esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Heuristic Approaches
### Fix-and-Relax Heuristics
#### 5.1.1 Principle  
FR heuristic procedure involves an Observation Window (OW) of length \(\sigma_{k}\) overlapping \(\delta_{k+1}\) periods between two successive steps \(k\) and \(k+1\). In this study, the OW length and the number of overlapping steps remain fixed, thus, for all \(k\)\(\sigma_{k}=\sigma\) and \(\delta_{k}=\delta\). Two successive steps of the procedure are illustrated in Figure 2.  
At each step \(k\), the decision variables are partitioned into 3 sets according to parameters \(a_{k}\) and \(b_{k}\): a Frozen Window (FW) comprising variables indexed between 0 and \(a_{k}-1\), an OW in which variables indexes fall between \(a_{k}\) and \(b_{k}\), and an Approximation Window (AW) for variables indexed after \(b_{k}+1\). At step \(k>1\), the values of the decision variables within the FW are known and integrated into the submodel \(Pr_{k}\). Moreover, constraints containing decision variables within the OW are completely taken into account, whereas constraints involving decision variables in the AW are either dropped or simplified.  
Steps of the FR heuristic are described in Algorithm 1. FR heuristic takes as inputs \(Pr\) the problem, \(\sigma\) the OW length and \(\delta\) the number of periods overlapping.  
Initial steps from lines 1 to 3 consist in setting \(k=0\) and fixing \(a=0\), \(b=\sigma-1\), while the main loop ends when \(b\geq T\). The loop instructions are described from lines 4 to 11. Line 5, the submodel \(Pr_{k}\) is solved. From lines 6 to 8, \(k,a_{k}\) and \(b_{k}\) are updated as follows : \(k\) is incremented by 1, \(a_{k}\) is set to \(b_{k}-\delta\), and the ending time of the OW \(b_{k}\) is incremented by \(\sigma-\delta\) steps. Line 9, the if-statement sets \(b_{k}\) to \(T\), preventing \(b_{k}\) to overflow. Finally, at line 13, the last model is solved.  
Figure 2: FR procedure between steps \(k\) and \(k+1\). (Reprinted from [56]).
## 2 Channel Model and UOWC System
### Full-Duplex Architecture  
As shown in Figure 2, the proposed full-duplex UOWC system consists of transceivers, which combine a blue laser diode (LD), a green LD, and two photo-detectors (PDs) within an independent unit. By using this method, blue and green LDs can be used to load the real and imaginary branches of inverse fast Fourier transform (IFFT), respectively. This results in the removal of HS in the optical OFDM scheme. In Figure 2d, the diagonal arrangement of a pair of light sources and a pair of photo-detectors can increase the stability for real applications because the beams can be captured more easily at the receiver side. When both sides adopt this design, a high-speed full-duplex UOWC system will be able to support the requirements of data interaction, simultaneously. For the transmitter and receiver signal processing shown in Figure 2a,b, the details will be introduced in the next section.
###### Abstract  
College students can be sedentary for a majority of the day, which may exacerbate mental health issues or lead to declines in cognitive task performance; however, interventions to address sedentary behaviors may not positively influence everyone. Therefore, the present study sought to identify inter-individual cognitive performance and mood changes of college students during the performance of a cognitive task battery, while seated, standing and with intermittent bouts of walking. Participants (n = 31, age = 25.80 \(\pm\) 3.61 yrs, 7 male) completed a series of baseline questionnaires including the Trait Mental and Physical Energy and Fatigue survey. Using a randomized controlled cross-over design, participants completed 3 separate testing sessions. At each session, they performed a series of three rounds of cognitive tasks for 27 min and self-reporting mood states for 1 min in the seated position. Each round of cognitive testing was followed by a 2 min break. Each testing day had participants spend the 2 min break in a different condition: sitting, standing, or walking. A series of mixed ANOVAs were used for the primary analysis and a combination of machine learning regressors and classifiers were used for the secondary analysis. Our results suggest that there are unique inter-individual responses to each of the interventions used during the 2 min break. Participants who were low-trait mental and low-trait physical energy benefited the most from the standing desk intervention, while also reporting significant benefits of intermittent walking. However, participants who were low-trait mental fatigue had significant negative consequences of using both standing desks and walking intermittently, while those who were high-trait mental fatigue saw no change in cognitive responses or moods in those conditions. Post hoc machine learning analyses had modest accuracy rates (MAEs < 0.7 for regressors and accuracy rates >60% for classifiers), suggesting that trait mental and physical energy and fatigue may predict inter-individual responses to these interventions. Incorporating standing desks into college classroom settings may result in some students receiving cognitive benefits when inter-individual variability in mood and cognitive responses are accounted for.  
