esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 WGN for RNNs  
### Experimental Model  
In this paper, the analysis primarily focuses on time-series models such as RNNs and LSTMs. RNNs (as shown in Figure 2) are a type of recurrent neural network with a structure that incorporates temporal feedback, enabling the modeling of sequential data. Denoting time step \(t\) and given an input vector (\(x^{1}\), \(x^{2}\),..., \(x^{t}\)), the generation of a hidden state vector (\(h^{1}\), \(h^{2}\),..., \(h^{t}\)) is defined as Equation (1):  
\[h^{t}=\emptyset(W_{hh}\times h^{t-1}+W_{ih}\times x^{t}) \tag{1}\]  
where \(W_{hh}\) denotes the weights between neurons at different time steps, \(W_{ih}\) denotes the weights between inputs and neurons, and the \(\emptyset\) function is the activation function.  
LSTM is an improved network structure based on RNNs, which effectively addresses the issue of long-term dependencies. The core of LSTM is the memory cell, which can store information and retrieve it when needed. Information within the memory cell can be added, deleted, or updated through gate control, thereby achieving the effect of long-term memory. The structure of LSTM includes input gates, forget gates, and output gates, corresponding to controlling the flow of input information, controlling the flow of forget information, and controlling the flow of output information, respectively. These gates can be adjusted in their open or closed states through learning.  
\[\begin{pmatrix}\tilde{f}_{t}\\ \tilde{i}_{t}\\ \tilde{\sigma}_{t}\\ \tilde{g}_{t}\end{pmatrix}=W_{h}\times h_{t-1}+W_{x}\times x_{t}+b \tag{2}\]  
\[c_{t}=\sigma(\tilde{f}_{t})\odot c_{t-1}+\sigma(\tilde{i}_{t})\odot tanh( \tilde{g}_{t}) \tag{3}\]  
\[h_{t}=\sigma(\tilde{\sigma}_{t})\odot tanh(c_{t}) \tag{4}\]  
where \(\tilde{f}_{t}\) denotes the forget gate, \(\tilde{i_{t}}\) represents the input gate, \(\tilde{\sigma}_{t}\) signifies the output gate, \(\tilde{g}_{t}\) stands for the new memory gate, \(c_{t}\) denotes the cell state, \(h_{t}\) represents the hidden layer output at time step \(t\), \(W_{h}\) represents the weight matrix of neuron connections, \(W_{x}\) is the input weight matrix, \(x_{t}\) stands for the input, and \(b\) represents the biases of the respective gates.
###### Abstract  
The complex geological environment in western Sichuan, China, leads to frequent debris flow disasters, posing significant threats to the lives and property of local residents. In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The models were trained with data in Songpan County and used for debris flow susceptibility prediction in Mao County, using small watersheds as assessment units. Seventeen key feature factors based on multi-source remote sensing data encompassing topography and geomorphology, geological structures, environmental elements, and human activities were selected as input parameters after assessment with Pearson correlation analysis. Model performance was rigorously evaluated through ten-fold cross-validation, and hyperparameter optimization was employed to enhance predictive accuracy. To assess the models' robustness, the trained models were applied to the neighboring Mao County for cross-regional validation. The results consistently indicate that elevation, seismic nucleation density, population density, and distance to roads are the primary controlling factors influencing susceptibility. Comparative analysis between the Songpan and Mao County reveals that the RF model significantly outperforms SVM and XGBoost in accuracy and robustness. Therefore, the RF model is better suited for debris flow susceptibility assessment in western Sichuan. Although the effectiveness of this model may be limited by the relatively small sample size of debris flow events in the dataset and potential variations in environmental conditions across different regions, it still holds promise for providing a scientific basis and decision-making support for disaster mitigation in comparable areas of western Sichuan.  
