esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
## 4 Results
### Quality Parameters to Assess the Model  
This section briefly summarizes the parameters used to assess the performance of the method. First, it is defined what true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) mean in the context of earthquake prediction:  
1. TP. The methodology predicts that a large earthquake is occurring within the next five days and it does occur.
2. TN. The methodology predicts that a large earthquake is not occurring within the next five days and it does not occur.
3. FP. The methodology predicts that a large earthquake is occurring within the next five days but it does not occur.
4. FN. The methodology predicts that a large earthquake is not occurring within the next five days but it does occur.  
From all these statistics, several well-known measures can be calculated. In particular, sensitivity (\(S_{n}\)), specificity (\(S_{p}\)), positive predictive value (PPV), negative predictive values (NPV), F-measure or balanced F-score (F), Matthew's correlation coefficient (MCC), geometric mean (GM) and AUC. Their formulas are listed below:  
\[S_{n} = \frac{TP}{TP+FN} \tag{1}\] \[S_{p} = \frac{TN}{TN+FP}\] (2) \[PPV = \frac{TP}{TP+FP}\] (3) \[NPV = \frac{TN}{TN+FN}\] (4) \[F = \frac{2\cdot PPV\cdot S_{n}}{S_{n}+PPV}\] (5) \[MCC = \frac{TP\times TN-FP\times FN}{\sqrt{(TN+FN)(TP+FP)(TP+FN)(TN+ FP)}}\] (6) \[GM = \sqrt{S_{n}\cdot S_{p}}\] (7) \[AUC = \frac{1+S_{n}+FP/size(Dataset)}{2} \tag{8}\]
## 1 Introduction
The smuggling/theft of important trees such as sandalwoods in the forest poses a serious threat to forest resources, causes significant economic losses, and ultimately causes considerable destructive effects on the environment around the world. Therefore, in the literature [40; 41], the authors used MEMS technology and the forest sector to use renewable solar energy vibration sensors to monitor special trees such as sandalwoods. [PERSON] et al. [42] provided a method that included three units, a tree unit, a sub-server unit, and a forest officer, to prevent the smuggling of valuable trees such as sandalwoods and red sandalwoods in forest areas. In the tree unit vibration sensor, the sensor is deployed using continuity. An RFID receiver and a ZigBee transmitter are connected to the microcontroller. One subserver was used for every 50 trees. In the subserver, GPS, GSM, and ZigBee receivers are connected. In the above concept, the information collected by deploying various traditional sensors and types of monitoring equipment can be received through different wireless communication methods such as Zigbee, WiFi, 2G, 3G, or 4G. However, they are all limited by transmission distance, power consumption, and transmission costs. The aim of this work is to implement an intelligent forest Internet of things (IoT) using the wireless network communication technology of a low-power wide-area network (LPWAN). The proposed contributions include the following: 1. We selected two LPWAN technologies; long range (LoRa), which uses unlicensed frequency bands, and narrow-band Internet of things (NB-IoT), which uses licensed frequency bands, combined with their respective sensors, including a LoRa-support solar micro-weather station and NB-IoT-support sensors. 2. A multi-hop technique for the LoRa-based sensors and communications was proposed to solve the problem of long-distance communication without 3G/4G. 3. For the forest without electricity, we installed solar panels to supply electricity to the sensors and other equipment. In order to avoid the problem of insufficient solar panels due to insufficient sunlight in the forest, we also connected each solar panel to a battery to store electricity. Thus, sensing data on parameters such as the light intensity, air pressure, ultraviolet intensity, and CO\({}_{2}\) were transmitted successfully to a LoRa gateway connected to 3G/4G. Finally, the real-time sensing data were transmitted to a web server. The rest of this work is organized as follows. Section 2 provides the background related to the forest sensors and communication technology proposed in Section 3. The proposed forest sensors and communication technology are presented in Section 3. Section 4 shows the implementation and experimental results. Section 5 gives a conclusion.
#### Article
###### Abstract  
As is already known, some currently stable landslides may have been activated in the past along a pre-existing sliding surface and reached the residual strength there, as a consequence of high-cumulative displacements. After a fairly long period of quiescence, these landslides can reactivate due to a temporary increase in destabilising forces capable of mobilising the residual strength along the same sliding surface again. Some recent studies have suggested that, under certain conditions, the strength mobilised upon reactivation may slightly exceed the residual value and then decay towards the latter as the displacement progresses. Regarding this matter, many previous studies have hypothesised that some geotechnical variables could affect the recovered strength more significantly: the length of the ageing time, the vertical stress, the stress history, and the speed with which the reactivation occurs. The aim of this research is to confirm whether such recovery of strength upon reactivation is possible and which geotechnical parameters have the greatest influence on the process. To this end, laboratory tests were carried out with the Bromhead ring shear apparatus on normally consolidated saturated samples of both natural soils and clays provided by industry (bentonite and kaolin). The coupling effect of the ageing time, the vertical stress, and the reactivation speed on the mobilised strength upon reactivation were investigated, starting from a pre-existing residual state of these samples. Within the limits of this research, the results seem to confirm that all three geotechnical variables are influential, with a greater impact on the reactivation speed and, subordinately, on the ageing time for long quiescence periods. Therefore, it is concluded that a quiescent landslide could show a reactivated strength slightly higher than the residual value if the destabilising action could arise with a certain rapidity. Conversely, if the destabilising action occurs very slowly, the mobilised strength could correspond to the residual value. The experimental results of this research may find some application in the design of strengthening works for a stable quiescent landslide that could experience a fairly rapid increase in destabilising actions, such as in the case of seismic stress, morphological modification of the slope, or a rising water table.  
