esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to more effectively reduce the regulation costs of power grids and to improve the automatic generation control (AGC) performance, an optimization mathematical model of generation command dispatch for AGC with an electric vehicle (EV) charging station is proposed in this paper, in which a cost consensus algorithm for AGC is adopted. Particularly, virtual consensus variables are applied to exchange information among different AGC units. At the same time, the actual consensus variables are utilized to determine the generation command, upon which the flexibility of the proposed algorithm can be significantly enhanced. Furthermore, the implement feasibility of such an algorithm is verified through a series simulation experiments on the Hainan power grid in southern China, where the results demonstrate that the proposed algorithm can effectively realize an autonomous frequency regulation of EVs participating in AGC.  
electric vehicle charging station; cost consensus algorithm; automatic generation control; autonomous frequency regulation +
Footnote †: journal: Applied Sciences
# Comparison of 2- and 3-Dimensional Cultured Periodontal Ligament Stem Cells; a Pilot Study
## 4 Discussion
First, the decreased diameter and reduced proliferation ability of the 3D-cultured PDLSCs may be due to inherent characteristics of the PDLSCs or may be attributed to the morphologic characteristics of the spheroid, which make it difficult to supply adequate nutrients and oxygen and discharge waste products. Results of this study are similar to other studies that have used 3D pellet cultures, low-adhesive plates, or scaffolds. In this regard, it may be necessary to compare other 3D methods, such as a bioreactor. Second, the assays that were optimized for a 2D environment may not have been optimal for evaluating 3D-cultured cells. For example, the CCK-8 assay is a method of measuring the color change caused by the dehydrogenation of water-soluble tetrazolium salts by living cells [37]. Therefore, it is not ideal to compare the absorbance of 2D and 3D cultures quantitatively because the number of cells contacting the solution differs between the culture environments. It is difficult or impossible to find protocols and assays optimized for 3D-cultured PDLSCs, and therefore in conjunction with the increasing popularity of 3D culture, future research is necessary to establish optimized methods for analyzing 3D-cultured cells. Third, this was a pilot study for comparing 2D- and 3D-cultured PDLSCs, and as such, it had a limited sample size. Despite these limitations, it confirmed that the 3D-cultured PDLSCs display different gene expression, morphology, and physiology compared to identical 2D-cultured cells. Moreover, the results of this study will contribute to developing optimized 3D-culture methods for dental stem cells.
## 5 Comparison with Recent Similar Works  
In their recent work, [PERSON] et al. have presented an extensive analysis over the performance of different heuristic and metaheuristic algorithms on some TSPLib instances [21]. In addition, [PERSON] et al. [22] proposed an improved hybrid VNS algorithm for solving TSP instances. In this section, a comparison between our proposed GVNS schemes (GVNS_1 and GVNS_2) and those algorithms presented on papers [21,22] is performed.  
Table 18 shows the comparison between our GVNS_1 and GVNS_2, within the Best Improvement search strategy and a time limit of 120 s, with all metaheuristic solution approaches presented in the work of [PERSON] et al. [21]. The results show that our methods produce better results than the previously mentioned metaheuristics, except the case of instance rat195.tsp in which the GA and the TS perform better than GVNS_2.  
Figure 5: Statistical test for nTSP (1/2). (**a**) Statistical test box plots for nTSP 1 min FI; (**b**) Statistical test box plots for nTSP 1 min BI.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Instance** & **OV** & **GVNS\_1** & **GVNS\_2** & **GA** & **SA** & **TS** & **ACO** & **TPO** \\ \hline eil51.tsp & 426 & 426 & 426 & 454.1 & 439.13 & 439.1 & 467.46 & 437.26 \\ berlin52.tsp & 7542 & 7542 & 7542 & 7946.4 & 7960.67 & 7740.1 & 7922.32 & 7705.8 \\ st70.tsp & 675 & 676 & 675 & 700.72 & 696.33 & 690.27 & 756.55 & 697.12 \\ kroA100.tsp & 21282 & 21282 & 21312 & 22726.2 & 22277.5 & 22521.64 & 22941.68 & 22463.6 \\ ch130.tsp & 6110 & 6137 & 6208 & 6610.8 & 6558.7 & 6717.06 & 6913.99 & 6515.28 \\ rat195.tsp & 2323 & 2349 & 2448 & 2414.52 & 2537.99 & 2373.94 & 2465.11 & 2573.47 \\ a280.tsp & 2579 & 2630 & 2725 & 2789.83 & 2830.18 & 2800.79 & 2867.85 & 2790.54 \\ rd400.tsp & 15281 & 15684 & 16272 & 16567.29 & 16816.65 & 20723.56 & 19259.06 & 18190.84 \\ pcb442.tsp & 50778 & 52381 & 54108 & 55718.9 & 57421.04 & 83123.01 & 63436.7 & 60750.43 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Comparisons between GVNS_1 and GVNS_2 with BI for 2 mins with the recent work of [PERSON] et al.  
