esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Topology Control
### Post-Deployment
#### 4.3.2 Link Quality  
Increasing the network connectivity by power control is one way to improve the WSNs' resilience, but the individual link quality is also important for resilient WSNs. Many power control approaches are employed to improve the link quality, thereby improving the reliability of the link. Higher transmission power helps to improve the data transmission reliability, but also introduces higher energy consumption and interference. There is a tradeoff among energy efficiency, link quality and interference.  
There are papers devoted to conduct practical experiments to improve link quality, such as [38, 91, 92, 93]. Adaptive and Robust Topology control (ART) [38], an adaptive topology control protocol, is based on extensive empirical study of the link quality in the indoor environment. It adapts the transmission power in response to variations in the link quality triggered by either environmental changes or varying degrees of network contention. ART monitors the outgoing packages and records the transmission failures during a defined time window. If the transmission failure is out of the defined threshold, ART increases or decreases the transmission power. One of the advantages is that ART does not always increase the transmission power to improve the link quality, because, in practice, it is not always true under high contention and interference. Instead, it may decrease the transmission power under high contention. The work in [91] proposes a protocol that applies a control-theoretic approach to control the packet reception ratio (PRR) directly. The receiver monitors all incoming packages and records the package transmission failures and then computes the transmission power needed for the next round. This information will be sent to the sender side. The work in [93] adjusts the transmission power by measuring the average received signal strength indicator (RSSI) and comparing with upper and lower thresholds to decide to increase or decrease the nodes' transmission power. Adaptive transmission power control (ATPC) [92] employs a feedback-based transmission power control algorithm to dynamically maintain individual link quality over time. It makes proposals to the protocol with the idea of minimizing the power level by choosing the proper transmission power for each package transmitted, while guaranteeing the desired link quality. First, [92] reveals the relation between the transmission power and link quality indicator RSSI and link quality indicator (LQI), which is generally monotonic and continuous, but not deterministic. There are fluctuations in a small range at any transmission level. Based on empirical results, [92] formulates a predictive model to characterize the relation between transmission power and link quality. With this predictive model, the sender node adjusts the transmission power in order to guarantee a specific receiver the desired link quality. The work in [94] provides a localized configurable topology control (CTC) algorithm to achieve the desired path quality bounds in lossy WSNs.
## 5 Conclusions  
Based on the described research, it can be concluded that:  
* A strong correlation was found between M\({}_{\rm{r}CTT}\) and M\({}_{\rm{r}FWD}\) for both base layers as well as subbase layers.
* The indicative values of CBR\({}_{\rm{sat}}\) were also determined. For the monitored unbound mixtures, they ranged between 117% and 231%.
* The values of M\({}_{\rm{r}CTT}\) and M\({}_{\rm{r}FWD}\) for the subbase layers are comparable.
* Using the innovative FWD device, it is possible to reveal locally non-durable points at the edge of the pavement during pavement diagnostics.
* It was verified that the innovative FWD device was usable specifically for pavements with lower traffic load, at which the problematic durability of the pavement edges can be assumed.
* Additionally, during the phase of deflections evaluation, it was possible to label a point as non-durable if there were differences in deflections in the horizontal and transversal directions larger than approximately 20%. This criterion can be used in technical regulations dealing with pavement diagnostics during their reconstructions.  
An auxiliary cross frame can have various uses in the measurement of durability. It provides advantages when enlarging the width of roads with various pavement structures in the cross section or in pavements with neglected maintenance from the perspective of correct drainage function.  
Conceptualization, D.S. and L.M.; methodology, M.V., P.H. and O.D.; software, R.K. ([PERSON]), J.S. and P.V.; validation, D.S., P.H., O.D., L.M. and M.V.; formal analysis, M.V. and K.S.; investigation, K.S.; resources, P.H. and O.D.; data curation, M.V. and R.K. ([PERSON]); writing--original draft preparation, D.S., P.H., O.D., L.M., R.K. ([PERSON]), R.K. ([PERSON]), J.S., P.V., K.S. and M.V.; writing--review and editing, D.S., P.H., O.D., L.M., R.K. ([PERSON]), R.K. ([PERSON]), J.S., P.V., K.S. and M.V.; visualization, R.K. ([PERSON]), J.S., P.V. and K.S.; supervision, M.V.; project administration, O.D.; funding acquisition, D.S., P.H. and O.D. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the Technology Agency of the Czech Republic with Project CK01000158 \"Use of higher amounts of reclaimed material in asphalt mixtures with PmB bitumen\", Project CK03000169 \"Development of road construction layers with optimized grain size replacing insufficient aggregate fractions\", and Project TH04030342 \"Recycled building materials for transport infrastructure construction\".  
