esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Case 1: Maximizing Decentralization and Security  
Given \(D=D_{max}=1\) and \(S(f)=1\) for \(f_{max}<f\), the goal is to achieve maximum scalability \((C=1)\). To maximize decentralization and security, full node participation is required, meaning that every node participates fully in the consensus process \((p_{i}=1\ for\ all\ i)\), ensuring equal influence among nodes and preventing centralization. The protocol must also tolerate up to \(f_{max}\) fraction of malicious nodes, requiring thorough validation and communication to maintain security.  
The impact on scalability can be analyzed as follows. Each node must broadcast messages to all other nodes, resulting in a total of \(M=N\times(N-1)=O\big{(}N^{2}\big{)}\) messages per consensus round. In a fully decentralized network, consensus protocols like PBFT require all-to-all communication for agreement.  
For latency analysis, the propagation delay (\(t_{propagation}\)) represents the time for a message to reach all nodes and is given by where \(N\) is the number of nodes and \(\delta\) represents the base propagation delay.  
\[t_{propagation}=\delta\times\log\ (N) \tag{35}\]  
In optimal conditions, information dissemination in a network follows a logarithmic pattern due to message forwarding. The processing delay (\(t_{processing}\)) is the time for nodes to process received messages and is calculated as follows:  
\[t_{processing}=\frac{M\times s_{msg}}{B} \tag{36}\]  
depending on the message size (\(s_{msg}\)) and node bandwidth (\(B\)). The consensus delay (\(t_{consensus}\)) involves additional rounds of communication and protocols requiring \(k\) rounds, it is given by:  
\[t_{consensus}=k\times\big{(}t_{propagation}+t_{processing}\big{)} \tag{37}\]  
Therefore, the total latency (\(L\)) is at least as follows.  
\[L\ \geq t_{propagation}+t_{processing}+t_{consensus} \tag{38}\]  
As \(N\) increases, \(L\) grows at least logarithmically, potentially linearly or quadratically, depending on protocol specifics. Therefore, we will have the following.  
\[Complexity(L)\geq O(Log(N))>O(1) \tag{39}\]  
Regarding throughput limitations, each node can process up to \(P\) transactions per second, which means that the network throughput (\(T\)) is limited by \(T\leq P\), as the slowest node becomes the bottleneck. Adding more nodes increases communication overhead without increasing overall network throughput due to processing and bandwidth constraints.  
In conclusion, due to high communication complexity \(\big{(}O\big{(}N^{2}\big{)}\big{)}\) and increased latency, the system cannot achieve \(C=1\) when \(D=D_{max}\) and \(S(f)=1\). Thus, maximizing decentralization and security inherently limits scalability, leading to a contradiction.
# Speech Enhancement Algorithms: A Systematic Literature Review
## 3 Method
### Research Questions (RQs)  
Identifying and constructing the Research Questions (RQs) is the first and most essential process in any literature review. In this systematic literature review, RQs were developed by starting with the broad topic of Speech Enhancement Algorithms (SEAs), conducting preliminary research on specific issues related to SEAs, and narrowing down the focus within this field. The soundness of the potential questions was then evaluated by identifying the main challenges in existing SEAs. The following seven RQs were established to facilitate an in-depth examination of the field:  
**RQ1:**: What are the most commonly used recent research approaches in the field of SEAs?
**RQ2:**: What are the main types of transform-based SEA used in the existing studies?
**RQ3:**: What is the number of channels presented in the current works?
**RQ4:**: What are the major models used to identify speech and noise signals?
**RQ5:**: What are the well-known datasets used in existing papers?
**RQ6:**: What are the most effective measurements for evaluating speech intelligibility and quality?
**RQ7:**: What are the current limitations and challenges in the reviewed SEAs?  
The construction of these RQs was formulated carefully. RQ1 aims to identify recent research topics and then conduct preliminary research on the field of SEAs, which is the subject of interest in this work. Then, the definition of essential issues that need to be addressed in this field is provided based on RQ2, RQ3, and RQ4. Specifically, RQ2 focuses on the recent types of transform-based SEAs, RQ3 highlights the number of channels presented in the current SEA, and RQ4 provides an overview of the major types of models for speech and noise signals. After that, RQ5 and RQ6 narrow the focus and scope of the research by demonstrating the most well-known datasets applied in the field and the most effective measures used for evaluating the intelligibility and quality of enhanced speech signals, respectively. Finally, RQ7 aims to investigate the current limitations and challenges in the reviewed studies on SEAs. The concept of RQ formulation has significant implications for our methodological process, and has been carefully implemented to guide researchers through these questions in order to provide broader benefits to the field of SEAs.
