esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey
## 6 Machine Learning-Based Methods
### ML Methods Applied to 3D Calcium Imaging Stacks
The Neurofinder challenge dataset was improved by having it analysed by two expert human annotators. They compared their approach with four other state of the art neuron segmentation approaches namely CalmA [93], Suite2P [67], HNcorr [43], and UNet2 DS [78]. They have reported a superior true positive detection performance as compared to the states approaches at lower PSNR values; with their approach detecting 80% true positives at PSNR = 10 while the mentioned approaches achieved 65%, 52%, 47% and 41%, respectively. This approach achieved an average F1 score of 69% on the Neurofinder challenge. [PERSON] et al. proposed a deep learning powered pipeline for neuron segmentation which explicitly makes use of the temporal and shape information of the neurons [94]. First, the input calcium imaging videos is split in temporal segments. Then, correlations between temporal traces are calculated over all temporal segments. The pixel-wise correlations and a mean summary image is then fed into a CNN which aggregates the information over temporal segments. The outputs of this CNN are fed into a UNet which is then trained to not only produce a foreground background mask, but also produce affinities between neighbouring pixels. The authors have validated their approach on the Neurofinder benchmark and has achieved the best F1 of 67%. [PERSON] et al. proposed a CNN-based online neuron segmentation and trace extraction pipeline for processing mesoscopic imaging datasets which they call CITE-On (Cell identification and Trace Extraction Online) [95]. This approach is capable of identifying thousands of neurons in real time. The greyscale images are first downsampled, and then upsampled to reduce noise factor and then converted into three channel RGB-like images. These images are then fed into a CNN-based on RetinaNET architecture. The RetinaNET architecture is a single stage detector which uses a focal loss to deal with the class imbalance problem in object detection tasks [96]. The RetinaNET CNN puts bounding boxes around neuron like shapes which are then projected on a summary image. The pixels in each bounding box are then used to create a temporal trace of that neuron. In an online environment, the starting frames are first used to create the initial bounding boxes and traces. When new images become available, the bounding boxes are updated by including the new frames in the imaging stack while removing an equivalent amount of images from the beginning of the imaging stack. The authors claim an average F1 greater than 0.85 for imaging datasets of different calcium indicators, suggesting cross domain generalization capability. In offline settings, the author claim that the performance of CITE-On is on par with STNeuronNet [90] on never before seen datasets.
# Location Information-Assisted Robust Beamforming Design for Ultra-Wideband Communication Systems
## 2 System Model
### Geometrical Model  
The complex channel vector between the \(n^{th}\) antenna of the \(j\)th BS and the \(k\)th user is expressed as follows:  
\[h_{j,k}^{n}(t)=\sum_{m=1}^{M-1}\beta_{jk,m}^{n}\beta(t-\tau_{jk,m}^{n}), \tag{2}\]  
where \(\tau_{jk,m}\) denotes the TOA of the \(m\)th path between the \(k\)th UE and the \(j\)th BS. The complex channel fading amplitude \(\beta_{jk,m}^{n}\) of the \(m\)th path is modeled as [22]:  
\[\beta_{jk,m}^{n}=\left\{\begin{array}{ll}\frac{\lambda}{4\pi d_{jk,m}^{n}}e^{ j\frac{2m_{jk,1}^{n}}{\lambda}},\,\text{LoS},\\ \frac{\lambda\sqrt{\tau_{jk,m}^{n}}}{4\pi d_{jk,m}^{n}}e^{j\frac{2m_{jk,m}^{n} }{\lambda}},\text{reflector},\end{array}\right. \tag{3}\]  
where \(\Gamma_{\text{R}}\) denotes the reflection coefficient, \(d_{jk,1}^{n}=\left\|\mathbf{u}_{j}-\mathbf{p}_{k,n}\right\|\) denotes the direct shooting distance, \(d_{jk,m}=\left\|\mathbf{a}_{jk,m}-\mathbf{p}_{k,n}\right\|\) denotes the distance from the \(j\)th BS and the virtual point. According to the application of the image-source model for a first-order MPC, \(\mathbf{a}_{jk,m}\) denotes the mirror position of the \(j\)th BS on the \(m^{th}\) path, which is described in detail below.  
