esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Initial Results
### Blockchain Operations That Address Ethical Issues in AI
#### 3.3.4 Interplay of Federated Learning and ZKP--Compatibility Logic  
Another critical issue that persists is the secure interaction between the federated learning (FL) components and the blockchain layer through zero-knowledge proof (ZKP). This interoperability is crucial to prevent local data from leaving local devices as well as to enable a distributed governance architecture for validating updates of models and operations on data without accessing the raw inputs.  
The technical pipeline is as follows: the FL clients locally train the models on the private subsets of their data and calculate the intermediate updates (i.e., gradients or weights). These updates are subsequently encoded into small cryptographic attestations with zk-SNARKs, for example Groth16 or PLONK, which convince that the updates were computed correctly with respect to pre-agreed training parameters, while withholding the underlying information. The ZKPs are then published to the blockchain alongside the commitments of model hashes, giving validators or governance actors the means to verify that the calculation is not compromised, without transgressing privacy.  
For the purpose of proving forgery or client malbehavior, each FL-ZKP bundle is bound by a per-session identifier and signed by the participant's identity key organized under a decentralized identity (DID) framework. Smart contracts are used on the blockchain to verify the proof and for future trust audit hashing. The DAO governance module can meanwhile utilize this proof of reliability to invoke reward distribution, punish hostile updates or invoke retraining procedures.  
The architecture is designed in a way so that the heavy computations (FL model training and ZKP generation) are separated to off-chain infrastructures and the on-chain operations only entail lightweight proof checking and logging, which prevents the issue of performance bottleneck. Furthermore, modularity of design allows the FL model can be iterated independently of blockchain upgrades, improving its maintainability over the long term as well as its extension for cross-domain.  
Another technical issue left as a challenge is the interoperability between diverse blockchain systems (e.g., due to data sovereignty, jurisdiction diversity, and governance diversity, it is expected that the multi-chain deployment will become a common practice). Even though CF-BIAI-SXT does not directly indicate communication protocols between the chains, this could be provided by decentralized interoperability protocols such as Cosmos IBC or Polkadot's relay-chain logic to establish secure and verifiable communication of user credentials and consent data between chains.
# Constructing Enhanced Composite Solid-State Electrolytes with Sb/Nb Co-Doped LLZO and PVDF-HFP
## 1 Introduction
The introduction of fillers minimizes the binding between Li+ and TFSI-, thereby maximizing the Li+ migration number and Li+ conductivity of the electrolyte. Except for the doping of one single element increasing the ionic conductivity of LLZO to varying degrees, several studies have shown that co-doping is more helpful and synergistic in enhancing the performance of LLZO [27]. [PERSON] et al. [28] found the structure of Li6.7 La3 Zr1.5 Sb0.3O12 to be stable and more favorable for improving ionic migration, and some studies have shown that Nb can form the garnet structure for co-doping with other elements [29; 30]. Based on this consideration, the isovalent dopant Nb2O5 was chosen to further enhance the ion conductivity of LLZSO. Herein, cubic-phase LLZO co-doped with Sb and Nb was synthesized using a solid-phase sintering method; based on previous research, the content of Sb was fixed at 0.3 mol invariable, and the effects of the niobium dopant concentration on the relative density, ionic conductivity, and composite performance were investigated. Subsequently, composite solid electrolytes, with the co-doped garnet powder as a filler mix in the polymer matrix, were prepared using a solution casting method, and the batteries with the composite solid electrolytes were assembled for electrochemical performance and safety analysis. The prepared CSEs show high ionic conductivity at room temperature, wide electrochemical windows, good interface stability with lithium anode, and good safety.