anxiety; standing desks; interindividual differences; fatigue; college students; sedentary behavior; moods +
Footnote †: journal: Applied Sciences
## 4 Discussion  
To reduce the loss of image information and obtain high-resolution dehazed images in icing wind tunnel environments, we proposed Dense-HR-GAN. Firstly, in the network architecture, the structure of \"Dense_Block+Trans_Block\" in the encoder part can concatenate the obtained features on the channel. This can achieve the effect of feature reuse and avoiding feature loss. Secondly, using instance normalization in the network structure can independently normalize each feature channel of each sample. This better preserves the details of the image and improves the robustness of the model. Thirdly, the feature maps extracted from the encoder will be skip-connected with those from the decoder. Skip connections can effectively address the problem of deep networks struggling to learn low-level information by connecting shallow feature maps with deep ones, thereby improving the performance and effectiveness of the model. This helps preserve the original important information of the image in the dehazing process.  
As analyzed in Section 3.3, our dehazing results are satisfactory, and the evaluation metric \(\overline{r}\) and \(NIQE\) achieve good values. As shown in Figure 6, compared with traditional dehazing algorithms, our model can better restore the colors of the images and has obvious dehazing effects. Figure 9 shows a partial comparison of our model with a deep learning-based dehazing algorithm. As seen in the third row of images, our model produced dehazed images with higher contrast and more thorough haze removal under LWC = 1.0. Moreover, in the case of LWC = 0.5, our model produced dehazed images that allow for clearer observation of icing on the wings and effectively preserves the original shape of the ice to a great extent, as shown in the red boxed regions in the first two rows of images. (The blue box area is an enlarged version of the red box area in the image).
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 5 Proposed Triple-Mixed Gibbs Sampling
### Mixing Ratio Choice
```
Input:\(\mathbf{y},\mathbf{H},\mathbf{x}^{(0)},t_{\max},q_{\infty},q_{1},R\)  Define: \(f(\mathbf{x})\rightarrow\) ML cost function (Equation (7)) \(\phi(\mathbf{x})\rightarrow\) Normalized ML cost function (Equation (14)) \(\theta_{s}(\mathbf{x})\rightarrow\) Stalling limit function (Equation (15)) \(p(\mathbf{x},a|\theta)\rightarrow\) Transition probability distribution (Equation (6)) \(c_{b}\rightarrow\) Cost of best candidate so far; set initial \(c_{b}\) to \(\infty\)  Compute: for\(r=1\) to \(R\)do  Initial candidate: \(\mathbf{z}=\mathbf{x}^{(0)}\); Initial cost: \(\beta=f(\mathbf{z})\) \(t=0\) while\(t<t_{\max}\)do for\(i=1\) to \(n\)do  generate \(r_{1},r_{2}\sim U[0,1]\) if\(r_{1}<q_{\infty}\)then  generate pmf \(p(x_{i}^{(t+1)}=\omega)\sim U[0,1],\forall\omega\in\mathcal{A}\)  sample \(x_{i}^{(t+1)}\) from this pmf else if\(r_{2}<q_{1}\)then \(a^{2}=1\) else \(a^{2}\propto\) SNR (according to Equation (17) and Table 1) endif \(x_{i}^{(t+1)}\sim p(x_{i},a|x_{1}^{(t+1)},...,x_{i-1}^{(t+1)},x_{i+1}^{(t)},...,x_{n}^{(t)})\) endif endfor \(\gamma=f(\mathbf{x}^{(t+1)})\) if\(\gamma\leq\beta\)then \(\mathbf{z}=\mathbf{x}^{(t+1)}\); \(\beta=\gamma\) endif \(t=t+1\); \(\beta_{\circ}^{(t)}=\beta\) if\(\beta_{\circ}^{(t)}==\beta_{\circ}^{(t-1)}\)then  compute \(\theta_{s}(\mathbf{z})\) if\(\theta_{s}<t\)then  if\(\beta_{\circ}^{(t)}==\beta_{\circ}^{(t-\theta_{s})}\)then break while endif endif endwhile if\(f(\mathbf{z})<c_{b}\)then \(\mathbf{z}_{b}=\mathbf{z}\); \(c_{b}=f(\mathbf{z}_{b})\) endif  compute \(P=\lfloor\max(0,0.5\log_{2}M\phi(\mathbf{z}_{b}))\rfloor+1\) if\(P<r\)then break for endif endfor Output:\(\mathbf{z}_{b}\)
