esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 5 Performance Evaluation
### Parameter Effect  
In this subsection, we present the performance of our sleep control mechanisms in various \(N_{i}|N_{i}|\), \(\rho\), and \(q_{th}^{ON}\) when the bandwidth of a backhaul network is much higher than the computing capability of a MECS (i.e., \(b_{i,j}>>b_{i}\)). In Figure 8, we display the total energy consumption in a scenario, in which all MECSs can directly communicate with each other. The workload rate of the system varied from 0.6 to 1.5. We used ten and twenty MECSs in the system. The results revealed that the proposed method reduced the total energy consumption by approximately 31%, on average, compared with the case where no sleep control is used. However, the variance in the energy consumed was larger than that when sleep control was not used. This is because the difference in energy consumption between the active and the sleeping MECSs increased when sleep control was activated. In the figure, _sleep ctrl with a 50% threshold_ and _sleep ctrl with a 70% threshold_ indicate that \(q_{th}^{ON}\) is set to 50% and 70% of \(Q_{max}\), respectively. In our experiments, we set \(Q_{max}\) to 100. We observed that the energy consumed in the network with different values of \(q_{th}^{ON}\) were similar.  
In Figures 9 and 10, we display the total energy consumed in the system when \(|N_{i}|\leq|N|\). In these figures, we configure \(q_{th}^{ON}\) as 0.5 or 0.7. When the range of the neighborhoods (i.e., \(|N_{i}|\)) changes, the average and the standard deviation of the energy consumption do not differ significantly. A similar pattern is observed when the number of MECSs increases. The standard deviation of the energy consumption becomes larger as \(q_{th}^{ON}\) increases. This is attributed to that \(q_{th}^{ON}\) determines the time when an active MECS wakes up sleeping neighbors.  
In Figures 11 and 12, we compare the \(Q_{i}\)s under different workloads for a neighborhood range of four and six. In the figures, \(q_{th}^{ON}\) is set to 0.5 or 0.7 for the MECS under  
Figure 8: Total energy consumed when all MECSs were able to directly communicate with each other. (**a**) when the number of MECSs is 10; (**b**) when the number of MECSs is 20.  
Figure 7: Impact of network bandwidth on the task service delay. (**a**) 10 MECSs; (**b**) 20 MECSs.  
sleep control. With each \(q_{th}^{ON}\), we measure the length of the queue of each MECS when the workload is 1.0 or 1.5. The first to the third quartile values of each \(Q_{i}\) are located below 50% or 70% of \(Q_{max}\). As the workload increases, the sleeping MECS numbers decrease, and the non-sleeping MECS loads become relatively even. When \(q_{th}^{ON}\) is 0.7, the sleeping MECS numbers decrease with respect to the case in which \(q_{th}^{ON}=0.5\), and the standard deviation of the queue length decreases.
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
## 5 Test Verification
### Stability Tracking Verification  
In order to verify the stability of the minimum-variance self-tuning controller, the p-type single-crystal silicon processing experiment was carried out by using the minimum-variance self-tuning control on the improved EDM control system. During the test, the motor was in position control mode and the electrode movement speed was 2 cm/s. In actual processing, in order to prevent short-circuit phenomena, after repeated tests, the absolute distance of the electrode positive feed does not exceed 40 \(\upmu\)m. Since the detection range of the interelectrode gap model established in the first section was 10-80 \(\upmu\)m, the output range of the control system was limited to 10-80 \(\upmu\)m. The tracking input signal was separately a straight line, a square wave, a sine wave, and the output was a time constant \(\tau\). Equation (9) was used to further convert \(\tau\) into the interelectrode gap \(d\). Figures 10-12 show the tracking effect of the input signal and parameter identification effects.  
Figure 9: EDM control system schematic.  
Figure 11: Square wave tracking: (**a**) The original image; (**b**) partial enlargement; (**c**) parameter identification result.  
Figure 10: Straight line tracking: (**a**) The original image; (**b**) partial enlargement; (**c**) parameter identification result.  
