esa-sceva/satcom-chunk-collection huggingface dataset:
# Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
## 3 Educational Data Analytics
### Gathering and Processing Activity Logs for Students in the Cloud  
Figure 2 describes the architecture of the platform created in order to automatically process the activity logs that trace the actions performed by the students in AWS while carrying out the lab activities.  
The platform is defined as a completely serverless application, where the cloud provider manages the capacity allocation for the underlying services employed [32]. The activity logs of the students performing the lab sessions are automatically generated by AWS CloudTrail and delivered up to 15 min after the actions are carried out. These are compressed JSON files that include records about each time an API (Application Programming Interface) is invoked, that is, every time a user performs an action with an AWS service that is supported by CloudTrail. Each record contains valuable information that includes:  
* _WHO_. The AWS IAM (identity and access management) user--in our case, the student--that made the action.
* _WHAT_. The specific action performed, that is, the API call invoked for each AWS service.
* _WHEN_. The timestamp that indicates when the action was carried out.
* _WHERE_. The client tool used to perform the action, typically a web browser or a command-line tool.  
These logs are automatically uploaded as files to an Amazon S3 bucket, which triggers the execution of a Lambda function (_Store_) responsible for storing the events (actions carried out by the student) in a NoSQL table in AWS DynamoDB. In order to minimize the amount of information stored in the database, actions that do not modify the infrastructure are discarded. Only those actions that involve creating or modifying a resource in AWS are logged, as required in the hands-on activities proposed. A REST API to query this information is created via the API Gateway, which, upon a request, triggers a Lambda function (_Query_) responsible for querying the DynamoDB table and formatting the results in JSON.  
The application has a web-based graphical front end coded as a Yue.js application that is compiled into a static website (only HTML, CSS, and JavaScript), which is hosted in an Amazon S3 bucket and exposed via a custom DNS (domain name service) entry through Amazon Route 53. The web application, running in the user's browser, relies on Amazon Cognito to perform user authentication  
Figure 2: Distributed platform to collect, store, process, and visualize educational analytics for cloud training in Amazon Web Services (AWS).  
via the user's credentials. This is also employed to obtain valid access tokens to query the REST API to provide programmatic access to the collected data.  
This application architecture has been fully released as an open-source platform to gather activity logs, which can be exploited as a learning dashboard aimed at self-regulation of students, as demonstrated in the work by [PERSON] et al. [31]. This work analyzes the insights obtained from the course-related data collected through both CloudTrail-Tracker and the LMS.
## 4 Discussion  
In this paper, a low-power multimode cavity microwave oven is used in the microwave heating test, and a reduced-scale TBM cutter is used in the cutter cutting rock breaking test. In practical engineering applications, because of the limitation of the microwave cavity, the cavity equipment cannot be directly applied to the engineering site. In order to realize the engineering application of microwave-assisted TBM cutter rock breaking, the open-type microwave cracking device of hard rock should be adopted.  
As for energy consumption, some scholars have analyzed the energy consumption of indoor microwave-assisted rock breaking [30]. For the actual field application, the energy consumption is complicated, and it should be analyzed according to the actual situation of the field. Regarding the construction process, two construction methods can be proposed based on actual geological conditions. One is to apply microwave radiation and cutter action concurrently if the rock mass is readily fractured. The other is to irradiate the rock mass to prefracture it and then use the TBM cutter head to cut the rock. Apart from these, technical issues such as the power and water supply, dustproofing, shockproofing, and equipment stability also need to be considered. If necessary, the structural fabrication of the TBM cutter head also requires modification.
## 2 Four Channels Generator
### Marx Modulator Pulse Burst Sequence Programming  
The control system includes three main components, as shown in Figure 1:  
* The computer user interface, which allows the user to define the timing and pulse amplitude sequence for the four channels, using a simple line command, using Pause in ns for the time before the pulse, Pulse in ns for the pulse time, and Mask for the amplitude control that defines the stages to trigger in each Marx;
* The main control board that receives the information from the computer user interface and sets the pulse timing sequence and amplitude for each Marx modulator, sending this information by UART communication to each Marx. Additionally, once the trimming sequence and Mask is programmed in each MM, the trigger signals are sent to each modulator. The minimum step possible is 100 ns and the maximum pulse width, or burst time, is 100 \(\mu\)s;
* The drive board \(k\) in each Marx that receives the timing sequence for all the pulse time of each Marx and the corresponding information when to turn-on or turn-off each stage during pulse trigger signal.  
The main control board generates a time sequence with the time periods when there are pulses, which matches the trigger signal generation, for each Marx modulator. In parallel, the main control board sets for every pulse time period the Mask for each Marx.  
