esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
### Satellite Product Intercomparison  
#### 5.1.1 Product Completeness  
The global map of the percentage of gaps (i.e., values with missing data) during one whole year of data (2014) for the PROBA-V SA v1.0 products (Figure 5) shows poor spatio-temporal continuity over latitudes higher than 45\({}^{\circ}\) north and over the equatorial belt, with a percentage of missing values up to 100% in some pixels over these areas. Note that the information from the quality flags was not considered here in the computation of the gaps (i.e., a gap or missing value corresponds to a fill value in the product). C3S products are not provided over areas out of the region from latitudes of 60\({}^{\circ}\)S to 75\({}^{\circ}\)N.  
The temporal evolution of missing values (Figure 6a) shows the highest percentage of missing PROBA-V SA v1.0 values during wintertime in the northern hemisphere. The percentage of missing values ranges, on average, from approximately 12% (July and August 2014) to 32% (January and December 2014). PROBA-V SA v1.0 shows a similar temporal trend of missing data to MCD43A3 C6. Better completeness was found for SPOT/VGT SA v1.0 with almost no missing data during the months from March to May and a percentage up to 10% in wintertime in the northern hemisphere.  
The distribution of the temporal length of the missing values (Figure 6b) allows one to better understand the impact of the gaps for monitoring the temporal variations. PROBA-V SA v1.0 shows that approximately 65% of the gaps are shorter than 30 days and approximately 35% are shorter than 10 days (time resolution of the product). Similarly, typically, approximately 35-40% of the SPOT/VGT SA v1.0 gaps are shorter than 10 days, and approximately 75% of gaps are shorter than 30 days during the overlap period between SPOT/VGT and PROBA-V. MCD43A3 C6 has a shorter length of gaps, with approximately 40% of the gaps corresponding to 5 days.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 3 Consideration of Local Scour  
To study the influence of local scour on monopile, the scour characteristic must be clarified, which is the basis and reference for the scour effect. On the basis of the Dafeng Offshore Wind Farm in Jiangsu Province, scour around large diameter monopile was surveyed and data including mudline altitude, erosion or deposition volume, maximum scour depth, and topographic fluctuation changes were collected. The water depth is 8-14 m, the wave height is 1-2 m, and designed pile outer diameter is 5.5 m. Besides pile 49#-52#, which were not investigated owing to weather conditions, a total of 68 sets of data were obtained.
# Efficient False Positive Control Algorithms in Big Data Mining
## 3 PFWER-Based Distributed False Positive Control Algorithm
### Problem Definition  
**Definition 1**.: _Let \(\{l^{0},l^{1}\}\) be two class labels, and the transaction dataset is \(D=\{(T_{1},l_{1}),\ (T_{2},l_{2}),\ldots,\ (T_{n},l_{n})\}\), where each transaction \(T_{i}\) is composed of a set of items set, i.e., \(T_{i}=\{t_{1},\ t_{2},\ldots,\ t_{k}\}\). Each transaction \(T_{i}\) in the transaction dataset carries a binary class label \(l_{i}\in\{l^{0},l^{1}\}\)._  
**Definition 2**.: _Let the pattern \(S\) be a set of items, i.e., \(S=\{t_{1},\ t_{2},\ldots,\ t_{i}\}\), \(t_{i}\in\{1,\ldots,m\}\). Let \(\sigma(S)\) denote the number of dataset \(D\) containing pattern \(S\), \(\sigma_{1}(S)\) denote the number of dataset \(D\) labeled \(l^{1}\) as containing pattern \(S\), \(\sigma_{0}(S)\) denote the number of dataset \(D\) labeled \(l^{1}\) as containing pattern \(S\), and \(\sigma_{0}(S)\) denotes the number of datasets \(D\) in which the label \(l^{0}\) is the number of containing patterns \(S\). Based on the above two definitions, a \(2\times 2\) column-linked table can be constructed, as shown