esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The global expansion of 5G networks has intensified the demand for compact, high-performance antenna that operate across the sub-6 GHz 5G New Radio (NR) spectrum [1]. Unlike ultra-fast millimeter-wave (mmWave) frequencies that exhibit high path loss and a limited range, sub-6 GHz bands balance the data transfer rate with coverage [2]. The 5G NR Sub-6 GHz spectrum includes both low-band (below 1 GHz) and mid-band (3-6 GHz) frequencies, with the latter being particularly critical in terms of 5G deployment, given the balanced trade-off between capacity and the propagation distance [3].  
Among the various frequencies, the n77, n78, and n79 bands have emerged as the most widely adopted for mid-band 5G applications, offering gigabit-class data transfer rates and broad coverage [4]. Table 1 provides an overview of these key frequency bands, detailing their frequency ranges and typical applications. These bands serve as the backbone of global 5G connectivity, supporting mobile broadband and Internet of Things (IoT) applications, and other wireless services [5; 6]. In response to these demands, this study proposes a multi-port coupled antenna that enhances both efficiency and multiple-input multiple-output (MIMO) performance. The form factor remains compact. The antenna can be integrated within the metallic chassis of commercial smartphones.  
The design of such antenna systems is challenging given the limited space and the presence of metal housings that often trigger strong electromagnetic coupling among antenna elements. Such interactions degrade isolation, increase the envelope correlation coefficient (ECC), and negatively impact overall MIMO performance [4; 5; 6]. To address these issues, various antenna topologies such as distributed slot arrays, loop-type elements, and shared aperture configurations have been explored. Although some show promise, many suffer from narrow bandwidths, high ECC values, or low radiation efficiency when implemented into metal frame smartphones [7; 8].  
Moreover, in practice, smartphones often assign different radiation efficiency targets to each antenna port because of layout constraints and the need to avoid user hand blockage. Typical efficiency values range from \(-4.5\) to \(-10\) dB depending on the port location and the proximity thereof to other internal components [9; 10].  
In this paper, we introduce a compact multi-port antenna system for sub-6 GHz operation. This is based on a shared coupling structure integrated with the smartphone metal frame. The development strategy is two-staged. Initially, a four-port configuration is used to assess feasibility, followed by transition to a two-port design optimized in terms of isolation and a reduced ECC. Simulations and measurements confirm that the antenna exhibits wideband matching (voltage standing wave ratio [VSWR] \(\leq 3\)), a high radiation efficiency (\(>\)\(-6\) dB), isolation that exceeds \(-12\) dB, and an ECC below 0.2 across the n78 band, validating the applicability thereof for practical smartphone platforms.
# Additive Manufacturing of PH 13-8 Mo Family: A Review
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Material** & **Ref. (Data from)** & **Material Form** & \multicolumn{2}{c}{**Solution Treatment (ST)**} & \multicolumn{2}{c}{**Boilding Direction**} & \multicolumn{2}{c}{**Utilinate Tensile Strength (MPa)**} & \multicolumn{1}{c}{**Yield Strength**} & \multicolumn{1}{c}{**Elongation (\%)**} & \multicolumn{1}{c}{**Hardness**} \\  & **(Data from)** & \multicolumn{2}{c}{**Material Form**} & \multicolumn{2}{c}{**Aging (A)**} & & & & & & & \\ \hline PH 13-8 Mo & [27] & \multirow{2}{*}{Wrought} & ST: 927 \(\pm\) & A: 510 \(\pm\) & \multirow{2}{*}{**Cooling**} & ST: Cool to & & & & \\  & & 15\({}^{\circ}\)C & 6\({}^{\circ}\)C for 4 h & & below 60 \({}^{\circ}\)C & - & 1515 & 1410 & 10 & 45 HRC \\ \hline EOS Stainless Steel & [31] & Additive & ST: 850 \(\pm\) & A: 525 \(\pm\) & 10 \({}^{\circ}\)C & ST: Rapid & Vertical & 1740 & 1670 & 6 & \\  & Manufacturing & 10 \({}^{\circ}\)C for 30 min & 10 \({}^{\circ}\)C & 10 \({}^{\circ}\)C & 10 \({}^{\circ}\)C & & air cooling, & & & \\  & CX & & 30 min & for 2 h & A: Air cooling & & & & & \\ \hline Uddeholm AM Corraa\({}^{\text{\textregistered}}\) & [32] & Additive & ST: 850 \({}^{\circ}\)C & A: 525 \({}^{\circ}\)C & A: Cool in air & & & & & \\  & Manufacturing & & for 30 min & for 4 h & to room & - & 1700 & 1600 & 10 & 50 HRC \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Heat treatment procedures and resulting mechanical properties of PH 13-8 Mo family from ASTM A693 standard and manufacturers. MPHSSs are suitable candidates for AM due to good printability [38; 41]. Therefore, different powder manufacturers (EOS GmbH, Krailing, Germany and Uddeholms AB, Hagfors, Sweden) have recently developed MPHSS alloys (EOS Stainless Steel CX (EOS GmbH, Krailing, Germany) and Uddeholm AM Corrax(r) (Uddeholms AB, Hagfors, Sweden)) for the AM market that could be considered under the PH 13-8 Mo family due to their similarities in chemical composition [31; 32]. Their chemical compositions are shown in Table 1, and the suggested heat treatment regime (solution treatment and aging) for the highest hardness and strength established by the manufacturers, as well as the resulting mechanical properties of those, are shown in Table 2.  
This work aims to identify and review the existing literature on the AM of the PH 13-8 Mo family, with a focus on its microstructure and properties. As detailed below, both PBF and DED techniques have been employed to process these alloys. To date, most research has focused on PBF using a laser beam (PBF-LB) and DED using an electric arc (DED-Arc). These studies predominantly focus on understanding these alloys' microstructures and mechanical properties for the as-built and heat-treated conditions. However, only a few studies have addressed the use of DED with a laser beam and powder (DED-LB/p). The paper concludes by summarizing the findings and identifying potential research directions for further exploration of this alloy family.
# Diagnosing the Machine Set Rotor Using Its Relative Vibrations
## 3 Qualities and Symptoms of the Wear Margin of the Machine Set Rotor
### Symptoms of Misalignment of the Machine Set Rotor with a Crankshaft  
The machine set may include piston machines. The rotor shaft of a piston machine is a crankshaft, while a crank is part of the crankshaft. The crank has a specific elasticity and can be subject to deflection. The distance between the crank webs may vary depending on the dimensions of the crank, the value of radial force acting on the crankspin and the main journal eccentricity. The measure of distance change between the crank webs is crank deflection, defined as the difference \(\Delta a\) of the distance value \(a\) between two positions of the crank in one plane (Figure 6):  
* vertical plane \(\Delta a_{v}=a_{\text{TDC}}-a_{\text{BDC}}\) (TDC--top dead center, BDC--bottom dead center);
* horizontal plane \(\Delta a_{h}=a_{\text{SB}}-a_{\text{PS}}\) (SB--starboard side, PS--port side).  
The value of distance between crank webs changes as the function of the shaft rotation angle. Variations of the distance are approximately sinusoidal, with the average value equal to \(\Delta a/2\) (Figure 6). It can be demonstrated that there is a relationship between shaft alignment and axial vibrations of the shaft free end. For this purpose, performing a thought experiment, we should fix one main journal of each crank, starting from the flywheel end, and sum up the changes of the distances \(\Delta l=\Delta a\), accounting for the sign and angular displacement between the cranks. The effect of summing up \(\Sigma\Delta l\) is the value of axial displacements of the free end of the crankshaft with respect to the rotation angle [47]. The variations of axial displacement as the function of the rotation angle can be considered as a symptom of crankshaft alignment. The measures of this symptom are amplitude value and phase shift relative to TDC of the first crank and average value [48] (Figure 7). The signal of axial displacement is contained in the axial vibration signal of the shaft free end.  
