esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Attosecond Gamma-Ray Source from ICS
The emitted photons exhibit a comb-like structure with a scale similar to that of the electron beam, as shown in Figure 4c. The single micro-pulse of the photons has an FWHM length of approximately 260 as to 300 as, with time intervals between sub-pulses ranging from 1.10 fs to 1.45 fs, which is about half of the wavelength of the driving laser and consistent with the micro bunched characteristics from LWFA. Figure 4d illustrates the spatial and temporal characteristics of the ICS radiation, which reveal a micro-sized attosecond radiation source with an rms divergence of about 4.25 mrad. The energy spectrum of photons in Figure 4e shows us a continuum spectrum. The tail of the integrated spectrum can be approximately fit by a modified Bessel function [37] as \(dN/dE\sim\int\limits_{E/E_{c}}^{\infty}K_{5/3}(x)dx\), with a fitted critical energy of \(E_{c}=\hbar\omega_{c}\simeq 2.0\pm 0.2\)MeV. Let us now proceed to estimate the peak brilliance of our source. The source size is comparable to the electron beam diameter of 2 um at interaction, whereas the divergence of the gamma-photon beam is about 4.25 mrad. For the gamma-ray pulse with an FWHM of 267 as, as marked in Figure 4b, we have approximately 2.23 \(\times\) 10\({}^{2}\) photons in a 0.1% bandwidth around 2 MeV, implying a peak brilliance of ~1.03 \(\times\) 10\({}^{22}\) photons s\({}^{-1}\) mm\({}^{-2}\) mrad\({}^{-2}\) 0.1% BW. This brilliance is competitive with laboratory-based multi-MeV \(\gamma\)-ray sources using moderate-intensity laser pulses (\(a_{0}=1.5\)). For the comparison, such electrons (\(\gamma\approx 600\)) oscillate in a similar plasma density of 5 \(\times\) 10\({}^{18}\) cm\({}^{-3}\), with a typical oscillation amplitude at the micrometer level. The critical energy of betatron radiation is about a few tens of keV. Additionally, the bremsstrahlung radiation spectrum shown in Figure 4e indicates emission with a cutoff energy of <2 MeV as the electrons pass through the plasma foil. In contrast, the cutoff photon energies from ICS can extend to 10 MeV. Furthermore, the total photon numbers from betatron and bremsstrahlung radiation are nearly two orders of magnitude smaller than those from the ICS gamma-ray photons. Figure 4: The temporal and spatial characteristics and energy spectrum of ICS radiation. (**a,b**) the density distributions of electrons and gamma photons around \(x=2059\) μm. (**c**) Comb-like structures on the axis density profiles for both electron beam and gamma photons. (**d**) inherit temporal and spatial distribution of the emitted gamma photons. (**e**) The energy spectrum of photons from ICS and bremsstrahlung; the critical energy of ICS is about 2.0 MeV.
# Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer
### EVs in Cancer  
As explained in the preceding section, EVs carry a significant amount of information and can also be used for drug delivery. These two hallmarks make them an important weapon against cancer. Their implications in cancer are very intricate and are still being characterized. Certainly, their role as cellular information mediators makes them capable of modifying both local and distant tumoral microenvironments [36]. Tumor-derived EVs (TEVs) carry cell-derived molecules, such as coding or noncoding RNAs and DNA fragments, contributing to tumor progression and malignancy [37]. So, in both a paracrine and systemic manner, TEVs promote cancer progression, transferring aggressive phenotype information and conferring drug resistance [37; 38; 39]. They also interfere with the antitumor immune response and mediate intercellular communication among stromal cells and bone marrow. On the other hand, TEVs are able to promote the formation of pre-metastatic niches to encourage the outgrowth of incoming cancer cells distant from the primary tumor site [37; 40; 41]. Microvesicles derived from tumoral endothelial miR-9 cells were reported to promote not only MV-induced endothelial cell migration but also tumor angiogenesis [42; 43; 44; 45; 46; 47]. Some authors revealed that oral squamous cell carcinoma cell-derived extracellular vesicles (OSCC_EVs) were enriched in microRNA (miRR)-21-5p and that EVs were associated with enhanced resistance to cisplatin, an increased metastatic phenotype, stemness, and a poor outcome in OSCC patients [43]. By conducting liquid biopsies in a minimally invasive or noninvasive way, it is easy to observe circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), which are useful for monitoring patients and tumor evolution [45; 46]. Similarly, EVs were found to be a source of prostate cancer biomarkers obtainable via liquid biopsies [47]. Another group correlated the pro-metastatic role of circulating EVs induced when undergoing neoadjuvant chemotherapy treatment with an enrichment in annexin A6 (ANXA6) in breast cancer patients [44].
