esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion of Theoretical and Experimental Results  
Table 5 reveals that the average root mean square voltage of the first three modes are 1.726, 2.184, and 2.229 volts, yielding a ratio of 1:1.265:1.291. Following dimension conversion, the root means square voltage values predicted by theory for the first three modes are displayed in Figure 27, measuring 1.773 volts, 2.282 volts, and 6.658 volts, respectively. The relative errors between the theoretical and experimental voltage outputs are approximately 2.717%, 4.497%, and 198.688%, respectively. The theoretical voltage output ratio for the first three modes is 1:1.287:3.755. From the results, it is evident that the experimental data for the first and second modes align well with the theoretically predicted values. However, discrepancies in the third mode may be attributed to the inability of the magnet at the free end of the elastic steel sheet to synchronize with the frequency of the magnet on the rotating shaft. Consequently, irregular motion in the slapping of the elastic steel sheet and the lever led to a mismatch between the third mode voltage output and the theoretical value.  
To verify that the power generation efficiency of the system improves with the addition of slapping force compared to without, we conducted theoretical and experimental analyses on the first and second modes of the system without the addition of slapping force. As irregular motion was observed in the third mode, we have excluded it from further discussion. The theoretical considerations and root mean square data from five experiments for the first and second modes without slapping the root are presented in Table 6. The theoretical and experimental voltage diagrams for the system are illustrated in Figures 28 and 29. An analysis of Table 6 and Figures 28 and 29 reveals that the theoretical and experimental errors for the first and second modes without adding slap force fall within 10%. A comparison of voltage root means square values with and without slapping force in Table 7 indicates a significant increase in generated volts when slapping force is applied. To underscore the practical applicability of this model, this study also summarizes the power generation and voltage output data for 60 s in the experiment in Table 7. Consequently, it can be confirmed that applying slapping force on the piezoelectric sheet enhances voltage generation efficiency.  
In summary, this fundamental slap-type wind-driven power generation system effectively enables piezoelectric patches to generate electricity through deformation and slapping forces. In the past, most PZTs only utilized either deformation to generate electricity. Therefore, we believe that this innovative approach holds great promise for the future.
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 1 Introduction
### Normal Copula Model for Correlated Item Residuals  
We now review the normal copula model for correlated item residuals for the 2 PL model [31]. There is an underlying latent item response \(X_{i}^{*}\) for each item \(X_{i}\) (see (11)). The item residuals in (11) follow the logistic distribution. Let \(\varepsilon=(\varepsilon_{1},\ldots,\varepsilon_{I})\) be the vector of item residuals. In the normal copula model, it is assumed that the vector \(\boldsymbol{e}=(e_{1},\ldots,e_{I})\) of transformed item residuals follows a multivariate normal distribution with a zero mean vector \(\mathbf{0}\) and a correlation matrix \(\boldsymbol{\Sigma}^{*}\). Note that all variables \(e_{i}\) have standard deviations of \(1\). In more detail, each component \(i\) (\(i=1,\ldots,I\)) of \(\boldsymbol{e}\) is defined as  
\[e_{i}=\Phi^{-1}(\Psi(\varepsilon_{i}))\, \tag{15}\]  
where \(\Phi\) denotes the distribution function of the standard normal distribution.  
