esa-sceva/satcom-chunk-collection huggingface dataset:
The resulting measurements are sent to dedicated platforms (e.g., AirCasting, AirVisual) for global collection and data sharing [23]. These widespread devices may provide high temporal and spatial resolution if we really understand and critically evaluate the capabilities and characteristics of the data they provide, allowing the development of PM\({}_{1}\), PM\({}_{2.5}\), and PM\({}_{10}\) concentration maps at a global level to drive local policies for PM abatement. To assure their effective usefulness as aerosol monitoring devices, they must be tested in parallel to reference instruments. Measurements should be performed indoors and outdoors to characterize different aerosol sources and meteorological conditions that can greatly impact low-cost sensor performance [25; 30]. Optical devices can be affected by high relative humidity conditions that could lead to an underestimation of PM. Furthermore, the instability of the flow of a low-cost sensor can alter the quantity of particles being sampled. An intercomparison of parallel aerosol measurements allows a critical evaluation of the potentialities of a large-scale implementation of low-cost sensors since data of poor or unknown quality are less useful than no data at all [31]. To the best of our knowledge, the literature reports mainly intercomparison studies of outdoor aerosol monitoring among optical devices (standard analyzers and/or low-cost sensors) and gravimetric samplers used as reference. A more comprehensive study including devices with different working principles and analysis methods for PM determination is needed. In this paper, measurements taken with optical low-cost sensors, gravimetric samplers, and a cascade impactor are directly compared for quantitative determination of PM\({}_{2.5}\) concentration and to estimate detection limits. Our research aims to assess the performance of low-cost sensors in comparison with other technical devices to better understand potentialities and drawbacks, and their usefulness for health impact assessment.
## 1 Introduction
[PERSON] et al. [52] studied the impact resistance of NSC and glass FRP (GFRP)/carbon FRP (CFRP)-confined concrete via a drop-weight testing machine. The results show that FRP confinement can effectively improve the impact resistance of the concrete cylinders. Generally, the fracture strain of the FRP is lower than that from quasi-static compression and the fracture strain of the GFRP is much larger than that of the CFRP. [PERSON] et al. [30; 31] conducted a series of static and dynamic tests on aramid FRP (AFRP)-confined concrete, with the investigated parameters including the strain rate and the AFRP thickness. It was determined that the dynamic compressive strength, ultimate axial strain, and energy absorption of the concrete were heavily influenced by the strain rate. The external confinement provided by the FRP significantly improved the concrete properties under dynamic loadings and a dynamic compressive strength model for the AFRP-confined concrete was proposed. [PERSON] et al. [32] reported an experimental program on the impact behavior of CFRP-confined concrete using a Split-Hopkinson Pressure Bar (SHPB) apparatus with a diameter of 155 mm. The effects of the specimen length, strain rate, CFRP thickness, and concrete strength were investigated and the SHPB results revealed that the DIF was not dependent on the strain rate. However, the impact resistance of high-strength concrete confined with different types of FRPs has not yet been studied and the existing dynamic compressive strength models are only based on NSC. On the other hand, while the current studies mainly involve the number of FRP layers and the strain rate as test parameters, they rarely take the type of FRP jacket into consideration. Based on the above background, it is necessary to consider the effects of the FRP types, FRP thickness, and strain rate on the impact resistance of the FCHC. To this end, a total of 180 FCHC specimens were designed and an SHPB impact device was used to complete the impact tests on FCHC. The effects of the FRP type, FRP thickness, and strain rate on the dynamic peak stress, dynamic increase factor (DIF), and energy absorption of FCHC are studied and discussed. Subsequently, a new dynamic compressive strength model for FCHC is established based on the available experimental results and its accuracy is carefully examined.
## 4 Simulation Results  
### Loop Heat Pipe Model Validation  
In accordance with the geometrical properties provided in Section 3.1, a validated loop heat pipe model [33] was chosen as a reference for this article. To ensure the accuracy and credibility of the presented model, a validation study was conducted by comparing our analytical model with experimental tests performed on a loop heat pipe [33]. Through this rigorous process, we obtained comparable results, thereby validating the lumped parameter model. In Figure 5, the result is visible in the experimental test from [33], and the current model is also visible, where \(\dot{Q}_{evap}=10\) W is applied for 1.4 h on the evaporator (\(T_{8}\)), and the external boundary temperature condition is \(T_{ext}=23\)\({}^{\circ}\)C.  
