esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Small-Molecule Treatment and Emerging Biological Therapy for Poriasis
## 3 Biomacromolecules
### Protein and Peptide
#### 3.1.2 Gene Editing  
Protein-based therapeutics have gained significant traction in the pharmaceutical sector since the FDA's initial approval of recombinant human insulin for diabetes management in 1982. The range of protein drugs, mainly monoclonal antibodies and cytokines, also including enzymes and peptides, has expanded rapidly. However, the inability of proteins to penetrate cell membranes limits their effectiveness to extracellular targets, posing a challenge for intracellular applications. A major focus in therapeutic development is creating protein drugs with high target efficiency. CRISPR-Cas9, a transformative genome-editing technology, has brought to light the critical demand for effective delivery systems capable of transporting Cas9 ribonucleoprotein complexes into the nuclei of target cells. In the last ten years, researchers have developed a range of delivery strategies, encompassing both viral and non-viral methods, to facilitate Cas9 RNP-mediated gene editing. Despite the high efficiency of viral vectors, they are associated with significant risks, including immune system activation, unintended genomic integration, and the potential to induce oncogene expression. In contrast, non-viral systems, including lipids, inorganic nanoparticles, and polymers, are safer and have gained widespread use in Cas9 RNP genome editing [97].  
[PERSON] et al. developed functional microneedle arrays to co-deliver dexamethasone PLGA nanoparticles and NLRP3 specific-Cas9 ribonucleoprotein (RNP) polymer nanocomposites in 2021, targeting the NLRP3 gene to disrupt NLR inflammosomes. Disrupting inflammosomes can alleviate the common off-target problem appearing in corticosteroid treatment. On the other hand, Dex promoted the expansion of nuclear poles, which provided convenient entry of gene editing agents. The presence of Dex increased the indel frequency from 29.6% to 36.2% in DC2.4 cells and 19.1% to 31.7% in 3T3 cells. When microneedle patches were administered to mice, the results showed that the indel frequency was 17.2%, and the skin disorders were vastly relieved [98]. Three years later, the team collaborated with [PERSON] et al. to identify a highly efficient dendrimer delivery vehicle constructed with phenyl-boronate and lipoic acid, also targeting NLRP3 for psoriasis. They screened a series of ratios of phenyl-boronate and lipoic acid to achieve the highest intracellular delivery efficiency. Pharmacodynamics results showed that 33.7% of NLRP3 locus had been disrupted, and psoriasis symptoms had obviously improved equally to calcipotriol ointment [99].
# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 3 Digital Twin Technologies for Electromobility
### Digital Twin and Internet of Things  
Recently, IoT technology has been used in smart and electric mobility. In the electric mobility revolution, DT technology applications are facilitated by advanced data analytics and IoT. Digital and physical interactions are changed by integrating DT and IoT platforms. The IoT enables connections and intelligent access to physical devices, and the DT can handle challenges related to integrating IoT and data analytics, which facilitate rapid real-time analyses and decisions. In electromobility, the IoT has established a wide platform with connected vehicles that can send data from physical devices to the cloud or local  
Figure 4: DTN architecture for electromobility.  
servers. The role of the DT lies in dealing with this information, simulating resources by creating DT models, establishing virtual connections, and integrating with artificial intelligence. Therefore, DT technology is a substantial technology used to innovatively improve performance in electromobility and advances monitoring, analytics, and predictive capabilities [25].  
