esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### QR Code Segmentation and Decoding  
In this study, the QR code area in the video taken with the monocular camera mounted on the drone is detected using the segmentation model. If the QR code is detected using the object detection model, the minimum bounding box containing the QR code is detected [24,28]. The bounding box, which is a result of the object detection model, includes a background. The background in the bounding box hinders distortion correction and decoding of the QR code. On the other hand, since the segmentation model is detected in units of pixels, the detailed edge of the detected object can be easily obtained. Thus, we used the segmentation model to detect the QR code area, and to obtain the edge. UNet, UNet++, PSPNet, DeepLabV3, and DeepLabV3+ were compared as the segmentation model for the proposed framework. Finally, UNet++, which had the best QR code decoding performance, was used.  
The QR code attached to the floor and inventory is square, but perspective transformation occurs due to the photographed angle. To perform decoding by correcting the perspective transformation, four vertices of the detected QR code area are required. However, since the edge of the detected QR code contains noise, unlike the actual edge, the edge is approximated as a square using the Douglas-Peucker algorithm [49]. The approximation level of Douglas-Peucker algorithm is determined by a predefined epsilon. In this paper, the epsilon is set to 2% of the outline length. It is assumed that the ground QR code has information on the 3D position, while the inventory QR code has only information on the inventory, but not position. The type of detected QR code is determined as a result of decoding. In addition, the vertex of the data zone corresponding to the inside of the green line in Figure 4 was obtained through decoding, while the orientation of the QR code was obtained using the alignment pattern corresponding to the inside of the red line in Figure 4.  
Errors may occur in the vertex coordinates of the QR code area detected by the segmentation model, depending on the performance of the model. However, as shown in Figure 5, if QR code decoding is possible, the coordinates of the vertex of the data zone can be obtained relatively uniformly, even if the error occurs in the vertex coordinates of the detected QR code area. Therefore, the drone's 3D position estimated using the vertex of the  
Figure 4: Alignment patter (red) and data zone (green) of the QR code.  
Figure 3: Architecture framework of the proposed 3D position estimation model of the object in the video frame.  
data zone is smaller in error than the drone's 3D position estimated using the vertex of the QR code detected by the segmentation model.
# Optical Trapping, Sizing, and Probing Acoustic Modes of a Small Virus
## 2 Materials and Methods
### Ear  
The two lasers, each with a power of 10 mW as measured at the fiber launcher input to the trapping set-up, were turned on to trap the particles. The two lasers were tuned to the same wavelength of 853.08 nm during trapping, so that any beating due to interference would be of low frequency so as not to disturb the trapping process. Virus and polystyrene spheres were each trapped multiple times across many days. After trapping a particle, one laser was held fixed at 853.084 nm wavelength, while the other was tuned over a range of 853.273 to 853.084 nm, by varying the temperature via the controller, with an automated Matlab process. Each step was 0.01 K, corresponding to a \(\sim\)0.001-nm wavelength change, as measured by the optical spectrum analyzer. For each wavelength step, the signal of the APD was accumulated for 15 s at 5 kHz acquisition frequency via a (Labjack U3-LV) data acquisition module. The normalized standard deviation of the APD signal was computed for each wavelength step. This measure of noise level increases as the beat frequency of the interfering lasers approaches the characteristic frequency of the trapped particle, which shows up as a peak in the noise level of the signal at the resonance value.  
As a simple model to predict the resonance frequencies of PhiX174, [PERSON]'s theory [55; 56; 57; 58] was used. The icosahedral geometry of the virus studied in this work, PhiX174, can be simplified further by taking it to be a sphere. Despite much discussion and study on the elastic properties of viruses in existing literature, the wave speeds of viruses are generally not well established. Consistent with best estimates from previous works [43; 44; 47], for PhiX174, the longitudinal wave speed and Poisson'sratio were here taken to be \(\sim\)1800 m/s and \(\sim\)0.3 (corresponding to a transverse wave speed of 900 m/s), respectively. Using these values, the frequencies of the L0 mode were calculated to be 30 GHz.
## 2 Basic Theory
### Deep Belief Networks  
Deep belief networks consist of some restricted Boltzmann machines [26] (RBMs) stacked sequentially. The learning process of DBNs is divided into unsupervised learning and supervised learning. Unsupervised learning, also known as pre-training, is implemented using the contrastive divergence [27] (CD) algorithm. Supervised learning is implemented using the backpropagation (BP) network in the last layer, which is used to fine-tune the weight parameters of unsupervised learning. The mechanism of the DBN is to use multi-layer RBM to extract and process the features of the target, and then use a classifier for classification. The structures of DBN are shown in Figure 2.  
