esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 3 Results
### Global Point Cloud Acuracies After 1 cp Transformation  
The RMSEICP-ALL better characterizes the systematic error (bending) of the individual clouds, see Chapter 2.6.4 (Table 3). For all scanners, ICP transformation achieved better results, confirming that the systematic shape error occurs (to some extent) in all measurements. It is most evident in the Trimble X7 measurements, where the RMSE value dropped by about 50% after ICP transformation, while the least pronounced systematic error was found in the Leica RTC 360 (an RMSE drop by only about 15%) indicated a predominance of the random component of the error). A noticeable drop in RMSE values occurred also in the case of SLAM scanners, most notably the GeoSLAM ZEB Horizon RT. However, despite this improvement, the accuracy of this SLAM scanner remained still the poorest among all scanners. The data also show that in the case of SLAM scanners and the Leica RTC 360, the random error generally predominates.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scanner**} & \multirow{2}{*}{**Trimble X7**} & \multirow{2}{*}{**Leica RTC 360**} & \multicolumn{2}{c}{**Emesent**} & \multirow{2}{*}{**GeoSLAM ZEB**} & \multirow{2}{*}{**FARO Orbis**} \\  & & & & **Hovermap ST-X** & & \\ \hline RMSESPHER [mm] for & & & & & \\ individual & 5.4 & 1.2 & 5.9/6.6/9.1 & 31.5/35.3/41.2 & 9.0/8.2/9.3 \\ measurements & & & & & \\ RMSESPHER® [mm] & 5.4 & 1.2 & 7.3 (1.7) & 36.2 (4.9) & 8.8 (0.6) \\ (StDev) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Absolute accuracies of individual clouds relative to the reference cloud (after initial transformation to spherical targets) and standard deviation (StDev in the brackets).  
Figure 8: Examples of spherical targets scanned by individual scanners. (**a**) Trimble X7; (**b**) Leica RTC 360; (**c**) Emesent Hovermap ST-X; (**d**) GeoSLAM ZEB Horizon RT; (**e**) FARO Orbis (**f**) Leica ScanStation P40.
## 3 Results and Discussion
### Spirulina Effects on Mozzarella Cheese's Chemical Composition
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Amino Acid (mg/100 g of Sample)** & \multicolumn{2}{c}{**Group**} & \\ \hline _Abbreviation_ & _Common Name_ & **C** & **S** & **SEM** \\ \hline Total EAA & & 6031.10 & 7040.75 & 736.86 \\ His & Histidine & 388.00 & 448.76 & 46.68 \\ Arg & Arginine & 552.39 & 633.09 & 55.73 \\ Thr & Threonine & 520.98 & 608.85 & 60.38 \\ Val & Valine & 793.33 & 931.31 & 103.68 \\ Met & Methionine & 278.41 & 333.20 & 48.91 \\ Lys & Lysine & 1039.48 & 1226.28 & 134.79 \\ Ile & Isoleucine & 645.86 & 753.29 & 83.53 \\ Leu & Leucine & 1176.33 & 1373.75 & 155.64 \\ Phe & Phenylalanine & 636.33 & 732.23 & 79.97 \\ Total NEAA & & 7150.25 & 8360.97 & 930.26 \\ Asp & Aspartic acid & 857.74 & 1015.54 & 114.24 \\ Ser & Serine & 742.36 & 873.18 & 94.15 \\ Glu & Glutamic acid & 2868.14 & 3361.24 & 389.88 \\ Gly & Glycine & 255.70 & 297.84 & 33.40 \\ Ala & Alanine & 364.21 & 424.53 & 43.46 \\ Pro & Proline & 1305.20 & 1520.22 & 171.65 \\ Tyr & Tyrosine & 733.81 & 844.68 & 85.86 \\ Cys & Cysteine & 23.09 & 23.74 & 6.92 \\ \hline \hline \end{tabular} SEM, Standard Error of the Mean; EAA, Essential Amino Acid; NEAA, Not Essential Amino Acid.  
