esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
## 4 AN Maximizing Relay's ASER Performance  
Region II (\(A\leq E_{z}/N_{0}<B\)):When the ANR is medium, the power to transmit rotated QAM AN symbols \(x_{2}^{*}\) reaches a constant, and the probability to transmit the AN symbols increases as \(E_{z}/N_{0}\) increases. The corresponding SER in Figure 5 is higher than those using the deterministic power \(\overline{P}\).  
Region III (\(B\leq E_{z}/N_{0}<C\)):In this region, the probability to transmit AN symbols with the rotated QAM phase selection reaches 1, and the corresponding transmitting power reaches the highest \(x_{2}^{*}=\overline{P}\). In other words, for the AN symbols, the deterministic power usage and the rotated QAM phase selection achieve the maximum SER, which can also be observed in Figure 5.  
Region IV (\(C\leq E_{z}/N_{0}<D\)):The ANR arrives at a threshold \(E_{z}/N_{0}=C\), above which the optimal AN design is to use both of the two kinds of AN symbols: the rotated QAM phase selection with power \(x_{1}^{*}\) and the QAM phase selection with power \(x_{2}^{*}\). The probability to transmit the QAM phase selection symbols increases as the ANR increases.  
Region V (\(E_{z}/N_{0}\geq D\)):When the ANR is large, the QAM phase selection with a deterministic power \(\overline{P}\) achieves the maximum SER. In particular, when \(E_{z}/N_{0}\) is sufficiently large, i.e., \(E_{z}/N_{0}\geq 17\) dB in Figure 5, the SER converges to the non-informative SER, which was mathematically proved in Equation (17).  
To intuitively understand the above observations, again take the constellation point \"5\" in Figure 2 as an example. When the AN power is low, the optimal AN generation scheme is to burst the limited power to move \"5\" towards the adjacent points \"1\", \"4\", \"13\" and \"7\" in order to introduce decoding errors. As the AN power becomes sufficiently large, some of the power can be used to move \"5\" towards the points \"0\", \"12\", \"3\" and \"15\" in order to induce more decoding errors. Until the AN power becomes significantly large, beaming all the power to the directions of points \"0\", \"12\", \"3\" and \"15\" yields the maximum SER.  
In Section 3, we have obtained the optimal phase and amplitude distribution of the AN based on the instantaneous channel knowledge of the source-to-relay and relay-to-destination links. In this section, we consider the optimal distribution of the AN symbol power\(|z|^{2}\) that maximizes the ASER performance of the \(M\)-QAM modulation at the relay provided that the long-term statistical CSI is known to the destination. Following a similar approach to Section 3, we first present an analytical expression for the aforementioned performance metric over Rayleigh fading channels. The optimal phase and power distributions of the AN that maximizes this performance are then determined.
###### Abstract
For instance, highly sensitive and recyclable touch sensors made from paper composed of cellulose nanofibrils have been additively manufactured via a cheap method called inkjet printing [9]. The estimated cost of each sensor is EUR 0.06, which demonstrates the cost effectiveness of flexible materials and their ease of fabrication. Advances in additive manufacturing (AM) has enabled the flexible sensor industry to advance greatly. AM enables complex shapes to be moulded easily by controlling the CAD file. It allows weight reductions and innovative designs to be made that are otherwise impossible with conventional methods, such as moulding and surface coating. With the introduction of various AM methods including powder bed fusion, fused deposition modelling, laser sintering, inkjet printing, aerosol jet printing, stereolithography printing (SLA) and others, various materials can be processed with differing degrees of resolution depending on the intended use. Figure 1 presents some of the recent flexible sensors in literature. These include humidity, chemical, temperature and pressure sensors. Sensors connect humans to the internet enabling the monitoring of phenomena such as the heart status of an elderly person at any time and location, which enables the early detection of emergencies. In Figure 2 we see such an example of the use of flexible sensors in today's digital age [10]. Some researchers are focused on self-healable flexible sensors. Self-healing sensors are able to recover their mechanical and electrical properties after damage such as cracks, scratches or cuts. The ability to self-heal prolongs the life of the sensor which translates to material/cost savings and a decrease in environmental damage due to the disposal of waste products from the manufacturing processes. Self-healing properties enable the sensor to be stable under harsh conditions which translates to a more accurate and reliable sensor and opens up opportunities for innovative uses of the sensor. Besides flexible versus non-flexible sensor categories, sensors have also been categorised in many ways including intrinsic or extrinsic, active or passive, analogue or digital, absolute or relative, contact type or non-contact type and natural or man-made. One of the most famous ways of categorising sensors is by the stimuli they measure, giving categories such as temperature sensors, humidity sensors, pressure sensors, chemical sensors, light sensor, speed sensor and so on. Sensors can also be named by a special feature they possess, such as self-healable sensors. Recent research in flexible sensors has led to the developments of bio-integrated devices, wearable health monitoring devices and electronic skins, to mention a few. Flexible sensors, coupled with the Internet of Things (IoT), has allowed for remote health care monitoring and human-machine interfaces [11].
