esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and formulations
### Forced Response of the Finite LR Plate with Elastic Boundary Conditions
When considering the damping characteristic of the springs, the force equilibrium equation of both kinds of resonators can be written as:  
\[m_{Ri}\tilde{w}_{Ri}+k_{Ri}(1+j\eta_{Ri})[w_{Ri}-w(x_{is},y_{t})]\ =\ 0,\ i\ =\ 1,\ 2, \tag{20}\]where \(\eta_{Ri}\) is the damping loss factor of the spring damper, and then the displacements of two kinds of resonators can be expressed by:  
\[w_{Rist}\ =\ \frac{k_{Ri}(1+j\eta_{Ri})}{k_{Ri}(1+j\eta_{Ri})-\omega^{2}m_{Ri}}w (x_{is},y_{t}),\ i\ =\ 1,\,2. \tag{21}\]  
Thus, \(F_{ist}\) in Equation (2) are given by:  
\[F_{ist}\ =\ -m_{Ri}\ddot{w}_{Rist}\ =\ Z_{Ri}(\omega)w(x_{is},y_{t}),\ i\ =\ 1,\,2, \tag{22}\]  
where \(Z_{Ri}(\omega)\ =\ \frac{\omega^{2}m_{Ri}k_{Ri}(1+j\eta_{Ri})}{k_{Ri}(1+j\eta_{Ri})- \omega^{2}m_{Ri}}\). The substitution of \(F_{ist}\delta(x-x_{is})\delta(y-y_{t})\) into Equation (17) gives:  
\[F_{ist}\delta(x-x_{is})\delta(y-y_{t})\ =\ \sum\limits_{m\ =\ 0}^{M}\sum\limits_{n \ =\ 0}^{N}F_{mnist}\cos(\lambda_{am}x)\cos(\lambda_{bn}y),\ i\ =\ 1,\,2, \tag{23}\]  
where \(F_{mnist}\ =\ (F_{ist}/L_{mn}L_{bn})\cos(\lambda_{am}x_{is})\cos(\lambda_{bn}y_{t})\). Substituting Equations (4), (9), (19), and (23) into Equation (1) derives equations written in a matrix form as:  
\[(\mathbf{Ra}+\mathbf{Sp})-\omega^{2}(\mathbf{Za}+\mathbf{Tp})-[\mathbf{U}(\omega)\mathbf{a}+\mathbf{V}( \omega)\mathbf{p}]\ =\ \mathbf{F}, \tag{24}\]  
where \(\mathbf{F}\ =\ \left[\cdots,\,F_{mm0}+\sum\limits_{s\ =\ 1}^{S}\sum\limits_{t\ =\ 1}^{T}(F_{mm1 st}+F_{mm2 st}),\cdots\right]^{T}_{(M+1)(N+1)\times 1}\). The coefficient matrixes \(\mathbf{R}\), \(\mathbf{S}\), \(\mathbf{Z}\), \(\mathbf{T}\), \(\mathbf{U}(\omega)\), and \(\mathbf{V}(\omega)\) are defined in Equations (A6)-(A10) in Appendix A. The substitution of Equation (16) makes Equation (24) be expressed as:  
\[\mathbf{\left[K-\omega^{2}M-W(\omega)\right]}\mathbf{a}\ =\ \mathbf{F}, \tag{25}\]  
where \(\mathbf{K}\ =\ \mathbf{R}+\mathbf{SH}^{-1}\mathbf{Q}\), \(\mathbf{M}\ =\ \mathbf{Z}+\mathbf{TH}^{-1}\mathbf{Q}\), and \(\mathbf{W}(\omega)\ =\ \mathbf{U}(\omega)+\mathbf{V}(\omega)\mathbf{H}^{-1}\mathbf{Q}\). For a given frequency \(\omega\), by solving Equation (25), the coefficient vector \(\mathbf{a}\) can be achieved, and then \(\mathbf{p}\) can be determined by Equation (16). The transverse displacement of the base plate can be further obtained by Equation (4).
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
### Location Aided Routing (LAR)  
The LAR protocol is utilized as a form of location-based and reactive routing in wireless ad hoc networks. It does this by making use of three distinct packets, namely, the route request, the route reply, and the route error, in order to send and receive information between nodes and maintain stable connections. LAR is an on-demand routing protocol that operates in a manner analogous to that of DSR (dynamic source routing). In contrast to DSR, the LAR protocol makes use of location data in order to define the boundaries of the \"request zone,\" which is the area in which it is possible to discover new routes. As a direct consequence of this, the route requests will be sent only by the nodes that are part of the request zone, as opposed to being broadcast to the entirety of the network. The originator, using data from the past, makes a prediction about a spherical region called the predicted zone, which is the location in which the target object is most likely to be found at this same moment. The traveler's average speed, in addition to the time and date associated with the traveler's previous locations, is used to calculate not only the location of the pinpoint but also the size of its surrounding area. Find the smallest rectangle that encompasses both the targeted zone and the source in order to identify the request zone. There have been many different attempts made to raise performance standards by altering the dimensions and layout of the request zone.  
