esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Defining the Use Cases: The Gaia-X-Med Project  
A total of four university research institutes, four clinics and eight companies were involved in the Gaia-X-Med project. Through this composition, we made sure that the results were not purely theoretical, but transferred and evaluated by clinical and industrial partners.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 3 Data Collection II: Stationary Target Reflection
### Target Detection Test Criteria  
We rule a successful target detection as satisfying a negative-positive-negative (neg-pos-neg) test. In this test, the MF is first computed without the presence of a target. This constitutes a negative target detection and sets the MF threshold for the positive test. A reflector (Figure 6) is then placed broadside to the transmit array at a nominal distance in the far field, and the MF response is calculated. A positive target detection requires that the maximum of the MF be at least 3 dB higher than the threshold set in the preceding negative test. The target is removed and the MF is calculated again. The maximum of the MF should again be approximately equal to the threshold set during the first negative test.
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 1 Introduction
Once the feature points have been detected, a describing process is performed to extract the corresponding points between the point clouds. The description process analyses each point in relation to its neighbours, focusing on its geometric or radiometric properties. Thus, the signature of histograms of orientations (SHOT) descriptor [37; 38] splits into several bins a sphere that is centred at a feature point and collects the histogram of normal angles in each bin to build the descriptor. Other descriptors such as USC or 3 DSC [39] also consider a sphere superimposed on the feature point and divides it into smaller segments. In these cases, the number of points contained is computed at each segment and weighted inversely by the segment density. These descriptors are not robust enough in environments with presence of noise [39]. [PERSON] [40] developed the point feature histogram (PFH) descriptor, which besides searching the correspondences between points, classifies them in primitive groups such as corners, edges or planes [29]. It should be highlighted that the scenarios of construction works present similarities and repetitive patterns, especially in the early stages of the works where the predominant elements are pillars, walls, doors and windows, all presenting similar shapes. Moreover, the scans of these scenarios are affected by illumination changes. All these factors complicate the process of detection and characterization of corresponding points between point clouds, provided that two different points from different point clouds can be assigned very similar descriptions and be incorrectly considered as corresponding due to their similar characteristics. In order to avoid all the previous drawbacks and to develop a methodology that is easy to use, the detector selected was Harris 3D [41; 42] in combination with the point feature histogram (PFH) descriptor. The paper is structured as follows: after the Introduction, Section 2 presents the methodology developed and the technology used; Section 3 shows the results obtained after the application of the methodology to the quality control of the construction works based on statistical analysis with robust estimators. Section 4 describes the conclusions extracted during the work.
## 2 Impedance Measurement in Electrical Drive Systems  
### Electrical Machine Impedance Definition  
Several types of impedance measurements can be performed on a permanent magnet synchronous machine (PMSM). The most common types in the literature are common mode (CM) and differential-mode (DM) measurements. On an electrical machine with a star-connected winding, these CM and DM measurements are carried out by connecting the different phases of the machine, as shown in Figure 1a,b. For condition monitoring purposes and to locate the fault in the windings, it is possible to successively analyze the behavior of two of the machine phases, as shown in Figure 1c. Finally, with access to the neutral point, it is possible to characterize the behavior of each phase individually, as described in Figure 1d.  
Similarly, for machines with delta-connected windings, the CM and DM impedance behaviors are measured by connecting the machine phases as shown in Figure 2a,b. Figure 2c describes the connection scheme for characterizing the impedance behavior of each of the machine phases.  
Finally, in the case of an open-end winding, the behavior of each machine phase can be straightforwardly characterized according to the measurement scheme of Figure 3.
