esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Healthcare sector's carbon footprint is a significant environmental concern, with regional variations: 4.6% in France, 7.6% in the United States, and 5.1% in Australia [1]. Diagnostic imaging, particularly Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), is a major contributor due to high energy consumption and waste generation [2, 3, 4]. This has driven the rise of green radiology, highlighting growing environmental awareness [5] and decarbonization policies [6].  
Despite these challenges, MRI remains essential for non-invasive diagnostics. In France, there are 15.8 MRI units per million population, compared to 40.1 in the United States [7]. However, rising electricity costs pose sustainability challenges, emphasizing the need for energy-efficient strategies.  
A significant portion of an MRI machine's energy consumption occurs during idle periods, underscoring the importance of initially selecting energy-efficient models, as retrofitting is often not feasible [8]. Simple measures, such as turning off workstations and monitors, can reduce unnecessary energy consumption [9,10]. For instance, turning off scanners overnight and on Sundays can save approximately 14,000 kWh/year [11].  
However, MRI scanners equipped with superconducting magnets cannot be completely shut down. In response, manufacturers offer various standby modes [12]. Carbon footprint reduction also extends to data preprocessing, with strategies such as optimizing workflows like functional MRI preprocessing tools [13].  
Advances in artificial intelligence and deep learning, including technologies such as Siemens' Deep Resolve [14], General Electric's Air Recon DL (deep learning) [15], Canon's AICE (Advanced intelligent Clear-IQ Engine) [16], and Philips' SmartSpeed [17], have notably advanced MRI capabilities and significantly reduced scan times.  
Previous research has mainly focused on image quality and clinical relevance, particularly in musculoskeletal imaging [18]. However, the significant reduction in scan times not only benefits non-cooperative patients (e.g., pediatric patients, those in pain) [19] by minimizing sedation needs and enhancing comfort but also improves the overall accessibility and efficiency of imaging services. Moreover, the effect of these advancements on power consumption has yet to be fully explored.  
This study aims to optimize MRI energy usage by analyzing non-productive energy modes, refining operational workflows, and assessing the financial implications of increased productivity through a theoretical scheduling model that accounts for electricity pricing.
## 2 Methodology
### Least Squares Model with Outlier Detector
The estimated quantities for \(\mathbf{\Delta\Phi}\) can be derived by:  
\[\mathbf{\Delta\Phi}=\mathbf{A}\Big{(}\mathbf{A}^{T}\mathbf{P}^{dd}\mathbf{A}\Big{)}^{-1}\mathbf{A}^{ T}\mathbf{P}^{dd}\mathbf{\Delta\Phi}, \tag{10}\]  
The residuals of the measurements can be derived by:  
\[\mathbf{r}=\mathbf{\Delta\Phi}-\mathbf{\Delta\hat{\Phi}}, \tag{11}\]  
The variance matrices of the estimated quantities can be derived by:  
\[\mathbf{Q}_{\hat{x}\hat{x}}=\Big{(}\mathbf{A}^{T}\mathbf{P}^{dd}\mathbf{A}\Big{)}^{-1} \tag{12}\]  
After the LS solution for each arc, an outlier detector is applied to check if the phase measurements for the arc have phase ambiguities [39]:  
\[\text{Max}(|\mathbf{r}_{i}|)>\text{c}\sqrt{\text{Max}((\mathbf{Q}^{dd})_{ii})}+2\sqrt {\text{Max}((\mathbf{Q}_{\Delta\hat{\Phi}\Delta\hat{\Phi}})_{ii})}, \tag{13}\]  
where \(\text{Max}(\cdot)\) denotes the maximum value in a vector or matrix and c is a constant, usually chosen as 3. Both the LS residuals and variance components for the arc are used for such a quality check. For subsequent analysis, the unacceptable arcs are discarded. After deriving the parameters (i.e., elevation-error and deformation-rate increments) of all the valid arcs, we can determine the parameters (i.e., elevation errors and deformation rates) at all TCPs with respect to a reference point.
# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## EDM Process Details
### Various EDM Process Variants
#### 2.4.4 Powder-Mixed EDM  
As the name implies, powder of suitable material, such as nickel or even ceramics, is mixed with dielectric fluid. The presence of powder makes the process mechanism totally different from the conventional EDM process [97]. In fact, powder particles fill the gap between the electrode and workpiece. When voltage is applied during machining, the presence of particles forces the electrode and the workpiece to move away from each other at a small distance to adjust the gap area reduction filled by the powder particles. The gap between electrode and workpiece may increase by 100% to 300% or from 25-50 \(\upmu\)m to 50-150 \(\upmu\)m [98]. Furthermore, the presence of powder particles between  
Figure 3: Schematic diagram of wire EDM.  
the electrode and workpiece also leads to early explosion and faster sparking, which cause a higher erosion rate.
By employing fuzzy logic, the optimal OFDM-IM index parameter combinations for different vehicular network scenarios are derived, providing an effective technical solution for the achievement of novel, efficient communication systems in vehicular networks. The rest of this paper is organized as follows. Section 2 presents the related work. Section 3 establishes a V2V communication transmission model based on OFDM-IM technology in vehicular networks. Section 4 proposes a user demand- and scenario-adaptive OFDM-IM method for vehicular network scenarios. Section 5 verifies the effectiveness of the proposed method through simulation experiments. Finally, Section 6 summarizes the entire paper.
## 2 Dataset  
In order to work as efficiently as possible, we utilized a morphological imaging dataset from Recursion Pharmaceuticals with 305,520, 5-channel images. In April of 2020, Recursion developed the dataset, where cells were treated in 1536 various microplates, with fixation through 5% paraformaldehyde, permeabilization through 0.25% Triton, and staining with a variety of different dyes. Three different cell treatments were involved, with mock cells, which served as a control, cells with irradiated and, therefore, deactivated SARS-CoV-2 virions, and cells infected with active SARS-CoV-2 at a ratio of 4 virions to 10 cells. These cells were then treated with a large number of candidate compounds, which came from a library of drugs including FDA approved drugs, European Medicines Agency (EMA) approved drugs, and compounds in clinical trials for SARS, which were compiled into the RxRx19a dataset [17]. Each compound was given in concentrations, varying in 6 different concentrations, each separated by half-log increments, with each concentration of each compound being repeated 6 times. The SARS-CoV-2 virus targets human kidney cells, with EM observations indicating the presence of virus-like particles within the kidney cells of coronavirus infected patients. The cytopathic nature of the SARS-CoV-2 virus suggests direct tubular damage through cytotoxicity. This knowledge is applied through the usage of continuous renal replacement therapies on COVID-19 patients due to the observation of acute kidney injury in patients, but was also crucial in the creation of the RxRx19a dataset, as tubule damage meant that the infected kidney cells of a COVID-19 patient possess characteristics that would distinguish them from mock cells. Recursion used human renal cortical epithelial cells, or HRCE cells from proximal and distal tubules, as well as VERO cells from the kidney cells of the African green monkey as a control in the dataset. An example of the images described above can be found in Figure 1[5].  
Recursion also released another set of data with the same treatment conditions named RxRx1, but with a 384-well plate density, as opposed to the 1536 plate density of RxRx19, an image resolution of 512 \(\times\) 512 with 6 channels instead of the image resolution of 1024 \(\times\) 1024 with 5 channels of RxRx19, and 1138 classes of siRNA as opposed to the 1672 compounds of RxRx19. The cells used in the previous experiment were treated with siRNA instead of target compounds. The siRNA dataset shared similar features to the SARS-CoV-2 dataset, but with 1139 classes of siRNA applications instead of the 1672 molecules and three viral conditions of the RxRx19a dataset. This allows us to use the siRNA dataset, a relatively larger dataset, to train the classification model first, and then use the viral/mock cells in the SARS-CoV-2 dataset to retrain the model [5]. Details on this will be given in Sections 3.3 and 4.
## 2 Background of Mobile _Ad Hoc_ Network Routing  
Many routing protocols have been proposed for Mobile _Ad Hoc_ Networks (MANETs) [1]. In this section we review two standard MANET routing protocols, _i.e._, Destination Sequenced Distance Vector (DSDV) routing protocol and _Ad Hoc_ On Demand Distance Vector (AODV) routing protocol.
# Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies +
###### Abstract  
Description of the physical behavior of electric guitars is still not very widespread in the scientific literature. In particular, the physical models describing a nonlinear behavior of pickups still requires some refinements. The study presented in this paper is focused on nonlinear modeling of the pickups. Two main issues are raised. First, is the currently most used nonlinear model (a Hammerstein model) sufficient for the complex nonlinear behavior of the pickup? In other words, would a more complex model, such as a Generalized Hammerstein that can deal better with the nonlinear memory, yield better results? The second troublesome issue is how to measure the nonlinear behavior of a pickup correctly. A specific experimental set-up allowing for driving the pickup in a controlled way (string displacement perpendicular to the pickup) and to separate the nonlinear model of the pickup from other nonlinearities in the measurement chain is proposed. Thanks to this experimental set-up, a Generalized Hammerstein model of the pickup is estimated for frequency range 15-500 Hz and the results are compared with a simple Hammerstein model. A comparison with experimental results shows that both models succeed in describing the pickup when used in realistic conditions.  
Keywords:guitar pickup; nonlinear systems; distortion +
Footnote †: This paper is an extended version of our paper published in the 19 th International Conference on Digital Audio Effects (DAFA-16), Brno, Czech Republic, 5–9 September 2016.
## 4 System Reliability Modeling Considering Planetary Transmission Behavior Characteristics  
### Conditional Probability Expectation Algorithm for Part Fatigue Reliability Calculation  
The traditional \"load and strength interference\" analysis method is extended to establish a conditional probability expectation algorithm for calculating the fatigue reliability of parts based on the probability distribution of stress level and the life distribution under specified stress level. For the static strength failure of parts under one load, the reliability can be regarded as a function of stress, and the conditional reliability model under specified stress is established. That is, under the condition of stress \(\sigma\), the probability calculation formula of static strength \(S\) greater than the stress is  
\[\zeta(\sigma)=\int_{\sigma}^{\infty}f(S)\mathrm{d}S \tag{13}\]  
where \(f(S)\) is the probability density function of static strength \(S\).  
Furthermore, the static strength reliability model of parts that can reflect the effect of load uncertainty can be expressed as  
\[R=\int_{0}^{\infty}h(\sigma)\cdot\zeta(\sigma)\mathrm{d}\sigma \tag{14}\]  
where \(h(\sigma)\) is the probability density function of stress \(\sigma\).  
Equation (14) is the traditional \"load and strength interference model\", then the basic meaning is extended from probability perspective. Based on the total probability calculation principle of continuous random variable, Equation (14) can be interpreted as the mathematical expectation of random function \(\zeta(\sigma)\) in the definition domain of random variable \(\sigma\).  
For fatigue reliability, because it is difficult to obtain the fatigue strength distribution under specified life directly, a mathematical expression for calculating the fatigue reliability of parts directly based on life distribution can be constructed according to the above extended thinking. Assuming that \(\zeta(n,\sigma)\) is the conditional fatigue reliability function  
Figure 7: Statistical transformation of test data. (**a**) Probability life transformation for tooth; (**b**) P\(-\)S\(-\)N curves fitting for tooth.  
under specified stress level, that is, under the condition of stress level \(\sigma\), the probability calculation formula of the life \(N\) greater than the number of load cycles \(n\) is  
\[\zeta(n,\sigma)=\int_{n}^{\infty}f(N|\sigma)\mathrm{d}N \tag{15}\]  
where \(f(N|\sigma)\) is the life probability density function at stress level \(\sigma\).  
Correspondingly, under the action of random stress level \(\sigma\), the fatigue reliability model of parts can be expressed as  
\[R(n)=\int_{0}^{\infty}h(\sigma)\cdot\zeta(n,\sigma)\mathrm{d}\sigma \tag{16}\]  
The fatigue reliability index of parts under random constant amplitude cyclic load can be directly calculated by Equation (16).
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
## 4 Quantitative Assessment Methods of Early Enamel Caries with OCT
### Quantitative Assessment Based on the Volume of Residual Enamel  
Demineralization caused by caries lesions changes the volume of residual enamel. [PERSON] et al. [82] obtained cross-sectional images of sound, partially demineralized and completely demineralized teeth in vitro by using 1310-nm SD-OCT with an axial/lateral resolution 6/25 \(\upmu\)m, and measured the volume of residual enamel with an automated calculation method based on pixel intensity. The volumetric evaluation algorithm is shown in Figure 5. For the precise selection of residual enamel, an image window is applied to the 2D OCT images, as shown in Figure 5a. Then, the pixels that satisfy the pre-determined intensity threshold range are selected as shown in Figure 5b. Finally, a 3D OCT volumetric image is obtained as shown in Figure 5c. The volume of residual enamel is determined by:  
\[V_{tot}=\left(N_{1}\times l_{x}\times l_{y}\right)\times l_{z}+\left(N_{2} \times l_{x}\times l_{y}\right)\times l_{z}+\cdots+\left(N_{n}\times l_{x} \times l_{y}\right)\times l_{z} \tag{7}\]  
where \(N_{i}(i=1,2,\ldots,n)\) is the number of pixels in each window that satisfy the predetermined intensity cut-off points, \(n\) is the number of 2D images contained in the 3D image. \(l_{x}\), \(l_{y}\) and \(l_{z}\) are the pixel sizes in the x, y and z directions, respectively.  