## 3 Materials and Methods
### Overall Architecture  
Given a low-resolution image \(I_{LR}\in R^{H\times W\times C_{in}}\), \(H\), \(W\), and \(C_{in}\) represent the height, width, and number of channels of the input image, respectively. \(I_{LR}\) is processed through a \(3\times 3\) convolutional network that extracts basic visual features like edges, textures, and colors, mapping it to a higher-dimensional feature space \(F_{0}\in R^{H\times W\times C}\); it can be formulated as follows:  
\[F_{0}=H_{SF}(I_{LR}), \tag{1}\]  
where \(H_{SF}(\cdot)\) denotes the shallow feature extraction network. \(C\) represents the number of channels in the intermediate feature space \(F_{0}\) with \(C\gg C_{in}\). Subsequently, \(F_{0}\) is passed to the deep feature extraction network, where it undergoes multiple Dense Residual Fused Blocks to extend the shallow features into global structural and semantic levels, thus enabling the learning of deeper high-level visual features \(F_{DF}\in R^{H\times W\times C}\). The Dense Residual Fused Block (DRFG) is composed of several Mamba-like linear attention blocks  
Figure 1: The overall architecture of DRFAN and the structure of DRFG. Each DRFG module integrates multiple MLLA blocks for global context modeling and a GAB block for local grid attention. Below, the MLLA and GAB modules and representative attention maps are visualized, highlighting their respective receptive behaviors. The FWB module shown here adaptively reweights concatenated multi-level features to reduce channel redundancy, helping to maintain compact and efficient feature representations throughout the DRFG. To improve interpretability, we annotate the input and output tensor shapes for each module (e.g., \(3\times\mathrm{H}\times\mathrm{W},180\times\mathrm{H}\times\mathrm{W},( \mathrm{H}\times\mathrm{W})\times 180\)), clearly illustrating the end-to-end data flow.  
(MLABs), a Grid Attention Block (GAB) and residual connections. The computation process can be formulated as follows:  
\[F_{DF}=H_{DF}(F_{0}), \tag{2}\]  
where \(H_{DF}(\cdot)\) denotes the deep feature extraction network, composed of N groups of DRFGs and a \(3\times 3\) convolutional layer. The computation process of deep feature extraction is formulated as follows:  
\[F_{i}=H_{DRFB_{i}}(F_{i-1}),i=1,2,\ldots,N, \tag{3}\]  
\[F_{DF}=H_{Conv}(F_{N}), \tag{4}\]  
where \(H_{DRFB_{i}}(\cdot)\) represents each DRFG. \(F_{1}\), \(F_{2}\), \(\ldots\), \(F_{i}\), as well as \(F_{DF}\) are the intermediate features extracted by the network. \(H_{Conv}(\cdot)\) denotes a single \(3\times 3\) convolutional layer. After aggregating shallow features \(F_{0}\) and deep features \(F_{DF}\) via a global residual connection, the super-resolution image \(I_{SR}\in R^{H\times W\times C_{in}}\) is reconstructed by an upsampling operation. The reconstruction process can be formalized as follows:  
\[I_{SR}=H_{Re}(F_{0}+F_{DF}), \tag{5}\]  
where \(H_{Re}(\cdot)\) denotes the image reconstruction function, consisting of \(3\times 3\) convolution layers and a sub-pixel convolution layer.
