esa-sceva/satcom-chunk-collection huggingface dataset:
# Cutting Force and Cutting Quality during Tapered Milling of Glass Magnesium Board
###### Abstract  
In this paper, the effects of tool geometry and cutting parameters on cutting force and quality were investigated during the tapered milling of glass magnesium (MGO) board with diamond cutters. The results were as follows: firstly, both the cutting force and roughness of the machined surface were positively correlated with the taper angle of the cutters and the cutting depth, but negatively related to the spindle speed. Then, the cutting depth had the largest influence on the cutting force and surface roughness, followed by the taper angle and spindle speed. Thirdly, the taper angle had a significant influence on the cutting force, but not on the surface roughness. The contribution of the spindle speed to both the cutting force and the surface roughness were significant, while the cutting depth had an insignificant effect on the cutting force and the surface roughness. Finally, the optimal cutting condition for the tapered milling of glass magnesium board was found to be a taper angle of \(15^{\circ}\), a spindle speed of 5000 rpm (cutting speed of 36.63 m/s), and a cutting depth of 0.5 mm, which are proposed for industrial production in order to achieve greater cutting quality and economic benefit.  
tapered milling; glass MGO board; PCD cutter; orthogonal testing design; optimization Article
# A Low BER DB-PAM4 Adaptive Equalizer for Large Channel Loss in Wireline Receivers
## 2 The Principle and Realization of the Adaptive Equalizer
### Theoretical Analysis and Implementation of Adaptive Equilibrium  
At a transmission rate of \(112\) Gb/s, the half-baud rate attenuation of PAM4 signal modulation through backplane communication reaches at least \(-30\) dB. It is difficult for traditional equalization methods to effectively compensate for such a large channel attenuation. In this paper, the new DB-PAM4 transmission method is adopted, and the duobinary coding of the entire channel is realized by adjusting the equalization strength of the equalizer at the receiving end. This method includes part of the channel loss, the required equalization intensity is reduced, and the bandwidth requirement is reduced.  
The equalization processing is divided into two steps: the first step is pre-equalization, which tests and shapes the unknown channel to adapt to the duobinary processing results. The second step is digital equalization, which is to finely adjust the tap coefficients of the digital equalizer to reduce the bit error rate of data transmission. The two-step adjustment method greatly improves the adaptability of the adaptive equalizer. Compared with the traditional method of adding redundant channels or protocols, the processing method in this paper greatly reduces the hardware overhead and ensures the accuracy of the equalization results.
## 3 Current Scenario  
As it has been previously said, we have studied the impact and identification of critical nodes in smaller networks with an MIP model capable of delivering exact results in terms of both latency and lifetime. Specifically, networks up to \(N=100\) mica2 nodes and critical nodes subsets of \(|N_{c}|=\{1,2\}\) have been studied. It has been decided to utilize mica2 motes as the basis of our study due to the amount of configurable transmission levels they enable.  
It is worth mentioning that, in this scenario, the removal of critical nodes have been performed iteratively. That is, in the case of two critical nodes, the developed framework firstly computes and removes the most critical node and, then, from the remaining network, the second most influential critical node is identified and studied.  
This decision has been taken due to the high complexity that exact models carry. MIP execution time for networks with \(N=100\) sensors can take up to 2 min, versus the few seconds that GRASP offers. Moreover, when increasing the size of the problem both in terms of network sensors and number of critical nodes to identify, the inner exponential complexity of the MIP model makes it unfeasible for larger networks. For the creation of the GRASP, we have decided to take advantage of the reduction in complexity to modify our approach. One may think that the iterative removal of critical nodes covers enough scenarios that could happen in real systems. However, an even worse scenario could appear in which many critical nodes fail or are attacked at the exact same time. Imagine a system that has its nodes distributed across many buildings, with independent power sources. If critical nodes happen to be all inside the same building, a power outrage in such building could completely shut down the whole WSN. To solve this problem, instead of removing nodes one by one, GRASP identifies the set of nodes that, jointly, produce the most impact in the WSN. It is worth stating that, by the inner characteristics of heuristics, GRASP does not always offer the optimal solution. Instead, sometimes a local optimum is delivered as the best possible result. That is, while trying to improve the solution during the local search phase, it can happen that the explorable neighborhood does not improve the solution, and, thus, GRASP finalizes stuck in a local optima. To check whether our approach is good enough, an initial comparison between MIP results and GRASP results for small networks is shown.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 2 Advanced Technologies and Mechanisms in RF MEMS Switches
### Actuation Mechanism  
RF MEMS switches can be divided into electrostatic, piezoelectric, electromagnetic, and electrothermal based on traditional actuation methods. Figure 1 illustrates the typical structures of these four types of switches.  
