esa-sceva/satcom-chunk-collection huggingface dataset:
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 5 Proposed Triple-Mixed Gibbs Sampling  
Here, we propose an algorithm that at each iteration picks a different value for the TP from the set \(\{\infty,\text{SNR}/\log(n),1\}\) with probabilities \(q_{\infty}\), \((1-q_{\infty})q_{\text{SNR}}\), and \((1-q_{\infty})q_{1}\), respectively. In this manner, at each iteration there is a chance \(q_{\infty}\) that a random walk is performed; otherwise, the algorithm chooses \(\alpha^{2}=1\) or \(\alpha^{2}=\text{SNR}/\log(n)\) with probabilities \(q_{1}\) and \(q_{\text{SNR}}\), and samples accordingly. Note that since we are interested only in the high SNR regime (\(\text{SNR}\to\infty\)) and to avoid dealing with imaginary TP, the term inside the square root operator has been despised. For each sampling process, choosing \(\alpha^{2}=1\) ensures fast convergence to a local minimum, \(\alpha^{2}\propto\text{SNR}\) guarantees a quick mix to steady state and \(\alpha^{2}=\infty\) is necessary to avoid \"deep\" local minima. Whilst yielding the same complexity, we find that with a suitable choice for \(q_{\infty}\) and \(q_{1}\) (\(q_{\text{SNR}}\) is redundant, as \(q_{1}+q_{\text{SNR}}=1\)), it is possible to effectively mitigate the stalling effect and achieve near-ML performance. The pseudocode related with the proposed variant, denoted as triple MGS (T-MGS), is presented in Algorithm 2, where \(U[0,1]\) denotes the uniform distribution between 0 and 1, and pmf stands for probability mass function.
## 3 Precise Orbit Determination Strategy
### Models  
As the BeiDou system is very similar in signal structure and frequencies to GPS, the observation models and satellite force models for GPS can be utilized directly for BeiDou with very slight modifications. Therefore, similar observation models and dynamical models to the operational International GNSS Service (IGS) data processing at GFZ are selected for each three-day solution and they are listed in Tables 2 and 3, respectively.
## 3 Proposed Methods
### Method for Determining Spoofed Signals  
The method described in Section 3.1 can quickly determine carrier-phase integer ambiguities from received GNSS signals using two short-baseline common-clock antennas. On this basis, we directly determine spoofed signals by using the inter-station single-difference carrier-phase measurements after integer ambiguity compensation and the information provided by the MIMU of the carrier. Due to the dynamic nature of the observation scenario in this paper, there are certain carrier-phase measurement errors, as well as extrapolation errors from the MIMU of the carrier.  
Therefore, the attitude transformation matrix from the \(e\) system (CGCS2000) to the \(b\) system (carrier body coordinate system) provided by the MIMU in the trusted environment of satellite navigation before spoofing is \(\mathcal{C}_{e,imu}^{b}\) and \(\mathbf{r}_{1,imu}\). If there is no spoofing, we can derive the following equation from Equation (5):  
\[\lambda\cdot\Big{(}\Delta\widehat{\phi}_{12}^{i}+N_{12}^{i}\Big{)}=\frac{ \begin{bmatrix}L&0&0\end{bmatrix}\cdot\mathcal{C}_{\text{e,imu}}^{b}\Big{(} \check{\mathbf{r}_{1}}^{i}-\mathbf{r}_{1,imu}\Big{)}}{\Big{|}\Big{(}\check{\mathbf{r}_{1} }^{i}-\mathbf{r}_{1,imu}\Big{)}\Big{|}} \tag{14}\]  
The information on the right side of the above equation is obtained based on the MIMU measurements and ephemeris files, while the information on the left side of the  
Figure 5: Domains and standardised points of inter-station single-difference integer ambiguities on B\({}_{1}\) and B\({}_{3}\). The square area represents the range of values for carrier phase ambiguities N\({}_{1}\) and N\({}_{3}\). Due to the integer constraints for N\({}_{1}\) and N\({}_{3}\), the feasible candidates for (N\({}_{1}\), N\({}_{3}\)) can be illustrated as the dots in the square area.  
equation is solved based on the satellite navigation signal measurements. Therefore, we define \(\delta\phi_{\text{error}}\) as follows:  
\[\delta\phi_{\text{error}}=\lambda\cdot\left(\Delta\widetilde{\phi}_{12}^{i}+N_{ 12}^{i}\right)-\frac{\begin{bmatrix}L&0&0\end{bmatrix}\cdot\mathcal{C}_{\text{ e,imu}}^{b}\left(\widetilde{r}_{1}^{i}-\mathbf{r}_{1,imu}\right)\\ \left|\left(\widetilde{r}_{1}^{i}-\mathbf{r}_{1,imu}\right)\right|\end{bmatrix} \tag{15}\]  
where \(\lambda\) is known; \(L\) can complete the calibration work in advance; \(\mathbf{r}_{1,imu}\) and \(\widetilde{r}_{1}^{i}\) vary not much in two consecutive epochs; \(\Delta\widetilde{\phi}_{12}^{i}\) is the measured value; \(N_{12}^{i}\) is the carrier-phase integer ambiguity revolved in Section 3.1.  