In the DBNs, \(v=v_{1},v_{2},\cdots v_{n}\) and \(h=h_{1},h_{2},\cdots h_{n}\) denote the states of the visible variables and hidden variables, respectively. \(W^{R}\) denotes a \(m\times n\) weight matrix connecting the neurons between the visible and hidden layers. The joint probability distribution \(P(v,h;\theta)\) and the energy function \(E(v,h;\theta)\) between the hidden and visible layers of the RBM are shown in Equations (8) and (9). The purpose of the greedy layer-by-layer training is to maximize the joint probability distribution.  
\[P(v,h;\theta)=\frac{1}{Z}e^{-E(v,h;\theta)} \tag{8}\]  
\[E(v,h;\theta)=-\sum_{i=1}^{m}a_{i}v_{i}-\sum_{j=1}^{n}b_{j}h_{j}-\sum_{i=1}^{ m}\sum_{j=1}^{n}v_{i}w_{ij}^{R}b_{j} \tag{9}\]  
where \(Z\) is the partition function (normalization factor), \(w_{ij}^{R}\) is the connecting weight of the RBM, parameter set \(\theta=(W^{R},a,b)\) composed of \(w_{ij}^{R}\), \(a_{i}\) and \(b_{j}\), \(a_{i}\) and \(b_{j}\) are the bias of the visible and hidden neurons, respectively. The update of \(\theta\) is shown in Equation (10).  
\[\theta_{l+1}=\theta_{l}+\eta\Delta\theta_{l} \tag{10}\]  
where \(\eta\) is the learning rate.  
Figure 2: Structure of a DBN.  
The backpropagation of the DBN belongs to supervised learning implemented using the BP network. The cross-entropy loss [26] can serve as the objective function for fine-tuning all layers in the BP network, as shown in Equation (11).  
\[E=-\frac{1}{N}\sum_{i=1}^{N}y_{i}^{k}\log p_{i}^{k} \tag{11}\]  
where \(N\) represents the number of samples, \(y_{i}^{k}\) represents the true label of the \(i\)th sample as \(k\), and \(p_{i}^{k}\) is the predictive probability that the \(i\)th sample belongs to category \(k\).
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 3 Methodology
### Adaptive Filtering
To cancel the backward leakage, this algorithm is done with the switching of the input and reference signals, where the final result is the summation of the two generated outputs of the adaptive filters.
## 4 Joint Recommendation Selection and Visiting Time Management Problem
### Recommendation Selection Based on Reinforcement Learning  
The action probability vector of museum visitor/learning automaton \(i,i\in\aleph\) is \(\mathbf{Pr}_{i}(\tau)=\{\Pr_{i,A}(\tau),\Pr_{i,B}(\tau),\Pr_{i,C}(\tau)\}\) where \(\Pr_{i,x}(\tau)\), \(x=A,B,C\) expresses the probability of selecting the \(R_{x}^{th}\), \(x=A,B,C\) recommendation. The model of learning automata is expressed below.  
\[\Pr_{i,x}(\tau+1)=\Pr_{i,x}(\tau)+b\cdotp p_{x}(\tau)\cdot(1-\Pr_{i,x}(\tau)), \;x^{(\tau+1)}=x^{(\tau)}, \tag{4a}\] \[\Pr_{i,x}(\tau+1)=\Pr_{i,x}(\tau)-b\cdotp p_{x}(\tau)\cdot\Pr_{i,x}(\tau),x^{( \tau+1)}\
eq x^{(\tau)}, \tag{4b}\]  
where \(b,\;0<b<1\) is a step size parameter that controls the convergence time of the learning process [21, 22]. The impact of parameter \(b\) on the convergence time of the algorithm is numerically studied in Section 6. Equation (4b) represents the probability that visitor \(i\) will select a different type of recommendation \(x^{(\tau+1)}\) in time slot \(\tau+1\) compared to the one in time slot \(\tau\), i.e., \(x^{(\tau)}\) while Equation (4a) reflects the probability of museum visitor \(i\) continuing to prefer the same type of recommendation, i.e., \(x^{(\tau+1)}=x^{(\tau)}\). It is noted that the stochastic learning automata approach need the following information: (a) visitor's probability of recommendation selection at the previous time slot and (b) the penetration of each recommendation, which is expressed in Equation (3). The latter characterizes each recommendation and considers the total QoE of visitors who selected a specific recommendation over the total QoE of all visitors within the museum at the examined moment. The latter two types of information can be easily available by a mobile application where the overall framework is envisioned to reside.  