Figure 6: Model of the crank of the unilaterally fixed crankshaft (with one crank) and displacement of unfixed (free) end of crank: \(\Delta a\)—crank deflection; \(\Delta l\)—crank (journal of crank) free end displacements.
# A Pipeline Approach to Context-Aware Handwritten Text Recognition
## 4 Experimental Results
### Comparisons with State-of-the-Art Methods  
To validate the effectiveness of the proposed model, a comparison is made with LSTM [27] and Visual Transformer (ViT) [28] models. LSTM was selected for benchmark comparison as it is the state-of-the-art sequential processing method in HTR. Moreover, ViT was investigated to demonstrate the superiority of the proposed ResNet-101T method over the sole Transformer model. For a fair comparison, the same setting is imposed in all the experiments, such as the dataset split ratio. ViT was trained with a total of 1600 steps and evaluated at every 200 steps. The LSTM model achieved a CER and WER of 11.55 and 26.64, and ViT had a CER and WER of 10.60 and 18.41, where the performance of both models was inferior to ResNet-101T, as presented in Table 5. In terms of speed comparison, the LSTM model used the least computational time for training while ViT took a much longer time to train. Note that although LSTM has a faster training speed due to its simpler architecture, its accuracy is much lower than the proposed ResNet-101T model. Figure 11 shows the training and validation loss of the models throughout training.
## 1 Introduction
### Climate Change Indicators: Glaciers
Conducting a comprehensive examination of the long-term trend in upper air temperature within the high-altitude regions and evaluating its interplay with climatic dynamics would facilitate the comprehension of the fluctuations observed in the spatial distribution of snow and glacier coverage across the HKH region (Table 2). \begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Glacier/Region** & **Location** & **Period** & **Retreat Pattern** \\ \hline
[44] & Qomolangma (Mount Everest) & Central Himalayas & 1960-2006 & 5.5 to 9.5 ma\({}^{-1}\) \\ \hline
[45] & Tuanjiefeng Peak, Qilian Mountains & China & 1966-2010 & 9.9 \(\pm\) 3.9\% deglaciation \\ \hline
[46] & Yehelong Glacier & China & 1966-2000 & 23.2\% deglaciation \\ \hline
[47] & Geladandong area & China & 1969-2000 & 1.70\% deglaciation \\ \hline
[48] & Ak-shirak Range, central Tien Shan plateau & China & 1977-2001 & 20\% deglaciation \\ \hline
[9] & 885 glaciers of Bhutan Himalaya & E. Himalaya & 1977-2010 & 23.3 \(\pm\) 0.9\% glacial area loss (\(-\)1980 and 2010) \\ \hline
[49] & Zemu Glacier, Sikkim & E. Himalaya & 1977–1984 & 27.7 ma\({}^{-1}\) \\ \hline
[50] & Siachen & E. Karakoram region (N. & 1929–1958 & Higher during the 20 th century than 1862–1909 \\ \hline
[51] & Imja Glacier & Nepal Himalayas & 2001–2006 & 370 m retreat \\ \hline
[52] & 57 glaciers of Ghunsa Khola & Nepal Himalayas & 1958–1992 & 50\% retreat, 38\% stationary, and 12\% advancing \\ \hline
[53] & Langtang sub-basin & Nepal Himalayas & 1977–2009 & 26\% deglaciation \\ \hline
[54] & Manaslu & Nepal Himalayas & 1970–2019 & 0.26 \(\pm\) 0.0001\% a\({}^{-1}\) \\ \hline
[55] & Batuart Glacier & Northwest Karakoram, Pakistan & 1992–2000 & 17 km\({}^{2}\) deglaciation \\ \hline
[56] & Bada Shigri, Himachal Pradesh & NW. Himalayas & 1890–1906 & 20.0 ma\({}^{-1}\) \\ \hline
[49] & Bada Shigri, Himachal Pradesh & NW. Himalayas & 1977–1995 & 36.1 ma\({}^{-1}\) \\ \hline
[49] & Chota Shigri, Himachal Pradesh & NW. Himalayas & 1986–1995 & 6.70 ma\({}^{-1}\) \\ \hline
[56] & Kolhani, Jammu and Kashmir & NW. Himalayas & 1857–1909 & 15.0 ma\({}^{-1}\) \\ \hline
