6a-d shows the calculation of BER for the two antenna heights \(H_{1}\) and \(H_{2}\) and a combination of the parameters that define BER: three values of data rate \(R_{b}\) (56 Kbps, 115.2 Kbps and 250 Kbps), two values of power noise \(N_{0}\) for bandwidths of \(B=100\) MHz (full 2.4 GHz band) and \(B=20\) MHz (channel bandwidth for the 2.4 GHz band), and number of symbols \(M\) (BPSK, 8 PSK and 16 QAM).  
A high variability is observed between the different cases, with higher values of BER achieved for more complex modulation schemes. Additionally, it is noticed the difference between the considered data rates, leading to a lower BER for the lowest data rate values. Finally, as \(N_{0}\) is higher (larger bandwidth \(B\)), the BER values are lower.  
It is also observable that the cases for \(H_{1}\) show a BER value under \(10^{-6}\) if the distance from transmitter stays under 20-25 m. An 802.11 certified device must carry out a receiver sensitivity threshold of \(-83\) dBm and BER of \(10^{-4}\). For the antenna height \(H_{2}\) this inflection point shows up at a further distance, over 35 m.  
Then, we can state that the near-ground scenario is more restrictive than an OLoS link, and would require the use of more robust modulations or error correction codes to equal the performance of LoS based networks. These results become helpful for optimization of the design and deployment of a near-ground WSN depending on the involved parameters: modulation, data rate and the level of \(N_{0}\)
## 2 Materials and Methods
### Methodology
#### 2.4.3 Semantic Similarity Calculation  
This research applied the Doc2 vec model to calculate the semantic similarity. The calculation method uses the SAO structure of the patent text and the evolutionary benefits keywords dictionary to compare the similarity. We define Pt as the patent text keyword and Eb as the evolutionary benefit keyword, and then rewrite Equation (1) to into Equation (2) as shown below.  
\[sim\big{(}Pt_{i},Eb_{j}\big{)}=cos\theta=\frac{Pt_{i}\cdot Eb_{j}}{\big{|}|P_{ i}||\cdot|\big{|}Eb_{j}|\big{|}} \tag{2}\]  
As shown in Figure 5, the method is to compare the similarity between the text keyword 1 from the patent and the evolutionary benefits keywords dictionary keyword 1, as well as the text keyword 2 from the patent and the evolutionary benefits keywords dictionary keyword 2.  
Figure 4: Dependency parsing of patent texts.  
Figure 3: Patent text.  
Text keywords 1 and 2 themselves are words with connected relations, and the similarity calculation with evolution benefit keywords needs to sum up the semantic similarity of the two as expressed in Equation (3). \(Pt_{(1)i}\) is the ith word of the patent text keyword 1, and \(Eb_{(1)j}\) is the jth word of the evolutionary benefit keyword 1. \(Pt_{(2)i}\) is the ith word of the patent text keyword 2, and \(Eb_{(2)j}\) is the jth word of the evolutionary benefit keyword 2. Therefore, the sum of the similarity calculation for \(sim\big{(}Pt_{i},Eb_{j}\big{)}\) are between \(-2\) and \(2\). We illustrate the relative relationship of the similarity calculation in Figure 6.  
\[sim\big{(}Pt_{i},Eb_{j}\big{)}=cos\theta=\left(\frac{Pt_{(1)i} \cdot Eb_{(1)j}}{\Big{|}\Big{|}Pt_{(1)i}||\cdot|Eb_{(1)j}\Big{|}\Big{|}}\right)+ \left(\frac{Pt_{(2)i}\cdot Eb_{(2)j}}{\Big{|}\Big{|}Pt_{(2)i}||\cdot|Eb_{(2)j} \Big{|}\Big{|}}\right) \tag{3}\]  
Once the similarity value reaches a certain threshold, it can be determined that the evolutionary benefit is related to the patent text, thereby recommending relevant patents. After experiments and analysis, this study defines that the threshold is 1.55, which means that the patent text is related to the evolutionary benefits. Conversely, less than 1.55 is considered irrelevant. The flowchart of the above-mentioned is shown in Figure 7.  
