esa-sceva/satcom-chunk-collection huggingface dataset:
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 6 Discussion  
Remote asset management is important for future industries and requires flexible and reliable network services that can be provided by means of network virtualization. However, the underlying  
Figure 12: Average path utilization for increased FG size.  
Figure 13: Acceptance ratio for increased CPU capacity and FG length of 5.  
network infrastructure imposes several performance, security, and resilience challenges. To address these challenges, NFV that supports flexible composition and deployment of SFCs can be used. However, these NFV procedures shall be automated for large industries based on the application requirements and network service provider policies, such as the location of VNFs and required security mechanisms.  
This paper presents a preliminary model that flexibly creates and deploys EVNs and SFCs based on these requirements and policies. The model applies a set of rules sequentially on an AR while using graph transformation. The resulting graph is an EVN that includes the application nodes and the composed SFCs based on its requirements. The paper also proposes a topology-aware and path independent heuristic in order to embed the SFC based on path proactive verification. We implement the developed methods and our use case in Alevin and present an exemplary mapping result. This result shows that our system can correctly compose and map the EVN and SFCs while satisfying all policies. A preliminary evaluation of the chain embedding heuristic, while using a typical random topology, shows that it is promising for such environments in terms of admission, resource utilization, and performance. A main challenge for future work is evaluating the entire approach using random topologies that hold the required properties of entities. Furthermore, determining and implementing comparable solutions from the state of the art is required. Another challenge is more coordination of the stages by SFC recomposition according to the embedding results.  
Conceptualization, W.M. and H.d.M.; methodology, W.M., J.R., and H.d.M.; software, J.R., M.R., and W.M.; Evaluation, J.R., W.M., and M.R.; resources, W.M. and J.R.; writing--original draft preparation, W.M. and J.R.; writing--review and editing, W.M. and H.d.M.; visualization W.M., J.R., and M.R.; supervision, H.d.M. and W.M.; project administration, H.d.M. and W.M.; funding acquisition, H.d.M. and W.M. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the German Federal Ministry for Education and Research (BMBF), under grant agreement no. 16 KIS0578, within the project Future Industrial Network Architecture (FIND).  
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.
## 4 Case Study and Economic Analysis
### Results of Economic Dispatch  
The State Grid of China adheres to the principle of unified dispatch and hierarchical management, and the peak-shaving standards are not unique in different regions. Figure 4 is the wind power utilization of UPI and UOI at different peak-shaving levels. The result shows that UPI and UOI have the comparable peak shaving capability, because different combustion-supporting technology of UPI and UOI does not greatly influent the peak-shaving ability.  
Figure 3: Daily load level and wind power prediction.  
The operation states of the 455 MW unit and the 80 MW unit are quite different from each other when participating in peak shaving. In Figure 5 the 455 MW unit in both cases keeps in ON state for 24 h, while in Figure 6 the 80 MW unit only operates during peak-demand hours and in RPS and DPSW periods. This is because the start/stop cost of the 80 MW unit is much lower than that of the 455 MW unit; and for the 80 MW unit, it is more economical to be shut down than being operated in DPSW and DPSC mode during the off-peak hours. With increased levels of peak-shaving, both UPI and UOI are more frequently to engage in deep peak shaving. To fully exploit renewable resources, thermal power plants should be encouraged to actively engage in deep peak shaving. However, deep peak shaving will increase environmental pollution and influence the marginal price of coal-fired units. Therefore, the economy of deep peak shaving should be comprehensively evaluated from different perspectives, and comparative economic analysis of UPI and UOI should be investigated.
# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 2 Materials and Methods
### Study Area  
As one of the rapidly developing urban areas in China, the CLQ in Guangdong province often changes dramatically due to the influence of anthropogenic and environmental factors [13]. Thus, we selected Guangdong province as the study area for establishing the CLQ estimation model (20\({}^{\circ}\)09\({}^{\prime}\)\(\sim\)25\({}^{\circ}\)31\({}^{\prime}\) N, 109\({}^{\circ}\)45\({}^{\prime}\)-117\({}^{\circ}\)20\({}^{\prime}\) E) (Figure 2a). Referring to the Guangdong Statistical Yearbook in 2021, the area has an average of 1767 h of sunshine, an annual average temperature of 22.8 \({}^{\circ}\)C, and annual precipitation of 1505 mm. Guangdong Province is an important grain production area in China, with a cultivated land area of 4.28 \(\times\) 10\({}^{4}\) km\({}^{2}\). The study area for the regional-scale CLQ estimation was the Conghua district within Guangzhou City (Figure 2b). The area of cultivated land in Conghua covers 2.05 \(\times\) 10\({}^{2}\) km\({}^{2}\).  
Figure 1: The flowchart of method.  
Figure 2: (**a**) Location of Guangdong province with the soil sampling distribution; (**b**) the DEM of the study area in Guangzhou city; (**c**) the distribution of samples for regional-scale CLQ estimation in the Conghua district (the plots for model training are black, the plots for mapping model testing are blue, and the plots for mapping accuracy validation are pink).
## Materials and Methods
### 2.5. Cultivation Studies
#### 2.5.6 Spinner Cultivation: Analyses  
Off-line samples were taken daily to measure the substrate (_Glc_) and metabolite (_Lac, Amm_) concentrations using a BioProfile 100 Plus (Nova Biomedical, Waltham, MA, USA). After the cells had detached from the MC surface (15 min TrypLE Select), the cells were counted using a NucleCounter NC-200 (ChemoMetec, Denmark). Based on the regular measurements of cell density and substrate/metabolite concentration, growth-dependent parameters Equations (1)-(6) were calculated:  
(I) Specific growth rate \(\mu\) (Equation (1))  
\[\mu=\frac{ln(X_{A}(t))-ln(X_{A}(0))}{\Delta t} \tag{1}\]where \(\mu\) is the net specific growth rate. \(X_{A}(t)\) and \(X_{A}(0)\) are the cell numbers at the end and the beginning of the exponential growth phase, respectively, and \(t\) is the time.
* Doubling time \(t_{d}\) (Equation (2)) \[t_{d}=\frac{ln(2)}{\mu}\] (2) where \(t_{d}\) is the doubling time, \(ln(2)\) the binary logarithm of 2, and \(\mu\) the specific growth rate.
* Population Doubling Level \(PDL\) (Equation (3)) \[PDL=\frac{1}{log(2)}\cdot log\bigg{(}\frac{X_{A}(t)}{X_{A}(0)}\bigg{)}\] (3) where \(PDL\) is the number of population doublings, and \(X_{A}(0)\) and \(X_{A}(t)\) are the cell numbers at the beginning and the end of the cultivation, respectively.
* Expansion factor \(EF\) (Equation (4)) \[EF=\frac{X_{A}(t_{max})}{X_{A}(t=0)}\] (4) where \(EF\) is the expansion factor, \(X_{A}(t_{max})\) is the maximum cell number, and \(X_{A}(t=0)\) is the inoculated cell number.
* Lactate yield from glucose \(Y_{Lac/Glc}\) (Equation (5)) \[Y_{Lac/Glc}=\frac{\Delta Lac}{\Delta Glc}\] (5) where \(Y_{Lac/Glc}\) is the lactate yield from glucose, \(\Delta Lac\) is the lactate production over a specific time period and \(\Delta Glc\) is the glucose consumption over the same time period (the exponential growth phase).
* Specific metabolite flux \(q_{met}\) (Equation (6)) \[q_{met}=\bigg{(}\frac{\mu}{X_{A}(t)}\bigg{)}\bigg{(}\frac{C_{met}(t)-C_{met} (0)}{e^{\mu t}-1}\bigg{)}\] (6) where \(q_{met}\) is the net specific metabolite consumption or production rate (for \(Glc\), \(Lac\), \(Amm\)), \(\mu\) is the specific cell growth rate, \(X_{A}(t)\) is the cell number at the end of the exponential growth phase, \(C_{met}(t)\) and \(C_{met}(0)\) are the metabolite concentrations at the end and the beginning of the exponential growth phase, respectively, and \(t\) is the time.
