esa-sceva/satcom-chunk-collection huggingface dataset:
# Secrecy Rate of Resource-Constrained Mobile Relay Model under Two-Way Wiretap Channel
## 2 Materials and Methods
### Achievable Secrecy Rate Problem Formulation  
The achievable secrecy rate region of the resource-constrained two-way mobile relay system is the average of all combinations of the worst case uplink-downlink achievable secrecy rate; therefore, the achievable secrecy rate \(R^{Sec}\) requirement that the system needs to meet is  
\[R^{Sec}\leq\frac{1}{T}\sum_{m=1}^{M}t_{m}min(R_{m}^{UP-Sec},R_{m}^{DL-Sec}) \tag{7}\]  
where \(T\) is the time duration, consisting of all transmission time \(t_{m}\) and total moving time. Moreover, the transmit power and energy requirements must also satisfy  
\[\sum_{m=1}^{M}q_{m}t_{m}\leq Q,\quad q_{m}\geq 0 \tag{8}\] \[\sum_{m=1}^{M}p_{m}t_{m}\leq(E-E_{G}),\quad p_{m}\geq 0 \tag{9}\]  
where \(q_{m}\) and \(Q\) are transmit power and initial available energy at Alice, respectively. In addition, \(p_{m}\), \(E\), and \(E_{G}\) are the transmit power, initial available energy, and motion energy of the relay, respectively. If the relay is fixed, \(E_{G}=0\); otherwise, \(E_{G}\
eq 0\), which is determined by a selected visiting path. Having the power allocation and graph mobility constraints satisfied, the achievable secrecy rate problem is formulated as P1.  
\[\text{P1}: \max_{R^{Sec},\mathbf{s},\mathbf{Z},\{\lambda_{n}\},\{t_{m},q_{m},p_{m}\}} R^{Sec}\] s.t. \[\sum_{m=1}^{M}\Big{[}t_{m}log_{2}(1+\frac{q_{m}||\mathbf{h}_{m}|| ^{2}}{\sigma_{t}^{2}})-t_{m}log_{2}(1+\frac{q_{m}||h_{e}|^{2}}{\sigma_{e}^{2}}) \Big{]}\geq TR^{Sec} \tag{10a}\] \[\sum_{m=1}^{M}\Big{[}t_{m}log_{2}(1+\frac{p_{m}||\mathbf{g}_{m}|| ^{2}}{\sigma_{u}^{2}})-t_{m}log_{2}(1+\frac{p_{m}||\mathbf{g}_{e,m}||^{2}}{ \sigma_{e}^{2}})\Big{]}\geq TR^{Sec}\] (10b) \[\frac{\Theta(\mathbf{Z})}{a}+\sum_{m=1}^{M}t_{m}\leq T\] (10c) \[(\mathbf{s},\!\mathbf{Z})\in\mathcal{A}(\mathbf{s},\!\mathbf{Z})\] (10d) \[\sum_{m=1}^{M}p_{m}t_{m}+\zeta(a)\Theta(\mathbf{Z})\leq E\] (10e) \[\sum_{m=1}^{M}q_{m}t_{m}\leq Q\] (10f) \[(1-s_{m})(t_{m}+p_{m}+q_{m})=0,t_{m}\geq 0,p_{m}\geq 0,q_{m} \geq 0\quad\forall m=1,\cdots,M \tag{10g}\]  
where the constraints mean:  
1. (10a) and (10b) are for secure transmission.
2. (10c) ensures that the mobile ground vehicle moving and data transmission are completed in T seconds.
3. (10g) is the constraint that the stopping time and power allocation are zeros provided that the vertex is not visited.  
However, problem P1 is a MINLP problem due to the discontinuity of integer constraints of (10d), which is NP hard and nontrivial to solve[18]. Moreover, the secrecy rate depends on the transmit power \(\{p_{m},q_{m}\}\) and transmission time \(\{t_{m}\}\), which present unknown and nonlinear coupling with each other.
