esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Logic-Based Performance Enhancement of FSO Systems Under Adverse Weather Conditions
## 2 Modeling of Rain and Fog Impairments in FSO System  
The FSO communication link has many advantages. Unlike the radio frequency spectrum, it operates without the need for a license. Its installation is possible in regions where optical fibers or cables cannot be installed due to their geographical location [1]. However, FSO signals are highly sensitive to atmospheric conditions, resulting in degradation of received power and attenuation losses due to rain, fog, haze, and dust [2]. Rain and fog are among the most critical environmental impairments affecting FSO performance. In this study, we focus on modeling the impact of rain and fog on signal quality and transmission distance. The modeling approach is supported by both empirical studies and standard recommendations such as ITU-R P.1814 and ITU-R P.1817, which provide prediction methods and propagation data essential for designing robust terrestrial FSO systems [19; 20].
# A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control
## 2 Traffic Signal Timing Optimization Algorithm
### Dynamic Programming Algorithm
#### 2.1.1 Forward Recursion  
1. Set \(j=1\), \(s_{j-1}=0\) and \(v_{j}(0)=0\).
2. Calculate \(S_{j}\).
3. For \(s_{j}\) in \(S_{j}\) { Calculate \(X_{j}\bigl{(}s_{j}\bigr{)}\). \[v_{j}(s_{j})=\operatorname{Min}_{x_{j}}\bigl{\{}f_{j}\bigl{(}s_{j},x_{j}\bigr{)} +v_{j-1}\bigl{(}s_{j-1}\bigr{)}\bigl{|}x_{j}\in X_{j}\bigl{(}s_{j}\bigr{)} \bigr{\}}\] record \(x_{j}^{*}\bigl{(}s_{j}\bigr{)}\) as the optimal solution in Step 2.
4. If \(\bigl{(}\sum_{k=1}^{(j+1)}X_{k}^{\min}\leq T\bigr{)}\), \(j=j+1\), go to Step 2. Else STOP.  
For each barrier group, DP calculates the optimal decision \(x_{j}^{*}\bigl{(}s_{j}\bigr{)}\) for each state variable \(s_{j}\). The performance measure (objective function) \(f_{j}\bigl{(}s_{j},x_{j}\bigr{)}\) used to determine the state variable is passed to the lower optimization level with the constraint of control variable \(x_{j}\). The stopping criteria will be met if the sum of the minimum time length in all barrier groups is larger than \(T\). The justification of the stopping criterion is different from that in the COP algorithm [14], which does not consider the constraint of the maximum green time of a phase group. In addition, considering that pedestrians need to cross the street, barrier groups are not allowed to be skipped in this study.  
After all decisions are made for all barrier groups, the optimal decision \(x_{j}^{*}\bigl{(}s_{j}\bigr{)}\) of each barrier group can be retrieved in the backward recursion as follows.
## 3 Key Technology of the A2S Integrated MIoT
### Spatial Location Optimization
#### 3.4.2 Deployment Optimization  
In spatial location optimization of the A2S integrated MIoT, the deployment optimization of multi-ABSs is an important aspect. The deployment optimization involves deploying multiple ABSs to the optimal location in an MIoT system for efficient communication coverage and DT. Figure 10 shows the deployment model of multiple ABSs in an A2S integrated system. The objective of multi-ABS deployment optimization is to determine the appropriate BS deployment location under the premise of meeting certain constraints, so as to maximize the communication coverage and transmission efficiency. Table 5 is a list of some common optimization goals and methods [143].  
In A2S integrated scenarios, deployment optimization is widely used in many fields. Wireless communication network operators can use deployment optimization to optimize the location of BSs. By analyzing user needs, geographic information, a signal propagation model, and other factors, the system can generate the best BS deployment plan to improve signal coverage, reduce interference, and optimize network capacity and user experience. In the field of video surveillance, deployment optimization can be used for camera layout planning. The system can generate the optimal camera layout scheme according to the monitoring target area, monitoring requirements, equipment performance, and other factors to achieve comprehensive monitoring coverage, and improve security and monitoring effects. In IoT applications, deployment optimization can help to rationally deploy IoT devices. For example, in the field of smart homes, the system can generate the best device layout scheme according to a user's needs, the IoT device type and communication distance, and other factors to achieve efficient connection and intellig  
Figure 10: Multi-ABS deployment optimization model.
