esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This research investigates the energy sustainability of CLT and straw residential buildings in the Cold and Severe Cold Regions of China. Overall, the calculated heating energy-saving ratios of CLT buildings for Hailar, Harbin, Urumchi, Lanzhou, and Beijing are 3.04%, 7.39%, 7.43%, 12.69%, and 13.41%, respectively, when compared with RC. The heating energy-saving ratios for straw bale buildings in these cities are 8.04%, 22.09%, 22.17%, 33.02%, and 34.28%, respectively. Although RC is the most frequently used building material in these climate regions, there is great potential for the use of CLT and straw in this construction market.  
This analysis also provides an assessment of the influence of orientation on building energy consumption. From the aspect of heating energy conservation, south-facing is the preferred orientation for buildings located in the Cold and Severe Cold regions of China. South-facing buildings require  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Hailar**} & \multicolumn{3}{c}{**Harbin**} & \multicolumn{3}{c}{**Urumchi**} & \multicolumn{3}{c}{**Lanzhou**} & \multicolumn{3}{c}{**Beijing**} \\ \cline{2-13}  & **RC** & **CLT** & **Straw** & **RC** & **CLT** & **Straw** & **RC** & **CLT** & **Straw** & **RC** & **CLT** & **Straw** & **RC** & **CLT** & **Straw** \\ \hline
1F & 201.92 & 199.57 & 194.87 & 202.24 & 197.30 & 186.04 & 177.56 & 173.61 & 164.45 & 121.01 & 116.90 & 108.46 & 122.08 & 117.95 &  
5%-7% less energy than those facing east or west. In terms of building height, as the building height increases, the household energy consumption decreases gradually.  
The analysis confirms that, as bio-based building materials, CLT and straw are more energy efficient than RC in the Cold and Severe Cold regions, reducing the heating energy requirements. There is great potential for the promotion of the bio-based building material in Severe Cold region and Cold region in China.  
The energy-saving potential of the bio-based building materials may also be presented in the regions sharing similar climatic regions with northern China worldwide. As more energy-efficient buildings are in need to reduce environmental impacts of building industries in the cold regions worldwide, the applications of bio-based buildings materials, including CLT, Straw Bale, and so forth, will have similar energy-saving potential as in China.  
Conceptualization, H.G. and X.Y.; methodology, S.Z. and L.H.; software, S.Z. and L.H.; validation, T.Q., W.S.; formal analysis, S.Z.; investigation, T.Q.; resources, W.S.; data curation, S.Z.; writing--original draft preparation, H.G.; writing--review and editing, H.G.; visualization, S.Z.; supervision, X.Y.; project administration, S.Z.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
This research is funded by National Natural Science Foundation of China, grant number 51608144; and Heilongjiang Provincial Natural Science Foundation of China, grant number LH2019E110.  
The authors declare no conflict of interest.
## 5 Performance Evaluation
### Simulation Results
#### 5.2.10 Changes in Packet Delivery Ratio vs. Duty Cycle  
Figure 16 illustrates the packet delivery ratio across varying node densities under different duty cycle conditions. Since all the compared protocols employ opportunistic routing by transmitting packets through available relay nodes, the initial packets are successfully delivered to the sink. The primary distinction lies in the extent to which redundant packet transmissions are minimized. The graph indicates that in low-density networks, the proposed protocols exhibit suboptimal performance due to a higher ratio of redundant packet deliveries. However, in high-density scenarios, our protocols effectively mitigate redundant transmissions, thereby improving overall network efficiency.  
Figure 16: Comparison of the packet delivery ratio with varying non-uniform densities and duty cycles.  
Figure 15: Comparison of the end-to-end packet delivery delay with varying non-uniform densities and duty cycles (for a single packet transmission).
# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 3 PV Model Architecture
### Flowchart of the Model  
The flowchart of the PV prediction model based on GBDT is shown in Figure 1.  
Refer to Algorithm 1, the prediction process can be divided into two parts. The first part is to train the model with the sample data. The other one is to predict future power output with the trained model.  
At the beginning, we should input the training data which record the history weather and power condition, and then pre-process the data based on the method introduced in Section 3.3. After the initialization of GBDT function according to (8), the loop process begins and the model comes in to the iteration structure.  