## 3 Experiments and Result Analysis
### Analysis of Experimental Results
#### 3.2.1 Model Evaluation
The experimental findings show that the RMSE for latitude, longitude, and depth in BiLSTM are 0.00286, 0.00185, and 0.47666, respectively, in the absence of feature discrimination. The RMSE without the AWOA optimization algorithm is 0.00182, 0.00081, and 0.21234 for latitude, longitude, and depth, respectively, after separating position information from physical attitude. This indicates better prediction accuracy for latitude and longitude. The RMSE for location information becomes 0.00026, 0.00037, and 0.03992 for dimension, longitude, and depth, respectively, after incorporating the AWOA method. A comparative investigation shows that AWOA-DBiLSTM significantly outperforms BiLSTM and DBiLSTM in terms of prediction accuracy for latitude, longitude, and depth. AUV trajectory prediction is different from land two-dimensional trajectory prediction, in which not only longitude and latitude but also depth should be taken into account when predicting the trajectory. Due to the complexity of the underwater environment, it is not feasible to only rely on the historical position trajectory when predicting the AUV trajectory, and other factors affecting the trajectory should also be taken into account. From the experiments as a whole, both from the predicted trajectory visualization and the evaluation metrics, RMSE, MSE and MAE, it can be concluded that the AWOA-DBiLSTM model proposed in this study improves the AUV trajectory prediction accuracy and achieves a significant improvement in the prediction objectives. This is because the classification and extraction of influencing factors can effectively improve the prediction accuracy. In addition, the neural network parameters also occupy important influencing factors. The DBiLSTM model achieved better experimental results in trajectory prediction, but there is still room  
Figure 15: The trajectory of the AUV is predicted using the AWOA-DBiLSTM model: (**a**) represents the prediction of latitude by AWOA-DBiLSTM. (**b**) represents the prediction of longitude by AWOA-DBiLSTM. (**c**) represents the prediction of depth by AWOA-DBiLSTM. for improvement when comparing it with the AWOA-DBiLSTM model; this is because the AWOA-DBiLSTM model adjusts the importance of the input features by optimizing the weight and bias parameters in the neural network to achieve trajectory prediction.
## 3 Categorization of Security and Privacy Mechanisms in ITS  
In ITS, privacy mechanisms involve proper verification, registration, and communication. In order to provide a trustworthy system, there must be effective authentication of vehicles and messages. With effective security and privacy schemes, an adversary can easily be identified. Once an adversary (malicious vehicle) is identified, it must be revoked and proper accountability can be performed. The effective revocation and accountability approaches can save the ITS from maximum damages and gain trust in return from ITS users. In order to establish a complete trustworthy system for transportation, confidentiality, integrity, authentication, and non-repudiation properties must be considered. In view of the aforementioned features, several researchers have made their best efforts to design security schemes for ITS. In this survey paper, security and privacy schemes in ITS as shown in Figure 6 are mainly categorized into (i) group/ring signature-based (GSB or RSB) schemes, (ii) pseudonym-based (PB) schemes, and (iii) hybrid schemes.  
In order to critically analyze the security and privacy schemes in ITS, the following parameters are considered: (i) scalability, (ii) security/privacy, (iii) computational cost, (iv) latency, and (v) communication overhead. The parameters are obtained from the literature as discussed in the next sections of this paper.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 7 Experimental Results
### Energy Consumption Measurement  
To determine the battery performance of USV, energy consumption is calculated. The measurement of energy consumption is retrieved every second when the USV sail is in autonomous mode, as plotted in Figure 18. The figure shows that to complete an autonomous mission in 64 s with a predetermined path, the energy consumption is 5900 J. From this data, it is concluded that the USV is able to sail for 6.02 min with 0.18 km cruising capability.  
Figure 16: Measured longitude and latitude.
# Physicochemical Characterization of Hyaluronic Acid and Chitosan Liposome Coatings
## 3 Results
### Physicochemical Properties of the Coated Liposomes
#### 3.1.3 FTIR-ATR Analysis  
Characterization by FTIR-ATR spectroscopy was carried out to examine the influence of a polysaccharide coating on the liposome structure. Figure 4 shows the spectra of a DOPC/DOPE system before and after the coating with the polysaccharides (Figure 4a) and the overlay of the spectra (Figure 4b).  
In the 3000-4000 cm\({}^{-1}\) spectral range, the band at around 3380 cm\({}^{-1}\) that was formed due to the stretching of the aminic N-H belonging to liposomes, was completely embedded into the broadband due to the strong stretching of the hydroxyl groups and additional NH stretching of the amide groups of the polysaccharides. Symmetric and asymmetric C-H vibrations in the 2850-2855 cm\({}^{-1}\) range did not show any shift due to the presence of polysaccharide. These wavenumbers have been routinely used to study the phase behavior of lipids because their shift to higher values is attributed to the lipid transition from the gel phase into the liquid-crystalline phase, causing the hydrocarbon chains of the lipids to become more disordered [36]. Regarding the very intense band at 1738 cm\({}^{-1}\), attributed to C=O stretching of the lipid ester group, the absence of change in terms of wavenumber and  
Figure 3: \({}^{1}\)H-NMR spectra of: **(a)** DOPC/DOPE liposome; **(b)** DOPC/DOPE coating with CH.  
band shape confirms the very low interaction between the lipids and polysaccharides and, consequently, there is no modification of the hydrogen bonds structures in the interfacial lipid carbonyl groups.  
