esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
Figure 3 illustrates the model training process proposed in this study. The model training process can be divided into two steps: Step 1 involves creating the response spectra using the complex transfer function of the target structure and the wave spectrum, while Step 2 consists of training the CVNN with the response spectra as the input and wave direction, wave height, and wave period as the output.  
Figure 2: Basic structure of CVNN.  
Among the six degrees of freedom (DOF) of the floating body, heave, roll, and pitch are considered appropriate for sea state estimation [4]. Specifically, by using the heave, roll, and pitch transfer functions obtained through numerical analysis, three cross spectra can be generated, namely heave-roll, heave-pitch, and roll-pitch spectra, as shown in Equation (4). Since auto spectra are real-valued rather than complex-valued, they are excluded from the training of this model.  
Figure 4 shows the structure of the data used for model training. The types of input data are represented by symbols: H, R, and P represent heave, roll, and pitch, respectively, while C represents cross-correlation. Therefore, HRC refers to the cross spectrum between heave and roll. The three cross spectra, along with frequency, serve as the input data, while the corresponding wave direction, wave height, and wave period are the output data.  
Figure 5 illustrates the process of applying the trained CVNN prediction model. To input data into the prediction model, they must be in the form of a response spectrum. Therefore, the measured time-series data must first be converted into frequency-domain data. After performing cross-correlation on the three types of motion data--heave, roll, and pitch--the data are transformed into the frequency domain using the Fourier transform, resulting in heave-roll, heave-pitch, and roll-pitch cross spectra. These cross spectra are then fed into the CVNN as the input, which predicts the corresponding wave direction, wave height, and wave period for the given motion spectra.  
Figure 4: Structure of database for model training.  
Figure 3: Flow chart for the model training.
# New Four Points Initialization for Digital Image Correlation in Metal-Sheet Strain Measurements
## 3 Materials and Methods
### Method  
The proposed method has the purpose of measuring the surface deformation in a metal sheet specimen used for truck exteriors, and it was divided into ten stages from (a) to (j). The peculiarity of this procedure consists of the incorporation of an initial condition based on points normalization to perform the matching using 2D-DIC. The general procedure is as follows:  
1. Stamping of known circle grid on the unformed metal sheet.
2. Deformation of the metal-sheet through the mechanical stamping process.
3. Calibration of cameras.
4. Illumination of the piece with LED blue light for measuring.  
Figure 4: Setting of stereo cameras with illumination and metal sheet.  
Figure 3: (a) Circles on metal sheet without deformation; and (b) circles on formed metal sheet.  
* Capture of stereo images.
* Selection of four landmarks.
* Search for neighbor's centroid.
* Calculation of the NCC in the proposed neighborhood.
* Triangulation of the points to obtain their position in 3D space.
* Strain estimation from averaging the centroids' differences with the four neighbors using Equation (6).  
The process includes the stamping of the known circle grid on the unformed metal sheet and the deformation of the sheet through the mechanical stamping process, because it is important to delimit the application of the proposed method.  
The individual camera calibration can be performed with the \"calib\" function, while the stereoscopic calibration can be performed with the function \"stereo_gui\". Both are from the library \"Camera Calibration Toolbox for Matlab\" published by Jean-Yves Bouguet [40]. The illumination can be performed through a blue LED set. It is important that the image capture is done with the calibrated camera system.
## Appendix A Statistical metrics used in our study are:  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(GPP_{(pre)i}-GPP_{(ref)i} \right)^{2}}\] (A1)  
\[rRMSE=\frac{RMSE}{GPP_{(ref)max}-GPP_{(ref)min}}\] (A2)  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\left|GPP_{(pre)i}-GPP_{(ref)i}\right|\] (A3)  
\[rMAE=\frac{MAE}{GPP_{(ref)max}-GPP_{(ref)min}}\] (A4)where \(GPP_{(pre)}\) is predicted GPP values from the ARIMA model, \(GPP_{(ref)}\) is reference GPP values from different products; \(GPP_{(ref)max}\) represents the maximum values of reference GPP, while \(GPP_{(ref)min}\) represents the minimum; \(n\) is the number of research months.
