esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Due to the fact that PVA fibers enhance the bridging action and cracking strength [22], they are regarded as an attractive option because of these beneficial effects. [PERSON] et al. [23] investigated 12 mm long PVA fibers; they found an increase in compressive strength and elastic modulus within a particular fiber dosage limit. In contrast, split tensile strength was increased by increasing the fiber content. [PERSON] et al. [24] studied the mechanical and structural properties of 6 mm and 12 mm length PVA fiber reinforced concrete. PVA fiber increased tensile and flexure properties with constant compression strength up to 0.25 volume fraction, while PVA fiber also increased ductility in reinforced concrete (RC) beams. [PERSON] et al. [25] conducted a detailed study on adding PVA fibers to concrete; the research parameters included two different lengths (6 mm and 12 mm). Cement replacement by 30% fly ash maintained the constant slump of concrete. [PERSON] et al. [25] observed an improvement in the post-cracking response of the fiber-reinforced matrix. They also observed enhanced ductility and higher mechanical properties when fiber content was kept at 0.25%. When the fiber content was increased to 0.375%, they observed declining effects on concrete properties. As discussed above, fiber-reinforced concrete was initially developed to improve the flexural performance of structural members under service conditions. [PERSON] et al. [26] observed that, until recently, most of the researchers were biased towards investigating material parameters of FRC and reducing the cost of the end product. These research studies provided enough data for designing the FRC members for serviceability requirements. However, only a few research studies have focused on the FRC matrix's non-linear behavior, and they mostly studied steel fibers [27]. [PERSON] et al. [28] have recently published research on the post-crackingand fracture behavior of engineered geopolymer concrete using PVA fibers. The novelty of the current work is the detailed investigation of post cracking performance of concrete containing copper coated steel and PVA fibers. Such types of concrete are widely used for large constructions. For example, the developed concrete was used in post-tensioned beams for enhancing the bursting strength at the anchorage edge. This study aims to investigate the effects of low volume fraction (0.3%) of PVA and CCS fibers on the mechanical properties, modulus of elasticity, ductility, and concrete post cracking characteristics. Two sets of concrete are studied, one with 100% cement and the other with 80% cement and 20% fly ash. In comparing PVA and CCS fibers, a 0.3% volume fraction of fibers was fixed. The fiber aspect ratio was 100 and 90 for CCS and PVA fibers, respectively. The post cracking characteristics included first crack strength; toughness index; I\({}_{5}\), I\({}_{10}\), and I\({}_{20}\); flexural toughness; and the ductility index.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 5 Smart Grid Attacks
### Attack Source
Strategic targets such as vitalpoints like ports, oil refineries, power stations, ports, telecommunications, power stations, and bridges to hinder force mobilization, exemplified in conflicts such as Iraq [147; 148], Lebanon [149; 150], Gaza [151], and West Bank [152]. * **Cyber Operations:** CyberOps (CyOps) utilize hacking to attack the smart grid's cyber-physical infrastructure [153] as a new cyber-warfare concept [154; 155], which is similar to cyberattacks on Estonia in 2007, Georgia in 2008 [156], and Ukrainian electricity companies in 2015-2016 using BlackEnergy malware [157; 158; 159]. Specific incidents include the deployment of the Havex RAT by the Russian APT group \"Energetic Bear or Dragonfly\" for global cyber espionage targeting energy, defence, and pharmaceutical sectors in the US and Europe [160]. Notable attacks such as Stuxnet (2010), under \"Operation Olympic Games\", targeted Iranian nuclear facilities by manipulating Siemens PLCs. NotPetya (2017) [161], followed by the BlackEnergy [162; 163], caused widespread damage globally. * **Psychological Operations:** PsyOps use cyber deception [164; 165] in conjunction with psychological warfare [166], sabotage, and espionage operations to compromise a nation's security and resilience [167; 168]. One prominent instance is the succession of unexplained fires and explosions that occurred in Iran between 2020 and 2021 and targeted vital infrastructure [169], including events at Parchin military installations as well as other power stations and industrial locations all around the nation. Reminiscent of recent Ukrainian counteroffensive operations, especially on the Donetsk front, propaganda videos such as \"Plans Love Silence\" emphasized Operational Security (OpSec) [170]. These events demonstrate the merging of CoOps and CyOps into PsyOps [171; 172].
