esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Research Background  
In South Korea, structural design standards have been continuously improved since the enactment of the provisions of the seismic design law in 1988. Of late, there has been increasing interest in light gauge steel frames--which have robust seismic performance--as the seismic design law was recently extended, such that two or more single-family or multi-family developments are subject to earthquake-resistant design rules. This has particularly been the case since the 2016 Gyeong-ju and 2017 Po-hang earthquakes. Light gauge steel frames are built with cold-formed steel (CFS) members. These members are lightweight, and thus bring about good seismic properties [1; 2; 3]. As light gauge steel frames are constructed for low-rise buildings with varied demand, on-site panel construction methods are more predominantly employed than factory panel construction methods. However, on-site panel construction has a drawback, in that material transportation and deformation are likely to occur at the time of construction, thereby decreasing the initial rigidity of CFS members [4].  
In light gauge steel frames, steel strap-braced and steel-sheathed wall systems are generally used to ensure seismic performance. The members of light gauge steel frames have screwed joints that are replaced with bolted joints in the steel structure. Many screw joints adversely affect workability, so an integrated member has been developed with details that minimize the use of screw joints. The integrated panel proposed in this research replaces the studs and steel sheet of a steel-sheathed wall system, as shown in Figure 1. The integrated panels developed for this purpose are presented in the paper, and are studied to compare the effects of stud reinforcement and the steel-sheathed panel on a designed testing rig. Of particular importance is identifying the behavior of the integrated panel, and predicting the nominal strength. Unlike steel-sheathed panels in which the steel sheet and studs are screwed together, an integrated panel--in which a single member resists lateral forces--is expected to be more stable to lateral forces.
## Materials and Methods
### Data Evaluation and Marker Extraction  
EEG epochs were segmented using the gait events \"IC left\" and \"IC right\" as onset markers that were synchronized with the EEG recordings (see Section 2.4 for details). EEG epochs were cut into segments from 1000 ms before until 1000 ms after the event (IC left or IC right) and the epochs were baseline corrected using the \(-\)1000-0 ms time window before the IC onset as a baseline. The length of the individual epochs (\(-\)1000-1000 ms) was chosen to include the whole gait cycle (left foot and right foot) within one epoch. As described above, each participant walked at a pace of 3.5 km/h. Two consecutive ICs of the same foot have a time lag of at least 1000 ms, ICs of different feet at least 490 ms. There were no breaks, stops, or inter-trial periods of \"no activity\" between gait cycles, which could have been used as a baseline. Thus, so as to not confound ERPs elicited by the left IC with the activity elicited by the preceding right IC and vice versa, baseline correction included the average time window of the previous gait cycle. This attenuated any systematic and temporal baseline differences between the left and right IC, respectively. Similar procedures of using the whole pre-stimulus trial time window for baseline corrections are common in so called rapid serial (visual) presentation designs, in which stimuli are presented at rates of 1 Hz, 3 Hz, or even faster to avoid the effects of component overlap [26; 27]. The baseline-corrected epochs were then averaged across all available epochs for each subject for sufficient data quality and a good signal to noise ratio. It is principally possible to split the EEG epochs into the different seven walking conditions. It is also possible to analyze the data separately for left and right initial contact (IC) or other gait events (e.g., TO). The analysis of gERPs reported here was first performed for all available epochs, irrespective of the different walking conditions (spontaneous or instruction guided). Next, single analyses were performed for each of the walking conditions and the results are reported descriptively for the averaged data of a single subject and the grand averaged data of the three volunteers.
## 1 Introduction
### Literature Review  
So far, much research has been developed, mainly focusing on the optimal operation scheduling under different loading conditions and objectives. An IEMS (intelligent energy management system) is proposed for an optimal CHP-based micro grid in [3], and the IEMS aims to find the optimal set points of renewable distributed units, the CHP unit and energy storage systems. In [4], a hybrid PSO algorithm based on a CLS (chaotic local search) mechanism and FSA (fuzzy self-adaptive) structure is utilized to improve the optimization process. In [5], the author reported his work on microgrid operation at the UC Irvine campus during January 2009-December 2013. In [6], a DER-CAM (distributed energy resources customer adoption model) optimization technique is proposed to minimize the cost of a microgrid with CHP system.  