The comparison of results in Figure 5 shows a good approximation between the proposed analytical model and experimental tests [33], where the temperature difference at steady-state conditions is approximately 0.1 \({}^{\circ}\)C and \(-0.5\)\({}^{\circ}\)C, respectively, at \(T_{8}\) and \(T_{2-3}\). After 1.4 h, the power is turned off, i.e., \(\dot{Q}_{evap}=0\) W, and the loop heat pipe model returns to the initial temperature state of 23 \({}^{\circ}\)C.
# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 1 Introduction  
The rise of new technologies, such as artificial intelligence, real-time video analytics, and virtual reality, has increased the expectations for the computing power, energy efficiency, and responsiveness of mobile devices within the Internet of things (IoT). Due to constraints in storage, computing capabilities, and battery life, IoT devices face significant challenges in locally handling demanding tasks. Multi-access edge computing (MEC) has emerged as a promising solution, enabling users to shift their computationally intensive tasks away from mobile devices to MEC servers by exploiting wireless connections [1].  
However, the proliferation of IoT devices, including smartphones, tablets, and smart vehicles, which are driven by advancements in smart cities and intelligent transportation systems, has dramatically increased the volume of computation-intensive applications [2]. Thus, there has been a corresponding surge in workload and network traffic within MEC servers. In this paper, we propose the TRUST-ME model, which is a novel framework that enables users to autonomously choose an optimal MEC server for processing their computational tasks by considering their trust levels of the servers' quality of service (QoS),communication capabilities, and computing characteristics. Additionally, the TRUST-ME model facilitates the MEC servers in efficiently allocating their computing resources to meet the users' latency constraints in a fair and computationally effective manner.
# Triaxial Compression Performance Research of Steel Slag Concrete on the Unified Strength Theory
## 3 Test Results and Discussion
### Triaxial Strength  
According to Table 3, the peak stress and peak strain of this experiment have been summarized. The coefficient of variation for each group in Table 3 was not greater than 15% (most of that were not more than 10%). As shown in Table 3, compared with uniaxial strength, SSC's strength under triaxial compression has been greatly improved. The constraints in other directions have an obvious effect on the improvement of \(\sigma_{3}\). The value of stress \(\sigma_{3}\) can improve to 108-189 Mpa due to the constraint of \(\sigma_{2}\) and \(\sigma_{1}\). The relative value \(\sigma_{3}/f_{m}\) (triaxial strength to uniaxial strength) is between 2.93 and 5.05, which means that SSC's triaxial compressive strength is obviously enhanced.  
It can be seen that the influence of stress ratio on the value of \(\sigma_{3}/f_{m}\) in Figure 5. When the stress ratio \(\sigma_{1}/\sigma_{3}\) is constant, \(\sigma_{3}/f_{m}\) increases first and decreases with the increase of \(\sigma_{2}/\sigma_{3}\). Most of (except for the SSC-30 at a stress ratio of \(\sigma_{1}/\sigma_{3}=0.1\)) the maximal value of\(\sigma_{3}/f_{m}\) was obtained at \(\sigma_{2}/\sigma_{3}=0.5\). It can be seen that the influence of the first principal stress \(\sigma_{1}\) on \(\sigma_{3}/f_{m}\) is visible. When the stress ratio \(\sigma_{2}/\sigma_{3}\) is constant, the values of \(\sigma_{3}/f_{m}\) at \(\sigma_{1}/\sigma_{3}=0.1\) is larger than that at \(\sigma_{1}/\sigma_{3}=0.05\), which proves that the stress ratio \(\sigma_{1}/\sigma_{3}\) also can influences the triaxial strength. The reason is that the first principal stress \(\sigma_{1}\) constrains the lateral expansion of the concrete in the \(\sigma_{1}\) direction and effectively slows the crack extension. The greater stress \(\sigma_{1}\) is, the more obvious the constraint is. Using SSC-100 as an example, when stress ratio \(\sigma_{2}:\sigma_{3}=0.75\):1, \(\sigma_{3}/f_{m}\) growth can reach 159% from \(\sigma_{1}/\sigma_{3}=0.05\) to \(\sigma_{1}/\sigma_{3}=0.1\).  
