esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Qualitative Validation--Results and Analysis
### Qualitative Validation  
It is important to assess and validate methods qualitatively so as to develop a better understanding of the issues presented by different operational fault scenarios [27]. This implies that the information can be gathered by differentiating the performance outcome of each scenario and accordingly setting up an expected baseline for more efficient system health evaluation.  
Accordingly, a rubric scoring-based qualitative validation method is developed to evaluate the performance of the EBS under different scenarios with fault modes. An ascendant numerical qualification using numbers from 1 to 4 is given to describe the behaviour of the signal compared to the normal condition, with 4 representing the greatest change. The sum of the results obtained is compared to a range of values which define the fault mode. Table 3 describes the numerical assessment and the order used. Depending on the failure, a different combination in results can be expected and the failure identification process can be made. As seen in the previous section, the OC, IOC, and jamming have very different outcomes. Whilst the OC and IOC have electric power cut and no temperature change, with a jamming condition the same cannot be expected.  
Figure 10: The impact of fault modes of the electric power of the EMA electric motor (**a**) motor power in Normal Operation condition; (**b**) motor power in open circuit (OC); (**c**) motor power in intermittent open circuit (IOC); (**d**) motor power in jamming.  
Table 4 shows the qualitative validation of the fault modes by comparing various related output signals. It is worth noting that each of them displays a specific value that can help identify the change each signal experiences. Comparing with the normal condition, the change in every signal is very noticeable, as all of them suffer from medium-to-great increase. The OC shows a complete lost signal as the electric connection of the actuator is disconnected.
## 2 Theory of VMD
### Reduced Frequency Aliasing Effect  
Mode aliasing or mode mixing is a common phenomenon in the process of EMD, which is defined as signal components in the same intrinsic mode function (IMF) mode containing different disparate scales or components in the same scale residing in different IMF components [(26)]. Actually, mode aliasing will affect the feature extraction seriously. In order to verify a reduction in the effects of frequency aliasing of VMD, a multi-harmonic signal is analyzed in this subsection. The multi-harmonic signal can be written as follows:  
\[\begin{split} x_{1}(t)=1.5\cos(2\pi*30t)+\cos(2\pi*150t+\varphi)+ 0.5\cos(2\pi*200t)+\cos(2\pi*300t)+1.5\cos(2\pi*500t),\\ \varphi\in(-\pi,\pi),t\in(0,0.5).\end{split} \tag{3}\]  
The sampling frequency of \(x_{1}(t)\) is 2000 Hz, while the number of sampling points \(N\) is 2000. The temporal waveform of the simulated signal and its Fast Fourier Transform (FFT) spectrum are both shown in Figure 2. Obviously, there are five frequency components, namely 30, 150, 200, 300, and 500 Hz, in the spectrum.  
When the simulated signal was decomposed with VMD, the number of modes \(K\) was set to 5, and two kinds of initialization of the center frequency \(\omega_{k}\), denoted as \(\mathcal{P}_{u}:\omega_{k}^{1}=\frac{k-1}{2K}(k=1,\dots,K)\) and \(\mathcal{P}_{u}:\omega_{k}^{0}=0(k=1,\dots,K)\), were adopted in the verification, respectively. Finally, five BLIMFs achieved with VMD and their corresponding spectra are illustrated in Figure 3a-d, respectively. It can be seen in Figure 3a,b that all of the components in the signal can be separated very well without any redundant components and mode mixing when \(\omega_{k}\) was initialized by \(\mathcal{P}_{u}\). All five components of the original signal can be also detected successfully in Figure 3c,d when the initialization of \(\omega_{k}\) was chosen as \(\mathcal{P}_{z}\) in conjunction with a small value of \(\alpha\). Meanwhile, the IMF components obtained by EMD and their spectra are given in Figure 3e,f. However, it is found that there is no ideal harmonic component in Figure 3e because of the severe mode aliasing of EMD. In addition, it should be noted that the decomposed BLIMFs are very different from the IMFs shown in Figure 3f, since VMD is no longer a recursive decomposition algorithm.  