A packet that contains the four corner coordinates is transmitted whenever a route discovery method is started up. Only those in this particular region will be able to listen to RREQ broadcasts. As a result, the node that is part of the request zone will proceed with the normal forwarding of the packet after it has received RREQ. However, if a node that is not within the request zone receives an RREQ, the group will be discarded by that node immediately.
## 4 Discussion
### Gait Analysis  
Under different speeds, the step frequency of one able-bodied subject with or without a prosthesis is measured as shown in Figure 10b. It is found that when the subject walks with a prosthesis the step frequency is higher. Because of the instability the prosthesis brings, the subject accelerates the frequency of adjusting the gait to increase the dynamic stability. However, when the speed is 4 km/h, the step frequencies of both are almost the same because the strides of both are near to upper limit.  
The strides of the prosthetic wearer's sound leg and prosthetic leg are measured as shown in Figure 10a. The figure shows that the stride of the sound leg is smaller than the prosthetic leg under the same speed. We guess that because the subject trusts his sound leg more, he shortens the stride of the sound leg to increase its support time. The difference between the two strides also may come from the mechanical design.  
From the above discussion, gait analysis can help make comments and compare the performance of a prosthesis. Moreover, walking speed is used to evaluate impaired mobility [19] and assess the physical condition [20] of amputees.
# Synthesis and Structural Confirmation of the Thiazole Alkaloids Derived from _Peganum harmala_ L.
## 1 Introduction  
_Peganum harmala_ L. (_Zygophyllaceae_), known as the Syrian rue, is an abundant wild-growing flowering plant. The seeds, roots, and aerial parts of the plant have been used as popular folk medicine in China and Middle-east Asia to treat several diseases. Many studies have been conducted using extracts from each part of the plant to evaluate the pharmacological properties and identify the bioactive compounds thereof. For instance, many alkaloids isolated from this plant, including \(\beta\)-carboline derivatives, exhibit diverse therapeutic effects, including anti-inflammatory, anticancer, and antibacterial activities [1; 2; 3].  
Recently, a Chinese research group isolated the natural thiazole alkaloids peganum A (**1**) and B (**2**) from the seeds of _Peganum harmala_ L. They also evaluated their antiproliferative activities against HL-60, PC-3, and SGC-7901 cancer cell lines, but **1** and **2** did not show significant activities. It was claimed that thiazole was a rarely observed moiety in natural products and was reported for the first time in the genus Peganum [4]. They proposed that 1 and 2 have \(2H\)-thiazole or 2-methylthiazol moieties connected with 4-hydroxy-3,5-dimethoxy by a methylene bridge, respectively (Figure 1). Unlike 2, it was difficult to distinguish **1** by two-dimensional NMR studies from its possible reigisomer **1**. Therefore, in the previous paper [4], the structure of peganum A was not clearly defined and was proposed as **1** via \({}^{13}\)C NMR chemical shift calculations by quantum chemical methods.  
Natural and unnatural thiazole compounds have been widely explored by synthetic organic and medicinal chemists because of their broad range of biological activities, including antioxidant, analgesic, antimicrobial, anticancer, antiallergic, antihypertensive, anti-inflammatory, and antipsychotic activities [5; 6; 7]. As part of our research interest in biologically active natural products, and because of their interesting structural features, we considered peganumals as attractive synthetic targets. To establish a synthetic procedure for **1** and **2**, which could be used for the structural confirmation of peganum A, we planned a Hantzsch thiazole synthesis, one of the most reliable routes to2,5-disubstituted thiazoles [8; 9], between \(\alpha\)-bromoaldehyde **3** and the corresponding thioamides. \(\alpha\)-Bromoaldehyde **3** was expected to be obtained from commercially available syringaldehyde **4** by aldol reaction, hydrogenation, and \(\alpha\)-bromination. In this paper, we describe the first total synthesis of peganumals and the structural confirmation of peganumal A.
