esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Effects of UF, \(O_{3}\), and Their Combination on Reduction of Fluorescence Intensity  
Effects of O\({}_{3}\), UF, and their combination on the three-dimensional EEM spectra of secondary effluent were investigated and the results are illustrated in Figure 3. It could be observed that the four EEM spectra exhibited similar peak information. Two main peaks could be identified from the fluorescence spectra of samples. The first main peak was located at excitation/emission wavelengths (Ex/Em) of 225-250/325-400 nm (Peak A), while the second main peak was observed at the 275-300/325-400 nm (Peak B). Peak A had been considered as aromatic protein-like substances and peak B had been considered as soluble microbial metabolites and humic acid-like substances [22]. Fluorescence intensity (FI) of peaks could be used for quantitative analysis. The fluorescence parameters of the spectra including peak location and FI were summarized in Table 3. It could be seen that FI of protein-like and humic acid-like substances were obviously lower in the combined process, indicating that lower concentrations of protein and humic acid substances were present. The result indicated that the removal efficiency of protein-like and humic acid-like substances was significantly enhanced by the combination of O\({}_{3}\) and UF.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Peak** & **Ex/Em** & **Influent FL (A.U.)** & **O3 FL (A.U.)** & **UF FL (A.U.)** & **O3 + UF FL (A.U.)** \\ \hline \multirow{6}{*}{Peak A} & 230/350 & 1854 & 1535 & 1883 & 1183 \\  & 230/400 & 2406 & 1921 & 1880 & 1450 \\  & 235/390 & 1098 & 995 & 992 & 680 \\  & 240/350 & 776 & 716 & 724 & 467 \\  & 225–250/325/400 & 1533 & 1291 & 1369 & 945 \\ \hline \multirow{6}{*}{Peak B} & 280/350 & 1642 & 1489 & 1578 & 892 \\  & 275/400 & 2074 & 2094 & 2056 & 1000 \\ \cline{1-1}  & 280/400 & 2060 & 1971 & 2049 & 1095 \\ \cline{1-1}  & 285/376 & 1469 & 1320 & 1410 & 801 \\ \hline \end{tabular}
\end{table}
Table 3: Fluorescence spectral parameters of samples.
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
## 2 Experimental Investigation
### Mix Design and Preparation of Specimens  
In this investigational study, the focus was on developing SCC with five different mix proportions. The workability characteristics of the trial mixes were adjusted in accordance with grading guidelines to meet the specified criteria. The reference mixes were formulated with a fixed composition consisting of 59% OPC, 31% FA or GGBFS, and 0% EPA, based on the findings of the trial mixes. To comply with EFNARC directives, the mixtures of FA and GGBFS in the control mixes were adjusted to the same level as in the trial mixes. The control mix designation depended on the SCM used, with FA referring to Fly Ash and GGBFS indicating Ground Granulated Blast Furnace Slag. In the second phase, the influence of EPA was examined by incorporating it into the mix and replacing 2.5% of the fine aggregate in the control mixes. The final mixes, FA/EPA and GGBFS/EPA were prepared using 59% OPC, 31% FA or GGBFS, and 2.5% MS, which EPA replaced through volume batching. The mix designations followed a naming convention where FA and GGBFS denoted Fly Ash and Ground Granulated Blast Furnace Slag, respectively, and the numerical value followed by EPA represented the percentage of expanded perlite aggregate used as an alternative for the fine aggregate.  
Each mix was designed to have a powder content (total cementitious material) of 464 kg/m\({}^{3}\) and a CA content of 804 kg/m\({}^{3}\). The water-powder ratio was maintained at 0.42, with a consistent 218 kg/m\({}^{3}\) water content across all mix combinations. The specific mix proportions can be found in Table 4. The CA, MS, and EPA were mixed thoroughly for one minute, after which the binder materials were added to an electric laboratory concrete mixing machine. The dry components were blended for approximately 2 min to achieve a uniform mixture. Subsequently, 70% of the water, along with the superplasticizer (SP), was added gradually to the mixture, followed by a 2-min mixing and 2-min resting period. The remaining water was then introduced, and the mixer drum was rotated for an additional 3 min to complete the mixing process. The freshly prepared SCC mixture was carefully poured into a steel mould to assess its performance under load. To prevent water evaporation and facilitate the setting process, the mould containing the concrete was securely covered with polythene sheets for 24 h. After the specified period, the specimens were demoulded and transferred to a curing tank to initiate the hydration reaction. The samples were allowed to undergo the curing process in the tank for 28 days.
