esa-sceva/satcom-chunk-collection huggingface dataset:
# Hiding Stealth Optical CDMA Signals in Public BPSK Channels for Optical Wireless Communication
## The Theories and Principles Background of the Proposed Stealth Communication Approach
### Free Space Optical Communication (FSO)  
FSO is an optical communication technique for transmitting data in free space, such as air, vacuum, or outer space. The setup of an FSO system is similar to that of an optical fiber cable (OFC) network. The only difference between them is that the optical beams in an FSO network are transmitted through free air compared with glass in OFC networks. As shown in Figure 4, the fundamental structure of an FSO system comprises a laser, an electro-optic modulator (EOM), a lens design, and a detector. The laser is modulated using the EOM and the laser beam is converged onto the receiver by the lens.  
FSO system performance is dependent on the transmission medium because of the presence of foreign elements such as rain, fog, haze, physical obstructions, scattering, and atmospheric turbulence [28].
## 4 Verification and Validation
### Implementation of the Proposed Model in a Construction Example
#### 4.2.1 Overview of the Hypothetical Scenarios
Since it is a building information model, all details, including the exact locations of the structural elements, are included in the file. The file has a \"medium\" criticality in terms of confidentiality since it includes intellectual property. The most critical aspect of the file is its integrity since a stealthy attacker could tamper with the structural design details and mislead the site teams about the execution. This could potentially cause a reduction in the strength of the structural elements, which might cause a catastrophic failure of the building during the operational phase. Considering that the building is a hospital, the criticality of properly implementing the correct structural design further increases. Finally, the availability of the file has \"medium\" importance since the unavailability might cause disruptions to site activities, which might indirectly lead to financial loss. The accessibility of the file is \"medium\" as there are a considerable number of project participants, such as the structural design team, quantity surveyor, and cost estimator, who need this information to perform their tasks. From the malicious insider's perspective, the file is not as valuable as the cost and schedule forecast file. However, he/she can attack the file with ransomware and threaten the project by permanently destroying it or leaking its content. Considering that the file has medium-level sensitivity in terms of confidentiality and availability, it is also of medium importance to the malicious insider. The \(\omega\) values for each cybersecurity attribute of each file were assigned based on Figure 9 and the average \(\omega\) was the arithmetic mean of the three different \(\omega\) values. The cost of cyberattack \(\left(C_{cyb}\right)\) was calculated using the method presented previously. For the cost and schedule forecast file in Scenario 1, the file security level was calculated by multiplying the project security level, 0.7, by the file accessibility level, 0.9, which resulted in 0.63. Since the malicious insider had an expertise level of \"Advanced beginner\", which corresponded to a value of 0.3 (see Table 3), subtracting this from the file security level, 0.63, gave us the value for the cost of the cyberattack \(\left(C_{cyb}\right),0.33\). This means that the malicious insider will need additional resources to compromise the cost and schedule forecast file in Scenario 1. The values for the structural design file in Scenario 1 are given in Table 6 and the values for both files in Scenario 2 are presented in Table 7. The BNE corresponding to these scenarios were computed based on the flowchart of BNE (see Figure 4) and are also presented in Tables 6 and 7.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 2 System Description  
The BOC signal is received using dual estimation loops, including a code phase tracking loop and the subcarrier phase tracking loop, which are subject to sampling distortion. The sampling distortion of the BOC signal includes resolution errors and zero-bias, which are modeled as follows.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 5 Evaluation  
In this section, we provide a detailed evaluation of the two introduced approaches for OSDC with BLE, considering the influence of the main BLE parameters and the contact time, for a time between two consecutive contacts of one day. We first use the current consumption measurements on real BLE devices described in the previous section to feed the sensor node current consumption analytical model, for both the advertisement- and connection-based approaches. Results are used to derive the sensor node lifetime in each case. We then compute the theoretical maximum amount of collected data per contact interval, and determine data collection efficiency in terms of energy consumed per collected data bit. We also evaluate and discuss the influence of BER on all considered performance parameters. Finally, we investigate the limitations that real hardware poses to the maximum amount of collected data per interval, by performing experiments. Packet and payload sizes considered for the evaluation are shown in Table 3.
