esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Results
### Pressure Pulsation Analysis
This indicates that the optimization has changed the phase value of the pressure pulsation at the inlet of the secondary space guide vane. Figure 15: Pressure fluctuation characteristic diagram of the Y2 monitoring point before and after optimization. (**a**) Time domain; (**b**) frequency domain. As can be seen in Figure 16b, the dominant frequency of pressure pulsation before and after optimization is around the doubled impeller blade frequency. The optimized individual with the better head has a 20.2% reduction in pressure pulsation amplitude compared to the initial individual, and the individual with the better efficiency has a 30.2% reduction in pressure pulsation amplitude compared to the initial individual. Figure 17 shows the pressure pulsation characteristics at monitoring point Y4 at the outlet of the secondary spatial guide vane for the three models before and after optimization. In Figure 17a, the pressure pulsation amplitude at this monitoring point is significantly reduced compared to the pressure pulsation amplitude at the inlet of the space guide vane, which is due to the weakening of the pressure pulsation amplitude after the fluid is guided through the space guide vane. The regularity of the pressure pulsation at this point is significantly reduced by the effect of multiple dynamic interference couplings. As can be seen in Figure 17b, the pressure pulsation amplitude reaches a maximum around doubling the shaft frequency, which is due to the fact that at the guide vane outlet, the effect of dynamic interference is weaker and the influence of the shaft frequency  
Figure 16: Pressure fluctuation characteristic diagram of the Y3 monitoring point before and after optimization. (**a**) Time domain; (**b**) frequency domain. Figure 17: Pressure fluctuation characteristic diagram of the Y4 monitoring point before and after optimization. (**a**) Time domain; (**b**) frequency domain. dominates. The pressure pulsation amplitude of the optimized individual with the better head increased by 75.9% compared to the initial individual, while the pressure pulsation amplitude of the individual with the better efficiency decreased by 46.5% compared to the initial individual. The increase in pressure pulsation for the higher-head individual compared to the pre-optimized individual is probably due to the influence of multiple dynamic and static interference couplings at the guide vane outlet and the higher static pressure energy at the guide vane outlet for the higher-head individual, which is more strongly influenced by the dynamic and static interference coupling. The pressure pulsation coefficient for the higher-head individual is approximately 0.02, which is still relatively small compared to the other locations and is within acceptable limits.
# Tree Planting Density and Canopy Position Affect 'Cerasuola' and 'Koroneiki' Olive Oil Quality
###### Abstract
[PERSON] [17] indicated that to maximize orchard production and efficiency, both interception of maximum amount of radiation and optimization of the radiation distribution within the canopy are important factors. In olive trees, fruits located on the periphery of the canopy which exploited more solar radiation were bigger and with higher oil contents compared to fruits from internal parts of the canopy [19]. In 'Arbequina' hedgerows, fruits from the upper part of the canopy showed more advanced maturity and larger size. Furthermore, oil content increased by nearly 50% from lower to upper layers ([PERSON] et al., 2009b). Hence, it can be concluded that intercepted radiation determines some of these differences, such as fruit size and oil content. In addition, differences in oil quality can result from rapid growth and early maturation in the upper layers of the tree canopy. Indeed, previous studies indicated that irradiance received in different hedgerow positions and orientations influenced fruit development and oil quality in olive. Fruits receiving more radiation showed the highest fruit weight, mesocap oil content, maturity index, and total polyphenols in virgin olive oil [20]. Recently a study of 'Arbequina' also found that oil extracted from the upper layers presented higher concentration of oleuppein and ligstroside aglycone [12]. These compounds, besides having high antioxidant capacity, also contribute to the bitter and pungent flavor of the oil [21]. On the other hand, several studies reported that biomass production was directly related to canopy light interception in other fruit trees such as apple and peach [22; 23; 24; 25]. In 'Arbequina' olive trees trained to 2.5-2.9-m hedgerows, the fatty acid composition of the extracted oils was significantly affected by the amount of intercepted light [26]. The aim of this study was to evaluate the influence of fruit position in the canopy and planting density on the production, fruit characteristics, and oil quality parameters such as phenol contents and volatile compounds from 'Koroneiki' and 'Cerasuola' olives. 'Koroneiki' was included as a reference cultivar since it is already implemented in super-intensive planting systems while 'Cerasuola' is a major Sicilian cultivar usually grown in traditional planting systems and appreciated for its quality oils.
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 4 Architecture and Ontology Models
### Detailed Architecture  
Figure 2 shows the detailed architecture of the SemKoRe.  
