esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Cement and Fly Ash on the Improvement of Fine Sand Soil
## 4 Conclusions  
This study investigates the effects of cement and fly ash additives on the geotechnical properties of fine sand soil through experimental testing. Samples are created by adding fixed 3% cement and 5, 10, 15, 20 and 25% fly ash by weight to the fine sand soil. Compaction tests are performed to obtain the optimum water content and maximum dry unit volume weight values for the samples with and without additives. The CBR, direct shear box and falling head permeability tests are conducted on the samples at their optimum water contents to evaluate the effects of the additives on the geotechnical properties of the soil. Based on the extensive experimental study carried out:  
1. The optimum water contents of treated soils increases continuously with the increase in fly ash amount. However, the dry unit weight of the samples decreases with the addition of fly ash, contrary to the addition of cement.
2. Fly ash and cement additives improve the internal friction angle, cohesion and CBR value of the fine sand soil in the experiments. A 28 day curing period results in nearly a two-fold increase in the CBR ratio compared to a 7 day curing period. The relative density of the sand influences both the increase in the friction angle and cohesion. The friction angle shows a greater increase in loose sand, while the cohesion exhibits a larger increment in dense sand compared to loose sand.
3. The permeability coefficient of the sand decreases with the addition of fly ash and cement at all percentages of additives.  
These findings indicate that fly ash can be used as a stabilizer for soil improvement applications of sand soil, as it positively affects the geotechnical properties of the soil. Further experiments can be conducted on sand particles of varying sizes to broaden the investigation.  
Investigation, E.Y. and Y.S.U.; methodology, Y.S.U.; resources, E.Y.; supervision, Y.S.U.; validation, E.Y.; writing--original draft, E.Y.; writing--review and editing, Y.S.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 6: Falling head permeability test results.
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 4 Model Building
### Transition System for Workflow  
**Definition 1** (Transition system).: _A transition system \(\mathcal{W}\) is the tuple \((W,w_{\text{ini}},\longrightarrow,AP,L)\) where_  
* \(W\) _is a set of states;_
* \(w_{\text{ini}}\in W\) _is an initial state;_
* \(\longrightarrow\subseteq W\times W\) _is a transition relation;_
* \(AP\) _is a set of atomic properties;_
* \(L:W\to 2^{AP}\) _is a labelling function that assigns a subset of atomic propositions to each event._  
A trace in \(\mathcal{W}\) is a finite sequence \(\varphi=w_{1}w_{2}\ldots w_{n}\) such that \(w_{i}\longrightarrow w_{i+1}\) for all \(1\leq i\leq n\). We present a transition system in Figure 3 as the model of SimpleFlow. In general, to model a workflow as a transition system \(\mathcal{W}\), the following rules are applied:  
* The set \(AP\) includes the names of some stages in the workflow. The excluded stages (such as the control stages \"fork\", \"join\" and \"ok?\" in SimpleFlow) have almost instant elapsed times, which are negligible.
* The labelling function \(L\) assigns \(\{\text{start}\}\) to \(W_{\text{ini}}\) and \(\{\text{end}\}\) to a state without outgoing transitions, respectively.
* The parallel threads in the workflow are represented by branches in \(\mathcal{W}\). For example, the two \"fork-join\" threads in SimpleFlow are represented by the two branches of \(w_{1}\) and \(w_{2}\) from \(w_{\text{ini}}\).  
The last rule is worthy of further explanation. Because some threads are temporarily blocked before entering a \"join\" stage in a workflow execution, only the _last-completed_ thread determines the execution time. As our focus is on time analysis, we can resolve the parallel structures in the workflow by the branching structures in \(\mathcal{W}\). Thus, the traces  
Figure 3: Transition system for SimpleFlow \(\mathcal{W}_{SF}\)  
Figure 2: Overview of the approach.  
in \(\mathcal{W}\) model the traces of last-completed threads in the workflow only. In \(\mathcal{W}_{SF}\), the path \(w_{\text{ini}}w_{2}\ldots w_{2}w_{3}\) (for any positive number of \(w_{2}\)) models an execution trace of SimpleFlow where the thread of Task2 is the last one completed. This can avoid the exponential growth in the size of the transition system as the number of threads in the workflow increases. For convenience, we also use \(\mathcal{W}\) to denote the workflow that it models from now on.
# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 3 Results
### Comparison of GPP Estimates at Three Spatial Resolutions
In the irrigated region, the highest CVs of the GPP\({}_{\text{L}}\) and GPP\({}_{\text{M}}\) were 1.2 and 1.15, respectively (Figure 5D,F). Moreover, the GPP\({}_{\text{M}}\) showed almost no significant spatial difference in this region. Figure 5: A typical agroecosystem near Mead, Nebraska, USA (41.1651 N, 96.4766 W). The research area is in the green box of subplot (**A**). Subplot (**B**) shows the seasonal change in GPP\({}_{\text{L}}\), GPP\({}_{\text{M}}\), and GPP\({}_{\text{A}}\) from 2014–2018. The dashed line represents the standard deviation of each pixel GPP in the research area. Subplots (**C**,**E**) are the mean GPP for five years of each pixel from GPP\({}_{\text{L}}\) and GPP\({}_{\text{M}}\), respectively. Subplots (**D**,**F**) are the temporal change in GPP, which is quantified by the coefficient of variation (CV) of each pixel. Dryland ecosystems include savanna, open shrubland, and deserts. In savanna ecosystems, the landscape includes random areas of grass and trees, which leads to high spatial heterogeneity in the GPP. Although the mean values of the GPP were similar to the \(\mathrm{GPP_{L}}\), \(\mathrm{GPP_{M}}\), and \(\mathrm{GPP_{A}}\) at the research site (Figure 6), the spatial variance in the GPP was quite different. The \(\mathrm{GPP_{L}}\) showed the highest \(\mathrm{GPP_{STD}}\), of around 3.378 g C m\({}^{-2}\) d\({}^{-1}\), while the \(\mathrm{GPP_{STD}}\) in the \(\mathrm{GPP_{M}}\) and \(\mathrm{GPP_{A}}\) were 0.134 and 0.005 g C m\({}^{-2}\) d\({}^{-1}\), respectively (Figure 6D). The seasonal variations in the GPP showed different patterns (Figure 6C) of pixels representing trees (CV \(\sim\)0.25) and pixels predominantly representing grass (CV\(\sim\)0.4). For the desert, the \(\mathrm{GPP_{L}}\) captured the seasonal change in GPP in an oasis (Figure 7). However, since the GPP driven by MODIS data misclassified the vegetation type in this region, the GPP in this region was close to zero. The \(\mathrm{GPP_{M}}\) had a maximum GPP of only around 0.002 g C m\({}^{-2}\) d\({}^{-1}\) in 2016, which suggested a significant underestimation compared to the \(\mathrm{GPP_{L}}\). Figure 6: A savanna ecosystem near Gregory, Queensland, Australia (139.34 E, 18.89 S). The research region is in the green box of subplot (**A**). Subplots (**B**,**C**) show the mean GPP and seasonal change in GPP at each pixel from 2014 to 2018, respectively. Subplot (**D**) shows the seasonal change in \(\mathrm{GPP_{L}}\), \(\mathrm{GPP_{M}}\), and \(\mathrm{GPP_{A}}\), while the dashed curves represent the \(\pm 1\) standard deviation of GPP in the entire research region in each series. Figure 7: Subplot (**A**) shows an oasis in the desert at Qiancaozi, Xinjiang, China (94.65\({}^{\circ}\)E, 42.63\({}^{\circ}\)N). Subplot (**B**) shows the seasonal change in \(\mathrm{GPP_{L}}\), \(\mathrm{GPP_{M}}\), and \(\mathrm{GPP_{A}}\), while the \(\mathrm{GPP_{M}}\) and \(\mathrm{GPP_{A}}\) are close to 0 at the red cross representing an oasis (Figure 7A).
# A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions
## 2 Materials and Methods  
### Longitudinal Speed Estimator of Four-Wheel Drive Slipping Vehicle on Split Roads  
At a high level, the wheel rotational speeds and longitudinal acceleration are derived directly from the wheel speed encoders and the acceleration sensor after filtering the data. Then they are input to the logic controller. The estimated vehicle speed (\(v_{E}\)) is the result after calculation by the logic controller. The comparison between estimated vehicle speed and reference vehicle speed from a production car is achieved by an in-vehicle network. An overview of the strategy is shown in Figure 1.  
