esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 6 Further Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**CSO**} & \multicolumn{2}{c}{**VSCCPSO**} & \multicolumn{2}{c}{**ALO\_GWO**} & \multicolumn{2}{c}{**ACABC**} \\ \cline{2-9}  & **BABCGWO** & **BABCWOA** & **BABCGWO** & **BABCWOA** & **BABCWOA** & **BABCGWO** & **BABCWOA** \\ \hline \multirow{2}{*}{LSVT} & 0(+) & 0(+) & 0.10(=) & 0.03(\(-\)) & 0(+) & 0.33(=) & 0(+) & 0(+) \\ Yale & 0(+) & 0(+) & 0(+) & 0.57(=) & 0(+) & 0.05(=) & 0(+) & 0(+) \\ colon & 0(+) & 0(+) & 0(+) & 0.03(+) & 0(+) & 0.02(+) & 0(+) & 0(+) \\ SRBCT & 0(+) & 0(+) & 0.04(+) & 0.16(=) & 0(+) & 0.01(+) & 0(+) & 0(+) \\ DBWorld & 0(+) & 0(+) & 0(+) & 0(+) & 0.01(+) & 0(+) & 0(+) \\ Leukemia1 & 0(+) & 0(+) & 0(+) & 0.05(=) & 0(+) & 0.04(+) & 0(+) & 0(+) \\ DLBCL & 0(+) & 0(+) & 0.01(+) & 0.34 & 0(+) & 0.01(+) & 0(+) & 0(+) \\ ALLAML & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\ Pixraw10P & 0(+) & 0(+) & 0(+) & 0(+) & 0.03(+) & 0.02(+) & 0(+) & 0(+) \\ Prostate & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0.19(=) & 0(+) & 0(+) \\ Leukemia2 & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\ GLI\_85 & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Wilcoxon rank sum test on the numbers of features selected by algorithms. In conclusion, the proposed framework is effective. The exploration ability of the ABC algorithm is successfully combined with the updating mode of the algorithm with a strong exploitation ability, such that the BABCGWO algorithm and BABCWOA algorithm can find optimal solutions with lower error rates and fewer feature numbers in a shorter period of time.
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 4 Results
### Waveband Analysis  
[PERSON] et al. [12] proposed a longitudinal segmentation approach for the inverse design of Y-junction splitters using PSO over the 1500-1580 nm band. This segment-based approach resembles our proposal. However, it requires additional post-processing, such as geometry smoothing. Figure 5 depicts the waveband comparison between [12] and our CMA-ES best result. Our approach shows an improved transmittance of \(<\)1550 nm wavelengths. The footprint of our device is only 5% larger than [PERSON]'s device, and it does not need additional processing. These features motivate us to evaluate our method's performance on a broader band.  
Based on our definition of the FOM, which considers the integrals over the SCL and OESCL frequency domain, we expect planarity from the SCL band, given that it represents the narrower band between the two.  
At first glance, the results shown in Figure 6 confirm our expectation. Note that the figure depicts the best and worst executions for each algorithm. Additionally, we calculated the difference between the minimum and maximum values of transmittance in the waveband for each of the three different runs. We make two key observations. First, in the SCL band, PSO is \(\leq\)1%, GA is \(\leq\)1.5%, and CMA-ES is also \(\leq\)1%. All executions show  
Figure 4: Similarity of the FOM for each algorithm (GA, PSO, and CMA-ES) in every optimization process. (**Top**) SCL-band. (**Bottom**) OESCL-band.  
Figure 5: Waveband performance of our work compared with [PERSON]’s device [12]. Our work shows the best result using CMA-ES.  
similar transmittances, and their differences are around the fabrication-error limit. Second, the OESCL results show that the GA is up to 4%, whereas PSO is \(\leq\)1.4% and CMA-ES is \(\leq\)2%, respectively.  
Figure 7 depicts the electric field distribution at 1330 nm and 1550 nm. It shows that the gap between the arms (\(\delta\) in Figure 2) is more constraining than the angles of the junction. This is because of our _angle-based parametrization_ method. We observe a smooth transition from the MMI region through the two spline-shaped arms in both bands. Fabrication is feasible using photolithography.  