Figure 2: The simulated multi-harmonic signal: (**a**) temporal waveform (**b**) its Fast Fourier Transform (FFT) spectrum.  
Figure 3: Cont.
## 2 Low-Cycle Reversed Loading Test
### Test Results  
#### 2.3.1 PRC Tube Column Specim  
Some of the cracks generated in the column specimens during loading are presented in Figure 6. When the forward loading (displacement downward) reached 60 kN, the mid-span displacement of the column specimen was 3.77 mm, and the first crack appeared near the mid-span of the lower part for the column body. When the reverse loading (displacement upward) of 60 kN was applied, the first crack could be completely closed, and no cracks appeared in the upper part of the column body. After that, the loading mode of the displacement-controlled method was adopted. The mid-span displacement of the column specimen formed by the first-level forward displacement loading was 12 mm, and that formed by the first-level reverse displacement loading was \(-\)10.68 mm. Under the first-level forward displacement loading, two cracks appeared on the left and right sides of the lower part for the column body, respectively. Under the first-level reverse displacement loading, three cracks appeared on one side of the upper part for the column body, and two cracks appeared on the other side. Moreover, the crack in the lower part of the column body caused by the first-level forward displacement loading extended to the upper part of the column body. After that, 2-3 new cracks were produced in the upper and lower parts of the column specimen under each level of forward and reverse displacement loading, respectively. Among them, the concrete in the mid-span of the column specimen was crushed under the fifth-level forward displacement loading, and the steel bars in the column specimen were fractured during the ninth-level forward displacement loading.  
Figure 5: The arrangement of strain gauges.  
Figure 6: Some of the cracks generated in the PRC tube column specimen during loading.
Sensors_**2012**, _12_, 17295-17319; doi:10.3390/s121217295  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Towards a Hybrid Energy Efficient Multi-Tree-Based Optimized Routing Protocol for Wireless Networks**  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) INRIA Lille-Nord Europe, 59650 Villeneuve d'Ascq, France;  
E-Mails: [EMAIL_ADDRESS] (T.R.); [EMAIL_ADDRESS] (D.S.-R.)  
\({}^{2}\) SITE, University of Ottawa, Ottawa, ON K1N 6N5, Canada; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-359-577-846.  
_Received: 24 October 2012; in revised form: 10 December 2012 / Accepted: 12 December 2012 / Published: 13 December 2012_  
**Abstract:** This paper considers the problem of designing power efficient routing with guaranteed delivery for sensor networks with unknown geographic locations. We propose HECTOR, a hybrid energy efficient tree-based optimized routing protocol, based on two sets of virtual coordinates. One set is based on rooted tree coordinates, and the other is based on hop distances toward several landmarks. In HECTOR, the node currently holding the packet forwards it to its neighbor that optimizes ratio of power cost over distance progress with landmark coordinates, among nodes that reduce landmark coordinates and do not increase distance in tree coordinates. If such a node does not exist, then forwarding is made to the neighbor that reduces tree-based distance only and optimizes power cost over tree distance progress ratio. We theoretically prove the packet delivery and propose an extension based on the use of multiple trees. Our simulations show the superiority of our algorithm over existing alternatives while guaranteeing delivery, and only up to 30% additional power compared to centralized shortest weighted path algorithm.  
**Keywords:** geographic routing; guaranteed delivery; energy efficiency
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning
### What is SON?
The aim of self-optimization is to maintain the quality and performance of the network without the need for human intervention or manual work. Self-healing deploys trigger and detection methods in order to protect the network from failure. The purpose of self-healing is to automatically detect and address any network operation problems. Self-protection maintains the safety and security of the network from cyber-attacks or intruders. The aim of self-protection is to detect patterns that could lead to potential threats. 5G SONs should have the capability to adjust the network if needed in certain situations, the ability to find minimum network parameter values to acquire a high optimization level, and an awareness of the current network status and any changes happening within the network. All of these built in capabilities in SON will make it more suitable and adaptable for future wireless networks.