[PERSON] et al. [26] introduced IoT-based and DT-based models that enable tracking solutions for safety management. The proposed framework allows for a real-time safety tracking mechanism for detecting stationary behavior and self-learning genetic positions to recognize abnormal conditions and obtain accurate locations. The combination of IoT and DT technologies can help operators and stakeholders in taking the necessary technological improvements for making electromobility smarter, by connecting DTs of smart vehicles, simulating and managing the EV fleet data network for value-added services that improve the driving and charging experiences of users, and generating benefits for the entire sector. Being able to simulate the connection of EVs through IoT can, for instance, allow for optimally scheduling the recharging of such vehicles by prioritizing the charging operations based on real-time states of charge, available infrastructure, and/or user preferences. Finally, DT technology facilitates smart functionalities by leveraging data gathered consistently from IoT devices. Moreover, DT enables features such as predictive maintenance, network analysis, energy efficiency optimization, streamlined resource distribution, and real-time network supervision. Additionally, seamless interactions between various networking devices are possible, as their digital counterparts are platform-agnostic and can be managed using standardized methods without concern for the specific technical details of individual devices [27].
## 3 Factors Influencing Volatile Composition
### Postharvest Handling
#### 3.4.3 Chemical Application  
In addition to CA, other gaseous treatments of fruits and vegetable have been reported. Use of ethylene to synchronize ripening has been practiced for years on banana and tomato, and for depreening of citrus. Ethylene treatment of tomato fruit alters volatile levels [116]. 1-methylcyclopropene (1-MCP) treatment of peach fruit \"Tardibelle\" altered the supply of alcohol and acyl-CoA precursors, leading to significant changes in the emission of some volatile esters, particularly of the straight-chain type [8].  
Other chemical treatments of fresh product may also affect flavor. Calcium treatment of fruit is a widely used practice aimed mainly at avoiding the development of bitter pit. Calcium treatment of commercially mature \"Golden Reinders\" apples notably enhanced the production of aroma volatile compounds after mid-term storage under air and, to a lesser extent, under standard CA. Aroma volatile production was severely depleted in ultra-low oxygen atmosphere stored samples, and calcium treatment could not overcome this inhibition [117]. Methyl jasmonate (MJ) alone and in conjunction with ethanol, is able to modify the biochemical pathways of volatile compounds [118]. Reports in the literature have described the impact of MJ on biosynthesis of volatile compounds in both climacter fruit, such as apples and non-climateric fruit, such as strawberries [119; 120; 121; 122]. The application of MJ may avoid undesirable alterations in the volatile fraction occurring postharvest and during storage, as well as minimizing aroma losses. Postharvest MJ treatments in combination with ethanol on the formation of aroma constituents in berryfruit (raspberries, strawberries and blackberries) showed different effects according to berry species. In contrast to raspberries, which exhibited a significant decline in the total amount of volatiles after treatment, a significant enhancement of total volatile compounds was observed in strawberries, while no significant effect was found in blackberries. Esters and terpene compounds responded similarly in strawberries and blackberries suggesting similarity in the biochemistry of their aroma synthesis. In contrast, raspberry volatile compounds showed a different pattern, reflecting different biosynthetic pathways for aroma formation in raspberry. The natural volatile compounds, MJ and ethanol, seemed to have either promoting effects on the formation of the (-)- enantiomers of chiral terpen and ionones or inhibitory effects on the synthesis of the corresponding (+)-enantiomers [123].
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 3 Proposed Algorithm
### Data Encryption Process
After that, a key is implemented on the cipher matrix text obtained from the cipher matrix algorithm so that only authentic users can access the data, for which the Cipher XORation technique is used. In Cipher XORation, the user-defined key and the cipher matrix text are XORed, and a secure ciphertext is obtained. When the data are secured according to different phases and rules, it is impossible to break the security of the data no matter how much the attacker develops a secure algorithm.
