esa-sceva/satcom-chunk-collection huggingface dataset:
# Output Feedback Tracking Control for Vessel with Collision-Avoidance and Performance Constraints
###### Abstract
[30] introduces a collision-avoidance function for a recursive control design, in which dynamic obstacles can be avoided through the control design. However, input constraints are neglected in the controller design process, which has a great impact on the collision-avoidance ability. To get around this, Ref. [31] proposes a new control strategy combined with modified artificial potential functions for dynamically positioned vessels with input saturation. In view of the experience of navigation practice, the capabilities of collision avoidance have a great impact on the safety of navigation. On one hand, the capabilities of collision avoidance are limited to the performance of the actuator, and balancing the avoidance collision ability and the limited performance of the actuator is a challenging subject. On the other hand, the performance of the control system is closely related to navigation security. Poor performance of the control system leads to great tracking errors, and collisions may occur during transportation, especially during narrow waters. As far as is known, no results have been reported on vessels with collision avoidance in the simultaneous presence of input constraints and prescribed performance. This paper aims to propose a collision avoidance switch control (CASC) strategy for vessels. A dynamic calculation of collision risk (DCCR) is introduced to evaluate whether there exists a risk of collision avoidance. Based on the analysis of the DCCR, the DCCR can choose between a collision-avoidance controller (CAC) and a prescribed performance controller (PPC). Then, an ADS is constructed to handle the input constraints. The collision-avoidance controller (CAC) is designed based on an artificial potential function, the ADS, and the ESO. The prescribed performance controller (PPC) is designed based on an error constraint function, the ADS, and the ESO. The system's stability is proved based on the Lyapunov direct method. Simulation outcomes verify the effectiveness of the presented control strategy. The main contributions are summarized as follows. (1) Compared with the existing related results [21; 22; 24], input saturation and collision avoidance are considered simultaneously in the controller design. (2) In contrast to the control approach proposed in [31], the CASC introduces a dynamic calculation of collision risk, which is used for generating decisions between the CAC and PPC. The CASC is valid with a faster simulation rate and less error, regardless of the trajectory tracking or collision avoidance. The remaining sections are as follows. Section 2 gives the problem formulated. Section 3 gives an observer design. Section 4 presents the collision-avoidance switch control strategy. The simulation is designed in Section 5. Conclusions are given in Section 6.
## 2 Materials and Methods
### Modelling of a TCSC SMIB System Installed with PIDF Controller  
The block diagram of the PIDF controller is shown in Figure 3. The transfer function of the PIDF controller with parameters \(K_{P}\), \(K_{D}\), \(K_{I}\), and \(N\) in Figure 3 is given as  
\[\Delta\sigma=(K_{P}+\frac{K_{I}}{s}+K_{D}\frac{Ns}{s+N})\Delta\omega \tag{35}\]  
where \(K_{P}\), \(K_{D}\), \(K_{I}\), and \(N\) are the proportional gain constant, derivative gain constant, integral gain constant, and derivative filter coefficient of the PIDF controller, respectively.  
A block diagram of the TCSC PIDF controller with a speed deviation in the SMIB system is shown in Figure 4.  
Figure 3: Block diagram of the PIDF controller.  
The values of Equation (26) are placed in Equation (35), and Equations (36)-(42) are the extended Phillips-Heffron model of the single-machine infinite-bus power system installed with a PIDF controller and TCSC stabiliser.  
