esa-sceva/satcom-chunk-collection huggingface dataset:
# Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique
## 5 Conclusions and Future Work  
In this paper, we make an effort to provide the best CNN architecture for brain tumor classification using MRI images by comparing all the performances of 15 configured CNN architectures. Based on the objective function value, we selected the ideal model, which was easily trainable and detected tumors faster than other architectures, with a low computational cost. The parameters and configuration of the selected CNN model were simpler than those of the other 14 architectures. Based on the outcomes of this study, we recommend that the simpler architectures perform best and are more stable and reliable than the complex ones. We admit that architecture 13 can perform better, and can gain a higher level of accuracy and stability if we perform small adjustments and experimentation. We also admit that the dataset was very small for testing. In the real-world implementation, we require more MRI scans for training and testing of various state-of-art deep learning models.  
In terms of future work on brain tumor detection, various transfer learning architectures can be trained for classification, and architectures, such as ResNet, VGG and many other ImageNet models can be implemented. Therefore, brain tumor detection, which is a sensible case, can provide the best outcome via transfer learning and can better diagnose tumors. A large dataset can be used instead of image augmentation which can lead to better stability of architectures for the classification of MRI scans.  
Workflow pipeline creation, M.S.; dataset gathering, H.G.; data preprocessing, M.P., L.F.E.-A.; results and analysis, V.S., E.L.-C.; writing--review and editing, S.S.R.; implementation and supervision, M.K.G. All authors have read and agreed to the published version of the manuscript.  
Author [PERSON] acknowledges support from the Chilean Government through FONDECYT initiation grant No. 11190056. Research supported by Red Sistemas Inteligentes y Expertos Modelos Computacionales Iberoamericanos (SIEMCI), project number 522 RT0130 in Programa Iberoamericano de Ciencia y Tecnologia para el Desarrollo (CYTED).  
**Informed Consent Statement:** Not applicable.  
Figure 9: The receiver operating characteristic (ROC) curve for the 13 th CNN architecture. The area under the curve (AUC-ROC) is calculated to be 0.737 units.  
Data Availability Statement: The dataset was collected from Kaggle, and we have also explained the dataset preparation in Section 3.1 Dataset Used. For more reference, the dataset link is below. Dataset Link: [[https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection](https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection)]([https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection](https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection)) accessed on 19 September 2020.  
The authors declare no conflict of interest.
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 3 Novel Pooling Methods
### Rank-Based Average Pooling  
The proposed pooling evaluates the average performance for practically zero negativity activation functions, which could also cause the loss of racist and discriminatory data by downplaying higher activation levels. Likewise, in max pooling, non-maximum activations are eliminated, leading to data loss. A rank-based average pooling layer can overcome the challenges of information loss imposed on both max pooling and average pooling layers (RAP) [56]. The outcome of the RAP can be stated as Equation (8):  
\[\text{S}_{j}=1\div t\sum_{i\in\text{K}_{j,\ r_{i}<t}}^{n}a_{i} \tag{8}\]  
The ranks boundary, which defines the categories of activations used during averaging, is represented by \(t\). In feature maps, \(R\) stands for the pooling regions \(j\), and \(t\) stands for the index of each activation inside of it. \(\text{S}_{j}\) and \(a_{i}\), within this order, reflect the rank of activation I and the value of activation I. When \(t=1\), max pooling is established. According to [PERSON] et al. [37], limiting \(t\) to a median value achieves good performance and a good balance between max pooling and average pooling. Therefore, RAP has better discriminative power than traditional pooling methods and is a perfect combination of maximum and average pooling. Figure 7 depicts a simulation of rank-based pooling in operation.
## 5 Cache Allocation
### Global Weight  
Based on content domain weights, data path weights, and node weights, we calculate the global weights of each node in the network. Since different domains contain the same node, and so do different paths, we need to analyze both cases specifically.  
For a node that belongs to multiple different content domains at the same time. If a node belongs to a content domain, the content of that content domain needs to be cached. Because different content domains contain different content, nodes need to store the content contained in each content domain to which they belong. So we overlay the weights that nodes are assigned from different content domains to get the following \(NG_{n}\):  
\[NG_{n}=\sum_{a=1}^{CDC}NCD_{a} \tag{6}\]  
In Expression (6), \(NCD_{a}\) is the value of the weight that Node n is assigned in content domain a. \(CDC\) is a collection of all content domains that contain the node.Within content domain a, the calculation of the weight value \(NCD_{a}\) assigned to a node takes into account that the node belongs to multiple different data paths at the same time.  