We consider that multipath signals are reflected from the plane when the prior information of the position is known, such as walls, doors, and tables, which are represented as wall segments below [23; 24]. Each wall segment \(s\in\mathcal{S}=\{1,...,S\}\) is described by its location \(\mathbf{p}_{s}\in\mathbb{R}^{2}\) (an endpoint of the wall segment) and orientation \(l_{s}\mathbf{e}_{s}\), with \(l_{s}\) as length and the unit-vector \(\mathbf{e}_{s}\in\mathbb{R}^{2}\) as the direction of the wall segment. To relate the estimated path delays to the environment, we employ a geometric model, as shown in Figure 2. The application of the image-source model for a first-order MPC is equivalent to mirroring the BS position \(\mathbf{u}_{j}\) at segment \(s\in\mathcal{S}\), expressed as:  
\[\mathbf{a}_{j,s}=\mathbf{u}_{j}-2\mathbf{T}_{s}\big{(}\mathbf{u}_{j}-\mathbf{ p}_{s}\big{)}. \tag{4}\]  
where \(\mathbf{T}_{s}=\mathbf{U}_{\frac{\pi}{2}}\mathbf{e}_{s}\mathbf{e}_{s}^{\top} \mathbf{U}_{\frac{\pi}{2}}^{\top}\) is the rotation matrix, and \(\mathbf{U}_{\frac{\pi}{2}}=\left[\begin{array}{cc}0&-1\\ 1&0\end{array}\right]\).
# A Brief Review of Multipath TCP for Vehicular Networks
## 4 Cross Layer MPTCP, MPTCP with SDN, and Energy Efficient MPTCP
### Energy Efficient MPTCP  
An energy-aware congestion control algorithm, namely, ecMTCP, is proposed in [93]. The ecMTCP mainly consists of two parts. The contribution of ecMTCP is to achieve energy-savings by shifting the traffic from the most congested path to a less loaded path.  
However, if the energy consumption is the most important criterion for MPTCP transmissions, the goal can be achieved by sacrificing other network metrics such as throughput and latency slightly. In [94], [PERSON] et al. proposed a multipath scheduler using MPTCP to improve energy efficiency. They achieve energy efficiency with slight scarification of throughput. The scheduler takes the energy model of different radio interfaces, communication history of the device user, and application model into consideration and switches between multiple paths properly to save energy. Similarly, [PERSON] et al. [95] proposed an algorithm that takes a tradeoff between the throughput and energy consumption for MPTCP by shifting a traffic to an energy efficient path.  
Likewise, an energy-aware MPTCP, namely, eMPTCP, was designed in [96]. This approach combines power-aware subflow management and delayed subflow establishment to reduce power consumption with a small negative impact on latency. The power-aware subflow management is for selecting path dynamically to maximize energy efficiency. The delayed subflow establishment is for avoiding power consumption for small size file transfers. Since web browsing applications are delay-sensitive, browsers can determine whether to enable MPTCP based on whether the secondary path supports a small latency.
# Learning Deceptive Strategies in Adversarial Settings:
## 3 Game-Theoretic Reward Shaping
### Neural Network Architectures for \(\theta_{\text{R}}\) and \(\theta_{\text{P}}\)  
Each agent's DQN maps a fixed-length feature vector into estimated Q-values over its discrete action set. Both the robber and the police officer use three fully connected hidden layers, but the robber's network includes dropout for regularization whereas the police officer's does not. The input and output dimensions differ according to each agent's role, but the hidden-layer structure is otherwise identical in depth.  
Overall Structure: Let \(d_{\text{in}}\) denote the input dimension (determined by the maximum number of goals \(G_{\text{max}}\) and obstacles \(O_{\text{max}}\); see Section 4.2) and let \(|A|\) denote the size of the agent's action set (4 for the robber's four movements; \(G_{\text{max}}\) for the police officer's choice of goal). Then,  
* Robber's network (\(Q_{\theta_{\text{R}}}\)): \[\text{FC}\big{(}d_{\text{in}},\,256\big{)}\xrightarrow{\text{ ReLU}}\xrightarrow{\text{Dropout}(p=0.2)}\] \[\text{FC}\big{(}256,\,128\big{)}\xrightarrow{\text{ReLU}} \xrightarrow{\text{Dropout}(p=0.2)}\] \[\text{FC}\big{(}128,\,64\big{)}\xrightarrow{\text{ReLU}}\] \[\text{FC}\big{(}64,\,|A|\big{)}\,.\] Here each \"FC\" is a fully connected layer, and each dropout layer uses probability \(p=0.2\).
