esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids
## 4 Preliminaries
### Secret Sharing  
Secret sharing is a sophisticated technique used to protect sensitive information by breaking it into multiple fragments and distributing those fragments to various entities, thereby concealing the original secret. The process of secret sharing involves allocating portions of the secret value to different individuals, and the shared value is obtained by summing up the contributions from each participant. Consequently, each party only gains access to a limited portion of the shared value, ensuring that no single entity possesses the complete secret. Depending on the specific scenario, it may be necessary to retrieve all or a predefined number of shared values to reconstruct the original secret. [PERSON]'s secret sharing is an exemplary method that relies on the polynomial equation theory to achieve a high level of information security. Additionally, it effectively utilizes matrix operations, leading to enhanced computational efficiency and accelerated processing [30]. This approach is widely recognized for its robustness and practicality in safeguarding confidential data.  
The secure aggregation algorithm proposed in this paper uses secret sharing based on [PERSON]'s secret sharing, which allows users to decompose their desired secret \(s\) into \(n\) fragments and distribute them to \(n\) other individuals for safekeeping. In this way, when the number of collected secret fragments is less than a threshold \(t\), no one can obtain information about the secret \(s\). However, when \(t\) individuals present their own secret fragments, the secret \(s\) can be reconstructed. The secret sharing comprises two processes: the sharing process, executed by the algorithm \(\textbf{SS.share}(s,t,\mathcal{U})\rightarrow(u,s_{u})_{u\in\mathcal{U}}\), produces a collection of secret fragments \(s_{u}\) for each user \(u\) involved in the sharing. These fragments are uniquely associated with each user. Similarly, the recombination process, performed by the algorithm \(\textbf{SS.recon}((u,s_{u})_{u\in V},t)\to s\), combines the fragments \((u,su)_{u\in V}\) and generates the reconstructed secret \(s\).
## 3 Cross Correlation Analysis to Diagnose the Sizes of Wheel Flats
### Diagnosis of a Flat Size from Flat Signals Including Severe Field Noses  
The normal wheel signal including severe field noises was obtained from a field vehicle test under operating conditions with variable speed, as shown in Figure 13. By the way, the wheel flat signal was obtained by test in the KRRI (Korea Railroad Research Institute).  
Figure 17: Results of the cepstrum analysis using the testing data of the roller rig.  
Then, a final flat signal including severe field noises was obtained by superimposing the above two signals which are the field operation signal on a normal wheel and the tested one on a wheel flat.  
Subsequently, cross correlation analyses with kernel function (g) for the final flat signal, combined with the order analysis, were performed, as shown in Figure 18. It is found in Figure 18 that the cross correlation peaks using the one flat signal as a kernel function are significantly amplified compared to the noise signal peaks. However, it is somewhat difficult to distinguish the flat signal peaks from the noise signal ones because the peak magnitude of the amplified flat signal is a little similar to the noise ones.  
In Figure 19, four flat signals were selected as kernel function g. After that, the cross correlation analysis was performed again. In this analysis, a flat signal was selected as the representative kernel function g for four flat signals of 2\(\pi\) cycle, using the characteristic that the cycle of the flat signal is constant as 2\(\pi\) rad in the angle domain.  
The cross correlation result of peaks on the four-flat signals is about two times larger than that of the noise signal. As shown in Figures 18 and 19, the cross correlation result using the kernel function (g) with the four flat signals makes the flat signal more clearly visible compared to the cross correlation result with one flat signal. As the number of the flat signals used in the g-function increases, the amplification rate of the flat signal also increases. Moreover, the cross correlation result of the noise signal is not amplified regardless of the number of the flat signals used in the g-function.  
Figure 20 shows the results of the cepstrum and cross correlation analyses for the six different flat sizes which are normalized by the analysis result of the flat size of 10 mm. When comparing the analysis results of the cepstrum to those of the correlation, the cross correlation results indicate a linearly increasing trend according to the flat sizes. It means that the cross correlation is more effective than the cepstrum in diagnosing wheel flat sizes.  
