esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficiency Analysis of Roadway Lighting Replacement in a Selected Polish Municipality
###### Abstract  
The paper addresses the problem of the efficiency of road lighting replacement in a selected municipality. The following question arose: can the replacement of lighting bring measurable financial benefits in the short or long term, and does increasing the comfort of road use in terms of lighting involve higher costs? During the global energy crisis, reducing the operating costs of road lighting has become the goal of many analyses and studies. Professionally selected lighting of streets and sidewalks is a factor that significantly affects the safety of road users. The problem of many studies is the omission of safety considerations, lighting comfort and budgetary possibilities of municipalities. The authors conducted comparative analyses of solution variants, examining both road lighting comfort and costs as an innovative element of research. The DIALux application was used to analyse the changes in road lighting depending on the luminaires used and the changes in pole spacing influenced by the height of the light point. Variant and scenario analyses were incorporated into the efficiency analyses, while the calculations themselves were based on detailed cost analyses with reference to Polish catalogues of material inputs and market prices. The authors conducted cost analyses of lighting dismantling and installation, including the subsequent operating costs over 20 years for seven variants of poles with their systems and four variants of luminaires. The results were compared with the existing lighting system in use. An original element of the study is the use of BIM analyses with design variants, combined with analyses of the technical condition of the existing lighting network and an illuminance analysis with estimates of the height and spacing of poles. Numerous studies indicate that it is very cost-effective to replace old lighting systems, especially those based on high-intensity discharge (HID) sodium lamps, with more modern LED lamps additionally equipped with twilight dimmers. The analyses also demonstrated that cost-efficient lighting replacement could go hand in hand with improved road lighting comfort without the need to incur additional costs, which is often overlooked in various studies. The analysis performed for a model lighting network indicates that savings of nearly 60% are possible over 20 years.  
efficiency analysis; BIM; LED; roadway lighting Article
## 3 Validation of Proposed CEDM
### Discussion of Proposed CEDM for Future Research  
Although the proposed CEDM exhibits reliable predictive capability for compacted zones with different degrees of density due to DC, some inherent limitations are evident when the model is applied to real-world scenarios. These limitations mainly stem from the following simplifications which were made in the model assumptions: (1) Because the soil was treated as homogeneous and isotropic, the elliptical model assumed a symmetric densification pattern around the tamping point; however, this is often not evident in real  
Figure 7: Comparison of predicted and simulated improvement range after (**a**) 1 st, (**b**) 3 rd, (**c**) 5 th and (**d**) 7 th tampings.  
world scenarios, especially those involving irregular or heterogeneous soil conditions. (2) The model assumed idealized soil properties; however, in practice, soil behavior may be influenced by many factors, including moisture content, particle size distribution, and previous compaction history. (3) The model assumed linear or smoothly varying density gradients. In reality, soil compaction may occur in more discontinuous or nonlinear patterns, particularly in irregular or non-homogeneous soils. (4) While the elliptical model captured overall densification, it does not explicitly account for dynamic stress wave interactions (e.g., wave reflections, nonlinear damping) that can affect compaction and the spatial distribution of improvement zones.  
To address these issues, some possible adjustments might be made to improve the applicability of proposed CEDM for broader engineering applications in real-world scenarios, as follows: (1) The model could be adapted to incorporate layered or spatially varying soil properties. This could be achieved by dynamically adjusting the soil parameters (e.g., density, friction angle) based on local measurements or geotechnical surveys. Correspondingly, the contour of the improvement range could be modified through piecewise treatment or by applying the spatial interpolation technique. (2) Introducing nonlinear compaction relationships or adjusting the compaction gradient function could provide a more accurate representation of how soil stiffness evolves during compaction. (3) Some dynamic parameters could be introduced to modify the elliptical model. For example, the attenuation of dynamic stress waves with time and distance could be considered by introducing an attenuation coefficient that varies with time and distance.
