esa-sceva/satcom-chunk-collection huggingface dataset:
# Concrete Silos: Failures, Design Issues and Repair/Strengthening Methods
## 2 Failure of Reinforced Concrete Silos  
The failure of reinforced concrete silos is attributed to material, design, design codes, and durability issues [9]. In the present context, the term durability denotes the ability of a material or structure to maintain the structural, aesthetic, functional, and mechanical properties over its useful life. The durability of RC silos and other concrete reinforced structures, can be predicted by the S \(\leq\) R relationship where R is the acoustics, fire durability, crack width limits, limit deflections, and construction resistance, construction resistance. The symbol S represents the loading, which influences thermal, chemicals, acoustics, movement of metal reinforcements, and the loading variables [2]. If the conditions outlined in the above equation are maintained, there is a marginal risk of structural failure over time T. However, periodical loading and unloading in silos contribute to stress on the building structures. Considering that each material has a defined number of cycles to failure, an increase in the frequency of loading and unloading elevated the risk of failure. Even though high durability is desirable, the properties might be adjusted through the modification of the computational resistance parameters in the Serviceability Limit State (SLS) and the Limit State Design (LSD) [2]. In each of the two states, the S and R parameters could be modified. On the downside, the modification of these parameters has to take into consideration other material and non-material factors such as the physical properties of the bulk solids, and the type of flow (mass and funnel flow).  
Apart from durability, the integrity of the structure is also influenced by secondary variables such as filling and discharge pressure [3], wind, and thermal actions linked to ambient fluctuations in the temperature [10]. The filling and discharge pressure in the silo, which is predicted by the flow patterns (mass flow and funnel flow), determines the risk of silo wall rotation and displacements and other forms of structural deformation [11]. In brief, the filling and discharge pressures are the most important secondary factors in the design of silos. The contribution of each of these variables to the failure of reinforced concrete structures is reviewed in the following subsections; Section 2.1 investigates the role of material factors on reinforced concrete silo failure.
# A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks
## 8 Analysis of Simulation Results  
In order to test and compare the BER performance and complexity between the S-T-Max-log-MPA algorithm and the original MPA algorithm in IWSN, simulation experiments are carried out. In the experiment, the selected parameters are J = 6, K = 4, M = 4, N = 1000. The overload factor is 150%, and the channel is a Gaussian white noise (AWGN) channel. The codebook used is a 4-dimensional codebook published by Huawei in reference [47].
## 4 Experiment  
### Data Acquisition  
Gene expression data and clinical information were obtained from two cohorts of bladder cancer patients with neoadjuvant treatment retrieved from the GEO database ([[https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)]([https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/))(accessed on 24 September 2023)) with the accession numbers GSE169455 [44] and GSE69795 [45]. The criteria for study inclusion were as follows: (1) The cases were diagnosed with MIBC. (2) All patients received neoadjuvant treatment. (3) The cases had complete expression data. (4) For analysis of clinical characteristics, the patients had corresponding clinical and prognosis information. The dataset encompasses comprehensive clinical details and gene expression information, as delineated in the Supplementary Materials (see Tables S1 and S2). Furthermore, a concise summary of the principal attributes of the dataset, including sample size, platform, and data sources, is systematically tabulated in Table 1.  
The integration of multiple datasets typically resulted in batch effects or non-biological variations. To tackle these effects, we utilized the empirical Bayes algorithm provided in the ComBat function of the SVA package during our data preprocessing phase [46]. This method was crucial for reducing batch-related discrepancies, therefore enhancing the overall quality and reliability of our data analysis.
