esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In this study, we analyzed a range of ChR2 stimulation conditions in experiments with primary hippocampal cultures.  
We have determined an optimal frequency of light stimuli for generating APs in the current-clamp recording configuration. The optimal frequency range was between 1-5 Hz. Stimulation with frequencies less that 1 Hz was insufficient to generate APs, and stimulation with frequencies over 5 Hz results in the loss of fidelity of responses.  
We have observed that the amplitude of ChR2 currents depends non-linearly (right-skewed bell-shaped curve) on the pulse duration. ChR2 photocurrent amplitudes were stable in the range of 10-30 ms, however, at low (1-5 ms) and large (100-500 ms) pulse duration values, the amplitudes changed in an almost stochastic manner (Figure 2).  
Amplitude differences at low (1-5 ms) values of pulse duration are presumably explained due the fact that ChR2 only reaches the \(O_{1}\) state and cannot change to \(O_{2}\) with maximum bandwidth. At the same time, the observed amplitude differences at large pulse durations are explained due to the desensitization and degradation of ChR2 and the \(P_{4}\) state [7]. Thus, we can conclude that the optimal interval of light stimuli is in the range of 10-30 ms.  
The relationship between the photocurrent amplitude and the intensity of the light stimulus can be explained by the fact that increasing intensity leads to an increase of \(O_{2}\) channel state duration. At intensities above 35 mW mm\({}^{-2}\), the amplitude of the photocurrent of ChR2 may decrease; this assumption needs further research.  
Thus, we found that the following parameters of light stimulation: frequency (F) = 1-5 Hz, t = 10-30 ms, I \(\leq\) I\({}_{\text{max}}\) are optimal for multiple light stimulation, i.e., the activity of neurons expressing ChR2 will be stable throughout the stimulation period. This is also shown in Figure 9.  
Figure 9 demonstrates the cumulative effect of duration and intensity on the activity of neurons expressing ChR2. Thus, the photocurrent amplitude is almost constant for the pulse duration, which is equal to 10 ms in the full range of intensities, while there is a wide variation of the amplitude values at other durations (1 ms and 300 ms) and intensities.  
Figure 9: Graph representing the relative difference between the first two current spike amplitudes as a function of the light impulse duration and their relative intensity. The maximal difference is normalized to unity; the relative light intensities are indicated by markers. This graph was designed as follows: the chosen pulse duration was assigned to a value of the difference between amplitudes of the first and second pulses.
###### Abstract  
Convolutional Neural Networks (CNN) have been applied to diverse machine learning tasks for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw waveform-based approach has been explored to directly learn hierarchical characteristics of audio. However, the majority of previous studies have limited their model capacity by taking a frame-level structure similar to short-time Fourier transforms. We previously proposed a CNN architecture which learns representations using sample-level filters beyond typical frame-level input representations. The architecture showed comparable performance to the spectrogram-based CNN model in music auto-tagging. In this paper, we extend the previous work in three ways. First, considering the sample-level model requires much longer training time, we progressively downsample the input signals and examine how it affects the performance. Second, we extend the model using multi-level and multi-scale feature aggregation technique and subsequently conduct transfer learning for several music classification tasks. Finally, we visualize filters learned by the sample-level CNN in each layer to identify hierarchically learned features and show that they are sensitive to log-scaled frequency.  
onvolutional neural networks; music classification; raw waveforms; sample-level filters; downsampling; filter visualization; transfer learning Article
# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
## 2 Sensor Fusion Architecture  
Figure 1 depicts the proposed sensor fusion architecture and the diagnoser. In the proposed approach, each virtual sensor gives an estimate of the entire set of measured system outputs, and every virtual sensor is merged, using a classical fusion stage, with the measured system output generating a cleaner estimated signal. Afterwards, a voter is used to select the most reliable estimated signal. This single output of the architecture is a reliable estimate of the system output that may be used by a diagnoser or any other device demanding reliable measures of the system. It is worth remarking that every virtual sensor is generated from a subset of the system outputs using an element that we name a _homogenizer_.  
