esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Performance of R-Type Steel Damper Used in Reinforced Concrete Members
## 2 Experimental Program
### Phase II Test Program  
#### 2.2.1 Design of Phase II Specimens  
In the Phase II test, the proposed R-type damper was applied to an RC member to evaluate its structural performance. The proposed damper can be applied in various manners wherever a passive vibration damper can be applied, for example, a wall type, a coupling beam connecting the shear wall, and a brace type, etc. In this study, an RC coupling beam was selected as the structural member as it is relatively simple and can be used to verify the performance of the proposed damper. The coupling beam is a structural member that exists between the shear walls and is easily damaged by lateral load. The experimental results in this study can be applied to many places where shear force due to lateral load is transmitted to the damper as well as the coupling beams.  
As shown in Figure 3, Specimens M1 and M2 without and with the proposed damper, respectively, were designed for the Phase II test. The specimens had stiff stubs at both ends to realize relatively high rigidity compared to that of the coupling beam because many reinforcing bars are placed in the shear wall to withstand the lateral and axial forces. The beam of the specimen had a width, height, and length of 200 mm, 450 mm, and 1000 mm, respectively. Furthermore, the stub has a cross section of 400 \(\times\) 850 mm\({}^{2}\) and length of 1200 mm.  
Figure 3: Details of Phase II test specimens (unit: mm): (**a**) M1; (**b**) M2.  
Figure 2: Test setup of Phase I test specimen (unit: mm): (**a**) overall view; (**b**) setting of LVDTs.  
A compression test revealed that the concrete used in the specimens had a strength of 26.0 MPa and the damper was made of the same steel as that used in the Phase I test. As shown in Figure 3, in Specim M1, eight D19 (286.7 mm\({}^{2}\)\(\times\) 8 ea.) rebars were placed in a section of the test region as the main reinforcement and the effective height of the beam was 370 mm. The stirrup was designed to dominate the shear by placing D10 (71.3 mm\({}^{2}\)) rebars at 150 mm intervals.  
In Specim M2 with the proposed R-type steel dampers, the yielding of the damper is designed to precede the flexural yielding of the specimen. In addition, four D22 (387.1 mm\({}^{2}\)\(\times\) 4 ea.) steel bars were placed in the test region of specimen M2 as the main reinforcing bars, reflecting the reduced factored-external load by installing dampers. The T-shaped steel plate to which the damper is fastened and the main reinforcing bar were welded together. In addition, for simplifying the construction, the stirrup was spirally reinforced with D10 reinforcing bars at 150 mm intervals.
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
## 4 Deterioration of CH Artifacts: Causes and Process  
First of all, we want to focus attention on the fact that many CH artifacts are made with different materials. This means that deterioration causes and processes may be different for different parts of the artifact, and their responses to decontamination or restoration treatments are also different. It may be the case that the use of some restoration material or procedure on certain components works as a catalyst for water absorption, or directly as an attractor of biodeteriogens for other components (the case of glue in book manufacturing). It may also be the case that the best practices in preserving some materials are inefficient or even dangerous for others.  
Materials inevitably degrade; we cannot avoid this, but we can interfere with the speed of this process. To do so, we need an in-depth knowledge of the following:  
1. The constituent(s) of the material(s) the manufacture is made of;
2. The level of deterioration;
3. How the constituents interact with the deterioration agent(s) (hence the mechanism of deterioration processes).  
While point 3 is part of the acquired scientific knowledge [94; 95], points 1 and 2 are specific to each case, and that knowledge must be acquired during the study and preliminary diagnosis phase. Since our interest is in CH artifacts, we will consider only materials, deterioration processes, and their causes circumstantial to our field of interest. It must also be noted that the archeological excavations themselves alter the stability of CH manufacturing, since they expose them to solar radiation, atmospheric phenomena, fine dust, and CO\({}_{2}\)[96]. Additionally, restoration interventions usually present recurrent phenomena, such as sulfur and iron-containing efflorescence. Figure 3 shows a case of recurrent efflorescence on the pinewood of a 14 th-century vessel stored indoors (Museu Martitim de Barcelona) [97].  
