esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Solution Process  
Linear programming used as a method in this paper can be solved by the simplex method. The simplex method can be utilized to solve linear programming [32]. The simplex method is based on forming the inverse of the basic matrix and updating the inverse [33]. That is, it is the repetition of searching for feasible solutions, calculating the value of the objective function, and comparing the derived value and the optimal value until finding the optimal solution. The process of the simplex is shown in Figure 2. First of all, all equations should be converted into standard equation form after inserting slack variables and create an initial simplex table. After this, find the entering variables. When solving the maximization problem, the variable with the largest absolute value of the coefficient becomes the entering variable. The next thing is to find leaving variable. To achieve this, the ration between entering variable and solution should be calculated. At this time, the variable with the lowest ration value becomes the leaving variable. After deciding on entering variable and leaving variable, the intersection point of the entering variable and leaving variable becomes the pivot element and all values of the row in the simplex table with the pivot element should be divided by the pivot element. The rest of the rows should be updated by using the new pivot row made previously. Lastly, if there is no new leaving variable, iterations terminate. If not, go back to the step of finding the entering variable. Various computer packages based on the simplex method were already developed. A developed mathematical model in the paper is also solved by one of the packages. The package used in the paper is CPLEX, which is the mathematical optimization software package. The version is 20.1.0. The darker parts in Figure 2 are processed by CPLEX.  
Figure 2: Solution process of the simplex method.
## 4 Performance Analysis
### Analysis of Experimental Results
#### 4.4.1 F7 (2D Vincent)  
The F7 function is shown in Figure 9. It has 36 peaks with different sizes and contains multiple different coarse and fine peaks, with complex terrain and landforms. This function challenges the capabilities of the multimodal optimization algorithm.  
Figure 10 shows the range of potential peaks predicted by the TCDE algorithm in rounds 1, 10, 20, and 30 of the optimization of the 2D Vincent function by means of terrain contours. From Figure 10, it is evident that the TCDE algorithm identifies some of the peaks in the first round, and, as the algorithm iterates, more potential peaks are discovered. In other words, the number of individuals in the potential peak area also increases. This means that peaks are located more accurately.  
Figure 9: Three-dimensional image of 2D Vincent function.  
The distribution of individuals in the TCDE algorithm's topographical exploration after setting taboo areas is shown in Figure 11. It can be seen that, as the algorithm iterates, the taboo regions also constantly change. This causes the population to constantly move away from the positions where the peaks have been explored during the evolution of the algorithm. In this way, the TCDE algorithm can explore areas that were previously underexplored more carefully.  
Through the above two techniques, the TCDE algorithm can effectively address the problem of coarse and fine peaks in the _2D Vincent_ function. Thus, all peaks are identified.  
Figure 11: Individual distribution of 2D Vincent function with TS for topographical exploration when applying DE (**a–d**).  
Figure 10: Four stages of optimizing the 2D Vincent function. (**a–d**) show the optimization process at different iterations.
## 3 Results
### Reliability and Availability, Monetary Flows and upper and lower Boundaries to Power Consumption
When the bid size exceeds half the portfolio capacity (5 MW), the lower boundary will become larger than the upper boundary, making it impossible for the portfolio to remain between the boundaries and deliver the required flexibility. In these cases, the availability drops to 0%, which results in NA-fines for every measurement. Since the boundaries resulting from the 'always available' strategy are extreme, they are not displayed in Figure 5.  
