esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem formulation and related literature review
### Mathematical model  
In this part of this section, we establish the context of this research. This research aims to develop an efficient method for automatically optimizing the hyperparameters and designing a CNN structure for a specific image classification task.  
More details regarding the CNNs layers, evolution, and functional principles can be retrieved from [20; 76].  
The performance of the CNN architecture highly depends on the hyperparameters' values. Different real-world visual tasks require different network structures; thus, each network has to be configured independently, and the appropriate set of hyperparameters varies from task to task. Finding the right set of hyperparameters for a given problem is a very time-consuming process, and it requires knowledge from the domains of deep learning and CNNs. To address this issue, we propose an enhanced swarm intelligent-based method to search automatically for the right set of hyperparameters to build a structure that will produce high classification accuracy and that can be used by users who do not have prior knowledge from the area and can apply it to a specific problem of interest.  
Since the optimization of all CNN hyperparameters would be a very time and computational resource consuming process, in the proposed research, we optimized the following CNN hyperparameters: the number of convolution layers, the number of filters, the filter size, the number of FC layers, and the hidden units in the FC layer.  
In this work, the optimal CNN architecture was encoded as a set of hyperparameters, which is defined as:  
\[h=\{h_{conv},h_{fc}\} \tag{1}\]where \(h_{conv}\) denotes the set of convolutional layer parameters and \(h_{fc}\) is the set of FC layer parameters.  
The set of convolutional layer parameters is defined as:  
\[h_{conv}=\{C_{0},...,C_{n-1}\} \tag{2}\]  
where the number of convolutional layers is denoted by \(n\).  
Each convolution layer in the \(i\)th layer contains a two-tuple:  
\[C_{i}=(k_{count},k_{size}) \tag{3}\]  
where the pairs \(k_{count}\) and \(k_{size}\) are the number of filters and the size of the filters of the \(i\)th layer, respectively.  
The set of the fully-connected layers is formulated as:  
\[h_{fc}=\{s_{0},s_{m-1}\} \tag{4}\]  
where \(s\) represents the hidden unit number in the FC layer of the \(i\)th layer and \(m\) is the number of FC layers.  
By \(\mathcal{H}\), we denote the possible set of CNN architecture configurations. The aim is to find a configuration \(h\in\mathcal{H}\) that minimizes the error rate of a given task. Thus, in our research, we took the classification error rate (_err_) as an objective function.  
This problem belongs to the class of NP hard challenges, and therefore, exact (deterministic) optimization algorithms are not applicable. However, swarm intelligence algorithms showed very promising results in solving NP hard optimization problems, and that was the basic reason why we implemented and adapted swarm intelligence algorithms for CNN hyperparameters' optimization.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 2 Dielectric Permittivity Measurements
### Materials
#### 2.2.4 Permittivity Measurements of Porous Polymers  
Regarding the porous samples, Table 2 shows the results for the materials at the two extreme frequencies. The samples were manufactured with gyroid or Voronoi meshes. From these data, the graphs in Figures 6 and 7 were extracted, enabling a wide range of permittivities within the same material (at 2.45 and 24.5 GHz, respectively).  