residual strength; landslide reactivation; ageing; displacement speed Article
## 4 Discussion  
### Novelty of the Work  
In northwestern Hubei, the mountainous terrain spawned the high forest cover and rich plant species, which makes the region an important carbon sink. However, the fine distribution and variation in the photosynthetic carbon sequestration from regional plants still remain unclear and lack in-depth exploration, mainly due to the complex and high-dynamic topography and environments in the region requiring finer spatial and temporal resolution monitoring. However, conventional methods based on single-satellite data struggle to fulfill the requirements. This study integrates high-temporal-resolution MODIS NDVI data (16-day intervals) with Landsat data with fine spatial resolution to create a 30 m resolution GPP time series in northwestern Hubei, and analyzed the spatiotemporal variation and climatic responses of photosynthetic carbon sequestration in the region. Compared with previous studies in the same region, this research uses finer data and produces clearer results [35; 37; 48]. This approach overcomes the spatial limitations of MODIS and the issues of hard-to-obtain time series for Landsat due to cloud cover and longer revisiting cycles. Based on previous studies, we modified the light use efficiency parameters in the CASA model and combined the model with multisource fused remote sensing data to obtain high-resolution GPP estimation in this heterogeneous landscape. By leveraging more detailed spatial data, this research enhances vegetation productivity estimation accuracy and its spatial variation representation, as well as gained insights into climate-vegetation interactions across elevations. Benefiting from the fine spatial resolution, we analyzed the GPP distribution gradients and the variation trends along with elevation, which would greatly impact the temperature and other environmental conditions. Furthermore, we also further explored the climatic controls across different altitudinal gradients at different time scales, as well as their intra-annual and inter-annual variations. Some conclusions were obtained by further considering fine-scale ecological variations, which can provide insights into small-scale mountainous ecological variations and support regional ecological conservation and land management.
## 3 Discussion and Example  
In this section, we first calculate the sum secrecy capacities of the noncausal and causal cases. Then, we compare our results with some existing results and present a binary example to illustrate the tradeoff between the sum secrecy rate and the information embedding rate under the secrecy constraint.
# Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets
###### Abstract  
Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.fitfitl, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.  
j  
2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 4 Feature Parameter Extraction
### Root Mean Square  
Similar to transmitted waves, reflected waves contain considerable crack geometry information. In the time domain, the root mean square of the reflected wave amplitudes is extracted and analyzed. Figure 8 shows a 3D map with angle as the \(X\)-axis, depth as the \(Y\)-axis, and normalized root mean square of the reflected waves as the \(Z\)-axis.  
Figure 6: Feature extraction flowchart. X: WP coefficient peak; Y: WP energy peak; Z: WP entropy.  
Figure 7: Three-dimensional map of the angle–depth–transmitted wave peak.  
Figure 8 shows that the root mean squares have a rising, falling, and subsequently rising trend as the angles increase; however, they gradually increase when the depths increase. An obvious linear correlation between the root mean square of reflected waves and the geometric information of cracks is obtained. Therefore, the root mean squares of reflected waves are a good feature parameter for the SVM method.
# Hands-On Deformation of Volumetric Anatomical Images on a Touchscreen
## 5 Methods: Direct Handle Manipulation
### Problem Statement and Formulation  
We pose handle manipulation as a multi-finger interaction. The user touches a handle at multiple locations simultaneously, and slides the fingers on the screen to convey the motion of the projected handle. This projected motion is automatically transformed into an optimal 3D motion, providing the user with a sense of natural and intuitive manipulation of the handle directly in 3D.  
When the user touches a handle, we identify the screen-space positions of the fingers, and we map them along their viewing rays to 3D positions on the visible isosurface, same as for handle selection as described in Section 4.1. We initialize these 3D positions as the handle points. Once a handle is transformed during manipulation, the positions \(\{\mathbf{q}_{j}\in\mathbb{R}^{3}\}\) denote the updated positions of the handle points in view space, i.e., expressed in the reference system of the camera. We define as \(\{\mathbf{p}_{j}\in\mathbb{R}^{2}\}\) the target 2D projections for handle points, expressed in normalized device coordinates. When the user moves the fingers on the screen, we record their current positions as the target 2D points.  
