esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, we report on a free-hand motion capture study in which 32 participants 'traced' 16 melodic vocal phrases with their hands in the air in two experimental conditions. Melodic contours are often thought of as correlated with vertical movement (up and down) in time, and this was also our initial expectation. We did find an arch shape for most of the tracings, although this did not correspond directly to the melodic contours. Furthermore, representation of pitch in the vertical dimension was but one of a diverse range of movement strategies used to trace the melodies. Six different mapping strategies were observed, and these strategies have been quantified and statistically tested. The conclusion is that metaphorical representation is much more common than a 'graph-like' rendering for such a melodic sound-tracing task. Other findings include a clear gender difference for some of the tracing strategies and an unexpected representation of melodies in terms of a small object for some of the Hindustani music examples. The data also show a tendency of participants moving within a shared'social box'.  
motion; melody; shape; sound-tracing; multi-modality  
voice, melodies going 'up' and 'down'. [PERSON] et al. suggest that this mapping might be more strongly present in Western cultures, while the use of other metaphors in other languages, such as thick and thin pitch, might explain pitch using other non-vertical one-dimensional mapping schemata [8]. The vertical metaphor, when tested with longer melodic lines, shows that we respond non-linearly to the vertical metaphors of static and dynamic pitch stimuli [9]. Research in music psychology has investigated both the richness of this vocabulary and its perceptual and metaphorical allusions [10]. However, the idea that the vertical dimension is the most important schema of melodic motion is very persistent [11]. Experimentally, pitch-height correspondences are often elicited by comparing two or three notes at a time. However, when stimuli become more complex, resembling real melodies, the persistence of pitch verticality is less clear. For'real' melodies, shape descriptions are often used, such as arches, curves and slides [12].  
In this paper, we investigate shape descriptions through a sound-tracing approach. This was done by asking people to listen to melodic excerpts and then move their body as if their movement was creating the sound. The aim is to answer the following research questions:  
1. How do people present melodic contour as shape?
2. How important is vertical movement in the representation of melodic contour in sound-tracing?
3. Are there any differences in sound-tracings between genders, age groups and levels of musical experience?
4. How can we understand the metaphors in sound-tracings and quantify them from the data obtained?  
The paper starts with an overview of related research, before the experiment and various analyses are presented and discussed.
## 2 Materials and Methods
### Hybrid A* Algorithm Improvement
#### 2.5.1 Node Extension Improvement  
The node extension of Hybrid A* is guided by two heuristic methods. One is the 2D heuristic method in the traditional A* algorithm, which considers the information of obstacles in the map, but ignores the physical attributes of the vehicle itself. Its node extension only contains coordinate information and direction information, as shown in Figure 9a. Another is the non-holonomic-without-obstacles heuristic method. This method considers the vehicle non-holonomic constraints and assumes that there are no obstacles in the map, assumes the goal state of \(\left(x_{g},y_{g},\theta_{g}\right)=(0,0,0)\), computes the shortest path to the goal from every point \((x,y,\theta)\). The schematic extension diagram is shown in Figure 9b. The second method uses the maximum of the two methods cost as the heuristic cost. Also, in order to save computing time, the algorithm uses Reed-Shepp (RS) curve to judge the current node and the goal node periodically. If there is no obstacle between the two points, an obstacle-free RS path will be generated directly between the two points. We set a few parameters, such as number of motion primitives (NMP), motion length (ML), and extension interval (EI), to control the node extension. NMP determines how many next nodes will be calculated based on the current node, by way of example, in Figure 9b, the NMP is 5, so there are five forward and five backward points. ML is the length of each extension determined by the minimum turning radius. EI determines how many loops to perform RS curve detector.  
When the Hybrid A* algorithm estimates the moving cost, it usually takes the grid value of the center point as the estimation cost. We improve the \(g\)-value estimation function of the Hybrid A* algorithm, add a new term to estimate the cost of tire contact with the ground when the vehicle is moving. The improved node extension diagram is shown in Figure 10, the yellow area is the coverage area of the tire when extending, and the occupancy grid value within the range is taken as the extension cost.