## 2 DEA Lens
### Fresnel Zone Plate Lens  
In addition to the above lenses driven using DEAs, a DEA film can be used to prepare a binary phase Fresnel zone plate (FZP) lens [49]. The DEA film is patterned with a series of concentric zones, and the electrode is compatible with the DEA film. The DEA film is stretched on an external rigid frame. The side-view structure of the device is shown in Figure 6. In the voltage-off state, the radius of the \(n_{th}\) zone (\(r_{n}\)) is at its maximum (Figure 6a). The focal length (f) of the lens is defined by \(f=r_{n}^{2}/(n\lambda)\), where the integer \(n\) indicates the \(n_{th}\) Fresnel zone, \(r_{n}\) is the radius of the zone, and \(\lambda\) is the wavelength of the incident light. Therefore, in the voltage-off state, the focal length is the longest. When a sufficiently high voltage is applied across the electrodes, the FZP undergoes contraction, leading the radius of the zones to reduce (Figure 6b). Therefore, the focal length (\(f^{\prime}\)) of the FZP lens is reduced. Because it does not employ any liquid, the device is compact and mechanically stable. This device has potential use in optical communication and sensing. Similar to a conventional FZP, the diffraction efficiency of the FZP lens is low (the theoretical limit is 40.5%). Moreover, the fabrication procedure is quite complicated.
## 3 Performance Analysis  
For the communication system, BER is an important index to measure the communication performance of the system. In the uplink, the bit error rate of the system using hexagonal quadrature amplitude modulation (HQAM) and cross quadrature amplitude modulation (XQAM) can be approximately expressed as [19]:  
\[P=\int\limits_{-\infty}^{\infty}f_{\gamma}(\gamma)P_{e}(\gamma)d\gamma \tag{8}\]  
\(P_{e}(\gamma)\) is the error probability expression of XQAM and HQAM, which can be expressed as:  
\[P_{XQAM}(\gamma)=\bigg{(}4-\frac{2}{M}-\frac{2}{N}\bigg{)}Q(\tau\sqrt{\gamma} )-\bigg{(}4-\frac{4}{M}-\frac{4}{N}\bigg{)}Q^{2}(\tau\sqrt{\gamma}) \tag{9}\]  
\[P_{HQAM}(\gamma)=K_{NN}Q\bigg{(}\sqrt{\xi\gamma}\bigg{)}+\frac{2}{3}K_{NN}Q^{2 }\Bigg{(}\sqrt{\frac{2\xi\gamma}{3}}\bigg{)}-2K_{CNN}Q\Big{(}\sqrt{\xi\gamma} \bigg{)}Q\Bigg{(}\sqrt{\frac{\xi\gamma}{3}}\Bigg{)} \tag{10}\]  
In order to simplify the analysis, the expression part of \(Q^{2}()\) can be ignored, so \(P_{XQAM}(\gamma)\) and \(P_{HQAM}(\gamma)\) can be approximately expressed as [29]:  
\[P_{HQAM}(\gamma)=K_{NN}Q\bigg{(}\sqrt{\xi\gamma}\bigg{)} \tag{11}\]  
\[P_{XQAM}(\gamma)=\bigg{(}4-\frac{2}{M}-\frac{2}{N}\bigg{)}Q(\tau\sqrt{\gamma}) \tag{12}\]  
In the ground-HAP-satellite uplink, the average bit error rate of the system can be expressed as [16]:  
\[P_{BER}=P_{g-h}+P_{h-s}-2P_{g-h}P_{h-s} \tag{13}\]  
where \(P_{g-h}\) and \(P_{h-s}\) represent the bit error rate of the ground-HAP link and HAP-satellite link, respectively:  
\[ \begin{array}{l}P_{g-h(HQAM)}=\int\frac{exp(-\gamma)}{\sqrt{\gamma}}\frac{1 }{2\gamma_{\gamma}^{2}}\sum\limits_{i=1}^{\infty}w_{i}\frac{z_{i}\beta_{1}}{ \eta_{i}}\bigg{(}\frac{\sqrt{\gamma}}{\eta\sqrt{\gamma}}\bigg{)}^{\beta_{1}-1 }exp\bigg{[}\gamma-\bigg{(}\frac{\sqrt{\gamma}}{<I>\eta\sqrt{\gamma}}\bigg{)} ^{\beta_{1}}\bigg{]}\times\bigg{\{}1-exp\bigg{[}-\bigg{(}\frac{\sqrt{\gamma}} {<I>\eta\sqrt{\gamma}}\bigg{)}^{\beta_{1}}\bigg{]}\bigg{\}}^{\alpha_{1}-1}K_{ NN}Q(\sqrt{\xi\gamma})d\gamma\\ \\ g(a_{m})=K_{NN}Q\Big{(}\sqrt{\xi a_{m}}\Big{)}\frac{\alpha_{1}\beta_{1}}{2\eta _{1}\sqrt{\gamma}}\bigg{(}\frac{\sqrt{a_{m}}}{\sqrt{\gamma}<I>\eta_{1}}\bigg{)} ^{\beta_{1}-1}e^{a_{m}-[-(\frac{\sqrt{a_{m}}}{\sqrt{\gamma}<I>\eta_{1}})]^{ \beta_{1}}}\bigg{(}1-e^{-(\frac{\sqrt{a_{m}}}{\sqrt{\gamma}<I>\eta_{1}})} \bigg{)}^{\alpha_{1}-1}\\ \\ H_{m}=\frac{\Gamma(n+1/2)a_{m}}{n!(n+1)^{2}\big{[}L_{n}^{-0.5}(a_{m})\big{]}^{2} }\end{array}
