esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Identification of Children with ADHD from EEG Brain Waves
## 1 Introduction
Simple EEG linear features are emphasized in our proposed model development, whereas other works were only based on complex nonlinear features. 4. The model was trained on a large dataset of 120 children (the highest of other models was 49) collected from two different sessions at two different places, eliminating the measurement bias in data collection. Also, the experimental setup was child-friendly, easy to reproduce in local settings, and could be employed for future ADHD detection. 5. We also performed rigorous validation (unlike other works) to ensure that our model is not impacted by bias and overfitting, which commonly appear in the ML pipeline. The rest of the paper is organized as follows. First, recent and related works are presented in Section 2. Materials and method information is given in Section 3, along with the dataset description. Section 4 describes the preprocessing methods. Section 5 describes the feature extraction and feature selection. Section 6 represents the results. Lastly, Sections 7 and 8 give the discussion and conclusion of this study. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-Band** & **Frequency Range** & **Associated Brain Function** \\ \hline Delta & 0.5–4 Hz & Deep sleep or unconsciousness \\ Theta & 4–8 Hz & Sleep or drowsiness and recall \\ Alpha & 8–13 Hz & Eye closed and visual stimuli are limited \\ Beta & 13–30 Hz & Attentive to stimuli or problem-solving \\ Gamma & 30 Hz and above & Movement, emotional processing, \\  & & and high-level mental activity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: EEG sub-bands are associated with different brain functions [11, 12].
# All-Digital Computing-in-Memory Macro Supporting FP64-Based Fused Multiply-Add Operation
## 2 Overview  
### Digital CIM Macro  
The digital CIM macro is typically constituted by a memory array, a parallel adder tree, and a partial-sum accumulator, as shown in Figure 1. The memory array contains memory units and logic operation gates. The parallel adder tree is a full-adder array that aims to add several narrow bit-width numbers to obtain a wider bit-width number. The partial-sum accumulator contains a shifter and a full adder. Taking the multiplication of unsigned 64 \(\times\) 64 \(\times\) 4b weights and 64 \(\times\) 4b input activations as an example, the operation of the digital CIM macro will be introduced and detailed as follows. This macro contains 64 columns of sub-CIM units; each unit consists of a memory array that contains 64 \(\times\) 4b weights and 64 \(\times\) 4 bit-wise multipliers, a 6-stage parallel adder tree, and a partial-sum accumulator. In CIM operation, 64 \(\times\) 4b input activations are simultaneously fed into the memory array in a bit-serial manner. The memory array performs 64 multiplications of 1b input activation and 4b weight in one computation cycle, while the results of multiplications are fed into the 6-stage parallel adder tree to generate a 10b partial sum. A partial-sum accumulator is required to accumulate the partial sums of each computation cycle, after the above four computation cycles, the partial-sum accumulator shifts and adds four 10b results to get a 14b result. Therefore, an extra computation cycle is required to output the accumulated result, and a total of 5 computation cycles are required to complete the MVM for input activations with 4b precision [24].
## 1 Introduction
Parallel measurements of sunshine duration by manual and automatic devices are relatively limited in Cyprus. There are only three locations (synoptic stations) where both CS and three different automatic sensors (Kipp & Zonen pyranometer, pyrheliometer and CSD3 sunshine recorder) are operating at the same site or at least at a close distance from the selected locations. It has to be noted that the CSD3 sensors are located in neighboring automatic weather stations (AWSs). Up to now, there are no relevant studies comparing sunshine duration measurements. As indicated earlier, several studies in different parts of the world have shown both negative and positive differences. The purpose of the present study is to compare the manual with the three automatic sensors at the selected locations. The established relationships could be useful in extending the existing daily time series of CS measurements since the CS recorders were replaced with automatic instruments. Section 2 gives information about the sunshine duration instruments operating at the synoptic stations of Larnca A/P, Pafos A/P and Athalassa during the periods 2009-2012 and 2012-2015. The manual observations are based on Campbell-Stokes sunshine recorders, while the automatic instruments are manufactured by Kipp & Zonen. Section 3 compares the daily sunshine duration measurements obtained from the said instruments on a seasonal basis and under various skies conditions and discusses the reasons for the observed positive or negative differences. Furthermore, linear relationships are established to convert the automatic measurements to a CS time series. To assess the differences between the manual and automatic sensors, parametric (T tests) and non-parametric (Mann-Whitney) statistical tests are applied. Finally, the global radiation is estimated using the Angstrom-Prescott equation based on the daily records of both devices. The final section presents the conclusions of the study.
# Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
## 4 Discussion
### Effect of Wildfire Impacts of Grassland GPP As Predicted by Data-Driven Models  
The GPP shows great promise for monitoring grassland condition, due to its responsiveness throughout the season. Additionally, since it is based on physiological principles, it provides information about the rate of biomass production. The very high GPP rates in spring following burning are likely related to the high interception of radiation by young, green, rapidly growing leaf areas. There may also be shifts in species composition that could affect the duration of differences between burned and unburned plots. However, data are not available in this study to evaluate species composition pre- and post-fire. While the greater productivity on burned compared to unburned areas may be counterintuitive, [PERSON] (2019) reported the same response on the Konza Prairie Research Natural Area in Kansas, which he attributed to soil warming, increased surface light intensity, and nitrogen availability.
## 5 The Results of Analysis  
### HRIR Dataset and Pre-Processing  
The binaural HRIRs of a KEMAR artificial head with DB-060/061 pinnae, which were calculated by using the boundary element method, were used in the analysis and algorithm [24]. More specifically, a laser 3D scanner (UNIscan) associated with a software (VXelements) was used to acquire the geometrical surfaces of a KEMAR artificial head with DB-060/061 pinnae, and then boundary element method was used to calculate the near-field HRTFs. The theory of boundary element method and details of implementation could refer to [24]. The calculated dataset involves HRIR at seven source distances of \(r=0.2\), 0.25, 0.3, 0.4, 0.5, 0.75, and 1.0 m, and 2520 directions at each source distance. The directions range from elevation \(\phi=-85^{\circ}\) to \(85^{\circ}\) and azimuth \(\theta=0^{\circ}\) to \(355^{\circ}\), with elevation and azimuth intervals of \(5^{\circ}\). The length of each HRIR is 882 points at sampling frequency of 44.1 kHz.  
A bilinear directional interpolation is applied to the calculated HRIRs [25], resulting in binaural HRIRs at 7 source distances and 64,440 directions (with both elevation and azimuthal resolutions of \(1^{\circ}\)). Minimum-phase approximation and truncation by a time windows yield a set of 128-point minimum-phase (binaural) HRIRs at 7 source distances and 64,440 directions (with tensor representation of \(\mathbf{h}_{128\times 64440\times 7}\)), which are used for the analysis and algorithm.
# Wavelet Time-Frequency Analysis on Bridge Resonance in Train-Track-Bridge Interactive System
## 3 Time-Frequency Interaction Analysis on the Train-Track-Bridge System
### Time-Frequency Analysis from Excitation to Bridge Response  
#### 3.3.1 On the Resonant Bridge  
In order to study the time-frequency evolutionary characteristics on the resonant bridge from excitation to response, the speed conditions of 195, 292.5 and 585 km/h were selected, and the corresponding first-order loading frequencies are 2.17, 3.25 and 6.5 Hz, respectively. The CWT scalograms of mid-span bridge excitation and vertical acceleration response are shown in Figure 7 and it can be seen that:  
1. For the excitation scalogram in Figure 7a,c,e, there are only three loading frequency orders in yellow under all the speed conditions, reflecting that the influence of higher-order loading frequency is basically negligible. Frequency values of each order are integer multiples of the first-order loading frequency, respectively, while the order with the frequency band equal to the bridge natural frequency (6.5 Hz) is defined as the resonance-triggering band. Among them, the first-order loading frequency is the largest, containing the highest signal energy, followed by the third-order and second-order. The scalogram value corresponding to each loading frequency order  
Figure 6: Mid–span bridge vertical vibratory acceleration signal energy in the function of train speed: (a) Local view; (b) Global view.  
firstly increases and then decreases with the process of entering and leaving the bridge. The scalogram value of each loading frequency order will not change with the increase in speed, but its frequency and the bandwidth between each order frequency will increase with the increase in speed.
