esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Rebars' Detection Methodology and Results  
The accurate detection of reinforcement bars within concrete structures is essential for structural diagnostics and integrity assessment, particularly when employing non-destructive testing methods such as active microwave thermography. However, the interpretation of thermographic images obtained through microwave excitation is inherently complex. As shown in the previous section, the observed temperature patterns are not a direct representation of the reinforcement layout; instead, they result from intricate interactions between the electromagnetic field and the heterogeneous structure of reinforced concrete. As mentioned earlier, microwaves do not directly heat steel bars more efficiently than concrete. Rather, the presence of reinforcement alters the electromagnetic field distribution, often intensifying heating in the concrete above the bars. This effect, combined with diffraction and interference phenomena, leads to thermographic patterns that are highly dependent on the depth, orientation, and spatial configuration of the rebars. Consequently,  
Figure 11: Comparison of time–temperature characteristics for regions containing two selected reinforcement bars—rebar 4 (R4) and rebar 5 (R5)—based on experimental and numerical results for the configuration with a 45\({}^{\circ}\) inclination: (**a**) identification of the regions encompassing both rebars on a selected experimental thermogram, (**b**) corresponding indication of these regions on a representative numerical thermogram, (**c**) comparison of the time–temperature profiles for rebar 4 (blue: numerical result; red: experimental result), and (**d**) analogous comparison for rebar 5.  
the resulting thermal images are difficult to interpret using conventional image analysis techniques. To address this challenge, this study explores the use of convolutional neural networks (CNNs) for automated rebar localization in thermographic data. As a first step, we assess the network's ability to detect the position of reinforcement bars based on thermal patterns. A distinctive aspect of our methodology is the use of a digital twin--a high-fidelity numerical model replicating the experimental setup--to generate synthetic training data. This approach enables the production of diverse and well-annotated datasets without the need for extensive physical experiments, facilitating efficient and scalable model training.  
The following section presents the proposed methodology for rebar detection, outlining the data preparation, network architecture, and training strategy, with a focus on leveraging simulation-based data for real-world applications.
# Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments
## 3 Resource Profiling and Performance Modeling
### Resource Profiling and Prediction Models
#### 3.2.1 Online Linear Regression (OLR)
\[\hat{\beta}_{0}^{w}=\overline{Y}_{w}-\hat{\beta}_{1}^{w}\overline{X}_{w}\]  
_where \(i=(1,2,\ldots,n-w+1)\)_  
\[\hat{\beta}_{1}^{w}=\frac{\sum_{j=i}^{w+i-1}(X_{j}-\overline{X}_{w})(Y_{j}- \overline{Y}_{w})}{\sum_{j=i}^{w+i-1}(X_{j}-\overline{X}_{w})^{2}} \tag{10}\]  
For example, suppose we have 100 days (here \(n=100\)) of SCOUT profiling information including the number of available CPU cores in each computing element. The Online Linear Regression (OLR) method calculates the regression coefficient parameter \(\beta_{0}\) and \(\beta_{1}\) using the most recent \(w\) days of profiled data. The length \(w\) can be 1 week, 10 days or so on depending on user preference for prediction. Therefore, the online linear regression model equation can be calculated as follows:  
\[\hat{Y}_{w+i}=\hat{\beta}_{0}^{w}+\hat{\beta}_{1}^{w}X_{w+i} \tag{11}\]  
Algorithm 1 describes the OLR approach in depth. Here, \(L\) is the collection of all computing elements in a VO, \(n\) denotes the number of datapoints stored in the database, \(w\) denotes the user's preferred window size (the size of the window can be adjusted), \(i\) refers \(n-w+1\), and \(D\) denotes all profiling data gathered by the SCOUT system. ```
Result: Return OLR Input: List of CEs \(L\), window length \(w\), \(i\) and Data (\(D\)) of each CE for Each CE in \(L\)do \(\bar{X}\) = Computed mean of \(CE_{l}\) from D \(\bar{Y}\) = Computed mean of \(CE_{l}\) from D endfor\(j\) = \(i\) to \(w\)+\(i\)-\(1\)do \(Beta\_Numerator\) + = \((x_{j}-\bar{X})\) (\(y_{j}-\bar{Y}\)) \(Beta\_Denominator\) + = \((x_{j}-\bar{X})^{2}\) end  Compute: \(\beta_{1}=Beta\_Numerator/Beta\_Denominator\)  Compute: \(\beta_{0}=\bar{Y}-\beta\_{1}^{*}\bar{X}\)  Calculate: OLR using Equation (11)
