esa-sceva/satcom-chunk-collection huggingface dataset:
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 7 Conclusions  
Few lightweight, underwater acoustic network authentication schemes have been designed due to the change in the data transmission environment and propagation medium. Thus, this work proposed a lightweight authentication and key agreement scheme for UANs, which adopted PUF to protect the secret information stored in the gateway and sensors, used the fuzzy verifier to achieve two-factor secrecy, and used the semi-group property of Chebyshev polynomials to achieve lightweight authentication and perfect forward secrecy. We used the widely accepted formal security proof in the random oracle model to prove the security of our scheme. Compared to existing schemes, the proposed protocol had higher security and improved the computational efficiency by 39.52% compared to the best existing solutions, with perfect forward security. As a result, the proposed scheme is efficient and more suitable for battery-powered devices in the underwater acoustic networks.  
Q.X., resources, writing--review and editing, supervision, funding acquisition; Y.Y., formal analysis, writing--original draft. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Hangzhou Joint Fund of the Zhejiang Provincial Natural Science Foundation of China (Grant No. LHZSZ24F020002) and the National Natural Science Foundation of China (Grant No. U21A20466).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors have no conflicts of interest/competing interests to disclose.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Registration (512 Bits, TLS)** & **Verification (2048 Bits, Public)** \\ \hline Phone Server & 11.382 \(\upmu\)s & 7.142 \(\upmu\)s \\ Sensor Server & 13.451 \(\upmu\)s & 7.129 \(\upmu\)s \\ Phone Sensor & - & 6.974 \(\upmu\)s \\ \hline \end{tabular}
\end{table}
Table 5: Transmission delay.
## Appendix A
Please note that the following transformation must be applied for the secondary winding:  
\[{Z^{\prime}}_{\text{SC2}}=k^{2}Z_{\text{SC2}}, \tag{12}\]  
where: \(k\)--the turns ratio. Cases of three-winding transformers and autotransformers are more complex. The equivalent circuit of a three-winding transformer is given in Figure A2a. In contrast, autotransformers have a different structure. An autotransformer's primary winding is connected in parallel with the grid, the secondary is connected in series (Figure A2b) [58]. Both windings belong to the common coil and are linked not only magnetically, but also electrically. It is not an easy task to produce mathematical equations for an autotransformer, thus the equivalent scheme is rarely found in the literature. This procedure has been found in [73,74], and the scheme is given in Figure A2c. The interaction of a common coil with the tertiary winding can be regarded as a casual transformer with T-model since only the magnetic link is present. The winding configuration is \(Y_{0}/\Delta\)-1 [75]. For power transmission lines, the well-known models can be used--either the nominal \(\Pi\)-model (Figure A2d) or the distributed-element model (Figure A2e). The distributed element model, containing partial derivatives, is more complex, requires a longer computational time. Since the longest length in the test scheme is 100 km, the difference between simulation accuracies is not significant for this research. It is noteworthy that the phase velocity of the wave in the power transmission line depends on the product of inductance and capacitance [76; 77]. The velocity must not be greater than the speed of light:  
\[v_{p}=\frac{\lambda}{T}=\frac{1}{\sqrt{LC}}\leq c, \tag{101}\]  
where: \(v_{p}\)--the phase velocity, m/s; \(\lambda\)--the wavelength, m; \(T\)--the period, s; \(L\)--the line inductance per unit length, H/km; C--the line capacitance per unit length, F/km; \(c\)--the speed of light.
## 2 Materials and Methods  
### Experimental Setup  
The optical system for concentration measurements was based on DHI. The principles and mathematical equations of DHI are well known in the literature [5; 6]. In DHI, phase difference maps were obtained from the correlation between two holograms. The experimental setup we used to record the holograms is shown in Figure 1. The optical system had a He-Ne laser light _LA1_ (CrystaLaser, Reno, NV, USA) with a peak wavelength of \(\lambda\) = 543 nm, and with a maximum output power of 15 mW. The laser beam was divided into two beams by a beam splitter _BS1_. One beam (object beam) was sent to the _L1_ and _L2_ lenses to be expanded and collimated, respectively. Then, the object beam was scattered by a diffuser _D1_, and passed through a common glass tube that contained the liquid sample _S1_ to be analyzed.  
