esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Observer-Based Estimation
### Vehicle Models
\begin{array}{l}ma_{x}=m\big{(}\dot{u}-vr\big{)}=F_{x11}\cos\delta_{11}+F_{ x12}\cos\delta_{12}-F_{y11}\sin\delta_{11}-F_{y12}\sin\delta_{12}+F_{x21}+F_{x22} \\ ma_{y}=m\big{(}\dot{v}+ur\big{)}=F_{y11}\cos\delta_{11}+F_{y12}\cos\delta_{12 }+F_{x11}\sin\delta_{11}+F_{x12}\sin\delta_{12}+F_{y21}+F_{y22}\\ J_{z}\dot{r}=\big{(}F_{x12}\cos\delta_{12}-F_{y12}\sin\delta_{12}+F_{x22}-F_{ x11}\cos\delta_{11}+F_{y11}\sin\delta_{11}-F_{x21}\big{)}\frac{t}{2}\\ +\big{(}F_{y11}\cos\delta_{11}+F_{y12}\cos\delta_{12}+F_{x11}\sin\delta_{11}+F _{x12}\sin\delta_{12}\big{)}a_{1}-\big{(}F_{y21}+F_{y22}\big{)}a_{2}\end{array}  \tag{8}\]  
where \(\delta_{11}\) and \(\delta_{12}\) are the wheel steering angles, respectively the front left and front right, \(F_{xij}\) and \(F_{yij}\) are the longitudinal (\(x\)) and lateral (\(y\)) road-tyre forces at each corner (\(i=1\) for front and \(i=2\) for rear, \(j=1\) for left and \(j=2\) for right). Although some papers use relatively complete vehicle models, several studies introduce simplifying hypotheses, such as:  
* the use of single track vehicle models (also known as the bicycle model), e.g., [16; 24; 120] by [PERSON] et al., [PERSON] et al. and [PERSON] et al. ;
* the assumption of the availability of the vehicle longitudinal speed, so that the first equation in Equation (7) is not used, e.g., [16; 24; 26] by [PERSON] et al., [PERSON] et al., [PERSON] et al. ;
* the assumption of small steering angles and/or small sideslip angles, e.g., [16; 26] by [PERSON] et al. and [PERSON] et al. ;
* no road inclination and/or bank angle (only a few papers address these issues, e.g., [32; 58; 121] by [PERSON] et al., [PERSON] et al., and [PERSON] et al. A set of equations similar to Equation (7) needs to be coupled with a tyre model, so as to express the forces as functions of the relevant slip parameters, e.g., longitudinal slip ratio and slip angle, respectively for longitudinal and lateral forces. In the literature, there are several approaches, e.g., the use of linear models, Pacejka models (also known as Magic Formula models), alternative tyre models (e.g., rational tyre model [122], Duggoff model [123], Burckhardt model [124]). The use of a dynamic model can lead to a good VSA estimation, yet results are accurate only if the tyre model truly reflects the actual conditions. Unmodeled effects such as road conditions and tyre wear can dramatically worsen the reliability of the estimation [125]. Attempts to address this issue include algorithms providing an online update of tyre parameters (e.g., Pacejka coefficients, cornering stiffness, and rational tyre model coefficients, respectively [16; 120; 122]). Figure 2: Dynamic modelling of a vehicle, with road-tyre force [113].
# Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics
## 3 Experiments
### Data Description and Preprocessing  
We downloaded organ data publicly accessible from the \"Multi-Atlas Labeling Beyond the Cranial Vault--Workshop and Challenge\" (BTCV) dataset [27]. The dataset was compiled with the objective of developing efficient segmentation algorithms and was gathered during a workshop and challenge hosted by MICCAI in 2015. It comprises 50 abdominal CT scans, collected under the supervision of an Institutional Review Board. BTCV labeled the following organs: spleen, right kidney, left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, portal vein and splenic vein, pancreas, right adrenal gland, left adrenal gland as well as non-annotated organs. Abbreviations for these organs are as follows: SP for spleen, RK for right kidney, LK for left kidney, GB for gallbladder, ES for esophagus, LV for liver, ST for stomach, AO for aorta, IV for inferior vena cava, PVSV for portal vein and splenic vein, PA for pancreas, RA for right adrenal gland, LA for left adrenal gland, and BG for non-annotated areas.  