\end{table} 
Table 3: Amino acid profiles of mozzarella cheese from control (C) and Spirulina (S) groups. The mozzarella cheese's mineral content was not affected by the diets, as shown in Table 5. As it is well known, the major mineral that is present in buffalo milk, and thus in buffalo mozzarella cheese, is calcium, while the second most predominant mineral is phosphorus. [PERSON] et al. [52] reported that the mineral content is influenced by the cheese process, with the ash content reducing proportionally as the milling pH decreases. They explained that acid development solubilises micellar calcium phosphate, leading to mineral expulsion in whey. Specifically, the calcium levels decrease in line with the milling pH reduction, although there is uncertainty about the influence of this cheese-making step on the potassium and sodium levels. The buffalo milk mineral content is subject to variation due to several factors, including the season, environment, diet, stage of lactation, animal breed, and genetics [53]. The Mediterranean buffalo has been reported to possess the highest magnesium content in milk, whilst the greatest amount of minerals found in buffalo milk occurred during the summer season [54]. In our study, it was found that the mineral content was consistent across the samples because both groups were homogeneous, consisting of the same breed, and were fed the same diet. Therefore, the inclusion of Spirulina powder did not affect the mineral percentage in the buffalo mozzarella cheese.
# Confidence Measures for Deep Learning in Domain Adaptation
## 5 Results
### Early Stop of the GAN Training  
In domain adaptation process, the labels of the target domain are not available. For this reason, a validation set cannot be used to evaluate the training progress; normally, a fixed number of training step is set to stop the domain adaptation phase. This criterion does not guarantee stopping the training properly, especially if the training is not stable. The proposed confidence measures proved to be related to the performance of a classifier and, in addition, they did not require the labels of the target dataset to be computed. For these reasons, our idea was to use them to monitor the domain adaptation process and to stop the training properly. In particular, the proposed confidence measures could be used to stop the training of the GAN in Step 2. In this GAN, the generator \(E_{I}\) was initialized with the pre-trained weights of \(E_{S}\) and was fed with a combination of images from both the target and the source domain. \(E_{I}\) was trained to learn the feature distribution produced by \(S\) (the generator of the GAN in Step 1). This particular configuration seemed to guarantee much more stability than a classic GAN framework. To prove the effectiveness of the proposed measures in GAN the training, the following experimental setup was employed:  
* The training was stopped according to the confidence measures (\(e\), \(s\), \(d_{img}\), \(g\), \(d_{feat}\), \(mix_{1}\) and \(mix_{2}\)) computed by the reliability estimator \(M\).
* The GAN was trained for a fixed number (200,000) of iterations (Fix Iter.).
* The early stop was based on the accuracy calculated on the validation set (Max Acc.)  
Figure 3: ROC curves obtained using each measures to predict the accuracy on the CIFAR validation set. Measures \(e\), \(s\), \(d_{img}\), \(g\), \(d_{feat}\), \(mix_{1}\) and \(mix_{2}\) are, respectively, plotted in (**a**–**g**).  
The experiments aimed at comparing the performance of the classifier \(C\), when the GAN was stopped according to these different strategies. It is worth noting that the stopping criterion based on the use of a validation set was not feasible in domain adaptation and, in this work, it was used only for comparison purposes, since it provided an ideal optimal performance. The experiments were carried out on two domain adaptation tasks: SVHN \(\rightarrow\) MNIST and CIFAR10 \(\rightarrow\) STL.
## 4 Control Mechanism Transformation in New Heterogeneous Traffic Flow Environments on Urban Roads
### New Heterogeneous Traffic Flow Characteristics and Model Construction  
These fundamental transformations in control mechanisms are enabled by advances in data acquisition capabilities. Vehicle-infrastructure networking environments enable acquisition of traffic data with broader spatiotemporal dimensions and more refined and comprehensive characteristics [31]. An important feature of new heterogeneous traffic flow is the mixed operation of conventional vehicles and intelligent connected vehicles, with significant differences in traffic flow characteristics under different penetration rates. [PERSON] et al. analyzed the research status of vehicle group collaborative decision-making from the perspectives of vehicle group collaborative decision-making mechanisms, collaborative decision-making methods, and typical application scenarios in vehicle-infrastructure cooperative environments, with research results providing references for the management and control of new heterogeneous traffic systems in vehicle-infrastructure cooperative environments [32].  
Regarding heterogeneous traffic flow detection and identification, [PERSON] et al. reviewed data collection research in urban traffic control, analyzing the evolution process from fixed non-identifiable data, mobile detection data, to fixed identifiable data, indicating that practical application factors such as changes in data error rates due to detection environment differences and changes in traffic flow arrival patterns represent challenges facing current research [33].  