In each loop, firstly, we update the \(\overline{y_{i}}\) based on \(F_{m-1}(\mathbf{X})\). Then we traverse all the split points in the CART algorithm and calculate the loss function for every split point according to (13). After that, we need to choose the split point, where the loss function reaches the minimum value, and confirm the basis function \(h(\mathbf{X}_{i};j_{m},s_{m})\) of this iteration. After the best split point is found, we calculate the step length according to the minimum loss function norm.  
At last, we should update the \(F_{m}(\mathbf{X})\) function according to basis function and step length and then judge the number of iterations and convergence. If the number of iterations reaches the maximum value or the algorithm has converged, the final function \(F(\mathbf{X})\) is conformed which means the training part ends and the prediction model begins.  
In the second part, we input and pre-process the weather forecast data, and then calculate the prediction value based on the function \(F(\mathbf{X})\) trained in the first part. Next, we restore and output the predicted power values, and then wait for the new data. After the new dataset comes, we need to analyze the errors of the known data, and repeat the above-mentioned steps until no new weather data is entered and the whole process terminates.  
Figure 1: The flowchart of photovoltaic prediction model based on the Gradient Boost Decision Tree (GBDT).
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Number of Vehicles  
#### 4.3.1 Simulation Results of Friis Propagation Model regarding Number of Vehicles  
The mRSU outperforms sRSU in the Friis propagation model. Since the reception power is inversely proportional to the distance between the mRSU and the node, the mRSU has the lowest delay at all points on the x-axis. As the mRSU remains in movement with the vehicles, the length does not rise substantially, and the delay remains minimal, while the throughput of the mobile RSU increases as the number of vehicles rises and the throughput of the static RSU gradually decreases. In the vast majority of situations, the packet delivery ratio of the mRSU is the best.
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 5 Conclusions  
We proposed the _angle-based parametrization_ of a 3 dB Y-junction splitter that produces foundry fabrication-ready devices and optimized new geometries evaluating three evolutionary algorithms: PSO, GA, and CMA-ES. We evaluated the algorithms considering their convergence rate, the similarity of FOM, and the performance of their waveband analysis.  
We found that although CMA-ES is a global optimization algorithm, it can be applied to photonic devices' topological optimization just by defining the population size. Our analysis shows that CMA-ES parametrizations produce similar transmittance results to PSO but in fewer generations. This result persists in optimizations executed in the SCL and OESCL-bands, whereas GA and PSO executions fluctuate.  
Conceptualization, R.P.-G. and J.L.G.-G.; methodology, J.G. and R.E.R.-N.; software, J.L.G.-G.; validation, J.G. and R.E.R.-N.; formal analysis, R.P.-G.; investigation, R.P.-G.; resources, R.E.R.-N. and J.G.; data curation, J.L.G.-G. and R.P.-G.; writing--original draft preparation, R.P.-G., J.L.G.-G., J.G. and R.E.R.-N.; writing--review and editing, F.J., M.A.-A., R.E.R.-N. and J.L.G.-G.; visualization, F.J., M.A.-A., R.P.-G. and J.L.G.-G.; supervision, M.A.-A., J.G. and R.E.R.-N.; project administration, J.G. and R.E.R.-N.; funding acquisition, R.E.R.-N. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge funding support from CONCYTEC PROCIENCIA under the call E041-01 [contract number 015-2018-FONDECYT/BM].  
Not applicable.  
The numerical's scripts used to run the experiments can be found at [[https://github.com/TISparta/angle-based-parametrization](https://github.com/TISparta/angle-based-parametrization)]([https://github.com/TISparta/angle-based-parametrization](https://github.com/TISparta/angle-based-parametrization)).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 3 OpenCL on HARPv2
### OpenCL Kernel Types  
OpenCL kernels can be subdivided into 2 different types: _single work-item kernels_ and _NDRange kernels_. In a single work-item kernel, only one instance of the kernel is executed, while in an NDRange kernel, multiple instances of the kernel are executed in parallel corresponding to the iteration space of a n-dimensional loop, i.e., the N-Dimensional Range. The difference between these two types of kernels is illustrated by the examples of Figure 2. In both code fragments, the sum of vectors \(a\) and \(b\) is calculated and written to vector \(c\). Even though both kernels calculate the same vector-summation, they execute in an entirely different way. The code in the single work-item kernel is implemented as a single compute unit (the functionality of the kernel is implemented only once, and this single implementation processes all data). Because the implemented for-loop iterates serially over the indices \(i\), only a single element of vector \(i\) will be calculated at each time step. An NDRange kernel, on the other hand, consists of _multiple_ compute units, allowing execution in a SIMD way. As can be seen in Figure 2b, no for-loop is present. Instead of sequentially calculating the sum of vectors \(a\) and \(b\), the OpenCL framework assigns a specific index to each compute unit by using the function get_global_id(0).  