```  
**Algorithm 1**Online Linear Regression (OLR).
## 4 Performance Evaluation
### Simulation Results and Analysis
#### 4.2.2 End-to-End Delay  
Figure 13a illustrates the end-to-end delay concerning the number of nodes. MJTAR exhibits the lowest end-to-end delay compared to other protocols. This result is attributed to MJTAR's ability to leverage higher connectivity stemming from increased traffic density as the number of nodes rises. Figure 13b plots the end-to-end delay against vehicle travel speed. Two factors contribute to higher delays with increased vehicle speeds. First, higher vehicle speeds result in shorter durations spent at junctions. This phenomenon  
Figure 12: Comparison between proposed GSR, GyTAR, and MJTAR for packet delivery ratio; (**a**) number of vehicles (5 packets/s); (**b**) speed of vehicles (300 nodes and 5 packets/s); (**c**) packet size (300 nodes and 5 packets/s).  
occurs because elevated speeds heighten the likelihood of missing a Hello packet between neighboring vehicles at a junction, the data packet is buffered, and the junction selection mechanism is executed at the subsequent junction. Second, vehicle speeds exceeding the neighbor table update rate exacerbate delays. Specifically, when a vehicle forwards packets to a neighbor that may overtake it in the opposite direction, temporary looping can occur as the overtaking process unfolds. This temporary looping phenomenon contributes to increased delays.  
Figure 13c depicts the end-to-end delay for various packet sizes while nodes travel at 30 km/h. Interestingly, MJTAR demonstrates that as the packet size increases, the end-to-end delay does not substantially change despite the occurrence of packet collisions.
## 2 Materials and Methods
### Feature Variable Selection for Model Construction  
In the January 15 data, five points were removed due to the presence of NONE values in the spectral indicators \"CRSI\". Similarly, in the August 26 data, one point was removed for the same reason. For the PLSR model, a Pearson correlation analysis, as discussed in Section 2.5, was conducted on the remaining points. In the January 15 data, 18 features with a correlation greater than 0.4 with soil salinity were selected, while 30 features with a correlation greater than 0.4 were chosen in the August 26 data. Since the random forest model is composed of multiple decision trees, it has a strong resistance to noise. Additionally, the BP neural network (BPNN), as a deep learning model, can automatically learn and select features. Therefore, all variables were used for these two models.