# Fast and High Precise Spatial Documentation of Traffic Accident Site Using Only Low-Cost RPAS
###### Abstract
These parts can be accelerated by better infrastructure--for instance, more IRS and car rescue service stations or better-equipped emergency vehicles. The time acceleration of these steps is strongly dependent on a huge supply of money. In other words, every second of speeding up would be disproportionately expensive. Regarding the economic cost alone, it is not as efficient and feasible as optimizing the second step. This step can be significantly accelerated with minimal expenses, using low-cost RPAS instead of conventional methods. RPAS utilization decreases the time of traffic accident site inspection (the second step) and enhances the overall awareness of the site situation. Using statistical data from the national database to justify the differences and improvements, this communication focuses on the situation in the Czech Republic. To elucidate the situation in the Czech Republic, we have included some brief statistical information about traffic accidents as follows. According to [9], 89,446 traffic accidents (where traffic police were involved) occurred in the Czech Republic in 2020, resulting in 470 fatalities, 1536 serious injuries, and 19,270 minor injuries. The total direct costs were almost \(\copyright\)212 million, although these numbers are also affected by the COVID-19 pandemic (during which time traffic flow decreased). In comparison, 102,220 traffic accidents with total direct costs of over \(\copyright\)353 million occurred in 2019. According to [10], the cost of a closed highway is approx. \(\copyright\)145,000 per hour in the Czech Republic. If the time spent on traffic accident documentation could be shortened, a lot of hours and money could be saved. The methodology in general can indeed be applicable outside the Czech Republic too. Herein, we do not mention or discuss legislation for using RPAS. Firstly, legislation varies in different states throughout the world. Secondly, the IRS may use slightly different rules for using RPAS. The main goal of this communication is proof of concept. It demonstrates that new methodology using RPAS for traffic accident documentation can substitute conventional methods with many benefits, such as faster acquisition of more accurate data, and faster, efficient, more effective, and precise data processing, data reusability, etc.
## 3 \(V_{oc}\) Losses Analysis
### Measured \(V_{oc}\) on Test Samples  
To verify the above calculated \(V_{oc}\), test samples based on epi on FZ wafers were designed. These test samples had a similar structure to that of the UTSi solar cell, excluding the fact that the ultrathin silicon in the UTSi solar cell was supported by a steel substrate and the epitaxial layers in the test samples were grown directly on FZ thus no foreign support was needed. Figure 4 shows the structure of these test samples. Again, in the FZ substrate, bulk recombination is negligible and surface recombination dominates the final \(iV_{oc}\). These test samples were processed together with the UTSi solar cells and control samples to eliminate extraneous variables during fabrication. Table 5 lists the lifetime and \(iV_{oc}\) values of the test samples measured using a Sinton lifetime tester. These measured \(iV_{oc}\) values are between 682-703 mV, and their mean value is 691.7 mV, consistent with the calculated value of 687.0 mV in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Steps** & **Component** & \(\mathbf{J_{0}}\) **(A/cm\({}^{2}\))** & \(\mathbf{V_{oc}}\) **(mV)** & Steps \\ \hline
**1** & \(J_{0b}+J_{0e}\) & 4.76 \(\times\) 10\({}^{-15}\) & 767.0 & Maximum \\
**2** & \(J_{0 bs}\) & 4.23 \(\times\) 10\({}^{-14}\) & \(>\)710.1 & Front SiO\({}_{x}\)N\({}_{y}\) \\ \(-\) & \(J_{0 es}\) & 6.12 \(\times\) 10\({}^{-14}\) & \(-\) & \(-\) \\
**3** & \(J_{0 bs}+J_{0 es}\) & 1.04 \(\times\) 10\({}^{-13}\) & 687.0 & Front SiO\({}_{x}\)N\({}_{y}+\) Rear SiO\({}_{2}\) \\ \hline \end{tabular}
\end{table}
Table 4: \(J_{0}\) and \(iV_{oc}\) of the UTSi solar cell when passivated by SiO\({}_{x}\)N\({}_{y}\) and SiO\({}_{2}\). \(J_{0b}+J_{0e}\) is from Table 1, and \(J_{0 bs}\) is the reverse saturation current density of the base and \(J_{0 es}\) is the reverse saturation current density of the emitter when taking SRV into consideration.