This fusion architecture generates more reliable signals, allowing the use of sophisticated diagnosers (such as those based on sliding modes) capable of diagnosing faults in a short period of time after their occurrence, and drastically reducing the number of false fault detections.  
Figure 1: Sensor fusion architecture and the diagnoser applied in a diagnoser scheme. The sensor fusion architecture encompasses the homogenizer, EKF, and voter stages. The homogenizer generates virtual outputs, while the EKF merges real and virtual signals to generate more reliable signals, the filter reduces the noise from the signals, and the voter selects the best signal (in this work, the minimum variance signal). The diagnoser leverages these reliable signals to detect, locate, and identify concurrent and nonconcurrent faults.  
Since in this work the output of the proposed fusion architecture is used by a fault diagnoser, we considered the system model introduced in [14]:  
\[\dot{x}=f(x)+g(x)u(t)+l(x)m(t),\hskip 14.226378 pty=h(x) \tag{1}\]  
where the state is \(x\in\mathbb{R}^{n}\), the control input is \(u\in\mathbb{R}^{c}\), the output is \(y\in\mathbb{R}^{z}\), and the fault magnitude is \(m\in\mathbb{R}^{d}\), while \(f(x)\in\mathbb{R}^{n}\), \(g(x)\in\mathbb{R}^{n\times c}\), \(l(x)\in\mathbb{R}^{n\times d}\), and \(h(x)\in\mathbb{R}^{z}\) are smooth functions defined on an open set \(\Omega\subset\mathbb{R}^{n}\).  
**Assumption 1**: _No one \(m(t)\) belongs to the kernel of \(l(x)\). In other words, any fault occurrence always affects the system state._
## 2 Materials and Methods
### Incorporated Quality Control System Using Computer Vision
#### 2.5.3 YOLO Object Detection  
This investigation employed YOLOv8 n, s, m, l, and x and YOLOv9 m, c, t, s and e models to detect strawberry defects across defined classes. Table 1 presents the training parameters utilized for the YOLOv8 n, s, m, l, and x and YOLOv9 m, c, t, s and e models. Following the generation of bounding boxes around strawberries using the LabelImg 1.8.6 module, these bounding boxes were subsequently fed into the YOLOv8 n, s, m, l, and x and YOLOv9 m, c, t, s and e models to identify the strawberry defect class. The input image sizes for YOLOv8 n, s, m, l, and x and YOLOv9 m, c, t, s and e were configured as 832 \(\times\) 832, aiming to preserve the key features of objects (strawberries with varying degrees of defects) within the images. Table 1a,b provide a comprehensive summary of all defined parameters. Figure 2: The sample image utilized for training computer vision algorithms. (**a**) unlabeled defected strowberries; (**b**) labeled defected strowberries. The table compares YOLOv8 n, s, m, l, and x and YOLOv9 m, c, t, s and e models. These models share a standardized input image size of 832 \(\times\) 832 pixels, ensuring uniformity in image processing. They vary, however, in their maximum training batch sizes: YOLOv8 n, s, and m and YOLOv9 m, c, t, s and e are configured with a batch size of 4 images, while YOLOv8 l operates with 2 images, and YOLOv8 x with just 1. This difference influences training efficiency and GPU memory utilization. Each model is designed to detect 2 classes, focusing on a binary classification task. They all initialize training using pre-trained weights, with varying degrees of weight utilization: YOLOv8 n starts with 319 out of 355 weights, YOLOv8 s with 349, and YOLOv8 m, l, and x with 589 out of 595 weights. YOLOv9 m starts with 901 out of 907 weights, YOLOv9 c with 931 out of 937, YOLOv9 t, and s with 1303 and 1333 out of 1339 weights and YOLOv9 e with 1805 out of 1811 weights. This initialization strategy accelerates the convergence of training. All models employ a consistent learning rate of 0.01 and a momentum of 93.7%, optimized with the AdamW optimizer. Training is conducted over 20 epochs for each model, ensuring comprehensive exposure to the dataset for robust learning and accurate object detection. These configurations collectively underscore the models' versatility and capability in handling object detection tasks across various scales and computational capacities. The architecture of the proposed YOLOv8 n, s, m, l and x models consists of a series of convolutional layers, Cross-Stage Partial Network (CSP) with Two Flow Paths (C2f) blocks, a Spatial Pyramid Pooling Fast (SPPF) block, and Concat and Upsample layers. The convolutional layers are designed to extract basic features from input images, while the C2f blocks enable deeper learning of complex features through residual connections. Using the SPPF block further enhances the model's ability to detect objects of various sizes by combining information from different spatial pyramid levels. The Concat layers integrate
