esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Results of Gaze Direction Recognition  
The 3000 images of the gaze direction recognition dataset were divided into training, validation, and test sets in a 7:2:1 ratio. The experiment trained the gaze direction recognition dataset for 300 epochs, stored the training weights every 10 epochs, and continued until the model was trained for 300 epochs to obtain the optimal training weight value. Table 4 gives the accuracy results of nine gaze direction recognitions on the test set. It can be seen that among the nine gaze directions, the recognition accuracy of upleft, down, up, and normal reached 100%; the accuracy of left and upright exceeded 90%; and the accuracy of downleft and right was between 80% and 90%. This indicates that when the gaze is upward, the eyes are relatively open, and the position and size of the pupil can better represent the gaze direction. Furthermore, when the gaze is downward, the eyes are relatively closed, and it is difficult to represent the gaze direction with the position and size of the pupil, leading to the relatively weak expression ability of the downleft and downright gaze directions.  
Figure 11: Abnormal objects of different frames in dataset from different perspectives.  
Figure 12: Examples of three-perspective cheating behavior determination.  
By observing the area of the confusion matrix indicated with red frames shown in Figure 13, it can be seen that 17% of the downleft direction is incorrectly identified as downright, 21% of downright is incorrectly identified as downleft, and 10% of left is incorrectly identified as right. Because downleft, downright, left, and right are abnormal gazes in the overhead perspective, some confusion between these four directions will not affect the accuracy of final cheating behavior identification.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 2 Distinctive Characteristics of Tumor Microenvironment and Heterogeneity of EPR
### Components of Tumor Microenvironment
\begin{table}
\begin{tabular}{c c l} \hline \hline  & \multicolumn{2}{c}{**Components of the Tumor Microenvironment**} \\ \hline \multicolumn{2}{c}{**Cellular Components**} \\ \hline \multicolumn{2}{c}{_Component_} & \multicolumn{2}{c}{_Characteristics_} \\ \hline \multirow{3}{*}{\begin{tabular}{c} Cancerous \\ [31] \\ \end{tabular} } & \(\bullet\) & Autonomous proliferation. \\  & \(\bullet\) & Metastatic capability in healthy tissues (formation of cancerous sites). \\  & \(\bullet\) & Immunological tolerance. \\  & \(\bullet\) & Resistance to cell death. \\ \hline \multirow{3}{*}{\begin{tabular}{c} Fibroblasts \\ [31; 45] \\ \end{tabular} } & \(\bullet\) & Key component of the tissue. \\  & \(\bullet\) & Normal fibroblasts: Secretion and remodeling of the ECM, regulation of cellular \\  & & growth and survival. \\  & \(\bullet\) & Cancer-associated fibroblasts (CAFs): Secretion of various factors (growth factors, angiogenesis factors, etc.) for tumor growth, angiogenesis, ECM remodeling, \\  & & metastasis, and immune suppression. \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Endothelial cells \\ [31; 45] \\ \end{tabular} } & \(\bullet\) & Under hypoxic conditions, tumors activate hypoxia-inducible factors (HIFs), which promote the secretion of VEGF, leading to the formation of new blood vessels from endothelial cells. \\  & \(\bullet\) & Immature vessels in tumors exhibit leakage and irregular blood flow. \\  & \(\bullet\) & Endothelial cells are converted into CAFs under the influence of TGF-\(\beta\) and BMP, promoting cancerous infiltration. \\  & \(\bullet\) & Endothelial cells assist the entry of cancer cells into the bloodstream by disrupting the endothelial barrier for cancer cell dissemination. \\  & \(\bullet\) & Lymphatic endothelial cells are responsible for the collection and transport of lymph from tissues back into the bloodstream. They participate in angiogenesis and influence the management of lymphatic flow. \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Pericytes \\ [31] \\ \end{tabular} } & \(\bullet\) & Pericytes surround blood vessels and provide structural support, contributing to their stability. \\  & \(\bullet\) & Their alteration impairs the vascular barrier and facilitates the proliferation of endothelial cells and tumor angiogenesis. \\ \cline{1-1}  & \(\bullet\) & Promote immunological tolerance and affect the therapeutic efficacy of \\ \cline{1-1}  & \(\bullet\) & Increase ECM synthesis to create a pre-metastatic niche for cancer cells. \\ \cline{1-1}  & \(\bullet\) & Paracrine communication with cancer cells for angiogenesis and immunological \\ \cline{1-1}  & \(\bullet\) & Lymphatic pericytes have a similar role. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the key components of a tumor’s microenvironment and their roles.
# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
## 3 The Proposed Indoor Localization Method
### Single Point Positioning  
If the number of the ranging results was more than 2 after filtering, the LS algorithm was used for the single point positioning. The observation equation for the ranging results can be expressed as follows:  
\[d_{RTT}^{i}=\rho^{i}+d_{D}^{i}+d_{NLOS}^{i} \tag{12}\]  
In the equation, \(d_{RTT}^{i}\) is the ranging result collected from the i-th FT, \(\rho^{i}\) is the Euclidean distance between the i-th FT and the MT, \(d_{NLOS}^{i}\) is the ranging error caused by the NLOS error from the i-th FT; and d is the ranging error caused by the standard time deviation. According to Equation (12), we can derive the error observation as follows:  
\[V^{i}=\rho^{i}+d_{D}^{i}+d_{NLOS}^{i}-d_{RTT}^{i} \tag{13}\]  
Assuming that the approximate coordinate of MT is X = (X\({}_{0}\), Y\({}_{0}\)) and that the Euclidean distance from the coordinate of the i-th FT to the approximate coordinate of MT is \(\rho 0\), the Taylor series of the above formula is:  
\[V^{i}=\rho_{0}^{i}+\frac{X_{0}-X_{FT}^{i}}{\rho_{0}^{i}}dX+\frac{Y_{0}-Y_{FT}^ {i}}{\rho_{0}^{i}}dY+d_{D}^{i}+d_{NLOS}^{i}-d_{RTT}^{i} \tag{14}\]  
If we set the following:  
\[L^{i}=d_{RTT}^{i}-\rho_{0}-d_{D}^{i}-d_{NLOS}^{i} \tag{15}\]  
Then the equation could be changed as follows:  
\[V^{i}=\frac{X_{0}-X_{FT}^{i}}{\rho_{0}^{i}}dX+\frac{Y_{0}-Y_{FT}^{i}}{\rho_{0} ^{i}}dY-L^{i} \tag{16}\]  
Figure 8: Fitting results of the two methods. (**a**) Fitting result and the raw data of methods A and B. (**b**) Residuals of the fitting plans of methods A and B.  
where dX and dY are the corrections of the approximate coordinates of MT, and \(X_{FT}^{i}\) and \(Y_{FT}^{i}\) are the two coordinate values of the i-th FT. Thus, Equation (14) can be expressed in a matrix form as follows:  
\[\underset{m\times 1}{V}=\underset{m\times 2}{A}\times\underset{2\times 1}{ \Delta}\times\underset{m\times 1}{L} \tag{17}\]  
where m is the number of ranging results after filtering; V is the vector of the correction of ranging results; L is observation error constant matrix; \(\Delta\) is an unknown parameter in Equation (15); and A is the cosine coefficient matrix in Equation (19).  
\[\Delta=[dX,dY] \tag{18}\]  
\[A^{i}=[\frac{X_{0}-X_{FT}^{i}}{\rho_{0}},\frac{Y_{0}-Y_{FT}^{i}}{\rho_{0}}] \tag{19}\]  
The error observation equation can be established using the ranging results from different FTs, and then the parameters of the coordinate can be obtained by solving the equation with the least square method, as shown in Equation (20).  
\[\Delta=(A^{T}A)^{-1}\times A^{T}\times L \tag{20}\]  
The pseudocode of single point positioning is shown in Figure 9. The observation equation was constructed using the inputs to calculate \(\Delta\). Through iteration, \(\Delta\) gradually approached 0, where the approximate coordinate of MT was gradually steady. When \(\Delta\) was smaller than a threshold or the iteration number was more than a threshold, the iteration could meet the condition of stopping.