# A Reconfigurable SRAM CRP PUF with High Reliability and Randomness
## 4 Implementation of SRAM CRP PUF System  
Figure 5 depicts the mixed-signal system architecture, an Application-Specific Integrated Circuit (ASIC) consisting of two blocks: a digital controller and an analog SRAM CRP PUF array (9 \(\times\) 16-bit). The digital controller was designed using the Synopsys tool, while the analog SRAM CRP PUF array and the integration of these two blocks were implemented using the Cadence Virtuoso tool. The controller generates signals to control the operation of each PUF cell with the state control unit block designed to follow the proposed timing revision. The counter block is used to select each row in the PUF array and count the clock cycle to generate the corresponding control signals, _EN_BL_, _PRE_<_8:0_>, _EN_<_8:0_>, _C_<_n_-_1:0_>, and _WL_<_8:0_>. The 9 \(\times\) 16-bit SRAM CRP PUF array is divided into two parts: the upper part and the lower part. Each part comprises 9 rows and 8 columns of PUF cells, resulting in a 72-bit response. In each column, two tristate buffers are connected to the two outputs of each PUF cell to read the response output. The 1-bit response is obtained from one output among two tristate buffers. The 16-bit response per row is the final response output, _PUF_OMIT_<15:0_>.
## 6 Conclusions  
The need to improve SUs's channel usage experience (i.e., transmission opportunities on the scarce spectrum resources), especially on channels where the presence of PUs may be dominant, means investigating users' performance under the hybrid spectrum access model becomes important. In this paper, we explored the performance of the hybrid spectrum access mode and compared it with overlay and underlay modes. We derived closed-form expressions for the selected metrics and demonstrated the ability of the hybrid spectrum access model to improve SUs channel access experience, while ensuring that the QoS of PUs is also satisfied. The obtained results showed that the hybrid spectrum access scheme can significantly improve the channel access experience of SUs without compromising the QoS constraints of PUs in massive cognitive networks. The analyses presented in this paper are useful in modeling the performance of large-scale CRN, where multiple PUs and SUs can access any channel concurrently. In the future, it may be interesting to obtain various detection probabilities conditioned on packet arrival and delivery rates. This may provide more insights regarding the performance of the hybrid spectrum access-based CRNs.  
Conceptualization, S.D.O. and B.T.M.; methodology, S.D.O.; software, S.D.O.; validation, S.D.O. and B.T.M.; resources, B.T.M.; writing--original draft preparation, S.D.O.; writing--review and editing, S.D.O. and B.T.M.; supervision, B.T.M.; funding acquisition, B.T.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the SENTECH Chair in Broadband Wireless Multimedia Communications (BWMC), Department of Electrical, Electronics, and Computer Engineering, University of Pretoria, South Africa.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors will like to appreciate the contributions of [PERSON] to this research work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Key and Protocol Dependency  
It is important to note that cryptographic keys are interdependent. In order for a certain protocol to be executed, nodes should have the appropriate keys. In this section, we recapitulate the dependency between the keys for each of our protocols.  
Figure 8 presents the dependency between keys for each of our protocols, where \(K_{1}\&K_{2}\xrightarrow{A:B}K_{3}|K_{4}\) denotes that \(K_{3}\) or \(K_{4}\) are delivered from \(A\) to \(B\) and encrypted with \(K_{1}\) and \(K_{2}\). The public/private keys of nodes are used to compute the shared symmetric keys using Diffie-Hellman without interaction described in Section 2.1. All of these keys, besides the network key \(NK\), are predistributed to nodes before the deployment. Therefore, all protocols in Figure 8 plus KR can be executed at any time after  
Figure 7: Protocols for the multihop shared key. (**a**) Protocol MSK\({}_{a}\); (**b**) protocol MSK\({}_{b}\).  
deployment, except protocol RSK. Indeed, protocols DJS and IJS end with establishing a session key between the initiator and its neighbor. Therefore, protocol RSK must be executed after DJS and IJS in order to renew the session key.  