We conclude that even when the FOM integral of the OESCL band defined in Equation (2) goes up to more than 2.5 times the value of the integral of the SCL band,  
Figure 6: Waveband analysis of the best (solid lines) and the worst (dashed lines) executions for each algorithm. (**Top**) SCL-band and (**Bottom**) OESCL-band.  
Figure 7: Normalized electric field distributions achieved with the best parameters after the the CMA-ES optimization at the O- and C-bands. Note that at 1330 nm (**left**), the evanescent field has a smaller magnitude than at 1550 nm (**right**) resulting in smaller loss at the gap between the two arms.  
only the transmittance of the GA executions is affected. The effect on the other two methods remains under the fabrication error limit.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 1 Introduction
### Non-Standard or Colloquial Language in a Diverse Patient Population  
The standard language model, which is integrated into the tool, contains a set of several hundred colloquial expressions in both English (EN) and Polish (PL). Future development plans include expanding the language database to encompass phrases that can be recognised in individuals with speech impediments. For instance, we need to address the underpronunciation of the 'r' sound and formalise the recognition of sentences spoken by individuals who stammer.  
Currently, the application includes a detection module adapted to the population of Poland, where society is relatively homogeneous. However, we are aware of the challenges of implementing such a tool across diverse nations. To address the issue of hard-to-recognise descriptions, the tool allows patients to clarify their symptoms by offering a list of ailments to select from, thus improving accuracy and accessibility for all users.
# A SAR-Based Index for Landscape Changes in African Savannas
## 3 Results
### Annual Changes
#### 3.1.2 Natural Resource Depletion  
Based on the derivation of NR values for each grid cell their impact upon environmental integrity (EnvInt, 35%) and human security (HumSec, 65%) can be estimated. Their summarized temporal development is demonstrated in Table 6: The mean change of resources (NRD\({}_{\text{w mean}}\)) ranges between \(+0.379\%\) (2015-2016) and \(-0.421\%\) (2011-2015). This means that positive and negative changes widely balance within the study area. At no time are changes dominated in one direction only. This is even more clearly shown by the median, which is not given in the table, as it levels at 0.0 throughout all years. Still, there are some years which show a slightly higher impact than others. If the NRD\({}_{\text{w}}\) values of all grid cells are added together per year (NRD\({}_{\text{w net}}\)), values between 6.79 and \(-7.55\) emerge. These reveal a more distinct view on the impact of changes over time. While the overall changes in the study area are around \(\pm 3\), a more pronounced impact of \(-7.55\) can be assigned to the period between 2011 and 2015 as a consequence of missing data for an annual change detection.  
However, the large increase between 2015 and 2016 (+6.79) indicates that the remarkably low value in 2015 of (\(-7.55\)) is not only caused by the longer time span but also depicts a local negative peak which needs to be discussed in Section 4.  
The main advantage of the NRD\({}_{\text{w}}\) is that a value of percentaged increase or decrease of landscape quality can be assigned to each grid cell. This allows for the spatial characterization of landscape changes and their impacts. The results are provided as maps for the image pairs throughout the investigated period as demonstrated in Figure 6. It shows where the environment within the study area changed during the investigated years and in which directions changes occured regarding the expert-based weights shown in Table 5. At a first impression, no clear trends can be recognized in the maps. In particular, the regions around Camp Djabal and Goz Beida show no considerable decrease in environmental resources during the last ten years. Also, the Hadjer Arkop massif is predominantly stable during the investigated period. Variations in NR can well be observed around Wadi Aouada and its areas of higher and more developed vegetation. Whether these are just of seasonal nature or a steady development cannot be clarified by comparing single conditions within time-series. This will be addressed more specifically in Section 3.2. In general, the areas of strongest developments are shrubland in the Southeast of the study area and grassland-covered plains in the North.  
Figure 6: Weighted Natural Resource Depletion Index (NRD\({}_{\text{w}}\)) for the study area during the investigated period. Note that, due to lack of available L-band SAR data, an annual investigation was not possible between 2011 and 2015.
