esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
## 2 Related Work  
### 3D Shape Completion  
In this section, we review recent work applying deep learning technology to shape completion, and then introduce the feature pyramid network and its application to point cloud completion.  
Figure 1: Our method directly predicts missing point clouds (blue points) with incomplete point clouds (gray points) as input. We designed an encoder–decoder architecture with a Channelwise Attention Module (CAM) and a Mixed Attention Module (MAM). The decoder utilizes a bidirectional point pyramid attention network and skip connections to generate multiscale predictions (primary center points, secondary center points, detailed points).  
In the field of shape completion, PCN [15] is the first deep learning network using point clouds without any voxelization. It presents an encoder-decoder architecture and FoldingNet to generate a dense point cloud in a coarse-to-fine fashion. Subsequently, many models have been developed to achieve high-resolution completion and strong generalization performance. By combining reinforcement learning with a generative adversarial network (GAN), RL-GAN-Net [16] and Render4 Completion [17] are a novel set of architectures for 3D point cloud representation learning and generation whose reconstruction results had better robustness. [PERSON] et al. [18] proposed a new recurrent forward network (RFNet) for point cloud completion and achieved the most advanced performance, which consisted of three separate modules: recurrent feature extraction (RFE), forward dense completion (FDC), and raw shape protection (RSP).
## 7 Performance Evaluation  
The performance of the rocket engine and the nozzle is dependent on parameters such as thrust (T), specific impulse (I\({}_{\text{sp}}\)), thrust-specific fuel consumption (TSFC), thermal efficiency (\(\eta_{\text{th}}\)), propulsive efficiency (\(\eta_{\text{p}}\)), and overall efficiency (\(\eta_{\text{o}}\)). This section includes the estimation of all the mentioned parameters and presents a performance comparison of configuration C1 with the baseline configuration [27; 28; 29].  
Figures 26 and 27 represent the trend of thrust and a specific impulse of  
The efficiency of a rocket engine is described as internal and external efficiency. Internal efficiency is called thermal efficiency, whereas external efficiency is called propulsive efficiency, and a combination of both efficiencies is known as the overall efficiency of the system [26]. Figure 28b-d shows estimates of C1's thermal, propulsive, and overall efficiency plotted against the baseline configuration.  
Thermal efficiency specifies the effectiveness of converting power input into the system into power output. Figure 28b shows the trend of thermal efficiency versus nozzle radius with and without thrust regulation via GG. It is observed that the thermal efficiency is increased by about 24% from 0 mm to 21 mm radially.  
The propulsive efficiency was estimated against a Delta-V (\(\Delta\)V) of 1 km/s, equivalent to a rocket travelling at Mach 3. Figure 28c plots the trend of propulsive efficiency against the ratio V/C, which is the ratio of rocket flight velocity (V) to exhaust velocity (C). It can be seen from the plot that 100% propulsive efficiency can be achieved in both cases and the rocket can be operated with an exhaust velocity less than its flight velocity. However, with thrust regulation in GG, V/C can be extended up to a ratio from 0 to 1.7.  
Figure 28d plots overall efficiency versus nozzle radius. Figure 28d shows that without thrust regulation, overall efficiency could reach 10% and was distributed erratically along the nozzle radius. While with thrust regulation, the overall efficiency increased by up to 24% and was highest in the region from 0 mm to 10 mm.
# Microwave Pyrolysis of Biomass: The Influence of Surface Area and Structure of a Layer
## 1 Introduction
### Motivation, Novelty, and the Main Objectives of the Study  
A review of scientific achievements in the field of microwave pyrolysis illustrates its active development. At the same time, the pyrolysis process itself and its characteristics continue to require deeper insight in order to develop optimal technologies that maximize the production of useful end products. The purpose of this study was to determine the effect of the surface area and structure of the biomass layer, its distribution and dispersion on the characteristics of microwave pyrolysis. The following tasks were solved: (1) development of an experimental procedure and design of an experimental setup; (2) determination of the nomenclature and optimal concentrations of promising biofuel components; (3) formulation of generalizing criterion expressions and comparative analysis of biomass pyrolysis efficiency with varying surface area.