In the range between 1700 and 1500 cm\({}^{-1}\), the lipids showed weak bands that were completely embedded in the strong absorption due to the polysaccharide carboxylic/amidic groups.  
The intense bands at 1240 cm\({}^{-1}\) and 1170 cm\({}^{-1}\) can be attributed to the asymmetric stretching of phosphate diester (PO\({}_{2}\)) and the symmetric stretching of ester groups (CO-O-C) in the lipids. The coating by the polysaccharides induces a shift of the ester absorption to 1160 cm\({}^{-1}\) for the CH coating and to 1158 cm\({}^{-1}\) for the HA coating. These shifts could be attributed to partial hydrogen bond rearrangement due to the high sensitivity of these vibrations in the hydrogen bond network.  
Considering the FTIR analysis as a whole, the presence of polysaccharides is well documented by the bands assigned to amides and saccharides in the 1500-1700 cm\({}^{-1}\) and 800-1100 cm\({}^{-1}\) regions, respectively. In addition, the absence of wavenumber shifts and changes in band shape suggests that the polysaccharide coating did not induce major changes in lipid structure.
###### Abstract  
Unmanned aerial vehicles (UAV) have attracted increasing attention in acting as a relay for effectively improving the coverage and data rate of wireless systems, and according to this vision, they will be integrated in the future sixth generation (6G) cellular network. Non-orthogonal multiple access (NOMA) and mmWave band are planned to support ubiquitous connectivity towards a massive number of users in the 6G and Internet of Things (IOT) contexts. Unfortunately, the wireless terrestrial link between the end-users and the base station (BS) can suffer severe blockage conditions. Instead, UAV relaying can establish a line-of-sight (LoS) connection with high probability due to its flying height. The present paper focuses on a multi-UAV network which supports an uplink (UL) NOMA cellular system. In particular, by operating in the mmWave band, hybrid beamforming architecture is adopted. The MUltiple Signal Classification (MUSIC) spectral estimation method is considered at the hybrid beamforming to detect the different direction of arrival (DoA) of each UAV. We newly design the sum-rate maximization problem of the UAV-aided NOMA 6G network specifically for the uplink mmWave transmission. Numerical results point out the better behavior obtained by the use of UAV relays and the MUSIC DoA estimation in the Hybrid mmWave beamforming in terms of achievable sum-rate in comparison to UL NOMA connections without the help of a UAV network.  
hybrid precoding; millimeter wave; non-orthogonal multiple access scheme; massive MIMO; unmanned aerial vehicles; direction of arrivals (DoA); MUSIC algorithm Article
## 3 Methods
### DIVA Model  
Inspired by the VAE [6] (proposed in 2013), the DIVA model uses the VAE structure in its design to combine a path search and path reasoning. The encoder compresses the input image \(x_{i}\) by encoding. It is converted to a low-dimensional implicit variable \(z\) conforming to a priori distribution \(p(z)\), and the process is expressed by (1). Next, the decoder decodes the hidden variable and upgrades it into a generated image \(\hat{x}_{i}\) with the exact dimensions of the original image. The process is expressed by (2). During training, the parameters of the encoder and the decoder are updated simultaneously to improve the encoding quality and enhance the generating ability of the decoder. For the sample \(x_{i}\), the loss function is shown in (3). DIVA is partially adjusted in the process of applying the VAE. Finally, the KG reasoning task can be described as finding the probability distribution of the potential relationships between entity pairs, as shown in (4). DIVA relates the posterior distribution in the VAE to the path search module \(q_{\varphi}(l|(e_{s},e_{d}),r)\), and the learning efficiency is improved by adding the feature vector \(r\) of the annotated relationship in the search decision. After the learning of the posterior distribution is complete, the prior distribution \(p_{\beta}(l|(e_{s},e_{d}))\) approaches the posterior distribution \(q_{\varphi}(l|(e_{s},e_{d}),r)\) by (5), and finally, the training process of the path search module is completed.  
