esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of Automated Chromosome Recovery in the Reconstruction of Ancestral Gene Order
## 6 Simulations
### Parameters for Simulations  
The simulation proceeds along the branches of the evolutionary tree with the given topology and the known occurrences of whole genome doubling. In addition, we are given the number of chromosomes in each extant genome and statistics on the gene families, with \(J\) families in total, that were used in the reconstruction of ancestral genomes. For each family \(j\), \(M(h,j)\) is the number of genes in the family for each of the extant genomes, where \(h\) indexes the six genomes as in Section 5. Note that the identities of the gene families, compiled using SynMap [6; 7] as described in [5], are retained across all the genomes. The number of genes per gene family is usually 0, 1 or 2 per genome, though some families have more. There are no families with more than 10 genes in any particular genome or more than 50 for all six genomes; such families are excluded at the outset, from both the RACCROCHE analysis and the simulation, as they would tend to degrade the reconstruction. No information on chromosome content, gene order or gene adjacencies is input to the simulation. The numbers of inversions (\(\sim\)150) and translocations (\(\sim\)50) were estimated from RACCROCHE to have affected each extant genome [5]. These numbers are included as parameters in the simulation. Not all gene families are present in all of the extant genomes. We can deduce which gene families are present in each of the ancestral chromosomes through the phylogenetic validation criterion described in [4; 5], to wit, that a gene family is present in an internal node of the tree if and only if it is present in some terminal node in at least two of the three subtrees, ancestral or descendant, subtended by that node (cf. Section 2, item 3). The parameters that must be fixed for use in the simulation include four non-negative cost parameters \(a,b,c\) and \(d\), and a matrix \(N(I,J)\) of estimated gene frequencies, where \(I\) is the number of ancestors and \(J\) is the total number of gene families with representatives in at least two genomes. Algorithm 2 estimates the optimal gene family sizes to be used in the simulation. The topological structure of the phylogenetic tree is input through the arrays \(\mathbf{anc}\) and \(\mathbf{anc}\), which define the ancestor-descendant relations. In the monocor example, \(\mathbf{anc}= \begin{pmatrix}1&1&2&3&4&4\end{pmatrix} \) summarizes the ancestor-extant descendant relations in the tree, while \(\mathbf{anc}= \begin{pmatrix}1&1&2&3\end{pmatrix} \) summarizes the ancestry relations among the ancestors. Ploidy changes along the branches of the tree are counted using the input parameters \(r_{h}\) and \(r_{k}\). These parameters are integers that indicate the number of WGDs between an ancestor and its descendant \(h\) or \(k\), for extant genomes and ancestors, respectively. For example, \(r_{h}=1,2\) or 4 indicates that there are zero, one or two WGDs from an ancestor to its descendant \(h\).
# The Biology of _Pichia membranifaciens_ Killer Toxins
## 2 Isolation of _Pichia membranifaciens_ Killer Strains  
The frequency and distribution of yeasts in fermentations of several olive brines is well referenced [77; 78], but investigations have habitually been related with negative effects [79; 80; 81]. Scarce relevance has been given to the potential positive roles that most significant yeast species can develop in olive brines, including interactions among different yeast species and with other microorganisms, particularly the predominant bacterial populations responsible for the lactic acid fermentation of olives. The majority of yeasts isolated from olive brines bore the killer character, and the strains of _P. membranifaciens_, the dominant species, were found to be particularly active [43; 82]. In this anthropic environment, halotolerant yeast populations develop [43] at pH values around 4.5 and temperatures that have been observed to be compatible with the killer character shown by most yeast isolates of such environments. The presence of NaCl, in concentrations similar to that found in olive brines, increase the killer character and augment the killer spectra of yeasts isolated from this environment, assessing the relevance of the killer phenomenon and indicating that the killer character could be a competitive advantage in this environment [44]. Furthermore, the presence of killer yeast in olive brines is found to be very high (39%), indicating that, in these particular fermentative conditions, the killer character could be particularly relevant for competition [83].  