Not applicable.  
The authors declare no conflict of interest.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 4 Experimental Results
### Remote Sensing Image
Figure 9 shows contour line overlay images of remote sensing image classification results, where each column represents algorithms FCM, FCM_S, FGFCM, RFLICM, GK-WFLICM, MSHLICM, VT-HMRF-FCM, and the proposed FVT-FCM. The red line can reflect the fitting effect of the boundary in the classification results. It shows that pixel-based algorithms perform better in fitting boundaries than region-based. In these two region-based algorithms, VT-HMRF-FCM, and the proposed FVT-FCM, the proposed FVT-FCM has a better ability to fit uncertain boundaries than VT-HMRF-FCM, as shown in Figure 9(h1-h6). Table 5 shows the quantitative evaluation of the remote sensing image classification results; the accuracy comparison of each image is also shown in Figure 10. It can be seen that Kappa's trend is the same as that of OA. For OA, pixel-based algorithms are sensitive to different images. For complex images with texture features, similar colors, and strong noise, the OA of pixel-based algorithms can be as low as 50%, Kappa is around 0.4. In contrast, the accuracy of region-based methods is generally stable at a high level, exceeding 90%. The proposed method with a simple model can achieve or even outperform the accuracy of the VT-HMRF-FCM with a complex model. Figure 9: Contour line overlay images of remote sensing image classification results. Table 6 shows the boundary accuracy of the remote sensing image classification results, and the comparison of boundary accuracy trends of various algorithms is shown in Figure 11. Figure 11a-\(f\) represents image1-6, respectively. As PA increases, UA generally shows a downward trend. When PA and UA are in equilibrium, OA reaches the maximum value. Normally, the UA and PA of VT-HMRF-FCM and the proposed algorithm can balance at a relatively high value. Thus, their OA is higher than other pixel-based algorithms. The proposed method adopts a hierarchical fuzzy membership model, which can further accurately describe boundary effects. Thus, it has higher boundary accuracy, as shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c
## 2 Preliminary Design Based on Theoretical Calculation
### Theoretical Calculation Based on Subsection Model  
Supercritical carbon dioxide cannot be treated as a constant property's fluid during the theoretical design process since the thermophysical properties vary greatly with temperature in a supercritical state. In order to simulate the variation of the properties of SCO\({}_{2}\), a subsection model based on equal heat transfer power is used for the theoretical calculation. Figure 1 shows the principle of the subsection model. The heat exchanger (HE) is initially divided into several heat exchanger units (HEUs) with the same heat flux where the properties of both hot and cold fluids are considered to be constants. The input parameters of the back HEU are given by the front one. In the subsection process, each unit is assumed to have undergone the same amount of heat exchange.  
The thermodynamic model of HE introduced in Section 2.2 and the subsection model based on equal heat transfer power are written into a program by MATLAB language to carry out the design calculation of the preliminary HE. The working condition of the preliminary HE is listed in Table 1. In the program, the thermophysical properties of liquid lead are calculated according to correlations in Table 2, and the thermophysical properties of SCO\({}_{2}\) are from the NIST database. In addition, the flow velocity of liquid lead is limited  
Figure 1: Diagram of subsection model.  
to less than 1 m/s, the flow velocity of SCO\({}_{2}\) is lower than 5 m/s, and the pressure drop on both the tube-side and shell-side is below 50 kPa are restrictions in the design.  
The preliminary designed results on HE are shown in Table 3.  
To verify the accuracy of the preliminary design results of HE, the subsection model based on equal length was used. The total length of HE obtained from the design calculation is set as the input parameter in the subsection model based on equal length. This model assumes that the length of each HEU is the same and calculates the total heat transfer rate by the length of HE. Theoretically, the total heat transfer rate calculated by the subsection model based on equal length should be the same with the heat transfer rate in Table 1 since the thermodynamic model and boundary conditions are the same. The verification process of the preliminary HE is also written into a program by MATLAB language based on the subsection model based on equal length and the results are shown in Table 4.  
It can be seen from Table 4 that the error between the verification result and the boundary condition in Table 1 is within 3%. This result verifies the preliminary design of HE based on the subsection model with equal heat transfer power so that the preliminary HE can be used for subsequent optimization design.