## 5 Conclusions  
In this paper, we propose a lightweight real-time object detection network SSD7-FFAM for embedded devices that can be used in specific scenarios from scratch. The proposed novel feature fusion and attention mechanism method can effectively improve the accuracy of object detection. Firstly, through deconvolution and feature fusion, low-level feature maps and high-level feature maps are combined to obtain feature maps with  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **mAP (\%)** & **\#Boxes** \\ \hline Faster R-CNN(VGG-16) & 73.2 & 300 \\ R-FCN(ResNet-101) & 79.5 & 300 \\ SSD300(VGG-16) & 77.5 & 8732 \\ YOLOv3(DarkNet-53) & 79.6 & 10,647 \\ SSD300-FFAM(VGG-16) & 78.7 & 9032 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the proposed SSD300-FFAM with state-of-the-art detectors on the Pascal VOC 2007 test set. All models were trained with VOC 2007 trainval and VOC 2012 trainval.  
more substantial semantic information for prediction, which is conducive to detecting small objects. Secondly, the channel attention module and the spatial attention module are used to enhance the contextual information of the detection target so that the detector focuses more on the object instead of the background. The experimental results on the NWPU VHR-10 dataset show that the proposed SSD7-FFAM can reach 83.7% mAP and predict an image at an average of 0.033 s. The proposed SSD7-FFAM has a parameter value of 1.66 M, which is easier to deploy to embedded devices than other lightweight object detection networks because it does not require a pre-trained model.  
In our future work, in addition to addressing the above limitations, we will transplant the SSD7-FFAM to embedded devices such as drones, and we will test more methods and further optimize our model to enhance scalability.  
Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, visualization, and writing--original draft preparation, Q.L.; resources, writing--review and editing, supervision, project administration, and funding acquisition, Y.L. and W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2020 YC0832700.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Test Materials and Program
### Test Methods
#### 2.3.2 Uniaxial and Triaxial Compression Tests  
The primary equipment used in this test consisted of a rock mechanics testing machine (TW-1000) (Shanghai Institute of Civil Engineering and Architecture, Shanghai, China) and an acoustic emission monitoring system (Express-8) (Beijing Huace Tianyu Technology Co., Ltd., Beijing, China). During the uniaxial and triaxial compression tests on rock specimens, deformation control was applied at a rate of 0.05 mm/min. Stress-strain curves and other mechanical parameters were obtained. Simultaneously, an acoustic emission monitoring system was used to record acoustic emission signals throughout the compression process, capturing valid AE signals. The AE signal sampling rate was set to 2 MHz, with the AE threshold and preamplifier both set to 40 dB. For the acoustic emission measurements, four RT-50 AE sensors were attached to the surface of the rock specimen. To ensure optimal coupling between the sensors and the specimen, vacuum silicone grease was applied to the sensor surfaces. The acoustic emission receiver, which has a cylindrical body with a concave end surface, was positioned along the curved boundary of the specimen using custom-designed steel springs. The degree of concavity in the receiver's end surface was precisely matched to the curvature of the cylindrical rock specimen, ensuring a secure and stable fit. This concave design facilitated an optimal contact with the specimen's surface, further enhancing the stability and accuracy of the measurements during the testing process. Each acoustic emission sensor was equipped with two specially designed grooves--one at the top and one at the bottom--to securely accommodate the steel springs. This dual-groove configuration ensured that the steel springs were effectively fixed, thereby maintaining the stable positioning of the sensors throughout the test. The confining pressure was varied during the test at 0, 3, 6, and 9 MPa to simulate the stress conditions of the rock mass in various geological environments. Thus, 0 MPa represents the initial state without confining pressure, while 3, 6, and 9 MPa represent low, medium, and high confining pressures, respectively, covering the typical range of ground pressures found in real-world projects. The test setup and rock specimen installation are shown in Figure 4. A detailed diagram of the acoustic emission installation and auxiliary components is shown in Figure 5.  