Figure 3: A waterlogged medieval timber hull. (**a**) View of the vessel Sorres X (Museu Martitim de Barcelona). (**b**) Detail of efflorescence on pine wood. (**c**) A thin cross section of the same pine wood sample. (**d**) Detail showing crystal accumulations. [97]. [PERSON] [PERSON] et al. Distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license https://creativecommons. org/licenses/by/4.0/ (accessed on 2 January 2025). No changes were made to figure except for choosing only the sections of interest from the original figure, changes were made to caption.  
A consideration needs to be made: together with the material itself (its elemental composition) and the manufacturing process, the most relevant element to be considered is the surface. Porous materials are more penetrable and, hence, intrinsically more sensitive to external factors, whether they are damaging factors or preservation treatments and investigations.  
Different causes interact synergistically, to the point that [PERSON]. [PERSON] et al. speak of Integrated Pest Management (IPM) [98].
## 4 Fatigue Life Estimation of PC Composite Box Girder Bridge with Corrugated Steel Webs
### Fatigue Life Estimation
#### 4.2.2 Fatigue Simulation Analysis Principle of MIDAS/FEA  
Fatigue failure is mainly brittle fracture of structural materials under the fatigue load cycle, which is caused by cumulative damage of materials. The commonly used fatigue analysis methods include the stress-life method and strain-life method. The stress-life method is used for fatigue analysis in MIDAS/FEA NX 2022.  
In general, the fatigue calculation in MIDAS/FEA NX 2022 is based on different materials, the bridge materials are Q355 corrugated steel web and C55 concrete. Observe the life cycle of box girder, that is, the number of repeated loads when fatigue failure occurs under repeated fatigue loads, and calculate the fatigue life of all positions, and the minimum value in the data is the fatigue life of the whole structure.  
The S-N curve in MIDAS/FEA NX 2022 is the load amplitude with the same difference between the peak value and the valley value. The relationship between the stress ratio S under cyclic action and the number of loading cycles N when the model reaches failure. After inputting the material properties, an ideal S-N curve will be automatically generated inside the program [29]. The S-N curve is shown in Figure 29 below. The upper part is the point where 90% (\(S_{\text{u}}\)) of the maximum stress amplitude is cycled 1000 times, and the lower part is the point where the fatigue limit stress amplitude (\(S_{e}=0.5S_{\text{u}}\)) is cycled 1,000,000 times. The curve connects the two points.  
Figure 29: Concrete S–N curve.  
The average stress in the material at the time of damage is much smaller than that at fracture damage, so the degree of fatigue damage to the structure at different stress levels should be considered in the fatigue performance analysis. The higher the average stress, the shorter the fatigue life. In order to analyze the effect of average stress on fatigue life, the software also provides two modified methods, Goodman and Gerber.  
\[\begin{split}\text{Goodman modified formula}:\frac{\sigma_{\text{a} }}{S_{\text{e}}}+\frac{\sigma_{\text{m}}}{S_{\text{u}}}=1\\ \text{Gerber modified formula}:\frac{\sigma_{\text{a}}}{S_{\text{e}}} +(\frac{\sigma_{\text{m}}}{S_{\text{u}}})^{2}=1\end{split} \tag{11}\]  
where \(\sigma_{\text{a}}\)--stress amplitude, \(\sigma_{\text{a}}\) = (\(\sigma_{\text{max}}-\sigma_{\text{min}}\))/2; \(\sigma_{\text{m}}\)--mean stress, \(\sigma_{\text{m}}\) = (\(\sigma_{\text{max}}+\sigma_{\text{min}}\))/2; \(S_{\text{e}}\)--fatigue limit stress range; and \(S_{\text{u}}\)--maximum stress amplitude.  
Load curve reference [29] selects a toothed waveform with a period of 0.3077 s, as shown in Figure 30 below.