Following [PERSON] et al. [9], we pose handle manipulation as the computation of the optimal 3D transformation of handle points \(\{\mathbf{q}_{j}\}\) such that their projections match the target projections \(\{\mathbf{p}_{j}\}\). We express the 3D transformation with a translation vector \(\mathbf{t}\) and a rotation matrix \(\mathbf{R}\), and the 3D-to-2D projection as a function \(\text{Proj}(\cdot)\). Then, we define the following error term for each handle point:  
\[\mathbf{f}_{j}=\text{Proj}\big{(}\mathbf{R}\;\mathbf{q}_{j}+\mathbf{t}\big{)} -\mathbf{p}_{j}. \tag{2}\]  
And we formally define handle manipulation as the optimization of an objective function \(f\), i.e.,  
\[\{\mathbf{t},\mathbf{R}\}=\arg\min f,\quad f=\sum_{j}\mathbf{f}_{j}^{T}\; \mathbf{f}_{j}. \tag{3}\]  
The projection function consists of a linear transformation \(\mathbf{P}\) from 3D to 2D, followed by a division by the 3D Z coordinate. With a unit vector \(\mathbf{k}=\left(0,0,1\right)^{T}\), the projection can be written as  
\[\text{Proj}(\mathbf{v})=\frac{1}{\mathbf{v}^{T}\;\mathbf{k}}\;\mathbf{P}\; \mathbf{v}. \tag{4}\]
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 3 Opportunistic User Scheduling with Information-Hiding Power Control
### Overall Scheduling Procedure
#### 3.2.1. **Initialization** (Broadcast Receive Random Vectors & Pre-Determined Threshold Values)  
The base station first constructs \(S\) orthogonal random vectors, i.e., \(\mathbf{U}_{0}=\left[\mathbf{u}_{0}^{[1]},\cdots,\mathbf{u}_{0}^{[S]}\right]\) and broadcasts the information of \(\mathbf{U}_{0}\), \(\eta_{1}^{\star}\) and \(\eta_{\mathrm{L}}^{\star}\) to all transmitters (users or IoT devices).  
2.2. **Stage 1** (Information-Hiding Power Control Based on Scheduling Parameters & Feedback Information)  
For given an expected scheduling beam index \(l^{\star}\), each transmitter estimates its scheduling parameters in (9) and compares them with \(\eta_{1}^{\star}\) and \(\eta_{\mathrm{L}}^{\star}\). If estimated generating interference or information leakage exceeds the maximum allowable level of the system, i.e., \(\eta_{1}^{[n,l^{\star}]}\geq\eta_{1}^{\star}\) or \(\eta_{\mathrm{L}}^{[n]}\geq\eta_{\mathrm{L}}^{\star}\), transmitter adjusts its transmit power for satisfying the threshold values (\(\eta_{1}^{\star}\) and \(\eta_{\mathrm{L}}^{\star}\)). Therefore, for \(n\)-th transmitter with an expected scheduling beam index \(l^{\star}\), its transmit power is determined as  
\[P^{[n,l^{\star}]}=\min\left\{1,\frac{\eta_{1}^{[n,l^{\star}]}}{\eta_{1}^{\star }}\frac{\eta_{1}^{[n]}}{\eta_{\mathrm{L}}^{\star}}\right\}\times P_{0}. \tag{10}\]  
Note that the main objective of power control in (10) is to hide transmitter's information to other receive beams and eavesdroppers by decreasing transmit power which is upper bounded by the maximum transmit power \(P_{0}\). The concept of using power control in the opportunistic user scheduling was introduced in [33] without considering eavesdroppers (i.e., conventional wireless multiuser networks).  
Figure 2: The overview of the proposed scheduling process and power control mechanism.  
The effective SNR of \(l^{*}\)-th receive beam for \(n\)-th user is defined as  
\[\hat{\rho}^{[n,l^{*}]}\triangleq\frac{\eta_{\mathrm{Q}}^{[n,l^{*}]} \times P^{[n,l^{*}]}}{\sigma_{0}^{2}}, \tag{11}\]  
which is fed back from each transmitter to the base station as a scheduling metric. For each user, feedback information in (11) can be calculated for all beam indices to provide accurate scheduling metrics or for some selected beam indices to reduce feedback overhead. Proper beam indices can be selected by using pre-determined positive threshold value (e.g., \(\eta_{\mathrm{Q}}^{*}\)) which is determined at the base station for guaranteeing the minimum signal quality. Each transmitter finds the index \(l^{*}\in\{1,\cdots,S\}\) satisfying \(\eta_{\mathrm{Q}}^{[n,l^{*}]}\geq\eta_{\mathrm{Q}}^{*}\).
