esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Algorithm Performance
#### 3.4.1 Paris Integration Domain  
As previously explained, random incidence sound absorption is calculated using Paris' integration formula with a selected range of angles. As a consequence, the angular step may affect the final result. Figure 10 presents diverse integration steps in the range [0-90\({}^{\circ}\)]. As Paris' equation has a sinusoidal term in it, angles of 0\({}^{\circ}\) and 90\({}^{\circ}\) have no weight when diffuse field absorption is calculated. Given this circumstance, 0\({}^{\circ}\) and 90\({}^{\circ}\) simulations were not computed, thus saving computational time. The figure illustrates the effect of adopting different angle steps in the estimation of the random incidence sound absorption, clearly showing that a 10\({}^{\circ}\) step is enough to accurately perform this calculation.  
An additional condition of [PERSON] formula is the integration limits, which are also defined by nature from 0 to 90\({}^{\circ}\). Figure 11 shows the differences when choosing several integration limits summarized in Table 3, with a 5\({}^{\circ}\) step and, as suggested in Reference [35], the case in which only the calculation at 55\({}^{\circ}\) is carried out for locally reactive materials is also compared. The figure shows good agreement with analytical data, so [PERSON] integration can be performed with a 10\({}^{\circ}\) to 80\({}^{\circ}\) range, with 10\({}^{\circ}\) steps, without losing information.  
Figure 10: Diffuse absorption coefficient calculated for a 60 mm melamine sheet using oblique incidence with Paris’ formulation using different angle integration steps. The insert shows a zoomed area from 630 Hz to 5 kHz and absorption coefficient amplitude from 0.7 to 1.
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
## 5 Results
### Case Studies  
To illustrate effectiveness, we present three case studies comparing performance on original BIRD versus refined counterparts, highlighting how logical alignment, schema enhancement, and prompt guidelines improve accuracy.  
1. Case Study 1: Logical Alignment in formula_1 Domain (Question 880, Figure A4 vs. Figure 15): Refinement clarified ambiguity and aligned SQL with intent, improving accuracy (GPT-4o: 43.68% to 51.23%) and showcasing O1-Preview's optimized solution, validating logical alignment's gain.
2. Case Study 2: Schema Enhancement in the california_schools Domain (Question 83, Figure A3 vs. Figure 16): Refining schema (Figure 5) resolved data inconsistencies, enabling correct interpretation and contributing to accuracy gains by reducing data handling errors.  
Figure 14: Error comparison by type for six models.  
Figure 13: Error percentage by difficulty level for six models.  
Figure 15: Case Study 1: Logical alignment resolves ambiguity in formula_1. The original GPT-4o output sums speeds, missing the intent, while refined outputs correctly compute the percentage difference of fastest lap times. O1-Preview’s concise join-based solution aligns with its 80% error reduction in this domain (Cross-round Accuracy).  
* [3] Case Study 3: Prompt Guidelines in the superhero Domain (Question 835, Figure 4 vs. Figure 17): Guidelines prompted the correct use of LEFT JOIN and NULL handling, correcting omissions and improving accuracy by reducing edge case errors.
* [4] These case studies confirm that our method resolves BIRD's issues, enhancing LLM reasoning and supporting accuracy uplift.
* [5] Improvements in Runtime Performance: Refinements also enhance SQL execution efficiency (1). For instance, simplifying Question 477 (Figure 11) reduced execution time by 65.16% (2). We measured the times for 20 representative pairs across models (3). Figure 18 shows that refined SQL consistently reduces execution time (e.g., GPT-4o's average drop of 46.12%) (4), particularly in complex domains (5). Improvements stem from logical alignment and prompt guidelines reducing computational overhead (6). Schema enhancements have a minimal direct runtime impact (7). This demonstrates practical runtime gains critical for real-world applications (8).  
Figure 16: Case Study 2: Schema enhancement in california_schools. Original GPT-4o output risks errors with Magnet under ‘Y/N’ description. Refined schema ensures 0/1 consistency, and O1-Preview’s DISTINCT cuts errors from 5 to 4 (Figure 12).