# High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
## 2 Depth Camera Modeling and Implementation
### Depth Noise Modeling  
It was pointed out that the depth noise from Intel Realsense D435 can be modeled as a Gaussian model [25; 30]. The standard deviation of the noise model is calculated based on the depth data obtained in Section 2.2 as follows. Since the true distance value is known, the depth error at each pixel can be calculated as in Equation (3).  
\[Depth_{error}=Depth_{measured}-Depth_{true} \tag{3}\]  
The standard deviation value of the \(460\times 320\)\(Depth_{error}\) values can then be computed. Figure 4 illustrates the calculated standard deviation values of the depth error for all three materials. It was observed that the standard deviation values for each material increase exponentially with distance. This trend is exhibited by all three materials, albeit with different specific patterns. Based on this observation, multiple candidate functions, including exponential functions and other polynomial functions, were tested to find the best fit for the data. The exponential function model in Equation (4) was ultimately selected because it yielded the smallest Root Mean Square Error (RMSE) values.  
\[std_{noise}=0.005e^{m\times d} \tag{4}\]  
where \(std_{noise}\) is the standard deviation of the depth error, m is the material value with the unit of \(1/\text{m}\), d is the distance value with the unit of meter, and 0.005 is the fitting coefficient with the unit of meter.  
Figure 4: \(Cont\).  
The graph depicted in Figure 4 illustrates how the standard deviation of the depth value varies with different material values. Additionally, there is another type of error known as the fill-rate error, which arises when the depth camera cannot calculate the depth value for certain pixels due to the absence of a detectable dot pattern. These pixels are assigned a depth value of 0, and this error occurs randomly in 1% of the depth camera pixels [32], as demonstrated in Figure 5. This fill-rate error is simulated by setting 1% of the depth values to 0.
# Progress in THz Rectifier Technology: Research and Perspectives
### THz Diode Technology: I-V Characteristics and Figures of Merit (FOM) for MIM Diode
#### 2.2.5 Asymmetry (Asym)  
To achieve rectification, an ideal diode must show an asymmetrical I-V curve. This parameter is referred to as asymmetry. The asymmetry is defined as the absolute ratio of the forward current \(I_{F}\) to the reverse current \(I_{R}\).  
\[Asym\ \ (V)=\bigg{|}\frac{I_{F}(V)}{I_{R}(V)}\bigg{|};\text{Asymmetry}\ >\ 1 \tag{6}\]  
A value of 1 obtained by considering a diode of equal metals on both sides of the insulator layer indicates full symmetry and hence no rectification. If the diode consists of different metals on both sides of the insulator layer, the structure is asymmetric and the value is greater than 1. According to Equation (7), to achieve asymmetry and high current density, metals with a high difference in work function, good conductivity, and a large barrier height are desirable:  
\[Asym=10^{3.3137(\Psi_{\text{M2}}-\Psi_{\text{M1}})} \tag{7}\]  
where \(\Psi_{\text{M2}}\) represents the work function of the top metal (M2) and \(\Psi_{M1}\) represents the work function of the bottom metal (M1). The asymmetry also depends on the insulator thickness; as it increases, the asymmetry increases [7; 23; 27; 28; 29].
# Characteristics of Urban Heat Island in China and Its Influences on Building Energy Consumption
###### Abstract  
Urban heat island (UHI) draws more attention as it affects not only the health of residents but also the energy consumption of buildings at the city scale. To achieve carbon neutrality goals, it is crucial to better understand the mechanism of the UHI influences on building energy consumption. The characteristics of urban heat island intensity (UHII) and the relationship between the UHII effect and building electricity and related coal consumption were analyzed, based on the long period of monitoring data with hourly weather data from 1 January to 31 December 2019. Results show that a strong correlation between the annual mean UHII and the median daily mean UHII exists. The synthetic diurnal UHII of most cities presents a U-shaped variation trend. In different building climate zones in China, namely, severe cold region (SCR), cold region (CR), hot summer cold winter region (HSCWR), hot summer and warm winter region (HSWWR), and mild region (MR), the influences of UHII on building energy consumption were analyzed. The existence of UHI reduces building energy consumption in 96.7% of SCR cities and 60.8% of CR cities, while in HSCWR, HSWWR, and MR cities, the percentage of cities where the building energy consumption is increased by UHI is 69.4%, 80% and 63.6%, respectively. Urban climate strongly influences building energy consumption, indicating that it should be considered and analyzed in detail for making future urban development or carbon emission reduction strategies.  