In space applications, RF MEMS switches or structures, compared to PIN and optical switches, may demonstrate more ideal performance due to their non-involvement with radiation-sensitive electronic components [22]. In terrestrial wireless communication systems, the choice and number of switches depend on the required operational speed and specific requirements of the entire system. Silicon, a common substrate material, shows no significant degradation in mechanical properties even under radiation doses exceeding 100 Mrad. The radiation sensitivity of devices is largely influenced by their actuation mechanism, structural design, and material choice [23; 24], primarily related to the disturbance caused by charge trapping in dielectrics under radiation. Notably, electrostatically driven MEMS devices are extremely sensitive to charge accumulation in the dielectric layer. In contrast, MEMS devices using other actuation mechanisms, such as thermal and electromagnetic, exhibit higher radiation resistance.
It was found that there were physical and chemical adsorption and decomposition reactions between water molecules and (001) facets, but only physical adsorption on (101) facets. Since separated water can facilitate the transfer of photogenerated carriers and the formation of active free radicals, it may make (001) facets have a stronger redox effect than (101) facets, causing the (001) facets to exhibit higher photocatalytic activity [15]. Many of the Fe\({}_{3}\)O\({}_{4}\) and TiO\({}_{2}\) composites studied now are core-shell structures. When the outer surface of Fe\({}_{3}\)O\({}_{4}\) powder is coated with a layer of TiO\({}_{2}\), it inhibits the transport of photoexcited electrons to a certain extent. In this paper, the photocatalytic performance of TiO\({}_{2}\) was improved by increasing the exposure ratio of (001) high-energy facets and compounding with Fe\({}_{3}\)O\({}_{4}\) to change its band gap. HF was used as an inhibitor, which on the one hand destroyed the spherical structure of the previous Fe\({}_{3}\)O\({}_{4}\) and on the other hand inhibited the growth of (101) surfaces. TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@TiO\({}_{2}\) composite materials were prepared by the solvothermal method as photocatalysts. TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@TiO\({}_{2}\) were characterized by X-ray diffraction spectroscopy, field emission scanning electron microscopy, ultraviolet-visible diffuse reflectance spectroscopy and Raman spectroscopy. Acid Red 73 (AR73) solution was used to simulate dye wastewater. The degradation ability of TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@TiO\({}_{2}\) can be observed through the experiment of photocatalytic degradation of Acid Red 73. Fe\({}_{3}\)O\({}_{4}\)@TiO\({}_{2}\) was recycled by magnetic recovery to degrade AR73. The photocatalytic degradation mechanism was studied.
## 4 Test Results and Mechanism Analysis  
### Effect of Nano-SiO\({}_{2}\) on the PSD of Soil Samples  
The measurements of \(T_{2}\) distributions for different SiO\({}_{2}\) contents are depicted in Figure 5. As shown in Figure 5, each \(T_{2}\) spectrum curve had three peaks, defined from left to right as main peak, subpeak 1, and subpeak 2, corresponding with adsorbed water, capillary water, and free water, respectively. With increasing SiO\({}_{2}\) content, the relaxation time of the main peak gradually decreased. The main peak was significantly higher than the subpeak 1 and subpeak 2 in each \(T_{2}\) spectrum curve. With increased SiO\({}_{2}\) content, the maximum value of the main and subpeak 1 noticeably increased. The area under the \(T_{2}\) distribution curve represented the amount of water content in the soil sample, indicating the proportion of pores at a given radius. With increasing SiO\({}_{2}\) content, the main peak moved to the left and the span of the main peak gradually decreased. This phenomenon indicated that the thickness of adsorbed water decreased with increased SiO\({}_{2}\) content.  