To obtain an accurate speed estimate for vehicles with a high slip rate of two or more wheels, the vehicle network is used to handle the four wheels' speeds and longitudinal acceleration signals. Since wheel longitudinal speed is calculated from wheel angular speed, it can be easily affected by tire pressure. To enhance the efficiency of wheel longitudinal speed, it is reasonable to introduce a tire rotation factor (\(\eta\)) into this estimator. The controller is divided into two layers. The function of the first layer is to provide two relatively-accurate estimations of longitudinal vehicle speeds based on four wheels by a fuzzy controller and acceleration sensor by integration rather than using the final value of the fuzzy logic. These relatively-accurate speed estimates, however, are not correct for high levels of slip.  
Estimating longitudinal speed based on wheel speed and estimating based on acceleration are quite different. The former is more accurate without wheel slip while the latter is better without frequent switching between acceleration and deceleration. To optimize their advantages, we propose the local vehicle speed estimator to combine the estimates of two different sensors by introducing a weighting coefficient. Then the second layer is developed as a final fuzzy controller to obtain an accurate speed estimate in extreme slip conditions.
# CiteSpace-Based Bibliometric Review of Pickup and Delivery Problem from 1995 to 2021
## 6 Conclusions  
This paper attempts to review the academic achievements and evolution trend in the field of PDP with the assistance of CiteSpace and Web of Science. Based on the 819 relevant documents included in Web of Science core collection, this study firstly identifies the basic status of PDP research in terms of posting trend and category distribution. In addition, the results of scientific knowledge maps and bibliometric analysis are shown from six dimensions, namely, author/cited author, cited journal, country, institution, keyword, and reference. Finally, this paper discusses the evolution trend in the PDP research judging from the historical development of research hotspots and knowledge base.  
The main conclusions are as follow: (1) The annual number of publications related to PDP research shows an overall upward trend, and this trend will continue in the next few years. The statistical results of category distribution display that PDP research has the characteristics of comprehensive, multidisciplinary, and interdimensional. Among them, \"Operations Research & Management Science\", \"Engineering\", and \"Computer Science\" are the most closely related categories. (2) Author and cited author analysis show that [PERSON] is the most productive author, [PERSON] is the author with the most citations. Both of them come from HEC Montreal, which is the most influential institution in the PDP research. (3) The results of country analysis reveal that the main contributing forces of PDP research are mostly located in North America and Europe, except for China, the country has the largest number of publications in the world. Moreover, the results of institution analysis indicate that the breadth and depth of international partnership between academic communities need to be further reinforced. (4) Research topics involving \"algorithm\", \"model\", \"VRP\", \"time window\", \"to many operation\", and \"a ride problem\" have received extensive attention for a long time. The emerging keywords are determined as \"LNS\", \"metaheuristic algorithm\", \"hybrid algorithm\", \"cost allocation\", and \"simultaneous pickup\", which are also the current hotspots in the research on PDP. (5) Knowledge clusters on methodologies have become predominant among several professional areas, especially with respect to column generation, heuristics, and metaheuristic approach. (6) Judging from the historical development of research hotspots and knowledge base, the evolution trend of PDP research is divided into three stages: Start-up stage (1995-1999) when simple but appropriate mathematical models were developed on the basis of the results of VRP research; initial development stage (2000-2010) when the issues considered by scholars became more and more specific, and the variant and extension of PDP-related theoretical models were the main research subjects; rapid development stage (2011-2021) when the development path is closely related to Internet, e-commerce, mobile technology, sharing platforms, intelligent transportation, and artificial intelligence.
