esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 The 5G Spectrum Standardization
### Application and Device Benchmarking in 5G Networks  
In a typical 5G network, application developers and device manufacturers should be able to test and benchmark new mobile applications, devices, and services. A critical aspect of the mobile communications market is to guarantee the correct and efficient behavior of dense applications and massive devices to satisfy the requirements of end users. Although these mobile devices have been standardized, there is no adequate consensus on the benchmarking or testing methodologies at the application level. Thus, the need to identify reference deployment scenarios and the definition of new KPIs and QoE metrics becomes imperative.  
It is worthwhile considering three distinct areas for testing and benchmarking. These include applications, devices, and mobile network operators. Interestingly, Small and Medium Enterprises (SMEs) provide these applications and often find accessing various testing processes in real-time wireless networks difficult. Another critical challenge for SMEs is understanding the requirements of standardized testing schemes and their associated products. Unfortunately, the enormous testing costs and the need for technical expertise often discourage SMEs from conducting robust tests.  
A typical testbed scenario is shown in Figure 14. The architecture comprises the Orbit Management Framework (OMF), Orbit Measurement Library (OML), and OMF Experiment Description Language (OEDL). The OMF controls the experimental execution in testbeds. The OEDL technically describes the execution of an experiment and specifies the results in readable formats. Also specified is the Experiment Controller (EC) that interprets and orchestrates the OEDL scripts. Finally, the Resource Controller (RC) manages each resource in the testbed, and there are RCs for managing smartphones, among others.  
Last, the Advanced Message Queuing Protocol (AMQP) manages communication between the EC and RCs and measures data obtained through the OML. Finally, the test automation platform manages the DC power analyzer and the eNodeB emulator for design verification and signaling testing. In comparison, the DC Power Analyser helps to characterize power consumption in mobile devices.
## 6 Conclusions  
The proposed IGA-BEM got consistent results, while solving the problem of Figure 8, compared to those obtained from the proven methods FEM and BEM. Although the displacement values were different, the deformation generated was similar in all three methods. However, fewer degrees of freedom were required in proposed IGA-BEM, because a single NURBS was used to model the  
Figure 18: Close-up to the contact area between two bodies analyzed with the IGA-BEM and the optimization proposed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Contact Area Width** & **Variation with Respect to** & **DOF to Solve the** \\  & **(mm)** & **the Analytical Method** & **Problem** \\ \hline Analytical (Hertz model) & 0.56 & 0 & - \\ \hline FEM & 0.65 & 16\% & 4368 \\ \hline Proposed IGA-BEM of & 0.62 & 11\% & 48 \\ contact & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of contact area widths.  
completed body. Moreover, the load/field points used in the conventional BEM were separated from the points, to represent the geometry.  
Regarding the nonlinear contact problem, the contact-zone widths obtained with FEM and with the proposed contact method varied 16% and 10%, respectively, compared to the one obtained analytically, with the Hertz method. Nevertheless, the DOF used by FEM to solve the contact problem were 4368, while the proposed method only used 48 DOF.  
Decreasing degrees of freedom is very significant, for example, when solving multiphysics problems, where it is required to invert matrices. Furthermore, as seen in [32], the propagation error is tied to the size of the matrix: The more elements the matrix has, the higher the error.  
Although the results are promising, future work is required to prove that the methodology is valid in other situations where displacements are not symmetrical or additional factors, such as friction, are considered.  
J.C.J.C. conceptualized the topic of this research; the software was programmed by S.V.C.G.; and the formal analysis was made by [PERSON], S.V.C.-G., A.D.-G., and R.A.G.-L., as well as the review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
The authors thank CONACYT for the support.  
The authors declare no conflict of interest.