The pores in saturated soil were filled with water, thus, the \(T_{2}\) distribution curve of saturated soil can reflect the pore distribution. Based on Equation (5), the coefficient  
Figure 4: Relationship between hydraulic conductivity and different superhydrophobic nano-SiO\({}_{2}\) contents.  
\(\rho_{2}\) of the soil sample was calculated. According to Equations (6) and (7), the PSD and pore-volume distributions of samples with different SiO\({}_{2}\) contents were calculated through \(T_{2}\) spectrum curves obtained from the NMR test.  
The pore-volume distribution curves are plotted in Figure 6. For analysis, pores corresponding with the main peak, subpeak 1, and subpeak 2 were defined as micropore, medium pore, and macropore. As shown in Figure 6, the pore radius of the micropore was primarily distributed from 0.003 \(\upmu\)m to 0.6 \(\upmu\)m, whereas the pore radius of the medium pore was distributed from 0.6 \(\upmu\)m to 4 \(\upmu\)m. With increasing nano-SiO\({}_{2}\) content, the range of pore radius of micropore gradually decreased from 0.003-0.6 \(\upmu\)m to 0.003-0.2 \(\upmu\)m, and the range of pore radius of medium pore gradually decreased from 0.6-5 \(\upmu\)m to 0.6-2.2 \(\upmu\)m, as shown in Figure 7.  
The total pore volume of the sample with different SiO\({}_{2}\) contents was 68.5, 67.4, 66.3, 63, 61.7, and 59.6 cm\({}^{3}\), respectively. The volume difference of different samples is caused by the different expansion amount during saturation. Moreover, the volume of the main peaks and subpeak 1 decreased with increased SiO\({}_{2}\) contents. The pore volume of the samples significantly varied under different SiO\({}_{2}\) contents. Table 3 shows a clearer comparison of the PSD of samples under different SiO\({}_{2}\) contents.  
Figure 5: The \(T_{2}\) spectrum curves of samples with different SiO\({}_{2}\) contents after hydraulic conductivity test.  
Figure 6: The pore volume distributions of soil samples with different SiO\({}_{2}\) contents after hydraulic conductivity test.
## 4 Results and Discussion
### Real-World Signals  
Following the synthetic signals, four types of operational voltage disturbance signals were measured and provided online at the IEEE Working Group on Power Quality Data Analytics [44], including sag, swell, sag and harmonic, and swell and harmonic. For each disturbance, there are 20 sample signals, of which the fundamental frequency is 60 Hz and which are sampled at 7.6 kHz. The number of sample points in one cycle is 128 and there are eight cycles in every signal. Several operational signals are needed to participate in the training process to fine-tune the parameters of the existing mode. To choose an optimal number of operational signals, a different number of synthetic signals of each disturbance from the training data used in the previous section were randomly replaced by the real-world signals. Then, the new training data were used to fine-tune the CNN model. Finally, the fine-tuned model was validated by using the operational data. The results are shown in Figure 8. It can be seen that a low classification rate (20%) is obtained without the fine-tuning process. Then, the classification rate improves (97.5%) with the five real-world signals used. The classification rate reaches the highest point (98.8%) when the number of operational signals is seven. Hence, in order to improve the performance of the classification of operational voltage disturbances, seven synthetic signals of each disturbance from the training data were randomly replaced with real-world signals. The validation confusion matrix is shown in Figure 9. The features extracted by the convolution layers are presented in Figure 10. In total, 20 events for each disturbance were used for validation and the total classification rate was 98.8%, as shown in Figure 9. It is observed that the proposed method combining PSR and CNN has good performance for both synthetic and operational PQD classifications.  
Figure 7: The features extracted by the convolution layers of the synthetic signals: (**a**) the first convolution layer; (**b**) the second convolution layer; (**c**) the third convolution layer.
