esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors
## 3 Dataset and Data Representation
### Coordinates of Sensor Locations Graphical Representation Method  
For the method analyzed in this study, the same dataset and sensor location matrix were used. The Scatter Plot style was used for graphic representation. An image is \(227\times 227\) in size. A graph represents a window of data from the time set. Each active sensor in the window is graphically represented by a point whose coordinates represent the sensor's location in the matrix. To create a linear relationship of activities, the next displayed active point is joined to the previous one by a line. Several sets were generated with different data window parameters, but also the introduction of a normalized temporal side, more precisely using the data order to normalize the temporal one. The temporal aspect was introduced by scaling the coordinates on the x-axis with a constant value of 3 units. The exact time value was not used because some activities may last longer and others less so, and the data window limits the activity in time. An example of generated images can be seen in Figure 4.  
In Algorithm 2, the pseudo-code used to generate images using the scatter plot is presented. The first step is to read the input dataset file. For each line item in the dataset file, we initialize the activity group that will store multiple line items. We then add the current data item to the activity group. Then for each item, from the current position to any item inside the defined data window (using the time in seconds), we add the next rows to the activity group. When the activity group is full of items, more than two, we create the target folder and path, map the activity group items to a list of 2D points, and leverage the ScottPlot library to plot the scatter image type and save a PNG image.  
Figure 4: Graphical representation of data generated from Kyoto dataset Multiresident ADL Activities: (**a**) window of 10 s without overlapping; (**b**) window of 30 s without overlapping; (**c**) window of 50 s without overlapping; (**d**) window of 50 s without overlapping without temporal normalization.
## 6 Results
### Start-Up Speed
#### 6.2.1 Inflighting Bytes  
We set the initial send window to one. We tested the bandwidth and total link delay of different bottleneck links and recorded the changes in flight data over time in the links of the four schemes. Flight data were sent by the source server but not received (i.e., the data transmitted in the connection). The experimental results are shown in Figure 5.  
Figure 5 shows the variation in inflighting bytes over time. The subtitles show the bottleneck bandwidth and total delay of the links in the experiment. The graph shows that under the four experimental bandwidths and conditions, the Nodctl scheme had the fastest start-up speed and the slightest fluctuation in flight data.  
Table 3 shows the time taken for the flight bytes of the four schemes to reach the BDP. The table shows that the impact of delay was more significant than the bandwidth. The Nodctl scheme took much less time to achieve the BDP than the other three schemes, and its advantages became more apparent with the increase in bandwidth and latency. After completing the connection, Nodctl initiates rate negotiation to determine the transmission  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Test Platform & NS-3 \\ Tested Scheme & Nodctl, NewReno, Cubic, Bbr \\ Server Access Link Bandwidth, Delay & 200 Mbps, 5 ms \\ User Access Link Bandwidth, Delay & 200 Mbps, 5 ms \\ Bottleneck Link Bandwidth, Delay & 10–90 Mbps, 1–140 ms \\ Queue Length & 100 packets \\ Receive Cache Size & 10 MB \\ Send Cache Size & 10 MB \\ Packet Size & 1052 B \\ Transfered Data Amount & 0–90 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameter set-up.  
rate, which takes approximately 2 RTTs. NewReno and Cubic use similar slow start methods, and thus their time to reach the BDP is the same. The BBR scheme was the slowest to reach the BDP.
# Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System
## 3 Fisher Discriminative Sparse Representation (FDSR) of the Features Extracted from DBN
### Computation of Fisher Discriminative Sparse Representation  
The computation of FDSR is mainly divided into three steps: DBN training, eigenvector solving, and dictionary learning. In this paper, the DBN consists of three RBMs, set neuros in each RBM as \(m_{1}\), \(m_{2}\) and \(m_{3}\), the inputs are \(N\) samples with \(M\) parameters, \(K_{1}\) is the number of iterations. The computational complexity of the first, second, and third RBM can be represented as \(F_{1}\), \(F_{2}\) and \(F_{3}\) respectively, and the computational complexity of fine tuning is defined as \(R\).  