# A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments
## 4 Results
### Range Errors Comparison on Car Dashboard and Roof  
In a realistic application of smartphone positioning, drivers usually mount their devices on the dashboard for navigation or chatting purposes. However, much of the published research conducted experiments under open-sky environments or mounted smartphones on car roofs. Considering that the car roof may also influence the GNSS observation quality, this subsection assesses how the distribution of range errors will be affected when the smartphone is placed on the car roof compared to the car dashboard. Figure 11 depicts the probability distribution of range errors, in which the blue and green bins denote the range errors on the roof and dashboard, respectively. The STD values of range errors on the car roof are 5.1 m and 5.3 m on the dashboard for P1, while they are 2.2 m and 2.4 m for P5, respectively. A clearer and higher centralization for the range errors on the roof is observed compared to those on the dashboard from Figure 11, which infers that the GNSS observation quality is slightly better when the phone is placed on the car roof, and different code precisions may need to be applied when the smartphones are in different places of the car in the data processing.
## 4 Concept Stability Calculation Based on Maximal Non-Generator  
In this section, the MNG-SC algorithm that can accurately calculate the concept stability is introduced in detail. Unlike traditional algorithms that use minimal generators to calculate stability values, we provide a novel perspective of stability calculation by linking it to maximal non-generator. Then thanks to the proved equivalence between the maximal non-generator and the lower neighbor concept, the maximal non-generator is directly obtained form the existing concept lattice. At last, a novel Maximal Non-Generator-based Stability Calculation (MNG-SC) algorithm is proposed.
## 2 Modeling of Skewed Integral Abutment Bridges  
### Three Dimensional Bridge Model  
The prototype bridge was a one-span SIAB with a span of 37.0 m from bearing to bearing and a skew angle of 15\({}^{\circ}\), which is the angle between the longitudinal direction of the bridge and the normal direction of the abutment. The bridge superstructure consisted of five steel girders with a center to center spacing of 3 m, supporting a concrete deck with a thickness of 0.22 m and a width of 14.2 m. The bridge substructure consisted of two abutments, each of which was 0.9 m thick and 3.9 m high, and supported by five HP 310 \(\times\) 125 steel piles with the strong axis parallel to the bridge longitudinal direction. Figure 1 shows the elevation view, plan view and details of the skewed integral abutment bridge.  
A three-dimensional finite element model of the bridge was established using the design and analysis software SAP2000, as shown in Figure 2. In addition to the prototype bridge, bridge models with a skew angle of 0\({}^{\circ}\), 30\({}^{\circ}\) and 45\({}^{\circ}\) were also established to study  
Figure 1: A skewed integral abutment bridge.  
the effects of skew angle on bridge seismic behavior. The bridge deck and abutments were modeled using four-node thin shell elements and the girders and piles were modeled using two-node beam elements. In order to consider the composite action, rigid link elements were used to connect the nodes on the deck and the steel girders, and rigid connections were assumed between the abutment-girders and abutment-piles. The bridge deck and abutments were constructed with concrete with a nominal compressive strength of 27.6 MPa, and the girders and piles were constructed with Grade 345 steel with a nominal yield strength of 345 MPa. The girders were assumed to be elastic, since there was no report of superstructure damage in integral abutment bridges in previous seismic events. The maximum stress in the girder during the time-history analyses was checked, and there was no sign of yielding in the girders. Plastic hinges were placed at the top of the piles to consider material nonlinearity, and vertical displacements were constrained at the bottom of the piles. The damping coefficient of the model was taken as 0.05.