Both kernel types have different characteristics and the decision to choose one of both heavily depends on the hardware: a single work-item kernel is not suited for a GPU because only one GPU-core is used while all others are idle. An NDRange kernel allows simultaneous execution of different compute units on different cores. The FPGA can implement data-level parallelism; however,  
Figure 2: (**a**) Single work-item kernel adding 2 vectors \(a\) and \(b\). (**b**) NDRange kernel for vector addition. The n-dimensional range is given in the call in the host computer.  
single work-item kernels are preferred because it enables _pipeline parallelism_. In pipeline parallelism, a computation is split into different pipeline stages. Intermediate results are stored in registers, thereby splitting lengthy calculations in several smaller computation steps. Several iterations are then executed simultaneously in different pipeline stages. Such pipelined implementation often leads to a high performance on FPGA.
## 3 Results and Discussion
### Variation of Doppler Signal
#### 3.6.1 The Profile of Electron Concentration  
The profile of electron concentration (\(Ne\)) was evaluated each day, starting from 28 February 2024 and up to 4 March 2024, for the time between 05:00 and 13:00 UT, with a step of 2 h. Note that such a time span corresponds to daytime solar time, which is defined as UT+5. The calculation was made immediately at the website [43] for the median point on the radio path and in accordance with the model IRI2020. An example of the calculation result obtained for the date of 28 February 2024 is shown in the left plot of Figure 9.  
The \(Ne\) profile presented in Figure 9 indicates that over the course of 8 hours, during the time interval of 5:00-13:00 UT on 28 February, the maximum electron concentration in the ionosphere was changing from \(1.578\cdot 10^{6}\) cm\({}^{-3}\) to \(1.048\cdot 10^{6}\) cm\({}^{-3}\). On another day, 4 March 2024, the electron concentration in the same daytime period have varied from \(1.614\cdot 10^{6}\) cm\({}^{-3}\) to \(1.112\cdot 10^{6}\) cm\({}^{-3}\), which is 2.2% above the \(Ne\) value seen on 28 February.  
Figure 8: Schematic of the radio path ”Dushanbe-orzu—Institute of Ionosphere (Almaty)” relative to the epicenter of the earthquake on 4 March 2024. TX—the transmitter of the broadcasting radio station in Dushanbe-orzu (\(N\) 37.533\({}^{\circ}\), \(E\) 68.700\({}^{\circ}\)); \(RX\)—the receiver of the Doppler ionosonde in Almaty (\(N\) 43.175\({}^{\circ}\), \(E\) 76.953\({}^{\circ}\)). Red ellipse indicates the middle of the radio path of sounding radio wave (the sub-ionospheric point); black ellipse marks projection of the area of radio wave reflection onto the ground. Orange circle indicates the boundary of the Dobrovolsky region (\(R_{D}=419.7\) km).
# Bioactive Compounds and Nanodelivery Perspectives for Treatment of Cardiovascular Diseases
## 4 Therapeutic Effect of Bioactive Compounds on Cardiovascular System
### Lycopene  
Lycopene is a hydrocarbon carotenoid that is oxygenated and has quite a similar structure to \(\beta\)-carotene. Lycopene has an antioxidant property due to the presence of conjugated double bonds. This fat-soluble pigment imparts red color to a good variety of food items such as tomato, guava, watermelon, and others. Smoking is a major CVD risk factor. Smoke introduces free radicals into the human body, causing LDL oxidation, foam formation, and leading to atherogenesis. The severity of atherosclerosis is linked with an increase in LDL oxidation inclination. Lycopene prevents the oxidation of LDL and protects humans from coronary heart disease (CHD) [122].  