We clip pixel values of CT images in the range from \(-135\) to \(215\). In addition, we normalize pixel values and resize them to \(256\)\(\times\)\(256\). We split \(75\%\) and \(25\%\) for training and test sets by patient. Because there is no image which has all organs at the same time, we select images which have more than seven organs. Consequently, we acquire 821 images for training and 228 images for testing. In Figure 6, we present a plot showing the count of images corresponding to each organ. As illustrated in the figure, the background appears (BG) in all 821 images in the training set. In contrast, the esophagus (ES) appears in only 120 images, and the two adrenal glands (RA and LA) appear in 242 and 279 images, respectively.
## 1 Introduction
By applying additional regularization constraints to reduce noise and discontinuities in the registration process, and to make the deformation results smoother and more realistic, our main contributions in this paper are as follows:  
1. We have successfully applied the dilated convolution combination and attention mechanism to the registration of 3D brain MRI images, so that the network can expand the receptive field without adding too much computational cost and network parameters and obtain multi-scale features, and the network can better understand the global structure and local details of the image and improve the ability to accurately detect the deformed region. 2. Through the cascading network architecture, the deformation registration of the input image from coarse to fine is realized, which greatly improves the accuracy of registration. This method of step-by-step refinement of registration results can effectively improve the accuracy of registration results and can handle registration tasks of different scales and complexities. At the same time, the double regularization constraint is used to ensure the smoothness and authenticity of the image registration, avoid the excessive deformation and discontinuity of the registration results, and make the registration results more in line with the actual situation. 3. The proposed network model adopts an unsupervised end-to-end training method. It is not limited by ground-based facts and enables near-real-time brain MRI registration between patients. Compared with traditional algorithms and existing deep-learning-based algorithms, the algorithm in this paper is able to perform alignment efficiently and accurately, and also promotes alignment smoothness and suppresses severe bending deformations under the constraints of dual regularization, which greatly improves the performance in all aspects.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 2 Progress of Traditional Aquaculture
### Conventional Technologies and Solutions
#### 2.2.2 Use of Antibiotics or Medicines  
Due to the unfavorable environment caused by water deterioration, in aquaculture practice, antibiotics or medicines are used to control the diseases of aquatic animals [29]. Commonly used antibiotics and medicines for aquaculture include ampicillin, oxacillin, penicillin, ceftazidime, cefazolin, and so forth [30]. However, the diseases in aquaculture are controlled by antibiotics at the expense of human health, environmental protection, and ecological stability. First, one of the problems created by the abuse of antibiotics is the presence of residual antibiotics in commercialized aquaculture products, of which the consumption is increasing continually in recent years. Second, antibiotics-resistance caused by the residual antibiotics has been considered as a trigger of ecological disasters. According to the survey of [PERSON] et al. (2012), in some cases antibiotics are no longer effective in treating bacterial diseases as the aquaculture pathogens have become antibiotics-resistant. Therefore, it is not a sustainable and eco-friendly way to control aquaculture diseases by the overuse of antibiotics or medicines. Previous studies mainly focused on the removal of antibiotics or medicines by conventional technologies, such as anaerobic and aerobic biological treatment processes [31; 32], while did not devote much effort in the source reduction of antibiotics or medicines in aquaculture.
## 4 Functional Description of D-MSR Algorithms in Different Protocol Layers
### D-SAR Protocol
The receiver of the _Release_ message deletes the communication schedule established with the sender and a _Release Complete_ message is sent to the sender. The communication schedule is specified by the traffic ID. Next, the receiver of the _Release_ message forwards the _Release_ message to the next hop in the route toward the end-point of the connection. Upon receiving the _Release Complete_ message from the next hop, it will then delete the communication schedule constructed with the next hop, which is specified by that traffic ID. This process continues until the _Release_ message reaches the end-point of the connection. This procedure ensures that all nodes along the route release all the resources previously allocated to the connection. In this case, the D-SAR module in each node deletes the related links and superframes from the communication table. The details of the D-SAR protocol for the source, the intermediate, and destination node are provided in [24].