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 4 Performance Analysis
Finally, regarding reliability, Azure DevOps offers stronger uptime guarantees and disaster recovery options, making it better suited for mission-critical applications. At the same time, GitHub remains a developer-friendly option with robust, although occasionally inconsistent, service reliability [27]. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Subcategory** & **Azure DevOps** & ** GitHub** & **Remarks** \\ \hline \multirow{2}{*}{Efficiency} & \multirow{2}{*}{Build Execution Speed} & Optimized for large-scale, multi-stage deployments & Faster for small to medium-sized projects & Azure DevOps excels in enterprise-scale builds; GitHub is better for agile workflows. \\ \hline \multirow{4}{*}{Caching Mechanisms} & \multirow{2}{*}{Caching Mechanisms} & Advanced caching for distributed workloads & Basic caching for incremental builds & Azure DevOps provides better caching for complex pipelines. \\ \hline \multirow{4}{*}{Resource Utilization} & \multirow{2}{*}{Resource Utilization} & Fine-tuned control over compute resources & Lightweight, optimized for rapid iteration & Azure DevOps offers more granular resource control. \\ \hline \multirow{4}{*}{Scalability Times} & Pipeline Execution Times & Longer for complex, enterprise-scale projects & Shorter for small, agile workflows & GitHub is faster for smaller projects; Azure DevOps handles complexity better. \\ \cline{2-5}  & & Yes (globally distributed infrastructure) & Limited (primarily cloud-native) & Azure DevOps supports multi-region deployments better. \\ \hline \multirow{4}{*}{Scalability} & Hybrid Cloud Integration & Strong support for hybrid cloud strategies & Limited hybrid cloud optimizations & Azure DevOps is better for hybrid cloud environments. \\ \cline{2-5}  & Large Repository Handling & Efficient for large monolithic repositories & Slower for large repositories with complex pipelines & Azure DevOps handles large repositories more efficiently. \\ \hline \multirow{4}{*}{Reliability} & Concurrent Builds & Handles high-volume CI/CD workloads effectively & Struggles with high-volume, dependency-heavy projects & Azure DevOps is superior for high-volume workloads. \\ \cline{2-5}  & \multirow{2}{*}{Uptime SLA} & 99.9\% (Microsoft global infrastructure) & 99.95\% (GitHub Enterprise Cloud) & Both offer substantial uptime, but Azure DevOps has better failover mechanisms. \\ \hline \multirow{4}{*}{Service Disruptions} & Rare, with robust failover mechanisms & Periodic disruptions during peak usage & Azure DevOps is more reliable for mission-critical applications. \\ \cline{2-5}  & \multirow{2}{*}{Disaster Recovery} & Advanced disaster recovery options & Limited disaster recovery capabilities & Azure DevOps provides better disaster recovery solutions. \\ \hline \multirow{4}{*}{Reliability} & Mission-Critical Suitability & Highly suitable for mission-critical applications & Suitable but less consistent for mission-critical workflows & Azure DevOps is preferred for mission-critical use cases. \\ \cline{1-1} \cl
# Carbonyl Composition and Electrophilicity in Vaping Emissions of Flavored and Unflavored E-Liquids
## 3 Results and Discussions
### Levels of Target Carbonyls in _E-Liquids' Vaping Emissions_
Study conducted by [PERSON] and [PERSON] [11] shows higher concentrations of carbonyls in vaping aerosols of flavored e-liquids than unflavored e-liquids. In this study, unflavored e-liquids (PG-VG) produced higher levels of formaldehyde, acetaldehyde, and acrolein than flavored e-liquids. Yet, all flavored e-liquids produced higher levels of acetone/propionaldehyde. Moreover, at least one flavored e-liquid produced butyratedehyde or benzaldehyde; these compounds were undetected or below detection limit (S/N < 3) in PG-VG emission samples. For example, _trans_-2-hexenol-flavored e-liquid emitted the highest levels of butyraldehyde. Oxidative decomposition could be a possible pathway contributing to the increased formation of butyldehyde (C4H8O) from the degradation of _trans_-2-hexenol (C6H12O) in the presence of oxygen [53; 54; 55]. This shows degradation of flavoring chemicals in e-liquids during vaping leads to additional production of carbonyls.