For a node that belongs to multiple different content domains at the same time. From the content storage perspective, the content that needs to be cached is the same between different data paths in the same content domain, so it seems that the maximum value of node weight on each data path can satisfy the requirement of content caching. For example, a node should allocate 3 cache units in Path 1 and 5 cache units in Path 2. Since the content is the same, allocating 5 cache units to a node can meet the requirements of Path 1 as well as Path 2. However, from the perspective of traffic load, a node belonging to different data paths has a high traffic load, so we choose to add the weights allocated by the nodes in different data paths as the weights of the nodes in the content domain, as shown below, which can also avoid the complex calculation caused by maximizing:  
\[NCD_{a}=\sum_{b=1}^{DPC_{a}}NDP_{ab} \tag{7}\]  
In Expression (7), \(NDP_{ab}\) is the value of the weight that the node is assigned on data path b in content domain a. \(DPC_{a}\) is a collection of all data paths that contain this node in content domain a. With Expression (7), we can get the weight of a node in the content domain based on the weight that the node is assigned on the path. In combination with the Expression (6), we can further calculate the global weights of nodes based on their weights in the content domain.  
\[NG_{n}=\sum_{a=1}^{CDC}\sum_{b=1}^{DPC_{a}}NDP_{ab} \tag{8}\]  
In Expression (8), \(CDC\) represents the set of all content domains containing that node, \(DPC_{a}\) is the set of all data paths containing that node in content domain \(a\), and \(NDP_{ab}\) is the weight assigned to that node on the data path \(b\) in the content domain \(a\). The size of cache space allocated to a node is proportional to its global weight.
# Roles of D-Amino Acids on the Bioactivity of Host Defense Peptides
## 7 Toxicity  
One important aspect of d-AA-containing HDPs that is under addressed in the current literature is the toxicity of this class of HDPs against healthy mammalian cells. Low toxicity against healthy mammalian cells is vital for the clinical applicability of a peptide. However, the toxicity of virtually all tested D-AA-containing HDPs against healthy mammalian cells was simply inferred from its IC\({}_{50}\) value with no discussion on how and why off-target toxicity occurs. To the best of our knowledge, no dedicated mechanism study exists on the toxicity of D-AA-containing HDPs against healthy mammalian cells. Although the toxicity against healthy mammalian cells by D-AA-containing HDPs (i.e., whether as diastereomer or all-d enantiomer) were generally lower than their all-l counterparts. However, it should be noted that toxicity was not absent as toxicity toward healthy control cells was on par or even higher than the toxicity against target cells [45; 66; 122], thus highlighting the need for further study in this area. It should also be noted that the reported bioactivity of D-AA-containing HDPs together with their all-l counterparts were not often found as D-AA-containing HDPs were typically tested alone. Another major issue in the exploration of off-target toxicity of D-AA-containing HDPs is the limited spectrum of healthy mammalian control cells tested by many studies. Often, only a few control cell lines were used. Considering the importance of low off-target toxicity and the absence of dedicated mechanism study, further research into how and why toxicity against healthy mammalian cells by d-AA-containing HDPs occurs could be of vital importance in facilitating further clinical applications. A recent study from [PERSON] et al. [123] addressed this nascent and important area of research on d-AA-containing HDPs by investigating the in vivo toxicity of the d-isoform of the 8K-wild-type-NBD peptide (i.e. where NBD is an acronym for NEMO-binding domain and NEMO is an acronym for NF-xB essential modulator). At the onset, [PERSON] et al. [123] hypothesized that the d-isoform may potentially have higher therapeutic effect than their l-isoform owing to their longer persistence in vivo due to their inherent protease resistance. Their findings indicated that although the d-isoform afforded a similar level of bioactivity as the l-isoform but to their surprise the renal toxicity of the d-isoform was significantly higher and this was attributed to inherent toxicity of d-Ser as described previously by [PERSON] et al. [124]. However, it should be noted that the d-isoform of the 8K-wild-type-NBD peptide is an all-d peptide comprising of a staggering number of nineteen d-AA (i.e., eight d-Lys and eleven d-AA on the NBD). Such a high number of d-AA on the HDP has been suggested by [PERSON] et al. [96] to be unfavorable for the bioactivity. Particularly, their study observed that when the number of d-AA in the peptide was more than three, deterioration of the anticancer activity was observed.
# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 3 Selection of the Control Strategy
### Upper-Level Control Strategy
#### 3.1.2 Controller Results Analysis  
The braking system is modeled in MATLAB according to the previous section and the respective effects of the PID controller (Figure 9) and the fuzzy-PID controller (Figure 10) are analyzed in combination.  
The response curves of the output system are shown in Figure 11 for the EHB system with unit step signal (a) input by fuzzy-PID control, PID control and no control. In the figure, the cyan curve is the unit step response curve of the EHB system with no control, the pink curve is the unit step-response curve of the EHB system after PID regulation, and the red curve is the unit step response curve of the EHB system after fuzzy-PID control with a step signal input to the system at 1.00 s. After comparison, the regulation effect of both controls is very obvious, the overshoot of the system is obviously reduced, the regulation time becomes shorter, and the fuzzy-PID controller has better control effect than the PID controller, and can reach stability within 0.02 s.  
Figure 10: Fuzzy self-adjusting PID controller.  
Figure 9: PID controller in Simulink.
# A Noise-Tolerant Carrier Phase Recovery Method for Inter-Satellite Coherent Optical Communications
## 3 The Noise-Tolerant Method
### Feedback Estimation Stage  
The feedback loop obtains an estimation of the carrier phase by driving the phase error \(\theta_{d}\) towards zero. The phase \(\theta_{\text{in}}\) is calculated as follows:  
\[\theta_{\text{in}}[\![k]\!]=\arctan(r_{B}[\![k]\!]). \tag{7}\]  
The output \(K_{d}\theta_{d}\) from the phase detector is subsequently smoothed by a second-order loop filter, mitigating the impact of noise on phase estimation. The gain factors \(K_{1}\) and \(K_{2}\) affect the loop filter's capacity to eliminate noise. The NCO generates the estimated value of the carrier phase, \(\hat{\theta}_{1}\), based on the output from the loop filter \(\theta_{\text{loop}}\). The iterative function of the feedback loop is represented as  
\[H(z)=\frac{K_{d}K_{1}z^{-1}+(K_{d}K_{2}-K_{d}K_{1})z^{-2}}{1+(K_{d}K_{1}-2)z^{- 1}+(K_{d}K_{2}-K_{d}K_{1}+1)z^{-2}}. \tag{8}\]  
In Equation (8), three parameters, \(K_{d}\), \(K_{1}\), and \(K_{2}\), are involved, which are associated with the natural frequency \(\omega_{n}\) and the damping factor \(\xi\) of an analog feedback loop [28]. If the product of the natural frequency and the symbol period \(T\) is significantly less than 1, specifically \(\omega_{n}T\ll 1\), the relationships among these three variables and \(\omega_{n},\xi\) can be derived as follows:  
\[K_{1}\approx\frac{2\xi\omega_{n}T}{K_{d}}, \tag{9}\]  
\[K_{2}\approx\frac{(\omega_{n}T)^{2}}{K_{d}}, \tag{10}\]  
where \(K_{d}=4\) is designed to eliminate the impact of QPSK modulation on the estimation of the carrier phase [17]. An empirical value for \(\xi\) is \(\sqrt{2}/2\). Therefore, \(K_{1},K_{2}\) in Equation (8) are determined by \(\omega_{n}\), whose empirical condition is  
\[\omega_{n}<\frac{1}{10T}. \tag{11}\]
# Performance-Based Analysis of Single-Layer Cylindrical Steel Reticulated Shells in Fire
## 6 Effect of Structural Parameters on the Responses of Reticulated Shells During a Fire
### Influence of the Rise-Span Ratio  
The rise-span ratio not only affects the height of the latticed, reticulated shells, but also affects the air temperature field inside the structure. In this section, we examine what happens when the fire source is located in the center at ground level with a maximum fire source power of 20 MW and a rise-span ratio of 1/2, 1/3, or 1/4. We analyzed displacement in the N40 node above the fire source to investigate the influence of different rise-span ratios on the fire resistance performance of the latticed shell structure. A time-history curve of the vertical displacement of node N40 (above the fire source, with different rise-span ratios) is shown in Figure 24.  
Figure 23: Time-history curve of vertical displacement (influence of the members).  
Figure 22: Time-history curves of axial force of key members in different fire conditions. (**a**) Fire location I (1863s); (**b**) fire location II (582s); (**c**) fire location III (567s).  