Measuring the dielectric properties at 24.5 GHz presents a technological challenge, as methods such as the bi-reentrant cavity and split cylinder do not provide reliable results due to the small size of the samples and cavities and the manufacturing tolerances, so the rectangular waveguide method is used instead. Although this method is reliable for characterizing the dielectric constant, its uncertainty in calculating the loss tangent presents challenges, as stated in [30; 31], so that the results of the loss tangent at that frequency are missing in this study. However, this uncertainty does not affect the phase, only the attenuation, so the impact of losses is limited to a reduction in gain and radiation efficiency of the antenna set.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**f = 2.45 GHz**} & \multicolumn{2}{c}{**f = 24.5 GHz**} \\ \cline{2-6}
**Resin** & **Porosity (\%)** & **Dens. (g/cm\({}^{3}\))** & \(\epsilon_{r}\) & \(\tan(\delta)\) & \(\epsilon_{r}\) & \(\tan(\delta)\) \\ \hline CX & 0.25 & 1.14 & 3.25 & 0.13232 & 2.87 & - \\ CX & 0.50 & 0.76 & 2.29 & 0.07884 & 2.08 & - \\ CX & 0.75 & 0.38 & 1.62 & 0.02978 & 1.61 & - \\ Dental & 0.25 & 0.98 & 2.55 & 0.04162 & 2.36 & - \\ Dental & 0.50 & 0.65 & 1.83 & 0.03021 & 1.80 & - \\ Dental & 0.75 & 0.33 & 1.40 & 0.01467 & 1.37 & - \\ PA11 & 0.25 & 0.76 & 2.29 & 0.03770 & 2.29 & - \\ PA11 & 0.50 & 0.51 & 1.86 & 0.02767 & 1.71 & - \\ PA11 & 0.75 & 0.25 & 1.44 & 0.01763 & 1.26 & - \\ Ultrasint PP & 0.25 & 0.67 & 1.98 & 0.02609 & 1.91 & - \\ Ultrasint PP & 0.50 & 0.45 & 1.73 & 0.02306 & 1.67 & - \\ Ultrasint PP & 0.75 & 0.22 & 1.38 & 0.01101 & 1.24 & - \\ Rigid 10K & 0.25 & 1.22 & 2.67 & 0.02609 & 2.63 & - \\ Rigid 10K & 0.50 & 0.82 & 2.12 & 0.01740 & 2.08 & - \\ Rigid 10K & 0.75 & 0.41 & 1.56 & 0.00870 & 1.54 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variable density sample measurements.  
Figure 6: Variable density sample measurements at 2.45 GHz.
## 6 Conclusions and Future Works  
In this paper, a hierarchical structure of DN integration of multi-RMGs is presented while considering the household prosumers cluster as an RMG. The RMG integrating into future distribution system has significant potentials to improve energy efficiency and economic benefits for the RMGs and ADN by coordinating optimal energy consumption scheduling and operation management between the two entities. A distributed coordination and hierarchical optimization method based on ATC is proposed for residential microgrids integrated into the distribution system while considering the demand responses of EVs and flexible loads in households and the underlying power flow balance constraints in distribution network level. Based on that, the optimization problems of RMGs and ADN are completely separated from each other and solved independently. A case study is provided to prove the feasibility and effectiveness of the proposed method. The test results show that the  
Figure 12: The convergence of the ATC-based distributed method: (**a**) Feasibility, (**b**) Optimality.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Total Objective Function \(f_{dis}\)** & **Total Objective Function \(f_{can}\)** & **Relative Error** \\
**(Proposed Method)** & **(Centralized Method)** & _(\(f_{dis}-f_{can}\))/\(f_{can}\)_ \\ \hline \hline
526,519 & 526,137 & 0.0146 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison Results.  
method performs an effective function on peak-load shifting and jointly maximizing the welfare of both household prosumers and distribution system operator.  
There are several directions that the proposed hierarchical optimization framework can be further extended.  
* Energy consumption behaviors of households are considered to be the same in small-scale residential areas and constant household profiles are adopted, which is simplified. The proposed hierarchical optimization framework will be extended to a stochastic bi-level programming problem considering individual household profiles in our future work, which can be tackled by using the scenario-based optimization approach.
* Energy interactions among the individual household prosumers within RMG while taking into account their differences in energy consumption behaviour will be incorporated into the coordinating energy management between the RMGs and ADN.
* There are stochasticity and uncertainty in loads. Popular optimization techniques such as stochastic optimization and robust optimization [33] to deal with load uncertainty in coordinating energy management is an interesting research topic for further works.  