Figure 4: Rock mechanics tester and rock specimen installation.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
### Literature Review and Contribution
Therefore, we integrate the scheduling policy into the T-GA-based global path planning process, which can continuously adjust the scheduling policy according to the changes in time and environment. In summary, in this paper, we propose a two-layer path planning method for multiple AGVs that can efficiently solve the three problems faced by AGVs. The main contributions of this paper are as follows:  
1. To solve the problem of dynamic obstacle avoidance and unknown environment pathfinding for AGVs, we design an improved Q-learning path optimization algorithm based on Kohonen networks (K-L for short). 2. In order to obtain improved path planning results, we propose a two-level path planning method by combining a K-L algorithm and an improved genetic algorithm (referred to as GA-KL). We use a genetic algorithm (T-GA) based on path smoothing factor and node busy factor optimization as a global planning algorithm to generate the global optimal set of path nodes. 3. To effectively accomplish dynamic scheduling of multi-AGVs, we integrate the scheduling policy into global path planning and achieve dynamic scheduling of multiple AGVs by analyzing the congestion of the current working environment and combining it with a weight-based voting algorithm.
# Fluid Net Models: From Behavioral Properties to Structural Objects
## 2 Basic Concepts and Notations
### Petri Net Structure and System  
**Definition 1**.: _A Petri net structure is a bipartite digraph defined by the four tuple \(N=\langle P,T,\textbf{Pre},\textbf{Post}\rangle\), where \(P\
eq\emptyset\) and \(T\
eq\emptyset\) are finite non-empty disjoint sets of nodes named places and transitions, respectively, and \(\textbf{Pre}:P\times T\rightarrow\mathbb{N}\cup\{0\}\) (\(\textbf{Post}:P\times T\rightarrow\mathbb{N}\cup\{0\}\)) is the pre-incidence function specifying the weighted arcs directed from places to transitions (post-incidence function that specifies the weighted arcs directed from transitions to places)._  
A _subnet_ of \(N\), \(N^{\prime}=\langle P^{\prime},T^{\prime},\textbf{Pre}^{\prime},\textbf{Post}^ {\prime}\rangle\), is a Petri net structure where \(P^{\prime}\subseteq P\), \(T^{\prime}\subseteq T\) are subsets of places and transitions of \(N\), respectively; and \(\textbf{Pre}^{\prime}=\textbf{Pre}[P^{\prime},T^{\prime}]\) and \(\textbf{Post}^{\prime}=\textbf{Post}[P^{\prime},T^{\prime}]\) are the pre- and post-incidence functions of \(N\) restricted to \(P^{\prime}\) and \(T^{\prime}\). The _preset_ and _postset_ of a node \(v\in P\cup T\) are denoted as \({}^{\bullet}v\) (set of input nodes) and \(v^{\bullet}\) (set of output nodes), respectively. These definitions can be naturally extended: let \(V\subset P\cup T\) be a set of nodes, while \({}^{\bullet}V\) (respectively, \(V^{\bullet}\)) denotes the union of the preset (respectively, of the postset) of every node \(v\in V\). A _siphon_ is a set of places \(\Sigma\subseteq P\) such that \({}^{\bullet}\Sigma\subseteq\Sigma^{\bullet}\).  