The Mask is calculated by adding the value corresponding to the power of two, with index from 1 to 8, equal to the number of stages, of the stages that are on. For example, if all the stages are on, the Mask value is 510, if the first four stages are on, the Mask value is 30, if the first stage is on only, the Mask value is 2, and so on and so forth.  
In order to generate a burst pulse sequence in each Marx, the user must define a command line as shown in Table 1, as an example, where the total pulse burst time, \(t_{\text{B}}\) (equal to the sum of pause and pulse times), is identical in all the four channels. If, for one channel, the pulse sequence is not defined, or defined for a lower time, the output voltage is set to zero to the end of \(t_{\text{B}}\).  
Figure 5 shows the output result, for one channel, of the pulse sequence defined in Table 1, where a train number of 3, for pulse sequence repetition, and a frequency \(f\) between each train number was defined, also, as shown in Figure 6.  
For the example shown in Figure 5, the pulse time sequence defined was t\({}_{\text{B}}\) = 2000 ns, equal to the sum of all the Pause and Pulse times of the burst sequence, shown in Table 1. The time sequence is sent to the Marx generator with the Mask definition, which defines for each pulse time, which stages are on and off.  
Although the minimum step is 100 ns, preliminary results were taken for a minimum pulse time of 200 ns, 2.5 MHz maximum frequency, for safety switching margin at Marx modulators. The user can define a time for the burst operation, t\({}_{\text{B}}\), within certain limits, which is the same for each channel, from 0 to 4.
## 3 Proposed Method  
Figure 1 illustrates the architecture of our proposed model. In the image processing layer, the DA-S technique is applied to augment a dataset by adjusting the saturation of each image included in the original DB-T. Specifically, two augmented images are generated by applying saturation weights 0 and 2 to the original images. These positions in the diagram are visually simplified to emphasize the generation of two distinct augmented images representing different saturation levels, rather than strictly following a chronological sequence. The augmented images are normalized to a range of from 0 to 1 to represent saturation levels as percentages (from 0% to 100%). The resulting database (DB) consists of the original dataset, Original DB-T (11,192 images), and the augmented dataset, DA-S DB (\(w~{}=~{}0,w~{}=~{}2\), totaling 22,384 images). Together, the complete dataset comprises a total of 33,576 images, which are used as input data to train the proposed model. The ResNet18-based model is trained in the classification layer, categorizing emotions into four categories (happiness, anger, sadness, and neutrality) in the output layer. Through this architecture, our model applying the DA-S technique exhibits improved performance in speech emotion classification.
## 3 Proposed MTU-Net-Based Speech Enhancement
### Mask-Based VAD Method  
According to previous studies on the relationship between the IBM and the performance of speech recognition [31; 32], the binary mask was found to be highly related to the human auditory system, as show in [31]. Human labelers for VAD annotation tasks listen to each noisy speech signal directly and then mark the voice-activated intervals for each signal, a method that is similarly used to determine the binary mask for the noisy input speech signal.  
Figure 3: The procedure of obtaining three different estimates of clean speech from the noisy input speech and the outputs of the proposed MTU-Net.  
The proposed mask-based VAD is described in this subsection based on the relationship between the voice-activated intervals and the binary masks. Figure 4 provides two different block diagrams showing how to combine the SE and VAD. Specifically, Figure 4a shows a combined approach in which the SE module is used as a front-end for VAD. Thus, the VAD method in this category requires large amounts of manually labeled data to train a VAD model. In contrast, the proposed approach shown in Figure 4b does not require any manually labeled data for VAD. Instead, the voice-activated intervals are estimated by using the IRM that has already been estimated from the MTU-Net-based SE, as described in Section 3.3.  
To detect voice activity, the pre-estimated IRM in the inference stage, as described in Section 3.3, is utilized to estimate the speech presence probability at the \(i\)-th frame, \(\gamma_{i}\), which is defined as  
\[\gamma_{i}=\frac{1}{K}\sum_{k=0}^{K-1}H_{i}^{IRM}(k). \tag{8}\]  
Finally, the decision about whether the \(i\)-th frame is voiced or not is done using \(\gamma_{i}\), as follows:  
\[VAD(i)=\left\{\begin{array}{ll}1&\text{if }\gamma_{i}>\theta_{VAD}\\ 0&\text{otherwise}\end{array}\right. \tag{9}\]  
where \(\theta_{VAD}\) is a threshold for VAD. This threshold can be set to minimize the equal error rate (EER) in the training dataset as done in [33].
