esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigated the effects of different fiber contents by volume (0%, 1%, 2%, and 3%) on flowability, entrapped air content, and compressive and flexural strength of ultra high performance concrete (UHPC). Four water-to-binder ratios (0.18, 0.20, 0.22, 0.24) were used and four different dosages of superplasticizer were introduced for preparing each water-to-binder ratio mixture. The experimental results reveal that the increased content of fiber decreases the flowability and entrapped air content of fresh UHPC mixtures. The compressive strength of UHPC increased with a greater addition of steel fiber, from 1% to 3%. The flexural strength was increased slightly by the addition of 1% steel fiber and increased remarkably with the addition of 2% and 3% steel fiber. For every water-to-binder ratio mixture, with and without steel fiber, a good linear relationship can be found between compressive strength and entrapped air content. Therefore, it is suggested that lowering the entrapped air provides a significant contribution to increasing compressive strength of UHPC containing higher steel fibers.  
UHPC; steel fiber; flowability; entrapped air content; mechanical properties +
Footnote †: journal: Applied Sciences
## 3 Two-Tier Camera-Based WMSNs Topology  
Considering the mentioned applications for Camera-Based WMSNs, monitoring is the common aspect of the most of these applications. High coverage, high reliability, low cost, and high functionality are essential for achieving higher performance in monitoring applications [15].  
According to the literature, almost all of the single-tier sensor networks satisfy merely one of the mentioned essential items that are necessary for improving performance in monitoring applications. For example, using many low cost cameras as sensor nodes increases the coverage and decreases the functionality because of limited capabilities of these nodes. On the other hand, using more powerful and expensive nodes increases the functionality but achieving the reliability by increasing the network density would be really costly.  
Multi-tier topologies have succeeded in creating a balance between cost, functionality, reliability, and coverage. For example, the lowest tier contains cheap sensors with poor functionality and quality while the higher one compensates for the lack of these items by stronger and more expensive nodes. Some practical examples of Multi-tier WMSNs are low-resolution-camera-based sensor nodes such as low-power Motes [16] nodes that are equipped with Cyclops cameras [17] in the lowest tier, stronger camera-based sensor nodes like Imote2 [18] nodes equipped with CMUCam3 [19], Mesheye [20], and Panoptes [21] which are placed in the higher tier and finally the nearest tier to Sink includes strongest camera-based sensor nodes like Stargate-XScale nodes [22] with Web-Cam. Also, in [23] an experimental multi-tier architecture has been introduced which is called SensEye.  
All in all, the purpose of multi-tier topologies is providing a balance between cost, functionality, coverage, and reliability to realize many WMSNs applications. Therefore, the presented topology is a multi-tier topology which unlike typical WMSNs topologies has only two tiers. The main differences between this topology and the others are that it has just low cost camera sensor nodes in lower-tier and its upper-tier consists of stronger nodes without camera that perform tasks like simplified image stitching and lower-tier management. Obviously, presented two-tier topology which has no camera in upper-tier cannot acquire a high-resolution image without using Image Stitching. The structure of upper-tier, lower-tier and the communication methods are explained in subsections 3.1 and 3.2 respectively.
# Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior
## 2 Related Work
### Defect of DCP  
Through a large number of experimental results, it is known that for some images containing large bright areas such as the sky and water surfaces, the dehazing results based on the dark channel prior will show significant color distortion in the bright areas. In fact, for outdoor clear images, the bright areas have a large pixel value, and channels with pixel values close to zero cannot be found in the area, so the dark channel priors will invalid in these areas [28]. As shown in Figure 1, the sky area and some bright white areas of the scene in the dark channel image have large pixel values.  
If the dark channel prior is not considered, the transmission derived from Equation (5) is  
\[t_{\text{actual}}(x)=\frac{1-\min_{y\in\Omega(x)}(\min_{c\in[r,g,b]}(\frac{I^{ c}(y)}{A})}{1-\min_{y\in\Omega(x)}(\min_{c\in[r,g,b]}(\frac{I^{c}(y)}{A})}) \tag{9}\]  
Figure 1: Outdoor haze-free image and its dark channel image.  