## 5 Case Studies
### Case 2: Wind Speed of 8.0 m/s, Wind Penetration of 30%, Disturbance of 100 MW  
To investigate the scenario with high wind penetration, the SG2 is set to be out of service while the capacity of the DFIG-based wind farm increases to 150 MW. Since one SG is out of service, the system inertia and primary frequency response capability become low. As a result, the maximum ROCOF and the frequency nadir reduce to \(-0.823\) Hz/s and  
Figure 10: Results for Case 1: (**a**) Frequency; (**b**) Active power; (**c**) Rotor speed.  
58.822 Hz, respectively, when no frequency response is implemented in the DFIG controller. For the conventional frequency response scheme and the proposed enhanced frequency support scheme, the frequency nadir increases to 59.297 Hz and 59.402 Hz/s, respectively, and the maximum ROCOF increases to \(-\)0.768 Hz/s and \(-\)0.754 Hz/s, respectively, due to the proposed power reference (see Figure 11).  
Furthermore, the setting frequencies with no frequency response, conventional frequency response, and proposed enhanced frequency support scheme are 59.539 Hz, 59.561 Hz, and 59.568 Hz, respectively, since the output power of the proposed enhanced frequency response is more than the others after supporting the frequency.  
As shown in Figure 11b, the output power of the enhanced frequency support scheme increases to the torque limit referred to power, which is larger than conventional frequency  
Figure 11: Results for Case 2: (**a**) Frequency; (**b**) Active power; (**c**) Rotor speed.  
response so that the frequency nadir and maximum ROCOF are better. Furthermore, the DFIG releases more energy so that the rotor speed reduction for the enhanced frequency support scheme is larger, as illustrated in Figure 11c.  
As illustrated in Figure 12, the proposed enhanced frequency support scheme effectively boosts the frequency response capability including the maximum ROCOF, frequency nadir, and settling frequency. Furthermore, with the growing penetration of wind power, the indices of the frequency support performance with no frequency response become worse, whereas the indices of the conventional and proposed frequency support schemes become better.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Correlation
#### 2.3.6 Updating the SFPs  
\(\mathbf{P}_{update}\) is obtained by composing the inverse of \(\Delta\mathbf{P}\) with \(\mathbf{P}\) as  
\[\mathbf{P}_{update}=\mathbf{\omega}(\mathbf{P})\ \mathbf{\omega}(\Delta\mathbf{P})^{-1}. \tag{21}\]  
Here \(\mathbf{\omega}\), being dependent on the SF order, uses the SFPs to populate a square matrix as [43]  
\[\mathbf{\omega}^{SF0}\bigl{(}\mathbf{P}^{SF0}\bigr{)}=\left[\begin{array}{ cccc}1+u_{x}&u_{y}&u\\ v_{x}&1+v_{y}&v\\ 0&0&1\\ \end{array}\right] \tag{22}\] \[\text{and}\ \mathbf{\omega}^{SF2}\bigl{(}\mathbf{P}^{SF2}\bigr{)}=\left[ \begin{array}{ccccc}1+A_{1}&A_{2}&A_{3}&A_{4}&A_{5}&A_{6}\\ A_{7}&1+A_{8}&A_{9}&A_{10}&A_{11}&A_{12}\\ A_{13}&A_{14}&1+A_{15}&A_{16}&A_{17}&A_{18}\\ \frac{1}{2}u_{xx}&u_{xy}&\frac{1}{2}u_{yy}&1+u_{x}&u_{y}&u\\ \frac{1}{2}v_{xx}&v_{xy}&\frac{1}{2}v_{yy}&v_{x}&1+v_{y}&v\\ 0&0&0&0&0&1\\ \end{array}\right],\]  