Although the first application project dates back to 1991 [23], BIPV systems are relatively under-researched, which is evidenced by approximately 1500 publications as compared to more than 100,000 on the subject of PV, according to a search in Scopus. As field data for BIPV projects are rarely available, there exists a lack of practical insights. Simulation tools and modelling approaches are therefore required to generate information about the energy production, architectural design features and cost aspects of BIPV systems. Several simulation tools exist with which to assess the energy production of BIPV systems [24; 25], yet none of them fully take into account all variables relevant for \"good design\", such as (beside energy performance) technical design features, financial aspects, CO\({}_{2}\) emission reduction potential, aesthetics, user perception and appreciation, and the effects of regulations and laws. Moreover, architects often qualify those tools as \"difficult to use\", \"complex\", and \"cumbersome\" [26], and hence they require expertise from energy researchers and engineers during the assessment. To enable clear communication between the various stakeholders and to optimise the different features involved in BIPV applications, multicriteria optimisation as a building information modelling (BIM) approach and modelling tools are needed to facilitate collaboration and gather more insights. Given the context sketched above, the energy balance, architectural design features and cost aspects of 24 BIPV projects will be determined and evaluated for three different building categories, namely; residential, office and historical buildings. In this study, we used BIMsolar together with Sketchup, as well as our own written MatLab algorithms to quantify energy and cost-related aspects, so as to evaluate various types of buildings on different locations in order to:  
* Map the feasibility of BIPV projects: how do investment costs, electricity prices, incentives, type of buildings, their geometry, and their geographic location impact feasibility in terms of the energy, cost and design features of a BIPV project? * Energy balance of BIPV: what potential exists regarding increased self-consumption and self-sufficiency of BIPV systems in NZEBs in different geographic locations? * Design features: how could design features of the buildings be respected or even enhanced by BIPV projects? This paper is structured as follows. Section 2 will present the method applied to the evaluation of 24 selected BIPV projects, with a focus on their description. Section 3 will report the cumulated data of 24 BIPV projects and compare them with each other. Finally, in Section 4, a discussion about the results will take place and conclusions will be drawn.
## 2 Methodology
### Watermark Embedding
#### 2.3.5 Watermarking Evaluation  
According to the applied method for watermarking, the performance can change dramatically. To find out the performance of a given watermark method, one can measure the method's transparency. As artifacts are introduced during the watermarking process, the host image's quality is affected. Transparency is defined as the amount of information disclosed after the watermark operation. In practice, transparency is usually measured by evaluating the imperceptibility of the algorithm, which in turn is measured by an objective image quality metric called peak signal-to-noise ratio (PSNR). Measuring the PSNR of a method is straightforward and simple, and as a result, PSNR has become a widely used metric for watermarking methods. The PSNR is evaluated by comparing the host image \(I\) of size \(N\times M\), with the watermarked image \(K\), as follows [6]:  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{\text{Max}^{2}}{\frac{1}{M\cdot N} \sum_{i=1}^{M}\sum_{j=1}^{N}(I(i,j)-K(i,j))^{2}}\right) \tag{5}\]  
where  
* Max: Maximum possible pixel value (e.g., 255 for 8-bit images).
* M and N: Dimensions of the image (rows and columns).
* I(i,j) Pixel value at position (i,j) in the original image.
* K(i,j): Pixel value at position (i,j) in the distorted or compressed image.  
Alongside transparency, the degree of similarity between the watermarked image and the original image is also crucial in assessing the performance of a watermarking technique. Evaluating and measuring the similarity between the extracted watermark and the original one is essential in determining the level of distortion introduced by the method. Normalized cross-correlation (NCC) is used to measure image similarity between the original and extracted images in watermarking.  
NCC is computed as follows: W, W\({}^{\prime}\) are the original watermark image and the extracted watermark, respectively, while \(\mu_{\text{W}\cdot\text{HW}^{\prime}}\) represent the means of the original and extracted watermark.  
\[\text{\emph{NC}}\left(\text{\emph{W}}.\text{\emph{W}}^{\prime}\right)=\frac{ \sum_{i=1}^{M}\sum_{j=1}^{N}[\text{\emph{W}}(\text{\emph{i}}\cdot\text{\emph{ j}})-\mu_{\text{\emph{W}}}][\text{\emph{W}}(\text{\emph{i}}\cdot\text{\emph{ j}})-\mu_{\text{\emph{W}}}]}{\sqrt{\sum_{i=1}^{M}\sum_{j=1}^{N}[\text{\emph{W}}^{( \text{\emph{i}}\cdot\text{\emph{j}})}-\mu_{\text{\emph{W}}}]^{2}}\sqrt{\sum_{ i=1}^{M}\sum_{j=1}^{N}[\text{\emph{W}}(\text{\emph{i}}\cdot\text{\emph{j}})-\mu_{ \text{\emph{W}}}]^{2}}} \tag{6}\]  
NCC is particularly useful in evaluating the accuracy of the watermark extraction process.