For bright areas that do not satisfy the dark channel prior, the value of the dark channel cannot be approximated to 0, so the denominator of the above formula is less than 1. From this, it can be inferred that the actual transmission \(t_{actual}(x)\) of the bright regions is greater than the transmission \(t(x)\) estimated based on the dark channel prior. Therefore, the transmission map estimated based on the DCP algorithm is limited to the bright region, and the pixel channel of the sky region is divided by the relatively small \(t(x)\) (as shown in Equation (6)), which significantly increases the small difference between the pixel channels of the sky area. This causes the color of the restored image to be distorted.
## 3 Multi-Hop IoT Systems for Patient Monitoring  
### The IoT in Healthcare  
Integration of the IoT into the healthcare sector represents a pivotal development with far-reaching implications for patient care, operational efficiency, and health outcomes. One of the key areas where the IoT is making a significant impact is patient care. Wearable sensors and implantable devices equipped with IoT capabilities enable continuous monitoring of vital signs. This real-time tracking not only enhances the quality of patient care but allows proactive interventions, particularly in managing chronic conditions [3]. Patients can benefit from personalized, data-driven insights into their health, fostering a more engaged and informed approach to well-being.  
Beyond traditional healthcare settings, the IoT facilitates remote patient monitoring, as shown in Figure 6. This is particularly crucial in remote or under-served areas, where access to healthcare may be limited [31]. Devices that measure vital signs or ensure medication adherence empower individuals to actively participate in managing their health, irrespective of geographic constraints.  
Figure 5: Heart rate and oxygen level detection through a photodetector using red and IR LEDs creates a photoplethysmogram [30].  
The data generated by IoT devices serve as a goldmine for healthcare analytics. ML algorithms can analyze this vast amount of health data, leading to improvements in diagnostic accuracy, resource allocation optimization, and the development of personalized treatment plans [32]. Predictive modeling becomes a powerful tool for anticipating and preventing health issues, contributing to more effective healthcare strategies.  
On the operational front, implementing smart healthcare infrastructures enabled by the IoT streamlines hospital and clinic operations. Automated inventory management, predictive equipment maintenance, and optimized energy usage contribute to cost-effectiveness and create a seamless healthcare environment. These operational efficiencies, in turn, can enhance the overall quality of healthcare services.  
However, integration of the IoT in healthcare comes with its own set of challenges. Privacy and security concerns loom large as sensitive health data are transmitted and stored. Robust cybersecurity measures, strict adherence to regulations, and continuously monitoring for vulnerabilities are imperative to addressing these concerns, ensuring the integrity and confidentiality of patient information.
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 2 System Model  
In this paper, a fault diagnosis model that meets practical production requirements for smart energy network of smart city is considered. The discrete state-space model of which is given as follows:  
\[\left\{\begin{array}{l}x(k+1)=f(x(k))+\Gamma(k)v(k)\\ z(k+1)=h(x(k+1))+e(k+1)\end{array}\right., \tag{1}\]  
where \(k\geq 0\) is a discrete time variable, \(x\in R^{n}\) is a state vector, \(z\in R^{m}\) is an output vector, \(f:\ R^{n}\to R^{n}\) and \(h:\ R^{n}\to R^{m}\) denote nonlinear transition functions, \(\Gamma\in R^{n\times n}\) and \(v_{k}\in R^{n}\) and \(e_{k}\in R^{m}\) are uncorrelated, zero-mean, Gaussian white noises with covariance \(V_{k}\) and \(E_{k}\), respectively. The meter of the smart energy network records consumption of electric energy in certain time intervals and communicates that information to the utility for monitoring and billing. Based on the normal and observed values of different meters, the data is modeled as a normal distribution with specific mean and standard deviation. Due to the harsh environment, each meter may malfunction with a probability \(p\). In this paper, we aim to solve the fault detection problem with a high accuracy and a low message complexity in a nonlinear time-varying stochastic system. All symbols and descriptions throughout the paper are given in Table 1.