Protocol KR is done based on information given by an IDS in order to revoke the keys in possession of compromised nodes. In executing KR, the sink informs the nodes in the neighborhood of the compromised node to delete all shared keys. Therefore, protocols RAK and MSK can be executed regularly or after the revocation process in order to maintain the security level of the application in the case of RAK or to create more secure links between nodes in the case of MSK. Protocol RNK must be renewed in a short interval or after a revocation process.
# A Review of Degradation and Life Prediction of Polyethylene
## 2 Aging Degradation of Polyethylene
### Types of Degradation
#### 2.2.1 Biodegradation  
The process of biodegradation happens when microbial populations, other decomposing organisms, or abiotic forces work together to break down biodegradable materials into minute parts [44]. Three primary processes make up the biodegradation of polyethylene: (1) biodegradation, which occurs when microorganisms grow on the polyethylene's surface or within it, altering its mechanical, physical, and chemical qualities; (2) biodegradation, which is the process of having microorganisms break down a polymer into oligomers and monomers; (3) assimilation, which is the process by which microbes acquire the requisite carbon, energy, and nutrients from the breakdown of polymers and transform the carbon in the material into carbon dioxide, water, and biologically necessary chemicals [45]. The chemical composition, molecular weight, and crystallinity of the polymer, as well as other physical, chemical, and biological aspects, all affect how effectively a substance degrades [46].  
Biological factors that may cause the biodegradation of polyethylene include bacteria, fungi, and microorganisms. Over the past few decades, numerous bacterial strains have been found to interact with polyethylene, and research studies have shown that there are already several genera of bacteria and a small number of genera of fungus that are able to degrade polyethylene. Some of the categories are shown in Table 1. In reality, enzymes choose particular functional groups. Generally, shorter chains, more amorphous parts, and less complicated structures in polymers make them more susceptible to microbial biodegradation [47].
## 3 Results
### Vegetation  
The average vegetation cover in the Control subplots reached 5.6%, significantly higher than in the other subplot types: Disk and No Disk trails (Figure 6a). The vegetation cover in the Disk subplot was slightly higher than in the No Disk subplot (2.5% and 0.9%, respectively) (Figure 6a). Likewise, the average number of plant individuals per square meter was significantly higher in the Control subplots (88 individuals) than in the Disk and No Disk trail subplots (36 and 27 individuals per square meter, respectively) (Figure 6b). There was no significant difference in average plant height between the various subplots (Figure 6c).  
The average species numbers per square meter in the Control and the Disk trail subplots were 6.8 and 8.6, respectively. This differed significantly from the species richness in the No Disk trail, where the average number of species was 4.3 per square meter (Figure 6d). Additionally, the average species diversity \(\mathrm{H}^{\prime}\) was significantly higher in the Disk subplots than in the Control and No Disk trail subplots (Figure 6e).  
Plant assemblages in the Disk trail subplots differed significantly from those in the No Disk and Control subplots. The latter two assemblages were almost similar (Figure 7a, Appendix A).  
Figure 5: The amount of rainfall in the study area during 2002–2003 and the percentage of soil moisture in the top 10 cm of the soil in the winter between December 2002 and April 2003.  
The dominant perennial species in the Control plots are Hs (_Haloxylon scoparium_, Amaranthaceae), a zero-halophytic dwarf shrub species, and Ec (_Erodium crassifolium_, Geraniaceae), a herbaceous geophyte, accompanied by an annual dominant species Gm (_Gymmarthena micrantha_, Asteraceae), a sprawling species. The No Disk trail was dominated by three herbaceous plants, Gm and Aw (_Avena wiestii_, Poaceae) annual plants, and the geophyte Ec. The annual species Rp (_Reboudia pinnata_, Cruciferae) dominated the Disk trail, followed by the other three annual species Ma (_Malva aegyptia_, Malvaceae), Ca (_Calendula arensis_, Asteraceae), and Nc (_Nasturtiopsis coronopifolia_, Cruciferae) as well as and the geophyte Ec (Figure 7b).
