esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Multiobjective Optimization Approach
### Multiobjective Optimization Modeling
#### 5.1.2 Constraints and Objective Functions
\tag{30}\]  
where \(W_{1}\) is the total energy consumption of the FPTLIR in the flight on-line mission interval, \(W_{2}\) is the total energy consumption of the FPTLIR in the flight off-line mission interval, and \(W_{\text{vni}}\), \(W_{\text{hni}}\), \(W_{\text{hfi}}\), and \(W_{\text{vfi}}\) when subscript \(i=1,2,3\) are the energy consumptions of the FPTLIR in the acceleration, uniformity, and deceleration intervals of the four path intervals of the FPTLIR in the order of priority: the VPON, the HPON, the HPOF, and the VPOF, respectively. The time-saving metric ensures the shortest mission execution time under the corresponding energy consumption in both the on-line and off-line flight intervals as well as meeting the specified time requirements. Equation (31) includes the objective function of the flight time for both on-line and off-line mission intervals, along with the constraints on flight time within these intervals. \[\left\{ \begin{array}{l}f_{\text{on2}}(t)=\sum\limits_{i=1}^{3}\left(t_{ \text{vni}}+t_{\text{hni}}\right)\\ \sum\limits_{i=1}^{3}\left(t_{\text{vni}}+t_{\text{hni}}\right)\leq t_{\text{ on}}\\ \left\{\begin{array}{l}f_{\text{off2}}(t)=\sum\limits_{i=1}^{3}\left(t_{ \text{vfi}}+t_{\text{hfi}}\right)\\ \sum\limits_{i=1}^{3}\left(t_{\text{vfi}}+t_{\text{hfi}}\right)\leq t_{\text{ off}}\end{array}\right.\end{array} \right. \tag{31}\]  
In this case, the total time for the FPTLIR to complete the on-line mission is maximized to \(t_{\text{on}}=20\) s and the total time to complete the off-line mission is maximized to \(t_{\text{off}}=30\) s, according to the inspection requirements. The performance indicators mentioned above are quantified as constraints and optimization objectives in the optimization model based on the flight energy model. Each performance index has a distinct focus depending on specific missions. In this paper, the main focus is on the reduction of the mission execution time while ensuring stability and safety indices, position accuracy index, and energy saving.
# Coupling Denoising to Detection for SAR Imagery
## 3 Results
### Dataset Settings  
We acquired 60 TerraSAR-X raw scenes from German Aerospace Center [34] and 55 COSMO-SkyMed raw scenes from Italian Space Agency [35]. The raw scenes go through multiple stages like preprocessing, Doppler centroid estimation (DCE), and focusing to obtain single look slant range complex (SSC) images. The SSC images are then converted to multi-look ground range detected (MGD) images by multi-looking procedures. With the MGD images, we create patches of size 800\(\times\)800 via sliding-window operation, within each patch containing at least one target object which belongs to airplane (A), etcetera (E), or ship (S) categories. Finally, we randomly split patches into 80% for training, and 20% for testing.
# Model for Quantifying the Quality of Secure Service
## 3 Components of a Security Model
### Probability of Confidentiality  
Numerous researchers have attempted to quantify confidentiality with varying success [8, 9]. Confidentiality is the aspect of a network that protects against unauthorized message receipt, i.e., preventing an eavesdropper from either receiving or decoding messages. One approach to quantifying confidentiality is to redefine it as a probability so that  
\[P(C)=1-P(l) \tag{3}\]  
where \(P(C)\) is the probability of confidentiality and \(P(l)\) is the probability of leakage. Leakage refers to an untrusted listener having access to an \"information flow from secret inputs to public outputs\" [10]. Inspired by Perfectly Secure Message Tranmission (PSMT) [11], the set of all adversarial listeners, \(A_{L}\), maps to a set of wires (channels), \(\sigma\), that the listeners have access to; if one of the members of \(A_{L}\) has access to the information, then the probability of leakage exists.  
For leakage to occur, a listener must intercept the message, decrypt it (if applicable), and then decode the data contained in the message. The probability of interception, \(P(int)\), quantifies the probability that a listener with channel access will receive the message. The probability of decryption, \(P(dcr)\), quantifies the probability that the adversary will decrypt it4. Finally, the probability of decoding, \(P(dco)\), quantifies the probability that an adversary will decode the message5.  
