esa-sceva/satcom-chunk-collection huggingface dataset:
# Reverse Electrodialysis: Potential Reduction in Energy and Emissions of Desalination
###### Abstract  
Saliity gradient energy harvesting by reverse electrodialysis (RED) is a promising renewable source to decarbonize desalination. This work surveys the potential reduction in energy consumption and carbon emissions gained from RED integration in 20 medium-to-large-sized seawater reverse osmosis (SWRO) desalination plants spread worldwide. Using the validated RED system's model from our research group, we quantified the grid mix share of the SWRO plant's total energy demand and total emissions RED would abate (i) in its current state of development and (ii) if captured all salinity gradient exergy (SGE). Results indicate that more saline and warmer SWRO brines enhance RED's net power density, yet source availability may restrain specific energy supply. If all SGE were harnessed, RED could supply \(\sim\)40% of total desalination plants' energy demand almost in all locations, yet energy conversion irreversibility and untapped SGE decline it to \(\sim\)10%. RED integration in the most emission-intensive SWRO plants could relieve up to 1.95 kg CO\({}_{2}\)-eq m\({}^{-3}\). Findings reveal that RED energy recovery from SWRO concentrate effluents could bring desalination sector sizeable energy and emissions savings provided future advancements bring RED technology closer to its thermodynamic limit.  
salinity gradient; renewable energy; electro-membrane process; global warming potential; waste-to-wealth; sustainability +
Footnote †: journal: Applied Physics Letters  
1
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 4 BB84 Simulation Model  
This section describes the implementation of the BB84 simulation model within QuantumSolver (QS) [35], with a specific focus on its _Crypto_ module. QS is a toolset developedin Qiskit [36] that enables the simulation of QKD protocols, such as BB84, using IBM simulators and real quantum computers.  
Figure 1 shows a simplified overview of the program's internal operations.  
In more detail, the program flow can be divided into three distinct phases presented in the following subsections:  
1. Key generation.
2. Key checking.
3. Validation.
## 2 Background
### Feature Selection for Regression
A set \(\mathcal{F}\) of solutions for a multi-objective problem is non dominated (or Pareto optimal) if and only if for each \(\bar{U}\in\mathcal{F}\), there exists no \(\bar{V}\in\mathcal{F}\) such that (i) there exists \(i\) (\(1\leq i\leq z\)) that \(f_{i}(\bar{V})\) improves \(f_{i}(\bar{U})\) and (ii) for every \(j\), (\(1\leq j\leq z\), \(j\
eq i\)), \(f_{j}(\bar{U})\) does not improve \(f_{i}(\bar{V})\). In other words, a solution \(\bar{U}\) dominates a solution \(\bar{V}\) if and only if \(\bar{U}\) is better than \(\bar{V}\) in at least one objective, and it is not worse than \(\bar{V}\) in the remaining objectives. We say that \(\bar{U}\) is non-dominated if and only if there is no other solution that dominates it. The set of non-dominated solutions from \(\mathcal{F}\) is called the Pareto front. Optimization problems can be approached in several ways; among them, multi-objective evolutionary algorithms are a popular choice (see, e.g., Refs. [31; 32; 35]). Feature selection can be seen as a multi-objective optimization problem, in which the solution encodes the selected features, and the objective(s) are designed to evaluate the performances of some model-extraction algorithm; this may entail, for example, instantiating (10) as:  
\[\left\{ \begin{array}{ll}\max\ Performance(\bar{U})\\ \min\ Cardinality(\bar{U}),\end{array} \right. \tag{11}\]  
where \(\bar{U}\) represents the chosen features; (11) can be seen as a type of wrapper. When the underlying problem is a regression problem, then (11) is a formulation of the feature selection problem for regression.