The volume of residual enamel for carious samples, partially demineralized samples and sound samples ranged from 12.26 to 28.72 mm\({}^{3}\). The progression of dental caries is determined by detecting changes in the volume of residual enamel. When reduction in tooth volume is identified, medication can be taken immediately to inhibit the development of caries. The key in this method is the determination of threshold parameters, which requires the evaluation and standardization of volumetric information for multiple in vivo teeth to enhance accuracy.
## 2 Materials and Methods  
A commercially available, undoped CdTe wafer with a (001) orientation and cubic zincblende structure was used for this study. CdTe exhibits a direct bandgap of 1.5 eV at room temperature [25]. One side of the wafer had a mirror-like polished finish, while the other side remained unpolished. Because of the flatness of the polished surface, roughness measurements were exclusively performed on the unpolished backside. Both surfaces were cleaned with methanol before analysis.  
Surface morphology was characterized using a NanoSurf EasyScan 2 AFM (NanoSurf AG, Zurich, Switzerland) system operating in contact mode under ambient conditions. In this mode, the AFM cantilever is held near the sample surface, allowing for the measurement of repulsive interatomic forces. AFM topographic images were acquired across a 10 \(\times\) 10 \(\upmu\)m\({}^{2}\) area using a standard silicon tip. The root-mean-square (RMS) roughness, a quantitative measure of surface texture, was determined from the AFM images using the instrument's software (version 3.10.0.36). The RMS value, expressed in nanometers, is sensitive to peak and valley heights and is calculated as follows: RMS (nm) = [\(\Sigma\) (z\({}_{\mathrm{i}}\) - \(\mathrm{z_{ave}}\))\({}^{2}\)/N]\({}^{1/2}\), where z\({}_{\mathrm{i}}\) represents the height at a given point, z\({}_{\mathrm{ave}}\) is the average height in the scan area, and N is the number of points of scanning on the surface [26].  
PL spectra were recorded at room temperature using a spectrometer equipped with a 532 nm laser (energy of 2.3 eV and power of 2.5 mW) as the excitation source. The emission light was analyzed with a monochromator configured with a 1200 gr/mm diffraction grating and a thermoelectrically cooled CCD used as a detector.  
Micro-Raman spectra were obtained using an Edinburgh Instruments (Edinburg, UK) RM5 Raman microscope equipped with a 785 nm laser excitation (photon energy of \(\hbar\omega\) = 1.57 eV) and a 1200 gr/mm grating. The laser power at the sample was approximately 1 mW. The spectra were collected in the backscattering geometry with an integration time of 30 s. The laser beam, with a spot diameter of about 10 \(\upmu\)m, was focused on the sample with a 50\(\times\) microscope objective. The spectrometer was calibrated using the 521 cm\({}^{-1}\) Raman line of a silicon wafer.  
The measured PL and Raman spectra were obtained in areas with different surface roughness, previously measured with AFM, as described below.
## 5 Mathematical Formulations for Vehicle Travel and Charging
### Time Cost
#### 5.1.2 Queuing Time  
The arrival of AEVs at the charging infrastructure can be modeled as a time-dependent stochastic process, which can be represented as a Poisson process. The parameter \(\lambda(t)\) of the Poisson process describes the average arrival rate of AEVs at a charging infrastructure during time slot \(t\). The probability that \(n\) AEVs arrive at the charging infrastructure during time slot \(t\) is given as [42],\[P\{n\}=\frac{e^{\lambda(t)}(\lambda(t))^{n}}{n!},n=0,1,2\cdots. \tag{8}\]  
At the CS, the number of charging slots in service and the remaining time of charger service can be obtained by CS smart devices. \(f^{u}_{cs_{i}}(t)\) represents the remaining service time of the \(u\) th charging slot. The queue number of vehicles on \(cs_{i}\) at time \(t\) can be given by  
\[Z_{cs_{i}}(t)=max\{0,Z_{cs_{i}}(t-1)+\lambda(t)-p_{cs_{i}}(t)\}, \tag{9}\]  
where \(Z_{cs_{i}}(t)\) represents the number of AEVs queuing at \(cs_{i}\) at time \(t\). \(p_{cs_{i}}(t)\) denotes the number of vehicles that have been fully recharged and leave the CS during time slot \(t\).  