## 2 Problem Statement of Multi-Robot Robust Motion Planning
### Multi-Robot Robust Motion Planning Problem  
The multi-robot motion planning (MMP) problem is generally defined as a multi-objective optimization problem with different types of constraints [4; 5]. Thus, the general form of multi-robot robust motion planning is modeled as a multi-objective optimization problem in this paper. Section 2.1 shows the robot model constraint, and Section 2.2 gives safety constraints. What is more, the control input of each robot is constrained by its maximum velocity, maximum acceleration, maximum angular velocity, and maximum angular acceleration. Meanwhile, the position and yaw at the initial time constrain the initial state of each robot. Based on the above discussion, the multi-robot robust motion planning problem can be defined as follows.  
\[\min J(X,U) \tag{7a}\] \[s.t.:\mathbf{x}_{m,k+1}=f(\mathbf{x}_{m,k},\mathbf{u}_{m,k})+\mathbf{N}_{m,k}^{ noise},\forall m\] (7b) \[\mathcal{P}(\mathbf{x}_{m,k}\
otin C_{mo,k})\geq 1-\delta_{o},\forall m\] (7c) \[\mathcal{P}(\mathbf{x}_{m,k}\
otin C_{inter\_mm,k})\geq 1-\delta_{r},\forall m\] (7d) \[0\leq v_{m,k}\leq v_{\max}^{m},\left|w_{m,k}\right|\leq w_{\max}^{m },\forall m\] (7e) \[\left|v_{m,k+1}-v_{m,k}\right|\leq a_{\max}^{m},\left|w_{m,k+1}-w_{m,k}\right| \leq a_{\max}^{m},\forall m\] (7f) \[\mathbf{x}_{m,0}=\mathbf{x}_{m}(0),\forall m \tag{7g}\]  
In Equation (7), \(m\in\{1,2,\dots,N\}\). Equation (7a) is the cost function. In this paper, we define \(J\) as a cost function that forces the robot to stay near the reference path and track it synchronously. The exact form is given in Section 4.3.3. In Equation (7a), \(X=\{[\mathbf{x}_{1,0},\dots,\mathbf{x}_{N,0}],[\mathbf{x}_{1,1},\dots,\mathbf{x}_{N,1}],\dots, [\mathbf{x}_{1,M},\dots,\mathbf{x}_{N,K}]\}\) and \(U=\{[\mathbf{u}_{1,0},\dots,\mathbf{u}_{N,0}],\)\([\mathbf{u}_{1,1},\dots,\mathbf{u}_{N,1}],\dots,[\mathbf{u}_{1,M-1},\dots,\mathbf{u}_{N,K-1}]\}\). The kinematic model of the robots with disturbances is presented as Equation (7b). Equation (7c,d) model the safety constraints. Equations (7e,f) provide control input constraints. The initial state constraint is denoted by Equation (7g).  
The key difficulty in solving this problem is how to deal with the safety constraints (7c,d). The safety constraints (7c,d) are nonconvex constraints represented by the integral of a multivariate Gaussian distribution. These types of constraints are called chance constraints. They are intractable to handle in the usual case. To make the problem easy to solve, double-layer corridors are devised. Then, the problem can be transformed into a deterministic optimization problem.
## 5 Proposed Method
### Identifying Traffic Conditions
#### 5.1.1 Calculating the Connection Quality of Two Vehicles
Equation (8) is used to calculate this estimation recursively:  
\[WMEWMA^{t}=\beta\left(WMEWMA^{t-1}\right)+(1-\beta)X^{t} \tag{8}\]  
Figure 6: The movement of vehicles in the opposite direction (**a**) \(Vehicle_{i}\) approaches \(Vehicle_{j}\). (**b**) \(Vehicle_{i}\) gets away from \(Vehicle_{j}\). where \(X^{t}\) is the value of \(X\) at the moment \(t\). Note that Equation (8) can be rewritten as Equation (9):  
\[X(l)=\beta\frac{\sum\limits_{k=t-L_{window}}^{t-1}X^{k}}{L_{window}}+(1-\beta)X^ {t} \tag{9}\]  
WMEWMA has two control parameters, namely window size (\(L_{window}\)) and the control coefficient \(\beta\) so that \(0\leq\beta\leq 1\). Each window records the last \(L_{window}\) values. When \(\beta\) has a higher value, this estimation is more dependent on historical values. This means that the estimation is more stable. In contrast, if \(\beta=0\), the average value depends only on the last value. This scale can create a stable estimation and is calculated easily. * **Hello packet reception rate (\(\eta_{ij}\)):** The quality of the link between \(Vehicle_{i}\) and \(Vehicle_{j}\) is measured based on the ratio of the hello packages received by \(Vehicle_{i}\) to all hello packets transmitted by \(Vehicle_{j}\) at the time interval \(\Phi\). Each node uses a counter to count the number of hello messages received from its neighbors. Moreover, the hello broadcast time is equal to the specified time frame \(\tau\). As a result, the number of hello messages sent by \(Vehicle_{j}\) can be calculated in a certain time \(\Phi\). Therefore, each vehicle can calculate the hello reception rate according to Equation (10): \[\eta_{ij}=\frac{\sum_{l\in\Phi}R_{hello}(i,j)}{\sum_{l\in\Phi}S_{hello}(i,j)}\] (10) where \(R_{hello}(i,j)\) and \(S_{hello}(i,j)\) are equal to the number of received hello messages and the number of sent hello messages, respectively. Then, each vehicle uses Equation (9) to refresh the hello reception rate (\(\eta_{ij}\)) using the WMEWMA method.
