esa-sceva/satcom-chunk-collection huggingface dataset:
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 4 Discussion
### Current Limitations and Future Works  
The current pseudo-side-scan sonar generation method only regards the side-scan sonar image as an image style but ignores the imaging mechanism of the side-scan sonar. For example, although the shadow part was retained in our generated image (Figures 7 and 8), the directions of the shadows were random. Moreover, according to the characteristics of sonar data acquisition, a shadow is often accompanied by an evident increase in the intensity of a target.  
However, the pixel-level transformation of CycleGAN was unable to fully replicate the imaging physics of side-scan sonar. The real side-scan images could reflect the physical relationships between sound wave frequency, sound scattering angles, and the scattering properties of seabed materials. To enhance the physical consistency of the generated images, physical-driven constraints could be introduced into the loss function, or a module simulating physical processes (such as sound wave propagation) could be embedded into the generator. Additionally, physical parameters (such as the sound frequency, the  
Figure 12: Comparison of detection accuracy (precision–recall curves) on URPC2021 sonar dataset using the original (**a**) and improved YOLOv8 model (**b**).  
incidence angle, and the water depth) could be added to the input parameters. However, a simplified physical model may still lead to deviations between the generated images and real data, and the increased computational complexity may affect training efficiency.  
In future work, hybrid frameworks combining physical models with deep learning can be explored to enable the model to generate pseudo-side-scan sonar images with physical features similar to those of real sonar images. By continually expanding the side-scan sonar dataset, updating target detection models, and optimizing model lightweighting, the effectiveness, accuracy, and speed of target detection in side-scan sonar images can be further enhanced.
# Flocculation Harvesting Techniques for Microalgae: A Review
## 3 Bio-Flocculation
### Algal-Bacterial Bio-Flocculation  
Bacteria generally co-exist during conventional algae cultivation under reactor-scale and/or outdoor conditions. Their interactions are species-specific and can be either mutually benefiting or inhibiting according to the nutritional and environmental conditions [56; 57]. Interestingly, some algae-associated bacteria such as _Flavobacterium_, _Terrimonas_, and _Sphingobacterium_ have been proven to promote the formation of large flocs which can settle thereby enhancing algae flocculation. A xenic culture of _C. vulgaris_ with bacteria exhibited high harvesting efficiency of \(\sim\)94%, while an axenic (bacteria-free) _Chlorella_ culture showed only 2% flocculation efficiency [52]. Bacterium _Solibacillus silvestris_ isolated from activated sludge has been reported to secrete a less-toxic bio-flocculant (composed of 75.1% proteoglycan and 24.9% protein), inducing electrostatic bridging and patching between algal cells [53]. However, this bio-flocculant (dosage, 0.2 g/L) showed only a relatively low harvesting efficiency of 76.3% for marine alga _N. oceanica_.  
Fast growth rates of bacteria can be beneficial relative to other microbial flocculants such as yeast and fungus. However, species-specific reactivity and environmental safety issues should be carefully considered and resolved for large-scale applications [58].
## 3 Procedure, Data and Methodology
### Automatic Recognition of Lexical Situation Types  
Next, we will utilize the relevant models to train the dataset and achieve the prediction of situation types. This section primarily introduces the models, classifiers and related methods used in the prediction experiments.  
[PERSON] et al. [46] proposed the Vector Space Model (VSM) in the field of information retrieval. In the study of computational linguistics, the VSM has been widely applied and exhibits robust capabilities in handling tasks involving large-scale textual data. Leveraging the VSM, we base our experiment on the constructed linguistic feature vector matrices and embedding vector matrices as the basis for our data, and utilize multinomial logistic regression (MNLogit), support vector machine (SVM) and artificial neural network (ANN) as three common classifiers. The reason for selecting these classifiers is as follows:  
MNLogit is a relatively simple traditional classifier. As a linear model, it decomposes multi-class classification problems into multiple binary classification tasks, and has been widely applied in studies involving multi-class classification and prediction problems.  
SVM is among the best-performing traditional classifiers. This algorithm, based on the maximum margin principle, constructs a maximum margin hyperplane in the feature space to maximally separate data of different classes.  
ANN is a widely used and high-performance computational model. By mimicking the behavior feature of biological neural networks and simulating the structure and function of the human brain using engineering techniques, ANN is capable of handling complex prediction problems.  
