esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Channel Capacity  
The concept of the Shannon capacity of a channel is centered on the maximum data rate that can be reached within a certain bandwidth (BW) and at a specific signal-to-noise ratio. This theoretical capacity suggests a decrease in bit error rate (BER) that is challenging to achieve in real-world scenarios. However, as link level design techniques evolve, the actual data rate for noise channels is approaching this theoretical boundary, as discussed in [24]. Often measured in bits per second (bps)/Hz or equivalent units, a high channel capacity denotes the communication system's capability for swift and effective data transmission. It is formulated as  
\[C=log_{2}det\Big{[}I_{M_{R}}+\frac{P_{t}}{P_{n}M_{T}}HH^{H}\Big{]} \tag{22}\]  
Here, \(I_{M_{R}}\) is the identity matrix with dimensions (\(M_{R}~{}\times~{}M_{R}\)), H characterizes the channel response sized size (\(M_{T}~{}\times~{}M_{R}\)), (\(*\))\({}^{H}\) is the conjugate transpose, and \(P_{t}/P_{n}\) quantifies the signal noise ratio (SNR). In estimating the capacity of the channel, our examination included channel responses obtained through the LS method, the prevalent MMSE method, and three additional approaches rooted in machine learning applications, namely ELM, RELM, and ORELM. The formula for calculating the estimated capacity within the CNN-SD model is prescribed as follows (6) to produce (23).
###### Abstract  
Understanding the response of vegetation and ecosystem resilience to climate variability and drought conditions is essential for ecosystem planning and management. In this study, we assessed the vegetation changes and ecosystem resilience in the Horn of Africa (HOA) since 2000 and detected their drivers based mainly on analysis of the Moderate Resolution Imaging Spectror-diameter (MODIS) products. We found that the annual and seasonal trends of NDVI (Normalized Difference Vegetation Index) generally increased during the last two decades over the Horn of Africa particularly in western parts of Ethiopia and Kenya. The weakest annual and seasonal NDVI trends were observed over the grassland cover and tropical arid agroecological zones. The NDVI variation negatively correlated with Land Surface Temperature (LST) and positively correlated with precipitation at a significant level (\(p<0.05\)) account for 683,197 km\({}^{2}\) and 533,385 km\({}^{2}\) area, respectively. The ecosystem Water Use Efficiency (eWUE) showed overall increasing trends with larger values for the grassland biome. The precipitation had the most significant effect on eWUE variation compared to LST and annual SPEI (Standardized Evapotranspiration Index). There were about 54.9% of HOA resilient to drought disturbance, whereas 32.6% was completely not-resilient. The ecosystems in the humid agroecological zones, the cropland, and wetland were slightly not-resilient to severe drought conditions in the region. This study provides useful information for policy makers regarding ecosystem and dryland management in the context of climate change at both national and regional levels.  
vegetation dynamics; ecosystem resilience; MODIS NDVI; water use efficiency; climate change; climate variability; drought; precipitation; LST; SPEI +
Footnote †: journal: Remote Sensing  
_Remote Sensing_, **2021**, 13, 1668. [[https://doi.org/10.3390/rs13091668](https://doi.org/10.3390/rs13091668)]([https://doi.org/10.3390/rs13091668](https://doi.org/10.3390/rs13091668))  
[[https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing)]([https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing))
## 3 Materials and Methods
### Prediction Module  
#### 3.3.1 CIoU Loss Function  
The loss function of object detection usually is constructed from a classifying loss function and a regression loss function for boundary box regression. Because the initial bounding boxes for the truth and bounding boxes for candidates overlap, their intersection ratio is IoU. Scale invariance is a desirable characteristic of IoU. This indicates that the two bounding boxes are considered together with their width, height, and placement. The area of the forms, regardless of their size, is the focus of the normalized IoU measure. The change from \(Ln\prime\)s norm loss to IoU loss in boundary box loss functions is based on the application of the metric as a loss function. Thus, the IoU's mathematical formula is given:  
\[IoU=\frac{\left|B\cap\ B_{gt}\right|}{\left|B\cup\ B_{gt}\right|} \tag{6}\]  
This relevant loss function is:  
\[L_{IoU}=1-\frac{\left|B\cap\ B_{gt}\right|}{\left|B\cup\ B_{gt}\right|} \tag{7}\]  
The prediction box is \(B\) after training. \(B_{gt}\) is called a ground truth bounding box, which is also the premarked target ground. If the extracted candidate object is not covering the box delimiting the main claim, LIoU is 1. The Generalized Intersection over Union (GIoU) loss [50] will put a large box outside, which is equivalent to a penalty item. DIoU loss [51] considers the distance between the overlapping area and the center point. If the target frame contains a prediction frame, the distance between them can be calculated directly. However, specific to our prediction task, this study must also consider the geometric factor  
Figure 9: Architecture of SPP-MHSA, FPN, and PANet.  