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
## 4 Simulation Experiments
### Robustness Validation Experimental Methodology  
In order to evaluate the robustness of the method proposed, arc segments in LowEleArc and HighEleArc, shown in Figure 11, were selected as experimental objects. Gaussian noise conditions of 10 dB, 15 dB, and 20 dB were added to the ISAR image sequence, respectively, and under each noise condition, 100 Monte Carlo experiments were performed for each experimental arc segment to evaluate the robustness of the proposed method in the robustness validation experiment.  
The flowchart of the robustness validation experiment is shown in Figure 12. Step 1: Set the ISAR imaging parameters. Set the search scope of the look angle to 15-55\({}^{\circ}\). Set the search step length to 0.026\({}^{\circ}\). Set the random noise conditions to 10 dB, 15 dB, and 20 dB. Set the number of Monte Carlo experiments to 100. Set the actual look angle of the spaceborne SAR antenna to 35\({}^{\circ}\). Step 2: Obtain all the visible arcs when the ground-based radar observes spaceborne SAR, within 24 h from the spaceborne SAR orbit epoch time, and select the LowEleArc and HighEleArc as experimental arcs. Step 3: According to the sliding window shown in Figure 11, obtain two experimental arc segments. Step 4: Obtain  
Figure 10: Illustration of the sliding window within the visible arc.  
Figure 9: Flowchart of the feasibility verification experiment.  
the random noise parameters. Step 5: Generate the ISAR image of spaceborne SAR during the experimental arc segment. Step 6: Extract the range and Doppler values of the scatterers on the parabolic antenna edge. Step 7: Obtain the position vectors of scatterers on the parabolic antenna edge using (27). Step 8: Quantify the search scope according to the search step length to obtain a search set. Step 9 : Calculate the values of the objective function (30) in the search set. Step 10: Find the minimum value of the the objective function (30) to obtain the estimated look angle. Step 11: If the Monte Carlo experiment has not ended, go to Step 5 ; otherwise, if the traversal of different noise conditions has not ended, go to Step 4; otherwise, the experiment ends and all the estimated look angles are obtained.  
Figure 11: Arc segments used for Monte Carlo experiments in the low and high elevation arcs. LowEleArc: low elevation arc. HighEleArc: High elevation arc.  
Figure 12: Flowchart of the robustness validation experiment.
# Non-Destructive Study of the Microstructural Effects of Sodium and Magnesium Sulphate Attack on
## 1 Introduction
While the effects of reactions involving the sulphate anion have been extensively studied, the microstructural changes produced in the pore network have received less attention, especially in terms of porosity and permeability related values. These parameters are important as they affect the service properties of structures, affecting the mechanical strength of concrete itself, as well as increasing the risk of corrosion of embedded reinforcement bars, for example by affecting the resistivity of the concrete or the ease of chloride ion ingress and promoting the easier ingress of sulphate salts. Many consider the corrosion of reinforcement bars to be the most important threat to real reinforced concrete structures [20]. Some authors have observed that at early ages, the formation of reaction products actually decreases the porosity due to the formation of reaction products that may be accommodated within the hardened cement's pore structure [14; 27; 28]. Similar microstructural changes have been observed during chloride migration experiments through concrete [29]. However, it appears that no current literature has considered the effect on electrical resistivity. As such, the main objective of this research is to investigate the use of the non-destructive impedance spectroscopy technique in studying the aforementioned effects caused by sulphate attack on the microstructure of mortars with different contents of silica fume, compared to mortars containing ordinary Portland cement only. In order to check the validity of the impedance spectroscopy results, they have been contrasted with those results obtained using the more well-known and non-destructive \"Werner\" resistivity test. The use of non-destructive techniques could allow for early detection of sulphate attack in concrete structures, with the consequence of being able to intervene at an early stage, avoiding possible disasters and the economic cost of repairs at an advanced stage of the attack. A classical technique, mercury intrusion porosimetry, has also been used as a contrast technique in this study. Finally, the compressive strength of mortars has been determined, in order to check the relationship between this property and the microstructural changes observed. In addition, compressive strength is very commonly used in the literature [19; 25; 30] (as mentioned previously), for analyzing the effect of sulphate media on the behaviour of cement-based materials.