It can be seen that during a fire, the vertical displacement of the N40 node can be roughly divided into three processes. In the development stage of fire (0-650 s), the node temperature increases gradually, and the thermal expansion of steel at a high temperature makes the N40 node gradually produce upward displacement. When the fire is stable (650-2400 s), the displacement of the node remains stable at 2400 s. The initial displacement of the system begins to decrease with a decrease in fire and thermal expansion. Because the maximum displacement of nodes during the fire does not exceed the limit of displacement of the shell (0.5 m), the reticulated shell does not fail and can continue to bear loads after the fire.  
On the contrary, when the rise-span ratio is 1/2, the initial vertical displacement of node N40 at a normal temperature is greater than 0.12 m. The upward displacement of the node is the smallest. When the rise-span ratio is 1/4, the vertical displacement of node N40 with a normal temperature loading is the smallest. However, in the fire development stage, the upward displacement of N40 increases rapidly, and the displacement value is large. When the rise-span ratio is 1/3, the maximum displacement of node N40 is between the first two values. The reason is as follows: with a normal temperature load, the displacement value of node U3 in a reticulated shell structure with a large rise-span ratio is larger because of the large roof load.  
In the meantime, during a fire from the same fire source, the lower the temperature of the node of the structural component with a large rise-span ratio is, the less obvious the thermal expansion effect of that component is. Therefore, in designing a latticed shell structure, the rise-span ratio should be selected to minimize the absolute value of displacement of the superstructure for the duration of a fire.
# A Study of 5G Edge-Central Core Network Split Options
## 5 Testbed Implementation and Evaluation Results
### Autonomous Edge Network  
For the autonomous edge network, the complete system was deployed in a single virtual machine with the network functions running as parallel programs, separated in Linux namespaces. In fact, because the synchronization of the user profiles in the UDM is executed asynchronous and using database specific synchronization tools, no connectivity with the central entities was required. This resulted in the complete communication to be localized at the edge, acting the same as a local campus network, covering only a limited concern area.  
As noted in Table 3, such a solution has a minimal delay in all the control plane procedures, due to the much-reduced latency of the communication between the different network functions, practically virtual interfaces within the same virtual machine. In light of the very low delays obtained, it is to be further considered if for such small edge deployments, the current 5G architecture still is the most viable or if a more compact option consuming less resources and with less network management requirements should be used instead.  
As all the core network components were co-located within the edge node, such a solution is very good for services which need a very low delay in the control plane as well as for the services which cannot rely on a continuous or resource-stable backhaul. However, it is to be noted that such a solution is not good for large size edge networks, as the grouping of functionality is missing the flexibility capabilities which a system with multiple network functions is offering.
# Production of a Human Metabolite of Atorvastatinin by Bacterial CYP102A1 Peroxygenase
## 3 Results and Discussion
### Hydroxylation of Atorvastatin
Comparison of Atorvastatin 4-Hydroxylation Activity of CYP102A1 Supported by External Addition and In Situ Generation of Hydrogen Peroxide  
A continuous low-level supply or in situ generation of hydrogen peroxide is important for peroxygenase's stability [27; 28]. The overall productivity can be increased due to enhanced enzyme stability at tailored H\({}_{2}\)O\({}_{2}\) generation rates. Here, we compared the peroxygenase activity of CYP102A1 supported by externally added H\({}_{2}\)O\({}_{2}\) (10 mM) or by in situ generation of H\({}_{2}\)O\({}_{2}\) via glucose and glucose oxidase. The catalytic activity of atorvastatin 4-hydroxylation supported by externally adding 10 mM H\({}_{2}\)O\({}_{2}\) increased rapidly from a 2-10 min reaction time, after that, 4-OH formation rates reached a plateau (Figure 6). After 30 min, the reaction rate decreased slightly. When glucose oxidase and glucose were used to produce H\({}_{2}\)O\({}_{2}\) continuously, the atorvastatin 4-hydroxylation activity gradually increased from a 2-30 min reaction time. The product formation reached a plateau at 30 min and then increased slightly up to 60 min. The results show that in situ H\({}_{2}\)O\({}_{2}\) generation is more suitable for 4-OH atorvastatin formation than that of externally adding H\({}_{2}\)O\({}_{2}\) to support the peroxygenase activity of CYP102A1. When the concentrations of H\({}_{2}\)O\({}_{2}\) with glucose oxidase and glucose were determined under the same experimental conditions with M371, the H\({}_{2}\)O\({}_{2}\) concentrations increased to 2.7 mM after 5 min, was constant until 10 min, and then decreased to 1.3 mM after 60 min (Figure S6). These results show the distinctive effects of the two H\({}_{2}\)O\({}_{2}\) systems, the external addition and in situ generation, were observed on the atorvastatin 4-hydroxylation activity of CYP102A1.