[PERSON] and [PERSON]-[PERSON] conceived and designed the study. [PERSON]: conceptualization, methodology, data analysis and writing original draft; [PERSON]-[PERSON]: conceptualization and checked the overall logic of this work; [PERSON] and [PERSON] thoroughly revised the paper.  
This research was funded by the National Natural Science Foundation of China, grant number 51777116.  
The authors declare no conflict of interest.
# Machine Learning-Based Text Classification Comparison: Turkish Language Context
## 5 Findings
### Performance Metrics  
The prediction accuracy, loss, validation loss, and validation accuracy of ML algorithms are often used to assess them. Taking these parameters into account, we examined the output of the LSTM-trained model. The values were then chosen to illustrate whether or not the model was overfitting. A model has overfitted if the validation loss is greater than the training loss. It may be considered that an accurately fitted model has been built if both validation values are equal or extremely close to one another. Accuracy is a straightforward and widely used assessment metric. Accuracy aids in determining an algorithm's performance as a classifier. When fresh data are received, it offers the likelihood of prediction. When analyzing the performance of an ML algorithm, it is also useful to consider three additional metrics, termed precision, F1-score, and recall, in addition to its accuracy.  
The equations below show the formulae for deriving F1-score, precision, accuracy, and recall. The F1-score is determined based on both precision and recall values. Accuracy, however, tells us that the number of projected classes is correct. Accuracy alone is insufficient, especially in datasets where certain classes contain a large amount of data. The recall value indicates the rate of successfully recognizing a class. For instance, the dataset in the detection of cancer, which is a common working field, particularly in ML research, is divided into two groups. A major portion of the sample is made up of healthy persons. Typically, the system will forecast that the incoming data will come from healthy persons. Because of the uneven nature of our dataset, analyzing merely the accuracy is insufficient.  
\[Accuracy=\frac{T_{positive}+T_{negative}}{T_{positive}+T_{negative}+F_{positive }+F_{negative}} \tag{2}\]  
\[Precision=\frac{T_{positive}}{T_{positive}+F_{positive}} \tag{3}\]  
\[Recall=\frac{T_{positive}}{T_{positive}+F_{negative}} \tag{4}\]  
\[F1\_score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{5}\]  
Here, True positives are samples that were appropriately classified as positive. False positives are samples that were wrongly classified as positive. False negatives are negative samples that were wrongly classified. True negatives are samples that were appropriately classified as negative.
However, studies have also demonstrated that some AMPs have intracellular targets such as inhibition of transcription, translation, or other processes (e.g., DNA, RNA, guanosine tetraphosphate (ppGpp) and protein synthesis), or can interfere with bacterial cell wall synthesis [34]. AMPs have been widely investigated as antimicrobial agents, especially against planktonic bacteria. Within those studies, AMPs have shown reduced development of resistance. Though resistance to these molecules seems to be less common than to conventional antibiotics, it was recently found in several pathogens during the co-evolution of host and pathogens [35]. A number of AMPs are currently undergoing clinical and pre-clinical trials [36]. During those trials, a number of candidates failed due to high susceptibility to enzymatic degradation and fast renal clearance leading to a short peptide in vivo half-life or unexpected in vivo toxicity. Apart from the mentioned drawbacks, AMPs inherently possess other disadvantages such as low bioavailability, pH- and/or salinity-dependent activity, and loss of activity due to binding to serum proteins [33; 37]. As a solution, rational design including synthetic optimization of the active peptide, such as altering length, backbone, side chains, hydrophobicity, charge and amphipability can be used to overcome limitations, yielding in peptide mimics (also called peptidominetics) [33; 36]. The resulting mimics usually retain the activity profile of the original AMP, while displaying significant improvement in bioavailability and overall stability. Within recent years, development of peptidominetics has allowed for manipulation of the pharmacological profile of AMPs, resulting in a deeper understanding of the relationship between structure and antimicrobial activity. Additionally, many AMPs have also been shown to modulate the immune responses in the host, like interfering with unwanted inflammation and recruit immune cells to the site of infection [26]. Thus, AMPs and their mimics are considered to have a great potential for local delivery from implant coatings. This review discusses recent developments in the field of antib Biofilm peptides and peptidominetics as well as presents an overview of their applications for coating the surface of medical implants, as well as methods for their immobilization.
