esa-sceva/satcom-chunk-collection huggingface dataset:
# Particle Number of Aerosol in Specific Conditions of Biotechnology Laboratory Cleanroom
## 2 Materials and Methods
### Sampling Procedure  
Particle count measurements were performed using a Fluke 985 laser particle counter (FLUKE, Everett, WA, USA). The particle counter had a valid calibration until February 2023. The measurements were performed on a total of six channels (0.3 \(\upmu\)m, 0.5 \(\upmu\)m, 1.0 \(\upmu\)m, 2.0 \(\upmu\)m, 5.0 \(\upmu\)m, and 10.0 \(\upmu\)m). The basis for determining the measurement method was the technical standard ISO 14 644-1 (hereinafter referred to as the technical standard) [19]. The ISO 14 644-1 air purity classes are listed in Table 1. A network of eight measurement points was established in each room. These points were located in the center of gravity of the equally sized areas into which each room was divided. The number of points was increased compared to the requirements of the technical standard for the statistical processing of measurement data. The measurements were performed at a vertical height of \(h=1500\) mm. The mouth of the measuring probe of the device was directed vertically upwards. The volume of the aspirated air for each measurement was \(V=5.7\) dm\({}^{3}\). The suction time was \(t=120\) s. The classification of the air purity class of the laboratory and the office was carried out according to part 4 of the technical standard ISO 14 644-1.  
In addition, room temperature \(t\) [\({}^{\circ}\)C], relative humidity \(\phi\) [%], and outdoor air velocity \(v\) [m\(\cdot\)s\({}^{-1}\)] were measured using KIMO AMI 300 multifunction equipment (KIMO Instruments, Saint-Priest, France). Absolute atmospheric pressure was measured using a Greisinger GPB 3300 barometer (Greisinger Electronic GmbH, Regenstauf, Germany), with a measurement deviation of \(\pm 2\) hPa. The values related to the microclimatic conditions were measured a total of three times: namely, at the beginning of the measurement of the number of particles, approximately in the middle of the measurement, and at the end of the measurement. The arithmetic mean was then calculated from the individual values. The variability of the measured values was very low (coefficient of variation less than 0.01%). Two people were present in the room during the measurement, i.e., a full operation measurement strategy was chosen.  
In total, three sets of measurements were performed in different months of the year--February, May, and July. These measurements will be referred to hereafter as measurements A, B, and C.
###### Abstract  
One of the continuous challenges related to the growing popularity of mobile devices and embedded systems with limited memory and computational power is the development of relatively fast methods for real-time image and video analysis. One such example is Optical Character Recognition (OCR), which is usually too complex for such devices. Considering that images captured by cameras integrated into mobile devices may be acquired in uncontrolled lighting conditions, some quality issues related to non-uniform illumination may affect the image binarization results and further text recognition results. The solution proposed in this paper is related to a significant reduction in the computational burden, preventing the necessity of full text recognition. Conducting only the initial image binarization using various thresholding methods, the computation of the mutual similarities of binarization results is proposed, making it possible to build a simple model of binary image quality for a fast prediction of the OCR results' quality. The experimental results provided in the paper obtained for the dataset of 1760 images, as well as the additional verification for a larger dataset, confirm the high correlation of the proposed quality model with text recognition results.  
