esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Platforms and Devices Materials and Methods
### Reliability Verification  
To ensure the reliability of the proposed experimental techniques, specific experimental conditions are selected. Initially, a measuring cylinder and a thermometer are placed in an empty tank. The jet switch is then turned on, and the volume and temperature of the jet in the measuring cylinder are measured after 1 min. This process is repeated three times to obtain accurate results. The findings from these experiments are presented in Table 2. The difference between Number 1 and Number 2 lies in the difference in the initial jet velocity. The jet velocity used for testing in Number 1 is 0.1 m/s, and the jet velocity in Number 2 is 0.3 m/s, but the initial jet temperature in testing Number 1 and 2 is the same. The test is repeated three times at the same speed and temperature. The purpose of the test is to verify the stability of the jet generator. The speed and temperature are selected according to the typical working conditions.  
In terms of the error in jet velocity and temperature, both are within acceptable limits, indicating good stability of the thermal jet generator.  
The reliability of the thermally-stratified water tank is primarily demonstrated through its minimal change of jet velocity and temperature over time. Figure 2 illustrates the variation in depth and temperature difference of the thermally-stratified water tank across different working times.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Volume (L)** & **Velocity (m/s)** & **Error (\%)** & **Temperature (\({}^{\circ}\)C)** & **Error (\%)** \\ \hline \multirow{3}{*}{1} & 0.173 & 0.102 & 2.0\% & 35.1 & 0.3\% \\  & 0.166 & 0.098 & 2.0\% & 35.3 & 0.9\% \\  & 0.171 & 0.101 & 1.0\% & 34.9 & 0.3\% \\ \hline \multirow{3}{*}{2} & 0.517 & 0.305 & 1.7\% & 35.3 & 0.9\% \\  & 0.523 & 0.308 & 2.7\% & 35.0 & 0.0\% \\ \cline{1-1}  & 0.511 & 0.301 & 0.3\% & 35.1 & 0.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal jet Generator Experimental Results.  
Figure 2: Temperature stratification change curve.  
According to the change curve, it is evident that the temperature of the upper water body remains relatively stable over time, while the temperature of the lower water body fluctuates within a range of 1 \({}^{\circ}\)C. As a result, the temperature water tank meets the experimental requirements in terms of maintainability, and its impact on the temperature field of the thermal jet water surface can be considered negligible.
# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 5 Results  
### Sl-Mlg  
The overall performances of the three GPT methodologies (Zero-Shot, RAG, and RAG ICL) and two BERT configurations (standard and hyperparameter-tuned) in SL-MLG classification were compared (Table 4). The evaluation focused on accuracy and total runtime to assess the models' ability to prioritize a single most relevant category from multi-label ground truth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MODEL** & **TUNING** & **TRAINING** & **INFERENCE** & **ACC.** \\ \hline \hline \multicolumn{2}{c}{**RAG ICL**} & & & 1 m 14 s & 79.07\% \\ \multicolumn{2}{c}{} & & 1 m 51 s & 86.05\% \\ \multicolumn{2}{c}{} & & 3 m 12 s & **90.70\%** \\ \multicolumn{2}{c}{} & & 3 m 29 s & 9.30\% \\ \multicolumn{2}{c}{} & & 3 m 14 s & **90.70\%** \\ \multicolumn{2}{c}{} & & 1 m 3 s & 86.05\% \\ \multicolumn{2}{c}{} & & 1 m 8 s & 79.07\% \\ \hline \hline \multicolumn{2}{c}{**RAG**} & & & 1 m 9 s & 58.14\% \\ \multicolumn{2}{c}{} & & 1 m 21 s & 69.77\% \\ \multicolumn{2}{c}{} & & 1 m 44 s & 86.05\% \\ \multicolumn{2}{c}{} & & 1 m & 44.19\% \\ \multicolumn{2}{c}{} & & 1 m 45 s & 81.40\% \\ \multicolumn{2}{c}{} & & 1 m 49 s & 65.12\% \\ \multicolumn{2}{c}{} & & 1 m & 62.79\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: SL-MLG results of BERT and GPT models. Total runtime is split into three columns: TUNING, TRAINING, and INFERENCE. Only BERT models include values for TUNING and TRAINING due to the need for hyperparameter tuning and supervised training. GPT models rely solely on prompt-based inference, resulting in blank entries for TUNING and TRAINING columns.  
