esa-sceva/satcom-chunk-collection huggingface dataset:
# \(k\)-Circle Formation and \(k\)-epf by Asynchronous Robots
## 3 Impossibility Result  
In this section, we characterize the initial configurations for which the \(k\)-circle formation problem cannot be solved deterministically. Notice that if \(k\) is an odd integer and the initial configuration \(C(0)\in\mathcal{I}_{5}\), then \(|F|\) must be even. For an initial configuration \(C(0)\) which is symmetric about the \(y\)-axis, if both the values of \(k\) and \(|F|\) are odd, then \(R_{y}(0)\
eq\emptyset\). As a result, \(C(0)\) cannot possibly belong to \(\mathcal{I}_{5}\).  
Theorem 3.1: _If the initial configuration \(C(0)\in\mathcal{I}_{5}\) and \(k\) is an odd integer, then the \(k\)-circle formation problem is deterministically unsolvable._  
Proof of Theorem 3.1.: If possible, let algorithm \(\mathcal{A}\) solve the \(k\)-circle formation problem starting from the given initial configuration \(C(0)\in\mathcal{I}_{5}\) when \(k\) is odd. Consider the scheduler to be semi-synchronous with the additional property that whenever a robot \(r\) is activated, \(\phi(r)\) is also activated. We assume that all the robots move with constant speed (which is the same for all robots) without transient stops. We also assume that the distance traveled by \(r\) is the same as that by \(\phi(r)\). First, consider that both \(r\) and \(\phi(r)\) have opposite notions of positive \(x\)-axis direction. As a result, their views would be identical. Since they run the same algorithm, their destinations and the corresponding paths would be mirror images. Even with non-rigid motion, if they travel the same distance, their final positions would be mirror images of each other. Since we started with a symmetric configuration, no algorithm can break the symmetry under this setup. Let \(f\) be a fixed point on the \(y\)-axis. Since the overall configuration is symmetric, the robot positions on \(C(f,\rho)\) must be symmetric around the \(y\)-axis. As \(k\) is odd, \(C(f,\rho)\) must contain a robot position on the \(y\)-axis. Since the initial configuration did not have any robot position on the \(y\)-axis and all the robots move in pairs, having a robot \(r\) moved to the \(y\)-axis would mean moving \(\phi(r)\) to the same point. As a result, a point of multiplicity will be created, from which it is  
Figure 1: Square represents the center of gravity, black circles represent robot positions, and crosses represent fixed points. (**a**) \(\mathcal{I}_{1}\)-configuration. (**b**) \(\mathcal{I}_{2}\)-configuration. (**c**) \(\mathcal{I}_{3}\)-configuration.  
Figure 2: (**a**) \(\mathcal{I}_{4}\)-configuration. (**b**) \(\mathcal{I}_{5}\)-configuration.  
deterministically impossible to separate \(r\) and \(\phi(r)\). Hence, the \(k\)-circle formation problem is deterministically unsolvable.  
Notice that the unsolvability criterion (Theorem 1) for the \(k\)-circle formation problem would never be satisfied when \(k\) is an even integer. Even for odd values of \(k\) and the symmetric configurations in \(\mathcal{I}_{3}\cup\mathcal{I}_{4}\), the unsolvability criterion (Theorem 1) for the \(k\)-circle formation problem would never be satisfied.
###### Abstract  
While infrastructure providers are expanding their portfolio to offer sustainable solutions for beyond 10 Gbps in the access segment of optical networks, we experimentally compare several modulation format alternatives for future passive optical networks (PONs) aiming to deliver 25+ Gbps net-rates. As promising candidates, we consider the intensity modulation direct detection (IM/DD) schemes such as electrical duobinary (EDB) and 4-level and 8-level pulse amplitude modulations (PAM-4/8). They are more spectrally efficient than the conventional non-return-to-zero on-off-keying (NRZ-OOK) used in current 10G PONs. As we move to higher rates, digital equalization enhances the performance by smoothening the systems imperfection. However, the impact that such equalization has on the optical power budget remains unclear. Therefore, in this article, we fairly compare the optical power budget values of a time division multiplexed PON (TDM-PON) exploiting a linear digital signal equalization at the receiver side. We consider the conventional PON configuration (20 km of single-mode fiber (SMF), 1:N optical power splitting) with IM/DD and net-rates above 25 Gbps. Furthermore, we focus on a downstream transmission imposing the bandwidth limitations of 10G components using a digital filter before the detection. The obtained results show that the use of a digital post-equalization with 43 feed-forward (FF) and 21 feedback (FB) taps can significantly improve the signal quality enabling new alternatives and enhancing the optical power budget.  
