esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Recent electron microscopy (EM) imaging techniques make the automatic acquisition of a large number of serial sections from brain samples possible. On the other hand, it has been proven that the multisynaptic bouton (MSB), a structure that consists of one presynaptic bouton and multiple postsynaptic spines, is closely related to sensory deprivation, brain trauma, and learning. Nevertheless, it is still a challenging task to analyze this essential structure from EM images due to factors such as imaging artifacts and the presence of complicated subcellular structures. In this paper, we present an effective way to identify the MSBs on EM images. Using normalized images as training data, two convolutional neural networks (CNNs) are trained to obtain the segmentation of synapses and the probability map of the neuronal membrane, respectively. Then, a series of follow-up operations are employed to obtain rectified segmentation of synapses and segmentation of neurons. By incorporating this information, the MSBs can be reasonably identified. The dataset in this study is an image stack of mouse cortex that contains 178 serial images with a size of 6004 pixels \(\times\) 5174 pixels and a voxel resolution of 2 nm \(\times\) 2 nm \(\times\) 50 nm. The precision and recall on MSB detection are 68.57% and 94.12%, respectively. Experimental results demonstrate that our method is conducive to biologists' research on MSBs' properties.  
Keywords:electron microscopy; multisynaptic bouton; convolutional neural network; image processing; synapse; neuron +
Footnote †: journal: Applied
## 4 Discussion
The non-removal of peptide sequences in this study did not affect protein expression and in vitro activity tests, as in other studies [74; 75; 76; 22; 77]. Previous studies have shown that 1-1.5 h is the optimal reaction duration for collecting enzymatic reaction products [22; 34]. In this study, most of the main products were produced within 1 h, whereas longer reaction durations only significantly increased the number of by-products. Therefore, under a consistent metal ion concentration and incubation temperature, it is recommended to complete the reaction of TPSs within 1-1.5 h. The products of mono-TPSs mainly possess chirality, with several trace products that have a related stereo-structure [78]. Heterologous expression of monoterpene synthases reported in _Pinus_ is usually named after their main products, such as (+)-\(\alpha\)-pinene synthase and (-)-\(\alpha\)-pinene synthase from _P. taeda_, (+)-\(\alpha\)-pinene synthases, and (-)-\(\beta\)-pinene synthase from _P. contorta_ and _P. banksiana_, as well as (-)-\(\alpha\)-pinene synthase and \(\beta\)-pinene synthase from _Pinus elliotti_[22; 34; 79]. In this study, the main product profiles of the Pm TPSs were similar to those reported above but at different concentrations. Meanwhile, as observed in the TPSs of _P. taeda_[22], most Pm TPSs only generated certain enantiomers. Four Pm mono-TPSs were heterologously expressed. Among them, Pm (-)-\(\alpha\)-pinene produced the largest number of products and formed the largest number of hydrogen bonds with GPP. The varied amounts of by-product linalool produced by Pm mono-TPSs suggest that Pm (-)-\(\alpha\)-pinene may have a higher catalytic specificity than Pm (+)-\(\alpha\)-pinene and Pm (-)-\(\beta\)-pinene. Therefore, the number of active sites and hydrogen bonds between the substrate and enzyme may be positively related to product specificity. [PERSON] et al. [40] analyzed the volatiles produced by _P. massotiana_ fed on by _M. alternatus_ adults. The ratio of \(\beta\)-pinene to \(\alpha\)-pinene in the volatiles was approximately 1:100, which is very close to the ratio of these two products in in vitro activity tests. However, the chemical composition of the products in the in vitro activity tests differed from that of the volatiles. This may be due to the catalytic functions of downstream enzymes, such as P450 enzymes, which in plants catalyze the transformation of some TPS products to other compounds [80]. For example, limonene in _Mentha canadensis_ can be converted into (-)-carvone or (-)-isomentadiene by two P450 enzymes [81]. Notably, although the limonene synthase gene was upregulated in _P. massoniana_ fed upon by _M. alternatus_ adults, and limonene was the main heterologous expression product, this compound was not detected in volatiles. The limonene produced may have been converted to pinocarvone by downstream P450 enzymes [40].