## 6 Conclusions  
We used Sentinel-2 optical remote sensing images with the sub-pixel offset technique to derive the time series displacement of lake ice from 7 December 2020 to 17 June 2021, in order to characterize the spatial-temporal distribution and movement of ice in Lake Khovsgol, Mongolia. Sentinel 2 was found to perform well in the near-infrared band (B8), which could achieve an accuracy of approximately 1/10 pixel for the horizontal displacement of lake ice movement. In our study, the ice cover dynamics in Lake Khovsgol were found to be similar to those observed in other locations. We observed that lake ice has been appearing later due to the long-term effects of global warming. We additionally observed that the amount of annual lake ice is influenced by regional climatic factors, such as local temperature and wind speed. The monitoring results showed that the first stage of lake ice formation lasted 26 days, the second stage lasted nearly 4 months, and the third stage lasted 2 months. During the second stage, the lake ice movement was obvious, with horizontal displacement values of up to 12 m. In addition, the displacements in the NS direction were larger than those in the EW direction. During the third stage, POT performed poorly, as measurements were negatively affected by the physical properties of the ice-covered lake's surface. It is worth noting that ice ridges and ice fractures occur during the accumulation period, during which the ice-covered Lake Khovsgol is not a safe and stable land transport route--especially after late spring when the lake ice is accompanied by freeze-thaw cycles. The authors of this study were the first to monitor lake ice on the Mongolia Plateau. Thus, we have provided not only a warning for residents to perform a series of activities on the ice during the freezing season but also some reference measurements for future studies of lake ice in this region.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the experiment data. [PERSON] and [PERSON] gave suggestions for the discussion; [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (42174004, 41704005).  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The Sentinel-2 optical ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)), accessed on 3 January 2021) data were provided by provided the ESA through their open data policy. Most figures were plotted using Generic Mapping Tools (GMT).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Numerical Simulation  
### Finite Element Model  
The influence area caused by tunnel excavation ranges from 1 to 2 times the diameter of the circular tunnel excavation [28]. Hence, the boundary settings for the model in this study extend beyond four times the tunnel diameter to avoid the boundary influence in numerical calculations. Due to the complexity of the engineering project, which involves 12 steel arch culverts and 2 tunnel structures, the computational workload for the finite element model is substantial. Therefore, some simplifications were applied to the establishment of the finite element model. The following assumptions were made:  
1. Composite strata parameters were chosen for the non-subway reinforced areas from a safety perspective.
2. The pile-reinforced area was supposed to be a homogeneous horizontal layer.
3. The road surface of the multi-arch culvert bridge was treated as horizontally flat, while the geological layers were simplified to be horizontally distributed.
4. The spatial effects of the shield tunnel excavation were not considered.  
This study combines the same type of soil layers due to the complexity of layering. Because the mechanical properties of the same type of soil are not significantly different, the impact on the simulation results is minimal. A three-dimensional finite element model was established based on the actual conditions of the project, which is shown in Figures 3 and 4. The dimensions of the model are 250 m in length, 100 m in width, and 57 m in height. The average spacing between the two shield tunnels is 6.0 m. The mesh of the multi-arch culvert bridge, tunnels, and foundation reinforcement areas was refined to ensure computational accuracy. The C20 concrete foundations were simulated using liner structural elements with contact elements set on both sides. The final model comprises a total of 224,199 nodes and 210,972 elements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Soil Layer** & **Natural Weight** & **Cohesion** & **Friction** & **Poisson Ratio** & **Compression** & **Deformation** \\
**\(\boldsymbol{\gamma}\) (kN/m\({}^{3}\))** & **C (kPa)** & **\(\boldsymbol{\varphi}\) (\({}^{5}\))** & **\(\boldsymbol{v}\)** & **Modulus** & **Modulus** \\  & & & & & & **\(\boldsymbol{E_{s}}\) (MPa)** & **\(\boldsymbol{E_{0}}\) (MPa)** \\ \hline Fill \⃝1 & 18.8 & 20 & 10 & 0.4 & 3.5 & - \\ Mud \�1\({}_{1}\) & 16.5 & 6.8 & 3.4 & 0.35 & 2.4 & - \\ Silty clay \�2\({}_{2}\) & 19.7 & 24.7 & 11.2 & 0.32 & 5.7 & - \\ Medium sand \�3 & 19.2 & 0 & 30.0 & 0.25 & 15.0 & 12.0 \\ Strong weathering & 21.0 & 40.0 & 25.0 & 0.28 & 12.0 & 80.0 \\  &
## 5 Performance Evaluation Metrics
### Mean Absolute Percentage Error (MAPE)  
MAPE expresses the average absolute error as a percentage of the true values and its predicted values [50], and is calculated as follows:  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|y_{i}-\hat{y}_{i}\right|}{y_{i}} \times 100\% \tag{8}\]
## 3 Results and Discussion
### Lean Design and Its Functionalities
#### 3.2.3 Functionality of Lean Tools in the Building Design Process
Once again, LPS, BIM, and ADM were identified as the main tools for control. Finally, in a global evaluation, tools such as TVD, ADM, BIM, LPS, LAP, IPD, and A3 Report demonstrated a strong capacity to meet administrative functionalities in building project design. Table 10 presents the combinations of tools along with their respective percentages to identify which tool combinations achieve the highest functionality coverage from the experts' perspective. Most research on Lean applied to design focuses on discussing tools individually, such as LPS, TVD, SBD, and BIM [14; 20; 21]. However, Table 8 presents a perspective where these tools can be combined efficiently. When tools complement each other, they can cover more functionalities, as seen in the cases of BIM-LPS or BIM-ADM. The literature suggests that to increase