esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Geometric Model for Tree Height Measurement  
A specific geometric model was developed to measure tree heights by considering the length of the shadows, the terrain's slope and aspect, and the Sun's position, which were derived from the image's metadata.  
In particular, the Sun's azimuth and elevation were considered (Figure 8) and defined as follows:  
* Sun's azimuth (\(\theta_{\text{S}}\)): the angle between the north direction and the Sun's direction projected onto a horizontal plane (143.7\({}^{\circ}\) for this specific acquisition);
* Sun's elevation (\(\varphi_{\text{S}}\)): the angle between the horizontal plane and the direction of the Sun (62.2\({}^{\circ}\) for this specific acquisition).  
The terrain slope and aspect were extracted from the DEM using QGIS software v. 3.34.10 [39]. Additionally, the image was visualized in false color (Figure 9a) with QGIS to enhance the identification of the tree canopy, minimizing the influence of shadows and the surrounding terrain. The following was conducted for each tree investigated in the study area:  
Figure 7: Area 1 UAV point cloud obtained using DJI Phantom 4 multispectral RTK (**a**); Area 2 (**b**) and Area 3 (**c**) point clouds obtained using DJI Phantom 4 photogrammetric RTK.  
* The shadow length was estimated directly on the orthophoto along the Sun's azimuth direction by creating a shape file containing a segment with an inclination of 143.7\({}^{\circ}\) from the shadow's endpoint to the corresponding point on the canopy (Figure 9b);
* The segment length was measured using the \"Add geometry attributes\" tool;
* The two extreme points of the segment were then extracted using the \"Extract vertices\" tool of the vector menu (Figure 9c);
* The first point of the segment (ground point) was used to measure the terrain's aspect and slope using the \"Extract value by point\" tool;
* The second point of the segment (canopy point) was used to identify the elevation of the corresponding point on the canopy from the UAV point cloud, which was later used to validate the tree height measured by the model.  
Finally, the following equation (Equation (1)) was used to estimate the tree height (\(H\)) (Figure 10):  
\[H=h+\Delta=d\times tan\varphi_{5}+d\times\cos(\beta-\theta_{5})\times tan\alpha \tag{1}\]  
where  
* \(d\) represents the shadow length measured along the Sun's azimuth direction;
* \(\varphi_{5}\) represents the Sun's elevation angle;
* \(\theta_{5}\) represents the Sun's azimuth angle;  
Figure 8: Sun’s azimuth (\(\theta_{5}\)) and Sun’s elevation (\(\varphi_{5}\)).  
Figure 9: Study area in false color visualization (**a**); cyan segment represented the shadow length (**b**); “ground point” and “canopy point” (cyan dots) were extracted from the shadow segment (**c**).  
\(\alpha\) represents the terrain's slope;
* \(\beta\) represents the terrain's aspect. In particular, \[h=d\times tan\varphi_{S}\] (2) is the tree height without the terrain slope effect; \[\Delta=d\times\cos(\beta-\theta_{s})\times tan\alpha\] (3) is the difference between \(H\) (actual tree height) and \(h\) (Figure 11).
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 5 Experiments
### Training of D1-DCAE Model  
The optimization of the trainable parameters in deep neural networks heavily relies on the training process, which directly impacts their performance. Hence, to achieve the highest possible performance of the 1D-DCAE model, the training process was conducted with extreme care. The main aim was to ensure the model's high reconstruction capacity and discriminative ability. In addition, various experiments were implemented using different training hyperparameters. In order to minimize the loss (i.e., reconstruction loss), the training process was designed such that it utilized the minibatch gradient descent optimization approach and employed the Adam update rule [35] considering its fast convergence rate and lower memory requirements. Finally, a batch of 32 training samples (i.e., minibatch) and a learning rate of 0.001 are utilized.  