## 2 Principal component aggregation in wireless sensor networks
### Applications
#### Event detection  
The use of PCA for identifying at the network scale unexpected events which are not detectable at the node level has been discussed in the literature [19, 20]. Detection of such events can be achieved by focusing on the low variance components. In fact, such components are expected to bear coordinates close to zero, as they typically account for sensor equipment noise. In this context, the principal component aggregation scheme can be used to deliver the value of low variance components, and the evaluator function takes the form of a statistical test checking if the coordinates on one or more low variance components is different from zero.
## 2 Materials and Methods
### Impact Tests in Compression and Combined Compression and Shear  
Impact tests were performed on a free fall guided impact machine (1000_00_MIMAT, Cadex, St-Jean-sur-Richelieu, QC, Canada) equipped with a 6-axis load cell (MC6-2000, AMTI, Watertown, MA, USA) (capacity z-axis: 9 kN, sampling rate 100 kHz). The compression setup consisted of a 5.7 kg free-falling flat mass dropped onto the foam sample positioned horizontally on the load cell (Figure 1a). The combined compression and shear setup was similar to the one described by [PERSON] and [PERSON] [20]. Two identical foam samples were glued with adhesive transfer tape on two surfaces facing each other and having a 45\({}^{\circ}\) angle with respect to the horizontal plane. Both foams were simultaneously impacted by a V-shape impactor of 6.3 kg. The surface of the impactor was covered with sandpaper (gri 80) to limit relative motion at the foam-impactor interface. The load cell was positioned under one of the two impacted samples (Figure 1b).  
In both setups, the impactors were dropped from multiple heights starting at 200 mm, up to the maximal height by increments of 200 mm. The maximal height corresponded either to 2.6 m or to the height at which the load reached the maximal load cell capacity (9 kN). The resulting impact speed ranged from 2-7 m.s\({}^{-1}\), corresponding to typical impact speeds applied to a helmet during a football game, a hockey game or a ski accident [9; 12; 28]. All impacts were filmed with a high-speed camera MotionBLITZ EoSens(r) mini (MIKROTRON, Unterschleissheim, Germany) at a sampling rate of 10 kHz. The displacement of the impactor was measured in the footage by automatically tracking markers placed on the impactor with digital image correlation using GOM Correlate. The synchronization of the load cell measurement and the video footage was done by matching the time of the first load measurement above 50 N with the time of the first video footage showing a contact between the impactor and the foam. High-speed video frame was also used to ensure that there was no slippage between the foam and both the laying surface and the impacting surface.  
Figure 1: Experimental setup of the impact tests in (a) compression, (b) combined compression and shear.
# Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery
## 2 Method
### Full Convolution Neural Networks Based Method  
#### 2.2.1 Network Description  
In this section, we present a specifically designed FCN method inspired by the work of [PERSON] et al. (2019). Figure 4 shows the schematic representation of our network. We perform convolutions aiming at both extracting features from the data and segmenting the needle out of the image. The left part of the network consists of a compression path, while the right part decompresses the signal until original sizes are reached.  
In order to reduce the size of the network and consider existence of the needle in B-scan image sequence continuously, we take three adjacent B-scan images in OCT cube as input and resize the image with a factor of \(\chi\). The left side of the network is divided into different stages operating at different resolutions. Similar to the approach demonstrated in [PERSON] et al. (2019), each stage comprises one to two layers. The convolution layer performed in each stage uses volumetric kernels having a size of \(7\times 7\times 3\) voxels with stride 1. The pooling uses the max-pooling operation in FCN with \(7\times 7\times 3\) voxels with stride 2, thus the size of the resulting feature maps is halved. PReLu non linearities are applied throughout the network. Downsampling allows us to reduce the size of the input information, and furthermore increase the receptive field of the features being computed in subsequent network layers. Each pair of the convolutional and pooling layers computes two times higher features more than the one in the previous layer.  
