esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Disturbance Observer-Based Terminal Sliding Mode Control
: _Because the formulation developed earlier in this paper is for second-order systems only, it is desirable to generalize it to an nth-order formulation for different applications. Therefore, the results obtained in this work are extended for a class of nonlinear nth-order systems of the form:_  
\[\left\{ \begin{array}{l}\dot{x}_{i}=x_{i+1}+d_{i}(t),\ \ \ \ i=1,2,\ldots,n-1\\ \dot{x}_{n}=f(x,t)+g(x,t)u+d_{n}(t)\end{array} \right. \tag{33}\]  
_To estimate the mismatched disturbances \(d_{i}\left(i=1,2,\ldots,n-1\right)\), an nth-order disturbance observer is applied here as follows:_  
\[\left\{ \begin{array}{l}\dot{z}_{1i}=v_{1i}+x_{i+1}=\varphi_{1i}(z_{1i}-x_{i}) +z_{2i}+x_{i+1}\\ \dot{z}_{2i}=v_{2i}=\varphi_{2i}(z_{2i}-v_{1i})+z_{3i}\\ \ldots\\ \dot{z}_{(n+1)i}=\varphi_{(n+1)i}\Big{(}z_{(n+1)i}-v_{ni}\Big{)}\end{array} \right. \tag{34}\]  
_where \(\varphi_{ji}\) are nonlinear functions of the form:_  
\[\varphi_{ji}(s)=-\lambda_{ji}L_{i}^{1/(n-j+2)}|s|^{(n-j+1)/(n-j+2)}\text{sign} (s)-\mu_{ji}(s) \tag{35}\]  
_and \(z_{1i}=\dot{x}_{i}\), \(z_{2i}=\hat{d}_{i}\), \(z_{3i}=\hat{\dot{d}}_{i}\), \(\ldots\), \(z_{(n+1)i}=d_{i}(\hat{n}-1)\), \(L_{i}>0\), \(\lambda_{ji}\), \(\mu_{ji}>0\), \(j=1,2,\ldots,n+1\). Consequently, an nth-order disturbance observer-based TSMC is designed as:_  
\[\sigma=\dot{x}_{n}+\sum_{k=2}^{n}\big{(}c_{k}|\psi_{k}|^{\mu_{k}}\text{sign}( \psi_{k})\big{)}+c_{1}|x_{1}|^{\alpha_{1}}\text{sign}(x_{1})+\chi_{2} \tag{36}\]  
_where \(\psi_{k}=x_{k}+z_{k1}+z_{(k-1)2}+\ldots+z_{(k-1)}\), \(\chi_{2}=z_{3(n-1)}+z_{4(n-2)}\ldots+z_{(n+1)1}\). \(c_{k}\) and \(\alpha_{k}\) are positive constants such that the polynomial \(p^{n}+\sum_{k=2}^{n}\Big{(}c_{k}p^{k-1}\Big{)}+c_{1}\) is Hurwitz and the following equation set is satisfied [30; 47]:_  
\[\left\{ \begin{array}{l}\alpha_{1}=\alpha,\ \ \ n=1\\ \alpha_{k-1}=\frac{\alpha_{k}\alpha_{k+1}}{\alpha_{k+1}-\alpha_{k}},\ \ \ k=2,\ldots,n\ \ \forall n\geq 2\end{array} \right. \tag{37}\]  
_where \(\alpha_{n+1}=1\), \(\alpha_{n}=\alpha\), \(\alpha\in(1-\varepsilon,1)\), \(\varepsilon\in(0,1)\)._
# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 4 Results
### Comparison with Standard nnUNet  
To assess the performance of both models, the following evaluation metrics were used: Dice coefficient, accuracy, recall, IoU, and precision. The results are shown in Table 1, highlighting the improvements achieved by the SPCA-enhanced nnUNet. The boxplot illustrates the performance comparison between the baseline nnUNet and the SPCA-enhanced nnUNet across four key evaluation metrics, as follows: Dice, precision, recall, and IoU. The SPCA-enhanced nnUNet consistently outperforms the baseline model, achieving higher median scores in all metrics. The improvement is particularly pronounced in recall and IoU, where the SPCA-enhanced model demonstrates not only higher median values but also reduced variance, indicating more stable and reliable performance. For Dice and precision, while the enhancement is less substantial, the SPCA-enhanced nnUNet still maintains a clear advantage. These results underscore the efficacy of integrating SPCA into nnUNet, leading to significant gains in segmentation performance, especially in metrics that emphasize model sensitivity and spatial overlap accuracy. Figure 8 demonstrate a comparison between nnUNet and SPCA-enhanced nnUNet using different metrics.  