## 5 Results and Analysis
### Statistical Test: Two-Tailed \(t\)-Test  
This study further investigates a statistical test, i.e., a two-tailed \(t\)-test that is used to determine if two sample means are equal [80]. This test is more suitable when the sample drawn from a population is sufficiently large (specifically more than 30), assuming that the data are normally distributed. In a two sample \(t\)-test, null hypothesis (\(H_{0}\)) indicates that  
Figure 23: Visual representation of UA for wavelet-based models (Teifi at Glanteifi), (**a**) LB, UB and Mean; (**b**) SE and ME; (**c**) WCB. Figure 22: Visual representation of UA for wavelet-based models (Fal at Tregony), (**a**) LB, UB and Mean; (**b**) SE and ME; (**c**) WCB. the true mean difference between the paired is zero, i.e., \(\mu_{1}-\mu_{2}=0\), while the alternate hypothesis (\(H_{A}\)) indicates that the true mean difference between the paired samples is not equal to zero, i.e., \(\mu_{1}-\mu_{2}\
eq 0\). Therefore, the procedure for implementing the two-tailed \(t\)-test is as follows:  
\[\text{Null hypothesis}:H_{0}:\mu_{1}-\mu_{2}=0 \tag{28}\]  
\[\text{Alternate hypothesis}:H_{A}:\mu_{1}-\mu_{2}\
eq 0 \tag{29}\]  
where \(\mu_{1}\) and \(\mu_{1}\) are the mean of two different samples. The test statistics can be calculated using the expression given by:  
\[t_{0}=\frac{(\overline{x_{1}}-\overline{x_{2}})-(\mu_{1}-\mu_{2})}{\sqrt{S_{p }^{2}\Big{(}\frac{1}{n_{1}}+\frac{1}{n_{2}}\Big{)}}} \tag{30}\]  
where:  
\[S_{p}^{2}=\frac{(n_{1}-1)S_{1}^{2}-(n_{2}-1)S_{2}^{2}}{n_{1}+n_{2}-2} \tag{31}\]  
where \(x_{1}\) and \(x_{2}\) are the two observations under consideration, \(n_{1}\) and \(n_{2}\) are the total number of observations; \(S_{1}\) and \(S_{2}\) the standard deviation of two different samples, and \(S_{p}\) is the pooled standard deviation. Calculated results at a significance level 5% (i.e., \(\alpha=0.05\)) assuming equal variances are presented in Tables 7 and 8 for Fal at Tragony and Teifi at Glanteifi phases, respectively. Note that, the absolute values of \(t\) Stat less than 1.96 (for the two-tailed test) indicate that there is no significant difference in mean between the samples. However, the obtained value closer to ideal value (i.e., \(z_{0.025}=1.96\approx\)\(\mid\)t stat \(\mid\)) indicates a more reliable model. As can be seen, the EO-ELM and WDNN satisfy the conditions in R-R modeling in Teifi at Glanteifi in the testing phase, while EO-ELM and WDNN satisfy the conditions in the Fal at Tragony testing phase. From the above analysis, it may be concluded that the proposed EO-ELM is a better alternative integrated machine learning model with or without wavelet which gives better accuracy and generalization capability for R-R modeling in place of ELM, KELM, PSO-ELM, SVR, ANN, GBM and in UK. However, in the wavelet-based R-R modeling approach, WDNN performed better compared to other models. This was because increasing of the number of features in the dataset provides better efficacy in the proposed WDNN.
## 6 Conclusions and Future Research  
GNSS provides high-accuracy real-time localization services in outdoor open-sky environments, but in GNSS-denied urban or indoor environments, accurate localization is still an unsolved problem due to the unavailability or degradation of GNSS signals. For localization technologies based on mobile communication networks or other wireless technologies, obstructions between the source and receivers will result in NLOS propagation, which significantly degrades the localization performance. Accurate localization in the presence of NLOS propagation has attracted considerable research focus due to it is the premise of many technologies and applications. In this paper, for GNSS-denied urban or indoor NLOS environments and localization technologies based on mobile communication networks or other wireless technologies, a hybrid TOA/AOA virtual station localization algorithm based on scattering signal identification is proposed. Without requiring any assumption or prior knowledge about MS, scatterer and BS, and only based on the geometric relationship of MS, scatterer and BS, a two-step one-bounce scattering signal identification algorithm is proposed. Simulation results demonstrate that the identification rate of the one-bounce scattering signal from the same scatterer can up to 95%, which provide a solid foundation for the following virtual station localization algorithm. On this basis, regarding four scatterers as virtual stations, we propose a novel virtual station localization algorithm, which almost eliminates localization error resulting from scattering signal misidentification. Simulation results show that the MS localization performance of proposed algorithm outperforms the state-of-the-art hybrid TOA/AOA localization algorithms, as far as we know. In this paper, we explore false alarm probability and alarm dismissal probability versus identification threshold by simulation, and choose the identification threshold D, which minimizes the sum of false alarm probability and alarm dismissal probability, for our simulation scenario. For future work, the specific relationship between identification threshold D and false alarm probability will be investigated.  