Figure 6: Statistical test for nTSP (2/2). (**a**) Statistical test box plots for nTSP 2 min FI; (**b**) Statistical test box plots for nTSP 2 min BITable 19 shows the comparison between our GVNS_1 and GVNS_2, within the Best Improvement search strategy and a time limit of 120 s with the results obtained by a hybrid VNS in the second mentioned work [22]. It is clearly observed that our methods outperform the hybrid VNS. More specifically, the GVNS_1 outperforms the hybrid VNS algorithm in all tested instances, while the GNVS_2 produce better results than those achieved by [PERSON] et al. approach in seven out of 10 problem instances.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Technological Considerations  
This subsection deals with technologies that might be considered as part of making the case for a BVLOS operation. The idea is that, after consideration, an operator may decide which technologies are appropriate for taking a risk-based approach towards the operation while still achieving the desired operational results. These considerations are also not exhaustive but, rather, present some common technologies and how they might be used.
## 2 Route Generation Methodology
### Lower-Level Path Generation: Ant Colony Optimization (ACO)  
In the following approach, the lower-level path generation method was used to pre-compute several routes within each sub-area. The paths are dependent on the start location selected; however, several start locations can be used to create a collection of candidate paths. Eventually, the higher-level path generation method will select the optimal start location to yield the least total distance.  
The lower-level path generation method is based on the ACO algorithm used to solve the CSP. The basic principles of the ACO and its modifications are explained in this section.
# Quantitative Immunofluorescence Mapping of HSP70's Neuroprotective Effects in FUS-ALS Mouse Models
###### Abstract  
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease, often linked to mutations in the FUS gene, leading to toxic protein aggregates. This study investigates the role of HSP70, a molecular chaperone, in mitigating neurodegeneration in FUS-ALS mouse models. Using quantitative immunofluorescence microscopy, we mapped cellular changes in the primary motor cortex of double transgenic FUS/HSP70 mice and compared them to single FUS-transgenic controls. Our results reveal that double transgenic mice exhibit significantly reduced neuronal damage and increased levels of mature neuronal (NeuN) and microglial (Iba1) markers, indicating a protective effect of HSP70. Intracellular HSP70 expression proved more effective than extracellular release, suggesting that targeted HSP70 delivery to neurons may offer a promising therapeutic avenue for ALS. This study underscores the potential of quantitative immunofluorescence for mapping neuroprotective pathways and highlights HSP70's impact on mitigating FUS-related pathology in ALS.  
heat shock proteins; HSP70; HSPA1A; neurodegeneration; primary motor cortex; amyotrophic lateral sclerosis; ALS; fused in sarcoma; FUS; transgenic animals; immunofluorescence; microscopy
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 4 Experiments and Analysis
### Effect of Patch Size  
In order to investigate the effect of patch size on the final prediction accuracy, we used four different patch sizes (64, 112, 224, and 384). As shown in Table 3, the patch size of 64 and 112 shows better performance in SRCC and PLCC. For each test, the best is shown in bold. Taking into account that when the patch size is too small, it is not conducive to the quality assessment of large-size images. Thus, a patch size of 112 was used in the following experiments.  