## 6 Results
### Load Transfer
The main reason for the difference is the choice of modelling the pile group as three pile rows. The offset in longitudinal road direction of every other pile (Figure 3) in each pile row resulted in larger GR deflection and tensile load along the borders of the model for the triangular pile arrangement than for the square pile arrangements. Subsequently, adding more pile rows would decrease the influence of the tensile load along the model border on the average tensile load of the entire model. As seen in Figure 18, \(T_{GR}\), was well below the GR tensile strength of 112 kN/m  
Figure 17: (**a**) The average pile head load (\(Q_{head,av}\)) and (**b**) the average pile efficacy as percentage (\(Q_{head,av}\)%) with increasing \(s\). for the modelled cases. The largest resulting value of \(T_{GR,max}\) for all modelled cases was almost 25 kN/m (1.1% strain), in the case of the triangularly arranged semi-floating pile group with \(s=2.0\) m. [PERSON] and [PERSON] [54] observed up to 0.7% strain in both geogrid layers at \(\alpha=2.8\%\), similar to the maximum simulated strain of roughly 0.6% at \(\alpha=3.1\%\) (\(s=1.0\) m). The simulation results of \(T_{GR,max}\) and \(T_{GR,av}\) were larger on average by 20% and 23%, respectively, for the semi-floating pile groups than for the floating pile groups. The GR deflection and subsequent tensile load was greater for the semi-floating pile group than for the floating pile group due to the aforementioned difference in relative displacement between the two types of pile groups. Increased GR deflection leads to greater membrane effect and more load transferred onto the pile heads, as observed by [PERSON] and [PERSON] [6], adding to the difference in \(Q_{head,av}\) and \(Q_{head,av}\%\) in Figure 17 between the floating and semi-floating pile groups. These results lead to the conclusion that the maximum allowable value of \(s\) in TK Geo 13 [16] can be increased from 1.2 to 1.6 m without reaching the characteristic pile bearing capacity. In addition, the arching and membrane effect in a lightly piled embankment is not significantly influenced by the pile arrangement (square or triangular).
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 2 The Concepts and Ideas Behind Courcelle's Theorem
### Describing The Problems: Monadic Second-Order Logic
#### 2.1.1 The Need for Metatheorems  
To better appreciate why we need a unifying framework for talking about some algorithmic results, please have a look at the following quotation from a paper by [PERSON] (pp. 7-8, [11]) from 1989 (just a year before the first algorithmic metatheorem was presented by [PERSON]):  
**Theorem 4.4**: _Each of the following problems is in NC when restricted to graphs with tree width \(\leq K\), for constant \(K\): vertex cover [GT1], dominating set [GT2], domatic number [GT3], chromatic number [GT4], monochromatic triangle [GT5], feedback vertex set [GT7], feedback arc set [GT8], partial feedback edge set [GT9], minimum mammal matching [GT10], partition into triangles [GT11], partition into isomorphic subgraphs for fixed \(H\)[GT12],..._  
\(\vdots\)  
\(47\) (!) further problems  
\(\vdots\)  
_...maximum length-bounded disjoint paths for fixed ] [ND41], maximum fixed-length disjoint paths for fixed ] [ND42], chordal graph completion for fixed \(k\), chromatic index, spanning tree parity problem, distance \(d\) chromatic number for fixed \(d\) and \(k\), thickness \(\leq k\) for fixed \(k\), membership for each class \(C\) of graphs, which is closed under minor taking._  
Researchers desperately wanted to replace such page-long listings of problems with just one phrase: \"Each problem that _can be described in a certain way_ is in NC, when restricted to graphs with tree width \(\leq K\), for constant \(K\).\" The obvious question, which remained unresolved for some time, is of course: What is the \"certain way\"?
# Seismic Response and Damage Analysis of an Isolated Bridge Subjected to Near-Fault Ground Motion
## 3 Results and Discussion
### Seismic Response Analysis of Bridge
#### 3.1.2 Pier Displacement  
The displacement (at the top of the pier) time histories of pier P2 are shown in Figure 11. The peak displacements of pier P2 of the non-isolated bridge and the isolated bridge in the X-direction are 123.1 mm and 271.0 mm, respectively. The isolation bearing did not decrease the displacement of the pier but increased the peak displacement of pier P2 by 1.2 times. It is shown that the isolation bearing may not be suitable for the near-fault ground motion. In addition, pier P2 of the isolated bridge produces a residual displacement of 27.3 mm in the X-direction. In the Z-direction, the isolation bearing decreases the peak displacement of pier P2 from 136 mm to 70.5 mm. The decreasing percentage of the acceleration peak value is 48.2%.  