###### Abstract
The IIoT has been proposed to dramatically enhance quality of traditional industries, break regional limitations to achieve remote monitoring, perform autonomous production and provides real time information to users [19]. For example, the medical industry can also benefit from the IoT where industrial sensors and actuators are used as wearable devices to collect users' physiological data, such as blood pressure, electrocardiogram (ECG), temperature, and so on. This physiological data are usually sent to the nearby devices or servers of the user to carry out further data collection, aggregation, and then sent for diagnosis and input to a industrial provider using an open channel, i.e., the Internet. However, due to the round-the-clock networking of nodes in this IoT network, it is vulnerable to various security issues, such as message tampering, eavesdropping, and denial of-service attacks [20]. In the healthcare industry, this raises major security issues as the misuse of data can result in the incorrect diagnosis and can cause life-threatening scenarios for the patients under observation [21]. Moreover, data stored on a blockchain are highly reliable and available through replication. In this subsection, we enumerate the contributions of our proposed methodology, which are as follows:  
1. A detailed literature review of the state-of-the-art schemes of patient and participants detection based on encryption ad security algorithm;
2. Novel cross-domain and access control policies are proposed using homomorphic encryption;
3. Moreover, a new privacy-preservation and intrusion detection framework is designed by using blockchain and deep learning technique that enables two-level privacy;
4. In first level of data security and privacy preservation, a blockchain and smart contract scheme is suggested to enable immutable data exchange and to avoid data poisoning attacks;
5. We have achieved an optimum security and anonymous keyword search in the hyper-ledger fabric framework;
6. Our proposed research method provides an alternative private key in case a key is lost;
7. We have achieved the efficiency as compared to the existing methods as these methods exhibit more communication and encryption cost as these methods need to encrypt the data. Our proposed scheme provides an efficient solution to the users. The remaining structure of the paper is as follows: Section 2 discusses the literature review, Section 3 explains the proposed methodology, Section 4 defines the results, and Section 5 is the conclusion of the overall paper.
## 4 Simulation Results
### Numerical Simulation Results
#### 4.1.3 Formation Tracking with Disturbance  
In order to verify the anti-disturbance ability of the ESO in interference environments, the following simulation experiment was designed: Add different frequency interference signals to the three robots. Then,, \(disturbance_{1}(t)=disturbance_{3}(t)=3\sin(0.01\pi)t\) is added to Robot 1 and Robot 3. Next, \(disturbance_{2}(t)=3\sin(0.04\pi)t\) is added to Robot 2. The initial states of the three robots and the formation shape were set as per the following:  
\[d_{12} =d_{21}=[0,2,0]^{T},d_{13}=-d_{31}=[0,2,-2]^{T},d_{23}=d_{32}=[0,-2,-2]^{T}\] \[\mathbf{q_{1}} =[0,0,0,0,0,0]^{T}\] \[\mathbf{q_{2}} =[-0.5,-3,1,0,0,0,0]^{T}\] \[\mathbf{q_{3}} =[0,-2,1,0,0,0,0]^{T}\]  
The multi-small spherical robots formation tracking result is illustrated in Figure 15. As shown in the figure, the three robots respectively formed the expected formation and tracked the expected trajectory. The real trajectory of each of the robots possessed slight fluctuations, which was caused by added disturbances. Table 4 lists the root mean square error of the trajectory tracking and the root mean square error of the formation. Compared with Table 3, the root mean square error of the yaw angle increased, which was due to the higher-frequency sinusoidal interference added to Robot 2. The maximum error in a three-dimensional formation with interference is 0.66 BL (body length), which is less than the diameter of the robot and demonstrates the effectiveness of the formation control method with the ESO.  
Figure 14: Control inputs of each of the robots based on backstepping.  
Figure 15: Triangle formation trajectory in three-dimensional space with disturbance.