# Antimicrobial Effect of Submicron Complex Oxide Particles CsTeMoO\({}_{6}\) under Visible Light
## 4 Discussion
On the other hand, the difference in the degree of survival of microorganisms under the action of CsTeMoO\({}_{6}\) under dark and light conditions may depend on the physiological and biochemical features in the studied cultures, namely, the presence of possibly different mechanisms of resistance to this complex metal oxide. One of the mechanisms of the antimicrobial action of biocidal compounds is their influence on the cell wall and cytoplasmic membrane, resulting in the disruption of permeability processes through these structures. Therefore, the specific structure of the cytoplasmic membrane and cell wall of microorganisms plays an important role [40]. It is known that the structure and composition of the fungal cell wall differ from that of bacteria. The structure of the cytoplasmic membrane of bacteria also differs from that of fungi (the latter containing ergosterols in the membrane, which can affect its permeability) [41; 42; 43; 44]. Furthermore, the chemical composition and structure of the cell walls of Gram-positive bacteria (_S. aureus_) are markedly distinct from those of Gram-negative bacteria (_E. coli_) [45]. It is known that the structure of peptidoglycans in E. coli and _S. aureus_ differs in the nature of interpeptide bridges [46]. Metal oxides and their modifications destroy biological membranes, which leads to the death of living organisms. The membrane effect of these compounds, for example, in relation to bacteria such as _Escherichia coli_, _Staphylococcus aureus_, _Pseudomonas aeruginosa_ and _Bacillus subtilis_, is mainly associated with a change in the charge of the bacterial cell wall [10]. There are also disparities in the structure of the cell wall between the vegetative mycelium and fungal spores, with the latter having a more robust, two-layered structure. Additionally, in species of the _Penicillium genus_, the spores' cell wall exhibits additional cutinization. It is recognized that the chemical composition of the mycelial cell wall differs between pp. Penicillium and pp. _Aspergillus_. The anatomical and morphological characteristics of the microorganisms under consideration, along with the diverse mechanisms of the inhibitory action of metal oxides on microbial metabolism, enable us to partially elucidate potential differences in the antimicrobial efficacy of the compound under investigation. This applies to its impact on various biological entities under both light and dark conditions. Furthermore, it is known that microorganisms may exhibit unequal susceptibility to toxic compounds due to the presence of enzymes that facilitate the conversion of biocides into non-toxic forms. These enzyme systems may vary in their activity, impacting the microorganisms resistance to antimicrobial agent [47]. The elucidation of the specific mechanisms of the inhibitory effect of this compound lies outside the scope of this article and requires additional special research.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
###### Abstract  
High-precision relative-state measurement technology is one of the key technologies for achieving the precision formation flying of distributed spacecraft. This paper conducts a comprehensive analysis of the precision formation-flying projects of distributed spacecraft in various countries. In the context of practical application, the specific mission configuration, orbit distribution, measurement technology, and payload of the project are summarized. On this basis, the relative state measurement techniques are outlined for the first time, using non-autonomous measurement techniques, autonomous measurement techniques, and new composite relative measurement techniques. A detailed analysis of GNSS (Global Navigation Satellite System)--relative measurement, laser measurement, infrared measurement, RF measurement, visible light visual measurement, and multiple composite measurement methods is conducted. The applicable scenarios of each measurement method are thoroughly discussed from several aspects, such as the technical scheme, system design, accuracy requirements, advantages, and shortcomings. In addition, this paper proposes the concept of adopting a multidisciplinary optimization architecture from the perspective of the overall design of the precision formation of the distributed spacecraft. It enables relative-state measurement payload selection and property indicator optimization, on the premise of optimizing the overall formation performance. Finally, the optimization direction and future development trend of the spacecraft precision formation flight project and relative state measurement technology are established.  
distributed spacecraft; precision formation flying; relative state measurement; multidisciplinary optimization +
Footnote †: journal: Aerospace
## 3 Results  
### ANN Analysis Results  
We recorded experimental data from the PEA where the input was a triangular signal of 145 V with a 4 s period. Furthermore, the displacement was acquired through the strain gauge reader in 40 s experiments with a sampling time of 1 kHz. From this data, we used the displacement as an input and the applied voltage as an output because the aim is to achieve an inverse model.  