## 4 Oscar: Tsch Optimized Cell Allocation Algorithm
### Design
#### 4.1.2 Rescheduling Algorithm
On the other hand, when an Rx slot is added to meet a need due to increased traffic, the corresponding Tx slots are re-established. Similarly, the parent sends a packet to all of its children to inform them that the slot is available again. Figure 1 shows an example of the classification of nodes according to their distance from the root. It shows the network consisting of 11 nodes distributed into 6 classes used as an example in this work. The network is composed of a root and 10 nodes distributed in a tree topology. During each time interval, the nodes all wake up to transmit or receive packets according to their class ID. In addition, different channels are used by two pairs of adjacent layers in order to mitigate interference. The allocation of the number of slots is done as described in Figure 2. For example, in normal Orchestra operation which has a slotframe size of six, node X will have slot numbers 1, 7, 13, 19, 25, 31, and 37. With the proposed implementation in OSCAR, node X keeps its slots as 1, 7, 13, 19, 25, 31, and 37 only if it is located in class 0. However, if it is located in class 1, it will only have slots 1, 7, 13, 19, 25,..., 37. Slot 31 is removed because it is a class 1 node and does not belong to the node closest to root. If node X was class 2, two of the six slots would be removed, i.e., slots 25 and 31, and so on as the class number increases. For the nodes that are far from the root where the rank is important, less energy will be consumed because less slots will be allocated. For those closest to the root, there will be more consumption, but it is minimal. The other advantage of this mechanism is that it does not cause network congestion as the nodes closest to the root, improving latency.
## 3 Results and Discussions
### Sensorial Quality Evaluation
The samples that were most highly regarded were those that were cooked through convection baking (A2--8.41 points), followed by those that were cooked sous vide (A1--8.15 points) and those that were cooked through microwave baking (A3--7.91 points). Figure 3: Two-dimensional t-SNE plot illustrating clustering for the eight samples of fish dishes: A1: Sous vide cooking—cook–serve (SV-CS), A2: Convective cooking—cook-serve (CC-CS), A3: Microwave cooking—cook–serve (Mw-CS), A4: Convective cooking—cook-hill (CC-CC), A5: Convective cooking—cook–freeze (CC-CF), OA1: Enhanced sample Sous vide cooking—cook-serve (SV-CS), OA2: Enhanced sample convective cooking—cook-serve (CC-CS), and OA3: Enhanced sample microwave cooking—cook–serve (Mw-CS). Figure 2: The determination of clusters optimized in accordance with the Bayesian Information Criterion (BIC) for the eight samples of fish dishes: A1: Sous vide cooking—cook-serve (SV-CS), A2: Convective cooking—cook–serve (CC-CS), A3: Microwave cooking—cook–serve (Mw-CS), A4: Convective cooking—cook-chill (CC-CC), A5: Convective cooking—cook–freeze (CC-CF), OA1: Enhanced sample Sous vide cooking—cook-serve cooking—cook-serve cooking—cook-serve (Mw-CS). Akaike Information Criterion (AIC); within sum of squares (WSS). Furthermore, a notable observation is that among all of the samples analyzed, those produced in the cook-serve system were the most highly regarded. The samples produced in the cook-chill and cook-freeze systems were found to be less appealing in terms of external appearance (A4--7.67 points, A5--7.2 points) and internal appearance (A4--7.47 points, A5--7.77 points), in comparison with those produced in the cook-serve system. The gradual cooling of the samples and the temporary storage at a temperature of 4 \({}^{\circ}\)C (A4), respectively at a temperature of \(-\)18 \({}^{\circ}\)C (A5), can be considered to justify slight dehydration of the products, with a direct influence on the color and external appearance of the products. Concerning the optimization proposal, it was observed that the sous vide (SV) samples (OA1--7.93 points) did not yield a positive result and were undervalued in comparison with the initial samples (A1--8.15 points). The technical optimization proposal, in this case, failed to enhance the sensory quality, as evidenced by the contraction of the packaging resulting from the internal vacuum, which mechanically impacted the product's integrity. The internal and external appearance of the samples were influenced by the consistency of the non-thermally treated products and the mechanical actions applied during the preparation, formation, and thermal treatment. Moreover, it was observed that the samples subjected to convective baking (OA2) and microwave baking (OA3) exhibited higher scores in comparison with the initial samples (A2 and A3, respectively).
