esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Telerabilitation Program  
Home-based telerehabilitation was implemented as a comprehensive program incorporating all core elements of cardiac rehabilitation. All patients, along with their partners, took part in an educational program developed and conducted by the study team, which consisted of a physician, physiotherapist, nurse, and psychologist. Exercise training was designed following established guidelines for patients with HF [15]. The workload was tailored to each individual's tolerance based on data achieved in CPET, taking into account patients' perceived exertion according to the Borg scale and the target heart rate (HR). The training HR was calculated using the heart rate reserve (HRR) method, which involves taking a percentage of the difference between the maximum HR and resting HR and then adding this value to the resting HR. For this program, the target HR range was set at 40-70% of the HRR. Each training session comprised three phases: a warm-up period of 5-10 min that included breathing, light resistance, and strength training, a main exercise session lasting 15-45 min based on walking training, and a five-minute cool-down phase. For patients with implantable cardioverter defibrillators (ICDs) and CRT-D devices, the maximum training heart rate (HR) was set at 20 bpm below the defibrillator activation threshold to ensure safety. Patients trained five times weekly.
## 4 Simulation Experiments  
A circular path or a straight line was used in the simulation study with constant reference velocities, which is a simplification in comparison to the environment that TMRs encounter in real applications. The performance of the controller cannot be fully investigated by using such a reference trajectory because the controller does not output any signal after a certain point. For simulation purposes, an eight shape was given as the reference trajectory in this paper. The desired signals were presented as  
\[\begin{cases}x_{d}=10\sin(t/20),\\ y_{d}=10\sin(t/10),\\ \theta_{d}=actan2(\dot{y}_{d},\dot{x}_{d}),\\ v_{d}=\sqrt{x_{d}^{2}+y_{d}^{2}},\\ \omega_{d}=\frac{\ddot{y}_{d}\dot{x}_{d}-\ddot{x}_{d}\dot{y}_{d}}{x_{d}^{2}+y_ {d}^{2}},\\ t\in[0,120].\end{cases} \tag{42}\]  
The initial values of the TMR were given as \(\left[\begin{array}{cccc}x&y&\theta\end{array}\right]^{T}=\left[\begin{array} []{cccc}0&0&0\end{array}\right]^{T}\). The FTESO's design parameters were chosen as \(\mu_{1}=\epsilon_{1}=90\), \(\mu_{2}=\epsilon_{2}=270\), \(\mu_{3}=\epsilon_{3}=2700\), \(\mathrm{Y}=0.1\), \(\alpha_{1}=0.8\), \(\alpha_{2}=0.6\), \(\alpha_{3}=0.4\), \(\beta_{1}=1.2\),\(\beta_{2}=1.4\), \(\beta_{3}=1.6\). The initial states for the FTESO were selected as \(\hat{z}=\left[\begin{array}{cccc}0&0&0\end{array}\right]^{T}\), \(\left[\begin{array}{cccc}\hat{\rho}_{3}&\hat{\rho}_{4}\end{array}\right]^{T}= \left[\begin{array}{cccc}0&0&0\end{array}\right]^{T}\). The FTESO-based FXZNN model parameters were given as \(\gamma=150\), \(l_{1}=1.5\), \(l_{2}=2\), \(g_{1}=1\),\(g_{2}=0.1\), \(g_{3}=10\). The lumped bounded disturbance was given as \(d=[2+cos(t/10)+cos(t/3)\), \(1+2 sin(t/5)+cos(t),1]^{T}\). Some common noise forms are shown in Table 2[19]. The considered noise in this paper is given as \(n(t)=\left[\begin{array}{cccc}0.5\cos(2\pi t)+0.5&0.5 exp(-t)+0.5\end{array} \right]^{T}\). Simulation experiments were conducted to explore the performance of the proposed scheme under noise and noise-free conditions, respectively. The TMR parameters are listed in Table 3[40]. All the simulations were conducted using MATLAB R2020b/Simulink software, and the ode45 (DormandPrince) solver was used for the differential calculations with a relative tolerance value of 0.001.
# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
## 3 System Model and System Architecture
### Wireless Communication Module
MFSK signals can be represented as  
\[w(t)=e^{i(w_{c}t+2\pi f_{i}t)} \tag{9}\]  
where \(f_{i}\) represents the frequency of the modulation. The AM and FM spectra of single tone modulation are shown in Figures 3 and 4. Distributed sensors are equipped with more powerful data processing units, enabling them to process data independently and filter out valid information. Additionally, they are equipped with high-power transmission antennas that rely on WiFi networks to transfer collected industrial data to a central computer in the cloud through a hotspot device. To ensure the uninterrupted operation of the sensor network for an extended period without replacing batteries, it is not practical for the collector to rely solely on battery power. We must also employ advanced power technologies, such as solar panels and lithium batteries. The wireless communication module is capable of transmitting networked information and serves as the entry point for various smart terminals to access the Industrial Internet of Things. It plays a vital role in connecting the perception layer and network layer of the Industrial Internet of Things [36; 37]. The wireless communication module serves as the system terminal, belongs to the hardware link at the bottom, and is irreplaceable. We connect a high-power external antenna to the wireless communication module to efficiently transfer industrial data to cloud servers using the 5G network.
# An Intelligent Solution for Automatic Garment Measurement Using Image Recognition Technologies
## 4 Discussion  
For more extensive experimental purposes, three other convolutional neural network (CNN) models have been examined, namely MobileNetV2 [39], ResNet50 [40] and DeepPose [41] (see Figure 8). All models were trained with the same dataset and for the same time. Some models have employed pretrained weights while others used fixed, not trainable backbone part of neural network. Table 4 presents the experimental results including similarity metrics. Therefore, some widely accepted quantitative metrics are used in the study to measure the similarity between two images [42; 43]. Mean squared error (MSE) is commonly used to estimate the difference between two images by directly computing the variation in pixel values. The smaller value of MSE represents better similarity. Its value is defined as:  
\[\text{MSE}=\ell(x,y)=\text{sum}(L),\quad L=\{l_{1},\dots,l_{N}\}^{\top},\quad l _{n}=(x_{n}-y_{n})^{2}, \tag{2}\]  
where \(N\) is the batch size, \(x\) and \(y\) are tensors of arbitrary shapes with a total of \(n\) elements each and the reduction is the sum operation.  
From the results, we can notice that the MobileNetV2 fully trained model has provided better results (MSE loss = 0.009 and Dice = 0.985) compared to the fixed MobileNetV2 model using pretrained data. The worst results have been achieved using DeepPose with MSE loss = 0.039, Dice = 0.935, Dice loss = 0.065 and RMSE = 0.190.  
Clothing segmentation allows identifying the garment's location and distinguishing it from other objects in the photos. However the key points detection approach can be used not after the segmentation, but instead of segmentation, thus omitting one step and facilitating the calculation of dimensions [44]. Determining the coordinates of tens of points is an easier task than classifying all the pixels in a photo. This allows the use of a smaller artificial neural network model and an output layer with fewer neurons. A few instances of the results of predicted key points positions are provided in Figure 8.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **MSE Loss** & **Dice** & **Dice Loss** & **RMSE** \\ \hline MobileNetV2 (fixed) & 0.032 & 0.944 & 0.056 & 0.186 \\ ResNet50 (fixed) & 0.032 & 0.948 & 0.052 & 0.181 \\ MobileNetV2 & 0.009 & 0.985 & 0.015 & 0.095 \\ ResNet50 & 0.022 & 0.962 & 0.038 & 0.150 \\ DeepPose & 0.039 & 0.935 & 0.065 & 0.199 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of accuracy metrics.  
Figure 7: Examples of specific cases: (**a**) in the blazers dataset regarding to shoulders line and (**b**) in skirts dataset regarding to the bottom line.