## 2 Materials and Methods  
### Chemicals and Samples  
All reagents were of analytical grade, and ultrapure water (18.2 M\(\Omega\)/cm) was generated with a Millipore system (Millipore, Billerica, MA, USA). Hydrochloric acid (37%), acetonitrile (99.9%), acetone, formic acid (98-100%), and ethanol of HPLC grade as well as trichloroacetic acid (TCA) and urea were obtained from Scharlab Chemie (Barcelona, Spain). Methanol (99.99%) and butanol of HPLC grade were from Fisher Scientific (Leicestershire, UK). Polyethylene glycol (8000 Da), polyethylene glycol (4000 Da), twin20 (1228 Da), ethylene glycol (62 Da), dextran (50,000 Da), gallic acid, epicatechin, \(\alpha\)-mangostin, procyanidin B\({}_{2}\), vanillin, iron (III) chloride, iron (II) chloride, sodium carbonate, hydrogen peroxide, 6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox), potassium ferric cyanide, 4-dimethylaminocinnamaldehyde (DMAC), potassium persulfate, 2,2'-azinobis(3-ethylbenzothazoline-6-sulphonic acid) diammonium salt (ABTS), ferrous sulfate, glycerol, 1,10-phenanthroline, citric acid, D-sorbitol, choline chloride, lactic acid, ethylene glycol, and formic acid were from Sigma-Aldrich (Saint Louis, MO, USA). Sodium dihydrogen phosphate dihydrate and dipotassium hydrogen phosphate were from Merck (Darmstadt, Germany).  
Mangosteen fruits (_Garcinia mangostana_ L.) were acquired in a local market (Alcala de Henares, Madrid, Spain). The fruit pieces were washed, and the peel was separated from its edible pulp and its mesocarp. Next, the peels were ground in a commercial blender and stored at \(-20\)\({}^{\circ}\)C until their analysis.
## 4 Results
### Completion of Practical Exercises and Evaluation Scores  
As practical exercises and evaluations were conducted only for the Ecuadorian training workshops, here we show results for each week's CS evaluations. The overall score was calculated from these evaluations, with a combined total of 75.5% correct answers over two weeks. Detailed scores for individual CS, derived from separate question sets, are presented in Figure 9.  
In the first week, participants achieved an overall score of 82.8% correct answers across the CAMS, CLMS, and C3S services, with CAMS averaging the highest at 97.8%, followed by C3S at 79.3% and CLMS at 71.6%. Issues with specific questions highlighted misunderstandings about CLMS's non-commercial operation and the socio-economic pathways described by CMIP6 in C3S. The second week saw an overall score of 72.6% across the CEMS, CSS, and Sentinel sessions. CSS averaged the highest at 85%, with Sentinel data (69.5%) and CEMS (69%) close behind. The results slightly miss the training objectives, particularly for CEMS. Challenges included understanding CEMS objectives, the GloFAS methodology, and differentiating commercial from open Sentinel data services.  
The completion of the workshop exercises was documented using data from posit.Cloud and QFIELD usage statistics, providing quantitative insights into the 16 participants registered on the platforms (Figure 10).  
Figure 9: Evaluation results for the training workshops in Ecuador.  
For the CAMS exercises involving atmospheric pollution forecasting with Sentinel 5P, 56% of participants successfully replicated the task, completing 136 h of computing. In the CLMS module, which focused on land cover and deforestation mapping using Sentinel-1 and 2, 62% of participants replicated the exercise, taking between 236 to 288 h of computing. In the C3S exercises, which involved rescaling climate models and assessing crop resilience with future climate data, we saw a higher engagement, with 75% of participants replicating the task, taking between 288 and 455 h of computing. Meanwhile, the CEMS tasks, which included reviewing emergency information from the 2016 Ecuador earthquake and constructing collaborative maps with QFIELD, had a lower replication rate of 50%, with participants taking between 29 and 59 h of computing. Additionally, the AHP exercise for hazard assessment was replicated by 100% of the participants, taking between 29 and 155 h of computing. This reduced computing time may have been due to most of the exercises being conducted outside of posit.Cloud. The CSS module on modeling illicit crops and identifying associated infrastructure had the lowest engagement, with only 25% replication and 206 h of computing. In the Sentinel session, as it was conducted mainly using SNAP, replication of exercises could not be evaluated.
# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
## 1 Introduction
[PERSON] et al. [38] employed both of the linear regression and neural network to predict three conceptions associated with energy consumption, cooling, and heating energy demands. The results of their studies proved that the neural network was superior to other models. [PERSON] et al. [39] explored the potential of neural network-based solutions in forecasting the diurnal cooling energy load; this study used recorded data of the five days before the day of the experiment to estimate the energy consumption; the outcomes demonstrated that the ANN approach is very effective. Moreover, [PERSON] et al. [40] performed a comparative analysis between different machine learning techniques such as radial basis function neural network (RBFNN), general regression neural network (GRNN), traditional backpropagation neural network (BPNN), and support vector machine (SVM) in predicting the hourly cooling load of a normal residential building. There are few studies (e.g., [PERSON] et al. [41] and [PERSON] et al. [42]) on the machine learning-based modeling application on the prediction of heating load. Nevertheless, using machine learning paradigms for optimizing the answers determined by the best artificial intelligence-based models is the chief aim of the actual study. To help engineers obtain an optimized design of energy-efficient buildings without any further experiments, this knowledge gap should be addressed. Hence, the basic purpose of this work is to estimate the amount of heating load in energy-efficient buildingsbuildings by various new machine learning-based approaches. In the following, several machine learning techniques such as multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) are employed to estimate the amount of heating load (HL) in energy-efficient buildings.
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
###### Abstract
In the whorl pattern, the ridges are circularly rendered around a central point on the finger [6]. It should be noted that the minutiae-based matching approach encounters several issues, particularly when fingerprint samples are degraded or rotated, resulting in poor performance results. On the other hand, the image-based matching approach is  
Figure 1: Fingerprint verification vs. fingerprint identification. shown to be very robust against these degradation issues. Therefore, this research adopts the image-based matching approach. This work has several key contributions involving fingerprint enhancement, feature extraction, and matching. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature vectors of two feature points is utilized as a feature matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points.
## 4 Discussion
The results showed a significant reduction in insertion torque in all groups with a long cutting flute in comparison to the corresponding screws with a short flute. Longer cutting flutes were also shown to increase the pullout strength. It is possible that the bone could rebound into the flutes during insertion [19], forming bone chips that accumulate around the threads of the flute and provide greater friction and resistance to pullout. The long cutting flutes could also carry more bone chips towards the proximal part of screw, creating a greater compressive force on the screw shaft. Although double-lead screws demonstrated greater penetration into the test block with each revolution, the double-lead design and greater helix angle may cause more material damage and reduce the pullout strength. Longer cutting flutes were found to significantly increase the pullout resistance in the double-lead screw groups, but the results were still considerably lower than those of the corresponding single-lead screws. When immediate stability is required, the risk of pedicle screw pullout may be reduced by using a single-lead design. There are some noteworthy limitations to this study. This study used an artificial bone model simulating the physical properties of real bone, but the material is distinct in that it has an entirely homogeneous structure. While not truly representative of real bone, the use of a bone model negated any variation between samples and allowed the investigation to focus on a comparison of different screw designs in terms of insertion torque and pullout strength. Artificial bone does have some notable advantages, including negligible inter-specimen variability, low cost, ready availability, and minimal specimen preparation. The use of synthetic bone complements the aim of this study to compare the pullout properties of different pedicle screws [19; 20; 21]. It should also be recognized that this study only assessed the thread design of one commercially available pedicle screw. Future studies may consider a wider range of pedicle screws. The test environment was also different than in vivo implantation in that testing was performed in a dry environment at room temperature. Similarly, the loading conditions did not accurately represent physiological loading, hence this testing did not account for the mechanical environment at a specific fixation site or when using different implantation methods.