We assume that the RSVTM framework has no prior knowledge of the reward probability \(p_{x}(\tau)\), \(0\leq p_{x}(\tau)\leq 1\), \(x=A,B,C\) and the action probability \(\mathbf{Pr_{i}}(\tau)=\{\Pr_{i,\text{A}}(\tau),\Pr_{i,\text{B}}(\tau),\Pr_{i,\text{C} }(\tau)\}\). Therefore, the initial selection of recommendation by the museum visitors could be made with equal probability, i.e., \(\Pr_{i,\text{x}}(\tau=0)=\frac{1}{3}\). Lastly, it is noted that there is a convergence towards the type of recommendation that will mitigate visitors waiting time before starting a museum tour and provide them with increased satisfaction related to their personal visiting time limitations. The description of the recommendation selection reinforcement-learning algorithm is presented in Section 5.
## 4 Results and Discussion
### Clustering and Tool Condition Identification
2 & 0.05 & Initial wear & I & 7.01 & 0 & Initial wear \\ NO. 3  
single cluster. However, the \(War\) of cluster V and VI are 0.25 and 0.27, respectively. The monitoring of tool wear condition is unaffected. In this Test III, samples of the turning process are fed into CAGS, where the noise coefficient is set to 0.2, the resolution coefficient is set to 1, the halo coefficient is set to 0.5, and the merge coefficient is set to 0.1. Clustering results are shown in Figure 18, where the five groups of samples are finally clustered into four classes. Based on clustering results, the \(War\) of each cluster is calculated and listed in Table 4. The predicted wear conditions fully match with the actual tool wear conditions, although the \(War\) of cluster III has a difference with the VB of NO. 3 and NO. 4. The results of this test demonstrate the robustness of the proposed TCM system, which is effective for variable cutting processes such as milling and turning. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test ID**} & **Cutting** & \multirow{2}{*}{**VB (mm)**} & **The Actual** & \multirow{2}{*}{**Clustering**} & **Cluster** & \multirow{2}{*}{_War_} & **The Predicted** \\  & & & **Tool Wear** & & **Label** & **Density** & **Tool Wear** \\  & & & **Condition** & & & **Condition** \\ \hline \multirow{4}{*}{A} & NO. 1 & 0 & Initial wear & I & 11 & 0 & Initial wear \\  & NO. 2 & 0.07 & Initial wear & I & 11 & 0 & Initial wear \\  & NO. 3 & 0.09 & Initial wear & I & 11 & 0 & Initial wear \\  & NO. 4 & 0.26 & Middle wear & V, VI & 4.57, 4.33 & 0.25, 0.27 & Middle wear \\  & NO. 5 & 0 & Initial wear & I & 11 & 0 & Initial wear \\  & NO. 6 & 0.07 & Initial wear & II & 8.85 & 0.04 & Initial wear \\  & NO. 7 & 0.11 & Middle wear & II & 8.85 & 0.04 & Initial wear \\  & NO. 8 & 0.29 & Middle wear & IV & 4.21 & 0.29 & Middle wear \\  & NO. 9 & 0 & Initial wear & I & 11 & 0 & Initial wear \\  & NO. 10 & 0.07 & Initial wear & I & 11 & 0 & Initial wear \\  & NO. 11 & 0.11 & Middle wear & I & 11 & 0 & Initial wear \\  & NO. 12 & 0.31 & Severe wear & III & 4 & 0.31 & Severe wear \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tool wear condition monitoring results of Test II. Figure 17: Clustering results of Test II.
## 7 Discussion
### Comparison with Traditional Models  
To evaluate the performance of our proposed model comprehensively, we compared it with three other traditional optimization models: deterministic, probabilistic, and fuzzy. The comparative results showed that traditional deterministic models had 20% higher costs, and on-time delivery rates were approximately 30% lower [22]. Meanwhile, the stochastic programming (SP) and robust optimization (RO) models achieved a 15% cost reduction and a 10% increase in on-time delivery performance, though they were computationally demanding. The fuzzy models improved decision-making, reducing costs by 12% and increasing delivery performance by 15% [6]. In contrast, the integrated GP, DCC, and HGSA model reduced costs by 20% and improved delivery rates by up to 17% compared to the traditional models. This demonstrates the model's superior performance in reducing costs and improving deliveries while maintaining computational efficiency. These results confirm the utility of our approach in enhancing supply chain resilience and efficiency under high uncertainty.