Footnote 4: The probability of decoding is \(P(int)\), \(P(dcr)\), and \(P(dco)\) is  
Consider the relationship between the probabilities of interception, decryption, and decoding. For data leakage to occur, an adversary must be able to achieve all three actions, i.e., decryption is irrelevant if the adversary is unable to receive any messages. Conversely, receiving every transmission ever sent is irrelevant if an adversary is unable to decrypt or decode the messages. The logical binary relationship of how \(P(l)\) relates to \(P(int)\), \(P(dcr)\), and \(P(dco)\) is captured in Table 1. The proposed equation to describe \(P(l)\) in terms of \(P(int)\), \(P(dcr)\), and \(P(dco)\) is  
\[P(l)=P(int)\times P(dcr)\times P(dco). \tag{4}\]
## 2 Taxonomy for the Protection and Detection of Infringing Multimedia Content
Taking into account the defined objectives, proposed hypotheses, abstracts reviewed, methodologies applied, and results achieved, the research is categorized into the following domains and subdomains. * The _safeguarding domain_[6; 7; 19; 48; 49] outlines protection strategies related to copyright laws, international and domestic regulations, legal bodies, global and national benchmarks, as well as technical methods implemented to guarantee that multimedia content on the IoE is used only with the explicit permission of the copyright owners. The research identifies the following specific subdomains
* _Non-technical protection_[50; 51; 52] involves examining the legal and non-technical domain of jurisdictional frameworks that govern the presentation and regulation of copyrighted material, ensuring it occurs with the consent of the rights holder. * _Technical protection_[25; 53; 54; 55; 56; 57; 58; 59; 60] refers to computational techniques known as digital locks or anti-circumvention mechanisms. These are designed to safeguard multimedia works by digitally enforcing copyright laws. * AI/ML-driven protection [61; 62; 27; 63] is an intelligent scheme that utilizes one or more AI/ML models to enhance the protection of multimedia objects in the IoE, in conjunction with non-technical or technical processes. * The _active domain_[25; 64] are processes and methodologies that actively seek, either manually or automatically, multimedia files across the IoE, to identify, assess, and address potential infringements to which the given object is bound. From these, the following subdomains emerge:
* _Non-technical_[65; 66; 67] contains tasks that reside within the DMCA notice-and-take-down process, an act that sets limitations for multimedia content providers on the IoE. If these mandates are not adhered to, it may result in a partial or total removal of the reb resource. * _Technical_[28; 29; 68; 69; 70; 71] involves any technical procedure that allows for traversing the IoE to identify websites, links, URLs, P2P platforms, File Transfer Protocol (FTP) endpoints, torrents, and storage clouds where an identified infringing multimedia resource is residing. * AI/ML-driven [28; 29; 30; 31; 32; 33; 34; 35; 72; 73; 74; 75] includes advanced schemes that harness the power of AI/ML algorithms to efficiently perform cyber patrolling by traversing the IoE using NLP techniques, Supervised Learning (SL), Non-Supervised Learning (NSL), and DL analyses to analyze, discover, and present potential infringing multimedia content. Figure 2: Systematic search filters for records according to PRISMA [37]. Figures 3 and 4 illustrate the taxonomic structure, covering domains, subdomains, and categories, of the research trajectories within the safeguarding and active domains. Hence, Tables 1 and 2 provide a more detailed view of the aforementioned. Figure 4: Active domain a taxonomy related to identifying, countering, and removing material that has been infringed over the IoE. Figure 3: Safeguarding domain, a taxonomy related to safeguarding copyrighted material over the IoE.
## 3 Application to a Case Study
### Analysis of the Factors Affecting the Health of an Offshore Platform Structure
#### 3.5.1 Scouring  
Scouring is a natural phenomenon causing denudation. Local scouring causes the soil near the pile foundation to peel off; this reduces the penetration depth of the pile, reduces the constraint of the soil on the pile foundation, and reduces the lateral stiffness of the platform structure and thus affects its natural frequency. The change in the scouring depth over time can be expressed as [39]:  
\[h(t)=h_{i}(t-t_{0}), \tag{9}\]  
where \(h(t)\) is the scouring depth at time \(t\), \(h_{i}\) is the annual scouring rate, and \(t_{0}\) is the time at which scouring began.  
Figure 14: The diagram of overall natural frequency variation of the offshore platform over a whole year.  
Figure 13: The diagram of time-varying effect on frequency under marine life growth. (**a**) Frequency change rate under marine life growth. (**b**) Frequency variation under marine life growth.  