## 2 Integration of Artificial Intelligence (AI) in Robotic Surgery
### Enhancing Surgical Skill Development  
AI in robotic surgery not only aids surgeons during operations but contributes to the education and training of new surgeons. Systems that analyze surgeon performance, such as the video analysis of skill and technique (VAST), evaluate surgical actions based on video feeds from robotic consoles [3]. In VAST, videos of 11 surgeons performing bladder neck anastomosis were analyzed. The goal was to train a computer vision system to evaluate aspects like instrument speed, trajectory, smoothness, and positioning. Surgeons were classified based on skill level, and the system's performance was validated against expert peer assessments using the GEARS tool. The algorithm achieved 83% accuracy using individual instrument data, which increased to 92% with joint movement, and reached 100% when incorporating the contralateral instrument. The study also identified a strong correlation between surgical expertise and variables, such as joint position, acceleration, and velocity. Similarly, [PERSON] et al. developed an AI tool for skill evaluation and measure ment, incorporating video annotation of 25 robot-assisted radical prostatectomy (RARP) procedures using the Proximie augmented reality platform [5]. A novice surgeon annotated the 12 procedural steps, with 17 videos deemed suitable for analysis and 8 excluded due to poor quality or excessive length. The temporal tagging accuracy ranged from 85.6% to 100%, averaging 93.1% [5].
## 3 Methods
### Data Analysis Procedure  
For the data analysis, structural equation modeling (SEM) was employed using partial least squares (PLS-SEM) with SmartPLS v.4 software. This approach was selected due to its ability to model complex relationships between latent and observed variables, allowing for the simultaneous assessment of the validity of the UTAUT2-based theoretical model and the influence of its constructs on university students' acceptance of artificial intelligence.  
First, the validity and reliability of the measurement model were assessed through factor loadings analysis, considering a minimum threshold of 0.70 for item acceptance. Additionally, the internal consistency of each construct was verified using Cronbach's alpha and composite reliability (CR), with an acceptance criterion set above 0.70. Convergent validity was confirmed using average variance extracted (AVE), ensuring values above 0.50.  
To ensure discriminant validity, the Cornell-Larcker criterion was applied, comparing the square root of AVE with the correlations between constructs. After validating the measurement model, the structural model analysis was conducted, evaluating the significance and magnitude of path coefficients using the bootstrapping procedure with 5000 resamples. Determination coefficients (R\({}^{2}\)) were calculated to estimate the explained variance of endogenous variables, and \(\hat{P}\) values were used to assess the effect size of each predictor.  