## 5 Deployment  
This section discusses the design details of each parallelized functional block. Figure 6 shows how the memory transfer and the interaction between CPU and GPU are managed across the entire pipeline. The dashed rectangles correspond to data, while the colored rectangles contain instructions (CUDA kernels and their launchers).  
In more detail, the CPU allocates memory on the GPU and initializes a vector of random states leveraged in the random number generation operations. Then, each functional block is implemented in the form of one or two kernels that start their execution on the GPU once a dedicated launcher is executed. The interaction within the kernels is akin to what is described in Figure 5. Once all the operations are terminated, the pose estimates and the map estimates are copied back to the host memory to be fed as input to the _Particle Prediction Launcher_ at the next iteration.  
The pseudocode relevant to the parallelization of the functional blocks in the following sections is available in Appendices A.2-A.6. This section is aimed at discussing the design peculiarities of our approach; for the methodological and mathematical foundations of FastSLAM 2.0, please refer to Section 2.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 6 Conclusions  
In this paper, four evaluation indicators (sensitivity, selectivity, correlation, and repeatability) were selected to evaluate the sensor array in multiple dimensions, and a comprehensive evaluation model for sensor array optimization based on EWM-TOPSIS algorithm was proposed. In order to verify the effectiveness of the as-proposed model, it was applied to optimize the E-nose system containing 10 sensors, and a comparative investigation was carried out on the recognition ability of the E-nose with the sensor array before and after optimization. The main results are summarized as follows:  
1. The optimization of the sensor array for E-nose according to a single evaluation indicator has limitations.
2. Different evaluation indicators contribute differently to the overall performance of the E-nose. Therefore, the weights of different evaluation indicators should be considered when comprehensively evaluating the sensor array.  
Figure 13: Comparison of recognition accuracy of E-nose with different sensor array optimization methods.  
3. The proposed comprehensive evaluation model based on EWM-TOPSIS can accurately reflect the contribution of different evaluation indicators to the overall performance of the sensor array, and the recognition accuracy of the E-nose with the sensor array optimized by this model can be significantly improved.  
Conceptualization, Y.Z. ([PERSON]); Methodology, Z.P. and Y.Z. ([PERSON]); Software, Z.P. and Y.Z. ([PERSON]); Validation, Z.P., Y.Z. ([PERSON]), J.Y., P.P., F.B., X.L., Y.G. and Q.R.; Formal analysis, Z.P. and Y.Z. ([PERSON]); Investigation, Y.Z. ([PERSON]), X.L., Y.G. and Q.R.; Resources, Y.Z. ([PERSON]); Data curation, J.Y., P.P., F.B. and Y.Z. ([PERSON]); Writing--original draft, Z.P. and Y.Z. ([PERSON]); Writing--review & editing, Y.Z. ([PERSON]); Visualization, Y.Z. ([PERSON]); Supervision, Y.Z. ([PERSON]); Project administration, Y.Z. ([PERSON]); Funding acquisition, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by Major Special Science and Technology project of Anhui Province (No. 202103a07020007) and Key Research and Development Program of Anhui Province (No. 202104a05020057).  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 2 Overall UAV Design
### Wing Design
#### 2.4.3 Analysis of Wing Aerodynamics
Consequently, a wing installation angle of 2\({}^{\circ}\) is chosen. Figure 8: NACA 4412 airfoil model, wing model, and pressure distribution cloud map. Figure 9: Aerodynamic simulation of the airflow of the NACA4412 profile to create a wing model. 2.4.4. Finite Element Analysis of Outer Wing and Middle Wing Connector  
To save space and enhance portability, this study designs the UAV with foldable wings, dividing the wing into the midsection and outer sections. The midsection is directly fixed to the fuselage, while the outer sections are equipped with propulsion systems. To prevent risks such as mid-air disassembly, it is crucial to analyze the strength and stability of the connector under the aerodynamic loads on the wings. The three-dimensional model of the connector is imported into finite element analysis software, and the model undergoes a geometric cleanup. As shown in Figure 10, the connector consists of two parts, each fixed to the spar of the midsection and outer sections of the wing. Once the left and right parts of the connector are joined, bolts are used to secure the upper and lower holes. Subsequently, the geometric model of the connector undergoes finite element mesh division, as illustrated in Figure 11.  