After three convolutional layers with image size of \(4\times 8\times 3\), the network increases the low-resolution input by de-convolution combining the pooling results from previous layers [PERSON] et al. (2019). The two feature maps computed from the last convolutional layer, having \(1\times 1\times 1\) kernel size and producing the same size as input images which will be converted to probabilistic segmentations of the foreground and background regions by using soft-max operation. In order to obtain the original resolution of the needle foreground, the segmentation result from FCN is used to fuse with the original image after binarization and 4-connected components labeling. The labeled area with a certain number of pixels voting from the output foreground of FCN will be considered as the needle fragments. All needle fragments will be covered by one bounding box to indicate the ROI.  
Figure 4: The architecture of full convolution neural networks (FCN) based needle segmentation method.
## 3 Introduction of 2D-DPRCML
Since there are two dimensions of 2D-PECA, the different odd and even clock cycles allow the iterative results of both dimensions to be utilized. The second is to extend the perturbation period further. Although the ECA is long-periodic, its period is always limited by the number of cells to less than \(2^{M}\), so the added perturbation cannot exceed this period. If different perturbations are added by odd-even transformation, and the perturbations are obtained from different ECAs in the 2D-PECA, the perturbation period can be significantly extended, theoretically reaching a maximum period of \(2^{2M}\). The introduction of perturbation enhances the complexity and effectively alleviates the degradation in the chaotic system [12]. It should be noted that the number of cells \(M\) in \(x\) or \(y\) is larger than the largest of 64, \(R\) and \(L\). \(\delta_{n}\) is a sign matrix calculated as Equation (13), in which \(S_{x}^{t}(c_{1},c_{2},c_{3},\cdots,c_{R})\) and \(S_{y}^{t}(c_{1},c_{2},c_{3},\cdots,c_{L})\) are row vectors in GF. (2), and its result is an \(R\,\times\,L\) matrix containing the elements '\(-1\)' and '\(1\)'. In Equation (13), we intercept the first \(R\) and the first \(L\) cells in ECA \(x\) and ECA \(y\), respectively, to generate the sign matrix, ensuring that every lattice in the system can correspond to a sign in the sign matrix. In addition to reducing dynamical degradation, another purpose of adding perturbations is to reduce the correlation between different lattices [12]. If we simply add the same perturbation to all lattices in each iteration without the sign matrix, the lattices will still be strongly correlated due to the coupling. The purpose of designing the sign matrix is to add perturbations with different and varying signs to each lattice during the iterations of the system, which successfully diminish the correlation between different lattices. The process of \(n\)th iteration in the 2D-DPRCML system is portrayed in Figure 2.  
As shown in Figure 2, the choice of coupled lattices is determined by the iterative results of 2D-PECA. 2D-PECA is iterated once to update the value of \(a_{1}\), \(a_{2}\), \(b_{1}\) and \(b_{2}\) before calculating the value of one lattice. Consequently, when 2D-PECA is in chaos, \(a_{1}\), \(a_{2}\), \(b_{1}\) and \(b_{2}\) are pseudo-random and dynamic rather than fixed as in Refs. [29; 30].
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
## 2 Design, Materials, and Methods
### Zrle  
Preprocessing with delta coding for optimal selective Huffman coding is used to achieve lossless ECG compression [32]. Huffman coding creates variable-length codes with shorter code words for higher probabilities, and each is represented by an integer bit number. Huffman coding is relatively simple, and it is the best coding scheme possible when coded words are restricted to integer lengths [32; 33]. Section 2.2 describes how zero separation increases the zero-value probability of high bits, and this can then be compressed using Huffman. For real-time encoding and minimal implementation complexity, a new method based on RLE [33] was designed with a high probability of zero bases. RLE is a simple technique that is used for digital data compression and represents successive runs of the same value in the data when the value is followed by a counter, rather than the original run of values. ZRLE is modified from RLE to encode zero values only. This method replaces continual zero values as one zero and a number of consecutive zeros, and it requires a quantity of consecutive zeros greater than two. In addition, it fails to compress a single zero with a pair of 0 and 1. The resolution of high bits defines the maximum number of consecutive zeros. For example, four-bit high-bit resolution presents a maximum number of consecutive zeros of 16, or two to the fourth power. However, a lower resolution corresponds with more consecutive zeros in high bits, but an increasing low-bits resolution reduces the total CR. Although a QRS complex is of medium-frequency and has a higher dynamic data range, it provides fewer periods in a single heartbeat. Since it is not possible to know the encoded size of high bits, two consecutive zeros represent the end tag that identifies the end of one block. When a block of data is encoded, the end tag is appended to the last record (that is not a  
Figure 4: MIT/BIH Arrhythmia Database recording 100 original ECG volumes and delta volumes.  