# Compact MIMO System Performances in Metallic Enclosures
## 4 Experimental Results and Discussions
### EVM Measurement-Moving Stirrer  
EVM measurements of two USRP channels were measured when the stirrer was continuously moving. The movement of the stirrer created a time-varying channel, which had a direct implication on the two-channel EVM. Figure 4 shows the QPSK constellation diagram of two USRP channels. It can be seen that the QPSK constellation diagrams are also time-varying; as the mode stirrer rotates, the received power value changes. The effects are visualized in the form of a constellation diagram with low and high gain values. This means that the BER and EVM are also time-varying. Figure 5 shows the time-varying EVM of two USRP channels for Tx gain values from 2 to 8 dB, respectively. It can be seen that USRP channel 1 EVM had high variations in the max and min values of EVM. USRP channel 2 was more stable. For the Tx gain value of 2 dB (see Figure 5a), the standard deviation of the channel 1 EVM was \(\sigma_{ch1}=1.93\) and the standard of the channel 2 EVM was \(\sigma_{ch2}=1.01\). Similarly, for the Tx gain of 8 dB (see Figure 5b), the standard deviation of channel 1 was \(\sigma_{ch1}=1.11\) and the standard deviation of channel 2 was \(\sigma_{ch2}=0.414\). The standard deviation of channel 1 was higher for all of the Tx gain values. Note that the possible cable faults were ruled out by exchanging the two USRP channels, which resulted in exactly similar performances. In other words, the USRP channel 1 EVM was better than the USRP channel 2 EVM. In this way, we precisely restricted our two-channel EVM and BER values to be functions of the propagation environment.  
Figure 4: Channel 1 and channel 2 QPSK constellation diagram at 5.6 GHz in a mode-stirrer metal enclosure. AGC gain = 60 dB, Rx gain = 0 dB, and Tx gain varied from 2 dB to 8 dB with the step of 2 dB.
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 4 Optimization of the Skeleton Cross-Section
### Optimal Design of the Skeleton Cross-Section
#### 4.2.2 Solution to the Constrained Variational Problem via Euler's Method  
A Lagrange multiplier \(\lambda(r)\) is introduced to transform the original constrained variational problem into a free variational problem [21; 22]. The resultant free variational problem is  
\[I^{*}[z(r),\lambda]=\int_{R_{1}}^{R_{3}}\bigg{\{}\sqrt{1+[z^{\prime}(r)]^{2}} +\frac{z_{0}}{R_{3}-R_{1}}+\lambda\frac{1}{r}z(r)-\lambda\frac{A}{R_{3}-R_{1} }\bigg{\}}\mathrm{d}r. \tag{28}\]  
Applying the Euler method to Equation (28) yields  
\[\frac{\lambda}{r}\;-\;\frac{\mathrm{d}}{\mathrm{d}r}\left(\frac{z^{\prime}(r) }{\sqrt{1+[z^{\prime}(r)]^{2}}}\right)=0. \tag{29}\]  
Integrate Equation (29) to obtain  
\[z^{\prime}(r)=\pm\Big{\{}\lambda^{-2}[\ln(C_{1}r)]^{-2}-1\Big{\}}^{-1/2}, \tag{30}\]  
where \(C_{1}\) is a constant. Based on the previous discussion of the nature of the curve \(z(r)\), Equation (30) can be rewritten as  
\[z^{\prime}(r)=\left\{ \begin{array}{lcl}&\Big{\{}\lambda^{-2}[\ln(C_{1}r)]^{- 2}-1\Big{\}}^{-1/2}&(R_{1}<r\leq R_{2})\\ &-\Big{\{}\lambda^{-2}[\ln(C_{1}r)]^{-2}-1\Big{\}}^{-1/2}&(R_{2}\leq r<R_{3}) \end{array} \right.. \tag{31}\]Integrating Equation (31) yields  