The object beam passed through a rectangular aperture _A1_ and was collected by a positive lens _L5_. Then, it was sent to a cubic beam splitter _BS2_ that was placed in front of an 8-bit charge-coupled device camera (CCD) (Pixelink, Rochester, NY, USA). In addition, the liquid sample with respect to the acquisition camera had a distance of 25 cm. Otherwise, the reference beam was reflected by the _M1_ and _M2_ mirrors. Later, the reference beam was sent to the _L3_ and _L4_ lenses to be expanded and collimated, and then, the output beam was sent to _BS2_ where it could interfere with the object beam, which was right in front of the CCD camera.  
A small angle \(f_{CX}\) was introduced between the object and the reference beam on the Mach-Zehnder configuration to achieve the off-axis holography geometry. The CCD was a monochromatic sensor with \(1280\times 1024\) pixels (1.3 MP) with an 8-bit dynamic range. The pixel size was 5.2 \(\upmu\)m. The hologramswere continuously recorded at 13 fps, while the liquid sample passed through the glass tube at a rate of 12 mL/min. This allowed us to create a large image dataset by recording 13 different holograms per second. The liquid sample was continuously injected by a syringe Infusion Pump KDS 200 (KD Scientific Inc., Holliston, MA, USA).
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 3 Our Proposed Architecture
### Feature-Attended LSTM  
The structure of feature-attended LSTM includes two parts, feature grouping and training process. First of all, the FIoT devices receive traffic data as a time series and form a dataset, then the dataset will be divided into several proper groups according to the feature grouping algorithm based on the correlation of features. In the training process, feature-attended LSTM firstly constructs multiple streams as inputs. Next, a corresponding number of Information Extraction Units (IEUs) with the same hidden layer structure as LSTM are established on each stream. These IEUs can extract the information according to the grouped features. Then, each stream sends the output to the reconstruction layer. The final output result is at last achieved based on the weighted impact factors.  
In this way, the time correlation of each feature and its influence on the final model is fully considered to optimize the model performance. Furthermore, the grouping strategy of features is optimized to achieve a balance between computational cost and training efficiency.
## 5 Experimental Evaluation
### A3c-L4s Evaluation
#### 5.2.1 Convergence of A3C Model  
We can see in Figure 25 the convergence of the A3C model. The figure plots the average epoch or episode reward against the epoch index. The average reward gradually increases, quietly approaching the limit, demonstrating A3C's ability to minimize queue delay for each agent. We can improve A3C with more training time and with more data. Especially in online learning, the A3C model can extract higher performance out of the environment. The A3C model is computationally more expensive and time-consuming than other models. We might want to train our data on a much bigger dataset and for longer.  
Figure 23: Case 2: L4S (ECN disabled)—Scenario 2 with Bandwidth = 1 Mbps, Delay = 20 ms. (**a**) Throughput; (**b**) Congestion Window; (**c**) Smoothed TCP RTT.  
Figure 24: Network topology utilized for data collection for A3C-L4S model.  
PIE-based AQM algorithms like our modified L4S AQM recalculate its ECN marking probability at regular time intervals (_tupdate_), which is set to 15 ms by default unless explicitly configured differently. Likewise, periodically, after a certain number of actions or steps (controlled by _args_update_interval_), each worker thread transmits its gradients to the global network, thereby determining the frequency of global network parameter updates. This dual strategy allows for adaptive control over the stability and convergence of the A3C algorithm, balancing computational efficiency with performance optimization. In highly dynamic and unstable network environments, increasing the update frequency for both A3C and L4S proves beneficial in improving the stability and learning efficacy of the A3C algorithm. Future refinements based on comprehensive testing across diverse scenarios, including mobile networks and IoT environments, may involve optimizing these intervals to update global network parameters more frequently and recalculate ECN-marking probabilities.  