As noted, scouring is the reduction of the soil depth at the pile foundations year by year; that is, a certain length of soil spring is deleted with increasing service time. Here, we assume that the depth of the steel pipe pile under the mud surface is reduced by 0.2 m every year [39].  
The diagram of time-varying effect on frequency under scouring is shown in Figure 11. It shows that the platform frequency generally decreases with the increasing of service time. Within the service life, the effects of scouring on the frequency is 3-15%, so scouring has a greater impact on the higher-order modes.
## 3 Generation of Equations
### Graph Encoding
#### 3.1.2. Graph  
A graph \(G=\{N,B\}\) is defined by two lists of _nodes_\(N\) (also called _vertices_) and _branches_\(B\) (also called _edges_), with \(B\subset N^{2}\) (each element of \(B\) is an object defined on two elements of \(N\), see [33] for details). The dictionary (Appendix B) encodes the graph of each elementary component. The branches of such an elementary graph contain the constitutive laws of the corresponding component:  
* _Dipoles_ are made of two nodes and a single branch, defining a single couple of state \(x\) and storage function \(h(x)\) (storage component), or dissipative variable \(w\) and scalar relation \(z(w)\) (dissipative components).
* More generally, _n-ports multipole_ are made of \(n\) nodes and at least \(n-1\) branches, defining \(n-1\) couples of variables and functions. Typically, the graph for the bipolar junction is made of two branches (base-emitter and base-collector). The graph corresponding to a given circuit is derived from its netlist description in two steps:
* build the _internal_ graph by connecting the elementary graph of the components from the first block of the netlist,
* introduce a reference node \(N_{0}\) (or datum, see [27] SS10) to define the _external_ branches from the second block.  
Typically, \(N_{0}\) corresponds to the ground or any local electrostatic potential which does not impact the currents nor voltages. Then, \(N\) is built from the list of nodes appearing at least once in the netlist, plus the reference node \(N\triangleq[N_{0},N_{1},\cdots,N_{n_{N}}]\). According to Section 2, the set of branches is organized as \(B=\{B_{S},B_{D},B_{P}\}\), with \(B_{S}\) the \(n_{S}\) energy storage branches, \(B_{D}\) the \(n_{D}\) dissipative branches, and \(B_{P}\) the \(n_{P}\) sources.  
As an example, the construction of the graph in Figure 2 from its netlist 1 is as follows. Firstly, the internal graph is built. It is made of \(n_{N}=5\) nodes \(\{N_{1},\cdots,N_{5}\}\) and four branches \(B_{S}=\{C_{1}\}\) and \(B_{D}=\{R_{1},Q_{1,kc},Q_{1,kc}\}\). Secondly, we introduce the (virtual) reference node \(N_{0}\) to define the four branches corresponding to the external ports \(B_{P}=\{IN,Vcc,OUT,GRD\}\).
## 5 Performance Evaluations
### Simulation Metrics and Scenarios  
We use three metrics to evaluate the performance. The average delivery delay, energy consumption and packet delivery ratio. The size of the sensor network is set to 200 m \(\times\) 200 m where 100 nodes are uniformly distributed. An obstacle is created at the center of the area; _i.e._, an area is set with no nodesinside to simulate an obstacle in a realistic environment. We select IEEE 802.11 as our MAC protocol and the radio range of nodes is 40 m. The sink is located at the left side of the obstacle. We give two comparison sets to show the performance. The first one is based on the transmission rate of the source node is gradually increased until it reaches 100 packets per second. The second one is based on an expanding obstacle area. During the process of simulation, there is always some node that gains a forwarding opportunity in one game. In other words, some node would be the forwarding node. Therefore, after the destination received the packet from the source node, each forwarding node would change their own benefit according to the payoff function.