Keywords:image quality assessment; Optical Character Recognition; image binarization; OCR prediction; image analysis +
Footnote †: journal: Applied sciences
# Recognition of Driving Behavior in Electric Vehicle's Li-Ion Battery Aging
## 3 The Recognition of Driving Behavior
### Deep Learning Driving Behavior Recognition
#### 3.3.3 Fusion Models
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Category** & **Methods** & **Used Features** & **Recognition Level** & **Data Window** & **Accuracy** \\ \hline \multirow{3}{*}{HMM} & HMM [9] & Steering gas, and brake * & Driving action & 0.3-5.2 s & 82-90\% \\  & HHMM [12] & CAN data * & Sequence of driving state & 2-5.5 s & 93-99.85\% \\  & AR-HMM [14] & Speed, pedal stroke * & Driving state & 0.1 s & - \\ \hline \multirow{3}{*}{GMM} & GMM [15,16] & Following distance, all CAN & Driving action & 0.8-20 s & 72.9–92.65\% \\  & GMM-Fusion [18] & IMU * & Driving action, sequence of & Entire trip & - \\ \hline \multirow{3}{*}{SVM} & SVM [21] & Radar, video, GPS, CAN data \(\dagger\) & Single event & 10 s & 80.8\% \\  & SVM-hybrid [24] & CAN data, GPS \(\dagger\) & Driving action & 0.1 s & 97\% \\  & SSVM [26] & Steering, acceleration, brake, & Driving style & instant & 86.6\% \\  & & gear * & Driving style & 0.5 s & 92\% \\ \hline FL & FL-variant [36] & IMU \(\dagger\) & Driving style & 0.5 s & 92\% \\ \hline \multirow{3}{*}{KNN} & KNN [38] & Brake, GPS, throttle, traffic & Single event & 0.1s & 90.1\% \\  & & light * & & & \\ \hline \multirow{3}{*}{RNN} & LSTM [50] & IMU, CAN data * & Driver identification & 30 s & 74.7–82.3\% \\  & Stacked-LSTM [49,51] & IMU, GPS, camera *,\(\dagger\) & Driving style & 6.4 s & 86–96\% \\ \hline \multirow{3}{*}{CNN} & CNN [42] & CAN data \(\dagger\) & Driver identification & 100 s & 96.85\% \\  & CNN image transform [45] & CAN data \(\dagger\) & Driving style & 20 s & 83.8–99\% \\ \hline \multirow{3}{*}{Fusion} & CNN-LSTM-Attention [63] & IMU, GPS \(\dagger\) & Driving action and state & 1 s & 95.85\% \\  & CNN-RNN-Attention [62] & CAN data \(\dagger\) & Driver identification & 60 s & 97.9–98.4\% \\ \cline{1-1}  & CNN-BiGRU [61] & CAN data, GPS, Driver \(\dagger\) & Driving style & 3 s & 97.33\% \\ \hline \end{tabular}
* Simulated data. \(\dagger\) Real vehicle collected data. Driving action: non-stop straight road, stop, left turn, right turn, etc. Driving state: stop, acceleration, coasting, braking, etc. Driving style: normal, aggressive, passive, etc.  
\end{table} 
Table 5: Comparison of conventional and deep learning recognition.
## 4 Results
### Systematic Analysis
#### 4.2.4 Q7. How Can Enterprises Successfully Integrate Digital TPM?
In parallel, cross-functional collaboration between departments, such as maintenance, production, IT, and data science, is essential to ensure a unified and effective implementation [41]. When leadership and workforce are aligned, the organization is well-positioned to fully realize the benefits of digital TPM. Another critical pillar is the establishment of strong data governance. As sensor networks generate large volumes of data, companies must ensure that this data is accurate, standardized, and interoperable across platforms [55, 61]. Without consistent and reliable data, predictive models lose effectiveness. To address this, organizations should implement centralized data lakes or structured maintenance databases that enable efficient analytics and informed decision-making. In terms of execution, a gradual and iterative approach is recommended. Instead of launching a full-scale transformation all at once, companies should begin with pilot projects targeting critical machines or production lines [51, 53]. These early initiatives act as proof of concept, generating quick wins that build momentum and strengthen organizational buy-in [51]. Lastly, to ensure sustainability and long-term impact, companies must cultivate a culture of continuous improvement. Feedback loops should be embedded into maintenance processes, allowing teams to regularly analyze performance data, identify gaps, and update predictive models as needed [69, 70]. By fostering a mindset of learning and innovation, organizations ensure that their digital TPM systems evolve in step with future challenges and technological advancements.