# Hybrid Load Forecasting Using Gaussian Process Regression and Novel Residual Prediction
## 6 Conclusions  
Accurate electricity load forecast plays an essential role in planning for utilities and electricity markets in electricity power networks. This paper proposes a novel hybrid GPR forecasting method of the aggregated energy load of a mixture of residential, commercial, and industrial clients. The relatively new EWT method was used for data preprocessing. To ensure a precise load prediction, some of the most important influencing factors were selected. Among these, the most influential were temperature, working/non-working day, and humidity. The hybrid method assumes the combination of an autoregressive time series model and GRP. Using the same methodology, further forecasting of the residuals was added. Following implementation of the algorithm, the method was validated on a new set of data. It was observed that a better approach would be to further apply an error prediction for the residuals. The proposed forecasting method was evaluated using three performance metrics. For the presented load data, this algorithm presents good results that mimic the characteristics of the aggregated plot. The novelty of the approach consists of applying EWT to preprocess the data, combining an AR method with GPR, and using the generated residuals as predictors to increase the algorithm's precision. A future development of the method may be developed by adding a closed-loop system methodology in which the method is executed until the residuals decrease no more.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metrics** & **Simple Forecasting** & **With Error Prediction** \\ \hline MAPE (\%) & 5.04 & 4.28 \\ RMSE (MWh) & 205.4 & 158.7 \\ MAE (MWh) & 188.7 & 138.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance metrics for July testing.  
Figure 10: Validation plot for July load forecasting.  
Figure 9: Comparison between historical data, predicted value, and forecasting value with error prediction.  
**Author Contributions:** All the authors contributed to this study. C.D., H.G.B., D.D.M.: conceptualization, investigation and writing of the original draft; T.A., S.P., I.B.: funding acquisition and project administration; T.A., S.U.: software and data curation; C.D., H.B.: resources; D.M., S.P., S.D.C.: supervision and writing of review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI - UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0404 / 31 PCCDI/2018, within PNCDI III.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Control System Design of the MSR Plant
### Controller Design
#### 3.2.1 Controller for the Thermal Power  
The controller for the thermal power has three inputs and one output. The schematic of the working principle is illustrated in Figure 4. Based on the demand from the grid, the desired power is determined and then transformed into the corresponding neutron flux as the set value. The flux error (between the desired value and the measurement) is transmitted to the PID controller as an input to be processed. Then, the output signal is transmitted to the controller of the control rod for the determination of the movement (speed and direction) of the control rods. Finally, the signal of movement is given to the control rod drive servo to execute the desired movement. The proportional gain (\(K_{P}\)), integral term (\(K_{I}\)), and derivative term (\(K_{D}\)) of the PID controller are set to 0.025, 0.0079, and 0.0028, respectively. Taking into account the criticality of safety, the speed of the introduced reactivity should be bounded by \(\pm\)10 pcm/s. To prevent the frequent execution of the control rod servo caused by noise or tiny deviations, a dead zone is introduced to avoid the unnecessary movement of the control rod. If the error between the measurement and the desired value is less than the threshold defined by the dead zone, the control system is locked by a special signal to make the controller output value zero so that the unnecessary execution of the control rod drive servo is prevented.  
Figure 3: Control schematic diagram of the whole plant system.  
The deviation between the measurement and set values of the outlet temperature is converted to a 0-1 switch signal, which is used to control the output signal of the PID controller. Since the mass flow rate of the secondary salt is adjusted based on the calculations of the governing equations under various loads, the heat transfer capability of the secondary circuit is fixed at various loads. Based on this fact, the core outlet temperature (operating temperature) is also fixed at various loads, which means that if the core outlet temperature reaches the desired value, the power must be equal to the desired power. So, once a new set value of power is given, a desired outlet temperature is given. This desired outlet temperature is then compared to the measured temperature, and this error is used to switch on the output of the PID controller to insert the reactivity by moving the control rod to regulate the thermal power. Once the temperature error is smaller than \(\pm\)0.05 K, which means that the system has already reached the desired state, the PID controller will be locked (setting its output value to zero) to prevent unnecessary movement of the control rod so that its drive servo can have a longer service life. In the process of thermal power control, the feedforward controller is used to adjust the secondary salt flow rate to avoid temperature hysteresis based on the feedforward tuning function, as illustrated in Figure 4.  
Figure 4: Schematic of the simulation platform of the MSR plant with control systems.
## 2 PCMs: Basic Characterization  
Latent heat is present in every form of phase change: solid-solid, solid-liquid (solidification and melting), liquid-gas (boiling and condensation) or solid-gas (sublimation). In comparison with sensible heat, a large amount of energy is stored in phase changes, with small temperature variation. Phase change processes that involve a gas phase, such as boiling, condensation, and sublimation processes, are characterized by a considerable variation in the material volume [2; 19]. Thus, these processes need pressure vessels, which make the thermal energy storage system more complex and less feasible [15]. The transition between two solid phases or between solid and liquid implies less volume variation [1] so that the increase in pressure is also moderate; therefore, solid-solid and solid-liquid phase changes of PCMs make the energy storage system less complex. Furthermore, PCMs with solid-solid transitions present as their main advantage the possibility of direct contact of the heat transfer fluid (HTF) with the PCM, as the shape of the solid remains during the phase change [33; 34]. However, it is worth mentioning that fewer materials present a solid-solid transition, having commonly lower latent heat than that corresponding to the solid-liquid transition [13; 18].