[56] & Kolhani, Jammu and Kashmir & NW. Himalayas & 1912–1961 & 16.0 ma\({}^{-1}\) \\ \hline
[6] & Pir Parjal (12,243 glaciers), Greater Himalaya, Shamaswari, Zanaskar, Ladakh, Karakoram & NW. Himalayas & 2000–2012 & 0.35\(\pm\) 0.33 ma\({}^{-1}\) \\ \hline
[57] & Glaciers in China & Qinghai-Tibetan Plateau & 1960–2008 & 26.7\% deglaciation \\ \hline
[58] & Naimonanyi, Himalayas & Tibet & 1950–2004 & No net accum. of ice mass \\ \hline
[59] & Su-lo Mountain & Tibetan Plateau, China & 1966–1999 & Area loss of 34.7 km\({}^{2}\) \\ \hline
[60] & Kunlun shan (278 glaciers) & Tibetan Plateau, China & 1970–2001 & 0.40\% deglaciation \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the major glaciers and snow-covered regions in the Hindu Kush-Himalayan (HKH) region.
## 3 Experiments and Analysis
### Objective Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **UICM\(\uparrow\)** & **UISM\(\uparrow\)** & **UIConM\(\uparrow\)** & **UIQM\(\uparrow\)** \\ \hline UDCP & 6.251 & 5.422 & 0.046 & 1.942 \\ Fusion-based & 7.853 & 6.829 & 0.141 & 2.745 \\ UGAN & 6.522 & 6.623 & 0.202 & 2.863 \\ Water-Net & 5.408 & 6.850 & 0.254 & 3.083 \\ CWR & 4.911 & 6.401 & 0.233 & 2.862 \\ MSBDN-DFF & 4.371 & 5.715 & 0.181 & 2.457 \\ FFA-Net & 4.014 & 5.670 & 0.214 & 2.551 \\ Ours & 6.314 & 7.258 & 0.268 & 3.278 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average UICM, UISM, UIConM, and UIQM results for enhanced images. \(\uparrow\) represents that higher is better. Red represents the best, while blue represents the second best. the image fusion network Water-Net. In terms of GFLOPs, our method significantly outperforms the second-ranked multiscale feature fusion network, MSBDN-DFF. This observation highlights the considerably lower computational resource requirements of our approach, making it more suitable for deployment on AUVs with a constrained GPU performance and battery capacity. To further assess efficiency, we compared the time required to enhance a single image among the seven methods. The results were averaged using the test data. Our method achieves an impressive average time of 0.069 s for enhancing each underwater image, providing a significant advantage over image fusion and multi-scale feature fusion algorithms. Collectively, the multiple experiments conducted in this section prove that the method proposed in this paper effectively balances performance and real-time processing capabilities.
## 3 Proposed Framework  
The proposed cattle tracking and recovery system consists of a rumen sensor module, a WSN control unit, a Worldwide Interoperability for Microwave Access (WiMAX) gateway, WiMAX base stations, and a data center. The rumen sensor module acquires the real-time location data of the animal and sends the data to the wireless control unit. The WSN Control Unit (WCU) module, which is a multi-protocol WSN coordinator, controls the rumen sensor module and transfers the data to the data center for storage using WiMAX connectivity. The Database Management System (DBMS) will be used for storing the extracted data in a database. The system's database is based on TinyDB. TinyDB is a lightweight document oriented database, which facilitates data collection and storage from the sensors using power-efficient in-network processing algorithms. Below is the proposed framework for the livestock identification, tracking and recovery (Figure 1). It is a solution for livestock identification and tracking that meets the requirements of the people and security personnel in charge of livestock protection and recovery and which provides insight into anti-stock theft network deployments.  