\[\begin{array}{l}F_{1}=K_{1}N\big{(}4 Nm_{1}+2m_{1}^{2}+M^{2}+M+m_{1}\big{)}\\ F_{2}=K_{1}N\big{(}4m_{1}m_{2}+2m_{2}^{2}+m_{1}^{2}+m_{1}+m_{2}\big{)}\\ F_{3}=K_{1}N\big{(}4m_{2}m_{3}+2m_{3}^{2}+m_{2}^{2}+m_{2}+m_{3}\big{)}\\ R=K_{1}N(Mm_{1}+m_{1}m_{2}+m_{2}m_{3})\end{array} \tag{17}\]  
Then, the computational complexity of DBN \(C_{DBN}\) is:  
\[C_{DBN}=F_{1}+F_{2}+F_{3}+R \tag{18}\]  
\(K_{2}\) is the iterations of FDSR method with sparsity \(L\), then the computational complexity of dictionary learning and eigenvector solving is \(O\big{[}\big{(}(K_{2}+L)MN^{2}\big{)}\big{]}\), \(O\big{[}\big{(}(2K_{2}+L)MN^{2}\big{)}\big{]}\), respectively. As a result, the complexity of FDSR is \(O\big{[}(3K_{2}+L)MN^{2}\big{]}\).
## 4 Simulation Experiment and Discussion
### Discussion  
The obtained results in this study demonstrate the superiority of CC-MPSO over the well-known and widely used algorithms considered in this paper. The primary factor contributing to this effectiveness stems from the following aspects:  
Firstly, the algorithm is directed to explore the motion space rather than the Cartesian space by the innovative motion encoding mechanism. This method effectively stops the algorithm from generating useless paths throughout the search phase, resulting in fewer search failures. Meanwhile, it can significantly increase algorithm search performance and accelerate convergence speed, making it more suitable for dealing with dynamic target search problems.  
Secondly, the algorithm employs a problem decomposition strategy to break down the multi-UAV target search problem into several subproblems, with each subproblem being addressed by a solitary particle. Decomposition is beneficial in handling the intricacy of the problem and enhancing the scalability of the method.  
Thirdly, the implementation of the co-evolution mechanism enables various subproblems to collaborate and achieve global search by exchanging knowledge. This aids in circumventing the occurrence of local optima and enhancing the algorithm's ability to search globally.  
Based on the statistical analysis, the CC-MPSO algorithm successfully optimizes 52 out of 75 statistical elements. It also shows suboptimal performance in the remaining statistical items, with the majority of suboptimal outputs having a difference of barely one to two percent compared to the optimal ones. These differences are almost insignificant in actual applications. Furthermore, the simulation results have shown that the target search method, which is based on RI-MAC, also has fine performance. The reason for this can be linked to the algorithm's implementation of an association mechanism for prior information. In this mechanism, the target probability graph is continuously modified based on the association rules matched by various sorts of targets. It can be utilized to augment the performance of the CC-MPSO algorithm in the same way. Nevertheless, the pressing task at hand is to improve the target association rule base in order to align it more successfully with real-world application circumstances.
## 2 Governing Equations  
The equations governing the flow of an incompressible fluid, under isothermal conditions, are the continuity,  
\[\
abla\cdot\mathbf{u}=0 \tag{5}\]  
and the momentum equations,  
\[\rho\frac{D\mathbf{u}}{Dt}=-\
abla p+\
abla\cdot\boldsymbol{\sigma}+\mathbf{F}, \tag{6}\]  
where \(\mathbf{u}\) is the velocity vector, \(p\) is the pressure, \(\boldsymbol{\sigma}\) is the stress tensor (to be defined later), \(\rho\) is the mass density, and \(\mathbf{F}\) represents the external forces. Note that all variables are dimensionless, with: \(\mathbf{x}=\mathbf{x}*/H\), \(\mathbf{u}=\mathbf{u}*/U\), \(t=t*\)\(U/H\), \(p=p*/(\rho U^{2})\) (the \(*\) represents the dimensional variable).  
In order to achieve a closed system of equations, a constitutive equation for the extra-stress tensor, \(\boldsymbol{\sigma}\), is required. Recently, [PERSON] et al. [27] proposed a new differential model based on the Phan-Thien-Tanner constitutive equation [26] (see also [28]). This new model considers a more general function for the rate of destruction of junctions, the Mittag-Leffler function, where one or two fitting parameters are included, in order to achieve additional fitting flexibility.  
The Mittag-Leffler function is defined as,  
\[E_{\alpha,\beta}(z)=\sum_{j=0}^{\infty}\frac{z^{j}}{\Gamma(\alpha j+\beta)}, \tag{7}\]  
with \(\alpha\), \(\beta\) being real and positive. \(\Gamma(\cdot)\) is the gamma function, given by:  
\[\Gamma(t)=\int_{0}^{\infty}x^{t-1}e^{-x}dx. \tag{8}\]  
when \(\alpha=\beta=1\), the Mittag-Leffler function reduces to the exponential function, and when \(\beta=1\) the original one-parameter Mittag-Leffler function, \(E_{\alpha}\) is obtained.  