## 4 Conclusions
Deconvolution of average control spectra (I) in the high wavenumber region (3600-2600 cm\({}^{-1}\)), (II) in the fingerprint region (1800-900 cm\({}^{-1}\)), (III) in the Amide I band region (1760-1580 cm\({}^{-1}\)) and (IV) in the Amide III band region (1320-1180 cm\({}^{-1}\)); for the analysis of the Amide I and Amide III peaks a further piece-wise baseline subtraction within the spectral range of interest was performed before deconvolution. Figure S2. Variations of secondary protein structure contributions to Amide I band (1740-1550 cm\({}^{-1}\)) with dose, for cells fixed immediately and 24 h, 48 h and 72 h after irradiation; the ratios between the secondary structure peak areas and the area of the entire Amide I peak are reported as Mean \(\pm\) SD. Asterisks indicate when a significant difference with respect to the corresponding control value occurred at \(p\leq 0.05\). Table S1. Average FTIR peaks position for control and samples treated with different doses fixed at different times after irradiation; in the first column peak assignments in accordance with Figure 2 are reported. The shifts with respect to the non-irradiated sample for every fixation time, in terms of units of wavenumber are indicated in brackets (red bold values stand for shifts greater than the spectral resolution of the instrument 4 cm\({}^{-1}\). Table S2. Amide I deconvolution results for control and irradiated sample fixed at different times after irradiation, with assignments in accordance with the data reported in the literature [58,59]. The ratios between the area of the different subcomponents and the area of the entire Amide III band are reported as a mean value of percentage (A%) \(\pm\) SD. Table S3. Amide III deconvolution results for control and irradiated sample fixed at different times after irradiation, with assignments in accordance with the data reported in the literature [39]. The ratios between the area of the different subcomponents and the area of the entire Amide III band are reported as a mean value of percentage (A%) \(\pm\) SD. Conceptualization, M.L. ([PERSON]) and V.C. ; methodology, G.P. ([PERSON]), M.L. ([PERSON]), M.P., F.P.C. and P.P. ; software, V.R. ; formal analysis, V.R. and M.P. ; investigation, M.P., V.R., M.L. ([PERSON]), F.P.C., P.P., L.M. and G.P. ([PERSON]); data curation, G.P. ([PERSON]), V.R. and M.P. ; writing--original draft preparation, M.L. ([PERSON]); writing--review and editing, M.L. ([PERSON]) and V.C. ; funding acquisition, V.C. and L.M. All authors have read and agreed to the published version of the manuscript. This work was funded by the INFN-funded project ETHICS (Pre-clinical Experimental and THeoretical studies to Improve treatment and protection by Charged particleS). Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.
## 3 Enabling Technologies for Smart and Resilient Grids
### Matrix Converter Applications in Hybrid Transformers  
Hybrid transformers enable AC voltage regulation in distribution grids. Traditionally, this regulation could be done with tap changers in conventional transformers [16], with poor dynamic response and a narrow adjustment range. matrix converters can empower traditional distribution transformers with AC voltage compensation with fast dynamic response in the mitigation of voltage sags and swells.  
The advantages of a 3 \(\times\) 3 direct matrix converter based dynamic voltage restorer (DVR) as well as a suited modulation for sag and swell compensation is presented in [17]. A DVR also without storage devices with an indirect 3 \(\times\) 3 matrix converter is proposed in [18], capable of compensating balanced and unbalanced voltage sags and swells. A hybrid transformer with a 3 \(\times\) 3 direct matrix converter that can work as a DVR and additionally control the power flow by means of phase shifting is proposed in [19; 20] providing both simulation and experimental results. A similar proposal for an active distribution transformer that is also able to perform the function of a DVR with the ability to mitigate sags and swells up to 20% together with power flow control is proposed in [21] where a sliding mode controller is used to guarantee the tracking of the input and output currents. The topology for this work is presented in Figure 4 serving as an example of the application of matrix converters in hybrid transformers. Finally a comprehensive comparison for DVRs using matrix converters is given in [22].  
To highlight the matrix converter capabilities in hybrid transformer applications, experimental results are shown in Figure 5 for a voltage sag compensation and local power factor compensation using the topology from Figure 4.  
The presented results demonstrate the application of the matrix converter in relevant future challenges of the electrical grid where both disturbance mitigation and active power quality contributions are of major importance.
## 3 Enhanced Water Quality Index
### Sub-Indices Aggregation  
Once the weights are assigned and the sub-index values are calculated, the EWQI is computed by aggregating the values using either geometric or arithmetic mean, logarithmic function or root square. The formula for the EWQI is given in Equation (3). Here, \(n\) is the number of the parameters selected which are mentioned in Section 3.1, \(q_{n}\) is the quality rating or sub-index of the nth parameter which is calculated by Equation (1), and \(W_{n}\) is the relative weight of the nth parameter calculated by Equation (2).  