The SemKoRe consists of three entities:  
1. A SemKoRe Agent: Runs on industrial IoT gateways connected to the machines. It collects data when failures occur in the connected machines. According to the chosen architecture, the collected data are then shared with either the SemKoRe Server or Local SemKoRe. The SemKoRe Agent is designed to fit in all architectures (see Figure 1).
2. A SemKoRe Server: Running on the cloud, it manages several failure data producers, i.e., Local SemKoRes or SemKoRe Agents. The collected failure data are validated by an expert and aggregated and afterwards shared with the SemKoRe Agents and/or Local SemKoRes.
3. Local SemKoRe: Lightweight instance of the SemKoRe Server deployed on a local server to manage the machines located in a site or factory. It collects data produced by the SemKoRe Server (if available) and the SemKoRe Agents on local gateways. The data aggregation is done locally, and only aggregated data are shared with the SemKoRe Server. The cloud then merges its aggregations with the Local SemKoRe aggregation, and pushes back the updates to the corresponding entities.  
Figure 2: SemKoRe detailed architecture.  
We use in the cloud a message broker to connect the machine gateways to the SemKoRe Server for bi-directional data sharing. We also developed a REST (Representational State Transfer) interface on the SemKoRe Server side to directly call remote services, e.g., commissioning service.
## 1 Introduction
Finally, the dependent variable is the final cost. The initial cost, estimated completion time, actual completion time, delay time, initial demolition-drainage costs, final demolition-drainage costs, cost of expenses, initial plumbing costs, final plumbing costs, initial heating costs, final electrical costs, final electrical costs, initial masonry costs, final masonry costs, initial construction costs of plasterboard construction, final cost of plasterboard construction, initial cost of bathrooms, final cost of bathrooms, initial cost of flooring, final cost of flooring, initial cost of frames, final cost of frames, initial cost of doors, final cost of paint, final cost of paint, initial cost of kitchen construction, and final cost of kitchen construction are the independent factors. First, the radial basis function (RBF) is the process that is used. With up to 6% sum of squares error and nearly 0% relative error in the training sample--which makes up approximately 70% of the entire sample--the model's efficiency throughout training and testing was assessed and analyzed. Secondly the multi-layer perceptron (MLP)is the approach that is applied. The model's efficiency throughout training and testing was evaluated and analyzed, with the sum of squares error to be ranged between 1% and 5% and 0-7% relative error in the training sample, which accounts for roughly 70% of the total sample. As an instance, it has been shown that ANNs are a very useful technique for estimating total costs, especially for building renovation projects. Specifically, there is the highly precise RBF approach, in which the error value in the model with 43 units in the hidden layer remains steady. In this regard, and centered on what is presented above, in the construction sector, building renovation is a primary importance, yet accurate cost estimation is a major challenge. To decrease economic disparities, the usage of artificial neural network (ANN) models is highlighted for forecasting the costs of renovations. ANN models were developed and tested, according to the substantial real project cost data that Greek contractors provided. Radial basis function (RBF) and multi-layer perceptron (MLP) are two ANN techniques that were evaluated in the present research. The RBF model was far more accurate in cost estimation than the MLP model. The sections of this article are organized as follows: A comprehensive assessment of the literature is given in Section 2 regarding RBF and MLP ANNs as a tool for building projects in particular and the construction industry in general. The problem formulation is presented in Section 3. The methodology as the data collection and analysis processes for developing the RBF and MLP ANN models to predict and compare the ultimate costs of building renovations are outlined in Section 4. Section 5 present the results as well as clarifying them. Conclusions, limitations, and recommendations for additional study are included in Section 6.
# Semantic 3D Mapping from Deep Image Segmentation
## 2 Related Work  
### Semantic Mapping  
RBG-D cameras are the main tool for semantic mapping. These provide affordable volumetric segmentation supported by techniques usually focused on conventional images. Based on that initial segmentation process, authors like [PERSON] [6] perform additional tasks such as including scene elements and predictions over the frames, to finally integrate information about 3D shapes, location, and, if available, semantic classes into a global volume.  
Semantic mapping is a central component for robot localization, so it is usually integrated with SLAM implementations, such as in [7]. Predictions from a Convolutional Neural Network can be combined into a dense SLAM map, like in [8], and even later include geometric information from depth images into the scene processing [9]. Other works like [10] work at object-level, building dense semantic maps based on preexisting 3D RBG-D-based SLAM frameworks like [11].  