* Police officer's network: \[\text{FC}\big{(}d_{\text{in}},\,256\big{)}\xrightarrow{\text{ ReLU}}\] \[\text{FC}\big{(}256,\,128\big{)}\xrightarrow{\text{ReLU}}\] \[\text{FC}\big{(}128,\,64\big{)}\xrightarrow{\text{ReLU}}\] \[\text{FC}\big{(}64,\,|A|\big{)}\,.\] No dropout layers are inserted for the police network.  
Figure 3 shows the robber's DQN (with dropout), and Figure 4 shows the police officer's DQN (without dropout). Both diagrams are scaled to fit within typical journal margins.  
To encourage the robber's network to learn broadly generalizable deceptive strategies rather than memorizing specific exit configurations, we intersperse dropout layers after the first two hidden-layer activations. By randomly dropping a fraction of hidden units during training, the robber's DQN is forced to distribute its internal representations acrossmany pathways, reducing overfitting to particular grid layouts or goal placements and thus improving transfer to novel exit arrangements. The police officer's network, in contrast, must develop highly precise value estimates to detect and counteract subtle deceptive cues; introducing dropout there tended to destabilize its ability to capture fine-grained distance-change and resource-allocation patterns. Empirically, we found that regularizing only the robber network strikes the right balance between robustness and sensitivity for our adversarial setting.
## 4 Simulation and Analysis of the DLLFP
### DLLFP with Simple Positive Wave Distribution  
In order to observe the internal sound field distribution of a DLLFP in the acoustic-structural coupling domain, the corresponding numerical simulation analysis is also performed in this paper. The excitation frequency \(f_{0}\) is parametrically scanned from 10 Hz to  
Figure 7: Relationship between wavenumber–thickness product and angular frequency of DLLFP. (**a**) Pressure acoustic domain. (**b**) Solid mechanics domain. (**c**) Acoustic–structural coupling domain.  
\(f_{\max}\) = 25 kHz, with the scan step set to 249.9 Hz. The mode analysis frequency is given as \(f_{0}\) = 10,000 Hz for the interior of the pipe, the mode search method is set to the manual search method, the APPACK solver is selected as the mode solver, and the mode search base \(k_{0}\) = 2\(\pi\)\(f_{0}\)/\(c_{0}\). Numerical simulations are solved using the MUMPS direct solver to calculate the acoustic pressure stress distribution. The partial sound pressure distribution clouds are given for the scanning frequencies of \(f\) = 5008 Hz and \(f\) = 20,002 Hz.  
After the sound pressure distribution clouds are summarized, some of the sound pressure distribution clouds under the coupled domain sound field are shown in Figure 8. The (2,0) mode simple positive wave shown in Figure 8c is found to occur intensively in the high-frequency phase of \(f\) = 17,503-25,000 Hz. The (1,0) mode simple positive wave shown in Figure 8b appears intensively in the intermediate phase from \(f\) = 12,755-19,502 Hz. While the sound pressure distribution is shown in Figure 8a for the through mode, the (2,1) mode simple positive wave in Figure 8f is shown almost throughout. Figure 8 also shows that the sound pressure distribution of the straight-through mode is plotted as a cloud with a frequency \(f\) = 5008 Hz and a wavenumber \(k\) = 10.619. The straight-through mode appears again in a frequency \(f\) = 20,002 Hz and a wavenumber \(k\) = 41.412, which proves that the straight-through mode is present across the full spectrum of frequencies. A comparison between Figure 8b, reveals that the phase of the sound pressure distribution cloud varies with frequency. Different propagation modes appear in different frequency bands because of the difference in their respective cut-off frequencies. As the wavenumber increases, the sound pressure increases along the radius; in the high-frequency part, the overlap of different propagating guided wave modes makes the sound pressure distribution more complex.
# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
## 3 Results and Analysis
### Comparison of ITD Technique and DWT with ROI Estimation  
The ROC results between ITD and DWT with ROI are shown in Figure 13 using the stated dataset. The classification accuracy of ITD was 96.875%, with a sensitivity of 0.9625 and a specificity of 0.975. The classification accuracy for DWT was 89.765%, the sensitivity was 0.8925 and the specificity was 0.89125.
## 1 Introduction
A limitation in previous works has been that the modulation frequency was very low, sometimes at the kHz level. For an FM interferometer, the low modulation frequency means a low measurement speed. In this paper, a high modulation frequency of 1 MHz is applied to the LD, and its frequency is tuned around the transition 6-3, P(33) of \({}^{127}\)I\({}_{2}\). The hyperfine structure of the transition is detected using a high bandwidth lock-in amplifier (LIA), up to 50 MHz. The frequency of the LD is locked to the b\({}_{21}\) hyperfine component and then employed for the FM displacement-measuring interferometer. Moreover, a suitable modulation excursion is determined to obtain a high signal-to-noise ratio of both the I\({}_{2}\) absorption and the harmonic intensities. The intensity fluctuation of the LD source is removed using the ratio of two consecutive harmonics, and the direction of movement is tracked in real-time. To verify the measurement error, the displacement-measuring result of the proposed interferometer is compared with that of a capacitive sensor. In order to prove the improvement of the new system, a comparison between the new results and our previous result is shown in detail. Additionally, the performance of the FM interferometer in the presence of vibration and environmental disturbance is examined. The interferometer is used to measure the axial error of a high-speed spindle at a speed of 500 rpm. The capability of the interferometer for high-speed measurement is confirmed as well. In summary, the main contributions of this paper are listed as follows: (1) the use of a high modulation frequency of 1 MHz to improve both the frequency stability for the LD and the measurement speed of the FM interferometer; (2) the determination of modulation excursion to enhance the signal-to-noise ratio of the I\({}_{2}\) absorption signal and the strength of the harmonic signal; (3) the verification of the measurement resolution of the stabilized LD-based FM interferometer at a high modulation frequency; and (4) the measurement of the axial error of a high-speed spindle at 500 rpm using the proposed interferometer. This study shows that a high measurement speed and a nanometer resolution of the displacement measurement can be achieved using a frequency-stabilized LD-based interferometer. This is very significant because LD sources are commonly used in industrial applications. The proposed interferometer can be used for many fields of technology such as nanotechnology, semiconductor technology, and precision manufacturing technology. The paper is organized as follows. Section 2 describes in detail the design, the methods, and materials used. Section 3 provides the experiment and results using the interferometer. Lastly, conclusions and future works are given in Section 4.
## 2 Design of the Solar-Powered ECG Device  
The ECG monitoring device is illustrated in Figure 1. In this design, up to five electrodes are used to collect three-channel ECG signals from the human body [17]. Five electrodes are attached to the patient's chest and abdomen. The simple paste method is used to attach the electrodes to the patient. The ECG was designed with four main modules. The first one is the solar energy harvesting module, including a solar panel, solar energy harvester BQ25570, and a supercapacitor to store energy from the solar panel. The second module is an analog front-end module with the use of low power integratedanalog front-end ADS1293. The third module is Bluetooth with a Bluetooth low energy (BLE) module RN4020. The final module is MCU with the use of an extreme low power (XLP) device (PIC16 LF19186) for optimal power consumption.  