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 2 Requirements for the Externally-Injected-Electron Bunch
### Requirements on the Electron Beam Energy  
Assuming a constant plasma density, the total energy gain in a single laser-driven plasma stage can be expressed as \(\Delta E=\epsilon E_{z}L_{acc}\), where \(E_{z}\) is the longitudinal accelerating voltage and \(L_{acc}\) is the acceleration length. The latter quantity is limited by several phenomena such as bunch dephasing, laser pump depletion and diffraction.  
In this paragraph we will focus only on the dephasing issue. The e-bunch and the wake-field generated by the laser travel in general with different velocities. The velocity of the e-bunch \(v_{e}\) depends on the energy of the electrons and approaches the velocity of light, \(c\), while they are accelerated by the field. The velocity of the laser wake-field is instead constant and similar to the group velocity of the driving laser that can be expressed as \(v_{g}=c\cdot\sqrt{1-\omega_{p}^{2}/\omega_{0}^{2}}<c\) where \(\omega_{p}\) is the electron plasma frequency and \(\omega_{0}\) is the laser frequency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plasma Density [cm\({}^{-3}\)]** & **Wavelength [\(\mu\)m]** & **Period [s]** & **Field Gradient (Lower Boundary) [GV/m]** \\ \hline \(10^{18}\) & 33 & \(110\,\times\,10^{-15}\) & 96 \\ \(10^{17}\) & 104 & \(348\,\times\,10^{-15}\) & 30 \\ \(10^{16}\) & 330 & \(1.1\,\times\,10^{-12}\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of electric field parameters for different plasma densities estimated using the equations above.  
For the external injection experiments those two velocities can be matched at the entrance of the plasma chamber [20], e.g., a plasma wake-field obtained inside a plasma with density \(n\simeq\) few \(10^{16}\,\mathrm{cm}^{-3}\) will be perfectly synchronized with an e-beam with about 100 MeV energy. It is important to specify that this is only a soft requirement for the design of the accelerator since, when the electrons are further accelerated in the plasma, their velocity will increase (approaching \(c\)) and therefore slippage will be anyway introduced. More specifically the dephasing length \(L_{d}\) is defined as the length that the electrons must travel before they slip by one half of a period with respect to the plasma wave. For highly relativistic electrons and with the assumption \(\frac{\omega_{0}}{\omega_{p}}>>1\), it can be roughly estimated as \(L_{d}\simeq\frac{\omega_{0}^{2}}{\omega_{p}^{2}}/\lambda_{p}\). There are methods for reducing the phase-slippage in the acceleration channel, such as the transverse modulation of the plasma density [21], the so called _laser guiding_ technique.  
Apart from considerations related to the synchronization between the electrons and the laser, injecting in the plasma a beam of hundreds of MeV energy has the advantage (compared to injection at lower energies) of allowing a better control of the space charge force, which is damped at high energy and affects the maximum peak current that it is possible to inject in the plasma bubble.
## 2 Materials and Methods
### Processing of WorldView-3 Images  
For each site, two cloud-free scenes taken by the WorldView-3 satellite were utilized in this research. The bi-seasonal WorldView-3 scenes acquired in the summer and autumn seasons for the Hakkoda (20 September 2019 and 10 June 2016), Zao (26 October 2017 and 8 September 2017), and Shiranuka (16 October 2020 and 1 June 2021) sites were utilized in the research. The summer and autumn seasons were chosen to capture phenological variations among the vegetation types because leaves are photosynthetically active during summer and deciduous shrubs and trees change color during autumn in the study sites located in cool temperate zones. Due to lack of cloud-free scenes available in the same year, the images were acquired from different years in two sites. However, the study sites were usually devoid of human disturbances and landscape changes and thus the acquired images were still appropriate.  
WorldView-3 is a very high-resolution commercial imaging satellite which acquires 11-bit data in nine spectral bands (panchromatic, coastal, blue, green, yellow, red, red edge, near infrared 1, and near infrared 2), and additional 14-bit data in eight shortwave infrared bands. The nominal ground sample distances of the acquired images were 0.5 m for panchromatic and 2.0 m for multi-spectral images. Ortho-rectification of the WorldView-3 images was done using 30 m digital elevation model data [56] to remove geometric distortions [57; 58] and topographic correction was done using a modified sun-canopy-sensor model [59; 60]. The band-wise radiometric calibration factor and effective band width data were read from the metadata of the given WorldView-3 products and top-of-atmosphere radiance was calculated from the pixel-wise digital numbers. Then, the top-of-atmosphere reflectance was calculated accounting for the earth-sun distance, band-averaged solar spectral irradiance, and solar zenith angle [61].