From the tracking effect in the actual machining process, it can be seen that the system had a certain adjustment time of 2 s in the initial stage of processing. Then, the interelectrode distance \(d\) could stably track the different interelectrode gap expected value \(d_{r}\). The right side of the tracking diagram is a local enlarged view. It can be seen from the red area in the local enlarged view that if the interelectrode gap \(d\) increases and the electrode is retracted, while \(d\) decreases, the electrode is fed to the workpiece. Therefore, the variation of the interelectrode gap is consistent with the actual advance and retreat of the electrode. It can be seen from the figure that the estimated values of parameters \(a\) and \(b\) are stable. The verification results show that the controller performs well and the parameters identified on-line are stable.
# Ultimate Compressive Strength of H-Section Stub Columns Subject to Random Fitting Corrosion Damage
## 1 Introduction
Unlike general corrosion, pitting corrosion consisting of random corrosion pits easily led to buckling in the compressive web area of corroded H-shaped beams, and the reduction in local bearing capacity of the web was simultaneously related to the losses of thickness and mass caused by corrosion damage [14]. It was stated that for random pitting corrosion, the reduction factor of elastic buckling strength may vary by up to more than 20% due to the change in corrosion location under the same degree of corrosion damage [15]. In addition, the increasing use of high-strength steel makes the cross-section of structural members slimmer. Corrosion damage may render a high risk of instability in this type of steel member [16; 17]. Although regular rather than random corrosion pits with an identical depth were introduced by mechanical drilling in the tested high-strength-steel columns, they resulted in a large strength reduction, even up to 40.4%, with 15% volume loss due to the corroded material [18]. All current codes standards, such as DET NORSKE VERITAS (DNV) specification [19], widely recommend the equivalent thickness method for residual strength assessment, but they are based on the uniform thickness loss defined upon the whole member. Nonetheless, the ultimate compressive load of a randomly pitted member is always lower than a uniformly corroded one with the equivalent thickness. Furthermore, it is a challenge to accurately define the equivalent thickness for an actual corroded member, given the presence of highly uneven corrosion and variations in thickness across different positions of the member [20]. Importantly, the random nature of corrosion pits in real long-term corrosive environments, in terms of their size, shape and distribution, was not taken into account simultaneously. Resultantly, a simple and applicable method for predicting the ultimate strength of randomly pitted H-section columns is greatly needed. This paper investigates the mechanisms behind the reduction in compressive strength and failure in randomly pitted H-section steel stub columns. To represent the random nature of actual corrosion pits accurately, a numerical modeling method is introduced, considering the variations in their size, shape and distribution simultaneously. The study focuses on analyzing the effects of the corrosion pattern, the pitting intensity and the width-to-thickness ratio of the plate element on the ultimate strength of the H-section steel stub columns. Additionally, the equivalent thickness method for random pitting corrosion is proposed, verified in terms of both the Chinese and European Standards.
# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 4 Findings and Discussion
### Issues of Recommender Systems
(2016) created low-dimensional hidden representations of context factors derived from sensors using autoencoders to address sparsity in context-aware recommender systems brought on by adding high context features [68; 69]. Furthermore, different studies, such as [70; 71], attempted to apply a social approach using an attention mechanism to eliminate the \"cold-start problem\". * Context-aware problem: Different studies have attempted to address this issue using user attribution, text information, and other context data as the auxiliary source to determine the context in an environment [69; 72]. However, three techniques are possibly used to eliminate this issue: facial expression detection, speech recognition and interpretation, and analyzing physiological signals. * Privacy solution: The best technique to eliminate this issue is applying cryptography and omitting third-party or peer users to keep the data private and only used for what is intended. Ref. [69] is an example of an improved cryptography solution. * Shilling assaults solution: Different approaches can effectively detect these attacks such as behavior monitoring and hit ratio. To categorize the attacks there are three criteria used to identify possible assaults such as the intention of attacking, the necessary knowledge to issue such an attack, and the possible size of attack [73; 74]. * Synonymy solution: The most appropriate technique to overcome this issue is using descriptive text rather than terms on a product by using an ontology, SVD (single value decomposition), and LSI (latent semantic indexing) [75]. * Latency solution: To address this issue generalization techniques are applied to users to reduce computing. Furthermore, collaborative filtering approaches based on the model are also used to improve performance [76; 77; 78]. In this section the results of a group of previous literature studies on this topic are presented in Table 1:The trending of sentiment analysis usage in the past years is growing as shown in Figure 7.