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
## 3 The Developed Fault Location Algorithm
### Determining a Physical Solution  
The proposed equations for fault location are second-order polynomial equations in terms of \(x\), where \(x\) represents the fault's distance. As a result, two new distances for the fault are obtained for each iteration of the explained algorithm. One of them is a positive and is coordinated with the section under evaluation, and the other one is purely mathematical and has no physical meaning.  
\[\alpha_{2}x^{2}+\alpha_{1}x+\alpha_{0}=0 \tag{23}\]  
The fault's distance \(x\) is obtained from the following equations, which show the correct physical solution:  
\[\left\{\begin{array}{ll}x=\frac{-\alpha_{1}+\sqrt{\alpha_{1}^{2}-4\alpha_{2 }\alpha_{0}}}{2\alpha_{2}},&\mbox{$If\ (\alpha_{1}>0)$},\\ x=\frac{-\alpha_{1}-\sqrt{\alpha_{1}^{2}-4\alpha_{2}\alpha_{0}}}{2\alpha_{2}},& \mbox{$If\ (\alpha_{1}<0)$}.\end{array}\right. \tag{24}\]  
The flowchart of the proposed method is shown in Figure 3.  
Figure 3: Flowchart of the proposed method.
# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
###### Abstract  
Deepfakes have become widespread and have continued to develop rapidly in recent years. In addition to the use of deepfakes in movies and for humorous purposes, this technology has also begun to pose a threat to many companies and politicians. Deepfake detection is critical to the prevention of this threat. In this study, a Choquet fuzzy integral-based deepfake detection method is proposed to increase overall performance by combining the results obtained from different deepfake detection methods. Three different deepfake detection models were used in the study: XceptionNet, which has better performance in detecting real images/videos; EfficientNet, which has better performance in detecting fake videos; and a model based on their hybrid uses. The proposed method based on the Choquet fuzzy integral aims to eliminate the shortcomings of these methods by using each of the other methods. As a result, a higher performance was achieved with the proposed method than found when all three methods were used individually. As a result of the testing and validation studies carried out on FaceForensics++, DFDC, Celeb-DF, and DeepFake-TIMIT datasets, the individual performance levels of the algorithms used were 81.34%, 82.78%, and 79.15% on average, according to the AUC curve, while the level of 97.79% was reached with the proposed method. Considering that the average performance of the three methods across all datasets is 81.09%, it can be seen that an improvement of approximately 16.7% is achieved. In the FaceForensics++ dataset, in which individual algorithms are more successful, the performance of the proposed method reaches the highest AUC value, 99.8%. It can be seen that the performance rates can be increased by changing the individual methods discussed in the proposed method. We believe that the proposed method will inspire researchers and will be further developed.  
deepfake; manipulation; deepfake video detection; faceswap; face reenactment; Choquet fuzzy integral Article
## 2 Irrigation System Modeling
### Crop Evapotranspiration  
Crop evapotranspiration (\(ET_{c}\)) is the combination of two processes, when water is lost from the soil by evaporation and crop transpiration. Evaporation is when liquid water is converted to water vapor and removed from the surface by evaporating. Transpiration is the vaporization of liquid water contained in plant tissues into the atmosphere [29].  
Factors affecting evapotranspiration are subject to climate conditions and crop characteristics. According to the crop coefficient approach the crop evapotranspiration is determined by the product of the reference crop evapotranspiration, \(ET_{o}\) and the crop coefficient \(K_{c}\), as shown in Equation (6) [29].  
\[ET_{c}=K_{c}\cdot ET_{o} \tag{6}\]  
where \(ET_{c}\) is the crop evapotranspiration in mm per day, \(K_{c}\) is the dimensionless single crop coefficient and \(ET_{o}\) is the reference crop evapotranspiration in mm per day.  
