esa-sceva/satcom-chunk-collection huggingface dataset:
# Deterioration Process of Cementitious Material Properties under Internal Sulphate Attack
###### Abstract  
As one of the important factors affecting the structural durability of cementitious materials, sulphate erosion not only reduces the service life of the material but also poses a threat to the structural safety of the project. At present, scholars around the world have conducted extensive research on topics related to external sulphate attacks and have achieved fruitful results. However, the process and mechanism of attack degradation of the gelling material are not clear. In this paper, gypsum sand was introduced into the mortar. The effects of gypsum content, gypsum particle size and curing temperature on physical properties such as expansion and strength of specimens were investigated. X-ray diffraction (XRD), thermogravimetry (TG), differential scanning calorimetry (DSC), Scanning Electron Microscopy (SEM) and energy dispersive spectrometry (EDS) were used to analyse specimens' phase composition and microstructure evolution at different attack ages. Finally, cementitious materials' degradation process and mechanism under internal sulphate attack (ISA) were analysed. Through the above research, the following main conclusions are drawn: (1) The swelling rate of cement mortar specimens all increased with the increase of gypsum dosing; (2) The expansion of cement pastes and mortars does not increase with the increase in curing temperature; (3) The gypsum particle sizes are smaller, the expansion of mortar specimens is larger, and the loss of compressive and bending strength of mortar specimens is large; and (4) The main reaction product of ISA is Ettringite (AFt). The gypsum formed during the ISA process does not cause expansion, while AFt is the main reason for the destruction of the cementitious material.  
internal sulphate attack; cement; deterioration; mechanism +
Footnote †: journal: Applied Sciences  
1
## 3 Experimental Results
### PDT Results
#### 3.2.2 Characteristic of Lateral Deformation  
The overall lateral deformation mode and distribution characteristics of drift ratio along the structural height are important to the seismic behavior of the structural system. Figure 11a shows the maximum lateral displacement curves under different excitation. It can be found that the lateral deformation pattern of the specimen belongs to shear mode. Moreover, the figure clearly shows that the lateral displacement curve was divided into two significant part at the corbel (5.125 m). This was due to the rigid-upper-flexible-bottom characteristics of the vertical stiffness distribution condition that the stiffness of the upper part of the corbel is much larger that of the pipe columns.  
Figure 11b shows the maximum drift ratio versus the specimen height. It was found that drift concentration of the specimen occurred on the column, where the peak of maximum drift ratio occurred at the height of corbel (5.125 m) after PGA came up to 0.30 g.  
Figure 11: Lateral deformation curves of the specimen under PDTs: (**a**) maximum lateral displacement and (**b**) maximum drift ratio.  
Figure 10: Roof drift ratio time-histories of the specimen under PDTs.
## 1 Introduction
Some studies have explored the use of the finite element analysis software Ansys 2015 to simulate the airflow field inside an outdoor ring-shaped main cabinet, and studied the effects of wind speed, shell size, and vent shape on ventilation effectiveness [13]. A measurement method has been developed for optimizing the temperature fluid flow field by considering grid control, boundary conditions, and heat sources as switch devices [14]. However, only a few recent studies have addressed the mechanical flow characteristics in RMUs [15]. Research has shown that different airflow distributions can be analyzed through numerical simulation of the thermal and humidity environment inside the switchgear [16]. However, due to its simplified structure, there was a lack of consideration for the heat source inside the room. Following this, Figure 1 illustrates the entire research process, focusing on two parts: experimental and simulation. The novelty of this paper lies in using CFD-EMAG coupling simulation to study the distribution of multiple physical fields inside a cable room and providing support for the research through mutual verification between experiments and simulations. Based on experiments and simulations, optimized dehumidification methods and measures have been proposed, providing support for their safe operation. A simulation model containing cables was established for the CFD-EMAG coupling simulation, taking into account the unique cable compartment structure inside the RMU. Since the cable room cannot be opened for data collection after it is put into operation, a cable room device was made for a dehumidification experiment. Electric heating wires were used as heat sources to replace cables and influence the temperature and humidity fields. At the same time, a simplified simulation model containing electric heating wires was also established for comparison with the experiment. The accuracy was enhanced through mutual verification via experiments and simulations, and the pipeline layout was improved to achieve a more uniform internal gas flow. Based on this, and the literature [17; 18; 19], references have been provided for the cable structure, materials, and humidity settings inside the cable room, and the anti-condensation and gas flow distributions of the dehumidification method in the cable room were further simulated using a more comprehensive cable-containing model with a complete internal structure. Through experimental verification and analysis, optimization measures have been proposed to prevent condensation in cable rooms.
