esa-sceva/satcom-chunk-collection huggingface dataset:
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 2 Related Works
### State-of-the-Art Limitations  
As shown in this section, while depth-from-focus methods enable estimation of accurate depth maps from digital holograms, they require a set of hyperparameters to be carefully chosen. In addition, since multiple numerical reconstructions need to be computed, their computational complexity increases for scenes with a large depth range. On the other hand, phase-space-based methods do not require computing numerical reconstructions. Nevertheless, state-of-the-art works were limited to scenes composed of a small number of particles, and their application to complex scenes with a large depth range was never studied in the literature.  
In this work, we propose using these two approaches together: we use a phase-space-based method to extract regions of interest (ROIs) from the input hologram, which are then exploited to determine the depth range to apply a DFF method to extract the scene depth map from the hologram.
# Assessment of Seasonal and Diurnal Variations of Alkaline Phosphatase Activity in Pasteurized Milk
## 2 Materials and Methods
### Description of the Newly Developed Test \"AP Test\"
#### 2.3.1 Chemical Action Mechanism of the Method Using the \"AP test\"  
Alkaline phosphatase (ALP) catalyzes the hydrolysis of p-nitrophenyl phosphate at pH 10.4, releasing p-nitrophenol and phosphate, according to the following chemical reaction:  
The percentage of p-nitrophenol is compatible with the catalytic concentration of phosphatase in the sample to be tested (with a sensitivity limit of 1.5 mU/L), and the sensitivity of this percentage is enhanced by the enzyme catalyst contained in reagent R3 of the \"AP test\".
## 6 Open Research Challenges and Future Directions  
As integration of VRUs into ITS is being researched, there remain several open research issues. In this section, we discuss these issues and also, possible future directions as follows:  
* 3D localization has been widely researched in the research community. However, to the best of our knowledge, there have been no efforts for 3D localization of VRUs in V2P systems.
* Current self-driving vehicle efforts are focused on using standalone technologies, such as, computer vision, Radar, and LiDAR. The results of how the self-driving vehicles respond to V2P-enabled VRU detection remain unseen.
* A V2P-capable vehicle simultaneously need to detect and track (anonymously) multiple VRUs (V2P-capable) that are present in its vicinity. However, current V2P efforts have not fully explored this aspect yet. The number of VRUs that can be detected and tracked (anonymously) simultaneously and the factors that may affect this capability, such as, limitations of object tracking algorithms, are currently unexplored.
* The VRU and vehicles, that are potentially on the verge of collision, may need to communicate with each other. This requires a higher and on-demand QoS in real-time. The algorithms for on-demand QoS for crucial V2P communication, in the presence of rest V2X communication, may pose interesting research problems.
* Mobile Edge Computing (MEC) is currently being researched for V2X networks. MEC may be considered in the design of V2P systems. The role that MEC can perform, in safety as well as convenience V2P applications, is currently unexplored. It may enable V2P safety communication and also, help reduce network congestion caused by VRU-generated safety messages.
* Integration of V2P systems with Geographical Information Systems (GIS) may help enable predictive warnings about VRUs. For example, V2P system may request the information from GIS, such as, school location or bus-stop information, and warn drivers beforehand about VRUs' presence. GIS may help improve efficiency of V2P systems, including safety as well as convenience, in a specific area.
###### Abstract  
This work proposes a matching data science approach for the laser ablation quality, \(r_{cb}\), the study of Si\({}_{3}\)N\({}_{4}\) film based on supervised machine learning classifiers in the CMOS-MEMS process. The study demonstrates that there exists an energy threshold, \(E_{ib}\), for laser ablation. If the laser energy surpasses this threshold, increasing the interval time will not contribute significantly to the recovery of pulse laser energy. Thus, \(r_{cb}\) enhancement is limited. When the energy is greater than 0.258 mJ, there exists a critical value of interval time at which the \(r_{cb}\) value is relatively low for each energy level, respectively. In addition, the variation of \(r_{cb}\), \(\Delta r_{cb}\), is independent of the interval time at the invariant point of energy between 0.32 mJ and 0.36 mJ. Energy and interval time exhibit a Pearson correlation of 0.82 and 0.53 with \(r_{cb}\), respectively. To maintain \(\Delta r_{cb}\) below 0.15, green laser ablation of Si\({}_{3}\)N\({}_{4}\) at operating energies of 0.258-0.378 mJ can adopt a baseline interval time of the initial baseline multiplied by \(1/\sqrt[3]{2}\). Additionally, for operating energies of 0.288-0.378 mJ during Si\({}_{3}\)N\({}_{4}\) laser ablation, \(\Delta r_{cb}\) can be kept below 0.1. With the forced partition methods, namely, the k-means method and percentile method, the XGBoost ( v 2.0.3) classifier maintains a competitive accuracy across test sizes of 0.20-0.40, outperforming the machine learning algorithms Random Forest and Logistic Regression, with the highest accuracy of 0.78 at a test size of 0.20.  