## 4 Genome Editing for Autosomal Dominant Retinitis Pigmentosa
### Rpi  
_RP1_ is another highly prevalent gene for RP, whose prevalence differs geographically but can account for up to 10% of adRP cases [17]. RP1 protein is localized in the connecting cilium of photoreceptors. It is a photoreceptor-specific microtubule-associated protein (MAP) and it is necessary for the organization of the membrane discs [80]. To date, 190 RP1 mutations have been linked to RP, which are categorized into four classes. The majority of _RP1_ mutations causing adRP are class II mutants [17; 18]. These mutations are concentrated in a hotspot in exon 4 of _RP1_ (nt position 500-1053) and result in premature stop codons due to either nonsense or frameshift mutations. The corresponding transcript escapes NMD but the resulting protein is missing a large part of the C terminal end.  
Many of the dominant _RP1_ mutations show incomplete penetrance, which depends on the patient's genetic background. Haploinsufficiency has been excluded because carriers of null alleles are mostly asymptomatic, whereas homozygous individuals present with the disease [81]. The mechanism of action of _RP1_ mutations is considered to be dominant-negative [17; 18]. Mice homozygous for a dominant nonsense _RP1_ mutation show outer segment disorganization and photoreceptor degeneration. However, this phenotype can be prevented by the expression of exogenous wild type RP1 [81]. It must be noted though that, like for rhodopsin, the levels of wild type protein need to be within a specific range, as too much RP1 will also result in degeneration [81].  
Despite the high prevalence of this gene, to our knowledge there have been no other relevant gene therapy studies reported to date. In order to determine the most suitable approach, it is necessary to further elucidate the mechanisms of the disease. Nevertheless, currently, we can presume that all three approaches of gene therapy (correction of the mutant allele, invalidation of the mutant allele and/or wild type supplement) could be candidates for adRP associated with class II RP1 mutations.
###### Abstract  
Cisplatin (CDDP) is an anticancer drug that is frequently used to treat head and neck cancers; however, it may cause oral mucositis. The discontinuation of CDDP may be required for some patients with a severe status, and the control of oral mucositis is extremely important. \(\beta\)-Cryptoxanthin (\(\beta\)-cry), a carotenoid, exerts anti-inflammatory effects. Its inhibition of 5-FU-induced inflammatory responses was recently demonstrated. However, the effects of \(\beta\)-cry on CDDP-induced oral mucositis remain unclear. In the present study, we stimulated human oral mucosa-derived keratinocytes (hOMK) and fibroblasts (hOMF) with CDDP, added \(\beta\)-cry, and examined its effects, with a focus on the production of inflammatory cytokines, matrix metalloproteinase (MMPs), and reactive oxygen species (ROS). CDDP increased the mRNA expression and production of inflammatory cytokines and MMPs both in hOMK and hOMF. However, increases in IL-6 and MMP-9 mRNA expression levels and IL-6 production in CDDP-treated hOMK and hOMF were inhibited by \(\beta\)-cry. Furthermore, the production of ROS and the rate of SA-\(\beta\)-gal-positive cells were increased by CDDP, but were not affected by \(\beta\)-cry. CDDP may induce oral mucositis by increasing the levels of inflammatory cytokines, MMPs, and ROS. \(\beta\)-cry partially inhibited CDDP-induced increases in inflammatory cytokines and MMPs, suggesting its potential to attenuate the symptoms of chemotherapy-related oral mucositis.  
