esa-sceva/satcom-chunk-collection huggingface dataset:
# Qualitative Assessment of Hazardous Gas Emissions from Sewage Sludge-Derived Biochar
## 1 Introduction
### Volatile Organic Compound Emissions from Biochar  
Biochar production from sewage sludge carries the risk of emitting harmful volatile organic compounds (VOCs) [13]. These important occupational safety considerations are rarely addressed in the literature. Large-scale biochar production involves sewage sludge handling, storage, transportation, and application involving workers who are at risk of inhaling both gaseous VOCs and VOCs sorbed to biochar, which, in turn, can be hazardous as airborne particulate matter [14; 15]. Ventilation of the biochar storage area can affect the quantity and composition of emitted gases [13].  
A VOC is defined as any organic compound and creosote fraction that has a vapor pressure of 0.01 kPa or more at 293.15 K or has a corresponding volatility under its specific conditions of use [14]. The common VOCs are aromatic hydrocarbons, halogenated hydrocarbons, alkanes and cycloalkanes, alcohols, aldehydes, and ketones or terpen [15]. Short-term exposure to VOCs can irritate the mucous membranes of the eyes and upper and lower respiratory tracts and cause headaches and dizziness [16]. Long-term exposure increases the risk of respiratory and cardiovascular diseases, congenital fetal defects, and neurotoxic disorders. Long-term exposure significantly increases the risk of cancer (mainly lung and leukemia) and damage to the nervous system, kidneys, and liver.  
Figure 1: Sewage sludge management options [8; 9; 10].  
To date, the safety of biochar production from sewage sludge is poorly understood. However, some common-sense approaches should be tested, leading to safer practices in biochar handling and storage and improving end-user conditions. This research investigates the mechanistic effects of two such approaches: (1) headspace-to-biochar volume ratio in storage and (2) ventilation of stored biochar headspace on hazardous gas emissions.
## 2 Mathematical Background
 \tag{28}\]  
Figure 1: Data-flow diagram of the RDFT algorithm for \(N=3\). Since \(\phi_{4}=2\pi/4=\pi/2\) then \(\cos\phi_{4}=0\), \(\cos 2\phi_{4}=-1\), \(\cos 3\phi_{4}=0\), \(\cos 4\phi_{4}=1\), \(\cos 6\phi_{4}=-1\) and \(\sin 3\phi_{4}=-1\), \(\sin 6\phi_{4}=0\), \(\sin 9\phi_{4}=1\), so the \(\mathbf{R}_{4}\) matrix will take the form  
\[\mathbf{R}_{4}= \begin{bmatrix}1&1&1&1\\ 1&0&-1&0\\ 1&-1&1&-1\\ 0&1&0&-1\end{bmatrix}  \tag{29}\]  
When we calculate the product of this matrix by the real input vector \(\mathbf{x}_{4}\) we obtain  
\[ \begin{bmatrix}y_{0}\\ y_{1}\\ y_{2}\\ y_{3}\end{bmatrix} = \begin{bmatrix}(x_{0}+x_{2})+(x_{1}+x_{3})\\ x_{0}-x_{2}\\ (x_{0}+x_{2})-(x_{1}+x_{3})\\ x_{1}-x_{3}\end{bmatrix}  \tag{30}\]  
Figure 2 shows a data-flow diagram corresponding to this calculation. When the vector \(\mathbf{z}_{4}=\mathbf{a}_{4}+i\mathbf{b}_{4}\) of complex coefficients of DFT for the input vector \(\mathbf{x}_{4}\) is needed, it can be easily obtained from the output vector \(\mathbf{y}_{4}\) of RDFT, according to (15)  
\[\mathbf{a}_{4}= \begin{bmatrix}a_{0}\\ a_{1}\\ a_{2}\\ a_{3}\end{bmatrix} = \begin{bmatrix}y_{0}\\ y_{1}\\ y_{2}\\ y_{1}\end{bmatrix} \quad\mathbf{b}_{4}= \begin{bmatrix}b_{0}\\ b_{1}\\ b_{2}\\ b_{3}\end{bmatrix} = \begin{bmatrix}0\\ -y_{3}\\ y_{3}\end{bmatrix}  \tag{31}\]  
The algorithm of the RDFT for \(N=4\), presented in Figure 2, can be described by the following matrix-vector procedure, in which the matrix \(\mathbf{R}_{4}\) has been factorized:  
\[\mathbf{y}_{4}=\mathbf{C}_{4}\mathbf{D}_{4}\tilde{\mathbf{A}}_{4}\mathbf{A}_ {4}\mathbf{x}_{4} \tag{32}\]  
where  
\[\tilde{\mathbf{A}}_{4}= \begin{bmatrix}1&0&1&0\\ 0&1&0&1\\ 1&0&-1&0\\ 0&1&0&-1\end{bmatrix} \quad\tilde{\mathbf{A}}_{4}= \begin{bmatrix}1&1&0&0\\ 1&-1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \quad\mathbf{D}_{4}=\mathbf{I}_{4}\quad\mathbf{C}_{4}= \begin{bmatrix}1&0&0&0\\ 0&0&1&0\\ 0&1&0&0\\ 0&0&0&1\end{bmatrix}  \tag{33}\]  
and \(\mathbf{I}_{k}\) denotes the identity matrix of size \(k\). According to this algorithm, we need only 6 additions of real numbers to calculate the output vector \(\mathbf{y}_{4}\). It should be noted that the matrix \(\mathbf{D}_{4}\) is responsible for multiplications and that, in this case, this matrix is equal to identity matrix, so we need not any multiplications of real numbers.