urban heat island; building energy consumption; carbon emission; urban climate +
Footnote †: journal: Applied Sciences
# Distributed Uniform Streaming Framework:
## 3 DUST Core for Event Streaming  
The DUST-Core library implements the common functionality that is required for every streaming component (see Figure 1). This includes functionality such as event-streaming and configuration of the application component but does not include the functionality of the dynamic placement of application components. The dynamic placement is implemented in the DUST Coordinator which is described in Section 4. The DUST-Core architecture (see Figure 2) is divided into three main software modules. The first software module is the application interface which is designed to create the required abstraction to migrate application components and to change the middleware during runtime (e.g., use shared memory when application components are on the same physical device). The second module is the definition of the middleware implementation and requirements for event-streaming. The last software module is the DUST Core, which implements the functionality such as communication with the coordinator, message batching, etc. These software modules are described in the following subsections
# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 6 Discussion  
This approach only functions for a small number of users. As the number of users increases, the accuracy rate drops drastically, even when adding model branches. This is mainly due to the insufficient spatial resolution of WiFi signals, which leads to the mixing of signals from multiple users. Currently, there are two potential solutions: one is to improve the spatial resolution of the system by using more advanced antenna technology [39]; the other is to limit the direction of signal transmission in order to better spatially discriminate the signals of different users.  
Environmental factors can affect system performance, and currently, we can only conduct experiments in trained environments, not across domains. When the environment changes, dynamic CSI containing human activity information may be obscured by environmental reflections of static CSI. Our team successfully achieved cross-domain activity recognition through the utilization of adversarial networks and domain classifiers for environment-independent activity identification [40]. Next, we will attempt to apply this approach to legitimacy recognition.  
This approach will not work when CSI is unavailable or if there are errors in detecting that information.When channel estimation errors or factors such as pilot contamination render the received CSI unusable, the system may fail to operate smoothly. Noncoherent (NC) detection technology [41; 42] may offer an effective solution to this problem. This technique does not require the receiver to accurately understand the channel's state information, but rather relies on the statistical properties of the signal for demodulation. In the face of complex channel conditions or communication environments, noncoherent detection technology can simplify system design and enhance system robustness.
## 1 Introduction
At the same time, ECC is very suitable for large-area bridge decks due to its low cost [12]. In recent years, studies on steel-ECC composite structures have focused on the performance of pinned shear bonds [13,14], interfacial properties [15,16], and flexural properties [17,18]. Among them, flexural performance research mainly studied ECC and steel beams to form a combined beam, and [PERSON] et al. [17] found that the stiffness of steel-ECC beams was improved by 50% compared to steel-NC beams by flexural tests. In addition, [PERSON] et al. [19] considered the tensile properties after incorporating ECC cracking, and each member was able to derive stable load-carrying values. Most of the works above use I-beams as the main structure and the stress distribution also does not match the stress characteristics of orthotropic anisotropic steel bridge plates, while the study of a ECC-steel combined bridge deck plate has not been fully developed. Some experts at Southeast University have also tried to apply it to bridge decks to improve the fatigue life of bridges [20]. However, none of these scholars and experts have considered the setting of shear-resistant connector keys, so no overall combined structure was formed to work together, and no related research was produced thereafter. This paper takes the steel-ECC combined deck sheet as the research object and conducts a four-point bending static loading test by microcomputer-controlled electro-hydraulic servo long column press so as to test the bending load capacity of a steel-ECC combined deck sheet under a negative bending moment, and it investigates the influence of the deck thickness and bolt spacing of the steel-ECC deck sheet on the bending resistance performance, thereby providing a theoretical and experimental basis for the steel bridge panel to adopt ECC for rigid deck pavement reinforcement.