Portfolio-availability represents the proportion of the week in which 100% flexibility can be delivered with the portfolio, whereas the reliability represents the fraction of the week in which the portfolio responded correctly given the frequency and corresponding RFP. The major difference between portfolio availability and reliability is that the reliability is strongly influenced by the frequency and RFP, whereas the portfolio availability is solely dependent on the power consumption of the portfolio and the bid size. Figure 6 shows a steep decrease in availability, dropping from 100% availability at a reliable bid size of 3.1 MW to 0% availability at a bid size of 5.0 MW. Availability reduction to 0% at a bid size of 5.0 MW is explained by the fact that the portfolio will not be able to deliver 100% flexibility on a symmetrical market when the bid size exceeds half the maximum capacity. Therefore, in the model, the availability is always reduced to 0% when the bid size exceeds 5.0 MW. In contrast to the availability, the reliability will not drop to 0%. Even at extremely high bid sizes, when the frequency is 50 Hz, zero flexibility is required, and the portfolio is still able to respond correctly. This frequency-dependency is the main reason that the reliability shows a less-steep decline compared to the availability. Prevalence of frequency deviation occurrence is discussed in Section 3.3. Figure 5: Power consumption (blue line) of the portfolio and boundaries (red dotted lines) at the reliable bid size of 3.1 MW.
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 2 Materials and Methods
### Model Evaluation
#### 2.4.2 Evaluation of Grain Counting Model  
The accuracy, precision, and F1 score were used to evaluate the performance of the grain counting model. The F1 score is an indicator used to measure the accuracy of the dichotomous (or multitask dichotomous) model in statistics, which takes into account effectively both precision and recall of the classification model. For the evaluation of the grain counting model, true positive (TP) represents the number of spikelets that correctly predicted the grain quantity category, false negative (FN) represents the number of spikelets that predicted the current category as the other categories, and false positive (FP) represents the number of spikelets that predicted the other categories as the current category. Furthermore, the receiver operating characteristic curve (ROC) can visually display the performance of the grain counting model, with each point on the curve reflecting the  
Figure 7: Process of counting spikelet grains.  
sensitivity to the same signal stimulus. The area under the ROC curve is defined as AUC. The higher the AUC value, the better the overall performance of the grain counting model.
# Speckle Noise Reduction in Digital Holography Using a DMD and Multi-Hologram Resampling
## 5 Speckle Reduction Evaluation  
We considered two metrics to quantify the level of speckle reduction. The speckle contrast coefficient \(C\) and the correlation coefficient \(\gamma\).  
The speckle contrast coefficient is the most commonly used metric to have an understanding of the speckle level present in a reconstructed image [25]. The coefficient is computed by  
\[C=\frac{\sigma_{I}}{\langle I\rangle}, \tag{10}\]  
where \(\sigma_{I}\) represents the standard deviation and \(\langle I\rangle\) the mean of the intensity values reconstructed. The evaluation of the coefficient is done over a homogeneous intensity region so that the changes on \(C\) are due only to the speckle contribution. The coefficient follows a theoretical behavior of \(1/\sqrt{h}\) for \(h\) reconstructed images [25], the lower its value is, the less speckle noise is present in the image.  
As a second metric, we considered the correlation coefficient for a pair of reconstructed images \(l_{l}\) and \(l_{s}\) as an indication of the level of uncorrelation between them. The coefficient is obtained from the equation  
\[\gamma_{l,s}=\frac{\sum_{p}^{M}\sum_{q}^{L}[l_{l}(p,q)-\langle I_{l}\rangle][l _{s}(p,q)-\langle l_{s}\rangle]}{\sqrt{\sum_{p}^{M}\sum_{q}^{L}[l_{l}(p,q)- \langle l_{l}\rangle]^{2}\sum_{p}^{M}\sum_{q}^{L}[l_{s}(p,q)-\langle l_{s} \rangle]^{2}}} \tag{11}\]  
The correlation coefficient is near a unit value for images with similar speckle distributions and near zero for uncorrelated images. Being an indicator of the relation between two images, we can compute a matrix of coefficients for the set of all registered holograms indicating which pairs of reconstructions are more uncorrelated.
# Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method
## 3 Experiment
### Experimental Scheme  
To validate the reliability of vibration measurement of the bridge model by UAV combined with CNN and KLT optical-flow method, the measurement results of the fixed camera are taken as a reference. Moreover, the measurement results of DIC are taken as a reference to validate whether KLT optical-flow method is feasible to replace DIC in bridge vibration measurement.  