# Research on Grouting Pressure of Earth-Rock Dams Reinforced by Polymer Based on Discrete Elements
## 6 Simulation Result Analysis
### Influence of Different Grouting Pressures
#### 6.2.2 Changes in Overall Porosity  
The change in porosity during the grouting process reflects both the slurry's ability to fill pores and the resulting compactness of the material post-grouting.  
From the above figure (Figure 14), it can be seen that the porosity under different grouting pressures after grouting is reduced. The porosity decreases by 6.3% before and after grouting under a pressure of 0.1 MPa, by 7.1% under 0.3 MPa, by 7.9% under 0.5 MPa, by 9.0% under 0.7 MPa, and by 9.7% under 0.9 MPa. Analyzing the results reveals a direct correlation between grouting pressure and porosity changes. As grouting pressure progressively increases, there is a corresponding rise in the degree of porosity reduction. At a lower grouting pressure, such as 0.1 MPa, the decrease in porosity is relatively modest because this pressure level does not provide sufficient force to propel the slurry deeper into smaller pores. However, with higher grouting pressures, the slurry can penetrate more effectively into the finer pores of the rockfill, leading to a more significant reduction in porosity.
To overcome these limitations for the case of a small Doppler angle between the horizontal direction and the sample velocity, and thereby a highly dominant transverse velocity component, the present approach proposes the matching of the 2D-scanner in the sample arm to the transverse velocity component of the sample motion, comparable to a tracking shot where the camera moves alongside the object to be detected. Consequently, the backscattering signals of the matched oblique sample movement will be highly correlated, whereas those of static sample structures and slowly moving scatterers will be less correlated and damped, depending on the scanner velocity. The method of the so-called lateral resonant Doppler OCT (LR-DOCT) is validated by means of in vitro flow phantom studies with diluted Intralipid and human blood.
# Behaviour of Uniform Drava River Sand in Drained Condition--A Critical State Approach
###### Abstract  
Numerous triaxial tests on sand and sand-like materials have been performed worldwide during the past several decades. Their results provided a development of the advanced soil constitutive models and laboratory testing devices, as well as the establishment of a worldwide database of different types of uniform sandy materials. From such research, the critical state and steady state theory has emerged as one of the most useful tool for the modelling of a soil behaviour. This paper presents the results of static drained tests performed on the uniform Drava River sand from the Osijek region in Croatia. The main aim was to determine the shear behaviour and critical state, given that these characteristics are mostly unknown for the tested sand material. A series of detail triaxial tests were performed in drained conditions for three different initial relative densities, \(D_{R}\), and two different loading directions; e.g., axial compression and axial extension. In total, 18 drained tests were performed. The study indicated that the value of 33.75\({}^{\circ}\) is the critical friction angle for the tested sand. The relative density of 57% is determined as the critical relative density. Additionally, the study confirmed the difference in critical state for compression and extension loading. In addition, the results indicate that the sample preparation procedure has an important impact on the critical state of loosely prepared sandy samples. These results give the first insights into the behaviour of the Drava River sand, which can generally contribute to the worldwide sand behaviour knowledge base.  
uniform sand; stress-strain behaviour; critical state; triaxial test; compression; extension Article
# Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
## 3 Results
### Random Forest
#### 3.1.1 Feature Importance  
* We found that the ability to draw a clock (cg9 dclkdraw), the number of people in the social circle (sn9 dnumsn), and self-perception of control had a significant impact on the onset of dementia (hc9 depresan1, wb9 agrwstmt1).