Figure 2 represents, in a general way, the curve of the crop coefficient \(K_{c}\), according to the development stage of a crop, where the evolution of the plant from the initial growth stage, is the crop coefficient \(K_{c\,\,ini}\), the \(K_{c\,\,md}\) is the crop coefficient during the middle stage of crop growth and \(K_{c\,\,fin}\) is the crop coefficient at the final stage [29].  
There are different approaches given by the Food and Agriculture Organization of the United Nations (FAO) that allow the calculation of reference evapotranspiration by means of equations that use meteorological variables. As a standard method, the empirical method of Equation (7), corresponding to the FAO Penman-Monteith equation for the calculation of \(ET_{o}\), is recommended [27; 29].  
\[ET_{o}(t)=\frac{0.408\Delta(t)\cdot(R_{n}(t)-G(t))+\gamma\frac{900}{T(t)+273}u _{2}(t)\cdot(e_{s}(t)-e_{a}(t))}{\Delta(t)+\gamma(1+0.34u_{2}(t))} \tag{7}\]  
where \(ET_{o}\) is the reference evapotranspiration of mm per day, \(R_{n}\) is the net radiation at the crop surface \([\mathrm{MJ}/\mathrm{m}^{2}/\mathrm{day}]\), \(G\) is the heat flux density \([\mathrm{MJ}/\mathrm{m}^{2}/\mathrm{day}]\), \(T\) is the daily average air temperature at 2 m height \([^{\circ}\mathrm{C}]\), \(u_{2}\) is the wind speed at 2 m height \([\mathrm{m}/\mathrm{s}]\), \(e_{s}\) is the saturation vapor pressure \([\mathrm{KPa}]\), \(e_{a}\) is the actual real vapor pressure in \([\mathrm{KPa}]\), \(\Delta\) is the temperature vapor saturation slope \([\mathrm{KPa}/^{\circ}\mathrm{C}]\) and \(\gamma\) psychometric constant \([\mathrm{KPa}/^{\circ}\mathrm{C}]\).  
Figure 2: Generic curve of the crop coefficient (\(K_{c}\)).
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 4 Numerical Experiments
### Impact of the Correlation Structure of the Arrival Process
#### 4.2.1 Frame Synchronous Coded-Slotted ALOHA  
Figure 12 presents the numerical results for frame synchronous CSA. We cannot notice in the graphs differences in performance with respect to the case with no correlations (i.e., Poisson point arrivals, cf. Figure 4). Figure 12 was produced with \(H=0.75\)--a moderate value for LRD--and \(r_{1}=0.5\) --also an intermediate value for the short-range correlation, but the results obtained with other combinations of \(H\) and \(r_{1}\) are identical. This can be explained with the same reason conjectured in Section 4.1.1: since all the arrivals during a frame must wait until the start of the next one for transmitting their replica packets, and since these replica packets are uniformly distributed over the frame, the original correlation structure vanishes and only the average number of new packet arrivals matters. So, in this case, the synchronous structure and gated service make the correlations possibly inherent in the input traffic inessential for the system performance. This insensitivity is not unexpected, yet its empirical verification is still interesting. As it is also shown in Figure 4, MUD has a clear advantage over single detection as long as the normalized traffic load is light (\(\lambda_{a}/k\leq 0.5\), approx.), but increases quickly beyond that threshold. This is reflected in the average delay in the same fashion, since the delay reaches a maximum near that threshold for the input traffic, too, and then starts to decrease and eventually stabilizes. This decay is due to the high fraction of discarded (non-decoded packets), obviously.
## 4 Discussion
UAV data acquired from oblique and facade viewing angles were compared with nadir images in a steep and complex mountainous terrain, and the produced point clouds were compared with TLS data [24]. It was proved that the combination of oblique and facade images, compared to nadir data, increased the geometric accuracy of the derived point cloud data [24]. In a respective test [25], it was demonstrated that the combination of nadir and oblique images ameliorates the accuracy of 3D surface models in an agricultural area when no ground control points or a small number of ground control points are used. Nadir and oblique imageries were compared for 3D building representation [26]. The results proved that oblique data ameliorates the obtainable accuracy of the derived point cloud and of the produced 3D model. As described in [28], supplementing nadir image blocks with oblique images consistently mitigates the systematic error patterns within complex topography. The results from many scenarios proved that the combination of nadir and oblique images increased precision and accuracy, and reduced data gaps. Those previous results are in full accordance with our results. In all the studied areas, it was also proved that the simultaneous processing of the nadir and oblique imagery decreases the RMSE error and increases the geometric precision.