Firstly, the fixed camera and UAV are placed about 2 m away from the bridge model, and the UAV is used to shoot the static model (the shooting time is 8 min). The reference-point coordinates tracked by DIC and KLT optical-flow method are used as the input of CNN, and the target-point coordinates are used as the network output to train the network. Secondly, the bridge model is excited to vibrate, the fixed camera and UAV are used to shoot (the shooting time is 80 s for 5 times of excitation), and the points are tracked by DIC and KLT optical-flow method again. The reference-point coordinates measured by the UAV are still taken as the input of CNN, the false displacement of the target points caused by the UAV will be outputted by the trained CNN according to the characteristic relationship between the reference points and the target points, and the real displacements of the target points measured by the UAV are obtained by subtracting the output value from the uncorrected displacement of the target point. The natural frequency of the model  
Figure 9: Reference points and experimental layout: **(a)** reference points; **(b)** experimental layout.  
is obtained by OMA from the time-history curves. The specific process is shown in Figure 1, and the process of correcting displacement measured by a UAV through the CNN is shown in Figure 10. Finally, the tracking effects of DIC and KLT optical-flow method are compared under the fixed-camera and UAV measurements to validate the feasibility of KLT optical-flow method replacing DIC, and the results of fixed camera and UAV are compared under the KLT optical-flow method to validate the reliability of UAV data combined with CNN and KLT optical-flow method. The DIC, KLT optical-flow method, and CNN are realized using MATLAB (MathWorks Inc., Natick, MA, USA) software.
## 3 Testing Results  
### Deformation and Elastic Modulus  
#### 3.1.1 Stress versus Strain Curves  
Figure 2 shows the relation curves between axial, circumferential, and volumetric strains and axial stress of the No. 4 coal specimen subjected to cyclic loading and unloading. In Figure 2, the symbol \"\(\varepsilon\)\" represents the strain, and the \"\(\sigma_{1}\)\" represents the axial stress. From Figure 2, the three curves of the coal specimen in each loading-unloading cycle do not form the enclosed hysteresis loops. These show that there are many microcracks, microdefects, and holes inside the coal specimen. The coal specimen has a loose texture and poor resistance to deformation.
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 3 Materials and Methods
### Preprocessing of CT Image Data
#### 3.3.3 Data Augmentation  
In the MSD Task03 Liver dataset, each scan is accompanied by a tumor mask delineating the liver and tumor's respective positions. Data augmentation techniques, such as rotation, zoom, RandGaussianNoised, and RandAffined, are applied. Employing this procedure results in improved network accuracy. Figure 4 shows image slices with different transforms we mention here, and Figure 5 represents the transformed image result. The following data augmentation techniques were used.  
* Flipped: Flipping enhances the robustness and diversity of the training dataset by providing additional variations of input images without changing semantic information. By using flipping in the training dataset, the model learns to recognize and segment objects from different orientations.
* Rotated: This technique involves rotating the input images along different angles, introducing variations that the model needs to learn and adapt to during training.
* Zoomed: Zoomed aims to enhance the model's ability to handle scale variations, improve generalization, and increase robustness, leading to better performance on unseen data.
* RandGaussianNoised: This technique involves adding random Gaussian noise to the images. When the training data are relatively small, RandGaussianNoised can artificially increase the size of the dataset.  
Figure 4: Slice with different transforms.  
Figure 5: Due to simple transformation, the same slice of the patient can show two different body parts.  
Figure 3: Windowing steps to obtain an isolated liver.  
* RandAffined: This involves applying random combinations of translation, rotation, scaling, and shearing operations to input images during the dataset's training. Introducing noise and variability into the training dataset can help prevent overfitting.
## 3 Control Strategies  
The performance of the MG integrated HSAPF depends on the control strategy designed. In the proposed study, for improving the reliability of the proposed system, two different control strategies are taken, one for controlling the maximum power in MG and the other for controlling the VSI of HSAPF. The LINC technique is employed as an MPPT technique in PV and WT system, while the HSAPF is controlled using the AMDWPT based SRF technique. A flow chart showing the working of an MG integrated HSAPF interfaced with utility network is presented in Figure 2.  