\[\Delta\dot{\delta}=\omega_{o}\Delta\omega \tag{36}\]  
\[\Delta\dot{\omega}=\left[-K_{1}\Delta\delta-\mathrm{D}\Delta\omega-K_{2}\Delta \dot{E}_{q}-K_{p}\Delta x_{tscc}\right]/M \tag{37}\]  
\[\Delta\dot{E_{q}}=\frac{1}{\hat{T}_{do}}(-K_{4}\Delta\delta-K_{3}\Delta\dot{E}_ {q}-K_{q}\Delta x_{tscc}+\Delta\dot{E}_{fd}) \tag{38}\]  
\[\Delta\dot{E_{fd}}=-\frac{\Delta\dot{E}_{fd}}{T_{A}}-\frac{K_{A}}{T_{A}}(K_{5} \Delta\delta+K_{6}\Delta\dot{E}_{q}+K_{p}\Delta x_{tscc}) \tag{39}\]  
\[\Delta\dot{\sigma}=\Delta f \tag{40}\]  
\[\Delta\dot{f}=G\Delta\delta+H\Delta\omega+I\Delta\dot{E_{q}}+J\Delta\dot{E_{ fd}}-L\Delta\sigma-N\;\Delta f\;+K\Delta x_{tscc} \tag{41}\]  
\[\Delta\dot{x}_{tccx}=\frac{1}{T_{tsc}}\Delta f-\frac{1}{T_{tsc}}\Delta x_{ tccx} \tag{42}\]  
where \(G\), \(H\), \(I\), \(J\), \(K\), \(L\), and \(N\) are constants.  
\[\dot{X}=A_{PIDF}\mathrm{.X}+B_{PIDF}\mathrm{.U} \tag{43}\]  
\[\dot{X}=\left[\Delta\dot{\delta}\;\;\Delta\dot{\omega}\;\;\Delta\dot{E_{q}}\; \;\Delta\dot{E_{fd}}\;\;\Delta\dot{\sigma}.\;\;\Delta\dot{f}\;\;\Delta\dot{x}_ {tccx}\right] \tag{44}\]  
\[\mathrm{X}=\left[\Delta\delta\;\;\Delta\omega\;\;\Delta\dot{E_{q}}\;\;\Delta \dot{E}_{fd}\;\;\Delta\sigma\;\;\Delta f\;\;\Delta x_{tscc}\right] \tag{45}\]  
Figure 4: Block diagram of the TCSC PIDF controller.  
\[A_{PIDF}=\begin{bmatrix}0&\omega_{o}&0&0&0&0&0\\ -\frac{K_{1}}{M}&-\frac{D}{M}&-\frac{K_{2}}{M}&0&0&0&-\frac{K_{p}}{M}\\ -\frac{K_{4}}{T_{d_{o}}}&0&-\frac{K_{3}}{T_{d_{o}}}&\frac{1}{T_{d_{o}}}&0&0&- \frac{K_{4}}{T_{d_{o}}}\\ -\frac{K_{4}K_{5}}{T_{A}}&0&-\frac{K_{4}K_{6}}{T_{A}}&-\frac{1}{T_{A}}&0&0&- \frac{K_{4}K_{5}}{T_{A}}\\ 0&0&0&0&0&1&0\\ G&H&I&J&-L&-N&K\\ 0&0&0&0&0&\frac{1}{T_{\rm{rec}}}&-\frac{1}{T_{\rm{rec}}}\end{bmatrix} \tag{46}\]  
\[B_{PIDF}=\begin{bmatrix}0\\ \frac{1}{M}\\ 0\\ 0\\ 0\\ 0\end{bmatrix} \tag{47}\]  
\[U=[\Delta T_{m}] \tag{48}\]
# A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images
## 3 Results and Discussion
### Comparison Experiments
#### 3.3.1 Comparision Experiments for DE Module
Some small-sized objects (such as cars in shadows) can also be distinguished from their surroundings. However, due to a lack of details, the distinction between the outline of the car in the images reconstructed by SRCNN and ESPCN and the surrounding scenery was not high. The reconstruction effects of DBPN and our algorithm were similar. In the red boxes of Figure 5, the ridges in the fields can be clearly seen in the image reconstructed by our DE module. The DBPN algorithm performed slightly better, but the fields in the red box area of the reconstructed image of the SRCNN and ESPCN algorithms are mixed and difficult to distinguish. This demonstrates that the proposed module can effectively enrich the details of the reconstructed image; especially the boundaries between some insignificant adjacent objects, which can also be clearly distinguished. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**U** & **ESPCN** & **SRCNN** & **EDSR** & **DBPN** & **DE** \\ \hline
4 & 26.3522 dB & 27.5891 dB & 23.7169 dB & 27.3679 dB & 27.5876 dB \\
3 & 27.6836 dB & 29.0024 dB & 29.6821 dB & 29.7826 dB & 29.7317 dB \\
2 & 31.9551 dB & 32.8070 dB & 33.3641 dB & 33.9941 dB & 33.9959 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different methods using the Satellite dataset II (East Asia). Among them, ESPCN and our DE module were mentioned above; their PSNR values are also listed here. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**U** & **ESPCN** & **SRCNN** & **EDSR** & **DBPN** & **DE** \\ \hline
4 & 26.0611 dB & 27.0830 dB & 23.3839 dB & 27.2730 dB & 27.3529 dB \\
3 & 27.9854 dB & 28.7625 dB & 29.2732 dB & 29.3326 dB & 29.6001 dB \\
2 & 31.4781 dB & 32.9577 dB & 33.2932 dB & 33.3457 dB & 33.3720 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different methods using the Inria aerial dataset. Among them, ESPCN and our DE module were mentioned above; their PSNR values are also listed here.