The _token-flow_ matrix (_incidence_ matrix if the net is _self-loop free_) of a net \(N\) is defined as \(\textbf{C}=\textbf{Post}-\textbf{Pre}\). A transition \(t_{i}\) with more than one input place (i.e., \(|{}^{\bullet}t_{i}|>1\)) is named a join transition; a place \(p_{j}\) with more than one output transition (i.e., \(|p^{\bullet}_{j}|>1\)) is named a choice place. A column vector \(\textbf{y}\
eq\textbf{0}\) is called a _\(P\)-flow_ of \(N\) if \(\textbf{y}^{T}\textbf{C}=\textbf{0}\); if the non-null entries of such vector are positive, then it is called a _\(P\)-semiflow_. A basis of the left kernel of \(C\) is a basis of P-flows and is denoted by \(\textbf{By}_{y}\), so that \(\textbf{B}^{T}_{y}\textbf{C}=\textbf{0}\). Similarly, a column vector \(\textbf{x}\
eq\textbf{0}\) is termed a _T-flow_ of \(N\) if \(\textbf{Cx}=\textbf{0}\); if the non-null entries of that vector are positive, then it is termed a _T-semiflow_. If there is a P-semiflow (T-semiflow) such that \(\textbf{y}>\textbf{0}\) (\(\textbf{x}>\textbf{0}\)), the net is said to be _conservative_ (_consistent_), denoted as Cv (Ct).  
**Definition 2**.: _A net \(N\) is Mono-T-Semiflow (MTS) if it is conservative and consistent with a unique minimal (defined in the naturals, and least common multiple equals one) T-semiflow._
# UAV-Borne Profiling Radar for Forest Research
## 2 FGI-Tomoradar Development
### FMCW Principle  
FMCW is a commonly used radar technique for range measurement. A continuous carrier signal is frequency-modulated using a lower-frequency triangular or saw-tooth waveform, whereby the frequency variation should be as linear as possible over time. The modulated RF signal is amplified and then transmitted by an antenna. The echo reflected from a target is collected with a receiver antenna. Due to the frequency modulation and time difference caused by the signal's flight time to the target, there is a frequency difference (i.e., beat frequency \(f_{b}\)) between the transmitted RF signal and received RF signal. The beat frequency is directly proportional to the time delay \(\Delta t\). Therefore, the distance to the target can be measured as in Equation (3):  
\[\mathrm{f_{b}}=\frac{\mathrm{df}}{\mathrm{dt}}\cdot\Delta t=\frac{\mathrm{df}} {\mathrm{dt}}\cdot 2\mathrm{R/c}\to\mathrm{R}=\frac{\mathrm{f_{b}}\cdot\mathrm{c}}{2 \frac{\mathrm{df}}{\mathrm{dt}}} \tag{3}\]  
where \(\frac{df}{dt}\) is the ramp rate of the frequency modulation and \(\mathrm{c}\) is the speed of light. From Equation (3), it can be observed that the linearity of the ramp rate directly affects the range measurement. The range resolution \(\Delta R\) is determined by  
\[\Delta R=\mathrm{c}/2\Delta f \tag{4}\]  
where \(\Delta f\) is the sweep bandwidth and (in the Tomoradar case) the sweep bandwidth is 1 GHz, resulting in a 15 cm range resolution.
# Research on Image Feature Extraction and Environment Inference Based on Invariant Learning
## 3 Image Feature Extraction Method Based on Invariant Learning
### Environment Configuration and Parameter Settings  
The environmental setup for the animal classification task in this study includes both hardware and software configurations. The experimental hardware configuration includes an NVIDIA A800-SXM GPU and an Intel(R) Xeon(R) Platinum 8358 CPU. The software environment consists of CUDA 11.6 and Python 3.9.7, running on the Ubuntu 20.04 operating system, utilizing PyTorch 1.12.1 as the deep learning framework.  