## 2 Materials and Methods  
### Tested Optical Fibers  
For the present investigation, three MM optical fibers with pure silica cores, fluorine-doped claddings and polyimide coatings have been manufactured by iXblue (Lannion, France) [29]. The three fibers have been drawn from three distinct preforms that are characterized by different contents of hydroxyl groups bonded to the silica network (Si-OH) with 0.41 ppm (derived by the attenuation peak at 1385 nm), 27 ppm and 873 ppm (from the peak at 1240 nm). Hereafter, we will refer to these fibers as _low-_, _medium-_, and _high_-OH, respectively. The spectral attenuations of the three pristine fibers, obtained by cut-back measurements, are shown in Figure 1. Each sample has a numerical aperture of 0.22 and a diameter of 125 \(\upmu\)m for the cladding and 150 \(\upmu\)m for the coating, while the core diameter is 105 \(\upmu\)m for the _low_- and _high_-OH fibers, and 112 \(\upmu\)m for the _medium_-OH one. The main parameters of the three tested samples are listed in Table 1.  
The number of OH groups affects the transmission properties of silica due to the fundamental OH absorption band at 2.72 \(\upmu\)m and its corresponding overtones, whose major absorption peaks are located at 1.38 \(\upmu\)m and 0.94 \(\upmu\)m. It is known from the literature that, as far as the 0.94 \(\upmu\)m overtone, the OH content affects the attenuation in the order of 1-2 dB km\({}^{-1}\) ppm\({}^{-1}\)[30; 31; 32; 33]. Furthermore, in Figure 1 we observe the absorption band centered at 630 nm due to DID NBOHCs [14] for both the _low_- and _medium_-OH optical fibers. This same band is absent in the _high_-OH fiber due to the excess of hydrogen atoms that passivate the defects.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
###### Abstract
Therefore, it extracts more features than lateral algorithms or other previously proposed bilateral algorithms. For reconstruction, it simply averages two lateral results, thus being more interpretable than other bilateral algorithms. Third, it is rooted in 2 DPCA and A2 DPCA; so, the computational time to extract features by DB2 DPCA will be no longer than those of other bilateral algorithms. The contributions of our work are summarized in the following points:  
1. A novel, bilateral 2 DPCA--i.e., DB2 DPCA--is proposed by fusing two unilateral 2 DPCA algorithms. We concatenate the projection results of 2 DPCA and A2 DPCA together for projection and take the average reconstruction results of 2 DPCA and A2 DPCA for reconstruction. By this way, we can extract more features from images and obtain a more interpretable reconstruction model. 2. We evaluate DB2 DPCA in the tasks of image recognition and reconstruction on four widely used benchmark databases. The experimental results demonstrate that the proposed algorithm outperforms competing algorithms. 3. Additional experiments on reordered and reshaped images further demonstrate the superiority of DB2 DPCA in image recognition. The remainder of this paper is organized as follows. Section 2 reviews the recent advances of 2 DPCA. Section 3 describes the competing algorithms and the proposed DB2 DPCA algorithm. Section 4 conducts experiments of image reconstruction and classification to evaluate the performance of DB2 DPCA. Section 5 shows the experimental results. Section 6 provides discussions on this study and related works. Finally, Section 7 presents the conclusions.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 3 Experimentation Environments
### Megalos  
Kathara supports different virtualization managers, particularly when using Kubernetes [43] as virtualization technology is called Megalos [44], which inherits all the advantages of using containers and Docker images.  
As well as Kathara, Megalos supports the implementation of virtual network scenarios consisting of virtual devices, which in this context are named by the authors as Virtual Network Functions (VNFs), where each VNF may have several L2 interfaces assigned to virtual LANs. By exploiting Virtual Extensible LAN (VXLAN) and EVPN BGP, Megalos guarantees the segregation of each virtual LAN traffic from the traffic of other LANs and Internet traffic. In addition, this key characteristic permits distributing VNFs (i.e., devices) in a cluster of servers, where the L2 overlay permits separating emulation traffic from the cluster traffic. The emulation distribution into multiple nodes permits Megalos to emulate large-scale network infrastructures where a significant quantity of VNFs and virtual LANs are required.  