## 4 Optimization of Battery Pack Area Based on Stud Genetic Algorithm
### Establishment of Objective Function and Constraint  
According to the analysis in Section 3, if \(\Delta T_{\text{max}}\) was within 5K when the inlet airflow rate was the smallest, it would be satisfied under other large inlet airflow rates. Therefore, the volume of the battery was optimized when the inlet airflow rate was minimized (Q0 = 0.04). As can be seen from Figure 1, the three-dimensional volume can be simplified to a two-dimensional area (Figure 2b) for optimization.  
According to the analysis in the previous section, it can be concluded that \(d_{0}\) and \(\Delta T_{\text{max}}\) are positively correlated. The value of \(\Delta T_{\text{max}}\) increased first and then decreased as \(w_{\text{dp}}\) increased, and the minimum \(\Delta T_{\text{max}}\) was taken when \(w_{\text{dp}}\) was at a minimum. Figure 2a shows that the battery pack area and the five structural parameters were positively related. Therefore, both \(d_{0}\) and \(w_{\text{dp}}\) were minimized, which reduced \(\Delta T_{\text{max}}\) and saved space. Thus, \(d_{0}\) was set to 0.5 mm and \(w_{\text{dp}}\) was set to 5 mm. The battery area \(S\) can be expressed by:  
\[S=(w_{\text{cp}}+w_{\text{dp}}+l_{b})\cdot(9d_{0}+8d_{c})+0.5(\tan\theta_{\text {cp}}+\tan\theta_{\text{dp}})\cdot(9d_{0}+8d_{c})^{2} \tag{25}\]  
where \(l_{\text{b}}\) is the length of battery with a value of 65 mm, and \(d_{c}\) is the diameter of a single cell with a value of 18 mm.  
As \(\theta_{\text{cp}}\), \(\theta_{\text{dp}}\) and \(w_{\text{cp}}\) increased, the maximum temperature difference gradually decreased, but meanwhile, the battery pack area increased. The target of minimizing the area of the battery pack was optimized under the condition of Q0 = 0.04 m\({}^{3}\)/s. The mathematical description of the optimization problem can be expressed by:  
(1) Objective function:  
\[A\text{rea}=\text{min}S(w_{cp},\theta_{\text{dp}},\theta_{\text{cp}}) \tag{26}\]  
Figure 13: Flow chart of stud GA.  
(2) Structure variables:  
\[\left\{\begin{array}{l}w_{\text{cp}}\in[3\text{mm},30\text{mm}]\\ \theta_{\text{dp}}\in[10^{\circ},20^{\circ}]\\ \theta_{\text{cp}}\in[10^{\circ},20^{\circ}]\end{array}\right. \tag{27}\]  
(3) Constraint:  
\[\Delta T_{\text{max}}\leq 5K \tag{28}\]  
The initial capacity of the sample was designed to be 50. Through the above steps, a new generation of individuals could be obtained, and the pros and cons of individuals in the population could be judged. Thus, when GA evolved to the K th generation, the calculation was stopped if the population converged, otherwise the iterative calculation was continued until the termination condition was satisfied. A flow chart of battery pack optimization is shown in Figure 14.