The isolated _P. membranifaciens_ strains were screened against a collection of several sensitive strains, for the presence of different _P. membranifaciens_ killer phenotypes, determining the presence of, at least, two clearly different ones [20] and confirming the fact that different toxins can be produced by strains of the same yeast species. Two strains of _P. membranifaciens_, CYC 1106 and CYC 1086, were selected because of their different killer spectrum of resistance/sensitivity and their higher activity against the panel of potentially sensitive yeasts used. In conclusion, the strains of _P. membranifaciens_ of our laboratory produced two different toxins that were named PMKT (secreted by _P. membranifaciens_ CYC 1106) and PMKT2 (secreted by _P. membranifaciens_ CYC 1086). These toxins are the main objective of the investigations recovered in the present work.
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 4 Experimental Setup and Scenario-Based Simulation  
This section describes the experimental setups and simulations used to evaluate the proposed mechanism. Algorithms are implemented, and scenarios are simulated using CloudSim Plus [32], a toolkit for modeling and simulating infrastructures and facilities in cloud computing. The proposed algorithms are analyzed from users' and SaaS providers' viewpoints. It assesses how many requests are accepted while maintaining the QoS and how many user requests are handled effectively in a unit of time (it calls average response time). From the viewpoint of the SaaS vendors, it shows how fewer resources can be used to operate the workload version. Consequently, three performance assessment metrics, namely the average response time for requests, the throughput, and the PE cores, are used during vertical scalability.
# An Application of Elastic-Net Regularized Linear Inverse Problem in Seismic Data Inversion
## 6 Conclusions  
The quality of the inverted reflectivity series directly affects the quality of impedance. Sparse-spike inversion adopts a sparse regularization to impose sparsity on the inverted reflectivity series. The high resolution of the sparse-spike-like reflectivity series is obtained at the cost of sacrificing small reflectivity. However, the reflectivity series of underground formations from the actual impedance well log is not strictly sparse. It contains not only  
Figure 10: The inverted impedance profile.  
the sparse major large reflectivity, which corresponds to the main stratigraphic sequence interface of underground formation, but also small reflectivity, which represents weak reflectors of a thin interbed between major reflectivity. The large reflectivity is sparse, but the small reflectivity is dense.  
In this paper, we adopt elastic-net regularization to replace sparse regularization. The elastic net is, in fact, the efficient combination of sparse regularization and dense regularization. Hence, ENRI combines the advantages of sparse regularization and dense regularization. From the synthetic seismic trace tests, we can see that the L2 RI swings some major large reflectivity series to make the inverted reflectivity series and impedance over oscillate; the L1 RI can obtain more blocky impedance, but will suppress many small reflectivity series; and in the inverted reflectivity series by ENRI, both large and small reflectivity series are all recovered well. From the field seismic data application, we can conclude that ENRI provides an effective new alternative method to invert impedance.  
Conceptualization, R.D.; methodology, R.D.; software, D.P.; validation, R.D.; formal analysis, C.Y.; investigation, R.D.; resources, D.P.; data curation, D.P.; writing--original draft preparation, R.D.; writing--review and editing, C.Y.; visualization, D.P.; supervision, D.P.; project administration, C.Y.; funding acquisition, C.Y. 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 (No. 42204123), the Natural Science Foundation of Sichuan Province, China (No. 23 NSFSC5255), the Science and Technology Cooperation Project of CNPC-SWPU Innovation Alliance (No.2020 CX010203), and the Research and Innovation Team of China West Normal University (No.CXTD2020-5). Data sharing is not applicable. The data used to support the findings of this study have not been made available because the author did not get permission to share the data, especially the real seismic data. The authors would like to thank the Research Institute of Exploration and Development, Southwest Oil and Gas Field Company, and PetroChina for their technical support during this research. We would also like to sincerely thank the reviewers for their helpful and constructive comments that clearly contributed to improving this manuscript. The authors declare no conflict of interest.