# A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging
## 2 Materials and Methods
### Evaluation Metrics
#### 2.7.7 Maximum (Max)  
The maximum value represents the largest observed value in the dataset:  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline DSC (Unref\_GT-Ref\_GT) & 0.3827 & 0.1676 & 0 & 0.2725 & 0.4100 & 0.5075 & 0.7500 \\ \hline DSC (Unref\_GT-Predict) & 0.3317 & 0.1783 & 0 & 0.2025 & 0.3500 & 0.4600 & 0.7800 \\ \hline DSC (Ref\_GT-Predict) & 0.6730 & 0.1938 & 0 & 0.5900 & 0.7300 & 0.8100 & 0.9400 \\ \hline Unrefined GT Volume (mm\({}^{3}\)) & 338.34 & 650.96 & 0 & 56.08 & 133.93 & 311.89 & 6785.58 \\ \hline Refined GT Volume (mm\({}^{3}\)) & 285.27 & 435.32 & 0 & 61.14 & 127.53 & 331.41 & 4082.03 \\ \hline Predict Volume (mm\({}^{3}\)) & 303.81 & 556.35 & 0 & 39.04 & 115.56 & 311.61 & 5363.77 \\ \hline Volume Ratio (Ref\_GT/Unref\_GT) & 1.4572 & 1.7242 & 0 & 0.6700 & 0.9900 & 1.4800 & 15.23 \\ \hline Volume Ratio (Predict/Unref\_GT) & 1.3245 & 2.0852 & 0 & 0.5600 & 0.8750 & 1.3475 & 25.32 \\ \hline Volume Ratio (Predict/Ref\_GT) & 0.9380 & 0.5843 & 0 & 0.6525 & 0.8800 & 1.0900 & 7.14 \\ \hline \end{tabular}
\end{table}
Table 1: Summary statistics of experimental results for radiomics feature extraction, including mean, standard deviation, minimum, 25%, 50%, 75%, and maximum values for Dice similarity coefficients (DSCs), segmented volumes, and volume ratios across different ground truth datasets (Unrefined GT and Refined GT) and predicted segmentations.  
\[\text{Max}=\max(x_{1},x_{2},\dots,x_{N}) \tag{12}\]  
These statistical measures provide a comprehensive view of the segmentation performance by quantifying the variability, distribution, and central tendency of the Dice similarity coefficient (DSC) values and volumetric measurements. They enable objective comparisons between the unrefined, refined, and predicted ground truth datasets, supporting the validation and improvement of the proposed segmentation approach.
# Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
## 5 Experimental Results
### Gait Style Analysis  
We can classify the motion data according to content embedding statistics and identify a synthesized walking series according to ID embedding statistics (Table 1). We investigate the clustering performances on different feature vectors: (1) skeleton joint position vector consisting of absolute coordinates (\(\mathbf{C}_{a}\)) and relative coordinates (\(\mathbf{C}_{r}\)); (2) hidden unit vectors of auto-encoder corresponding to absolute (\(\mathbf{H}_{a}\)) and relative (\(\mathbf{H}_{r}\)) cases; (3) Gram matrix reshaped vectors corresponding to absolute (\(\mathbf{G}_{a}\)) and relative (\(\mathbf{G}_{r}\)) cases. We also test with different distance metrics including \(l_{1}\)-norm (\(\mathbf{L}_{1}\)), \(l_{2}\)-norm (\(\mathbf{L}_{2}\)), and the Mahalanobis distance (\(\mathbf{L}_{m}\)). If we regard the motion of the same type as one cluster, the average distance between all sample points reflects the cluster size. To better differentiate different motion types, we aim to maximize the distance between two clusters and define the following measure:  
\[D=\frac{D_{I-J}}{D_{I}+D_{J}} \tag{12}\]  
where \(D_{I}\), \(D_{J}\) are the average distance of all samples in each cluster \(I\), \(J\), and \(D_{I-J}\) is the distance between the centroids of two clusters. We define the measures \(D_{\mathrm{C}}=\frac{D_{W-B}}{D_{W}+D_{R}}\), \(D_{P}=\frac{D_{P1-P2}}{D_{P1}+D_{P2}}\) for the content and ID embedding in Table 1.  
