esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Neural Network Models  
#### 2.1.1 Methodology Description  
A neural network is an information-processing system [29] that has certain performance characteristics in common with biological neural networks and is modeled after the human brain, which computes some relationship between its input and output [6]. Usually, accurate information about the system is unknown and the observations from the system are the only thing that can be utilized. A fixed architecture of neural networks can be built and control parameters are modified according to some algorithms until a certain loss function is minimized. Learning is carried out based on a training dataset that consists of several data samples observed from the actual system [30; 31]. In this study, a standard feed-forward network with a backpropagation BPNN model and an improved BP neural network by a genetic algorithm GABP model is employed for comparison with the deep learning LSTM model.  
BP neural network is a multi-layer feed-forward network using a learning algorithm called backward error propagation or simply backpropagation in one-way, the network consists of three layers, namely the input layer, the hidden layers (which can be multi-layer), and the output layer [32]. The prediction process is as follows: Firstly, the network's weights and thresholds are initialized, then the output and error of each layer are calculated, and then the weights and thresholds are corrected according to the calculated loss value, finally, the new output and errors are recalculated until the error is small enough and the training is over. The BPNN algorithm is based on gradient descent with the characteristic of rapid forecasting and recognition speed, and good performance of fault tolerance, but it is easy to fall into \"local minimization\" [33], and may not get the optimal solution.  
A genetic algorithm (GA) is a computational model that mimics the process of [PERSON]'s biological natural evolution to search for optimal solutions. The GABP model uses GA to optimize the initial weights and thresholds of BP in the neural network to avoid falling into \"local minimization\", which can give better output [17; 18; 19; 20]. The forecasting process is shown in Figure 1:  
(1) Initialize the weight, threshold, and population of the BP neural network, set the population size and genetic algebra;  
(2) Calculate the fitness of each individual in the population and replace the least-fit population with new individuals;  
(3) Perform selection, crossover, and mutation operations to obtain the next generation individuals;  
(4) Update to obtain the optimal weight and threshold of the population and perform BP neural network forecasting as mentioned in the last paragraph.  
The flowchart of the GABP neural network algorithm is shown in Figure 1.  
Figure 1: BP neural network optimized by the genetic algorithm flowchart.
# An Effective AUSM-Type Scheme for Both Cases of Low Mach Number and High Mach Number
## 4 Theoretical Analyses on Low Speed Issues
### Theoretical Demands of the AUSM-Type Schemes for Low Speeds
#### 4.3.1 The Demand of the Mass Flux at Low Speeds  
In general, all the AUSM-type schemes' mass fluxes in Equation (14) can be written in the following form [31]:  
\[\begin{array}{l}\hat{m}_{i+1/2}=\frac{1}{2}\left(\!f_{c,i}+f_{c,i+1}\right)+ D^{\rho}\Delta\rho+D^{V_{n}}\Delta V_{n}+D^{p}\Delta p\end{array} \tag{103}\]  
For example, the coefficients in AUSM+'s mass flux are as follows:  
\[\begin{array}{l}D^{p}_{AUSM+}=|V_{n}|\;\;D^{p}_{AUSM+}=0\;\;D^{p}_{AUSM+}=0 \end{array} \tag{104}\]  
Combining Equation (103) with Equation (14) and using the non-dimensional method in Section 4.1, we can find that the coefficient \(D^{p}\) has a strong influence on the scheme's robustness against the checkerboard decoupling.  
If \(D^{p}\) is of the order \(O(1)\), the mass flux makes the scheme satisfy Equations (101) and (102) at the same time. Furthermore, if \(D^{p}\) is of the order \(O(M)\), the mass flux makes the scheme satisfy Equation (101).  
Based on analyses above, we can obtain the following lemma:  
**Lemma 1**.: _The coefficient \(D^{p}\) in the mass flux should be of the order \(O(M)\) or \(O(1)\) to suppress the checkerboard decoupling._
# AI-For-Mobility--A New Research Platform for AI-Based Control Methods
## 3 Sensor Architecture
### Vehicle Environment  
The vehicle environment includes not only static objects such as traffic signs, buildings, trees, etc., but also dynamic objects such as other traffic participants. The perception of the vehicle environment is one of crucial information sources for automated vehicle guidance. Based on this information, trajectories can be planned and dynamically adjusted. In addition, perception sensors provide information about the ego-vehicle motion, e.g., speed relative to a static object.  