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 5 Related Work
### Distinct Counting  
On the other hand, distinct counting is also of interest to a lot of researches. However, it is mainly discussed in Vehicular Ad Hoc Networks, like [5; 7], rather than a more general mobile sensing scenario. In the mean while, in order to solve distinct counting problem, there are many studies [5; 28; 29], adopting different algorithms, including FM sketch.  
In [6], [PERSON] et al. present a self-adaptive algorithm, Self-Adaptive LogLog, which is proposed based on Refined LogLog, to adapt to cardinalities of different scales automatically. They focus on the accuracy of the method, while the application scene is not clear and they rarely take the collection process into consideration.  
[PERSON] et al. in [30], use FM sketches to accomplish a kind of robust in-network aggregation in sensor networks. The application situation is believed to result in packet loss or node failures. They consider the coordinated collection of information towards a sink in the sensor network. However, the security problem is overlooked during the entire aggregation process. In [31], the FM sketch is used to integrate with spatio-temporal indexes to solve the problem: \"How many objects were in region \(x\) over the time interval \(t\)?\" Like [30], [PERSON] et al. in [31], do not mention the privacy protection of user data.  
In [32], [PERSON] et al. propose an event-exchanging and data-gathering scheme based on FM sketches in vehicular networks. The sketches can be exchanged without loss of information and can be insensitive, so as to allow manipulating the same physical repository for all vehicles. Their aggregation structure reduces the time needed to actually find a parking space and increases the percentage of vehicles finding such a resource in a bounded time in congested situations.  
[PERSON] et al. propose a secure data aggregation scheme in Vehicular Ad Hoc Networks which is based on the FM sketch in [7]. They also consider the security problem, while their security goal is to enable the traffic monitoring center to verify whether an aggregate sensing report is correct or not. Their security refers to the aggregator's aggregated security rather than the user's privacy protection.  
In general, there are few works that have studied both privacy protection of users and distinct counting for mobile sensing simultaneously. Our scheme, PPDC, integrating the idea of homomorphic encryption into FM sketch, provides a useful solution to solve this research problem. PPDC not only reduces the consumption of storage space and other resources by calculating the number of different elements, but also has practicality due to guaranteeing user privacy, which makes it worthy to be studied and applied for practical applications.
## 2 Materials and Methods
### Shear Velocity Inversion  
An iterative least-squares 1D inversion method [19] was applied to obtain the shear wave velocity profile across the Meishan Fault from the Rayleigh wave phase dispersions. At each station along the array, a constant velocity model was used as the initial model and the uncertainties were applied as weights in the inversion process. During the inversion iteration, the \(\mathrm{Vp}\)/Vs ratio was fixed at 1.80, and the \(\mathrm{Vp}\) and \(\mathrm{Vs}\) were updated in each iteration, as well as the density according to the empirical relationship between \(\mathrm{Vp}\) and density [20]. It was found that the changes in \(\mathrm{Vs}\) between iterations are not always obvious.  
Figure 3: The maximum amplitude of the envelope of the stacked waveforms with respect to source beam slowness and receiver slowness for Stations MS22 and MS34, taken as an example. The color bar represents the amplitude of the stacked waveforms for each slowness, and the white cross indicates the location of the maximum amplitude.  
After this inversion process was performed at each location to obtain a series 1D Vs model, the 2D Vs profile was finally constructed from all 1D Vs models.  
Figure 4: Histograms of repeated measurements of the slowness at Station MS22 at periods of 0.3 s, 0.5 s, and 0.7 s.
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 3 Systematical Design of the Receiver
### Noise Figure  
The design strives to not use external LNA in some weak interference scenarios, reducing costs, so the target NF is 2.5 dB, which is lower than the required 5 dB proposed by [10]. The corresponding cascade noise factor is about 1.7783. According to the cascade noise factor equation [15; 16] and the system architecture, it is mandatory to increase the gain of the first-stage LNA while keeping its NF as low as possible. So the noise factor of the first-stage LNA is 1.7. The corresponding NF is 2.3 dB. The more backward the sub-circuit, the greater the cascade gain. Hence, the later-stage sub-circuits with larger NFs can reduce the design difficulty. As a result, the noise factors of ACTBA, GMT, BPF, and PGA are 6.31, 22.39, 3162.28, and 3162.28, respectively, and the corresponding NFs are 8 dB, 13.5 dB, 35 dB, and 35 dB, which are summarized in Table 1.
