esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Toll plaza staffing schedules at seven bridges (see Figure 1) in the San Francisco Bay Area were recently revised to better allocate resources to address traffic demand. These schedule revisions accompanied changes in the number of cash and FasTrak lanes at the toll plazas. Currently, drivers using the FasTrak lane are required to pay the toll through use of an electronic transponder installed on the vehicle, while drivers using the cash lane can pay either with cash or via the electronic transponder. Due to these changes, the traffic near two bridges experienced delays while those at other bridge locations remained unchanged. Under normal operation, dynamically changing toll plaza capacity might be unnecessary. However, during the closure of a major freeway corridor due to construction or catastrophic failure, there may be a sudden shift in demand for alternate routes. This sudden shift in demand, coupled with limited government resources, may necessitate dynamically determining toll plaza operations. To this end, this study presents an analytical method for dynamically adjusting toll plaza capacity based on real-time demand and reports on the validity of the proposed method which has been evaluated using empirical data.  
[PERSON] [1] was one of the first studies to utilize empirical data to investigate the relationship among flows, number of toll booths, and level of service. Based on findings from analyzing data at the Lincoln Tunnel, he proposed a method for determining the number of toll booths required and recommended toll collectors' schedules. Recent studies have primarily relied on simulated data; [PERSON] and [PERSON], [PERSON] _et al._, and [PERSON] developed a microscopic simulation model to evaluate toll plaza systems [2; 3; 4; 5; 6]. Researchers focused on evaluating both the performance of each toll lane, and the entire system using commercial microscopic simulation software including MODSIM, Paramics, and VISSIM [7; 8; 9; 10]. A toll plaza simulation model, SHAKER, was recently developed to determine the hourly maximum capacity with different lane types [11; 12]. [PERSON] and [PERSON] formulated a model to optimize staffing at toll plazas through investigation of traffic parameters at a border checkpoint [13]. This model compared wage costs for staff with border waiting times for travelers.  
One of the main challenges in implementing the findings from these simulation studies is that the data needed to implement the proposed strategies are often lacking in practice: Often, loop detectors in the field are not located at strategic locations and may be non-existent in the vicinity of toll plazas. To address this issue, this paper proposes a proxy measure for dynamically determining toll plaza capacity based on discharge rate at the toll plazas and segment travel times detected by probe vehicle.
# Assessing Evolution of Microservices Using Static Analysis
## 4 Evaluation Case Study
### Data View Metric Results  
The results of the five data view metrics (D1-D5) highlight the evolution across the three versions of TrainTicket, with a focus on both persistent and transient data entities and their evolving relationships. Additionally, these metrics emphasize the overlap and potential merging of entities across microservices as the system evolves from one version to the next.  
**D1: Number of Persistent Data Entities (\(\#\mathbf{PDE_{s}}\))**.  
It remained relatively constant across all versions, starting at 31 in v0.0.1 and remaining consistent at 27 in both other versions. This indicates stability in the storage approach and data model for the microservices.  
**D2: Number of Transient Data Entities (\(\#\mathbf{PDE_{s}}\))**.  
It increased significantly from 0 in v0.0.1 to 182 in v0.2.0 and dramatically decreased to 81 in v1.0.0. This suggests the introduction of additional entities for customizing responses and data transfer purposes, which decreased in the latest version to optimize the usage of entities across microservices.  
**D3: Number of Relationships between Data Entities (\(\#\mathbf{RDE_{s}}\))**.  
It started at 0 in v0.0.1, grew to 41 in v0.2.0, and 43 in v1.0.0, suggesting the evolution of the data model complexity and the establishment of relationships between entities.  
**D4: Number of Merge Candidate Data Entities (\(\#\mathbf{MDE_{s}}\))**.  
It increased from 11 in v0.0.1 to 139 in v0.2.0 and decreased to 32 in v1.0.0. This indicates a growing complexity in the bounded contexts and the need for merging similar entities.  
**D5: Number of Merge Candidate Relationships between Data Entities (\(\#\mathbf{MRDE_{s}}\))**.  
It started at 0 in v0.0.1, increased to 17 in v0.2.0, and further increased to 19 in v1.0.0. This indicates the emergence of candidate relationships for merging based on similar entities.  