###### Abstract
However, the application of these new coatings has been limited by their lack of selectivity. Molecularly imprinted polymers (MIPs) have the advantage of selective recognition of specific target analytes. This type of polymer was first proposed by [PERSON] et al. and has rapidly developed in areas such as pharmaceutical analysis [26], biosensors [27], and enzyme catalysis [28]. Nowadays, MIPs are widely applied in sample pretreatment processes due to their high selectivity in complex samples. There are also reports of using MIPs as stir bar coatings for trace target analyte adsorption and extraction. However, with most extraction techniques using MIPs as extraction coatings, such as stir bar sorptive extraction and solid-phase microextraction, it is challenging to form uniform and mechanically robust coatings during the preparation process. In this study, a sol-gel method was successfully used to obtain a homogeneous and stable MIPs coating on the surface of a stir bar. The adsorption and selectivity of the MIPs-coated magnetic material for BPA were investigated. Furthermore, the MIPs-coated stir bar was utilized for the adsorption and extraction of trace BPA from spiked real water samples, followed by successful determination using HPLC. This approach allows for the efficient and selective analysis of trace BPA in complex environmental water samples.
## 4 Results and Discussion
### Ultrasonic Propagation Time, Distance, and Wave Velocity  
A pulse-echo method is applied to investigate the acoustoelastic effect in GIS epoxy specimens under uniaxial stress. Under applied compressive stress, the propagation time and strain in specimen A were measured. Under applied tensile stress, the ultrasonic propagation time and strain in specimen B were measured. Then, the ultrasonic wave velocities were derived.  
The ultrasonic distance is measured using Equation (5), and the wave speed under loading stress is measured using Equation (4). Ultrasonic tests were performed on specimen A under 0-100 MPa compressive stress and specimen B under 0-50 MPa tensile stress, using 0.5 MHz transverse ultrasonic wave polarized vertically to the stress and 0.5 MHz transverse ultrasonic wave polarized parallel to the stress. The measured propagation time, distance, and wave speed of the #A-1 specimen and #B-1 specimen are demonstrated in Figures 9 and 10, respectively.  
In Figure 9, in the range of 0-100 MPa compressive stress, the propagation times \(T_{v}\) and \(T_{p}\) both decrease slightly with the stress increasing, and the decreased magnitude of \(T_{v}\) (320 ns) is less than that of \(T_{p}\) (480 ns). The propagation distance \(L\) increases linearly with compressive stress increasing due to linear deformation under the stress loading. The wave velocities \(V_{v}\) and \(V_{p}\) increase, and the increment magnitude of \(V_{v}\) (22 m/s) is less than that of \(V_{p}\) (31 m/s).  
In Figure 10, in the range of 0-50 MPa tensile stress, the propagation times \(T_{v}\) and \(T_{p}\) both increase slightly with the stress increasing, and the increased magnitude of \(T_{v}\) (71 ns) is less than that of \(T_{p}\) (121 ns). The propagation distance \(L\) decreases linearly with tensile stress increasing due to linear deformation under the stress loading. The wave velocity \(V_{v}\) and \(V_{p}\) both decrease, and the reduction magnitude of \(V_{v}\) (11 m/s) is less than that of \(V_{p}\) (19 m/s).  
Therefore, the variation in propagation time and velocity of transverse waves with polarization parallel to stress are more significant than those of transverse waves with polarization vertical to stress.  
Figure 10: Propagation time \(T\), propagation distance \(L\), wave velocity \(V\) of specimen #B-1 in the range of 0–50 MPa tensile stress: (**a**) 0.5 MHz ultrasonic transverse waves polarized vertically to the stress; (**b**) 0.5 MHz ultrasonic transverse waves polarized parallel to the stress.  
Figure 9: Propagation time \(T\), propagation distance \(L\), wave velocity \(V\) of specimen #A-1 in the range of 0–100 MPa compressive stress: (**a**) 0.5 MHz ultrasonic transverse waves polarized vertically to the stress; (**b**) 0.5 MHz ultrasonic transverse waves polarized parallel to the stress.