machine learning; XGBoost classifier; laser ablation; CMOS-MEMS; k-means Article
## Materials and Methods
### Variational Mode Decomposition (VMD)
#### 2.1.2 Solve the Variational Problem  
(1) Introduce the second-level penalty factor \(C\) and the Lagrange multiplication operator \(\theta(t)\) to change the constraint variational problem into a non-binding variational problem. Among them, \(C\) guarantees the reconstruction precision of the signal and \(\theta(t)\) maintains the strictness of the constraint condition. The expanded Lagrange expression is characterized by  
\[\begin{split} L(\{u_{k}\},\{\omega_{k}\},\theta)& =C\sum\limits_{k=1}^{K}\left\|\partial_{t}\Big{[}\Big{(}\delta(t)+ \frac{j}{\pi t}\Big{)}u_{k}(t)\Big{]}e^{-j\omega_{k}t}\right\|_{2}^{2}\\ &+\|f(t)-\sum\limits_{k=1}^{K}u_{k}(t)\|_{2}^{2}+\Big{\langle} \theta(t),f(t)-\sum\limits_{k=1}^{K}u_{k}(t)\Big{\rangle},\end{split} \tag{4}\]  
(2) VMD uses the alternating direction multiplication operator method to solve Equation (4) (the variational problem). In the expanded Lagrange expression, the \"saddle point\" is found by alternately updating \(u_{k}^{n+1}\), \(\omega_{k}^{n+1}\) and \(\theta^{n+1}\), where n denotes the number of iterations and where \(u_{k}^{n+1}\) can be transformed into the frequency domain using the Fourier isometric transformation  
\[\begin{split}\hat{u}_{k}^{n+1}&=\operatorname*{ argmin}_{\hat{u}_{k},u_{k}\in X}\Big{\{}C\|j\omega\{[1+\text{sgn}(\omega+ \omega_{k})]\hat{u}_{k}(\omega+\omega_{k})\}\|_{2}^{2}\\ &\qquad\qquad+\|f(\omega)-\sum\limits_{k=1}^{K}\hat{u}_{k}( \omega)+\frac{\theta(\omega)}{2}\|_{2}^{2}\Bigg{\}}\end{split}, \tag{5}\]  
where \(\omega\) is the random frequency and \(X\) contains all desirable sets of \(u_{k}\). Replace \(\omega\) with \(\omega-\omega_{k}\), and the non-negative frequency interval integral form is  
\[\hat{u}_{k}^{n+1}=\operatorname*{argmin}_{\hat{u}_{k},u_{k}\in X}\Bigg{\{} \int_{0}^{\infty}\Bigg{[}4C(\omega-\omega_{k})^{2}|\hat{u}_{k}(\omega)|^{2}+2 \Bigg{|}\hat{f}(\omega)-\sum\limits_{k=1}^{K}\hat{u}_{k}(\omega)+\frac{\hat{ \theta}(\omega)}{2}\Bigg{|}^{2}\Bigg{]}d\omega\Bigg{\}}. \tag{6}\]  
Thus, the solution to the quadratic optimization problem is  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum\limits_{k=1}^{K}\hat{u}_ {k}(\omega)+\frac{\hat{\theta}(\omega)}{2}}{1+2C(\omega-\omega_{k})^{2}}. \tag{7}\]  
According to the same process, the method for updating the center frequency is solved via  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega|\hat{u}_{k}(\omega)|^{2}d\omega }{\int_{0}^{\infty}|\hat{u}_{k}(\omega)|^{2}d\omega}. \tag{8}\]  
Among them, \(\hat{u}_{k}^{n+1}(\omega)\) is equivalent to the Wiener filter of the current residual quantity and \(\hat{f}(\omega)-\sum\limits_{k=1}^{K}\hat{u}_{k}(\omega)\); \(\omega_{k}^{n+1}\) is the barycenter of the power spectrum of the current modal function. When an inverse Fourier transform is applied, we end up with \(\hat{u}_{k}(\omega)\), where its real-part is \(\{u_{k}(t)\}\).