Analysis of these metrics offers valuable insights into the system's evolution, shedding light on shifts in microservice counts, connections, diverse data entities, and their relationships across the entire system. A more profound understanding can be achieved when considering the relationships between these metrics and exploring the cause behind these metrics indicators.
## 4 Model Development
### B. Regularization  
Ridge, Lasso, and Elastic Net belong to a family of regression techniques that use L1-norm and L2-norm regularization penalty terms; a tuning parameter \(\lambda\) controls the strengths of these penalty terms. These techniques were used as an alternative to the best  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Mean** & **Median** & **Range** & **95\% CI for M** \\ \hline BTS (MPa) & 2.5819 & 2.5250 & 2.9200 & (2.40, 2.762) \\ G\({}_{\text{s}}\) & 2.1619 & 2.1500 & 0.3000 & (2.142, 2.181) \\ I\({}_{\text{d2}}\) (\%) & 36.29 & 42.90 & 52.40 & (31.93, 40.64) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the BTS.  
Figure 3: Probability plot of the BTS.  
subsets. Ridge regression was introduced by [47; 48] to improve the prediction accuracy of the regression model by minimizing the following loss function:  
\[\hat{\beta}=\text{arg}min_{\beta}||y-X\beta||_{2}^{2}+\lambda||\hat{\beta}||_{2} ^{2}\,\quad||\beta||_{2}^{2}=\sum_{j=1}^{p}\beta_{j}^{2} \tag{1}\]  
If \(\lambda=0\), the resulting estimates are the ordinary least squares of the MLR. In Ridge regression, the L2-norm penalty term was used to shrink regression coefficients to nonzero values to prevent overfitting, but it did not play the role of feature selection.  
Lasso regression was developed in the field of geophysics in 1986 and 1996 [49; 50; 51; 52]. Lasso regression performs both feature selection and regularization penalty to improve prediction accuracy. It combats multicollinearity by selecting the most important predictor from any group of highly correlated independent variables and removing all the others. An L1-norm penalty term was used to shrink regression coefficients, some to zero, thereby guaranteeing the selection of the most important explanatory variables. Another advantage of Lasso is that if a dataset of size n is fitted to a regression model with p parameters and p > n, the Lasso model can select only n parameters [53]. The following loss function is minimized to obtain estimates of the regression:  
\[\hat{\beta}=\text{arg}min_{\beta}||y-X\beta||_{2}^{2}+\lambda|\beta|_{1}\,\quad(\ |\hat{\beta}|_{1}=\ \sum_{j=1}^{p}|\beta|_{1}) \tag{2}\]  
Elastic Net is a variant of Ridge and Lasso and was introduced by [54]; its penalty term contains a mixture of the Ridge and Lasso penalty terms and has the following loss function:  
\[\hat{\beta}=\text{arg}min_{\beta}||y-X\beta||_{2}^{2}+\lambda\ (\alpha||\beta||_{2}^{2}+(1-\alpha)|\beta|_{1}) \tag{3}\]  
where \(0\leq\alpha\leq 1\); \(\alpha=0\) denotes Lasso whereas \(\alpha=1\) denotes Ridge regression [54]. Some of the coefficients can be shrunk as in Ridge, and some coefficients can be set to zero as in Lasso.
## 3 Mathematical Modeling of Precipitation Effects
### Stochastic Measurement Noise  
However, with the underlying assumptions, this term does not cover the backscattering of large, sensor-close particles, which lead to stochastically distributed false-positive detections in the sensor frustum (see Figure 1, measurement effects). Due to their operating principle, Flash LiDAR systems are particularly sensitive to scattering particles that are close to the sensor aperture. Since Flash LIDAR systems rely on a diffuse laser beam with comparatively strong beam divergence (see Figure 2), the ratio between their cross-section and the size of an occurring scattering particle becomes larger in direct proximity to the laser aperture. Consequentially, the irradiance on that particle is significantly higher than that of a more distant particle. Accordingly, the proportion of backscattered power is high  
Figure 2: Projective Flash LiDAR model, adapted with permission from Ref. [13], 2024, IEEE. Assuming full transmitter beam and receiver frustum crossover, the illuminated section \(A_{t,u,v}(L_{u,v})\) per detector element \((u,v)\) can be assumed as the respective observed area \(A_{u,v}(L_{u,v})\) and, hence, calculated as a function of the inverse intrinsic camera model \(\{\mathbf{K_{cam}}^{-1},\mathbf{\delta_{dis}}^{-1}\}\) and the target distance \(L_{u,v}\). In general, the size ratio of an occurring particle and \(A_{u,v}\) decreases significantly with \(L_{u,v}\), which makes Flash LiDAR sensors rather sensitive to aperture-close false-positive detections.  