The constitutive equation is given by:  
\[K(\sigma_{kk})\boldsymbol{\sigma}+Wi\overset{\square}{\sigma}=\frac{2(1- \zeta)}{Re}\mathbf{D}, \tag{9}\]  
where \(\sigma_{kk}\) is the trace of the stress tensor, \(Wi=\lambda U/H\) is the Weissenberg number, \(Re=UH/\
u\) is the Reynolds number (\(\
u=\mu_{0}/\rho\) is the kinematic viscosity), \(\mathbf{D}=\frac{1}{2}\left(\
abla\mathbf{u}+\left(\
abla\mathbf{u}\right)^{ t}\right)\) is the rate of deformation tensor, \(\boldsymbol{\sigma}\) is the elastic stress, and \(\zeta=\frac{\mu_{S}}{\mu_{0}}\) is the viscosity coefficient, where \(\mu_{0}=\mu_{S}+\mu_{P}\) is the total shear viscosity (\(\mu_{S}\) is the solvent/Newtonian viscosity, \(\mu_{P}\) is the polymer viscosity) and \(\overset{\square}{\sigma}\) represents the Gordon-Schowalter derivative.  
The stress function, \(K(\sigma_{kk})\), is given by a new formulation that imparts more flexibility and accuracy to the model predictions, as discussed in [27; 29; 30]. It is given by:  
\[K(\sigma_{kk})=\Gamma(\beta)E_{\alpha,\beta}\left(\frac{eRe\;Wi\;\sigma_{kk}}{ (1-\zeta)}\right), \tag{10}\]where \(\varepsilon\) represents the extensibility parameter, \(\Gamma\) is the Gamma function, and the normalisation \(\Gamma(\beta)\) is used to ensure that \(K(0)=1\), for all choices of \(\beta\).
## 3 Agwan: A Generative Model for Labelled, Weighted Graphs  
In this section, we present our generative model, Agwan (Attribute Graph: Weighted and Numeric). The model is illustrated in Figure 1.  
Consider a graph \(G=(V,E)\) with discrete vertex label values drawn from a set \(L\) (Figure 0(a)). For each combination of vertex attributes \(\langle i,j\rangle\), the corresponding mixture model \(\Omega^{ij}\) parameterises the distribution of edge weights, with an edge weight of 0 indicating no edge (Figure 0(b)). \(u,v\in V\) are vertices and \(w_{uv},w_{vu}\in\mathbb{N}\) are edge weights. Edges \(e\in E\) are specified as a 3-tuple \(\langle u,v,w_{uv}\rangle\). Thus, the Agwan model is parameterised by \(\pi\), the set of prior probabilities over \(L\); and the set of edge weight mixture parameters \(\Theta=\{\Omega^{ij}|i,j\in L\}\). For directed graphs, \(|\Theta|=|L|^{2}\) and we need to generate both \(w_{uv}\) and \(w_{vu}\). For undirected graphs, \(\Omega^{ij}=\Omega^{ji}\), so \(|\Theta|=O(|L|^{2}/2)\) and \(w_{vu}=w_{uv}\).  
\(\Omega^{ij}\) is specified as a BMM:  
\[\Omega^{ij}=\sum_{m=1}^{M}\omega_{m}^{ij}\cdot\mathrm{Beta}(a_{m}^{ij},b_{m}^ {ij}) \tag{1}\]  
Figure 1: Agwan parameters. (**a**) Vertex Labels; (**b**) Edge Weights.  
where \(\omega_{m}^{ij}\) is the weight of each component; and \(\mathrm{Beta}(a_{m}^{ij},b_{m}^{ij})\) is the beta distribution with shape parameters \((a_{m}^{ij},b_{m}^{ij})\). The PDF of the beta distribution is:  
\[\mathrm{Beta}(x;a,b)=\frac{1}{\mathrm{beta}(a,b)}x^{a-1}(1-x)^{b-1},\;a,b>0 \tag{2}\]  
where \(\mathrm{beta}(a,b)\) is the beta function \(\mathrm{beta}(a,b)=\frac{\Gamma(a)\Gamma(b)}{\Gamma(a+b)}\) and \(\Gamma(\cdot)\) is the gamma function defined as \(\Gamma(z)=\int_{0}^{\infty}t^{z-1}e^{-t}dt\). As the support of the BMM is \((0,1)\), we use a constant \(s^{ij}\) to scale the data into this range before fitting the BMM parameters. During graph generation, we draw values from the BMM and multiply by \(s^{ij}\).  