```  
**Algorithm 2** Triple mixed Gibbs sampling algorithm with multiple restarts and stopping criteria.
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 3 Methodology  
Figure 1 is a block diagram of the proposed wavelet-based algorithm for the detection of the QRS complex. The purpose of the signal preprocessing is to segment the ECG recording and remove the baseline drift. The proposed wavelet-based algorithm includes signal preprocessing, a four-level dyadic wavelet transform, a search of candidate extreme points, a determination of candidate extremum pairs, an evaluation of noise, a calculation of candidate QRS peak positions, and a determination of QRS Peaks. The details are described in the following sections.  
Figure 1: Block diagram of the proposed wavelet-based algorithm for detection of the QRS complex.
# Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems
## 2 Simulation Setup Description
### Machine Learning Model
Quantum neural networks, for example, can exploit the properties of quantum mechanics to perform certain computations more efficiently than classical computers. Similarly, graph neural networks can exploit the structure of graph data to improve the accuracy of predictions. In this work, the dataset size is small, and the kernel function was carefully chosen, thus avoiding the aforementioned limitations.
## 2 The Quick Preliminary Design Model
### Pressure Hulls  
The pressure hulls are watertight cylinders with hemispherical dished heads, capable of withstanding the external hydrostatic pressure (see Figure 2). In this study, the bow and stern pressure hulls are not internally reinforced with frames. This design requires thicker pressure hulls, but it offers clean spaces that facilitate equipment installation with moderate hull thicknesses due to the relatively small diameter of the cylinders. Conversely, the central pressure hull is reinforced by a framing system, made of T-section frames, allowing thinner hulls. The framing system (see Figure 3) is defined by the thickness of the pressure hull _thk_; the distance between frames \(l_{s}\), the height of the web of the frame \(h_{sw}\), the thickness of the web of the frame \(a_{sw}\), the thickness of the flange of the frame \(h_{sf}\), and the width of the flange of the frame \(a_{sf}\). Regarding the construction material, DMFC-AUV\QpDM allows the user to define it by means of its density \(\rho_{ph}\), Young's modulus \(E_{ph}\), and Poisson's coefficient \(\sigma_{ph}\).  
To evaluate the suitability of each pressure hull, DMFC-AUV\QpDM makes use of the specific regulations for AUVs elaborated by the American Bureau of Shipping (ABS) [25]. These regulations allow easy implementation in a mathematical model and assure the basic viability of the structure in a quick process. The ABS method defines the maximum allowable working pressure, that is, the maximum external hydrostatic pressure due to the water column above the AUV, based on different strength components and establishing additional requirements for frames construction,namely, minimum inertia and maximum slenderness ratio. In this case, and in accordance with the arrangement of the pressure hulls, the strength components evaluated are: the inter-stiffener strength, the longitudinal stress at the frame, the overall buckling strength, the stiffener stresses, the circumferential tripping stress on the stiffener, and the limit pressure for spherical shells.
## 4 Experimental Datasets and Evaluation
### Datasets
#### 4.1.3. Varied Drone Dataset (VDD)  
The Varied Drone Dataset (VDD) comprises 400 high-resolution aerial photographic images captured utilizing DJI MAVIC AIR II unmanned aerial vehicles from 23 geographical locations within Nanjing, China. It incorporates pixel-level semantic annotations across seven categories, labeled utilizing LabelMe software. In contrast to other datasets focused solely on urban environments, VDD incorporates diverse rural and natural landscapes, encompassing agricultural lands, villages, forests, hills, and bodies of water. With its large-scale annotated imagery spanning urban, industrial, rural, and natural areas captured from varying angles and conditions, VDD furnishes rich semantic details. VDD is freely available on the website [[https://vddvd.com/download/](https://vddvd.com/download/)]([https://vddvd.com/download/](https://vddvd.com/download/)) (accessed on 13 November 2023) [46]. We extracted the portion of the VDD dataset belonging to rural buildings to enrich our China Rural Building Unmanned Aerial Vehicle Imagery Dataset to verify the extraction potential for rural buildings within the unmanned aerial vehicle image dataset utilizing the model. Figure 8 illustrates an example of input images and their corresponding labels in the VDD dataset.  
Figure 6. Image and label selected from Building Image Dataset of Typical Rural Areas in China.  
Figure 7. Image and label selected from China Rural Building UAV Imagery Dataset.