The loss computed over the minibatch is used in backpropagation to balance the trade-off between the model's robustness and efficiency. The MAE is considered a reconstruction loss because it results in the best performance. Notably, the [PERSON] method [36] is used to initialize all of the model's trainable parameters in order to keep the backpropagated gradients and activation values within a tolerable range. The 1D-DCAE model is trained using normal SIP messages solely after applying the feature extraction steps explained in Section 3.2. As every batch of normal SIP messages in the training dataset flows through the model's deep network during each iteration (i.e., epoch) of the training process, the model's trainable parameters are updated in such a way as to minimize the reconstruction loss. After many epochs (typically more than 150 epochs), the reconstruction loss reaches its minimum, and the encoder part learns how to encode the relevant features of a normal SIP message into a lower dimensional latent representation \(H\) with a 50 times decrease in dimension. Furthermore, the decoder part of the model also learns how to reconstruct a normal SIP message from its encoded features in the latent representation \(H\).
# Anthropomorphic Grasping of Complex-Shaped Objects Using Imitation Learning
## 5 Collecting Data for Imitation Learning
### Three-Dimensional Data  
As we can see in Figure 5, at the beginning of the process, the platform first captures depth images of the manipulation object from three different viewpoints, and these images are converted to point clouds. By transforming these point clouds according to the captured pose and merging the transformed data into a single point cloud, we can make a 3D model of the object. However, we cannot directly use the result of this process as it may contain points from backgrounds.  
In order to remove the background points behind the object, we use the following filtering process. We first capture the depth image of the objects in the up pose, where the camera faces downwards, and first remove the points with \(z\)-coordinate lower than the preset threshold. Then, we calculate the minimum and maximum \(x,y\) values of the remaining points, which are used to filter out points when the system captures the depth  
Figure 5: System overview.  
image of the objects sideways. Although this method can remove most of the background points, it is hard to remove the point cloud of the tripod stand the object is placed upon. To solve this problem, as shown in Figure 6, we wrapped the tripod stand with black tape so that it was not detected by the IR-based RealSense sensor.  
The 3D data acquisition algorithm is summarized in Algorithm 1, where \(T_{up}\), \(T_{left}\), and \(T_{right}\) are defined as a transform matrix [33] for the up pose, left pose, and right pose, respectively. The output of the above algorithm is shown in Figure 7. After creating a 3D model with this algorithm, it is labeled with the object name, trajectory data, and grasping pose. The following section explains this task.  
```
1:capturing_pose \(\leftarrow\) up
2:while capturing object do
3:point_cloud \(\leftarrow\) convToPointCloud(depth_image)
4:point_cloud \(\leftarrow\) removeIndexStand(point_cloud)
5:if capturing_pose is upthen
6:3d_model.setEmpty()
7:transform_mat \(\leftarrow\)\(T_{up}\)
8:max_x,max_y,min_x,min_y \(\leftarrow\) setXYCond(point_cloud)
9:xy_cond \(\leftarrow\) [max_x,max_y,min_x,min_y]
10:capturing_pose \(\leftarrow\) left
11:elseifcapturing_pose is leftthen
12:transform_mat \(\leftarrow\)\(T_{left}\)
13:capturing_pose \(\leftarrow\) right
14:elseifcapturing_pose is rightthen
15:transform_mat \(\leftarrow\)\(T_{right}\)
16:endif
17:point_cloud \(\leftarrow\) filtering(point_cloud,xy_cond)
18:point_cloud \(\leftarrow\) transform(transform_mat,point_cloud)
19:3d_model.append(point_cloud)
20:ifcapturing_pose is right then
21:break
22:endif
23:endwhile
```  
**Algorithm 1** Three-Dimensional Reconstruction Algorithm  
Figure 6: Five objects used for the experiment. (**a**) Scissors. (**b**) Brush. (**c**) Cup. (**d**) Clamp. (**e**) Spray bottle.
# On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
###### Abstract  
The Internet of Energy is the deployment of IoT technology within energy systems (including distributed power monitoring and measuring points, energy plant sensors, points of distribution) to increase the efficiency of the whole infrastructure while decreasing energy waste. Due to criticality and the extension of the Internet of Energy, it needs an underlying network with vast coverage and high-efficiency parameters. In this paper, we argue that the 5G network is suitable for the Internet of Energy and present a concrete 5G implementation based on Open RAN that may gain in flexibility while reducing costs. In our simulations, we model and validate beamforming mechanism in Open RAN 5G and show that beamforming may achieve high-efficiency parameters that the Internet of Energy requires.  