The GPT models exhibited high accuracy overall. Under the RAG ICL setting, the Llama3.3-70b and Llama3.1-70b models achieved the highest performance, both reaching 90.70% accuracy. In comparison, Gemma2-27b and Llama3.1-7b followed closely with 86.05% accuracy under RAG ICL. The RAG methodology without category definitions also yielded competitive results, with Llama3.3-70b achieving 86.05% accuracy, demonstrating that retrieval-based approaches can enhance classification performance.  
Zero-Shot configurations offered the best computational efficiency, with runtimes as low as 9 s per inference, but at the cost of lower accuracy, peaking at 76.74% for Llama3.3-70b. This suggests that while Zero-Shot prompting enables rapid inference, its lack of contextual grounding affects classification precision.  
BERT models performed competitively, though with lower accuracy than GPT models. The hyperparameter-tuned bert-large model achieved the highest accuracy of all the BERT configurations at 83.72%, slightly outperforming the standard bert-large version (74.42%). However, the tuned model required significantly longer runtimes (1243 m 48 s vs. 28 m 31 s), indicating that while hyperparameter optimization yielded accuracy gains in this specific SL-MLG setting, this also came with a significant time increase.
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 1 Introduction
[13] proposed a hybrid FSO/RF system that manipulates adaptive switching techniques to form IM/DD and coherent heterodyne detection; finally, the expression of outage probability under various atmospheric conditions was obtained and was used as a criterion to compare single FSO systems with hybrid systems. Ref. [14] proposed a link-switching mechanism based on ML in a hybrid FSO/RF system, the system utilized ML to predict the link margin and achieve link prediction based on weather conditions. As far as we know, the issue of ML-based modulation switching and link threshold for hybrid links considering weather data has not been addressed. This work is based on link thresholds and uses ML to select modulation for communication links based on current atmospheric conditions. The rest of this paper is organized as follows: Section 2 examines the proposed a hybrid FSO/RF model of an ML-based switching system and discusses the different atmospheric effects. Section 3 discusses the ML model in determining link availability, deriving expressions for its spectral efficiency, link budget (LB), and bit error rate (BER). Section 4 describes the simulation results, and useful concluding remarks are drawn in Section 5.
## 4 Strain Measurement and Calculation of Wings
### Virtual Strain Bridge
#### 4.2.1 Arrangements of Virtual Strain Bridges  
The arrangement of strain bridges should ensure accurate and reliable measurement of the structure's strain. This requires selecting appropriate placement positions and orientations.  
The upper and lower spar caps of the wing beam primarily withstand the bending moment generated by the characteristics of wing's structural force. Bending moment strain gauges No. 1 and No. 4 were positioned along the length of the spar caps, while strain gauges No. 2 and No. 3 were oriented perpendicular to strain gauges No. 1 and No. 4. The ribs and web of the wing beam, along with the wing skin, primarily experienced shear forces generated by the wing. Each shear strain gauge comprised four interconnected 90  
Figure 5: Schematic wing diagram of the FEM and bridge arrangements: (**a**) schematic diagram of the wing FEM; (**b**) arrangement of bending moment bridge; (**c**) arrangement of the shear bridge; and (**d**) arrangement of the torque bridge.  
strain gauges. The arrangement of shear strain gauges is illustrated in Figure 5c. The wing box primarily experienced torsional forces generated by the wing. Each torsion strain gauge comprised four interconnected 90\({}^{\circ}\) strain gauges. The arrangement of torsion strain gauges is illustrated in Figure 5d.