where\[A_{1}=2u_{x}+u_{x}^{2}+uu_{xx}, A_{2}=2 uu_{xy}+2(1+u_{x})u_{y},\]  
\[A_{3}=u_{y}^{2}+uu_{yy}, A_{4}=2u(1+u_{x}),\]  
\[A_{5}=2 uu_{y}, A_{6}=u^{2},\]  
\[A_{7}=\frac{1}{2}(vu_{xx}+2(1+u_{x})v_{x}+uv_{xx}), A_{8}=u_{y}v_{x}+u_{x}v_{y}+vu_{xy}+uv_{xy}+v_{y}+u_{x},\]  
\[A_{9}=\frac{1}{2}(vu_{yy}+2(1+v_{y})u_{y}+uv_{yy}), A_{10}=v+vu_{x}+uv_{x},\]  
\[A_{11}=u+vu_{y}+uv_{y}, A_{12}=uv,\]  
\[A_{13}=v_{x}^{2}+vv_{xx}, A_{14}=2 vv_{xy}+2v_{x}(1+v_{y}),\]  
\[A_{15}=2v_{y}+v_{y}^{2}+vv_{yy}, A_{16}=2 vv_{x},\]  
\[A_{17}=2v(1+v_{y}) \text{and }A_{18}=v^{2}.\]
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
## 1 Introduction  
In the past few years, the Fourth Generation (4G) of mobile telecommunication has been significantly changing and improving our lives. Meanwhile, the exponentially increasing rate requirements throw down the gauntlet to the latest standard for the mobile telecommunication. Nowadays, research interest has already been drawn on the the Fifth Generation (5G) of mobile telecommunication which can achieve substantially higher throughput than 4G. Massive multiple-input multiple-output (MIMO) and DEVICE-to-device (D2D) communication are two promising candidate technologies of 5G. Massive MIMO employing a few hundreds of antennas at the BS can achieve a higher data rate through focusing the energy into a narrow beam towards the receiver. D2D is another emerging technology to meet the high-rate data requirements due to the short-range direct transmission without going through the base station (BS).
## 3 DR and AGC Unit Capabilities
### Modeling of Residential Smart Appliances
#### 3.1.2 WH Model  
The water heater model is a temperature-based model rather than an energy-based one. This means that the duration of the ON period of the heating coils depends on the temperature set point and the current water temperature. The WH working power is presented in Equation (6):  
\[p_{\text{WH}}(t)=\left\{\begin{array}{ccc}0,&T_{\text{WH}}(t)\geq T_{\text{ WH}}^{s}+T_{\text{WH}}^{d}/2\\ P_{\text{WH}},&T_{\text{WH}}(t)\leq T_{\text{WH}}^{s}-T_{\text{WH}}^{d}/2\\ p_{\text{WH}}(t-1),&T_{\text{WH}}^{s}-T_{\text{WH}}^{d}/2\leq T_{\text{WH}}(t) \leq T_{\text{WH}}^{s}+T_{\text{WH}}^{d}/2\end{array}\right. \tag{6}\]  
where \(p_{\text{WH}}(t)\) is the working power of water heater in time interval \(t\) (kW); \(P_{\text{WH}}\) is the rated power of WH (kW); \(T_{\text{WH}}(t)\) is the water temperature in time interval \(t\) (\({}^{\circ}\)F); \(T_{\text{WH}}^{s}(t)\) is the desired temperature set point in time interval \(t\) (\({}^{\circ}\)F); \(T_{\text{WH}}^{d}\) is the thermostat dead band (\({}^{\circ}\)F).  
The WH is controlled by changing the water set point \(T_{\text{WH}}^{s}(t)\). Decreasing the water set point to some value can stop the WH from working. The controlled formula is presented in Equation (7):  
\[T_{\text{WH}}^{s}(t)=s_{\text{WH}}(t)\cdot T_{\text{WH}}^{set} \tag{7}\]  
where \(s_{\text{WH}}(t)\) is the DR control signal which is received from the in-home controller, and \(T_{\text{WH}}^{set}\) is the water set point (\({}^{\circ}\)F).  
The water temperature in the tank is calculated as shown in [23].