As schematized in Figure 1, the recorded signals from ECG devices are transmitted to a smartphone or personal computer (PC) via Bluetooth [6; 10; 20]. Application software is developed based on smartphones to observe patients' ECG signals in real-time. The patient's ECG signals can also be collected by PC and then data analysis software is used to find abnormalities in the heart rate with a high accuracy. Doctors can use these results for further diagnosis.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 3 Case Study
### Hyperparameter Optimization for ST-GAT Neural Network  
Hyperparameter selection is crucial for the prediction performance of the proposed ST-GAT neural network. Specifically, 70% of the dataset is allocated for training with the last 20% of the training set reserved as a validation set for automatic hyperparameter tuning. The remaining 30% of the dataset serves as an independent testing set to evaluate the model performance. Grid search is employed for the tuning process, as it ensures that every combination in the search space is explored, preventing the model from becoming trapped in local optima. The range of hyperparameters and the optimal values are shown in Table 1. In the optimal model, the hyperparameters include two GAT layers with the first layer having 16 output features and the second layer having 32 output features. The GRU layer has an input size of 32 and a hidden size of 32, which is followed by a fully connected layer with an input size of 32 and an output size corresponding to the number of classes (four in this case). The sliding window size is set to 4. The model is trained using the Adam optimizer with a learning rate of 0.0001, and the loss function used is the mean squared error (MSE).  
The model training is performed on a personal computer featuring an AMD Ryzen 7 5800U processor, 16 GB of RAM, and an NVIDIA GeForce RTX 3060 6 GB GPU. The training environment is configured with Python 3.7.16 and PyTorch 1.9.1 using the PyTorch framework. MSE and mean absolute error (MAE) are used to evaluate the prediction  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Range** & **Optimal Value** \\ \hline Number of GAT layers & 1–3 & 2 \\ GAT Layer 1 output features & 8–64 & 16 \\ GAT Layer 2 output features & 16–128 & 32 \\ GRU input size & 16–128 & 32 \\ GRU hidden size & 16–128 & 32 \\ Fully connected layer input size & 16–128 & 32 \\ Sliding window size & 2–10 & 4 \\ Learning rate & 0.00001–0.001 & 0.0001 \\ Validation MSE & - & 0.579 mm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grid search hyperparameter tuning.  
Figure 11: Partial autocorrelation for wall deflections at different lags.  
accuracy of the model. Smaller MSE and MAE values indicate smaller errors. R2 is used to evaluate the goodness of fit for the model. The formulas are as follows:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{15}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right| \tag{16}\]  
\[\text{R}^{2}=1-\frac{\sum\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum\left(y_{i}- \overline{y}_{i}\right)^{2}} \tag{17}\]  
where \(n\) denotes the total number of samples, \(y_{i}\) is the measured wall deflections, \(\overline{y}_{i}\) is the mean of the measured wall deflections, and \(\hat{y}_{i}\) is the model prediction.
## 2 Related Work  
Deraining problem can be addressed as either a video-based problem or a single image-based one. Video based methods may make use of temporal information present in subsequent frames. On the hand, single image-based deraining is a more challenging task as it lacks the temporal redundancy knowledge and less information is available [14; 15]. In order to handle this problem, more research attention has focused on developing different algorithm designs. Moreover, single image-based approaches can be divided into three methods [16]:  
* Filter-based [17; 18; 19; 20]: It is based on identifying different rain properties and designing appropriate filters to achieve rain-free images;
* Prior-based [21; 22; 23; 24; 25; 26]: It uses prior knowledge of rainy and clear images. Prior-based methods includes, but not limited to, morphological component analysis (MCA) [27], histogram of oriented gradients (HOGs) [28], structural similarities, and sparse representation models [26];
* Deep-learning-based models [29; 30].  
Several approaches have been proposed to achieve rain detection and removal [31; 32; 33; 34; 35; 36; 37]. They have been summarized in Table 1.  
However, most of them did not consider AVs system requirements, and suffered from the below listed deficiencies:  
* High computation time [33; 35; 38];
* Only addressed the problem of rain detection;
* Assumes static scenarios [39]; ref. [40]'s approach does not work with highly dynamic raindrops. The approach of [41] fails to separate dynamic textures and moving objects;
* Not applicable for real-time scenarios: 1. The approach of [31] requires the involvement of a human operator to process the captured frames, also it is bound by certain scenes (lane and building scenes); 2. The approach of [32; 33] exploits temporal information during the recovery process; 3. The approach of [34] makes use of previously cleaned frames; 4. The approach of [35] has high computational time;
* Limited to certain degradation factors: [36; 37; 38; 42].