# Mechanical Properties and Microstructure of Polyvinyl Alcohol (PVA) Modified Cement Mortar
## 3 Results and Analysis
### FTIR Spectroscopy  
The FTIR analyses of cement mortar containing 0.6% and 2.0% PVA were conducted after 28 days of curing, and the spectra are shown in Figure 10 for comparison. As shown in Figure 10, all curves have obvious characteristic peaks at 3643, 1643, 1452, and 973 cm\({}^{-1}\). The absorption peak near 3643 cm\({}^{-1}\) corresponds to the characteristic peak of free hydroxyl in the Ca(OH)\({}_{2}\) molecule produced by cement hydration. The characteristic absorption peak of Si-O of C-S-H of cement hydration products is at near 973 cm\({}^{-1}\). The peak at 1452 cm\({}^{-1}\) corresponds to the stretching of CaCO\({}_{3}\). The FTIR spectra of PCM2 containing 0.6% PVA shows more intense absorption peaks at 973 cm\({}^{-1}\) as compared to CM, indicating the positive effect of PVA on accelerating the hydration rate of the cement matrix. However, this peak of PCM4 was much lower than that of PCM0, indicating the excessive addition of PVA impacted the hydration of the cement matrix in a negative way. Moreover, Figure 10 shows that the addition of PVA reduced the spectral data at 3643 cm\({}^{-1}\), which corresponds to the -OH band of Ca(OH)\({}_{2}\). This result coincides well with the results of DSC.  
Figure 10: Fourier transform infrared spectroscopy (FTIR) analysis of cement mortar with different PVA content.  
Figure 9: Differential scanning calorimetry (DSC) analysis of cement mortars at 28 days. (a) DSC curves and (b) bound water and Ca(OH)\({}_{2}\) content.
## 3 Results and Discussion  
### Chemical Composition, Acidity, and Water Activity of Cheese  
The basic chemical composition of the fresh cheeses is given in Table 1. A significant interaction (\(p\leq 0.05\)) between the effects was only found for fat content. Milk source had a significant effect on fat (\(p\leq 0.001\)) and NaCl (\(p\leq 0.05\)) contents, while wild garlic addition affected the amount of protein (\(p\leq 0.001\)) and ash (\(p\leq 0.05\)). HF cheeses contained more fat and NaCl than RP ones. Nevertheless, the differences, although statistically significant, were somewhat small. Cheeses with wild garlic demonstrated higher protein and ash contents than cheeses without herbs. Likewise, a higher ash content due to wild garlic addition was previously reported in herby pickled cheese, and it was concluded that this herb is an important source of mineral matter [2].  
The water content, acidity, and water activity of cheeses during storage is presented in Table 1. Neither milk source nor storage duration had any significant effects on the water content, pH, or water activity. The addition of wild garlic only had an influence on water content (\(p\leq 0.001\)), causing its decrease. No significant interactions were found between the effects.  
The decrease in water content of the cheese with wild garlic was expected. In general, dried leaves of herbs are characterized by high dry matter contents. Thus, the incorporation of wild garlic caused an increase in the dry matter content of the cheeses examined in this study. Likewise, [PERSON] et al. (2018) reported the higher dry matter content of double cream cheese supplemented with _Allium roseum_, in comparison to the control. Moreover, mixing the cheese curd with wild garlic leaves, although gently performed, could have promoted whey release. Nevertheless, there was no whey release during refrigerated storage.
## 3 Data Sources and Preprocessing
### Data Preprocessing  
As the original MCD12Q1 product is stored in hierarchical data format (HDF) and with the sinusoidal projection, data pre-processing is necessary, including format conversion, reprojection, resampling, image mosaicking, and sub-area masking. The MODIS Reprojection Tools (MRT) professional projection conversion system is employed for this purpose. Here, the MODIS HDF data format is converted into Geotiff. At the same time, the data projection is converted from SIN to WGS84/UTMand the image mosaicking and subsetting are also completed. Conversely, the acquired GlobeLand30 are just processed by mosaicking and subsetting in ENVI 4.7 (the ENvironment for Visualizing Images software), due to the original Geotiff format and UTM projection. Additionally, to compare the MCD12Q1 and GlobeLand30, the spatial resolution of GlobeLand30 is resampled at 500 m using the nearest neighbor resampling method, which keeps and basically does not destroy the gray values of the original image, in comparison with the bilinear interpolation and the cubic convolution interpolation method.