By defining the threshold at \(\delta\phi_{\text{error}}\geq\delta\phi_{\text{max}}\), we can determine that the measured signals of the satellite are spoofed signals. The value of \(\delta\phi_{\text{error}}\) is defined by the maximum error of satellite two-dimensional direction determination in the following paper.
## 2 Materials and Methods
### Vis-NIR Multispectral Reflectography  
Multispectral analysis was performed with the scanner developed at the National Research Council--National Institute of Optics (CNR-INO), allowing for the simultaneous acquisition of 32 narrow-band images (16 VIS + 16 NIR images) through whiskbroom scanning in the range 390-2500 nm [5]. The optical head, i.e., the lighting system and catoptric collecting optics, is placed in a 45\({}^{\circ}\)/0\({}^{\circ}\) illumination/observation geometry, moving with a step of 250 \(\upmu\)m and speed of 500 mm/s. The light reflected from the scanned surface is collected by a square-shaped fiber bundle and delivered to a set of Si and InGaAs photodiodes, each equipped with an individual interferential filter. The autofocus system maintains the optimal target-lens distance during scanning thanks to a high-speed triangulation distance meter and custom-made control software. The instrument's output is a set of perfectly superimposed monochromatic images, which are aberration-free and metrically correct.  
The multi-spectral image cube was processed with principal component analysis (PCA) to compress the informative content of large amounts of data in a new, reduced, non-redundant dataset [29]. PCA allows for the expression of the original spectral datasetwithin a new reference space identified by orthogonal and uncorrelated coordinates, called principal components (PCs), corresponding to linear combinations of the original variables (i.e., the different wavelengths). Since PC variables are hierarchically ordered, the few first PC images are typically representative of the substantial information of the original dataset [1]. Given our research aim, the initial 32 images were reduced to four significant PC images that effectively summarized the salient spectral variations of the drawing. Vis, NIR, and PC images were used to produce false-color (FC) images by combining either a near-infrared, a red, and a green image (NRG-FC), or three PCs (PC-FC). In the first case, traditional NRG\(\rightarrow\)RGB mapping was used (namely, N\(\rightarrow\)R, R\(\rightarrow\)G, and G\(\rightarrow\)B, with \"R\", \"G\", and \"B\" that indicate the red, the green, and the blue channel, respectively, and \"N\" the near-infrared spectral band). In the second case, three PCs were combined in the trichromatic RGB space. The resulting image provided a detailed outline of the drawings and their similarities--when present, which would otherwise remain undetected during a simple visual inspection, and to a traditional FC.
# Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs
## 3 Proposed Schemes
### Throughput Maximization
According to these values, from Equation (27), \(E_{tx}\) is 1.6 joules when \(P_{tx}\) is 10 watts and 0.16 at 1 watt. Similarly, \(E_{rx}\) varies with the \(P_{rx}\); it is 0.08 joules when \(P_{rx}\) = 0.5 watt, and at 0.1 watt, it is 0.016 joules. Now, Equations (26) and (27) can written as (represented in joules):  
at \(P_{1}\): \(0.16+0.016=0.176\),  
at \(P_{2}\): \(0.16+0.08=0.24\),  
at \(P_{3}\): \(1.6+0.016=1.616\),  
and at \(P_{4}\): \(1.6+0.08=1.68\),  
The intersection of lines L1, L2, L3, L4 and L5 is shown in Figure 7. The bounded region computed by these lines is the optimal region that is also called the feasible region; it satisfies all of the points and shows that a minimum energy will be consumed with the premise of that region resulting in a high network lifetime, leading to a comparatively better packet delivery ratio.