Figure 8: Comparison of evaluation metrics between standard nnUNet and SPCA-enhanced nnUNet. The green one demonstrates standard nnUNet, and the orange one demonstrates SPCA-enhanced nnUNet.  
Figure 7: Performance comparison. **Top** plot demonstrates the training and validation loss of SPCA-enhanced nnUNet. **Bottom** plot demonstrates Dice comparison between SPCA-enhanced nnUNet and standard nnUNet.
###### Abstract
reported the effective control of fouling during CEB at high backwashing frequencies using NaCIO concentrations of 0.5-1.5 mg/L with the minimum adverse effect on bacterial communities in membrane bioreactors (MBRs) [22]. [PERSON] et al. found that the function of NaCIO-assisted CEB could be correlated to the polysaccharide in foulants for the in situ application of CEB in anammox MBR [23]. However, few attempts have been made to examine NaCIO-assisted CEB associated with HF NF performance in drinking water treatment plants. Previous studies have provided a preliminary indication regarding the potential of employing the NaCIO-assisted CEB technique for NF membrane cleaning. In this study, a novel thin-film composite (TFC) HF NF membrane was fabricated to remove natural organic compounds from the surface water. To the best of our knowledge, this is the first attempt to subject HF NF membranes to CEB for membrane fouling control. Physical and chemical cleaning methods, membrane surface morphology, and water quality analyses were systematically performed to assess the impact of organic foulants. In addition, we intended to optimize the CEB process using NaCIO to minimize the negative impact of NaCIO on membrane properties and perform fewer pretreatment processes to achieve high fouling control with low investment and operational costs in real water treatment plants.
## 2 Materials and Methods
### Performance Metrics and Methodology  
In this paper, to evaluate the SEs, three performance metrics were used, i.e., the coefficient of determination (\(R^{2}\)) [25], the mean absolute error \(MAE\)[18] (Equation (2)), and the root-mean-squared error (\(RMSE\)) [26]. The \(R^{2}\) is in the 0 to 1 range, where 0 represents the worst-possible value, while the value of 1 represents the best-possible value and is aimed for. Regarding the \(MAE\) and \(RMSE\) values, the goal is to obtain as low a value as possible.  
As stated in the description of the GPSR algorithm, the \(MAE\) metric was used to evaluate the population members (fitness function). After each SE is obtained, it is evaluated to determine the \(R^{2}\), \(MAE\), and \(RMSE\) values. To calculate the mean and \(\sigma\) values of the performance metrics in GPSR with RHS and the 5-CV, the following steps are performed:  
* During the 5-CV, calculate the performance metric values on the training and validation sets.
* After the 5-CV process is complete, calculate the performance metrics (mean values obtained on the training dataset).