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
###### Abstract  
Pneumonia remains a leading cause of respiratory morbidity and mortality, underscoring the need for rapid and accurate diagnosis to enable timely treatment and prevent complications. This study introduces PELM (Pneumonia Ensemble Learning Model), a novel deep learning framework for automated pneumonia detection using chest X-ray (CXR) images. The model integrates four high-performing architectures--InceptionV3, VGG16, ResNet50, and Vision Transformer (ViT)--via feature-level concatenation to exploit complementary feature representations. A curated, large-scale dataset comprising 50,000 PA-view CXR images was assembled from NIH ChestX-ray14, CheXpert, PadChest, and Kaggle CXR Pneumonia datasets, including both pneumonia and non-pneumonia cases. To ensure fair benchmarking, all models were trained and evaluated under identical preprocessing and hyperparameter settings. PELM achieved outstanding performance, with 96% accuracy, 99% precision, 91% recall, 95% F1-score, 91% specificity, and an AUC of 0.91--surpassing individual model baselines and previously published methods. Additionally, comparative experiments were conducted using tabular clinical data from over 10,000 patients, enabling a direct evaluation of image-based and structured-data-based classification pipelines. These results demonstrate that ensemble learning with hybrid architectures significantly enhances diagnostic accuracy and generalization. The proposed approach is computationally efficient, clinically scalable, and particularly well-suited for deployment in low-resource healthcare settings, where radiologist access may be limited. PELM represents a promising advancement toward reliable, interpretable, and accessible AI-assisted pneumonia screening in global clinical practice.  
pneumonia; deep learning; early diagnosis; clinical decision support; medical imaging; automated diagnosis; ensemble learning
## 2 Materials and Methods
### Research Subject
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & \(m\) [kg] & \(SO\) [m\({}^{2}\)] & \(TSO\) [m\({}^{2}\)] & \(VO\) [m\({}^{3}\)] \\ \hline \multirow{3}{*}{\(L_{\text{abs},up}=50\) mm} & \(d_{gt}=3\) mm & 0.946 & 0.048 & 0.151 & 0.00226 \\ \cline{2-5}  & \(d_{gt}=4\) mm & 1.283 & 0.0496 & 0.156 & 0.00241 \\ \cline{2-5}  & \(d_{gt}=5\) mm & 1.632 & 0.0512 & 0.161 & 0.00257 \\ \hline \multirow{3}{*}{\(L_{\text{abs},up}=75\) mm} & \(d_{gt}=3\) mm & 1.361 & 0.068 & 0.214 & 0.00454 \\ \cline{2-5}  & \(d_{gt}=4\) mm & 1.836 & 0.0696 & 0.219 & 0.00475 \\ \cline{2-5}  & \(d_{gt}=5\) mm & 2.323 & 0.0712 & 0.224 & 0.00497 \\ \hline \multirow{3}{*}{\(L_{\text{abs},up}=100\) mm} & \(d_{gt}=3\) mm & 1.775 & 0.088 & 0.276 & 0.0076 \\ \cline{2-5}  & \(d_{gt}=4\) mm & 2.389 & 0.0896 & 0.281 & 0.00788 \\ \cline{2-5}  & \(d_{gt}=5\) mm & 3.014 & 0.0912 & 0.286 & 0.00816 \\ \hline \multirow{3}{*}{\(L_{\text{abs},up}=125\) mm} & \(d_{gt}=3\) mm & 2.19 & 0.108 & 0.339 & 0.01145 \\ \cline{2-5}  & \(d_{gt}=4\) mm & 2.942 & 0.1096 & 0.344 & 0.01179 \\ \cline{1-1} \cline{2-5}  & \(d_{gt}=5\) mm & 3.706 & 0.1112 & 0.349 & 0.01213 \\ \hline \end{tabular}
\end{table} 
Table 3: Geometric performances of the FAP in SGVTC construction, depending on the simulation scenario.