The system architecture is a three-tiered mobile infrastructure. It has the following key components: The mobile embedded sensor (MES), the WSN control unit, WiMAX gateway, WiMAX base stations, and the application data center. The MES will be inserted in the target animal using the right applicator tool. The source of power for most sensors has always been limited. One suggestion on how to address this has been to use Wireless Power Transfer solution which provides a means for electronic devices to obtain power wirelessly without any physical connectivity to the WSN nodes attached to the animals [22]. The WSN control unit and WiMAX gateway will have an infinite energy source, high computation power, and large storage. The MES does not communicate with its neighbors always and it does not deliver data until contacted by WSN control unit.
# Semi-Analytical Planetary Landing Guidance with Constraint Equations Using Model Predictive Control
## 5 Simulation Results
### Controllability Set Analysis  
In this example, the constrained controllability set of the lander is analyzed and visually displayed. The aim of this simulation example is to verify the reachability determination method presented in Section 3.2 from a special point of view. The reachability determination process is reversely used to determine the controllable initial states and finally generate the complete controllability set. For visualization, we set the initial velocity and the initial mass of the lander fixed, aiming to generate a position-based constrained controllability set.  
The problem of whether an initial state is controllable is equivalent to the problem of whether the target point \([0,0,0,0,0,0]^{T}\) is reachable under our control mode in such an initial state. Therefore, we can obtain the controllable initial states through the reachability determination method. We first set the initial position of the lander \([x_{0},y_{0},z_{0}]^{T}\) to \([0,0,Z]^{T}\), where \(Z\) is an arbitrary positive real number. Rotate the positive direction of the X-axis, as shown in Figure 11. Then, the initial velocity in the new coordinate system is expressed as:  
\[v_{x0}^{\prime}=v_{x0}\cos\theta+v_{y0}\sin\theta,v_{y0}^{\prime}=v_{y0}\cos \theta-v_{x0}\sin\theta \tag{41}\]  
When the value of \(\theta\) is given, the relation between \(Z\) and \(x_{l}\), the detailed definition of which is presented in Section 3.2, can be obtained. When \(\theta\) increases from 0 to \(2\pi\), a closed surface is formed, the inner of which is exactly the constrained controllability set. We set the initial velocity \(v_{0}=[-50;-10;-75]\) m/s, and the other parameters are listed in Table 1. The position-based constrained controllability set is shown in Figure 12. When the lander  
Figure 10: Control profile comparison of Example 1.  
Figure 9: Trajectory comparison of Example 1.  
with the specific initial velocity is in the controllability domain, the target landing point can always be safely reached.  
Figure 11: Rotation of the positive direction of the X-axis.  
Figure 12: Controllability set of Example 2.  
Some details should be mentioned when solving the relation between \(x_{1}\) and \(Z\). After the value of \(\theta\) is given, the first step is to determine the maximum and minimum controllable \(Z\), denoted as \(Z_{\text{max}}\) and \(Z_{\text{min}}\), respectively. When the value of \(Z\) is relatively large, \(t_{1}\) is equal to \(t_{\text{f}}-m_{\text{fuel}}/\eta\); when \(Z\) is relatively small, \(t_{1}\) is equal to 0. Therefore, there exists a critical initial height \(Z_{\text{mid}}\) making \(t_{1}=t_{\text{f}}-m_{\text{fuel}}/\eta=0\). When the initial \(v_{x0}\) is fixed, the value of \(Z_{\text{mid}}\) is also fixed and can be obtained by solving Equations (28) and (29). Then, the dynamic equations are decoupled, and the values of \(t_{\text{f,max}}\), \(\mu_{\text{z,min}}\), \(\mu_{\text{y,min}}\), \(\mu_{\text{x,max}}\), and \(x_{1}\) can be solved in succession.