Figure 10: The acceleration time histories of the girder B2 point. (**a**) X-direction. (**b**) Z-direction.  
Figure 9: The acceleration spectra of the TCU068 ground motion.
###### Abstract
The G protein-coupled receptor family C group 5 member A or GPRC5A, is an orphan receptor involved in cellular processes, such as proliferation, differentiation, and apoptosis. Recent advances have highlighted GPRC5A as an important biomarker in several human cancers, with its expression status correlated with clinical outcomes [23]. However, its role in TME and precise physiological role remains unclear in GBM. In cancer, GPRC5A expression is often dysregulated, contributing to tumorigenesis. For instance, GPRC5A is highly expressed in colorectal, breast, lung, and gastric cancers, among others, and is associated with poor prognosis [23]. The deletion of GPRC5A often leads to decreased cell growth, adhesion, and spreading in several cancers, including colorectal, breast [24], and pancreatic cancer [25]. Moreover, GPRC5A can act as a tumor suppressor, where its loss leads to increased malignancy and immune cell dysfunction, especially in lung cancer [26; 27; 28]. These contrasting roles underscore the context-dependent function of GPRC5A in cancer biology. In GBM, GPRC5A expression is elevated in GBM patients [29], and its depletion reduces cell growth [30]. In this study, we investigated the role of GPRC5A in GBM by examining its expression patterns, prognostic significance, and interactions with key components of the tumor microenvironment. Using data from the Gepia and TCGA databases, we investigated the correlation between GPRC5A expression and patient outcomes, as well as its association with genetic mutations commonly found in GBM. We further employed gene set enrichment analysis (GSEA) to identify relevant pathways linked to GPRC5A, focusing on its relationship with immune cell infiltration and stromal components. Our analysis revealed that GPRC5A is significantly upregulated in GBM compared to normal tissues and correlates with poorer overall survival (OS) and progression-free interval (PFI). We observed that high GPRC5A expression is associated with specific genetic mutations, particularly _NF1_ and _PTEN_, and is predominantly associated with a mesenchymal stem-like cell phenotype. Additionally, GPRC5A showed strong correlations with inflammatory immune cells, high extracellular matrix (ECM) environments, and pseudopalisading cells, characteristic of hypoxic regions in GBM. These findings offer valuable insights into the potential role of GPRC5A as a biomarker and therapeutic target, highlighting its impact on GBM tumor biology and patient management.
## 4 Discussion
### Performance Metrics for Evaluating the Platform's Effectiveness  
To ensure the effectiveness of SPARK-IT in bridging the gap between technical innovators and business mentors, the performance can be evaluated using key metrics that assess recommendation accuracy, system efficiency, and user engagement. The accuracy of expert recommendations is measured through precision and recall based on user feedback that compares AI-generated mentor matches with successful mentorship engagements. By analyzing the percentage of mentor-innovator pairings that lead to productive collaborations, the platform continuously refines its AI-driven matchmaking engine. Additionally, SPARK-IT monitors user satisfaction by gathering structured feedback on mentorship quality, business advice relevance, and overall collaboration outcomes. Mentor retention rates serve as an indicator of expert engagement, showing how effectively the platform incentivizes and sustains long-term participation.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 3 Xperience Similarity-Based Q-Learning (XSQ-Learning)
### Parameter Selection Guidelines  
* \(\delta\): Typically initialized to 0.7- 0.9 (higher values enforce stricter similarity) and annealed over time as the agent becomes more confident in its estimates.
* \(\iota\): Small constant (\(10^{-5}\)) prevents division by zero while being negligible compared to typical TD-error magnitudes.
* \(c\): Upper bounds update magnitude (recommended 0.2-0.5) to maintain stability. Lower values are preferable in highly stochastic environments.  
Each parameter contributes to the update process in a specific way: \(\delta\) selects which pairs are considered for updates, \(c\) restricts the update magnitude, and \(\iota\) helps maintain numerical stability. Their optimal values depend on the environment's reward structure and state space geometry.