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 3 Graph Construction and Dynamic Graph Neural Network
### Dynamic Graph Neural Network
#### 3.2.2 Multi-Head Graph Attention Network  
Multi-head attention facilitates the capture of neighborhood features across multiple subspaces, thereby enhancing the model's expressive capacity. For a GATv2 Conv layer, assume the input dimension is \(d_{in}\) and the output dimension is \(d_{out}\). For node \(i\) and its neighbor \(N(i)\), a linear transformation is applied as follows:  
\[h_{i}=Wx_{proj,i} \tag{14}\]  
Here, \(W\in\mathbb{R}^{d_{attn}\times d_{in}}\). The attention score \(e_{ij}\) is defined as follows:  
\[e_{ij}=a^{T}\delta\big{(}h_{i}+h_{j}\big{)} \tag{15}\]  
Here, \(j\in N(i)\), \(a\in\mathbb{R}^{d_{attn}}\) represent the learnable parameter, while \(\delta\) denotes the LeakyReLU activation function; \(h_{i}\) and \(h_{j}\) are the projection features of node \(i\) and its neighbor \(j\). Subsequently, Softmax normalization is applied:  
\[\alpha_{i,j}=\frac{\exp\big{(}e_{ij}\big{)}}{\sum_{k\in N(i)}\exp(e_{ik})} \tag{16}\]  
In the formula, \(\alpha_{i,j}\) represents the normalized attention coefficient of node \(j\) with respect to \(i\). Finally, neighborhood aggregation is performed:  
\[x^{\prime}_{i}=ELU\left(\sum_{j\in N(i)}\alpha_{\partial ij}h_{j}\right) \tag{17}\]  
In the formula, \(x^{\prime}_{i}\) is the new feature representation of node \(i\) after aggregating neighborhood information; \(ELU\) represents nonlinear activation, enhancing expressive ability. When employing multiple attention heads H, each head independently learns its own set of weight parameters \(W^{(r)}\) and \(a^{(r)}\) and extracts partial features. If a concatenation strategy is adopted--where the outputs of all attention heads are concatenated--the final output dimensionality is \(H\times d_{out}\); alternatively, if an aggregation strategy is utilized, where a weighted summation is performed, all attention head outputs are integrated into a \(d_{out}\) dimensional representation.
## 3 Graph Generation Methods of Articulated Manipulators
### Graph Generation Methods for Manipulators with 2-DOF Non-Fractionated Intermediate Kinematic Chains  
The graphs for manipulators of this type can be generated by connecting a fixed base and an end-effector each to a vertex of a graph of 2-DOF non-fractionated intermediate KCs which can be obtained from the databases [1,6]. If the two vertices connected to the base and end-effector constitute a symmetrical or asymmetrical vertex pair, it is necessary to select only usable vertex pairs that satisfy the structural conditions 1 and 2 of articulated manipulators described above.  
For example, consider graph \(X\) shown in Figure 5a. Among the pairs in \(SP_{X}\) and \(AP_{X}\), the vertices of \(SP_{X1}\) and \(SP_{X2}\) are in the same 1-DOF independent loop, and the vertices of \(AP_{X1}\), \(AP_{X2}\), and \(AP_{X7}\) are directly connected. Therefore, these pairs cannot be used to generate articulated manipulator graphs, but the other pairs can.  
Figure 4: Articulated manipulators and their graphs: (**a**) A KC of a 4-DOF palletizing robot; (**b**) graph representation of (**a**); (**c**) KC of a 6-DOF heavy payload robot; (**d**) graph representation of (**c**).  
Suppose \(USP_{X}\) and \(UAP_{X}\) denote the sets of usable symmetrical and asymmetrical vertex pairs which can be used to generate the graphs of articulated manipulators among \(SP_{X}\) and \(AP_{X}\), respectively. Then, for graph \(X\) in Figure 5a, \(USP_{X}\) is {(3-1, 3-2)}, \(UAP_{X}\) is {(1-1, 3-2), (1-1, 4-1), (2-1, 3-1), (2-1, 4-1)}, and the numbers of pairs in \(USP_{X}\) and \(UAP_{X}\) are \(\mid USP_{X}\mid\) = 1 and \(\mid UAP_{X}\mid\) = 4, as shown in Figure 5b,c, respectively. By connecting the fixed base and the end-effector to two vertices of usable pairs in an intermediate graph, feasible manipulator graphs can be obtained.  