Figure 2: Flowchart showing working of an microgrid (MG) integrated hybrid shunt active power filter (HSAPF) interfaced with utility network.
# Storing Set Families More Compactly with Top ZDDs
## 6 Concluding Remarks  
We have proposed top ZDD to compress a ZDD by regarding it as a DAG. We compress a spanning tree of a ZDD by the top DAG compression and compress other edges by sharing them as much as possible. We showed that the size of a top ZDD can be logarithmic of that of the standard ZDD. We also showed that navigational operations on a top ZDD are done in time polylogarithmic to the size of the original ZDD. Experimental results show that the top ZDD always uses less space than the standard ZDD, and uses less space than the DenseZDD for most of the data. In previous work on decision diagrams, it was not possible to share and compress substructures of different heights even if they were similar. In addition, the existing methods for top trees could not handle DAGs. In trees, it is easy to number nodes linearly and to divide the whole tree, but this is not the case in DAGs. These are the main reasons for the novelty of this research.  
Future work will be as follows. First, in the current construction algorithm, we create a spanning tree of ZDD by a depth-first search, but this may not produce the smallest top ZDD. For example, if we choose all 0-edges, we obtain a spanning tree whose root is the terminal \(\top\), and this might be better. Second, in this paper, we considered only traversal operations and did not give advanced operations such as choosing the best solution among all feasible solutions based on an objective function. Third, we will apply our method to other methods that use ZDDs. It would be useful to compare the performance of Top ZDD and DenseZDD in a real case study to deepen our knowledge. For example, we can make the ZDDs smaller that represents Fault Trees for reliability assessment of systems [23]. Lastly, we considered only compressing ZDDs, but our compression algorithm can be used for compressing any DAG. We will find applications of our compression scheme.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Construction Time (s)**} & \multicolumn{2}{c}{**Traversal Time (\(\mu\)s)**} \\ \cline{2-5}  & **Top ZDD** & **DenseZDD** & **Top ZDD** & **DenseZDD** \\ \hline \(n=11\) & 0.038 & 0.015 & 17.184 & 0.778 \\ \hline \(n=12\) & 0.335 & 0.065 & 21.581 & 0.900 \\ \hline \(n=13\) & 1.722 & 0.419 & 20.173 & 1.099 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Families of solutions of the \(n\)-queen problem.  
**Author Contributions:** Conceptualization, K.M. and K.S.; methodology, K.M., S.D. and K.S.; software, K.M.; validation, K.M.; formal analysis, K.M.; investigation, K.M.; resources, K.M. and S.D.; data curation, K.M.; writing--original draft preparation, K.M.; writing--review and editing, S.D. and K.S.; visualization, K.M.; supervision, K.S.; project administration, K.S.; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study
###### Abstract  
EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, using the phase lag index (PLI) and the phase locking value (PLV) methods, we investigate how the performance of a connectivity-based EEG biometric system varies with respect to different time windows (using epochs of different lengths ranging from 0.5 s to 12 s with a step of 0.5 s) to understand if it is possible to define the optimal duration of the EEG signal required to extract those distinctive features. All the analyses were performed on two freely available EEG datasets, including 109 and 23 subjects, respectively. Overall, as expected, the results have shown a pronounced effect of the time window length on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase in the biometric performance as the time window increases. Furthermore, our initial findings strongly suggest that enlarging the window size beyond a specific maximum threshold fails to enhance the performance of biometric systems. In conclusions, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as an EEG fingerprint and that, in this context, it is essential to establish an adequate time window capable of capturing subject-specific features. Furthermore, we speculate that the poor performance obtained with short time windows mainly depends on the difficulty of correctly estimating the connectivity metrics from very small EEG epochs (shorter than 8 s).  