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Immunofluorescence-Mediated Identification of mTOR Signaling Components  
Association of certain mTOR signaling components with mTOR was discovered by a direct visualization of their colocalization with mTOR in cells using high-resolution confocal microscopy, which includes the discovery of lysosome as the mTORC1 activation site and LRS (leucyl-tRNA synthetase) as a cellular leucine sensor to promote mTORC1 activation.
# Fast Target Recognition Based on Improved ORB Feature
## 3 Feature Extraction and Matching Based on an Improved ORB Algorithm
### KNN Matching Algorithm to Eliminate Mismatching  
The core idea of the K-nearest-neighbor (KNN) algorithm [34; 35; 36; 37; 38; 39; 40; 41] is to search the top \(K\) feature points with the highest similarity in another feature space as candidate features.  
In this paper, the \(K\) = 2 KNN algorithm is adopted to find the ratio \(R\) between the distances of the nearest neighbor and the second-nearest neighbor:  
\[R=\frac{D(V_{p},V_{mq})}{D(V_{p},V_{mq})} \tag{12}\]  
where \(V_{p}\) is the feature vector of the feature point \(p\), \(V_{mq}\) is the feature vector of the nearest neighbor \(q\) in an image, \(V_{mq}\) is the feature vector of the second-nearest-neighbor feature point in an image, and \(D\) is the distance between the vectors.  
For each feature point, the optimal feature matching and sub-optimal feature matching are obtained and the distance between them is recorded as m and n, respectively. Each feature point matching pair is screened according to the ratio \(T\) (0.7 in this paper), and matching with the excessive ratio is considered as mismatching.  
Based on the above, this paper puts forward the idea of combining the image enhancement technology with the ORB algorithm and uses adaptive histogram equalization to process the input images in advance. The experimental results show that the ORB algorithm combined with the image enhancement technology improves feature extraction and matching. The specific process is shown in Figure 5 below.  
Figure 5: Traditional ORB feature matching (**left**) and improved ORB feature matching (**right**).
## 3 Parameter Optimization for Better Sound Absorption Performance
 \\ \hline Thickness of the porous metal \(D\) (mm) & 93.2769 & 99.9000 & 100.0000 & 0.0000 \\ Length of cavity backing the porous metal \(D_{1}\) (mm) & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ Diameter of the microhole \(d\) (mm) & 0.6920 & 2.0000 & 0.0000 & 0.1425 \\ Thickness of the panel \(t\) (mm) & 0.1000 & 0.1000 & 0.0000 & 0.1000 \\ Distance of the neighboring microblocks \(b\) (mm) & 8.8697 & 4.7522 & 0.0000 & 1.0000 The theoretical sound absorption coefficients of the investigated sound absorbers are summarized in Figure 2a-c, which correspond to the limited total thicknesses of 30 mm, 50 mm, and 100 mm, respectively. It can be observed from Figure 2a that when the limited total thickness was 30 mm, the sound absorption coefficients of the sound absorber consisting of microperforated panel + cavity + porous metal + cavity were completely consistent with those of the sound absorber consisting of microperforated panel + cavity because the obtained optimal structural parameters for these two sound absorbers are the same in Table 1. It can also be observed that the sound absorption coefficients of the porous metal were significantly determined by its thickness. The larger the thickness, the better its sound absorption performance, which is consistent with the normal sound absorption principle of porous material [7; 13; 28]. Meanwhile, it can be seen from Figure 2c that when the limited total thickness reached 100 mm, with the exception of the sound absorber consisting of microperforated panel + cavity, all sound absorbers achieved a similar sound absorption performance, which is consistent with the results in Table 3.  
