esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 3 Experimentation
### Simulation Models
#### 3.1.4 Dual Sourcing Simulation  
This model is included in the SimOpt distribution and represents a manufacturing location purchasing its input via regular or expedited suppliers with different ordering costs and delays while facing stochastic demand. Regular deliveries cost 100 per unit and take 2 days to arrive, while expedited deliveries cost 110 per unit but take 0 days to arrive. One simulation run will simulate 1000 days of operation.  
There are storage costs when the location carries stock from one day to the next day, and a penalty \"cost\" per unit of shortage whenever there is a lack of stock to meet demand. Satisfied demand has no benefit other than zero penalty, which implies that location is a substitute for a cost center that aims to minimize costs and penalties.  
The strategy followed by the location can be summarized as follows: It consists of having two inventory target levels, one for the regular supplier and one for the expedited supplier. The location will place an order with a specific supplier whenever the stock level falls below its associated threshold value. Although the goods being ordered are homogeneous when in stock, goods that are yet to arrive are not considered against the target level for the expedited supplier as they will only arrive after the expedited delivery.  
The two inputs of interest are these two threshold levels. The three objectives of interest are the three main types of costs to be considered: ordering costs, holding costs and penalties for running out of stock.
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 3 System Model Design
### Computation of Transmission Evaluation Value Based on CMFI
#### 3.4.1 The Fuzzy Component  
The objective of the inference component is to determine the membership degree and fuzzy subset of nodes. With the support of the method of tripartition, we develop the fuzzy component by three levels of membership functions (low, medium, and high). In general, a fuzzy input can generate three levels of membership degrees and each of them corresponds to one level of fuzzy subset (low, medium and high). There are many distribution models that can be adopted in several application scenes in the control engineering theory, such as the normal distribution, the \(F\) distribution and the parabolic distribution, etc. [13; 21] As the remaining energy or cache is below a certain lower limit, the node may be dead or unable to move; also, these two values must be below a network adaptable upper limit, so the two-factor fuzzy inputs follow a half trapezoid or trapezoid distribution. To this end, curve trapezoid membership function will be utilized to compute the membership degree of the two-factor fuzzy inputs [23; 25] in the proposed improved fuzzy system, namely the Curve-trapezoid Mamdani Fuzzy Inference System (CMFI). After the process of the fuzzy component, each of two-factor fuzzy inputs (energy or cache) can generate three fuzzy degrees and fuzzy subsets.
## 2 Materials and Methods  
### Image Acquisition  
A total of 194 OCT-A images, of both the SCP and DCP of 30 patients with CME and non-proliferative diabetic retinopathy, and with or without motion artifacts, were included in the analysis. Each patient was evaluated at the baseline and after 6 months. Inclusion criteria for the study were: male or female \(\geq\) 18 years of age with center-involving cystoid macular edema and good quality imaging. The study adhered to the tenets of the Declaration of Helsinki and was approved by the institutional Ethics Committee (CE123 2017); each patient agreed to participate in the study and signed a written informed consent.  
OCT-A images were taken using a commercially available swept-source instrument after pupil dilation. The device used was the swept-source DRI OCT/OCT-A Triton Plus (Topcon Medical Systems Europe, Milano, Italy). This instrument has a wavelength of 1050 nm and a scanning speed of 100,000 A-scans/second. Image processing relies on a motion contrast measure named OCT-A Ratio Analysis (OCTARA). OCT-A maps covering the central 3 mm \(\times\) 3 mm macular area were acquired for each subject at each follow-up visit. All images were carefully reviewed to check automatic segmentation of the different plexuses. Poor quality images were excluded from the analysis, with the exception of motion artifacts presence.  
The SCP slab was segmented between the inner limiting membrane (ILM) +2.6 um and the inner plexiform layer (IPL)/inner nuclear layer (INL) +15.6 um; the DCP slab was segmented between IPL/INL +15.6 um and IPL/INL +70.2 um.  