On the other hand, the SSA replacement ratio also has an effect on triaxial strength. When the stress ratio \(\sigma_{1}/\sigma_{3}=0.05\), the value of \(\sigma_{3}/f_{m}\) is very close at different replacement ratios, and the maximal value of \(\sigma_{3}/f_{m}\) is SSA replacement ratio of 100%. When the stress ratio \(\sigma_{1}/\sigma_{3}=0.1\), the maximal value of \(\sigma_{3}/f_{m}\) was obtained at the replacement ratio of 100%.  
Figure 4: Schematic diagram of failure form under triaxial compression: (**a**) Plate-splitting cracks, \(\sigma_{1}:\sigma_{2}:\sigma_{3}=0.05\):0.25:1, SSC-70; (**b**) 0.05:0.25:1, SSC-100; (**c**) 0.1:0.25:1, SSC-70; (**d**) Single-shear cracks, 0.05:0.5:1, SSC-70; (**e**) 0.05:0.75:1, SSC-100; (**f**) 0.1:0.75:1, SSC-100; (**g**) Double-shear cracks, 0.05: 0.75:1, SSC-70; (**h**) 0.05:0.5:1, SSC-100; (**i**) 0.1:1:1, SSC-100.
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 2 Related Work
### Scalability of Commercial Solutions  
Many UWB companies emerged in the last couple of years, which is another sign of growing attention in UWB localization. While most marketing attention is given to high achievable accuracies, some products also claim to have high scalability with thousands of tags per system. Among different chip manufacturers, DW1000 (Decawave Ltd, Dublin, Ireland) is probably the most used UWB chip. An example of a company that produces UWB devices based on DW1000 is OpenRTLS [16]. On their web page, they claim a maximum update rate of 8000 updates/sec/channel, but also state that the system can accurately localize max 7500 items per second in a 20 m radius. It is not mentioned which settings, localization mechanism and MAC protocol are used to achieve these numbers, nor can these claims be validated.A competitor called Sewio [17] commercializes an UWB TDoA system in which they advertise support for more than 1000 tags. The communication range is around 25-30 m and a table that reports the channel utilization is provided for different packet sizes and refresh rate, e.g., from 0.1 Hz up to 10 Hz. However, there is no information on how high user density scenario are supported and which protocols they implement. Decawave is not the only company that is investing in UWB microchip. Other products are also available in the market. An example is BeSpoon [18], which has developed an UWB microchip and implemented UWB indoor localization systems that support TWR, TDoA and AoA. Similar to previous examples, the figures of user density are somehow vague and there is no quantified support for their claims. In this case, an example of a classic office building with 250 employees is shown. Again, details on adopted techniques are left out of the description. Another commercial UWB chip alternative is Time Domain [19]. In contrast to most commercial chips, Time Domain provides proprietary MAC implementations such as ALOHA and TDMA. However, although the Time Domain datasheet describes the data throughput and expected range and accuracy in a wide range of conditions, the scalability and update rate of the system in each of these conditions is not available. A study that involves multiple Time Domain nodes is available [20]. In an industrial warehouse environment, 22 tags are used to create a low-complexity UWB-based collision avoidance system. Although range estimates are sufficiently accurate to identify dangerous situations, scalability of such a system is not investigated and the authors did not mention how multiple access is implemented, i.e., a suitable MAC protocol.  
An overview of the most significant studies and commercial solutions is reported in Table 1. This paper quantifies the scalability claims of the above systems. To this end, mathematical models for the overall scalability are derived that take into account the impact of design aspects such as PHY settings, MAC approach and positioning approaches.