\(\beta\)-cryptoxanthin; CDDP; inflammatory cytokines; MMPs; oral mucosal cells Article
## 4 Simulation Setup and Results
### Further Analysis on the Proposed Method  
In this section, we analyze the performance of the proposed unified EA with differential evolution [10] (DE and DE*) and the genetic algorithm [35] to demonstrate its superiority. We have presented the obtained input-current ripple (\(\Delta_{ig}\)) for the proposed hybrid approach, DE, DE*, and GA, in Table 3. As shown in Table 3, DE represents the original differential algorithm with the \"DE/rand/bin\" and binomial crossover. As mentioned earlier, in the proposed unified EA, the \"DE/current-to-best/1\" and binomial crossover are employed. For better analysis, we have included DE* in the simulation setup that replaces the \"DE/rand/bin\" mutation with the \"DE/current-to-best/1\" mutation. For a fair comparison, the crossover rate (CR) and mutation scaling factor (F) are maintained identically for DE, DE*, and unified EA as CR = 0.75 and F = 0.3. The GA employs a real-encoding crossover and a polynomial mutation. The mutation factor F is chosen from real random numbers as uniformly distributed between 0 and 1, and CR \(\in\) [0, 1], in GA. We set the population size and number of generations to 20 and 50, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Gain**} & \multicolumn{4}{c}{**Input-Current Ripple (\(\Delta_{ig}\))**} \\ \cline{2-5}  & **Proposed** & **DE** & **DE*** & **GA** \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: Comparison of proposed method with DE and GA algorithms.  
As shown in Table 3, the mean values of the input-current ripple over 30 runs are given. Based on the results in Table 3, the proposed method resulted in lower input-current ripple values than DE, DE*, and GA in all cases. The proposed method has better simulation results than DE, DE*, and GA algorithms because our proposed hybrid algorithm incorporates the properties of DE and GA. Additionally, the efficiency of the proposed hybrid algorithm is increasing with hybridization. The hybridization of the proposed method can potentially improve the current model's performance and minimizes input-current ripple. To conclude, a hybrid algorithm can improve the method's performance (convergence speed) and the quality of the solutions.
###### Abstract
In the literature [38, 39, 40], population-based models, such as particle swarms and genetic algorithms, have been proposed for path loss predictive modeling. Although the population-based modeling approach showed enhanced predictions, their overall performance could be limited when employed in high-dimensional input features, especially when the predictor number is far larger than the observation number [41]. Though datasets with high-dimensional input features provide more information, the redundant and irrelevant constituents of the data may lower the prediction accuracy of the model [42]. In the current contribution, Random Forest-Particle Swarm (RF-PS) optimization is proposed for adaptive modeling and predictive analysis of signal path loss with high-dimensional input features obtained from 4G cellular networks in the mid-southern part of Nigeria. The relative performance of the proposed RF-PS-based model has been compared to the standard PS and hybrid RBF-PS approach for benchmarking purposes. To this end, the vital contributions of this paper include:  
* Measurement-based acquisition of detailed signal data and computation of attenuation loss levels across selected urban LTE microcellular radio communication paths using professional TEMS investigation tools. * Effective application of the random forest technique for the most informative and important subset of features selection from measured signal data sets toward robust predictive analysis. * Development and application of an improved signal path loss model using hybrid random forest and particle swarm optimization for optimal cellular planning across the investigated locations. * Validation of the developed signal path loss models in other eNodeB (base stations) across the investigated locations to ascertain the level of their prediction accuracies. The remaining parts of this paper are organized as follows. Section 2 describes the methods and materials. Section 3 presents the results and useful discussions. Finally, the conclusion to the paper is given in Section 4.
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
## 4 Results and Discussion  
This section presents the results and analysis performed to assess the difference between diverse ISB stochastic models and their impacts on positioning accuracy and convergence time. As mentioned before, processing is re-initialized eight times per day to evaluate the influence on convergence time. One initialization is considered to be a cold start. A time slide window of 3 h is chosen to ensure enough time for converging into a certain accuracy in nearly all the cases and offer enough test samples. Considering the entire experiment over all stations, it gives 448 cold starts (8 stations \(\times\) 7 days \(\times\) 8 initializations).  