## 3 Deviation-Angle-Panel-Based Antenna Concept
### Rotation Angle  
From the immediate geometric relation in Figure 5a, we can obtain  
\[\alpha_{\rm m}=\arcsin\frac{r_{\rm c}}{\frac{D_{\rm a}}{2}}=\arcsin\frac{2r_{\rm c }}{D_{\rm a}}. \tag{1}\]  
It can be observed from Figure 5b that the deviation parameter \(k_{\rm d}\) increases and the panel becomes longer in height and narrower at the bottom, causing a decrease in VSE. However, as mentioned earlier, the height of the rocket fairing is much greater than its diameter, so this phenomenon has little influence on the storage of the antenna. In Figure 5a, assume that the polar angle of point \(D_{1}\) is \(\theta_{D_{1}}\); thus, its position vector on plane \(OXY\) is \(\left(r_{\rm c}\cos\theta_{D_{1}},r_{\rm c}\sin\theta_{D_{1}}\right)^{\rm T}\), and the position vector of point \(C_{1}\) on plane \(OXY\) is \(\left(r_{\rm c}\cos\left(\theta_{D_{1}}+\frac{2\pi}{n}\right),r_{\rm c}\sin \left(\theta_{D_{1}}+\frac{2\pi}{n}\right)\right)^{\rm T}\), where \(\theta_{D_{1}}\) is an unknown to be determined (\(\theta_{D_{1}}\) will be used in the next section). Referring to the law of sines [54], we have  
\[\bar{l}_{OD_{1}}\sin(\theta_{D_{1}}-\theta_{A_{1}})=\bar{l}_{A_{1}D_{1}}\sin( k\alpha_{\rm m}), \tag{2}\]  
where \(\bar{l}_{OD_{1}}\) and \(\bar{l}_{AD_{1}}\) are the lengths of projections of line segments \(OD_{1}\) and \(A_{1}D_{1}\) on plane \(OXY\), respectively, and \(\theta_{A_{1}}\) and \(\theta_{D_{1}}\) are corresponding polar angles of points \(A_{1}\) and \(D_{1}\), as well as \(\theta_{A_{1}}=-\frac{\pi}{n}\). Meanwhile, the explicit expressions of \(\bar{l}_{OD_{1}}\) and \(\bar{l}_{A_{1}D_{1}}\) are  
\[\begin{cases}\bar{l}_{OD_{1}}=r_{\rm c},\\ \bar{l}_{A_{1}D_{1}}=\sqrt{\left(\frac{D_{2}}{2}\cos\frac{-\pi}{n}-r_{\rm c} \cos\theta_{D_{1}}\right)^{2}+\left(\frac{D_{2}}{2}\sin\frac{-\pi}{n}-r_{\rm c }\sin\theta_{D_{1}}\right)^{2}},\end{cases} \tag{3}\]  
respectively. It can be found from Equations (2) and (3) that the equation about \(\theta_{D_{1}}\) is a complex nonlinear equation, so [PERSON]'s method [55] can be applied here to solve it. Solving Equation (2) obtains \(\theta_{D_{1}}\) and then the position vector of point \(D_{1}\). According to the manipulation demonstrated in Figure 6, the rotation angle \(\theta_{\rm r}\) can be expressed as  
\[\theta_{\rm r}=\frac{\pi}{2}-\arccos\frac{\mathbf{r}_{A_{1}D_{1}}\cdot\mathbf{r}_{A_{1 }B_{1}}}{|\mathbf{r}_{A_{1}D_{1}}||\mathbf{r}_{A_{1}B_{1}}||}. \tag{4}\]  
Figure 6: Process of pose adjustment. (**a**) Initial pose. (**b**) Intermediate pose. (**c**) Final pose.
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## 5 Control Design
### Control Design with Right-Coprime Factorization  
In this section, the stability of the entire control system is guaranteed by designing a controller based on operator theory for the control target with right factorization [16]. In addition, a controller that compensates for tracking to the reference input in the outer loop is designed to guarantee tracking. The designed controller is shown in Figure 4. Figure 4 shows the control system in vector, for example \(\mathrm{R}_{w}^{-1}=(R_{w1}^{-1},R_{w2}^{-1},R_{w3}^{-1})\). Here, the plants to be controlled are \(D_{a_{n}}^{-1}\) and \(N_{a_{n}}\) representing heat spreaders and \(P_{w_{n}}\) representing tubes. We further define a new operator \(\tilde{N}_{w_{n}}=P_{w_{n}}N_{a_{n}}\).  
In designing the control system, the controller design is performed under the conditions of \(n=(1.3)\), \(k=(1,3)\), and \(n=k\). This is because there are only two regions where the amount of heat absorption can be input from the Peltier device: \(\mathrm{Part\,A_{1}}\) and \(\mathrm{Part\,A_{3}}\). Therefore, the system cools and controls \(\mathrm{Part\,A_{n}}\) by heat transfer from \(\mathrm{Part\,W_{n}}\). All other heat transfers that occur between the other Part are interferences. In this section, the design is performed for the nominal model.  