## 5 Experiments and Discussion
### Experimental Comparison of Different Methods  
In the experimental comparison of the different methods with the same device, this paper uses the Kalman filter algorithm [19] and the nonlinear optimization algorithm proposed in this paper to collect point cloud data from the same forest area, and the reconstruction results are shown in Figure 9.  
At the initial stage of the experiment, both methods construct the 3D scene map of the forest area well, as shown in the right part of Figure 9a,b. With an increase in the amount of collected data, a scene shift occurs with the Kalman filter method. However, with the method proposed in this paper, we can still accurately collect the terrain data, as shown in the left part of Figure 9a,b. In this paper, the root mean square error is used as the accuracy evaluation standard between the point cloud maps. The experiments show that the proposed method reduces the root mean square error by 4.65% compared with the method based on the Kalman filter.  
In this paper, the time cost of each part in the point cloud processing is calculated as the evaluation index of the 3D reconstruction system, as shown in Table 5. The Kalman filter algorithm in the original literature iterates with the pose covariance matrix, and its calculation amount increases by the order of magnitude. Moreover, it is only optimized based on the pose of the last frame, lacking loopback and unable to eliminate cumulative error. Compared with the 3D reconstruction based on the Kalman filter, the method proposed in this paper has strong robustness and high precision, and its time cost meets the real-time requirements of a 3D point cloud reconstruction system.
# Numerical Modeling of Bowel Sound Propagation: Impact of Abdominal Tissue Properties
## 2 Theory and Model
### Transmission of Bowel Sounds
#### 2.2.2 Sound Propagation Models
(**c**) Overall geometric model of the abdomen. passes through it with the least resistance. Consequently, the fat layer allows sound to propagate with less reflection and greater transmission when coupled with other tissue layers, thereby facilitating the overall propagation of sound waves. These settings enable the software to automatically calculate the propagation, reflection, and refraction of bowel sounds as they interact with different tissue layers. This approach effectively captures the propagation characteristics of bowel sound waves in complex biological media, ensuring that the simulation results are both physically accurate and reasonable.
## 3 Model Development and Validation
### Validation of the Finite Element Model  
#### 3.3.1 Verification  
[PERSON] et al. [41] conducted a cyclic fatigue performance test of a low yield strength steel shear plate damper and the failure mode is depicted in Figure 5b investigating obvious buckling. Using the method above, the finite elemental analysis (Figure 5a) is conducted and the results including the failure mode and the cyclic curve are shown in Figure 5c,d. It can be seen that simulated buckling deformation is consistent with the tested. The simulated curve agrees well with the test curve but a little deviation of the initial stiffness could be observed which might be caused by failing to accurately model the actual loading boundaries. In addition, this finite element model(FEM) accurately predicts the bearing capacity in each loading circle. Therefore, this modelling method could be used to predict the behavior of the HESs and the results could be used to evaluate their performance.  
[PERSON] et al. [39] proposed a metal shear plate damper utilizing the low yield performance of BLY 160 materials for effective energy dissipation and conducted hysteric tests to evaluate its performance.  
Figure 4: The finite element model for the HES.  
Figure 5: The validation of the finite element modeling method. (**a**) Finite element model, (**b**) tested failure mode, (**c**) simulated failure mode, (**d**) hysteresis curves.  
This metal shear plate damper is composed of four components: shear panels, confined flanges, stiffening ribs and the roof/floor plates for connection (Figure 6b). To validate the simulating method used above, the above modelling method is employed to carry out a finite element analysis of the damper (Figure 6a). The failure mode, the buckling behavior and numerical simulated curves are shown in Figure 6c,d. It is found that the simulated curve has good coincidence with the test curve, but the load-displacement amplitude has some deviation. Some unpredicted slip occurred in the boundary during the experimental loading process, resulting in a deviation between the finite element loading displacement and the test. Therefore, the modelling method could simulate the hysteric behavior of the HESs and could be utilized to evaluate their performance.