## 2 Materials and Methods
### Definition of the Asphalt Mixtures to Be Studied and Their Aggregate Grading Curves  
The mineral aggregates and the filler were also characterized regarding their grading curves, according to the EN 933-1 standard. By knowing the gradation of all aggregates, including the PEX waste, it is possible to calculate the amount of each material to be used in order to fit the standard grading limits of the asphalt mixture to be produced in this work, i.e., an AC 14 surf mixture (Portuguese annex of standard EN 13108-1).  
The main objective of this work is to incorporate a significant amount of PEX in an asphalt mixture for a road pavement surface layer and compare its performance with a conventional mixture without PEX. Thus, two comparable asphalt mixtures were defined in this work to carry out the study: (i) a conventional mixture with only mineral aggregates; (ii) a new mixture incorporating PEX, with 5% of PEX by volume of the aggregates used in the mixture. Taking into account that polymer modifiers are typically included only as a small percentage of the bitumen, the amount of PEX used in this work is remarkably high, in order to highlight the outcomes of this solution.  
The calculated amounts of each aggregate fraction and PEX waste used in both asphalt mixtures, with or without PEX, in order to fit their gradation within the limits of an AC 14 surf mixture are presented in the Table 1.  
The aggregate grading curves of the different mineral aggregates, filler and PEX waste are presented in Figure 1a. Then, by using the amount of each aggregate fraction and PEX waste previously presented, the aggregate grading curves of the conventional mixture (without PEX) and the mixture incorporating PEX are presented in Figure 1b, perfectly adjusted to the standard limits of the AC 14 surf mixture.  
PEX waste presents a 0.5/4 dimension, which will partially substitute the coarser part of 0/4 mineral dust. It should be noted that these dimensions could be changed in future studies, namely by further reducing the PEX size in order to increase the interaction with bitumen, but the industry costs associated with this operation shall be taken into consideration.  
It can also be observed that the particle size distribution obtained for both asphalt mixtures, with or without PEX, is almost equivalent. Thus, the performance of both mixtures can be compared without direct influence of their gradation, which should only be influenced by PEX waste.
## 2 Development of the Positioning Procedure
### DGPS and Conditional DGPS  
DGPS is a positioning technique whereby the position of the rover is determined using code pseudorange corrections, calculated by the reference receiver. The achieved position accuracy is refined to a scale of 1 metre, whereas the accuracy of an autonomous position is around 10 metres [12]. DGPS assumes good operational conditions in the proximity of both receivers. DGPS does not function well under adverse conditions, because the noise and multipath are amplified in the differencing process [12].  
Since DGPS does not perform well under adverse conditions where signal reception is poor, so-called conditional DGPS (cDGPS) has been developed, in which only strong signals are used to secure the positioning. Strong signals are defined as those with a SNR value above 35 dBHz, which is usually the limit value for the signal tracking of regular GNSS receivers [3].The cDGPS is performed in each epoch with 6 or more strong signals which are also tracked by the reference receiver. If the cDGPS cannot be performed, the positions are determined by the custom autonomous positioning procedure. Another useful feature of the cDGPS is its ability to calculate residuals for \"no cDGPS\" epochs. At the epoch the conditional DGPS is viable again, the position is determined by the cDGPS and by the custom procedure. The difference between both positions is time-related distributed back to  
Figure 1: Flowchart of the custom positioning procedures.  
\"no cDGPS\" epochs. This feature has no bearing on real-time surveys. Nevertheless, it is useful for a post-analysis of the calculated positions. It should be noted that the cDGPS can only be performed when reference station data is available. Figure 1 displays the basic data flow of the custom developed autonomous and cDGPS positioning procedures.