## 2 Materials and Methods
### Description of the Dataset
#### 2.2.2 GPM IMERG Precipitation  
The GPM mission offers unprecedented information regarding precipitation that covers the majority of Earth's surface and is a valuable tool, especially in areas with little or no coverage by ground observations, for climate and weather models, for the management of water resources and for the planning of emergency services aiming to mitigate impacts from natural disasters like storm surges and droughts.  
The GPM mission, co-led by NASA and the Japan Aerospace and Exploration Agency (JAXA), comprises a satellite constellation centered on a \"Core\" satellite equipped with an advanced combined active/passive sensor. The mission integrates precipitation measurements from research and operational microwave sensors from ten partner satellites [47; 48], and releases gridded precipitation products at half-hourly, daily or monthly time steps at a spatial resolution of 0.1\({}^{\circ}\). IMERG is the algorithm that combines satellite-derived microwaves with infrared precipitation estimates, along with precipitation gauge data to obtain accurate precipitation estimates with a relatively long time series, suitable for research purposes. There has been remarkable progress in the quantification of spatial variability of precipitation using satellite products. TRMM (Tropical Rainfall Measuring Mission) has been used to compare gate gridded products over India [49] while biases corrected over the United States [50] and GPM (Global Precipitation Measurement Mission) have been used as input for hydrological simulations in river basins in Vietnam [51; 52; 53], and comparisons with streamflow illustrate that they perform better in simulations.  
Daily GPM IMERG final run data of the latest release, i.e., V06, were retrieved from [[https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3](https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3) IMERGM.06/]([https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3](https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3) IMERGM.06/) (access date 30 June 2021) [54]. The daily precipitation values were aggregated at the GRACE time steps to acquire a precipitation time series of the same time intervals with those of GRACE for the time period from June 2018 to March 2021. An analogous process was followed for the testing period from 2005 to 2015. To test the performance of the GPM IMERG dataset at the regional level, a comparison exercise was conducted for the two test sites with measured precipitation from meteorological stations operating in the test sites during the 2005 to 2015 period.
## 4 Construction Project Management  
Building information modeling (BIM) is becoming a global standard in construction project management. Information management in the architectural, engineering and construction (AEC) industry, supported by information and communication technologies (ICT), plays a key role in facilitating cooperation between stakeholders and maintaining interoperability between industries [13]. Its advantages, such as improved efficiency, cost reduction, process optimization and better information management, are increasing leading countries to introduce the use of BIM in public procurement. The implementation of BIM around the world is proceeding with varying intensity, but its importance in construction is growing. According to Boston Consulting Group, the implementation of digitalization processes is expected to have brought global savings of 13-21% by 2025 [14]. Article [15] presents a detailed analysis of BIM policy and implementation across Europe. Using data from government, academic and industry sources, key trends were identified and the effectiveness of BIM policies in advancing technology in construction was assessed.  
As building information modeling (BIM) technology is rapidly evolving in the AEC sector, there is a growing need for robust techniques to evaluate and improve the as-built BIM model to capture the completed project with all deviations and corrections from the original design. To address this, the authors of [16] introduce a new analysis framework for managing and improving BIM during the construction phase. Their approach is to construct a network of relationships based on geometric attributes extracted directly from Industry Foundation Classes (IFC) files. The results show that this method significantly improves the ability to analyze and compare the as-built BIM model through comprehensive model evaluation and matching. By leveraging global geometric information and interdependent relationships between elements, the authors provide a more holistic understanding of as-built BIM, enabling more effective management and optimization. In this way, network analysis is proving to be a powerful tool for managing structures and assets in the AEC industry, opening up new possibilities for evaluating and fitting as-built BIM models.  