2. For the response scalogram in Figure 7d, the triggered resonance band, corresponding to the second-order response frequency, is not completely dominant for the entire response with the existence of other orders of response frequency. In contrast, for Figure 7b,e, only the triggered resonance bands are visible and completely dominate the entire response. The scalogram value of the triggered resonance band in Figure 7b is more significant than the one in Figure 7e.
3. In general, the bridge resonant response is stationary while the train is crossing the bridge. The resonance-triggering band is included within each order of loading frequency, while the triggered resonance band will dominate the bridge response only when the signal energy of the corresponding triggering band is significant. Typically, the resonance-triggering band which is in the first-order loading frequency and equals the natural frequency of bridge will lead to the most intense bridge resonant response.
# Computer Tools to Analyze Lung CT Changes after Radiotherapy
## 4 Lung Images' Segmentation and Registration
### Registration Process  
Image registration is used to overlay multiple images of the same object or scene taken at different times with the use of the same or different devices [26]. It is used in different fields such as medicine, remote sensing (e.g., environmental monitoring, weather forecasting), cartography, and computer vision. Usually, some preprocessing steps like noise removal, normalizing image sizes, or smoothing filters are applied. As described in [27], medical image registration is an integration process applied in order to bring images acquired from different modalities into spatial alignment. An example of registration usage is radiotherapy treatment planning. Doctors can merge images from different devices and/or different times and prepare a proper dosing plan.  
A survey of pulmonary image registration methods was given in [28], and it discussed the following approaches:  
* Intensity based--registration relies on statistical criteria for matching different intensities between fixed and moving images.
* Feature based--registration is based on different geometrical structures, i.e., points or curves, very useful while registering pathological lungs.
* Segmentation based--in rigid registration, the same structures from both images are extracted; after that, they become the input parameters for the main registration method; unfortunately, registration accuracy is connected with the segmentation quality.
* Mass-preserving--registration relies on detecting density changes on CT images, which are related to different inhalation volumes (air volume in lungs).
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 4 Experimental Results
### Packing Performance Evaluation  
In addition to using BD-rate to evaluate the packing performance, we also calculated the reduction in occupancy map size as a measure of the packing performance. Figure 11a shows the occupancy map generated by the original method, while Figure 11b shows the occupancy map generated by our method. DAPP can generate smaller occupancy maps. Table 7 shows that the DAPP results in a maximum reduction of 20.14% in the area of the occupancy map. An average reduction of 6.92%. The reduction of the occupancy map also leads to an improvement in the performance of point cloud compression. As shown in Table 8, the BD-rate of D1 has an average gain of \(-0.91\%\). The average BD-rate gain of D2 is \(-1.16\%\). The BD-rate of Luma gain of \(-1.03\%\), the BD-rate of Cb gain of \(-0.45\%\) on average, and the BD-rate of Cr gain of \(-0.81\%\) on average.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Point**} & \multicolumn{2}{c}{**Geom.BD-Rate (\%)**} & \multicolumn{2}{c}{**Attr.BD-Rate (\%)**} & \multirow{2}{*}{**Time Reduction**} \\ \cline{2-2} \cline{4-7}  & **D1** & **D2** & **Luma** & **Cb** & **Cr** & **Rate (\%)** \\ \hline \hline \multirow{2}{*}{Loot} & \(1.45\) & \(1.20\) & \(0.68\) & \(2.53\) & \(0.65\) & \(23.05\) \\ \cline{2-7
## 5 Experimental Evaluation
### Ablation Study and Parameter Sensitivity  
Table 5 shows the ablation results of the trust components. Removing participation frequency \(\phi\) drops the accuracy to 70.5%; removing gradient consistency \(\psi\) further lowers it to 67.9%; excluding contribution effectiveness \(\omega\) gives 69.4%. This confirms the complementary roles of all three components in accurate trust assessment.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **FEMNIST (\%)** & **CIFAR-10 (\%)** & **Sent140 (\%)** \\ \hline FedAvg & 72.4 & 69.1 & 71.0 \\ FedProx & 73.2 & 70.5 & 72.3 \\ FedTrust & 74.8 & 71.0 & 73.4 \\ Krum & 75.6 & 70.8 & 72.7 \\ TAIM & 78.5 & 74.9 & 75.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy comparison under unified model architecture.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Local Training (s)** & **Comm. Volume (MB)** & **Server Aggregation (s)** \\ \hline FedAvg & 3.1 & 8.5 & 0.03 \\ FedProx & 3.4 & 8.5 & 0.05 \\ FedTrust & 3.9 & 8.5 & 0.04 \\ Krum & 4.2 & 8.5 & 0.15 \\ TAIM & 3.9 & 9.0 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: System resource overhead comparison.  