Keywords:internet of energy; 5G; open radio access network +
Footnote †: journal: _Energies_
## 1 Introduction  
The importance of foundational dynamic stiffness and damping in the vibration's assessment has been addressed in many literatures such as [PERSON]'s method [1], [PERSON]'s approach [2], and [PERSON]'s research [3].  
The conventional method for evaluating soil stiffness and damping is based on the classical theory of vibrations of a disk supported on top of an elastic half-space [4]. This theoretical solution is limited to few simple foundation configurations and soil profiles. The applications of the theory often overestimate the degree of soil damping, and, thus, underestimates the vibration amplitude. Field experiences show that the beneficial effects of radiation damping in mitigating foundational vibrations, as predicted by the elastic half-space vibration theory, may not always be effective [5]. Such cases include large compressor foundations, combustion turbine generator/steam turbine generator (CTG/STG) table-top foundations, and other large foundational structures that support high-speed (30 Hz or higher) vibratory equipment. In special soil media, such as rock stratum found very close to the bottom of the foundation, the radiation damping from the theory may be impractical to vibration analyses. This minimal radiation damping is due to the elastic rebounding of waves at the soft soil-rock interface, which is different from the theoretical solution. Currently, a few modelling guidelines [5] exist for assessing such a soil-structure interaction damping on foundation vibrations using the Finite ElementMethod. This paper proposes several state-of-the-art methods for evaluating effective soil damping that could be modelled in FE (Finite Element) software i.e., SAP2000 and GISTRUDL. For the formulation of modal damping ratios in the dynamic equation of motion, two different approaches, i.e., direct use of effective damping values and proportional damping (either mass-proportional or stiffness-proportional damping, or both) are employed in this study.  
The frequency-dependent interaction effect [6; 7] is accomplished by numerical simulations utilizing DYNA6 [8] and is incorporated in the frequency domain steady-state vibration analysis in this paper.
## 3 Results
### Validation of the PT-JPL Model
#### 3.1.2 Validation of PT-JPL Model Based on MODIS ET  
Figure 4 shows the annual ET data based on the PT-JPL model and MODIS products in the HLS, and the correlation analysis results between the two. Figure 4a shows the annual ET data based on the PT-JPL model. The simulated ET values of the model show obvious spatial differences over the range of the HLS. The ET value gradually decreases from the higher value (668.5 mm/a) in the north and middle to a lower value in the south. Figure 4b shows the annual ET data based on MODIS products. The ET values shown by MODIS products also have spatial heterogeneity, and the ET values gradually decrease from the higher values in the north and middle (815.7 mm/a) to the lower values in the south.  
Figure 3: The results for ET simulated by PT-JPL and the water balance method (including Bias, RMSE, and linear fitting R\({}^{2}\) as indicators) are verified.  
Figure 2: ET calculated by the water balance method in the HLS from 2000 to 2018. (**a**) The annual average runoff and its trend at the upstream and downstream gate stations. (**b**) The regional runoff of the HLS, obtained by subtracting the runoff of the upstream Toudaoguai station from the runoff of the downstream Longmen station. (**c**) The regional ET of the HLS, obtained by subtracting the regional runoff from the regional precipitation according to the water balance method.  
south (202.9 mm/a). Figure 4c shows the distribution of correlation coefficients between the PT-JPL model and MODIS products. The regions with strong correlations are predominantly located in the central and southwestern areas, where the correlation coefficients are generally high, around 0.94. This suggests a high level of consistency between the ET data from the PT-JPL model and MODIS products in these regions. Conversely, the areas with weaker correlations are mainly found in the southern and certain northern regions, with lower correlation coefficients, some even below 0.5, indicating notable differences in ET data between the PT-JPL model and MODIS products in these areas.  
Furthermore, our research compared the annual ET simulated by the PT-JPL model with the annual ET from the MODIS product. The validation results indicated an R\({}^{2}\) value of 0.78, a Bias of 20.94 (Figure 5), and an RMSE of 44.79. The comparison between the annual average ET values from the PT-JPL model and those from the MODIS product demonstrates that the model's simulation results are highly reliable.  