in Table 3._  
**Definition 3**.: _The null hypothesis \(H_{0}\) is that the pattern \(S\) is not significantly associated with the label \(l_{i}\) and let \(\delta\) be the corrected significance level, the null hypothesis is rejected, and the pattern \(S\) is considered to be significantly associated with the label \(l_{i}\) if and only if the \(p\)-value \(\leq\delta\)._  
**Definition 4**.: _A false positive is the probability of finding an incorrect association (Type I error) [5]._  
Section 2.1.4 has shown that the \(p\)-value calculation method used in this paper is the Fisher exact test. The Fisher exact test observes that the values \((n,n_{1},\sigma(S))\) of the edges of the \(2\times 2\) column table are fixed. Thus, under the null hypothesis that mode \(S\) and the labels \(l_{i}\) are independent of each other, the calculation of \(\sigma_{1}(S)\) follows the hypergeometric distribution, as shown in Equation (7).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Do Not Reject \(H_{0}\)** & **Reject \(H_{0}\)** & **Column Total** \\ \hline \(l_{i}=l^{1}\) & \(\sigma_{1}(S)\) & \(n_{1}-\sigma_{1}(S)\) & \(n_{1}\) \\ \(l_{i}=l^{0}\) & \(\sigma_{0}(S)\) & \(n-n_{1}-\sigma_{0}(S)\) & \(n-n_{1}\) \\ Row total & \(\sigma(S)\) & \(n-\sigma(S)\) & \(n\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: A \(2\times 2\) contingency table \[p^{F}(\sigma_{1}(S)=a|\sigma(S),n_{1},n)=\frac{\binom{n_{1}}{a}\binom{n-n_{1}}{ \sigma(S)-a}}{\binom{n}{\sigma(S)}} \tag{7}\]  
Let \(b\) be the observed value of \(\sigma_{1}(S)\) in the \(S\) column table, and the \(p\)-value obtained using [PERSON]'s exact test is shown in Equation (8). The \(p\)-value of [PERSON]'s exact test is the value of all \(\sigma_{1}(S)=a\) than the cumulative sum of probabilities that are lower.  
\[p^{F}_{S}(b)=\sum_{p^{F}_{S}(a|\sigma(S),n_{1},a)\leq p^{F}_{S}(b|\sigma(S),n_{ 1},n)}p^{F}_{S}(a|\sigma(S),n_{1},n) \tag{8}\]
## 5 Simulation Results and Discussion
### Test Systems and Results
#### 5.1.4 Test System 4  
This case study considers 140 generators belonging to Korea's power system. Twelve generating units possessing a cost function with the valve-point loading effect are utilized, while ramp rate limits, prohibited operating zones and system losses are neglected; the input parameters are available in [14]. The cheapest operating cost obtained using the RLGWO technique is shown in Table 7. The convergence characteristics for 140 generators using GWO and RLGWO methods for the fitness value are presented in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Unit** & \begin{tabular}{c} **Power Output** \\ **(MW)** \\ \end{tabular} & **Unit** & \begin{tabular}{c} **Power Output** \\ **(MW)** \\ \end{tabular} & **Unit** & \begin{tabular}{c} **Power Output** \\ **(MW)** \\ \end{tabular} & **Unit** & \
\begin{tabular}{c} **Power Output** \\ **(MW)** \\ \end{tabular} & **Unit** &
\begin{tabular}{c} **Power Output** \\ **(MW)** \\ \end{tabular} \\ \hline  
06515 \\ \multicolumn{10}{c}{Fuel Cost (§/h) = 1,559,390} & \multicolumn{10}{c}{Load demand = 49,342 MW} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Best simulation results of 140-unit system with loss (\(P_{D}=49,342\) MW).  
Figure 9: Results for different methods for the 110-unit system (50 trials): (**a**) boxplot of the final generation cost; (**b**) time and number of hits of the minimum solution.  
The statistical results, listed in Table 8, present the maximum, minimum, and average cost obtained by the OIWO method and the proposed RLGWO method over 50 trials. Figure 11 presents a visualization of Table 8. For this test problem, RLGWO produced a lower result than OIWO, as seen in Figure 10(a). Compared to OIWO, it found the lowest cost more times out of 50 trials, and was faster than OIWO. This may mean that the increase in problem size affects the RLGWO less than OIWO.