Figure 5: Calculation illustration between patent text keywords and dictionary keywords.  
Figure 6: Illustration of the similarity calculation between corresponding text keywords and dictionary keywords.
In most studies, a single bar is placed in a concrete specimen [8] or RC specimens without transverse bars being used [9; 10; 11]. That is, most existing studies are limited by the fact that they do not consider representative beam and column member reinforcement placement and, as such, are unable to consider the process of chloride ion permeation in a realistic way because the rebar diameter and cover thickness are small. Therefore, this study sought to better replicate RC columns by creating seven specimens (600 \(\times\) 600 mm cross-section) with D25-D38 longitudinal bars and three transverse bars and with varying compressive strengths, longitudinal bar diameters, and concrete cover thicknesses. Accelerated corrosion tests were performed to examine the concrete surface corrosion cracking and transverse bar strain behavior according to the amount of rebar corrosion. The accelerated corrosion tests were designed to mimic a chloride distribution similar to a RC member that exposed in the marine environment. This was done by controlling the current passing through the longitudinal bar and transverse bar and accounting for the differences in chloride ion permeation as a function of cover thickness.
## 4 Experiments and Evaluation
### Computational Efficiency  
The computational efficiency of the proposed approach, leveraging the Discrete Wavelet Transform (DWT) alongside image gradient scaling using Sobel edge detection for watermark embedding in the least significant bit (LSB), entails a multi-faceted process. Integrating Sobel edge detection adds a computational layer to identify image gradients  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & [13] & [25] & [26] & [27] & [6] & & [28] & & [29] & **Our Method** \\ \hline
**Attacks** & **NC** & **NC** & **NC** & **BER** & **NC** & **NC** & **NC** & **BER** & **NC** & **NC** & **BER** \\ \hline Median filter & 0.94 (kernel) & - & 0.58 (3 \(\times\) 3) & 0.41 & - & - & 0.89 (3 \(\times\) 3) & 0.24 (3 \(\times\) 3) & 0.86 (3 \(\times\) 3) & 0.13 \\ \hline Salt and Pepper & 0.96 (0.001) & 0.99 (0.01) & 0.048 (0.01) & 0.84 (0.01) & - & 0.95 (0.01) & 0.66 (0.01) & 0.97 (0.01) & 0.03 \\ \hline Gaussian & 0.32 (0.05) & 0.62 (0.02) & 0.49 (0.01) & 0.99 (0.01) & 0.99 (0.03) & 0.21 (0.01) & 0.48 (0.01) & 0.97 (0.05) & 0.01 \\ \hline Scaling & 0.71 (\(\times\)2) & - & 0.47 (0.47) & 0.47 (0.74) & 1 & 0.74 (0.32) & 0.52 (\(\times\)2) & 0.78 (\(\times\)2) & 0.12 \\ \hline Histogram Equalization & 0.87 (60.98) & - & - & - & 0.98 (0.98) & 1 & 0.89 (0.23) & 0.54 (0.8) & 0.08 (0.08) \\ \hline JPEG Compression & 0.98 (60) & - & 0.55 (90) & 0.44 (0.01) & - & 0.99 (20) & 0.13 (20) & 0.54 (20) & 0.98 (20) & 0.08 (20) \\ \hline Sharpening & 0.65 (01) & - & 0.50 (0.49) & - & 0.93 (0.88) & 0.21 (0.54) & 0.66 (0.1) & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of robustness (BER and NC) among different methods using a watermark of size 96 bytes (768 bits).  
efficiently, enhancing the security of watermark embedding. Considering \(N\) as the number of pixels in the image, \(M\) as the number of bits to be embedded, and \(L\) as the number of decomposition levels in the DWT, the overall complexity comprises three main components: DWT processing \(O(LN\log N)\), Sobel edge detection \(O(N)\), and LSB embedding \(O(MN)\). Thus, the total complexity is \(O(LN\log N)+O(N)+O(MN)\), with the dominant factor contingent on the interplay between \(L\), \(M\), and \(N\). Knowing that \(M<<N\), we can say with certainty that the increase of time consumption over the size of the watermark is Linear. In the subsequent Figure 5, we delineate the computational watermarking/extraction time over variations in the watermark size.  