productivity in the design phase, it is essential to strengthen the following five key areas: creating a consistent and reliable information flow [67; 82], optimizing value generation [83; 84], fostering effective collaboration among stakeholders [67; 79], achieving perfection through continuous improvement [85], and integrating design with construction [24]. These improvements can be achieved through various Lean practices and tools specifically designed for this purpose [24]. Based on the conducted analysis, it is concluded that the tools that can significantly enhance these five areas are BIM, LPS, and ADM, given their impact on optimizing building project design. During the design phase, it is crucial to use one or more Lean tools that, when combined with the BIM model, help optimize key areas such as planning, control, decision-making, and team management [86]. This is achieved by identifying non-value-added activities and improving processes for later construction stages [86]. In this context, BIM offers a broad range of functionalities that can generate highly efficient results in the design phase when combined with complementary tools. \begin{table}
\begin{tabular}{c c} \hline \hline
**Combinations** & **Covered Functionalities** \\ \hline LPS-BIM & 88\% \\ ADM-BIM & 88\% \\ BIM-LAP & 81\% \\ BIM-IPD & 81\% \\ LPS-A3 Report & 81\% \\ BIM-VM & 77\% \\ LPS-VM & 77\% \\ ADM-LPS & 73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Combinations of tools based on functionalities. For instance, automating LPS, which plays a key role in planning and progress control, could enhance its effectiveness when integrated with BIM, particularly through 3D visualization of elements. Implementing additional methods within the system would provide the necessary information to optimize workflow holistically [86]. Additionally, the importance of combining complementary tools becomes evident, as this maximizes their impact on the design process.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 3 Proposed Method
### Quantum Circuit for PRESENT Block Cipher
#### 3.1.3 Permutation of PRESENT Block Cipher  
The permutation operation changes the bit order as shown in Table 2. It can be implemented using only Swap gates. In Table 2, 4 bits colored in red (0, 21, 42, and 63) do not change the position, but all 60 bits except 4 bits colored in red change the position. Moreover, 60 bits are grouped into 20 3-bit (e.g., blue) to exchange bit positions with each other. This can be done with two Swap gates as follows.  
\[\text{Swap}(b_{1},b_{4}),\text{Swap}(b_{4},b_{16}) \tag{11}\]  
The permutation is completed by performing this step by 19 more times for the remaining 57 bits. Finally, 40 Swap gates are used. Otherwise, this can be implemented with qubits relabeling. Therefore, Swap gates are not counted as gate cost. Therefore, the quantum cost for Permutation of PRESENT block cipher is zero.
# Privacy Framework for the Development of IoT-Based Systems
## 4 Related Work
### AI-Based Approaches for IoTS Security and Privacy
Therefore, our contribution focuses on embedding privacy at the stages of IoTS application development to enhance system-wide security and privacy outcomes. As previously stated, given the growing concern over data privacy and security in IoTS environments, FL has emerged as a promising solution to address such challenges. However, FL must be designed in such a way that sensitive data is not shared across devices, thereby preserving privacy while maintaining high performance in IoTS. The exposure of data in IoTS creates opportunities for several adversarial activities, such as malware exploits, Denial of Service (DoS) attacks, phishing schemes, and IoT botnet intrusions [32]. As a solution, FL enables collaborative model training by allowing users to share local parameters without exposing private data, which inherently provides higher security compared to centralized training. However, this decentralization also introduces new security challenges. Recent experiments reveal that distributed parameters in FL can be vulnerable to attacks where malicious actors could attempt to retrieve private data, such as images from face detection devices or medical data from wearable devices [33]. These security threats undermine the privacy benefits of FL and hinder its adoption for critical IoTS applications. Despite these risks, FL continues to be seen as a promising method for improving privacy-preserving IoT security. For instance, [PERSON] et al. developed a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence (PEFLID-CSAAI) [33], which improved the intrusion detection capabilities in decentralized environments while ensuring data privacy. This innovative approach integrates FL to enable collaborative intrusion detection without compromising the confidentiality of sensitive data, ensuring compliance with privacy regulations, such as GDPR. Another proposal using FL to enhance privacy is presented by [PERSON] et al. [34], who propose an FL-based system that allows manufacturers to improve their products using user data while preserving user privacy. Taking these contributions into account, FL could serve as a complementary future step that supports privacy and security in IoTS. This technique could be integrated in the future into the stages of IoTS development to provide greater protection of personal data, thereby enhancing the effectiveness of the application and ensuring the privacy of users.
# Trends and Challenges in Network Covert Channels Countermeasures
## 5 Covert Channel Limitation, Elimination and Prevention
### Prevention  
An important aspect to consider to fully asses the security of network/computing infrastructures concerns how to completely prevent the possibility of setting up a network covert channel. As hinted, protocols known to be prone to steganographic attacks should be completely blocked [1; 8; 55]. Unfortunately, this is unfeasible in many scenarios and use cases. Since covert channels are the prime consequence of imperfect isolation, prevention mechanisms should aim at restoring the protocol data units to their basic form, in order to reduce the chance of leaking states or embedding additional information. In this vein, reverting fields to default values or inserting padding (or trimming messages) are the methodologies at the basis of many countermeasures [9].  