## 2 Transmitter Structure  
The transmitter structure of the proposed method is depicted based on the complex baseband model in Figure 1, where the number of transmit transducers and receiver hydrophones are both assumed to be one. It is shown that the information bit sequence \([b(nT_{a})]\) with the time interval \(T_{a}\) is firstly converted to several bit subsequences \([b^{m}(nT_{b})],m=0,\cdots,\text{M}-1\) by the serial-to-parallel (SP) converter, where \(b^{m}(nT_{b})\in\{0,1\}\) and \(T_{b}=T_{a}/M\). Then, each bit subsequence is independently encoded by the encoder through inserting redundant bits into information bits to protect information bits from single bit errors or short burst errors. The encoders at the transmitter can be different or the same. Next, the coded bit subsequences \(\{c^{m}(nT_{b})\},\text{m}=0,\cdots,\text{M}-1\) are interleaved, divided into Q-bit blocks \(\{\mathbf{d}^{m}(nT)\},\text{m}=0,\cdots,\text{M}-1,T=Q\cdot T_{b}\) and mapped to the symbol sequences \(\{x^{m}(nT)\},m=0,\cdots,\text{M}-1\) from the \(2^{\text{Q}}\)-ary symbol alphabet \(\mathbf{S}=\left\{\alpha_{1},\alpha_{2},\cdots,\alpha_{2^{\text{Q}}}\right\}\) with zero mean and unit energy, where \(\alpha_{i}\in\mathbf{S}\) is a complex number. Finally, the mapped symbol sequences are modulated by the FMT modulator consisting of \(K\)-times up-samplers, transmit filters, carrier modulators, and a combiner, and then the transmitted signal can be obtained.  
Referring to Figure 1, the transmitted signal can be expressed as:  
\[\begin{split}\text{s}\Big{(}k\frac{T}{K}\Big{)}=\sum_{m=0}^{M-1 }\sum_{n=-\infty}^{+\infty}x^{m}(nT)g_{t}\Big{(}k\frac{T}{K}-nT\Big{)}e^{j \frac{2\pi}{M}mk}\end{split} \tag{1}\]  
where \(x^{m}(nT)\) denotes the \(n\)-th symbol transmitted on the \(m\)-th subband, and \(g_{t}(kT/K)\) denotes the time domain response of the transmit filter. In the FMT modulator, all transmit filters are the same and the selection of that must keep a balance between implementation complexity and spectral containment. In the proposed method, the root raised cosine (RRC) shaping filter is used as the transmit filter, and the value of the roll-off factor sets to \(\alpha=K/M-1\).
# Earthquake Hazard Mitigation for Uncertain Building Systems Based on Adaptive Synergetic Control
## 3 Adaptive Synergetic Control Design
### Design of Classical Synergetic Control  
Let us define the error \(e\) to be the difference between the actual state \(x_{1}\) and the desired state (displacement) \(x_{1d}\):  
\[e=x_{1}-x_{1d} \tag{9}\]  
In the present application, \(x_{1d}\) must be set to zero; then, \(e=x_{1}\). Taking the first and second derivatives of error results in  
\[\dot{e}=\dot{x}_{1}=x_{2}\]  
\[\ddot{e}=\dot{x}_{2}=-\frac{B_{s}}{M_{s}}\;x_{2}-\frac{K_{s}}{M}x_{1}+\frac{1} {M_{s}}v+\ddot{x}_{g} \tag{10}\]  
On the basis of the system dynamic described by Equation (2), one can define the macro-variable \(\psi(x)\) as follows:  
\[\psi(e)=c\cdot e+\dot{e} \tag{11}\]  
The first derivative of Equation (11) is given by  
\[\dot{\psi}(e)=c\;\dot{e}+\ddot{e} \tag{12}\]  
where \(c\) is a scalar design parameter. The macro-dynamic variable's evolution towards the manifolds is defined as follows [23; 27]:  
\[T\cdot\dot{\psi}(e)+\psi(e)=0 \tag{13}\]  
where \(T>0\) is the convergence rate of the evolved micro-variable of the system. This dynamic is dependent to ensure that the trajectories of all state variables achieve the desired manifold and remain on it for future time [23; 27].  
On the basis of Equations (9)-(13), one can obtain  
\[T\;\left(c\;\dot{e}+\ddot{e}\right)+\psi\;(e)=0\]  
or  
\[T\;\dot{x}_{2}+T\;\dot{e}+\psi\;(e)=0 \tag{14}\]  
Using Equation (2), Equation (14) becomes  
\[T\;\left(-\frac{B_{s}}{M_{s}}\;x_{2}-\frac{K_{s}}{M_{s}}x_{1}+\frac{1}{M_{s}} v+\ddot{x}_{g}\;\;\right)+Tc\;\dot{e}+\psi(e)=0 \tag{15}\]  
In order to satisfy Equation (15), the control signal is set as follows:  
\[v=B_{s}\;x_{2}+K_{s}\;x_{1}-M_{s}\ddot{x}_{g}-M_{s}\;c\;\dot{e}-\frac{M_{s}}{ T}\psi(e) \tag{16}\]
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis  
Diagnostic studies are categorized into diagnosability evaluation and diagnosability design. Diagnosability evaluation is the characterization of the fault diagnosis ability. For different evaluation purposes, diagnosability evaluation is divided into qualitative and quantitative evaluation. Diagnosability qualitative evaluation determines whether the fault can be detected or isolated. Diagnosability quantitative evaluation quantifies the difficulty of the fault diagnosis [29].