## 5 Empirical Validation: A Case Study
### System Under Test and Methodology  
The System Under Test (SUT) was a large-scale digital identity management platform developed for a municipal government, hosting sensitive data for approximately one million users. The platform is a critical piece of public infrastructure, hosted as a tenant on a major public cloud provider. Its multi-tier architecture consists of numerous servers for client-facing applications, transactions, and backend gateways, all running on CentOS 7.7, supported by MySQL and Redis databases, and serving mobile applications.  
The assessment was operationalized using a hybrid methodology that combined non-technical document reviews (21 items) with technical testing and evaluation (18 items) to gather quantitative data for the P-R-A-L indicators. A suite of mature security tools was employed to measure specific indicators. For instance, capabilities in the Prevention (P) dimension, such as threat data monitoring, were assessed using tools like Retina and Spiderfoot to simulate malicious traffic and evaluate asset discovery. To quantify Destruction Resistance (R), tools like BloodHound and Nemesis were used to map attack paths and evaluate the system's ability to discover and block attacks. Finally, indicators for Adaptive Recovery (A) were tested by probing for damage isolation and resource recovery mechanisms, while Evolutionary Learning (L) was evaluated by simulating attack scenarios to test the system's intelligent control and decision-making capabilities.
# A Virtual Experiment for Learning the Principle of Daniell Cell Based on Augmented Reality
###### Abstract  
Chemistry is a science emphasizing both theory and experimentation. After learning the theoretical knowledge, experimental operation can help students understand chemical concepts and transform them into practical knowledge. Considering the safety issue and the lack of teaching time and experimental equipment, some teachers often choose to demonstrate an experiment instead of letting students conduct it by themselves. This may affect their learning motivation as well as the construction of chemical concepts and hands-on skills. This study combined the augmented reality (AR) technology with the operating principle of the Daniell cell to develop a virtual experiment for the application in high school chemistry courses. Students can conduct the virtual experiment using mobile devices by selecting the required equipment and materials from a deck of cards to set up the experimental environment. In the virtual experiment, students can use the galvanometer to measure the current after mounting the salt bridge on the beakers containing zinc sulfate and copper sulfate solutions. They can also see the change of molecular structures and movement of electrons and ions during the redox reactions to understand the important concepts and knowledge. An empirical research has been performed, and the analytical results show that both the virtual experiment and the real experiment could improve students' learning achievement, but the former was more effective for the low-achievement students because they could explore autonomously to enhance cognition by observing the submicroscopic view of the redox reactions. The post-test results show that the average score of the low-achievement students in the experimental group (41.60) was significantly higher than that of the control group (27.67). Questionnaire results reveal that most students were satisfied with the learning contents, user interface, learning motivation, system reality, and practicality of the virtual experiment, and the average satisfaction score was 3.98 out of 5. The application of virtual experiments is not limited by time or space. Students only need to download the application (APP) software and print out the AR cards to practice at home, so it is suitable for large-scale promotion in rural areas.  