This paper is a collaborative work by all the authors. Conceptualization and methodology, R.L. and L.Y.; analysis, R.L., L.Y.; writing--original draft preparation, R.L. and L.Y.; writing--review and editing, Y.K. and P.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Nature Science Foundation of China [No.61871332], Sichuan Science and Technology Program [grant numbers:2021 YFG0169], [grant numbers: 2022 YFG0190] and Chengdu Normal University Science and Technology Project [no. 111/111159001].  
Figure 9: Localization error CDFs curve for ROS model. (**a**) The curve of proposed algorithm; (**b**) The curve of IGSA, HTA and NTA-CA.
## 3 Results and Discussion
### Analysis of Physicochemical Characteristics  
#### 3.4.1 Water Binding Capacity  
The WBCs of three chitosan samples are shown in Figure 3. It was observed that the WBC of CSS (10.8 g water/g chitosan) was significantly higher than that of CSC (5.2) and CS (3.3). This difference may possibly be attributed to the high temperature and pressure treatment degrading \(\beta\)-chitosan into smaller molecules more easily and increasing the chitosan surface area, and a higher DD, exposing more hydrophilic amino groups in the chitosan molecular chain, resulting in the formation of hydrogen bonds with water molecules and showing better hygroscopicity and moisture retention. [PERSON] et al. [40] observed that for hydrophilic polymers, polar groups can strongly interact with water molecules. Therefore, the more polar groups, the greater the water absorption, indicating that the water absorption of chitosan increases with the increase in DD.
# Ship Scheduling Algorithm Based on Markov-Modulated Fluid Priority Queues
###### Abstract
The Markov property [8] provides an important idea in solving this problem. By modeling the complex anchorage scheduling process as a decision-making process based on the current system state [9], the dynamic characteristics of the ship arrival and service process can be effectively captured. In particular, a continuous-time Markov chain (CTMC) model [10; 11] can accurately describe the random evolution law of the anchorage resource allocation state and provide theoretical support for scheduling decisions. A ship scheduling algorithm based on a Markov-modulated fluid priority queue (SSA-BMMFPQ) is proposed based on the CTMC strategy. The main contributions are as follows:  
(1) An anchorage resource scheduling framework based on fluid queue theory is proposed. The ship arrival and service process is modeled as a fluid input and output modulated by the state of the anchorage scheduling system. The exponential computation of the discrete queue model is avoided by continuous processing, and the scheduling computation complexity in complex scenarios is reduced. (2) A CTMC is introduced to model the state change of the anchorage scheduling system, and the ship arrival process is modulated by the state transition matrix and rate matrix to describe the coupling relationship between the service capability and arrival flow, so as to improve the accuracy of environment modeling and the scheduling response capabilities. (3) A priority queue service mechanism is designed to solve the resource competition problem in multi-priority ship scheduling at anchorage. By assigning independent fluid input and service weights to ships of different priority classes, the service rates of ships of different priority classes can be reasonably allocated under the condition of limited resources.