## 10 Experimental Analysis
### Statistical Comparisons on CEC2014 Problems  
According to [77], the CEC2014 benchmark problems include 30 optimization problems with one objective that are defined on real numbers with the search space dimensions of \(D=10,30,50\), and 100. Based on objective functions characteristics, the CEC2014 benchmark problems can be put into four groups:  
* Unimodal functions (1-3),
* Simple multimodal functions (4-16),
* Hybrid objective functions(17-22),
* Composition functions (23-30).  
In order to compare how well the algorithms performs, the solution error measure \(f(\mathbf{\hat{x}})-f(\mathbf{x}^{*})\) was created. This is defined as the difference between the solution \(\mathbf{\hat{x}}\), which has the lowest objective function value and was obtained in a single simulation run and solution \(\mathbf{x}^{*}\), which is previously known for the considered objective function. The termination criterion for the subsequent numerical experiments was set at \(10,000D\), 100 individual in each algorithm's population is employed, and each algorithm was run 51 times for each of the test functions. Two non-parametric statistical hypothesis tests, such as the Wilcoxon signed-rank test and the Friedman test, have been used to analyze and evaluate the quality of the solutions from a statistical perspective.  
The Wilcoxon signed-rank is used to compare the optimization performance of each of the algorithms under consideration. This test provides information whether the first algorithm performs statistically better than the second algorithm, and was performed with \(\alpha=0.05\) significance. Therefore, \"there is no difference between the mean results of the two samples\" [78] represents the null hypothesis. The hypothesis is rejected when \(p<\alpha\), and then \"there is a difference in the mean results of the two samples\" represents inverse hypothesis.  
Here, \(R^{+}\) denotes accumulated ranks where first algorithm had a superior performance to second one taken to test, and \(R^{-}\) denotes the accumulated ranks for the case when the second tested algorithm achieved better performance.  
The Friedman test was employed to determine differences in performance between each algorithm taken for comparison form the statistical point. This is achieved by obtaining the ranks of each algorithm over each CEC2014 problem on all considered \(D\). Here, the best performing algorithm is the one with the lowest rank. This test is taken with a significance level \(\alpha=0.05\), and the null hypothesis \"there is no difference among the performance of all algorithms\" is rejected for \(p\leq\alpha\). Conversely, the alternative is \"there is a difference among the performance of all algorithms\" [78].  
In the obtained results, the signs \(+\), \(\approx\), \(-\) (better, equal, and worse) denote: (\(+\)) that the first algorithm had significantly better optimization performance than the second, (\(-\)) that the first algorithm performed significantly worse than the second, and (\(\approx\)) that there is no significant difference in optimization performance between the two algorithms.
## 1 Introduction  
Peptide-based cancer vaccines are emerging as pivotal in the treatment of human tumors. They instruct the immune system to recognize tumor antigens as foreign [1]. When capable of eliciting a sufficient immune response that leads to the formation of memory cells, these antigens are classified as protective immunogens. This ability to provoke an enduring immune response is crucial for the effectiveness of cancer vaccines in providing long-term protection against tumor recurrence.  
Identifying suitable antigens is crucial in developing peptide cancer vaccines [2]. In the ideal situation, each potential antigen would be isolated or synthesized, and then its immunogenic qualities would be empirically evaluated. However, this strategy is hampered by several practical and logistical issues, such as the difficulty of replicating human immune responses in experimental settings, expensive expenditures, and time-consuming laboratory processes. Another significant challenge is that, to date, the pool of experimentally confirmed tumor antigens remains relatively small. Although plenty of in vitro validated data and animal studies performed in vivo, the direct applicability of these findings to human cancer patients is not straightforward due to the complex nature of the human immune system. In silico prediction algorithms can address both of these problems. By leveraging known experimentally confirmed immunogens, classification models that distinguish them from the non-immunogenic ones can be derived, significantly reducing the need for biological assessment of every putative immunogen. Thus, in silico prediction algorithms have emerged as innovative tools by rationalizing the antigen discovery process in recent years [3].  
Such models have already proven to be robust [4; 5; 6; 7]. Our recent work utilized many machine-learning methods to develop models for classifying immunogenic human tumor peptides [8]. The present study aims to train similar algorithms on a dataset of whole proteins and to compare the observed results. The models with the best performance metrics are projected to be implemented in the third version of the Vaxijen web server.  