## Results
### Evaluating the Performance of the GPP Estimates Based on Different Methods  
The biome-specific (crop and grass) GPP-SIF relationships were obtained by fitting a linear function to the SIF and tower GPP at the Daman and A'rou sites (at Daman: GPP_SIF\({}_{757}\) = 37.63 \(\times\) SIF\({}_{757}\) + 0.39, GPP_SIF\({}_{771}\) = 56.66 \(\times\) SIF\({}_{771}\) + 0.77, GPP_SIF\({}_{740}\) = 19.50 \(\times\) SIF\({}_{740}\) + 0.81, GPP_RSIF = 1.85 \(\times\) RSIF \(-\) 0.59; at A'rou: GPP_SIF\({}_{757}\) = 15.86 \(\times\) SIF\({}_{757}\) + 0.69, GPP_SIF\({}_{771}\) = 23.75 \(\times\) SIF\({}_{771}\) + 1.46, GPP_SIF\({}_{740}\) = 7.54 \(\times\) SIF\({}_{740}\) + 0.70, GPP_RSIF = 0.97 \(\times\) RSIF + 0.12). The predicted GPP values based on SIF\({}_{757}\), SIF\({}_{771}\), SIF\({}_{740}\), RSIF, and VPM and the tower GPP are shown in Figures 4 and 5. The GPP estimates from SIF\({}_{757}\), SIF\({}_{771}\), SIF\({}_{740}\), RSIF, and VPM effectively tracked the magnitude and seasonal dynamics of the tower GPP at the Daman and A'rou sites, especially RSIF, VPM, and SIF\({}_{757}\), which captured the beginning period, thriving period and end period of the growing season. In contrast, the SIF\({}_{771}\) and SIF\({}_{740}\)-based model exhibited similarly weaker performances than other three models in predicting GPP. GPP_VPM and GPP_RSIF exhibited the best performance in GPP estimation, closely followed by GPP_SIF\({}_{757}\), then GPP_SIF\({}_{771}\) and GPP_SIF\({}_{740}\), while GPP_SIF\({}_{757}\) had the lowest RMSE among the models (Figure 5, Table 2). The correlation coefficients between GPP predicted by the SIF (SIF\({}_{757}\), SIF\({}_{771}\), SIF\({}_{740}\), and RSIF) and tower GPP, and GPP predicted by VPM model and tower GPP were greater at the A'rou site than at the Daman site.  
Figure 4: Validation of GPP estimates from SIF\({}_{757}\), SIF\({}_{771}\), SIF\({}_{740}\), and VPM at the (**a**) Daman and (**b**) A’rou sites for the period 2014–2017.
## 3 Results  
### Inhibitory Effect of Cell Proliferation  
Cell metabolites were prepared from the lactic acid bacteria (_Lactobacillus_ sp. La1 and La2), using the ultrasonication method, and named as Pobt-La1 and Pobt-La2. The cell proliferation inhibitory effect of the prepared postbiotics (Pobt-La1 and Pobt-La2) on HT-29  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gene** & **Primer** & **Sequences (5’-3’)** & **Conditions** & **Cycle** \\ \hline \multirow{2}{*}{_caspase-3_} & F & TGCCTGTAACTTGAGAGTAGTG & 96 \({}^{\circ}\)C 30 s & \\  & & 60 \({}^{\circ}\)C 30 s & 40 \\  & R & CTTCACTTTCCTTGGGATG & 72\({}^{\circ}\)C 30 s & \\  & F & ACATGGACTGCTTCATTGGC & 96 \({}^{\circ}\)C 30 s & \\ \multirow{2}{*}{_caspase-8_} & R & AAGGACTTCAAACCAGTG & 72 \({}^{\circ}\)C 30 s & \\  & F & AGGGTTTCATCAGAGTCAGAG & 96 \({}^{\circ}\)C 30 s & \\ \multirow{2}{*}{_bax_} & R & ATCTTCTTCCAGATGGGAG & 72 \({}^{\circ}\)C 30 s & \\  & F & CCTCTATGCCAACACAGTG & 94 \({}^{\circ}\)C 1 min & \\ \multirow{2}{*}{\(\beta\)-actin_} & R & 60 \({}^{\circ}\)C 1 min & 35 \\  & R & ATACTCCTGCTTGCGTATCC & 72 \({}^{\circ}\)C 1 min & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primer set used for PCR amplification.  