These classifiers demonstrate certain advantages in processing high-dimensional data, effectively performing feature selection and classification tasks. Therefore, we select these three classifiers for tenfold cross-validation. We optimize the classifiers' parameters using grid search to achieve the highest F1 score as the criterion for model optimization,determining the optimized parameters for each classifier. Finally, we train the models based on the gold-standard dataset to evaluate the aspect information dataset.  
The classification experiments in this paper consist of two parts: prediction experiments based on linguistic features and based on embedding vectors. The following sections will separately introduce the data and methods used in these two experiments.
## 2 Decoupling Techniques (DTs) in Ultra-Wideband MIMO Antennas (UMAs)
### Placing AEs at 90\({}^{\circ}\) and 180\({}^{\circ}\)  
By locating AEs at an angle of 90\({}^{\circ}\) (orthogonal), isolation between AEs can be increased. In the literature survey of UMAs, the arrangement of AEs is mostly orthogonal. In [37], a sector was detached from a circular patch of the AE to improve impedance bandwidth, and a U-slot was added in the patch to reject interference from the Wi-MAX band, as presented in Figure 2a. For isolation of the MIMO antenna, the AEs were placed orthogonally and had separated ground planes to achieve greater isolation. In [38], the single AE contained a semi-circular patch and a triangular-shaped truncated ground surface, as displayed in Figure 2b. The upper corners of the ground plane were triangularly truncated to receive better impedance matching. Here, good isolation was obtained by just locating the AEs orthogonally. In [39], the circular patch was loaded with four circular slots of different radii, where the center of the circular slot coincides with the external edge of the circular disk, as shown in Figure 2c. The truncated corners of the ground planes were used for wideband matching. The four AEs were placed orthogonally to increase isolation. In reference [40], the AE consisted of a half-elliptical monopole patch on the upper side with a counterpart half-elliptical slot added to the ground surface, as presented in Figure 2d, which was called the quasi-self complementary (QSC) method. The antenna was excited by a tapered transmission line to achieve wide bandwidth. In the four-port MIMO structure, the isolation was enhanced by the orthogonal arrangement of the AEs. A C-slot was added to the semi-elliptical radiator to suppress the WLAN band. In paper [41], the antenna was designed using step etching on the ground plane, and an arrow-shaped slot was loaded on the radiating patch to enhance bandwidth, as displayed in Figure 2e. The homogeneous elements and angular variation techniques were applied to reduce mutual coupling between AEs. In reference [42], the antenna consisted of four monopole AEs, where each monopole had three concentric rings, as presented in Figure 2f. The orthogonal arrangement of the AEs provided good isolation and polarization diversity. In [43], the AE was designed using modified elliptical radiators, as shown in Figure 2g. The AE was further developed into a MIMO antenna configuration with four elements placed orthogonally to each other in a single plane. In paper [44], the MIMO antenna was composed of four octagonal-shaped
## 2 Circuit Design
### Signal Processing  
The obtained voltage output signal from the transimpedance amplifier is further processed by a bandpass filter consisting of a cascaded highpass and lowpass. While the signal in the wide bandwidth range is filtered at the cutoff frequencies \(f_{c}=0.5\,\mathrm{Hz}\) for the highpass and \(f_{c}=1\,\mathrm{kHz}\) for the lowpass, the smaller bandwidth range is filtered using the cutoffs \(f_{c}=7\,\mathrm{Hz}\) and \(f_{c}=24\,\mathrm{Hz}\), respectively. While the wide bandwidth allows a full representation of the morphology of the cardiac activity to be measured, the smaller bandwidth can be used to extract the QRS complex and to remove noise signal parts for further signal processing. After the bandpass, the filtered signal is further amplified using a differential amplifier and then digitized by the microcontroller, which stores the sampled signal into an internal flash memory. During recording, the signal can also be transmitted wirelessly via Bluetooth to an external device. For all evaluations in this work, we used the signal stored on the internal flash memory which was transferred to a PC after the measurement and then converted and handled in a comma separated values (csv) file.  
Figure 3: (**a**) Transimpedance amplifier converting the current input signal into a voltage output signal. (**b**) Structural model of the feedback loop to reduce DC/low-frequency interring noises.
# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion  
### Search Inclusion Criteria and Study Description  
The reported PubMed and Scopus searches identified 1824 papers, selected as described in the PRISMA flow diagram (Figure 2).  
