esa-sceva/satcom-chunk-collection huggingface dataset:
# Action Recognition Based on the Fusion of Graph Convolutional Networks with High Order Features
## 3 Proposed Graph Convolutional Network with High-Order Features
### High-Order Features Fusion  
**Joint Feature:** For both of NTU-RGBD and NTU-RGBD-120 datasets, the joint features are extracted from the 3D coordinates of the skeleton sequence. Joint features are fundamental and important features for the skeleton data. Joints coordinates contain abundant spatial and temporal information. Our baseline is a single stream of 3D joint. We also put the joint data into our neural networks to extract joint feature as shown in Figure 2a.  
Features extracted only by 3D joints are not enough for action recognition. We propose several pieces of high-order information as input which is effective for action recognition. In front of the input layer, a batch normalization layer is added to normalize the input data. A global average pooling layer is added at the end of the network to pool feature maps of different samples to the same size. Both the input and output of the network are graph-structures data in the graph convolution. The last graph convolution layer generates a discriminative feature and puts it into the standard soft-max classifier. The final score is the weighted summation of the scores of five streams, which is used to predict the action label. We believe that the information contained in the joints, bones, and relative distance is the most fundamental and important. Therefore, these features should be set large weights. The velocity and acceleration information are auxiliary features that strengthen the temporal relationship. These features should be set small weights. The weighted summation method can be formulated as Equation (7):  
\[S_{f}=S_{a}W_{a}+S_{b}W_{b}+S_{c}W_{c}+S_{d}W_{d} \tag{7}\]  
where \(S_{a},S_{b},S_{c}\), and \(S_{d}\) denote the score of joint, bone, joint and bone velocity, and relative distance, respectively. \(S_{f}\) denotes the final score. \(W_{*}\) denotes the weights of scores.
## 4 Case-Study  
### [PERSON]'s Steel Beam Splice Connection  
#### 4.1.1 Geometry  
One of the most well-known splice connections in steel girders is the one used by [PERSON] in an experimental programme held at the University of Texas at Austin [30]. This experimental programme is well-known for its role in providing a seminal work on the design of cover plate connections [58; 59]. Consequently, after a careful assessment of the programme's case representativity, Case 1 (out of 32) has been considered the ideal one for a reference connection [21] on which parts optimisation can be assessed and benchmarked.  
As shown in Figure 1, where the dimensions are in millimetres, two W24 \(\times\) 7 \(\times\) 55 beam segments in A36 steel [60] were joined with two flanges and two web cover plates to resist bending moments and shear loading. The flanges contained cover plates just on their outside surface, allowing them to withstand in-plane loading through the bolts' simple shear, as shown in Figure 1. These A572 Grade 50 steel [61] splices were 812.8 mm long, 12.7 mm thick, and 205 mm broad, which was 27 mm greater than the width of the beam flange. Their twenty A325 steel bolts were 19.1 mm in diameter and fit into 20.7 mm holes. The flange bolts were positioned in two columns with an expected spacing of 95 mm. Within each column, the bolt spacing was 63.5 mm, and the distance to plate edges was 76.2 mm.  
Figure 1: Splice connection layout.  
For the reference case, bolt tightening did not involve preloading. The load transfer mechanism was, therefore, ensured by bolt shear. Thence, flange splices are deemed to resist the total applied bending moment, and web splices will resist the applied shear if the flange splices can withstand the total applied moment, as confirmed with the laboratory testing [30].
## 3 Materials and Methods
### Mamba-like Linear Attention (MLLA) Block  
#### 3.3.1 Unified SSM Gating and Linear Attention Framework  
We draw upon the successful design of the MLLA mechanism, originally applied in image classification, and extend it to SISR, as illustrated in Figure 2. MLLA fundamentally integrates the complementary strengths of linear attention and Selective State Space Models (SSMs), while systematically addressing the limitations of standard self-attention in terms of scalability and temporal modeling.  
Linear attention reformulates traditional self-attention by replacing the softmax normalization with linear kernel approximations, reducing the complexity from \(O(N^{2})\) to \(O(N)\). This enables efficient pairwise interaction modeling across long sequences, but lacks mechanisms for adaptive memory control or forgetting.  
\[h_{i}=\widetilde{A}_{i}\bigodot h_{i-1}+B_{i}\left(\Delta_{i}\bigodot x_{i} \right), \tag{8}\]  
\[y_{i}=C_{i}h_{i}/1+D\bigodot x_{i}, \tag{9}\]  
where \(h_{i}\) is the internal hidden state at time step \(i\); \(\widetilde{A}_{i}\) acts as a forget gate, regulating how much of the past state \(h_{i-1}\) is retained; \(\Delta_{i}\) serves as an input gate, determining the strength of the current input \(x_{i}\); and \(D\bigodot x_{i}\) provides a residual connection from input to output.  