## 2 Related Works
### EH-Based Clustering Routing Protocols
#### 2.2.2 Clustering Routing Protocols for EH-CRSNs  
Addressing the joint resource allocation challenge in EH-CRSNs, with constraints on EH efficiency and communication outage probabilities, [32] aims to maximize network throughput and introduces an allocation algorithm for sub-channels, power, and leasing time that increases throughput and network robustness in complex communication settings. Reference [33] proposes a clustering routing protocol for RF EH-CRSNs based on residual energy and channel quality. This protocol features a dual-level node classification algorithm: at the first level, nodes are categorized into either transmission nodes or EH nodes based on their remaining energy, and subsequently, the algorithm pairs transmission nodes with their ideal transmission channels. At the second level, transmission nodes that can deliver data packets on determined channels within the specified time perform data reporting, while others engage in EH. Studies related to EH-CRSNs clustering protocols tend to concentrate on the distribution of network resources and address only the single-hop intra-cluster communication challenge, with the complex multi-hop routing between clusters not yet being effectively addressed. The characteristic analysis and comparison of the aforementioned clustering routing protocols is shown in Table 1.
# Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
## 8 Future Work  
In this study, our primary focus has been on analyzing the well-known NSF network topology. Extending the investigation to explore the effectiveness of the proposed machine learning models across other network topologies would be a valuable avenue for future research. On the other hand, exploring the performance of our machine learning models over varying prediction durations, whether longer or shorter than those used throughout this work, is also left for future exploration.  
We conducted a comprehensive analysis of the proposed Convolutional Neural Network (CNN) architecture, both in 1D and 2D configurations. Alternative structures and forms of CNN including hybrid models can be considered for future work.  
Conceptual discussions, all; methodology, F.N., E.W. and J.C.; software, F.N. and C.P.; validation F.N. and E.W.; investigation, F.N. and C.P.; writing--original draft preparation, all; writing--review and editing, F.N. and C.P.; supervision, E.W. and J.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Catalonia Government through the Agency for Management of University and Research Grants, grant SGR-2021-00598.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 4 State of Art about Data Availability in L2 Rollups
### Off-Chain (Validium Mode)
#### 4.2.1 Data Availability Committee (DAS)  
A Data Availability Committee (DAS) is a group of trusted nodes that store transaction data off-chain and provide cryptographic guarantees for data availability. This approach addresses the data availability problem while significantly reducing the costs and complexities of storing all transaction data directly on L1. A DAC is selected by forming a group of nodes to store and verify transaction data. These nodes are often determined based on network resilience, performance, and trustworthiness. The specific implementation of the rollup determines if the committee member selection process is permissioned or decentralized. Once a rollup processes a transaction batch, it transmits the corresponding data to the DAC members. Each member maintains a full copy of the transaction data, which ensures that DAC can maintain the data availability even if some nodes fail or become compromised. This helps to achieve redundancy and fault tolerance.  
To ensure data integrity and tamper resistance, DAC members generate cryptographic proofs that validate that the stored data are still available. DAC members regularly post these proofs on-chain to confirm the availability of data. The on-chain smart contract verifies proof provided by the committee members and validates the data availability and integrity.  
This approach implies that the rollup can trust the DAC. The cryptographic techniques can vary depending on the specific implementation of the rollup. Furthermore, DAC implements a challenge-response technique to improve the security. Any network member can challenge the batch if they believe the DAC is not correctly storing the data. After that, the DAC members must submit additional evidence to validate the data availability. Immutable X [31] supports both on-chain and off-chain solutions, allowing users to store data according to security and cost requirements. The DAC members sign and retain each batch of transactions. Users can still withdraw from the protocol if a compromised member is present. The Immutable DAC includes Immutable, StarkWare, Deversifi, Consensys, Netherrmind, Iqlusion, Infura, and Cephalopod [41].