## 4 The New Hybrid Optimization Algorithm
### The LM-D Algorithm
#### 4.3.3 The Interactive Part of Algorithm  
To integrate these algorithms, it is essential to implement suitable adjustments to the hybrid algorithm, incorporating the exchange of data between the L-M algorithm and the DIRECT algorithm. The L-M algorithm communicates data regarding the local optimum to the DIRECT algorithm and confirms if the algorithm has reached the maximum iteration. Concurrently, when the DIRECT algorithm acquires parameters that fulfill the criteria, it transfers these new parameters to the L-M algorithm via the interaction component. The comprehensive flow diagram of the hybrid algorithm is depicted in Figure 7.  
Moreover, it is crucial to emphasize that even with the hybrid algorithm, a definitive guarantee of achieving calibration parameters meeting the global convergence threshold cannot be ensured. If the hybrid algorithm ultimately falls short of meeting the criteria for the global optimum, it indicates potential challenges in the calibration process. These challenges might stem from the introduction of inaccurate data into the data set or the presence of excessive noise, leading to iterated results that fail to meet the required standards. Insuch instances, the calibration results are unreliable, necessitating either a recalibration or the elimination of erroneous data, noise reduction, or other corrective measures to satisfy the calibration accuracy requirements before progressing with the solution.
## 5 Research Methods
### Experiment Design  
In order to further verify the effectiveness of the joint estimator and compare the progressiveness of the CKF/GHCKF/AGHCKF algorithm, a modified vehicle driven by a rear in-wheel motor was used for experiments. As shown in Figure 3, the steering angle sensor collects the data of the steering wheel angle, the GPS/RTK module collects the data of the vehicle's longitudinal velocity and heading angle, and the INS module collects the data of the lateral acceleration, lateral acceleration, yaw rate, and yaw angle. The Hall sensor in the hub motor can measure the wheel speed in real time. The lateral velocity cannot be directly measured. The collected heading angle, yaw angle, and longitudinal velocity are calculated to indirectly gain the lateral velocity. The upper computer software collects and saves the measurement data and inputs them into the controller. The built-in AGHCKF/GHCKF algorithm of the controller estimates the vehicle state and road adhesion coefficient in real time.  
Considering the safety issues during the actual vehicle experimental process, in this paper, the double-lane change experiment was only conducted on high-adhesion coefficient roads, while the slalom experiment was conducted on a joint road at low speed.  
As shown in Figure 4a, the test vehicle was driven on a new asphalt road surface at high speed (80 km/h) in the double-lane change experiment, and the driving data were collected via sensors to estimate the vehicle state and road adhesion coefficient. Referring to the relevant literature, the adhesion coefficient of asphalt pavement under high-speed driving conditions is approximately 0.6-0.7. As shown in Figure 4b, the test vehicle was driven on an asphalt-gravel joint road surface at low speed (40 km/h) in the slalom experiment,and the driving data were also collected via sensors to estimate the vehicle state and road adhesion coefficient. Referring to the relevant literature, the adhesion coefficient of the asphalt pavement under low-speed driving conditions is about 0.6-0.8, and the adhesion coefficient of the gravel road is about 0.4-0.7.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 2 Theoretical Performance
### Crlb
#### 2.2.2 CRLB in Geodetic Coordinate System
\[\mathbf{A}_{2}=\mathbf{J}_{\alpha}^{\text{T}}\mathbf{C}_{t}^{-1}\mathbf{J}_{ \alpha}=\sum_{k=1}^{K}\mathbf{J}_{\alpha,k}^{\text{T}}\mathbf{C}_{t,k}^{-1} \mathbf{J}_{\alpha,k} \tag{32}\]  
\[\mathbf{U}_{2}=\mathbf{J}_{\alpha}^{\text{T}}\mathbf{C}_{t}^{-1}\mathbf{J}_{ t,nav}=diag\Big{\{}\mathbf{J}_{\alpha,1}^{\text{T}}\mathbf{C}_{t,1}^{-1} \mathbf{J}_{t,nav,1},\ldots,\mathbf{J}_{\alpha,K}^{\text{T}}\mathbf{C}_{t,k}^{ -1}\mathbf{J}_{t,nav,K}\Big{\}} \tag{33}\]  
\[\mathbf{V}_{2}=\mathbf{J}_{t,nav}^{\text{T}}\mathbf{C}_{t}^{-1}\mathbf{J}_{ \alpha}=diag\Big{\{}\mathbf{J}_{t,nav,1}^{\text{T}}\mathbf{C}_{t,1}^{-1} \mathbf{J}_{\alpha,1},\ldots,\mathbf{J}_{t,nav,K}^{\text{T}}\mathbf{C}_{t,k}^ {-1}\mathbf{J}_{\alpha,K}\Big{\}} \tag{34}\]  