Figure 5: MIT/BIH Arrhythmia Database recording 100 ECG volumes and rotated delta volumes.  
pair of zeros) and the counter. Thus, the decoding process restores the high-bit volume with the same predefined block size, or the zero value is used to make up for the shortage of records in a high-bit volume. The end tag affects the CR terminating the data string with 0, 0, and a suitable segmentation has an end tag to eliminate longer continual zeros in the tail. This experiment aimed to provide a high-bit resolution encoding scheme to achieve the optimal CR for use in dynamic-block mode.
## 2 Proposed Methodology  
An overview of the proposed method for classifying PQDs is shown in Figure 2. It consists of five essential steps: (1) PQD dataset generation; (2) time-frequency transformation; (3) DCNN-based transfer learning; (4) ensemble learning; and (5) evaluation. In the first step, various PQD signals are generated by using the open-source PQD signal generator given in [36]. Once the data are formulated, the signals are then transformed into a time-frequency spectrum of signals using CWT. In the third step, various pre-trained DCNN models including ResNet-50, VGG-16, AlexNet, and SqueezeNet are employed as classification models. After this, a voting-based ensemble model is exercised to achieve the final classification of PQDs. Finally, the model's performance is evaluated by considering various performance evaluation matrices. The details of each step involved in the proposed framework are described in the following sub-sections.
## 3 Results
### HY7602-FA Improves Muscle Hypertrophy and Strength  
To analyze the effect of HY7602-FA on exercise capacity, muscle grip strength and hindlimb skeletal muscle weight were measured. Muscle strength was measured weekly throughout the 4-week feeding period. The results showed that muscle strength in all groups was greater than that in the negative control group (non-exercise group).  
As shown in Figure 5a, grip strength in the control, NFA, creatine, and HY7602-FA groups was 1.19 \(\pm\) 0.09, 1.35 \(\pm\) 0.18, 1.18 \(\pm\) 0.13, and 1.37 \(\pm\) 0.19 N respectively, demonstrating that HY7602-FA increased muscle strength (\(p<0.05\)). Importantly, there was a significant difference in muscle strength between the HY7602-FA and NFA groups.  
Next, to further investigate the effects of HY7602-FA on skeletal muscle, we measured the weight of skeletal muscle in the hindlimbs. The ratio of skeletal muscle weight/body weight, expressed as the relative number for the soleus, EDL, and gastrocnemius muscles, was significantly higher in HY7602-FA fed mice than in the exercise control group (\(p<0.05\)) (Figure 5b).  
Figure 3: Intestinal effects of fermented antler (FA) on AMPK signal pathway-related genes (_Ampk_, _Ppargc1a_, _Sirt1_) associated with mitochondrial activity in skeletal myotubes. FA upregulates the expression of mRNA encoding _Ampk_, which is associated with mitochondrial activity. To check _Ampk_, _Ppargc1a_, and _Sirt1_ expression, total RNA was prepared from C2C12 cells incubated for 24 h with NFA (1 mg/mL), HY7602-FA (1 mg/mL), or creatine (1 mg/mL). _Ampk_, _Ppargc1a_, and _Sirt1_ were detected by RT-PCR. Data are expressed as the mean \(\pm\) SD of three determinations. _Asterisks_ indicate significant differences: **_p_\(<0.01\) vs. NT. _Ampk_, AMP-activated protein kinase; NT, no treatment; _Ppargc1a_, peroxisome proliferatorator-activated receptor-gamma coactivator 1 alpha; _Sirt1_, sirutin 1.  
Figure 4: Effect of HY7602-FA on exercise endurance. Exercise capacity was measured based on total exercise time (**a**), running distance on a treadmill (**b**), or total swimming time (**c**). Data are expressed as the mean \(\pm\) SD (\(n\) = 9 mice per group), and _asterisks_ indicate significant differences: * \(p\) < 0.05 and **\(p\) < 0.01 vs. the control group. Changes in (**d**) body weight and (**e**) food intake over 4 weeks. NFA, non-refmented antler; FA, fermented antler.  
Overall, these data suggest that HY7602-FA improves grip strength by inducing muscle hypertrophy.