We specify \(\Omega^{ij}\) such that the weight of the first component (\(m=0\)) encodes the probability of no edge: \(\omega_{0}^{ij}=1-P(e_{ij})\), where \(P(e_{ij})\) is the probability of an edge between pairs of vertices with labels \(\langle i,j\rangle\) and is learned from the input graph. The weights of the BMM components (\(m\in[1,M]\)) are normalised by \(P(e_{ij})\), so the weights of all the components form a probability distribution: \(\sum_{m=0}^{M}\omega_{m}^{ij}=1\).
# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 4 Experimental Results
We plot the PSNR value of the 960 Kbps coding rate under the network transfer condition with the magenta line (-o-), which shows that most of its pattern overlaps the 768 Kbps coding rate result. Here, only two sectors had obvious differences. The proposed model uses the average PSNR value for comparison. The average difference between the 768 Kbps and 960 Kbps results obtained by our model is 0.11, and the line of the 768 Kbps result is closer to the red line (+). This example shows that the proposed model may result in significant differences for some scenarios; however, the average value it can obtain shows very little difference compared to the real average PSNR. Figure 9b shows the results for scenario 56. Here, the moving object has a large area but a very slow speed. Compared to a moving object with same area but higher speed, this scenario demonstrates better video reconstruction results. We estimated that the coding bitrate in this experiment uses the largest coding unit (i.e., 960 Kbps). Figure 10 shows the frames in which the moving object appears. The first row is the frame number, and the second row shows the original images (64 Kbps without network transfer). The third row shows the reconstruction images after network transfer with coding at 960 Kbps by the proposed model. The fourth row shows the reconstruction images after network transfer with coding at 64 Kbps. Here, we found that the images reconstructed using a coding value obtained by the proposed model have a very similar quality compared to the original images. Figure 9: (a) In scenario 38, the moving object occupies a large area and moving at higher speed. When a moving object appears in the video, the PSNR value is reduced significantly. When we input this scenario into our model, we obtain an estimated coding bitrate of 768 Kbps. See the blue (\(-\)) line. Please note that there is a significant gap between the blue (\(-\)) and red lines (\(+\)). The red line is our experimental goal. In theory, it should fall in 960 Kbps, even if we set the limit to 1 Mbps. The magenta (-o-) represents a coding rate of 960 Kbps and shows that most of its pattern overlaps the 768 Kbps coding rate result. The proposed model uses the average PSNR value for comparison. (b) The area occupied by the moving object is a little bigger than that used in (a) but the object is moving at slower speed. The gap shown between the blue (\(-\)) and red (\(+\)) line is much smaller than (a).
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 4 Use Cases
### Use Case 2: Classic Voice Service Virtualization  
The network slicing mechanism employs numerous virtual networks within a shared physical network, each tailored to meet the unique requirements of individual users. This capability to isolate and restrict segments ensures that in the event of an enemy attack, not all network segments are affected. This offers significantly enhanced protection, security, reduced latency, and increased bandwidth, making it a crucial mechanism for tactical environments.  
While essential communication services, such as voice, data, messaging, and video, are staples for military troops, additional value-added services necessitate the utilization of network slicing mechanisms to ensure ultra-reliable communications. Some of them are listed below:  
1. **Push-to-talk (PTT)**: It is a method that allows conversations over semi-duplex communication lines, that is, bidirectional communication. PTT devices are always listening, and by using a button, it is possible to change the voice reception mode to transmission mode. The \"mission-critical push-to-talk (MCPTT)\" standard is a functionality that meets the requirements for mission-critical voice communication, which include high availability, reliability, low latency, and emergency calls, among others.
2. **Fixed mobile convergence (FMC)**: This service involves communication between military users connected through fixed and mobile military networks.
3. **Quality of service (QoS)**: This capacity allows the network to control traffic by adapting resources such as bandwidth and thus ensuring good performance of voice services in critical communications. This is important to preserve the basic voice service in tactical scenarios with total security and criticality required at all times. Defining different QoS, we can assign priority to voice traffic over other types of traffic, ensuring that voice packets are processed first.
4. **Satellite Backhaul**: In the event that the fiber or microwave backhaul connection is lost, satellite backhaul could be useful for certain scenarios and locations to guarantee secure communications for these basic services [68; 69; 70; 71; 72]. In addition, 3 GPP standardization [73] is defining the integration of the satellite in future terrestrial networks.