The definition of \(\mathbf{J}_{t,nav,k}\) is given in (20), and \(\mathbf{J}_{\alpha,k}\) is defined as  
\[\mathbf{J}_{\alpha,k}=\left[\frac{\partial t_{1,0,k}}{\partial\theta_{\alpha} };\frac{\partial t_{2,0,k}}{\partial\theta_{\alpha}};\ldots;\frac{\partial t _{M-1,0,k}}{\partial\theta_{\alpha}}\right] \tag{35}\]  
\[\frac{\partial t_{j,0,k}}{\partial\theta_{\alpha}}=\left[\frac{\partial t_{j,0, k}}{\partial\mathbf{u}_{k}}\times\frac{\partial\mathbf{u}_{k}}{\partial \alpha_{k}}\times\frac{\partial\alpha_{\alpha}}{\partial\alpha_{\alpha}},\frac{ \partial t_{j,0,k}}{\partial\mathbf{u}_{k}}\times\frac{\partial\mathbf{u}_{k}}{ \partial\alpha_{k}}\times\frac{\partial\mathbf{\alpha}_{k}}{\partial\alpha_{o}}\right] \tag{36}\]  
\[\frac{\partial t_{j,0,k}}{\partial\mathbf{u}_{k}}=\frac{\mathbf{u}_{k}^{\text{ T}}-\mathbf{s}_{j,k}^{\text{T}}}{c\Big{\|}\mathbf{u}_{k}-\mathbf{s}_{j,k} \Big{\|}}-\frac{\mathbf{u}_{k}^{\text{T}}-\mathbf{s}_{0,k}^{\text{T}}}{c\Big{\|} \mathbf{u}_{k}-\mathbf{s}_{0,k}\Big{\|}} \tag{37}\]  
\[\frac{\partial\mathbf{u}_{k}}{\partial\alpha_{k}}=\mathbf{H}(\boldsymbol{ \alpha}_{k})= \begin{bmatrix}-(N+h)\cos B_{k}\sin L_{k}&\frac{\partial x_{k}}{ \partial B_{k}}\\ -(N+h)\cos B_{k}\cos L_{k}&\frac{\partial y_{k}}{\partial B_{k}}\\ 0&\frac{\partial x_{k}}{\partial B_{k}}\end{bmatrix}  \tag{38}\]  
\[\frac{\partial x_{k}}{\partial B_{k}}=-(N+h)\sin B_{k}\cos L_{k}+\frac{ae^{2} \sin B_{k}\cos^{2}B_{k}\cos L_{k}}{\left(\sqrt{1-e^{2}\sin^{2}B_{k}}\right)^{3}} \tag{39}\]  
\[\frac{\partial y_{k}}{\partial B_{k}}=-(N+h)\sin B_{k}\sin L_{k}+\frac{ae^{2} \sin B_{k}\cos^{2}B_{k}\sin L_{k}}{\left(\sqrt{1-e^{2}\sin^{2}B_{k}}\right)^{3}} \tag{40}\]  
\[\frac{\partial z_{k}}{\partial B_{k}}=-\Big{(}N\Big{(}1-e^{2}\Big{)}+h\Big{)} \cos B_{k}+\frac{ae^{2}\sin^{2}B_{k}\cos B_{k}}{\left(\sqrt{1-e^{2}\sin^{2}B_{ k}}\right)^{3}} \tag{41}\]  
\[\frac{\partial\alpha_{k}}{\partial\alpha_{o}}=\mathbf{I}_{2\times 2},\frac{ \partial\alpha_{k}}{\partial\alpha_{o}}=(k-1)T\times\mathbf{I}_{2\times 2} \tag{42}\]
_Remote Sensing_**2010**, \(2\), 1702-1721; doi:10.3390/rs2071702  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
**Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet**  
**[PERSON] 1 and [PERSON] 2*  
1 Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research UFZ, Permoserstr. 15, 04115 Leipzig, Germany; E-Mail: [EMAIL_ADDRESS]  
2 Department of Geography, Ludwig-Maximilians-Universitat (LMU) Munchen, Luisenstr. 37, 80333 Munich, Germany  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 14 May 2010; in revised form: 18 June 2010 / Accepted: 28 June 2010 / Published: 6 July 2010_  
**Abstract:** Leaf area index (LAI) and water content (WC) in the root zone are two major hydro-meteorological parameters that exhibit a dominant control on water, energy and carbon fluxes, and are therefore important for any regional eco-hydrological or climatological study. To investigate the potential for retrieving these parameter from hyperspectral remote sensing, we have investigated plant spectral reflectance (400-2,500 nm, ASD FieldSpec3) for two major agricultural crops (sugar beet and spring barley) in the mid-latitudes, treated under different water and nitrogen (N) conditions in a greenhouse experiment over the growing period of 2008. Along with the spectral response, we have measured soil water content and LAI for 15 intensive measurement campaigns spread over the growing season and could demonstrate a significant response of plant reflectance characteristics to variations in water content and nutrient conditions. Linear and non-linear dimensionality analysis suggests that the full band reflectance information is well represented by the set of 28 vegetation spectral indices (SI) and most of the variance is explained by three to a maximum of eight variables. Investigation of linear dependencies between LAI and soil WC and pre-selected SI's indicate that: (1) linear regression using single SI is not sufficient to describe plant/soil variables over the range of experimental conditions, however, some improvement can be seen knowing crop species beforehand; (2) the improvement is superior when applying multiple linear regression using three explanatory SI's approach. In addition to linear investigations, we applied the non-linear CART (Classification and Regression Trees) technique, which finally did not show the potential for any improvement in the retrieval process.  