[PERSON] et al. proposed a trajectory control theory for connected automated traffic in their research, providing a theoretical foundation for vehicle coordination control in heterogeneous traffic flow environments [34]. This trajectory-level traffic control method will play an increasingly important role in future heterogeneous traffic flow environments, providing theoretical support for precise control and optimization.  
In terms of multi-objective optimization, [PERSON] et al. proposed a cooperative method for vehicle speed control and traffic signal optimization, achieving comprehensive optimization of intersection throughput efficiency and energy conservation and emission reduction through speed guidance for connected vehicles and signal timing optimization [35]. This method fully considers the trade-offs between traffic efficiency and environmental protection, providing new approaches for multi-objective control in heterogeneous traffic flow environments.
The control system is realized by isolating the structure top story mass of a four-story steel frame structure, converting it into tuned mass, and connecting it to the substructure with two high damping rubber bearings placed in series. Two objectives are to investigate aspects related to the dynamic structural response and to assess the seismic effectiveness of the non-conventional TMD for control purposes. Three structural configurations are in turn investigated concerning the dynamic response and control effectiveness: (1) the reference four-story structure, (2) the three-story intermediate structure, (3) the three-story structure equipped with the non-conventional TMD. The TMD design according to a well-established literature design procedure is preliminary illustrated. The input motion conditions, considered at different intensities, are: white noise, sine sweep, and natural earthquakes. The results of the experimentation at each input aim at demonstrating that a non-conventional TMD implemented via inter-story isolation is a smart control strategy useful for enhancing structural vibration mitigation. Moreover, since the adopted isolators, i.e., high damping rubber bearings, introduce nonlinearity in the system, the nonlinear effects provoked on the frame story structure are investigated throughout the paper. The paper is organized as follows: Section 2 describes the physical models tested, Section 3 summarizes the non-conventional TMD design, Section 4 describes the experimentation, Section 5 illustrates the results of the experimentation based on the tests conducted (white noise, sine sweep, and natural earthquakes), and Section 6 reports the main conclusions of the work.
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 6 Tensor-Based Approaches for Multilinear Systems
### Tensor Systems  
In this section, we introduce the notion of tensor system using the [PERSON] product [55]. In Table 12, we present two examples of tensor systems: one is linear in the unknown tensor variable \(\mathcal{X}\), while the other one is bilinear in the unknown tensor variables \((\mathcal{X},\mathcal{Z})\). **Example 2**: _To illustrate the notion of tensor system, let us consider the following equation:_  
\[\mathbf{Y}=\mathcal{A}\star_{2}\mathbf{X}. \tag{70}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Forms** & **Dimensions** & **Tensor Systems** \\ \hline Linear & \(\mathcal{Y}\in\mathbb{R}^{Lp}\), \(\mathcal{A}\in\mathbb{R}^{Lp\times L_{N}}\), \(\mathcal{X}\in\mathbb{R}^{Lp}\) & \(\mathcal{Y}=\mathcal{A}\star_{N}\mathcal{X}\) \\ \hline Bilinear & \(\mathcal{Y}\in\mathbb{R}^{Lp}\), \(\mathcal{A}\in\mathbb{R}^{Lp\times\mathbb{R}^{Lp}\times\mathbb{R}^{Lp}}\), \(\mathcal{X}\in\mathbb{R}^{Lp}\), \(\mathcal{Z}\in\mathbb{R}^{\mathbb{R}\mathbb{R}}\) & \(\mathcal{Y}=\mathcal{A}\star_{N}\mathcal{X}\star_{M}\mathcal{Z}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Examples of tensor systems. _This equation can be associated with the following map \(f:\mathbb{R}^{K\times L}\
i\mathbf{X}\ \longmapsto\ f(\mathbf{X})=\mathbf{Y}\in \mathbb{R}^{I\times I}\) such as_  
\[y_{i,j}=\sum_{k=1}^{K}\sum_{l=1}^{L}a_{i,j,k,l}x_{k,l} \tag{71}\]  
_with the associated fourth-order tensor \(\mathcal{A}\in\mathbb{R}^{I\times J\times K\times L}\)._  
