esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance of the Application of MODIS and BESS GPPs in Southwest China  
Compared with MODIS GPP, BESS GPP demonstrates higher consistency with flux observation GPP data (Figure 6A). The coefficient of determination (\(R^{2}\)) of seasonal BESS and flux GPPs from 2003 to 2010 reached 0.59 with an RMSE of 1.30 g C m\({}^{-2}\) d\({}^{-1}\). The \(R^{2}\) of MODIS and flux GPPs was only 0.01 with a high RMSE of 2.73 g C m\({}^{-2}\) d\({}^{-1}\). The low \(R^{2}\) value of MODIS and flux GPPs was largely due to the difference in their trends from March to September in one year (Figure 6B).  
MODIS and BESS GPPs presented an increasing trend in 2001-2015 (Figure 7A,B). Pixel counts with an increasing trend surpassed 72% and 76% of the total area in accordance with MODIS and BESS data, respectively. The trends of these pixels with a GPP increase were within the range of 0-0.15 g C m\({}^{-2}\) d\({}^{-1}\), with most of the pixels distributed around the southeastern area of Yunnan, adjacent places of the three provinces and Chongqing City and the northwestern region of the study area. Overall, the sum of the entire GPP trend was 12,581 g C m\({}^{-2}\) d\({}^{-1}\) and 13,406 g C m\({}^{-2}\) d\({}^{-1}\) for MODIS and BESS GPPs, respectively. In addition, nearly 90% of the pixels with significant changes (\(p<0.05\)) also exhibited an increasing trend (Figure 7C,D). The spatial distribution of p  
Figure 6: (**A**) Consistency analysis of MODIS GPP, BESS GPP and flux observations from Xishuang-banna and (**B**) comparison of their seasonal trends in 2003–2010.  
increasing trend was similar for MODIS and BESS GPPs and largely distributed around the southeastern area of Yunnan and adjacent places of the three provinces and Chongqing City.
# Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
## 3 Simulation Study and Discussion of Results
### Random Event Generation  
The workflow of the proposed Monte Carlo simulation is illustrated in Figure 2. The process begins by loading the input data, including meteorological datasets, wind turbine power curve, ANN model, PLM curve, load profile, battery profile and fixed daily prices. Next, each iteration of the simulation involves the execution of tasks in the following three modules:  
1. ANN forecasting model: Wind power is estimated using the ANN-based model presented in Section 2.3. In this stage, the initial (unadjusted) wind power forecast for the next 24 h is generated by applying the statistical transformations of the meteorological datasets to the ANN model. This unadjusted forecast is used for scenario (b).
2. Misclassification correction: This step is used in the scenario (c) to adjust the wind power forecasts using the PLM method. To simulate misclassified stochastic events, a random binary variable \(d=(d_{i})\) that follows a binomial distribution with probability \(p_{i}\) is defined, where \(p_{i}\) is the PLM value of the predicted power class at time \(i\). If the value of the random variable \(d_{i}\) is equal to 1, the forecasted power production in period \(i\) is considered to be higher than the real power value, and thus it is downgraded by one production class. For example, if the power production of period \(t\) was assigned to the power class C5 and the random variable \(d_{i}\) is equal to 1, then the forecast is downgraded to the power class C4. Under these conditions, it is expected that the performance of the system decreases, followed by a decrease in the battery SoC and a potential increase of system costs due to the purchase of extra electricity from the grid. The expected power loss at time \(t\) is calculated, in percentage terms, as: \[EL_{i}=\max(0,d_{i}\cdot p_{i}\cdot LGM),\] (13) where \(LGM\) is a constant defined as the percentage of power losses given an event of low misclassification. In this case, the output of the ANN model is adjusted by the factor 1-\(EL\) in order to calculate the expected power.
3. Storage battery operations: The battery operations are simulated taking into account the net load, calculated as the difference between the load demand and power production (the reader is referred to Section 2.2).
# NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation
## 3 NextDet
### Head  
The head module of a modern object detector determines bounding boxes and outputs detection results such as object class, score, location, and size. The general design of such systems incorporates multiple head modules in order to precisely detect objects in an input image and predict scores by exploiting a common feature set from earlier in the network. The design of the proposed NextDet object detection network utilizes 3 heads using the YOLO layer as seen in Figure 4.  
Each head of the proposed NextDet network incorporates a highly efficient Spatial Attention Module (SAM) block, introduced in Convolutional Block Attention Module (CBAM) [38], for attentive feature aggregation. In this approach, a spatial attention map is generated with a focus on more important parts. Average-pooling and max-pooling operations along the channel axis is utilized to obtain inter-spatial relationships of features and can be defined mathematically as follows:  
\[y=\sigma\Big{(}N_{f}(x)\Big{)}\times x \tag{6}\]  
Here, \(x\) denotes the input feature map, \(y\) denotes the output feature map and \(N_{f}\) denotes non-linear function for computing a SAM block's output. \(\sigma\Big{(}N_{f}(x)\Big{)}\) corresponds to the attention function which assigns a value up to 1 for spatial features of a higher priority and a value down to 0 for spatial features of a lower priority of the input \(x\).
# Scale-up and Economic Assessment of Biofunctional Particles Synthesis for Bilirubin Removal
## 3 Results and Discussion
### Economic Assessment
#### 3.2.1. _Bifunctionalized Particles Production Plant Capacity and Location_  
The production of HSA functionalized particles depends on the number of particles per cartridge as well as the number of treatments carried out in Spain. As commented before, 64,000 people in Spain needed renal replacement treatments and approximately 80% of them required hemodialysis treatments in 2019 [23]. On the other hand, normal values for total bilirubin in blood are between 0.3 and 1.2 mg/dL, while hyperbilirubinemic patientsusually present an average value of total bilirubin in blood of 12 mg/dL. Taking into account these data and that average volume of blood in human body is 50 dL, the bilirubin removed in each case should be 540 mg. On the other hand, according to the in vivo experiments performed by the research group, the average capacity of bilirubin removal of St-MMA-GMA-PEGMA is 1 mg BR/g particles. Thus, each cartridge used in the patient's treatment should contain 540 g of St-MMA-GMA-PEGMA particles. Besides, this treatment removes the bilirubin, but the liver problems will persist. So, it is expected that each patient needs 21 treatment sessions per year. All this information and the final calculation of the required annual production of HSA functionalized particles is summarized in Table 5.  
The production of particles will be carried out in lots, bearing in mind that the overall time to produce HSA functionalized particles is 48 h (batch), including several steps: monomers washing, polymerization reaction, particles purification and drying and the albumin immobilization. Considering 300 workdays per year and three work shifts of 8 h, the total amount of production per year is 7200 h. The capacity of the plant per batch must be 4 Tm.  
Regarding the plant location, it is planned to be located in Ciudad Real (Castilla-La Mancha) due to its central position and lower construction costs with respect to other regions in Spain. In addition, production in Ciudad Real is facilitated by proximity to the University Hospital of Ciudad Real (HGUCR) as a reference hospital for the treatment of patients with hyperbilirubinemia.