## 4 Application of ISSA in Node Location in HWSNs
### Localization Steps  
In this section, we replace the least-squares method of the localization algorithm with the meta-heuristic algorithm. The specific steps of the localization algorithm are as follows.  
Step 1. Calculating the intersection area of the incoming neighbor  
As shown in Figure 5a, when the intersection region of anchor nodes 1, 2, and 3 is used as the search space for meta-heuristic algorithms, the searching range can be narrowed, thus accelerating the convergence speed. However, because the intersection region of anchor points is an irregular graph, performing the calculation is difficult. To simplify computations, the communication region of each anchor point can be treated as a square, as illustrated in Figure 5b, and the intersection region of the incoming neighbor can then be calculated.  
Step 2. Calculating the distance from the unknown node to each anchor node  
In this step, the idea of the DV-Hop localization algorithm is adopted, and the product of hop count and hop distance between nodes is used as the distance from an unknown node to an anchor node. The hop distance formula is shown in Equation (10), and the distance from the unknown node to the anchor node is calculated in Equation (11).  
Figure 4: Heterogeneous wireless sensor network model: (**a**) network connections; (**b**) distance from all anchor nodes to unknown node 21.  
\[Hopsize_{i}=\frac{\sum_{i\
eq k}\sqrt{\left(x_{i}-x_{k}\right)^{2}+\left(y_{i}-y_{ k}\right)^{2}}}{\sum_{i\
eq k}h_{ik}} \tag{10}\]  
\[d_{ij}=Hopsize_{ij}\cdot h_{ij} \tag{11}\]  
where \((x_{i},y_{i})\), \((x_{k},y_{k})\) are the coordinates of anchors \(I\) and \(k\), respectively, and \(h_{ik}\) represents the number of hops between anchors \(I\) and \(k\). \(Hopsize_{i}\) is the hop distance of anchor \(I\), \(Hopsize_{ij}\) denotes the hop distance of anchor \(I\) with the least number of hops from unknown node \(j\), and \(d_{ij}\) is the distance between unknown node \(j\) to anchor \(i\).  
Step 3. Calculating coordinates of unknown node  
To begin with, the fitness function is defined as in Equation (12). Then, the swarm intelligence optimization algorithm is used to find the unknown node coordinates, with the coordinates with the lowest fitness values being selected as the unknown node coordinates. If the distance value in Step 2 is guaranteed to be constant, the higher the performance of the chosen swarm intelligence optimization algorithm, the more accurate the positioning.  
\[fitness_{j}=\sum_{i=1}^{N_{a}}\abs{\sqrt{\left(\tilde{x}_{j}-x_{i}\right)^{2 }+\left(\tilde{y}_{j}-y_{i}\right)^{2}}-d_{ij}} \tag{12}\]  
where \(j\) represents the unknown node numbers, \(N_{a}\) is the total number of anchors, and \((x_{j},y_{j})\) represents the coordinates of unknown nodes randomly generated.
## 3 Methodology  
### Methodology Architecture  
Figure 1 shows the wireless sensor system architecture for diabetic retinopathy prevention and treatment, aiming at and addressing the existing problems in the DR grading application, such as tedious information entry, resource consumption, and the inadequate diagnosis of retinal lesions based on fundus images. The architecture includes three parts: patient interface, service interface, and user interface. The left block represents the patient side of the system, which utilizes a portable retinal camera and blood glucose monitor to capture retinal images and measure blood glucose concentration, respectively. The tablet computer is used to send the electronic medical record (EMR) of patients to the database server by wireless network. The portal retinal camera can perform independent operations such as retinal illumination and focusing, and does not display retinal images but connects to a computer with a wireless card via a built-in WiFi wireless connection module, displaying the collected fundus image from the camera sensor in real-time on the computer screen. The middle block of Figure 2 shows the database server in our system. The database server is used for data storage, while the deep learning model equipped on the server can analyse the signals online. In addition, the uploaded EMR information is transmitted to hospital information system (HIS), supporting the studies of diabetes prevention and its complications. The right block lists several user interfaces of clinical applications of our system, including the central monitoring station, workstation for doctors and nurses, and automated screening interface for patients.
# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 5 Results and Discussion
### Results Comparison with Baseline Models
As traffic density increases, the congestion problems observed in other modes of transportation affect bicycle users less. The ability to move faster, especially at traffic lights, makes the bicycle stand out in this density range. * High Traffic Density: In high-traffic-density scenarios, the motorcycle is the most efficient mode of transportation. This is because the motorcycle can move faster both in congested traffic conditions and at traffic lights. Thanks to its high maneuverability, the motorcycle can more effectively overcome obstacles in the flow of traffic, which reduces the total travel time. In peak traffic scenarios, the motorcycle's flexibility and mobility give it an advantage over other modes. * Pedestrian Performance: Although the pedestrian mode is an effective option for short-distance travel, it is less effective than other modes in heavy traffic scenarios and over long distances. Traffic lights and distance significantly increase pedestrian travel times. The performance of transport modes is affected by a combination of factors such as traffic density, traffic lights, and mobility. The proposed model is able to dynamically respond to these factors and select the most appropriate transport mode in specific traffic scenarios. In particular, the superior performance of the bicycles in low- and medium-density traffic and the motorcycle in high-density traffic shows that the model can adapt to different conditions. In the future, performing more comprehensive evaluations will be made possible by incorporating additional factors such as user preferences, environmental impacts, and cost into the model.
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 4 Performance Evaluation
### Simulation Settings  
The 5G UDN topology used for performance evaluation is illustrated in Figure 10, and it is created by referring to the set up used in [20]. As elaborated in Section 3.1, Option 3 of the NSA networking mode was used. In an area measuring 300 m\({}^{2}\), three eNBs (blue circle dots) with fixed positions were deployed. Five, ten, and fifteen randomly distributed gNBs (red circle dots) were deployed. All the BSs communicate with each other through the X2 interface. Each eNB is connected to the EPC through S1-MME. Five UEs were randomly positioned in the simulation area with a two-dimensional random-walk mobility model [31]. The speed of each UE varied from 2 to 20 m/s. The simulation duration was 30 s. The other parameters and their values in the simulation are summarized in Table 5.
# Paper and Fiber-Based Bio-Diagnostic Platforms: Current Challenges and Future Needs
## 2 Major Challenges and Opportunities
### Chemical Characteristics of the Paper/Fiber Materials and the Need for Surface Modification
Porosity and permeability of the paper/fiber networks can be analyzed by bulk density method, gas permeability techniques or X-ray computed tomography (X-CT) [106; 107; 108; 109]. Other techniques such as Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) can provide detailed information regarding the chemistry of the surface. These analytical techniques are well established in the laboratory and industrial setups. Nonetheless, close control over material properties and reproducibility of the products can be achieved through fabrication strategies. The consistency of the physical and chemical characteristics of the developed paper/fiber materials eliminates the risk of errors in the analytical assays and provides with accurate detection outcomes. One alternative approach to overcome such limitations could be the use of and electrospinning technique (particularly three-dimensional (3D) electrospinning setup) in which the diameter range of the fiber and the porosity could be closely controlled, and different classes of bulk materials could be selected for fabrication of the fibers. If the chosen material or one of the combined materials has desirable active functional groups in its chemical structure, then the fabrication process can be finely adjusted in order to obtain and optimal concentration of the necessary functional groups without the need for surface modification strategies [5; 6]. Unlike the chemical modification techniques, this alternative approach would not harm the original nature of the paper material, and the stability of the surface functionalities is guaranteed, as they are an integral aspect of the chemical fingerprint of the constructed matrix, and would not lose their activity over time.
## 6 Our New Model: A Robust Trustless and Privacy-Preserving Framework for VANETs  
The suggested framework systematically addresses the intricate challenges of trust, privacy, and scalability inherent in VANETs. Building upon the foundational architecture depicted in Figure 2, our approach leverages the robust capabilities of DLT, with a particular focus on Ethereum and its Layer 2 scaling solutions such as Optimism [76], Arbitrum [49], zkSync [89], and Polygon ZKEVM [24]). These technologies have been carefully selected and integrated to forge a formidable framework that ensures secure, effective, and scalable communication within VANETs. The framework is divided into various layers (the physical layer, the P2P network layer, and the DLT layer).  
For data security and authentication, the architecture has a decentralized ledger layer built on EVM. Multiparty threshold signatures are what make this layer stand out. They make sure that transactions are only approved and recorded when a certain number of parties agree. Adding threshold Homomorphic encryption also lets the system analyze encrypted data while keeping personal data safe. A P2P network layer makes the design even better by making the network much more reliable and making data easier to reach. This layer is very important because it lets multiple nodes share files, alarms, and error reports instantly and without any problems. The Base Layer, which is also called the physical network layer, is the most important part of this design. It is made up of servers and RSUs. These parts are the network's basis; they keep the structure strong and the operations stable.  
Figure 2: Our proposed architecture: the system model.  