## 4 Results and Discussion
### CTAM Performances under Different Compaction Methods
#### 4.2.5 Temperature Shrinkage Coefficient  
The variations of the temperature shrinkage coefficients of Aggregates A and B are shown in Figure 14.  
The temperature shrinkage coefficients shown in Figure 14 increased with increasing temperature. The temperature shrinkage coefficient under Proctor compaction was generally the highest, followed by those under gyration and vibration compaction, which was in part attributed to the results of the aggregate degradation shown in Section 4.2.1, because finer aggregates were easier to shrink under the same conditions. In addition, the temperature shrinkage coefficient of Aggregate B was higher than that of Aggregate A, because Aggregate B had a higher air void, resulting in easier temperature shrinkage of the aggregate.
## 3 System Architecture  
On off keying (OOK) scheme is known as the simplest form of amplitude-shift keying modulation by using two statuses: ON/OFF to transmit data. In the simplest form of OOK, a binary one is presented by a carrier for a specific duration and a binary zero is presented by the absence of a carrier for the same duration. OOK almost is applied to transfer Morse code via RF waveform or the industrial, scientific, and medical (ISM) band to connect among computers in the computer network because of its simplicity.  
In Section 3, we provide the details of the MIMO-COOK scheme. Unlike the COOK mentioned in [22], we used RoI algorithms to detect light sources. To detect preambles and decode, we used a matched filter instead of the zero-crossing filter. Figure 1 illustrates the architecture of MIMO-COOK scheme. The operations of the blocks are presented below.
## 3 Results
### Transitions in Physiological States of STEC O157:H7 on Romaine Lettuce During Cold Storage
#### 3.3.4 Transition of Microbial Cells to the VBNC State During Cold Storage  
Log increase for the VBNC cells during the cold storage period ranged from \(-\)3.62 to 3.90. Whereas on lettuce harvested at 9 \({}^{\circ}\)C, a change of 0.002 log CFU/g was reported in VBNC over the cold storage period, lettuce harvested at 17 \({}^{\circ}\)C had \(-\)0.01 log. Among the strains, the Central Coast 2018 outbreak strain had the highest increase of 0.284 logs across the period of storage, while Yuma 2018 and Salinas 2019 outbreak strains had \(-\)0.269 and \(-\)0.003 logs, respectively. Over cold storage, the Central Coast 2018 outbreak strain had a relatively higher increase in transition to VBNC cells than other strains (Figure 5D). Correlation tests for trends  
Figure 7: Beta coefficients from beta regression model evaluating the effect of strain, harvest temperature and cold storage period on injured cells. Statistical significance (*) was established when the confidence interval was not equal to zero and the computed effect size of the model (R\({}^{2}\)) was 0.236. The baseline categories for strain was Central Coast, CA 2018.  
of transition to VBNC showed that period of storage was not correlated to formation of the dormant cells at either harvest temperature 9 \({}^{\circ}\)C (r = 0.03) or 17 \({}^{\circ}\)C (r = \(-\)0.11). ANCOVA tests showed that the interaction of harvest temperature and strain significantly (\(p\) < 0.001) affected the formation of VBNC cells. The Central Coast 2018 outbreak strain had increased VBNC formation during cold storage compared to the Yuma 2018 and Salinas 2019 outbreak strains (Figure S6, \(\eta^{2}\) = 0.19 [95% CI = 0.15, 1.00]). The Yuma 2018 outbreak strain showed higher formation of VBNC cells at 9 \({}^{\circ}\)C than at 17 \({}^{\circ}\)C.  
Regression tree analysis showed that the variance in VBNC formation was optimally explained by 8 splits (CP = 0.01, xerror = 0.78; Figure 9). The highest formation of VBNC cells during cold storage occurred for the Central Coast 2018 outbreak strain, stored for \(\geq\)1 day on lettuce harvested at 17 \({}^{\circ}\)C (1 log, probability 0.12). The least formation of VBNC cells (\(-\)1.1 logs, probability = 0.19) was reported for the Yuma 2018 outbreak strain on produce that was harvested at 17 \({}^{\circ}\)C and stored for \(\geq\)5 days. Cold storage days accounted for 47.6% of the variance in the formation of VBNC cells (Figure S5).