Design the operators according to the Bezout identity in Equation (A12). Designing the operators \(S_{n}\) and \(R_{n}\) so that they satisfy the Bezout identity compensates for the BIBO stability of the nonlinear system. Then \(S_{n}\) and \(R_{n}\) become right-coprime factorizations. In Equation (A12), \(I\) is the identity operator and outputs the input signal as it is.  
\[S_{w_{n}}\tilde{N}_{w_{n}}+R_{w_{n}}D_{a_{n}}=I \tag{26}\]  
The designed operators are shown in Equations (27) and (28). However, a constant \(B_{k}(0<B_{k}<1)\) is used as the design parameter.  
\[S_{w_{n}} =(1-B_{k})\tilde{N}_{w_{n}}^{-1} \tag{27}\] \[R_{w_{n}} =\frac{B_{k}}{m_{a}c_{a}} \tag{28}\]  
From the above, the BIBO stability of the inner loop is guaranteed. However, the design of Equation (A12) cannot perform reference input tracking. Therefore, the reference input tracking is achieved by designing a tracking compensation controller as in Equation (29). Here, \(K_{P_{n}}\) and \(K_{l_{n}}\) are the design gains.  
\[C_{w_{n}}(e_{n})(t)=K_{P_{n}}e_{n}(t)+K_{l_{n}}\int_{0}^{t}e_{n}(\tau)d\tau \tag{29}\]
## 4 Discussion
### Barriers, Bottlenecks and Gaps to Overcome
#### 4.1.1 TF1 Tools and Means  
Regarding tools to evaluate and implement circular design in construction, the relative weight of circular economy-related credits within the screened rating systems ranges from 3 to 39% of the whole. The most recurrent CE items in credits include life cycle impact, recycled material, waste management, and water reuse/management. Conversely, design for disassembly, flexibility and adaptability, and land reuse are less prevalent compared to others. This in fact reflects findings from the literature, where it is largely argued that most of the attention of policies and tools target only recycled materials or saving actions, rather than flexibility, dismantability, and other \"novel\" design concepts related to CE.  
Figure 10: Graphical synthesis of main barriers and drivers within the systematic implementation pathway of CE in construction.  
This observation is particularly valid for the Italian CAM, where most of the credits are focused on recycled content of materials and components used in the project, and little reference is made to disassembly potential, with no mention of flexible design.  
The reason why many tools (and players in the construction sector) focus mostly on 3R principles is probably related to cost and ease of implementation. In fact, compared to the others, design for disassembly might be more difficult to design and implement, and also may be more expensive in the short-term perspective. For example, \"design for disassembly\" is deemed an excellent way to reuse and recover the same structure or components of a building and to reduce waste after the end of life of buildings, but procedural cost still represents a major problem, as also the low level of knowledge of designers and construction companies on this topic. In this regard, green or selective demolition is still a niche of the construction market, and significant social and economic limitations still exist [66]. A hint for how to support deconstruction--which is more expensive than traditional demolition--is to promote the net financial and material benefits that can be retrieved at the end of the process [66].  
Besides GBRSs and Resilience Assessment tools, it emerges from the literature that LC-based approaches, practices, and tools are difficult to implement, as it is hard to collect reliable input data and details about existing buildings, which represents a significant barrier affecting tools' effectiveness. In particular, it is still challenging to measure embodied energy in existing buildings [44], as well as the residual value of materials and components already in place. Albeit the discourse about life cycle impact assessment tools has been focusing on the accuracy of software and results, this analysis highlights that more attention should rather be paid to the accuracy and availability of input data, which are often difficult to retrieve and unstable across the time. A harmonized database at the national or even international level could help raise transparency and availability on information for impact assessment.