It is crucial that the pilots of military aircraft have access to updated information about the location of friendly and hostile aircraft. This kind of information is provided through so-called _situation awareness (SA) data_. SA data contain, among other things, the location, direction and speed of friendly and hostile aircraft in an area.  
We define the concept of the _leaked packet_. A leaked packet is a unit of data transmitted with a radio carrier wave that can be detected by an adversary aircraft. A packet is considered to be leaked even if the received radio signal to noise ratio (SNR) is so low that the adversary cannot receive and decode the data. Data on tactical links is typically encrypted, which makes decoding infeasible. However, it is enough that the adversary can discern the radio signal carrying the packet from electromagnetic noise. Adversaries can use this information to track the position of the transmitting aircraft.  
In this paper, we present a routing protocol for airborne tactical networks. The protocol is called the _Hostile-Direction Aware Routing Protocol (HDARP)_. The protocol requires nodes to be equipped with directional antennas and makes use of SA data to route packets from the sender to the receiver (including multi-hop routing when needed), while at the same time avoiding adversary detection/positioning by eliminating leaked packets. HDARP is intended to be used together with the widely adopted TCP/IP stack, with minor adaptions for directed communication. HDARP is protected by patent application SE 2200136-6.
## 3 Results  
The length of the cutting flute was found to significantly influence (\(p<0.05\)) the screw insertion torque, with significant differences observed between groups with the same number of flutes but different flute lengths: A and C, B and D, E and G, and F, and H. A lower mean maximum insertion torque was observed for the screws with a longer cutting flute and with three cutting flutes instead of two (Figure 3). The double-lead thread design produced a larger mean maximum insertion torque than the single-lead thread (\(p<0.05\)), but was able to reach specific depths using half the number of revolutions (Figure 4). The number of cutting flutes on the screw was also found to significantly influence (\(p<0.05\)) the insertion torque, but only in Groups G and H (Figure 3).  
The mean pullout strength of Group D was significantly (\(p<0.05\)) greater than all other screw designs (Table 2, Figure 5). The cutting flute length, cutting flute quantity, and number of thread leads were found to significantly influence the pullout strength (\(p<0.05\)). Incorporating three cutting flutes into the screw design increased the pullout strength over the screw with two flutes when using the same cutting flute length and lead thread. The groups with a single-lead threaded screw were also found to produce a greater pullout strength than the double-lead screws.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Group**} & \multicolumn{3}{c}{**Average Insertion Torque (Nm) during Initial**} & \multirow{3}{*}{**Average Max. Insertion**} & \multirow{3}{*}{**STD**} & \multirow{3}{*}{**Average Pullout Strength (N)**} & \multirow{3}{*}{**STD**} \\  & & & & & & & \\ \cline{2-2} \cline{6-8}  & **1 st Rev.** & **2 nd Rev.** & **3 rd Rev.** & **4 th Rev.** & & & \\ \hline A & 0.100 & 0.294 & 0.586 & 0.902 & 2.365 & 0.093 & 1766 & 15.8 \\ B & 0.110 & 0.323 & 0.610 & 0.923 & 2.415 & 0.103 & 1809 & 20.7 \\ C & 0.100 & 0.294 & 0.561 & 0.833 & 2.2 & 0.034 & 1919 & 33.2 \\ D & 0.103 & 0.317 & 0.589 & 0.858 & 2.17 & 0.042 & 1967 & 34.0 \\ E & 0.299 & 0.928 & 1.605 & 2.143 & 2.974 & 0.115 & 1606 & 14.3 \\ F & 0.300 & 0.895 & 1.566 & 2.101 & 2.929 & 0.037 & 1645 & 18.8 \\ G & 0.259 & 0.847 & 1.415 & 1.906 & 2.568 & 0.091 & 1747 & 30.4 \\ H & 0.282 & 0.849 & 1.360 & 1.790 & 2.36 & 0.028 & 1791 & 35.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean results of insertion torque and pullout strength in each group.  