cells was measured by WST-1 assay. First, CCD-18 Co cells, a normal cell line, were treated with the highest concentration of Pobt-La1 and La2 to check whether the sample was toxic to cells. CCD-18 Co cells showed a higher growth rate in the test group than in the control group not treated with Pobt-La1, La2 (Figure 1a). The increase in cell viability observed in Pobt-La1 and La2 is considered a side result of the cell activity and immunity increase among the various physiological activity functions of probiotics. The results of the WST-1 assays measuring the cell viability of HT-29 cells are shown in Figure 2, and the viability of HT-29 cells according to the concentrations of postbiotics is summarized in Table 2. As a result of treating HT-29 cells with Pobt-La1 and La2 at different concentrations, we were able to confirm that the cell proliferation inhibitory effect appeared in a concentration-dependent manner (Figure 1b). In the case of treatment with Pobt-La1, an inhibition effect was shown at a maximum of 80% to a minimum of 39%. However, in the case of Pobt-La2, the inhibition effect appeared in the range of 68% to 49%. Subsequent experiments had been conducted to obtain the IC50 concentration, which shows a 50% survival rate of cancer cells, and the IC50 values were 176.6 \(\upmu\)L/mL for Pobt-La1, and 158.1 \(\upmu\)L/mL for Pobt-La2, respectively.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Open Issues and Research Challenges
#### 6.2.5 Control Overhead Minimization  
Increasing the hello packet broadcast frequency can aid in discovering the updated location of neighboring UAVs in FANETs. However, a low hello interval produces a high control overhead in FANETs, which may consume a significant amount of bandwidth and energy for UAVs. UAV swarms maintain a stable topology by utilizing a topology-control algorithm. However, in a few scenarios such as obstacle avoidance or formation splitting owing to the mission demand, the neighbor relationship changes in the UAV swarm. Therefore, the swarm can sense the degree of mobility and the degree of neighbor intimacy as the relative distance or link duration, according to which it should control the hello interval adaptively. A specific guideline for controlling the adaptive hello interval according to changes in the neighbor relationship is provided in [16; 21].
# Automatic 2D Floorplan CAD Generation from 3D Point Clouds
## 3 Proposed Method
### Data Pre-Processing  
We used unstructured 3D point clouds as an input data from LIDAR scanners. Therefore, our algorithm requires preprocessing of the 3D point clouds in order to save time during the process and to gain more insight for meaningful element extractions, such as for floors and walls. For data preprocessing, 3D point clouds are spatially re-sampled at 1 cm and rotated to follow the proper orientation for a wall detection algorithm.
## 2 Theoretical Formulation  
### Modal Analysis  
A multi-degree-of-freedom system vibrates at a certain natural frequency during free vibration, similar to a single-degree-of-freedom system. Unlike a single-degree-of-freedom system, a multi-degree-of-freedom system has multiple natural frequencies. Under this premise, a method is sought to decompose the free vibration into the superposition of several simple harmonic vibrations. Its kinetic equation is as follows in Equation (1) [26]:  
\[[M]\big{\{}\ddot{x}(t)\big{\}}+[C]\big{\{}\dot{x}(t)\big{\}}+[K]\big{\{}x(t) \big{\}}=\{0\} \tag{1}\]  
where \([M]\) is the mass matrix, \([C]\) is the damping matrix, \([K]\) is the stiffness matrix, \(\big{\{}\ddot{x}(t)\big{\}}\) is the node acceleration vector, \(\big{\{}\dot{x}(t)\big{\}}\) is the node velocity and \(\{x(t)\}\) is the node displacement vector.  
At any given moment \(t\), the equation can be regarded as a series of statics equilibrium equations taking inertial forces \([M]\big{\{}\ddot{x}(t)\big{\}}\) and damping forces \([C]\big{\{}\dot{x}(t)\big{\}}\) into account. The equation of its physical meaning is as follows in Equation (2) [26]:  
\[m\times a+c\times v+k\times x=F(t) \tag{2}\]Its derivative form is shown as follows in Equation (3) [26]:  
\[m\times\frac{d^{2}x}{dt^{2}}+c\times\frac{dx}{dt}+k\times x=F(t) \tag{3}\]  
For the equation of a second-order inhomogeneous differential equation, the first step in solving this equation is to assume that \(F(t)=0\) and convert them into homogeneous second-order differential equation, whose eigenvalue and eigenvector can be solved. The eigenvalues obtained are arranged in order from small to large, which is the natural frequency of each order, and the eigenvector corresponding to the eigenvalues is the vibration mode of each order. From the solution process, it is shown that there can be no external load in the modal calculation.