This definition allows the computation of item response probabilities for subsets of items within a testlet that accommodates testlet dependence. Assume that \(\operatorname{Cor}(e_{i},e_{j})=\rho_{ij}\) for items \(X_{i}\) and \(X_{j}\) within a testlet. Then, we have  
\[\begin{array}{rl}&\mathsf{P}(X_{i}=1,X_{j}=1|\theta)\\ =&\mathsf{P}(a_{i}\theta-b_{i}-\varepsilon_{i}>0,\,a_{i}\theta-b_{j}- \varepsilon_{j}>0\,|\,\theta)\\ =&\mathsf{P}(e_{i}<\Phi^{-1}(\[PERSON](a_{i}\theta-b_{i}))\,,e_{j}<\[PERSON]^{-1}(\[PERSON]( a_{j}\theta-b_{j}))\,|\,\theta)\\ =&\[PERSON]_{2}\big{(}\[PERSON]^{-1}(\[PERSON](a_{i}\theta-b_{i}))\,,\[PERSON]^{-1}(\[PERSON](a_{j} \theta-b_{j}));\rho_{ij}\big{)}\end{array}, \tag{16}\]where \(\[PERSON]_{2}\) is the distribution function of the bivariate normal distribution depending on the correlation \(\rho_{ij}\). For \(x,y\in\{0,1\}\), the item response probabilities can be evaluated as  
\[\begin{array}{rcl}&\mathsf{P}(X_{i}=x,X_{j}=y|\theta)\\ =&\Phi_{2}\Big{(}\Phi^{-1}\big{(}\Psi\big{(}(2x-1)(a_{i}\theta-b_{i})\big{)} \big{)},\Phi^{-1}\big{(}\Psi\big{(}(2y-1)(a_{j}\theta-b_{j})\big{)}\big{)};\rho _{ij}^{x+y}\Big{)}\end{array}. \tag{17}\]  
The extension to more than two items in a testlet follows the same principle [31]. However, the multidimensional normal distribution must be evaluated. There is increasing computational complexity in this calculation with an increasing number of items within a testlet.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 3 Circuit Implementation and Simulation Results
### Encoding Circuit A  
Scheme A is based on the standard charge packet counting circuit's front end, ignoring its asynchronous counterpart. Simultaneously, the frequency-encoding/temporal encoding switching control signal MODE is introduced: when MODE = 1'b0, the circuit operates in frequency-encoding mode; when MODE = 1'b1, the circuit operates in temporal encoding mode, and the generated pulse width has the TTFS form, as depicted in Figure 6.  
Scheme A is based on a frequency-encoding circuit and adds temporal encoding control via the NAND gate. The original frequency-encoding pulse retains only one pulse edge, and the following pulses are insulated (at the same time, the charge packet count is suspended for energy efficiency purposes).  
In frequency encoding's functioning mode, the integral capacitor must be periodically reset. Consequently, when designing the actual circuit, the latency of the comparator, the size of the reset switch, and the value of the integral capacitor are the most crucial factors that must be carefully considered. As depicted in Figure 7, a half-side Schmitt trigger has been implemented to reduce comparator latency. The input signal range and counting period must be utilized to optimize the size of the reset switch and the value of the integral capacitor. Figure 8 depicts the simulation results of the circuit in frequency-encoding mode and time-encoding mode, respectively.  
Figure 5: Circuit diagram of charge packet pulse.  
Figure 6: Circuit diagram of encoding circuit A.  
As depicted in Figure 7, the circuit uses a Schmitt trigger (half side), a push-pull stage circuit structure, and local positive feedback, which accelerates the output inversion, to reduce the comparator delay. However, the threshold voltage for the Schmitt trigger is determined by device size and cannot be adjusted externally. Meanwhile, the entire scheme A is modified by the frequency-encoding circuit, especially the integral capacitance value. The capacitance value is too small to encode temporal information. As a result, the TTFS pulses of even the weakest input current signal account for only a small fraction of the total counting period.  
Scheme B introduces an innovative comparator to solve the above two problems: a self-biased rail-to-rail comparator. In addition, for the temporal encoding mode, an additional integrating capacitor is added to the circuit.
## 1 Introduction
With the growth of laser amplitude, the temporal distribution presents a multi-peak structure, and the number of secondary peaks is constantly reducing; the height of the first peak relative to the main peak is constantly decreasing; and the temporal distribution becomes more concentrated. The 2D radiation spectrum gradually assumes a multi-peak distribution with the enhancement of laser amplitude, and the energy is concentrated in the main peak and the first peak in the low-frequency band.
# Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach
## 4 Experiments and Discussions
### Simulated Speckle Image Experiments
#### 4.2.3 Comparative Analysis of Model Calculation Accuracy  
A number of test samples are selected, and the preset true displacement values in the horizontal u direction are from 0 pixels to 1 pixel, with an increment of 0.05 pixels, which are predicted by the ST-SDM model and the CNN-SDM model, respectively, and the absolute values of the mean displacement errors (AVMEs) and relative errors between the predicted values and the true values are shown in Figure 9. As shown in Figure 9, the AVMEs for the ST-SDM are from \(5.2\times 10^{-3}\) to \(6.5\times 10^{-3}\) pixels, with the mean error and the RMSE of the AVMEs are \(5.89\times 10^{-3}\) and \(0.42\times 10^{-3}\) pixels, respectively. The AVMEs for the CNN-SDM are from \(6.1\times 10^{-3}\) to \(8.2\times 10^{-3}\) pixels, with the mean error and the RMSE of the AVMEs being \(7.18\times 10^{-3}\) and \(0.84\times 10^{-3}\) pixels, respectively. Accordingly, the relative errors of the ST-SDM range from \(0.55\%\) to \(12.79\%\), while the relative errors of the CNN-SDM range from \(0.63\%\) to \(15.94\%\). The maximum and minimum values of the AVMEs and the RMSEs of two deep learning-based algorithms (the CNN-SDM and the ST-SDM) as well as several traditional algorithms (SF, GC, FANR) from the literature [16] are shown in Table 1. As shown in Table 1, the accuracy of the two deep learning-based algorithms, the CNN-SDM and the ST-SDM, outperforms that of the traditional algorithms SF and GC. However, their accuracy is lower than that of the traditional algorithms FANR, IC-GN, and IV-ICGN. Additionally, the ST-SDM demonstrates higher accuracy than the CNN-SDM.  
Figure 8: The variation in accuracy of the third level classification task.
## 3 Results and Discussion
### Practical Aspects  
The mixing studies indicate that both welding processes benefit from the use of larger groove cross-sections. The use of a wider root gap in the I-groove welds, for example, helped to introduce filler metal into the bottom of the groove. In practice, with open square preparation where a wide root gap is used in thick-section welding, the diameter of the laser beam spot needs to be wide enough and the power density of the beam must simultaneously be sufficient to ensure both proper melting of the joint fusion faces and to enable keyhole formation. It should be noted that the mixing results for the thick-section welds described in this work represent one transversal cross-section sample per test weld joint produced. Welding in general and laser-arc/cold wire-based keyhole welding in particular, are dynamic processes. Consequently, some natural variation in mixing and the resulting weld metal composition along the solidified weld joint can be expected even if all the welding experiments are carried out in the proper parameter window and in a robust manner. However, the results presented  
Figure 20: Filler metal mixing results from EDS stepwise characterization. (**a–d**) Filler metal mixing-% profiles of measurement lines 1–4 (ML1–ML4) from test weld LCW8 (**e**). In figures (**a–d**) one step between adjacent measurement points in the horizontal axis equals 100 micrometres.  
offer some indication of how mixing can be considered in welding parameter planning for thick section welding with laser-arc or laser cold wire-based processes.
## 2 Materials and Methods
### Evaluation Procedures  
In order to compare observed precipitations with IMERG estimates, some interpolation needs to be done. To avoid the modification of the actual observation data, an alternative grid to point interpolation technique is used in this study. The procedure consists of using a bilinear interpolation method on the IMERG estimates data to obtain the precipitation at the specific points corresponding to the meteorological stations where the precipitation data were collected. Hereafter, these points will be referred to simply as meteorological stations. The bilinear interpolation method uses the distance-weighted average of the four nearest grid nodes values to give an estimate at a point of interest. This method also assumes high correlations between data points, which can be expected in this case, due to the relatively high resolution of the satellite precipitation product. [PERSON] et al. [14] showed that the application of the bilinear interpolation to precipitation fields with spatial resolution of about 10 km may introduce bias since it does not conserve total precipitation and results in smoothing of the precipitation field by increasing the minima and reducing the maxima. However, this interpolation method has been satisfactorily used in previous studies [15; 16].  
Statistical measures can be divided into different categories. In this study, continuous and categorical statistics are used to characterize IMERG from hourly, daily and seasonal precipitation estimates.
# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
## 5 Results  
In this article, we used twelve algorithms to find the most effective model for predicting energy consumption from the input (Table 2). Figure 2 shows a graphical comparison between the actual energy values and the predicted energy for each part and according to its different orientations using these algorithms.  
Figure 2 shows that some models predict results closer to the actual values.  