## 4 Overview of the Proposed Error Recovery Mechanism
### Network (WBSN) Scenario  
We have chosen the hierarchical architecture of WBSN, which consists of different biomedical sensor nodes, Personal Server(PS)/monitoring station (MS) and relay nodes in between sensor nodes and PS (e.g., Figure 2b) [44; 45]. The network can be modeled as a connectivity graph using the following equation,  
\[G=(V,E) \tag{1}\]  
where \(V\) is the union of \(S\), \(R\) and \(PS\), \(S\) is the set of all \(N\) biomedical sensor nodes \(S=\{s_{1},s_{2},.\,s_{N}\}\) and \(E\) is the set of all wireless links between any biomedical sensor node and a relay node, any two relay nodes and/or between relay node and base station or sink, \(E=\{e_{1},e_{2},e_{3},..,e_{N}\}\), while \(R\) is the set of \(n\) relay node, \(R=\{r_{1},r_{2},r_{3},...,r_{n}\}\). Similar to [PERSON]'s work [13], this work applies an NC-based error recovery mechanism for the wireless links between relay nodes and PS/MS.  
All of the medical sensor nodes in the considered WBSN use fixed and limited transmission power while communicating with relay nodes. Furthermore, the relay nodes use fixed and limited transmission power (higher than medical sensor nodes) during their communications with biomedical sensor nodes and PS/MS. In the considered WBSN, the medical sensor nodes act as source nodes and the relay nodes act as forwarding nodes. Typically, the placement of sensors in or on a human body is deterministic, hence the physical topology of the network. Thus, the WBSN, as in Figure 3, seems static, but in reality, it could be dynamic, due to its application environment (e.g., hospital, home) and in-body and on-body dynamic path loss [12]. The path loss modeling is out of the scope this paper, and we have considered an existing one similar to [12].  
The appropriate number of relay nodes \(n\) depends on the application and the wearer's physical structure. Like [PERSON]'s work, this work has considered that for a WBSN of \(3n\) body sensors (S) and one monitoring station (MS or PS) with \(n>=2\), \(n\) relay nodes are sufficient. For the implementation of the proposed scheme, using simulation and analysis, if we considered a scenario that includes \(n=4\) or four relay nodes, 12 sensor nodes are sufficient (Figure 3) for the application.  
Figure 3: The network scenario used.
# Releasing Differentially Private Trajectories with Optimized Data Utility
## 3 Preliminaries
### Differential Privacy  
Stemming from the area of statistical disclosure control, differential privacy [7] has become a widely accepted privacy standard. In general, differential privacy requires that the outcome of any query to a dataset is insensitive to the change (e.g., addition and removal) of a single record in that dataset. The formal definition of differential privacy is given as follows.  
**Definition 1** (\(\epsilon\)-differential privacy).: _A privacy protection algorithm \(f\) can provide \(\epsilon\)-differential privacy if and only if any two neighboring datasets \(D_{1}\) and \(D_{2}\) differing on at most one record and for any possible output \(O\in Range(f)\):_  
\[\Pr(f(D_{1})=O)\leqslant e^{\epsilon}\times\Pr(f(D_{2})=O) \tag{1}\]  
_where the possibility is taken over the randomness of \(f\), \(Range(f)\) denotes the set of possible outputs of the mechanism, and \(\epsilon\) is called the privacy budget._  
In this work, \(f\) is the trajectory synthesis algorithm, UDPT, which takes an actual trajectory dataset \(D_{A}\) as input and outputs an obfuscated trajectory dataset \(D_{O}\), i.e., \(f(D_{A})=D_{O}\). The any two neighboring databases \(D_{1},D_{2}\) are expressed as any two trajectory datasets \(D_{A},D^{\prime}_{A}\) differing on at most one location record \(l_{t}\in D_{A}\), i.e., \((D_{A}-D^{\prime}_{A})\bigcap(D^{\prime}_{A}-D_{A})=\{l_{t}\}\). The \(Range(f)\) means the set of all possible outputs of \(f\), and \(O\) the any possible output in \(Range(f)\). According to Equation (1), when the output of UDPT is insensitive to the change of any single location record in the actual trajectory dataset, we say that UDPT is \(\epsilon\)-differentially private. In other words, untrusted analyzers cannot derive the existence of any single location record, including the record on sensitive locations, e.g., home and hospital, in the actual dataset \(D_{A}\) by observing the obfuscated trajectory dataset \(D_{O}\), so individual location privacy is protected.  