# Quality Assessment of Organic Kefirs Made with Kefir Grains and Freeze-Dried Starter Cultures
## 3 Results and Discussion
### Analysis of Organic Kefirs During Refrigerated Storage
#### 3.3.4 Texture Parameters  
Texture is an important determinant of the quality of fermented milk and should remain stable throughout the refrigerated storage [30]. Table 5 presents the firmness, consistency, cohesiveness, and viscosity index of experimental kefirs. The firmness of the kefirs varied among the samples. Kefirs G24 and L12 had lower firmness compared with kefirs G12 and L24. The statistical analysis showed that firmness was affected only by the type of kefir, while storage time did not influence this parameter (Table A1). A lower range of kefir firmness during the 21-day refrigerated storage (17.39-18.37 g) was recorded by [PERSON] et al. [71]. The authors noted that firmness was positively affected by total solids and casein content in milk. Regarding cohesiveness, this parameter was influenced by both the type of beverage and storage time (Table A1). Kefir L12 had significantly lower cohesiveness values during most of the storage period (days 1-14) while kefir L24 had the highest. For kefir L24, there was a gradual decrease in cohesiveness during storage. An increase in the cohesiveness of kefir during refrigerated storage was noted by [PERSON] et al. [71], which was contrary to our findings. In the cited study, the value for this parameter ranged from \(-\)11.54 g to \(-\)12.02 g. As with cohesiveness, kefir L12 had the lowest consistency values (the least compact), while kefir L24 had the highest (the most compact). The consistency values did not change significantly during storage (Table 5). The consistency of kefir was in the range 383.01-396.47 g-s, which was similar to the L24 variant recorded by [PERSON] et al. [71]. The final texture parameter evaluated was the viscosity index (g-s). Similar to consistency and cohesiveness, this parameter was influenced by both storage time and the type of kefir (Table A1). Kefir L12 had the lowest viscosity index values (the least negative values), while L24 had the highest (the most negative values). [PERSON] et al. [71] recorded a viscosity index range similar to kefir G12 during the 21-day storage (\(-\)1.24 g-s to \(-\)1.76 g-s). The low values of the firmness, cohesiveness, consistency, and viscosity index of kefir L12 are presumably the result of its insufficient acidification. When comparing kefir L24 and G24, a significantly higher
###### Abstract  
The purpose of this study was to assess the accuracy of positional data and the inter-unit reliability of an ultra-wide-band (UWB) tracking system. Four well-trained males performed five courses designed for the analysis of x- and y-coordinate accuracy analysis, specifically related to the positional distance variation between the UWB data and the fixed reference lines of a basketball court. This was achieved using geographic information system (GIS) mapping software that calculated, for each interval and participant, the distance from the main axis of displacement and from the opposite side of the court each 0.5 s (x and y coordinate). The accuracy of the results was satisfactory, with a mean absolute error of all estimations for the x-position of 5.2 \(\pm\) 3.1 cm and for the y-position of 5.8 \(\pm\) 2.3 cm. Regarding inter-unit reliability, the intra-class correlation coefficient (ICC) value was high for the x-coordinate (0.65) and very high for the y-coordinate (0.85). The main findings of the study were: (i) The accuracy of UWB tracking systems can be considered suitable for practical applications in sport analyses; (ii) position estimations are very precise and acceptable for tactical analyses; (iii) the error of the position estimations does not change significantly across different courses; and (iv) the use of different devices does not significantly affect the measurement error.  
s 2019 9 939 00.3390/app9050939
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 3 Methodology  
### The FL-SDUAN Model  
The FL-SDUAN model consists of three parts: the underwater data nodes, the surface aggregation nodes, which act as controllers, and the coastal base stations (BSs). The DN consists of a buoy, an anchor chain, and an anchor. The sensor is mounted on the buoy, and the anchor chain connects the anchor to the buoy. When the DN is deployed, the anchor sinks to the bottom, and the anchor chain expands and contracts to a fixed length. Finally, all the buoys form a 3D network structure. The FL-SDUAN model is shown in Figure 1.  
The data plane is built by virtualization, in which Open vSwitch (OvS) is installed on the DNs to implement the OpenFlow switching function [36]. The controller is set on the water surface, and the OpenDayLight (ODL) system is installed on it to build the control plane. A high-energy rechargeable battery powers the controller. As an application plane, the associated applications run on the BS. The multi-application system is an agent management system based on Flowvisor [37].  
In FL-SDUAN, it uses an SDN architecture with the separation of data communication and control communication. Different hydroacoustic signal frequencies are designed for control communication and data communication. Single-hop mode is used for control communication, and multi-hop relay mode is used for data communication. RF signals are used for communication between BSs and surface controllers. When the UAN is deployed, the controller triggers all underwater nodes in the management area through control communication. At a given time, the underwater nodes are connected to the controller via one-hop control communication and then report their status information, such as location and resource status, at any time. When the underwater node sends a routing request, the controller executes the routing algorithm and assigns routing decisions to the corresponding node.