## 4 Theoretical Analysis of Maximum Crack Width in Negative Moment Zone
### Calculation of Maximum Crack Width  
According to the GB50010 [1], the maximum crack width \(\omega_{\rm max}\) can be calculated by the following formula:  
\[\psi=1.1-0.65\frac{f_{\rm t}}{\rho_{\rm te}\sigma_{\rm s}}=0.84 \tag{9}\]  
\[E_{\rm s}=2.0\times 10^{5}\;\rm MPa \tag{10}\]  
\[d_{\rm eq}=32\;\rm mm \tag{11}\]  
\[c_{\rm s}=60+7=67\;\rm mm \tag{12}\]  
\[\omega_{\rm max}=\alpha_{\rm cr}\psi\frac{\sigma_{\rm s}}{E_{\rm s}}l_{\rm cr} =\alpha_{\rm cr}\psi\frac{\sigma_{\rm s}}{E_{\rm s}}\left(1.9c_{\rm s}+0.08 \frac{d_{\rm eq}}{\rho_{\rm te}}\right)=0.35\;\rm mm \tag{13}\]  
where \(\psi\) is the uneven coefficient of strain in longitudinal tensile reinforcement between cracks, \(\rho_{\rm te}\) is the reinforcement ratio of tensile reinforcement calculated from effective tensile concrete cross-sectional area, \(\sigma_{\rm s}\) is the calculated stress value of the reinforcement, \(\alpha_{\rm cr}\) is the stress characteristic coefficient, and \(c_{\rm s}\) is the distance from the outer edge of the outermost longitudinal tension reinforcement to the bottom edge of the tension zone.  
On the whole, for the reinforced beam, the greater the reinforcement ratio of the beam section, the stress of the steel bar, and the crack spacing, the greater the maximum crack width. According to the calculation results of the above formula, the theoretical maximum crack width appears at the intersection section of the beam and column at the farthest distance from the loading point. The calculated theoretical width is 0.35 mm, which is 0.10 mm different from the experimental result of 0.25 mm. The formula needs to be further modified.  
The crack spacing \(l_{\mathrm{cr}}\) is the data that can be directly obtained in the test. It is closely related to the calculation method of the maximum crack width. At the same time, the stress characteristic coefficient \(\alpha_{\mathrm{cr}}\) of the component is the value taken considering the maximum crack width under long-term load. However, this paper only aims at short-term load, so it is necessary to modify the stress characteristic coefficient \(\alpha_{\mathrm{cr}}\) of the component. Therefore, this section focuses on the influence of crack spacing \(l_{\mathrm{cr}}\) and component stress characteristic coefficient \(\alpha_{\mathrm{cr}}\) on the maximum crack width.