Original OCT-A images were exported with a resolution of 320 \(\times\) 320 pixels (9.4 um lateral resolution).  
The diagnosis of ME was made by means of fundus examination, performed by the ophthalmologist expert in retinal disorders (S.V.), and structural OCT, performed with the same instrument as OCT-Aon the same day by a trained ophthalmologist (C.T.). On OCT, a 6-mm radial scan (consisting of 12 scans 15\({}^{\circ}\) apart) centered on the fovea was acquired.
# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
## 3 Periodic Defect Detection Algorithm Based on CNN + LSTM
### Periodic Defect Recognition Based on LSTM
#### 3.2.2 The Specific Process of Recognition of Periodic Defects of LSTM  
(1) Constructing an input vector of the LSTM by using the feature vector of the periodic defect extracted by the CNN.  
Figure 8 shows a corresponding relation of extracted feature vectors and the original image. A long rectangular image is inputted into the feature extraction network to obtain the feature matrix above. The information contained in one column of the original image corresponds to a multi-dimensional matrix composed of a plurality of columns in the same proportional position. An extracted feature matrix is divided into a plurality of small matrices horizontally, and each of the small feature matrices represents a corresponding matrix in the original image, which is a slicing operation.  
Figure 7: Flowchart of the convolutional neural network and long short-term memory (CNN + LSTM) detection method.  
The structure of LSTM is shown in Figure 9. Its input is a set of time-sequenced vectors. The corresponding output is also a set of time-sequenced information. In Section 2.2, the defect image and the background image are periodically arranged and spliced into a long rectangular image to obtain a periodic defect sample. This sample is inputted into the designed CNN, and the output of the convolutional layer is extracted as the feature vector of the sample. Then, the feature vector is sliced into the input vector of LSTM.  
(2) Training Using LSTM  
LSTM was implemented using a Tensorflow [17] framework in this study. The inputted format of LSTM in this framework was {batchsize, timestep, dims}, where batchsize is the training batch; timestep is the total number of time steps (each single defect enters the network at each timestep); and dims is the dimension of the input data.  
The dimension of the extracted feature vectors was (7, 70, 512). We sliced it into a set {{7,7*512}, [7,7*512].......[7,7*512]} of feature vectors as the input of LSTM, i.e., \(X_{0}\), \(X_{2}\)....... \(X_{t}\). There are 70/7 = 10 timesteps of features. We added X\({}_{t+1}\), \(X_{t+2}\), timestep = 12, and dims = [7,7*512].  
As shown in Figure 9, the sliced feature vectors were inputted into the LSTM according to the timestep, and the output H of the last cell was the final result. H is a Boolean value. H = 1 means that the input sample is a periodic defect sample, H = others means that the input sample is not a periodic defect sample. The defect recognition network was trained according to the above rules.
## 6 Discussion
### Threats to Validity
#### 6.3.2 External Validity  
One major threat to external validity is related to the software systems on which the research is based, including sample size, selection bias, and subject characteristics. To address these threats, our research relied on a version of 45 different software systems and all versions of 3 software systems, a sample generally larger than that in related work. The selection was random, and our selected subjects represent a heterogeneous group of software systems in terms of software characteristics, including size, complexity, code documentation, maturity, and domain of use. However, the results could have been different for a different group of software systems, as there is no guarantee that the selected software systems are good representatives. All systems studied were open-source and based on the Java programming language. Therefore, it is plausible that the results are different for closed-source software and may not be representative of the overall population, but only for open-source software. Furthermore, the results of the study may not generalize to the broader population of object-oriented systems based on programming languages other than Java. Even though the findings are likely valid for software based on other object-oriented languages, further research is needed to confirm them.  
Another threat to external validity is the set of Index variants studied. We limited our selection to well-defined variants (i.e., variants with an adequate description of the metrics needed for computation, the process of the metrics' collection, and the process of Index calculation) that are commonly used in existing research on object-oriented software. Replication studies are needed to confirm and generalize the results to a more extensive set of Index variants.