Figure 18: Cross correlation result of the flat wheel using one cycle signal for one flat.
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 3 Triple Modular Redundancy in FSMs  
The use of triple redundancy in sequential systems requires deeper analysis. The operation of such systems is determined not only by the input state, but also by the previous state. In view of this, an error in a circuit/module can cause a whole series of incorrect states of such a circuit (Figure 2). Placing a voter as in Figure 6 does not solve all of these problems. Consideration needs to be given to what architecture with voter/voters will effectively achieve the objectives.  
Figure 5: Triple modular redundancy of individual modules in the block with redundant A module (**a**), with redundant B module (**b**), and with A and B redundancy (**c**).  
Figure 6: Impact of SET on internal FSM state (with TMR).  
Let us consider an example of the implementation of an FSM according to [PERSON]'s architecture. Figure 7 also shows the three points at which errors will be injected, assuming they are single errors.  
Let the FSM follow the simple graph shown in Figure 8. Let us consider the behavior of this FSM when an error is injected at three different locations (as in Figure 7). Let us assume that the FSM is in state \(ST1\) at the beginning each time and the input is state in 1.  
Let an error be injected in the output block \(\lambda\) of the FSM (1) on MSB position. In this situation, an incorrect output is realized--the output state is 101. For the second time, let an error be injected into the transition block \(\delta\) (2) on the least significant bit: from 1 to 0. In this situation, the FSM will transition to \(ST2\) state instead of transitioning to \(ST3\) state. The third time, let an error be injected into the state register DFF (3) on the least significant bit: from 1 to 0. In this situation, the FSM behaves as if it was in the \(ST0\) state instead of the \(ST1\) state, so the transition to the \(ST1\) state will be realized.  
Consider these error injection options in FSMs with triple modular redundancy according to different architectures. For simplicity, let us assume that the initial conditions are always the same: the FSM/FSMs is/are in state \(ST1\) and the input is in state 1. Error injector numbers (1)-(3) will be marked as shown in Figure 7. The considerations presented in the following subsections include assumptions of the occurrence of a single error and do not take into account the situation in which the voter is subject to errors. Depending on the placement of the voter and the choice of FSM blocks subjected to redundancy, several error effects mitigation models can be distinguished.  
The models presented in the following sections are shown for [PERSON]'s FSM. Of course, these models are analogous to [PERSON]'s structure, but drawing an additional connection would obscure the individual figures unnecessarily. However, the experiments presented in Section 5 include both [PERSON] and [PERSON] benchmarks.  
Figure 8: State transition graph and state assignment for example FSM.  
Figure 7: [PERSON] FSM with error injection points.
## 4 Simulation Verification
### Verification with Traditional Measurement Method  
Until now, there has not been any commercial measurement system available for identifying the 6-DOF geometric errors of rotary tables without using three-axis machine tools. As a result, a comparison test was performed by using the traditional dial indicator method and the proposed measurement method. Figure 6 shows the verification experiment when using the traditional dial indicator method for rotary table measurements. However, the dial indicator is only capable of measuring in a specific direction. In other words, the real positions of the rotary table in the \(x\), \(y\), and \(z\) directions can be measured and obtained only by using the dial indicator method. As a result, the comparative test results  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(O_{y\mathbf{a}}\) & \(O_{z\mathbf{a}}\) & \(S_{y\mathbf{a}}\) & \(S_{z\mathbf{a}}\) \\ \hline Unit & \(\upmu\)m & \(\upmu\)m & arcsec & arcsec \\ Measured value & 59.4 & 192.6 & 12.78 & 108.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measured results of set-up errors.  
Figure 4: Flowchart of measurement experiment.  