In addition, \(\epsilon\) is privacy budget of the data curator. Generally, a smaller \(\epsilon\) leads to larger randomness, which further results in a stronger privacy guarantee while a poor preservation of data utility. Therefore, \(\epsilon\) can be used to tune the trade-off between privacy and data utility of UDPT.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 2 Study Site and Database  
### Description of the Study Site  
The study site is located on the Kairouan plain (9\({}^{\circ}\)23\({}^{\prime}\)-10\({}^{\circ}\)17\({}^{\prime}\) E, 35\({}^{\circ}\)1\({}^{\prime}\)-35\({}^{\circ}\)55\({}^{\prime}\) N; Figure 1), in Central Tunisia [30]. This region is exposed to a semi-arid Mediterranean climate, characterized by a dry summer season, with precipitation occurring mainly in autumn and spring, leading to an annual average rainfall of approximately 300 mm. This precipitation is highly variable in time and space, with frequently occurring drought events [30]. The average annual temperature is 19.2 \({}^{\circ}\)C, with the minimum occurring in January and the maximum in August. The average potential evapotranspiration is close to 1600 mm. The landscape in this area is mainly flat and the vegetation is dominated by agricultural plants (cereals, olive trees, and market gardens). Various different cereal crops are grown, and their rotation is typical of semi-arid regions. The Kairouan plain aquifer is the largest in central Tunisia. Surface and groundwater streams are drained into the Sebkha Kelbia, a large salt lake.
## 4 Analysis and Results
### Informal Security Analysis
#### 4.1.2 Resistance to Replay Attacks  
In the proposed scheme, a replay attack is prevented due to the random numbers involved in or entered into the formation of every transmitted message during the authentication and session key phases, where random numbers cannot be extracted by the adversary. For example, let us assume that the adversary captures and replays the message containing the ciphers C1, C2, C3, \(\text{AID}_{\text{C}}\), and Hash2 to [PERSON]. Upon responding to this message by the AM, the adversary will receive the Verifier, which is generated from the random numbers R2 and Na. In this case, the adversary is unable to extract Na because it cannot obtain R2. Furthermore, R2 cannot be computed because it is generated from H (\(\text{MID}_{\text{C}}\)), which is unknown to the adversary. Therefore, the adversary is unable to compute Hash3 and Verifier and cannot proceed to start the mutual authentication with B. This scenario of the attack is depicted in Figure 10.  
Figure 10: Replay attack.  
Figure 9: Impersonation attack.
## 3 Result and Discussion
### FE-SM with EDAX  
In Figure 1d, pictures of the synthesized \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles obtained using FE-SEM are displayed. The \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles in the FE-SEM image are distributed uniformly and have a spherical shape. The \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles had an average particle size of 28.17 nm. The fabrication of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles is depicted in Figure 1e as a histogram of the particle size distribution. Figure 1f displays the EDAX pattern. The components that made up the resulting \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles were iron (Fe) and oxygen (O).