# On Hyperbolic Complex Numbers
## 3 Spherical Hyperbolic Complex Numbers
### Four Dimensional Space-Time Structure
: _We define the geometric hyperbolic product of the vectors \(\mathfrak{r}_{1}=Hyp(r_{1},\varphi_{1,1},\varphi_{2,1},\psi_{1})\) and \(\mathfrak{r}_{2}=Hyp(r_{2},\varphi_{1,2},\varphi_{2,2},\psi_{2})\) as_  
\[\mathfrak{r}_{1}*_{h}\mathfrak{r}_{2}=Hyp(r_{1}r_{2},\ \varphi_{1,1}\circ \varphi_{1,2},\ \varphi_{2,1}\diamondsuit\varphi_{2,2},\ \psi_{1}+\psi_{2}) \tag{25}\]  
_where_  
\[\varphi_{1}\circ\varphi_{2}=(\varphi_{1}+\varphi_{2})I_{[0,\pi]}(\varphi_{1} +\varphi_{2})+(\varphi_{1}+\varphi_{2}-\pi)I_{(\pi,2\pi)}(\varphi_{1}+\varphi_ {2}).\]  
**Theorem 6**. : _The analytical hyperbolic vector product of \((t_{1},x_{1},y_{1},z_{1})^{T}=Hyp(r_{1},\varphi_{1,1},\varphi_{2,1},\psi_{1})\) and \((t_{2},x_{2},y_{2},z_{2})^{T}=Hyp(r_{2},\varphi_{1,2},\varphi_{2,2},\psi_{2})\) equals the geometric hyperbolic one._  
Proof. : The proof of this theorem follows the line described in Section 2. **Example 3**. : _Since_  
\[\left( \begin{array}{c}t\\ x\\ y\\ z\end{array} \right)\odot_{h}\mathfrak{e}=\left( \begin{array}{c}t\\ x\\ y\\ z\end{array} \right) \tag{26}\]  
_and_  
\[\mathfrak{j}_{1}\odot_{h}\mathfrak{j}_{1}=\mathfrak{j}_{2}\odot_{h}\mathfrak{ j}_{2}=\mathfrak{j}_{3}\odot_{h}\mathfrak{j}_{3}=\mathfrak{e} \tag{27}\]  
_the vectors \(\mathfrak{e}\) and \(\mathfrak{j}_{1},\mathfrak{j}_{2},\mathfrak{j}_{3}\) are called the hyperbolic multiplicative neutral element and the hyperbolic units. Note that (27) is closely related to, but nevertheless different from, (16) in [25]._  
It is supposed to, respectively, mean \(\oplus\) and \(\bullet\) vector addition and scalar multiplication in the usual sense. **Definition 15**. : _We call \((\mathfrak{X},\oplus,\bullet,\odot_{h},\mathfrak{0},\mathfrak{e},\mathfrak{i} _{1},\mathfrak{j}_{2},\mathfrak{j}_{3})\) with \(\mathfrak{e},\mathfrak{j}_{1},\mathfrak{j}_{2},\mathfrak{j}_{3}\) having the properties (26) and (27) a four dimensional hyperbolic complex algebraic structure._  
We recall once again from [23] that changing the basis elements of an event space can be very useful and does not affect the basic properties of the complex algebraic structure under consideration.
## 4 Verification
### Case Study  
In this section, we supplement the investigations with an array of simulation cases involving various stiffened prototype plates, aimed at scrutinizing the validation of the similarity procedure in addressing distorted model II. In compliance with shipbuilding structural guidelines, a series of prototype variations characterized by diverse dimensions of T-type stiffeners were meticulously crafted. An overarching scaling factor of 1/10 was consistently applied to all instances, and the detailed dimensions of these perfectly similar stiffened plates are summarized in Table 12. In each instance, the reference stiffened plates were subjected to an impact event initiated by a hemispherical indenter, endowed with a mass of 0.4 t and an initial velocity of 3.0 m/s. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Item** & **Unit** & **Model 1** & **Model 2** & **Model 3** & **Model 4** & **Model 5** \\ \hline Distorted model I & Plastic displacement & mm & 39.78 & 39.70 & 39.51 & 39.75 & 39.63 \\  & Peak acceleration & m/s\({}^{2}\) & 198,136 & 201,432 & 203,220 & 205,054 & 205,238 \\ \hline
**No.** & **Item** & **Unit** & **M1-E** & **M2-E** & **M3-E** & **M4-E** & **M5-E** \\ \hline Distorted model II & Plastic displacement & mm & 40.70 & 40.24 & 40.66 & 40.79 & 40.64 \\  & Difference & - & 2.30\% & 1.36\% & 2.91\% & 2.61\% & 2.55\% \\  & Peak acceleration & m/s\({}^{2}\) & 198,729 & 200,924 & 202,999 & 205,366 & 205,343 \\  & Difference & - & 0.30\% & \(-\)0.25\% & \(-\)0.11\% & 0.15\% & 0.05\% \\ \hline \end{tabular}
\end{table} 
Table 11: Numerical results and difference from corresponding distorted model I.  