The energy storage system (ESS) is designed to store energy when production exceeds demand and to make it available at the user's request. Much research focusing on the modeling and scheduling of storagefacility in microgrids has been carried out. In [7], a typical dispatch model is proposed to minimize the total operation cost of the microgrid. In [8], an energy management system for ESS in microgrids is presented. Furthermore, the optimal charging schedule of EVs has attracted much research attention. In [9], a closed-form solution is proposed for the stochastic optimization problem that is formulated to schedule EVs' charging with uncertain departure times under hourly time-of-use pricing tariffs. In [10], a detailed review of vehicle-to-grid (V2G) technology in conjunction with various charging strategies of EVs is presented.  
In microgrids, it is an existing challenge to adopt appropriate DSM programs to balance the power supply and demand effectively [11; 12]. The benefits of DSM are quantified in terms of the ability of demand-responsive loads to relieve upstream network constraints and to provide ancillary services, _i.e._, operating reserve [13]. An energy management strategy from both the demand side and generation side to realize the objectives of meeting the electricity demand is addressed in [14].  
The PSO algorithm is widely used in micro grid scheduling optimization.In [15], the PSO algorithm is employed for system optimization scheduling. In [16], Monte Carlo simulation of the particle swarm optimization (MCS-PSO) algorithm is employed to study the uncertainty of multidimensional network operation optimization scheduling. However, it is proven that the PSO algorithm is apt to fall into local optima in the optimization process. This paper proposes the MTPSO algorithm, which has a stronger global and local search ability, faster convergence and higher accuracy to solve the dispatch optimization issue in micro grid. The contribution of this paper is two-fold. First, we construct the approximate theoretical model of a micro grid system for scheduling optimization issue study. Second, we present a case study in areal micro grid located in North China to compare the performance of MTPSO and PSO in scheduling optimization and to prove the performance of the MTPSO algorithm.
## 4 Discussion
### Pressure Impulse Process  
From Figure 12, it can be observed that after the seal ring is subjected to pressure impulse, it moves towards the low-pressure side until it acts on the side of the seal groove at the bottom. With the increase in impulse pressure, the O-ring deforms in a concave shape. The reason for this is the friction occurring between the step ring and the O-ring. The interaction between the O-ring, the sealing groove, and the step ring hinders the movement of the O-ring, effectively reducing the risk of leakage due to changes in the relative position between the step seal ring and the O-ring. Under different compression ratios and pressure impulses, the deformation of the step seal ring is relatively small, while the O-ring undergoes significant changes in shape. This is determined by the material properties of both components. This reciprocating sealing arrangement utilizes this property of the O-ring to achieve sealing under pressure impulses.  
From Figure 13, it can be seen that when the compression ratio is 10% and the seal ring is subjected to pressure impulse, there is a severe misalignment between the O-ring and the  
Figure 16: Maximum thickness of cross-section after step seal ring wear: (**a**) step sealing ring thickness measurement diagram, (**b**) thickness of stepped seal after wear.  
step seal ring. Moreover, after the pressure impulse ends, they do not return to their original positions. In practical use, when the seal ring encounters significant pressure impulses, it can result in the displacement or even twisting of the seal ring, causing irreversible damage. This situation should be avoided. When the compression ratio reaches 15% or higher, the severe misalignment between the O-ring and the step seal ring disappears. This is because as the compression ratio increases, when it exceeds 15%, the strain on both the step seal ring and the O-ring increases, and the contact surface between them widens. This leads to a more even distribution of forces, better repetitive positioning, and effectively prevents leaks caused by the movement of the seal ring.