Figure 1: Steps of the RT patient-based workflow, in which AI-based techniques can be applied.  
Figure 2: PRISMA flowchart diagram summarizing the study selection. (§) The list of included papers for quantitative synthesis and their classifications according to the AI subgroups (i.e., type of methods) and main aims are reported in the Supplementary Materials (Table S1). (*) The list of excluded papers from quantitative synthesis and reasons for exclusion are reported in the Supplementary Materials (Table S2).  
After the screening of 691 duplicates, 204 of 1133 papers were excluded for the following reasons: 45 reviews; 19 no English; 13 no AI application; 65 no application to clinical RT; 6 comments, commentary, or letters; 15 conferences; 3 ethical or philosophical perspectives; 8 harmonization or standardization; 5 surveys; 2 corrections or corrigendum; 6 errata or retracted; 16 editorial or book chapter; 1 general recommendation. In addition, 8 of 929 papers were excluded after the inspection of the full text for the following reasons: 6 overviews, 1 no application to clinical RT, 1 commentary. The details about the excluded papers are reported in the Supplementary Materials (Table S2).  
Out of the 921 included papers reporting the types of methods employed (i.e., IO, ML, or DL), 321 papers (34.9%) used ML methods and 596 (64.7%) used DL methods. Four papers (0.4%) investigated the application of IO approaches to image and chart reviews and the Pinnacle treatment planning system. In particular, the Pinnacle auto-planning module is based on the ability of a digital computer to perform tasks commonly associated with human intelligence through a system of iterative optimization [8].  
Figure 3 shows the distribution of the published papers, including IO, ML, and DL methods per year. From the figure, it is possible to notice how the number of papers using AI approaches in RT is growing over time. In particular, the number of papers per year using DL shows a steeper growth compared to ML approaches.  
The numbers of papers reporting applications of AI subgroups according to the different RT workflow steps (highlighted in Figure 1) are reported in Figure 4. Among the AI approaches, DL methods were mostly implemented for semi-automatic organs at risk (OARs) or tumor segmentation studies (254 papers) and for synthetic image generation (127 papers). ML methods were mostly used for the outcome prediction (183 papers).  
Figure 3: Papers per year according to the type of AI approach. The blue, cyan, and dark blue bars represent DL, ML, and IO methods, respectively. The grey line represents the total count (i.e., the sum of DL, ML, and IO methods) per year.
## 2 The Seismic Reliability Analysis Process for the Substation System
### QMC Simulation  
QMC improves the problem of the slow convergence of the MC method in the sampling process and improves the accuracy of operations and has been applied to many fields that require a Monte Carlo simulation, such as statistical circuit analysis [38], structural reliability assessment [39], and security pricing [40]. The basic idea of both the QMC method and the MC method is to obtain the probability of the possible occurrence of the desired event by test sampling, but the theoretical bases are different; the QMC method uses a low-dispersion low deviation series (LDS) instead of the pseudorandom series in the MC method, which is the core idea of the method.  
Halton sequences and Sobol sequences are commonly used LDS sequences with easy construction methods and good sequence uniformity. It has been shown that Sobol sequences possess higher convergence and more stable output results compared with Halton sequences, except for the first dimension [41]. Therefore, in this study, the Sobol sequence with better convergence performance was used as the original sampling sequence for QMC simulation, and considering that the Sobol sequence obtained by the traditional method causes problems, such as low sequence bias when the number of sampling points is small, to ensure its dual characteristics of randomness and low bias, the Sobol low bias sequence is randomized based on the linear matrix scrambling technique that is divided into the following three steps.  
(1) Obtain Sobol sequences using the basic principle of Sobol sequences:  
\[r_{n}^{(t)}=g_{0}(n)v_{1}\oplus g_{1}(n)v_{2}\oplus\cdots\oplus g_{n-1}(n)v_{n} \tag{5}\]  
where \(r_{n}\) is the \(n\)-th number of the Sobol sequence, \(v_{n}\) is the \(n\)-th directional number, \(g_{n-1}(n)\) is the binary representation of the grey code with \(n-1\) as the variable, only one binary digit of the adjacent grey code is different, and the calculation adds only one XOR operation.  