Figure 2: _Cont._
## 3 Results
### Adaptability Assessment Tools and Models
#### 3.1.5 Preliminary Assessment Adaptation Model (PAAM), 2014  
Preliminary Assessment Adaptation Model (PAAM) [42] is a predictive model to support the decision-making regarding adaptations of existing office buildings. The research by [PERSON] [42] addresses six levels of adaptations designated as level 1'minor', level 2 'alterations', level 3 'change of use', level 4 'alterations and extensions', level 5 'new build', and level 6 'demolition'. Together with six labels of property attributes influencing adaptation (physical, social, economic, environmental, legal, and technological), these levels are utilized to form the PAAM conceptual model. The model aims to conduct an initial assessment of building suitability for level 4 of adaptation, i.e., alteration and extensions. The author built upon previous models (e.g., [PERSON] (1981) [47]) and case study approach, governmental data, and building permits to identify adaptation criteria that fall into the labels of the property attributes database. The author relied on cross-sectional data of adaptation events corresponding to the period between 1998 and 2008 in a central business district in Melbourne, Australia. The Principle Component Analysis (PCA) mathematical technique was applied to 5290 adaptation events to create loadings of the individual factors of each property attribute category and therefore identify an order of relative importance on the different attributes. The PCA identified a checklist of 12 attributes falling into three categories of attributes: physical and size, land, and social. The answers to the checklist questions determine the adaptation potential ranging from very low to very high. The PAAM's checklist is applied to an illustrative case study of a 27-story existing office tower to demonstrate its practical application. The checklist delivers a simple instrument for stakeholders and non-experts to determine the overall potential of an existing office building focusing on the property attribute that develops the most variance in adaptation. However, the tool is oriented to the Australian context. Another limitation is that the PAAM model does not address functional or use changes; it rather provides an assessment tool for minor adaptation actions.
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 3 Proposed Method for Signal Detection
### Derivation of \(\omega^{*}\) and \(\gamma^{*}\) Using Novel PSO
```
1:G: Channel matrix
2:r: Received signal vector
3:\(K_{\max}\): Maximum iterations for OAOR
4:\(\epsilon\): Convergence threshold for OAOR
5:\(\beta\): Regularization parameter
6:\(N\): Number of particles for PSO
7:\(t_{\max}\): Maximum iterations for PSO
8:Estimate transmitted signal vector
9: Compute \(\mathbf{G}^{H}\mathbf{G}\) and its decomposition: \(\mathbf{G}^{H}\mathbf{G}=\mathbf{D}-\mathbf{L}-\mathbf{U}\)
10: Compute \(\mathbf{D}^{-1}\)
11: Initialize \(\mathbf{x}^{(0)}=\mathbf{D}^{-1}\mathbf{G}^{H}\mathbf{r}\)
12:\(k\gets 0\)
13: The OAOR iterative method updates the estimate \(\mathbf{x}^{(k)}\) using:
14:\(\mathbf{x}^{(k+1)}=\mathbf{x}^{(k)}+\omega^{*}(\mathbf{D}^{-1}\mathbf{G}^{H} \mathbf{r}-\mathbf{D}^{-1}\mathbf{G}^{H}\mathbf{G}\mathbf{x}^{(k)}+\gamma^{* }(\mathbf{D}-\mathbf{L}-\mathbf{U})\mathbf{x}^{(k)})\)
15:while\(k<K_{\max}\)do
16: Optimize \(\omega^{*}\) and \(\gamma^{*}\)
17: Initialize PSO parameters: \(g_{\mathrm{NO}}\gets N\), \(w_{\max}\gets 1.2-\left\lfloor\frac{g_{\mathrm{NO}}-50}{150}\right\rfloor\)
18:for\(i=1\) to \(N\)do
19: Initialize \(\mathbf{x}_{i}=(\omega_{i},\gamma_{i})\) randomly within bounds
20: Initialize \(\mathbf{v}_{i}\) randomly
21: Evaluate fitness \(f(\mathbf{x}_{i})\) with current \(\mathbf{x}^{(k)}\)
22: Set \(\mathbf{p}_{\mathrm{best},i}\leftarrow\mathbf{x}_{i}\)
23:endfor
24: Set \(\mathbf{g}_{\mathrm{best}}\leftarrow\arg\min_{i}f(\mathbf{p}_{\mathrm{best},i})\)
25:for\(t=1\) to \(t_{\max}\)do
26:\(w(t)\gets w_{\max}\cdot e^{-\frac{t^{2}}{2(0.5t_{\max})^{2}}}\)
27:\(c_{1}(t)\gets 2+0.5\cos(\frac{nt}{2})\)
28:\(c_{2}(t)\gets 2-0.5\cos(\frac{nt}{2})\)
29:for each particle \(i=1\) to \(N\)do
30: Update velocity and position:
31: Evaluate fitness \(f(\mathbf{x}_{i}(t+1))\) with current \(\mathbf{x}^{(k)}\)
32:if\(f(\mathbf{x}_{i}(t+1))<f(\mathbf{p}_{\mathrm{best},i})\)then
33:\(\mathbf{p}_{\mathrm{best},i}\leftarrow\mathbf{x}_{i}(t+1)\)
34:if\(f(\mathbf{p}_{\mathrm{best},i})<f(\mathbf{g}_{\mathrm{best}})\)then
35:\(\mathbf{g}_{\mathrm{best}}\leftarrow\mathbf{p}_{\mathrm{best},i}\)