## 4 Centralized Scheduling Algorithm for the IEEE802.15.4e TSCH Network  
The IEEE802.15.4e standard explains how the MAC layer executes a schedule, but it does not specify how to create/make the schedule. Scheduling can be done by a centralized or distributed system. In a centralized scheduling system, there is special node responsible for generating and maintaining network schedules. This particular node is called master node. Each node on the network reports its latest condition to the master node. The conditions that are reported can contain connectivity information or the amount of data queuing in each node [27]. Information entered at the master node will be the basis for the master node to do scheduling. After the scheduling decision is made, the master node will inform the schedule to all nodes in the network.  
As noted in the introduction, there have been many centralized scheduling algorithms for TSCH networks. This section will explain some of the centralized scheduling algorithms that will be tested for performance using the TLS-VaD. The scheduling algorithms are IRByTSA, the first top scheduling algorithm (FTSA), and first leaf scheduling algorithm (FLSA). FTSA and FLSA are relatively simple new algorithms used to compare with IRByTSA. Other researchers who are developing link-scheduling algorithms can use the TLS-VaD as a performance test tool.
## 3 Results and Discussion  
### Determination of RHCM Mold Temperature  
The mold temperature is to be heated to around the polymer glass transition temperature in RHCM. The glass transition is an inherent property of amorphous polymer material, and it is also a macroscopic reflection of the transformation of macromolecular motion forms. According to [PERSON]'s free volume theory [23; 24], below \(\mathrm{T_{g}}\), the volume expansion of the polymer is only the expansion of the occupied volume as the temperature increases, while above \(\mathrm{T_{g}}\), the free volume begins to expand. Therefore, \(\mathrm{T_{g}}\) can be obtained by measuring the inflection point on the polymer-specific volume-temperature curve. Between 525 and 250 K, the NPT ensemble was used to simulate the phase-cooling of long-chain and short-chain PC systems. The data of the PC-specific volume changing with temperature was extracted for linear fitting to obtain two straight lines, whose intersection point was \(\mathrm{T_{g}}\)  
Figure 1: Simulation models. The blue lines indicate the simulation cell. (**a**) Short-chain polycarbonate (PC) model; (**b**) interface model between short-chain polymer and nickel mold insert; (**c**) long-chain PC model; and (**d**) interface model between long-chain polymer and nickel mold insert.  
As can be seen from Figure 2, the glass transition temperatures of long-chain and short-chain PC materials are significantly different. The molecular weight of the N = 25 system has exceeded the critical molecular weight, and the T\({}_{\text{g}}\) reaches 417 K, which is close to the experimental value [25]. However, the T\({}_{\text{g}}\) of the N = 5 system is only about 358 K, which is because the system with low molecular weight contains more chain ends, and the chain end has a larger free volume and stronger activity than the chain middle, and so the glass transition of low molecular weight polymer takes place at a lower temperature. As the molecular weight increases, the T\({}_{\text{g}}\) increases. When the critical molecular weight is exceeded, the chain end concentration is already very small, and T\({}_{\text{g}}\) will not change obviously. Thus, it can be seen that the simulation results are reliable, and the mold temperature in RHCM is set to 415 K.
