esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Comparison of Different Optimization Algorithms  
This subsection compares the performance of various single-objective (GA, DE, ES, SRES, ISRES, and PS) and multi-objective (NSGA-II, NSGA-III, R-NSGA-II, R-NSGA-III, UNSGA-III, MOEA/D, AGE-MOEA, C-TAEA, SMS-EMOA, and RVEA) algorithms when applied to the dynamic balance optimization of a six-bar Watt linkage.  
The optimization algorithms were implemented in Python using the Pymoo library [47]. A consistent set of hyperparameters was employed for all algorithms: a population size of 150 individuals and 500 generations. All other hyperparameters were set to their default values.  
The multi-objective optimization algorithms were executed once, generating solutions that constitute the Pareto front. Conversely, each single-objective optimization algorithm was executed 200 times, each run employing a randomly selected value of \(\gamma\) (ranging from 0 to 1) to define the objective function, as described in Equation (16).  
Figures 4 and 5, along with Table 2, present the performance of the single- and multi-objective optimization algorithms with respect to \(ShM\) and **ShF**, visualized through the Pareto fronts of the obtained solutions. Figure 5 shows that the multi-objective algorithms identified a larger number of solutions. Most solutions found by the single-objective algorithms lie along or near the Pareto front. Visual comparison of Figures 4 and 5 does not readily reveal a single best-performing algorithm. However, analysis of the multi-objective algorithms suggests that NSGA-III, R-NSGA-III, AGE-MOEA, and SMS-EMOA demonstrate strong performance. Table 2 presents the hypervolume metric calculated from the Pareto front solutions obtained by each algorithm. We defined the reference point as [1.0, 1.0]. This metric identifies SMS-EMOA as the most effective algorithm for the optimization of the \(ShM\) and **ShF** objective functions.  
Table 3 presents the execution times for both single- and multi-objective optimization algorithms. All the experiments were executed on a Dell Latitude 7440 laptop with a 13 th Gen Intel(R) Core(TM) i7-1365U processor, 1.80 GHz, and 32 GB RAM. A significant difference in execution times is observed between the two categories. This disparity arises because single-objective algorithms require multiple executions to generate a set of solutions approximating the Pareto front. Conversely, multi-objective algorithms can generate a diverse set of solutions within a single execution. Among the tested algorithms, C-TAEA and MOEA/D exhibited the shortest execution times; however, as illustrated  
Figure 3: Unbalanced dynamic response.  
in Figure 5, their performance in terms of solution quality was not optimal. In contrast, SMS-EMOA, which demonstrated the best performance with respect to _ShM_ and **ShF** (see Table 2), had an execution time of 10.44 min--considerably faster than the single-objective algorithms, which required over 500 min to complete their multiple runs.
## 3 Deep-Learning-Based Channel Estimation Methods
### Data-Driven Deep-Learning-Based Channel Estimation Methods  
Figure 13 illustrates the structure of a simple channel estimator based on data-driven deep learning. The core idea of the data-driven approach is to consider neural networks as \"black boxes\", replacing the original traditional communication system structure with them and perpetually updating their parameters through training with massive amounts of data. Next, we will summarize the data-driven deep learning channel estimation methods by categorizing them according to different kinds of neural networks.
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 4 Proposed Observer and Control Structure
### Fundamentals of the Luenberger Observer  
Generally, the Luenberger observer is designed for linear systems with the given block diagram of Figure 2. This figure consists of three main blocks of (i) the real system, (ii) a dynamic model of the system, and (iii) Matrix L. The goal of the observer is the accurate estimation of the state variables of the system. The best dynamic response and accurate estimation is achieved if the dynamic model of the system accurately models the real system. In other words, it is proved that by providing the accurate state-space dynamic equation of the system, the correct initial conditions, and the inputs of the real system into the simulated state-space equation, the response of the simulated system, including the output and state variables, are equal to those of the real system.  