Lycopene and plasma levels in cardiovascular disease have been investigated by researchers. The findings were analyzed. The higher intake of lycopene was compared with reduced levels of estone. Here, a 17% of reduction of CVD was linked with lycopene. It works by several mechanism such as the reduction of oxidation of biomolecules, the antiangiogenic effects, the reduction of cholesterol levels, the stimulation of apoptosis, and the reduction of inflammation [123].
## 4 Numerical and Simulation Results
As the value of \(\chi\) increases, more power is reserved for information processing while no power is left for energy harvesting which  
Figure 4: The outage probability of \(D_{1}\) versus transmit SNR. Figure 5: The outage probability of \(D_{2}\) versus transmit SNR. increases the outage probability. Furthermore, when the satellite to the relay channel is relatively good, the dip in outage probability is more prominent. By contrast, for the case of HS, the outage probability curves are higher which is due to the impact of shadows. To further investigate the performance of NOMA-enabled HSTRNs, Figure 7 demonstrates the ergodic capacity as a function of transmitting SNR. Generally, one can note that an increase in the value SNR improves the ergodic capacity of the users. At lower values of SNR around 5 dB, the outage probabilities for HS and AS are almost the same. However, as the SNR increases, the ergodic capacity for the case of AS reaches higher values than the ergodic capacity of HS. This shows that the shadowing has a greater effect at larger values of transmitting SNR. Furthermore, one can also note that the ergodic capacity curves of \(D_{2}\) continue to increase with an increase in SNR. On the contrary, the ergodic capacity curves reach a ceiling due to the effect of interference as the SNR goes beyond 30 dB. This signifies that a large transmit SNR favors the ergodic capacity of \(D_{2}\) more than the ergodic capacity of \(D_{1}\) independent of relaying protocol. Figure 6: The outage probability versus transmit \(\chi\). Figure 7: The ergodic capacity versus transmit SNR with different values of \(R_{th}\), where \(\zeta=0.01\), \(\phi=0.1\), \(\delta=0.1\), \(\eta=0.9\) and \(m=1\). Figure 8 shows the ergodic capacity against the increasing values of SNR. Similar to Figure 7, we note that the ergodic capacity increases with increasing SNR in Figure 8. For the larger value of \(N_{1}=N_{2}\), the gap between AF and DF curves is larger, whereas, this gap reduces at smaller values of \(N_{1}=N_{2}\). Additionally, as opposed to Figure 7, the gap between \(D_{1}\) and \(D_{2}\) curves is more prominent at smaller values of SNR, wherein, \(D_{1}\) outperforms \(D_{2}\) in terms of ergodic capacity. However, the performance of \(D_{1}\) is affected by the interference which results in a ceiling at higher values of SNR.
## 6 Network Simulation and Results  
Window jitter, proposed in Section 3.2, is implemented, studied and evaluated by way of network simulations of several different network scenarios and parameter configurations. These simulations permit validating the theoretical results obtained in Section 4, as well as the algorithmic evaluation described in Section 5, by considering all network layers, including the transport layer, MAC (Medium Access Control) layer and the physical propagation model.  
This section compares the performance of LOADng RREQ flooding when using the original \"standard\" jitter (_i.e._, uniform jitter according to RFC 5148 [9]) and the proposed window jitter. Simulations also allow one to identify the networking and jitter elements that have an impact on this performance. Section 6.1 presents the setting of the performed network simulations. Section 6.2 describes the main results obtained in the experiments.  
Figure 14: Route discovery delay, shortest-path and shortest-delay RREQ flooding.  
Figure 13: Route overhead per route discovery, shortest-path RREQ flooding. (**a**) The number of RREQ (re)transmissions; (**b**) The number of RREP (re)transmissions.
# An Analytical Model for the Prediction of Emptying Processes in Single Water Pipelines
## 5 Discussion
Water utilities increasingly seek to implement digital twin systems within water distribution networks. As draining processes in pressurised pipelines can potentially lead to pipeline collapse, the proposed analytical solution offers a rapid means of estimating these effects. Real-time data are essential for accurately calibrating the phenomenon under study to establish a reliable digital twin. As physical equations govern the draining process examined in this study, variations in the initial parameters do not affect the model's applicability. However, the model is specifically designed for drainage operations and unsuitable for filling processes, where different parameters and governing variables come into play.