## 3 Simulation and Analysis
In weak atmospheric turbulence, the signal is much larger than crosstalk. Also, under the conditions of the moderate-to-strong turbulence, the ratio of signal to crosstalk is lower than that in weak atmospheric turbulence, but the signal is still the main part (more than 50 dB). Figure 1:0 Intensity and phase distribution of electromagnetic vortex waves with transmission distance of (**a**) 1 km, (**b**) 5 km and (**c**) 10 km for multiplexing \(l=1,3,5\) under moderate turbulence conditions, with adaptive compensation. Figure 1:1 Crosstalk distribution of electromagnetic vortex waves with transmission distance of (**a**) 1 km, (**b**) 5 km and (**c**) 10 km under moderate-to-strong turbulence conditions. In the wireless communication system based on OAM-modes modulation, the atmospheric turbulence will scatter the energy of the transmitted OAM mode into another mode. Figure 11 shows the variance of channel capacity with different OAM modes in weak-to-strong atmospheric turbulence. It is evident that channel capacity increases with the number of transmission OAM modes. In Ku band the channel capacity could reach the ideal situation under weak and moderate atmospheric turbulence, and channel capacity, through the fluctuating of strong atmospheric turbulence, has entered into a downtrend. Thus, we could improve the capacity of the wireless communication system by using vortex electromagnetic waves.
# Visual Ranging Based on Object Detection Bounding Box Optimization
## 3 Methods  
### YOLOv5 Object Detection  
#### 3.1.1 Methods and Evaluation Indicators  
The two methods proposed in this paper are solutions to existing problems in the field of visual ranging based on object detection, and both of these methods are built upon object detection. In order to verify the effectiveness of the proposed methods, the first step is to perform object detection. Therefore, in this paper, the YOLOv5 algorithm is adopted for object detection.  
Figure 1: The pixel spacing caused by non-fit phenomenon between the object detection bounding box and the object.  
YOLOv5 is the fifth generation of the YOLO algorithm, which is a typical generation in the YOLO series. It has numerous applications in the field of object detection due to its high detection accuracy and fast detection speed. Five versions of YOLOv5 are offered officially: YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. In this paper, the lightweight model YOLOv5s is used for object detection in order to achieve high detection accuracy and faster detection speed.  
In order to obtain a good object detection model and to better apply the two methods proposed in this paper, precision (P), recall rate (R), and mean average accuracy (mAP) are used as evaluation measures to assess the training results of YOLOv5. P represents the proportion of correctly detected objects to the total number of detected objects, while R represents the percentage of correctly detected objects among all actual objects. In addition, mAP represents the average accuracy (AP) across all training classes.  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\%, \tag{1}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\%, \tag{2}\]  
\[\text{mAP}=\frac{\sum\left(\text{AP}\right)}{\text{N}}. \tag{3}\]  
where TP represents the number of correctly detected objects, FP represents the number of error detections that treat non-objects as objects, and FN represents the number of missed detection objects. mAP = AP, which is due to the fact that the dataset produced has only one category (N = 1).
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 4 Performance Evaluation
### Results
The performance improvement is more pronounced when two threads are used than when one thread is used. This is due to the rate at which the model is updated becoming faster as a large number of agents are created in accordance with occurring QoE degradation and network fluctuations rapidly in the early stage of online learning (0 to 10 h) and the number of threads increases. Figure 6: The degree of performance improvement over time in online learning. Figure 7: The degree of performance improvement according to time and number of threads in online learning. Figure 9 shows the results of the experiment by changing the threshold value, which is the criterion in generating the agent, from 0.3 to 0.9 by comparing the normalized QoE. Experimental results show that the normalized QoE value increases as the threshold increases. However, if the threshold value increases, the performance evaluation criteria for the neural network model decrease, and the number of agents generated accordingly also increases. Since the increase in the number of agents leads to serious computational load, it is necessary to solve the trade-off between QoE improvement and load. To this end, we set the threshold at 0.7.