western Sichuan; debris flow susceptibility; transfer learning; hyperparameter optimization
## 5 Conclusions  
This paper introduced PSMD-SLAM, a pioneering SLAM method that combines panoptic segmentation and multi-sensor fusion for dynamic environments. Leveraging the LVI-SAM framework, our approach significantly boosts system performance. Key contributions include the introduction of PSMD-SLAM, dynamic object detection and removal through panoptic segmentation, and a robust 6D pose estimation module. The PSMS-SLAM framework integrates IMU, LiDAR-inertial, panoptic segmentation, visual-inertial, and back-end modules, enhancing each other's performance. The visual-inertial system, treating frames with panoptic segmentation as keyframes, reinforces dynamic object pose estimation and the SLAM process. Overall, PSMD-SLAM offers a promising solution for robust SLAM in dynamic environments by effectively incorporating panoptic segmentation and multi-sensor fusion. During the research process of PSMD-SLAM, the key challenge encountered is the accuracy and real-time issues of scene semantic recognition. In response to this challenge, future research will focus on improving the accuracy and real-time performance of panoptic segmentation. This research direction will contribute to enhancing the robustness and practicality of SLAM systems in dynamic environments.  
Figure 8: Visualization results of the dynamic target tracking. The visualization results of dynamic target tracking encapsulate the outcomes of our PSMS-SLAM method’s performance in tracking dynamic objects within the environment.  
**Author Contributions:** Conceptualization, J.C. and C.S.; Methodology, C.S. and B.Z.; Software, C.S. and B.Z.; Validation, F.W.; Investigation, F.H.; Data curation, F.W. and F.H.; Writing--original draft, C.S. and B.Z.; Writing--review & editing, J.C. and F.H.; Visualization, B.Z.; Project administration, C.S.; Funding acquisition, C.S. and J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by Guangdong Major Project of Basic and Applied Basic Research (2023B030300016), Shenzhen Technology Project (JCYJ20220818101211025) and CAS Key Technology Talent Program.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request. The data presented in this study are available on request from the corresponding author, C.S or B.Z. We plan to release our code at the appropriate time.  
**Conflicts of Interest:** Author [PERSON] was employed by the company China Mobile Financial Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 Conclusions  
This manuscript introduces a hollow concrete gravity dam containing saturated sandy soil, constructs a three-dimensional model thereof, and utilizes a viscous fluid approach to simulate the liquefied saturated sandy soil. We conducted three-dimensional finite-element dynamic time history analyses on various models and performed shaking table tests to investigate the seismic damping characteristics of the new-type dam. The findings are summarized as follows.  
1. Theoretical analysis and resonant column tests demonstrate that saturated sandy soil serves as an effective damping material, significantly reducing the displacement at the crest of concrete gravity dams under seismic loads and exhibiting excellent energy dissipation capabilities.
2. The numerical simulation outcomes reveal that the crest displacement of a hollow concrete gravity dam containing saturated sandy soil, along the direction of water flow, experiences a decrease relative to that of a conventional gravity dam, with the reduction being around 20%. This reduction magnitude is inversely proportional to the characteristic wave speed of the saturated sandy soil, suggesting that increased thickness of the saturated sandy soil layer leads to greater displacement reduction and enhanced seismic damping performance.
3. Shaking table experiments indicate that both the displacement and acceleration at the crest of a hollow concrete gravity dam containing saturated sandy soil are reduced under various seismic intensities when compared to a conventional gravity dam. These findings, along with the results from numerical simulations, collectively validate the damping efficacy of saturated sandy soil.
4. Under seismic loading, the energy dissipation capability of saturated sandy soil exhibits a positive correlation with soil layer thickness and a negative correlation with characteristic wave velocity. Notably, the ratio of energy dissipation levels across different scenarios is approximately equivalent to the ratio of their soil thicknesses.
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 3 Methods
### Atl03  
Two models are made in this study using the ATL03 geolocated photons: one model finds the ocean surface to determine the waves, and another uses the same basic principles as ATL12 to act as a baseline.