Figure 4: Plot of screw insertion torque and displacement for Groups D and H.  
Figure 5: A bar chart of the pullout strength for each screw design. Error bars represent plus or minus one standard deviation.
## 4 Method for Detecting Contradictions
### Contradictions--Subcategories  
The suggested categories are shown in Figure 4:  
The terms Simplex, Idem, and Alius are described below. It should be noted that requirements can be formulated as \"condition + conclusion\" as well as inverted as \"conclusion + condition\".  
Simplex (lat. = simple) refers to contradicting requirements without conditions (non-causal):  
* The car must be red: \(x\stackrel{{!}}{{=}}k\).
* The car must be blue: \(x\stackrel{{!}}{{=}}c\).  
Idem (lat. = same) refers to contradicting causal requirements with the same conditions or pairs where only one requirement has a condition:  
* If the customer wishes, the car must be red: \(A\Rightarrow x\stackrel{{!}}{{=}}k\).
* If the customer wishes, the car must be blue: \(A\Rightarrow x\stackrel{{!}}{{=}}c\).  
Alius (lat. = different) refers to contradicting causal requirements with the different conditions (causal):  
* The car must be red if the customer wishes it to be: \(A\Rightarrow x\stackrel{{!}}{{=}}k\).
* The car must be blue if the car has four doors: \(B\Rightarrow x\stackrel{{!}}{{=}}c\).  
The last example is a contradiction because the conditions of both requirements can be fulfilled at the same time (they are independent of each other) and the conclusions would then contradict each. Also, this is an example where the condition and conclusion have been inverted in order.  
If one requirement is non-causal and the other is causal, the contradiction as a whole is said to be causal.  
\"Contradictory\" refers only to the effect, not to the requirement as a whole. If the effects are contradictory but the requirements as a whole would be contrary, we still refer to them as contradictory here.  
The following Table 1 lists all types of contradiction in their formalized form. The column \"Multiple condition\" shows examples of formalized requirements with multiple conditions. It is not an exhaustive list of all possible multiple conditions:  
Figure 4: Classification of contradictions.  
If a condition is composed of two or-statements, it can be split into two sentences, as intermediate. This will be shown in Section 5.2.4_Alius Contrary_. Each partial cause can then separately be considered with the effect. From \(A\lor B\Rightarrow x\stackrel{{!}}{{=}}c\) follows \(A\Rightarrow x\stackrel{{!}}{{=}}c\) and \(B\Rightarrow x\stackrel{{!}}{{=}}c\). This facilitates the comparison of requirements that consist of compound conditions.
# Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques
## 2 The principle and structure of CFRP-packaged FBG sensors  
### The Principle of the FBG Sensor  
The FBG sensor is a type of optical fiber sensor whose refractive index changes periodically along the fiber axis, and its structure is composed of cladding, grating, and optical fiber (see Figure 4). When broadband light passes through the FBG sensor, the incident light spectrum whose wavelength meets the fiber Bragg reflection condition will be reflected, and the rest will be transmitted through, from the other end of the fiber core [22; 23].  
The wavelength of the light spectrum that satisfies the reflection condition can be written as follows:  
\[\lambda_{B}=2n_{eff}\Lambda \tag{1}\]  
where \(\lambda_{B}\) is the center wavelength of the FBG sensor, \(n_{eff}\) is the effective refractive index, and \(\Lambda\) is the length of the period. When the stress to which the FBG sensor is subjected changes, the length of period \(\Lambda\) as well as the refractive index \(n_{eff}\) will change, resulting  
Figure 4: The structure of FBG sensors.  
Figure 3: CFPP-packaged FBG sensors in WTB full-scale structural testing.  
in a change in the center wavelength \(\lambda_{B}\). By measuring the center wavelength \(\lambda_{B}\) before and after external stress is applied, the unknown physical quantity being measured can be obtained. However, when the FBG sensors are packaged by CFRP and pasted on the blade surface, the stress to which the FBG sensor is subjected will be influenced by the packaging layer and bonding layer, and the effective refractive index \(n_{eff}\) and the length of the period \(\Lambda\) will not be equal to the original ones. Moreover, if the packaging layer and bonding layer are damaged or degraded, the measurement accuracy cannot be assured.