virtual experiment; augmented reality; Johnstone's triangle; inquiry-based learning; scaffolds; empirical research; achievement test; questionnaire survey +
Footnote †: journal: Applied Sciences
# Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse
## 4 Data Analysis and Discussions
### Machine Learning Clustering Results
When these events cluster along a specific plane or region, it suggests that the stress or pressure within that area has reached a critical point, potentially leading to the development of larger cracks or fractures. Therefore, Clusters 5 and 10 are considered more dangerous than Clusters 1 and 2. The number of microseisms per unit volume also reflects the stability of the region. During the calculation process, it was observed that the logarithms of event density are mostly concentrated around \(-2\). A low density of MS events can result from either dispersed distribution within the cluster or a large recognition range of the cluster. The clustering effect can be evaluated using the silhouette coefficient (sc) to assess the quality of clustering, but it is primarily applicable when cracks are fully developed. In the intermediate process where ideal clustering conditions may not be met, the sc value tends to be low. Hence, determining the number of clusters should be based on the actual onsite situation.
## 4 Experiment Results and Discussion  
### Datasets for Evaluation: VisDrone2019 and OTB-100  
There are various opensource datasets available for measuring and evaluating the suggested method, as well as comparing with other state-of-the-art models. Applying the identical image or video sets technique to the algorithm reveals the advantages and weaknesses of the recommended method while evaluating it among other state-of-the-art models in different criteria of the learning field. VisDrone2019 VisDrone2019 VisDrone2019 and OTB-100 [55] datasets are opensource dataset benchmarks for evaluating measures and competing in different challenges for object detection/tracking techniques.  
The VisDrone2019 VisDrone2019 dataset was collected by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. The dataset benchmark consists of 288 video clips formed by 261,908 frames and 10,209 static images, captured by various drone-mounted cameras and covering a wide range of aspects including location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and rural), objects (pedestrian, vehicles, bicycles, etc.), and density (sparse and crowded scenes). Notably, the dataset was collected using various drone platforms (i.e., drones with different models), in different scenarios, and under various weather and lighting conditions. These frames are manually annotated with over 2.6 million bounding boxes of targets of frequent interests, such as pedestrians, cars, bicycles, and tricycles. Some important attributes including scene visibility, object class, and occlusion are also provided for better data utilization.  
The full OTB-100 [55] benchmark contains 100 sequences from recent literatures that address an extensive evaluation of the state-of-the-art online object tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. The authors initially constructed a large dataset with ground-truth object positions and extents for tracking, and introduced the sequence attributes for the performance analysis. Additionally, they integrated most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Moreover, extensive evaluation was conducted by the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, an effective approach for robust tracking was identified and provided along with potential future research directions in this field.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 3 Plant State Space Models and Their Properties  
As we mainly employ state space methods, we start with a continuous-time state space model of the plant \(\Sigma_{G}\), then we develop its time discretization.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 8 Conclusions  
This article presents a comprehensive review of IoMT networks with a specific remote patient monitoring case study. After studying a number of research works in this area, this paper describes the importance of resilience by identifying the key requirements of resilience: monitoring, control, recovery, and diagnosis.  
Section 5 analyzes the resilience in the existing work. This study found how these key requirements can be potentially fulfilled by the integration of SDN, ML, and MSA technologies. This paper primarily demonstrates a case study in Section 6 that investigates the different vulnerabilities in the existing scenario of the IoMT networks during the remote patient monitoring of a heart patient and poses the problem of a single point of failure and proposes a solution for how to mitigate the problem and provide resilience in the IoMT networks.  
This paper proposes an architecture in the case study section that elaborates on how ML, SDN, and MSA, as a unified technology, can fulfill these requirements through one paradigm and bring reliability, scalability, and quality of service together.  
However, some practical challenges might occur during the implementation of the proposed architecture. The first challenge can be interoperability issues between these three technologies, ML, SDN, and MSA, during the execution of the proposed model. Another challenge might be to deal with the overall complexity of the proposed paradigm, as each of these technologies has quite different mechanisms. The computational cost will also be higher while using these technologies in a single model. Moreover, this paper also illustrates the potential challenges in the evaluation of performance along with development and testing. Not withstanding these challenges, the priority will therefore be to determine the best possible trade-off between the overall cost and benefit of the final proposed model.  
In the future, a strategy has been planned to create a physical testbed to address all these challenges and verify the efficacy of the proposed methodology. We are planning to conduct the experimentation in real-world conditions so that we can systematically evaluate the effectiveness of our proposed architecture. It will consist of a set of interconnected networks to test the resilience in case of route failure. This strategy will conduct anomaly detection through ML and reflect the changes on traffic routes through SDN in real-time.  