## 4 Maximum Power Point Tracking (MPPT) Algorithm  
An MPPT algorithm is implemented to keep track of the changes caused by the environment and to extricate high power from the solar array. In a system with MPPT, voltage collapse is shunned when Maximum Power Point (MPP) is near to the operating point. The benefits of using the MPPT algorithm lie in its robustness and simplicity in implementation. In this paper, P&O, fuzzy logic and GWO algorithms are compared to extricate high power from the PV system. There are different control algorithms available to monitor the maximum power point. Among these, P&O and Fuzzy logic are better choices of control algorithms. The main aim of this control algorithm is to adjust the variation of the voltage, current and power at the maximum power point obtained under the specific values of irradiance and temperature.
# Point Deflection in Topological Interlocking Plates
## 4 Apparent Negative Stiffness and Post-Peak Softening  
We can now derive the force-deflection relationship. We start with a configuration shown in Figure 8d and consider the balance of moments created by the applied point force and the constraint force, Figure 10. We also introduce the following dimensionless variables:  
\[v=\frac{u_{y}}{l},\ \ \beta=\frac{b}{l} \tag{1}\]  
Consider the right part of the beam. Given that \(\mathbf{sin}\frac{\theta}{2}=\mathbf{v}\), the moment created by the reaction force, \(Q/2\), about point \(O\) is as follows:  
\[M_{Q}=\frac{Q}{2}\Big{[}\sqrt{1-v^{2}}+2\beta v\Big{]}l,\ \ \ v<1 \tag{2}\]  
We assume the constraining force linearly depending on the length increase caused by deflection:  
\[F=F_{0}+2\beta kv \tag{3}\]  
Here, \(F_{0}\) is the initial constraining force applied by either a cable (post-tensioning) or peripheral constraint frame; \(k\) is the effective stiffness of the constraint. Subsequently, the moment created by the constraining force, \(F\), about point \(O\) is as follows:  
\[M_{Q}=(F_{0}+2\beta kv)\Big{[}2\beta\sqrt{1-v^{2}}-v\Big{]}l,\ \ \ v<1 \tag{4}\]  
Equating these moments, one obtains the following force-deflection relationship:  
\[\frac{Q}{4\beta k}=\Big{(}\frac{F_{0}}{2\beta k}+v\Big{)}\frac{2\beta\sqrt{1- v^{2}}-v}{\sqrt{1-v^{2}}+2\beta v},\ \ \ v<1 \tag{5}\]  
Figure 11 shows the normalized force-deflection curves for different values of the normalized initial force, \(\frac{F_{0}}{2\beta k}\), and beam half-thickness, \(\beta\). They start with \(F_{0}=0\), which corresponds to negligible pre-tension.  
It is seen that the force-deflection curves show stages of considerable post-peak softening, that is, after the peak value the force-deflection relationship exhibits apparent negative stiffness. The peaks are reached at a critical deflection:  
\[v_{cr}=\frac{2\beta}{\sqrt{1+4\beta^{2}}} \tag{6}\]  
The critical deflection is only controlled by the block geometry (the block aspect ratio in the case considered).  
The beam deflection is stable when \(v<v_{cr}\) and unstable (requires indentation with displacement control) when \(v>v_{cr}\). This critical deflection is an increasing function of the beam normalized thickness, \(\frac{b}{l}\), and is independent of the initial constraining force.  
Figure 10: Deflection, \(\mathbf{u_{y}}\), of a fragmented beam under point load, \(Q\), applied at the center.  
Thus, the relative rotations of neighboring blocks constitute a mechanism of negative stiffness, observed in [46, 47]. (We note that the total energy of the system TI beam/plate plus the peripheral constraint is positive-definite.) It is also seen that the beam thickness \(\beta=\frac{b}{T}\) has the strongest effect on the force-deflection curves, while the effect of constraining force \(\frac{F_{0}}{2 pk}\) is minor.  