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 2 Materials and Methods
### Simulation Model
#### 2.1.1 Simulation Methods  
As shown in Figure 1, the lateral deviation caused by crosswind is calculated by using the path-tracking preview algorithm through the road line information, the path trajectory provided by TruckSim, and the deviation of the driver's prediction point. Then, the lateral deviation, sideslip angle deviation, and yaw rate deviation are fused with the gain matrix calculated by the LQR algorithm to obtain a steering wheel angle, which is then added to the compensated steering angle obtained by the feedforward controller to calculate the final steering wheel angle, and this wheel angle is input to TruckSim. TruckSim will provide various parameters of the vehicle in motion to the algorithm built by Matlab/Simulink, such as yaw rate, lateral acceleration, front wheel angle, and deviation between path trajectory and driver's prediction point, and then a closed-loop control is formed. Finally, through hardware in the loop experiments, the proposed LKAS control strategy is used to control the vehicle driving in the middle of the road line, and experiments are conducted on three operating conditions: \"without crosswind\", \"crosswind without LQR control\", and \"crosswind with LQR control\", to verify the effectiveness of the LKAS control strategy.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 3 The Proposed Method
### The Segment-Based Space Partitioning Tree (SSP-Tree)
#### 3.2.3 SSP-Tree Manipulations
: Given a new query segment \(qs\), Insert involves finding the leaf node \(N\) from the root to store \(qs\) and updating the count variables of the non-leaf nodes along the path from \(N\) to the root, both of which take time linear to the depth of the SSP-tree, and thus \(t_{insert}\approx\alpha_{1}\,\log_{2}(\frac{|QS_{set}|}{\theta})+\,\alpha_{2} \log_{2}(\frac{|QS_{set}|}{\theta})\). Please note that (i) updating full list \(N.FL\) maintained in each node \(N\) and (i) updating QT, QRT, and SRT take constant expected time because they are implemented as hash tables. Given an overflowed leaf node \(N\), SplitNode checks \(\theta+1\) tuples against each of two newly generated leaf nodes \(\hat{N}_{new}\). (Note: \(\hat{N}_{new}\) is a child of the newly generated non-leaf node \(N_{new}\).) In addition, for each tuple \((qs,flag)\), if \(flag\) is not set, SplitNode further checks \(\textit{q\_list}\) of the tuple \((qs,\textit{q\_list})\) in SRT. These take time at most \(2\cdot\alpha_{3}(\theta+1)\cdot|\textit{qs\_list}|\). Because SplitNode also involves updating the count variables of the non-leaf nodes along the path from \(N_{new}\) to the root, \(t_{split}\approx 2\cdot\alpha_{3}(\theta+1)\cdot|\textit{qs\_list}|+\log_{2}( \frac{|QS_{set}|}{\theta})\). To delete an existing query segment \(qs\), Delete finds the leaf node \(N\) from the root, deletes \(qs\) if necessary, and updates the count variables of the non-leaf nodes along the path from \(N\) to the root; therefore, \(t_{delete}\approx\alpha_{5}\log_{2}(\frac{|QS_{set}|}{\theta})+\alpha_{6}\log_{ 2}(\frac{|QS_{set}|}{\theta})\). Finally, MergeNode checks each tuple \((qs,flag)\) stored in two mergeable leaf nodes against a new leaf node, and thus \(t_{merge}\approx\alpha_{7}\theta\).
# Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
## 5 CDS Implementation Case Study: OFDM Long-Haul Fiber Optic Communications
### Simulation Results and Discussions
#### 5.2.3 CDS Learning Curve  
The CDS learning curve is shown in Figure 14a and corresponding BER is shown in Figure 14b. In Figure 14, the PACs 1, 2, 3, and 4 correspond to focus level 0 actions which correspond to the data rate of 208, 236, 232, and 236 Gb/s, respectively (see Algorithm 1). The PACs 5, 6, 7, 8, and 9 are the CDS focus level 1 actions as data rates of 236, 252, 260, 272, and 280 Gb/s, respectively.  