**Surround Camera System**  
A total of eight high resolution Gigabit Multimedia Serial Link (GMSL) cameras, more specifically four 60\({}^{\circ}\) field of view (FOV) and four 120\({}^{\circ}\) FOV cameras, are mounted on the roof rack of the AFM. The cameras provide the vehicle with a 360\({}^{\circ}\) view of the environment and enable the detection and classification of surrounding objects in real-time. Three 60\({}^{\circ}\) FOV cameras point in the driving direction of the vehicle, where one camera is centered in the middle of the vehicle and the remaining two cameras represent a stereo pair. One 60\({}^{\circ}\) FOV camera observes the environment at the back of the vehicle. The four 120\({}^{\circ}\) FOV cameras are placed on the roof rack such that the observation of the left and right space next to the vehicle is enabled, with two cameras on each side. The camera setup is illustrated in Figure 11. All cameras are connected to the multi-GPU-accelerated computing system, where the images are processed and forwarded to the neural network-based detection and classification. The cameras are intrinsically and extrinsically calibrated on the multi-GPU-accelerated computing platform with a commercially available software framework [20].  
**High Resolution Lidar Sensors**  
Two 360\({}^{\circ}\) lidar sensors are mounted on the roof rack at the front of the vehicle, which enables a more comprehensive perception of the environment. The lidar information is used to scan the surrounding objects and obstacles and to measure their distances to the vehicle. This enables the generation of a 3D map of the vehicle's environment, which can be used for a variety of the control algorithms, such as longitudinal and lateral control.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 8 Behavior-Based Methods to Detect False Base Stations  
In the previous two sections, the task of enhancing the PHY layer security of 5G networks has been seen from the perspective of machine/deep-learning methods and location-based techniques, or their combination. However, there are other families of techniques that do not fall into these two categories. Often, the securing of 5G networks is understood as a classification task aiming to distinguish between legitimate and rogue devices (UE or BS). In a technical report for 3 GPP, [170] defines several solutions to be implemented on higher layers to protect against connection to a false BS, such as digitally signing the broadcasted system information, or identifying a false BS from active UE measurement reports. For the later case, the report also states that if a false BS copies the identity of a legitimate one, it is difficult to detect which one, as measured by the UE in the measurement report, belongs to a genuine base station and which one is false.  
Such approaches belong to a relatively large family of methods able to detect illegitimate devices and comprises methods based on studying the discrepancies in the behavior between usual legitimate devices and their false counterparts. Note that, in many cases, at the final stage of such a method, the classifier, either a classical one or a machine-learning-based one, performs the final decision. In some cases, the behavior of the device under consideration is not studied by a single node, but rather in a network-assisted manner, in which several neighboring devices share their observations. Several exemplary suspicious marks of BS behavior have been sketched in dissertation [68], such as the following:  
* sudden peaks in RSRP/SINR;
* changing the transmission power without coordination with the network;* increased handover failure rate;
* moving base station.  
Other misbehavior marks have been described in [171], from which we have selected the following examples:  
* PCI outside the range of a given area;
* operating cells that have historically been powered-off outside working hours;
* received signal quality greater than usual in a given area;
* invalid PLMN identification.  
Similarly, the method from [172] relied on a custom-designed state machine that analyzes portions of the RRC message logs exchanged between the base station and the UE, as well as the handover request history. According to the authors, the method outperforms several classical machine-learning-based classifiers, but was not compared with any deep-learning ones. Note that other examples of behavior methods can rely on the analysis of the measurement reports, such as [166], but as the gathered information is used to tri-laterate the position of the BS, we consider this method as the most location-aware one.
# Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020
## Appendix A Investigation and analysis of soil samples and calculation of SOCD.  
To increase the sample density, sampling sites were selected to be evenly set up in different types of land use (forest, grassland, and cropland) in June to September 2022 in the Tibetan Plateau, the Yunnan-Guizhou Plateau, and the southern hills.  