The following section will consider a simplified model of oscillations of TI beams with point deflection damped by air squeezing accounting for the discontinuous nature of the damping.
## 1 Introduction
In addition, because the actual operation continuously fluctuates according to the changes in the outdoor temperature and cooling load, the extraction of steady-state data for predicting the refrigerant charge amount requires a long operation period, which may reduce the accuracy of the predictions. In this paper, a prediction model based on dynamic experimental data for overcoming these deficiencies is proposed. Dynamic experimental data for the model development are easy and fast to obtain; thus, the acquisition time is reduced. In the study of the dynamic characteristics of refrigeration systems, physical and data-based models are applied [14; 15; 16; 17]. Moreover, more and more researchers study dynamic models with machine learning owing to the improved computational speed [23; 24; 25; 26; 27; 28; 29; 30]. In this study, the dynamic behavior of a refrigeration system was modeled with a regression support vector machine (r-SVM, which is a data-based model). The model was used to confirm that the initial dynamic characteristics of the refrigeration system vary according to the refrigerant charge amount, which enables the prediction of the latter.
## 2 Methodology
### Decode Module  
During the decoding phase, if the final output feature map is directly upsampled as the prediction result, the segmentation region will be coarse, because although the deep feature has rich semantic information, it has low resolution and thus loses a certain amount of spatial information. The feature map in the early stage of encoding contains rich position information, so we can improve the final prediction effect by fusing the feature map of different coding stages. The internal structure of the decoder in this study is shown in the left area of Figure 6, which will pass through the \(3\times 3\) convolutional kernel, BN layer, and ReLU activation function from the deep and shallow input feature maps of the coding stage, respectively. The input feature map from the deep layer is then upsampled, and the dimensions are restored to match the dimensions of the shallow feature map. Finally, the two Concat input feature maps were spliced together and then passed through the \(1\times 1\) convolution kernel, BN layer, and ReLU activation function to adjust the channel size. Through the decoder, the model in this study fuses the features of the four coding stages layer by layer to form the final output feature map, and then adjusts the number of channels to the number of categories through the segmentation head to obtain the prediction effect map. The segmentation head in the decoding stage consists of a \(1\times 1\) convolutional kernel and an upsampling function (increasing the size to match that of the input image), as shown in the right area of Figure 6. The segmentation head is used to convert the feature map into a predictive image.  
Figure 5: Cloud heat map: (**a**) original image; (**b**) Transformer; (**c**) CNN; (**d**) the model of this study.
# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 3 Weighted Ensemble Classification  
After feature extraction, we adopted a feature fusion strategy to integrate them for classification. Generally, a fusion of features in deep learning is an integration method that combines different information extracted for the same input (or sample) to improve the overall model performance. Extracted MR features are initially weighted and then presented as input to a classifier. The weighing of the features is employed to highlight the impact of various features on tumor classification compared to traditional fusion techniques that employ equal feature weighing.  
Currently, three base-feature extractors (CNN, hand-crafted, and ViT) are employed to provide individual classification or prediction of the input sample (an MR image). Each model's output for a given input sample \(y_{i}\) is a probability vector \(P_{k}\), where \(k\in[1,\dots,mc]\) and \(mc\) represent the number of classes. The feature extractor modules were trained individually, and to improve the system's overall accuracy, the final system performance is based on the weighted combined features using weights \(w_{i}\in[0,1]\)[39]. To weigh the features vector before ensemble classification, the weights were assigned based on the models' priority. The model with the best performance out of the three was prioritized using the following equation.  
\[w_{i}=\frac{P_{i}}{\sum_{k=1}^{3}P_{i}} \tag{3}\]  
Weighted ensemble prediction can be performed after each model's weights are assigned. After that, test data can be utilized to evaluate the model by comparing its performance to the predicted output. The last step of the analysis pipeline is the classification using the weighted feature vector. A multilayer perceptron (MLP) classifier was used for classification. The classifier choice is based on the fact that MLP can learn intricate nonlinear patterns by magnifying pertinent aspects of input data while suppressing irrelevant information [40].