However, in order to generate only non-isomorphic graphs, the methods should be determined depending on whether the usable pair is symmetrical or not. If the two vertices are symmetrical, the generated graphs will be the same even if the fixed base and the end-effector exchange connection positions. Conversely, if the pair is asymmetrical, a new graph will be created by exchanging connection positions. Therefore, it is possible to create one manipulator graph for every pair of \(USP_{X}\) and two manipulator graphs for every pair of \(UAP_{X}\). Consequently, the total number of articulated manipulator graphs with any non-fractionated graph \(X\) as the intermediate graph is given by  
\[N_{X}=1\times\mid USP_{X}\mid+2\times\mid UAP_{X}\mid. \tag{1}\]  
For example, the number of articulated manipulator graphs generated using graph \(X\) in Figure 5 as an intermediate graph is  
\[N=1\times 1+2\times 4=9. \tag{2}\]
# Analysis on the Isostatic Bipod Mounts for the HERA Mission LIDAR
## 1 Introduction
The isostatic mount presented by [PERSON] [3] had three different components: the flexure A, which is permanently bonded onto the mirror's boss; the flexure B, which is fastened to flexure A and is the support flexure; and shims that are placed between the flexures. In [5], an adjustable bipod flexure for a large aperture mirror is presented, formed by two flexure bars with tangential and radial blades. The connection between the flexure and the mirror is made by an invar connector since the invar's CTE matches with the material of the mirror, and it is glued to the mirror with epoxy adhesive. In this paper, the isostatic bipod mounts design of PALT is presented. It follows a similar approach to the flexure presented by [PERSON] [3; 4], which means that the apex of the triangle formed by the flexure should point to the center of mass of the mirror to minimize surface distortion [3; 4]. Each bipod flexure has a symmetric combination of two tangential blades and the radial blade: tangential blades give tangential compliance, and radial blades give radial compliance to the mirror. Thus, when the mirror is accelerated in lateral directions or perpendicular to the mirror's optical axis, the tangential flexures compensate for this effect; however, radial expansion of the mirror due to thermal loads can be compensated for by the radial flexures [4]. The major differences from previous implementations lie in the mirror size and in the isostatic mount bipod mechanical design. This paper is structured as follows: Section 2 presents the previous research. Section 3 presents the PALT design. Section 4 explains the assembly and bonding procedures. Section 5 reveals the thermoelastic simulations and respective results. Finally, in Section 6, the conclusions are outlined.
## 3 Simulation Results
However, what is worth noting is that the shape of the zone affects the progress of changes in this distance. The relatively smoothest progress is obtained when the vehicle and obstacle safety zones are elliptical. Figure 9 shows an example of a steering wheel angle value obtained in a computer simulation, based on the presented model. The estimation of model coefficients is a complex issue. In order to correctly determine such coefficients, it is necessary to identify the coefficients based on the recorded waveforms for the actual accident situation. In this publication, the model coefficients were selected based on one run. They were initially selected by trial and error, taking into account the research experience of the authors. The main criterion was to obtain a similar course of the steering angle characteristics in relation to the actual course in a similar situation. The purpose of their presentation was to present the possibility of obtaining a turning characteristic similar to the characteristics obtained during previous real tests on a car track (Figure 9) [64] for a similar road situations. The values of the initially adopted parameters for the simulation are presented in Table 1. However, to use this model, it is necessary to perform a full identification of the parameters. It may turn out that the optimal parameters determined for such a situation may not coincide with those determined for a different, more complex situation. While in the case of reconstruction programs it is possible to imagine entering different parameters for different considered situations, it is not possible in assistant systems, when situations change dynamically. In the simulation, the parameters of the safety zone with an elliptical shape were used. Along with the penetration of an obstacle into the vehicle's driving area, the value of the steering wheel angle gradually increases. After reaching the appropriate inclination of the test vehicle, the stage of decreasing the value of this angle takes place. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \(W_{1}\) & 0.0001 \\ \(W_{2}\) & 0.00001 \\ \(W_{3}\) & 0.0005 \\ \(W_{4}\) & 0.002 \\ \(W_{5}\) & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of model used in simulation. Figure 9: Progress of the steering wheel angle value.