\begin{table}
\begin{tabular}{c c c} \hline
**TID2013** & **LIVE** & **Distortion Types** \\ \hline \(\surd\) & \(\surd\) & Additive Gaussian noise \\ \(\surd\) & & Additive noise in color components \\ \(\surd\) & & Spatially correlated noise \\ \(\surd\) & & Masked noise \\ \(\surd\) & & High-frequency noise \\ \(\surd\) & & Impulse noise \\ \(\surd\) & & Quantization noise \\ \(\surd\) & \(\surd\) & Gaussian blur \\ \(\surd\) & & Image denoising \\ \(\surd\) & \(\surd\) & JPEG compression \\ \(\surd\) & \(\surd\) & JPEG2000 compression \\ \(\surd\) & & JPEG transmission errors \\ \(\surd\) & & JPEG2000 transmission errors \\ \(\surd\) & & Non- eccentricity pattern noise \\ \(\surd\) & & Local block-wise distortions of different intensity \\ \(\surd\) & & Mean shift (intensity shift) \\ \(\surd\) & & Contrast change \\ \(\surd\) & & Change of color saturation \\ \(\surd\) & & Multiplicative Gaussian noise \\ \(\surd\) & & Comfort noise \\ \(\surd\) & & Lossy compression of noisy images \\ \(\surd\) & & Image color quantization with dither \\ \(\surd\) & & Chromatic aberrations \\ \(\surd\) & & Sparse sampling and reconstruction \\  & \(\surd\) & Fast-fading \\ \hline \end{tabular}
\end{table}
Table 1: Image distortion type.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Epoch Numbers in Step 1** & **5** & **10** & **15** & **20** \\ \hline Performance & SRCC & 0.980 & **0.983** & 0.982 & 0.979 \\ in step 2 & PLCC & 0.985 & **0.988** & 0.986 & 0.982 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the performance of step 2 with different training epochs in step 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Patch Size** & **64** & **112** & **224** & **384** \\ \hline SRCC & **0.983** & **0.983** & 0.979 & 0.963 \\ PLCC & **0.988** & **0.988** & 0.985 & 0.968 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the SRCC and PLCC with different patch size.
# ndInIoT-FC: IoT Devices as First-Class Traffic in Name Data Networks
## 3 ICN Architecture for IoT Environments
### ndnIoT-FC Architecture  
The main focus behind our architecture is to address IoT services by means of extending the NDN concept, introducing publisher-subscriber mechanisms allowing for a reduction to in-network traffic, and implementing changes at the NDN forwarding module level. This provides versatility on how data packets are managed to better accommodate such communication model. To this extent, various characteristics inherent to NDN are crucial and need to be taken into account.  
Packet naming, as one of the most important factors in Named Data Networks, must allow applications to subscribe to specific content without restricting how data should be forwarded.  
In scenarios following a publisher-subscribe methodology data can be seen as extremely volatile, i.e., the lot of data periodically generated by IoT devices are for immediate consumption. This characteristic intensifying the need for Interest packets to be kept at a minimum, which in turn puts emphasis in how publisher nodes react to subscription requests and how frequent content should be updated.  
Likewise, this volatile nature of the data results in a high burden on the caching of intermediate nodes, as several updates for the same content are received and thus need to be maintained, which puts a lot of weight behind the design of efficient caching methods towards these occurrences, or can result in the disability of caching altogether.  
In the forwarding spectrum, PIT entries validity and availability are major factors, as performance losses are evident in the event of lasting entries for nodes which are no longer available. Lastly, in the case of highly dynamic networks, as is the case of vehicular networks, paths should be updated frequently to ensure availability of content and allow efficient routing of data.  
With the main objective of meeting the characteristics above described, we propose a set of mechanisms in the NDN base architecture that are described below.  
Figure 2: IoT data gathering typical scenario.
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 6 Results Discussion
### Assessing the Scalability of Mid1 and Mid4 Models  
The graph in Figure 11 presents the PDR for Mid1 and Mid4 models in a 10 \(\times\) 10 grid network against the 15 \(\times\) 15 grid network.  
In a larger network, the differences between the two models are evident. The Mid4 model outperforms and is 38.1% more efficient than the Mid1 model toward reliable data collation. Figure 11 shows a small rise in control packets for the Mid4 model in a scaled-up network. However, Mid1 suffers from a 40.9% increase in control overhead when compared to the same model in a smaller network.  