(2) Replace the binary vector \(v_{j}\) with a disturbed \(v_{j}^{\prime}\):  
\[v_{j}^{\prime(t)}=v_{j,1}^{(t)}\cdot 1_{1}^{(t)}+v_{j,2}^{(t)}\cdot 1_{2}^{(t)}+\cdots \tag{6}\]  
where \(t\) is the dimension variable of the Sobol sequence and \(1_{k}^{(t)}\) is a binary vector based on two values.  
(3) Add a binary independent random vector \(\mathrm{e}^{(t)}\) to each new vector:  
\[r_{n}^{(t)}=\mathbf{e}^{(t)}\oplus\mathrm{g}_{0}(n)v_{1}^{\prime(t)}\oplus\mathrm{ g}_{1}(n)v_{2}^{\prime(t)}\oplus\cdots\oplus\mathrm{g}_{n-1}(n)v_{n}^{\prime(t)} \tag{7}\]
## 3 Results
### Multi-Channel EMG and Muscle Fatigue  
In Figure 4, a typical EMG time course is shown for the five muscle heads recorded; a contraction had a nominal time length of 10 s, with nominal pauses of 5 s. In Figure 5, we show an example of regression for MF. A regression line connects the MF values in following epochs. This signal was used to detect fatigue. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MVC\({}_{\text{PRE}}\)** & **MVC\({}_{\text{POST}}\)** & **N REP\({}_{\text{PRE}}\)** & **N REP post** \\  & **(N)** & **(N)** & **N REP\({}_{\text{PRE}}\)** & **N REP post** \\ \hline S1 & 300 & 290 & 20 & 20 \\ S2 & 230 & 272 & 20 & 20 \\ S3 & 240 & 455 & 20 & 19 \\ S4 & 268 & 189 & 20 & 20 \\ S5 & 660 \({}^{\ast}\) & 346 \({}^{\ast}\) & 20 & 20 \\ S6 & 170 & 330 & 20 & 20 \\ S7 & 141 & 196 & 20 & 20 \\ S8 & 326 & 413 & 20 & 20 \\ S9 & 211 & 216 & 20 & 20 \\ S10 & 226 & 349 & 20 & 20 \\ S11 & 568 & 458 & 14 & 19 \\ S12 & 275 & 366 & 20 & 20 \\ S13 & 201 & 221 & 20 & 20 \\ S14 & 210 & 240 & 20 & 20 \\ S15 & 283 & 326 & 20 & 20 \\ S16 & 550 & 400 & 20 & 20 \\ S17 & 247 & 329 & 20 & 20 \\ S18 & 252 & 3 Figure 4: Typical time courses from EMG measurements. EMG raw signal is in blue; red squares indicate the phase segmentation (contraction—no contraction). Figure 5: Typical time courses for median frequency. The \(m\) (angular coefficient) of the regression line was used to determine the detection of fatigue. In Figure 6, the \(m\) coefficients for MF are reported for each subject and muscle before the rehabilitation (gray) and after the rehabilitation (light blue). When \(m\) is positive, MF is increasing during contractions; when \(m\) is negative, MF is decreasing; and fatigue may be detected if \(m<-0.5\) Hz/_epoch_. Table 2 shows the muscle heads in fatigue condition (indicated by 1) and those that are not in fatigue conditions (indicated by 0). The non-parametric test showed that there was not a significant difference in the number of fatigued muscles PRE and POST therapy (\(p=0.175\)), with a slight tendency towards fatigue reduction, but visible only on few patients. This result is achieved with a simultaneous increase in the MVC exerted by each subject. Even with different thresholds for detecting fatigue, statistical tests do not show statistically significant differences (data not reported). Figure 6: \(m\) coefficients indicate the increase (positive \(m\)) and the decrease (negative \(m\)) of the median frequency of each muscle. Values are compared between pre-therapy phase (gray) and post-therapy phase (light blue).
## 2 Measurement Setup and Technique
### Recording Method  
An exponential sine sweep (ESS) signal of 2 s duration and 20 Hz to 25 kHz frequency range generated at a sampling rate of 48 kHz was used as the excitation signal. We chose an ESS as it is widely used for RIR measurements due to its large dynamic range, excellent signal-to-noise ratio (SNR), and ability to reject harmonic distortions and nonlinear effects [13; 17; 29]. The sweep signal was preceded and followed by 1 s silence periods to avoid time-aliasing problems.  