Cooperation with the developers of both Kathara and Megalos has recently produced Sibyl [45], a software framework based on these tools, tailored to perform a large number of experiments on several virtual fat-tree configurations. Sibyl implements a methodology specifically devised for testing routing protocols in fat-tree networks, including _VFTGen_[46], a tool to generate fat-tree topologies, _RT Calculator_, a tool to detect protocol convergence, and utilities for collecting control plane data and processing results. These tools, which have been used as inspiration for the methodology developed for the rest of the emulation environments, will be further referred to in the rest of the paper.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 6 Applications and Outcomes from Spaceborne LiDAR Data
### Bathymetry--Deriving Underwater Topography  
Bathymetry, the science of measuring and mapping underwater topography, is essential to understanding coastal dynamics, marine habitats, and navigation safety. NASA's Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the Advanced Topographic Laser Altimeter System (ATLAS), uses green-wavelength laser pulses to penetrate clear coastal waters and measure seafloor depths of up to 40 m under optimal conditions. Although bathymetry was not part of the original objectives of ICESat-2's mission, the satellite has been proven to be capable of providing valuable bathymetric data, particularly in the coastal and near-shore regions [162; 163]. Multiple recent studies utilizing ICESat-2 data have been published:  
Ref. [164] describes the creation and evaluation of ATL24, a global bathymetric data product for coastal and nearshore measurements provided by ICESat-2. The team analyzed the mapping performance of the data set and validated its precision at eight different sites. The findings help researchers understand the bathymetric capabilities of ICESat-2 and guide the use and future improvement of ATL24 for scientific and practical applications.  
Ref. [162] provides a comprehensive review of the algorithms used to extract bathymetry data from ICESat-2 satellite measurements. It covers key steps including water surface detection, bottom return classification, refraction correction, accuracy evaluation, and integration with optical imagery. The aim is to guide the development of a standardized global bathymetry product (ATL24) and assist researchers in refining and testing new algorithms for nearshore mapping.  
Ref. [165] presents a new method to measure the depth of Arctic supraglacial lakes using ICESat-2 and Sentinel-2 satellite data, overcoming the limitations of traditional methods. By combining spectral analysis and a refined regression model, the method improves the accuracy of the depth estimation. The tests in Greenland lakes showed error reductions of up to 14%, which improved the ability to monitor lake volume changes and provided better information on the impacts of climate change.
###### Abstract  
Seeds from several _Ribes_ taxa were surveyed for phenolic compounds and in vitro antiproliferative activity against HT-29 colorectal cancer cells. Total phenolic compounds were analyzed through the Folin-Ciocalteu procedure, while LC coupled to a single mass spectrometer (MS) Orbitrap using an electrospray interface (ESI) was performed to determine the phenolic profiles. Antitumor effects were established using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzoIUM bromide) assay. Total phenolics ranged from 11.4 in _R. alpinum_ to 94.8 mg of caffe acid equivalents (CAE)/g in _R. alrigum_ 'Koksa'. Concerning phenolic compounds, four were hydroxylated benzoic acids, four cinnamic acid derivatives, eight flavonoids, and nine flavonoid glycosides. The growth inhibition against HT-29 cancer cells was exercised much better by _R. aligrum_ 'Koksa' and _Ribes_ 'Erkeeni' (GI\({}_{50}\) 37 and 42 ug/mL). All _Ribes_ extracts, except for _R. aligrum_ 'Hara katarik', showed higher activity than _R. rubrum_ (GI\({}_{50}\) at 72 h: 99 ug/mL). Interestingly, the extract from _Ribes_ 'Erkeeni', which exhibited high bioactivity, contains all detected phenolic compounds, unlike _R. aligrum_ 'Koksa', which lacks only populin. Therefore, the high bioactivity found for such extracts could be due to a synergy of all detected compounds. This work constitutes a comprehensive action for expanding knowledge on the phenolic profiles and antitumor activity of GLA-rich _Ribes_ seeds.  