However, the model of the system may have some uncertainties. In addition, the initial values of the state variables in the real system are not available, and using them in the simulated system is not feasible. To compensate for this deficiency, the error between the measurable output of the real system and that of the simulated system is calculated and multiplied with a matrix, i.e., observer matrix L. The results are fed back to the simulated system, which finally reduces the error values. In an observable system, it is proved that all of the errors between the state variables of the real system and the simulated system are driven to zero by choosing a proper value for matrix L. Accordingly, the time-domain waveforms of the state variables of the simulated system become equal to those of the real system. By this procedure, the state variables of the real systems are obtained without using a direct measurement tool. The details of the Luenberger observer design procedure can be found in [24], which is not further elaborated for the sake of brevity. The application of the Luenberger observer for the control of the GFL-inverter is provided in the following subsection.  
Figure 2: State-space variable observer and its operation mechanism beside the real system.
## 2 Case Description
### Construction Processes  
Figure 3 presents the construction processes of the PRSS. Firstly, the supporting piles, the walings and the steel plates are manufactured in the factory in a standardized manner (Figure 3a). After boring holes at their designed locations (Figure 3b), the supporting piles are lifted into the boreholes, which are backfilled with sand (Figure 3c). When all the supporting piles are installed, the crown beams are subsequently assembled to connect all the supporting piles into an integral structure. Then, a slot is cut between two supporting piles, and the steel plates are inserted into the soil along the slot (Figure 3d). Thus, a supporting structure is formed by the supporting piles, crown beams and steel plates. Then, the excavation can be carried out (Figure 3e). However, before the earth excavation, the underground water level is lowered to 2 m below the excavation depth using the dewatering wells outside the excavation. After the soil is excavated to the designed elevation, the walings are installed to connect the supporting piles, followed by the insertion of wooden  
Figure 1: Schematic diagram of the PRSS: (**a**) aerial view; (**b**) cross section view.  
strips between the walings and the steel plates (Figure 3f). Next, expanded polyurethane is grouted behind the steel plates (Figure 3g). The above steps are repeated (i.e., dewatering, excavation, installing walings, and grouting) until the excavation reaches the final designed elevation of the UGS base.  
Figure 2: Typical soil profile and geotechnical properties. Note: \(\gamma\) represents unit weight, \(w\) represents water content, \(e\) represents the void ratio, \(\varphi^{\prime}\) represents effective internal friction angle, \(c^{\prime}\) represents effective cohesion, \(W_{\rm P}\) represents plasticity limit, \(W_{\rm L}\) represents liquidity limit, \(E_{\rm s}\) represents compression modulus, \(q_{\rm c}\) represents cone resistance in CPT, \(N\) represents the blow count of SPT, and \(K\) represents permeability coefficient.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 3 LIMS System: fs-Laser Ablation Ion Source and Time-of-Flight Mass Analyser
### TOF Mass Analyser
Nevertheless, in orthogonal acceleration, the initial ion kinetic energy distribution of the ions is converted into narrow ion velocity distribution in the ion extraction direction. Ions in an orthogonal acceleration experiment acquire kinetic energy approximately a hundred times higher than their initial kinetic energies and, consequently, time dispersion of the ion bunches can be significantly reduced. Orthogonal acceleration increases the mass resolution of heavy ions; the resolution of light ions decreases as the square root of the mass-to-charge value [130]. While the initial velocity spread in the orthogonal direction is minimised, there is a spatial distribution of ions across the detector surface. Typically, an orthogonal accelerator has a system of grids, which results in a decrease in ion transmission. The repetition time cannot be less than the finite flight-time of the heaviest ion; otherwise, light ions from one cycle may be registered before heavy ions from the previous cycle. The mass resolution derived from the mass spectra is affected by the temporal width of the ion packets arriving at the detector, \(\Delta\)t, which has three major contributions: \(\Delta\)t\({}_{\text{tof}}\), a spread in ion source and flight through the mass analyser; the spread due to the ion detection and coupling to the transmission line system \(\Delta\mathbf{t}_{det}\), and the finite time resolution of the acquisition system (\(\Delta\mathbf{t}_{acq}\)). They affect mass resolution according to the equation:  
\[\mathbf{R}=\frac{\mathbf{m}}{\Delta\mathbf{m}}=\frac{\mathbf{t}}{2\Delta \mathbf{t}}=\frac{1}{2}\frac{\mathbf{t}}{\Delta\mathbf{t}_{\text{of}}+\Delta \mathbf{t}_{\text{det}}+\Delta\mathbf{t}_{\text{acq}}} \tag{1}\]  
where t is the time of flight measured at the peak's maximum and \(\Delta\mathbf{t}\) is its full width at half maximum (FWHM). The pulse width (\(\Delta\mathbf{t}\)) of a particular atomic (isotope) ion package is determined in the measurement. While the ion packet spread by the ion optical settings is proportional to the time of flight t, the other terms are constant. The contribution of \(\Delta\mathbf{t}_{acq}\) is typically determined from the data acquisition system specification, while \(\Delta\mathbf{t}_{det}\) can be obtained by measuring single ion events and \(\Delta\mathbf{t}_{tof}\) is dependent on the ion-optical design of the instrument. All three terms should be minimised during the design process of the instrument to maximise the mass resolution. For most LIMS systems, \(\Delta\mathbf{t}_{tof}\) is much larger than other contributions. Recently developed high-resolution LIMS \(\Delta\mathbf{t}_{tof}\) becomes comparable with other terms and improvements of the other spreads are required [24].