Figure 8 illustrates the sensitivity of the TAIM model to hyperparameter configurations, specifically analyzing the sigmoid suppression steepness parameter \(k\) and the trust component weights \((\lambda_{1},\lambda_{2},\lambda_{3})\).  
The left subfigure shows how the steepness parameter \(k\) in the sigmoid suppression function affects the final model accuracy. When \(k\) is set to a small value (e.g., 2.5), the model accuracy drops to 70.8% because the resulting trust scores are overly smooth, which fails to distinguish high- and low-trust clients effectively. As \(k\) increases, the accuracy gradually improves, reaching a peak of 75.2% at \(k=10\), then slightly decreases but remains relatively high (e.g., 74.6% at \(k=15\)). This suggests that moderately enhancing the steepness helps amplify trust-based differentiation in aggregation, while excessively steep functions may overfit trust estimates and impair generalization. Overall, the system demonstrates robustness to a wide range of \(k\) values. The right subfigure examines the influence of different trust component weight configurations. The result shows that, with an equal weight setting (equal: 0.33, 0.33, 0.33), the model achieves 73.8% accuracy, indicating that each trust dimension independently contributes to performance. However, when one dimension is overly emphasized--such as participation frequency (\(\lambda_{1}=0.6\))--the accuracy significantly drops to 72.1%. In contrast, emphasizing gradient consistency (consist. heavy: 0.2, 0.6, 0.2) leads to a better result of 74.3%, highlighting the importance of gradient-level behaviors in robust modeling. TAIM's default setting (0.3, 0.4, 0.3) achieves the highest accuracy of 75.2%, confirming the effectiveness of the proposed joint modeling strategy in balancing trust dimensions.
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 10 Mitigation Techniques of Beam Systematics at the Neutrino Detectors
### The Narrow-Band Off-Axis (NBOA) Technique  
A technique similar to PRISM has been proposed in [48] for short-baseline experiments and, in particular for the cross-section measurements and the study of oscillation patterns in sterile neutrinos. The PRISM technique is mostly conceived for high-intensity long-baseline experiments and requires movable detectors of moderate mass (500 tons or more). Its implementation is too costly and complex for a cross-section experiment, whose anticipated investment is two orders of magnitude smaller than DUNE or HK. Since the ideal setup for a monitored neutrino beam is a narrow-band beam, the intrinsic width around \(E^{Max}\) (see Equation (15)) is much smaller than a wide band. This width creates a much tighter correlation between the neutrino emission angle \(\theta\) and its energy. In particular, the ENUBET collaboration has recently shown that the measurement of \(\theta\), which corresponds just to the identification of the interaction vertex of the detector, provides the energy with a precision that improves with the energy itself. For the current ENUBET design (mean secondary momentum at 8.5 GeV/c and 10% momentum bite), the precision ranges from 8% at 3 GeV to 20% at 1 GeV (see Figure 15). This is definitely a valuable result: at high energy (e.g., for the DUNE cross-sections), \(E_{\
u}\) is known very precisely and practically all biases due to energy reconstruction are removed. At low energy, the events are low-multiplicity elastic scatterings or events with resonant/coherent production of mesons. In this case, the missing particles in the energy reconstruction produce an inconsistency between the a priori measurement of \(E_{\
u}\) from the NBOA and the measurement based on final state particles. For pure quasi-elastic events, where this inconsistency never occurs, the precision is dominated by the detector measurement through the usual quasi-elastic (QE) formula [156]:  
\[E_{rec}=\frac{ME_{\mu}-m_{\mu}^{2}/2}{M-E_{\mu}+|\mathbf{p}_{\mu}|\cos\theta_ {\mu}} \tag{16}\]  
where the nucleon of mass \(M\) is at rest in the laboratory frame (Fermi motion and nuclear effects are neglected) and \((E_{\mu},\mathbf{p}_{\mu})\) is the four-momentum of the outgoing muon.