enough to trigger a false-positive detection in one or more pixels of the FPA, whereas more distant particles are not exposed to sufficient irradiance to induce a false-positive detection over the backscattered optical power. This is a major difference to scanning LiDAR systems, which work with a collimated laser beam with comparatively very small divergence. Hence, scattering particles can potentially induce false-positive detections over the whole measurement range. Accordingly, this effect may be considered with an additional probabilistic modeling approach, as suggested by [PERSON] et al. [33], for example, by incorporating it as probabilistic target response \(H_{\text{T},\mu,\
u}^{\text{X}}(L^{\text{X}})\) in a random distance \(L^{\text{X}}\)[13].  
Both of the described effects--The overall precipitation induced signal attenuation, and more significantly, the stochastic false-positive detections, due to large, sensor-close particles, can be observed as measurement effects in the dataset and were in magnitude even found to be correlated with the precipitation rate and type [13]. This finally motivates the exploitation of these undesired effects by employing data-driven models which are capable of reconstructing the present precipitation condition from it.
## 1 Introduction
controlled the redox potential during succinic acid production by engineered _Escherichia coli_ through the use of dithiothreitol (DTT) and potassium ferricyanide (K\({}_{3}\)Fe(CN)\({}_{6}\)) as respective reducing and oxidizing agents [10]. Additionally, the selection of carbon sources with varying oxidation states has been demonstrated to be a viable method for redox control. This strategy was effectively applied in the fermentation process of _A. succinogenes_ NJ113 to produce succinic acid, showcasing the versatility of redox management in bio-production systems [12]. In the present investigation, we sought to implement a robust feedback control mechanism in MECs to augment the yield of succinic acid as well as to elevate the electron utilization efficiency. This encompassed the strategic selection of regulatory parameters, the meticulous design and refinement of the feedback control system, and the elucidation of the feedback regulation mechanism. Therefore, in this study, we have explored the practicability of leveraging the ORP as a feedback regulatory parameter within MECs. In tandem, we examined the effects of NR, serving as an electron mediator within the MECs, on the efficacy of the control system. Furthermore, we refined the electron provisioning strategy by manipulating the applied electrical potentials, thereby elevating the regulatory system's efficiency. This study also explores the use of a fed-batch fermentation approach, which could potentially enhance succinic acid production by providing a steady supply of substrates and maintaining optimal growth conditions throughout the fermentation process. The results from such research could have significant implications for industrial biotechnology. By demonstrating the feasibility of ORP manipulation to improve biochemical synthesis in a bioelectrochemical system, this work contributes to the development of more efficient and sustainable methods for producing valuable chemicals like succinic acid.
# An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark
## 5 Implementation Details
### Sgemm  
The SGEMM application has two implementations. The first implementation (labeled as OPENNACC_TILE) uses the tile directive which enables the tilling of the two outermost loops. The final value of a cell is calculated using the reduction directive. The second implementation (labeled as OPENNACC_CACHE) is based on the idea given by [PERSON] and [PERSON] and presented in [47]. A part of one matrix is cached using the cache directive and then it is used to calculate the values of the cells relevant to it. In addition, two main loopsare collapsed to achieve a greater level of parallelism. The code for the OPENACC_TILE implementation is given in Listing 4.  