# Health Impact Assessment from Rice Straw Production in Cambodia
## 3 Results and Discussion
### Health Impact (DALYs)  
In this study, the total health impact of rice straw production in Cambodia was 13,093.50 DALYs. Rice production is the main causal factor in terms of provincial impact. Considering the impact on health, open burning contributed 98.50% of the total figure, which is consistent with the results of other studies in Thailand, where open burning of rice straw contributed 95% (2018) of the impact overall. We found that fertilizer and diesel combustion did not have much influence on human health. The health impact from RSOB at the provincial level in Cambodia is shown in detail in Table 12.  
Figure 4 illustrates how measurements of health impact varied in line with the total levels of rice produced in individual provinces. Only the process of open burning resulted in a serious health impact (12,904.69 DALYs), according to our results. In contrast, in terms of health impact, fertilizer and combustion contributed only 15.07 DALYs (0.12% of the total impact) and 173.74 DALYs (1.33% of the total), respectively. In addition, we applied a value for fraction of dry matter subjected to burning (Bk) to all 25 provinces at the same level (0.51), although the alternative straw management techniques actually practiced do not precisely match this definition. Thus, the value of Bk impacted the result of both pollutant emission and health impact. The characterization factor (CF) that we used to calculate health impact is a global factor that is not specific to Cambodia, and so would have had a notable influence on our health impact calculations. However, secondary PM25 formation from fertilizer and combustion in the emission inventories of NH3, NOx, and SO2 (presented in ton/year) contributed 23.89-38.02% of the total emission, but in line with the calculated HIs for each impact, we state that the open burning of straw contributed up to 98% of the total health impact.  
Open burning was the main contributor to air pollution. In this study, we found that 98.50% of the total health impact was from the open burning of straw residue, while emissions from fertilizer and combustion did not produce any negative impacts on health. Some policies have been proposed and implemented to reduce these emissions. Improvements in straw usage rates and a decrease in open-field burning are just two of the suggestions for  
Figure 4: Health impact from rice straw production in Cambodia.  
supporting a circular bio-economy that uses agricultural straw as a resource [33]. Another solution to avoid the open burning of straw is to recycle rice straw in soil as a potential step toward zero burning in the future. Recycling straw can increase the organic matter content in the soil by incorporation into the land [34]. In Cambodia, educating people not to open-burn straw may be the best solution to reducing air pollution [35].
## 4 LeSSA--Implementation and Technical Details
### Feature Engineering Layer  
The construction of appropriate feature vector creation from the unstructured textual data is a significant task for well-learning performance in sentiment classification. For this purpose, we develop the FE layer, which consists of two components: Text Pre-processing (TPP) and Sentiment Features Extraction and Selection (SFES). The TPP is employed to generate appropriate feature vector for better learning performance in sentiment classification. The TPP component consists of four types of modules. These modules are sentence parser, lower case, noise remover, tokenizer.  
First, the review dataset is loaded, then the review text is split into words by using sentence parser and tokenizer, respectively. In the next step, the noise remover module is invoked to remove noise from the text if any, and the same are then transformed to lower case by using case transformer module.  
The TPP then inputs the data to the SFES sub-module where the POS Tagger assigns POS tags with the purpose of determining and extracting the likely words such as adjectives, adverbs, verbs and nouns. After that, these words are searched in the induced wide-coverage sentiment lexicon to select the effective sentiment words and filter-out non-sentiment words. Next, the feature vector is created and selected using term frequency-inverse document frequency scheme (TF-IDF) and pruned words below 'absolute = 3' and above 'absolute = 3000' from the feature vector space.  
Further, we selected high-quality top rank sentiment features using the Minimum Redundancy Maximum Relevance (MRMRR) [60] feature selection approach. The extraction and selection criteria for high-quality sentiment features have a great impact on the performance of supervised and semi-supervised textual sentiment classification [1; 61]. We employed the aforementioned text-mining techniques to all the proposed sentiment learners.
## 5 Results and Discussion
(**b**) The energy consumption of the water pump. If the energy consumption during 2500 seconds of desiccant cooling system (three fans + desiccant wheel + water pump) operation is evaluated, the total energy consumed by the cooler with the developed control strategy is 1238.29 KJ (135 KJ for the fans, 1100 KJ for DW and 3.29 KJ for WP). If the classical ON/OFF control was applied in the control of DPEC, the total energy consumed during the operation would be 2095.17 KJ. Thus, with the developed control strategy, 40.90% energy savings were achieved compared to the classical ON/OFF control. On the other hand, if the automatic Stop/Run feature was not added to the control strategy, it was determined that 1571.29 KJ energy would be consumed during the operation. It was observed that 21.19% energy saving is increased with this feature, which automatically turns the DPEC on and off without disturbing the room comfort.