# Comparative Study of the Seismic Response Characteristics of Three Special Soils
## 1 Introduction
The recommended values of this article are given through a comparative analysis. Accordingly, the research results of this paper have certain reference significance for further research on the seismic response characteristics of special soils. Regionality is an important feature of special soils. To thoroughly analyze the seismic response characteristics of special soils, three soils are investigated. First, in terms of soft soil, Tianjin, located on the west side of Bohai Bay, has a silty coastline stretching 153 km. Soft soils are widely distributed in the Tianjin area and are somewhat representative of the region, so the soft soil in Tianjin is selected as a research object. Second, since most of the collapsible loess in China is concentrated atop the Loess Plateau, the School of Disaster Prevention Science and Technology has conducted detailed research since 2015 involving an investigation of the geological disasters associated with the Haiyuan earthquake. A large amount of actual data have been accumulated, so the loess in the Haiyuan area is selected as a research object. Third, laterite is a special regional soil that is distributed mainly in Southwest and Central-south China. Because the laterite in Liuzhou, Guangxi, has been studied in the most detail, the laterite in this area is chosen as a research object. In this paper, three special soil homogeneous single-soil-layer calculation models are established, and the SOILQUAKE soil layer seismic response analysis program is used for the calculation and analysis. The influence of the dynamic shear modulus ratios, damping ratio and shear-wave velocity on characteristic parameters of the design response spectrum is discussed. In addition, on the basis of actual drilling data of typical areas, three soil layer calculation models of actual special soil sites are established, which are used to calculate the dynamic characteristics of the special soil that are studied in terms of peak acceleration and characteristic parameters.
# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 6 Conclusions  
A novel multimodal power management strategy based on machine learning was proposed in this study. To evaluate its performance with real-time biosignal data, a wearable sensor node prototype was designed and constructed. The decision tree algorithm was implemented on the nRF52840 MCU and achieves a good balance between the saved energy and the duty cycle transmission. In the proposed test case scenarios, the ML-decision tree achieves at least 2.4\(\times\) more transmissions as well as avoids the system's power-off saving up to 39.6% of energy than the uniform power management. The energy analysis reveals a total consumption of 25.74 J per day to operate without interruption. Considering an efficiency of 35% for the flexible PV solar cell and only five hours of irradiance at standard conditions (STC) [29], the energy generation capacity was 99.72 J per day. This means that the system can generate 3.87 times more energy than what is required to operate without interruption.  
Conceptualization, A.C.-A., J.V.-C. and J.O.-A.; Formal analysis, A.C.-A., J.V.-C., E.O.-d.-l.-R., A.C.-A. and R.C.-A.; Investigation, A.C.-A., J.V.-C. and A.D.; Methodology, A.C.-A., J.V.-C., G.B.-N. and J.O.-A.; Project administration, J.V.-C. and J.O.-A.; Validation, G.B.-N., A.C.-A., E.O.-d.-l.-R., J.V.-C. and R.C.-A.; Writing original draft, A.C.-A., J.V.-C. and J.J.E.-L.; Writing review and editing, J.J.E.-L., J.V.-C., A.C.-A., R.C.-A. and A.D. All authors have read and agreed to the published version of the manuscript.  
[PERSON], [PERSON] and [PERSON] acknowledge the support of the Quintana Roo Science and Technology Council (COQCYT).  
Not applicable.  
The authors declare no conflict of interest.
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 4 Experimental Section
### Evaluation Metrics  
Several recall metrics (R@K) are mainly used to evaluate the model's performance for the two types of tasks, SGCls and PredCls, of the scene graph, and K = 20, 50, 100 are taken for evaluation.  
R@K [43]: Recall@K is a widely used metric in scene graph tasks to calculate the recall of the top-n for each model prediction triad (s, p, o). Triads are correctly predicted only when the subject, predicate and object are correctly predicted.  
ngR@K [44]: In the traditional recall calculation, only one relationship category of a pair of objects can participate in the final calculation; however, ngR@K allows all relationship categories (relationships) of a pair of objects to participate in the sorting.  
mR@K [45]: For Mean Recall@K, due to the long-tail phenomenon of category labeling in the dataset, the mean recall is considered for evaluating the prediction performance of the model for the non-uniformly distributed categories. The mR@K calculates the recall for all the predicate categories separately and then calculates the mean value, which can fully account for the importance of all the categories and will be relatively more informative.