Figure 4: ET simulated by the PT-JPL model was verified by Modis ET. (**a**) Average ET simulated by the PT-JPL model from 2000 to 2018; (**b**) average Modis ET, 2000–2018; (**c**) the correlation between the multi-year average ET simulated by the PT-JPL model and the multi-year average ET of Modis was analyzed.
# Behavior of Circular Hollow Steel-Reinforced Concrete Columns under Axial Compression
## 2 Experimental Procedures and Results
### Failure Modes  
Figure 4 presents the measured load-deflection (\(N\)-\(u_{\text{m}}\)) curves. The bearing capacity decreases by 20.1% from the measured value as the column height (\(H\)) increases from 1600 mm to 3200 mm; The bearing capacity decreases by 0.3% as the diameter (\(d\)) of the built-in circular hollow steel tube increases from 80 mm to 100 mm. When the load increases to about 50% for specimen 1-1-1, 70% for specimen 2-1-1, and 60% for specimen 1-2-1 of the ultimate load, the concrete in the right mid-height area of the specimen started to crush, accompanied by a cracking sound. When the load reached about 80% for specimen 1-1-1, 90% for specimen 2-1-1 and 70% for specimen 1-2-1 of the ultimate load, the whole specimen began to deform, and the concrete fragmentation area on the right side of the specimen continued to expand. As the ultimate bearing capacity was reached, the concrete in the mid-height area on the right side of the specimen was crushed and peeled off. As the load decreased to approximately 90% of the ultimate bearing capacity, the concrete in the left area of the specimen started to crack and burst, and the concrete in the mid-height area on the right side of the specimen was completely crushed. Figure 5 presents the failure process of the testing. The crushing area measured in the concrete of the axial compression specimen was compared with the strain distribution provided by FEM in Figure 6. The distribution of cracks in the concrete predicted by the finite element method is very similar to the experimental distribution of cracks, indicating that the proposed finite element model can represent the compression conditions of the concrete sample well. Figure 6 also illustrates the failure modes of the specimen as a whole, including the concrete, steel bars, and internal steel tube, determined through experiments and finite element methods. The inward buckling of the CHSRC axial compression specimen occurs at the middle height of the column; Generally speaking, there is good consistency between predicted data and experimental results.  
Figure 4: The measured load–deflection curves (\(N\)–\(u_{\text{m}}\)) of the specimens.  
Figure 5: The failure process of the specimens.  
Figure 7 plots the measured data curves of height against the lateral deflection of the columns, indicating that the deflection curves of the columns are consistent with half-wave sine curves.
## A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification  
[PERSON] *, [PERSON], [PERSON] and [PERSON]  
Integrated Management Coastal Research Institute, Polytechnic University of Valencia,  
Camino Vera s/n, 46022, Valencia, Spain; E-Mails: [EMAIL_ADDRESS] (M.G.);  
[EMAIL_ADDRESS] (D.B.); [EMAIL_ADDRESS] (S.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-609-549-043; Fax: +34-962-849-313.  
Received: 29 September 2009; in revised form: 15 October 2009 / Accepted: 20 October 2009 /  
_Published: 30 October 2009_  
**Abstract:** Forest and rural fires are one of the main causes of environmental degradation in Mediterranean countries. Existing fire detection systems only focus on detection, but not on the verification of the fire. However, almost all of them are just simulations, and very few implementations can be found. Besides, the systems in the literature lack scalability. In this paper we show all the steps followed to perform the design, research and development of a wireless multisensor network which mixes sensors with IP cameras in a wireless network in order to detect and verify fire in rural and forest areas of Spain. We have studied how many cameras, sensors and access points are needed to cover a rural or forest area, and the scalability of the system. We have developed a multisensor and when it detects a fire, it sends a sensor alarm through the wireless network to a central server. The central server selects the closest wireless cameras to the multisensor, based on a software application, which are rotated to the sensor that raised the alarm, and sends them a message in order to receive real-time images from the zone. The camera lets the fire fighters corroborate the existence of a fire and avoid false alarms. In this paper, we show the test performance given by a test bench formed by four wireless IP cameras in several situations and the energy consumed when they are transmitting. Moreover, we study the energy consumed by each device when the system is set up. The wireless sensor network could be connected to Internet through a gateway and the images of the cameras could be seen from any part of the world.