## 2 Experimental Procedures
### Characterization  
The wetting angles between L-PCS solutions and a standard graphite sample were tested by wetting angle measuring instrument (VAF-30, Beijing Huahai Zhongyi Industrial Furnace Co. LTD, Beijing, China). The viscosities of L-PCS precursors at room temperature were analyzed by a rotating viscometer (DV-II+Pro, Brookfield Ltd., Brookfield, USA). X-ray diffraction (XRD, X'Pert Pro MPD, Almelo, Holland) was used to identify the crystalline phase of cured and pyrolyzed samples. The Raman spectra were measured in a Renishaw spectrometer by irradiation with a 532 nm laser, and the bonding energy of samples were determined by Nicolet is50 infrared spectrometer (FT-IR, Nicolet is5003190721, New York, USA). The surface and fracture morphologies combined with element mapping of each sample were obtained by a HELIOS Nanolab 600i scanning electron microscope (SEM, Quanta 200 FEG, Hillsboro, USA). Python was used to process images from SEM to obtain the volume of pores, and the porosities were the ratio of volume of pores to the corresponding samples. The density was calculated by dividing the mass by the volume.
# Cost Analysis and Optimization of Modern Power System Operations
## 4 Results and Discussion  
This paper presents a comprehensive analysis of four distinct case studies for solving the unit commitment (UC) problem in a power system, aimed at determining the optimal day-ahead scheduling of generating units to meet dynamic load demand while minimizing operating costs.  
* Case 1 examines a traditional power system with 10 thermal units, resulting in an operating cost of USD 4.8524 \(\times\) 10\({}^{5}\), with fuel, start-up, and shut-down costs amounting to USD 484,797, USD 442.1, and USD 0, respectively. This forms the baseline for comparison.
* Case 2 incorporates spinning reserve requirements (assumed at 40%) for enhanced reliability. With a 55% load reduction, the operating cost drops significantly to USD 2.1090 \(\times\) 10\({}^{5}\), and start-up/shut-down costs are reduced to USD 199.4 and USD 213.4, respectively, highlighting the cost of maintaining reserve margins.
* Case 3 transitions to a modern power system configuration, involving only four thermal units, wind energy, and an energy storage system (ESS). This integration results in a notably lower operating cost of USD 2.2336 \(\times\) 10\({}^{5}\), emphasizing both economic efficiency and enhanced flexibility from renewables and ESS.
* Case 4 explores the impact of demand-side flexibility (DSF). By enabling 10% demand flexibility, the system achieves a further cost reduction to USD 2.0965 \(\times\) 10\({}^{5}\), demonstrating the potential of smart grid technologies and responsive consumption in optimizing operations.  
The results of the case analysis are summarized in Table 2 below.  
Figure 11: Comparison between original demand and 10% flexible demand.  
Figure 12: The variation in total operating cost vs. demand side flexibility.
## 2 Materials and Methods
### Groups and Irradiation Protocol  
The prepared 96-well plates were randomly divided into 4 study groups and a control group (no laser radiation). Before irradiation, each well was placed in a separate stand to protect other wells from receiving laser radiation. Group 1 was diode laser radiation at a wavelength of 635 nm, 220 mW power, 0.33 w/cm\({}^{2}\) power density, irradiation time of 3, 5, 7, and 12 s, and energy densities of 1, 1.5, 2.5, and 4 J/cm\({}^{2}\). Group 2 was diode laser radiation at a wavelength of 660 nm, 150 mW power, 0.25 w/cm\({}^{2}\) power density, irradiation time of 4, 6, 10, and 16 s, and energy densities of 1, 1.5, 2.5, and 4 J/cm\({}^{2}\). Group 3 was diode laser radiation at a wavelength of 808 nm, 250 mW power, 0.4 w/cm\({}^{2}\) power density, irradiation time of 2, 4, 6, and 10 s, and energy densities of 1, 1.5, 2.5, and 4 J/cm\({}^{2}\). Group 4 was diode laser radiation with a wavelength of 980 nm, power of 100 mW, a power density of 0.25 w/cm\({}^{2}\), irradiation time of 4, 6, 10, and 16 s, and energy densities of 1, 1.5, 2.5, and 4 J/cm\({}^{2}\). All irradiation was performed at the edge of the well (the tip area of 0.5 cm\({}^{2}\)) (Table 1). All irradiation was in continuous mode 1 mm above the wells in a fixed position. Then, cell viability and proliferation were investigated 24 h, 72 h, and five days after laser irradiation. A total of 24 wells were considered for each wavelength (6 wells for each energy density).