_Equation (70) can be solved with respect to the unknown matrix \(\mathbf{X}\) by minimizing the LS criterion \(\min\limits_{\mathbf{X}}\lVert\mathbf{Y}-\mathcal{A}\star_{2}\mathbf{X} \rVert_{F}^{2}\). This minimization is carried out after a vectorization of Equation (70) using the unfolding \(\mathbf{A}_{IJ\times KL}\) of the tensor \(\mathcal{A}\) and the vectorized forms \(\mathbf{x}_{KL}\) and \(\mathbf{y}_{IJ}\) of the matrices \(\mathbf{X}\) and \(\mathbf{Y}\), which leads to a standard system of linear equations in matrix form, with a coefficient matrix. The LS criterion then becomes_  
\[\min\limits_{\mathbf{x}_{KL}}\lVert\mathbf{y}_{IJ}-\mathbf{A}_{IJ\times KL} \,\mathbf{x}_{KL}\rVert_{2}^{2}. \tag{72}\]  
_Minimizing this criterion with respect to the unknown vector \(\mathbf{x}_{KL}\) gives the following normal equations:_  
\[(\mathbf{A}_{IJ\times KL}^{T}\mathbf{A}_{IJ\times KL})\hat{ \mathbf{x}}_{KL} =\mathbf{A}_{IJ\times KL}^{T}\mathbf{y}_{IJ} \tag{73}\] \[\Downarrow\] (74) \[\hat{\mathbf{x}}_{KL} =(\mathbf{A}_{IJ\times KL}^{T}\mathbf{A}_{IJ\times KL})^{-1} \mathbf{A}_{IJ\times KL}^{T}\mathbf{y}_{IJ} \tag{75}\]  
_if the matrix \(\mathbf{A}_{IJ\times KL}^{T}\mathbf{A}_{IJ\times KL}\) is invertible, i.e., if \(\mathbf{A}_{IJ\times KL}\) has full column rank, which implies the necessary but not sufficient condition that \(IJ\geq KL\)._  
**Remark 3**. : _Let us consider the inner product of the Nth-order real tensors \(\mathcal{A}\in\mathbb{R}^{I\! {\text{N}}}\) and \(\mathcal{X}\in\mathbb{R}^{I\!
## 3 Motivating Scenario  
### System Model  
The system model in a post-disaster rescue is presented in Figure 1, which mainly includes two entities: UAVs and exteriorly parked vehicles that have not been damaged after a disaster.  
UAV: Suppose the set of UAVs in the monitoring area is \(U=\{1,\,2,\,3,\,\ldots,\,u\}\). After taking off from a random position on the ground, each UAV selects a position at a height \(H\) from the ground and hovers horizontally at this position for area monitoring. \(H\) denotes the minimum altitude fit for area monitoring to prevent recurrent going down and going up. Suppose the coverage radius of each UAV is \(R_{0}\), and the coverage regions of the UAVs do not overlap with each other. Multiple UAVs can be organized into a UAV ad hoc network, and each UAV can transfer data to other UAVs through the network. Suppose that the communication radius of the UAV in the formed ad hoc network is \(R\). According to [30], we have \(R_{0}<R\). The UAVs aim to monitor the disaster area and obtain information from the target area at the first time.  
Parked vehicle: vehicles that park outside can be found everywhere in a city. Parked vehicles have the characteristics of short radio range, low bandwidth, limited storage and computational capacities, and wide distribution. Moreover, since parked vehicles are relatively stable, the end user's contact time with a parked vehicle is usually much longer than with a moving vehicle. Hence, compared to mobile vehicles, parked vehicles are more suitable for uploaded task execution. Suppose the set of parked vehicles in the monitoring area is \(N=\{1,\,2,\,3,\,\ldots,\,n\}\). Although a number of vehicles parked on the ground have been damaged in the disaster, a large number of these vehicles have not been damaged. These undamaged ground parked vehicles can serve as natural roadside computing facilities, and supply data offloading and computing services for UAVs in a collaborative manner.  
Cluster: There may be multiple parked vehicles in the monitoring area of the UAVs. To ease the management of the parked vehicles and optimize the allocation of parking resources, undamaged ground parked vehicles form clusters according to the method presented in Section 3.3. The clusters aim to offer computing service and even storage service for UAVs if necessary.  
Moreover, we assume parked vehicles and UAVs are equipped with OBUs through which they can send data to one another. UAVs are also equipped with another communication model (such as IEEE 802.11\(n\)) to enable a high rate of task data delivery among the  
Figure 1: System model in post-disaster rescue.  