keywords: hyperspectral remote sensing; spectral index; water stress; soil moisture; LAI; CART +
Footnote †: journal: Journal of the American Statistical Association
# Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods
## 1 Introduction  
The frequencies from 0.1 to 30 THz form the Terahertz domain of the electromagnetic (EM) band region and act as a gap of convergence between the microwave and the infrared band in the EM spectrum, as shown in Figure 1.  
Terahertz is a non-ionizing frequency which has high penetration in non-dielectric materials. Several non-crystalline materials are transparent for THz rays, for example, cloths, plastic, material, paper, etc. Its impact on organic tissues implies that it cannot damage DNA, making it safe for human applications such as medical imaging. Terahertz rays suffer from the reflection and absorption of metal surfaces and polar liquids such as water, respectively [1]. Recently, terahertz technology and deep learning have been a focus of research. The applied areas in the domain of object detection include the detection of agricultural products [2, 3, 4, 5], breast cancer and other medical conditions [6, 7, 8], and hidden object detection [9, 10, 11, 12]. Nevertheless, terahertz suffers from low image resolution  
Figure 1: EM spectrum band.  
due to blurriness and noisy dark-spotted images, which stems from low-energy power sources [13; 14; 15] consequently affecting the detection accuracy and rate. Therefore, it stands to reason that any attempt to increase the detection rate and accuracy must first address the challenge of low resolution while at the same time revamping the deep learning model.
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 6 Experimental Results and Discussion
### Clutter Model Fitting and Performance Evaluation
#### 6.1.2 False Alarm Rate Error
However, high sea state does not prevent the vessel detection if it is properly illuminated by the radar (i.e., if the illumination is not prevented by high sea waves) and, if the line-of-sight velocity component is large enough so that the corresponding echo signal is shifted to the exo-clutter region (cf. (1) and Figure 1a).
## 1 Introduction
Additionally, most of the related studies used various influencing factors and an assumed weight for the corresponding factor to map the potential groundwater recharge areas or high groundwater yield zones. Related studies rarely apply groundwater level data to derive the weights of influencing factors. Whether the weighting coefficient of each factor altered with seasonal and annual groundwater level changes has not been addressed. Therefore, this study evaluates the groundwater resource potential in Taiwan's mountainous areas based on the seasonal and annual changes in groundwater levels. In this study, the groundwater level data of 18 well stations monitored from November 2011 to October 2019 in the Taiwan mountainous region of the Choushui River Basin and Pei-Kang River Basin were used to evaluate and discuss variations in the groundwater level during the wet and dry seasons in different years. Possible influencing factors that affect groundwater level fluctuation potential were proposed and investigated. Furthermore, a technique for mapping the spatial groundwater level fluctuation potential was developed. Meanwhile, the weightage of each influential factor to control the degree of groundwater level fluctuation was determined. Weighting coefficients of influencing factorsin the wet/dry seasons for different hydrological years were also discussed. Finally, the groundwater level monitoring data were utilized to verify the estimated groundwater level fluctuation potential.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 2 Theoretical Background
### Multiple Scattering Theory (MST)
We should note that Equation (8) relates only to the case of far fields, i.e., the case when \(\mathbf{\hat{k}}_{\text{out}}\) is the same for all canopy voxels. This limitation is removed by introducing the new combined PO/MST model in Section 2.3.