## 2 Background and Related Work
### Security Vulnerabilities in Motor Control
#### 2.2.2 Limitations of Current Countermeasures  
Existing protection mechanisms exhibit critical limitations in addressing sophisticated attacks. CRC-based detection proves to be ineffective against carefully crafted attacks that preserve checksum validity, while static thresholds cannot adapt to dynamic operational conditions or evolving attack patterns. Existing hardware solutions impose prohibitive weight and complexity penalties for UAV applications, and reactive approaches detect attacks only after damage occurs, making them insufficient for real-time safety requirements [18; 19].
## 3 Results
### Defining Damage Variables and Initial Damage
Since some residual strength exists in some specimens, which still makes the value of the damage variable change after the macroscopic damage of the specimen occurs, only the stage before peak intensity is considered in the calculation of the damage variable based on the wave velocity, and the damage of the specimen is considered to have occurred when the stress is loaded to the peak intensity of the specimen and \(D_{\mathrm{V}}=1\). For the initial damage existing inside the natural rock, according to the change rule of microfracture inside the rock under external loading, when the stress level reaches the crack closure stress, the fracture inside the rock is completely closed, and the intactness of the specimen is maximized, so it can be approximated that the corresponding crack closure wave speed \(V_{\mathrm{Sc}}\) at this time is the transverse wave speed of the intact and undamaged damage \(V_{\mathrm{Sf}}\). Therefore, by substituting the initial transverse wave velocity of the rock specimen, \(V_{\mathrm{S0}}\), into Equation (5), the initial damage \(D_{0}\) of the rock can be quantitatively expressed. \[D_{0}=1-\left(\frac{V_{\mathrm{S0}}}{V_{\mathrm{Sc}}}\right)^{2} \tag{7}\]  
\(D_{0}\) is the initial damage of the rock; \(V_{\mathrm{S0}}\) is the initial transverse wave speed of the rock; and \(V_{\mathrm{Sc}}\) is the crack closure wave speed of the rock.
# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 2 Materials and Methods
### Classification and Definitions
#### 2.4.5 Structural Health Monitoring-Based Methods  
Systems for monitoring the structural health of machines and structures are reliant on sensors and structural prototypes. Standards established on categorization and prediction data, including machine-learning algorithms in SHM systems, require the collection of trustworthy data, which is driven by computational breakthroughs in sensing devices and the computational capacity of embedded gadgets [88]. For this reason, it is important to map out a set of techniques that predict FL based on the data acquired therewith as exemplified in Figure 10. In this case study for OSDs, [PERSON] et al. [89] evaluated fatigue reliability using long-term strain monitoring, the Rainflow algorithm, probabilistic models, the fatigue limit state, and an explicit formula, or MCS.  
Meanwhile, as an illustration, structural health monitoring along with fatigue analysis of retrofitted web stiffeners on steel highway bridges were performed by [PERSON] et al. [90]. When measures of deflection and strain were contrasted with FEA estimates. It was discovered that the genuine fracture depth and damage assessments based on strains at the key hot area are highly connected. In a similar vein, [PERSON] and [PERSON] [91] used structural risk analysis and Bayesian pre-posterior decision analysis to evaluate the worth of structural health monitoring. Accordingly, the case studies discussed the impact of the performance-related uncertainty of SHM on the value of SHM.  
In an investigation, [PERSON] et al. [92] collated stress signals from a bridge monitored by the Fiber Bragg Grating (FBG) system. Furthermore, the Bayesian criteria, wavelet multi-resolution analysis, Kolmogorov-Smirnov (K-S) goodness-of-fit test, finite mixture distribution, FBG sensors, and genetic algorithm were used in this research. The monitoring data were also used to evaluate the SCF's stochastic property, which demonstrated that it followed a normal distribution.  
[PERSON] et al. [93] quantified the ensuing benefits of SHM campaigns. Regression models of gauged stresses, road temperatures, and traffic densities paved the way for pre-posterior decision support to be used to optimize the design of monitoring campaigns for comparable bridges. The overall objective was to demonstrate the cost effectiveness of SHM approaches compared to civil infrastructure management. [PERSON] et al. [94] created a two-scale non-deterministic time-variant fatigue model for offshore steel wind turbines utilizing records from SHM. [PERSON] et al. [95] created nanotechnologies for controlling material strength. This nanotechnology arbitrarily records the accrued damages in the material both prior to and after the formation of cracks in the waiting phase for its next leap.