* Perform the last evaluation of the trained model (five SEs) on the testing dataset, and calculate the performance (evaluation) metric values (values achieved on the testing dataset),
* Calculate the mean and \(\sigma\) values of the performance metrics from the obtained values on the training and testing datasets.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 2 Advances in Irrigation Technologies and Water Management
### Optimizing Irrigation for Water-Scarce Regions  
In the North China Plain, water resources are limited, and traditional flood irrigation methods are usually inefficient and unsustainable, posing a major challenge to agricultural productivity. The findings in the paper have far-reaching implications, showing that crop water use can be reduced without sacrificing yields using micro-sprinkling irrigation with optimized border widths due to following several advantages:  
Uniform soil moisture distribution: By delivering water evenly across the field, the uniformity coefficient of soil moisture increased by 25.8%. These micro-sprinkling systems minimize water wastage and ensure consistent soil moisture levels across the field, reducing the risk of over- or under-irrigation.  
Optimized irrigation quotas: The study demonstrates how adjusting irrigation quotas based on crop growth stages can lead to significant water savings without compromising yield. Adjusting irrigation quotas in response to crop growth stages leads to improved WUE and higher grain yields.  
Economic viability: The simplicity and cost-effectiveness of micro-sprinkling systems make this technology accessible to smallholder farmers, who are often the most vulnerable to water scarcity, particularly in water-stressed regions.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Evaluation
#### 6.1.3 Throughput  
Throughput of the entire network is measured in terms of total number of packets received at BS (Base Station). The comparison for total number of packets successively received at BS is shown in Figure 8. ESDR outperforms its counterparts in terms of throughput by considerable margin. This is because packet loss in ESDR is minimum due to packet segregation strategy. Two third of generated packets are sent to courier nodes available in underwater environment. There is very minute packet loss in this transmission since it requires maximum one hop communication. Only one third of total generated packets are broadcasted to surface sink. In previous protocol holding time value was  
Figure 6: Average end to end delay: Comparison.  
Figure 7: Delay comparison in ESDR.  
depending on forwarding function which itself was having inconsistent values. In ESDR Improved forwarding function results into optimum values of holding time which eventually ensures decreased collision and lesser packet loss. So overall comparison, depicted in Figure 8 results into increased throughput of ESDR.  
Another parameter to get the insight in terms of throughput is Packet Delivery Ratio (PDR) shown in Figure 9. PDR throughout the lifetime is calculated downsampled and plotted for all comparing schemes. Improved forwarding function and efficient adaptive mobility of courier node cause the increase PDR value for proposed protocol. Moreover since approximately 66% of the generated packets are transmitted to courier nodes which may cause delay but ensure reliable delivery of packets destinations resulting with improvement in PDR and throughput as in Figure 9.
# Topology Optimization Based on SA-BESO
## 2 The Joint Topology Optimization Method Based on SA-BESO
### SA-BESO Algorithm Flow  
The SA-BESO algorithm finds the optimization direction based on the shift of the structural compliance value and uses the temperature parameter T to control the optimization process in stages.  
Based on the initial structure and a given initial temperature \(T_{0}\), the algorithm performs a double encoding of the structure elements, fresh binary codes are randomly generated from the crossover mutation, and the addition and deletion of elements are judged by the binary code and the original density values of the elements to form the new structure.  
Whether the current structure is superior to the original structure is judged according to the positive and negative of the structure compliance difference \(\Delta C\); the new structure is accepted with probability \(P\), where \(P\) is shown in Equation (7). That is, \(\Delta C<0\) indicates that the current structure is superior to the original structure, and the improved structure is selected; otherwise, the current structure is still accepted with some probability.  
\[P=\begin{cases}1&\Delta C<0\\ exp\big{(}-\frac{\Delta C}{\Gamma}\big{)}&\Delta C>0^{\prime}\end{cases} \tag{7}\]  
After a sufficient search at this temperature, the temperature \(T\) is lowered, and the search for the improved structure is continued. A reasonable and efficient cooling criterion is the key to the success of the SA-BESO algorithm. In this paper, a heuristic temperature update function is used.  
\[T_{k}=\frac{T_{0}}{k^{m}},k=1,2,\ldots, \tag{8}\]where \(k\) is the number of iterations, \(T_{k}\) is the control temperature after the \(k\) th iteration cooling, and \(m\) can be generally 3-5, which can consider the calculation accuracy and time.  