We can also see the reward data for each worker agent and the global network. Each agent's reward increases steadily and shows no adverse performance issues. It also does not seem to have hit a constant reward yet, so we might need to run the model for longer. This graph tells us our A3C model is feasible, and we can start with it and fine-tune it with more training.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Other Projects for Energy Management through ICT Tools
#### 4.4.1 Eepos  
Such an example is the \"EEPOS\" project, which included participants from four countries. The project focused on energy management within the neighborhood and maximum utilisation of local distributed energy resources (DER) so as to have \"energy positive\" neighborhoods. The scope was to  
Figure 4: A smart building and its various energy management options.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Project** & **Aim** & **Characteristics** & **SM Technologies Used** & **Other Technologies** \\ \hline EnergyTIC & In-home energy management & Customer information about energy consumptions & & \\ \hline eESEH & Energy awareness and management & Remote adjustment of load by the DSO, Data visualization for electricity, gas, heating & NB-PLC, wireless (ZigBee)—first link, GPRS for second link & \\ \hline Smart Build & Energy management in buildings & Collect data from sensors, meters and forward to control center via a hub & & Wireless connections among sensors \\ \hline SportE2 & Energy management in Sport facilities buildings & Interfaces for control of cooling, heating, lighting, deployment of Sensor network & PLC, wireless (ZigBee, GSM) for smart metering & Ethernet, ZigBee for sensor network \\ \hline VERYSchool & Energy management in school buildings & Smart Metering to examine energy consumption, data forwarded to Database & & \\ \hline FINSENY & Energy management in buildings, monitoring and control of microgrids & Design ICT solution for integration of DER, Smart Metering for energy management & PLC (first link)—PRIME in some cases, GPRS (second link) & ZigBee for Home Network \\ \hline Smartspaces & Energy saving in public buildings & ICT tools to observe and effectively manage energy consumption in public buildings & ZigBee, GSM/GRPS, Ethernet (first link—some cases) & \\ \hline Encourage & Energy management in buildings & Optimise building energy consumption, communication with external buildings/utilities & Wireless: ZigBee, GPRS, WLAN & Within the building: ZigBee, 802.11n protocol, PLC HomePlugGreenPhy \\ \hline Sunshine & Energy assessment of buildings & Reduce energy waste by heating/cooling, control public illumination based on AMR & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._achieve energy management in a neighborhood level and to take advantage of the energy produced by RES, by load shifting to time peaks of their energy surplus. In general, when energy surplus was detected, it was intended to utilize this amount of energy wherever it was required within the neighborhood. The NEMS platform has been built to fulfill the project objectives. The information from houses/buildings has been transmitted to this platform, where data about weather conditions have also been registered. From the technological point of view, for the in-home network the ZigBee protocol was mainly used [92].
# High-Reliability Underwater Acoustic Communication Using an M-ary Cyclic Spread Spectrum
## 2 Proposed Method
### Principle of MCSS
The main advantage of MCSS is that, unlike conventional CSK, the peak position in MSS and the peak position of the \(C_{0}\) cross-collation can both be found within one symbol, making it more resistant to Doppler spread. One symbol is modulated per sequence in conventional DSSS systems. The \(i\)-th signal is as follows:  
\[\mathbf{x}(i)=\mathbf{s}(i)\mathbf{c}. \tag{3}\]  
The \(i\)-th transmitted symbol is \(\mathbf{s}(i)\), and the spreading sequence is \(\mathbf{c}\). The cyclic shift matrix can be written as:  
\[\mathbf{T}= \begin{bmatrix}0_{1\times(L-1)}&1\\ I_{L-1}&0_{(L-1)\times 1}\end{bmatrix} . \tag{4}\]  
where \(I_{L}\) denotes the \(l\times L\) identity matrix and \(L\) is the length of the \(PN\) sequence. As shown in Figure 2, cyclically shifted \(C_{0}\) and noncyclically shifted \(C_{q}\) are combined to form one symbol, where \(q\in[1,\,2...,\,Q]\). \(q\) represents M bits of information and \(n\) represents N bits of information. In the \(i\)-th symbol \(\mathbf{x}(i)\), M bits indicated by \(q\) are \(q(i)\), and N bits indicated by n are \(n(i)\). As a result, the \(i\)-th symbol represents (M+N) bit information. The symbol \(\mathbf{x}(i)\) can be expressed as:  
\[\mathbf{x}(i)=\sum_{j=1}^{2}C_{(j-1)\times q(i)}\mathbf{T}^{(2-j)\times n(i)\times m} \tag{5}\]  
where the symbol \(\mathbf{x}\) is an \(i\times L\) matrix and the passband is expressed as:  
\[\mathbf{s}_{i}(t)=\ \text{Re}\left\{\left[\sum_{l=1}^{L}\mathbf{x}_{i}(n)g(t-iT_{s}- IT_{c})\right]e^{2\pi/\ell}\right\} \tag{6}\]  
\[\mathbf{s}(t)=\ [\ \mathbf{s}_{1}(t),\ \mathbf{s}_{2}(t)\,\ldots,\mathbf{s}_{i}(t)] \tag{7}\]  
where \(T_{s}\), \(T_{c}\) are the symbol and chip duration time, \(f_{c}\) is the carrier frequency, and \(L\) is the length of the symbol. \(g(t)\) is the impulse response of the pulse-shaping filter. Equation (6) means the convolution between the pulse-shaping filter and the symbol. A raised-cosine filter was utilized with a roll factor of 0.5. Figure 2: Block diagram of transmitter.