## 1 Introduction
### Research Significance  
Research on non-conventional lanes has important theoretical significance and practical value. From a theoretical perspective, the implementation of non-conventional lanes disrupts the existing mixed traffic flow patterns at intersections, changing not only motorized vehicle traffic patterns but also affecting the movement of non-motorized vehicles and pedestrians. This necessitates reconstructing the organic relationships of complex mixed traffic flows within intersections and seeking new optimization solutions.  
From an application perspective, although the adoption of non-conventional lanes has played a considerable role in improving urban road traffic, changes in traffic patterns have also led to new problems [5]. For example, after repositioning some left-turn lanes to the right, issues arise, such as insufficient capacity of exit lanes and intensified traffic conflicts;contralow left-turn lanes often only consider their own approach while ignoring the impact on the entire intersection system, resulting in decreased traffic operation efficiency for the whole intersection. Current theoretical gaps manifest in three specific areas: (1) the absence of unified safety evaluation frameworks that systematically assess risk factors across different non-conventional lane types beyond conventional conflict analysis, (2) the lack of integrated spatiotemporal optimization theory that simultaneously considers geometric parameters and signal variables rather than treating them separately, and (3) insufficient driver behavioral adaptation models that predict compliance rates and learning curves in dynamic lane environments. These theoretical deficiencies explain why field implementations often underperform simulation predictions by 15-25%. By systematically analyzing the basic forms and impacts of various non-conventional lanes at typical intersections and establishing a scientific adaptability evaluation system, a decision-making basis can be provided for the rational implementation of non-conventional lanes.  
This research addresses the changes in traffic patterns after the introduction of non-conventional lanes by analyzing key elements such as longitudinal and lateral matching of intersection approaches and exits, dynamic lane function conversion, and signal phase timing, forming systematic design and control methods. The research results will provide innovative ideas for the development of intelligent traffic control systems, and the promotion of technological upgrades in the field of traffic design and control has theoretical significance and practical value.  
The \"Urban Road Intersection Channelization Design Manual\", compiled by the Traffic Management Research Institute of the Ministry of Public Security [6], provides technical guidance for non-conventional lane design. The urban road traffic cooperative control cases compiled by [PERSON] [7] showcase the effects of non-conventional designs in practical applications. These achievements provide important references for the promotion and application of non-conventional lanes but still require more systematic theoretical research support.
## 3 Results and Discussion
### Sensitivity Analysis of Piping Distance  
From the results shown in Figure 10, when the piping distance reached 5.59 km, the annual costs for Cases 3 and 4 were the same, and it was found that when the piping distance reached 5.71 km, the annual costs of Cases 1 and 2 were the same. The CO2 emissions were superior to the case of using heat, but the annual cost may be higher, so establishing a waste incineration plant assuming that heat will be used after introducing the MBT system at a point more than 5.59 km away from the heat supply destination, was difficult. Similarly, [PERSON] et al. stated that the cost-effectiveness deteriorates when the distance between the waste incineration plant and the industrial facility increases, and the profitability decreases when the distance is 1 km or more while evaluating Aichi Prefecture in 2021 [6]. Since the piping distance that maintained profitability varied depending on the unit price of heat sales, the appropriate piping distance value varied regionally. However, when considering heat utilization, the distance between the waste incineration plant and the industrial facility could be a crucial factor in evaluating economic efficiency.
## 3 The Results of the Bibliometric Analysis
### Keywords and Term Analysis
#### 3.4.2 Term Analysis  
A total of 11,270 terms, derived from keywords, titles and abstracts, were identified using the VOSviewer from the 402 retrieved articles. Among these, 9486 terms only appeared for one time and 448 terms appeared more than five times. To identify the hot topics in USVs, a restriction was set to include terms that appeared at least 10 times; a total of 109 terms, as 60% of the most relevant terms, were extracted, mapped and clustered in two-dimensional space.  