digital post-equalization; electrical duobinary (EDB); intensity modulation direct detection (IM/DD); non-return-to-zero (NRZ); optical power budget; passive optical networks (PON); pulse amplitude modulation (PAM) +
Footnote †: journal: Applied Signal Processing
## 5 Factor Affecting the Properties of GPC
### Activator to Binder Ratio (A/B)  
The activator to binder ratio is inversely related to strength parameters. The compressive strength of GGBFS-based GPC diminishes with increase in alkaline solution to slag ratio [2]. [PERSON] et al. [134] studied the rheology and strength characteristics of fly ash/slag geopolymer pastes and concluded that the compressive strength of fly ash-based GPC decreased with the increase of activator to binder ratio. At lower activator to binder ratio, the mix was not workable, resulting from insufficient wetting of particles, while at a higher ratio, the hydroxylation of particles of fly ash was affected due to less Al and Si species in the aqueous phase, that in turn, resulted in lower compressive strength. This trend is shown in Figure 28. [PERSON] et al. [166] studied the behavior of FA and bottom ash-based GPC. It was found that better workability can be maintained with greater FA/ bottom ash and liquid/binder ratios.  
The flow charts presented in Figures 29-31 are proposed which summarize the properties to be considered while determining the notable factors such as molarity, curing temperature, curing time, silicate to hydroxide ratio, and activator to binder ratio. Fresh properties of GPC significantly depend silica to alumina ratio, activator content and its molarity. However, curing temperature affects the hardened properties, strength development, and crack generation. Moreover, each influential  
Figure 28: Relation between activator to binder (A/B) ratio and compressive strength at 2:1 fly ash/slag ratio, modified from [134], Elsevier, 2019.  
Figure 27: Effect of curing age and curing temperature on compressive strength of FA-based GPC, modified from [165], Academic journals, 2010.  
parameter effecting the GPC properties are presented in Table 9 with its advantages and disadvantages. The pros and cons of each influential parameter are highlighted that help in determining the different ratios in the mix design of GPC.  
Figure 29: Fresh and hardened properties considered while determining the ratios of GPC.  
Figure 30: Fresh and hardened properties considered while determining activator to binder content and molarity.  
Figure 31: Fresh and hardened properties considered while determining curing conditions.
## 5 Performance Evaluation
### Parameter Settings  
In the UAV-assisted SEC environment, we consider that multiple IoTDs are arbitrarily dispersed within a 500 m \(\times\) 500 m region, where UAVs and LEO satellites, equipped with ESs, are capable of offering computational support to these IoTDs. Each IoTD generates an MLLM task request that necessitates processing, either via task offloading to UAVs or LEO satellites. We set the number of IoTDs \(K=10\), the number of UAVs \(M=3\), and the number of LEO satellites \(N=4\), wherein UAVs fly at an altitude of \([40,60]\) m and LEO satellites at an altitude of 500 km. In this simulation, each UAV is assumed to be equipped with NVIDIA Jetson Orin NX with 1.88 TFLOPS and 16 GB memory, and the LEO satellite is equipped with NVIDIA Jetson AGX Orin with 3.33 TFLOPS and 32 GB memory [39].  
We implement the proposed algorithm based on the HuggingFace [40]. To evaluate the inference performance of our algorithm, we implement a text generation benchmark. Utilizing the MMMU dataset provided by HuggingFace, we randomly selected a specific subset where each input sequence contains \([16,32,64,96]\) tokens and a picture; then, the server generated \([16,32,64,96]\) tokens based on these inputs. The duration of each time slot was set as 3 s. The MDs' request arrivals were characterized by a Poisson distribution, with the rate parameter ranging from 1 to 1.5 requests at each time slot. The actor and critic learning rates were \(LR\_A=0.0003\) and \(LR\_C=0.0003\), respectively. The buffer size \(\mathcal{C}=10\),000, the soft update coefficient \(\tau\) was 0.01, and the batch size is 128. The other simulation parameters are presented in Table 1. Similar experimental configurations were adopted in [18,41]. The hyperparameters of the AD-SAC algorithm are listed in Table 2. The simulations were conducted on a platform featuring an Intel(R) Xeon(R) Platinum 8370C CPU 2.80 GHz and an NVIDIA GeForce GTX 4090 graphics.  
_Benchmark:_ In our study, we evaluated the efficacy of our enhanced AD-SAC algorithm compared to four benchmarking schemes:  
* Random: This method selects actions uniformly at random, serving as a baseline to assess the effectiveness of learning-based approaches.