# Analysis of Damage and Permeability Evolution of Sandstone under Compression Deformation
###### Abstract  
A large number of experimental studies have demonstrated that the permeability and damage of rock are not constant but rather functionally dependent on stresses or stress-induced deformation. Neglecting the influence of damage and permeability evolution on rock mechanics and sealing properties can result in an overestimation of the safety and stability of underground engineering, leading to an incomplete assessment of the risks associated with surrounding rock failure. To address this, the damage and permeability evolution functions of rock under compression were derived through a combination of experimental results and theoretical analysis, unifying the relationship between porosity and permeability in both porous media flow and fractured flow. Based on this, a fluid-solid coupled seepage model considering rock damage and permeability evolution was proposed. More importantly, this model was utilized to investigate the behavior of deformation, damage, and permeability, as well as their coupled effects. The model's validity was verified by comparing its numerical results with experimental data. The analysis results show that the evolution of permeability and porosity resulted from a competitive interaction between effective mean stress and stress-induced damage. When the effective mean stress was dominant, the permeability tended to decrease; otherwise, it followed an increasing trend. The damage evolution was primarily related to stress- and pressure-induced crack growth and irreversible deformation. Additionally, the influence of the seepage pressure on the strength, damage, and permeability of the investigated rock was evaluated. The model results reveal the damage and permeability evolution of the rock under compression, which has a certain guiding significance for the stability and safety analysis of rock in underground engineering.  
rock mechanics; permeability; damage; coupled model; triaxial compression test 2024202024147368 +
Footnote †: journal: Applied sciences
# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 4 Experimental Evaluation
### Results and Discussion
The analysis shows that the stretch of the proposed model is 26.2%, 27.5%, and 23.8% better than CC executed on Tree, Abilene, and GEANT topologies, respectively. DCS is 15.0%, 10.6%, and 13.5% more efficient than TCS executed on the same configuration of parameters and network topologies. Figure 5b shows the comparative results of DCS with CC, as well as TCS in terms of stretch. For experiments, 1000 elements of cache size and a 0.8 value of content popularity are considered and executed on Tree, Abilene, and GEANT network topologies. Results analysis shows that the stretch of DCS is 26.22%, 27.5%, and 23.8% more efficient than CC executed on Tree, Abilene, and GEANT topology, respectively. The proposed model  
achieved a percent improvement gain of 15.09%, 10.6%, and 13.5% as compared to TCS executed on the cache size of 1000 and content popularity rate of 0.8 on different network topologies. Figure 5c shows the comparative results of DCS with CC, as well as TCS in terms of stretch for the cache size of 500 with content popularity of 1.2 on Tree, Abilene, and GEANT network topologies. The analysis shows that the proposed model achieved a percent improvement gain of 25.3%, 30.0%, and 30.4% as compared to CC executed on Tree, Abilene, and GEANT topology, respectively. DCS is 14.5%, 27.5%, and 22.5% more efficient than TCS when executed on the same configuration of parameters and network topologies. Figure 5d shows the comparative results of DCS with CC, as well as TCS in terms of stretch. For these experiments, 1000 elements of cache size and a 1.2 value of content popularity are considered and executed on Tree, Abilene, and GEANT network topologies. The analysis shows that the stretch of the proposed model DCS is 33.9%, 20.3%, and 19.0% more efficient than CC executed on Tree, Abilene, and GEANT topology, respectively. DCS is 13.3%, 12.2%, and 8.9% more efficient than TCS when executed on the same configuration of parameters and network topologies. DCS moves the popular content near the user while the central position caching node is facilitated to provide the data item earlier than the source node because of the smaller stretch. Results clearly show that when the data item is placed near the user, it results in a decrease of the stretch.
## 6 Verification and Results
### Episode 2: Resilience and Availability (req: v, vii)
#### 6.2.4 Availability of Control Plane Components  
The aforementioned key distribution is not only performed during provisioning, while the encryption keys change periodically. This makes the AuC service a critical component. Killing the AuC service does not affect the end-user traffic. It only stops the encryption keys from being renewed. Hence, an additional key monitoring agent must run along with the encryption and decryption services. This monitoring agent detects if a new key is not received within a certain expire time. If the expiry time is reached before a new key is received, the EF deletes its' Security Association.  
Instead of shutting down the AuC, we simulated this key protection feature by manipulating the key expire time in the encryption service. We experimented with setting the expire time to 10 s in the encryption service and the AuC re-keying time to 20 s. Next, we ran a simple Iperf bandwidth test for 60 s and used tcpdump to monitor if the packets traversed the intermediate SF. We confirmed the functionality by observing that the EFs periodically stopped working every for 10 s.  
This declarative SDN approach for the AuC also applies to the network controller. When the SDN controller has distributed the flow rules to the SFFs, it is expected that the SFFs continue forwarding packets even after the SDN controller becomes unavailable. Consequently, network topology changes and VM migration together with a non-functional SDN controller results in non-functional SFCs. We confirmed this behaviour by shutting down the SDN controller.