of the target frame aspect ratio; thus, CIoU has also made improvements to this point, for example, by increasing the compliance limit.  
\[L_{\text{CIoU}}=1-IoU+\frac{\rho^{2}\left(b,b_{gt}\right)}{c^{2}}+\alpha v \tag{8}\]  
Positive weight parameters are denoted by \(\alpha\). The function measuring the consistency of the aspect ratio is denoted by \(\
u\). The width of the target bounding box is \(w\). The target bounding box's height is \(h\).  
\[\alpha=\frac{v}{\left(1-IoU\right)+v} \tag{9}\]  
\[v=\frac{4}{\pi^{2}}(arc\tan\frac{w^{gt}}{\mu^{gt}}-arc\tan\frac{w}{h})^{2} \tag{10}\]  
This study should consider the overlap area, the distance from the center, and the aspect ratio. This study applies the objective box for the regression loss function as the CIoU loss. This loss function speeds up and improves the regression accuracy of prediction boxes.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 2 Problem Formulation  
### Electrical Energy Forecasting  
To ensure the secure and economic integration of wind turbines into a power system, accurate wind power and load demand forecasting has become critical of energy management systems [24]. In this paper, the back-propagation algorithm based on the artificial neural network is employed to fit the power curve of the difference between wind generation and load. The total capacity of the ESS is determined by the predicted wind power and loads.  
Due to the uncertainties of renewable energy, it is difficult to predict the change in power and capacity of the ESSs. In this paper, the optimal storage capacity problem is formulated as a time series forecasting problem.  
The outputs of the generation comprise two parts:  
\[\left\{\begin{array}{l}P_{d}=P_{s}+P_{dev}^{d}\\ P_{w}=P_{F}^{w}+P_{dev}^{w}\end{array}\right. \tag{1}\]  
Similarly, the load can be separated into two components as follows:  
\[P_{l}=P_{F}^{l}+P_{dev}^{l} \tag{2}\]  
Supposing that  
\[P_{s}+P_{F}^{w}=P_{F}^{l} \tag{3}\]  
The power difference between the actual power and the forecasting power is compensated by the ESS.  
According to the requirement of a power system, the power balance in the hybrid wind/diesel/ESS system should be followed as Equation (4).  
\[P_{d}+P_{e}+P_{w}=P_{l} \tag{4}\]  
Consequently, the charging or discharging power of the ESS can be calculated by Equation (4) and the capacity of the ESS can be obtained herein, which is described by Equation (5).  
\[P_{e}=P_{l}-P_{d}-P_{w}=\left(P_{F}^{l}+P_{dev}^{l}\right)-\left(P_{s}+P_{dev }^{d}\right)-\left(P_{F}^{w}+P_{dev}^{w}\right) \tag{5}\]  
\[C_{E}=E_{E}^{\max}/u_{\min}=P_{e}^{\max}\times\Delta t/u_{\min} \tag{6}\]  
Noted that the main effect of the ESS is to compensate for the power deviation from the forecast; as a result, the largest forecasting error is the total capacity of the ESSs.
# Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems
## 5 Results
### Step 2--Defining SQL Query Patterns
#### 5.3.3 Select Transaction Instances
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case ID** & **Event ID** & **Activity Name** & **Timestamp** \\ \hline PO1 & PO1 & Create Purchase Order & 20-06-2017 \\ PO1 & R1 & Posting Goods Receipt & 23-06-2017 \\ PO1 & R3 & Posting Goods Receipt & 25-06-2017 \\ PO1 & IN1 & Book Invoice & 23-06-2017 \\ PO1 & IN2 & Book Invoice & 25-06-2017 \\ PO1 & 1 & Approve Purchase Order & 20-06-2017 \\ PO2 & PO2 & Create Purchase Order & 29-06-2017 \\ PO2 & R2 & Posting Goods Receipt & 13-07-2017 \\ PO2 & IN1 & Book Invoice & 13-07-2017 \\ PO2 & 2 & Approve Purchase Order & 29-06-2017 \\ PO3 & PO3 & Create Purchase Order & 08-07-2017 \\ PO3 & R4 & Posting Goods Receipt & 10-07-2017 \\ PO3 & R5 & Posting Goods Receipt & 12-07-2017 \\ PO3 & IN709 & Book Invoice & 10-07-2017 \\ PO3 & IN710 & Book Invoice & 13-07-2017 \\ PO3 & 3 & Approve Purchase Order & 08-07-2017 \\ PO4 & PO4 & Create Purchase Order & 13-07-2017 \\ PO4 & R7 & Posting Goods Receipt & 17-07-2017 \\ PO4 & IN707 & Book Invoice & 17-07-2017 \\ PO5 & PO5 & Create Purchase Order & 15-08-2017 \\ PO5 & R8 & Posting Goods Receipt & 25-08-2017 \\ PO5 & IN708 & Book Invoice & 25-08-2017 \\ PO5 & 4 & Approve Purchase Order & 17-08-2017 \\ PO6 & PO6 & Create Purchase Order & 15-08-2017 \\ PO6 & R9 & Posting Goods Receipt & 27-09-2017 \\ PO6 & R19 & Posting Goods Receipt & 30-09-2017 \\ PO6 & R21 & Posting Goods Receipt & 01-10-2017 \\ PO6 & IN710 & Book Invoice & 30-09-2017 \\ PO6 & IN712 & Book Invoice & 01-10-2017 \\ PO6 & IN723 & Book Invoice & 27-09-2017 \\ PO7 & PO7 & Create Purchase Order & 15-08-2017 \\ PO7 & R6 & Posting Goods Receipt & 20-08-2017 \\ PO7 & IN713 & Book Invoice & 20-08-2017 \\ PO7 & 5 & Approve Purchase Order & 17-08-2017 \\ PO8 & PO8 & Create Purchase Order & 21-08-2017 \\ PO8 & R10 & Posting Goods Receipt & 28-08-2017 \\ PO8 & IN714 & Book Invoice & 28-08-2017 \\ PO8 & 6 & Approve Purchase Order & 21-08-2017 \\ PO10 & PO10 & Create Purchase Order & 17-08-2017 \\ PO10 & PO11 & Posting Goods Receipt & 20-08-2017 \\ PO10 & IN715 & Book Invoice & 21-08-2017 \\ PO10 & 7 & Approve Purchase Order & 17-08-2017 \\ PO11 & PO11 & Create Purchase Order & 24-09-2017 \\ PO11 & R12 & Posting Goods Receipt & 30-09-2017 \\ PO11 & IN716 & Book Invoice & 30-09-2017 \\ PO11 & 8 & Approve Purchase Order & 25-09-2017 \\ PO12 & PO12 & Create Purchase Order & 02-10-2017 \\ PO12 & R13 & Posting Goods Receipt & 17-10-2017 \\ PO12 & IN731 & Book Invoice & 17-10-2017 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A fragment of the event log extracted from the real data of a NAV ERP system.
## 2 Materials and Methods
### Internal Dataset  
To create the internal dataset, a random selection was made from the DICOM format radiologic images of 600 patients in the FastMRI database. The selected images were limited to radiologic images of 471 patients after the application of the exclusion criteria (Figure 2). T2 and PD-weighted images were selected. The age range of the selected patients was from 2 to 85 years, with a mean age of 47.79 years; 44.58% (n = 210) were female, 42.02% were male, and 13.38% (n = 63) had no gender information. Right-knee MRI images were used in 53.08% (n = 250), and left-knee MRI images were used in 46.92% (n = 221) of the patients. While 16.35% (n = 77) of the patients had no meniscal tear, 83.65% (n = 394) had meniscal tears. In the group with meniscal tears, 53.81% (n = 212) of the tears were in the right knee, 46.19% (n = 182) in the left knee, 67.51% (n = 266) in the medial meniscus, and 32.49% (n = 128) in the lateral meniscus.  
In total, 849 images in portable network graphics (PNG) format were obtained from radiologic images of 471 patients. An average of 1.8 images were extracted for each patient. Of these images, 21.08% (n = 179) were healthy and 78.92% (n = 670) had meniscal damage. Of the healthy images, 51.96% (n = 93) belonged to the lateral meniscus and 48.04% (n = 86) to the medial meniscus. Of the images with meniscus damage, 32.24% (n = 216) belonged to the lateral meniscus and 67.76% (n = 454) to the medial meniscus. The medial meniscus was torn approximately three times more frequently than the lateral meniscus due to  
Figure 2: The process of creating the internal dataset.  
reduced mobility caused by meniscocapsular attachment points [23]. The difference in distribution between the lateral and medial meniscus in images with meniscal tears is due to the anatomical structure and limited mobility of the medial meniscus.  