\[z(r)=\left\{ \begin{array}{ccc}\int_{R_{1}}^{r}\left\{\lambda^{-2}[\ln(C_{1}r)]^{ -2}-1\right\}^{-1/2}\mathrm{d}r+C_{2}&,&(R_{1}<r\leq R_{2})\\ -\int_{R_{2}}^{r}\left\{\lambda^{-2}[\ln(C_{1}r)]^{-2}-1\right\}^{-1/2}\mathrm{ d}r+C_{3}&,&(R_{2}\leq r\leq R_{3})\end{array} \right., \tag{32}\]  
where \(C_{2}\) and \(C_{3}\) are constants introduced during the integration. Obviously, from \(\lim\limits_{r\to R_{1}^{+}}z(r)=z_{0}\) and \(z(R_{2})=z_{\max}\), we conclude that \(C_{2}=z_{0}\) and \(C_{3}=z_{\max}\). Next, we sequentially determine \(C_{1}\), \(R_{2}\) satisfying \(z(R_{2})=z_{\max}\), the Lagrange multiplier \(\lambda\), \(z_{\max}\) and \(z_{0}\). Since \(z(R_{2})=z_{\max}\), the derivatives of \(z(r)\) satisfy \(\left.\frac{\mathrm{d}z}{\mathrm{d}r}\right|_{r=R_{2}^{+}}=\left.\frac{ \mathrm{d}z}{\mathrm{d}r}\right|_{r=R_{2}^{-}}=0\), which can be substituted into Equation (31) to conclude \(\lambda^{2}[\ln(C_{1}R_{2})]^{2}=0\), i.e., \(C_{1}R_{1}=1\). Therefore, we have  
\[C_{1}=\frac{1}{R_{2}}. \tag{33}\]  
Thus, Equation (32) can be recast as  
\[z(r)=\left\{ \begin{array}{ccc}z_{0}+\int_{R_{1}}^{r}\left\{\lambda^{-2} \biggl{[}\ln\frac{r}{R_{2}}\biggr{]}^{-2}-1\right\}^{-1/2}\mathrm{d}r&(R_{1}< r\leq R_{2})\\ z_{\max}-\int_{R_{2}}^{r}\left\{\lambda^{-2}\biggl{[}\ln\frac{r}{R_{2}} \biggr{]}^{-2}-1\right\}^{-1/2}\mathrm{d}r&(R_{2}\leq r\leq R_{3})\end{array}  \right.. \tag{34}\]  
When \(r=R_{1}\) and \(r=R_{3}\), the first-order derivatives of \(z(r)\) are \(\left.\frac{\mathrm{d}z}{\mathrm{d}r}\right|_{r=R_{1}}=+\infty\) and \(\left.\frac{\mathrm{d}z}{\mathrm{d}r}\right|_{r=R_{3}}=-\infty\), respectively. Then, it is straightforward to see from Equation (31) that  
\[\left\{ \begin{array}{ccc}\lambda^{2}[\ln(C_{1}R_{1})]^{2}=1\\ \lambda^{2}[\ln(C_{1}R_{3})]^{2}=1\end{array} \right.
## 5 Implementation and Results
### Maneuverability and Fuel Economy  
Different values for weighting factor \"\(\overline{\text{W}}\)\" in Equation (21) control the change of control input at each time step. The obtained results show as this value becomes closer to 0, a faster transient response can be obtained (Figure 4) with a larger amount of over-fueling (Figure 5). In other words, the smaller values for the weighting factor provide more violent control actions to the change of engine performance. In a close-up view, a slower transient response and a smaller amount of over-fueling are achieved by selecting larger values for the weighting factor (Figure 6). Too small values can reduce the control stability and increase the difficulty of limiting engine parameters within their constraints as well. However, the weighting factor value must be kept sufficiently small for the satisfaction of the time requirement (maximum 5 s) for the transient operations over the entire operating range according to the regulations [40]. In this case, a slightly smaller weighting factor (0.2) is selected for ensuring fast transient operations, and such a value still allows the MPC to constrain the engine parameters within their limits.  