The SA-BESO algorithm will continue to carry out the cyclic iterative process of \"reducing temperature--randomly generating new structure--calculating the difference of structure compliance--judging whether to accept the new structure--accepting or rejecting the new structure in the stage\". When the control parameter T is reduced to the desired minimum, the structure reaches the final target volume and satisfies the convergence condition, the loop exits, and the final optimized structure is output. The process of the SA-BESO joint algorithm is shown in Figure 1.  
Figure 1: Flowchart of the SA-BESO method.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### Performance Metrics Analysis
#### 4.3.3 System Utilization (U) Enhancement  
* With High Bulk Service, system utilization remains stable within [0.960, 2.540].
* Lower vacation results in consistent resource use, preventing energy wastage.
* Cost Set III exhibits optimal system utilization, as solar power provides a steady energy source.
## 4 Discussion  
Figures 15 and 16 show comparisons of the polarization curves and power density curves of the straight flow channel model, the rectangular baffle model, and the trapezoidal baffle model. As can be seen from the figures, the performance difference between the three models was insignificant at higher voltage. However, at lower voltage with higher current density, the performance of the rectangular baffle model visibly surpassed that of the other two. The power density was 8.3% higher than in the straight flow channel model at a voltage of 0.3 V. In the optimized trapezoidal baffle model, it was 4.0% higher than that in the straight flow channel model.  
Figure 14: Water concentration at the cathode GDL/CL interface around the axis of symmetry.  
Figure 15: Polarization curves for three models.  
The rectangular baffle model had obvious advantages in term of the polarization curve and power density. However, it also caused more pressure loss, as shown in Figure 17. When the current density was 4 A/cm\({}^{2}\), the pressure drop of the straight flow channel model was 1377 Pa, while the pressure drop of the rectangular baffle model was about 12,938 Pa, which was 938.9% that of the straight flow channel model. Although the pressure drop of the optimized trapezoidal baffle model was also greater than that of the straight flow channel model, the pressure drop of 4895 Pa was only 37.83% that of the rectangular baffle model.  
Higher pressure losses accordingly require higher pumping power. In practice, the limited pump efficiency will further increase the practical pump power requirement. The net power output of the PEMFC after considering the pump power consumption is shown in Figure 18. The comparison is based on voltages of 0.4 V and 0.6 V; the former yielded the highest power density, and the latter was closer to the practical requirement of an on-road PEMFC. At 0.4 V, due to the large power density of the PEMFC and the limited change in pump power, the influence of pump efficiency was relatively low. With pump efficiency higher than 30%, the net power output of the rectangular baffle model was better than that of the optimized trapezoidal baffle model. However, at 0.6 V, the advantage of the trapezoidal baffle significantly increased; the model had a net power increase of 1.47%  
Figure 16: Power density curves for the three models.  
Figure 17: Pressure drop of flow channels in three models.  
compared to the rectangular baffle model at 50% pump efficiency and 3.94% at 30% pump efficiency. Considering the pump efficiency in reality, the optimized trapezoidal baffle model could improve the performance of the fuel cell better than the rectangular baffle model and showed an oxygen distribution similar to that of the rectangular baffle model.
# Securing SDN-Based IoT Group Communication
## 3 Overview
### SDN-Based IoT Group Communication
#### 3.3.2 CoAP Group Request  
A CoAP client wishing to send a request to a CoAP group implements the related protocol described in [14]. From a high-level perspective the client executes the following steps:  
1. From the lookup table, it retrieves the \(Endpoint_{ID}\) identifiers of the CoAP servers that are members of the group;
2. If it knows a \(Fwd_{ID}\) for all retrieved \(Endpoint_{ID}\) identifiers, it proceeds to step 4;
3. It constructs a message that includes all \(Endpoint_{ID}\) identifiers for which it does not know a \(Fwd_{ID}\) and sends it to a \"special\" MAC address used for forcing SDN switches to forward a packet to the controller. The controller responds with a list of \(Fwd_{ID}\) that is eventually returned back to the client;
4. It creates a new \(Fwd_{ID}\) by ORing the \(Fwd_{ID}\) identifiers of the retrieved \(Endpoint_{ID}\) identifiers and forwards the CoAP request using the created \(Fwd_{ID}\). Due to the properties of Bloom filter-based forwarding (see [7] for more details), the CoAP request will be forwarded to the appropriate IoT devices.