## 3 Proposed Method
### Preliminaries
#### 3.1.2 Communication Model  
The MANET's mobile node moves about unpredictably. Despite the lack of dependability, the extent of node mobility is critical. The mobile node movement pattern, as well as the change in position, acceleration, direction, orientation, and kinetic energy of mobile nodes with time, is displayed. The mobility model is used to represent node mobility in the MANET. The communication between the nodes results in poor network quality whenever the intermediary nodes for packet transmission are not in the specified range [33]. They are a trace-based mobility model that offers movement patterns using a simple method and a synthesized mobility model that offers realistic movements. Let \(R\) be the mobile node transmission radius. The mobile node is transferred to a new site due to the mobility of the node \(V_{m}\). The dimension of the new position of the movable node can be described as Equation (1):  
\[R_{t}=m\ (X_{d},Y_{d}) \tag{1}\]  
where time is stated as \(t\); dimension is referred to as \(m(X_{d},Y_{d})\). Through the mobility \(V_{m}+1\), the mobile node is stimulated to a fresh position inside the communication limits. In the simulated MANET model, a threshold changes the size of the mobile node at the new destination. The connection distances of the nodes are determined to be inside the limit by adding or subtracting the threshold value from the measurement. It is provided by Equation (2),  
\[R_{t+1}=m\ (X_{d}\pm T,Y_{d}\pm T) \tag{2}\]  
Figure 1: MANET model.  
where \(T\) is the threshold value that allows the node to create a momentary sequence inside the communication range. Since it uses a battery-powered source, the lifetime of the MANET is extended by properly managing the energy linked to individual nodes. Since the lifespan of a system is specified as the time from the start of a program until the nodes in the MANET ran out of fuel, the MANET's energy model takes into account all radio operational parameters, including sending, receiving, inactivity, and sleep. The identification of the node-expended energy is feasible at the moment of the routing protocol for data transmission. For packet forwarding, the network routes between the nodes that utilize the least amount of energy. The routing table can be used to store such routes.
## Optical Setup and Image Formation Model
### Noisy Observations  
The measurement process in optics amounts to count the photons hitting the sensor's elements and is well modeled by independent Poisson random variables (e.g., [56; 57; 26]). In many applications in biology and medicine the radiation (laser, X-ray, etc.) can be damaging for a specimen. Then, the dose (energy) of radiation can be restricted by a lower exposure time or by use a lower power radiation source, say up to a few or less numbers of photons per pixel of sensor what leads to heavily noisy registered measurements. Imaging from these observations, in particular, phase imaging is called photon-limited.  
The probability that a random Poissonian variable \(z_{s,\lambda}(x)\) of the mean value \(y_{s,\lambda}(x)\) takes a given non-negative integer value \(k\), is given by  
\[p(z_{s,\lambda}(x)=k)=\exp(-y_{s,\lambda}(x)\chi)\frac{(y_{s,\lambda}(x)\chi) ^{k}}{k!}, \tag{10}\]  
where \(y_{s,\lambda}(x)\) is the intensity of the wavefront at the pixel \(x\).  