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
## 2 Background and Motivation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multirow{2}{*}{**WiFi (802.11)**} & \multirow{2}{*}{
\begin{tabular}{c} **DSRC** \\ **(802.11p)** \\ \end{tabular} } & \multirow{2}{*}{**LTE**} & \multirow{2}{*}{**LTE-Advanced**} & \multirow{2}{*}{**mmWave** **(802.11 ad)**} \\ \hline
**Maximum Range** & 100 m & 1 km & 100 km & 100 km & 10 m \\ \hline
**Maximum Bandwidth** & 20 MHz & 10 MHz &
\begin{tabular}{c} 1.4, 3, 5, 10, 15, \\ 20 MHz \\ \end{tabular} & 100 MHz & 2.16 GHz \\ \hline
**Connectivity** & Intermittent & Intermittent & Pervasive & Pervasive & Intermittent \\ \hline
**Capacity** & Average & Average & Very High & Very High &
\begin{tabular}{c} Extremely \\ High \\ \end{tabular} \\ \hline
**Frequency Band** & \begin{tabular}{c} 2.4 GHz, \\ 5.2 GHz \\ \end{tabular} & 5.9 GHz & \
\begin{tabular}{c} 700–2690 \\ MHz \\ \end{tabular} &
\begin{tabular}{c} 450 MHz–4.99 \\ GHz \\ \end{tabular} & 30–300 GHz \\ \hline
**Peak Data Rates** & 54 Mbps & 6–27 Mbps & 300 Mbps & 1 Gbps & 7 Gbps \\ \hline
**Support for Mobility** & Low & Moderate & \
\begin{tabular}{c} Very High \\ (350 km/h) \\ \end{tabular} &
\begin{tabular}{c} Very High \\ (350 km/h) \\ \end{tabular} & Low \\ \hline
**V2V Connectivity** & Ad hoc & Ad hoc & No & D2D & Ad hoc \\ \hline
**V2I Connectivity** & Yes & Yes & Yes & Yes \\ \hline
**Market Penetration** & Very High & Low & Very High &
\begin{tabular}{c} Potentially \\ High \\ \end{tabular} & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Potential wireless technological candidates for vehicular communication. Figure 2: A heterogeneous vehicular networking architecture.
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 3 Results
### Step 1: Regression Model Development  
In addition to the existing shape model (see Section 3.1), a non-linear 1D regression-based prediction is developed as well in this research. This implies that 3D scan data would no longer be needed in a commercial setup. This was again built upon the raw data of the WEAR/CAESAR database; however, this time, only the metadata and measurements data were used that were of higher quality and consumed less processing power to compile. Because of missing data, some subjects had to be excluded, but 4194 subjects (from the Netherlands, Spain, and Northern America) of the database remained (2083 male and 2111 female). The shape model needed both the metadata and the 3D scans to be of good quality.  
First, the dataset was split into male and female, and per set, the three input parameters (age, stature, and weight) as well as the desired output measurements (as recorded in the WEAR database) were gathered. Of these parameters, log and squared values were calculated. Then, a multiple regression analysis revealed the most optimal predictive formulae to predict the known measurement, see Table 1. The regression model was free to leave out non relevant parameters (i.e., none of the base values, nor the log or squared values were required to be used in the resulting formulae). This analysis gave a list of coefficients to multiply the individual measurements with. Doing so for all of them, and adding them together, provides the most probable prediction within these given input parameters.  
Figure 3: Schematic of the dataflow within the research.  
In comparison to the shape model, this regression method focuses on a set number of specific measurements that need to be predicted, rather than the full 100,000 points that are defined in the STL output of the shape model. Reducing the complexity by this much allows less linear models and a more flexible fitting to the complex variation of body sizes and shapes. In the further scope of this research, most focus will be on the garment specific measurements such as chest circumference and hip circumference. However, these regression models can be calculated for any of the measurements that are present in the WEAR database. Nevertheless, the resulting model would need to be analyzed in error to verify whether the input parameters could accurately predict the desired output measurement.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 5 Outlook on Key Technologies for Lunar Constellation Navigation and Orbit Determination
### Multi-Source Ranging Fusion Technology  
The high-precision orbit determination of the lunar navigation constellation is a prerequisite for providing users with high-precision navigation and positioning services. However, the existing 100 m level orbit determination accuracy in the near-lunar space cannot fulfill the meter-level orbit determination of the lunar navigation constellation itself in the future. Satellite-to-ground laser ranging and inter-satellite ranging are important means to improve orbit determination accuracy. These two methods have been used in precise orbit determination of near-Earth space navigation satellites. Near-lunar space and near-Earth space have many different characteristics, and there are huge differences in the geometric configuration of orbits. However, there are currently few studies on satellite-to-ground laser ranging and inter-satellite ranging and orbit determination in near-lunar space.  
Satellite-to-ground laser ranging in near-lunar space is difficult, but once achieved, extremely high ranging accuracy can be achieved. At present, navigation systems such as GPS and BDS are actively considering using inter-satellite laser ranging to obtain greater inter-satellite information to further improve the accuracy of orbit determination and the accuracy of autonomous constellation navigation. The improvement of orbit determination accuracy using satellite-to-ground lasers and inter-satellite ranging has been fully demonstrated in near-Earth space. The world's major space powers are conducting demonstrationson lunar navigation systems. How to use satellite-ground lasers and inter-satellite ranging to improve near-lunar space orbit determination accuracy is a development trend and key core technology in subsequent lunar navigation constellation and orbit determination.