* Proximal Policy Optimization (PPO): PPO is a policy optimization DRL technique that iteratively refines the policy to maximize expected cumulative rewards.  
* Dueling Double Deep Q-Network (D3 QN): D3 QN builds upon the Deep Q-Network framework by incorporating a dueling architecture to estimate state values and action advantages separately.
* Deep Deterministic Policy Gradient (DDPG): DDPG is a policy gradient algorithm that learns a deterministic policy alongside a value function for optimization.
* Hybrid Proximal Policy Optimization (Hybrid-PPO): Hybrid-PPO introduces a parameterized PPO method based on the hybrid action space to solve the collaborative partial task offloading problem at the edge [42].
## 4 Application Example with SmartSync  
### Study Case  
The study uses a fixed-wing drone as its applicative example. This drone is inspired by the Zipline company blood delivery drone [31]. The specific use of this drone is the delivery of blood packages to different hospitals and clinics across a large area from a blood storage center. This drone is a fixed-wing drone, meaning a drone similar to a plane. Its powertrain has two coaxial motors, each linked to a propeller. The drone also presents redundancy in its ailerons, used to control direction. A control processing unit is placed in the removable battery of the drone and is given the flight plan before each flight. A QR code allows for identifying the blood package once installed in the drone cargo compartment until it is parachuted down to the delivery site. The drone takes off by being catapulted; then, it flies to the drop area. Cargo can be parachuted up to an 80 km radius, with a maximum time to objective of 45 min. The drone then comes back to the storage site. It lands by being caught by a recovery system, a sling between two 10-m high towers that attaches to a hook on the drone. An illustration of the drone's external architecture and flight scenario can be found in Figure 12.  
We consider a SysML architecture model as the specification of the system. From this architecture, we derived three models for different intents:  
* Safety assessment: An AltaRica 3.0 model represents the model for safety assessment.
* Scenario: A SCOLA (SCenario Oriented LAnguage) model represents the functional scenarios.
* Multi-physics: A Modelica model represents the multi-physics behavior of the drone's power electronics and aerodynamics.  
We asserted consistency over this design using SmartSync to synchronize these three models with the architecture model.  
Figure 11: Structure for binary consistency relations.
## 3 Results and Discussion
### Life-Cycle Impact Assessment Based on Hydrogen Production
Therefore, the recovery of hydrogen from waste gas stream must be considered among the most environmentally friendly options, appearing competitive versus complex processes like supercritical water gasification, chemical looping, advanced methane cracking, or tri-reforming [25; 58; 59]. When the AP was investigated, which was the second most studied category just after GWP by the LCA studies, the impact of the proposed recovery process was below 3.0 g SO\({}_{2}\) eq. in the worst scenario, with negative values (avoided burdens) for most scenarios even without optimization. Therefore, from the perspective of this impact category, the recovery of the hydrogen from waste gas streams resulted in a completely preferable option when compared to alternative production processes, most of them with AP values above 10 g SO\({}_{2}\) eq. [60; 61]. \begin{table}
\begin{tabular}{c c c} \hline
**Production Process** &
\begin{tabular}{c} **GWP (kg CO\({}_{2}\) eq./kg** \\ **H\({}_{2}\))** \\ \end{tabular} & **Reference** \\ \hline Natural gas thermolysis & 37.11 & [62] \\ Steam methane reforming & 11.89 & [53] \\ Coal gasification & 11.29 & [53] \\ Electrolysis (wind energy) & 0.97 & [53] \\ Electrolysis (photovoltaic energy) & 2.41 & [5] \\ Steam methane reforming & 10.56 & [54] \\ Methane partial oxidation & 10.70 & [60] \\ Electrolysis (grid electricity) & 30.50 & [63] \\ Supercritical water reforming of glycerol & 3.77 & [64] \\ Electrolysis (wind energy) & 0.68 & [57] \\ Autothermal bioethanol reforming & 4.36 & [61] \\ Methane cracking (liquid-metal technology) & 1.90 & [59] \\ Steam methane reforming & 11.50 & [55] \\ Autothermal natural gas reforming & 10.80 & [55] \\ Syngas chemical looping & 12.30 & [55] \\ Chemical looping reforming & 9.10 & [55] \\ Chemical looping combustion thermally coupled & 3.01 & [25] \\ steam reforming & 2.41 & [58] \\ Supercritical water gasification (photovoltaic energy) & 28.60 & [56] \\ Electrolysis (USA mix electricity) & 23.70 & [56] \\ Coal gasification & 4.40 & [56] \\ Ethanol reforming & 12.20 & [56] \\ Methanol reforming & 17.90 & [56] \\ Methane reforming & 13.80 & [56] \\ Biomass gasification with methane tri-reforming & 1.35 & [65] \\ \hline \end{tabular}
\end{table} 
Table 4: Greenhouse gases (GHG) impacts related to different technologies that produce hydrogen.