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 1 Introduction
For example, in [25], there is a potential application of an IoT large-scale system that may benefit if it adopts the proposed circuit for RF energy harvesting. Additionally, other potential applications could include smart city [26], Internet of Vehicles [27; 28], and Industrial IoT [29; 30]. Rectennas are made to work at frequencies that are important for wireless communications and broadcast services. The fifth generation (5G) of cellular communications operates in Frequency Zone 1 (FR1), among others, at the n48, n77, and n78 bands (with a central frequency of 3.5 GHz, [31] ) and the n79 band (with a central frequency of 5 GHz, [32]). The FR1 band is defined as one of the frequency bands in which 5G technology functions [33]. The FR1 band was intended to define bands below 6 GHz, but with some additional spectrum allocations, it was extended to 7.125 GHz; hence, the FR1 zone frequencies begin from 410 MHz to 7.125 GHz. The FR2 range refers to frequencies up to 20 GHz [34; 35]. Figure 1: Complete RF energy harvesting system. In the literature, several papers present dual-band rectifiers. The main differences are in the design of the impedance-matching network and in the power conversion efficiency. In this work, we present a novel dual-band rectifier circuit for the 5G FR1 zone, and we provide a complete design framework for a low-cost RF-to-DC rectifier. The main contribution of this paper lies in the following. * We present a novel dual-band rectifier circuit for the 5G FR1 zone. * We discuss and provide a complete design framework for a low-cost RF-to-DC rectifier. * We propose a novel impedance-matching network that consists of two distinct branches, one for 3.5 GHz and the other for 5 GHz. Each branch consists of a T-shaped network with rectangular and radial stubs. * We design a circuit design that works at 2 sub-6 GHz 5G bands, (3.5 and 5 GHz) simultaneously. The proposed circuit achieves better power conversion efficiency than the others in the literature, as will be shown in the numerical results section. In addition to that, we have not been able to find a paper in the literature with a rectifier circuit that works at sub-6 GHz 5G bands, and only at 3.5 and 5 GHz simultaneously. This circuit can be part of an IoT node to harvest ambient RF energy, thus proving a green energy source. The IoT node may operate at a different frequency. The remainder of this work is structured as follows: Firstly, we present the materials and methods in Section 2. In Section 3 we present the design specifications. In Section 4, we present the complete design of the proposed circuit and the numerical results. Section 5 discusses the contributions of this work and its main results. Finally, Section 6 outlines the concluding remarks of our work.
## 3 Methodology  
### The Experimental Technique  
This research employed an experimental technique known as Latin Hypercube Sampling (LHS) to train and validate the data required for the BP neural network. The primary operational features of the LHS in this study consisted of two main steps: random sampling and probability distribution reconstruction. Random sampling involves selecting samples randomly from a multivariate parameter distribution to analyze a small yet representative set of samples. Probability distribution reconstruction is the process of creating a probability distribution from a limited number of samples, thereby yielding a representative dataset. This comprehensive experimental study comprised seven phases, ranging from data preparation to evaluating the performance of the models, as illustrated in Figure 4.
## 3 Interval Valued Type-2 Fuzzy TOPSIS Method (IVT2F-TOPSIS)
\(H_{p}(\widetilde{z_{i}^{j}})\) shows the membership function value of the \(a_{i(p+1)}^{j}\). \[M_{p}(\widetilde{z_{i}^{j}})=\frac{a_{ip}^{j}+a_{i(p+1)}^{j}}{2} \tag{7}\]  
\[S_{p}(\widetilde{z_{i}^{j}})=\sqrt[2]{\frac{1}{2}{\sum_{k-4}^{q+1}}\left(z_{ik }^{j}-\frac{1}{2}\sum_{k-q}^{q+1}\left(z_{ik}^{j}\right)\right)^{2}},\text{ where }p=1,\ldots,3 \tag{8}\]  