The queuing time at the \(t\) can be obtained by  
\[\tau^{w}_{cs_{i}}(t)=\begin{cases}0,&Z_{cs_{i}}(t-1)+\lambda(t)-p_{cs_{i}}(t) <0,\\ f^{Z_{cs_{i}}(t)+1}_{cs_{i}}(t),&Z_{cs_{i}}(t-1)+\lambda(t)-p_{cs_{i}}(t)\geq 0.\end{cases} \tag{10}\]
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 3 Gabor-Based Visual Feature Extraction  
Gabor filters are a widely used technique in image processing and computer vision, where an image is convolved with a bank of Gabor filters at different orientations and scales, resulting in a multi-channel representation of the image that captures both spatial and frequency information [43]. In the context of facial recognition, [PERSON] and [PERSON] [44] used Gabor filters to identify human faces, and found that horizontally oriented features were the most informative, forming a distinctive \"barcode\" mapping of the face, with clear distinctions between features such as the lips, teeth, philtrum, and mentolabial sulcus.  
This was extended in previous research by the authors [3] to capture more detailed mouth movement features with a novel Gabor feature extraction method for capturing 3D geometric lip features using Gabor-based image patches. This can effectively extract three-dimensional features from speakers such as mouth opening area and depth, for a better understanding of mouth movement during speaking. Recent studies [6; 11; 45] have demonstrated the effectiveness of the Gabor feature extraction method in visual speech recognition tasks. The authors previously applied Gabor feature extraction for English and Mandarin Chinese speech recognition [11], which achieved comparable performance to deep learning CNN-based approaches while maintaining system simplicity and explainability. Compared to other visual extraction methods such as Active Appearance Models (AAM) [46], 2D-DCT [47], and CNNs [48], Gabor features strike a good balance between interpretability and accuracy, enabling parameters to be easily adapted and used for detailed analysis [3]. They also offer the advantage of being explainable and robust enough to recover from errors, making them suitable for real-world speech analysis applications such as speech recognition, synthesis, tracking, and linguistics. By first applying a Gabor transform and then extracting geometric features, information about the movement and shape of the lips during speech production can be visualised. Our Gabor feature extraction method was introduced in [PERSON] et al. [49], and is summarised below:
## 4 Experiments
### Attack Isolation  
In another experiment, we trained random forest and XGBoost classifiers to distinguish attack types. The decision variable has four classes--no attack, the HopSkipJump attack, the PermuteAttack, and the ZOO attack. Table 5 shows the results of each scenario.  
We can see that attack isolation has poorer performance than attack detection. Using the XGBoost model architecture, we obtain higher balanced accuracy values than for random forest. The highest values are observed for the one-data-set-out scenario. This suggests that distinguishing the attack type for the new data set is easier than for the new model type.  
Figure 3: Mean rank of feature importance across classifiers for attack detection.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Scenario Name** & **RF** & **XGB** \\ \hline
10-fold cross-validation & 0.6560 (0.1542) & 0.7625 (0.0961) \\ one-data-set-out & 0.7443 (0.2213) & 0.8030 (0.1512) \\ one-model-out & 0.7102 (0.1476) & 0.7216 (0.1379) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Balanced accuracy with its standard deviation for each scenario in attack isolation.  
The analysis of feature importance ranks shows that, similar to those obtained for attack detection, they are highly correlated within the classifier type. The correlation coefficient between scenarios for the same classifier varies from 0.67 to 0.99. However, correlation values between the results of scenarios realized using RF and XGB are slightly higher than those presented in the previous section and are in the range of 0.41-0.51. Figure 4 presents the mean rank for the top 20 features used in analyzed classifiers.  
In the case of attack detection as well, the most important feature is balanced accuracy, calculated on the attacked data set. In subsequent places, we can observe variables connected with uncertainty measures. In the presented top 20 most influential features, the random forest classifier uses statistics calculated based on five diagnostic attributes, while the XGBoost classifier uses eight diagnostic attributes.