## 2 The Proposed Method and Theoretical Background
### Polynomial Frequency Modulation Integral Algorithm
\begin{array}{l}\mathrm{PCT}_{\dot{h}^{i}}\Big{(}\dddot{\overline{\dot{x}}}_ {i}(t)\Big{)}\quad=\int_{-\infty}^{+\infty}\big{(}\int\dddot{x}_{i}(t) \mathrm{dt}\big{)}\dot{h}^{i}_{t_{0},\omega_{i},\dot{\
u}_{i}}(t)\mathrm{dt}\\ =\big{(}\int\dddot{x}_{i}(t)\mathrm{dt}\big{)}\dot{h}^{i}_{t_{0},\omega_{i}, \dot{\
u}_{i}}(t)\bigg{|}\begin{array}{l}+\infty\\ -\infty\end{array}-\big{(}\int_{-\infty}^{+\infty}\dddot{x}_{i}(t)\big{)} \dot{h}^{i}_{t_{0},\omega_{i},\dot{\
u}_{i}}(t)\mathrm{dt}\bigg{|}\begin{array} []{l}+\infty\\ -\infty\end{array}\\ +\int_{-\infty}^{+\infty}\dddot{x}_{i}(t)\mathrm{dt}\end{array}  \tag{24}\]  
The first two terms in Equation (24) can be written as follows:  
\[\Big{(}\int\dddot{x}_{i}(t)\mathrm{dt}\Big{)}\dot{h}^{i}_{t_{0},\omega_{i}, \dot{\
u}_{i}}(t)\bigg{|}_{-\infty}^{+\infty}-\bigg{(}\int_{-\infty}^{+\infty} \dddot{x}_{i}(t)\bigg{)}\dot{h}^{i}_{t_{0},\omega_{i},\dot{\
u}_{i}}(t) \mathrm{dt}\bigg{|}_{-\infty}^{+\infty}=0 \tag{25}\]Equation (24) is updated as follows:  
\[ \begin{array}{ll}\text{PCT}_{\tilde{i}}\bigg{(}\overline{\tilde{\chi}}_{i}(t) \bigg{)}&=\int_{-\infty}^{+\infty}\big{(}\int\int\bar{x}_{i}(t)\text{dt}\big{)} \tilde{h}^{i}_{t_{0},\omega_{i},c_{i}}(t)\text{dt}\\ &=\int_{-\infty}^{+\infty}\dot{x}_{i}(t)\dot{h}^{i}_{t_{0},\omega_{i},c_{i}}(t) \text{dt}=\text{PCT}_{\tilde{h}^{i}}\big{(}\bar{x}_{i}(t)\big{)}\end{array}  \tag{26}\]  
In order to reconstruct the velocity and displacement signals, Equations (23) and (26) can be further rewritten as follows:  
\[\text{PCT}_{\tilde{i}}\bigg{(}\overline{\tilde{x}}_{i}(t)\bigg{)}=\int_{- \infty}^{+\infty}\overline{\tilde{x}}_{i}(t)\bigg{[}j\Big{(}\omega_{i}+c_{i}( t-t_{0})^{i}\Big{)}\bigg{]}\dot{h}^{i}_{t_{0},\omega_{i},c_{i}}(t)\text{dt}=- \text{PCT}_{\tilde{i}}\big{(}\bar{x}_{i}(t)\big{)} \tag{27}\]  
\[ \begin{array}{ll}\text{PCT}_{\tilde{i}}\bigg{(}\overline{\tilde{x}}_{i}(t) \bigg{)}&=\int_{-\infty}^{+\infty}\overline{\tilde{x}}_{i}(t)\bigg{[}jic_{i}( t-t_{0})^{i-1}-\Big{(}\omega_{i}+c_{i}(t-t_{0})^{i}\Big{)}^{2}\bigg{]}\dot{h}^{i} _{t_{0},\omega_{i},c_{i}}(t)\text{dt}\\ &=\text{PCT}_{\tilde{h}^{i}}\big{(}\bar{x}_{i}(t)\big{)}\end{array}  \tag{28}\]  
Then, the corresponding signals are rewritten as follows:  
\[\overline{\tilde{x}}_{i}(t)=\bigg{\{}-\frac{1}{C_{h}}\int_{-\infty}^{+\infty} \text{PCT}_{\tilde{i}}\big{(}\bar{x}_{i}(t)\big{)}\dot{h}^{i}_{t_{0},\omega_{ i},c_{i}}(t)\text{d}\omega\bigg{\}}/\Big{[}j\Big{(}\omega_{i}+c_{i}(t-t_{0})^{i} \Big{)}\Big{]} \tag{29}\]  
\[\overline{\tilde{\tilde{x}}}_{i}(t)=\frac{1}{C_{h}}\int_{-\infty}^{+\infty} \text{PCT}_{\tilde{h}^{i}}\big{(}\bar{x}_{i}(t)\big{)}\dot{h}^{i}_{t_{0}, \omega_{i},c_{i}}(t)\text{d}\omega/\Big{[}jic_{i}(t-t_{0})^{i-1}-\Big{(}\omega _{i}+c_{i}(t-t_{0})^{i}\Big{)}^{2}\Big{]} \tag{30}\]
Regardless of the modality, these studies showed that VBF has a positive influence on balance and that even small changes in feedbacks can make a difference in balance performances, probably due to the enhanced neuromuscular control [16]. In clinical and field settings, the assessment of balance can provide accurate and sophisticated information regarding the efficiency of the neuromuscular system. Several postural control assessments have been used to evaluate dynamic balance. Force plates [27] and functional tests [28; 29] are commonly used to evaluate balance performances due to their accuracy, validity, and reliability. However, because of the multifaceted features of the postural control tests, by clearly reflecting the complexity of this