## 6 Discussion on the Results
### Structural Efficiency of the ACPH System  
From an analytical perspective, the notable yet steady reduction in base shear observed in ACPH frames has substantial implications for the overall structural framework. This decrease in maximum lateral forces facilitates a relaxation of the foundation specifications and lessens the bending and shear demands on the columns, thereby indicating potential financial efficiencies in materials and construction practices. Moreover, the implementation of the artificial hinge mechanism is precisely engineered to constrain plastic inelasticity to a designated steel component, which effectively restricts concrete damage to minor surface cracking. Consequently, repair processes following a seismic event can be streamlined, focusing on the replacement or reinforcement of hinge plates rather than engaging in extensive restoration of larger sections of damaged concrete or dealing with re-lapping procedures for compromised rebar at the ends of beams. Additionally, the hinge unit's capacity for bolting or pinning to the main beam framework introduces an option for disassembly and transportation, which proves advantageous for prefabricated industrial or temporary structures. In summary, this design ethos not only meets performance criteria during significant seismic incidents but also simplifies post-earthquake maintenance, thereby promoting the potential for the entire structure's future reusability.
# Speaker Diarization: A Review of Objectives and Methods
## 1 Introduction
Applying SD to such cases may not seem useful, but annotating ordinary conversations is of interest, where people often speak over each other. Usually, such interruptions, which result in portions of audio with multiple speakers, are brief, e.g., less than a few seconds and with only two simultaneous speakers (i.e., the original speaker usually ceases talking once the interruption is noticed). Accurate SD would note all such cases. The SD task is similar to other sorts of auditory grouping, such as auditory scene analysis [11]. This paper reviews relevant ideas for SD, including a description of the nature of speech, methods to process (analyze) speech, how to estimate the times of speaker change, and methods to identify speakers. Major focus is on modern machine learning methods, which now dominate most applications of artificial intelligence, but earlier techniques are also discussed. The paper will not examine the related problem of _continuous speech separation_, which isolates a set of non-overlapped speech signals from an audio stream that contains multiple utterances that are partially overlapped [12; 13]. As this paper concentrates on classification (time stamps and speaker identities), it will not address peripheral tasks such as speech enhancement (de-noising and de-reverberation) [14], nor discuss joint SD and ASR [15; 16]. The work updates earlier reviews of SD [17; 18; 19] that were written prior to the recent wave of neural systems. It is similar to a recent detailed review [20] but is more aimed toward explanations of methods and description of speaker aspects to exploit. We assume one microphone as a source, as multi-microphone situations typically use beam-forming, which is beyond the scope of this paper [21; 22].
## 2 Materials and Methods
### Overall Workflow  
To classify the different intertidal habitat types, we combined individual bands and sets of band indexes derived from Sentinel-1 and Sentinel-2 imagery with a DEM, produced in this study from a time series of Sentinel-2 scenes representing different tidal stages, to use as potential predictors. Our workflow consisted of (1) an extensive collection of quantitative field reference data, including grain size analysis of sediment samples; (2) the defining of target shorebird intertidal habitats through a set of quantitative empirical thresholds; (3) the collecting and computing of a set of predictors from Sentinel-1 and Sentinel-2 imagery, including a Sentinel-2-derived DEM; (4) the employment of a modified recursive feature elimination algorithm to identify a minimal set of predictors; (5) the training of a random forest predictor with the selected predictors; (6) the validation of the quality of the final model against a fully independent dataset; (7) the production of a classification map for the entire study area; and (8) the employment of a post-classification filter to produce the final map. In addition, we assessed the benefits (in terms of classification accuracy) of combining data from different sensors by comparing the final model and the final map with models and maps produced by alternative models without Sentinel-1 and DEM layers.  
Figure 1: Map of study area, in the Bijagös Archipelago, showing the location of the sampling polygons used for field reference data collection, the limits of the Biosphere Reserve, and the extent covered by each satellite scene used in this study. (**A**,**B**) show RGB images of Sentinel-2 scenes using red, green, and blue bands; (**C**) shows a grey-scale RGB composite with two channels of Sentinel-1 VH and VV bands.