Figure 5: Identified geometric errors of rotary table (**a**) linear errors and (**b**) angular errors.  
of the proposed measurement method and the dial indicator method are shown in Figure 7. Moreover, for evaluating the effectiveness of the proposed measurement method, the \(E_{n}\)-score is adopted for estimating how closely the proposed measurement method agrees with the traditional measurement method. According to ISO 13528 [43], the \(E_{n}\)-score of the measurement results in the \(x\), \(y\), and \(z\) directions can be expressed as follows:  
\[E_{n}(k)=\frac{(x_{i}-\overline{X})}{\sqrt{U_{x_{i}}^{2}+U_{X}^{2}}},\,k\in x,y,z, \tag{13}\]  
where \(x_{i}\) and \(\overline{X}\) denote the measurement results from the proposed and traditional measurement methods, respectively, \(U_{x_{i}}\) and \(U_{\overline{X}}\) represent the expanded uncertainty of the measurement results from the proposed and traditional measurement methods. Accordingly, the average of the absolute value of the \(E_{n}\)-score in the \(x\), \(y\), and \(z\) directions is 0.77, 0.39, and 0.32, respectively. As shown for the calculated results, all the averages of the absolute values of the \(E_{n}\)-score are below 1. Accordingly, the comparison verification results indicate that the proposed measurement method is feasible and effective for identifying the 6-DOF geometric errors of rotary tables.  
Figure 6: Experimental setup for using dial indicator measurement: (**a**) in \(x\) direction, (**b**) in \(y\) direction, and (**c**) in \(z\) direction, respectively.
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 7 Emerging Stars in Disease and Therapeutic Modulation
Initially, researchers employed natural sup-tRNA\({}^{\text{Arg}}\) to restore the expression of E-cadherin from mutated CDH1 genes to retard disease progression in patients with hereditary diffuse gastric cancer (HDGC) [191]. By individually fine-tuning sequences to fit the physico-chemical properties of the amino acids they carried, natural tRNAs could also be altered to be an effective sup-tRNA that could fully bind alanine on the stop codon UGA, capable of restoring the production of the nonsense mutated functional protein in mice [29]. The researchers then screened Sup-tRNA using high-throughput assays to select the sequences best suited for in vitro and in vivo nonsense suppression. [PERSON] and others engineered inhibitory tRNA, known as anticodon-engineered transfer RNAs (ACE-tRNA), to bind to endogenous aminoacyl-tRNA synthetases and allow the corresponding amino acid to bind, forming aminoacyl-tRNA. They then introduced the normal protein's corresponding amino acid at the PTC site, forming a sequence and full-length accurate normal protein [192]. In addition, a groundbreaking study demonstrated the potential of Sup-tRNA in treating genetic diseases caused by nonsense mutations. Delivering Sup-tRNA via the adeno-associated virus (AAV) in mice effectively overcame these genetic mutations [30]. This innovative gene therapy approach utilizes Sup-tRNA to read through premature stop codons, thereby restoring the production of intact and functional proteins under endogenous gene regulation. Research indicates that this method is both safe and effective, with therapeutic benefits lasting over six months after a single treatment, without disrupting global protein synthesis or tRNA homeostasis [30]. Additionally, mutations in aminoacyl-tRNA synthetases have been implicated in Charcot-Marie-Tooth (CMT, a genetic neuropathy with muscle weakness and atrophy in the lower limbs), causing abnormal tRNA binding and impaired release. This disruption leads to a deficiency of tRNA in cells, which subsequently disrupts protein synthesis [193]. However, studies have shown that overexpressing tRNA in CMT disease models in fruit flies and mice can restore protein synthesis, alleviate neuropathic symptoms, and reduce cellular stress responses [193]. This research not only reveals a potential molecular mechanism underlying CMT disease but also suggests that increasing tRNA levels may hold therapeutic promise for this condition. These emerging insights into tRNA and tsRNA biology highlight their crucial roles both in regulating the pathogenesis of various diseases and in serving as potential diagnostic biomarkers and therapeutic targets. The intricate involvement of tRNA in protein synthesis and disease mechanisms, alongside the versatile regulatory functions of tsRNAs, reflects a growing understanding of RNA's broader impact on human health. These small RNAs could become stars in novel treatment strategies, offering precision medicine approaches that leverage their unique properties and interactions within cellular processes.