Table 3 presents the statistical performance indicators for each model in terms of MAE, RMSE, R\({}^{2}\), and explained variance  
Figure 2: Actual and predicted energy values for each part.  
From Figure 3 and Table 3, it is clear that the GPR model achieves the highest precision, having **99.16%** as the explained variance value. For the R-squared, it is **99.14%**. The respective values of MAE and RMSE are **3.88** and **5.79**. It can be seen that these values are the best in comparison with the other models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Model** & **MAE** & **RMSE** & **R-Squared** & **Explained Variance** \\ \hline
1 & Linear Regression & 5.251029 & 11.569446 & 0.965963 & 0.969285 \\ \hline
2 & RANSACRegressor & 5.167925 & 10.929534 & 0.969624 & 0.973401 \\ \hline
3 & Ridge Regression & 5.894558 & 13.867725 & 0.951097 & 0.956034 \\ \hline
4 & **GaussianProcessRegressor** & **3.881234** & **5.793596** & **0.991465** & **0.991602** \\ \hline
5 & Lasso Regression & 5.764058 & 13.284901 & 0.955121 & 0.960033 \\ \hline
6 & Elastic Net Regression & 5.920195 & 14.095655 & 0.949476 & 0.954896 \\ \hline
7 & Random Forest Regressor & 7.287192 & 19.347954 & 0.904808 & 0.904854 \\ \hline
8 & SVM Regressor & 9.683700 & 23.169309 & 0.863493 & 0.877995 \\ \hline
9 & Linear SVR Multi Output & 15.571200 & 22.334431 & 0.873154 & 0.893362 \\ \hline
10 & Linear SVR Chain Regressor & 15.588542 & 22.311816 & 0.873410 & 0.893883 \\ \hline
11 & KNeighborsRegressor & 13.197450 & 24.191009 & 0.851189 & 0.851421 \\ \hline
12 & DecisionTreeRegressor & 9.929268 & 23.336388 & 0.861517 & 0.862568 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation Metrics of used models.  
Figure 3: Actual versus predicted values of each prediction algorithm.
## 4 Methodology and Prototype Implementation
### Overall Prototype Concept
The five manufacturers of the SMGWs are Devolo AG/Kiwigrid GmbH (cooperation), EMH metering GmbH and Co. KG, Landis + Gyr AG, Power Plus Communications AG (PPC), and Theben AG. The PPC SMGW was the first to receive a certification according the SMGW Protection Profile BSI-CC-PP-0073 in December 2018, followed by Sagemcom Dr. Neuhaus in September 2019, and EMH metering in December 2019. All other SMGWs, as of March 2020, are still undergoing certification. Figure 4: Outline of the implemented prototype: The decentralized energy management system performs an optimization which is (i) motivated by a dynamic electricity tariff and a potential maximum power limit dynamically received from an active external market participant (aEMP) via the SMGW, and (ii) restricted by consumer inputs representing degrees of freedom for the optimization.
## 9 Real-World Challenges in AI Implementation: Regulatory and Ethical Considerations
### Regulatory Hurdles  
One of the primary challenges in deploying AI systems, especially in communication networks, is navigating the complex landscape of regulations, including data privacy laws and data sovereignty concerns. For example, GDPR imposes strict rules on data collection, processing, and storage, which may limit the ability of organizations to collect and process large volumes of data required for training AI models. To address these challenges, I propose the following strategies:  
* Federated Learning: Federated learning, where the model is trained across multiple devices without sharing raw data, can help comply with data privacy regulations. By keeping data local, federated learning enables privacy-preserving AI while allowing global model updates, making it a promising solution for industries subject to stringent privacy laws.
* Data Anonymization and Differential Privacy: When working with sensitive data, anonymizing personal information and employing differential privacy techniques can ensure compliance with regulations while retaining the utility of the data for training AI models.
* Collaboration with Regulatory Bodies: AI developers must collaborate with regulatory bodies to ensure that their solutions are aligned with current laws and to help shape new frameworks that allow for the ethical deployment of AI technologies.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Ethical Principle** & **Related Regulation** & **Network Requirement** \\ \hline Transparency & GDPR & Data usage disclosure \\ Accountability & CCPA & Traceable decision-making \\ Fairness & Various Anti-Discrimination Laws & Unbiased resource allocation \\ \hline \hline \end{tabular}
\end{table}
Table 17: Ethical principles and compliance requirements for AI in communication networks.