## 3 Results and Discussion
### Results and Analysis
#### 3.2.3 Effect of the Lubricating Oil Quantity on the Slip Rate and Temperature  
The test conditions are as follows: the inner ring speed is 1050 r/min; the radial load is 130 N; and the lubricating oil quantities are 3 mL/min, 4 mL/min, 5 mL/min, 6 mL/min, and 7 mL/min. The measured minimum load is 124 N, the benchmark is 130 N, and the error of them is 4%. Figure 8a shows that the temperature in the contact area continues to decrease with the increase of the lubricating oil. With the increase of lubricating oil quantity, the trend of slip has no obvious linear relationship. When the lubricating oil quantity is 4 mL/min, the slip rate drops. According to the load analysis, the load value under this condition is 127 N because of the inevitable fluctuation of the load. Meanwhile, the load is larger than the load value under other conditions, so the slip rate under this condition is the minimum. As can be seen from the variation trend in Figure 8a, with the increase of lubricating oil, the temperature decreases gradually, but there is no direct relationship between the lubricating oil quantity and slip rate. It can be concluded that the coupling effect between different loads and lubricating oil quantities has a certain influence on the slip rate. Figure 8b is the scatter diagram of load for the five cases.
## 3 Methodology
### Dry Beach Slope Measurement
#### 3.3.3 Calculation of Dry Beach Slope  
It can be divided into two steps to measure the dry beach slope at each point \(P(x_{P},y_{P},z_{P})\). The first step is to find the point \(Q(x_{Q},y_{Q},z_{Q})\) corresponding to point \(P\) in the point cloud of the boundary between the dry beach and the dam. The second step is to use the coordinates of these two points (\(P\) and \(Q\)) to measure the slope of \(P\). The schematic is shown in Figure 10.  
The process of finding the corresponding point \(Q\) of point \(P\) is also carried out in the plane XY. Thus, the entire point cloud is projected onto the XY plane, and the projection point \(P\) is \(p\left(x_{P},y_{P}\right)\). The corresponding point \(Q\) is then determined according to Equations (7) and (8).  
The direction vector \(\overrightarrow{n}=(1,k,0)\) of the line \(l:y=kx+b\) fitted in Section 3.2.3 is parallel to the axis of the dam. Take a line \(l^{\prime}\) through the point \(p\left(x_{P},y_{P}\right)\) and perpendicular to the vector \(\overrightarrow{n}\). The equation for line \(l^{\prime}\) is shown in Equation (7).  
\[l^{\prime}:y=-\frac{x}{k}+(y_{P}+\frac{x_{P}}{k}) \tag{7}\]  
Calculate the distance from each projection point \((x_{i},y_{i})\) of the boundary between the dry beach and the dam to the straight line \(l^{\prime}\) using Equation (8). The point with the shortest distance is the projection of the corresponding point \(Q\).  
\[d=\frac{|\frac{1}{k}x_{i}+y_{i}-(y_{p}+\frac{x_{P}}{k})|}{\sqrt{1+\frac{1}{k}}} \tag{8}\]  
After finding the corresponding point \(Q\) of point \(P\), the dry beach slope of point \(P\) is measured by using the two-point coordinates. The calculation formula can be seen in Equations (9) and (10).  
Figure 10: Dry beach slope.  
The vector between points \(Q\) and \(P\) is  
\[\overrightarrow{QP}=(x_{Q}-x_{P},y_{Q}-y_{P},z_{Q}-z_{P}) \tag{9}\]  
The vector perpendicular to the vector \(\overrightarrow{n}\) is \(\overrightarrow{m}=(k,-1,0)\). Thus, the slope at point \(P\) can be measured using Equation (10).  
\[cos\alpha=\frac{\overrightarrow{QP}\cdot\overrightarrow{m}}{\left| \overrightarrow{QP}\right|\left|\overrightarrow{m}\right|} \tag{10}\]