\[S_{p}(\widetilde{z_{i}^{j}})=\sqrt[2]{\frac{1}{4}{\sum_{k-1}^{4}}\left(z_{ik}^ {j}-\frac{1}{4}\sum_{k-1}^{4}\left(z_{ik}^{j}\right)\right)^{2}},\text{ where }p=4 \tag{9}\]\[\overline{Y_{w}^{*}}=\left(Rank(\widetilde{\widetilde{v}_{ij}})\right)_{m\times n} \tag{10}\]  
Step 6: Compute the negative ideal solution (NIS) for \(C^{-}=(z_{1}^{-},\,z_{2}^{-},\,\dots,\,z_{m}^{-})\) and the positive ideal solution (PIS) for \(C^{+}=(z_{1}^{+},\,z_{2}^{+},\,\dots,\,z_{m}^{+})\), respectively, as shown in Equations (11) and (12):  
\[z_{i}^{-}= \begin{cases}min\,\left\{\left\{Rank(z_{ij})\right\}\right\}\text{, if }c_{i}eC^{-}\\ max\,\left\{\left\{Rank(z_{ij})\right\}\right\}\text{, if }c_{i}eC^{+}\end{cases}  1\leq j\leq n. \tag{11}\]  
and  
\[z_{i}^{+}= \begin{cases}max\,\left\{\left\{Rank(z_{ij})\right\}\text{, if }c_{i}eC^{-}\right. 1\leq j\leq n\\ min\,\left\{\left\{Rank(z_{ij})\right\}\text{, if }c_{i}eC^{+}\end{cases}  1\leq j\leq n\right. \tag{12}\]  
where \(C^{+}\) and \(C^{-}\) represent the set of benefit and cost attributes, respectively. Step 7: Calculate the distances to NIS and PIS and find the degree of closeness \(DoC(x_{j})\) based on Equations (13)-(15), respectively. \[d^{-}(x_{j})=\sqrt{\sum_{i=1}^{m}\left(Rank(z_{ij})-z_{i}^{-}\right)^{2}}, \tag{13}\]  
\[d^{+}(x_{j})=\sqrt{\sum_{i=1}^{m}\left(Rank(z_{j})-z_{i}^{+}\right)^{2}}, \tag{14}\]  
\[DoC(x_{j})=\frac{d^{-}(x_{j})}{d^{+}(x_{j})+d^{-}(x_{j})} \tag{15}\]  
Step 8: Sort the rank order of \(DoC(x_{j})\) in a descending manner. The pseudo-code of the proposed decision support model is given in Table 2 as follows.
## 4 Technologies for Nanocellulose Production
### From lignocellulosic Biomass to Cellulose
#### 4.1.1 Physical Pretreatment of Lignocellulosic Biomass Ultrasound Pretreatment  
Ultrasound pretreatment, a green non-thermal physical method, has recently emerged as the pacesetter, and is widely implemented and studied by the scientific fraternity for LB pretreatment [10; 55]. Ultrasonic waves are longitudinal sound waves with frequencies above 20 MHz, which is higher than the hearing limit of a human. Essentially, ultrasound pretreatment is based on cavitation which breaks the \(\beta\)-O-4 and \(\alpha\)-O-4 linkages of lignin and separates lignin and polysaccharides leading to the formation of cavitation bubbles [56]. As these bubbles reach a threshold size, they violently collapse, increasing temperature and pressure by 2000-5000 K and 1800 atm, respectively, at a small temporal and spatial scale, which is called shockwaves/hotspots. These shockwaves bring the alterations in the morphology of the biomass by generating turbulence, and cause shear and shock [56; 57]. Ultrasonication results in both physical and chemical changes in the biomass, including morphological disruptions, reduction in the degree of polymerization and an increase in specific surface area. The cavitation also induces strong microturbulence, which results in the micromixing in the medium and eliminates mass transfer restrictions [30; 58]. Ultrasound treatment has been used alone or in combination with alkali, ionic liquids or deep eutectic solvents for the pretreatment of biomass [9; 54; 55; 59]. In a previous study, pretreatment of brewer's spent grain using ultrasound was executed for releasing fermentable sugars [59]. The pretreatment process was optimized using response surface technology that resulted in 325 \(\pm\) 6 mg/g biomass of reducing sugar yield, which was 2.1-fold more than the untreated brewer's spent grain. Ultrasound-assisted NaOH pretreatment of groundnut shells, coconut coir and pistachio shells enhanced the delignification to 71.1, 89.5 and 78.9% from 41.8, 45.9 and 38% for groundnut shells, coconut coir and pistachio shells, respectively [59]. Ultrasound technology has given a variety of bioconversion processes the much-needed boost. Ultrasonication has also been employed for NC production. The subsequent homogenization and high-intensity ultrasonication processes were employed for the preparation of CNF from cellulose extracted from rice straw. The CNFs had good thermostability with a diameter between 6 and 20 nm, and an aspect ratio of about 177 when ultrasonication was used at a 500 W power for 40 min [60].