The uncertainty of digital technology during its implementation seriously hinders the qualitative development of the construction industry. In [17], a system of uncertainty indicators of digital technology applications is proposed based on the LDA topic model and a literature analysis. The DEMATEL-ISM method is used to construct a multi-level hierarchical model of the structure of uncertainty indicators in digital technology applications, to study the mutual influence between indicators and find the key uncertainty indicators.
## 2 Methodology
### Determination of Periodicityand Response Time of Water-Level Fluctuation
#### 2.4.2 Delayed Response Time and Correlation  
The response of the groundwater level to the rise and fall of the river stage was analyzed using the cross-correlation analysis method. Through cross-correlation analysis, the response relationship between the two timeseries could be estimated, and the time shift could be quantified. For two discrete timeseries signals \(Y_{1}(m)\) and \(Y_{2}(m)\), their cross-correlation function \(R(\tau)\) is expressed in Equation (2) [40].  
\[R(\tau)=\sum_{m=0}^{M}Y_{1}(m)\cdot Y_{2}(m+\tau), \tag{2}\]  
where \(m\) is the moment, \(M\) is the length of the timeseries, \(\tau\) is the delayed time, and \(Y_{1}(m)\) and \(Y_{2}(m)\) are the timeseries of the river water level and the groundwater level, respectively. A larger \(R(\tau)\) value indicates a greater correlation between the two sequences. If \(Y_{1}(m)\) is equal to \(Y_{2}(m)\), then \(R(\tau)\) represents the self-correlation function value of the two sequences at \(\tau\). Normalization is usually performed on \(R(\tau)\), so that the self-correlation is equal to one at the delayed time of zero. The delayed response time of groundwater level to the rise and fall of the river stage corresponded to the maximum \(R(\tau)\).
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 8 Results of Sub-Band Backdoor Attack
### Results of Defense Mechanisms against Sub-band BadNets
#### 8.2.2 STRIP  
We applied STRIP to defend backdoored models where the trigger was embedded in the important bands and less important bands and results are listed in Table 5. For the case where triggers were embedded in the important bands, STRIP failed six times out of nine, while for the case where triggers were embedded in the less important bands, it failed four times out of the nine scenarios. STRIP faces greater challenges with both attacks as compared to the all-bands attack.
###### Abstract  
The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.  
**Keywords:** ensembles; unbalanced datasets; internet of things; rotation forests; milling +
Footnote †: journal: Applied Sciences
###### Abstract  
The conventional engineering robustness optimization approach considering uncertainties is generally based on a probabilistic model. However, a probabilistic model faces obstacles when handling problems with epistemic uncertainty. This paper presents an evidence-theory-based robustness optimization (EBRO) model and a corresponding algorithm, which provide a potential computational tool for engineering problems with multi-source uncertainty. An EBRO model with the twin objectives of performance and robustness is formulated by introducing the performance threshold. After providing multiple target belief measures (Bel), the original model is transformed into a series of sub-problems, which are solved by the proposed iterative strategy driving the robustness analysis and the deterministic optimization alternately. The proposed method is applied to three problems of micro-electromechanical systems (MEMS), including a micro-force sensor, an image sensor, and a capacitive accelerometer. In the applications, finite element simulation models and surrogate models are both given. Numerical results show that the proposed method has good engineering practicality due to comprehensive performance in terms of efficiency, accuracy, and convergence.  
Keywords:epistemic uncertainty; evidence theory; robust optimization; sensor design +
Footnote †: journal: Applied
# Effects of Physical Exercise on Substance Use Disorder: A Comprehensive Review
## 3 Results
### Assessment and Impact of Physical Exercise on Crawings in Individuals with SUD  
Craving is defined as an intense and irresistible desire to consume drugs, which can occur at any time [88]. It represents one of the main challenges in rehabilitating individuals with SUD and can be triggered by factors such as stress or exposure to environments or situations associated with prior substance use, complicating the ability to prevent relapse [89].  