Additionally, the standardized root mean square residual (SRMR) was incorporated as a global model fit indicator. An SRMR value below 0.08 was considered a good fit criterion, allowing for an evaluation of the discrepancy between the observed and estimated covariance matrices.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 7 Conclusions  
This paper proposed three different covariance matrices as the input features and two different learning strategies for the DOA task. For the matrices, there is a progressive relationship among the three covariance matrices. The second matrix can be obtained by processing the first matrix, and the third matrix can be obtained by processing the second matrix. Compared with TFD, SHD effectively filters out the effects of the microphone array and mode strength to some extent. Compared with SHD, AED more efficiently removes information irrelevant to location information. In terms of the strategies, the first strategy is a regular learning strategy while the second strategy is to split the task into three parts to be performed in parallel. Experiments were conducted both on the simulated and real dataset to show that the combination of the AED features and the segmentation strategy can achieve the best performance. Moreover, through theoretical analysis and elapsed time, it can be observed that the proposed method has lower computational complexity. Thus, it can be concluded that the proposed method can effectively resist reverberation and noise.  
Conceptualization, Q.H. and W.F.; methodology, Q.H. and W.F.; software, W.F.; validation, Q.H. and W.L Fang.; formal analysis, Q.H.; investigation, W.F.; resources, Q.H.; data curation, W.F.; writing--original draft preparation, W.F.; writing--review and editing, Q.H. and W.F.; visualization, W.F.; supervision, Q.H.; project administration, Q.H.; funding acquisition, Q.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61571279.  
Not applicable.  
The authors received the support of the National Natural Science Foundation of China with the grant number 61571279. Sponsors provided financial support only. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Cascaded Controller and ISTSMC for the VTOL System
### ISTSMC for VTOL System  
The ISTSMC-based control algorithm is applied to ensure robustness and reduce the tracking error magnitude so that the desired VTOL system's pitch level is maintained. The state equation for the VTOL system is derived as follows:  
\[\dot{x_{1}}=x_{2},\;\dot{x_{2}}=-4.7x_{1}-0.44x_{2}+24.71u(t) \tag{22}\]  
The \(u(t)\) is the input to the VTOL system in terms of the motor current, and \(y=x=\theta\) is the pitch of the VTOL system along the horizontal axis. To achieve the required performance, the following sliding surface is selected for the VTOL system [31; 32; 33; 34; 35; 36; 37; 38; 39].  
\[\sigma=\dot{e}{+}\lambda e=x_{2}+\lambda(x_{1}-x_{d}) \tag{23}\]  
The \(\lambda\) is the slope of the sliding surface, as the sliding surface implies that \(e(t)\) converges asymptotically to zero as  
\[t\rightarrow\infty\text{ as, }e(t)=e(0)e^{-\lambda t} \tag{24}\]  
The following control law is designed by taking the derivative of the sliding surface:  
\[\dot{\sigma}=-4.7x_{1}-0.44x_{2}+24.71u(t)+\lambda x_{2} \tag{25}\]  
The supertwisting-based reachability is proposed in Equation (26) to ensure the finite time convergence of the sliding mode [36].  