## 4 Discussion  
We studied the performance of healthy subjects with respect to their hamstring muscles during controlled, specific, open-chain motor tasks: hip extension with a fully extended knee in the supine position on a mat (Figure 2a,b) and knee flexion while seated on a table (Figure 2c,d). The first result of our research is that the isometrically deployed hamstring torque is more than two times greater in a hip extension (2.93 Nm/kg referred to the hip) than in a knee flexion (1.21 Nm/kg referred to the knee), both in open chain. This is due to the fact that the isometric effort in a hip extension involves the _gluteus maximus_ and other minor hip extension agonist muscles in addition to the hamstring [35]. The physiological cross sectional area (PCSA) involved in hip extension is therefore larger than that in knee flexion, when no agonist muscles assist the hamstring. The muscular force generation and the deployed torque depend upon several physiological, biomechanics, and neural factors, including PCSA, muscle length, pennation angle, moment arm, contraction speed, and the involved motor units [39]. The dependence of force and velocity of a muscle on its length has long been established [40; 41]. In our case, we devised hip extension and knee flexion movements in such a way as to maintain the same fiber length. The hamstring length-tension relationship is therefore the same in both motor tasks: hip extension with a fully extended knee (MIHE, Figure 2a) and knee flexion (MIKF, Figure 2c). The length of the bi-articulate hamstring muscle is the same during both motor tasks because the hip flexion angle in the supine position (80\({}^{\circ}\)) extends the muscle fibers in exactly the same way as in the sitting position (flexed hip to 90\({}^{\circ}\)) with a knee flexion angle of 10\({}^{\circ}\). Therefore, the augmented torque during MIHE with respect to MIKF can be attributed mainly to the augmented muscle PCSA [42] because the agonist muscles are also recruited during the hip extension, while they do not take part in knee flexion. The hamstring agonist muscles involved during hip extension are the gluteus maximus, adductor longus, and adductor magnus [35]. Referring to isometric hip extension with an extended knee (MIHE Figure 2a), we found that the weight-normalized maximum isometric torque does not differ between dominant and non-dominant limbs. In terms of normalized values, no gender-related difference in torque was found. With reference to Table 3, the normalized torque regression analysis shows no difference with gender (\(p\) = 0.590), limb dominancy (\(p\) = 0.266), and age (\(p\) = 0.890), while the
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & 5 th generation \\
6G & 6 th generation \\ ACs & Assisted communications \\ AFD, \(A_{\gamma}\) & Average fade duration \\ AWGN & Additive white Gaussian noise \\ BE & Burst error \\ BER, \(P_{BER}\) & Bit error rate \\ \(C_{\gamma}\) & Channel capacity \\ CBFSK & Coherent binary frequency shift keying \\ CBPSK & Coherent binary phase shift keying \\ CDF, \(F_{\gamma}\) & Cumulative distribution function \\ C-F & Closed-form \\ DBPSK & Differential binary phase shift keying \\ F-O & First-order \\ F-S \(\mathcal{F}\) & Fisher-Snedecor \(\mathcal{F}\) \\ FSO & Free-space optical \\ G & Gamma \\ G-G & Gamma-gamma \\ i.i.d & Independent and identically distributed \\ I-G & Inverse gamma \\ I-F & Integral-form \\ IM-DD & Intensity modulation-direct detection \\ LCR, \(N_{\gamma}\) & Level crossing rate \\ MGF, \(\mathcal{M}_{MGF}\) & Moment generating function \\ NBFSK & Non-coherent binary frequency shift keying \\ OOK & On-off keying \\ OP, \(P_{\gamma}\) & Outage probability \\ PDF, \(p_{\gamma}\) & Probability density function \\ RF & Radiofrequency \\ RIS & Re-configurable intelligent surface \\ RIS-D & Re-configurable intelligent surface to destination \\ RV & Random variable \\ SNR & Signal to noise ratio \\ SISO & Single-input single-output \\ SSI & Spherical scintillation index \\ S-O & Second-order \\ S-RIS & Source to re-configurable intelligent surface \\ TI fading & Turbulence-induced fading \\ VLC & Visible light communication \\ \end{tabular}
## 3 Results
### Sequence Alignment Analysis of DcTPSb1 from the D. chrysotoxum Genome  
The protein DcTPSb1 was subjected to a BLAST search in NCBI, which identified a complete alignment with KAH0460538.1 (IEQ34_011201) from the _Dendrobium chrysotoxum_ genome, thereby confirming their sequence identity. Subsequent BlastP analysis revealed that DcTPSb1 exhibited significant similarity to a terpene synthase, with the highest similarity of 91% observed between DcTPSb1 and Alpha-terpineol synthase of _Dendrobium catenatum_ (PKU83075.1). Additionally, similarities of 75%, 73%, and 68% were found between DcTPSb1 and the hypothetical proteins KFK09_013739 of _Dendrobium noble_ (KAI0507613.1), terpene synthase 10-like of _Phalanopsis equestris_ (XP_020590622.1), and terpenoid synthase of the _Oncidium hybrid cultivar_ (QIN90833.1), respectively. Based on the high sequence identity, four proteins were selected for multiple sequence alignment. The alignment revealed that all proteins exhibited two motifs rich in aspartic acid residues, DDxxD and NSE/DTE, located at the C-terminal. Notably, except for the hypothetical protein KAI0507613.1 from _Dendrobium noble_, all proteins contained an RRX8W (arginine-tryptophan motif) domain at the N-terminal. Previous research has highlighted the essential role of the DDxxD and NSE/DTE motifs in the binding of cofactors such as Mg\({}^{2+}\) or Mn\({}^{2+}\), which are critical for the cleavage of premyl diphosphate substrates, ultimately leading to the synthesis of monoterpenes [28; 29; 30]. Furthermore, the RRX8W domain has been associated with the cyclization process of monoterpene synthases [31], thereby reinforcing the classification of DcTPSb1 within the TPS-b subfamily, as illustrated in Figure 1.