\[q_{\theta}(z|x_{i}), \tag{1}\]  
\[p_{\varphi}(\hat{x}_{i}|z), \tag{2}\]\[l_{i}(\theta,\varphi)=-E_{z\sim q\theta(z|x_{i})}[\log p_{\varphi}(x_{i}|z )]+KL(q_{\theta}(z|x_{i})||p(z)), \tag{3}\] \[p\big{(}r\big{|}(e_{s},e_{d})\big{)}=p_{\beta}(l|(e_{s},e_{d}))p_{ \theta}(r|l),\] (4) \[KL\big{(}q(l|(e_{s},e_{d}),r)\big{|}|p_{\beta}(l|(e_{s},e_{d})) \big{)}. \tag{5}\]  
The process of the path reasoning module in generating the path characteristic matrix is shown in Figure 1, in which the solid circle represents the entity in the path, the arrow represents the relationship in the path, and the hollow circle and virtual connecting line represent the entity and relationship adjacent to the path entity. The path characteristic matrix is directly composed of the stacking of entities and relationship embedded vectors in the path.
## 3 Localization Algorithm
### Loosely Coupled Hybrid Algorithm  
The first version of the hybrid algorithm used in the system is loosely coupled. It means that the user localization is calculated separately using both technologies and then fused. Localizations based on BLE power measurements and ranging are calculated using algorithms presented in Sections 3.1 and 3.2 respectively. The obtained results are then fused using the following formula:  
\[p=p_{BLE}\frac{\sigma_{RAN}^{2}}{\sigma_{BLE}^{2}+\sigma_{RAN}^{2}}+p_{RAN} \frac{\sigma_{BLE}^{2}}{\sigma_{BLE}^{2}+\sigma_{RAN}^{2}} \tag{13}\]  
where:  
Figure 2: Localization using: (**a**) a single laser sensor, (**b**) multiple laser sensors.  
\(p_{BLE}\) is the localization calculated based on BLE power measurements (6) and \(\sigma_{BLE}^{2}\) is the corresponding variance taken from covariance matrix \(P\) (7),
* \(p_{RAN}\) is the localization calculated based on ranging results (6) and \(\sigma_{RAN}^{2}\) is its variance (12).  
When the localization calculated using BLE is very inaccurate, it is possible that the result obtained using the hybrid algorithm will be positioned outside the used proximity sensor beam. Since the sensor detected the user, it is almost certain that he/she is in its field of view. In such case, to improve algorithms accuracy, the obtained result is pulled to the closest point on the arc corresponding to the measured distance.
## 3 Results and Discussion
### Electromagnetic Analysis Results
Magnetic saturation mainly occurred at the corners where the cross-sectional area of the circuit changed rapidly, and the highest magnetic flux density was observed at the corners at both ends of the core, as shown in Figure 19.0 Additionally, the magnetic flux density of the flux ring calculated using the permeance method was compared with that calculated using ANSYS Maxwell, as listed in Figure 19.1 It was confirmed that the magnetic flux density of the two models had an error of up to 8.08%. Based on the above results, the relationship between the current applied to the MR damper and the yield stress of the MR fluid is shown in Figure 19.2 It was found that F_MR had a damping force of at least 3.37 kPa at 0.2 A and up to 16.38 kPa at 1.0 A. As the current increased, the damping force also increased. However, because the increase in the magnetic flux density gradually decreased, the slope of the damping force F_MR in the form of a high-order polynomial for the magnetic flux density also gradually decreased.
# Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
## 2 System Model with Chirp-BOK Modulation  
The schematic diagram of tropospheric scatter propagation is shown in Figure 1, with the transmitter and receiver far apart. In Figure 1, the \"scatterers\" in the troposphere can form a scattering effect on electromagnetic waves in the microwave band, thereby realizing OTH communication. The transmitter sends the signal toward the troposphere without being blocked by terrestrial obstacles. Although most of the transmitted signal passes out of the troposphere, the remainder scatters at the troposphere, which we call \"common volume\". In order to set up a tropospheric scatter communication link, an antenna facing a horizontal line should be placed with an elevation angle as low as possible to minimize the path length. As shown in Figure 1, a common volume is formed at the intersection of the antenna beam widths, which can scatter the radio waves to the receiver.