Consequently, smaller values for weighting factors enhance the maneuverability of the engine, which is suitable for short-range fighters' control. However, the larger values for the weighting factor are associated with better fuel economy, which could be a priority in a civil-engine controller design procedure.  
Figure 4: The percentage of gross thrust to the design point from the control of MPC with different weighting factors values.  
\begin{table}
\begin{tabular}{r} \hline \hline
0.002 kg/s \(\leq\) W\({}_{H}\)\(\leq\) 1.20 kg/s \\ \(-\)0.80 kg/s \(\leq\)\(\Delta\)W\({}_{H}\)\(\leq\) 0.80 kg/s \\ \(55\%\leq\text{CN}_{\text{L}}\)\(\leq\) 105\% \\ \(71\%\leq\text{CN}_{\text{H}}\)\(\leq\) 112\% \\ \(\text{PR}_{\text{HPC}\text{S}urge}=\text{f}(\text{CN}_{\text{H}})\) = 9.5625-\(\text{CN}_{\text{H}}\)\(-\) 2.1096 \\ \(\text{PR}_{\text{HPC}\text{I}\text{I}\text{e}\text{I}\text{e}\text{I}}=\text{f}( \text{CN}_{\text{H}})\) = 3.0198-\(\text{CN}_{\text{H}}\) + 0.0180 \\ \(680\text{K}\leq\text{TET}\leq\) 2100K or 1792K \\ \(-\)5\% \(\leq\text{GTR}_{\text{Overshoot}}\) or EPR or \(\text{CN}_{\text{L}}\)\(\leq\) 5\% \\ \(\text{SFC}\leq\) 0.03 or 0.02 kg/KN-s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The constraints to the engine parameters.
# Alpha Centauri: Disc Dynamics, Planet Stability, Detectability
## 1 Introduction  
Alpha Centauri stands as the closest known stellar system to our Solar System at a distance of 1.34 pc away from Earth [1]. As such, it constitutes a target of choice for numerous studies related to stellar and orbit characterisation, e.g., [2; 3; 4]. This system is composed of three stars: Alpha Cen A, Alpha Cen B, and Proxima Centauri (or Alpha Cen C), which are in a hierarchical orbital configuration. In other words, the binary AB semimajor axis is significantly smaller than the semimajor of C around AB [4]. The age of the Alpha Cen system is estimated to be \(5.3\pm 0.3\) Gyr [1; 5; 6], which is similar to our own Solar System, which is approximately 4.6 Gyr old. Recent discoveries have further heightened interest in the Alpha Centauri system. As a matter of fact, an Earth-sized exoplanet (called Proxima Centauri b) was discovered in the habitable zone of Proxima Centauri in 2016 [7]. Subsequent research has hinted at the presence of additional planets [8; 9; 10; 11], although confirmation is still pending.  
Regarding the primary components, Alpha Centauri A and B bear a striking resemblance to our Sun in terms of size, mass, and temperature [4; 12]. Alpha Centauri A is about 10% more massive (\(M_{\rm A}=1.0788\,M_{\odot}\)), whilst Alpha Centauri B is around 90% as massive as the Sun (\(M_{\rm B}=0.9092\,M_{\odot}\)). The binary orbit is highly eccentric (0.52), has a semimajor axis of 23.3 au, and has a period of \(P_{\rm b}\approx 80\) yr [1]. The values of periastron and apoastron are equal to 11 and 35 au, respectively, which are akin to the mean radialdistances of Neptune and Pluto from the Sun. The third member, Proxima Centauri, is a red dwarf star situated approximately 8200 au away from the central pair AB [1]. Thanks to high-precision absolute radial velocity measurements, it is now robustly established that Proxima Centauri is bound to AB and that it takes 511 kyr to complete a single orbit around the inner binary [1; 4]. Hence, the closest stars to Earth belong to a triple stellar system.  