Other works rely on an object database to create object-oriented maps recognizing those objects from RGB-D sources and supporting the SLAM process. The database can be built during the exploration, like in [12], based on geometric segmentation information acquired from the cameras. In general, semantic SLAM builds its own semantic objectsbased on the entities created in the mapping phase [13]. These semantic 3D mapping approaches face serious complexity issues when it comes to large, dynamic environments. [PERSON] et al. [14] proposed using octomaps to leverage the overall efficiency. In addition, [PERSON] in [15] used scrolling grids in dynamic scenarios.
## 1 Introduction
[PERSON] [16] studied the IPA/water separation and purification with EG as solvent from a feed of 1875 kg/h of an 80 wt.% IPA and 20 wt.% water binary mixture, in the TCED process, with help from the Aspen Plus simulator. The purpose of this study was to develop a control structure of the main operational parameters in order to obtain an IPA concentration in the distillate of the EDC of 99.91 wt.%. [PERSON] and co-authors [17] studied the feasibility of EG as an extractive agent for the separation of an equimolar IPA/water binary, from a 100 kmol/h feed flowrate. The authors developed a sensitivity analysis with help from the Aspen Plus simulator to depict the operating conditions of the EDC with the purpose of obtaining 99.974 mol% purity of IPA in the distillate flow of the EDC. In our recent paper [18], the ability of a new proposed solvent, namely, propylene glycol (PG), for IPA dehydration by conventional three-column extractive distillation (TCED) was investigated. The optimal design of each column from the TCED process was developed by simulation using the PRO/II process simulator. The second part of our recent paper proposed four flowsheets of a multiple-heat integration process. With the lowest TAC as the goal, the energy and financial benefits of each suggested process were determined and examined. In reviewing the literature studies on IPA dehydration by extractive distillation, it is observed that, in the past, only two solvents have been investigated, namely ethylene glycol (EG) and dimethyl sulfoxide (DMSO). However, no conclusion can be reached regarding their efficiency and economic viability, as the investigations were conducted for different flow rates and compositions of the IPA/water mixture feed. Until now, the design of the extractive distillation process and the control of the main operating parameters took into account different IPA purity targets obtained in two- or three-column processes. To overcome the differences between the approaches in the literature on extractive distillation for IPA dehydration, our study, as a novelty, was conducted to compare the efficiency and economic viability of three solvents for IPA dehydration, namely ethylene glycol (EG), dimethyl sulfoxide (DMSO), and propylene glycol (PG). Although PG is part of the glycolic solvent group along with EG, it has been previously studied [18] as an alternative that is more environmentally and human health-friendly, more biodegradable, and less volatile than its counterpart, EG. A systematic and comprehensive methodology was developed to design a three-column extractive distillation (TCED) with optimum design variables for a unique flowrate and feed composition and the same purity of IPA as the target. Moreover, an energy-saving flowsheet for sensible heat recovery from hot flows was also studied for the TCED process, with EG and DMSO as solvents. Finally, a comparison of the processes in terms of the total annualized cost (TAC) was made.
# Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area
## 3 Case Study
### Regression Results Analysis of Initial Ground Stress
#### 3.4.2 Comparison between Regression Fitting Value and On-Site Measured Value  
According to the multiple linear regression equation and the calculation results of the ground stress field of the three-dimensional geological model under each boundary condition, the regression calculation value of each stress component at each extracted point is obtained. The difference between the regression value and the measured value is called the relative error for the ground stress. The relative errors of ground stress inversion results at 13 measuring points are shown in Figure 4. The comparison of regression results of the three coefficient estimation methods is shown in Table 7.  
Figure 4: \(Cont.\)  
From the analysis of Figure 4, it can be seen that, including the samples of point 1 and point 2 which are not involved in the regression calculation, under the three coefficient estimation methods, most of the absolute value of relative errors between the measured values and the regression values of initial ground stress are controlled within 10%. Among them, there are only 6 data exceeding 10% under the OLS method and only 5 data exceeding 10 % under the LR method, accounting for 6.4% of all statistics. Meanwhile, the fluctuation of the absolute value of relative error is small, and the average value of the ridge method is the largest, at 4.99%. The mean relative error of OLS is close to that of LR, and the minimum is 4.73% with the OLS method, which indicates that the values of regressed and measured are relatively close. The regression fitting result of the ground stress is reasonable and reliable, which can well reflect the distribution of the initial ground stress field in the tunnel area. To sum up, when considering the small relative error, the OLS method is very close to the LR method, and the relative error of the LR method is less regarding the larger data value, which shows that the LR method has a good effect on the regression fitting of the initial ground stress field.