## 3 Results and Discussions  
### Surface Morphology  
The surface morphology of the films was studied by scanning electron microscopy (SEM). Typical images of the pure ZnO, 5% Co-doped (CZO) and Co/Al-co-doped ZnO (CAZO) are presented in Figure 2. For comparison, films doped with 5% Al (AZO) were also prepared and displayed in Figure 2. SEM images reveal that the surface of all films is granular, with the grain size depending on the type of the dopant. The measured grain size is presented in Table 1. It can be seen that cobalt doping leads to more than twice the decrease in particle size, from 143 nm for pure ZnO to 62 nm for CZO films, while the induced changes in surface morphology are negligible for Al doping (the grain size is 128 nm for AZO films). The surface morphology of CAZO films (Figure 2d) is closer to that of CZO films (Figure 2c), with an average grain size of 71 nm (Figure 2a). In all studied cases, particles are distributed homogenously on the surface without any cracks or pores. The possible reasons for the finer granular morphology for Co-doped films are further discussed in the next section.
# A Practical Approach to Deploying a Drone-Based Message Ferry in a Disaster Situation +
## 2 Overview of Locally Accessible Cloud System (LACS)
### Use-Cases of LACS  
LACS is a multipurpose system which can be used to deliver various services in a variety of services in the event of a disaster and normal daily life. An example of the use of LACS in the event of a disaster is shown in Figure 3. A summary of LACS use-cases are provided below:  
1. Disaster
2. Local Government--Broadcasting urgent and vital information to residents.
3. Disaster Shelter--Bulletin board, emergency inventory management, etc.
4. First Responders (e.g., firefighters, police, etc.)--Sharing and gathering information, etc.
5. Normal daily uses 1. Local Government--Disseminate critical and time-sensitive information to residents, equipment and inventory management system, etc.
6. School--Bulletin Board, record management, eLearning, etc.  
Figure 1: LACS prototype.  
Figure 2: LACS features.  
Office--Temporary file storage, record management, information bulletin board, etc.
* Residents' Association--Bulletin board, housing records management, instant messaging service, etc.
* First Responders (e.g., firefighters, police, etc.)--Installed in vehicle for sharing and gathering information, temporary storage and record management, etc.  
Moreover, LACS is also capable of providing one of the key features of a cloud-based system. If deployed in the form of resource pooling, LACS can serve multiple consumers, regardless of the services required. To confirm the possibility of deploying such services, a demonstration test was performed as part of a feasibility study [24; 25]. In the feasibility study, services such as e-education, tele-medicine solution, and other disaster relation scenarios were confirmed. In addition, the deployment of additional cloud-based system services and their features will be confirmed in a subsequent demonstration test. In comparison, LACS differs from an edge node because it is used as a local system and does not send its processed outputs to a central server like most edge nodes. However, as a result of the multipurpose use of LACS, it can be used as part of an Edge computing system [26] if it is connected to the internet.
## 4 Development of the Methodology
### Study Case 1: Development of Load Profiles Model Proposed in Simulink
#### 4.1.3 Collect Energy Demand Data for Households  
To develop this work, real data were obtained to compare with simulated load profiles. The data were obtained through smart meters in the houses under test in the selected community. Smart meters allow the measurement of total power consumption and energy demand for individual devices. The smart meter employed in this case was an Emporia Gen 2 Vue Energy Monitor, which has the following features:  
REAL-TIME ENERGY DATA: the smart meter connects to WiFi to monitor and send data power use to the database. APPLIANCE MONITORING: the smart meter has eight individual sensors that provide data for all monitored devices. APPLIANCES MONITORED: Air conditioner, Water-heater, Washer, Range (Oven), Fridge, Lights, TV, and others.  
Figure 9 shows an example of the smart meter installation in a house under test. The smart meter and sensor are connected in a power distribution panel.  
Figure 8: Modeling time ranges of use.  
Figure 9: Installs in circuit panels of most split-phase 120 Vac homes with clamp-on sensors.
# Passive Sampling and Analysis of Naphthalene
## 1 Introduction
### Objectives  
The objective of this study was to develop the method for quantification of naphthalene TWA concentrations in exhaust gases by passive sampling with retracted SPME and analysis on gas chromatography-mass spectrometry (GC-MS). The effects of (1) variability of SPME fibers quality,(2) losses during sample storage time, (3) sampling time, (4) concentrations, (5) fiber retraction depth, and the (6) interfering contribution of extraction by metallic parts of SPME needle assembly itself were studied. Finally, the new method was tested on exhaust gases from idling pickup truck and a tractor and compared side-by-side with a direct injection of sampled exhaust gas method. The working hypotheses were that (a) the contribution of adsorption by metallic SPME assembly parts is reproducible yet significant for naphthalene, a semi-volatile PAH with multiple aromatic rings, and greater than those observed [30] for the mono-aromatic group BTEX group and that (b) naphthalene can be quantified in vehicle exhaust gas using the retracted fiber TWA-SPME approach when the contribution to extraction via SPME metallic parts is accounted.  