## 2 A Brief Review of the Shapiro Delay Formulae and the Aims of This Paper
### Standard Shapiro Delay Formulae, Derived from the Null Cone Equation
#### 2.2.1 Case A--Plane Elliptical Coordinates of the Satellite
Initially, it was performed in the paper [21] and some results have been summarized (with some new small additions and interpretations) in the review paper [22]. The mathematical properties of the space-oriented transformations (9)-(11) enable the definition of these elliptic integrals in terms of the rapidly changing dynamical parameter during the motion of the satellite--the true anomaly angle \(f\). In fact, this should be considered as a first step in expressing the propagation time in terms of all the Kepler parameters \((f,a,e,\Omega,I,\omega)\), characterizing the transformations (9)-(11). This is a much more complicated problem and for the moment, it is not clear whether the integrals over the other five variables \((a,e,\Omega,I,\omega)\) will be elliptic ones or not. Naturally, during the propagation of the signal from one satellite to another, the true anomaly angle will be the most rapidly changing and the other five parameters will change very slowly. Further, the square of the radius-vector \(x^{2}+y^{2}+z^{2}\) will not contain any of the angles \(\Omega,I,\omega\). In fact, it can easily be calculated from the expressions (9)-(11) for \(x,y,z\) that \(x^{2}+y^{2}+z^{2}=r^{2}\), where  
\[r=\frac{a(1-e^{2})}{1+e\cos f} \tag{13}\]  
and also  
\[E-e\sin E=n(t_{cel}-t_{p})=M. \tag{5}\]0  
Thus, by its construction, \(r\) is a radius vector in the orbital plane, depending only on \(f\) as a dynamical variable. This made possible the calculation of the elliptic integral for the propagation time \(T\) in terms only of the variable \(f\). There is one more physical reason for the propagation time to be expressed only through the dynamical variable \(f\). As shall be shown further, finding the propagation time \(T\) as a function of the eccentric anomaly \(E\) for the plane elliptical orbit is physically justifiable because the eccentric anomaly \(E\) is related to the celestial time \(t_{cel}\) through the Kepler Equation (5) \(E-e\sin E=n(t_{cel}-t_{p})=M\). Celestial time \(t_{cel}\) can be introduced also for the motion of a satellite along a space-oriented orbit. If \(v\) denotes the celestial velocity and \(dt_{cel}\)-the infinitesimal celestial time, then \(vdt_{cel}\) can be written as  
\[E-e\sin E=n(t_{cel}-t_{p})=M. \tag{5}\]1  
\[E-e\sin E=n(t_{cel}-t_{p})=M. \tag{5}\]2  
where  
\[E-e\sin E=n(t_{cel}-t_{p})=M. \tag{5}\]3  
is the velocity, associated with the true anomaly angle \(f\).
# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
## 4 Transient Contents Caching in NDN
### Replacement Policies  
Traditional replacement policies, like LRU and LFU, can work also in presence of transient contents. However, the freshness may also drive the cache replacement operation. Contents that are ready to expire are reasonably the best candidates to be evicted from the CS, when the latter is full and a new packet should be cached. If packets carry lifetime and timestamp information, it is trivial to check when the packet must be dropped.  
Thanks to the FreshnessPeriod carried in each Data packet, NDN can natively recognize if a transient packet is stale. Indeed, the FreshnessPeriod is treated like a timeout by the CS, and, when it expires, the packet is marked as non-fresh.  
NDN consumers decide whether Interests can be answered by non-fresh Data packets, or not, by using the Interest MustBeFresh field. However, some designs, like the ones in References [41, 44], decide to immediately evict from the CS all the stale packets, thus freeing resources for new fresh information.  
When the lifetime is not known in advance, forecasting methods can be developed to estimate, at the consumer/cacher side, when a new version of the content will be generated at the source [50]. For example, the Least Fresh First (LFF) policy in Reference [50] has been conceived for transient traffic originated by sensors and evicts the items that are supposed to be replaced soon based on forecasting of sensors-related future events. In order to predict the residual lifetime of the packet, LFF leverages an Autoregressive Moving Average (ARMA) model for time series forecasting.