Recall that the mean and the variance of Poisson random variable \(z_{s,\lambda}(x)\) are equal and are given by \(y_{s,\lambda}(x)\chi\), i.e., \(E\{z_{s,\lambda}(x)\}=\text{var}\{z_{s,\lambda}(x)\}=y_{s,\lambda}(x)\chi\), here \(E\{\}\) is a mathematical expectation, \(\text{var}\{\}\) is a variance. Defining the observation Signal-to-Noise Ratio \((SNR)\) as the ratio between the square of the mean and the variance of \(z_{s,\lambda}(x)\), we have \(SNR=E^{2}\{z_{s,\lambda}(x)\}/\text{var}\{z_{s,\lambda}(x)\}=y_{s,\lambda}(x)\chi\). Thus, the relative noisiness of observations becomes stronger as \(\chi\to 0\) (\(SNR\to 0\)) and approaches zero when \(\chi\to\infty\) (\(SNR\to\infty\)). The latter case corresponds to the noiseless scenario: \(z_{s,\lambda}(x)/\chi\to y_{s,\lambda}(x)\) with the probability equal to \(1\).  
The parameter \(\chi>0\) in Equation (10) is a scaling factor defining a proportion between the intensity of the observations with respect to the intensity of the input wavefront. This parameter is of importance as it controls a level of the noise in observations. Physically it can be interpreted as an exposure time and as the sensitivity of the sensor with respect to the input radiation.  
In order to make the noise more understandable the noise level can be characterized by the estimates of \(SNR\)  
\[SNR_{\lambda}=10\log_{10}\frac{\chi^{2}\sum_{s=1}^{S}||y_{s,\lambda}||_{2}^{2 }}{\sum_{s=1}^{S}||y_{s,\lambda}\chi-z_{s,\lambda}||_{2}^{2}}\text{ dB} \tag{11}\]and by the mean value of photons per pixel:  
\[N_{photon,\lambda}=\sum_{x}z_{s,\lambda}(x)/N_{sensor}. \tag{12}\]  
Here \(N_{sensor}\) is a number of sensor pixels. Smaller values of \(\chi\) lead to smaller \(SNR\) and \(N_{photon}\), i.e., to noisier observations \(z_{s,\lambda}(x)\).
# On Blockchain-Based Cross-Service Communication and Resource Orchestration on Edge Clouds
## 3 Network Service Marketplace Architecture
### System Architecture
#### 3.2.2 Blockchain Layer  
The Blockchain layer handles through smart contracts all information required regarding users, services etc. This study leverages the Blockchain functionality, the smart contracts and the components described in [8]. Additionally, as it will be described in the following sections, we update and extend the above components. As shown in the middle part of Figure 1, the Blockchain layer architecture refers either to a single EC deployment or a multi-domain EC environment, which can be either single- or multi-administrative. In both scenarios, each EC is considered to be an individual organization in the Fabric Network. Each organization has instantiated a Fabric Peer with the smart contracts, a Certificate Authority to manage identities and a Ledger. The information on the Ledger is identical in every EC. All organizations join a common Fabric channel and finally the orderer handles the consensus mechanism and is responsible for ordering transactions, creating new blocks and distributing a newly created block to all peers in a channel. The Blockchain Layer also forwards all cross-service orchestration requests and information to the Service Orchestrator of the NFV layer, which is thoroughly analyzed in Section 4.2.
## 3 Results and Discussion  
### Compressive and Tensile Response Analysis Subjected to F-T Conditions  
The mechanical results for bitumen incorporating basalt fiber are plotted in Figure 4 for the whole F-T treatment process, including the single-axis compression test and splitting test. From Figure 4, as F-T cycles increase, the single-axis compressive strength gradually declines. Compared with the control group (that is, bitumen without treatment), after F-T conditions, the loss rates of the high-temperature single-axis compressive strength of the bitumen mix are 6.74%, 9.76%, 10.98%, 11.87%, 15.04%, 17.24% and 24.05%, respectively. In the F-T pre-treatment, the single-axis compressive intensity of the bitumen mix declines quickly and levels off in the period of 6-12 times. When freezing-thawing treatment reaches 15-18 cycles, the loss rate of single-axis compressive strength increases. From Figure 4, the splitting intensity for bitumen mix incorporating basalt fiber gradually declines as F-T treatment continues. Meanwhile, after F-T conditions, compared with the control group, the loss rates of the low-temperature splitting intensity for bitumen mix are 8.27%, 14.71%, 18.43%, 19.96%, 21.78%, 28.52% and 31.56%, respectively. In the F-T pre-treatment, the low-temperature splitting intensity continues to decline and gradually tends to be flat after F-T conditions, and splitting strength decreases obviously. The development law of frost damage in cleaving at a lower temperature condition is similar to the above single-axis compression at a higher temperature condition. These results corroborate the findings of much of the previous work on the performance degradation of asphalt mixture under F-T conditions.  