# A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra
###### Abstract
Raman spectroscopy was also used by [PERSON] et al. to investigate lymphocytes of patients with late radiation toxicity following radiotherapy treatment because of prostate cancer: the PLS-DA model developed to classify patients using known radiation toxicity scores achieved an accuracy value of 93% [24]. [PERSON] et al. reported PLS-DA results about first derivative FTIR data in nucleic acids spectral range collected from serum samples of patients with lung cancer and healthy people: they achieved 87.10% accuracy for discrimination of the two types of samples [25]. FTIR and PLS-DA were also used to develop a prediction model based on the spectra of blood serum samples collected from healthy people and patients affected by attention deficit and hyperactivity disorder: the model was able to distinguish ADHD patients from healthy individuals with an accuracy of 100% [26]. Overall, these two different classification models can have different performances when applied to the same dataset for diagnostic purposes. Therefore, it is important to compare the predicted results of both models in order to optimize the diagnostic phase. Hence, the aim of the present study is to evaluate and compare the performance parameters of the PCA-LDA and PLS-DA models for the class prediction of three different datasets of vibrational spectra: a simulated dataset and two experimental datasets of Raman and FTIR spectra, respectively. The comparison is performed by evaluating the values of accuracy, sensitivity and specificity obtained in the class prediction for a subset of each of the three datasets, used as a test set. The obtained results point out that both the classification models were able to predict the class of the different spectra with high values of accuracy (93% \(\div\) 100%), sensitivity (86% \(\div\) 100%) and specificity (90% \(\div\) 100%). So, if datasets of different types of spectra are available, the application of both classification models to the prediction of the class of unknown measured spectra is promising as a reliable complementary diagnostic tool in the clinical setting.
## 4 Conclusions  
The experiment results show that it is feasible to repair superheavy lead-contaminated protogenetic soil by leaching with inorganic acids and low-molecular-weight organic acids. Among them, HCl and CA had the best leaching effects, respectively. Under optimal conditions, the remediation efficiency of 0.2 mol\(\cdot\)L\({}^{-1}\) HCl could reach 70.3%, the Pb content in the leaching soil was 2388 mg\(\cdot\)kg\({}^{-1}\), and 0.05 mol\(\cdot\)L\({}^{-1}\) CA leaching could reduce the total amount of soil from 8043 to 3732 mg\(\cdot\)kg\({}^{-1}\), accounting for 53.7%. The remediation efficiency of CA is slightly lower than that of HCl, but the leaching concentration is only a quarter of that of HCl. Most importantly, the pH value of the soil after leaching with CA is 6.97, while that of HCl is 3.88, indicating that leaching by CA causes less damage to the soil.  
The results of lead speciation change before and after leaching indicate that the leaching mechanism of HCl can be speculated as being that HCl removes acid-soluble lead and reducible lead through the effect of acid dissolution and oxidizable lead by the complexation of Cl\({}^{-}\). CA mainly removes acid-soluble lead via its acid dissolution effect and oxidizable lead through its strong complexing ability. The removal rates of acid-soluble lead in soil by HCl and CA are 93% and 83%, and the soil \(M_{F}\) values are 7.74 % and 12.3%, respectively, meaning that removal capacity of active lead fraction in soil by low-concentration CA is equivalent to that of high-concentration HCl.  
As a result, CA is more suitable for leaching remediation of protogenetic lead-contaminated soil with high lead content than HCl. Due to the higher complexity and higher lead content of the protogenetic soil, it is difficult to achieve good results using a single CA for leaching. Therefore, in the future, we may try to use several leaching agents for compound leaching. At the same time, the waste solution generated after washing should be extracted and treated to avoid water pollution.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12083995/s1](https://www.mdpi.com/article/10.3390/app12083995/s1)]([https://www.mdpi.com/article/10.3390/app12083995/s1](https://www.mdpi.com/article/10.3390/app12083995/s1)), Table S1. Orthogonal experiment design of HCl leaching.