\[\dot{\sigma}=-k_{1}|\sigma|^{\frac{1}{2}}sign(\sigma)-k_{2}{\int}\;sign( \sigma)dt \tag{26}\]where \(k_{1}\) and \(k_{2}\) are the control gains. Now, comparing the value \(\dot{\sigma}\) from Equations (25) and (26), one obtains  
\[-k_{1}|\sigma|^{\frac{1}{2}}sign(\sigma)-k_{2}{\int sign(\sigma)dt}=-4.7x_{1}-0.4 4x_{2}+24.71u(t)+\lambda x_{2} \tag{27}\]  
Equation (26) is simplified to compute the controller input as  
\[u(t)=\frac{1}{24.71}\left[4.7x_{1}+0.44x_{2}-\lambda x_{2}-k_{1}|\sigma|^{ \frac{1}{2}}sign(\sigma)-k_{2}{\int sign(\sigma)dt}\right] \tag{28}\]  
Validation of Control Law  
The stability of the proposed ISTSMC for the VTOL system is based on the nonlinear Lyapunov stability theorem. The existence of a sliding mode can be proven by selecting the following Lyapunov candidate function:  
\[v=\frac{1}{2}\sigma^{2} \tag{29}\]  
The derivative of the Lyapunov candidate function must be negative definite within the state space of the VTOL system to converge the system dynamics to the desired set point. The first-order derivative of the Lyapunov candidate function is given as  
\[\dot{v}=\sigma\dot{\sigma} \tag{30}\]  
Equation (30) is expanded by taking the value of \(\dot{\sigma}\) from Equation (26),  
\[\dot{v} =\sigma(-k_{1}|\sigma|^{\frac{1}{2}}sign(\sigma)-k_{2}{\int sign (\sigma)dt}) \tag{31}\]  
Finally, Equation (31) is simplified as  
\[\dot{v}=-k_{1}|\sigma|^{\frac{3}{2}}sign(\sigma)-k_{2}\sigma{\int sign(\sigma )dt}) \tag{32}\]  
\(k_{1},k_{2}>0\). Now, if \(\dot{v}\) is negative definite, then the ISTSMC is a stable control strategy for the VTOL system. The closed-loop diagram for the VTOL system based on ISTSMC is shown in Figure 4.
## 5 Research Challenges and Future Directions
### Interference Management  
Since spectrum resources are limited and interference might occur, it is crucial to effectively manage the 6G spectra, which requires new approaches to spectrum management and sharing [168]. Maximizing resource utilization while maintaining or improving the quality of service requires effective spectrum management. Issues such as spectrum sharing and managing spectrum mechanisms in heterogeneous networks that synchronize broadcasts on the same frequency must be investigated in depth for 6G [169]. Additionally, researchers must look at how common interference cancellation techniques, including parallel interference cancellation and consecutive interference cancellation, may be used to eliminate the interference.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 7 Issues and Challenges  
Implementing the IoT in drug delivery systems is a complex and challenging task that requires addressing several issues. Addressing these challenges is crucial for the successful deployment of IoT-based drug delivery systems, which can revolutionise the healthcare industry by providing more precise and effective treatments to patients. IoT-based drug delivery systems have the potential to significantly improve patient care and treatment outcomes by providing personalised and timely medication delivery. However, integrating these devices with the existing healthcare infrastructure can be challenging, as there are often several different systems and technologies in place. This can lead to interoperabilityissues, which can be seen by the lack of standardization and compatibility among devices. Some issues faced by IoT-based drug delivery systems are:  
* Data security: robust security measures must be in place to prevent unauthorised access and data breaches.
* Regulatory standards: stringent regulatory standards must be met to ensure patient safety and efficacy.
* Ethical and social implications: ethical and social implications must be addressed through discourse to align with societal values and goals.
* Device reliability and accuracy: IoT devices must be reliable and accurate to ensure patient safety and treatment efficacy.
* Standardisation and compatibility: standardisation and compatibility are crucial for seamless communication and collaboration among devices, sensors, and platforms.
* Limited connectivity: limited connectivity can result in devastating patient consequences, and low latency must be taken into consideration in healthcare.
* High costs of adoption: the high costs of development, manufacturing, and adoption present a significant financial challenge for companies looking to enter this market.
* Inadequately trained IT departments: IT departments may struggle with managing safety, security, and maintenance when dealing with a large influx of IoT devices during implementation.
## 3 Embedded Battery Tile Design and Construction
### Embedded Battery Tile Prototype Construction
From the point of view of the mass, the design can be considered effective, since the batteries make up 45% of the total mass. The outer PCB and the inner PCB, that make up 21% and 19% of the total mass, respectively, should also be considered as part of the structure, since they contribute in some measure to the overall rigidity of the tile.