Figure 4: The compressive and tensile strength results under F-T conditions.  
Figure 3: The experimental procedure of this study.
## 6 Typical Application Cases and Empirical Research  
Research indicates that non-conventional lanes have progressed from conceptual exploration to practical application, playing a significant role in alleviating traffic congestion and improving road utilization efficiency. However, application experiences from different countries and regions have also revealed some common issues: safety considerations, driver adaptability, and system integration levels. Future application promotion should focus more on localized modifications, fully considering regional differences in traffic culture, driving habits, and management systems. Simultaneously, there is a need to establish standardized design specifications and implementation guidelines to create replicable and scalable application models. Additionally, international exchange and cooperation should be strengthened to learn from advanced experiences, avoid repetitive exploration, and promote the global development of non-conventional lane technology.
## 5 Conclusions and Future Work  
### Conclusions  
We introduced a novel method designed to enhance the outcomes of DR algorithms when dealing with high-dimensional datasets. The proposed technique is centered around facilitating visual exploration and has been validated through the utilization of clustering algorithms on the resultant data. The key advantage of our approach lies in its complete automation, without requiring fine-tuning, and its compatibility with various DR algorithms. Furthermore, the process requires only a global data analysis, incurring linear computational costs, thereby making it well-suited for standard computers. The data processing, implemented in C++, takes mere milliseconds to execute on a laptop. Another advantage of our technique is that it achieves positive results for a variety of DR methods. These results were obtained without any hyperparameter tweaking of the DR algorithms. We analyzed the efficacy of our method through the use of conventional models featured in articles that analyze DR algorithms. Our system showed favorable outcomes for the majority of such models. Furthermore, we successfully employed the same approach for visualizing documents using document embeddings.
## 2 General Features of ClyA  
### Prevalence of the clyA Gene in Bacteria  
The _clyA_ gene is a chromosomal gene that was first identified in the _E. coli_ strain K-12 [12; 13]. Although the gene is widely distributed in various _E. coli_ strains, sequencing analysis showed that it was disrupted in half of the tested strains, and that the defective forms could be classified into several inactivation patterns based on different insertions and/or deletions [21; 22; 23]. In addition, this inactivated _clyA_ gene is found exclusively in _E. coli_ phylogeny B2 strains, including enteropathogenic _E. coli_ and extraintsinal pathogenic _E. coli_, while the intact gene is relatively conserved in strains from other phylogroups [23].  
Homologs of ClyA are also found in the pathogenic bacteria _Salmonella_ and _Shigella_[24; 25]. _Shigella_ strains often harbor nonfunctional copies of _clyA_ that have been inactivated either by the integration of insertion sequence elements (_S. dysenteriae_, _S. boydii_, and _S. sounei_) or by a frameshift mutation (_S. flexneri_), similar to the inactivated forms of _clyA_ observed in _E. coli_[26]. Regarding _Salmonella_, [PERSON] et al. reported that the _S._ Typhi and _S._ Paratyphi A strains they tested each harbored an intact copy of _clyA_[27]. However, it has been shown that the _clyA_ gene is absent in strains of _S. enterica_ serovar Paratyphi B and serovar Paratyphi C, in various non-typhoidal _S. enterica_ subsp. _enterica_ serovars (Typhimuriumium, Enteritidis, Cholerasusius, Dublin, and Gallinarum), and in _S. enterica_ subsp. _arizonae_ and _Salmonella bongori_. Thus, the _clyA_ gene and its homologs are conserved in some bacteria from the _Enterobacteriaceae_ family.
# Planning of Optical Connections in 5G Packet-Optical xHaul Access Network
## 3 Main Assumptions
### Transmission Model
Eventually, in Table 2, we show the transmission distance (\(L\)) values obtained using Equation (1) and system parameters presented in Table 1, for different values of MUX loss and the number of OADMs present on the optical transmission path. The obtained values of \(L\) are used in the optimization model presented in the next section to determine the maximum number of OADMs that can be installed on a transmission path of a given length.