## 2 Methodology
### Content-Aware Multi-Resolution Disparity Estimation Using Semi-Global Block Matching
#### 2.2.2 Multi-Resolution Elemental Image Pyramid  
Typically, when constructing a pyramid with multiple resolutions for any objective, the procedure commences by taking the original image and reducing its size. However, when it comes to EIs, the images are already of low resolution. Creating a pyramid by progressively down-sampling them will result in extremely small images that lack significant information. Thus, in the instance of the EIs, the pyramid is formed by enlarging the EIs into two additional layers and down-sampling the image by one layer, as seen in Figure 5, enabling the algorithm to encompass characteristics that span from large-scale structures at lower levels to intricate details at higher resolutions. Start with the EI of the original size as the base level \(L_{0}\), with each level increased by a factor of two using bicubic interpolation [16]. A minimum resolution threshold is implemented to prevent further down-sampling of EIs with an extremely low resolution. If the value of the EI is less than \(40\times 40\), the down-sampling step is omitted.
# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 3 System Overview
### CSI Overview  
Our prototype leverages WiFi ambient signal as information source to analyze the influence of a driver's gesture on a wireless channel. Existing WiFi devices that exploit IEEE 802.11n/ac protocols typically consist of multiple transmitting (Tx) and receiving (Rx) antennas and thus support the widely used Multiple-Input Multiple-Output (MIMO) technology. CSI refers to fine-grained signal containing physical layer information based on Orthogonal Frequency-Division Multiplexing (OFDM).  
In our experiment, an IEEE 802.11n enabled Access Point (AP) was used as a transmitter and Intel 5300 NIC was used as a receiver to collect CSI data from the physical layer of the WiFi system, which supports 30 subcarriers for each CSI Tx-Rx antenna pair. It records the channel properties of each Tx-Rx antenna pair in OFDM subcarriers. The channel variations are readily available in the form of CSI measurements on commercial WiFi devices [45]. A typical narrowband flat-fading channel for packet index \(i\), exploiting MIMO and OFDM technology can be modeled as:  
\[Y_{i}=H_{i}X_{i}+\mathcal{N}_{i}\;i\in[1,N], \tag{1}\]  
where \(H_{i}\) is the CSI channel matrix for packet index \(i\), \(\mathcal{N}_{i}\) denotes the Gaussian noise vector, \(Y_{i}\) and \(X_{i}\) are the received and transmitted signals respectively, \(N\) refers to the total number of received packets.  
Let \(\mathbb{N}_{Tx}\) and \(\mathbb{N}_{Rx}\) be the number of transmitting and receiving antennas respectively, then the CSI matrix consists of \(\mathbb{N}_{Tx}\times\mathbb{N}_{Rx}\times 30\) complex values for each CSI stream. CSI matrix \(H\) for each Tx-Rx antenna pair can be written as:  
\[H_{i}=[h_{1},h_{2},\ldots,h_{30}]\;i\in[1,N]. \tag{2}\]  
Each \(h\) is a complex value, which carries the information for both amplitude and phase response; estimated as:  
\[h=|h|e^{jsin\{\bigtriangleup h\}}, \tag{3}\]  
where \(|h|\) denotes the amplitude and \(\bigtriangleup h\) indicates the phase information. CSI information on multiple channels is correlated, whereas all streams behave independently. We utilize both the amplitude and phase information to unleash the full potential of CSI measurements.
## 2 Methodology
### Improved Genetic Algorithm for Fault Localization
#### 2.3.3 Adaptive Genetic Operator  
Population diversity is the embodiment of the wide distribution of individual genes in the population, which is an aspect that the population needs to ensure in the process of overall evolution.  
Crossover is a very important part of the genetic algorithm. It replaces the structure of two-parent parts to generate a new individual. The crossover probability PC is the key parameter, which can affect the evolution of the population to a certain extent and ultimately the searchability of the genetic algorithm. This paper adopts the adaptive crossover probability, which mainly hopes to increase or save the crossover probability in the iterative process according to the diversity of the parent population.  
The value process of adaptive crossover probability is as follows. When the fitness function value of individuals in the current population is less than that of the previous generation population, it will be considered to increase the crossover probability value, specifically, we use the basic crossover probability value plus the value of probability change. When the fitness function value of the individual in the current population is greater than or equal to the previous generation population, the crossover probability value is not changed.  