## 5 Electrode Materials
### Carbon-Based Electrodes  
Carbon materials can be further separated, due to their architecture, into three groups: plane structures (carbon rods), packed structures (granulated carbon) and brush structures [88, 89]. Plane structures are often used in the laboratory and consist of carbon cloth/mesh/paper and graphite rods/blocks. Due to their excellent conductivity and chemical resistance, graphite rods are one of the most commonly used materials for the anode. The downside of this material is its limited surface area [88]. Carbon paper can also be difficult to use as it is thin and brittle, whereas roughened items such as carbon cloth/mesh are better due to their high surface area. Carbon mesh is also considerably less expensive than carbon cloth and generates a higher power density [89, 90]. Activated carbon cloth removes more sulfide than other electrode materials due to its higher surface area and absorption capacity [91]. Graphite felt improves on both carbon cloth and mesh due to its thick but loose structure allowing for more bacterial adhesion due to the increased surface area [89]. Carbon fibre veils and papers have also been investigated as electrode materials and found to improve current generation by up to 40% compared to graphite rods, attributed to the increased surface area available for microbial biofilm [92, 93, 94]  
Packed structures work to increase the surface area of the electrode, such as with granular graphite [89]. For the electrode to be properly conductive, the granular graphite must be packed tightly; however, 'dead zones' of current production can exist, along with clogging of the graphite, particularly after long-term running [89, 95, 96].  
Granulated activated carbon (GAC) has recently been studied as a potential anode material. It is inexpensive and provides a substantially increased surface area, which bacteria can adhere to [88]. GAC has also been used in composite electrodes together with multiple graphite rods in a single-chamber MFC (SCMFC) [97]. The GAC provides additional surface area for bacterial adhesion while the multiple graphite rods reduce the resistance of the MFC by the reduction of the distance for electron transfer within the GAC bed. The resultant GAC-SCMFC reached a maximum power density of 7.2 Wm\({}^{-3}\), several times higher than a standard two-chamber MFC (2 CMFC) [97].  
Building on the improvements of the packed structure for electrode development are brush structures. The use of brush structures as anodes was studies by [PERSON] et al [95]. Brush electrodes consist of a highly conductive, non-corrosive metal core that is wrapped in graphite fibres. These are wound in an open structure to increase surface area and reduce fouling [95]. Another advantage of brush electrodes is their scalability. In comparison to other electrode materials, brush electrodes can be scaled up and have been used successfully in naval applications as cathodes in benthic MFCs [2; 95]
## 5 Parameter Optimization
### Plackett-Burman Test
#### 5.2.2 Analysis of PB Test Results  
Design-Expert 12 software was used to analyze the ANOVA of the PB test, and the results can be seen in Table 2. According to the correlation coefficients of the PB test: \(R^{2}=0.999\), indicating that 99.9% of the test variance can be explained by the model, with a high degree of fit to the actual data; \(R^{2}_{\text{adj}}=0.9901\), which is close to \(R^{2}\), indicating a good correlation; and CV = 11.57%, which is relatively low, demonstrating high reliability of the results; Adeq Precision measures the signal-to-noise ratio, with a value ideally greater than 4. The precision of this model is 34.0778. In summary, these indicators suggest that the model is highly reliable and can be used to explain the effect of various parameters on the volume ratio of the posture adjustment mechanism.  