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 4 Experiment
### Comparison with State-of-the-Art Technology (BI)  
To further demonstrate the superiority of the proposed LNLCN model, we compared our LNLCN model with nine state-of-the-art SISR models: SRCNN [13], FSRCNN [40], VDSR [14], EDSR [28], DBPN [49], RDN [50], FPAN [51], RCAN [15], ESBT [32], HNCT [34], SAN [20], HAN [52], and NLSN [31]. For comparison, we measured the PSNR and SSIM values on the Y channel of the reconstructed image using a MATLAB [53] function. Table 3 shows the degradation model reconstruction indicators of different methods at scaling factors of two, three, and four. The proposed LNLCN model achieves the best performance at various scaling factors compared to the other SR methods. This is mainly because, compared with the traditional nonlocal modules, we used the learnable nonlocal contrastive attention module to sparsely aggregate image-related information more effectively so that the network could focus on discriminating valid information. In addition, our ATG target training strategy could alleviate the ill-posedness of the super-resolution task by enabling the network to generate slightly different outputs, which is meaningful for obtaining superresolution results that are satisfactory in reality and perception, especially in terms of generating pleasing images.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### User Equipment
#### 3.3.1 PHY Security
Their last method mentioned further aims to hinder the abilities of an eavesdropper by way of Fountain Coding [96], which is a method used to encode packets in a stream of continuous data without re-transmissions. Their implementation utilizes an adaptive power allocation policy that varies SNR, thereby creating an even more difficult scenario for an eavesdropper to capture meaningful information. Physical layer security thus can benefit all three sectors of thesecurity paradigm: confidentiality, integrity, and availability. This makes PLS so important for 5G and beyond and warrants continued research and exploration.
## 4 Results and Discussion
### Frequency Sweep  
As seen in Figure 3a,b, the \(tan\delta\) and the loss moduli followed similar trends as the frequency increases from 1 Hz to 100 Hz. In general, the values increased with increasing frequency. Peaks were seen around 25 Hz, 70 Hz, and 100 Hz for all the configurations. The \(tan\delta\) was improved from values ranging 45-85% for various nanofillers additions when compared to the raw carbon fiber sample, as shown in Table 3. The loss modulus values showed an increase of values ranging 32-70%. This implies enhanced damping due to the increase in interfacial surface area resulting from high aspect ratios of CNTs and ZnO nanorods. The ZnO nanorods were reported to exhibit enhanced damping capabilities in the presence of a polymer due to dissipation by friction between the matrix and ZnO, and due to the electromechanical coupling [41]. The numbers also indicate that at higher frequencies, the addition of just one nanofiller is more advantageous than adding both ZnO and CNTs for better dissipation capabilities.  
Figure 3c shows that the storage moduli are almost independent of frequency for all the configurations. However, there is a negative peak at around 70 Hz. This peak indicates these  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Change in Tan Delta** & **Change in Loss Modulus** & **Change in Storage Modulus** \\  & **(at 61 Hz, \%)** & **(at 61 Hz, \%)** & **(at 61 Hz, \%)** \\ \hline CF and ZnO & 84.88 & 54.15 & \(-\)16.62 \\ CNT and CF & 77.98 & 68.68 & \(-\)5.22 \\ CNT and ZnO & 47.69 & 32.45 & \(-\)10.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the viscoelastic performance of the various composite configurations with respect to a composite based on CF under frequency sweep.  
Figure 2: Evolution of the different composites’ viscoelastic properties in temperature sweep analysis at 1 Hz of various composites’ configurations. (**a**) Tan delta, (**b**) Loss modulus, and (**c**) Storage modulus.  
composites might see a degradation in performance when operated at this frequency. Also, there is a significant decrease in the values of storage moduli for the composites with nanofillers when compared to the raw fiber composite, as seen in Table 3 and Figure 3c. This might be due to their exposure to high moisture levels during the hydrothermal growth of ZnO nanorods and the agglomeration during spraying of CNTs.  
Figure 3: The viscoelastic properties evolution of the different composites in frequency sweep analysis at 30 \({}^{\circ}\)C. (**a**) Tan delta, (**b**) Loss modulus, and (**c**) Storage modulus.
## 3 Analysis and Discussion of Test Results
### Analysis of Capillary Water Absorption Properties of Recycled Concrete
#### 3.2.5 Late Water Absorption  
From the late period phase (T\({}^{1/2}\) = 207.8-777.7 s) of the unit's cumulative water uptake curve, a linear fit was made to obtain the late-period water uptake rates for the RAC, WRAC, GRAC, and WGRAC groups as a function of the number of freeze-thaw cycles for different media, as shown in the figure below.  
According to Figure 8, it can be seen that the water absorption rate of the specimen under three different media used for freezing and thawing is very small, and the change is very small, because in the late stage of freezing and thawing, the growth of internal cracks in the specimen gradually slows down, and the water absorption rate in the late stage also gradually tends to be leveled off.  