EEG; EEG-based biometrics; biometric recognition; connectivity; time window Article
# Surface Roughness Analysis in the Hard Milling of JIS SKD61 Alloy Steel
###### Abstract  
Hard machining is an efficient solution that can be used to replace the grinding operation in the mold and die manufacturing industry. In this study, an attempt is made to analyze the effect of process parameters on workpiece surface roughness (\(Ra\)) in the hard milling of JIS (Japanese Industrial Standard) SKD61 steel, based on a combination of the Taguchi method and response surface methodology (RSM). The cutting parameters are selected based on the structural dynamic analysis of the machine tool. A set of experiments is designed according to the Taguchi technique. The average \(Ra\) is measured by a Mitutoyo Surftest SJ-400, and then analysis of variance (ANOVA) is performed to determine the influences of cutting parameters on the given \(Ra\). Quadratic mathematical modeling is introduced for prediction of the \(Ra\) during the hard milling process. The predicted values are in reasonable agreement with the observation of experiments. In an effort to obtain the minimizing \(Ra\), a single objective optimization is employed based on the desirability function. The result shows that the percentage error between measured and predicted values of \(Ra\) is 3.2%, which is found to be insignificant. Eventually, the milled surface roughness under the optimized machining conditions is 0.122 \(\upmu\)m. This finding shows that grinding may be replaced by finish hard milling in the mold and die manufacturing field.  
surface roughness; hard milling; Taguchi method; response surface methodology +
Footnote †: journal: Applied Mechanics and Physics Letters
# NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus
## 7 Evaluation
### Scalability Comparison  
To evaluate the scalability improvement of NeuChain+ using sharding compared to NeuChain, the changes in throughput, abort rate, and latency were tested under the SmallBank workload and configurations of 4, 8, 12, and 16 nodes, respectively. Additionally, the sharding permissioned blockchain Meepo was included as another comparison. Note that NeuChain+ was tested under environments with 1 to 4 shards, respectively, while Meepo operated under environments with 4, 8, 12, and 16 shards.  
The experimental results are shown in Figure 9. NeuChain+ achieved excellent scalability, with a peak throughput of 247 kTPS. The throughput under two, three, and four shards was approximately 1.7\(\times\), 2.35\(\times\), and 2.9\(\times\) that of NeuChain, respectively. Benefiting from cross-shard transaction processing based on deterministic execution, latency remained at a relatively low level, with the highest latency of just 0.188s for the four-shard setup. However, with the increase in the number of shards, the proportion of cross-shard transactions also increased, coupled with fewer accounts per shard (50,000, 33,000, and 25,000 accounts under two, three, and four shards, respectively), exacerbating transaction conflicts. Compared to NeuChain, the abort rate increased by 11, 25, and 56 kTPS under environments with two, three, and four shards, respectively. Meepo benefits from the trustworthiness within the consortium member and the complete ledger held by each consortium member. Therefore, Meepo maintained remarkable efficiency in processing cross-shard transactions, with a throughput of 65 kTPS under 16 shards. Additionally, Meepo employs a mechanism to redo all failed transactions, resulting in the 0 abort rate, but it also brings higher transaction latency.  
In order to evaluate the improvement of storage pressure, we fixed the transaction arrival rate at the peak performance of NeuChain, 87.35 kTPS, and recorded the storage space consumption of a single node during a 5 min runtime of the system under the SmallBank workload. NeuChain+ runs on two, three, and four shards, respectively, and each shard deploys on four nodes of client proxies and block servers.  
Figure 8: Performance of NeuChain+ under varying network latencies (SmallBank).  
The experimental results, as illustrated in Figure 10, indicate that the baseline NeuChain consumes 7.6 GB of storage space after running for 5 min. NeuChain+ performs excellently in optimizing storage pressure. When running under two, three, and four shards for the 5 min, it only consumed 4.4 GB, 3.1 GB, and 2.4 GB of storage, respectively, which is approximately 58%, 41.2%, and 31.4% of NeuChain. Since cross-shard transactions need to be included in all involved shards, storage optimization is not entirely linear.