## 4 Results  
### Comparison of Canopy Cover and LAI Estimates  
Figure 2 shows the comparisons of canopy cover and LAI estimates between the ALS-based and DAP-based methods across different species and forest types. Overall, canopy cover estimates using ALS-based and DAP-based methods showed superior correlations with a coefficient of determination (R\({}^{2}\)) of 0.80 with R\({}^{2}\) ranging from 0.64 to 0.89 for different species. Accordingly, the LAI estimates using these two methods were also highly correlated with an R\({}^{2}\) of 0.76 with the lowest R\({}^{2}\) being 0.54 for the mixed broad-leaved forest and the highest R\({}^{2}\) being 0.87 for the mixed coniferous and broad-leaved forest (Table 2). Despite the high correlations, it can be noted that almost all DAP-based estimates of canopy cover and LAI are larger than ALS-based estimates. The differences in canopy cover estimates between the two methods are not consistent across the range of canopy cover levels. Particularly, the differences are especially large in the case of low canopy cover levels. Due to the gap probability theory in the LAI estimation, the differences in LAI estimates between the two methods are large in the case of high LAI levels corresponding to the low canopy cover levels. From the perspective of forest type, there is no obvious difference in correlation between the two methods among different tree species and forest types. DAP-based canopy cover estimation tends to be closer to 1, especially for _Eucalyptus robusta [PERSON]_ and _Acacia confusa Merr._ when canopies are dense. Correspondingly, the DAP-based LAI estimates even approach 7 which represents very dense canopies in tropical forests.  
Figure 2: Comparison of canopy cover (**a**) and leaf area index (LAI) (**b**) between DAP-based and ALS-based estimation methods across different forest types.
# Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation
## 2 Wide-Area and High-Resolution System Design
### Photoelectric Sphere and Swing Imaging Design
\[m(x,y)=\sqrt{\left(L(x+1,y)-L(x-1,y)\right)^{2}+\left(L(x,y+1)-L(x,y-1)\right) ^{2}} \tag{10}\]  
\[\theta(x,y)=tan^{-1}(L(x,y+1)-L(x,y-1))/\left(L(x+1,y)-L(x-1,y)\right) \tag{11}\]  
\(m(x,y)\): where represents the gradient value and \(\theta(x,y)\) represents the gradient direction. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Position Number** & **Rotation Angle (\({}^{\circ}\))** & **Horizontal Angle Coverage (\({}^{\circ}\))** \\ \hline
1 & \(-\)14.0 & \(-\)16.65\(\sim\)\(-\)11.35 \\
2 & \(-\)10.5 & \(-\)13.15\(\sim\)\(-\)7.85 \\
3 & \(-\)7.0 & \(-\)9.65\(\sim\)\(-\)4.35 \\
4 & \(-\)3.5 & \(-\)6.15\(\sim\)\(-\)0.85 \\
5 & 0 & \(-\)2.65\(\sim\)\(-\)2.65 \\
6 & 3.5 & 0.85\(-\)6.15 \\
7 & 7.0 & 4.35\(\sim\)9.65 \\
8 & 10.5 & 7.85\(-\)13.15 \\
9 & 14.0 & 11.35\(-\)16.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Photoelectric sphere rotation angle.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 2 Architecture for CR-Based Multi-Gateway Multi-Beam HTS Systems  
To satisfy the ever-growing capacity requirement within the limited spectrum, we introduce a CR-based HTS communication architecture, as depicted in Figure 1, which is comprised of a geosynchronous satellite for fixed satellite service (FSS) or high-density FSS (HDFS), multi-gatewaysconnected with each other through fiber/wireless communication links, multiple active users, and multiple terrestrial base stations (BSs) for fixed service (FS). Here, each gateway serves a set of adjacent beams. In this way, the overall data traffic for terrestrial users can be transmitted independently by geographically separated gateways to the satellite through the feeder links [29]. Regarding spectrum sharing, a full frequency reuse pattern is employed among beams. Moreover, as shown in Figure 2, except for the dedicated 500 MHz (19.7-20.2 GHz) in the downlink and 500 MHz (29.5-30 GHz) in the uplink, HTS shares the rest of the frequency bands used for FSS in the downlink (17.3-19.7 GHz) and uplink (27.5-29.5 GHz) with other radio services, such as the terrestrial FS, small cells with millimeter waves (mmWaves), and terrestrial backhauling links.  
The main characteristics of this cognitive HTS system architecture are summarized as follows.