Here, we use the station coordinates from the IGS weekly solutions as the references, and the positioning errors are defined as the increments between PPP results and the references. The statistical analysis of positioning errors are made with the methods of 68% and 95% quantiles. These two statistical parameters are chosen instead of mean and standard deviation due to the possible remaining biases that might cause results do not follow a normal distribution [32]. The convergence time is defined as when the positioning error is lower than 0.2 m (95%) and 0.1 m (68%) in the North, East, and UP components [15]. This statistical approach is also used in [PERSON] and [PERSON] [33], [PERSON], et al. [32], and [PERSON], et al. [34].  
Since different MGEX analysis center (GBM, or WUM) has its own processing strategy to obtain precise multi-GNSS orbit and clock products, during the product generation, different ISB stochastic models will be employed with different ACs. In this study, we will apply the precise products from GBM and WUM to validate the difference between these two kinds of products and analyze the potential influence of the different ISB estimation strategies used in different ACs on the positioning and convergence time.  
Figure 1: Distribution of the selected stations.
## 2 Materials and Methods
### Case Study  
The research is conducted on the case study of one of the existing DH systems in Rijeka, Croatia. Constructed in the 1970s, the DH system services eight multi-story buildings and one primary school, covering a total heated area of 48,455 m\({}^{2}\). TRNSYS Type 56 multi-zone thermal model [18] is used to perform simulations of buildings and determine the required energy for heating. The methodology for developing and validating consumer, distribution, and production models has been detailed in a prior study by the authors [3].  
The design load for heating of buildings was determined through dynamic simulation within the TRNSYS interface, consistent with the HRN EN ISO 12831-1:2017 standard [20]. The calculated design heating capacity was 2195 kW.  
The total number of apartments in the DH system is 601, and the calculated capacity for DHW heating is 569 kW. The simultaneity factor of the heat load for heating buildings is 0.8 according to standard DIN 4708-2 [21]. Network heat losses were determined by dynamic simulation using TRNSYS Type 951. The heat pipelines' annual heat losses amounted to 167 kWh/m, with a peak hourly loss of 17.16 kW. Accordingly, the total heat capacity requirement in design conditions is 2342 kW. Characteristics of the DH system are outlined in Table 1.  
The supply temperature in the DH network, under the assumption that all buildings undergo energetic renovation while retaining the current radiators, is 72 \({}^{\circ}\)C at an outdoor design temperature of \(-8\)\({}^{\circ}\)C. During the heating season, the temperature of the supply pipe in the network varies in response to the external temperature, ranging from a maximum of 72 \({}^{\circ}\)C to a minimum of 65 \({}^{\circ}\)C. It does not fall below 65 \({}^{\circ}\)C due to demand for DHW in buildings.  
In addition to the boiler and CHP-based systems, 15 variants of systems with implemented HP units were considered. These systems employ a gas-fired boiler, a CHP unit, and a water-to-water HP in different sizes. They are designed to meet the same heat load for space heating and DHW, maintaining a supply temperature within the DH network.
###### Abstract  
For the longitudinal tire force estimation problem of four-wheel independent drive electric vehicles (4 WIDEVs), traditional model-based observers have limitations such as high modeling complexity and strong parameter sensitivity, while pure data-driven methods are susceptible to noise interference and have insufficient generalization ability. Therefore, this study proposes a joint estimation framework that integrates data-driven and modified recursive subspace identification algorithms. Firstly, based on the electromechanical coupling mechanism, an electric drive wheel dynamics model (EDWM) is constructed, and multidimensional driving data is collected through a chassis dynamometer experimental platform. Secondly, an improved proportional integral observer (PIO) is designed to decouple the longitudinal force from the system input into a state variable, and a subspace identification recursive algorithm based on correction term with forgetting factor (CFF-SIR) is introduced to suppress the residual influence of historical data and enhance the ability to track time-varying parameters. The simulation and experimental results show that under complex working conditions without noise and interference, with noise influence (5% white noise), and with interference (5% irregular signal), the mean and mean square error of longitudinal force estimation under the CFF-SIR algorithm are significantly reduced compared to the correction-based subspace identification recursive (C-SIR) algorithm, and the comprehensive estimation accuracy is improved by 8.37%. It can provide a high-precision and highly adaptive longitudinal force estimation solution for vehicle dynamics control and intelligent driving systems.  