## 5 Conclusions  
In this experiment, we cloned the CDSs of _DXS_ and _FPS_ genes in _Z. bungeanum_. The CDS sequence length of the cloned _ZbDXS_ was 2172 bp, encoding 723 amino acids. The CDS sequence length of _ZbFPS_ was 1029 bp, encoding 342 amino acids. Bioinformatics analysis showed that _ZbDXS_ was most likely to be localized in chloroplast, and _ZbFPS_ was most likely localized in mitochondria. _ZbFPS_ is a hydrophilic protein but not secreted protein. Phylogenetic tree analysis showed that _ZbFPS_ has the closest relationship with _Mangifera indica_, _Durio zibetthinus_, _Rosa rugosa_, and _Siriatia grosvenori_. In vitro enzyme activity experiments showed that the purified proteins _ZbDXS_ and _ZbFPS_ had the functions of the _DXS_ enzyme and the _FPS_ enzyme. The transient expressions of _ZbDXS_ and _ZbFPS_ in the tobacco leaves increased the terpene content of tobacco. The relative expression of _ZbDXS_ and _ZbFPS_ was the highest in the fruit, among which the relative expression of _ZbDXS_ was the lowest in the root, and the relative expression of _ZbFPS_ was the lowest in the stem.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods11121746/s1](https://www.mdpi.com/article/10.3390/foods11121746/s1)]([https://www.mdpi.com/article/10.3390/foods11121746/s1](https://www.mdpi.com/article/10.3390/foods11121746/s1)), Figure S1: Amplification of CDSs of _ZbDXS_ and _ZbFPS_ in _Z. bungeanum_. _ZbDXS_ amplification; Figure S2: Deduced amino acid sequences of the conserved regions of _ZbDXS_; Figure S3: Deduced amino acid sequences of the conserved regions of _ZbFPS_; Figure S4: Analysis of protein sequence traits; Figure S5: Predicting subcellular localization of _ZbDXS_ (a) and _ZbFPS_ (b); Table S1: Primers used in the experiment; Table S2: Sequence information for _ZbFPS_; Table S3: Sequence identity, GMQE, and QMEAN for _ZbDXS_ and _ZbFPS_; Table S4: Relative expression levels of _ZbFPS_ and _ZbDXS_ in different tissues.  
**Author Contributions:** Conceptualization A.W., L.T. and J.S.; Writing--original draft L.T. and J.S.; Writing--review & editing L.T. and J.S.; Writing--review & editing L.T., J.S., L.Y. and A.W.; Investigation L.T. and J.S.; Formal analysis, L.T.; Visualization, data curation L.T.; Funding acquisition, A.W.; Project administration, A.W.; Supervision, A.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financially supported by the National Key Research and Development Program Project Funding (2018 YED1000605).  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article or Supplementary Material.  
**Acknowledgments:** We thank Research Centre for Engineering and Technology of Zanthoxylum for their help with this experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods  
### Measurement Campaign  
The dataset analyzed in this study was collected during the WINd Turbine Wake EXperiment -Wieringermeer (WINTWEX-W) [18], a collaborative measurement campaign between the University of Bergen, Christian Michelson Research and the Energy Center of the Netherlands (ECN). The campaign took place at ECN's Wind Turbine Test Site Wieringermeer [21] and was designed to study a single turbine wake of one of the 2.5 MW Nordex research turbines by static and scanning Doppler LiDAR measurements. The Nordex N80 turbines have a rotor diameter and hub height of 80 m and are located on farm land 5 m below sea level. In addition to some farm houses, other prototype wind turbines and meteorological masts disturb the larger-scale flow around our turbine of interest (WT6). The test site location, as well as the measurement set up are pictured in Figure 1. This study is based on wind measurements from three Leosphere Windcube v1, marked by colored squares, and two sonic anemometers mounted on top (108 m) and at hub height (80 m) on the met-mast (M3), marked as a yellow triangle in Figure 1. The black square further downstream, at around 12 D, indicates the position of a scanning wind LiDAR system (Leosphere Windcube 100S) that has, however, not been included in this study. The three Windcube v1, named by their serial numbers as wls37, wls65 and wls67, were orientated along the wake center line for the main wind direction of south-westerly winds (227\({}^{\circ}\)). For this setup, the upstream LiDAR (wls67, blue mark) is located at a distance of 3.2 D in front of WT6; the Windcubes in the wake are situated at 1.8 D (wls37, red) and 3.5 D (wls65, green) downstream. A more detailed description of the different expected wake locations with respect to the incoming wind direction can be found in Table 1.  