## 4 Simulation Analysis  
### Demand Sequence Stationarity Analysis  
The demand data in this project include thirty-seven beams, with a duration of 14 days and a time interval of five minutes. The demand for a single beam fluctuates approximately on a daily cycle over the course of 14 days. While there is some consistency in the trend of beam data, significant demand differences occur at certain times on certain days.  
The demands of different beams exhibit tidal effects, showing strong temporal correlations.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Surface Temperature (SST)
\[B_{\lambda}(T)=\frac{2 hc^{2}}{\lambda-5}\left(\frac{1}{e^{\frac{hc}{(\lambda kT )}}-1}\right) \tag{1}\]  
where \(B_{\lambda}\) refers to the Brightness Temperatures (BT); \(T\) is the sea surface at absolute temperature; \(h\) is [PERSON]'s constant; \(c\) is the speed of light (in the vacuum); \(k\) is the Boltzmann's constant; and \(\lambda\) is the wavelength [124]. According to [PERSON]'s Equation, radiance at a known wavelength should be measured to determine the emitting temperature from the sea surface [98]. Two types of spaceborne RS systems, both of which are passive systems, can be mainly used for SST measurement: TIR and microwave radiometers. The TIR spaceborne systems are onboarded on the Low-Earth Orbiters (LEO) and Geostationary (GEO) satellites, while the microwave radiometers are onboarded on the LEO orbiters. The advantages and disadvantages of each system for SST estimation are provided in Table 3. Moreover, the main satellites to estimate SST are demonstrated in Figure 5.
## 1 Introduction
On the basis of theoretical derivation, the simulation compares GSTBC-EFOGSM with other optical spatial modulation schemes in terms of ABER, data transmission rate, energy efficiency and computational complexity of the receiver. In addition, the effects of key factors, such as data transmission rate, encoding ratio, number of photodetectors (PD) and modulation order, on the ABER of the system are also analyzed via simulation. Monte Carlo (MC) simulation is used to verify the correctness of the numerical simulation. The rest of this paper is organized as follows. Section 2 proposes and introduces the EFOGSM scheme and the EFOGSM mapping rules. Section 3 proposes and introduces the GSTBC-EFOGSM system model. Section 4 presents the channel fading model for the M distribution. Section 5 derives a Meijer-G form closed expression for the GSTBC-EFOGSM system ABER. In Section 6, the ABER performance of GSTBC-EFOGSM and other OSM schemes are analyzed and compared through simulations, as well as the performance comparison analysis, such as data transmission rate, energy efficiency and computational complexity of the receiver. Section 7 concludes the paper.
# Occupancy Reward-Driven Exploration with Deep Reinforcement Learning for Mobile Robot System
## 4 The Proposed Occupancy-Reward-Driven Exploration
### Occupancy-Reward-Driven Exploration  
The occupancy reward is a function that encourages the robot to seek in unexplored areas to collect knowledge about the indoor environment. The information is gathered from the map with occupancy probability values. In this study, the reward is computed based on the occupancy map \(M(n,m)\) of size \(n\times m\). For each time step of an episode, the sum of all map values is summed up and stored in \(M\) variable using the following equation:  
\[M_{t}=\sum_{i=0,\;j=0}^{n_{t}\;m}m(i,j) \tag{7}\]  
In order to observe the amount of explored segment discovered in each time step, the \(M\) of the current time must be deducted from those of the previous time:  
\[E_{t}=M_{t}-M_{t-1} \tag{8}\]  
The function approximator with the reward that is computed only by the explored segments can satisfy the continuous robot driving. However, it was seen during the training process that the robot trajectory is not optimal and not power-efficient. The robot spins constantly.  