Soil sampling: Within the survey plots, one topsoil mixture (0-20 cm) and four deeper mixtures (20-40 cm, 40-60 cm, 60-80 cm, and 80-100 cm) need to be collected and the soil bulk weight of each layer determined. The topmost layer (0-20 cm), due to its high organic carbon content, loose soil texture, and high variability, needs to be sampled independently by directly using a soil auger with a depth of 20 cm and an inner diameter of \(>\)3 cm, randomly selecting six points within the sample plot, removing small soil bodies, and mixing them to form a single mixed sample. In the process of taking surface samples, two points should be noted: one is to try to maintain the integrity of each small soil body; the second is that the sample bag should be opened in the field to allow water to evaporate as early as possible. Four deep mixed samples (20-40 cm, 40-60 cm, 60-80 cm, 80-100 cm) were collected by digging 1 m deep soil profiles at no fewer than three randomly selected locations in the sample plots of the forest survey sites, and each 20-40 cm, 40-60 cm, 60-80 cm, and 80-100 cm depth soil sample was collected from the downslope side of each profile, and the three samples from the same layer depth were evenly mixed to form a mixed sample. The samples were air-dried, sieved, and sent to the Chemical Analysis Laboratory of the China Geological Survey Command Center for Comprehensive Investigation of Kunming General Survey of Natural Resources Center China Geological Survey for the determination of the SOC content after removing plant roots and residues.  
SOC content measurement: A total of 0.5 g of soil passed through a 0.15 mm sieve was added to a glass test tube that had been caueterized at 500 \({}^{\circ}\)C. Then, 20 mL of 0.5 mol/L HCI was added, shaken well, oscillated for 1 h, and left to stand for 12 h to remove inorganic carbon. Deionized water was added and centrifuged at 3000 r/min for 5 min, and excess HCl was washed several times until the solution was neutral. The recovered soil samples were freeze-dried, and 0.03 g was taken to determine the SOC content using an elemental analyzer (Thermo Scientific, Waltham, MA, USA).  
Calculating SOCD: The SOCD calculation formula is as follows:  
\[\text{SOCD}=\sum_{i=1}^{n}SOC_{i}\times BD_{i}\times SD_{i}\times\frac{(1-C_{i })}{100}\]  
where the \(SOCD\) unit is kg C\(\cdot\)m\({}^{-2}\); \(SOC_{i}\) is the SOC content of layer \(i\) (g\(\cdot\)kg\({}^{-1}\)); \
# Field Test Study on Vertical Bearing Characteristics of Coal Rock
## 3 Results
### Plate Load Test
#### 3.1.3 Deformation Modulus  
The plate load test on coal rock is equivalent to the case of partial loading on the surface of a semi-infinite elastic body. According to the Boussinesq solution, the deformation modulus of the coal rock can be calculated [26]:  
\[E_{0}=I_{0}\Big{(}1-\
u^{2}\Big{)}\frac{pd}{s} \tag{1}\]  
where \(E_{0}\) is the deformation modulus (MPa); \(I_{0}\) is the shape coefficient obtained according to the vertical displacement solution of Boussinesq, for the circular bearing plate it takes 0.785; \(\
u\) is the [PERSON]'s ratio, it takes 0.27; \(p\) is the pressure of the linear segment of the \(p\)-\(s\) curve (kPa); \(d\) is the diameter of the bearing plate (m); \(s\) is the settlement corresponding to \(p\) (mm).  
Equation (1) can be rewritten as:  
\[p=\frac{E_{0}}{I_{0}(1-\
u^{2})d}s \tag{2}\]  
It can be seen from Formula (2) that if \(p\)-\(s\) is proportional, \(E_{0}\) can be calculated from the slope of the \(p\)-\(s\) curve.  
According to the results of the plate load test, the pressure-settlement curve is close to a straight line in the range of the proportional limit load, i.e., 700 kPa. The curve fitting results are shown in Figure 10. It reveals that there is a good linear correlation between the pressure \(p\) and the settlement \(s\).  
The \(E_{0}\) values of PLT1, PLT2 and PLT3 calculated according to Formula (2) and Figure 10 are 149.3 MPa, 102.4 MPa and 126.2 MPa. The average value is 126.0 MPa, and it is taken as the deformation modulus of the coal rock under the working load.  
The deformation modulus generally decreases with the increase of pressure. For the loading data of the three tests, the \(E_{0}\) value corresponding to the \(p\) value can be calculated according to Formula (1). The results beyond the proportional limit load of 700 kPa are shown in Figure 11. It shows that the deformation modulus decreases with the increase in the pressure.  
Figure 10: Relationship between pressure and settlement at liner segment: (**a**) PLT1; (**b**) PLT2; (**c**) PLT3.  