# Roadway Backfill Mining with Super-High-Water Material to Protect Surface Buildings: A Case Study
## 3 Design for Roadway Backfill Mining under Surface Buildings
### Design of the Technical Parameters
#### 3.2.1 Layout and Support Design of Roadway  
This mining process includes roadway mining and backfilling. Due to the advantages of flexible and appropriate, an intermittent transportation mode was adopted during roadway mining. This mode involves a continuous miner, shuttle car, individual roof bolter, anti-explosive diesel scraper, and continuous haulage, which includes five steps: cutting, loading, transporting, cleaning and mechanical operation. Similarly, during the backfilling process, two roadways were mined successively in the first stage, and followed by backfilling within these excavated roadways using the super-high water material. When the strength of the backfill body becomes stable, and no seepage of the backfill body occurs in the coal pillar, the next cycle can begin.  
Based on the geological conditions, relevant regulations and other factors, the two observation lines (along the strike and inclination) were set up above the XV2214 working face. The Global Navigation Satellite System (GNSS) and Real-time kinematic (RTK) method, a method of GPS measurement, was used for monitoring the surface subsidence, with horizontal accuracy of 3 mm and vertical accuracy of 5 mm after static state. A schematic diagram indicating the backfilling process and layout of the observation lines is shown in Figure 3.
## 3 Results and Discussion  
### Conventional Extraction Method (CEM) and Microwave-Assisted Extraction (MAE)  
Table 2 shows the ability of the extracts to scavenge the stable free radical DPPH and the total phenolic content (TPC) of the extracts obtained by CEM and MAE using different ethanol/water solutions. It is apparent that the ethanol/water ratio affects the concentration of the extracted phenolic compounds as well as the antioxidant activity of the extract.  
The extract obtained from CEM at room temperature, using ethanol/water 95:5 \(v/v\), showed the highest antioxidant activity (IC\({}_{50}\) = 12.1 \(\mu\)g/mL) but one of the lowest TPC (4.1 mg\({}_{\text{GAE}}\)/g\({}_{\text{rosemary}}\)) among all the extracts. An interesting observation is that during CEM, the TPC increases with increasing the water content in the extracting solvent: the highest TPC (11.1 mg\({}_{\text{GAE}}\)/g\({}_{\text{rosemary}}\)) was obtained in ethanol/water 50:50 \(v/v\). This result is in agreement with the recent work from [PERSON] et al. [13], who observed the same trend for the extraction of phenolics from rosemary distillation residue. [PERSON] et al. [27] also reported that the use of hydroethanolic solvent leads to the extraction of both hydrophilic and lipophilic phenols from Mediterranean plants.  
Another point worth noting is that, in the case of CEM, the DPPH radical scavenging ability decreased with increasing TPC. This might seem strange as one could expect the opposite trend, considering that phenolic compounds are known for their strong radical scavenging ability. However, this result can be explained by the fact that there are some non-phenolic compounds that can react with the Folin-Ciocalteau reagent (such as glucose and citric acid) but which cannot interact with DPPH [28; 29]. Analogous observations have been made by [PERSON] et al. [30], who studied the TPC and antioxidant activity of the extracts of fat-free residues of five different oilseeds.  
As far as MAE is concerned, the difference in dielectric properties of the solvents commonly used in MAE is of great significance. More precisely, the mixture of water and ethanol has a higher dielectric constant (\(\varepsilon\)) and good heating efficacy (tan \(\delta\)), increasing the exerted pressure on the cell wall of the plants, causing its rupture and releasing its bioactive compounds. The results obtained from the MAE showed that by increasing the amount of water in the solvent mixture, the TPC and the DPPH radical scavenging ability both increase. This is an indication that MAE potentially leads to the extraction preferably of phenolics which are strong DPPH scavengers. MAE using ethanol/water 50:50 \(v/v\) provided an extract with TPC and DPPH radical scavenging ability values analogous to the extract obtained by CEM using ethanol/water 70:30 \(v/v\), in a significantly shorter extraction time (1 h for MAE and 24 h for CEM).