# Assessment of Milk Quality in Skopelos Goats Under Low- and High-Input Farming Systems
###### Abstract
Additionally, grazing systems have been associated with improved milk composition, including higher fat, protein, and total solids (TSs) contents, as well as enhanced nutritional indices [12]. However, the overall impact of farming systems on milk composition is influenced by management practices, including nutrition, breed, selection, and milking techniques [13; 14; 15; 16]. Furthermore, extensive farming systems have been linked to lower rates of intramammary infections compared with intensive systems, although this relationship is not always consistent across different studies [6; 17]. While numerous studies have examined the effects of farming systems on milk composition [18; 19; 20; 15], limited research has investigated the combined influence of farming systems and lactation stage within goats of the same breed, particularly under commercial conditions. Additionally, the genetic relatedness between intensively and extensively raised goats is often overlooked, despite its potential to provide valuable insights into milk quality variations. One of the major innovations of the present study is the inclusion of purebred Skopelos goats certified by the Skopelos Goat Association and sharing the same genetic background. Moreover, several studies have investigated the production traits [22; 23; 24; 25; 26] and genetic characteristics [27; 28; 29; 30] of the Skopelos breed, including gene polymorphisms and genome-wide variation; however, to our knowledge, no study has evaluated milk quality under both low- and high-input farming systems. In this study, two commercial farms (intensive vs. extensive) were involved, and all goats in the intensive system originated from the extensive farm. This shared genetic background between the two farms reduces genetic variability as a confounding factor, allowing for a more precise evaluation of how farming systems influence milk quality. Another important aspect in this study is the inclusion of only second- and third-lactation-stage animals. The farms were large enough to provide a sufficient number of second- and third-lactation animals for this study, thus further limiting animal variability. Consequently, the objective of this study was to evaluate the combined effects of farming systems (intensive vs. extensive) and lactation stage over two consecutive lactation periods on goat milk quality regarding the chemical and physical characteristics and the somatic cell count (SCC) from two commercial farms rearing the Skopelos goat breed.
# Browser Forensic Investigations of Instagram Utilizing IndexedDB Persistent Storage
## 3 Background for IndexedDB and LevelDB
### Client-Side and Server-Side Storage  
Two principal options are available for storing information for a web origin: client-side storage and server-side storage. Both options carry advantages and disadvantages [25; 26]. Predominantly, the security of the client-side information is inevitably dependent on the user and the web browser [26]. Nevertheless, server-side storage utilizing client-side code such as JavaScript must send data forth and back, a process that generates increased network traffic. Furthermore, the scalability of the applications is dependent on the choice of storage techniques. When a substantial amount of data are stored on the server, the performance is directly proportional to the changes in the number of active users [27]. The most suitable option depends on the specifications of the application. A commonly accepted practice is to utilize server-side storage for sensitive information when less vulnerable information is stored on the client-side [28].  
Legacy client-side storage technologies such as cookies provide small-scale capacity compared to newer technologies such as Web Storage and IndexedDB [29]. Therefore, their utilization and the types of data they contain differ considerably [30]. Table 1 gives a summary of the client-side storage technologies with their capacities. The emergence of persistent storage technologies with fast access to larger sizes of data profoundly changed the content of the data stored on the client-side. Therefore, its significance for forensic researchers has been heightened over time.  
\begin{table}
\begin{tabular}{c c c} \hline
**Technology** & **Storage Size** & **Notes** \\ \hline Cookies & 4 KB & Legacy client-side storage technology \\ \hline Web Storage & 5 MB & Predecessor of IndexedDB that is still actively in use \\ \hline Session Storage & 5 MB & Non-persistent storage \\ \hline IndexedDB & \textgreater{}50 MB & Bridge between Web Storage and Cache API \\ \hline Cache API & \textgreater{}500 MB & Currently does not have considerable content in Alexa top 20 websites \\ \hline \end{tabular}
\end{table}
Table 1: Client-Side Technologies and Storage Capacity.