Rules; blackcurrant cultivars; phenolic compounds; LC-MS; HT-29; MTT assay 202  
Moreover, the fruit industry generates an increased number of fruit by-products each year, increasing the tonnage amount of seeds, and the seeds of some _Ribes_ taxa contain valuable amounts of \(\gamma\)-linolenic acid (GLA, 18:3\(n\)-6), which can account for more than 20% of total fatty acids (FAs) [7; 8]. Furthermore, blackcurrant seed oil, rich in GLA, is also appreciated for its phenolic composition [9], which constitutes a large fraction of the unsaponifiable material (the lipid fraction that is not formed by fatty compounds) of most vegetable oils [7].  
The biochemical composition of European currants has been previously described, especially for _R. nigrum_ (black currants) and _R. rubrum_ (red currants), while data on the composition of phenolic compounds and biological activity of the remaining species of _Ribes_ taxa and cultivars are extremely limited. Considering the benefits of the unsaponifiable components in seed oil and the limited information on various _Ribes_ taxa, this study aimed to unravel the phenolic profiles and the in vitro antiproliferative activity against colorectal cancer cells of the phenolic-containing seed extracts from selected _Ribes_ taxa. All studied species/cultivars have been previously typified as potential GLA producers [7].
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 4 Discussion  
An ideal QPE post-processing algorithm would predict the phase accurately in the presence of noise, have non-varying performance across a variety of noise levels, possess minimal overfitting, and have a rapid prediction time so that lag is not induced into any quantum computing applications where QPE is an intermediate step. No algorithm was a clear winner when considering these four criteria, as the lowest-error model (Ax-tuned NN) was also the slowest predictor; the algorithm with the lowest overfitting (linear regression) and fastest prediction time had the highest error level and a high degree of variation of error with noise. The XGBoost ensemble method was judged to be the best tradeoff between these criteria, as it had the second-best error level, second-best prediction time and low variation of error with noise. It is worth noting that this is not necessarily a negative result, as not all applications require speed or maximum accuracy.  
A residual analysis for the 5-qubit XGBoost algorithm is shown in Figure 13, and it can be seen that the model struggles to predict the boundary phases near 0 and near 1 since the phase is an angular measurement that passes continuously between 0 and 1.  
The second-best algorithm was the Ax-tuned NN models as they had the lowest error level and variation of error with noise. Of the three optimizers compared, the stochastic gradient descent (SGD) optimizer was best for all datasets. Other generalizations noted across all datasets were that the best performance was achieved with high initial learning rates, three or four hidden layers, and batch sizes (128-522) that were low in comparison to the 52,500-210,000 row dataset size.  
Figure 12: For the 2-qubit dataset, performance of the final NN model using the Ax-optimized set of hyperparameters as shown in Table 3. The training curves on the train dataset are shown in blue, and the validation split is shown in orange (**left**), and the model performance on the training/test/entire datasets are presented for varying levels of noise (**right**).
## 5 Nonlinear Coefficients of Joints under Different Preloads
### Combined Results of Nonlinearities  
The dependence of the experimental and simulated amplitudes on the preloads was consistent. In terms of the linear components (Figure 8e), the LF amplitudes stayed at the same level for all stress stages. The HF amplitudes, affected by the nonlinear dissipation, increased slightly under the preload from 1 N\(\cdot\)m to 2 N\(\cdot\)m and subsequently decreased with the increase in the load. Additionally, the amplitudes of nonlinear components, viz., SOH, TOH, LS, and RS in Figure 8f show measurable reductions with the increase in the preload initially and then stay steady for the high loads (about 9 N\(\cdot\)m-13 N\(\cdot\)m). As a result (Figure 8h,i), the experimental and FEM nonlinear coefficients generally decreased with the increase in the preload, as predicted by CAN theory. However, the influences of MIN and DN on the nonlinear coefficients were unneglectable. The coefficients related to the HF amplitude took a dive at 2 N\(\cdot\)m and were affected by DN. Meanwhile, all the coefficients dominated by MIN remained at a steady stage over 9 N\(\cdot\)m, leading to difficulties in the early-stage warning for bolt loosening.  