## 4 Measurement Results of HR and HRV with Optical Homodyne and Heterodyne Technique
### Feature Extraction in VCG and Its Mapping with ECG
From the recordings of both the signals, the relationship between the ECG to the vibratory signal VCG in terms of heart rate variations is analyzed. The peak vibratory signal is due to chest wall motion. This signal is generated due to cardiac muscle contraction triggered by the electrical signal. The first local maximum value is labeled as \(V_{1}\) in the VCG, while the first local maximum in the ECG curve is labeled as \(R\). For the measurement of \(HR\) and \(HRV\), V-V interval (\(T_{VV}\)) is considered, and this measurement is compared with the R-R interval (\(T_{RR}\)) in the ECG trace [44]. By using Equation (23), the \(HR\) can be calculated from the VCG signal as,  
\[HR\ =\left(\frac{1}{T_{VV}}\times 60\right) \tag{25}\]  
Figure 27: Mapping of complete ECG wave with VCG wave. Figure 26: Mapping of T wave of ECG and \(V_{2}\) wave of VCG.
## 2 Description of the LMS-Based Algorithms
### Filtered-X LMS Algorithm Applied to a Feed-Forward Configuration
#### 2.1.1 Offline Estimation of Secondary Paths  
Assuming the system to be linear time-invariant, it is possible to obtain an estimation \(s^{\prime}_{j\bar{i}}(n)\) of the filters, characterizing the secondary paths, before the application of the active control. With reference to Figure 3, the procedure for the offline estimation is the following:  
* A sample of white noise \(y_{j}(n)\) is generated with the \(j\)-th control source and used as reference input for the adaptive filter \(s^{\prime}_{j\bar{i}}(n)\) and for the coefficients updating in the LMS algorithm;
* The signal \(e_{i}(n)\) is sensed with the \(i\)-th error microphone;
* The response of the adaptive model \(e^{\prime}_{i}(n)\) is computed as: \[e^{\prime}_{i}(n)=\sum_{m=0}^{M-1}s^{\prime}_{j\bar{i},m}(n)y_{j}(n-m);\] (5)
* The convergence error is evaluated as: \(r_{i}(n)=e_{i}(n)-e^{\prime}_{i}(n)\);
* The adaptive filter coefficients \(s^{\prime}_{j\bar{i}}(n)\) are updated by using the LMS algorithm: \[s^{\prime}_{j\bar{i},m}(n+1)=s^{\prime}_{j\bar{i},m}(n)+\mu r_{i}(n)y_{j}(n-m )\ \ \ m=0,1,\ldots,M-1.\] (6)  
This procedure is repeated until a good convergence is reached. The coefficients of the adaptive filter \(s^{\prime}_{j\bar{i}}(n)\) are saved and used for the active control procedure described in Section 2.1. Since the secondary paths are invariant during the ANC application, it is important to note that they do not consider modifications of the physical system, such as geometric, thermal, or boundary condition variations. To solve this problem, it is possible to apply an online estimation procedure [28] or a periodically used offline technique in order to update the secondary paths and take into account system variations. However, to limit the computational complexity, in this article, only the offline modeling of secondary paths is employed.
# A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation
## 2 System Requirement  
Figure 1 shows the simplified diagram of the proposed system with two transceiver sets. In the HD mode, one transceiver is configured as the transmitter and the other as the receiver. Since they are always working in opposite modes, there is only one RF stream available all the time, which is also named time division duplexing (TDD). While in the frequency division duplex (FDD) mode or the in-band full-duplex (FD) mode, both transceiver sets act as transmitters and receivers simultaneously. Two RF streams are available in the opposite direction, and they can be configured with either the same or different carrier frequencies. In FDD, different carrier frequencies are used, while in FD, the same carrier frequency is used. Figure 2a,b shows the spectrum conversion for both the FDD and FD modes.  
In the FDD operation mode shown in Figure 2a, the LO frequencies are set at LO1 and LO2 so that the transmitter and receiver are separated in the frequency domain. This is achieved by adopting wide-bandwidth amplifiers in the receiver chain to cover its adjacent band. The wanted signals are separated from the SI signal in the frequency domain, and they can be recovered using a high-pass filter. The adjacent channel power ratio (ACPR) and SI isolation are the dominant considerations for FDD operation. However, the channel utilization is low, as both transceivers occupy separate physical channels. The digital baseband processor needs to handle the signal with an IF carrier, which will increase the complexity of the data processing units. In the FD mode, as shown in Figure 2b, both transceivers work simultaneously using the same channel, such that two RF streams share the same physical channel but are in opposite directions. In this mode, a frequency reuse strategy can theoretically double the spectral efficiency. The technical difficulties for such an approach are the requirement for SI suppression or cancellation, as the SI and the wanted signal overlap at the baseband. An excessive SI may even result in a reduced capacity for the FD system that falls below the HD systems. As shown in Figure 2b, the signal-to  
Figure 1: Simplified diagram for the dual-stream system.  