UAVs. There are four types of wireless communication in our proposed system architecture: (1) UAV to parked vehicle; (2) parked vehicle to UAV; (3) UAV to UAV; and (4) parked vehicle to parked vehicle. The first two types of communication can occur if a parked vehicle is found to be within the communication range of an UAV. An UAV can deliver its packets to another UAV through the UAV ad hoc network. Furthermore, parked vehicles are able to transfer data to each other by using the IEEE 802.11\(p\) protocol.
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 2 Materials and Methods
### Deep Neural Network Approaches
#### 2.2.2 Recurrent Neural Network (RNN)  
RNNs are conventional neural networks consisting of one or more feedback loops [47]. RNNs have the ability to utilize their input memory to process entries [48]. In conventional neural networks, all inputs and outputs are considered to be independent of each other. This means that the output is not fed back to the network as an input; however, in the case of RNNs, output can be fed again with the input to be considered in future decisions [47; 48]. RNNs' basic architecture is shown in Figure 3.  
In Figure 3, the RNN consists of input (\(x_{i}\)), hidden state (\(h_{t}\)), and outputs (\(y_{t}\)). \(W_{x}\), \(W_{y}\), and \(W_{h}\) are weight matrices. The most important part of RNN is the hidden state (\(h_{t}\)), which is a vector that can also have an arbitrary dimension [48].  
\[h_{t}=F_{w}\ (h_{t-1},\ x_{t}), \tag{2}\]  
\[h_{t}=tanh\ \left(W_{h}h_{(t-1)}\ +W_{x}x_{t}\right), \tag{3}\]  
\[y_{t}=W_{y}h_{t}, \tag{4}\]  
Figure 3: Basic recurrent neural network (RNN).  
Figure 2: Multilayer perceptron network with single output.  
Figure 3 also shows the relationship between functions in RNN. In the functions, \(h_{\mathbf{(t-1)}}\) of previous hidden state contains information from the previous time step; \(F_{w}\) is an activation function as shown Equation (3) [47; 48].
## 4 Validation of Equivalent Field Source Model
### Experimental Validation of the Equivalent Model  
Measurements of radiation emissions from the axial flow fan are executed in a shielded darkroom. The radiated interference measurement process is based on the GJB-151B standard. The experiment layout is shown in Figures 13 and 14. The receiving antenna is placed 1 m from the device under test. Within the rated operating range of the fan, the antenna measures the intensity of the electric field in the frequency band from 30 MHz to 200 MHz. The device involved in the measurement includes a LISN, a receiving antenna, and a measurement receiver.  
The measurement procedures are as follows:  
a. Check the measurement system integrity.  
b. Verify that the ambient requirements specified meet the standard. Ensure that the ambient conditions do not compromise the test results.  
c. Turn on the measurement equipment, and allow sufficient time for stabilization.  
d. Turn on the equipment under test, and allow sufficient time for stabilization.  
e. Using the measurement path of Figure 13, determine the radiated emissions from the equipment under test and its associated cabling.  
According to the layout of the experiment, a detailed model of the radiation emission of the fan is established, including the axial flow fan model, DC lines, LISN, and the grounded  
Figure 14: Layout for radiation emission measurements.  
Figure 13: Measurement configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Type** &
\begin{tabular}{c} **Dimension of the** \\ **Calculation Domain** \\ \end{tabular} & **No. of Grid** & **Computation Time** \\ \hline Detailed model & 2000 \(\times\) 2000 \(\times\) 2000 & 4,572,216 & 42 h 59 min \\ \hline Equivalent model & 220 \(\times\) 220 \(\times\) 138 & 1,813,965 & 11 h 3 min \\  & 2000 \(\times\) 2000 \(\times\) 2000 & 126,720 & 7 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of grid parameters and computation time for two models.  
plane, as shown in Figure 15. The DC line current of the fan is used as the excitation source of cable radiation, which is measured by a current probe and an oscilloscope, as shown in Figure 16.  
A comparison is presented in Figure 17 between the measured electric field intensity results and the calculated results from both models. The evaluation results from the FSV analysis are tabulated in Table 3. It is evident that there is a high level of agreement between the results obtained from the equivalent model and the detailed model. Furthermore, the calculated results align well with the measured data.  
Figure 16: Conduction current measurement for DC lines. (**a**) Conduction current measurement; (**b**) current measurement results.  
Figure 17: Comparison of the measured results with the results of the two computational models.  
Figure 15: Detailed model of the radiation emission of the fan.