Figure 11: Performance in a 10 \(\times\) 10 grid vs. 15 \(\times\) 15 grid. (**a**) Data reliability in the network.  
Figure 10: Metrics for the communication models. (**a**) Average energy utilization of a single node. (**b**) Battery charge consumption for one hour.  
The data latency metrics for the two models are presented in Figure 12. The average time taken by the occupancy data packet latency to reach the data collator is very high, clocking over 12 s for the Mid1 model. Mid4 takes about 2 s for reaching the data collators and shows a clear superior performance. As the number of nodes in the network increases, the congestion causes a severe funneling effect around the root node. Hence, the performance of the Mid1 model is very low in a large network.  
A similar trend is shown in Figure 13a for the time taken to reach the data consumers, and Mid4 outperforms the Mid1 model. The energy consumption is also lower for the Mid4 model, and the same is illustrated in Figure 13b. It can be concluded that a multi-data collator model with the BR at the center of the network fits the efficient communication model requirement for an SPS.
# Effects of Bridge Piers on Flood Hazards: A Case Study on the Jialing River in China
Very accurate, inexpensive DEM data has been obtained easily by LiDAR, and the GPU technique significantly improved the computational efficiency of the numerical model and naked available very accurate numerical methods ([PERSON] et al., 2018; [PERSON] et al., 2019). [PERSON] et al. (2019) demonstrated the accuracy of LiDAR data for a numerical flood model. [PERSON] et al. (2019) developed a hybrid parallel code for the 1D-2D-coupled urban flood modeling using DEM data by LiDAR with 1-m resolution. However, some complex flow features such as shock-type flow discontinuities, transcritical flows, as well as wetting and drying over irregular topography were induced, and therefore a robust numerical scheme was required to produce accurate and stable numerical solutions ([PERSON] et al., 2019). For an engineering application of the 2D numerical models, [PERSON] et al. developed 1D and 2D numerical models based on the shallow water equations and the two models were compared to analyze the effects of two bridges on the Cracti and Corace rivers in Italy ([PERSON] et al., 2019). [PERSON] and [PERSON] developed a 2D numerical model based on the unstructured finite volume method for bridge pier flow ([PERSON] and [PERSON], 2019). [PERSON] and [PERSON] carried out 2D numerical modeling for flood control evaluation of the Tongjiang River Bridge in China ([PERSON] and [PERSON], 2019). In general, 2D numerical models are expected to display more details of flow fields in the vicinity of bridge piers, which are very importance in determining the management of flood risks ([PERSON] et al., 2018). In this study, the impacts on flood flows of four bridges in the Jialing River were investigated with a 2D numerical model. Shallow water equations were solved using the finite volume method and an unstructured triangular mesh system. We focused on the backwater effects and velocity fields in the vicinity of piers. The rest of this paper is structured as follows. The study areas and input data appear in Section 2, as well as, the numerical model, which includes the use of mesh generation, model parameters, and initial and boundary conditions, is developed, and a sensitivity analysis and model validation are discussed. In Section 3, the backwater effects of bridge piers and flow fields near the piers are investigated and these results are applied to discuss the effects of bridge piers on planning dikes. Some conclusions are drawn in Section 4.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 5 Performance Evaluation
### Metrics for Performance Analysis  
In the performance analysis, the metrics of FVHDA are compared with vertical handover decision algorithm (VHDA) [21; 22; 23]. The metrics for performance analysis is evaluated according to the following metrics.  
Average Handover Delay: Average of end-to-end delay from the origin to the destinations for all remaining data packets.  
Average Packet Delivery Ratio: The average packet delivery ratio is calculated by dividing the number of successfully received packets by the total number of packets sent.
# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
###### Abstract  
In combination with the expected traffic avalanche foreseen for the next decade, solutions supporting energy-efficient, scalable and flexible network operations are essential. Considering the myriad of user case requirements, THz and mmW bands will play key roles in 6G networks. While mmW is known for short-rate LOS connections, THz transmission is subjected to even severe propagation losses, resulting in very short-range connections. In this context, we evaluate a dynamic multi-band user association algorithm to optimize connectivity in coexisting RF/mmW/THz networks. The algorithm periodically calculates association scores for each user-base station pair based on real-time channel conditions across bands, accounting for factors like signal strength, link blockage risk and noise. It then reassociates users in batches to balance loads while considering user priorities and network conditions. We simulate the algorithm's performance within a realistic propagation model, where high path loss, molecular absorption, blockage, and narrow beam widths contribute to lower coverage at higher frequencies. Results demonstrate the algorithm's ability to efficiently utilize network resources across diverse operating environments. In addition, our results show that the choice of frequency band depends on the specific requirements of the application, the environment, and the trade-offs between coverage distance, capacity, and interference conditions.  