However, the increasing sophistication of exfiltration attempts as well as the complexity of modern network architectures require to consider covert channels from the early stage of the design phase. Thus, prevention should be considered an \"offline\" process and handled in a _security-by-design_ manner. To this extent, standardization surely plays a major role and security considerations on potential covert channels should be always present. Enforcing a flow to adhere to its standard implementation can be also exploited at runtime to sanitize the behavior of the protocol and disrupt secret messages, if present. Covert channels can be also impeded by selectively blocking some protocol features while pursuing functional tradeoffs acceptable for the specific scenario or application.
## 2 Proposed Architecture
### RSP16T Cell
#### 2.1.1 Hold Mode  
In this mode, WL/WWL are connected to VSS and VDD, respectively, ensuring that each node is isolated from the bit lines (BL/BLB). To minimize wake-up time, the bit lines remain in a precharged state. As depicted in Figure 1, the pull-up transistors P3 and P1 for nodes Q and S1 remain on, while the pull-down transistors for nodes QB and S0 are off, thus allowing each node to reliably maintain its initial state.
## 5 Conclusions  
In this study, we analyze the transmission structure of the MISP and study the relationship between the transmission structure and the change in the resonance frequency. Gear clearance in the transmission process causes the resonance frequency of the gimbal frame to be lower than the natural resonance frequency, which worsens the resonance amplitude.  
Then, an adaptive notch filter based on the improved forgetting factor RLS algorithm is proposed to suppress the resonance with variable frequency in the MISP operation. Compared with the constant forgetting factor, the improved adaptive forgetting factor based on error power reached a compromise between the fast convergence and low misadjustment in the parameter estimation. The proposed adaptive notch filter adjusts the frequency parameters according to the resonance-frequency estimation to achieve resonance suppression with a variable frequency.  
Through simulation, the proposed method is proven to estimate the resonance frequency accurately online when the transmission structure changes, and to achieve a better resonance suppression effect compared with the constant forgetting factor. The reduction of the resonance amplitude is helpful in maintaining the relative position between the frame and the platform stable in the dual-stage system.  
Methodology, W.S.; software, W.S.; validation, W.S; formal analysis, W.S and K.L; data curation, W.S.; writing--original draft preparation, W.S.; writing--review and editing, K.L and J.W.; supervision, K.L. and J.W.; project administration, K.L. and J.W.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shenzhen Science and Technology Program (grant no. JCYJ20200109142205924, 202001093000459).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.  
Figure 8: Resonance frequency estimation.
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 2 Materials and Methods  
### System Model  
In the context of drone power inspection, the drone inspection area is far from the ground server (or base station), resulting in weak data transmission. Moreover, the transmission performance of the drone data link is constrained by wireless channels. As shown in Figure 1, This study adopts a multi-hop single-link model based on the drone cluster relay system. In this model, electricity-related data needs to pass through multiple relay nodes from the source node to the target node, but each relay node still uses a single link for bidirectional data transmission. Because this article focuses on channel decoding algorithms, researching efficient and reliable channel decoding algorithms helps ensure reliable data transmission. The drone inspection area and base station (BS) serve as the source node and destination node, respectively [9]. This only discusses the channel decoding process of any one-way data transmission and will not affect our research content.  
The data link is divided into three parts: the ground-to-air uplink from the source node to UAV1, the air-to-air link from UAV1 to UAV2, and the air-to-ground downlink from UAVn to the target node. For multi-hop single-link settings, drones serve as communication relays to expand the communication range. The drone cluster as a data transmission relay provides an application background for our research. The focus of this article is to study channel decoding algorithms for data transmission between any two drones. Most communication occurs in the air-to-air channel between different drones. Therefore, to ensure the reliable transmission of collected data, it is necessary to focus on discussing the data transmission performance of the air-to-air link between drones. The air-to-air link between adjacent drones has direct and non-direct paths, so the air-to-air link channel is considered a small-scale fading Rician channel. This paper takes the BER performance and computational complexity as the optimization objectives of the system, aiming to achieve a balance between the optimal performance of the data link channel decoding algorithms and resource utilization. This will help improve the overall performance of the drone power inspection system, thereby ensuring reliable data transmission. The first objective is to ensure dependable data transmission, meeting the stringent requirements for data integrity and accuracy in power inspection scenarios. The second objective is to reduce  
Figure 1: Multi-hop single-link system model based on the drone cluster relay.  
computational complexity, allowing the system to adapt to the limited resources available on drones.