## 3 System Framework Overview
### Middleware Layer
#### 3.2.5 Security and Privacy  
The proposed model places great emphasis on ensuring security and privacy since an amount of the contextual data that is accumulated, processed, and managed inside the context-aware middleware cloud is sensitive. The security and privacy module, through the performance of security functions which detect and monitor possible irregularities or unauthorized accesses to data, ensures the privacy of contextual information. In this regard, precisely identifying access control as well as encrypting or authenticating contextual data during the transmission and storage processes, are the two basic mechanisms used to perform security functions.  
In the literature, several solutions are proposed concerning context security and privacy [103; 104; 105; 106] among which the One-Time Password (OTP) protocol [107; 108; 109] appears to be the most suitable one for controlling the access to the contextual data stored in the cloud since it can be integrated to mobile devices. This protocol differentiates from the one applying just a static password as it uses cryptographic algorithms to generate a new unique access token for each time a user requests login authentication. The OTP protocol is distinguished in two approaches:  
Counter-synchronized OTP, in which a counter is set for synchronizing the authentication server and the access token. In this approach, the counter value of the generated access token is the security factor, since by the completion of an authentication attempt made by a certain user, it increases and cannot be reused.  
Time-synchronized OTP, in which a timer is set for synchronizing the authentication server and the access token. According to this approach, the user is requested to provide the generated access token within a session of predefined duration as after it expires the access token is no longer valid.  
From the aforementioned approaches the Time-synchronized OTP is the one applied for it is the most appropriate one for providing an enhanced security and privacy mechanism. Moreover, the system model adopts a combination of Attribute Based Access Control (ABAC) and Role Based Access Control (RBAC) in order to contemplate all attributes and roles that must be taken into consideration for controlling the access of users, groups, or applications to individual contextual data based on their level of privacy.  
Several OTP generator applications are available such as Google Authenticator and Free OTP while some cloud computing platforms incorporate a variety of authentication features (i.e., Microsoft Azure platform integrates features as OTP, RBAC, shared key, etc.). For further developing the security and privacy in the system an OTP generator may be integrated within the end user mobile devices to generate the access tokens.
## 3 Results and Discussion
### Differential Scanning Calorimetry (DSC)
Therefore, the tested compounds may not have had a significant impact on the thermal properties of glutenins. Even if the added polyphenols reduce a certain amount of SS bonds formed between or within the protein complex, their presence may not affect the thermal properties of glutenins due to their size and large number of H bonds. However, in the case of gliadins, which are smaller proteins, the presence of additional hydrogen bonds formed between the protein and the added polyphenol could influence the thermal properties of these proteins, increasing the degradation temperature. [PERSON] et al. [35] claimed that due to the spherical structure of gliadins, the hydrogen bonds built into the protein are difficult to break during heating, which results in a higher degradation temperature of gliadins. At the same time, the decrease in the gliadin degradation temperature, depending on the polyphenol concentration compared to the control, may be due to the primary reduction in disulfide bonds caused by polyphenols. Due to their properties, these compounds can reduce SS bonds in gliadins, which makes their structure weaker and easier to decompose.
## 4 Numerical Methods for the Solution
### Parameters and Calculation Steps  
Because Equations (11) to (16) are a set of first-order ordinary differential equations with two-point boundary values, the Runge-Kutta method is applied to solve these equations. On the basis of the dynamic model of the ROV and the umbilical cable, the programming flow chart of cable simulation with the lumped parameter method is as follows (Figure 5):  
For solving the nonlinear equations, an initial value must be given. In the previous studies, most researchers considered the straight line running steadily to be the initial configuration, and the vertical line of the cable in the still water was assumed as its initial state in this study. Further, the program allows running steadily in advance as the initial configuration.  