## 5 Discussion and Future Work Directions  
In [35], we proposed the network infrastructure to simulate and investigate the delay-sensitive and loss-prone tactile industrial application. A simulator, known as IoTactileSim aims to provide a tool to examine the strict QoS and QoE requirement for tactile applications. Recent work [36] on QoS provisioning explored the haptic coding, control systems, and intelligent prediction techniques with a focus to deal with mission-critical and delay-sensitive stringent QoS and QoE requirements like ultralow latency, ultrahigh reliability, high availability, and ultrasecurity. This paper aims to propose an ITE framework to utilize the recently proposed haptic traffic reduction or compression methods to adjust the packet rate and packet size based on the level of congestion in the network and improve QoS and QoE. The proposed ITE observes network impairments during remote interaction and compares them with QoS/QoE specifications. The threshold-based approach is used to ensure the QoS and QoE requirements, and to perform the optimal action for selecting the best codec to control the threat of QoS violation. There should be an adaptive approach to defining the application-specific threshold corresponding to network resources. Moreover, haptic traffic communication between the master and the slave is performed over the TCP. However, some studies in the literature suggest that the user datagram protocol is preferable due to the low header size and processing cost. Therefore, we intend to implement such real-time application protocols in our future research to overcome the extra computational and communication cost. To demonstrate the fruitfulness of the proposed system, there is a need to integrate a real-world haptic device at the master to transmit the haptic data and perform the industrial remote task at the slave side. In this regard, in future work, a haptic device will be utilized instead of a haptic dataset to controls the real-world  
Figure 15: Commutative power (Reward=Throughput/RTT) of the connection determine during simulation experimentation for ITE and baseline model with different number of training epochs and external hosts; (**a**) External Host = 5, (**b**) External Host = 10, (**c**) External Host = 15, (**d**) External Host = 20.  
tactile enabled remote control industrial applications. It will help to understand the more practical challenges related to haptic-driven applications in real-world scenarios.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 4 Scheme Model  
As shown in Figure 2, the model of the scheme includes six entities: a healthcare management center, healthcare organizations, a data owner, a data user, a blockchain network, and a star file system.  
Figure 1: The process of generating zero-knowledge proof and verifying proof.  
Figure 2: Scheme model.  
Healthcare Management Center (HMC): The HMC comprises healthcare regulatory bodies responsible for qualifying, registering, and regulating participants, such as healthcare organizations, within the system. Any organization seeking to join the system must undergo registration with the HMC to ensure compliance with regulatory standards and guidelines.  
Healthcare Organizations (HOs): The HO include hospitals, medical examination centers, research institutes, and other healthcare-related institutions licensed by the HMC.  
Data Owner (DO): The DO is generally the patient. With the consent of the DO, the medical data portion of the EMR can be encrypted and uploaded to a shared network.  
Data User (DU): DUs primarily consist of patients, healthcare professionals requiring access to patients' historical medical records, and authorized members of the other organizations necessitating the sharing and utilization of medical data.  
Blockchain (BC): The blockchain employed in this study was deployed utilizing the Hyperledger Fabric framework.  
IPFS: The IPFS is used for the distributed storage of medical data ciphertext, wherein it generates a CID representing the hash address of the stored data. This CID is then provided back to the client. The client can query the corresponding data on the IPFS using the CID.
# Social-Aware-Based Resource Allocation for NOMA-Enhanced D2D Communications
## 3 Social-Aware-Based Resource Allocation
### Optimization Model
#### 3.2.3 Joint Optimization Method for Resource Allocation  
The resource allocation is a process of allocating channel and power for D2D users in a D2D cluster. The channel allocation and power control results are continuously iterated and eventually converge. According to the previous description, the joint optimization method for resource allocation is shown in Algorithm 4.  
```
1: Initialization: cellular users \(\{C_{1},C_{2},\ldots,C_{M}\}\), cellular channels \(\{SC_{1},SC_{2},\ldots,SC_{M}\}\), D2D content requesters \(\{DR_{1},DR_{2},\ldots,DR_{N}\}\), D2D content providers \(\{DT_{1},DT_{2},\ldots,DT_{K}\}\), D2D clusters \(\{D_{1},D_{2},\ldots,D_{N}\}\), and historical channel allocation \(\{H_{\text{i}}(t)\}_{\text{ieN}}=\varnothing\).
2: While Equation (1)
3: Perform channel allocation for D2D clusters in Algorithm 2
4: If channel allocation results \(\subset\{H_{\text{i}}(t)\}_{\text{ieN}}\)
5: Perform D2D power allocation in Algorithm 3 for \(P_{d}^{k*}\)
6: Break
7: Else
8: Save the channel allocation result to \(\{H_{\text{i}}(t)\}_{\text{ieN}}\)
9: Perform D2D power allocation in Algorithm 3 for \(P_{d}^{k*}\)
10: End if
11: End while
12: Output: \(\Big{(}\eta^{*},P_{d}^{k*}\Big{)}\)
```  
**Algorithm 4** Joint optimization method for resource allocation.  
**Theorem 4**.: _The joint resource allocation algorithm can converge to the optimal strategy._  
Proof.: It can be proved that there is a convergence solution for each subproblem according to the previous sections. Considering that the algorithms in each subproblem can ensure that the sum D2D rate will not decrease, and the resources in the cell are limited, we can draw a conclusion that the joint resource allocation algorithm which combines channel and power allocation will finally converge.  