In view of this undesirable motion, the reward function is proposed as follows:  
\[r_{t}=k\times v_{t}+q\times w_{t}^{2}+d\times s+f\times E_{t} \tag{9}\]  
where \(v_{t}\) and \(w_{t}\) are linear and angular velocities at time \(t\) received from the actor-critic network. The variable \(s\) denotes the range of the sensor rays. When the sensor does not meet with the obstacle, the reward obtains the most significant value; in contrast, when the obstacle comes near, the \(s\) decreases the reward function. \(k\), \(q\), \(f\), and \(d\) are coefficients for the normalization of the reward range.  
The reward function should have a range or threshold that can vary. Thus, the actor-critic network during the training process should distinguish between reward values for providing appropriate actions in the RL environment. As equation 9 shows, \(r_{t}\) consists of four factors: linear velocity, angular velocity, sensing ranges, and quantity of the explored segment discovered in one time step. Each has its own priority of how much this factor affects the reward function. The order of priorities in the reward function will be presented in the next section.  
Finally, all the rewards are summed as \(G\) at the end of the episode after a predefined competing number of time steps, as shown in Equation (10):  
\[G=r_{t}+r_{t+1}+\ldots+r_{t+1} \tag{10}\]  
Speaking about the reward, it is necessary to consider the penalty as well. In lines 24-26 of Algorithm 1, the code catches the occasion of obstacle collision. When this happens, the current episode should stop, and the neural network should learn about the unfavorable event. In this case, the reward is assigned a negative number as punishment.
## 2 Materials and Methods  
Three-dimensional reconstruction of an object, an ear in our case, by leveraging a dataset of photographs and applying the structure-from-motion (SfM) technique, requires a precise computation of camera positions.  
SfM generates 3D reconstructions of an object or a scene from a series of 2D images, taken from different viewpoints. The process also estimates the camera's position and orientation for each of the images. The overall SfM pipeline consists of several steps: feature extraction; feature matching; camera motion estimation; dense point cloud generation; mesh generation; and texturing.  
The key mathematical concepts underlying SfM are projective geometry and optimization. The relationship between the 3D point and its 2D projection on an image can be expressed using homogeneous coordinates and the camera projection matrix. The purpose of the optimization process is to find the 3D structure and camera parameters that best explain the observed 2D image points. This is usually formulated as a non-linear least-squares problem.  
Performing photogrammetry using the SfM technique for the reconstruction of the ear requires a sequence of actions that must be carried out. First, capturing the ear from various angles is a necessary step. This step provides a comprehensive set of images necessary  
Figure 1: The problem of camera 3D localization. Building the 3D model of the ear using the structure-from-motion (SfM) technique requires a precise computation of the camera positions (**a**). In this research, we propose augmenting the side region of the head with a suitable texture for the reconstruction process. The solution we are investigating aims to enhance the texture in the background of the image of the ear, such that it remains fixed relative to the ear. We are exploring the potential of using a cap (**b**), examining its efficiency in improving 3D camera localization for SfM-based object reconstruction.  
for the reconstruction process. Second, the removal of the background in the images. The background is unsuitable for the reconstruction process due to the head movements relative to the background during the capture process. An unfixed background can lead to inaccurate results in the reconstruction process. Third, calculating camera positions in 3D space is an essential process. These positions can be determined by associating feature points within the head region across different frames. However, a notable problem arises in the side area of the head--the texture is not rich, therefore, it is difficult to extract a sufficient number of distinctive feature points that can be accurately matched across the frames.  
In this research, we propose augmenting the side region of the head with a suitable texture for the reconstruction process. This research explores the potential solution of using a cap and its efficiency in enhancing the camera 3D localization for SfM-based object reconstruction.