## 3 Modeling  
The proposed model is used to optimally operate an MG, while considering operational costs, AS reserves, and resource allocation to minimize MG losses. AS constraints are driven by the utility target limits. The model can be used for the provision of ancillary services with different time resolutions. In comparison to other research papers covering this topic, the novelty of the model is in modeling power losses of the MG, as well as in modeling the AS request at the Point of Common Coupling (PCC) of MG to the distribution grid as a virtual load (VL). In other words, if a request for balancing of active power appears, as shown in Figure 2, where the MG has to increase its power exchange with the distribution grid, a VL is added to the node of PCC with the constraint to supply the VL using only MG assets (generating, discharging batteries or reducing MG controllable load). The VL has a load curve that corresponds to the balancing request. The objective of the problem is to minimize MG operational costs considering MG power exchange with the utility grid:  
\[min\!\!\sum_{t}\left[OC_{MG}\right)+(t)\ P^{grid}(t)-\rho^{as}(t)\ P^{VL}(t) \tag{1}\]  
\[OC_{MG}(t)=\sum_{i\in G}f_{i}\big{(}P_{gi}(t)\big{)}+\sum_{j\in L}f_{j}\Big{(}P _{lj}(t)\Big{)}+\sum_{k\in S}f_{k}(P_{sk}(t)) \tag{2}\]  
It should be mentioned that the load cost function is defined for the controllable part of the load. The objective is subject to technical constraints, including MG power losses and AS requirements. \[P_{G}(t)+P_{S}^{dch}(t)+\ P^{grid}(t)=\ P^{VL}(t)+P_{L}(t)+P_{S}^{ch}(t)+L(t), \ \forall t \tag{3}\]  
\[P_{gi}^{min}\ \leq\ P_{gi}(t)\ \leq\ P_{gi}^{max},\ \forall i\in G \tag{4}\]  
\[P_{lj}^{min}\ \leq\ P_{lj}(t)\ \leq\ P_{lj}^{max},\ \forall j\in L \tag{5}\]  
\[P_{sk}^{ch,min}\ \leq\ P_{sk}^{ch}(t)\ \leq\ P_{sk}^{ch,max},\ \forall k\in S \tag{6}\]  
\[P_{sk}^{dchmin}\ \leq\ P_{sk}^{dch}(t)\ \leq\ P_{sk}^{dch,max},\ \forall k\in S \tag{7}\]  
\[P_{gi}(t)-\ P_{gi}(t-1)\ \leq\ \Delta P_{gi}^{up},\ \forall i\in G \tag{8}\]  
\[P_{gi}(t-1)-P_{gi}(t)\ \leq\ \Delta P_{gi}^{down},\ \forall i\in G \tag{9}\]  
\[P_{lj}(t)-P_{lj}(t-1)\ \leq\ \Delta P_{lj}^{upp},\ \forall j\in L \tag{10}\]  
\[OC_{MG}(t)=\sum_{i\in G}f_{i}\big{(}P_{gi}(t)\big{)}+\sum_{j\in L}f_{j}\Big{(}P _{lj}(t)\Big{)}+\sum_{k\in S}f_{k}(P_{sk}(t)) \tag{2}\]0 Balancing requirements: (1) Time to delivery; (2) and (3) minimum and maximum time; (4) and (5) minimum and maximum power; (6) and (7) ramping and de-ramping rates [40].