```  
**Algorithm 4** Optimized AOR algorithm for massive MIMO signal detection.
## 3 Influence Metrics
### Effectiveness Analysis of the Proposed Metrics
#### 4.2.2 Global Metrics  
Using real-world datasets, we analyzed the distribution properties of suggested global influence indicators. Figure 8 displays the Complementary Cumulative Distribution Functions (CCDF) for the suggested global centralities, with the horizontal axis denoting the order of device effect. Here, it is clear that the distributions of the Closeness measure are not uniform when taking into account various centrality techniques. This suggests that the selection of the centrality approach affects the measures' distributions. These results help us determine the best tools to increase the effectiveness of data transmission in OppMSNs. These features can be used by routing algorithms to choose the most reliable device as a relay. As a result, the impact measures suggest various possible contributions of the same device to information propagation when combined with various centrality approaches. This connection between device impact and route design, in our opinion, is a key element in enabling effective information propagation inside OppMSNs.  
Figure 7: Effectiveness of local metrics: (a) Number of neighboring devices among the top-N devices; (b) average number of contacts among the top-N devices.
# An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications
## 2 Emerging Nuclear Sensor Technology
### Solid-State Nuclear Sensors  
Solid-state nuclear sensors based on metal oxide semiconductors have become a promising approach for detecting ionizing radiation. Materials such as zinc oxide (ZnO), tin oxide (SnO\({}_{2}\)), and titanium dioxide (TiO\({}_{2}\)) are widely used (e.g., [65; 66; 67]) because of their well-defined semiconducting properties and inherent material stability.  
In these sensors, the fundamental mechanism relies on the interaction of ionizing radiation with the metal oxide, which generates electron-hole pairs [68]. This generation of charge carriers leads to a measurable increase in conductivity. For example, when radiation excites electrons from the valence band into the conduction band, the resulting change in carrier density is directly correlated with the intensity and energy of the incident radiation [69].  
One of the key advantages of metal oxide sensors is their robustness and stability in harsh environments. Metal oxides are known for their chemical and thermal stability, making them suitable for applications in extreme conditions, such as high-temperature zones within nuclear reactors [70; 71; 72]. Metal oxide semiconductors have been used in wearable sensor applications, and show great potential for nuclear sensing applications [73].  
Metal oxide nuclear sensors have found applications across several domains. In NPPs, metal oxide sensors contribute to the safety and efficiency of reactor operations by monitoring neutron and gamma radiation levels [74]. Metal oxide sensors also exhibit sensitivity to low levels of radiation. In environmental monitoring, this property is extremely valuable for detecting radioactive contaminants in air, water, and soil [75]. In medical imaging, metal oxide sensors are being explored for their potential to improve the detection capabilities of imaging equipment [7; 76].  
Integration with advanced technologies is opening new avenues for metal oxide nuclear sensors. The combination of these sensors with wireless communication systems enables remote monitoring of radiation levels in inaccessible or hazardous areas [77]. Moreover, incorporating artificial intelligence and machine learning algorithms allows for sophisticated data analysis, improving the interpretation of sensor data and predictive capabilities [78].
## 2 Related Work
[16] studied resource allocation for a multiuser MEC system based on time-division multiple access and orthogonal frequency-division access. Moreover, multiuser cooperative computing [28; 29] is also envisioned as a promising technique to improve the MEC performance by offering two key advantages, including short-range transmission via D2D techniques and computation resource sharing. Ref. [30] proposed a method to jointly optimize the transmit power, the number of bits per symbol and the CPU cycles assigned to each user in order to minimize the power consumption at the mobile side. The optimal solution shows that there exists an optimal one-to-one mapping between the transmission power and the number of allocated CPU cycles for each mobile device. In addition to task offloading, the problem of dependent tasks scheduling on multiprocessor systems has been widely studied [31; 32; 33; 34; 35; 36]. [PERSON] et al. [31] proposed an efficient list-scheduling algorithm, called Dynamic Critical-Path scheduling algorithm (DCP), for allocating dependent tasks to homogeneous multiprocessors to minimize the makespan. One valuable feature of DCP is that the start times of the scheduled nodes are unfixed until all nodes have been scheduled. Our IDCP also applies this feature. The differences between DCP and IDCP are that DCP is a constructive heuristic and can only be used for homogeneous multiprocessor scheduling, while IDCP is an iterative heuristic which addresses the offloading problem as task migrations among heterogenous MEC platforms. Moreover, DCP assumed infinite computation capacity while IDCP relaxes this unrealistic assumption for MEC scenarios. In addition, [PERSON] et al. [33] proposed real-time algorithms for dependent task scheduling on time-triggered in-vehicle networks. Another work [36] presented adaptive scheduling algorithms for task graphs in dynamic environments. All the above literature studied constructive heuristic algorithms. In contrast, the algorithm proposed in our paper is an iterative heuristic, offering more flexibility for optimization, that works on heterogeneous edge computing systems.