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 1 Introduction
Second, in order to validate the effectiveness of the interception strategy, only the path of the interceptor is optimized, and the motion of the escaping target is simplified to linear motion, which is also inconsistent with an actual scene. Finally, at present, there are few experiments on the pursuit-evasion problem and the interception strategy is only validated by simulation. In view of the above challenges, we aim to design a strategy that can intercept escaping targets. Compared with previous work, the contribution of this paper is mainly reflected in the following three aspects. Firstly, considering the dynamic curve motion of the target, a surface interception strategy is constructed to intercept fast maneuvering targets. Secondly, this paper considers predictability based on the Kalman filter, which can improve the efficiency of interception. Thirdly, a disturbance observer is proposed, for which the accuracy of estimating a disturbance is independent from the designed pursuit algorithm. The rest of this article is organized as follows: in the Section 2, the basic assumptions of the pursuit-evasion problem are introduced, and the guidance algorithm is adopted to solve the interception problem of evaders. The kinematics and dynamics of USV and the Kalman filter algorithm are also given. In the Section 3, the controller and disturbance observer are designed, and a stability analysis of the system is presented. A simulation of the pursuit algorithm is presented in the Section 4. The Section 5 provides the experimental results. Finally, some conclusions are drawn in the Section 6.
## 4 Mixed Reality for Human-Robot Interaction
### Mixed Reality-Enhanced Telemanipulation  
#### Mixed Reality-Enhanced Intuitive Telemanipulation  
In recent years, mixed reality technologies have been widely applied in the field of robotics, and these research directions can be divided into three categories according to application aspects: (1) human-robot interaction (HRI): immersive teleoperation, intuitive telemanipulation, collaborative interaction, wearable robots, haptic effects, and virtual devices; (2) medical robotics: robot-assisted surgery (RAS), prosthetics, and robot-assisted rehabilitation and training devices; (3) robot motion planning and control: trajectory generation, robot programming, simulation, and manipulation. Table 1 outlines the main differences between this article and previous research in the realm of MR-enhanced robotic manipulation. Figure 3: [PERSON]’s reality–virtuality continuum [84]. MR interfaces enhance operator immersion through realistic 3D visualizations and interactive elements. To minimize fatigue, haptic feedback and intuitive gestures are integrated, enhancing the user experience. Additionally, optimizing rendering algorithms is essential. Techniques like foveated rendering, where high-quality graphics are focused on the user's gaze, ensure a smooth experience while conserving computational resources [101]. MR has been applied to robotic teleoperation systems to optimize operator immersion and enhance user perception of the remote side to enable immersive robotic teleoperation (IRT) [102; 103; 104]. A three-dimensional virtual world similar to the slave side can be simulated through MR and displayed to the user on the master side. By implementing MR as a human-robot interaction interface (HRI), the user experiences a physical presence in the remote environment and co-existence with the robotic platform via MR subspace, while guiding and monitoring the robotic platform at the local user space [105; 106]. MR interfaces blend virtual elements with the real world, providing operators with an immersive 3D visualization of the remote environment, such as a welding site [107]. This immersion allows operators to perceive the task space in depth, enhancing their understanding of the work area and minimizing cognitive load. By feeling present in the remote environment, operators can focus more effectively on their tasks, reducing distractions and mental fatigue [108]. MR-based user interfaces with natural control commands minimize fatigue by utilizing human instincts, such as motion and gestures, to interact with complex robotic systems [109]. MR-enhanced teleoperation allows direct mapping of control commands and actions between the user and the robot, presenting the potential for performance enhancements. It serves as an intermediary for integrating imitation-based motion mapping and 3D visualization techniques [110]. Recent research on MR-enhanced teleoperation systems has primarily concentrated on gathering demonstrations for robotic learning, incorporating haptic feedback and other sensory inputs, developing immersive manipulation, integrating AI and machine learning,