An ROV with the principal dimensions of 3.17 m (length) \(\times\) 1.81 m (beam) \(\times\) 1.76 m (depth) is adopted as the numerical model for the calculations. The ROV is neutrally buoyant in the water. All the corresponding hydrodynamic coefficients of the maneuvering characteristics of the ROV can be obtained from previous work [32]. The connected point at the free surface near the supported vessel is assumed to be fixed at (0 m, 0 m, 0 m), and the other point of the cable connected to the ROV is set at the gravitational center of the ROV. Parameters of the lumped parameter method for ROV simulation are shown in Table 2.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Device to Device Communication
#### 3.3.4 Security and Privacy
They are denial of service, man in the middle attack, impersonation attack, session hijacking, interference attack, data leakage, malware attack, free riding attack, data modification, privacy violation, forge attack, active attack on controlled data, and so on. All these need to be addressed in the future;
* Privacy and Security: D2D communication allows DUs to communicate directly with one another. However, identifying the trusted users and malicious users is a challenging issue. Therefore, efficient algorithms need to be proposed to identify the trusted users and malicious users before providing a secured connection;
* Data Confidentiality and Integrity: Data confidentiality protects the data being revealed from unauthorised users as well as preserves the user content and secures the privacy. Integrity ensures that data is not altered during the transmission. This is also one of the future challenging issues in providing an accurate data transmission;
* Session Key Agreement: This session key agreement is used to ensure the security of data transfer over air interface. It is mandatory to keep the session key secret to avoid any type of security issues;
* Non Repudiation and Traceability: Non repudiation prevents D2D users from being denied transmission and the reception of messages. Traceability is mandatory to detect the source of false messages in a D2D communication system.
## 3 Data and Methods  
This study used high-resolution 2D seismic data of about 13,000 km collected from 1989 to 1993 by the Guangzhou Marine Geological Survey and reprocessed in 2015. Seismic acquisition parameters: the shot spacing is 25 m, the track spacing is 12.5 m, the sampling rate is 2 ms and the maximum record length is 9 s. All seismic data are mainly distributed in the Wan'an Basin and the adjacent area. The seismic acquisition grid reached 2 \(\times\) 4 km in the main development areas of carbonate platforms in the central and southern parts of the basin and 4\(\times\)8 km in the adjacent areas. According to the layer velocity analysis of the strata, the vertical resolution of seismic data reached 30-50 m. The information on nine wells distributed in the development area of the carbonate platforms provides good support for the interpretation of formation age by well-seismic calibration. In addition, well logging, lithology, mineral composition and biofossils provide valuable information for the sequence division and growth of carbonate rocks.  
All seismic interpretations were completed in the Geoframe4.5 software launched by Schlumberger. Seven sequence interfaces were interpreted from bottom to top, T\({}_{g}\) (-56.6 Ma), T\({}_{6}\) (35.4 Ma), T\({}_{5}\) (23.3 Ma), T\({}_{4}\) (16.3 Ma), T\({}_{3}\) (10.4 Ma), T\({}_{2}\) (5.2 Ma), T\({}_{1}\) (1.6 Ma) in Figure 3[23]. The identification and spatial and temporal distribution of the RCPs were mainly completed by interpreting seismic data and analyzing drilling data. In the main tectonic units, the pseudo-wells were extracted through the common central point trace set of seismic data, and the tectonic subsidence history of the carbonate platform development area was analyzed. The estimation of water depth is mainly based on sedimentary facies and micro-body paleontology information in drilling and on drawing tectonic subsidence curves.  
Figure 3: Tectono-stratigraphic scheme for the Wan’an Basin, modified a [9,24].