\[EWQI=\frac{\sum_{i=1}^{n}q_{i}W_{i}}{\sum_{i=1}^{n}W_{i}} \tag{3}\]
###### Abstract  
For manufacturing components with thick plates, such as in the heavy equipment and shipbuilding industries, the gas metal arc welding (GMAW) process is applied. Among the components that apply the thick plate GMAW process, there are groove butt joints, which are fabricated through multi-pass welding. Various welding qualities are managed in multi-pass welding, and the root-pass weld is controlled to ensure complete joint penetration (CJP). Currently, the state of complete joint penetration during root-pass welding is managed visually, making it difficult to confirm the penetration condition in real time. Therefore, there is a need to predict the penetration condition in real time. In this study, we propose a convolutional neural network (CNN)-based prediction model that can classify penetration conditions using welding current and voltage data from the root pass of V-groove but joints. The root gap of the joints was varied between 1.0 and 2.0 mm, and the wire feed rate was adjusted. During welding, the current and voltage were measured. The welding current and voltage are transformed into a short-time Fourier transform (STFT) representation depicting the arc and wire extension lengths. The transformed dynamic resistance STFT information serves as the input variable for the CNN model. Preprocessing steps, including thresholding, are applied to optimize the input variables. The CNN architecture comprises three convolutional layers and two pooling layers. The model classifies penetration conditions as partial joint penetration (PJP), CJP, and burn-through, achieving a high accuracy of 97.8%. The proposed method facilitates the non-destructive evaluation of the root-pass welding quality without expensive monitoring equipment, such as vision cameras. It is expected to be immediately applied to the thick plate welding process using readily available welding data.  
CNN model; GMAW; prediction; STFT; penetration condition +
Footnote †: journal: Applied  
1 Flexible Manufacturing R&D Department, Korea Institute of Industrial Technology, Incheon 21999, Republic of Korea; [EMAIL_ADDRESS]  
2 Testing Team, Greencenter Testing Institute, The University of Suwon, Hwaseong 18323, Republic of Korea; [EMAIL_ADDRESS]
## 4 Layout and Post-Layout Experimental Results  
In this section, the post-layout experimental results are presented. First, the size and the layout of the proposed low voltage delay element for the cold start-up ring oscillator are presented in Section 4.1. Next, the post-layout simulation of the proposed low voltage delay element is shown in Section 4.2. Finally, the post-layout experimental results of the cold start-up ring oscillator based on the proposed low voltage delay element are illustrated in Section 4.3.  
Figure 4: Circuit-level of the proposed low voltage delay element and the corresponding schematic-level simulation results with high voltage (VDDH) = 100 mV: (**a**) proposed delay element; (**b**) voltage level shifter (VLS) reported in previous literature [25], (**c**) VTC under power supply voltage (VDD) = 20 mV; (**d**) DC gain under VDD = 20 mV; (**e**) simulated oscillation of 21-stage ring oscillator with the proposed delay element under VDD = 20 mV.
## 4 Results and Discussion  
### Sensitivity Analysis of Design Variables  
Figure 5 shows the sensitivity coefficients of the three target objectives for each variable. The results provided by the PCC and SRRC methods consistently assessed the positive and negative impacts of the sensitivity coefficients on the target objectives for the reference building. As expected and consistent with experience, the impacts of the design variables on the three objective functions differed. Increasing the thickness of external insulation and improving the external window glass material were negatively correlated with energy consumption but positively associated with life-cycle costs and carbon emissions. The external windows represented a much higher sensitivity to energy consumption and cost than the external insulation thickness and a lower sensitivity to carbon emissions. While external shading, such as overhangs, has been increasingly applied to address cooling energy demand, particularly in the context of climate change, their carbon emissions during production often outweighed their limited energy-saving potential. This trade-off underscores the importance of carefully evaluating shading design choices in building retrofitting projects, especially in severe cold climates where shading may play a less critical role in the overall energy performance.  
The sensitivity analysis results also indicate that the insulation thickness of the south and north walls had a significantly greater impact on energy performance than that of the east and west walls in the reference building, primarily because the latter orientations have comparatively smaller surface areas. Additionally, the roof insulation thickness was found to have the least impact in this case. This finding suggests that optimization strategies can prioritize insulation improvements for the larger orientations, such as the south and north walls in this case study, while simplifying considerations for the east and west walls. Additionally, using different external insulation materials had little impact on energy consumption but a significant impact on carbon emissions and costs, warranting careful analysis when applied.  
Figure 4: Comparison of monitored hourly data and simulated results.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Hypothetical Airtightness** & **0.0001** & **0.0002** & **0.0003** & **0.0004** & **0.0005** & **0.0006** & **0.0007** & **0.0008** & **0.0009** \\ \hline NMBE & \(-32.09\%\) & \(-21.44\%\) & \(-13.44\%\) & \(-7.22\%\) & \(-2.24\%\) & \(1.85\%\) & \(5.27\%\) & \(8.16\%\) & \(10.64\%\) \\ cv(RMSE) & 32.25\% & 21.73\% & 13.97\% & 8.28\% & 4.82\% & 4.87\% & 7.08\% & \(9.55\%\) & \(11.83\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calibration for the airtightness of the case study.