## 3 Results and Discussion
### Impact of Thermosonication on the In Vitro Antioxidant Capacity  
The results of antioxidant activity of the fennel extracts exhibited similar behavior to that observed for the total phenolic content (Figure 4a,b). Figure 4 presents the thermosonication effects on the fennel extracts' antioxidant activity. The nominal power (\(p\)-value \(<\) 0.001) and the working temperature (\(p\)-value = 0.001) promoted a significant effect on the antioxidant activity. Extracts obtained using higher nominal powers and higher working temperatures showed higher antioxidant activity. However, in the DPPH essays, the increase in nominal power from 300 W to 400 W at the working temperature of 60 \({}^{\circ}\)C did not increase the antioxidant activity of the fennel extract. The ABTS assays also demonstrated a reduction in the antioxidant activity of the extracts obtained by thermosonication performed at 400 W and 50 and 60 \({}^{\circ}\)C.  
High working temperatures may also have promoted the cushioning effect in the extraction processes and, consequently, the reduction of the extraction efficiency of the antioxidant compounds of the fennel. Further, high temperatures can cause denaturation of antioxidant compounds, as observed in thermosonicated functional carrot juice [25]. Therefore, the best thermosonication condition to extract fennel compounds regarding phenolic compounds and antioxidant capacity was 300 W and 60 \({}^{\circ}\)C. In this thermosonication condition, the highest yield of TPC (3670 \(\pm\) 67 \(\upmu\)g GAE/g) and high antioxidant activity, measured by DPPH (1195 \(\pm\) 16 \(\upmu\)g TE/g) and ABTS (2543.12 \(\pm\) 0.00 \(\upmu\)g TE/g), were obtained.
## 3 Field Test Analysis and Delay Adjustment Under Vibration Control
### Vibration Wave Arrival Time Sequence Analysis for Single Monitoring Point and Delay Adjustment
The decibel value was calculated to be reduced by 1.76 dB after the delay adjustment. In addition, the energy reduction of the vibration wave was calculated to be 33.31% using Equation (9). Figure 8:1  
Through the adjustment of the delay times described above, the potential for vibration amplification due to excessive simultaneous wave arrivals at each monitoring point is effectively mitigated. Building on this foundation, the proposed method is further applied to eliminate high-probability vibration hazard locations in the HH2 direction, thereby achieving comprehensive vibration control for multi-hole blasting across all directions.
## 2 Materials and Methods
### Performance Metrics  
To evaluate the prediction performance of classification models, metrics such as the confusion matrix, precision, recall, F1 score, and accuracy are used. The confusion matrix is one of the intuitive metrics used to determine the accuracy of a model [51]. It is a table produced by comparing the predicted values with the actual data expressing whether the model produces correct results using four metrics. These four metrics consist of TruePositive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) values. The confusion matrix is shown in Figure 4.  
The values obtained from the confusion matrix are as follows:  
* Accuracy indicates the proportion of correct predictions for the obtained data, as shown in Equation (3) [52]: \[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN}\] (3)
* Sensitivity (also known as recall) is a metric that shows how many of the cases that should have been predicted as positive were actually predicted as positive [52]. It is calculated as shown in Equation (4): \[\text{Sensitivity}=\frac{TP}{TP+FN}\] (4)
* Precision indicates how many of the values predicted as positive are actually positive [52]. It is calculated as shown in Equation (5): \[\text{Precision}=\frac{TP}{TP+FP}\] (5)
* F1 score is the most standard way to represent the balance between precision and sensitivity in a single performance measure. The F1 score is obtained by calculating the harmonic mean of these metrics instead of their arithmetic mean, as shown in Equation (6) [52]: \[\text{F1 score}=\frac{2*\text{Precision}*\text{Sensitivity}}{\text{Precision}+ \text{Sensitivity}}\] (6)
* Specificity is a measure of how well the classifier predicts the True Negative values. The formula is shown in Equation (7) [52]: \[\text{Specificity}=\frac{TN}{TN+FP}\] (7)