V.T.: Conceptualization, Literature Review, Analysis of Existing Works, Methodology Design, and Writing of the original draft. S.S.: Supervision, and Review on writing and editing. M.D.: Supervision, Review on writing and editing, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6222.  
The authors declare no conflict of interest.
# Practical Access to Dynamic Programming on Tree Decompositions +
## 2 Preliminaries
The set of \(\tau\)-formulas is inductively defined by (1) the set of atomic \(\tau\)-formulas; (2) Boolean connections \(\
eg\phi\), \((\phi\vee\psi)\), and \((\phi\wedge\psi)\) of \(\tau\)-formulas \(\phi\) and \(\psi\); (3) quantified formulas \(\exists x\phi\) and \(\forall x\phi\) for a first-order variable \(x\) and a \(\tau\)-formula \(\phi\); (4) quantified formulas \(\exists X\phi\) and \(\forall X\phi\) for a second-order variable \(X\) of arity \(1\) and a \(\tau\)-formula \(\phi\). The set of _free variables_ of a formula \(\phi\) consists of the variables that appear in \(\phi\) but are not bounded by a quantifier. We denote a formula \(\phi\) with free variables \(x_{1},\dots,x_{k},X_{1},\dots,X_{\ell}\) as \(\phi(x_{1},\dots,x_{k},X_{1},\dots,X_{\ell})\). Finally, we say a \(\tau\)-structure \(\mathcal{S}\) with an universe \(U\) is a _model_ of an \(\tau\)-formula \(\phi(x_{1},\dots,x_{k},X_{1},\dots,X_{\ell})\) if there are elements \(u_{1},\dots,u_{k}\in U\) and relations \(U_{1},\dots,U_{\ell}\) with \(U_{i}\subseteq U^{\operatorname{ar}(X_{i})}\) with \(\phi(u_{1},\dots,u_{k},U_{1},\dots,U_{\ell})\) being true in \(\mathcal{S}\). We write \(\mathcal{S}\models\phi(u_{1},\dots,u_{k},U_{1},\dots,U_{\ell})\) in this case. **Example 1**. : _Graphs can be modeled as \(\{E^{2}\}\)-structures with universe \(U=V(G)\) and a symmetric interpretation of \(E\). Properties such as \"is 3-colorable\" can then be described by formulas as:_  
\[\tilde{\phi}_{\text{3 col}}=\exists R\exists G\exists B\;(\forall x\;R(x)\lor G (x)\lor B(x))\land(\forall x\forall y\;E(x,y)\to\bigwedge_{ \begin{subarray}{ c}C\in\{R,G,B\}\end{subarray} }\
eg C(x)\lor\
eg C(y)).\]  
_For instance, we have \(\bigotimes\models\tilde{\phi}_{\text{3 col}}\) and \(\bigotimes\models\tilde{\phi}_{\text{3 col}}\). Here, and in the rest of the paper, we indicate with the tilde (as in \(\tilde{\phi}\)) that we will present a more refined version of \(\phi\) later on._  
The _model checking_ problem asks, given a logical structure \(\mathcal{S}\) and a formula \(\phi\), whether \(\mathcal{S}\models\phi\) holds. A _model checker_ is a program that solves this problem. To be useful in practice, we also require that a model checker outputs an assignment of the free and existential bounded variables of \(\phi\) in the case of \(\mathcal{S}\models\phi\).
## 3 Results
### Underwater-Image Color vs. Sediment Concentration  
The 200 and 1000 mesh experiments were excluded from the analysis because an insufficiency in the power supply resulted in a failure to maintain constant lightning conditions. Underwater images for similar concentrations in the 325, 500, and 2000 mesh experiments are presented in Figure 6a.  
At low concentrations, color variations were observed across the samples, depending on the light-source and filming conditions; these differences became smaller as the concentration increased. To quantitatively analyze these color changes, the RGB band value that changed as compared to the pixel and the corresponding change in the gray band value was identified (Figure 6b). This study (in contrast to previous research [14]) identified that the red band value was the most sensitive among the different light wavelengths of underwater  
Figure 5: Results of the (**a**) concentration as a function of turbidity and (**b**) diameter as a function of concentration for each particle size.  