This gated recurrence can be conceptually aligned with the linear attention formulation:  
\[S_{i}=1\bigodot S_{i-1}+K_{i}^{\top}\left(1\bigodot V_{i}\right), \tag{10}\]  
\[y_{i}=\frac{Q_{i}S_{i}}{Q_{i}Z_{i}}+0\bigodot x_{i}. \tag{11}\]  
In this form, \(S_{i}\) serves as a memory accumulator, similar to the hidden state in Equation (8), and the unnormalized additive accumulation reflects the recursive nature of SSM.  
Figure 2: The structure of MLLA block, including (**a**) MLLA, (**b**) Linear attention, and (**c**) SGFN.  
However, unlike SSM, linear attention typically lacks forgetting, gating, and residual memory control.  
MLLA bridges this gap by embedding the SSM's gating logic into the linear attention framework. Specifically, the forget gate \(\overset{\sim}{A_{i}}\) corresponds to a learnable decay that modulates \(h_{i-1}\), similar to selective memory decay; the input gate \(\Delta_{i}\) modulates the contribution of the current input \(x_{i}\), enhancing dynamic input selection; the residual connection \(D\bigcirc x_{i}\) preserves low-level features, stabilizing gradient flow. The derivation of MLLA from SSM is shown in Figure 3.  
This unified structure enables efficient sequence modeling with explicit mechanisms for structured forgetting and input integration--features absent in traditional linear attention. MLLA thus operates as a gated linear attention method, retaining the scalability of attention while embedding SSM-like memory control, making it particularly suitable for tasks like image super-resolution, where both local recurrence and long-range dependency are essential.
# Quasi-Yagi Slotted Array Antenna with Fan-Beam Characteristics for 28 GHz 5G Mobile Terminals
## 4 Conclusions  
In this paper, a quasi-Yagi slotted array antenna is proposed. The proposed antenna has fan-beam radiation characteristics for wide beam coverage with beam steering capability. The very wide HPBW of \(256.72^{\circ}\) with a peak gain of \(11.16\) dBi is achieved by a slot antenna array with directors. Low mutual coupling characteristics are achieved by inserting slits on the ground plane to suppress the flow of the surface current and surface wave. To account for the effect of the human body on the antenna performance, a simulation was conducted with a simplified skin phantom with various separation distances. The proposed antenna shows stable performance near the human phantom since the antenna is a magnetic type. The proposed antenna satisfies the RF EMF exposure limit established by ICNIRP. Therefore, the proposed \(1\times 8\) array antenna is a good candidate for \(28\) GHz 5G mobile terminal applications.  
The presented work was carried out in collaboration between all the authors; S.K. and J.C. supervised the research. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Leaders in the Industry-University Cooperation+ (LINC+) program of the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2020-A-G011-010100).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Coating Thickness Measurement
The intensity of the Zn I 344.56 nm characteristic spectral line was observed regarding its variation with the number of laser shots, as shown in Figure 7.  
From Figure 9, it can be noted that under conditions of 101 kPa and 50 kPa, the intensity of the Zn I 334.56 nm spectral line initially starts at 0 and suddenly appears during a specific laser pulse. The number of laser pulses required for the Zn element spectral line to become observable varies under different conditions. Comparing the number of laser pulses corresponding to the intensity of the Zn element spectrum under the conditions of 50 kPa and 101 kPa, the number of laser pulses at 50 kPa is 16, while at 101 kPa it is 23. This indicates that the coating thickness at 50 kPa is between the thicknesses corresponding to 15 and 16 laser pulses, and at 101 kPa the coating thickness is between the thicknesses corresponding to 22 and 23 laser pulses. Based on the fitting curve of ablation depth versus the number of laser pulses, the coating thickness at 50 kPa is between 219.3 \(\upmu\)m and 234.4 \(\upmu\)m, while at 101 kPa, the coating thickness is between 337.8 \(\upmu\)m and 350.2 \(\upmu\)m. The main reason for the difference between these two measurements is attributed to the non-uniformity of the coating thickness. Theoretically, the thickness values obtained at the same point under different pressures should be the same,  
Figure 8: Linear model of ablation depth of fireproof coating with respect to laser applications at different air pressures. with only the number of laser pulses differing. The LIBS technology used in this article, combined with the coating thickness testing method established by confocal microscopy, can theoretically effectively avoid interference from air pressure in coating tests.