# Gene Networks and Pathways Involved in _Escherichia coli_ Response to Multiple Stressors
## 3 Results
### Functional Categories Involved in E. coli Response to Multiple Stressors  
Functional categorization, using DAVID, of the DEGs that are shared between at least four stressors (Tables 2 and 3) revealed that 26.5% of the identified upregulated genes are involved in survival, general stress response pathways, and response to DNA damage and various stimuli, 15.5% in transport, 12% in various metabolic processes, 12% in biofilm formation, 6 % DNA repair, 5% in transcription regulation, and 1% in motility (Figure 4A). Intriguingly, the analysis delineated 15 genes with uncharacterized functions that are upregulated by at least four out of the five stressors (representing 18% of the total number of upregulated genes). In parallel, the downregulation of various metabolic pathways is a shared feature between the response to all included stressors and constituted 57.5% of the 127 gene expression changes, 22.8% involved in transport, 5.5% in motility, 4% in response to various stimuli, 3% in peptidoglycan biosynthetic processes, and 2.4% in transcription regulation of cellular processes (Figure 4B). Three uncharacterized genes (out of 127), _ympf_, _ydij_, and _yedE_, were found to be downregulated under all stressors except nitrosative stress. Of note, nitrosative stress had generally fewer represented functional categories than other stressors among transcriptionally altered genes (Tables 2 and 3). Figure 3: Venn diagram displaying the overlap of (**A**) upregulated and (**B**) downregulated genes within and between the five stress conditions.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 2 System Characterization
### Receiver Algorithms  
Four receivers' algorithms, such as Zero Forcing (ZF), MMSE, Equal Gain Combining (EGC), and Maximum Ratio Combining (MRC), are used to process the received signal after channel estimation [23]. ZF and MMSE receivers are computationally intensive, with ZF suffering from noise amplification at high noise levels. In contrast, MRC and EGC are simpler but introduce interference, requiring iterative suppression.  
Assuming a non-iterative receiver, the estimated frequency domain data symbols \(\widetilde{\mathbf{S}}_{k}=\left[\widetilde{\mathbf{S}}_{k}^{(1)},\ldots,\widetilde {\mathbf{S}}_{k}^{(R)}\right]^{T}\) become  
\[\widetilde{\mathbf{S}}_{k}=\mathbf{B}_{k}\mathbf{Y}_{k} \tag{2}\]  
Depending on the algorithm, \(\mathbf{B}_{k}\) can be computed as the following:  
Figure 1: Block diagram of LIS system with SC-FDE signals.  
ZF: this linear receiver inverts the channel matrix to cancel interference: \[\mathbf{B}_{k}=\Big{[}\mathbf{H}_{k}^{H}\mathbf{H}_{k}\Big{]}^{-1}\mathbf{H}_{k}^{H}\] (3) \(\bullet\) MMSE: balances interference suppression and noise amplification by minimizing the mean square error: \[\mathbf{B}_{k}=\Big{[}\mathbf{H}_{k}^{H}\mathbf{H}_{k}+\beta\mathbf{I}\Big{]}^{-1}\mathbf{H}_{k}^{H}\] (4) where \(\beta=\sigma_{N}^{2}/\sigma_{S}^{2}=\frac{E\big{|}|\mathbf{N}_{k}|^{2}/2}{E\big{|} |\mathbf{S}_{k}|^{2}/2}\) and where \(\mathbf{I}\) is an \(R\times R\) identity matrix. \(\bullet\) MRC: this receiver combines the received signals based on the channel gains, improving the SNR: \[\mathbf{B}_{k}=\mathbf{H}_{k}^{H}\] (5) \(\bullet\) EGC: combines signals from multiple antennas with equal weights but adjusts phase to maximize received power: \[\mathbf{B}_{k}=\exp\Big{\{}j\cdot\arg\Big{(}\mathbf{H}_{k}^{H}\Big{)}\Big{\}}\] (6) \(\bullet\) _Iterative Block Decision Feedback Equalization (IB-DFE)_ is an advanced equalization technique used in communication systems to improve the performance in multipath or interference-limited environments. It iteratively refines the received signal by leveraging feedback from previously detected data, combining concepts from block-based processing and decision feedback. \(\mathbf{C}_{k}\) is a feedback filter matrix, \(\mathbf{B}_{k}\) stands for the forward filter matrix, and \(\overline{\mathbf{S}}_{k}\) represents the detected signal from the previous iteration. \[\widetilde{\mathbf{S}}_{k}=\mathbf{B}_{k}^{H}\mathbf{Y}_{k}-\mathbf{C}_{k}\overline{\mathbf{S}}_{k}\] (7) The interference cancellation matrix \(\mathbf{C}_{k}\) can be computed as: \[\mathbf{C}_{k}=\mathbf{A}_{k}^{H}\mathbf{H}_{k}-\mathbf{I}\] (8) where \(\mathbf{I}\) is an \(R\times R\) identity matrix, and \(\mathbf{A}_{k}^{H}=\bigg{[}\frac{\mathbf{H}_{k}(\mathbf{I}_{k})}{\big{|}\mathbf{H}_{k}(\mathbf{I} _{k})\big{|}}\bigg{]}_{\mathbf{N}_{k}\times N_{T}}\).