# Analysis of Ruggedness of 4H-SiC Power MOSFETs with Various Doping Parameters
## 2 Materials and Methods
### Device Description  
Figure 3 illustrates the structure of a 1.7 kV 4H-SiC MOSFET. The MOSFET was designed using the Sentaurus TCAD simulation tool. The drift-layer doping concentration (\(N_{\text{Drift}}\)) and drift thickness (\(t_{\text{Drift}}\)) for a breakdown voltage of 1.7 kV were \(0.97\times 10^{16}\) cm\({}^{-3}\) and 13.4 \(\upmu\)m, respectively. To decrease the junction field-effect transistor (JFET) resistance and threshold voltage, the JFET and channel region were implanted [14]. The doping concentrations of the JFET, p-base (\(N_{\text{\emph{phase}}}\)), \(N^{+}\) source (\(N_{N+}\)), and P\({}^{+}\) source (\(N_{P+}\)) were \(8\times 10^{17}\), \(6\times 10^{18}\), \(1\times 10^{20}\), and \(1\times 10^{20}\) cm\({}^{-3}\), respectively. The depths of the p-base, \(N^{+}\) source, and P\({}^{+}\) source were 1.0, 0.2, and 0.4 \(\upmu\)m, respectively. The ranges of the \(N_{\text{\emph{phase}}}\), \(N_{\text{\emph{Nsource}}}\), and \(N_{\text{\emph{Psource}}}\) values were from \(3\times 10^{18}\) to \(9\times 10^{18}\), \(3\times 10^{19}\) to \(1\times 10^{20}\), and \(3\times 10^{19}\) to \(1\times 10^{20}\) cm\({}^{-3}\), respectively. These values were used to analyze the failure of the 4H-SiC power MOSFET.
# Numerical Modeling of Single Pile Behaviors Due to Groundwater Level Rising
## 2 Study Area
### Reference Pile  
Static pile test data were obtained from a test pile at Silom-Sathon, located in the inner city zone of Bangkok (13.706261 N, 100.536521 E). This 20 floor building was completed in 1999, during the period of maximum groundwater drawdown (1997-1999). The bored pile, constructed by using a wet process, had 1 m diameter and the pile tip was at \(-55\) m (below ground level). This pile was used as reference for the simulations: it was labeled T55D1.0.
## 4 Multi-Criteria Decision-Making Method for Emergency Communication Protocol (MCDM-ECP)
### Criteria Weights  
As aforementioned in Section 3, next-hop node selection depends on the following decision criteria selected: EC, CN, HC, and DIS. We use the AHP method to determine the weights of the four decision criteria in this section, and then perform a ranking of next-hop node opportunities through TOPSIS in the next section.  
The AHP algorithm [40] was developed with four steps: (1) problem definition, (2) construction of the hierarchy, (3) construction of the judgment matrix, and (4) calculation of the normalized weights.  
Our problem is defined, divided, and classified as follows: the objective, the criteria, and the selection. The objective is to select the most probable target nodes from routing tables for data dissemination. The criteria are the factors affecting the preference of the selection. We analyze the variables that can affect the process of data dissemination in DAWN, and then choose four variables of interest for the proposed MCDM-ECP due to their relevance. The four variables (EC, CN, HC, and DIS) are enough to assess the route conditions. The judgment matrix for the AHP method is given by:  
\[P=\left[\begin{array}{cccc}p_{11}&p_{12}&p_{13}&p_{14}\\ p_{21}&p_{22}&p_{23}&p_{24}\\ p_{31}&p_{32}&p_{33}&p_{34}\\ p_{41}&p_{42}&p_{43}&p_{44}\end{array}\right]\]  
where \(p_{ij}\)\((i=1,\ldots,4,\)\(j=1,\ldots,4)\) stands for the importance degree of characteristic \(i\) to \(j\). We normalize each column of \(P\) by:  
\[\overline{p_{ij}}=p_{ij}/\sum_{k=1}^{4}p_{kj}\;(i=1,\ldots,4,\;j=1,\ldots,4) \tag{1}\]  
Then, we summarize the normalized matrix by line, and get vector \(w_{i}(i=1,\ldots,4)\):  
\[w_{i}=\sum_{j=1}^{4}\overline{p_{ij}}\;(i=1,\ldots,4,\;j=1,\ldots,4) \tag{2}\]  
Again, we normalize \(w_{i}(i=1,\ldots,4)\) by:  
\[\overline{w_{i}}=w_{i}/\sum_{i=1}^{4}w_{i}\;(i=1,\ldots,4) \tag{3}\]  
Finally, we get the weights \(W\):  
\[W=\left(\begin{array}{cccc}\overline{w_{1}}&\overline{w_{2}}&\overline{w_{3 }}&\overline{w_{4}}\end{array}\right) \tag{4}\]where \(\overline{w_{i}}(i=1,\ldots,4)\) stands for the importance ratio of each criterion that we choose.