The acquired images were divided into training, validation, and test sets in a ratio of 7:2:1, respectively. Stratified random sampling was used to maintain the proportions of lateral meniscus, medial meniscus, healthy, and torn meniscus in each set. This approach prevented imbalances in data distribution and ensured homogeneous class representation in each set. To show the distribution of the dataset more clearly, Table 1 shows the number of healthy and torn meniscus images in the training, validation, and test sets.
# Thermal Stability Design of Asymmetric Support Structure for an Off-Axis Space Camera
## 2 Low Thermal Deformation Optical Mechanical Structure
### Design Theory of Zero-Thermal-Expansion Material  
The support structure of a remote sensing camera is traditionally made of aluminum alloy or titanium alloy. However, the requirements in this field are to significantly reduce the weight of the camera, improve its stiffness and enhance its environmental adaptability. The technology of using carbon-fiber-reinforced plastics (CFRP) has been developed and successfully applied in various remote sensing camera structures, because it has excellent specific stiffness, specific strength and low CTE. We choose CFRP to construct a support structure with near zero thermal expansion, so as to realize the low thermal expansion support structure design of the remote sensing camera. The basic design theory of the single- and multi-layer thermal expansion of various composite components is introduced in [24].  
For the single-layer composite, the stress-strain relationship caused by temperature is as follows:  
\[\{\sigma\}=[Q](\{\epsilon\}-\{\alpha\}\Delta t) \tag{1}\]  
where \(\{\sigma\}\) is temperature-induced stress, \([Q]\) is the stiffness matrix describing the relationship between stress and strain, \(\{\epsilon\}\) is the strain of the composite, \(\{\alpha\}\) is the coefficient of thermal expansion and \(\Delta t\) is the temperature change.  
According to the classical theory of composite laminates, the stress-strain relation-ship can be expressed as:  
\[\left\{\begin{array}{c}N\\ M\end{array}\right\}=\left[\begin{array}{cc}A&B\\ B&D\end{array}\right]\left\{\begin{array}{c}\epsilon\\ \kappa\end{array}\right\}-\left\{\begin{array}{c}N^{t}\\ M^{t}\end{array}\right\} \tag{2}\]  
where \([A]\), \([B]\) and \([D]\) are the tension compression stiffness matrix, coupling stiffness matrix and bending stiffness matrix, respectively and \(\{N^{t}\}\) and \(\{M^{t}\}\) are the internal stress matrix and internal bending moment matrix caused by temperature load, respectively.  
In engineering applications, laminated composites are usually designed with symmetrical folding to avoid torsion due to temperature changes [25]. For multi-layer composite structures with symmetrical layers, the coupling stiffness matrix is zero, and the ma-terial thermal deformation can be expressed as:  
\[\{N^{t}\}=\sum_{k=1}^{n}\int_{Z_{k-1}}^{Z_{k}}{[Q_{k}]\{\alpha_{k}\}\Delta tdz} \tag{3}\]  
For multilayer symmetrical laminates, the coefficient of thermal expansion along the center layer can be expressed as:  
\[\{\alpha_{c}\}=\left(\sum_{k=1}^{n}[Q]_{k}\right)^{-1}\left(\sum_{k=1}^{n}[T] _{k}^{-1}\right)\{[Q][\alpha]\} \tag{4}\]  
Carbon-fiber-reinforced plastics (CFRP) not only have a very low linear thermal expansion coefficient, but also have a negative axial linear thermal expansion coefficient. Therefore, composites with a thermal expansion coefficient of zero can be obtained by utilizing these characteristics.
## 1 Introduction  
Peri-implantitis is the most serious disease in implantology and often has severe consequences for rehabilitation treatments with implants [1]. To date, there is no defined and/or predictable treatment for this condition [1; 2; 3]. Peri-implantitis is an inflammation of the tissues around an implant, characterized by progressive loss of the peri-implant tissue level [1; 4; 5; 6]. The loss of bone support caused by peri-implantitis can vary among individuals; in addition, this loss may increase over time, usually progressively and non-linearly [1; 4; 7].  
Regarding the incidence of peri-implant diseases, there is limited information in the literature about their real prevalence [8]. Peri-implantitis has a multifactorial origin and is not well-determined [4]. According to [PERSON] and [PERSON] [7], the overall frequency of peri-implant mucositis and peri-implantitis was 63.4% and 18.8%, respectively. There are also reports in the literature indicating that the incidence of peri-implantitis can vary from 12% to 40% [9].  
Numerous methods to decontaminate implant surfaces have been used by clinicians. Comparisons of decontamination methods have not revealed any statistically significant difference in treatment results [10; 11; 12]. The establishment of a clean implant surface has been suggested as a prerequisite for successful regenerative treatment of peri-implantitis [13]. Contaminants such as bacteria and their secretions, calculus, and soft tissue cells should be removed without modifying the implant surface. However, it is still unknown to what extent these contaminants must be removed to achieve a successful treatment result [14].  