The results for the CDS at steady state are summarized in Table 4. The smart fiber optic link that used the focus level concept enhances the data rate efficiency by \(\sim\)43% in comparison to conventional fiber optic link. This is the summation of \(\sim\)35% data rate enhancement (i.e., enhancement of the data rate \(R_{1}=208\) Gb/s to \(R_{9}=280\) Gb/s) and 8% of HD overhead reduction (\(\sim\)24 Gb/s can be used for data carrying, instead of overhead redundancy) reduction, which is achieved by lowering the FEC threshold from \(1.02\times 10^{-2}\) to \(4.7\times 10^{-3}\). The final Q-factor improvement is 7 dB, which is a significant improvement for the long-haul fiber optic link. The reason for such significant improvement is that the fiber optic channel using memory and the focus level concept can model the fiber optic channel more accurately in comparison to a memoryless fiber optic link channel. Under the same conditions, the performance improvement using the digital backpropagation (DBP) method is typically 1-2 dB [42] and the computational cost and complexity is significantly higher compared to our proposed system. In addition, the DBP method does not provide data rate enhancement.
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 4 Architecture  
For estimating the \(G_{MMSE}\) we used a modified Wide-ResNet (WRN) architecture with multiple inputs based on 1-dimensional convolutions. The architecture was built on Pytorch toolkit. As the front end, we used a configuration of speech representations computed on a 25 ms Hamming window frame (overlap = 10 ms). For each frame segment, three types of acoustic feature vectors are computed and stacked to create a single input feature vector for the network: 512-dimensional FFT, 32 Mel filterbank, and 32 cepstral features. Finally, input data are normalized using the mean and variance of the training dataset.  
During training, input features are generated on the fly. This way, in a single forward pass, the system computes the mask predictions for every t-f region and obtains the average loss to calculate the gradients. For this process, each audio in the training set is divided into segments of 2 seconds (200 frames). Then, the system creates a batch with 32 of these segments for training the network. During the evaluation, the inference of the mask is computed for the whole utterance in the evaluation set.  
The architecture is composed of five blocks of WRN with an increasing number of channels. All the blocks are connected to the input features. Each block has four WRN, and only the first has kernel context in the convolutions as indicated in Table 1. The network employs causal convolutions such that only information from the past intervenes in the present result. For saving computations, we used groups in the convolutions. The AdamW algorithm was used to train the network [30,31] and PReLUs [32] as a parametric non-linearity. The cost function for a segment of contiguous frames is based on the mean square error (MSE).  
Figure 2: Noise reduction block based on Depp Learning.
## 4 System Design
### Physical Layer: Data Collection
#### 4.1.2 Mobile Nodes and Other Nodes (Miner Location Tracking)  
Figure 4e shows a smartphone (136.6 \(\times\) 69.8 \(\times\) 7.9 mm), which is a portable device carried by miners having a weight of 145 g. These smartphones have an Adreno 330 CPU and an Android 6.0.1 (Marshmallow) operating system. They were connected to SNs and RNs using Bluetooth communication, which is further explained in Section 4.2. They use Li-ion batteries (3000 mAh) and can be charged easily. MNs are capable of exchanging information with the nearest SN. Using an MN, the miner can send emergency signals related to his health and can run a location algorithm. Thus, these MNs are helpful for tracking the position of miners and tracking their health conditions. On the other hand, static RNs are fixed to provide a BLE backbone network in the mine. SNs are different from RNs because sensors modules are also attached to SNs. The main function of the RN is to provide connectivity and to acquire data from the subordinate SNs. RNs in this system are limited to a maximum of four SNs. Finally, gateway nodes are the nodes connecting the SN and RN to the base station. Bluetooth is the main communication protocol for the entire network. The BS is responsible for the detection and declaration of global events.
## 4 Analysis of Existing Solutions
### Analysis of Effectiveness and Challenges
#### 4.2.2 Integration of XR Technologies into the Curriculum  
The introduction of XR technologies into educational curriculum represents a significant step forward but comes with a range of challenges and issues. Advantages include the following:  
1. Increased Motivation and Engagement--XR technologies provide students with the opportunity for immersive learning, where they can literally \"dive\" into the content. This can enhance their motivation and engagement [50].
2. Improved Understanding of Complex Concepts--XR enables the visualization and manipulation of complex concepts that would otherwise be difficult to grasp, such as molecular structures or historical events [51].
3. Personalized Learning--XR can provide a personalized educational experience tailored to individual student needs [52].  
However, challenges remain:  
1. High Costs--Implementing XR technologies requires significant financial investment in equipment, software, and teacher training [53].
2. Technical Issues--Technical problems, such as device compatibility, technical support, and equipment maintenance, can limit the widespread use of XR technologies in education [54].
3. Potential for Information Overload--Too much information and sensory stimulation can lead to student overload, which can reduce learning effectiveness. In the context of XR technologies, there is a danger that excessive visual and auditory information at once may overwhelm students, making it difficult for them to focus on the key aspects of the lesson [55].