## 5 Simulation Results and Discussion
### Simulation Results for the Semi-Urban Network
Table 13 shows the values of \(Ratio\) for the components in the semi-urban network. For all components not listed in the table, the obtained values are less than 0.15 [1/yr]. The highest values of the \(Ratio\) are associated with replacing circuit breakers CB2-CB9 at the beginning of the feeders. Among these, circuit breaker CB7, located at the beginning of the feeder supplying the industrial consumer, has the highest value. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Component** & **CB2** & **CB5** & **CB7** & **CB9** & **L1-new** & **L2-new** & **L3-new** \\ \hline _Ratio_ [1/yr] & 1.4399 & 1.5167 & 4.0485 & 2.6311 & 0.4719 & 0.2226 & 0.6917 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Value of _Ratio_ for the semi-urban network options. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Available Fund** & **Indices** & **Baseline Scenario** & **Improvement of Indices** & **Indices After Implemented Options** \\ \hline \multirow{3}{*}{EUR 250,000} & _SAID_ [h/yr] & 3.743 & 2.772 & 0.971 \\  & _ENS_ [kwh/yr] & 39,050.9 & 30,427.9 & 8623 \\  & _COST_ [keUR/yr] & 224.84 & 181.3 & 43.54 \\ \hline \multirow{3}{*}{EUR 200,000} & _SAIDI_ [h/yr] & 3.743 & 2.688 & 1.055 \\  & _ENS_ [kwh/yr] & 39,050.9 & 29,671.3 & 9379.6 \\  & _COST_ [keUR/yr] & 224.84 & 178.2 & 46.64 \\ \hline \multirow{3}{*}{EUR 100,000} & _SAIDI_ [h/yr] & 3.743 & 1.456 & 2.287 \\  & _ENS_ [kwh/yr] & 39,050.9 & 15,999.3 & 23,051.6 \\  & _COST_ [keUR/yr] & 224.84 & 116.3 & 108.54 \\ \hline \multirow{3}{*}{EUR 20,000} & _SAIDI_ [h/yr] & 3.743 & 0.715 & 3.028 \\  & _ENS_ [kwh/yr] & 39,050.9 & 7467.5 & 31,583.4 \\ \cline{1-1}  & _COST_ [keUR/yr] & 224.84 & 54.01 & 170.83 \\ \hline \end{tabular}
\end{table} 
Table 12: Reliability assessment results for the semi-urban network model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Available Fund** & **Allocated Fund** & **Selected Investment Options** \\ \hline \multirow{3}{*}{EUR 250,000} & \multirow{3}{*}{EUR 250,000} & CB2, CB5, CB7, CB9, TS5-D1, TS5-D2, TS5-D-F, TS6-D1, TS6-D2, TS6-D-F, TS17-D1, TS18-D1, TS19-D-F, TS27-D1, TS27-D2, TS27-D-F, TS33-D1, TS33-D2, TS33-D-F, L1-new, L3-new, Feeder 7-ROD \\ \hline EUR 200,000 & EUR 200,000 & CB2, CB5, CB7, CB9, TS5-D1, TS5-D2, TS6-D1, TS6-D2, L1-new, L3-new \\ \hline EUR 100,000 & EUR 100,000 & CB7, CB9, TS5-D1, TS5-D2, L3-new \\ \hline EUR 20,000 & EUR 20,000 & CB7, CB9, TS5-D1, TS5-D2 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Selected investment options for _COST_ minimization for the semi-urban network model.