As can be seen from Table 2 and Figure 12, the influence ranking of each parameter on the range of posture adjustment is as follows: the drive trajectory of the second driving pair > the parameters of the moving and fixed platforms > the angles between the driving pairs > the drive trajectory of the first driving pair.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Mechanical Parameters** & **Low Level** & **High Level** \\ \hline \(X_{0}\) & Width of moving platform \(m\) & 700 & 1000 \\ \hline \(X_{1}\) & Length of moving platform \(n\) & 1250 & 1550 \\ \hline \(X_{2}\) & Width of fixed platform \(M\) & 800 & 1100 \\ \hline \(X_{3}\) & Length of fixed platform \(N\) & 1350 & 1650 \\ \hline \(X_{4}\) & Angle between P\({}_{3}\) axis and moving platform length direction \(\varphi\) & \(3\pi/16\) & \(5\pi/16\) \\ \hline \(X_{5}\) & Angle between P\({}_{3}\) and P\({}_{2}\) axis \(\theta\) & \(7\pi/16\) & \(9\pi/16\) \\ \hline \(X_{6}\) & Angle between P\({}_{2}\) and P\({}_{1}\) axis \(\psi\) & \(\pi/3\) & \(2\pi/3\) \\ \hline \(X_{7}\) & Minimum drive trajectory of the first driving pair of limb \(l_{\text{1 min}}\) & 600 & 800 \\ \hline \(X_{8}\) & Maximum drive trajectory of the first driving pair of limb \(l_{\text{1 max}}\) & 900 & 1100 \\ \hline \(X_{9}\) & Minimum drive trajectory of the second driving pair of limb \(l_{\text{2 min}}\) & \(-250\) & \(-150\) \\ \hline \(X_{10}\) & Maximum drive trajectory of the second driving pair of limb \(l_{\text{2 max}}\) & 150 & 250 \\ \hline \(V_{1}\)–\(V_{8}\) & Virtual factors & \(-1\) & +1 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of PB test.  
As can be seen from Table 2 and Figure 14, the influence ranking of each parameter on the range of posture adjustment is as follows: the drive trajectory of the second driving pair > the parameters of the moving and fixed platforms > the angles between the driving pairs > the drive trajectory of the first driving pair.
## 3 Verification Results  
The system for pre-processing of point data, described in previous chapter, was verified on three characteristic case studies:  
1. timing belt tooth,
2. human face model and
3. sports glasses lens.  
In the first case study 3D digitization was performed by laser scanning, while in other two case studies it was accomplished by contact scanning.
# A Cross-Chain-Based Access Control Framework for Cloud Environment
## 3 Preliminaries
### Architecture  
Our proposed framework, as shown in Figure 1, is based on the Cosmos industrial cross-chain solution, which integrates the IBC protocol to interconnect heterogeneous blockchains, including modules that define how packets and messages are constructed and interpreted by the sending and receiving cloud front- and back-end zones, in our case, BC lightweight and full nodes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Freshness** &
\begin{tabular}{c} **Block** \\ **Finality** \\ \end{tabular} & **Heterogeneity** & **Fine-Grained** & **Scalability** & **Adaptivity** \\ \hline
[7] & 2024 & * & * & * & No & Yes & No \\ \hline
[8] & 2024 &
\begin{tabular}{c} Moderate/ \\ high \\ \end{tabular} & Probabilistic & Low/moderate & Yes & Yes & No \\ \hline
[9] & 2023 & \
\begin{tabular}{c} Moderate/ \\ high \\ \end{tabular} & Probabilistic &
\begin{tabular}{c} Low/ \\ moderate \\ \end{tabular} & Yes & No & No \\ \hline
[10] & 2023 & * & * & * & No & No & No \\ \hline
[11] & 2023 & High & Instant & Moderate & No & Yes & No \\ \hline
[12] & 2023 & \
\begin{tabular}{c} Moderate/ \\ high \\ \end{tabular} & Probabilistic &
\begin{tabular}{c} Low/ \\ moderate \\ \end{tabular} & No & Yes & No \\ \hline
[13] & 2020 & Moderate & Probabilistic &
\begin{tabular}{c} Low/ \\ moderate \\ \end{tabular} & No & No & No \\ \hline
[14] & 2019 & High & Instant & Moderate & Yes & No & No \\ \hline Our approach & 2024 & High & Instant & High & Yes & Yes & Yes \\ \hline \hline \end{tabular}  
* not provided by the authors in the paper.  
\end{table}
Table 2: Comparison among decentralized, data-privacy-preserving BC-based access control schemes.  
This framework deploys a fine-grained modular AC in the receiving zone of the cloud architecture. It undertakes the operations of processing authorization transactions included in the transferred IBC packets during the interactions between cloud zones, where each received client request carrying access attributes must be processed semantically before enforcing an access decision.  