Figure 7: Freeze–thawing times of different media secondary capillary water absorption rate. (**a**) Fresh-water freeze–thaw cycle; (**b**) sulfate freeze–thaw cycle; (**c**) chlorine salt freeze–thaw cycle.  
Through the above data analysis and comparison, it can be seen that the initial water absorption rate of the concrete changes, S1, is large; the secondary water absorption rate, S2, does not change much; and the later water absorption rate, S3, has an even more minimal effect on the capillary water absorption of concrete. It can be found that the capillary water absorption capacity of concrete is mainly determined by the initial water absorption rate, S1, so the initial water absorption rate, S1, is mainly used in the actual project to measure the capillary water absorption capacity of concrete to judge the durability performance of concrete.  
Prediction Model of Initial Capillary Water Absorption of Recycled Concrete under Freeze-Thaw Environment with Different Media  
From the above analysis, it can be seen that the initial capillary water absorption has the greatest influence on the capillary water absorption performance of recycled concrete, so the initial capillary water absorption can be used to represent the capillary water absorption of recycled concrete. Therefore, the prediction model of the initial capillary water absorption of recycled concrete under freeze-thaw cycles with different media was established according to different modification methods of recycled coarse aggregates and by using the numbers of freeze-thaw cycles as variables.
# Novel Design of Expandable Spinal Cage for Efficient Lumbar Spine Fusion Operation
## 2 Design of an Expandable Cage
### Link Mechanism for Angle Extension  
The angle adjustment of the spinal cage is achieved through the linkage mechanism illustrated in Figure 2, with the angle of the cage primarily determined by the relative heights of joints 4 and 5. The height of Joint 5 varies depending on the position of Joint 3, while the positions of joints 1, 2, and 4 remain unchanged due to the linkage structure. In other words, once the height of Joint 4 is set through the height adjustment process, the cage angle is controlled exclusively by adjusting the position of Joint 3. Thus, the angular change of the spinal cage with respect to the position of Joint 3 can also be expressed mathematically. Defining the position of Joint 3 as its horizontal distance relative to Joint 2, as shown in Figure 2, the spinal cage angle \(\theta_{4}\) can be represented by the following equation:  
\[a=\frac{L_{3}^{2}-L_{2}^{2}-L_{4}^{2}-X_{2}^{2}}{2L_{2}L_{4}}-\frac{X_{2}}{L_{ 4}}\sqrt{H^{2}-L_{2}^{2}}-\sqrt{H^{2}-L_{2}^{2}}-\frac{X_{2}}{L_{2}} \tag{2}\]  
\[b=-\frac{2\text{H}}{L_{2}} \tag{3}\]  
\[c=\frac{L_{3}^{2}-L_{2}^{2}-L_{4}^{2}-X_{2}^{2}}{2L_{2}L_{4}}-\frac{X_{2}}{L_{ 4}}\sqrt{H^{2}-L_{2}^{2}}+\sqrt{H^{2}-L_{2}^{2}}+\frac{X_{2}}{L_{2}} \tag{4}\]  
\[\theta_{4}=2\text{tan}^{-1}\left(\frac{-b\pm\sqrt{b^{2}-4 ac}}{2a}\right) \tag{5}\]  
Using the equation above, it can be observed that spinal cage angle \(\theta_{4}\) may have two possible values depending on the positional change of Joint 3. However, since lumbar lordosis angles are generally positive, the cage angle \(\theta_{4}\) in practical design must also take a positive value, consistent with the lumbar lordosis angle. Therefore, Equation (5) should be modified as follows:  
\[\theta_{4}=2\text{tan}^{-1}\left(\frac{-b+\sqrt{b^{2}-4 ac}}{2a}\right) \tag{6}\]  
From Equation (6), we can determine the spinal cage angle based on the height, \(H\), of Joint 4, which is set through height adjustment, and the position of Joint 3. Finally, by defining the adjustable range of Joint 3's position, we can achieve a desired angular adjustment range of spinal cage and incorporate this into the design.