Figure 2: Frequency spectrum for the proposed dual-stream operation of the (**a**) frequency division duplex (FDD) and (**b**) full-duplex (FD) modes.  
interference and noise ratio (SINR) is the key parameter to estimate the capability of the FD system. In principle, the SI can be perfectly regenerated and compensated since the transmitted data are known inside the transceiver. However, the realistic SI is also affected by the non-linear effects of the transmitter and receiver chain and the unpredictable LO phase noise that makes it far from the estimation.
## 1 Introduction
To analyze the convergence and test the effectiveness of the proposed blind approach, a set of simulations is carried out for various signal to noise ratio (SNR) levels and the proposed approach is compared with adaptive existing algorithms [56] that use the input-output relations to identify the kernels of cubic systems. The outline of this paper is organized as follows. We start in the Section 2 with the problem formulation and some assumptions regarding the system model representation. Section 3 describes the main theoretical tools of HOC in the case of cubic systems. Theoretical development based using polyspectra are provided in the Section 4 to propose a blind approach based only on fifth-order cumulants. A set of performance results is supplied to support the theoretical development of the proposed blind approach, which is compared with an adaptive algorithm [56] in Section 5.
## 3 Model Design and Implementation
### Overview of FI-NL2 PY2 SQL  
The overall workflow of this model is illustrated in the diagram, which includes Pre-Processing, NL2 PY (Natural Language to Python), PY2 SQL (Python to SQL), and Post-Processing. First, we preprocess the query questions, evidence, database information, financial data models, and financial corpora to identify potential database tables. Next, we convert natural language to Python code using prompt-based methods and DeepSeek. The Python code is then iteratively optimized based on database and sample information. With the Python-generated code and database knowledge, SQL is generated using Prompt and DeepSeek, and the SQL queries are continually executed and refined by troubleshooting issues. Finally, the entire SQL is validated against checkpoints and then outputted. As shown in Figure 4.  
Figure 3: Financial industry corpus diagram. It includes almost all types of financial corpus, such as regulations, financial news, market analysis, etc.  
As shown in Figure 5, the input data mainly includes natural language queries and database schema information. Natural language queries are encoded as vector representations, and database schema information (table and column names, etc.) are encoded as embeddings as auxiliary context input. The model combines encoded information and generates corresponding SQL queries using a deep learning architecture. The model can understand the query intent and generate corresponding SQL statements by learning the mapping relationship between natural language and SQL. The encoding and fusion of input data help the model accurately understand and operate the database structure when generating SQL.  
The sample CSV converts each data table of all databases directly into a CSV file. This allows Python to read each data table file directly and use the Pandas data frame to represent each data table. In the future, the large model can generate Python code and directly calculate the data to check whether the final data meets the natural language question.  
The output of the large model is the SQL statement generated for the natural language question. Since there may be many variants of SQL for the same result, we cannot directly judge whether the SQL statement is precisely the same. Therefore, the final method to evaluate the SQL accuracy is to compare whether the query result is consistent with the result of the accurate SQL statement by executing the SQL statement. If the result set  
Figure 4: Overview pipeline of FI-NL2 PY2 SQL. It shows the model process, from data to final SQL output.  
Figure 5: Overview pipeline of NL2 PY2 SQL.  
r0 generated by the generated SQL statement is precisely the same as the result set r1 generated by the correct SQL, the result generated by the SQL is considered proper.
## 1 Introduction
Moreover, a 4-D tracking algorithm is developed. Furthermore, physical experiments and filed tests that synthesize three cases are performed. The results of FEM simulation and physical experiments are used to prove that the algorithm is feasible and accurate. Finally, the high robustness of the 4-D tracking algorithm is demonstrated.