## 4 Experiments and Discussion  
### Module Experiments  
To evaluate the performance of the robot module, preliminary experiments were performed. The robots were programmed to move and actuate in different cases and geometric and force-measurement tools were used to obtain the parameter values. The weight of an SMMRob module is about 900 g. After testing on flat ground, the maximum transnational speed of the module was 40 cm/s and the maximum payload was 8 kg. Its battery life is 120 min when it moved (other cases depend on joint actuators and computing costs). A variety of terrains were selected to test the terrain trafficability of a single robot, which showed that it could move on a tiled floor, asphalt road, cobbled road, and turf, as shown in Figure 11. It could move up a slope of 28.5\({}^{\circ}\) and climb over an 18 mm high obstacle. A video of all the testing experiments is provided in the Supplementary Materials.  
As shown in Figure 12, the yaw angle of the joint is \(\pm\)30\({}^{\circ}\) and the pitch angle of the joint is \(\pm\)45\({}^{\circ}\). It can lift two modules in serial with an actuation torque of 2.7 Nm. The mechanical tolerance of docking is \(\pm\)8 mm in the lateral direction, \(\pm\)2 mm in the longitudinal direction, and \(\pm\)5.7\({}^{\circ}\) in deflection. The maximum distance for color marker recognition is 200 cm and the update rate is 30 Hz. The maximum distance of Apriltag rec  
Figure 11: Trafficability of a single SMMRob on a variety of terrains and slopes.  
resolution (\(640\times 480\) pixels) and 10 cm in lower resolution (\(160\times 120\) pixels). Wireless networking including WiFi and Zigbee can provide communication within a range of about 10-20 m indoors and further outdoors. The main board can be accessed remotely using a router. In a head module, the stereo video can be obtained in real time using WiFi transmission. In Figure 13, video can be captured when SMMRobs move from far away to near the wall. The parameters are summarized in Table 1.
## 5 Simulations  
In this section, we investigate the performance of the estimation procedures used in this paper. To that end, we generated \(10,000\) samples with different sizes (\(n=10\), \(n=25\), \(n=35\), \(n=75\), \(n=200\), and \(n=400\)) from the \(PDZ\) model, and then computed the different estimators and their mean square errors (MSE) (values in brackets) for the unknown parameters using R statistical software when \(\theta=5\) and \(\beta=0.5\). The results are given by sample size in Table 3.  
Based on Table 2, it can be noted that the bias and MSE values of different estimators decrease as the sample size increases. For example, when \(\theta=5\) with \(n=10\) and \(n=400\), the MLE values are \(\hat{\theta}=4.8841\) and \(\hat{\theta}=4.9989\), respectively. In addition, for the same case of \(\theta=5\), \(n=10\), and \(n=400\), the values of the LSE are \(\hat{\theta}=5.0759\) and \(\hat{\theta}=5.0167\), respectively. In general, we can say that the LSE method performs better than other methods up to \(n\leq 35\), while the MLE is the best one for \(n\geq 75\) in terms of the MSE.
# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
###### Abstract  
Clean energy is extremely important not only because of economic purposes but also for health considerations. The use of photovoltaic (PV) systems is growing, with the increased needs for electricity. This requires more attention to research of PV systems. In this study, a method to predict the expected lifetime based on the reliability of system performance is proposed. Geographical data were collected near two different locations: Cairo, Egypt and Riyadh, Saudi Arabia. The PV system was simulated with inputs from collected data to obtain the device factors and system responses. To study the significance of inputs and device parameters on the system responses, the Taguchi OA method was used. The probability density function (pdf) of the time of acceptable performance was estimated from the simulation data. A reliability analysis method was applied to the obtained pdf to estimate the reliability function, lifetime or mean life, reliable life, and rate of failure of the used PV system as assessment factors. The results showed that the system efficiency is highly dependent on the ambient temperature, while the performance ratio depends on many variables. The reliability analysis revealed that the field orientation of 30\({}^{\circ}\) tilt and 20\({}^{\circ}\) azimuth and of 30\({}^{\circ}\) tilt and 30\({}^{\circ}\) azimuth are best for near Cairo and near Riyadh, respectively. These orientations lead to the longest mean life of 772.25 and 688.36 months for Cairo and Riyadh, respectively. It also resulted in the lowest failure rates of 0.001295 and 0.001228 per month for both regions.  
PV systems; reliability; performance analysis; Taguchi OA design 20221288520  
orientation of the PV system, it is important to study the performance of the PV systems at different orientations for a specific geographical region of the world. This can help estimate the best system alignment to obtain optimum system performance [6].  
Generally speaking, the PV cell is a transducer that converts solar (optical) energy into electrical energy. Utilizing the PV cells for power production requires more parts to form a PV system. Studying such systems can be achieved through their transfer function or by collecting and analyzing data about operational conditions, inputs, outputs of different parts of the device (device factors), and system responses. Different settings can be considered to collect data from different experiments. The following sections give brief explanations of such data components and models.
# Seismic Upgrade of an Existing Reinforced Concrete Building Using Steel Plate Shear Walls (SPSW)
## 1 Introduction
This way, the SPSW yields before yielding of the surrounding members occurs. The steel plate can be stiffened or unstiffened. To reduce the slenderness of the steel plate, stiffeners are added in a vertical or horizontal configuration, even in some cases in a diagonal manner, providing, thus, the buckling strength needed to allow shear yielding of the plate. The mechanics of a stiffened or unstiffened SPSW are described in [17], revealing the development of a tension field in the plate as the main mechanical characteristic. Unstiffened SPSWs are capable of resisting seismic loads by developing a diagonal tension field with an optimum inclination angle of \(\alpha=45^{\circ}\). In slender unstiffened SPSWs, the tension field is formed after the buckling of the plate but before its shear yielding, whereas in stiffened SPSWs, the plate yields and then buckles. However, depending on the spacing and thickness of the stiffeners, the tension-field action in stiffened SPSW may take place when buckling occurs and before shear yielding [17]. SPSWs do not significantly increase the mass of the structure and can be easily replaced in case of failure. Moreover, their erection process is faster and easier than with other conventional retrofitting techniques [24]. From the perspective of capacity design [25], the HBEs (beams) should resist the demands of the tension field yielding of the steel plate, and the VBEs (columns) should resist the demands of the tension field yielding and flexural yielding of the HBEs. Therefore, it is important that the boundary elements have adequate strength and stiffness to ensure the full development of the tension field. Retrofit of r/c buildings is a highly active research domain and covers several hazard scenarios such as fire, seismic events, and explosions, e.g., [26; 27; 28]. Attempting to remobilize the interest of practicing engineers and researchers regarding the application of SPSW for seismic retrofit and upgrade purposes, this paper investigates the effectiveness of the SPSW on an existing r/c building designed by older seismic provisions. In particular, expanding the knowledge gained by previous works in the field [18; 19; 20; 21; 22; 23], what this present study seeks to make clear is how the modeling and configuration of the SPSW have an impact on the desired seismic performance of the existing r/c building. To be sure in capturing the effects of modeling and configuration of SPSW, three-dimensional non-linear time-history (NLTH) analyses are conducted, thus, from a computational point of view, the best option available for seismic response purposes is selected. On the basis of specific seismic response results obtained by NLTH analyses, including plastic hinge formations, and letting aside any issues related to fabrication and cost, conclusions about the modeling, configuration, and effectiveness of the SPSW for r/c buildings similar to the type studied herein are drawn.