As anticipated from Figure 5, the consumed time exhibits a linear correlation with the size of the watermark. This linear relationship suggests that, as the size of the watermark increases, the time required for computation also increases proportionally. This stability implies that the proposed watermarking approach can efficiently handle varying sizes of watermarks, which is crucial for applications such as video processing where watermark sizes may vary.
# Optimal Arrangement Strategy of IoT Sensors in Urban Drainage Networks: A Review
## 3 Arrangement of IoT Sensors in the Drainage Network
### Optimization of Monitoring Point Locations
#### 3.3.5 Design Evaluation Methods Based on Observability Theory  
The observability of a complex system can be defined as the utilization of the minimum number of sensors to collect enough information for the prediction or estimation of the state of areas of the system that are not directly monitored [86]. This facilitates a better understanding of the system's operational status and improves situational awareness for risk management and decision making. Observability analysis can be used to establish a state space model to ensure that the system's state can be estimated using sensor measurements. The approach guides the location selection process of sensors in applications such as the evaluation of the pollutant concentration in river networks [11] and the identification of key nodes in flood monitoring networks [87]. In the water supply network, the system's state can be estimated based on indicators such as node water demand [88], disinfect (chlorine) concentration [89], and pollutant identification [90], and sensors can then be arranged accordingly. There are few studies on the design and evaluation of the optimized placement of IoT sensors in UDNs based on observability. [PERSON] et al. [78] used the matrix completion model to improve observability by recovering missing data from unmonitored locations. They quantified the deviation between the predicted value of the water level and the observed value to evaluate the optimized placement scheme of the sensors. The utilization of the minimum number of optimally arranged sensors in a complex network structure to improve the observability of the pipeline network has great application potential for the identification and early warning of abnormal states in the operation of a UDN. In Table 4, we summarize and compare the properties of the above methods in terms of accuracy, robustness, computational costs, and adaptability.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 5 Future Opportunities
### Dynamics on Intrinsic Length and Time Scales  
Dynamics of polymers or liquids are frequently probed indirectly using tracer particles in a microrheology approach [187; 188; 189; 190]. As example, the glass transition of poly-propylene glycol (PPG) was studied revealing the temperature-dependent dynamics of silica nanoparticles dispersed in PPG [108]. At room temperature, the dynamics are diffusive, i.e., the relaxation rate shows a \(q^{2}\) dependece as \(\Gamma\propto q^{2}\) and the KWW exponent is \(\gamma=1\). With decreasing temperature towards the glass transition, the viscosity of the PPG increases, resulting in a slowing down of the silica motion. Furthermore, the KWW exponent and \(q\)-dependence of \(\Gamma\) changed, indicating collaborative dynamics due to formation of dynamic domains.  
Figure 13: Length-time scale map of different scattering techniques. Raman, Brillouin and dynamic light scattering use optical lasers as source. IXS refers to inelastic X-ray scattering whose time resolution is given by the energy resolution. PETRA III and PETRA IV are exemplary used as storage ring sources. With PETRA IV, the brilliance is supposed to be increase by a factor of 500, leading to access of 500\({}^{2}\) faster time scales compared to PETRA III [48].  
This study made use of two advantages of the microrheology approach: (1) due to the size of the tracer particles the relevant \(q\) is changed to the SAXS regime instead of larger \(q\) for the polymer and (2) the time scales are increased by several orders of magnitude. While the diffusion of the colloidal particles could be covered within 10 ms to 1000 s, the polymer dynamics are in the range of (sub-)\(\upmu\)s in the liquid and several 1000 s in the glassy state, unaccessibly in standard XPCS experiments. However, the tracer particle provide only limited information, the full dynamics, in particular on multiple length scales of the polymers, are hidden. These restrictions will not exist at future sources, allowing direct access to polymer dynamics on many length and time scales.