An alternative used by some professionals in the treatment of contaminated implants that have peri-implantitis is the smoothing (wear) of the rough and irregular surfaces of the implant with rotating instruments [15]. Implantoplasty is performed to reduce new bacterial adhesion and to optimize cleaning, thus preventing the recurrence of peri-implantitis in the portion of the oral cavity which will be exposed later. When performing implantoplasty, different rotary instruments, such as carbide burs and diamond burs, can be used, and good polishing is important [15; 16]. However, studies have shown that mechanical resistance is affected with this type of treatment [17; 18]. The surface morphology of titanium, regardless of the roughness parameters, affects cell viability and adhesion. Any type of mechanical treatment performed on a dental implant will modify its physical-chemical structure [19].  
The peri-implant tissue adhesion on the implant surface is a fundamental condition to restore the health of these areas after treatment. In this study, we used four different bur models (simulating implantoplasty techniques) to evaluate the effect of titanium instrumentation (wear) on the topography characteristic alterations as well as on cellular viability and adhesion in these conditions.
### Information Entropy of Individual AE Waveforms
Three different methods to account for this new information about the probability distribution have been investigated and yield three formulations to derive AE information entropy. First, one can assume that the underlying AE voltage distribution is constantly changing and is independent between all the time steps. Therefore, a new probability distribution of the second AE signal's 2048 voltage values should be created to estimate the true distribution at that particular instant. With every new AE signal, a new probability distribution is constructed to represent the constantly-changing underlying distribution. The information entropy calculated from each of these distributions is referred to as _instantaneous entropy_. The second method is based on the theory that the underlying probability distribution changes very slowly, and the second AE signal received is from approximately the same distribution as the first AE signal. In turn, the 2048 voltage values from the first and second signal are combined, and a distribution is formed from the total 4096 voltage values. As more AE signals are received, all of the new and previous AE voltage values are concatenated, and the estimated probability distribution of the true underlying distribution is updated for each new AE signal. Another way to explain this process is that every AE signal's distributions are combined and have equal effect on the estimated distribution. Because
## 4 Conclusions  
In this study, the assembly process of \"pile-column integration\" has been proposed. The pile column structure of the bridge can be completed by prefabricated pile foundation, pier, and cap beam based on the integral assembly installation method. The vertical compressive static load test, horizontal static load test, and reliability test of pile-column connection were conducted. From the test results, the following conclusion could be drawn:  
(1) Through the vertical and horizontal static load tests of single pile, the vertical and horizontal bearing capacity limit values of single pile can be accurately determined, which can meet the requirements. In the design of pile foundation in practical engineering, the pile end bearing layer, pile length, pile diameter, concrete strength grade, and other designs could be determined to give full play to the inherent ultimate bearing capacity of the pile;  
(2) The displacement curve of single pile No. 5 at the flange connection position under various loads of the pile does not have an obvious angle break, and there is no sudden change in the slope, indicating that the flange connection quality is good. The strain data are suggested as auxiliary to the displacement results;  
(3) The findings of vertical and horizontal static load tests and connection reliability in this study proved the feasibility of this prefabricated bridge substructure \"pile-column integration\", which can provide a reference for the rapid construction of bridges.  
Conceptualization, H.W., L.W. and K.Y.; methodology, H.W., L.W. and W.W.; validation, S.X., G.W. and R.L.; formal analysis, H.W., L.W. and W.W.; investigation, K.Y., S.X., G.W. and R.L.; writing--original draft preparation, H.W. and L.W.; writing--review and editing, K.Y. and W.W.; project administration, H.W. and W.W.; funding acquisition, H.W. and W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Base and Talent Special Project of Guangxi Province (grant number: AD19245152), Science and Technology Key R&D Project of Guangxi (grant number: AB22035074), \"Yongjiang Plan\" of Nanning Leading Talents in Innovation and Entrepreneurship (grant number: 2018-01-04), National Key R&D Program of China (grant number: 2021 YFB2600604), High-Level Entrepreneurship and Innovation Talents Project of Nanning High Tech Zone (grant number: 2020008), China Postdoctoral Science Foundation (grant number: 2021T140262), Scientific and Technological Project of Science and Technology Department of Jilin Province (grant number: 20210508028 RQ), Scientific Research Project of Department of Education of Jilin Province (grant number: JJKH20221019 KJ).  
Not applicable.  
The authors declare no conflict of interest.