## Materials and Methods
### Adaptive Control Algorithm
#### 2.2.1 Fuzzy-Based Algorithm Design
Then, a crossover algorithm is initiated once the selection process is completed. The background operator in genetic algorithms is mutation. The parameters of the GA directly depend on the number of variables. In the recommendations provided by the authors of the GA, the number of generations should be equal to \(N_{\text{var}}\) * \(20+10\), and the population number is calculated as \(N_{\text{var}}\) * \(50\) by default. After manual tuning of the membership function parameters based on heuristic knowledge of the process, 10 membership function parameters were selected for further optimization with the GA, thus simulating 210 generations with 500 populations. The large number of generations did not bring significant changes, so it was decided to use 50 generations for testing. The mutation and crossover parameters were left at their default values. In Table 1, the parameters used for the GA are presented. This GA was used to search for the optimal parameters of the fuzzy controller membership functions. Each individual represents a simulation of the biotechnological process where the PI controller parameters are adapted using the generated fuzzy model. The structure of the controller is \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Generations** & **Individuals in One Generation** & **Mutation Probability** & **Crossover Probability** \\ \hline
50 & 500 & 0.1 & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Genetic algorithm parameters. Figure 2: Fuzzy model membership functions: model input membership functions (**a**), model output \(T_{i}\) membership functions (**b**), model output \(K_{c}\) membership functions (**c**). constant and does not change during the simulation of the process. The GA searches for the optimal fuzzy model structure by defining the membership function parameters. In the applied genetic algorithm, the only termination criterion was the predefined number of generations. This allowed avoiding early convergence and local optima. After 50 iterations the GA was stopped as shown in Figure 3.  
The developed PI controller with fuzzy-based adaptation was implemented in the model simulation and used for the performance evaluation of the system. The \(K_{c}\) and \(T_{i}\) dependencies are presented in Figure 4.
## 2 Materials and Methods  
### Patient Selection  
All patients undergoing a cartilage repair procedure were prospectively followed, and clinical outcomes were collected in an institutional database, approved by the Hospital Ethics Committee and Internal Review Board of the Rizzoli Orthopedic Institute, Bologna, Italy (prot. gen. n. 39667). Informed consent of all patients was obtained. Surgical indications for these procedures were as follows: focal cartilage knee defects graded III-IV according to International Cartilage Regeneration & Joint Preservation Society (ICRS) classification in patients complaining of clinical symptoms (pain, swelling, locking, and giving way) after failure of conservative treatments. Contra-indications for surgery were untreated limb misalignment or knee instability, diffuse arthritis or bipolar (\"kissing\") lesions, and other general medical conditions such as infectious, tumor, metabolic, rheumatic, and inflammatory diseases. A definitive diagnosis of chondral lesion and sizing was performed during the arthroscopic procedure. Patients who presented with an anterior cruciate (ACL) lesion at the time of surgery underwent the combined ACL reconstruction in the same surgical session with cartilage harvesting.  
A statistician, blinded to the treatment outcome, extracted two homogeneous groups of patients from the general database treated with mosaicplasty or MACT. These groups were comparable both in terms of patient demographics and cartilage lesion features. In detail, male or female patients were included, aged between 18 and 40 years, with body mass index (BMI) between 18.5 and 30, treated for lesions involving only femoral condyles (excluding trochlea and tibial plateau), with defect size between 1.0 and 3.0 cm\({}^{2}\), traumatic or degenerative etiology (excluding osteochondritis dissecans lesions), and evaluated up to a minimum 10-year follow-up. According to these criteria, 43 patients were included in the study: 20 mosaicplasty and 23 MACT. The specific characteristics of the two groups are reported in Table 1. Statistical analysis confirmed that the two treatment groups were homogeneous with regard to gender, age, BMI, activity level, defect size and location, etiology, combined and previous surgery.
## 4 Time-Domain Characteristics  
A significant aspect of UWB systems is the computation of the dispersion that happens when the antenna radiates and receives a pulse signal. The pulse-based UWB systems that utilize delivering very tiny pulses in time have a unique set of design requirements for antennas. As a result, more research and examination into the time domain behavior of a UWB antenna is needed. The pulse distortion and fidelity factor of the emitted pulse are the most important time-domain characteristics. They determine the amount of pulse distortion caused by the antenna.  