From the above analysis, it is still challenging to quantitatively measure the influences of these combined contributions on the nonlinear responses of a loose bolted joint, even though many efforts have been made by researchers. Hence, a new intelligent method that considers the local influences caused by multi-mechanisms on acoustic nonlinearity generation is needed to make full use of the nonlinear features.
## 2 Material and Methods
### Relatioship between Population Density and Total Imperviousness  
Total imperviousness derived from the LSMA approach at Landsat pixel was aggregated for each suburb (Figure 2). Among around 250 suburbs, 154 suburbs, where population density data are available, were selected for further analysis. Total imperviousness (%) and population density (persons/km\({}^{2}\)) for each suburb were used as dependent variable and independent variable, respectively. Linear and nonlinear regression models were developed. Performance of the regression models was assessed by the coefficient of determination (R\({}^{2}\)), mean absolute error (MAE), and root mean square error (RMSE).
# Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network
## 3 Results and Discussion
### Depth Measurement Results  
Based on the optimized hyperparameters, the convolutional neural network model was trained, and the resulting training and validation loss curves are shown in Figure 11. From the graph, it is apparent that, initially, the training loss was higher than the validation loss. Subsequently, there was a rapid gradient-descent phase, where the training loss decreased faster than the validation loss, indicating that the model converged rapidly. On the validation side, the validation loss also exhibited a descent pattern and quickly reached its minimum value.  
In the end, both the validation and training sets converged to their respective minimum values, and the epochs for convergence were around 25. This suggests that, at approximately epoch 25, the model had found a local minimum during the gradient descent process. To make a further assessment, the model accuracy needs to be considered.  
Figure 12 illustrates the accuracy curves for both the training and validation sets. The accuracy reached its peak at around epoch 25, reaching approximately 99.8% for the training set and around 91.4% for the validation set; compared to the ANN regression model [16] and similar CNN models [23] in the same field, the accuracy of our model is competitive. This confirms that, at epoch 25, the model, indeed, achieved global optimization. It is clear that our model achieves a better convergence effect in the early stage of training. Compared to similar models [15; 16; 17; 18], the training efficiency of the model is improved after the feature extraction of the data via CWT. Early stopping was employed to terminate training when the validation accuracy ceased to improve. Continuing training beyond this point would increase the computational load and potentially lead to higher error rates in the test set.  
The validation results of the model demonstrate that, at epoch 25, the loss function had reached its lowest gradient, and the model accuracy had peaked. At this stage, the convolutional neural network model was trained optimally, achieving an accuracy of 91.9% in recognizing signals from different depths of defects: 0.1 mm, 0.2 mm, 0.3 mm, 0.5 mm, and the comparisons of the depths measured using the proposed method and the designed values is shown in Figure 13; the deviation from the designed values is between \(-\)0.042 mm and +0.025 mm, and the mean deviation is \(\pm\)0.037 mm.  
Figure 11: The training loss of the test set and validation set.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Batch Size**Learning Rate & **0.01** & **0.005** & **0.001** & **0.002** & **0.0004** & **0.0002** & **0.0001** \\ \hline
64 & 88.7\% & 87.8\% & 88.2\% & 90.5\% & 81.9\% & 77.8\% & 86.0\% \\
32 & 88.7\% & 86.9\% & 88.7\% & 91.9\% & 83.7\% & 83.7\% & 82.8\% \\
16 & 87.8\% & 83.7\% & 88.7\% & 86.0\% & 73.8\% & 84.2\% & 80.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The test set accuracy for different batch sizes and learning rates.