## 4 Discussion
### Different Scattering of the Marker Clusters Has a Limited Impact on AoR Estimation Accuracy  
Finally, the simulations presented in Section 3.2, which considered different scattering of the marker clusters on the surface of each segment, showed that no significant dependency between dispersion parameters and marker disposition was observed for both algorithms. The larger variability in estimation accuracy was obtained for the 1-DoF motion configuration (A); however, it was not possible to derive a correlation with the marker scattering in each cluster. This aspect should be taken into account during experimentation since marker positioning has an impact on bone pose estimation, which could not be investigated with the modeling approach considered in this study.
# Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
## 2 Related Work and Motivations
These are solutions enabling global distribution between server rooms located in different geographic locations, e.g., GARD [38] and GARDIB [7], and also local approaches such as FARD [39] and FNRD [6] strategies. All systems presented in the solutions work in a way that minimizes the response time for each individual HTTP request. Our latest work [8; 9; 40; 41] proposed a two-layer cloud architecture. The results obtained showed significantly better performance for intelligent switches than for nonintelligent strategies. Figure 1 shows a typical architecture of cluster-based web system with load balancer and many web servers. This architecture was used while conducting the research in this article. Taking into account the results of the latest research [40; 41], we propose the design of a new load balancer that distributes the HTTP request among web servers placed in one location (one region) in this article. Furthermore, several solutions for request response time prediction using artificial neural networks were tested and applied in Load Balancer. The detailed description of the proposed approach is presented in the subsequent sections.
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 4 Experimental Evaluation
### Experimental Setup
#### 4.1.2 Platform  
We conducted our experiments on a small-scale HPC-like platform representative of industrial deployments, consisting of eight physical nodes hosted in our partner's data center. Each node was equipped with two Marvell ThunderX2 processors, creating a total of 64 ARMv8-A cores per node. These processors are widely adopted in low-power data center applications and support Scalable Vector Extension (SVE) instructions, which were central to our evaluation. All nodes were provisioned with 256 GB of RAM and ran Red Hat Enterprise Linux 8. On each node, we installed OpenJDK 16, which can compile bytecode to optimal vector hardware instructions on our CPU architecture, thus achieving superior performance compared to equivalent scalar computations. Finally, we used the Hi-Bench benchmark suite for big data applications to generate synthetic datasets and run standardized and easily reproducible experiments. Although our setup does not qualify as a full-scale supercomputing environment, our objective was to replicate HPC workloads using industrial-grade infrastructure and widely adopted big data frameworks to evaluate the feasibility of ARM-based solutions outside of traditional HPC facilities.
## 7 Comparative Discussion on Various Energy-Efficient Navigation Systems
### Hybrid Navigation Systems  
Both reactive and deliberative strategies are incorporated in hybrid navigation systems. In Hybrid navigation systems, reactive behaviors are utilized to avoid obstacles swiftly, and deliberative planning is implemented to optimize paths over a period. An infrared sensor and GPS navigation technique can be implemented together for the navigation of a MR. MR navigation systems might be used for an extended duration without being required to recharge their batteries because renewable energy can be used for data acquisition and interpretation [51]. With these technologies, real-time responsiveness and energy-conscious planning are balanced. Hybrid navigation systems can achieve efficient consumption of energy by continuously adapting the amount of awareness that depends on the complex nature of the surroundings.
## 1 Introduction
DC bus signaling (DBS) and droop control are frequently employed in DC nano and microgrids with various DERs operating in a decentralized way [12]. They are based on locally measured variables, such as DC bus voltage, and provide an effective means for setting power sharing among parallel units in steady-state conditions. This is achieved by defining two parameters: the droop (\(\Delta\)V/\(\Delta\)I) slope (factor) and the threshold (no-load) voltage. The first is usually defined based on the power ratings of the DERs and remains constant. The larger units have smaller droop slopes to take larger shares of the power/current required for balancing supply and demand, mitigating DC bus voltage variations (\(\Delta\)V). The threshold voltage is used for energy management and can be updated by a supervisory controller employing low bandwidth communication, but is not essential for system operation. Disregarding the latter, for high reliability, and assuming that he SC and battery are not co-located, the SC unit should present a droop factor lower than that of the battery's to be able to contribute more to dynamic voltage regulation. However, additional means are required to accommodate its low energy capacity constraint. For that, a HPF with a properly selected cut-off frequency is proposed in this paper. Simulation and experimental results are presented to show that this approach can provide an improvement in the dynamic regulation of the DC nanogrid voltage as well as mitigate the fast current components in the battery unit.