```
1#pragmaaccdatacopyin(A[0:aLength],B[0:bLength])copyout(C[0:cLength])
2{
3#pragmaaccparallellooptitle(16,16)
4 for(intmm=0;mm<m;+mm){
5 for(intnn=0;nn<n;+nn){
6 floattemp=0;
7
8#pragmaaccloopreduction(+:temp)
9 for(inti=0;i<k;++i){
10 temp+=A[mm+i*lda]*B[nm+i*ldb];
11
12
13C[mm+nn*ldc]=C[mm+nn*ldc]*beta+alpha*temp;
14
15
16 }
```  
Listing 4: OPENACC_TILE implementation of the SGEMM application.
## 3 Structural Analysis for Thermal Reconfigurability  
Structural analysis simulations were performed using COMSOL Multiphysics for the kirigami-inspired structure and SMA spring. Figure 6a shows the final design incorporating the SMA spring. Material properties were selected for Ti and Ni using standard COMSOL Multiphysics modules, with \(-2.7\times 10^{-4}\) K\({}^{-1}\) coefficient of thermal expansion obtained by optimizing the SMA spring experiment results from [37] using COMSOL. The ambient temperature for this simulation was set at 253.15 K. Figure 6b shows SMA spring total displacement after 12 s heating from that paper. Similar results were obtained for the SMA spring used in the current paper.  
Figure 7a shows the kirigami-inspired structure design with SMA spring in COMSOL. SMA spring properties were the same as simulated in Figure 6, and Ecoflex 00-30 parameters were [PERSON]'s modulus = 125 kPa and [PERSON]'s ratio = 0.49 [38; 39]. Figure 7b shows simulated von [PERSON] stress from applying 0.68 V at 3 A to the SMA spring for 19 s. Thus, 270 kN/m\({}^{2}\) was applied to the connection between the kirigami-inspired structure and the SMA spring. Figure 7c shows total displacement with respect to time for the arc shown in Figure 7a. Simulated results for arc displacement suggest 16 s to convert circular sector to circular disk mode.  
Figure 5: Comparison of antenna peak gain according to ground size.  
Figure 6: (a) SMA spring rendered in COMSOL and corresponding (b) simulated total displacement.
###### Abstract
These researchers designed and analyzed the structural and dynamic performance of a moving-coil linear motor integrated into a compressor. Through simulations using ANSYS Maxwell and MATLAB-Simulink, they demonstrated how this design achieves higher mechanical efficiency, reduced noise, and compactness compared to conventional compressors. The electro-pneumatic braking system (EPBS), commonly referred to as the braking system, plays a vital role in ensuring the safe and efficient operation of high-speed trains. [PERSON] et al. [14] showed that the AMESim co-simulation method for the dynamic modeling of the behavior of electro-pneumatic brakes faces slow simulation speeds caused by increased model complexity, data exchange overhead, and communication delays. They propose a fast simulation approach for EPBSs using equivalent models, significantly reducing the simulation time without compromising accuracy. Genetic algorithms have been effectively employed to optimize design parameters, enhancing the performance and efficiency of electromechanical systems such as proportional solenoids. [PERSON] et al. [15] utilized a genetic algorithm to optimize the key design parameters of a proportional solenoid for hydraulic throttle valves in tractor suspension systems, ensuring better control overflow rates in hilly terrains. Their simulations demonstrated improved electromagnetic force characteristics and validated the solenoid's performance through finite element analysis and Simulink modeling. Methods for reducing calculation time, such as those employing Look-Up Tables, have shown significant promise. For example, [PERSON] et al. [16] developed a technique using Look-Up Tables for the dynamic system modeling of solenoids, cutting simulation times by approximately 50% compared to fully analytical and differential equation-based modeling. This approach leverages air-gap-current-force data from static simulations to streamline the modeling process. However, this model uses approximate magnetic data, utilizing an analytical formulation. Building on this type of innovative approach (Look-Up Tables), our study uses ANSYS-Maxwell simulations to extract air-gap-current-force data and generate a Look-Up Table for the dynamic system of a solenoid. By reducing the computational time without sacrificing accuracy, this method provides a practical and efficient way of analyzing and optimizing the performance of proportional solenoids. This method has the advantage of providing more accurate magnetic data for dynamic modeling. In this research study, the effects of the design parameters of the proposed proportional electromagnetic actuator are investigated. We propose a systematic approach using FEM electromagnetic analysis results (as Look-Up Tables) in a dynamic system simulation. Our results demonstrate that this technique is faster and more accurate than calculating magnetic flux by analytical formulation in a dynamic model environment. The experimental tests prove that the proposed method, analysis, and simulations are in agreement with the measurement results.