The profiling LiDARs performed standard DBS measurements with four Line-Of-Sight (LOS) measurements at 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\) from north, an elevation angle of 60\({}^{\circ}\) and a sampling rate of 1 Hz at ten different altitudes (40, 52, 60, 80, 100, 108, 140, 120, 160, 200 m). The range gates centered at 52 and 108 m are set to match the measurement heights of the met-mast and are overlapping with neighboring range gates since the range gate size was 20 m. It should be mentioned that the sampling time interval of the Windcube wls37 was 1.273 s and with that, 0.273 s longer than the sampling interval specified by the manufacturer. The analyzed period includes data from 30 November 2013 to 10 May 2014.  
Figure 1: Map of the Netherlands with the location of the wind turbine test site Wieringermeer and the measurement setup. Blue, green and red squares and the yellow triangle indicate the location of the Windcube v1 and the met-mast relative to the research turbine WT6 illustrated by a full circle. Open circles show the rest of the research turbines.
# NMDA Receptor Model of Antipsychotic Drug-Induced Hypofrontality
## 2 Glutamatergic Signaling and Cognitive Deficits in Schizophrenia  
### An Outline NMDA Receptor Structure and Function  
Brain NMDARs determine the existence of higher mental functions playing a fundamental role in the mechanisms of consciousness, memory, learning, emotions, and even in some motor functions. NMDAR molecules are heteromers consisting of two obligatory NR1 and two or three NR2 subunits, as shown in Figure 1[19]. Several isoforms of NR1 and at least four classes of NR2 subunit--A, B, C, and D--were identified. Diverse NR2A classes may differentially affect NMDAR activity and its modulation by both agonists and antagonists. The NR1 subunit does expose the lifelong expression in almost all brain structures [20]. NR1, NR2A, and NR2B subunits are also widely distributed in the central nervous system (CNS), especially in the hippocampus, thalamus, and prefrontal cortex, while the NR2C and NR2D ones are preferentially expressed in the cerebellum, brainstem, and spinal cord. It appears that pharmacological and biophysical activity of NMDARs is determined mainly by NR2 subunits [21]. Several regulatory domains of the NR2 subunit can bind numerous endo- and exogenous factors, including drugs e.g., polyamines, protons, zinc ions, glutathione, neurosteroids, ifenprodil, eliprodil, or even haloperidol [19]. All NMDAR subunits are strictly structurally and functionally connected, therefore modulatory sites of NR2 affect the glycine/D-serine binding site of NR1 [22]. The NR3 subunit is detected in the CNS only in the prenatal period and early childhood. NR3 suppresses the NMDAR activation when the receptor molecule consists of NR1 and NR2 subunits. An integration on NR3 merely with NR1 subunits may cause the origin of glutamate insensitive NMDARs [23]. This kind of NMDAR that may play a role in the pathogenesis of schizophrenia occurs during brain development; however, their evidence in adults is not so far confirmed [24]. Glycine and glutamate binding sites are located in the homologous domains of NR1 and NR2A/B subunits, respectively [25]. Despite their separate location, both sites are strictly functionally connected. Glycine binding facilitates glutamate ligation, extends the channel opening, and delays NMDAR desensitization [26]. Most importantly, the glutamate binding is not enough for NMDAR activation; a simultaneous postsynaptic membrane depolarization to remove Mg\({}^{2+}\) from the channel pore and glycine or D-serine binding are strictly required [27]. NMDARs contain a nonselective cation channel, that enable Ca\({}^{2+}\) and Na\({}^{+}\) influx into the neuron. An increased calcium concentration in the neuoplasm causes activation of adenylate cyclase, Ca\({}^{2+}\), and calmodulin-dependent protein kinase II (CaMKII). A common property of all NMDARs is binding several, psychomimetic open channel blockers (OCB)--ketamine, esketamine, phencyclidine (PCP), and dizoclipine [26].
## 3 Proposed Framework
### System Operations  
The target animals could be in different locations within the village. At the beginning, each gateway propagates its location coordinates to the targets within its reach. We propose that only 10% of animals will be used as targets in any target environment. The assumption is that the target animals will be evenly distributed in any groupings of the entire animal population. Each target begins its movement at a random start location. It is assumed that the animals use random walk mobility model which puts no constraint on the target velocity or moving direction. The animal moves from its current location by randomly choosing a direction and speed in which to plod within the grazing area. The average animal walking speed is 4.5 km/h [3] and there is an indication that if the animal is being rushed, it could double this speed. The data is transferred from the boluses to the gateways over single hop communication after a given period of time. The gateways store the information and only relay the information to the databases periodically at set intervals from where the users can query the data remotely over the Internet using a computer or mobile telephone. Localization of the target can also be done using multiple gateways being relocated from one location to the other on a mobile platform for real time tracking.
# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
###### Abstract  
This paper proposes a fusion network for detecting changes between two high-resolution panchromatic images. The proposed fusion network consists of front- and back-end neural network architectures to generate dual outputs for change detection. Two networks for change detection were applied to handle image- and high-level changes of information, respectively. The fusion network employs single-path and dual-path networks to accomplish low-level and high-level differential detection, respectively. Based on two dual outputs, a two-stage decision algorithm was proposed to efficiently yield the final change detection results. The dual outputs were incorporated into the two-stage decision by operating logical operations. The proposed algorithm was designed to incorporate not only dual network outputs but also neighboring information. In this paper, a new fused loss function was presented to estimate the errors and optimize the proposed network during the learning stage. Based on our experimental evaluation, the proposed method yields a better detection performance than conventional neural network algorithms, with an average area under the curve of 0.9709, percentage correct classification of 99%, and Kappa of 75 for many test datasets.  
change detection; convolutional network; deep learning; panchromatic; remote sensing Article
## 3 Results
### 3.1.1 Filtration Rate  
#### 3.1.1 Effect of Sulfuric Acid Concentration and Filtration Temperature  
The effects of sulfuric acid concentration and temperature on the filtration rate are shown in Figure 2. In general, there is a significant decrease in filterability as the temperatureincreases. With no sulfuric acid added, the filterability of the solution after coagulation decreased from 5216 dm\({}^{3}\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\) at 40 \({}^{\circ}\)C to 2248 dm\({}^{3}\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\) at 45 \({}^{\circ}\)C and then to 21 dm\({}^{3}\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\) at 50 \({}^{\circ}\)C. The same tendency was observed with increasing sulfuric acid concentration in a solution from 0 to 14 kg\(\cdot\)m\({}^{-3}\). It appears that acidification has a negative effect on filterability, and the best condition was observed when no sulfuric acid was added. At 8.6 and 14 kg\(\cdot\)m\({}^{-3}\) of sulfuric acid, the filterability curves were similar. However, a large increase in filterability was observed when no acidification was applied. It is worth mentioning that, without acidification, the recoverable lignin was only the insoluble lignin in the concentrate. This lignin is characterized by its high molecular weight compared to the lignin that is still soluble in the pre-hydrolysate. This can explain the high filterability obtained when acidification was not applied. The soluble lignin is mainly stabilized because of the repulsive forces due to the presence of ionized phenolic hydroxyl and carboxylic groups. To recover higher lignin quantities, an acidification step should be used in the process. This leads to protonation of the dissociated phenolic hydroxyl groups of the lignin. As a result, repulsive forces decrease and more lignin precipitates [27]. With acidification, lignin with a lower molecular weight starts to precipitate [28], thus leading to a decrease in filterability of the solution. At 45 and 50 \({}^{\circ}\)C and after acid addition, the filterability was low, as indicated in Figure 2. At a temperature of 40 \({}^{\circ}\)C and any acid concentration, the filtration rate is significantly higher compared to those obtained at temperatures of 45 and 50 \({}^{\circ}\)C.
## 4 Velocity Dependence Behavior Explained Using Microphysical Model
These characteristics support a deformation mechanism involving granular flow coupled with enhanced diffusive mass transfer, as illustrated by model C in Figure 5.  
Under hydrothermal conditions, the extended CNS model provides a more physically consistent explanation of frictional behavior than classical RSF laws. It captures three velocity-dependent regimes--strengthening at low temperatures, weakening at intermediate temperatures, and restrengthening at high temperatures--by accounting for competing deformation and healing mechanisms (Figure 6). Unlike RSF or empirical flow laws, CNS incorporates the effects of slip velocity, effective normal stress, and evolving microstructures, such as porosity, grain size, and shear band thickness. These features make it well-suited for interpreting experimental observations of granite gouges across a range of temperatures and velocities [92].