In the proposed framework, directional sensors would be depicted using a specific modeling technique, for example, simulation or mathematical modeling to capture the angular detection range and communication of the vehicles in VANETs. These parameters include the detection angle, range, and resolution to enable the accurate location of an object and fast data transmission. The incorporation of directional sensors helps the network to control data flow in a particular direction to avoid collision and improve network communication.
# Postural Control in Children with Cerebellar Ataxia
## 4 Discussion
### Disease Progression and Postural Behavior  
When looking to the present results as a whole, children with SlowP seem to have a worse postural behavior with respect to both children of the NonP and H groups. This result is unlikelyrelated to the severity of the pathology since all patients had a homogeneous SARA score, which, in turn, indicates comparable motor deficits in clinical terms. Therefore, the difference might stem either from the kind of cerebellar lesion (generalized atrophy vs. vermian hypoplasia) or from the progressive or non-progressive nature of the pathology. In this regard, children with SlowP suffered from generalized cerebellar atrophy, which represents macroscopic neuronal death, and received an ascertained clinical and/or radiological diagnosis of slow progression. On the contrary, Joubert syndrome, affecting children of the NonP group, is a congenital malformation that causes anomalous organogenesis of both the cerebellar vermis and peduncles. Therefore, it has an intrinsically stable nature along with the growth of the subject. In fact, once the organogenesis is completed, the vermian hypoplasia remains stable throughout the patient's lifetime.  
It could be argued that our observation of worse postural control in children with SlowP may be related to the larger extent of their cerebellar compromission (generalized atrophy vs. vermian hypoplasia). However, literature reports an emblematic case that contrasts with this interpretation. In fact, [PERSON] et al. (2005) [49] published a case report in which a 17-year-old subject with complete cerebellar agenesis showed only mild ataxia with slight dysmetria and moderate mental retardation, but no difficulty in attaining very complex motor tasks. Such evident functional compensation could be explained only through the plasticity of the remaining brain areas, which had to cope with a lesion that is stable since embryogenesis. This report suggests restricting the hypothesis to the progressive nature of the pathology.  
Returning to the present study, we envisage that children with NonP could use the plasticity of their intact brain areas, which may include the cerebellar hemispheres, to effectively compensate for their stable lesion and attain an almost normal psychomotor development. On the contrary, children with SlowP suffer from a continuous cerebellar degeneration, which conflicts with the consolidation of compensatory functional strategies. This perspective not only fits with the gradual worsening of postural deficits we documented here when passing from healthy children to patients with NonP and to patients with SlowP but would also explain why patients with adult-onset cerebellar lesions show even more pronounced postural deficits [46]. Indeed, neural plasticity gradually but consistently decreases over the lifespan [50].
## 3 Major Agriculture Applications and Services
### Crop Disease and Its Management  
Famine in Ireland occurred due to _Phytophthora infestans_, causing the late blight of potato, which caused many crop losses. Due to this famine, about one million people in Ireland died [37]. In the USA, the southern corn leaf blight caused by _Cochliobolus heterostrophus_ brought famine and crop losses of nearly one billion USD. Later, this crop disease was spread to Canada [38]. The Food and Agriculture Organization (FAO) estimated 20% to 40% annual crop loss per year due to crop diseases [39]. Several agricultural management strategies were applied to recover these losses, such as the use of pesticides and fungicides. These approaches are practiced in advanced agriculture since the last century. It is estimated that approximately 500,000 tons of pesticides are used each year in the US alone. In contrast, in other countries of the world, about two million tons of pesticides are applied for disease management [40]. Although these pesticides are used for plant disease management to increase crop production, there are many other harmful aspects to animals and humans' health. It also disturbs the world's ecology and eventually causes severe environmental pollution to the entire ecosystem [5; 41].  
Many of the advanced technologies, such as the IoT, play a significant role in reducing the usage of these chemicals, which are hazardous to human health. Many crop disease monitoring programs such as crop modeling, the prediction of pests, and weather forecasts are monitored by these advanced and emerging technologies in agriculture. These technologies are considered effective approaches for plant/crop disease management [42; 43].  
Although treatment, perception, and assessment are important in disease forecasting, monitoring, and management, advanced technologies (e.g., the IoT, wireless sensors, and UAVs) are beneficial for disease identification and pest management. For example, remote sensing technology can be used to capture large areas of agricultural cropland economically. The remote sensors can be used to analyze crop processing, including disease and pests, plant health, and the environment [43; 44]. This remote sensing tool is low cost and has many advantages, such as a low cost for automatic activation and supporting recovery. For example, recently, the yields of many crops have been severely threatened by the poor pollination process [45].