## 4 Experimental Validation
### Metrics and Implementation Details
#### 4.3.4 Training  
Stochastic gradient descent algorithm with 0.9 momentum and 0.0001 weight decay is used with a batch size of 4. In total, 100 training epochs are considered. We use 0.01 as the initial learning rate, and an exponential learning rate scheduler is implemented: \(lr=lr_{init}\cdot 0.97^{epoch}\). We save the last 10 models from a training session and use the mean prediction as the result. All models are trained with Pytorch [53] on Nvidia GeForce GTX 1080 Ti 11 Gb GPUs (Nvidia, Santa Clara, CA, USA).
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 2 Models and Methods
### Computational Models
#### 2.2.2 Temperature Computation
\begin{array}{c}B(\textbf{r},t)=B_{0}(\textbf{r}),\qquad T(\textbf{r},t) \leq 39\,^{\circ}\text{C}\\ B(\textbf{r},t)=B_{0}(\textbf{r})\{1+S_{B}(T(\textbf{r},t)-39.0)\},\text{ 39 }^{\circ}\text{C}\leq T(\textbf{r},t)\leq 44\,^{\circ}\text{C}\\ B(\textbf{r},t)=B_{0}(\textbf{r})\{1+5S_{B}\},\quad 44\,^{\circ}\text{C}\leq T( \textbf{r},t)\end{array}  \tag{8}\]  
where \(B_{0}(r)\) is based on the blood perfusion in each tissue and \(S_{B}\) (=0.8 \({}^{\circ}\)C\({}^{-1}\)) denotes a coefficient that determines the change in the blood perfusion characteristics over time. The thermal parameters of the rat and human models were taken from [25; 43], respectively. The heat transfer coefficient \(H\) was set to 0.5 W/(m\({}^{2}\)\({}^{\circ}\)C) between the skin and air, and 8.1 W/(m\({}^{2}\)\({}^{\circ}\)C) between lung and inner air in the rat model [7]. The heat transfer coefficient \(H\) was set to 5 W/(m\({}^{2}\)\({}^{\circ}\)C) between the skin and air [44], and 20 W/(m\({}^{2}\)\({}^{\circ}\)C) between the eye and air [45] in the human model. Sweating by the human was not considered, as temperature elevation is confined around the surface in most cases, which is not sufficient to induce sweating [29; 46]. Variability of different modeling of the temperature elevation can be found in [47]; typically, the blood perfusion rate is the dominant parameter affecting local temperature elevation.
## 4 Results and Discussion
### Comparing QR Code with Aruco Markers  
In the first application, QR codes with dimension of \(10\times 10\) cm are printed and installed in the environment at regular intervals. Each QR code stores information about the current location. For pre-processing, the authors used an open-source CV library called OpenCV that implement many algorithms for image processing and CV. The authors also used an open-source library called Zxing for detecting and identifying QR codes. When PVI use this application, it activates the camera to capture photos until a QR code is detected. The position details stored in the detected QR Code and the distance are given to the PVI as voice commands.  
The second application is the same as the first one, but it uses Aruco markers with the same dimension instead of QR codes. The authors also used an open-source library called Aruco library for detecting and identifying markers. After testing the two applications in different situations, the authors made a comparison to select which one is the best, as shown in Figure 10.  
Figure 9: The proposed simplified CNN architecture.  
After testing these two applications in different situations, the authors found that Aruco markers can be detected from distances up to 4 m while QR codes were limited to 2 m only. To detect Aruco markers from long and short distances, there is no need for the camera to be in their line of sight. For QR codes, they cannot be detected from farther than 2 m and whether the camera was in their line of sight was irrelevant. From these results, the authors have deduced that Aruco markers are better than QR codes, which lead to the verification H1. So, the authors formulate Thesis T1: Aruco markers work better than QR codes. As a result, the authors selected Aruco markers as tags for our prototype.