For the test points shown in Figure 11, the exponential function can be used for data fitting:  
\[E_{0}=A_{1}e^{p/t_{1}}+y_{0} \tag{3}\]  
where \(A_{1}\), \(t_{1}\) and \(y_{0}\) are coefficients.  
Figure 11 shows that there is a good correlation between the fitting curve and the test points, i.e., the deformation modulus and the pressure have an exponential function relationship.
## 2 Materials and Methods
### Instrumentation  
PCB piezo-electric pressure transducers (gauges) were used to measure blast overpressure histories at the different points of interest. Each gauge was positioned such that the piezo-electric diaphragms were at heights of 540 mm and 405 mm with respect to the base plate (consistent with the charge centres) for the 100% and 75% scale tests, respectively at locations A, B1-B3. Pressure sensor diaphragms face vertically upwards to measure the unobstructed blast overpressure histories perpendicular to the incident blast propagation direction.  
Pressure gauges were mounted flush inside bespoke nylon bushings that were inserted into the top of S355 steel pipes (Figure 3) and fixed to the base plate at the specified locations. The nylon bushings helped isolate pressure gauges to reduce ringing caused by stress waves propagating through the steel assembly after detonation.  
Figure 3: (**a**) Exploded view (schematic) and (**b**) photograph of the experimental pressure instrumentation set-up.  
Figure 2: Photographs of the 75% experimental corner set-up.  
Different specification pressure sensors were allocated to different gauge locations based on the maximum operating pressure and sensitivity (Table 1), as different pressure magnitudes and sensitivity were expected based on location. A Butterworth 200 kHz low-pass filter was applied to all channels with a frequency cut-off, fc = \(-\)3 dB to remove unwanted high-frequency signal components. The manufacturer quotes a rise time of 1 us and a natural frequency of \(>\)500 kHz for the pressure sensors used [28; 29; 30].  
All pressure gauges were connected to a HBM Genesis 2 TB tethered data acquisition (DAQ) system via low-noise cables. The DAQ was fitted with a signal conditioner (HBM GN815) designed to capture data from IEPE pressure transducers at a sample rate of 2 MHz, ensuring high-fidelity pressure histories at each gauge location.  
A simple break-wire circuit, constructed from aluminium foil in direct contact with the explosive charge and powered by a 4.5 V DC battery, was connected to the DAQ to trigger instrumentation recording following detonation. For each trial, the time of detonation was synchronised to have a time of 0.0 ms at the moment of detonation. This reference time was then used to determine the subsequent blast wave times of arrival at each pressure gauge surrounding the assembly.
# Guided Semi-Supervised Non-Negative Matrix Factorization
## 2 Related Work
### Semi-Supervised NMF  
Besides topic modeling, one variant of classical NMF, Semi-Supervised NMF (SSNMF) [10; 11; 14] is designed to further perform classification. SSNMF introduces a masking matrix \(\mathbf{L}=[\ell_{1},\ldots,\ell_{n}]\in\mathbb{R}_{\geq 0}^{p\times n}\) and a label matrix \(\mathbf{Z}=[\mathbf{z}_{1},\cdots,\mathbf{z}_{n}]\in\mathbb{R}_{\geq 0}^{p\times n}\), where \(p\) is the number of classes and \(n\) is the number of documents. The masking matrix \(\mathbf{L}\) is defined as:  
\[\ell_{j}=\begin{cases}\mathbf{1}_{p},&\text{if the label of document $x_{j}$ is known}\\ \mathbf{0}_{p},&\text{otherwise},\end{cases} \tag{2}\]  
where \(\mathbf{1}_{p}=[1,\ldots,1]^{T}\in\mathbb{R}^{p}\) and \(\mathbf{0}_{p}=[0,\ldots,0]^{T}\in\mathbb{R}^{p}\). Note that the masking matrix \(\mathbf{L}\) splits the label information into train and test sets. Each column \(\mathbf{z}_{i}\) is a binary encoding vector such that, if the document \(\mathbf{x}_{i}\) belongs to class \(j\), the \(j^{th}\) entry of \(\mathbf{z}_{i}\) is \(1\), and otherwise it is set to be \(0\). The dictionary matrix \(\mathbf{W}\), coding matrix \(\mathbf{H}\), and label dictionary matrix \(\mathbf{C}\) can be found by solving the optimization problem  
\[\operatorname*{argmin}_{\mathbf{W}\in\mathbb{R}_{\geq 0}^{d\times k},\mathbf{H}\in \mathbb{R}_{\geq 0}^{k\times n},\mathbf{C}\in\mathbb{R}_{\geq 0}^{p\times k}} \underbrace{\frac{1}{2}\|\mathbf{X}-\mathbf{W}\mathbf{H}\|_{F}^{2}}_{\text{ classical NMF}}+\underbrace{\frac{\mu}{2}\|\mathbf{L}\odot(\mathbf{Z}-\mathbf{C}\mathbf{H})\|_{F}^{2}}_{ \text{label information}}, \tag{3}\]  
where \(\mu>0\) is a regularization parameter and \(\mathbf{A}\odot\mathbf{B}\) denotes entry-wise multiplication between matrix \(\mathbf{A}\) and \(\mathbf{B}\). Matrices \(\mathbf{W}\) and \(\mathbf{H}\) can be interpreted in the same way as classical NMF. Matrix \(\mathbf{C}\) can be viewed as the dictionary matrix for the label matrix \(\mathbf{Z}\).