# A Lightweight and Practical Anonymous Authentication Protocol Based on Bit-Self-Test PUF
## 4 Formal Security Analysis
### Security Analysis
Furthermore, the PUFs cannot be recreated as they are immune to cloning [23], and each device is equipped with a PUF; the proposed authentication protocol can be considered secure against cloning attacks. **Theorem 3**: _The proposed protocol accomplishes mutual authentication._  
Proof. : Now, to prove the legitimacy of \(A\), \(A\) must respond to the query from the server \(S\). Alternatively, \(A\) needs to generate a valid response message \(s_{1},m_{2},u,v,ID_{1}\) after receiving a message \(m_{1}\) from the server \(S\). In this case, \(A\) must know the PUF response (i.e., \(r_{1}\)) used for this authentication. However, according to Lemma 2, \(A\) cannot obtain the secret response \(r_{1}\). Since \(A\) cannot infer \(r_{i}\), it cannot generate valid parameters \(s_{1}\), \(m_{2}\), and \(v\) associated with \(r_{i}\). We know from Lemma 3 that \(A\) cannot associate devices, and it is known from Lemma 1 and Lemma 5 that the proposed protocol is resistant to DOS attacks, cloning attacks, or modeling attacks. Therefore, \(A\) cannot impersonate a legitimate server. In our proposed scheme, only legitimate devices and servers can authenticate each other.
# A Construction Method for the Random Factor-Based G Function
## 5 Simulation Results
### Non-Deterministic Encryption Performance  
In the context of non-deterministic encryption, the use of the same encryption strategy on the same original data under identical encryption conditions will result in a distinct ciphertext. This performance can be expressed by using the same G-function to send the same data, resulting in different frequency-hopping sequences and reducing its predictability. Typically, the encryption process incorporates some degree of randomness, either through the inclusion of a random factor or the introduction of random elements. It is possible that the ciphertext may vary even when the input data and the encryption algorithm remain constant, due to the presence of random elements. In comparison to deterministic encryption algorithms, non-deterministic encryption algorithms exhibit greater randomness, which renders ciphertext more difficult to crack and enhances the security of communication systems. The following table illustrates the theoretical values of different transmission paths when transmitting N identical data.  
The RFGF approach proposed in this paper, which is based on a random factor, has the distinct advantage that the transmission information only affects the changes in the subset. The system is capable of randomly selecting the frequency points within the subset for hopping. Given that a subset comprises K frequency points, there are K potential options for each generation of the frequency-hopping sequence. In contrast, the remaining three G function methods exhibit reduced flexibility when generating frequency-hopping sequences. The RS code is the only method that can be adjusted without affecting the transmission process; however, the range is limited to two states, 0 and 1. Consequently, when transmitting N bits of the same data, the number of different path choices is only 2\({}^{\text{N}}\). In conclusion, the G function based on the random factor markedly enhances the non  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RFGF** & **TVID-GF** & **SA-GF** & **MRCS-GF** \\ \hline Value & 4.9905 & 7.9046 & 15.7044 & 42.5748 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistics of the standard deviation of the frequency points.  
Figure 9: Frequency statistics histogram of two-dimensional continuity test by four methods.  
deterministic encryption performance of the frequency-hopping sequence by augmenting the randomness of frequency selection.  
As can be observed in Table 7, an increase in the K value results in a greater number of distinct paths being generated from the same data set, thereby enhancing the non-deterministic encryption performance of the resulting frequency-hopping sequence. When K is greater than 2, the RFGF is observed to exhibit superior security characteristics in comparison to the others.
## 3 Results
### Evaluation of City Parameters for Urban Areas
A correlation between the classes with the most significant changes (code 11000, code 12000, code 13100, and code 20000) and city parameters was observed. The correlation value between code 11000 and parameters such as population, housing, water usage, energy, waste, the number of doctors, hospitals, and SO\({}_{2}\) was between 0.699 and 1 (0.849 population/code 11000, 1 residence sales/code 11000, 0.864 water consumption/code 11000, 0.799 energy consumption/code 11000, 0.989 waste collection/code 11000, 0.699 doctor number/code 11000, 0.714 hospital number/code 11000, 0.952 maximum air pollution/code 11000). The correlation values between code 12000 and the population, energy consumption, water consumption, waste, and maximum air pollution parameters were 0.876, 0.881, 0.932, 0.999, and 0.985, respectively. The correlation values between code 13100 and the population, energy consumption, water consumption, and waste parameters were 0.723, 0.903, 0.997, and 0.899, respectively. The current situation indicated a decrease in arable land, and the negative correlations between code 20000 and the population, energy consumption, water consumption, and waste parameters were \(-0.806\), \(-0.945\), \(-0.997\), and \(-0.936\), respectively. This suggested a significant correlation among these variables that indicated a notable relationship between LULC and various urban parameters.