## 2 Dynamic Modeling
### Governing Equations
\tag{4}\]  
In (3), \(\mu\) denotes the lift to torque coefficient, \(\mathbf{I}_{B}\) denotes the moment of inertia of the quadrotor without rotors, \(\mathbf{a}_{o}\) denotes the acceleration of the quadrotor, \(\mathbf{r}_{ci}\) denotes the vector pointing from \(O\) to the \(C.G.\) of \(i_{th}\) propeller, \(\mathbf{I}_{p}\) denotes the moment of inertia of the propeller, \(\boldsymbol{\omega}_{pi}\) denotes the angular velocity of \(i_{th}\) propeller. In this paper, the propellers' dynamics are also taken into consideration, which makes the equation derivation complicated, although these additional terms may be negligible since many papers do not include the effect of the propellers and some of the terms relative to the propellers are relatively small comparing to the quad-rotor. Nevertheless, in this work, we are trying to model for the quadrotor in detail and observe the terms that would affect the rotational motion of the drone. Moreover, for flight properties analysis considering different rotor space configurations readers can refer to [35]. Equation (3) represents the body frame rotational dynamics, which means one could get the angular velocity at different instants. From the control point of view, the global orientation of the drone should be calculated. The relationship between the variation of the Euler angle and the body frame angular velocity is described by [36]:  
\[\underbrace{\left[ \begin{array}{c}\dot{\phi}\\ \dot{\theta}\\ \dot{\psi}\end{array} \right]}_{\dot{\eta}}=\underbrace{\left[ \begin{array}{ ccc}1&\sin\phi\tan\theta&\cos\phi\tan\theta\\ 0&\cos\phi&-\sin\phi\\ 0&\sin\phi\sec\theta&\cos\phi\sec\theta\end{array} \right]}_{\mathbf{H}} \underbrace{\left[ \begin{array}{c}\omega_{Bx}\\ \omega_{By}\\ \omega_{Bz}\end{array} \right]}_{\boldsymbol{\omega}_{B}} \tag{5}\]  
Differentiating Equation (5) gives:  
\[\ddot{\boldsymbol{\eta}}=\dot{\mathbf{H}}\boldsymbol{\omega}_{B}+\mathbf{H} \dot{\boldsymbol{\omega}}_{B} \tag{6}\]  
Substituting Equation (3) into Equation (6) gives the complete dynamics for quadrotor represented as follows:  
\[\ddot{\boldsymbol{\eta}}=\dot{\mathbf{H}}\mathbf{H}^{-1}\dot{\boldsymbol{\eta} }+\mathbf{H}\mathbf{J}^{-1}\Big{(}\boldsymbol{\tau}_{\eta}-\mathbf{P}+\mathbf{ f}_{\eta}\Big{)} \tag{7}\]
## 2 Mathematical Equations of the Coupled Dynamics Theory of a Moored FWT
It appears that, although the FEA-based models can reach a good accuracy in prediction of the loads over a wide range of conditions, they are not computationally cost-effective [23]. In a lumped-mass model, the cable mass is discretized into point masses at every separate node by assigning it half of the combined mass of two adjacent cable segments. The complete equation of motion for each node \(i\) is [23]  
\[(\mathbf{m}_{i}+\mathbf{a}_{i})\widetilde{\mathbf{r}}_{i}=\mathbf{T}_{i+(1/2)}-\mathbf{T}_{i-(1/2) }+\mathbf{B}_{i+(1/2)}-\mathbf{B}_{i-(1/2)}+\mathbf{W}_{i}+\mathbf{F}_{i}+\mathbf{D}_{p,j}+\mathbf{D}_ {q,i}, \tag{4}\]  
where \(\mathbf{m}_{i}\) and \(\mathbf{a}_{i}\) stands for the mass and added mass assigned at the node \(i\), \(\mathbf{T}\) and \(\mathbf{B}\) stands for the internal stiffness and damping, \(\mathbf{W}\) and \(\mathbf{F}\) stands for the line net buoyancy and seabed contact, \(\mathbf{D}_{p}\) and \(\mathbf{D}_{q}\) stands for the hydrodynamic drag force in the transverse and tangential directions, respectively. All of the above symbols in bold are \(3\times 3\) matrices. Regarding the difference of the two mooring-line modelling methods, studies have been done previously by many other researchers. [PERSON] et al. [15] stated that a quasi-static approach is not suitable to study the dynamic behavior of WECs when the mooring has significant nonlinearities, e.g., especially for synthetic ropes. It was later proved by their experimental testing of moorings for large floating wave energy converters in the wave basin at Aalborg University, in which a large 52% deviation was found by comparing with their measured line tension [24]. [PERSON] et al. [25] concluded that mooring dynamics have an influence on platform motions only when those motions are large, but are always important to the prediction of mooring line loads. [PERSON] et al. [26] performed a thorough quantified comparison between the dynamic mooring and the quasi-static methods, showing that the lumped-mass method predicts smaller platform motions but larger mooring line tensions in the extensive presented results.