# Exploring the Spatiotemporal Alterations in China's GPP Based on the DTEC Model
## 3 Results  
### Verification of GPP Simulation Accuracy  
The monthly GPP values simulated by the DTEC model (GPPDT) with GO-SIF GPP (GPPSF) and MOD17 GPP (GPPMOD) were compared at the site scale with flux measurement data (GPPEC). These accuracy validation results (Figure 3) showed that the three models simulated and captured the dynamics of the GPP well, but the simulation accuracy changed with flux site and ecosystem type. At the cropland site (YC), GPPDT (\(R^{2}\) = 0.84, RMSE = 68.37 g C m\({}^{-2}\) month\({}^{-1}\), bias = 0.52 g C m\({}^{-2}\) month\({}^{-1}\)) was more accurate than GPPSF (\(R^{2}\) = 0.90, RMSE = 124.33 g C m\({}^{-2}\) month\({}^{-1}\), bias = 79.98 g C m\({}^{-2}\) month\({}^{-1}\)) and GPPMD (\(R^{2}\) = 0.81, RMSE = 164.09 g C m\({}^{-2}\) month\({}^{-1}\), bias = 109.20 g C m\({}^{-2}\) month\({}^{-1}\)) (Figure 3b). Among the QYZ, CBS and DHS forest sites, GPPDT achieved the best simulation, and the CBS site had the highest accuracy (\(R^{2}\) = 0.95, RMSE = 27.52 g C m\({}^{-2}\) month\({}^{-1}\), bias = 0.37 g C m\({}^{-2}\) month\({}^{-1}\)) (Figure 3f). The GPPDT values were more accurate than the GPPMD and GPPSF values at the mixed forest (ALS) (Figure 3g) and savanna (YJ) (Figure 3c) sites. In general, GPPDT (\(R^{2}\) = 0.85, RMSE = 41.12 g C m\({}^{-2}\) month\({}^{-1}\), bias = 5.98 g C m\({}^{-2}\) month\({}^{-1}\)) was better than GPPSF (\(R^{2}\) = 0.65, RMSE = 64.04 g C m\({}^{-2}\) month\({}^{-1}\), bias = \(-2.94\) g C m\({}^{-2}\) month\({}^{-1}\)) and GPPMD (\(R^{2}\) = 0.56, RMSE = 74.00 g C m\({}^{-2}\) month\({}^{-1}\), bias = 23.33 g C m\({}^{-2}\) month\({}^{-1}\)) (Figure 3a).  
Figure 3: The accuracy validation results of the DTEC, GO-SIF, and MOD17 models at 10 flux sites in China, where the \(x\)-axis represents the measured data at the flux sites, the \(y\)-axis represents the simulated values of the three models, and R\({}^{2}\), RMSE, and BIAS were used as the coefficients to evaluate the accuracy of the models.  
This paper further compared GPPD that with GPPSIF and GPPMOD data to evaluate the precision and accuracy of the DTEC model from three aspects: multiyear average (Figure S1), spatial distribution (Figure S2), and GPP trend significance test (Figure S3). Overall, the DTEC model had the highest accuracy, could better reflect the status of GPP in China, and could provide more accurate data for subsequent analyses.