## 2 Materials and Methods: RF Waveguides and Cavities  
Waveguides and cavities excel in material characterization due to their inherent sensitivity [14], manifested in S11 for the waveguide and precise resonance signatures for cavities. These parameters depend on the permittivity (\(\epsilon_{\text{r}}\)) and loss tangent (tan \(\delta\)) of the dielectric and the conductivity of the conductor. The key lies in changing the dielectric medium (grown biofilm will change the \(\epsilon_{\text{r}}\) and tan \(\delta\)), inducing a \"perturbation\" in the electromagnetic field distribution. This translates as a measurable shift in the S11 (waveguide) and resonance frequency (resonator). By meticulously analyzing these alterations, we can glean crucial information about the material's presence and physical properties, paving the way for accurate and non-destructive characterization of biofilm growth. Waveguides and cavity resonators are theoretically based on [PERSON]'s equations of the electromagnetic field [15].  
Waveguides are conductive structures (rectangular or cylindrical) in which the RF wave is confined within the structure and propagates inside from end to end. Generally, the waves are described as either transverse electric (TE) or transverse magnetic (TM) for the purposes of studying the wave propagation [1]. In some cases, the entire hollow space or the inner wall of the waveguide can be filled with a dielectric material. This can affect the propagation characteristics of the wave in several ways such as a change in propagation constant (S11) by means of a shift in frequency and/or a shift in amplitude. The presence of a dielectric results in reduced phase velocity and increased attenuation or loss of the wave as the permittivity (\(\epsilon_{\text{r}}\)) and loss tangent (tan \(\delta\)) change. This shift in frequency and amplitude can be used to determine the presence of biofilm on the surface of the inner wall of the waveguide.  
Cavities, conversely, are fully enclosed and can essentially trap the RF field within the structure. Idealized cavities, devoid of sources and losses, resonate at specific frequencies, creating strong electromagnetic (EM) fields that build up in the cavity. However, dielectric media inside the cavity and losses inherent on the walls dampen and/or change these resonance frequencies.
## 5 Results and Discussion
### Axial-Flow Gallery Ventilation Scheme  
#### 5.2.1 Air Velocity Field  
Although the type and power of the axial-flow fans arranged at the shaft inlet are the same, the difference in diameter between shaft 1 and shaft 2 will cause different air velocity in the shaft and within a certain range of the cavern. Therefore, the average air velocity on the central lines of cavern 1 and cavern 2 were monitored in the CFD simulations, and the velocity distribution in the cavern is shown in Figure 12. There is a large velocity fluctuation at the intersection of the shaft in the two caverns, and the velocity in the upper excavation area is basically stable after 50 m. The values for cavern 1 and cavern 2 are 1.4 m/s and 1.0 m/s, respectively.  
The cross-section area increases from 135 m\({}^{2}\) to 363 m\({}^{2}\) after entering the middle layer excavation, causing a sharp drop in the air velocity. The value near the middle bench is approximate 0 m/s due to the height of the middle bench, and then it tends to be stable after about 50 m. The air velocity in the middle bench of the two caverns is not very different, about 0.4 m/s. The cross-section area further enlarges to 436 m\({}^{2}\) after entering the bottom layer excavation and the steady air velocity of the two caverns further decreases to 0.35 m/s. Since large-scale caverns generally adopt the method of layered excavation, the air velocity presents a more obvious step-like distribution. The current \"Technical Specifications for Construction of Highway Tunnel\" (JTG F60-2009) [37] and \"Safety Regulations for Coal Mines\" [38] stipulate that the minimum air velocity in the cavern must reach 0.15 m/s. Thus, the axial-flow gallery ventilation scheme, by using shafts for the fresh air, can meet the air velocity requirements of the relevant regulations.