## 3 Results
### Principal Component Analysis  
As already discussed in Section 2.4, principal component analysis constitutes a fundamental part of chemometric data processing. Figure 4a shows on the \(x\) axis the first principal component (PC1), which explains 42.97% of the variance and on the \(y\) axis, the third principal component (PC3), which explains 9.78% of the variance. In the orthogonal space defined by the two components (which account for about 53% of the original variance), a horizontal trend is evident along the PC1, which could be attributable to the maturation of the samples and the variations that occur in berries over the course of time, mainly evolution of skin pigmentation. The ripening trend is underlined by the labelling of the samples according to the content of soluble solids (TSS); from right to left, we observe the same trend also recognizable in the total readouts of the device.  
Therefore, the interpretation of scores plot suggests that the main source of variability is the progress of maturation. To fully understand the results of the PCA, it is also necessary to observe the loadings plots, as reported in Figure 4b,c. The second principal component (PC2) seems to be attributable to the use of two different sensors. PC2 is positive for the wavelengths of the first sensor (450, 500, 550, 570, 600 and 650 nm) and negative for the wavelengths of the second sensor (610, 680, 730, 760, 810, and 860 nm). This principal component could explain the information related to the acquisition methodology of sensors operating in two different ranges of the electromagnetic spectrum, i.e., visible and NIR, respectively.  
Figure 3: Total readouts of four sampling dates of both sampling years, labelled according to the reference parameters: TSS (**a**), pH (**b**) and TA (**c**).
## 4 Experiment Results and Discussion  
### Experimental Environment  
This study made use of a GPU server with the following specifications: The system configuration featured an Intel (R) Xeon (R) Silver 4110 CPU (Intel, Chandler, AZ, USA) operating at a frequency of 2.10 GHz, accompanied by a substantial 128 GB of RAM. Additionally, the setup incorporated five NVIDIA GeForce RTX 2080 Ti (NVIDIA, China) graphics processing units, each equipped with 11 GB of memory. Among these GPUs, one was allocated for the federated server, while the remaining four were dedicated to individual clients. The operating system employed in this study was Ubuntu 16.04.6 LTS, 64-bit. The programming environments utilized included Python 3.8, TensorFlow 2.8.0, and Flower 1.3.0 [63] (Federated Learning Framework). However, for practical deployment, edge GPUs such as the NVIDIA TX2 and Intel NUC with GPUs were deemed sufficient.
## 1 Introduction  
One of the key aspects of any real-world deployment of Internet of Things (IoT) devices is the security of the system. In the context of Internet of Things (IoT), security requires that messages from devices have not been modified on route, and that they originate from a valid sender, as well as often requiring that the messages are protected from eavesdroppers. The Secure Remote Update Protocol (SRUP) [1] proposes a mechanism to address these requirements. SRUP enables secure, and authenticated Command and Control (C2) communication for IoT devices. The protocol is built on top of Message Queuing Telemetry Transport (MQTT) and utilizes a signed, binary message pattern which is used to send operational messages (such as data, commands, or instructions to receive software or firmware updates) between a C2 server and a series of IoT devices.  
In addition to these message types, the protocol also supports a number of _management_ messages, including facilitating the secure joining of devices to a C2 network.  
In order to ensure the authenticity of both the message and the sender, all messages are signed by the sender. Messages may also be optionally encrypted by using MQTT overTransport Layer Security (TLS). In the current implementation of SRUP, both operations rely on the use of Rivest-Shamir-Adleman (RSA) public/private key-pairs (although the protocol permits any asymmetric cryptographic system to be utilized, providing all parties in the C2 network agree on which is being used).  
This paper builds upon a previous work [2] which described a scheme by which a human _observer_ could confirm that a specified _physical_ device was in possession of the _logical_ identity it claimed to hold, and describes the implementation of a scheme to enable the automatic matching of a physical device, to its logical identity, via a trusted third-party: an observer node.