In the standard ResNet18 network, the fully connected layer was configured with 10 neurons, with the input to this layer being a 512-dimensional variable after average pooling. Training samples were input into both the ERM-based ResNet18 and IRM-ResNet18 models. The model was trained in 500 iterations, using a batch size of 128, a regularization parameter of 0.001, and a network learning rate set to 0.003. For the IRM-ResNet18 architecture, the penalty coefficient was set to 100. To improve learning efficiency, the penalty coefficient was set to 1 for the first 100 iterations and then restored to 100 thereafter.
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 1 Introduction
The LSCF identification technique and the Least Squares Frequency-Domain (LSFD) [20] estimator in pole-residue modal model with upper and lower residual terms are briefly described in the first part. Next, the novel MLE-PMM is derived in the the second part and, finally, in the last part of the paper, the implementation of the MLE-PMM herein proposed is evaluated, both in terms of accuracy of the modal parameter estimates and precision of the predicted confidence bounds, using two simulated examples.
## 2 Materials and Methods
### Downscaling Model
#### 2.2.4 Evaluation  
The downscaling results in the same direction and time were combined spatially as whole images for evaluation purposes. The main evaluation metrics were image-wise \(R^{2}\)[18] and Root Mean Square Error (RMSE), which are defined as follows:  
\[R_{t}^{2} =1-\frac{\sum_{i=1}^{h}\sum_{j=1}^{w}(y_{i,j,t}-\hat{y}_{i,j,t})^{ 2}}{\sum_{i=1}^{h}\sum_{j=1}^{w}(y_{i,j,t}-\hat{y}_{t})^{2}} \tag{4}\] \[RMSE_{t} =\sqrt{\frac{1}{hw}\sum_{i=1}^{h}\sum_{j=1}^{w}(y_{i,j,t}-\hat{y} _{i,j,t})^{2}},\]  
where \(\hat{y}_{i,j,t}\) is the downscaled AOD value at \(i\),\(j\),\(t\) and \(y_{i,j,t}\) is the corresponding true value. The downscaled results of ASDM, ASDM with transfer enhancement (ASDMTE), SRDRN, dissever framework with GAM and LM as regressors were compared on the same test sets with the above metrics. The structure of SRDRN can be found in [PERSON] et al. (2021) [26].
indicated by lower image-wise \(R^{2}\) and higher RMSE. These similar inherent temporal associations of G5 NR and MERRA-2 provided a good foundation for ASDM to assume that local-scale AOD can be predicted not only by between-scale associations, but also by inherent within-scale associations. In addition, due to generative algorithm differences between G5 NR and MERRA-2 AOD data, G5 NR AOD has a universally higher mean value and standard deviation (0.316 (0.258)) compared to MERRA-2 AOD (0.294 (0.197)), which is the reason G5 NR had higher lagging \(RMSE\) and \(R^{2}\) (Figure 5).  
Model performance results comparing ASDM and ASDMTE against SRDRN, GAM and LM are shown in Appendix Figures A1-A4. Both ASDM and ASDMTE outperformed other methods, as indicated by higher image-wise \(R^{2}\) and lower RMSE across all seasons and directions. Among all of the test sets, ASDMTE had average maximum image-wise \(R^{2}\)=0.758 and average mean image-wise \(R^{2}\)=0.443. The ASDM performed similarly with average maximum image-wise \(R^{2}\)=0.735 and average mean image-wise \(R^{2}\)=0.431. The SRDRN, GAM and LM methods had average maximum image-wise \(R^{2}\)=0.313, 0.106 and 0.095 respectively. Notably, the downscaled AOD map generated by ASDMTE and ASDM on 29 July 2006 (Figure 6a,b) preserved very similar spatial characteristics as the true G5 NR data in Figure 6d, while SRDRN and dissever-based downscaling results (see Figure 6c,e,f) exhibit clearly different patterns.  
Figure 5: Sample images from G5 NR (**top-left**) and MERRA-2 (**top-right**) on 29 July 2006; temporal trend of image-wise RMSE (**bottom-left**) and \(R^{2}\) (**bottom-right**) with different lags (days).