After several tests to achieve the best MSE, we configured the ANN in 22 neurons with 5 input delays. The data were split into 70%, 15% and 15%, respectively, for training, evaluation and testing. Finally, the performance was measured with the mean squared error (MSE) in the validation set, where the value obtained was 0.017 in 12.000 iterations made in 4 min.  
Figure 5 shows the performance of the ANN to fit with the PEA hysteresis in a 4 s cycle. Although Figure 5a adapts with a decent effectiveness, Figure 5b exhibits the error where several features can be highlighted. Between 1.5 and 2.5, the error tends to increase with significant peaks; nevertheless, at 2 s, the deviation increases considerably due to the slope change as it is a complex transition to be projected by the ANN. Still, the calculated RMSE for this case provided 0.041 V in comparison to experimental data, which was acceptable.
## 2 Background and Related Work
### Time-Aware Recommender Systems
#### 2.2.2 Temporal Context
Deep learning algorithms have also recently been used in context-aware recommender systems. In [73], a convolutional neural network (CNN) is applied in a recommendation approach that considers the time context. The contextual information is part of the input to the network as well as the ratings and the user and item features. The method is validated against other basic time-aware methods with the MovieLens-1m dataset. Only two time contexts are considered in the experiments: weekend and weekday. Despite the large amount of work published on context-aware systems, there are few studies that have examined time as a contextual variable. In addition, and as far as we know, there are no other studies in which the temporal context and the evolution of preferences are addressed in combination.
# The Standardization Method and Application of the BIM Model for Interchanges
## 3 Classification and Coding of BIM Components for Interchanges
### Method of Coding Components  
Compared with BIM components of other professions, interchange BIM components are more diverse, and the articulation between different components is more complex, making classification more difficult. Moreover, the same types of components are repeatedly used in different ramps, making identification more difficult. Commonly used code types are described in Basic Principles and Methods of Information Classification and Coding [27]. Combining the analysis with the application requirements and the characteristics of interchanges, hierarchical codes and combination codes are the most suitable code structures for interchange BIM components. Hierarchical codes classify the code object into several segments according to the hierarchy, with each segment representing the hierarchy of the code object. Combined codes are a combination of different code segments, which are interdependent but different from each other.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
The relevant national specification standards adopt the method of hierarchical codes for coding, but there are problems, such as poor flexibility of the classification structure and difficulties in subsequent changes. To increase the application capability of interchange BIM component coding and increase the classification flexibility, it is more appropriate to adopt the method of combination codes for coding. The coding content further extends the classification code of highway engineering components [28]. The interchange classification code is 08, in addition to the space code and example code [29], a total of 21 bits (excluding hyphens), and the code composition is shown in Figure 3.  
The coding of interchange BIM components using the combination code approach does not violate the normative principles of coding in essence, but it is a further extension of the coding rules based on the norms. To increase the application capability of the code, the overall spatial code and the example code are added in full combination with the interchange's characteristics.  
The overall spatial code is divided into three levels, which are used to characterize the project name, project section, and unit works, respectively, using the coding rules of sequential codes to indicate the spatial information of the project. The classification code adopts the coding rules of hierarchical code, based on the classification of interchange BIM components, and is mainly used to characterize the categories of components. The example codes are coded using unordered codes to indicate each specific component of the project, and to distinguish each component to ensure its uniqueness.