The main objectives of our framework are:  
* Enabling cross-chain communication to eliminate trust issues related to third parties within the cloud environment.
* Implementing an access control mechanism on the cloud back-end zone, governed by the CSP, where the cloud service consumers maintain their access policies on the deployed node, ensuring its privacy and integrity.
* Enhancing privacy preservation and resource availability in the cloud environment.
* Processing access requests originating from cloud clients using an enhanced ABAC with semantics for a scalable, flexible, and granular AC.
* Tackling the communication interoperability issues between heterogeneous BC zones implementing the IBC protocol.
## 3 Methodology
### Optimal Temperature Using Deep Q-Network (DQN)  
DQN is a reinforcement learning algorithm that combines Q-learning with deep neural networks, enabling a system to learn optimal control policies through interactions with its environment [27]. The fundamental equation of DQN is defined by Equation (9):  
\[\text{DQN Input State }s_{t}: \tag{9}\]  
\(\hat{T}_{indoor,t+1}\): indoor temperature at the next point predicted by LSTM.  
\(H_{t}\): current measured average humidity.  
\(P_{t}\): power consumption (related to heater/air conditioner operation status).  
\(T_{set}\) : user-specified setting temperature.  
\[Q(s_{t},a_{t})\gets Q(s_{t},a_{t})+\alpha[r_{t}+\gamma_{a}^{max}Q(s_{t+1}, a)-Q(s_{t},a_{t})] \tag{10}\]  
where\(Q(s_{t},a_{t})\): expected reward for taking action \(a_{t}\) in state \(s_{t}\).  
\(\alpha\): learning rate.  
\(r_{t}\): immediate reward.  
\(\gamma\): discount factor for future rewards.  
\(\frac{max}{a}Q(s_{t+1},a)\): maximum possible Q-value in the next state.  
Here, Equation (10) represents the Q-value update equation used in DQN. Additionally, the DQN utilizes deep neural networks to approximate the Q-value function based on dynamically changing environmental data in real time, while enhancing learning stability through a replay buffer that stores past experiences and a target network that provides consistent Q-value targets during updates. In a temperature control system, the state (s) may encompass variables such as current indoor temperature, outdoor temperature, time, and occupancy, whereas the action (a) represents adjustments to the target setpoint temperatures for heating and cooling, as well as system control commands. The reward (r) is designed to strike a balance between energy consumption and the comfort level of the indoor air temperature. By utilizing temperature data generated by the LSTM, the DQN learns control strategies that optimize energy efficiency and user comfort across diverse environmental conditions. Specifically, in indoor temperature regulation, the DQN automates complex decision-making processes--such as optimizing preheating and precooling times, adapting to usage patterns, and detecting changes in thermal dynamics--thereby enabling greater real-time adaptability to ambient conditions compared to traditional rule-based control systems [28].  
The final control temperature calculation content, DQN, selects the optimal control action based on the state \(s_{t}\) to calculate the final control temperature \(T_{final\_setpoint}\). This can be expressed as a weighted average of the LSTM prediction and the DQN correction value, as shown in the following equation:  
\[T_{final\_setpoint}=(1-\beta)\hat{T}_{indoor,t+1}+\beta Q_{adjust}(s_{t}) \tag{11}\]  
\(\beta\): the weight of LSTM prediction and DQN correction.  
\(Q_{adjust}\): directly regressed as a temperature correction output by the DQN, without incorporating power consumption or cost metrics in the reward structure.  
Thus, the final control temperature output by the DQN is given by Equation (11), where \(Q_{adjust}(s_{t})\) includes user-defined control values from JSON input.
## 3 Experiments and Results
### Comparison with Existing Studies  
To assess the effectiveness of our proposed model, we conducted a comparison with the classification results reported in related works. As shown in Table 3, the results demonstrate that the proposed 1 DCNN-BiRAU model based on SegPool in this paper achieves impressive results in both the binary and four-category classification tasks at the individual level. The model achieves an average accuracy of 99.47% and 90.90%, respectively, which represents a significant improvement over existing work where the highest performance is 90.08% [22] and 90.00% [23]. These results strongly demonstrate the superior performance of the proposed model in differentiating brain states.