## 2 Materials and Methods
### Information Technologies Used in the Proposed Model  
#### 2.2.1 Fundamentals of the \"Random Forest\" Algorithm  
The Random Forest algorithm consists of decision trees capable of predicting the expected values of regressions and classifications.  
[PERSON] introduced the Random Forest method, and the technique has since become an essential data analysis tool. The algorithm is generally recognized for its accuracy and ability to handle small sample sizes, capacity resource spaces, and complex data structures [32; 33].  
Theoretically, the analyses are less conclusive, and despite its wide use in practical contexts, little is known about the mathematical properties of Random Forest. So far, most studies have focused on isolated parts, whose main theoretical result offers an upper bound for the generalization error of forests in terms of correlation and strength of individuals trees [34].  
The bagging procedures and the Classification and Regression Trees (CART) division criteria critically analyze the Random Forest [35]. Like a bootstrap aggregation contraction, bagging is a general grouping scheme that proceeds by generating subsamples from the original data set, building a predictor for each sample and deciding on the mean. Bagging is one of the most effective computationally intensive procedures to improve unstable estimates, especially for large and high-dimensional data sets. However, it is impossible to find a suitable one-step model due to the complexity and scale of the problem. On the other hand, the CART-split algorithm builds individual trees to choose the best cuts perpendicular to the axes. The best value is selected at each node of the tree based on Gini impurity and the squared error of the regression.  
In [PERSON]'s Random Forest [33], each leaf, a terminal node of the individual trees, contains a fixed, preset number of observations, (this parameter is usually between 1 and 5).  
CART was chosen for the induction of decision trees in this study. This choice was motivated by the fact that the algorithm does not restrict the nature of the explanatory variables because it generates results that are easy to interpret and easy to find interactions between the variables.
# Assessment of Cardiovascular Disease in Autosomal Dominant Polycystic Kidney Disease
## 7 Computed Tomography  
The study of the heart in ADPKD patients with CT is rarely performed because of the risk of contrast-induced nephropathy linked to the administration of contrast media in patients often affected by CKD. No randomized control trials or quasi-RCTs are available on heart CT scans in ADPKD patients.  
Rare cardiac manifestations of ADPKD have been described beyond valvular abnormalities, in particular aortic aneurysm, coronary arterial aneurysms (CAAs) and dissections of coronary arteries (CADs). Different prevalence rates were reported by gender, patients' characteristics, and AH. The male gender is prevalent in CAAs, while patients diagnosed with CADs are mainly female. Presentation before 50 years of age is also a characteristic of these patients. The frequent concomitant presence of atherosclerosis and AH was described in CAA patients. The left anterior descending artery was most affected in CADs, while right coronary artery predominance was described in CAAs [50].  
Coronary dissection showed female and left descending anterior artery predominance, with features similar to non-ADPKD patients, but the median diagnostic age was below the expected value (41 vs. 50 years old). Coronary aneurysms had male and right coronary artery predominance but a lower median diagnostic age (44 years old) and a higher rate of multiple vessel affection than that reported for non-ADPKD patients [50].  
Diagnoses of coronary artery disease are primary made using coronary angiography, which allows contemporary diagnosis and treatment via coronary angioplasty.  
Only one case of spontaneous coronary artery dissection (SCAD) underwent diagnosis via a coronary CT scan performed during a health check in a 59-year-old man. In this case, renal function was within the normal limits, and the CT scan helped in the diagnosis of SCAD [51]. Coronary artery dissection is a rare complication described in ADPKD patients, characterized by unstable angina, acute myocardial infarction, or even sudden cardiac death [50; 52].  
Only one more paper reported a CT angiogram of the chest revealing a Stanford type A and DeBakey type I aortic dissection involving the aortic valve and extending to the abdominal aorta with the coeliac, superior and inferior mesenteric, and left renal arteries arising from the false lumen [52]. The authors suggest that patients with ADPKD should be investigated for intracranial, coronary, and aortic vascular anomalies to prevent such devastating outcomes. A CT scan should only be used for the diagnosis of acute dissection.