This study employs carbon fiber plates for wing connectors, defined in finite element software for stress testing. During flights, these connectors face lift and aerodynamic forces, with simulated loads applied to model the wing-generated lift in the analysis, as illustrated in Figure 12.  
Figure 6.0 Finite element mesh division of middle wing and outer wing connectors. Figure 6.1 Geometric model of the connecting parts of the middle wing and outer wing. of the horizontal tail area and the rudder to occupy 25% of the vertical tail area. Table 4 illustrates the primary parameters of the tail configuration. The design drawings of the tail are shown in Figure 6.2
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 3 Problem Statement
### Use Case 2  
In the second use case, we have a number of audio receivers connected to loudspeakers located in the same room. The audio on the speakers should be played out at the same time so that the voice of one speaker could not interfere with the others as shown in Figure 3b.
## Appendix C Qualitative Analysis  
The qualitative analysis was conducted to evaluate the diagnostic outputs generated by three LLMs--Gemini, Mistral, and Medichat Llama 3--across a comprehensive set of parameters. Specifically, we examined five diagnostics produced by each model under every combination of temperature settings (low vs. high) and prompt techniques (zero-shot, few-shot, and RAG). This rigorous approach resulted in a total analysis of 60 generated diagnoses.  
The evaluation focused on several key aspects of the diagnostic outputs:  
* Clarity: We assessed how clearly the diagnoses were presented, paying attention to the structure, language, and overall readability. Clear and organized outputs are crucial for practical clinical application.
* Precision: The accuracy of the diagnoses was a central focus. We examined whether the generated outputs effectively linked clinical data to specific conditions, ensuring that the diagnoses were relevant and specific.
* Justification: The strength and validity of the reasoning behind each diagnosis were evaluated. We considered whether the outputs provided robust justifications, drawing on clinical evidence and data to support the diagnostic conclusions.
* Originality: We analyzed the range of diagnoses generated by each model, looking for a balance between focusing on the most probable conditions and the introduction of speculative or rarer diagnoses. This aspect is particularly important in understanding how temperature settings influence the breadth of diagnostic possibilities.
* Actionability: The clinical utility of the generated diagnostics was assessed, considering how relevant and applicable the outputs would be in real-world decision-making scenarios.  
The analysis was conducted solely by reviewing the generated diagnostic outputs without comparison to actual diagnoses. This approach allowed us to focus on evaluating the style and characteristics of the diagnostics based on the specific model and parameter settings. By maintaining this focus, we were able to gain a comprehensive understanding of how variations in temperature and prompt techniques influence the quality, clarity, and overall effectiveness of the diagnostic outputs produced by each LLM. Several common themes and notable differences emerge across zero-shot, few-shot, and RAG techniques at low and high temperatures. In this section, we first detail the impact of temperature on the prompt techniques. Then, we present how the prompt technique impacts the generated diagnostic.
# SimEx: A Tool for the Rapid Evaluation of the Effects of Explosions
## 2 SimEx Capabilities
### Other Calculation Assistants
#### 2.2.3 Primary Fragments  
SimEx incorporates assistants for calculating the mass distribution, ejection velocity, and ballistic trajectory of primary fragments. The corresponding interfaces are shown in Figures 5-7. The fragment size distribution is estimated using [PERSON]'s statistical theory for fragmentation of steel cylindrical shells [3; 26; 27; 28], as suggested by UFC-3-340-03 [16]. As shown in Figure 5, this model determines the average number of fragments and their average weight. It also provides the size of the largest fragment corresponding to a given Confidence Level (CL). SimEx also includes a ballistic trajectory assistant for primary fragments that, in addition to the flight path, provides the flight time, velocity, and maximum distance, as illustrated in Figure 6. The initial velocity of primary fragments is computed using [PERSON]'s analysis [56] for cylindrical, spherical, and symmetrical/asymmetrical sandwich charges. Although this analysis assumes that all fragments have the same the initial velocity, given the different fragment sizes, both their initial kinetic energy and their subsequent aerodynamic deceleration are different. The assistant thus includes an initial aerodynamic deceleration chart, shown in Figure 7, that provides the fraction of the initial velocity achieved at a certain distance, given the fragment mass and material, and the local air density, specified through the ISA \(\pm\)\(\Delta T\) model. The aerodynamic assistants assume  
Figure 4: Interface of the assistant for the calculation of craters. HOB denotes the height of burst.  