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
## 3 System Model and Performance Analysis
### Average Channel Capacity Analysis  
A key performance metric is the average capacity of a wireless channel under fading conditions. For the real-valued channel coefficient \(I\), the channel capacity in bits per second per Hz is given as follows:  
\[\bar{C}=\mathbb{E}_{I}\bigg{[}\frac{1}{2}\log_{2}(1+\eta I^{2})\bigg{]}=\int_{0} ^{1}\frac{1}{2}\log_{2}(1+\eta h^{2})f(I)\,dI. \tag{21}\]  
The integral in (21) does not generally yield a closed-form expression. However, using [PERSON]'s inequality [19], an accurate approximation can be obtained by evaluating the function at the mean of \(I^{2}\):  
\[\bar{C}\approx\frac{1}{2}\log_{2}\Big{(}1+\eta\,\mathbb{E}[I^{2}]\Big{)}. \tag{22}\]  
The second moment \(\mathbb{E}[I^{2}]\) of a Beta(\(\alpha,\beta\)) distribution is given as follows:  
\[\mathbb{E}[I^{2}]=\frac{\alpha(\alpha+1)}{(\alpha+\beta)(\alpha+\beta+1)}. \tag{23}\]  
Substituting (23) into (22) yields the approximate closed-form expression for the average capacity:  
\[\bar{C}\approx\frac{1}{2}\log_{2}\bigg{(}1+\eta\cdot\frac{\alpha(\alpha+1)}{( \alpha+\beta)(\alpha+\beta+1)}\bigg{)}. \tag{24}\]  
Note that while the Beta distribution provided the best overall fit among the candidate models, its minor deviation from the empirical data in the high-irradiance region may lead to slight differences between the analytically derived expressions presented in this section and practical performance metrics.
## 2 Mycobacteria Diversity and Evolution
### Evolutionary Drivers of the Mycobacterium Genus  
The increasing availability of data from genomics approaches brought insights into the evolutionary processes of _Mycobacterium_ genus, however, most works remain focused on specific species or complexes, namely those of high clinical importance, such as the _Mycobacterium tuberculosis_ complex (MTC).  
Pan-genome analyses accomplished by different research groups underlined an open pan-genome [17; 18], with the work conducted by [PERSON] and collaborators (2017) identifying 150 thousand unique gene families that reflect the potential functional repertoire of mycobacteria [17]. Another work studied the balance between gene gain and loss and revealed that slow growers had generally gained and lost more genes compared to the rapid growers. The loss by slow growers of genes responsible for the access to extracellular nutrients was attributed to their slow growth rate [18]. The number of species-specific genes and relative gene balance might have contributed to mycobacteria adaptation to different ecological niches and environments and the differential pathogenic capacities of some members.  
Other works evaluate the importance of phenomena like horizontal gene transfer (HGT) to _Mycobacterium_ genus evolution, being plasmid exchange one of those mechanisms. However, the majority of works focus a limited diversity of mycobacterial species that include MAC, _M. scrofulaceum_, and _M. fortuitum_[19]. Hybridization works revealed the relatedness of plasmids from _M. avium_, _M. intracellular_, and _M. scrofulaceum_, isolated from clinical samples and the environment [20], suggesting that those plasmids had moved across species in the environment. Moreover, work conducted with _M. avium_ and _M. intracellular_ show that plasmids occur more frequently among clinical isolates than environmental strains, suggesting a role in pathogenicity [21].  
Experimental procedures have demonstrated that plasmids can be transformed and maintained into other mycobacteria, such as _M. tuberculosis_ and _M. smegmatis_, indicating that the different environmental niches occupied by their hosts and the physical inability to spread might explain their limited host range [22; 23].  
The evolution within the _Mycobacterium_ genus was traditionally considered clonal, however recent studies show evidence of HGT events in _Mycobacterium_ genomes, particularly involving genes related with nutrient transport, metabolic processes, resistance against drugs, and defense mechanisms, that all together may have contributed to metabolic versatility, ecological adaptation, pathogenicity, and survival under harsh conditions [17; 24; 25]. In fact, plasmids isolated from _M. marinum_ and _M. abscessus_ contain mercury resistance genes [26; 27], plasmids from _M. scrofulaceum_ are associated with both mercury and copper resistance [28; 29], and plasmids from _M. ulcerans_ contain genes involved in toxin production [30].