## 2 Literature Review
### Decision-Making Methods
#### 2.2.1 Best-Worst Method  
The pairwise comparison method proposed by [PERSON] in 2015 [PERSON] (2015) is based on identifying the best and worst criteria from a set of evaluation criteria, followed by pairwise comparisons between these criteria and all others. The stages of the Best-Worst Method are briefly outlined below: (a) Determine the set of decision criteria; (b) Determine the best criterion (the most desirable, the most important) and the worst criterion (the least desirable, the least significant); (c) Determine the preference of the best criterion over the other criteria, using a number between 1 and 9; (d) Determine the preference of all criteria over the worst criterion, using a number between 1 and 9; (e) Calculate the optimal weights determined from the maximum absolute differences:  
\[\left|\frac{w_{B}}{w_{j}}-a_{Bj}\right|\;and\;\left|\frac{w_{j}}{w_{W}}-a_{jW}\right| \tag{1}\]  
For all, \(j\) is minimized, which results in the following min-max model:  
This model is equivalent to the following model:  
\[\min\xi \tag{2}\] \[\left|\frac{w_{B}}{w_{j}}-a_{Bj}\right|\leq\xi,\;for\;all\;j\]  
\[\left|\frac{w_{j}}{w_{W}}-a_{jW}\right|\leq\xi,for\;all\;j \tag{3}\]  
\[\sum_{j}w_{j}=1;\;w_{j}\geq 0,\;for\;all\;j \tag{4}\]  
The solution of this model could lead to multiple optimal solutions; a new model is proposed that minimizes the maximum between the set of \(\left\{\left|w_{B}-a_{Bj}w_{j}\right|,\left|w_{j}-a_{jW}w_{W}\right|\right\}\). The problem can be formulated as follows:  
\[\minmax_{j}\left\{\left|w_{B}-a_{Bj}w_{j}\right|,\left|w_{j}-a_{jW}w_{W}\right| \right\},\;\sum_{j}w_{j}=1;\;w_{j}\geq 0,\;for\;all\;j \tag{5}\]  
This problem is transformed into the following linear programming problem:  
\[\min\xi^{L} \tag{6}\] \[\left|w_{B}-a_{Bj}w_{j}\right|\leq\xi^{L},\;for\;all\;j\]  
\[\left|w_{j}-a_{jW}w_{W}\right|\leq\xi^{L},\;for\;all\;j \tag{7}\] \[\sum_{j}w_{j}=1;\;w_{j}\geq 0,\;for\;all\;j \tag{8}\]  
This problem has only one solution, and we obtain the optimal weights \(\left(w_{1}^{*},w_{2}^{*},\ldots,w_{n}^{*}\right)\) and \(\xi^{L*};\xi^{L*}\). It is regarded as an indicator of comparison consistency, with values close to zero reflecting a high degree of consistency.
## 4 Self-Selective Routing (SSR)
### Reliable and Secure Route Formation in ASAART  
In the ASAART routing protocol, the route discovery packets are transmitted by the source node through the packet broadcast mechanism [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. In this case, there is a need to control the continuous flooding of the route discovery packets, particularly in a very dense network. In our scheme, shown in  
Figure 2: Proposed autonomous self-aware and adaptive route repair technique in (ASAART).  
Figure 2, ASAART uses the sender node local information that is contained in the control packet. Upon receiving the packets from the sender node, it uses the local neighbor information to update its global network state information contained in the control packet header information. The node then computes the auto self-awareness back-off delay based on the expected number of hops to the destination, as given by Equations (4)-(10). During the computation of the auto-self-awareness back-off delay, the sender node listens to the channel and prevents the transmission of the data packets. This happens when the sender node overhears that one of its neighboring nodes with the expected hop count to the destination has already transmitted the data packets. However, if the sender node does not overhear its neighbor's transmission and its back-off timer fires, the sender node will now update its expected hop count in the data packets and then re-transmit the data packets to the destination. The sender node with the auto self-awareness back-off information will have the highest priority to forward the packets to the next intermediate or destination node. To reduce the problem of redundant transmission, after the destination node receives a route discovery packet, it sends a route discovery acknowledgement to the source node. ASAART uses an efficient flooding control scheme that minimizes the redundant transmission of network packets. In this case, the intermediate and destination nodes have the complete network state information for a reliable route formation based on the discovery packet's auto self-awareness back-off information received from the source node. This scheme will minimize the redundant transmission of packets and ensure reliable end-to-end data delivery. The concept of secure route formation is beyond the scope of this research paper. Interested readers should consult references such as [48,50] in the literature.