We here present two motion types (walking and running) and two persons. Experimental results show consistent findings for other motion types and more persons. The statistics in Table 1 reveal the following facts:  
* The use of absolute coordinates consistently outperforms the relative coordinates across most scenarios. The exceptions are only 5 out of 36 cases, in which the relative coordinates show minor advantages over the absolute ones. The superiority of absolute coordinates shows that the joint trajectories in the global world better reflect the motion content and personal style. We believe the movement of root joints is significant for differentiating walking and running as well as different subjects (people walk or run at different preferred speeds).
* The use of \(l_{2}\)-norm performs best in maximizing the distance in both content and ID embedding. This is shown in the maximal value of \(D_{C},D_{P}\). This shows that  
Figure 4: Variation of factors: (**a**) viewpoint variation; (**b**) scene variation; (**c**) appearance variation; (**d**) lighting variation; (**e**) extreme cases.  
such a distance metric follows the L2 principle. The potential reason for causing the under-performance of the Mahalanobis distance is a relatively small dataset.
* \(\mathbf{G}_{a}\) is optimal for both the content and ID embedding, as shown in the bold numbers. This confirms the usefulness of our auto-encoder in extracting the latent features for both the motion content and personal style.
## 4 Experiments, Results, and Discussion
### Experimental Setup  
We performed our experiments on Google Colaboratory using Python 3 as the programming language. In order to implement our approach, NumPy and other well-known libraries are employed, along with multi-dimensional arrays and matrices. Pandas offers powerful data-structure manipulation and analysis tools, which we also utilized. In addition, TensorFlow and Keras are used for ML and DL. Moreover, numerous supervised and unsupervised ML method implementations are available in Scikit-learn. Furthermore, we used SMOTE [31] to oversample minority classes in order to increase the overall model efficiency.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Machine Learning Used for Yield Prediction
#### 2.3.2 Performance Metrics  
The scikit-learn's GridSearchCV (version 1.6.1) was implemented towards optimizing hyperparameters for the ML models. Also, five-fold cross-validation was used, along with data shuffles to guarantee randomness [64; 65]. To assess overall performance, the following commonly used indicators were averaged after the cross-validation process was finished:  
\[MAE=\frac{\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert}{n}, \tag{16}\]  
\[MSE=\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n}, \tag{17}\]  
\[RMS=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n}}, \tag{18}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}}. \tag{19}\]  
In Equations (16)-(19), \(n\) signifies the total count of data points, \(\hat{y}_{i}\)  and \(y_{i}\)  correspond to the predicted and actual values of the \(i^{th}\) data point, respectively. Finally, \(\overline{y}\) denotes the mean of the actual values, while \(y_{min}\) and \(y_{max}\) stand for the minimum and maximum values of the actual data, respectively. \(R^{2}\) provides a measure of how well the model explains the variance in crop yield. The error-based metrics used in this study, namely \(MAE\), \(MSE\), and \(RMS\), capture the magnitude of prediction errors. Overall, by using both variance-based and error-based metrics, along with rigorous cross-validation and hyperparameter optimization, the study provides a reliable assessment of the crop yield prediction models.
## 2 Materials and Methods  
### Root Canal Instrumentation  
One hundred and forty-seven mandibular single-rooted premolars were withdrawn from a pool of extracted teeth. After radiographical examination of teeth in a mesiodistal and buccolingual direction, only teeth with a single straight canal were selected. Teeth with previous root canal treatment, calcifications, apical curvature, immature apices, or resorptions were discarded. A total of eighty-four teeth were included in the study. The specimens were stored in distilled water for no longer than 15 days before the experiment. The crowns were partially removed with a size 701 high-speed fissure bur (Komet Italia, Milano, Italy) under water irrigation to standardized root lengths at 18 mm. After access cavity preparation, a #10 K-file (Dentsply Maillefer, Ballaigues, Switzerland) was introduced into the canal until its tip emerged at the apical foramen, which was observed under a microscope at \(10\times\) magnification (OPMI PICO; Carl Zeiss Meditec Inc., Jena, Germany), and the WL was