## 2 Numerical Simulation
### Parameter Definition  
The three-component force coefficients are a set of non-dimensional parameters describing static wind load. It can be expressed in two ways: wind axis and body axis coordinate system, which can be converted to each other. In the wind axis coordinate system, the drag coefficient \(C_{D}\), lift coefficient \(C_{L}\), and moment coefficient \(C_{M}\) are defined as follows:  
\[C_{D}=\frac{F_{D}}{0.5\rho{U_{0}}^{2}HL} \tag{8}\]  
\[C_{L}=\frac{F_{L}}{0.5\rho{U_{0}}^{2}BL} \tag{9}\]  
\[C_{M}=\frac{M}{0.5\rho{U_{0}}^{2}B^{2}L} \tag{10}\]  
The wind pressure coefficient \(C_{P}\) can be expressed as:  
\[C_{p}=\frac{P-P_{0}}{0.5\rho{U_{0}}^{2}} \tag{11}\]  
where \(F_{D}\), \(F_{L}\), and \(M\) are the drag, lift, and overturning moment of the model, respectively, the air density \(\rho\) is 1.225 kg/m\({}^{3}\), the velocity of the airflow is \(U_{0}=10\) m/s, the length, height, and width of model are \(L\), \(H\), and \(B\), respectively, \(P\) is the static pressure on the surface of the model, and \(P_{0}\) is the reference pressure.  
Figure 2: Computational domain and boundary conditions.
## 3 Signal Processing and Artificial Intelligence for DT Development
### Emerging AI Technologies Integrated with DT for Gearbox Monitoring Needs  
Emerging technologies in machine learning, such as federated learning (FL) and edge computing, have been developed to address critical needs in data privacy, real-time decision-making, and resilience against cyberattacks.  
Federated learning is designed to enhance data privacy by preventing the transmission of raw data to a central server. Instead, model training takes place locally at the site level, and only model updates are sent to a central server for aggregation. This decentralized training approach is particularly valuable in the context of gearbox monitoring in wind turbines, where it improves model accuracy by leveraging multiple datasets while simultaneously preserving data confidentiality [107]. In the context of DT systems, FL enables the sharing of knowledge derived from physical models, obtained through simulation or modeling, without exposing sensitive raw data. This offers a major advantage, as it reduces the risk of revealing proprietary gearbox technology or modeling techniques [108].  
One of the key challenges in gearbox monitoring is the need for real-time fault detection, which is essential to minimizing system impact and avoiding costly failures. While modern AI approaches often seek to improve accuracy through increasingly deep neural networks, they typically rely on centralized data processing, transmitting large volumes of monitoring data to distant servers. This reliance can compromise real-time performance [109]. Edge computing addresses this issue by minimizing latency through local data processing. By deploying lightweight neural networks or compressed digital twin models near the monitoring stations, this approach enables faster reaction times, improves real-time anomaly detection, and enhances predictive maintenance capabilities. Overall, the integration of FL and edge computing with DT systems represents a significant step toward intelligent, autonomous, and privacy-aware industrial monitoring [110].
## 4 Reconfigurability Judgment Analysis and Autonomous Control Method
### Alignment Controllability Judgment Model
#### 4.2.1 Alignment Controllability Judgment Model under Hard Constraints
The system is supported in a rigid rather than the air spring vibration isolation system. The spatial configuration of HPDME-ASVIS is changed, which is also called hardware adjustment. The safety alignment goal is achieved by sacrificing the vibration isolation of the HPDME-ASVIS. 4. When the alignment system under hard and under soft constraints are judged as uncontrollable and the emergency protection device failure occurs, this state is classified as a **III** alignment control fault state (**III** state). At this time, the safety and stability of the device cannot be guaranteed. HPDME-ASVIS must be shut down to assure safety immediately. The reconfigurability of the HPDME-ASVIS will be described in Table 1.  