# Performance Evaluation of Active Noise Control for a Real Device Casing
###### Abstract  
Reduction of noise generated by devices is an important problem, both in industrial environments where high sound pressure levels may damage hearing, and in households where the sound pressure level is usually moderate, but may cause discomfort and stress. Classically used passive methods often have poor performance for low frequencies. Alternatively, active control can be used to improve noise reduction in this frequency range. In the proposed approach, noise generated by devices may be reduced by controlling vibrations of the casing. The authors previously confirmed the performance of the proposed active control approach using a dedicated noise-canceling casing. Herein, we describe further development and application of the method to an off-the-shelf washing machine. Electrodynamic actuators were installed on four walls of the washing machine. The performance of the control systems was experimentally evaluated during the real spinning phase and the results are reported here.  
active noise control; active casing; structural control; device noise control Article
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 3 Fabrication of Hydrogels
### Chemical Crosslinking
#### 3.3.1 Condensation Reaction  
Condensation reactions, such as EDC/NHS-mediated coupling, leverage functional groups like carboxyl (-COOH) and amine (-NH\({}_{2}\)) groups on the polymer chains [156, 157]. In typical condensation reactions, the reaction involves the removal of water molecules as the bond is formed between two functional groups. EDC/NHS coupling is widely utilized to form an amide bond between carboxyl and amine groups through an intermediate step involving carbodiimide activation. While the EDC/NHS still forms a byproduct like urea, it resembles more of a catalyzed reaction than a conventional condensation reaction [157].  
Generally, these reactions are known to be highly specific and efficient, as they form covalent bonds that contribute to robust mechanical properties of hydrogels. The spontaneity of this reaction simplifies the fabrication process, progressing without continuous external input other than thermal energy [158]. However, it poses challenges in controlling reaction kinetics and ensuring network uniformity [159]. To form a crosslinked network instead of linear polymerization, the presence of multifunctional groups capable of bonding with multiple polymer chains is essential for creating a stable, three-dimensional hydrogel matrix [143, 160]. Additionally, the nature of byproducts generated during condensation reactions, such as water, alcohol, or ammonia, can significantly influence the process. For example, volatile byproducts like alcohol or ammonia require careful removal prior to biomedical uses to maintain biocompatibility and structural integrity [161, 162].
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 4 Theoretical Basis for ANNs to Improve Lighting System Reliability
### Feasibility of Lighting Control Aided by ANNs  
In certain tunnel areas, natural light characteristics are mainly related to latitude and longitude, entrance locations, angle of the sun, season, weather, time periods, cloud layer, atmospheric transparency, ground reflection ability, surrounding vegetation and other natural factors. These characteristics follow certain statistical regularity and strong seasonality. The outside illuminance is heavily influenced by these natural factors. On sunny days, outside illuminance fluctuates significantly and changes from moment to moment. When cloudy (cloud coverage is 0.8-1), the illuminance changes relatively gently. The outside color temperature is comparatively stable, especially on cloudy and foggy days, and color temperature changes gradually. When sunny, the color temperature varies from low to high and then to low, its peak usually appears at about midday, although sometimes it is fairly high near the morning and evening.  
The essence of the tunnel lighting system is to seek a smooth transition and easy adjustment between natural light and artificial lighting through the variation of luminescence parameters of the tunnel luminaries so as to guarantee driving safety and comfort. If the natural light changes in a regular manner, then the controllability of artificial lighting will be of high confidence.  
Supervised learning neural networks can find the intrinsic distribution law of known label data by analyzing this large number of labels and fitting with some specific laws. The trained neural network model can be used to predict the reasonable output of other input data.  
During the training of the neural network, the data labels of the input layer and output layer can be constructed by collecting the actual light sensing data, targeting the inside lighting parameters and tunnel attributes (bidirectional or unidirectional, design speed, design traffic volume, etc.). Through the study of this large collection of data labels, if the lighting neural network can achieve an ideal fit to the actual lighting control system, then the predicted value of the simulation network can be used as the setting parameters of the actual lighting control system.