## 5 Conclusions  
This study presented a methodology via PRN artificial neural networks to obtain the operating conditions of power transformers (normal, thermal faults, or electrical faults) as a function of the combustible gases (H\({}_{2}\) (hydrogen), CH\({}_{4}\) (methane), C\({}_{2}\)H\({}_{2}\) (acetylene), C\({}_{2}\)H\({}_{4}\) (ethylene), and C\({}_{2}\)H\({}_{6}\) (ethane)) presented therein.  
In the two configurations presented (5 and 10 neurons in the hidden layer), the network had good training, a little better when using 10 neurons, with 10 s of processing and **MSE** at 0.0175. In the validation phase (data that was not part of the training), the **MSE** was 0.0057, and a correlation between the obtained output and the desired output was 0.9718, showing the effectiveness of the model.  
In the best results, the network was able to classify the samples for both training and validation with low error. The network presented only 14 errors out of 815 samples, all in the training phase. In the validation phase, the network presented 100% accuracy. In total, for 815 samples in both phases (training and validation), the hit rate was 98%.  
As a result, the methodology presented via ANN (PRN) proved to be efficient in classifying the operating conditions of power transformers (normal--class 1, thermal faults--class 2, and electrical faults--class 3) depending on the combustible gases created inside of the same (H\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{2}\), C\({}_{2}\)H\({}_{4}\), and C\({}_{2}\)H\({}_{6}\)).  
Energy utilities can improve their annual predictive and preventive maintenance planning process by implementing this proposed method. In Brazil, dissolved gas analysis (DGA) tests are often carried out annually or every six months. However, depending on the severity of the DGA results, the interval between these tests can be reduced, which can promote a change in maintenance planning, preventing further damage to the equipment and ensuring continuity of service.  
A.G. and A.B.N.: conceptualization; A.G. and A.B.N.: writing--original draft; A.N.d.S., R.P.d.M., M.A.I., E.G. and F.T.N.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research is funded by CAPES and CNPq under grant number 88887.704285/2022-00.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank the financial support from Sao Paulo State University--UNESP--Prope.
# Structural and Vibro-Acoustics Optimization of a Car Body Rear Part
## 1 Introduction
In [11], the car body was optimized considering NVH (Noise Vibration Harshness) and crash performances. In [12], an acoustic analysis of lightweight auto-body, based on FEM and BEM, was performed, similarly to what is performed in this work. In particular, the whole car body was analyzed, highlighting the critical contributing parts to the noise emission by a panel sensitivity analysis, whereas, in this work, the focus was on a single panel/component (rear part), with a change of base material, from steel to aluminum, and with related topological optimization. Structural optimization was also carried out in [13]; the objective function of the problem consisted of maximizing the torsion stiffness of the car to increase its speed performance. Previous work, such as from [PERSON] et al. [14] and [PERSON] et al. [15], involved optimization of the dynamic characteristics of an automobile body by maximizing the frequency of the first torsion and/or bending mode separately (the optimizer routine worked first on the torsion mode and after on the bending mode). The improvement in the natural frequency for torsion mode in [15] was obtained at the expense of an increased weight, where it required an additional 10% of the original mass to increase the frequency by 2 Hz. Toovercome this drawback, the method proposed in this work employs structural topology optimization, changing, in parallel, the component material. In [16], the design objectives attempted to improve the existing torsion and bending modes by using structural optimization, without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by reinforcing the structure at the critical locations previously identified. There are a number of cases where optimization was used to modify smaller parts of the vehicle such as rocker panels, mounting brackets, pillars, seat frames, suspension rings, and steering knuckles. Large parts such as engine blocks, chassis, and whole car bodies have also been used as the basis for optimization. In [17], the effectiveness of car body optimization was shown, considering a replacement of the baseline material and improvement of structure design. In this work, we show how a combined use of advanced topological and structural optimization tools, together with the capabilities of an unconventional manufacturing technology, such as 3D printing in aluminum, can guarantee an increase in the vibro-acoustics and structural car performances, together with a weight decrease. The method proposed is of general validity, thus it can be applied to any car (especially for sport and premium cars) and to any component/subassembly of complex geometry, to be added in critical areas for stiffening purposes. For the first time, such a kind of optimization was applied to such a large component of a car (rear part), coupling the synergetic possibilities offered by the 3D printing with the outcomes of an optimization software.