Figure 17: Acceleration and displacement time history curves. (**a**) Acceleration; (**b**) displacement. Subsequently, the similarity procedure was employed to design the corresponding distorted models II, wherein the dimensions of the web were calculated to satisfy Criterion 1, along with the consistent load cases mirroring the reference models. The detailed dimensions of these distorted models are listed in Table 13. Utilizing identical solver settings consistent with the previous sections, numerical simulations were conducted for both the reference models and their corresponding distorted models. The resulting plastic displacements and peak accelerations are summarized and compared with the corresponding reference models in Table 13. The impact masses were adjusted using Equation (24) by substituting the dimensions into the equation. Since the sectional area of these distorted models closely matched that of the corresponding reference models, the impact mass remained approximately equal to the initial impact mass of 0.4 t in the reference models. As a result, Figure 18 presents the displacement and acceleration results for all the cases. The reference models are indicated by solid lines, whereas the corresponding distorted models II adhering to Criterion 1 are represented by dashed lines of the same color. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 4 Simulation Environment and Performance Metrics
### Average End-to-End Delay  
The average end-to-end delay is the average amount of time that packets take to travel from their source to their destination. It also includes the delay caused by re-transmission, buffering, and queuing. It can be computed as:  
\[\text{EED}=\frac{1}{N\sum_{n=1}^{N}(r_{n}-s_{n})}\quad\text{sec.} \tag{12}\]  
Here, \(r_{n}\) shows the time when the packet is sent, \(s_{n}\) shows the time when a packet is received, and \(N\) shows the total number of packets received.
## 3 Retrieving Private Data with Optimization Processes on Homomorphic Encryption Schemes
### The Modulus Switching Process  
When performing homomorphic encryption operations, we will use a set of encryption parameters to encrypt the data, one of which is the modulus \(q\) which defines the algebraic structure of the ciphertext. In general, \(q\) is a product of multiple prime numbers, which determines the ciphertext noise tolerance for correct decryption. The larger the \(q\) value, the greater the noise tolerance, which means that more operations can be performed on the ciphertext, and the size of the ciphertext would be more extensive. When the noise tolerance of the ciphertext is less than or equal to 0, subsequent operations are prone to produce erroneous results. Therefore, at the beginning of encryption, we should choose a larger \(q\) to make the ciphertext capable of performing more operations and producing the correct result. When it is determined that the ciphertext will not be subjected to subsequent operations, the \(q\) value of the ciphertext can be switched to a smaller \(q^{\prime}\), thereby reducing the size of the ciphertext and the transmission cost. In addition, replacing \(q\) with \(q^{\prime}\) will not affect the correctness of the decrypted ciphertext. The above statement's correctness is because the ciphertext size is proportional to \(\log\left(q\right)\). Optimizing this step will reduce the ciphertext size by \(\log\left(q\right)/\log\left(q^{\prime}\right)\) times. Similarly, Table 7 depicts the complexity of the Labeled-PSI after applying the Modulus Switching Process.
# Design and Analysis of the Composite Stability Control of the Reflective Optoelectronic Platform
## 3 Design and Analysis of Composite Stabilization Control System  
### Control Scheme Design and System Model  
Figure 7 depicts the optimized combined stabilization control scheme. The coarse-stable part, located below the dotted line, achieves LOS stabilization through conventional control methods [13; 14]. The unique feature in this study is the fine-stable part, located above the dotted line. This section employs a fast reflection operation under the position closed-loop mode to utilize the residual errors from the coarse-stable control loop as the input instruction for the fast reflector after feed-forward correction. The combined outputs from both coarse and fine circuits determine the inertial attitude of the LOS. The disturbance torques \(T_{d1}\) and \(T_{d2}\) correspond to the coarse and fine stabilization loops, respectively, while the optical coefficient is denoted by \(\lambda\).  
The system model shown in Figure 7 can be optimized to achieve the results seen in Figure 8:  
* The coarse-stable channel and the current controller are controlled by \(C_{1}\) and \(C_{2}\). The driving magnification, torque coefficient, current sampling coefficient, and back electromotive force coefficient are represented by \(K_{1}\), \(K_{2}\), \(K_{3}\), and \(K_{4}\). The equivalent object of coarse-stable control and the gyro are represented by \(G_{1}\) and \(G_{g}\).