## 3 Results and Discussion
### Metabolism of Carbon Sources
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Isolate**} \\ \cline{2-4}
**Carbon Source** & **A1, A2, A4, A6, A10, A11, A13, A14, A15,** & **A3, A7, A8, A20, A23** & **A5, A9, A12, A17, A26** \\  & **A16, A18, A19, A21, A22, A24, A25** & & \\ \hline Glycerol & - & - & - \\ Erythritol & - & - & - \\ D-arabinose & - & - & - \\ L-arabinose & - & - & - \\ D-ribose & + & + & + \\ D-xylose & - & - & - \\ L-xylose & - & - & - \\ D-adonitol & - & - & - \\ Methyl \(\beta\)-D-xylopyranoside & - & - & - \\ D-galactose & + & + & + \\ D-glucose & + & + & + \\ D-Trutose & + & + & + \\ D-mannose & + & + & + \\ L-sorbose & - & - & - \\ L-rhamose & - & - & - \\ Dulcitol & - & - & - \\ Imositol & - & - & - \\ D-mannitol & - & - & - \\ D-sorbitol & - & - & - \\ Methyl & - & - & - \\ \(\alpha\)-D-mannopyranoside & - & - & - \\ Methyl \(\alpha\)-D-glucopyranoside & - & - & - \\ N-acetyl-glucosamine & + & + & + \\ Amygdala & + & + & + \\ Arbutyne & + & + & + \\ Esculin & + & + & + \\ Salicin & + & + & + \\ D-cellobiose & + & + & + \\ D-maltose & + & + & + \\ D-lactose & + & + & + \\ D-melibiose & + & - & + \\ D-saccharose & - & - & - \\ D-trehalose & - & - & - \\ Inulin & - & - & - \\ D-melezitose & - & - & - \\ D-affinose & - & - & - \\ Starch & + & + & - \\ Glycogen & + & - & - \\ Xylitol & + & - & - \\ Gentiobiose & + & - & - \\ D-turanose & + & + & + \\ D-likosse & - & - & - \\ D-tagatose & - & - & - \\ D-fucose & - & - & - \\ L-fucose & - & - & - \\ D-arabitol & - & - & - \\ L-rabitol & - & - & - \\ Potassium gluconate & + & + & + \\ Potassium 2-ketoglucate & - & - & - \\ Potassium 5-ketoglucate & - & - & - \\ \hline \hline \end{tabular} + positive reaction, - negative reaction.  
\end{table} 
Table 2: Results of carbohydrate fermentation by the _L. lactis_ isolates assessed with the API 50 CH test.
## 2 Improved Cohesive Model
### Friction Model  
The improved friction model was developed by integrating the conventional PPR cohesive model with the MC strength criterion. This integration ensures that the peak values of the cohesive traction and frictional traction of interface materials occur simultaneously at the same tangential crack separation. When elements on either side of the interface compress and intrude into one another, resistance is activated along the interface. The resistance magnitude is determined by the intrusion depth and the normal stiffness of the interface under compression, which is equal to the normal stiffness when the tensile crack separation is nearly 0. The improved model assumes that frictional traction is initiated when the interface is compressed and shear slip occurs between adjacent layers, leading to a smooth increase in resistance as the tangential crack separation grows. This process is described by Equation (8) [3].  
\[T_{f}=\mu\times\kappa(\Delta_{t})\times|T_{n}|,\ T_{n}<0\ \text{and}\ \Delta_{t}>0 \tag{8}\]  
where \(\mu\) is the friction coefficient of the interface; \(\kappa\) is the response factor that increases monotonically and continuously from 0 to 1 with the growth in shear displacement, and it can be expressed by Equation (9) [3], as follows:  
\[\kappa(\Delta_{t})=\left\{\begin{pmatrix}\frac{T_{\frac{1}{2}}(0,\Delta_{t})} {\text{tan}_{\text{max}}}\end{pmatrix}^{s},0<\Delta_{t}\leq\lambda_{t}\delta _{t}\\ 1\,\ \Delta_{t}>\lambda_{t}\delta_{t}\end{pmatrix}\right. \tag{9}\]  
where \(s\) is the transformation shape parameter controlling the growth mode of the friction, and the influence of \(s\) on \(\kappa\) can be seen in Figure 3a. Through the established friction mode, the peak cohesion and friction can appear at the same shear deformation, and a smooth transition between cohesion and friction can be realized. For the improved model, eight input parameters are required, i.e., \(\Gamma_{n}\), \(\Gamma_{t}\), \(\sigma_{max}\), \(\tau_{max}\), \(\lambda_{n}\), \(\lambda_{t}\), \(\alpha\), and \(\beta\). In addition, the friction growth shape parameter, \(s\), and the friction coefficient, \(\mu\), should also be used as inputs. The curve shape of the coupled cohesion-friction model is shown in Figure 3.  
Figure 2: Separation–traction relationship in the PPR cohesive model: (**a**) Mode I fracture; (**b**) Mode II fracture.