## 3 Results and Discussion
### Antifungal In Vitro Test-Assay on Mycelium
Figure 2: Antifungal effects (% growth inhibition, % GI) of the ethanolic extract (EE) and fractions (F-H/D: hexane/dichloromethane, F-E: ethyl acetate, and F-B: _n_-butanol) from M. pubescens’ roots against B. cinerea. Fosbel Plus was used as a positive control. Results are expressed as a percentage relative to the negative control. Data are presented as means \(\pm\) SD (standard deviation, \(n\) = 8); \(p\) < 0.05 (*). F-W (water) fraction was inactive (GI < 10%). Extract/fractions with a GI > than 20% at 1 mg/mL were assayed at lower concentrations (0.5 and 0.1 mg/mL). The F-W fraction showed low to non-activity on the three phytopathogenic fungi, revealing that the components in the plant responsible for antifungal activity have medium to low polarity. Moreover, although none of the fractions exhibited higher growth inhibition than Fosbel Plus, used as a positive control, these potential biopesticides are expected to be less persistent and harmful than synthetic ones, making their use more desirable.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### Anticipated Cooperative Collision Avoidance (ACCA)
#### 3.3.1 Automotive Requirements  
The ACCA use case is mainly based on the exchange of data between vehicles in order to anticipate dangerous situations that occur at positions that are not covered by the on-board sensors of each vehicle individually.  
In order to implement the ACCA use case, vehicles must be equipped with a set of sensors (e.g., radars, cameras, lidars, etc.) that are able to detect and recognize dangerous events in their proximity, an on-board unit (OBU) for V2X communications, and an advanced driver-assistance system (ADAS). The ADAS includes several components to assist the driver and improve safety, e.g., electronic stability control, anti-lock brakes, lane departure warning, adaptive cruise control and traction control, human-machine interface (HMI), etc.  
Vehicles exchange data via vehicle-to-network (V2N) with a geoservice that runs in a network server located in the V2X infrastructure. Vehicles send information to the geoservice about detected dangerous situations or about their general sensing, using standard-compliant European Telecommunications Standards Institute (ETSI) Intelligent Transport Systems (ITS) messages, e.g., decentralized environmental notification messages (DENM) or cooperative perception messages (CPM), encapsulated in IP datagrams. The network server extracts, aggregates, processes and reflects all information related to road events in a certain coverage area, and it provides the requested information to those vehicles that have interested knowledge about dangerous situations in this geographical area. The location of dangerous events is shown on a local dynamic map (LDM) at the HMI, as if it was received directly by V2V communications.  
Besides detection of dangerous situations by on-board sensors, vehicles can combine information received from the geoserver, in order to apply decision policies in the ADAS systems, e.g., warning the driver about dangerous situation and/or automatically activating brake system if needed.  
Figure 3: Typical scenario of the anticipated cooperative collision avoidance (ACCA) use case where an abnormal situation cannot be detected by on-board sensors. Cross-border situation where different mobile network operators (MNOs) have a partial view of the incidence.
## 3 Results and Discussion
A thin, long and flexible aluminum beam works as the appropriate conduit. Tests were repeated for 50, 60, 80, 100, 110, 150, 180 and 200 Hz. Continuous sweeps of frequency have also been recorded. The results of the case study support the selection of a low-stiffness beam as the sensing material. The sensor does not have a complex design. However, it can be successfully retrofitted on metal beams, cylinders and plates to pick up peripheral vibration frequencies. The metallic wedge can be chosen to be a cylindrical metallic pipe, a solid rectangular wedge or any other shape that supports a pre-strain in the FBG by allowing deformation. This leaves room for the sensor to expand and compress according to the vibrational frequency. Several tests have been repeated with a triangular wedge instead of beam 1. For the motor test, no wedge or intermediate beam was present as the sensing beam was simply placed in contact with the housing. Instead of a complex design that is difficult to maintain, this sensor consists of a simple low-stiffness beam so that the configuration is similar to that of a bridge and can consistently be strained. Modal studies related to the vibrations of the metal plate and cantilever have been carried out by several groups. However, in the case of this sensor, the boundary conditions are not limited to a specific orientation or alignment. Thus, the (theoretical) modal analysis is not specific to the sensor but to the application or the case study where a definite mounting method may be used to detect the vibrations. In the analysis, although \(\omega\) and \(L\) may have interdependence, the sensor will have similar responsivity for different values of \(\omega_{f}\). The underlying principle used in this type of test is that the vibrations from the metal beam attached with FBG act as a source of forced vibrations, which are detected by the FBG sensor. The frequency of the detected vibrations is the same as the source of the forcing vibration. The vibrating body forces/couples its own vibration on/to the passive beam bearing the FBG. The results are consistent with the general principle of harmonic forced vibration [18] where the detected frequency is unchanged but the measured amplitude varies with the stiffness and design. This FBG sensor can be applied to many areas requiring vibration measurements, such as measuring the vibrations near railway tracks, near drilling sites and on suspension bridges.