ability, new simple approaches [30; 31] are needed to accurately evaluate dynamic postural control. In particular, during large-scale evaluations, practical, inexpensive, administrable, and accurate tools are preferable. In this context, computerized wobble boards (WBs) have been suggested as reliable and simple tool to evaluate dynamic balance in healthy young subjects in field and laboratory settings [31; 32; 33]. Unstable platforms, such as WBs, are the most used tools to train human balance, showing their effectiveness in improving postural control among different populations [34]. Although the influence of anthropometrics, sex, and VBF on human balance is well documented, focusing on the effect of the above-mentioned factors and/or their interaction on computerized WB could provide useful information to adapt and tailor individualized balance training protocols. Therefore, the aim of this study is to evaluate the effect of anthropometric characteristics, sex, and VBF during dynamic balance performance assessed on a computerized WB in young healthy adults.
## 6 Discussion  
According to the observed performance indicators, Sukhovskaya station and Bailey Yard station both cope with the current load. Nevertheless, their throughput is close to maximum. This means that in case of emergencies, they may not be able to cope with the load. Examples of such situations are the breakdown of a locomotive in the sorting bowl and/or repair work on the departure tracks. This is a topical problem for Bailey Yard station since the possibilities for redirecting traffic are limited due to the large distance between neighboring stations.  
Note that in the presented simulation results (see Tables 2 and 4), there is a non-zero fault probability for both stations, although it is insignificant (less than 0.03). It appears due to inaccuracy of the model because it is allowed to receive more trains per shift (12 h) than the receiving yard can accommodate. For example, Sukhovskaya station cannot accept six or more trains per shift. At a real transport facility, this situation is unlikely due to dispatch control, which contributes to a more uniform load distribution during the day, which is not taken into account in the model. Nevertheless, the presence of a non-zero loss probability once again confirms our assumption that the load of both stations is close to maximum and there is the risk of switching to an irregular mode of operation.  
Based on the simulation results, the limiting elements for both stations are found. For the Sukhovskaya station, they are the cargo and receiving yards. To increase the capacity of this station, it is advisable to reduce the average service time in the cargo yard by 16%, in the receiving yard by 22%. Then the station will be able to serve an average of 7.76 trains per day, i.e., 55.2% more than the current level.  
For the west part of Bailey Yard, the limiting elements are three departure yards with insufficient capacity. The estimated system capacity can be increased by at least 12% by reducing the average service time in departure yards by 20%. It is not possible to assess the effectiveness of measures to reduce the service time in departure parks due to a lack of information on internal traffic flows of Bailey Yard. For the same reason, we can design a model of only the West part of this station. Thus, the Bailey Yard station operation model needs to be refined and expanded, but the available data was sufficient to make some preliminary conclusions.  
The results were discussed with the experts of the \"Russian Railways\" company and received prefactory approval.