This work builds on the development TWA method for determination BTEX in exhaust gases [30] and hot syngas process gas [37; 38] with retracted SPME fibers for lower molecular weight compounds. It was shown that the TWA model based on Ficks' law works reasonably well for BTEX when the contribution of extraction by metallic parts of the SPME assembly is accounted for [30]. This contribution is reproducible and dependent on the type of compound [30]. Thus, it is critically important to assess that contribution for other key air pollutants with larger molecular weight and more complex structure and properties so that the TWA-SPME approach has a wider applicability.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 3 Systematic Literature Review
### Research Questions  
According to [23], research questions we had to consider were population, intervention, comparison, and outcomes.  
In our case, this survey is useful for VANET researchers who need a robust routing protocol to obtain better results in the development of new emergency- and data-interchange-focused services. More specifically, we targeted the improvement of hop-to-hop decisions in vehicular communications of multimetric routing protocols compared to prior proposals (that usually use one metric for routing decisions). The outcome of our review was to validate how the use of several metrics in a routing protocol improves the decision-making process in vehicular communication.  
Based on the mentioned strategy, our Research Questions (RQ) are the following. With them, we aimed to know how important metric selection is in routing protocols for VANETs, and how multimetric use improves path selection.  
1. Which are the most used metrics in routing protocols for VANETs?
2. Which combination of metrics is the most used for improved routing decisions?
3. Which are the routing protocols with the most comparatives?
4. Which network simulator is most used currently?
## 4 System Model
### Problem Formulation  
In this section, we consider the issue of achieving efficient computation offloading for multi-player VR edge-cloud computing systems. Regarding the above communication and computation models,the computation offloading problem is formulated as the following constrained optimization formulation problem:  
\[\begin{array}{llll}\min_{x,y}&Z_{i,j}\\ \text{s.t}&\left[E_{i,j}-E_{i,j}^{L}\right]\leq 0,&\forall_{i,j}&C1\\ &\bar{T}_{i,j}\leq\tau_{i,j},&\forall_{i,j}&C2\\ &\sum_{k=0}^{2}\alpha_{i,j,k}=1,&\forall_{i,j}&C3\\ &\alpha_{i,j,k}\in\{0,1\},&\forall_{i,j}&C4\end{array} \tag{16}\]  
The objective function of the optimization problem is to minimize the sum cost of the entire system in terms of time and energy through the deployment of task offloading. The constraints C1 and C2 are upper bounds of energy and time consumption, respectively. The constraint C3 guarantees that each computation task \(j\) must be executed only one time. Finally, the last constraint C4 guarantees that the computation offloading decision variable is binary.  
As a result that the objective function is linear as well as all the constraints are also linear, the optimization problem (16) is an integer linear optimization problem in which the optimal solution can be obtained using the branch and bound method [42].
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 2 Background and Related Work
### Gaps in the Literature on Predictive Modeling for Performance Optimization in Apache Spark MLlib  
While investigating the current state of the art of using MLlib for performance prediction in the context of Apache Spark MLlib concerning performance improvement, several research gaps have been noted. These gaps are important for developing more efficient and easily applicable large-scale models in big data models.  
Unlike the previous studies that have extended only in regards to static optimization or limited scalability [16], this study extends the literature by providing a dynamic and predictive modeling framework that is responsive to different workload requirements.  
For example, [PERSON] et al. concentrate on the memory optimization of fixed service level agreements [14], while our approach uses machine learning techniques to dynamically forecast the execution times and resource requirements. Similarly, as opposed to the study by [PERSON] et al. where the research was aimed at predicting the performance of workflows applications [13], instead, our approach is developed to specifically optimize and enhance the performance of the MLlib operators to be deployed within the distributed environment with a focus on the crosscutting concerns of structured and unstructured data workloads.