Currently, many types of treatments are employed to reduce cravings and prevent relapses in individuals with SUD. The most common are pharmacological or psychotherapeutic treatments [90]. However, scientific evidence accumulated over the past decade suggests that physical exercise can serve as a valuable therapeutic tool complementing these approaches, effectively reducing both craving and substance use relapses [37; 42; 61; 72; 91]. The heterogeneity in the substances studied in the literature is vast. Some articles focus on a single substance, which can vary widely, such as opioids [53], methamphetamine [42; 92] (the most commonly studied substance), or alcohol [93]. On the other hand, some references involve heterogeneous samples as some individuals were added to multiple or different substances [85; 94; 95; 96; 97].  
Over the past eight years, the literature has employed various methods to measure cravings. While both qualitative and quantitative methods have been used [54], quantitative methods predominate in almost all the reviewed studies. Among these, the visual analog scale (VAS) is particularly prominent [98]. The VAS is a straight line divided into 100 mm with a scale comprising 11 craving levels, in which 0 indicates no craving and 10 indicates maximum craving. Another less commonly used quantitative method is the use of questionnaires or scales, such as the Craving Questionnaires-Short Forms [53] or the Substance Craving Scale (SCS) [62].  
Different scales are also used depending on the substance. For example, the Amphetamine Craving Scale [70] or the Craving Automated Scale for Alcohol [99]. However, the VAS is the most frequently utilized method in the recent literature.  
The use of physical exercise programs to reduce withdrawal symptoms has seen significant growth in the scientific literature, becoming one of the most studied variables. Meta-analyses provide clear evidence of the effects of physical exercise on this issue [37; 43; 51], showing significant reductions in the withdrawal syndrome (SMD = \(-0.43\); \(Z\) = \(4.31\); \(p<0.01\)) and relapse rates (Odds Ratio = \(1.69\); \(Z\) = \(6.33\); \(p<0.01\)) when exercise programs are used as treatment compared to standard therapy [11; 45].
## 2 Experimental Methods
The linearly polarized pump beam was coupled into the ridge waveguides using an aspheric lens (L\({}_{1}\), f = 4.5 mm, NA = 0.55), and the transmitted residual pump and the generated fluorescent light were collimated with another lens (L\({}_{2}\), f = 20.5 mm, NA = 0.15) at the waveguide end facet, as sketched in Figure 1. After filtering out the residual pump light with a long pass filter with a cut-on wavelength of 450 nm (Thorlabs FELH0450, US), the polarization-resolved fluorescence spectra were recorded with a spectral resolution of 0.05 nm using a monochromator (Bentham IDR300, UK) and a calibrated photomultiplier (Hamamatsu R928, Japan) as a detector. To record the fluorescence spectrum of the bulk Pr:YLF crystal for comparison, the pump beam was injected into the unmodified substrate part of the sample using a weaker lens (L\({}_{1}\), f = 14 mm, NA = 0.18). For the fluorescence lifetime measurements, the pump beam was chopped into rectangular pulses with a trailing edge of 25 ns using an electro-optic modulator (Sanger Optoelektronik, LM 0202P, Germany). The fluorescence decay was recorded with a temporal resolution of 5 ns using a 500 MHz digital oscilloscope connected to the analog output of the photomultiplier tube (see Figure 1). As signal light for small-signal gain measurements, laser emission at 607 nm and 639 nm wavelengths from a home-built bulk crystal Pr:YLF laser was used. The low-power signal beam was collinearly combined with the pump beam with a maximum available power of 55 mW using a dichroic mirror, as shown in Figure 2. Both signal and pump beams were coupled into the ridge waveguide. After exiting the ridge, the residual pump light was filtered out with a long-pass filter, and the transmitted power of the signal light was recorded at different pump power levels. Figure 1: Schematic of experimental setup for fluorescence measurements. E-O: electro-optic modulator; V: waveform generator; \(\lambda\)/2: half-wave plate; P: polarizer; L\({}_{1}\), L\({}_{2}\), L\({}_{3}\): lenses; F: long-pass filter; BS: beam splitter; PM: power meter; M: monochromator; PMT: photomultiplier tube; OS: oscilloscope.