## 3 Basics of ML Algorithms
### Reinforcement Learning Algorithms
The actor network takes as input the current state and outputs a real value that represents an action chosen from a continuous action space. The function of the critic network is to estimate the Q-value of the current state of the action given by the actor. The training consists of updating the weights of the actor-network values and calculating the gradient of the policy value function. The TD3 method minimizes the effects of overestimated values and function approximation errors in deep Q-learning applications. It follows the DDPG approach but uses two critic networks. This variant of double Q-learning aims to limit the possible overestimation. Nonetheless, the SAC algorithm is considered as _off-policy_, where the actor's target is to maximize the entropy while maximizing the reward. SAC uses typically four neural networks: the _actor_ network to select the actions from the environment whose inputs are the current state and the output is a probability distribution over the possible actions, two _critic_ networks to estimate the state-action pairs as inputs and then estimate the Q-value of the expected return and performing stabilization or mitigating overestimation in training, and finally, the _value_ network to estimate how good it is to be in a particular state to improve stabilization. DRL communications and networking approaches cover network control, adaptive rate, proactive caching, data offloading, network security and negative preservation, traffic routing, resource scheduling, and data collection, among others [52].
###### Abstract  
In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.  
complex network; distance distribution; multi-index fusion; relatively important node
# Planar Array Failed Element(s) Radiation Pattern Correction: A Comparison
## 2 Design Methodology
### Problem Formulation  
#### 2.1.1 Array Beam Pattern Generation  
Beam pattern is also known as spatial signal processing. It transmits and receives sound waves in the desired direction by phase shift and time delay. It is well known for its advantage of high gain, focused directivity, reduced interference, and improved device ability, producing better signal quality than omnidirectional signals. The phased array beamforming method [15] is used to generate a beam pattern with \(M\times N=(8\times 8)\) elements, an ultrasonic frequency of \(f_{c}=100\) kHz, and the speed of sound in air at 20 \({}^{\circ}\)C is \(c=344\) m/s, the inter-element distance \(d\) avoiding grating is:  
\[\lambda=\frac{c}{f_{c}}=\frac{344}{100,000}=3.44\text{ mm} \tag{1}\]  
\[d=\frac{\lambda}{2}=1.72\text{ mm} \tag{2}\]  
The array factor beam pattern was normalized with equal amplitudes of \(w(m)=1\). Planar array beam pattern \(G_{p}\) is formed by the superposition of two-beam patterns, \(S_{x}\) on the \(x\)-axis and \(S_{y}\) on the \(y\)-axis. The beamforming output for the planar array pattern is given in (3).  
\[G_{p}(\theta,\varphi)=S_{x}(\theta,\varphi)*S_{y}(\theta,\varphi) \tag{3}\]  
where  
\[S_{x}(\theta,\varphi)=\sum_{m=1}^{M}w_{x}(m)e^{i(2m-M-1)*arg_{x}} \tag{4}\]  
Figure 1: Flowchart of failed radiation pattern correction methodology.  
\[arg_{x}=\pi d_{x}\Big{[}cos\Big{(}\theta-\frac{\pi}{2}-\gamma_{x}\Big{)}sin\Big{(} \phi-\frac{\pi}{2}-\gamma_{y}\Big{)}\Big{]} \tag{5}\]  
and  
\[S_{y}(\theta,\varphi)=\sum_{n=1}^{N}w_{y}(n)e^{i(2n-N-1)*arg_{y}} \tag{6}\]  
\[arg_{y}=\pi d_{y}\Big{[}sin\Big{(}\theta-\frac{\pi}{2}-\gamma_{x}\Big{)}sin \Big{(}\phi-\frac{\pi}{2}-\gamma_{y}\Big{)}\Big{]} \tag{7}\]  
\(\theta\) and \(\varphi=-90\):\(1\):\(90\), are the scanning angle of \(x\) and \(y\) dimensions;  
\(\gamma\) is steering angle;  
\(w_{x}(m)=w_{y}(n)=w(m)=1\), uniform weight on each element;  
\(d\) is the inter-element distance between array elements;  
\(M\times N\), Array elements, and \(m,n\) is the index.  
Figure 2 shows \(M\times N\) elements in an array with uniform inter-element distance [15].