Besides its proximity, this system offers an unparalleled laboratory for astrophysical research--including the search for exoplanets [7] and the tantalising possibility of extraterrestrial life [13; 14]. In this work, we revisit Alpha Centauri in the context of protoplanetary disc dynamics and planet stability. In Section 2, we present an updated hydrodynamical simulation of the circumstellar discs in Alpha Cen using the most precise binary orbit available [1]. In Section 3, we compute stability maps for massless and massive (S-type) planets in Alpha Cen. In Section 4, we showcase the radial velocity (RV) signals of the hypothetical (stable) rocky planets in Alpha Cen. Finally, we discuss our results in Section 5.
# Early Ransomware Detection with Deep Learning Models
## 5 Experimental Evaluation
### Ablation Study
#### 5.7.4 Increasing the Number of Training Epochs  
This section examines the top models' performance with different numbers of training epochs, analyzed on the PARSEC-500 and PARSEC-5000 datasets. We examined how the scores were affected by selecting the number of epochs to be \(\text{ep}=10,20,30\). Table 10 shows the results of this evaluation, including test and train times. We can see that the best performance was achieved for \(\text{ep}=10\) and that there was no need to increase the number of training epochs beyond that. This decision decreased the training time significantly, especially for the PARSEC-500 dataset. We also observed that the test times were not affected by increasing the number of training epochs.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & \multicolumn{6}{c}{PARSEC-500 dataset} \\ \hline model & text repr & FLIST & sensitivity & specificity & F1 & W & \(\text{ep}\) \\ \hline CNN & 1-hot & OpsRes & 0.9978 & 0.9827 & 0.9903 & 7 & 10 \\ CNN & 1-hot & OpsRes & 0.9978 & 0.9827 & 0.9903 & 7 & 10 \\ CNN & 1-hot & OpsResDur & 0.6100 & 0.5818 & 0.6015 & 7 & 10 \\ CNN & 1-hot & OpsResDurDet & 0.1127 & 0.9859 & 0.2000 & 7 & 10 \\ \hline \multicolumn{6}{c}{PARSEC-5000 dataset} \\ \hline model & text repr & FLIST & sensitivity & specificity & F1 & W & \(\text{ep}\) \\ \hline LSTM & 1-hot & Ops & 0.9998 & 0.9885 & 0.9942 & 7 & 10 \\ LSTM & 1-hot & OpsRes & 1 & 0.9751 & 0.9877 & 7 & 10 \\ LSTM & 1-hot & OpsResDur & 0.4085 & 0.8332 & 0.5186 & 7 & 10 \\ LSTM & 1-hot & OpsResDurDet & 0.1127 & 0.9122 & 0.1877 & 7 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Scores of the top models on the PARSEC-500 and PARSEC-5000 datasets with different data features (the best scores are marked in gray).  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{6}{c}{PARSEC-500 dataset} \\ \hline model & text repr & FLIST & sensitivity & specificity & F1 & W & \(\text{ep}\) \\ \hline CNN & 1-hot & OpsRes & 0.9885 & 0.7017 & 0.8645 & 1 & 10 & 0.57 \\ CNN & 1-hot & OpsRes & 0.9963 & 0.9101 & 0.9551 & 3 & 10 & 0.34 \\ CNN & 1-hot & OpsRes & 0.9962 & 0.9431 & 0.9704 & 5 & 10 & 0.20 \\ CNN & 1-hot & OpsRes & 0.9978 & 0.9827 & 0.9903 & 7 & 10 & 0.20 \\ \hline \multicolumn{6}{c}{PARSEC-5000 dataset} \\ \hline model & text repr & FLIST & sensitivity & specificity & F1 & W & \(\text{ep}\) & \(\text{test}\) time(s) \\ \hline LSTM & 1-hot & Ops & 0.9957 & 0.6741 & 0.8578 & 1 & 10 & 6.29 \\ LSTM & 1-hot & Ops & 0.9971 & 0.8944 & 0.9484 & 3 & 10 & 2.55 \\ LSTM & 1-hot & Ops & 0.9997 & 0.9628 & 0.9816 & 5 & 10 & 1.74 \\ LSTM & 1-hot & Ops & 0.9998 & 0.9885 & 0.9942 & 7 & 10 & 1.42 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Scores of the top models on the PARSEC-500 and PARSEC-5000 datasets with different window sizes (the best scores are marked in gray).
# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 4 Experimental Results
### Ablation Experiments
To validate whether the Stage2 model can achieve fine-tuning tasks using only a single Transformer block, we tested the denoising performance with different numbers of Transformer blocks. We employed various network structures, as shown in Table 4, which consisted of networks with different numbers of blocks. Each configuration was run 10 times on images of sizes 256 \(\times\) 256 and 512 \(\times\) 512, respectively, with an iteration step of 3000 in each run (ensure model convergence) to calculate the average running time. Finally, the execution times with only one block for image sizes of 256 \(\times\) 256 and 512 \(\times\) 512 are 30.94 s and 110.33 s, respectively. The denoising performance obtained using only one Transformer block is comparable to using multiple blocks, with acceptable difference in denoising performance in terms of PSNR, but it indeed requires the least execution time \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **DnCNN** & **DAGL** & **DRUNet** & **IRCNN** & **SWinIR** & **U-Transformer (Stage1)** \\ \hline \(x_{p}\) & 30.35 & 30.86 & 30.94 & 30.37 & 31.01 & 31.04 \\ \(x_{f}\) & 30.65 & 31.04 & 31.10 & 30.57 & 31.17 & 31.23 \\ Improvement & +0.30 & +0.18 & +0.16 & +0.20 & +0.16 & +0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average PSNR values of different methods on Set12 dataset with a noise level at 25. \(x_{p}\) represents a pre-processed image. \(x_{f}\) represents the final output. The improvement row represents the PSNR value of the final output image that is boosted from the preprocessed image. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Fuction** \\ \hline \(L1=\lambda\cdot||x_{f}-y||_{2}+||x_{f}-x_{p}||_{2}\) & \(L2=||x_{f}-y||_{2}\) & \(L3=||x_{f}-x_{p}||_{2}\) \\ \hline
**31.23** & 30.91 & 30.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average PSNR values of different loss functions on Set12 dataset with a noise level at 25. The bold part represent the best PSNR result. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Noise Level** & **15** & **25** & **50** \\ \hline Weight & 0.2 & 0.4 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The optimal weights for different noise levels on Set12 and BSD68 datasets. (only between one-eighth and one-fifth of the maximum execution time). Therefore, the proposed Stage2 model in this paper utilizes only a single Transformer block.
# Designing Social Robots with LLMs for Engaging Human Interaction
## 6 Discussion
### Emotional Resonance and Ethical Considerations  
Although the system was primarily designed to support naturalistic, memory-driven conversations for social companionship, several older adult participants responded to the robot in emotionally expressive ways. Descriptions such as \"friendly\", \"comforting\", or \"like a pet\" were common, and three participants who reported feeling lonely stated that they would enjoy having the robot at home. Others saw its potential value for individuals with dementia or limited social contact.  
These responses highlight the role of memory, attentiveness, and persona continuity in fostering emotional connection--even without explicit affective modelling. By recalling prior conversations, referencing personal details, and sustaining a coherent persona, the system projected a sense of social presence that many participants found meaningful. This aligns with prior studies that have observed affective responses in elderly users interacting with LLM-based robots [14; 31]. Unlike systems limited to scripted empathy or shallow interaction, our robot's consistent tone and contextual continuity contributed to a perception of an ongoing relationship, without relying on explicit emotion modelling.  
However, emotional resonance introduces ethical complexity. Some users expressed a desire to confide in the robot or saw it as a source of emotional support. This blurs the boundary between utility and affective deception. If a robot gives the impression of emotional understanding without real empathy, it may foster over-trust or unmet relational needs--especially among vulnerable populations such as older adults or those experiencing loneliness. These concerns echo broader discussions in HRI around emotional deception, anthropomorphism, and the ethics of synthetic empathy [48; 50; 51].  
Future systems aiming to support older adults should incorporate safeguards for emotional transparency, while also recognising that warmth, responsiveness, and memory can naturally give rise to a sense of companionship--even in LLM-powered agents.