In the initial acoustic setting for the RIR measurement, all variable wall panels were set to felt, and the loudspeaker was positioned at S1. The loudspeaker volume was set to receive around 65 dB of loudness at 1 m distance. The ESS signal was played through the loudspeaker, and recordings were made at each receiver position using the em32 Eigenmike. Next, all the variable wall panels were switched to wood, and the measurements were repeated using the em32 Eigenmike. The process was repeated for source locations S2 and S3. The entire measurement procedure was then repeated using the Rode NT-SF1 microphone. Following each recording, the RIRs were estimated through deconvolution of the recorded sweeps with the inverse sweep signal. MATLAB(tm) R2021a was employed for controlling the source and receiver signals, as well as for performing the deconvolution process.  
Before each measurement for every room configuration and source position, microphones underwent calibration, and latency adjustments were performed. The room temperature was also recorded before each run and was very stable, with variations of less than 0.5 \({}^{\circ}\)C across all measurements.
# MCIDN: Deblurring Network for Metal Corrosion Images
## 4 Experiment
### Ablation Studies
#### 4.4.2 Study of Loss Functions  
To validate the effectiveness of our multiple loss functions, we conduct a series of ablation experiments. Table 6 presents the contributions of each component in the loss function, demonstrating that every part enhances the model's performance. When utilizing only the spatial domain loss function, the PSNR is 32.0136 dB. The inclusion of either the frequency domain loss or edge loss individually results in varying degrees of improvement in both PSNR and SSIM. This is because the frequency domain loss improves the network's capacity to recover high-frequency information, resulting in clearer texture details. Simultaneously, the edge loss preserves sharp image contours and reduces blurring. When employing the total loss function, the PSNR and SSIM reach their highest values. These results indicate that our loss function plays a crucial role in deblurring metal corrosion images, enabling the model to more accurately reconstruct the corroded structures and edge contours.
# The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research
## 3 Magnetic Hyperthermia  
Magnetic hyperthermia (MHT) is more commonly used in various biomedical applications [37; 38]. MH, a clinical alternative to tumor treatments, also became a powerful tool for cancer treatment by exposing tumor tissue to elevated temperatures to achieve a therapeutic effect [31]. It has been successfully applied to the treatment of different types of cancer including the brain [39], spine [40], lung [41], prostate [42], breast [43], and pancreas [31]. It is also a promising alternative to traditional cancer therapies, particularly in the case of aggressive brain cancer like glioblastoma [44]. MHT's huge advantages are connected with biosafety, deep tissue penetration, and a focused place of action [45]. It induces apoptosis by the magnetic carriers generating the heat in the external alternating magnetic field [46]. The heating of magnetic carriers occurs due to the relaxation that generates the heat going within the core within Neel and Brown modes, while several parameters need to be included to estimate the specific absorption rate (SAR) and the intrinsic loss power of the colloidal suspension (ILP), described by the following equations [47]:  
\[SAR=C/m_{np}\left(dT/dt\right) \tag{1}\]  
where \(C\) stands for a heat capacity of the fluid per unit mass of fluid, \(m_{np}\) is the mass of magnetic phase suspended in the fluid, and \(dT/dt\) refers to the initial slope of temperature rise \(T\), as a function of time, \(t\); and as follows  
\[ILP=SAR/H^{2}f \tag{2}\]  
where \(H\) defines the amplitude of the magnetic field, and \(f\) refers to the frequency of the AMF.  
The SAR and ILP depend on the parameters defining the properties of the magnetic carriers: chemical composition, magnetization saturation, shape and size of nanoparticles, the density of the colloidal suspension (magnetic carriers mass in the volume of suspension), dispersant-specific heat, nanoparticles density, the viscosity, and the amplitude of the alternating magnetic field (AMF), frequency of the AMF. They also depend on the hysteresis effect, relaxation effect, eddy current, domain wall, and natural resonance influence of AMF [48]. Thus, the physicochemical and biological properties of the magnetic carriers and the medium influence the efficiency of magnetic hyperthermia-based therapies, making it crucial to be optimized for effective therapy [49]. It is important to provide a high level of efficiency and safety for the magnetic particles used.  
Magnetic hyperthermia application for beating cancers is a relatively new technique facing some challenges, such as accurately determining local temperature growth or tumor heating efficiency [49]. Other important issues are optimizing magnetic nanoparticles' shape, size, and stability. For example, [50] has shown that applying nanoparticles in the form of oxide nanocubes increases the efficiency of magnetic hyperthermia. Therefore, AI tools can benefit MH research (see Figure 1).