# A Hybrid PAPR Reduction Method Based on SLM and Multi-Data Block PTS for FBMC/OQAM Systems
## 2 Fbmc/OQAM System  
### Fbmc/OQAM Signal Structure  
In Figure 1, we can see the block diagram of transmitter in FBMC/OQAM systems, in which \(N\) denotes the number of subcarriers and \(a_{m,n}\) is the \(n\)-th subcarrier of \(m\)-th QAM symbol. The data is processed with OQAM modulation, where \(A_{m}=[a_{m,1},a_{m,2},\ldots,a_{m,N-1},a_{m,N}]\). After serial to parallel operation, the real and imaginary parts of each symbol are transmitted to subcarriers, respectively. Then, after the prototype filtering and phase modulation, the FBMC/OQAM transmission data block can be got by superimposing all the subcarrier signals. The \(m\)-th time-domain FBMC/OQAM data block signal is given below [28]:  
\[\begin{array}{l}s_{m}(t)=\sum\limits_{n=1}^{N}a_{m,n}h_{m,n}(t)\\ =\sum\limits_{n=1}^{N}\Big{\{}\Re\{a_{m,n}\}h(t-mT)+j\Im\{a_{m,n}\}h\Big{(}t-mT -\frac{T}{2}\Big{)}\Big{\}}e^{j\phi_{m,n}}\\ mT\leq t<(m+\beta+\frac{1}{2})T\end{array} \tag{1}\]  
where \(\Re\{.\}\) denotes the real part of \(a_{m,n}\) and \(\Im\{.\}\) denotes the imaginary part of \(a_{m,n}\). \(T\) is the symbol period, \(\beta\) is the overlapping factor, and the response of the prototype filter is \(h(t)\), and \(\phi_{m,n}=n(\frac{2\pi t}{T}+\frac{\pi}{2})\) represents an additional phase term.\(s_{m}(t)\) is a single FBMC/OQAM data block signal formula. After adding all transmitted subcarrier signals, the time-domain signal \(s(t)\) of consecutive data blocks is given as:  
\[s(t)=\sum\limits_{m=1}^{M}s_{m}(t),\ 0\leq t\leq(M+\beta-1/2)T-1 \tag{2}\]  
where \(M\) is the number of data blocks.  
Figure 2 shows the overlapped structure of FBMC/OQAM signal. It can be found that each data block signal is composed of two parts, which are staggered by \(T/2\). Additionally, one data block length  
Figure 1: The block diagram of transmitter in FBMC/OQAM systems.  
is \((\beta+1/2)T\). The length of total \(M\) data blocks is \((\beta+M-1/2)T\). Obviously, \(s_{1}(t)\) overlaps with the next \(\beta-1\) data block signal.
# A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data  
[PERSON]  
1 Department of Signal Processing and Communications, University Carlos III of Madrid Leganes, 28911 Leganes, Spain; [EMAIL_ADDRESS] (A.B.-L.); [EMAIL_ADDRESS] (C.S.-S.)
2 Department of Bioengineering and Aerospace Engineering, University Carlos III of Madrid Leganes, 28911 Leganes, Spain; [EMAIL_ADDRESS]
3 CIBER of Mental Health (CIBERSAM), 28029 Madrid, Spain
4 Instituto de Investigacion Sanitaria Gregorio Maranon, 28007 Madrid, Spain  
L1-L2 regularizations to promote sparsity, such as Lasso and elastic-net algorithms [14; 15]. Nevertheless, in neuroimaging, we have to deal with large datasets, where the number of cases is significantly smaller than the number of variables, and many of these approaches fail in this scenario, tending to over-fit. To avoid this problem, some authors propose Bayesian approaches but work over a reduced set of features [16; 17; 18], whereas others point to the use of more refined techniques better adapted to the problem needs [19; 20; 21].  
To overcome these limitations, we present a novel formulation for the Bayesian Linear Regression model. Our proposal, called the Dual Bayesian Linear regression model with Feature Selection (DBL-FS), is formulated to work efficiently with a reduced number of samples characterized in high-dimensional spaces, e.g., neuroimaging data. For this purpose, the model is formulated in the dual space and simultaneously includes an Automatic Relevance Determination (ARD) prior over the primal weights to provide the model with FS capabilities so that it can remove irrelevant input features. Here, we have tested our formulation on rodent data in an animal model of schizophrenia that show similar brain anatomical deficits than patients with schizophrenia [22; 23; 24]. One advantage of using rodent data is a more solid knowledge of the ground truth due to the controlled experimental induction of the pathology.