# A Strong Noise Reduction Network for Seismic Records
## 3 Results and Discussion
### Denoising Analysis of Different Types of Noise  
The denoising test was conducted using our model on noisy seismic signals contaminated with low-frequency noise, high-frequency noise, mixed-band noise, and spike noise to ascertain the model's efficacy in noise abatement across various frequency bands. The outcomes are depicted in Figures 4-7. Figure 4 illustrates the impact of low-frequency noise, which induces significant fluctuations in the seismic signal. This noise is heavily superimposed on the seismic signal band, resulting in poor differentiation and complicating the denoising process. Our model successfully suppresses these fluctuations, enhancing the SNR from \(-9.46\) dB to \(12.85\) dB, increasing the correlation coefficient by \(96\%\), and reducing the RMSE by \(80.19\%\). Figure 5 illustrates the impact of high-frequency noise, which has a higher frequency and minimal overlap with the seismic signal band. This noise detrimentally affects the event detection accuracy. Our model successfully removes it, improving the SNR from \(-6.87\) dB to \(21.13\) dB, raising the correlation coefficient by \(110\%\), and lowering the RMSE by \(80.9\%\). Figure 6 illustrates the impact of mixed-frequency noise, which comprises multiple frequency components that severely overlap with the seismic signal band, posing noise reduction challenges. After the denoising process, the SNR is improved from \(-8.65\) dB to \(10.25\) dB, the correlation coefficient is enhanced by \(66.66\%\), and the RMSE is decreased by \(69.71\%\). Figure 7 illustrates the impact of spike noise, which impacts the seismic phase and arrival time extraction. Following denoising, the SNR is boosted from \(-8.5\) dB to \(14.5\) dB, the correlation coefficient is increased by \(108.5\%\), and the RMSE is reduced by \(70.66\%\). In summary, our model effectively eliminates various noise types, augments the SNR, preserves the effective signal, and minimizes the waveform distortion.  
Figure 3: Training and validation loss during the training process.  
Figure 4: Denoising performance of low-frequency noise: (**I,III,V**) are the original signal, the noise signal, and the denoised signal in the time domain; and (**II,IV,VI**) are the time–frequency domain data of (**I,III,V**).  
Figure 5: Denoising performance of high-frequency noise: (**I,III,V**) are the original signal, the noise signal, and the denoised signal in the time domain; and (**II,IV,VI**) are the time–frequency domain data of (**I,III,V**).
## 3 Stray Current Corrosion Morphology, Measurement Method, and Influencing Factors
### Factors Affecting Stray Current Corrosion
#### 3.4.2 Electrical Factors
[75] found, through electrochemical testing, that the continuous change in the AC current direction will cause the local potential to shift negatively, which will intensify the anodic dissolution of the defects of the buried pipeline coating. In addition to the above-mentioned current frequency and current direction causing more serious corrosion to the buried pipeline, the waveform also has a corresponding effect on the corrosion rate. Potential fluctuations cause the local potential to shift negatively and accelerate the dissolution of the anode. [PERSON] [28] studied the corrosion effect of AC current with different waveforms at the same frequency. The study found that when the current frequency is the same, the corrosion rate of the triangular wave is greater than that of the sine wave and the square wave. It is believed that the reason for this phenomenon is related to its peak voltage. The potential difference in the AC system fluctuates with the periodic change in the current. Unlike the DC system, where the potential difference between the soil and the pipeline is constant, the potential fluctuation seriously affects the intensity and nature of corrosion. [PERSON] [76] analyzed the effect of dynamic AC currents on the corrosion rate of pipeline steel by the electrochemical method. AC potential fluctuation caused great interference with the cathodic protection system. Under high current density, the corrosion continued to intensify with the increase in interference time. Based on this, a dynamic corrosion risk assessment method based on the cathodic protection level was proposed. [PERSON] [77] analyzed the stray current interference generated by the traction current of urban rail through field measurement and studied the effect of potential fluctuation between soil and pipeline on corrosion. The study found that when the potential fluctuates with the train operation cycle, the corrosion rate increases significantly. [PERSON] [18] used the dynamic potential measurement method to study the influence of potential fluctuation on the corrosion rate of pipelines and found that when the potential fluctuation period is short, the corrosion rate increases significantly. In addition to the above factors, like the DC system, the traction current, the applied voltage, and the insulation of the track also affect the AC system.