Figure 14 illustrates the clustering of terms within new technology in the USV research domain, with an additional temporal dimension incorporated. In this depiction, the average publication year of each term's appearance within the research domain has been computed and assigned to each corresponding node on the map. Warmer, redder nodes signify more recent term appearances. A comparative analysis of the temporal factors in the three clusters revealed that terms within Cluster 1, encompassing multiple USVs, control strategy, external disturbance and stability, along with Cluster 2, associated with machine autonomy, numerical simulation and communication, as well as Cluster 3, featured by environmental identification and monitoring, were relatively recent in their emergence.  
Comprehending the causes behind USVs constitutes a significant aspect of this research domain. The innovation of algorithms and the development of computer science  
Figure 14: Term clusters of USV domains.  
have long been pivotal components of investigating the origins of new technology in USVs. This research area has historically scrutinized technological variables such as robustness, effectiveness, optimization, validity and accuracy. In recent years, a surge in research activity has been observed within Cluster 1, dedicated to the exploration of new vehicle forms with AI, and the new technology in control, communication and detection contributing to USVs.
# Spring Failure Analysis of Mining Vibrating Screens: Numerical and Experimental Studies
## 2 Finite Element Model
### Simulation Results  
Simulations were carried out using ANSYS to calculate the system vibration responses. The vibration displacements of the mining vibrating screen were obtained, and the results are displayed in Figure 4.  
The overall structure vibration includes the longitudinal vibration (in the \(y\) direction) and horizontal vibration (in the \(x\) direction). The stable state overall maximum amplitude (peak-to-peak value of displacement) is 11.54 mm, which is located in the middle of the reinforced beam. Meanwhile, the overall minimum amplitude is 8.40 mm, which is located at the bottom of the lateral plate. Furthermore, the mining vibrating screen assembled by multiple beams and plates will produce partial elastic deformation during system vibration. Therefore, the overall structure has lateral vibration (in the \(z\) direction), and the maximum amplitude is 0.89 mm, which is located at the upper part of the lateral plate. These vibration amplitudes can meet the work requirements.  
The value of \(k_{1y}\) was reduced by 30% to simulate spring failure, the vibration displacements of the mining vibrating screen were obtained, and the results are displayed in Figure 5.  
Figure 4: Vibration displacement contour map of the mining vibrating screen. (**a**) Overall vibration displacement contour map of the overall structure. (**b**) Lateral vibration displacement contour map.  
Figure 5: Vibration displacement contour map of the mining vibrating screen with spring failure. (**a**) Overall vibration displacement contour map of the overall structure. (**b**) Lateral vibration displacement contour map.  
As shown in Figure 5, the overall structure vibration displacements have changed. The overall maximum displacement is 11.49 mm, the overall minimum displacement is 8.36 mm, and the maximum lateral vibration displacement is 1.26 mm. However, these vibration characteristics may not meet the work requirements, and the lateral vibration displacement must be less than 1 mm when the mining vibrating screen is working [36].  
According to the simulations and analyses above, the results indicate that the whole system vibrations include longitudinal vibration (in the \(y\) direction), horizontal vibration (in the \(x\) direction), and lateral vibration (in the \(z\) direction) under normal conditions. However, the lateral vibration displacements (in the \(z\) direction) may increase as a result of spring failure, which cannot meet the work requirements. The system displacements in the established finite element model are sensitive to variable spring stiffness changes.