## 2 Modeling Method
### Support Vector Regression
The formula can be transformed into a dual problem and the solution is shown as follows:  
\[f(\mathbf{\chi})=\sum_{i=1}^{m}(\alpha_{i}^{*}-\alpha_{i})\mathbf{x}_{i}^{T}\mathbf{\chi}+b. \tag{7}\]  
After mapping into higherdimension space, Equation (7) can be expressed as  
\[f(\mathbf{\chi})= \sum_{i=1}^{m}(\alpha_{i}^{*}-\alpha_{i})\kappa(\mathbf{x}_{i},\mathbf{x} _{i})+b \tag{8}\] \[= \sum_{i=1}^{m}\omega_{i}\kappa(\mathbf{x}_{i},\mathbf{x}_{j})+b,\]  
where \(\kappa(\mathbf{x}_{i},\mathbf{x}_{j})=\phi(\mathbf{x}_{i})\phi(\mathbf{x}_{j})\) is the kernel function that converts the nonlinear cases into linear ones. As one of the commonly used kernel functions, radial basis function (RBF) is used in this paper:  
\[\kappa(\mathbf{x}_{i},\mathbf{x}_{j})=exp(-\frac{1}{2}(\frac{\|\mathbf{x}_{i}-\mathbf{x}_{j}\| }{\sigma})^{2}), \tag{9}\]  
where \(\sigma\) is the parameter of RBF, which controls the smoothness of kernel function varied by a positive infinite domain. The schematic view of SVR is shown in Figure 2. In this figure,the input layer represents the environmental data, including pavement initial temperature, pavement average temperature, environment average temperature, humidity, radiation, wind speed, and water film thickness, while the output layer refers to the pavement icing time. It should be pointed out that the input variables limit the prediction accuracy; thus, it is necessary to optimize the input. RSM, a conventional method used to optimize the design parameters by manipulating the relationship between input and output variables, has been gradually used to improve the predict accuracy of SVR in recent studies [32]. Therefore, RSM is utilized in this paper to calibrate the input variables for SVR.
# Probiotics and Beneficial Microorganisms in Biopreservation of Plant-Based Foods and Beverages
## 4 Biopreservative Properties of Probiotics
### Plant-Based Milk Analogues and Other Miscellaneous Products  
Plant-based milk substitutes and analogous products are becoming highly attractive among consumers at present. However, one of the major hygienic concerns associated with the production of beverages from plant origin is high microbial contamination of the raw materials. For instance, one of the major safety concerns associated with boiled and fermented quinoa products is the spontaneous growth of opportunistic pathogens expressing thermal resistance (e.g., _Enterobacteriaceae_). However, after fermentation with _Lactipalitobacillus plantarum_ DSM9843 (at 30 \({}^{\circ}\)C for 2 days) viable counts of _Enterobacteriaceae_ in boiled quinoa milk (5.3 log CFU/mL) reduced below the detection limits (<1 CFU/mL). During the storage of 28 days at 4 \({}^{\circ}\)C, _Lb. plantarum_ was the predominant microorganism found in the medium which maintained around 10\({}^{6}\)-10\({}^{7}\) CFU/mL [74].  
_Bifidobacterium animals_ has successfully been employed to maintain the microbiological quality of cashew nut milk without affecting whiteness or the beverage's sensory acceptance. The probiotic bacteria were able to survive in the food matrix with counts above 10\({}^{7}\) CFU/mL over a 30-day storage period at 4 \({}^{\circ}\)C [75].  
[PERSON] et al. [76] demonstrated that probiotics Lactococcus lactis subsp. _lactis_, _Lactobacillus reuteri_, and _Pediococcus_ acidification were better utilized with \(\beta\)-glucooligosaccharides (\(\beta\)-GOS), derived from barley \(\beta\)-glucan, leading to an approximately 25% increase in nisin-Z production which would elevate the antimicrobial activity of the probiotics [76].  
[PERSON] et al. [77] showed that certain compounds produced during the fermentation of hemp milk selectively enhance the growth of beneficial microbes such as probiotics (e.g., _Lactobacillus fermentum_, _Lb. plantarum_ and _Bifidobacterium bifidum_), whereas certain compounds such as terpen inhibit the growth of harmful bacteria.  