determined subtracting 1 mm from the measurement. To reproduce in vivo conditions, the apex was sealed with cyanoacrylate [25]. Each tooth was instrumented in a crown-down manner by using the ProTaper Gold rotary system (Dentsply Maillefer, Ballaigues, Switzerland) to size 40 at the WL. After each instrument change, the root canal was rinsed with 1 mL 5% of NaCl(Niclor, Ogna, Muggio, Italy) with a side vented needle (27-gauge) inserted on a syringe (Max-i-Probe, Dentsply Rinn, Elgin, IL, USA) introduced before the binding point but at a distance greater than 2 mm from the WL. The final rinse after instrumentation was performed with 1 mL 5% NaOCl and subsequently 1 mL 17% EDTA (Ogna), which were both left in place for 1 min, and with 5 mL of sterile water (Baxter, Roma, Italy) delivered with a 27-gauge side-vented needle (Max-i-Probe). Sterile paper points (ProTaper Gold paper points F4, Dentsply Maillefer, Ballaigues, Switzerland) were employed to dry the root canals. As a negative control, two teeth were used that did not undergo any further treatment. CH paste (Calxyl(r), OCO praparate, Dirmstein, Germany) was placed inside root canals with a size #30 lentulo spiral (Dentsply Maillefer,Ballaigues, Switzerland) and sterile paper points. The access cavities were filled with cotton and a temporary filling material (Cavit, 3M ESPE, Segefeld, Germany). Two radiographs of all teeth were taken in mesiodistal and bucco-lingual directions to verify the complete filling of all canals with CH paste. The lingual and buccal surfaces were marked with a longitudinal groove each, that was made with a diamond disk without penetrating the canal to facilitate the subsequent split in two halves (Figure 1A). Then, the specimens were kept at 37 \({}^{\circ}\)C at 100% humidity for a month in a Heracell VIOS 250i incubator (Thermo Scientific, Waltham, MA, USA).
## 4 Research Methodology
### Client-Side Setup  
The communication scenario is built around the wireless transmission of access control card data using Wi-Fi. We developed a simple IoT access control system using the Arduino UNO NodeMCU v3, the ESP8266 Wi-Fi module, and the MFRC552 RFID sensor module. The client-side setup and operation are as follows:  
First, we include the header files ESP8266 WiFi.h, MFRC522.h, and config.h. The first two libraries correspond to the Wi-Fi Module and the RFID Reader, while config.h defines the Wi-Fi SSID, password, default AES-128 encryption key, server IP, port, and the base and modulus values for the D-H key exchange.  
Next, the setup() function is used to initialize the MFRC522 module, connect to the specified Wi-Fi network, and establish communication with the server. The loop() function continuously monitors the MFRC522 sensor for access card UID readings, records the number of data packets transmitted, and encrypts the UID string using a custom AES-128 function aes128(). Then the encrypted data are sent to the server for processing.  
To ensure transmission accuracy, the number of packets recorded by the client are compared with the number received by the server. After a certain number of transmissions, the client initiates a key negotiation with the server using the D-H protocol to generate the next AES encryption key. The actual client-side setup is illustrated in Figure 5.  
Figure 4: SCA system configuration [16].
# NBTI and Power Reduction Using an Input Vector Control and Supply Voltage Assignment Method
## 3 Methodology
### Supply Voltage Assignment  
In order to guarantee the circuit's performance, a supply voltage assignment is applied together with IVC to reduce NBTI-induced delay degradation. The basic idea of SVA is shown in Figure 3.  
Figure 3: The basic principle of supply voltage assignment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gate** & **Objective Function** \\ \hline INV & \(P=P_{0}b+P_{1}a\) \\ NAND & \(P=(2P_{0}+P_{11}-P_{01}-P_{10})+(P_{10}-P_{01})a+(P_{01}-P_{00})b-(P_{00}+P_{1 1}-P_{01}-P_{10})c\) \\ NOR & \(P=(P_{01}+P_{10}-P_{11})+(P_{11}-P_{01})a+(P_{11}-P_{10})b-(P_{01}+P_{10}-P_{00 }-P_{11})c\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: ILP objective function for leakage power of logic gates.  
Figure 3 shows that, different from the guardband method, which selects a high voltage at the start of a circuit's lifetime, an SVA will increase the \(V_{dd}\) gradually to reduce the power dissipation and slow down the aging process. By estimating the delay degradation in different periods of a circuit's lifetime, the controller can determine whether the circuit has aged enough and the propagation delay has exceeded the allowed timing constrain, at which point the controller will increase \(V_{dd}\) to guarantee that no timing errors will occur. This procedure will repeat until the end of the circuit's lifetime.