The HPDME-ASVIS has four reconfiguration stages. Combining Equations (25) and (27), the alignment FRD can be defined as  
\[\gamma=\left\{ \begin{array}{ll}1.0&if\ V=0\\ 0.9&if\ V\
eq 0\ \ and\ \ V^{\prime}=0\\ 0.1&if\ V\
eq 0\ \ and\ \ V^{\prime}\
eq 0\\ 0&otherwise\end{array} \right. \tag{30}\]  
where \(\gamma\) is artificially determined according to constraints and reconfiguration goal. Correspondingly, the SRR can be defined as:  
\[r=\gamma \tag{31}\]  
Combining Equations (28) and (29), the FRD of the **I** state(\(V\
eq 0\) and \(V^{\prime}=0\)) can be further expressed as  
\[\gamma=0.1\times\frac{\left[\frac{1}{H_{\min}},\frac{1}{H_{\max}},\frac{1}{H_ {\max}},\frac{1}{H_{\theta}},\frac{1}{H_{\theta}}\right]\left[1-\frac{c_{1}^{ \prime}\min}{H_{\min}},1-\frac{c_{2}^{\prime}\max}{H_{\max}^{2}},1-\frac{c_{2 }^{\prime}}{H_{\theta}^{2}},1-\frac{c_{2}^{\prime}}{H_{\theta}^{2}}\right]^{T }}{\frac{1}{H_{\min}}+\frac{1}{H_{\max}}+\frac{1}{H_{\theta}}+\frac{1}{H_{ \theta}}+\frac{1}{H_{\theta}}}+0.9 \tag{32}\]  
It is noteworthy that it can be obtained \(\gamma=1\) from Equation (32) when \(\left[\varepsilon_{\min}^{*},\varepsilon_{\max}^{*},\varepsilon_{x_{c}^{*}} ^{*},\varepsilon_{\beta^{*}}^{*},\varepsilon_{\varepsilon}^{*}\right]=\mathbf{0}\) which is the FRD value of the F state. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Condition** & **Constraints** & \
\begin{tabular}{c} **Reconfiguration** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Reconfiguration** \\ **Goal** \\ \end{tabular} & **State** \\ \hline controllable under hard & hard constraints & - & full goal & **F** \\ \hline uncontrollable under hard & & & & \\ constraints controllable & soft constraints & & & \\ under soft constraints & & & & \\ \hline uncontrollable under hard & & & & \\ constraints & & & & \\ uncontrollable under soft & - & & & \\ constraints & & & & \\ \hline other cases & - & - & unreconfigurable & **III** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The reconfigurability description of HPDME-ASVIS.
## 8 Discussion  
This study highlights the progress made in monitoring aspects of red blood cell deformability and aggregation and leukocyte aggregation and adhesion. There are many advantages of microfluidic systems in studying blood cell biomechanics, particularly at the single-cell level.  
The visualization of the microfluidics results allows researchers to observe morphological changes in individual cells under deformation, linking cell shape directly to mechanical behavior. These systems are effective for identifying molecular determinants of RBC deformability, assessing the impacts of pathological RBC conditions (e.g., abnormal hemoglobins, membranous, and enzyme deficiencies) on microcirculation, and studying how systemic inflammation or cardiovascular disease affects RBC mechanics.  
Microchannel-based methods are valuable for analyzing erythrocyte deformability and aggregation, the adhesion of leukocytes and their interactions with other cells, and how these properties change under varying shear rates and pressure conditions.  
In conclusion, microfluidic platforms offer a powerful, high-resolution approach to the exploration of the microrheological behavior of blood and are especially useful in researching cardiovascular risk factors and blood pathologies.  
The rheology of the blood, measured by a microchannel, is influenced by the deformability of red blood cells (RBC), leukocyte adhesion, platelet aggregation, and whole-blood and plasma viscosity. Notably, the relationship between blood rheology, assessed by the microchannel method, and other hemorheological factors remains to be clarified. The changes in the microrheological properties of the blood and their influence on the aggregation and deformability of red blood cells and the adhesion of leukocytes associated with vascular disorders in patients with significant diseases could be analyzed. The obtained parameters could serve as objective biomarkers for the diagnosis, monitoring, and prognosis of the disease. However, the data obtained from in vitro experiments indicate the possibility of correcting the existing shortcomings in the RBC microrheology of patients. A comparison of microfluidic assays with studies utilizing micropipettes, ektacytometry, and micropore cell-transit times shows that the microfluidics-based devices offer a low-cost, high throughput alternative to these previous, and now rather ancient, technologies.