[PERSON] et al. [60] have identified 18 isolates composed of _Lactobacillus plantarum_, _Weissella cibaria_, _Leuconostoc mesenterroides_, _Leuconostoc citreum_ and _Lactococcus lactis_ strains from 700 samples of 36 types of fruits, vegetables, ready-to-eat salad and fresh-cut individual products. More importantly, none of the bacteria promoted spoilage reactions such as browning, pectinolytic activity or production of off-odors. Among the different strains isolated, _Leuconostoc_ species showed the best antagonistic capacities over a wide range of microorganisms [60]. However, the use of _Leuconostoc_ species as a food additive is limited to the predominance of heterofermentative species, which may negatively influence the organoleptic properties of the final product [78].
## 4 Experimental Analysis and Verification
### Comparison and Verification  
During the routine operation of an energy storage station, the abnormal change in the voltage of the battery cell may occur occasionally due to the change in the working environment. However, if a cell is identified as abnormal for several consecutive days, attention should be paid to this cell. Therefore, in order to verify that the results of abnormal battery cells in Section 4.1 are not occurring by chance, this section uses the voltage curves of the same charging period on July 1 for a comparison and analysis with the battery pack during operation. The process and results are shown in Figure 9.  
As can be seen from the results in Figure 9d, battery cells numbered 3, 21, 33, 53, 58, and 108 are indeed abnormal cells. In addition, the same results are obtained on other dates.  
To further verify the effectiveness of the anomaly detection method for battery cells proposed in this article, the same lithium battery pack charging data are compared and verified against four other methods, including the Variance-3\(\sigma\) principle, Range-3\(\sigma\) principle, Euclidean Distance-3\(\sigma\) principle, and Signal Energy-3\(\sigma\) principle for battery cell anomaly screening.  
Variance can measure the dispersion of a set of data, describing the deviation of each data value from its mean.  
\[\sigma^{2}=\frac{\sum\left(X-\mu\right)^{2}}{N} \tag{17}\]  
The range is the difference between the maximum and minimum values in a data sample. The larger range is, the greater the fluctuation range of the curve is.  
\[R=x_{\text{max}}-X_{\text{min}} \tag{18}\]  
The Euclidean distance measures the \"straight-line\" distance between two points in a multi-dimensional space. The Euclidean distance of each point on the curve to the  
Figure 9: July 1. (**a**) The voltage of the battery cells. (**b**) Step 1. (**c**) Step 2. (**d**) Step 3.  
The results in Table 2 indicate that the abnormal likelihood of battery cells numbered 3, 21, 33, 53, 58, and 108 is very high. This also indicates that the anomaly detection method for battery cells based on the Average Deviation-3\(\sigma\) principle is reliable.
## 4 Simulation Results
It is seen that the achievable sum rate increases with the number of UEs first, and then decreases sharply and finally becomes stable. The performance peak occurs when the total number of UEs is equal to the number of receive antennas. The reason is mentioned in the previous paragraph. Moreover, a comparison between the proposed algorithm and the random power allocation algorithm is conducted. For the random power allocation algorithm, the transmit power follows a uniform distribution in the range of \([0,26]\) dBm. And we calculate the mean value of 20 transmit power sets. It is observed that our proposed algorithm performs better than the random power allocation algorithm. In Figure 5, the user association results are presented for various sets of backhaul link rate \(C\). The maximum transmit power is set as 26 dBm. It is seen that the distribution of the number of UEs accessing different terrestrial BSs is relatively even. As the backhaul link capacity increases, fewer UEs will access the satellite. This is because UEs will prioritize accessing terrestrial BSs with better channel conditions. However,  
Figure 4: Achievable sum rate versus the number of UEs. Figure 3: Achievable sum rate versus the maximum transmit power. capacity of terrestrial BSs is constrained, UEs will turn to accessing the satellite in order to guarantee the fulfillment of their QoS requirements. Figure 6 presents the achievable sum rate versus the backhaul link capacity. The total number of UEs \(K\) and receive antennas \(N\) are set as follows: \(K=10\), \(N=10\), \(K=20\), \(N=20\), and \(K=40\), \(N=40\). The maximum transmit power is set as 26 dBm. It is seen that the performance increases with the increase in backhaul link capacity. When the backhaul link capacity is relatively low, the sum rate for 10 UEs is higher than the sum rates for 20 UEs and 40 UEs. This is because when the backhaul link capacity is small, the number of UEs that terrestrial BSs can accommodate is relatively limited, and excess UEs will access the satellite. This results in severe interference among the UE signals received by the satellite, thereby impairing the overall performance. When the number of UEs is relatively low, terrestrial BSs are sufficient to provide service to all UEs even with limited backhaul link capacity. As the backhaul link capacity of these BSs continues to rise, the achievable sum rate approaches saturation, leading to a flattened trend in the sum rate for 10 UEs thereafter.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 3 Results
### Visualization Example
#### 3.1.1 Convergence Time Estimation  
The height error variation on 1 September 2017 at SAOOP station, 1 September 2017 at SJCU station and 29 May 2016 at the SNA0P station is shown in Figure 3. The convergence time at all three stations was approximately 30 min, where the height estimate reached a stable continuous accuracy of approximately 0.1 m. Thus, the convergence period of 30 min was applied for RMSE computation in the following results.  