The calculation formula of adaptive crossover probability is as follows:  
\[Pc_{i}=\left\{\begin{array}{ll}Pc_{\text{basic}}&V_{i}\geqslant V_{i-1}\\ Pc_{\text{basic}}&+Pc_{\text{variance}}\text{ sigmoid}\left(\frac{V_{i-1}}{V_{i}}\right)&V_{i}<V_{i-1} \end{array}\right. \tag{2}\]  
\(Pc_{\text{basic}}\) refers to the preset basic crossover probability, and \(Pc_{\text{variance}}\) refers to the preset probability change. The sigmoid function is used to calculate the numerical normalization. \(V_{i}\) refers to the fitness function value of the individual of the current population, and \(V_{i-1}\) refers to the fitness function value of the individual of the previous generation.  
A mutation is an auxiliary way of population evolution in a genetic algorithm. Changing mutation operators can affect population evolution to a certain extent and also affect the searchability of a genetic algorithm. This paper uses an adaptive mutation operator, which mainly hopes to increase or save the mutation probability in the iterative process according to the diversity of the parent population.  
The value process of adaptive mutation probability is as follows. When the fitness function value of individuals in the current population is less than that of the previous generation population, it will be considered to increase the mutation probability value. Specifically, we use the basic mutation probability value plus the value of probability change. When the fitness function value of the individual in the current population is
## 1 Introduction  
Nowadays, the use of experimental techniques for monitoring the development of microstructure, durability, and mechanical properties of cement-based materials, such as impedance spectroscopy [1; 2; 3] and electrical resistivity [4; 5], has become very popular. In that way, structural health monitoring (SHM) technology based on piezoelectric materials has been widely applied to concrete structures [6; 7]. Among them, piezoceramic based smart aggregate has been developed rapidly since being proposed and introduced by [PERSON] [8]. Some researchers investigated on the potential usage of smart aggregate (SA's) in SHM through laboratory tests. Some adopted SA based monitoring system to monitor early-age concrete strength by recording and analyzing the amplitude development of the response signals [9; 10; 11; 12]. [PERSON] et al. [13] and [PERSON] et al. [14; 15] studied the utilization of SA in water seepage monitoring in concrete. [PERSON] et al. [16] and [PERSON] et al. [17] conducted research on the situation of real-time damage detection of a circular reinforced concrete column under seismic and cyclic load, respectively. The results demonstrated that the piezoelectric based SA transducer is capable of monitoring the damage induced by different kinds of loads.  
The researches mentioned above emphasized on the monitoring of the change in concrete materials caused by cement hydration, water content, or damage, and discussions on the influence of stress state and history experienced in monitoring process on the sensing signals are rare. According to [PERSON] et al. [18], the existence of compressive stress lead to the change of ultrasonic velocity, which resulted in a change of 1.96 percent under compressive stress with magnitude of 16 MPa. It can be reasonably deduced that, even though there are no obvious damage for concrete at elastic phase, the velocities and amplitudes of monitoring signals under different stress states are not the same, and it affects the accuracy and effectiveness of evaluation indexes of the monitoring system. Some researchers noted that there exist a great deal of influencing factors in in-situ monitoring, like temperature and loading conditions, which may result in considerable impacts during the monitoring stage [19; 20; 21; 22; 23; 24; 25; 26]. Specifically, the construction load has not been taken into consideration during early age concrete monitoring process. Especially, for the important columns downstairs, there are a lot of load increments in the construction process of a high-rise building [27]. The question whether the construction load brings in negligible impact on the monitoring results of SA's is not fully discussed.  
This paper aims to investigate the effects of staged stress on the monitoring signals. The test results demonstrate that the amplitude of sensing signal varies obviously under incremental stress, even in elastic stage. Besides, the wave velocity propagating in concrete is highly stress-dependent, which can be potentially employed as an index to evaluate the stress state in concrete structures.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Denial of Service (DoS)  
The prevention of this attack involves the inclusion of a verification parameter in each message exchanged in the authentication phases (HUP, PUP, TP, CP). The verification parameter used in our protocol was a timestamp and its validity was verified before the recipient processed each message. Therefore, if an attacker uses an invalid timestamp, the entire procedure is interrupted in time to prevent the DoS attack.