## 3 Wireless Communication Technologies
### Long-Range Communication Technologies
#### 3.2.2 Long-Term Evolution (LTE)  
LTE offers secure wireless connectivity, mobility, and a high data rate, which can considerably enhance control and safety beyond visual line-of-sight (LOS) use cases. LTE is optimized for IP with scalable bandwidths of 20 MHz, 15 MHz, 10 MHz, and less than 5 MHz. It supports both frequency division duplexing (FDD) (paired) and time division duplexing (TDD) (unpaired) spectrums. The optimum cell size is 5 km, even though it can attain a reasonable performance within 30 km and provide an acceptable performance up to 100 km. Not surprisingly, utilizing an established LTE network for UAVs have seen a surge of activities in the last three years. In [19], low altitude UAVs are connected through LTE. The work points out that the existing mobile LTE network can boost network performance in UAV-based disaster response scenarios. [PERSON] et al. [20] demonstrated the effectiveness of UAV-based real-time video streaming and surveillance over the 4G-LTE system. The study investigates the performance of the network in relation to the physical aspects of wireless propagation using Network Simulator-3. Similarly, in [21], the authors considered two scenarios in which UAVs either work as a base station transmitting in the downlink or as user equipment transmitting in the uplink using LTE network. The work spotlights that the present LTE network does require important modifications for a smooth integration of LTE-enabled UAVs. [PERSON] et al. [22] also investigated the performance of aerial radio connectivity in a rural LTE network. The results show that some practical and relatively low complex mitigation schemes of interference have good potential for deployment in a rural LTE network.
## Materials and Methods
### Sample Preparation  
Carbon-coated (Carbon@Fe\({}_{3}\)O\({}_{4}\)) nanoparticles were prepared using a solvothermal method reported by [PERSON] et al. [34]. Briefly, FeCl\({}_{3}\bullet\)6H\({}_{2}\)O (0.8 g) was dissolved in 60 mL of ethylene glycol with stirring. After a clear solution was obtained, urea (3.0 g) and D-glucose (0.36 g) were added, and the mixture was stirred at room temperature for 30 min. Subsequently, the solution was transferred to a Teflon-lined stainless-steel autoclave and solvothermally treated at 200 \({}^{\circ}\)C for 12 h. Notably, previous works suggested that solvothermal temperatures of 160 \({}^{\circ}\)C and above are required to obtain a single phase of magnetite phase (Fe\({}_{3}\)O\({}_{4}\)) [29; 38; 39], and the boiling point of ethylene glycol is elevated due to the elevated pressure in the reactor and the colligative property of solutions [40; 41; 42]. After cooling down to room temperature and washing sequentially with ultrapure water and ethanol (each three times), the resulting carbon-coated Fe\({}_{3}\)O\({}_{4}\) nanoparticles were magnetically separated from the solution using a neodymium magnet and dried at 60 \({}^{\circ}\)C in a vacuum oven (SHEL LAB, Shelton Manufacturing, Inc., Cornelius, Oregon) for 12 h. Uncoated Fe\({}_{3}\)O\({}_{4}\) nanoparticles were synthesized from a solvothermal treatment of a FeCl\({}_{3}\)/EG/urea mixture (without addition of D-glucose). Cysteine-functionalized (Cys-C@Fe\({}_{3}\)O\({}_{4}\)) particles were synthesized using a modified version of the protocol previously utilized in functionalization of iron oxide materials [43; 44]. In a typical synthesis, an aqueous suspension of C@Fe\({}_{3}\)O\({}_{4}\) nanoparticles (1.0 g in 100 mL) was ultrasonically treated for 30 min, and then mixed with an aqueous solution of L-cysteine (250-1000 mg dissolved in 100 mL). The pH of the suspension was adjusted to 5.8 using 0.1 M HCl or 0.1 M NaOH, followed by stirring for 12 h under an N\({}_{2}\) atmosphere at room temperature. The solid products were then separated using an external magnet, and cleaned by washing sequentially with water and ethanol (each three times). The particles were then vacuum dried at 60 \({}^{\circ}\)C for 12 h and were denoted as either 250 Cys-C@Fe\({}_{3}\)O\({}_{4}\), 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\), or 1000 Cys-C@Fe\({}_{3}\)O\({}_{4}\), with the number reflecting the weight of L-cysteine added in the preparation. Scheme S1 (Supporting Information) summarizes sample preparation methods used in this study.