Figure 3: The height error variation on (**a**) 1 September 2017 at SAO0P station; (**b**) 1 September 2017 at SJCU station; (**c**) and 29 May 2016 at SNA0P station. The height error is in metres.
## 3 Methodology
### Methodology
#### 3.3.3 Hurricane Damage Prediction
The pre-trained model ResNet can be used as a feature extraction technique, and VGG 19 layers are used for the fine-tuning methodology. VGG 19 provides better accuracy than the ResNet pre-trained model. The images with very poor quality, fully black and very cloudy, are discarded from the dataset. Random flips and rotations on varying angles are done for the augmentation of data for training and validation. VGG 19 layers contain the consequent 2D convolution layers and 2D max pooling layers. The proposed model for hurricane damage prediction contains convolution and max pooling layers, followed by fully connected layers and the output layer. In the proposed model, VGG 19 layers are used for training, and better accuracy is achieved. Using the satellite remote sensing data, the number of flooded or damaged buildings is identified.
# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 3 Method
### Detection Heads
#### 3.2.4 Sparse R-CNN  
The famous deep learning object detectors such as Fast R-CNN, Faster R-CNN, Mask R-CNN, and Cascade Mask R-CNN all rely on dense object priors. These detectors have many anchor points containing anchor boxes that densely cover different spatial positions, aspect ratios, and scales. The dense coverage helps to predict the classes and bounding box coordinates. In the case of single-shot detectors, let \(W\) be the width and \(H\) be the height of a feature map. If each anchor point is responsible for predicting \(k\) bounding boxes, then we have a large number \(W\times H\times k\) of bounding boxes for each feature map.  
Further, in the case of two-stage detectors such as Faster R-CNN, Mask R-CNN, and Cascade Mask R-CNN, dense object priors are generated first and then, using the region proposal network (RPN), a sparse set of foreground proposal boxes are obtained from these dense object priors. Finally, these are fed into the classification and regression branches to produce bounding box and category predictions.  
The authors of Sparse R-CNN [15] propose a different paradigm that moves towards thoroughly sparse object priors. As seen in Figure 7, the authors avoid using an RPN. They instead use a small set of proposal boxes, usually 100 or 300. These proposal boxes are learnable. The authors introduce the dynamic head to obtain them.  
First, the initial set of randomly initialized proposal boxes in the dynamic head is used with the RoI align operation in extracting features from the network backbone. Next, each extracted RoI feature is convolved with a learnable proposal feature. The result is fed into its detection and classification head to predict the class and bounding box. Each head is essentially conditioned on these learnable proposal features. The proposal features essentially act as weights for the convolutions and give rise to the learnable proposal boxes.  
Figure 6: The detection pipeline for the Cascade Mask R-CNN detection head. Cascade Mask R-CNN has multiple heads and utilizes cascading among these heads to refine the bounding box predictions.
## 4 Proposed Hybrid FVIMDE Algorithm
### Mathematical Model of the Hybrid DE-FVIM Optimizer  
The Hybrid DE-FVIM optimizer is a two-phase optimization algorithm that combines Differential Evolution (DE) with the Four Vector Intelligent Mechanism (FVIM). The optimizer iterates over a predefined number of iterations, Max_iter, which is divided into two phases: the DE phase and the FVIM phase.