## 4 Discussion
### Limitations  
First of all, the accuracy of the decision tree algorithm adopted in this paper is only 58.22%. If higher classification accuracy is needed, more classification attributes need to be added. However, this will also lead to excessive stratification, which is not good for minimizing the masking signal [20]. Therefore, optimizing the algorithm to improve  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **TP** & **FP** & **FN** & **R** & **\(P\)** & **F** \\ \hline non-stratification & 7293 & 28,781 & 6661 & 52.26\% & 20.22\% & 29.16\% \\ gender-stratification & 8369 & 34,236 & 5585 & 59.98\% & 19.64\% & 29.59\% \\ age-stratification & 8977 & 35,591 & 4977 & 64.33\% & 20.14\% & 30.68\% \\ decision tree-stratification & 9605 & 38,225 & 4349 & 68.83\% & 20.08\% & 31.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of detection results of different stratification methods.  
the accuracy of classification without adding more attributes is what we need to do in the future.  
Secondly, while the signal detection method adopted in this paper was PRR, some other methods, such as ROR, IC and MHRA, also need further tested.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 2 Fundamental Aspects of Microseismic Monitoring Technique
### Microseismic Source Location and Sensor Layout  
There are two common approaches to locating microseismicity, namely, the arrival time approach and the triaxial sensor approach, and the former is widely used in most underground location cases [48]. Seismic waves radiating from the microseismic source can be detected by sensors installed at suitable positions, and the arrival time function for each sensor is listed as follows:  
\[\sqrt{\left(x_{i}-x_{0}\right)^{2}+\left(x_{i}-x_{0}\right)^{2}+\left(x_{i}-x_{ 0}\right)^{2}}=v_{p}(t_{i}-t_{0}) \tag{1}\]  
where, \(\left(x_{0},y_{0},z_{0}\right)\) and \(t_{0}\) are the coordinates and onset time of the microseismic source, \(\left(x_{i},y_{i},z_{i}\right)\) and \(t_{i}\) are the coordinates and arrival time of the \(i\)-th sensor, and \(v_{p}\) is the P-wave velocity. Classical methods, e.g., the Geiger method, the double-difference method, and the simplex method, have been commonly employed to identify the source location of microseismicity. However, these classical methods are heavily dependent on the sensor layout, the precision of arrival time picking, and the velocity model of seismic waves, which leads to the low accuracy of the microseismic source location [47]. The simplified elastic wave velocity model used to interpret seismic wave propagation in layered grounds of coal mines results in a reduction in the accuracy of identifying the microseismic location. [PERSON] et al. [49] proposed a novel inversion method for a stratified velocity model in layered ground based on the time difference location algorithm of microseismicities, and the method showed good performance at improving the accuracy of the microseismic location. The source location is of great importance for risk assessment of impending mining-induced instabilities. Several location methods have been developed to improve the microseismic location accuracy, e.g., the nonlinear method, methods without the pre-measured wave velocity or arrival time picking, the collaborative method based on analytical and iterative solutions, and the optimized relative location method [50; 51; 52; 53; 54].  
The sensor layout is a key contributing factor influencing microseismic location accuracy. Longwall extraction is the most popular mining method in China, and microseis  
Figure 2: Microseismic monitoring configuration in underground coal mine, revised from [47].  
mic sensors in coal mines are usually distributed in a plane around the longwall panels (Figure 3) [55]. The planar layout of the microseismic network may make it difficult for high-precision identification of the microseismic location. Some optimization theories of the microseismic network are developed, such as calculating network monitoring capability based on the Monte Carlo method [56] and designing the microseismic network based on the degree of seismic danger (D value) and spatial concentration degree of seismicities (C value) [57] to enhance the accuracy and the reliability of the microseismic location in coal mines.