* The feed-forward correction, fine-stable channel position control, and current controller are controlled by \(W_{1}\), \(W_{2}\), and \(W_{3}\), respectively. The driving magnification, torque coefficient, current sampling coefficient, and back electromotive force coefficient are represented by \(M_{1}\), \(M_{2}\), \(M_{3}\), and \(M_{4}\), respectively. The equivalent object of fine-stable control is represented by \(G_{2}\), and the position sensor is represented by \(G_{p}\).
* The optical coefficient is denoted by \(\lambda\), and \(T_{d1}\) and \(T_{d2}\) represent the disturbance torque of the two channels. The motion of the base is represented by \(\omega_{b}\). The dotted line separates the fine stabilization components.
* By using the coarse-stable control error as the input, error compensation control can be carried out through the feed-forward channel to achieve high precision control of the LOS. When the azimuth channel is selected, \(\theta_{A}\) and \(\lambda=1\) are represented by \(\theta_{A}/_{E2}\), and when the pitch channel is selected, \(\theta_{E2}\) and \(\lambda=2\) are represented.  
This control strategy utilizes current closed-loop systems in the coarse and fine channels to decrease motor delay and enhance the response speed from voltage to current, optimizing motor performance.  
Figure 8: The combined stabilization control system model.  
Figure 7: Combined stabilization control scheme.
# Uniform Linear Antenna Array Beamsteering Based on Phase-Locked Loops
## 3 N-Element Array Design
#### Defining Array Parameters  
The definitions for antenna-related parameters can be found in [2]. Some have already been defined in Section 2 and some which are relevant to arrays are defined below:  
Beamwidth: In a plane containing the direction of the maximum of a beam, the angle between the two directions in which the radiation intensity is one-half value of the beam.  
Side Lobe Level (SLL): A side lobe is a radiation lobe in any direction other than the intended lobe. Side lobes are normally the largest of the minor lobes. The level of minor lobes is usually expressed as a ratio of the power density in the lobe in question to that of the major lobe. This ratio is often termed the side lobe ratio or side lobe level.  
Number of elements: This is the number of individual antennas that make up the array, we have chosen 5 elements for the array; the greater the number of elements the greater the directivity.  
Phased array: An arrangement of antenna elements where the phase of each element is controlled to steer the beam.  
Phase excitations: The phase input to the elements on the arrays is termed as excitation phase.  
Maximum Beam scan angle: The maximum beam-scan angle for a linear array without much beam-shape degradation is around \(\pm 60^{\circ}\), in the absence of mutual coupling, from bore-sight.
## 5 Open Problems and Future Opportunities for UAV Wireless Networks
### UAV Deployment and Trajectory
Both users' position and UAVs' deployment are obtained by ML techniques, which are echo state network (ESN) based predetion algorithm and multi-agent Q-learning-based placement algorithm, respectively. In [89], the optimal deployment of UAV BS for communication service to multiple users is proposed to maximize the sum transmit rate using Q-learning. The algorithm can learn the network topology with no explicit information of the environment to improve the network performance. The integration of ML techniques into UAV wireless networks increases as it can provide an efficient solution for complicated problems considering multi-factors. Q-learning, artificial neural network (ANN), deep Q-network (DQN) are applied in [90; 91; 92; 93] to improve the throughput, sum rate, and spectral efficiency of UAV wireless networks thereby extending the service coverage and enhancing the network capacity, however, more research on various environments should be conducted for practical implementation.
## 5 Experimental Results and Discussion
### Test 3: Analysis of Residual Loss with Two GWs  
The final test is performed with considering the same scenario of Test 2 in the presence of an additional GW configured to receive only UP packets. Table 6 reports the total number of lost packets for ED8 for two different SFs. It is worth noting that in this case the residual PLR is by far less than 0.1% even for small SFs (7-8), which confirms that the double GW solution provides the required service levels in the considered scenario.It is also important to stress that the use of double GW is effective if and only if the proposed transmission scheme is adopted: this highlights the viability and importance of such scheme. In particular, the required service levels cannot be achieved just using two GWs.
# Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions
## 3 DFT-Based LS Channel Estimation Methods
### Improvements in the Related Methods  
The main reason of the further modifications of the DFT-based methods is the observation that among \(M\) nonzero coefficients of \(\hat{\mathbf{h}}_{C}\), there are often still the ones with relatively low amplitudes. These can be probably other noise-like components inside the channel part, as can be inferred from the channel models with possible sparse or compressible impulse responses. Methods that replace their low amplitudes with zero values are the simplest form of the channel characteristic refinement. However, the issue is how to select the coefficients from \(\hat{\mathbf{h}}_{C}\) to form a new impulse response? Two popular approaches are briefly outlined below.
###### Abstract  
Since the raw signal collected from the sliding bearing is contaminated with background noise, and it is difficult to obtain high-precision results for the traditional methods due to the low signal-to-noise ratio (SNR). Therefore, a stepwise intelligent diagnosis method based on statistical filter and stacked auto-encoder (SAE) that is established with several auto-encoders is proposed to identify several faults of sliding bearing in a rotor system. Firstly, the statistical filter is utilized to reduce the interference information for the different abnormal states and to increase the SNR. Secondly, the stepwise intelligent diagnosis based on SAE is performed to learn the useful fault features, and it can automatically complete the fault diagnosis which is contributed with the superiority binary classification to fully mine the relationship between the fault characteristics and the health condition of bearing. Finally, the diagnosis of the oil whirl and structural faults in a rotor system is cited as an example to demonstrate the effectiveness of proposed method. It can effectively illustrate the advantages of the stepwise diagnosis method to obtain the maximum diagnostic accuracy.  
sliding bearing; fault diagnosis; SAE; statistical filter; stepwise intelligent diagnosis Article
## 1 Introduction  
Nitrogen-containing wastewater is an important issue in high-tech industries such as optoelectronic and semiconductor manufacturing industries. For example, in thin-film transistor liquid crystal display (TFT-LCD) manufacturing processes, wastewater containing high concentrations of nitrogen compounds (such as ammonium (NH\({}_{4}\)\({}^{+}\)), monoethanolamine (MEA), or tetramethylammonium hydroxide (TMAH)) is generated [1; 2]. TMAH is a caustic developing fluid, widely used in the manufacture of TFT-LCD and light emitting diodes (TFT-LED) and in semiconductor industries as a developer or etchant [2; 3; 4]. For example, a TFT-LCD factory (sixth generation) could generate 30,000 cubic meter per day (CMD) of TMAH-containing wastewater [5]. In Japan, one factory could discharge roughly 2500 tons of TMAH per year [6]. In addition, MEA is used as a stripper in the manufacture of TFT-LCD [7]. Exposure to TMAH may result in muscle weakness, muscular paralysis, dyspnea, hyperglycemia, respiratory deterioration, alkaline chemical burns, and/or sudden death [8; 9; 10]. For example, Lin et al. [10] reported on three fatal incidents in Taiwan due to TMAH exposure. TMAH must be properly treated before discharge due to its health and environmental effects [11]. These pollutants in wastewater discharged from TFT-LCD manufacturing factories are already being legislatively regulated in Taiwan and Japan [9]. In addition, the TMAH effluent standard at the Hsinchu Industrial Science Park, Taiwan, was modified from 60 to 30 mg/L in 2015 due to human health and environmental concerns. High concentrations of TMAH solutions from factories could be physicochemically recovered and recycled, while wastewater containing lower concentrations of TMAH (in the order of hundreds of mg/L) can be treated in wastewater treatment facilities [9; 12].  
Common treatment processes for nitrogen-containing wastewater include adsorption [13; 14; 15], biological processes [9; 11], ozonation [16], chemical oxidation [17], electrodialysis [18], and UV/persulfate [19; 20]. Although wastewater containing high concentrations of TMAH can be treated by Fenton oxidation and catalytic oxidation processes, costs of these processes can be very high [11]. The TFT-LCD industry contributed more than 100 billion dollars annually due to the growing demand in electronic products [2]. There is a need to develop effective processes for the removal of nitrogen-containing wastewater because meeting the new TMAH effluent standard will not be an easy task. An alternative effective treatment process (MnCe-GAC/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\)) for the treatment of nitrogen-containing wastewater is proposed and investigated in this study.