## 3 Performance Analysis
### PEP Analysis  
PEP plays a central role in characterizing the diversity performance of MIMO systems because the overall detection error probability can be upper-bounded by a weighted sum of PEPs over all symbol pairs [28]. However, it is difficult to obtain a closed-form expression for the exact PEP because of the inherent nonlinearity introduced by the one-bit quantizer in (4). To address this issue, an upper bound on the PEP is derived using [PERSON]'s inequality and the Chernoff bound, which offers analytical tractability while preserving key performance insights. [PERSON]'s inequality is applied to handle the square root function, whereas the Chernoff bound is employed to bound the Q-function. This combination of techniques is commonly used to derive the mutual information and bit error rate expressions in communication theory. By leveraging these tools, PEP \(\mathcal{P}\{\mathbf{x}_{S}\rightarrow\mathbf{\tilde{x}}_{S}\}\) can be obtained by averaging over the channel, which is given by  
\[\mathcal{P}\{\mathbf{x}_{S}\rightarrow\mathbf{\tilde{x}}_{S}\}=\mathbb{E}\{ \mathbb{P}\{\mathbf{x}_{S}\rightarrow\mathbf{\tilde{x}}_{S}|\mathbf{H}_{SRD} \}\}, \tag{18}\]  
where \(\mathbf{H}_{SRD}=[\mathbf{H}_{SD}^{T},\mathbf{H}_{RD}^{T},\mathbf{H}_{SR}^{T}]^ {T}\). Given a specific channel realization \(\mathbf{H}_{SRD}\), the PEP that erroneously determines in favor of \(\mathbf{\tilde{x}}_{S}\) when \(\mathbf{x}_{S}\) is transmitted can be expressed as  
\[\mathbb{P}\{\mathbf{x}_{S}\rightarrow\mathbf{\tilde{x}}_{S}| \mathbf{H}_{SRD}\} =\sum_{\mathbf{q}_{SRD}\in\{-1,1\}^{4N_{D}+2N_{R}}}\mathbb{P}\{ \mathbf{q}_{SRD}|\mathbf{H}_{SRD},\mathbf{x}_{S}\}\] \[\times\mathbb{1}\left\{\mathbb{P}\{\mathbf{q}_{SRD}|\mathbf{H}_{ SRD},\mathbf{x}_{S}\}\leq\mathbb{P}\{\mathbf{q}_{SRD}|\mathbf{H}_{SRD},\mathbf{ \tilde{x}}_{S}\}\right\}. \tag{19}\]  
The indicator function can be upper bounded using the inequality \(\mathbb{1}\left\{A\leq B\right\}\leq\left(\frac{B}{A}\right)^{s}\), where \(s\in[0,1]\)[29].
## 3 Methods
### Data Preprocessing
Through one-hot encoding, the Traffic_Type feature was transformed into the target feature, as shown in Table 7. In this conversion, rows labeled 0 represent normal traffic, while those labeled 1 correspond to attack traffic. The second stage, normalization, standardizes the numerical values of features within a 0-1 range to prevent attributes with large numerical values from disproportionately influencing the model's outcomes. Normalization ensures that all features contribute equitably to the learning process, enhancing model stability and performance. After normalization, the dataset is prepared for use in machine learning models. This step is particularly critical in mitigating the dominance of high-value features, which could otherwise introduce biases in algorithmic calculations. Normalization was applied uniformly to all features, ensuring that their values remained within the 0-1 range. The third stage, feature selection, involves eliminating non-contributory features that increase computational complexity and strain local hardware resources. An excessive number of features can lead to higher processing costs, increased energy consumption, and extended computation times. Therefore, feature reduction is often implemented to enhance model efficiency by streamlining the dataset. In this study, random forest, logistic regression, and ExtraTreesClassifier algorithms were employed to evaluate the significance of each feature. The outcomes of these three feature selection techniques were compared, and the results indicated that feature reduction was unnecessary for this dataset. All 42 features were deemed relevant and retained for model training. By analyzing the impact of these features on classification accuracy, this study provides valuable insights for future research and contributes to the broader literature on intrusion detection in IIoT environments.
## 2 Experimental Apparatus and Methods
### The Corona-Stabilized Switch
#### 2.1.2 Gas Type  
The breakdown and insulation recovery characteristics of the gas medium exert a significant influence on the repetition performance of corona-stabilized switches. The gas commonly utilized in corona-stabilized switches is SF\({}_{6}\)[7; 8; 9], which presents excellent electrical and thermophysical properties. However, the use of SF\({}_{6}\) in switches can lead to severe electrode erosion and significantly impact the switch lifetime, especially in gas switches with highly inhomogeneous fields [19; 20].  
The erosion effect of dissociated SF\({}_{6}\) particles can be mitigated by diluting SF\({}_{6}\) with other gases [14; 21]. This study investigated the repetition performance of SF\({}_{6}\)-N\({}_{2}\) and SF\({}_{6}\)-air mixtures, exploring their potential as replacements for pure SF\({}_{6}\) in corona-stabilized switches with high PRFs. Additionally, the SF\({}_{6}\)-He mixture exhibits significant potential for high-PRF switches [8; 22] as it combines the high dielectric strength of SF\({}_{6}\) with the superior thermal conductivity and molecular velocity of helium. This is expected to result in a faster insulation recovery rate. Therefore, this paper also investigated the repetitive breakdowns and insulation recovery characteristics of SF\({}_{6}\)-He mixtures.