spherical fragments with a variable drag coefficient for all Mach numbers [57], although the model could be extended to account for more realistic (i.e., irregular) fragment shapes in future versions [58]. The results of these models are also used to estimate the lethality risk by the impact of primary fragment in the event of a strike on a person, which is found to depend on the speed and the mass of the fragment, as illustrated by Figure 5.  
Non-tabulated explosives or explosive mixtures can also be considered, with the Gurney constant being computed by the thermochemical assistant presented in Section 2.2.1. In this case, the user must select a \"custom\" explosive, and the thermochemical assistant will open to specify the desired explosive composition. Once the wizard is closed, the Gurney constant is automatically exported to the fragment wizard.  
Figure 5: Interface of the primary fragment mass distribution and lethality assistant.  
Figure 6: Interface of the primary fragment calculation assistant showing the ballistic fragment trajectory, flight time, velocity, and maximum distance charts. Fragments are assumed spherical.
## 9 Self-Injection Parameters at FLAME  
As anticipated above, the ultimate goal of the SITE experiment is the production of sub-GeV-class electron bunches from laser-plasma interaction using a gas-jet of a few millimetres, without external guiding for the laser entering directly into the so-called bubble regime [32, 45]. In the bubble regime a short (\(c\tau<\lambda_{p}/2\)) and intense (\(a_{0}>2\)) laser pulse expels the plasma electrons outward creating a bare ion column. The blown-out electrons form a narrow sheath outside the ion channel and the space charge generated by the charge separation pulls the electrons back creating a bubble-like wake. For sufficiently high laser intensities (\(a_{0}>3.5\div 4\)) electrons at the back of the bubble can be injected in the cavity, where the longitudinal accelerating field is of the order of \(\sim 100\sqrt{n[\text{cm}^{-3}]}\) V/m).  
The FLAME laser meets both conditions of short pulse length and high intensity. As a consequence when the laser pulse impinges onto the gas-jet, it promptly excites (without significant pulse evolution) a bubble wake where electrons are readily injected and so the entire gas-jet length can be utilised for the acceleration process. In order to have a \"controlled\" acceleration mechanism, which ensures a better bunch quality, the plasma and laser parameters must be chosen according to the phenomenological theory [46].  
The working point we have considered for the SITE is the one described in Table 2 where \(L_{gas\,jet}\) is the length of the gas along the laser propagation, \(n_{e}\) is the electron density of the ionised gas, \(I_{0}\) is the laser intensity inside the FWHM diameter spot, and \(w_{0}\) is the waist of the laser beam in the focus, _i.e._, the distance from the laser axis where intensity is \(1/e^{2}\) of the intensity on axis. In this case, following the phenomenological description given in [46], we expect to obtain a quasi-monochromatic (few % momentum spread) bunch with a charge of \(\sim 0.6\) nC and an energy of approximately  
Figure 12: Ionisation potentials of N and He. The second row gives the ionisation energy of N from neutral N to hydrogen-like N. The third row reports the corresponding ionisation energies for neutral He and hydrogen-like He.  
1.0 GeV after 4 mm (dephasing length). The acceleration process has also been investigated through 3D PIC simulations performed with the fully self-consistent, relativistic, electromagnetic PIC code AlaDyn [47,48]. The results are summarised in Figure 13 where we show the evolution of the electron density and energy spectrum.  
At the end of the simulation we obtained a bunch with an energy of 0.9 GeV and a momentum spread (rms) of 3.3%, the charge is 0.6 nC, the bunch length is 1.8 \(\mu\)m (the average current is \(\sim\) 50 kA) and the beam divergence (rms) is 2.8 mrad. The results are in good agreement with [46].