## 1 Introduction
Another cause of ISI is the residual energy caused in the line by signal reflections due to impedance mismatch, not at the ends where it can be controlled via internal impedance at the ports, but along the transmission line. The present paper is focused on evaluation of interconnects in terms of previously presented aspects and different equalization schemes are applied to prevent ISI degradation evaluated via time and frequency domain analysis, based on simulation results. First, in Section 2, materials are selected for substrate, a certain technology is associated to the transmission link, distributed parameters are associated to model the interconnect, several properties of the transmitted signals on traces affected by higher and higher data rates and by increased length so increased attenuation, followed by general aspects of the EQ. In the same section particularities of passive, active, and adaptive EQs are then highlighted and compared in terms of functionality and complexity. Section 3 presents results of the passive EQs starting with a synthesis of the lumped elements based schemes and distributed ones using microstrip resonant structures. For the first part of this section, the synthesis process starts from the insertion loss of the analyzed link with a derivative resistor-capacitor (RC), a series of resistor-inductor (RL), or a combination of the two in a resistor-inductor-capacitor (RLC)--based EQ are first evaluated and in the end a bridge T filter is proposed for constant input and output impedance optimization function. For the second part, microstrip-based EQs, open stub structure will make the high-pass effect of EQ rise earlier and two variants of this model are implemented with a new schematic elaborated. The final section of conclusions summarize the synthesis presented in this paper.
# On-Road Driver Emotion Recognition Using Facial Expression
## 2 Related Work
### Driver Facial Expression Recognition  
Driving a vehicle is a complex process involving visual cues, hazard assessment, decision-making, and strategic planning [49] for both the driver and the automotive [50]. Driving style and driving behavior have attracted research interest for driving safety [51; 52] and avoiding collision [53]. [PERSON] et al. [54] investigated the driver anger regulation in visual attributes. Driver facial expression mirrors driver emotional state [55]; therefore, it is important for emotion recognition and ensuring driving safety.  
In [56], a driver facial expression recognition-based emotional stress system was proposed. The data was collected in a static vehicle scenario, then feature extraction applied PCA and implemented a SVM classifier.  
Due to the influence of driving tasks, the driver's facial expression may be suppressed or subtle when experiencing emotional states [57]. In that case, driver facial expression recognition is vital for intelligent vehicle human-machine systems.  
Vehicle driver facial expression recognition is more difficult compared with a lab-controlled environment. Nonetheless, most of the research mentioned above relating to driver facial expression recognition did not consider the issue of real on-road driver facial expression recognition, which lacks datasets for model training. Furthermore, these methods, regardless of the scenario, mean the datasets are static life scenarios or wild settings. The studies related to driver facial expression recognition also did not collect on-road driving data and did not adopt a transfer learning strategy, which is frequently adopted in ordinary facial recognition tasks.  
Emotion perception-based human-computer interaction in a smart cockpit is an important topic. More and more applications of deep learning have been utilized in various fields, yet driver facial expression recognition during the driving task has never been studied. Facial expression recognition during the driving scenario is far more important than the static scenario for emotion recognition in the intelligent vehicle human-machine system. This paper proposes a novel deep learning-based framework for on-road driver facial expression recognition in an end-to-end manner. To address the above-mentioned dataset limitation, this study conducted and collected a driver facial expression dataset. Based on this research, more studies can be migrated to the field of autonomous driving and smart cockpits, such as drone-assisted crowd counting [58], face detection under risk situations [59], and image super-resolution [60].  
In this work, a model for on-road driver facial expression recognition based on transfer learning is proposed. To the best of our knowledge, this is a novel work about on-road driver facial expression recognition. This work integrates some excellent technology in computer vision and deep learning with careful design and modification to construct theproposed FERDERnet and apply it. Furthermore, this research also collected a driver facial expression dataset during on-road driving tasks.