###### Abstract
Furthermore, the shape and length of APT can be controlled and flexibly modified, forming structures with specific physicochemical properties and functions. Research of multifarious adenine-mediated technology (MAMT) suggests that the adsorption capabilities of the four bases of nucleic acid APT to gold follow the order A \(>\) C \(\geq\) G \(>\) T [16]. Among them, adenine has strong adsorption to gold, comparable to Au-S bonds, and Au can even denature the double strands of adenine and thymine. The strong adsorption of adenine to gold electrodes has promising applications in the design of thiol-free APT [17]. Adenine can be used as an anchoring group and density control group to regulate the conformation and grafting density of APT at the sensor interface, reducing the surface density of APT on the electrode with the increase of adenine length. Moreover, adenine preferentially binds to gold atoms, reducing the accessibility of other materials to gold, and significantly hindering the non-specific adsorption of other materials. These aptamer (APT) assembly techniques can be applied to the construction of biosensors. In recent years, with the development of MIT and APT assembly technology, the application of bionic recognition biosensors in the field of DN biomarker analysis has been effectively expanded. With the discovery of new biomarkers for DN, the development of more efficient, stable, and cost-effective novel biomimetic sensing platforms has significant implications. Not only does it improve our understanding of the pathogenesis of DN and further promote early diagnosis in diabetic nephropathy patients, but it also optimizes the treatment of these patients. Therefore, this article reviews the research progress in the construction of MIPs, APT assembly technology, and the detection of biomarkers for DN based on BRBS in the field of biomimetic sensors in recent years, and forecasts their development.
## 4 Proposed Protocol
### Authentication Phase  
In the authentication phase, there are two possibilities for authentication with the Certification Authority Server. Authentication can be done using e-mail or password or the authentication code obtained in the registration phase. After selecting any login method, \(t_{i}\) is computed by the OBU for updating the logs table. \(AT_{i}\) is passed to the server along with its corresponding parameters. The CA Server checks the authentication type. We set two threshold values, i.e., 0 for \(VCA_{s}\) and 1 for \(E_{i},PW_{i}\). If \(AT_{i}==0\); then, the CA server compares \(VCA_{s}\) database records. If the vehicle code already exists in the database,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**OBU** & **CA Server** \\ \hline \hline \multicolumn{2}{l}{Registration:} \\ \multicolumn{2}{l}{Selects \(ID_{i}|\), \(E_{i}\), \(PW_{i}\) and \(RN_{i}\)} \\ \multicolumn{2}{l}{Computes} \\ \(t_{i}\) & \\ \(SPW_{i}=h(PW_{i})\) & \\ \(NC_{i}=filter\_var(RN_{i})\) & \\ \(UC_{i}=(ID_{i}||NC_{i})\) & \\ \(REF_{i}=(ID_{i}||t_{i}||r_{1})\) & \\ \(\frac{\{ID_{i},E_{i},SPW_{i},UC_{i},REF_{i}\}}{\{NC_{s}=shuffle(UC_{s})\}}\) & \\ \(\frac{\{ID_{i},E_{i},SPW_{i},UC_{i},UC_{s},SV_{s},VCA_{s},REF_{i}\}}{\{NC_{s}= \{ID_{i}||t_{s}\}}}\) & \\ \(\frac{\{ID_{i},E_{i},SPW_{i},UC_{i},UC_{s},SV_{s},VCA_{s},REF_{i}\}}{\{NC_{s}= \{ID_{i}||t_{s}\}}}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Registration phase.  
the server sends a response with a success message and starts the OBU session. The CA Server also updates the logs table for log maintenance. Otherwise, the server sends a failure message in response and does not start the OBU session on failure. It is clear from threshold values that 0 is for \(VCA_{s}\), and 1 is for \(E_{i}\), \(PW_{i}\); that is why when the CA Server receives \(AT_{i}\)== 0, the server compares \(E_{i}\), \(PW_{i}\) database records. If records match, the server sends a response with a success message, starts the OBU session, and updates the log table. Otherwise, the CA Server sends a failure message in the response and does not start the OBU session. The server sends \(\{SK_{i}\), _Message_ } as a response to the OBU. The OBU checks the response; if \(Message==\) \"_Success_\" and \(SK_{s}<>null\) then OBU sets \(AS_{i}=\) \"_OK_\"; otherwise \(AS_{i}=\) \"_Failed_\" (see Table 6).