# Black Ice Classification with Hyperspectral Imaging and Deep Learning
## 5 Model Architecture
The model's final layer is where actual classification takes place. The \"softmax\" activation function is used in this dense layer, which has units equal to the number of classes. Overall, the input data are initialized as 3D data with height, width, and depth (number of bands in one hyperspectral image data), which is called a data cube. For a hyperspectral imaging system, every pixel counts as a data point because of different spectral properties, unlike other 2D images. 3D convolution was done to preserve the spectral information of the input data cube, and the 2D Convolutional Neural Network was applied because it discriminates the spatial information, which is one of the major components in hyperspectral imaging. For the second part of this paper, one of the existing methods was implemented to analyze the difference. It classified the colored (RGB) black ice image with data augmentation and the CNN [27]. That method uses data augmentation to increase the data and then applies the CNN. A similar process was implemented with the existing black ice images. The images were augmented and passed through the 2D CNN model to analyze the process. The first step of this process is feature extraction, and the next stage is classification.
## 3 Results
### Transcriptome Analysis  
#### 3.2.1 Assembly of Flower Hoya Species Transcriptomes  
The transcriptome cDNA library was generated from the flower section using Illumina HiSeq 4000 platform. After removing low-quality sequences, a total of 65.06 million high-quality readings were successfully generated from all three species. De novo assembly in Trinity software transcript filtering with expression value yielded 144,042 final unigenes covering 89.21 Mpb for _H. cagayanensis_, 156,830 unigenes with 101.87 Mpb for _H. lacunosa_ and about 102,914 unigenes with a total of 93.59 Mpb for _H. coriacea_. In addition, the values of N50 are 989 pb (_H. cagayanensis_), 1178 pb (_H. lacunosa_) and 1545 pb (_H. coriacea_). Other than that, the average values of unigene length were 619.35 pb, 649.54 pb, and 908.36 pb for _H. cagayanensis_, _H. lacunosa_, and _H. coriacea_ (Table 3).  
The net data readings were mapped back to the final unigene database using HISAT2 to evaluate the transcriptome quality of the de novo assembly. Next, the number of unigenes with complete sequence (full-length ORF) was also screened using a comparison between the transcriptome unigene sequence with the sequence found in several databases. The results showed that 32,385 unigenes represented full-length proteins for _H. cagayanensis_ and 27,839 unigenes for _H. lacunosa_ and 31,043 unigenes for _H. coriacea_.
## 5 Evaluation
### Simulation Environment  
This section describes the specifications and simulation environments of deep-learning workstations to evaluate the performance of DQN-based erasure coding network topologies. The deep learning simulation was conducted on a workstation running the Ubuntu 20.04.4 operating system, with a XEON 4110 (8 core \(\times\) 2) CPU, 128 GB of DDR4 memory, and four RTX 2080 graphics cards.  
OpenvSwitch was used to set up an environment containing virtual nodes and a set of links connected to the switch, and the SDN network controllers used open-source-based Ryu as an OpenFlow network controller. For network bandwidth fluctuations, the link bandwidth of each node and switch reached up to 1 Gbps and was configured to be randomly changed within the range of 300 to 600 Mbps. We randomly specified a 15% probability of switch failures. The traffic intensity was set to 30% to 50% because it would be necessary to assume that some data were being transmitted. The key variables and the set values used in the experiments are listed in Table 3.  
The network topology used to experiment with the DQN-EC performance was given a fat tree [42]. The fat-tree network topology is configured using parameter \(\alpha\) and consists of \(\alpha^{3}/4\) servers and \(5\times\alpha^{2}/4\) switches. In the experimental evaluation, parameter \(\alpha\) was specified as 8, resulting in a network topology with 128 servers and 80 switches. For 128 servers, RS (6, 3) erasure coding is applied to randomly generate six data blocks and three parity blocks, which are distributed and stored on nodes, and the size of the blocks stored is 128 MB. For various experimental evaluations, the test was conducted by increasing the probability of a switch failure from 0% to 15% when requesting a decoding.  
Figure 7 shows a topology example consisting of 16 servers and 20 switches by specifying parameter \(\alpha\) as 4 to help understand the fat-tree network topology with erasure coding. The topology configuration shows that green-shaded nodes are source nodes for transferring blocks in a decoding operation, and blue-shaded nodes are destination nodes for receiving blocks and performing decoding operations. In the above simulation environment and EC network topology, the DQN-EC-based network routing method was labeled DQN-EC and compared with the underlying network routing algorithm OSPF.