Vaxijen is the first server to offer an alignment-independent prediction of protective antigens from viral, bacterial, parasite, fungal, and tumor origin [9]. Identifying tumor antigens uses two distinct datasets, each containing 100 well-known antigens and 100 known non-antigens, each being a whole protein molecule. The method employs a machine learning approach where each protein is represented as a string of z-scales [10], which capture the main physicochemical properties of the amino acid residues. These strings are converted into uniform vectors using auto-cross covariance (ACC) [11]. A genetic algorithm (GA) [12] is used to identify relevant variables within these vectors. Finally, partial least squares-based discriminant analysis (PLS-DA) [13] is used to develop the prediction model.
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 4 Solution Approach
### Simulated Annealing Chimp Optimization Algorithm with Sine-Cosine Operator
#### 4.2.5 Simulated Annealing Mechanism  
Although the traditional CHOA uses randomness, its degree of randomization is not sufficient, and it is prone to getting trapped in a locally optimal solution during the search process and cannot jump out. To address this issue, this study proposes integrating the simulated annealing acceptance criterion into the CHOA. This approach allows the algorithm to accept a novel solution that is worse than the current solution with a certain probability as the current solution and subsequently continue searching for a better solution. If the algorithm discovers a superior solution to the local optima, it indicates that the algorithm has escaped from the local optimum condition. This method not only ensures the power of global exploration but also enhances convergence accuracy. The probability formula for the simulated annealing mechanism to accept the new solution is as follows:  
\[P=\begin{cases}\begin{array}{cc}1&f(S_{new})<f(S_{curr})\\ e^{-\left[f(S_{new})-f(S_{curr})\right]/T}&f(S_{new})\geq f(S_{curr})\end{array} \end{cases}. \tag{38}\]  
where \(f(S_{curr})\) represents the objective function value of the current solution and \(f(S_{new})\) represents the objective function value of the new solution.  
Figure 5: Distribution of nonlinear learning factor values for 200 iterations. During the early stage of the search (0–100 generations), a larger value is helpful for global exploration; during the later stage of the search (100–200 generations), a smaller value is helpful for improving local development capabilities.  
Figure 6: Pseudocode of the algorithm for updating the attacker’s position.  
As the number of iterations increases, the acceptance probability should decrease due to the need to reduce the search time. In other words, temperature T decreases with the number of iterations. See (39) for the formula, where \(\beta\) is the cooling factor \(0<\beta<1\).  
\[T_{gen+1}=\beta T_{gen}. \tag{39}\]
## 4 Methodology
These properties were then enriched using practical data from literature, product datasheets, and additional documentation, providing comprehensive descriptions and up-to-date overviews of each component. After comparing these properties with those defined in the IFC standard, necessary modifications were made. Further details--such as descriptions, value lists, and normative references--were also integrated into the datasets. Throughout this process, various areas of expertise contributed seamlessly to the property definition. For instance, essential parameters from the DALEC system [11]--largely based on ISO 13790 [35]--were prioritized to support the BIM2 IndiLight workflow between Revit and DALEC [36], with a focus on daylight and artificial light as core elements. In parallel, properties for control systems were assembled based on previous project experience and guidelines (originating from the VEC-Module [37]), ensuring compliance with standards such as EN 13790 [35] and EN 17037 [38]. This work also incorporated HVAC control properties based on standards like EN 13779 [39] and EN 16798 [40], strengthening strategies for energy efficiency. In the daylight domain, properties were sourced from research-specific documents, product literature, and standards like DIN 14501 [41] and DIN 410 [42]. Sensor and commissioning-related controls were included, while motor properties were cross-checked against DIN 60034-1 [42] and DIN 60043-5 [43]. Finally, in the field of artificial lighting, experts gathered properties from technical standards and norms. This effort evolved through collaboration with manufacturers, the Swiss lighting software community, the ZVEI BIM group, and a CEN task force [44; 45]. The resulting list comprehensively covers both photometric and electronic lamp properties, as well as detailed luminaire attributes--including naming, mounting, mechanics, electronics, lifetime, safety, and control aspects--ensuring robust support for simulation-assisted design from early-stage to detailed design.