# Subway Station Capacity Maintained by Optimizing a Maintenance Schedule of Key Equipment
## 4 Reliability Analysis of the Key Equipment
### Reliability Modeling of the Key Equipment  
Large amounts of data on the key equipment maintenance record is available from the subway operation company. The faults of the key equipment are analyzed and divided into several classes. For each class, the probability of the failure intervals can be approximated by a typical distribution function. The commonly used fault distribution models are exponential distribution, normal distribution and Weibull distribution [19]. In this paper, the maximum likelihood estimation method (MLE) is adopted to estimate parameters of fault distributions. The likelihood function indicates how likely the observed sample is as a function of possible parameter values. Therefore, maximizing the likelihood function determines the parameters that are most likely to produce the observed data.  
The Anderson-Darling statistic measures how well the data follow a particular distribution. For a particular data set and distribution, the better the distribution fits the data, the smaller this statistic will be. The Anderson-Darling statistic can be used to determine the optimal distribution of the component [19].  
The Anderson-Darling statistic can be formulated as follows:  
\[A^{2}=-n-\frac{1}{n}\sum_{i=1}^{n}(2i-1)[nF(x_{i})+ln(1-F(x_{n+1-i}))] \tag{10}\]  
\[F(x_{i})=\Phi\left(\frac{x_{i}-\overline{x}}{\sigma}\right) \tag{11}\]  
where \(n\) is the sample size, \(x_{i}\) is the value of the \(i\)th sample, and \(F(x_{i})\) is an empirical cumulative distribution function subject to standard normal distribution.
# Developing Security Assurance Metrics to Support Quantitative Security Assurance Evaluation
## 2 Related Work
For instance, [PERSON] and [PERSON] [17] introduced an aggregational method that can be applied in the calculation of the security assurance value of the whole system when combining several entities, which have been evaluated independently. The effects of the emergent relations are taken into account in the calculation of the security assurance value of an attribute in the context of a system. [PERSON] et al. [18] take advantage of quantitative risk measurement methodologies to develop metrics for IT infrastructure security assurance evaluation along with aggregation techniques, i.e., the assurance level of a system is a specific combination of assurance levels from underlying components. The main algorithms used for the operational aggregation include the recursive minimum algorithm, the recursive maximum algorithm, and the recursive weighted sum algorithm. Moreover, to help businesses address service security assurance, [PERSON] [19] presents a set of metrics that can estimate the level of confidence for both consumers and providers. The defined metrics can be categorized into three main areas: security-related metrics (existence, correctness, etc. ), security verification-related metrics (coverage of verification, depth of verification, etc. ), and privacy-related metrics (data confidentiality and service consumer anonymity). Some SA metrics methodologies use evidence and arguments over security measure adequacy in a security case to build an acceptable level of confidence in system security. For instance, [PERSON] et al. [20] propose the use of security arguments by facilitating security metrics that need to be complete and valid and propose a framework for argument assessment that generates and interprets security metrics on the example of software systems. Within the framework, security is quantified in terms of a level of belief, i.e., a confidence level of arguments. Several approaches take advantage of patterns to assess and evaluate system security. In this area, for instance, [PERSON] et al. [21] associate security metrics with patterns, and exploit the relationships between security patterns and security objectives to enable the interpretation of measurements. [PERSON] et al. [22] evaluate the security of architecture by considering different misuse patterns. They propose to analyze how many misuse patterns for architecture can be countered when adding security patterns to improve the architecture. The calculated value then represents the level of security for the applied security patterns. Lastly, [PERSON] et al. [23] evaluate system security based on threat enumeration and on verifying if these threats are or are not controlled in a specific software architecture. They also consider the effect of policies and the use of weights according to their impact.