# Seismic Behavior of Steel Plate-Concrete Shear Walls with Holes
###### Abstract  
Steel plate-concrete shear walls (SPSW) are used as the containment in nuclear power stations. However, the influence of holes and axial loading on the behavior of steel plate-concrete shear walls is neglected in most studies. Thus, it is necessary to understand the seismic behavior of SPSW members with holes in order to avoid the potential risks for nuclear power stations. In this study, a series of specimens were tested by low-cycle reciprocal loading. The specimens were designed with different holes to simulate real members in nuclear power stations. A hysteretic curve of specimens was obtained from a low-cycle reciprocal test to discuss the seismic behavior of steel plate-concrete shear walls (SPSW). Moreover, effects of axial compression ratio, hole size, thickness of the steel plate, and hole position on the hysteretic performance of SPSW were analyzed. The horizontal ultimate bearing capacity of SPSW specimens was estimated using the norms of the Architecture Institute of Japan and the calculation method of Ono reduction rate. Results provide theoretical references for the design and application of SPSW with holes.  
Keywords:SPSW; axial compression ratio; hole size; thickness of stiffening plate; hole position; hysteretic performance +
Footnote †: journal: Applied Sciences
## 5 Use Cases
### Example 2: Ethereum  
_Based Smart Grid Switch and Counter_  
We developed a new service definition (serviceDef.js) for LDAF server and the client was a Swether device configured to operate over LDAF. Upon service initiation the LDAF server connects to the  
Figure 5: Regular DNS transaction from NodeJS environment.  
Figure 6: Transaction between the LDAF client and the server.  
remote Ethereum node. Just as in case of DNS, also here the client initiates a WebSocket connection for communication between the client and the LDAF server. The LDAF in our case enables two optimization scenarios:  
1. Binary message content serialization by applying binary encoding of message content instead of textual (JSON). In this case all the messages from the Ethereum node are passed to the Swether. Bandwidth reduction is a consequence of more efficient encoding.
2. Application adaptation reduces the number of block notifications that are periodically sent from the Ethereum node to the connected devices. For Swether, we applied this information to check the block depth for security reasons. After receiving an event notification for an action to be executed in the Swether, it first waits for a predefined number of blocks to verify that the notification is actually placed deep enough in the chain. However, in most other cases the block number notification is not required by the end device.  
We therefore implemented block notification filtering at the LDAF (with an arbitrary filtering ratio), to reduce the number of messages. To maintain the application's security, event validation was implemented in the LDAF service, too. Only verified event notifications are passed to the Swether. As Swether does not have to verify them, block number notification is no longer essential data for application operation. Only occasional block number notifications are applied to check the node synchronization.  
For the first scenario we set the LDAF service to pass every (1 out of 1) block notification to the Swether. We captured all the traffic from and to the LDAF server, so as to include the WebSocket session setup and at least 10 consecutive block notifications. The traffic between LDAF server and Swether was 1104 B and between the LDAF server and the Ethereum node about 16 KB. Due to binary message content, encoding the traffic was thus reduced to ~7% of that from the original API of the Ethereum node. For DNS, this advantage was minimal due to the small DNS query sizes and UDP. For Ethereum the reduction is substantial, as the traffic is reduced nearly to 1/14.  
However, the traffic was even more reduced when the application adaptation was applied. As shown in Figure 7, we applied different filtering rates to reduce the number of block notifications for the Swether. If we reduced the number to one out of two messages, the traffic was ~3% of the initial sum. If we let every one out of 10 messages through, the remaining traffic was only 1%. Despite the evident traffic reduction, we did not make any tradeoffs in the application's functionality or security.