Keywords:state estimation; model construction; subspace identification; data-driven; estimation accuracy +
Footnote †: journal: _Algorithms_
# Underwater Drone Architecture for Marine Digital Twin: Lessons Learned from SUSHI DROP Project +
## 3 Results and Discussion
### Surveyed Habitats  
#### 3.3.1 Marine Biodiversity  
Thanks to the use of the raw data obtained from Blucy's optical sensors, it was possible to conduct a qualitative study, assess the presence or absence of marine species characteristic of an inspected ecosystem during the first surveys. Visual census analysis consists of the identification and counting of species (e.g., fishes, benthic species) observed within a defined area. Visual census can be used to estimate the variety, numbers and even sizes of common, easily seen, easily identified species in areas where the recorded quality images were very good. A first preliminary video assessment analysis is performed in real-time during navigation to better plan the mission. More detailed work is carried out in the laboratory by processing all the photographic frames taken by the high-resolution BottomCam and the video stream recorded by the PilotCam. The strength of ROV-imaging is the ability to explore the seabed without resorting to the use of scuba divers. In addition, it is a non-invasive technique that allows the evaluation of the ecosystem without impacting on the benthic species present, unlike what happens with the classic sampling techniques that require the removal of the individual from its natural environment. Thanks to the images provided by the UUV it was possible to census numerous benthic species such as: holothurian, sponges, hermit crab, cnidaria and Posidonia as represented in (Figure 13).  
Figure 12: Transecr performed by UUV partially above and partially under water surface.  
In our survey we focused on the seagrass meadow (e.g., Posidonia oceanica). Due to its ecological role is an EU priority habitat, it is provided important ecosystem services: they contribute to coastal primary production and nutrient cycling, providing food, shelter, nurseries and habitat for many vertebrates and invertebrate species. The shift from qualitative studies, as described above, to quantitative studies requires a data processing phase. By using telemetry information collected simultaneously by the subsystems on Blucy, it is possible to georeference large portions of the acquired images and produce metric products such as orthophotos of the seabed. Furthermore, exploiting Structure from Motion (SFM) from imagery or MBES data processing techniques, it is possible to obtain a three-dimensional reconstruction of the marine environment and an high-resolution DEM of the seabed (Figure 14).
# State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM
## 2 Materials and Methods  
### Extraction and Optimization of Health Indicators  
#### 2.1.1 Battery Aging Data  
The extraction and optimization of health factors rely on high-quality battery aging data. Therefore, it is first necessary to clarify the source and basic characteristics of the adopted battery aging data, laying a data foundation for the effective extraction of health factors in subsequent steps.  
The experimental data in this study are derived from the open-source lithium-ion battery aging dataset provided by the University of Maryland. Specifically [33], battery cells labeled CS2_35 to CS2_38 from the dataset are selected as the research objects. These cells belong to the CS2 battery type, and their technical specifications are shown in Figure 1.  
All CS2 batteries underwent the same charging protocol, namely, a standard constant current/constant voltage (CC/CV) scheme. The cells were charged at a constant current of 0.5 C until the voltage reached 4.2 V, followed by a constant voltage phase at 4.2 V, which continued until the charging current dropped below 0.05 A. Unless otherwise specified, the discharge cut-off voltage for these batteries was set at 2.7 V. The batteries were subjected to repeated charge and discharge cycles under the above conditions until their capacity degraded to the soft failure threshold, at which point the battery was considered to have failed. This experimental procedure was repeated to accelerate the aging of the lithium-ion batteries.