In the proposed design, a normalized 5 th order Gaussian derivative is set as an input signal. The pulse duration is 370 ps, as shown in Figure 5a, and it will have the wideband spectrum in the frequency domain as demonstrated in Figure 5b. The \(n\)th Gaussian pulse is represented in the time domain by Equation (2) where \(H_{n}(t)\) is the \(n\)th Hermit polynomial, and its fifth-order polynomial is given in Equation (3) [25]:  
\[G(t)=Ae^{-\frac{t^{2}}{2\sigma^{2}}} \tag{1}\]  
\[G^{n}(t)=\frac{d^{n}G}{dt^{n}}=(-1)^{n}\frac{1}{(\sqrt{2}\sigma)^{2}}\cdot H_{ n}\bigg{(}\frac{t}{\sqrt{2}\sigma}\bigg{)}\cdot G(t) \tag{2}\]  
\[H_{5}(t)=32t^{5}-160t^{3}+120t \tag{3}\]  
The Fourier transform of the input signal is depicted in Figure 5c and resembles the reflection coefficient generated in the frequency domain and described in Figure 3b. The output normalized signal is shown in Figure 5d, and it has a pulse width of 3.74 ns, in which the signal distortion is around 50% in the first 1 ps; then, it reduces rapidly after that.  
Figure 4: (**a**) Proposed antenna in the anechoic chamber. Simulated and measured antenna co-polarized radiation pattern for E- and H-planes at (**b**) 2.7 GHz, (**c**) 4.05 GHz, (**d**) 5.05 GHz, (**e**) 6.04 GHz, (**f**) 7.15 GHz, (**g**) 7.90 GHz, (**h**) 11.55 GHz, (**i**) 13.11 GHz, and (**j**) 3.1 GHz.  
Figure 5: _Cont._
# Potassium Recovery from Potassium Solution and Seawater Using Different Adsorbents
## 3 Results and Discussion
### K Sorption by Different Adsorbents
#### 3.1.2 Effect of pH on K Sorption  
In this study, pH conditions were changed to find out the effect of pH on K sorption with three adsorbents. Figure 3 compares the maximum K sorption capacity of the three adsorbents used in this study derived from the Langmuir isotherm model.  
The maximum K sorption capacity was greater under acidic condition for the zeolite-based adsorbents, while it was greater under neutral condition for the manganese nodule (Figure 3a). On the other hand, the affinity of the binding sites increased with increasing pH for the natural zeolite, while the opposite was observed with the treated zeolite and the manganese nodule (Figure 3b).  
Adsorption process can be affected by several factors including temperature, pH, and contact time [3]. As pH increases, the sorbent surface becomes more negatively charged resulting in more metal uptake [16]. Previous study reported a positive linear relationship between the ammonium ion (NH\({}_{4}\)+) sorption on natural zeolite and the pH condition [17]. The NH\({}_{4}\)+ sorption capacity increased by about 30% as pH increased from 4 to 7. Moreover, the sorption of lead (Pb) and copper (Cu) on zeolite increased with increasing pH from 3 to 5 [16; 18]. However, the opposite was observed with K in our study (Figure 3a). The K sorption capacity decreased with increasing pH from 4.57 to 6.99 (Figure 3a). The presence of other ions such as sodium (Na+) ion and lithium ion (Li+) can compete with K+ ions for sorption sites. A recent study reported that, under the coexistence of Li+, K+, and Na+ ions, EAB zeolite showed higher selectivity towards sorption of Na+ and K+ ions over Li+ ions, and the sorption of K+ ions was preferred over Na+ ions [19]. In this study, when pH was adjusted to neutral pH conditions, phosphate buffers (NaH\({}_{2}\)PO\({}_{4}\)-Na\({}_{2}\)HPO\({}_{4}\)) were used, and the Na+ ion added with the buffers may compete with K+ ion in K sorption.