The computational complexity of the joint resource allocation algorithm is composed of the complexity of the two subproblems. Assume that the maximum number of iterations between the two subproblems is \(G\), the computational complexity of the channel allocation subproblem is \(O_{1}\), and the computational complexity of the power control subproblem is \(O_{2}\). The computational complexity of the joint resource allocation is \(O(G(O_{1}+O_{2}))\).
## 4 Parametric Study
### Influence of Face Sheets Thickness on Energy Dissipation  
During the interaction between the explosion product and the structure, the energy of the explosion is transferred to the sandwich panel, and then dissipated through the panel deformation. The energy dissipated by each component is numerically obtained and indicated in a stack bar diagram in Figure 11. The energy dissipated by each component is essentially the difference between the total energy (ET) and initial energy (EI) of each component. Figure 11a shows the energy dissipated by each component of the RCSP for the first scenario. The figure demonstrates the contribution of the front plate, the inner core, and the rear plate in the energy dissipated by the RCSPs. The results imply that when using a front sheet with thickness of 0.6 mm the energy dissipated attain the highest value and then reduced till reaching the minimum when the front-plate thickness is 2.6 mm. It can be concluded that with increasing the front-plate thickness the energy dissipation decreases. Figure 11b illustrates the energy dissipated by the RCSP components for the second scenario. The results indicate that the rear plate has a negligible influence on the energy dissipated by the sandwich structure as the rear plate participates with a small portion in the energy dissipation of the structure relative to the other components (the front-face sheet and the inner core). The inner core layer has a major portion of the energy dissipated. Increasing the rear plate thickness boost the energy dissipation of the core layer; however, it does not enhance the total energy dissipated by the sandwich structure. The highest level of energy dissipation achieved when the front-plate thickness was 0.6 mm. Therefore, the sandwich panel with a thinner front-face sheet can improve energy-absorbing capabilities of the structure. However, under large blast loading, tearing damage may take place on the thinner front face.
## 6 Experiments
### Experiment Results
#### 6.2.3 Convergence of QMC  
More sample times can make the approximate Q-value function QMC converge and be close to Q*, but it needs more runtime. To illustrate the convergence of QMC, we gathered the statistics of the max QMC of Dec-Tiger with the sample time \(K\) increasing, as shown in Figure 4. The max values of QMDP, QPOMDP and QBC for Dec-Tiger with horizon \(h=3\) and \(h=4\) are also listed. We can see from the figure that the max QMC declines and converges as \(K\) increases. Note that QMC is as compact as QPOMDP with horizon \(h=3\). In horizon \(h=4\), QMC is more compact than QPOMDP but looser than QBC. The goal of Figure 4 is to show that QMC is an upper bound of Q* and we can use a less compact Q-value function to reduce time of computing, but it still can guide A* to search the optimal policy.  
Figure 4: Convergence of QMC. as the sample time \(K\) increases. QMC converges to QPOMDP, which means QMC is an upper bound for Q*, but is less compact than QBC.  
Figure 3: Max number of policies in the policy pool for different horizons. This indicator illustrates that ICE maintains a larger policy pool than CEMC, which may make the former expand useless nodes.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 2 Applications of FSSs
### FSS in Wireless Communications
#### 2.3.2 Sidelobe Level Reduction  
Sidelobes represent unwanted radiation in directions other than the main lobe, which can lead to interference. FSS-based antennas effectively reduce sidelobe levels through the careful design of the unit cell structure and element distribution. By incorporating multi-layer FSSs with sub-wavelength elements, researchers have demonstrated sidelobe reduction by up to 20 dB, significantly minimizing interference from adjacent frequencies [105]. The reduction in sidelobe levels improves signal clarity and enhances spectral efficiency in crowded communication environments such as urban areas [106].