## 3 Results
### Performance Validation Results of the Simulator
The average memory usage of 449.38 MB, equivalent to 1.37% of the total 32 GB (32,768 MB) memory, demonstrates extremely low memory consumption. This suggests that the simulator efficiently processes and transfers data while identifying and removing unreferenced data in real time, thus optimizing memory utilization. In terms of FPS and resource usage, the simulator demonstrates overall positive results, as it performs resource-intensive tasks such as model data processing, post-processing visualization, model cutting, and data exporting without placing a significant burden on the computer. This indicates that the maxillofacial reconstruction simulation process utilizes resources efficiently, suggesting that it can operate smoothly on other PCs as well. Additionally, the evaluation of key performance metrics related to the simulator's core functions showed that the cutting time for 10 iliac bone models averaged 3.28 s (minimum, 1.03 s; maximum, 4.95 s), and for 10 facial bone models, it averaged 2.93 s (minimum, 1.26 s; maximum, 4.88 s). The interaction recognition accuracy was recorded at an average of 97.6% (minimum, 95%; maximum, 100%). The average cutting time for both models was 3.11 s (standard deviation, 1.21 s), indicating that cutting tasks do not require significant time and that the variation in cutting times between models is minimal. This demonstrates that the cutting algorithm is consistent and stable, enabling predictions for cutting times of new models and facilitating efficient resource management. The interaction recognition accuracy of 97.6% implies a low error rate during model selection and state changes in the simulator, suggesting that it provides a smooth and seamless user experience during simulation.
## 3 Experimental Results
### Detection Results
applies the Top-hat operator with a disk-shaped structuring element at multiple scales over the original image. The single-scale Gabor filter proposed by [PERSON] et al. [13] defines the parameters with a full-search, resulting on a filter with parameters \(\tau=8\), \(l=2.9\), and \(\kappa=180\) orientations. The method of [PERSON] et al. [3] is based on a vesselness measure computed from the Hessian matrix of the original angiogram using multiple scales in the range \([1,1.25,1.5,\ldots,21]\) in order to detect vessels of different diameters. The comparative analysis between the proposed method, and the eleven blood vessel enhancement methods described above, is illustrated in Figure 7 using the test set of 30 images of the present database. The comparative analysis reveals that the proposed method presents the highest vessel detection performance with \(A_{z}=0.9775\), compared with the eleven methods from the state of the art. To visualize the vessel detection results, Figure 8 presents the responses obtained by applying the different methods over a subset of angiograms of the testing set.
# Feasibility Study for Using Piezoelectric-Based Weigh-In-Motion (WIM) System on Public Roadway
###### Abstract  
Weigh-in-Motion system has been the primary selection of U.S. government agencies as the weighing enforcement for decades to protect the road pavement. In recent years, the number of trucks has increased by about 40% and in 2017, they travel 25% more annually than in 2016. The lack of the budget has slowed down the expansion of weighing enforcement to catch up with the growing workload of vehicle weighing. Unsupervised pavement section suffers more pavement damage and increased repairing cost. In this work, a piezoelectric material based WIM system (P-WIM) is developed. Such a system consists of several piezoelectric material disks that are capable of generating characteristic voltage output from passing vehicles. The axle loading of the vehicle can be determined by analyzing the voltage generated from the P-WIM. Compared to traditional WIM system, P-WIM requires nearly zero maintenance and costs 80% less on capital investment and less labor and effort to integrate. To evaluate the feasibility of this technology to serve as weighing enforcement on public roadways, prototype P-WIMs are fabricated and installed at a weigh station. The vehicle loading information provided by the weigh station is used to determine the force transmission percentage of the installed P-WIMs, which is an important parameter to determine the vehicles' axle loading by generated voltage.  
Weigh-in-Motion; Piezoelectricity; pavement Performance; pavement Monitoring +
Footnote †: journal: Applied Sciences