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 2 Establishment of Fractional-Order Model and Parameter Identification
### Model Parameter Identification Based on HPSO  
The parameter identification link is very important before the model is successfully applied. The parameter identification results directly affect the accuracy of the model. Because of the obvious time-varying non-linear and complex structure of the lithium-ion battery system, there are many parameters involved in operation, and most parameter values cannot be directly measured. Therefore, parameter identification is a difficult problem in the battery modeling process. For high-dimensional complex optimization problems, this paper proposes an HPSO algorithm based on a genetic cross factor. The flowchart of the algorithm is shown in Figure 5.  
Referring to the genetic algorithm (GA), the genetic crossover factor is introduced into the classic particle swarm optimization (PSO). After sorting the fitness of the particles from high to low, it is used to select the particles with the fitness of the top half in each generation. As part of the next generation of updated particle swarms, the particles in the second half of fitness are paired as crossover factors. The position of the exchange between the particles is called the intersection, which is randomly set. At the intersection point, the particles pair and exchange with each other to produce two completely new offspring. By comparing the fitness of the offspring particles with that of their parent particles and sorting them, the top half of the particles with high fitness values are selected to enter the next generation of updated particle swarm so as to realize the update of the particle swarm. The introduction of genetic crossover factors enriches the diversity of population particles, solves the problem that the classical PSO algorithm is prone to fall into the local optimality, improves the global searching capability of the parameter identification algorithm, and further improves the fitting degree of the battery model.  
Figure 4: Open circuit voltage (OCV)–state of charge (SOC) fitting curve.
# Historically Based Room Acoustic Analysis and Auralization of a Church in the 1470s
## 3 Digital and Historical Reconstruction
### Recording Liturgy
#### 3.2.1 Recordings  
The recordings were made in the anechoic chamber at Engineering acoustics, LTH, Lund University. Four male and three female singers, familiar with the style of music and the material, were recruited for the recordings. They were recorded using four close mics model Milab VM-44, hanging from the walls.  
In order to reach a plausible number of voices in the recordings, the singers were recorded multiple times. During each recording, all participants belonging to the same  
Figure 5: 3D model of the monks’ choir with beams fitted in the wall niches to the left, supporting the platform. The back part of the stalls are modeled based on preserved stalls used by the monks. View from the west towards the east. The main altar is visible east of the platform.  
group were performing together in the anechoic chamber. One of them was wearing headphones, playing back any earlier recordings and fed with generic live 6 s reverb effect; this time length was chosen as the singers deemed it more helpful to achieve good results as compared to no, or 3 s, reverb. The others followed this individual. The variations caused by this method are thought to be consistent with the assumption that these individuals were performing a daily task, albeit as part of a service. Recording and mixing were done in Cubase 11.0.  
Singing in a anechoic chamber may pose challenges for singers both due to lack of support and response from the room, and due to the physical influence such environment may have on humans. Such circumstances have an impact on the performance itself, and may result in a presentation that is quite different from how the same material would be presented in a more traditional space, such as a church. The addition of artificial reverb, the choice of singers, and great care during the recording process were used to combat these effects, but it is not possible to guarantee that the presentation is not affected by the discrepancy between the recording conditions and the church. Such problems have been encountered in other research projects, and have not yet been solved [22; 49].  
A musicologist familiar with the material was present for the full duration of the recordings, ensuring that the performance was as accurate as possible. As the act of performing in an anechoic chamber can be very challenging, the singers took frequent breaks. During these breaks, the performers, the producer, and the musicologist listened together to the recording, both dry and with added reverb, and determined whether it was of an appropriate tempo and quality.  
In addition to elements of the sex, some additional sounds were recorded. These were background sounds including prayers, said by female and male voices, the sound of historically representative clothing, sounds from a rosary, sounds from walking with leather shoes against a stone surface, and coughing.
# Removal of Chromium from Synthetic Wastewater Using Modified Maghemite Nanoparticles
## 3 Results
### Effect of Initial Concentration on % Removal of Chromium  
The effect of initial concentration on the uptake of chromium was studied at different initial chromium concentrations. The effect of initial concentration on removal of chromium is shown in Figure 3b, c, which shows that an increase in initial chromium concentration decreases the chromium RE. From the figures it can be observed that with an increase in the initial chromium concentration from 5 to 35 mg/L, there is a decrease in the chromium RE from 95% to 75%. This can be attributed to the fact that that for a fixed adsorbent dosage, a finite and limited number of available adsorption active sites lead to a decrease in percentage removal of Cr(VI) ions with increasing initial concentration [36].  
[PERSON] et al. [37] made similar observations, i.e., with an increase in the initial chromium concentration there is a decrease in the chromium RE while using maghemite nanoparticles as the adsorbent.