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## Appendix B The Calculation of Off-Axis Angles \(\mu_{i}\), \(\
u_{i}\)  
The off-axis angle \(\mu_{i}\) and \(\
u_{i}\) are computed as the following.  
\[\mu_{i}=sign(x)\times arccos(\frac{x}{\sqrt{x^{2}+z^{2}}}) \tag{11}\]  
\[\
u_{i}=sign(y)\times arccos(\frac{y}{\sqrt{y^{2}+z^{2}}}) \tag{12}\]  
In Equations (11) and (12), \((x,y,z)^{T}\) denotes the unit vector, \(\overrightarrow{n_{LED}}\), in the beam coordinate system \(X^{\prime}Y^{\prime}Z^{\prime}\). Then, the coordinates of \(\overrightarrow{n_{LED}}\) in the beam rectangular coordinate system are derived. The coordinates of the NGSO satellite and the GSO satellite in the Earth coordinate system could be expressed as (10) and (12).  
\[r_{n}=(0,R_{n}cos(\psi),R_{n}sin(\psi)) \tag{13}\]  
\[r_{\bar{g}}=(R_{\bar{g}}sin(\sigma),R_{\bar{g}}cos(\sigma),0) \tag{14}\]Accordingly, the unit vector \(\overrightarrow{n_{GL}^{\prime}}\) in the earth rectangular coordinate system can be represented as (A9), and the conversion matrix from the beam rectangular coordinate system to the earth rectangular coordinate system is expressed as \(R\).  
\[\overrightarrow{n_{GL}^{\prime}}=\frac{r_{g}-r_{n}}{\|r_{g}-r_{n}\|}\] (A9)  
\[R=\left(\begin{array}{ccc}0&-sin(\psi-\alpha_{i})&cos(\psi-\alpha_{i})\\ -1&0&0\\ 0&-cos(\psi-\alpha_{i})&-sin(\psi-\alpha_{i})\end{array}\right)\] (A10)  
Since \(G\), \(L\) and \(D\) are inline, the coordinates of \(\overrightarrow{n_{LD}}\) can be represented as the following.  
\[\overrightarrow{n_{LD}}=R\overrightarrow{n_{GL}^{\prime}}\] (A11)  
Through the unit vector \(\overrightarrow{n_{LD}}\), we can obtain the off-axis angles according to (A5) and (A6).
## 3 Results and Discussion
### Studies on OGN Desorption
This effect is characteristic for the ion-exchange mechanism and ion competition for electrostatic interaction with adsorbent active sites. The influence of methanol content in the elution solvent on the extraction recovery. The influence of MeOH percentage in the elution solvent on the OGN desorption efficiency was also studied. The 10 mM solution of NH\({}_{4}\)OAc at pH = 9.5 was with MeOH mixed at appropriate volume ratio: 10, 30, 50, 70, and 90% \(v/v\). Other extraction parameters were as described in Section 2.4. Obtained results were presented in Figure 4c. Appropriate MeOH content was significant for desorption efficiency. The highest recovery of DNA-20 from MNP-Ac was obtained for 50% \(v/v\) of MeOH in elution solvent. It is equal to 94.1 \(\pm\) 2.4%. Both increasing and decreasing its content cause a decrease in the desorption efficiency (Figure 4c). An appropriate organic solvent addition allows for overcome of some additional forces causing the OGN retention, e.g., \(\pi\)-\(\pi\) bonds between aromatic rings of MNPs coating and heteroatom rings of nucleobases. Recovery losses in MeOH content above 50% \(v/v\) may relate to changes in the mechanism and increasing of participation of retention in high organic solvent content characteristic for HILIC separation. In opposite to MNP-Ac, this effect is dominant in the whole range of studied desorption conditions in the cases of two other MNPs: MNP-Sul and MNP-Mal (Figure 4c). The recoveries values decrease with an increase of MeOH percentage in elution solvent. The highest values were obtained for 10% \(v/v\) organic solvent addition and were equal to 90.9 \(\pm\) 1.7% and 82.7 \(\pm\) 1.0% for MNP-Sul and MNP-Mal, respectively (Figure 4c). The influence of desorption time on the extraction recovery. Finally, the impact of desorption time on its efficiency was studied. The desorption studies showed that the best results are obtained for MNP-Ac. Thus, this step of the research was performed only for this material. The desorption of DNA-20 was performed with 10 mM solution of NH\({}_{4}\)OAc at pH = 9.5 with addition of 50% \(v/v\) of MeOH at different times: 1, 5, 10, 30, and 60 min. Other extraction parameters were as described in Section 2.4. The obtained results are plotted in Figure 5. The largest amount of OGN was desorbed after 60 min. However, the recovery after 30 min was only \(\sim\)6% percentage points lower, and it was still higher than 90%. Thus, the 30 min desorption time was applied in the final procedure as a compromise between good recovery and sample preparation time.