Figure 6: Changes in the (**a**) color of the underwater image and (**b**) RGB and gray band values in response to changes in the suspended-sediment concentration.  
images and varied irregularly with the concentration. This result may be attributed to the complex impacts of the underwater lighting system and the introduction of the red-colored clay. The values of the other bands decreased as the concentration increased; notably, that of the green band rapidly decreased.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### Deep Bidirectional LSTM-Based Speech Synthesis  
Although the deep MDN speech synthesis model can solve the single modality problem of the objective function and predict the acoustic feature parameters accurately to improve the naturalness of the synthesized speech, there are still some problems as elaborated in the following. Firstly, MDN can only leverage limited contextual information since it can only model fixed time span (e.g., fixed number of preceding or succeeding contexts) for input features. Secondly, the model can only do frame-by-frame mapping (e.g., each frame is mapped independently). To address these problems, the authors in [46] proposed a modeling method based on recurrent neural networks (RNNs). The advantage of RNN is the ability to utilize context information when mapping inputs to outputs. However, traditional RNNs can only access limited context information since the effects of a given input on the hidden layer and the output layer will decay or explode as it propagates through the network. In addition, this algorithm also cannot learn long-term dependencies.  
To address these problems, the authors in [47] introduced a memory cell and proposed the long short-term memory (LSTM) model. To fully leverage contextual information, bidirectional LSTM [48] is mostly used for mapping the input linguistic features to acoustic features.
## 4 Precoding Schemes
### Precoding Schemes for VLC
#### 4.2.3 Other Precoding Techniques  
Other precoding designs consider criteria such as solving the max-min SINR. In [166], this approach is proposed using optimal linear precoding while calculating the precoding vectors through convex optimization. However, this solution is subject to a high complexity. An iterative methodology is also proposed in [166], in order to relax the complexity of the optimal solution. This way, the max-min SINR criterion is also considered [158]. This work considers ADRs so that each receiver is composed of several PDs allocated according to a geometrical pattern [76].
## 2 Hybrid DS/FH Spread Spectrum Signal Model  
The received hybrid DS/FH spread spectrum signal at the receiver's front end is modeled as follows:  
\[r(t)=\sqrt{2P}\cdot D(t-\tau_{t})\cdot C(t-\tau_{t})\cdot\cos\left[2\pi\left(f _{cj}+f_{dj}\right)t+\varphi_{t}\right]+n_{0}(t), \tag{1}\]  
where the constituent parameters are defined in Table 2.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Parameters** & \multicolumn{1}{c}{**Definition**} \\ \hline \(P\) & Received signal power \\ \(D(t)\) & Data bit, \(D(t)=\pm 1\) \\ \(C(t)\) & D5 code sequence with code rate of \(R_{c}\) \\ \(\tau_{t}\) & Code delay time in seconds \\ \(\varphi_{t}\) & Initial carrier phase in radians \\ \(n_{0}(t)\) & Zero-mean Gaussian white noise that has one-sided power spectral density of \(N_{0}\) \\ \(f_{cj}\) & Carrier frequency of \(j\)th frequency hopping time slot \\ \(f_{dj}\) & Doppler frequency offset in \(j\)th frequency hopping time slot caused by relative motion between transmitter and receiver \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters in hybrid DS/FH spread spectrum signal model.  
The carrier frequency \(f_{cj}\) in (1) can be specifically expressed as follows:  
\[f_{cj}=f_{c}+h(j)\cdot\Delta f, \tag{2}\]  
where \(f_{c}\) is the carrier center frequency, \(\Delta f\) is the minimum hopping interval, and \(h(j)\) is the hopping point corresponding to the frequency hopping pattern.  
It is assumed that the relative motion velocity between the transmitter and receiver is \(v\), which is small with respect to the speed of light \(c\). The approximation of the Doppler frequency \(f_{dj}\) corresponding to the carrier frequency \(f_{cj}\) is as follows:  
\[f_{dj}=\frac{v}{c}f_{cj}=\frac{v}{c}f_{c}+h(j)\cdot\frac{v}{c}\Delta f. \tag{3}\]  
Additionally, the FH code designed in this paper is correlated with the DS code, which means the DS code rate is a multiple of the FH code rate, and a frequency hopping time slot contains a fixed number of DS chips. Therefore, the synchronization of an FH code is completed at the same time as the DS code using a traditional delay locked loop (DLL), and there is no need to track the FH code phase independently.