Keywords:coverage probability; SINR distribution; user association; millimeter wave; terahertz; 6G wireless networks +
Footnote †: journal:
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
###### Abstract  
In recent decades Global Positioning Systems (GPS) have become a ubiquitous tool to support navigation. Traditional GPS has an error in the order of 10-15 m, which is adequate for many applications (e.g., vehicle navigation) but for many robotics applications lacks required accuracy. In this paper we describe a technique, FAGPS (Fiducial Augmented Global Positioning System) to periodically use fiducial markers to lower the GPS drift, and hence for a small time-period have a more accurate GPS determination. We describe results from simulations and from field testing in open-sky environments where horizontal GPS accuracy was improved from a twice the distance root mean square (2 DRMS) error of 5.5 m to 2.99 m for a period of up-to 30 min.  
GPS; robot navigation; DGPS; navigation +
Footnote †: journal: Applied Sciences
## 3 Results
### Controlling Factors of \(R_{\rm S}\)
#### 3.2.2 Relationship between \(\rm R_{\rm S}\) to Flux and Biophysical Controls
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{\begin{tabular}{c} **K** \\ **(W m\({}^{-2}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Q\({}^{*}\)** \\ **(W m\({}^{-2}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **T\({}_{\text{air}}\)** \\ **(\({}^{*}\)C)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **VPD** \\ **(hPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **ET** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **GPP** \\ **(\({}^{*}\)m\({}^{-2}\) s\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **SWC** \\ **(m\({}^{3}\) m\({}^{-3}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **TS** \\ **(\({}^{*}\)C)** \\ \end{tabular} } \\ \hline OG & \(-\)0.05 \({}^{*}\) & 0.04 & 0.35 & \(-\)0.39 *** & 0.66 ** & 0.77 *** & 0.65 *** & 0.26 \\ MS & 0.09 & 0.16 & 0.05 \({}^{*}\) & \(-\)0.39 *** & 0.57 & 0.43 & 0.64 ** & 0.29 \\ ES & 0.25 & 0.30 & \(-\)0.20 \({}^{**}\) & \(-\)0.30 ** & 0.67 ** & 0.65 ** & 0.27 & 0.16 \\ \hline \end{tabular}
\end{table} 
Table 6: Correlation coefficients of soil respiration with global radiation (K), net radiation (Q\({}^{*}\)), air temperature (T\({}_{\text{air}}\)), vapor deficit pressure (VPD), evapotranspiration (ET), gross primary production (GPP), soil volumetric water content (SWC) and soil temperature (TS) in old-growth (OG), mid-secondary (MS) and early-secondary (ES) tropical dry forests in northwestern Mexico. The \({}^{***}\), \({}^{***}\), \({}^{***}\) and “***” represent the 0.0001, 0.001 and 0.05 significance probability of \(p\)-value, respectively. Figure 2: Structural equation models along forest succession in a tropical dry forest from northwestern Mexico. The model illustrates hypothesized direct and indirect relationships of biophysical controls over R\({}_{\rm{G}}\) on an old-growth (\(\lambda\)), mid-secondary (\(b\)) and early-secondary (\(c\)) tropical dry forest. The path diagrams depict the positive (solid lines) and negative (dashed lines) effects with their standardized regression weights (SWB), Each site path diagram shows the Bentler and Bonett’s normed-fit index (NFI), the goodness-of-fit index (GFI), the root-mean-square error of approximation statistic (RMSE), a Chi-square (\(\chi^{2}\)) value, the significance probability of \(p\)-value, the degrees of freedom (\(\lambda\),\(\lambda\)) and sample size (\(u\)). Arrow widths are proportional to \(p\)-value scheme.