## 2 Proposed Design
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline Wavelength & DN: 1596, 1596.8, 1597.6, 1598.4, & nm \\  & UP: 1524, 1524.8, 1525.6, 1526.4 & nm \\ \hline Input power & 10 & dBm \\ \hline Bit rate & DN: 10 & \\  & UP: 10 & Gbps \\ \hline No. of channels & DN: 4 & \\  & UP: 4 & \\ \hline Mode & LP[0,1] & \\ \hline GIMMF range & 4.6–5.5 & km \\ \hline FSO range & 100 & m \\ \hline Weak turbulence & \(10^{-17}\) & m\({}^{-2/3}\) \\ \hline Weather condition & 0.22 (clear air) & dB/km \\ \hline Aperture diameter (Tx/Rx) & 15 & cm \\ \hline Fiber attenuation & 2.61 & dB/km \\ \hline Geomteric loss & Yes & \\ \hline Free space path loss & Yes & \\ \hline Core radius & 25 & \(\mu\)m \\ \hline Clad radius & 62.5 & \(\mu\)m \\ \hline Lens diameter & 5 & mm \\ \hline Lens focal length & 0.08–0.125 & m \\ \hline Lens reflectance & 1–19 & \% \\ \hline Responsitivity & 0.9 & A/W \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation specifications [16; 17]. Figure 2: Generated mode plots in the proposed system.
# Current Status and Economic Prospects of Alternative Protein Sources for the Food Industry
## 3 Economic Potential and Market Perspectives of Alternative Protein
### Cultured Meats  
Actual cultured meat products are still not marketed in the United States and several other countries, which makes it difficult to build reliable market predictions. However, some agencies expect a market size of around USD 140 million, by 2030, and USD 630 million, by 2040, including 35% of the world's demand for meat (AT Kearney, 2019) [102]. Cultured meat is expected to be in restaurants in the next years as this production method is supposed to become cost-competitive in the next decade ([PERSON], 2021) [110].  
Optimistic growth prospects rely on the similarity of cultured meat to animal-sourced meat regarding sensory and nutritional attributes ([PERSON], 2019) [111]. On the other hand, high production costs, disputable consumer acceptance of cultured and genetically modified foods, and sanitary regulations are some of the reasons why any realistic market growth might only occur after 2030 (McKinsey, 2020) [10]. However, the recent marketing authorization obtained by Eat Just in Singapore for cultured chicken meat after a lengthy regulatory process could favor the approval of this type of meat in other countries ([PERSON], 2021) [112]. In addition to meat grown for human consumption, there are companies dedicated to developing armed seafood products and pet food.
###### Abstract  
The optimization of caching mechanisms has long been a crucial research focus in cloud-edge collaborative environments. Effective caching strategies can substantially enhance user experience quality in these settings. Deep reinforcement learning (DRL), with its ability to perceive the environment and develop intelligent policies online, has been widely employed for designing caching strategies. Recently, federated learning, when combined with DRL, has been in gaining popularity for optimizing caching strategies and protecting data training privacy from eavesdropping attacks. However, online federated deep reinforcement learning algorithms face high environmental dynamics, and real-time training can result in increased training energy consumption despite improving caching efficiency. To address this issue, we propose a low-energy federated deep reinforcement learning strategy for caching mechanisms (LFDC) that balances caching efficiency and training energy consumption. The LFDC strategy encompasses a novel energy efficiency model, a deep reinforcement learning mechanism, and a dynamic energy-saving federated policy. Our experimental results demonstrate that the proposed LFDC strategy significantly outperforms existing benchmarks in terms of energy efficiency.  
cloud-edge collaborative environments; caching strategies; deep reinforcement learning (DRL); low-energy federated deep reinforcement learning strategy for caching mechanisms (LFDC); energy efficiency