* One of the interesting findings was that the ability to use a cell phone is a major indicator of cognitive well-being. This is a significant non-intrusively obtained lifestyle parameter. (te9 cellphone).
* Ability to recall the name of the head of state (cg9 presidna2) and active social life also had a significant impact (pa9 outfrenjy).
## 3 Methods
### Comprehensive Probabilistic Method for Fracture Identification  
The primary challenge in identifying fractures using a single conventional logging lies in the inherent limitations of fracture interpretation accuracy [15; 32; 33]. Since outliers on a logging curve arise from multiple interacting factors, fracture interpretation is intrinsically ambiguous [17; 19]. Consequently, relying on a single conventional logging curve for fracture interpretation is challenging; thus, integrating multiple logging curves is essential [15; 34]. A comprehensive fracture analysis was conducted by integrating multiple conventional logging curves:  
1. Conventional logging curves are selected to identify fracture development, and using the fracture identification criteria of each logging curve, the effective thickness \(h_{i}\) of the fracture development is determined through conventional logging.
2. The actual thickness \(H_{i}\) of fracture development in the well section containing imaging logging data was measured, and the identified fracture thickness was confirmed to be accurate.
3. The ratio \(p_{i}\) between the effective thickness and actual thickness is calculated.
4. The reliability of fracture identification for each conventional logging curve is calculated: \[m_{i}=\frac{p_{i}}{\sum_{i=1}^{m}p_{i}}\] (1) where \(m_{i}\) represents the reliability of any logging curve, defined as the weight of the curve; \(p_{i}\) denotes the fracture thickness identified through imaging logging.
5. The logging curves are normalized to eliminate the influence of various dimensions: \[x_{i}=\frac{X-X_{min}}{X_{max}-X_{min}}\] (2) where \(x_{i}\) represents the normalized conventional logging, \(X\) is the value at any point on the conventional log, \(X_{min}\) is the minimum value, and \(X_{max}\) is the maximum value of the conventional log.
6. Based on the normalized values of conventional logging curves, a weighted average is calculated, yielding a new value referred to as the comprehensive probabilistic index: \[P_{i}=\sum_{i=1}^{m}m_{i}\cdot x_{i}\] (3) where \(P_{i}\) denotes the comprehensive probabilistic index; \(x_{i}\) represents the normalized conventional logging; and \(m_{i}\) is the weight of the logging curve. Thus, a higher value of the comprehensive probability parameter signifies more developed fractures at that location.
## 7 Experimental Results
### Original BEE
#### 7.1.6 Multihop Inter-Clustering Supported BEE (BEEM)  
The communication between the CHs and the BS is called inter-cluster communication. When the network size scales, if the sensors do not support long distance communication, the connectivity of the network is hard to guarantee without multihop routing. Because of that, supporting multihop inter-cluster communication is essential. Furthermore, multihop inter-cluster routing can further reduce energy consumption through reducing long distance communication between the CHs and the BS. The experimental results for BEEM are shown in Figure 9. The energy cost of forwarding packets is set to be 10 pJ/bit/m\({}^{2}\). As we can see, BEEM can provide better network coverage and longer overall lifetime than BEE.  
Figure 8: Network sensing coverage.  
Figure 9: Network sensing coverage comparison between BEE and BEEM.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 3 Potential Applications  
A number of characteristics are enabling the network edge to present an ideal platform for providing a rich set of applications and services in the context of vehicular environments. These characteristics are geo-distribution, mobility and location awareness, low latency, heterogeneity, and support for real-time interactions [29]. In contrast with VANET, and from our point of view, VEC has the following potential benefits to ITS:  
* Connectivity: vehicles with no Internet connection can be supported with safety and convenience services on demand, by providing timely information through direct communication between the requesting vehicles and the nearest edge. In safety conditions, edges broadcast warning messages to vehicles within their coverage area. Moreover, vehicles can request the nearest edge for convenience service provision. The edge can connect to the Internet if necessary and provide a response to the requesting vehicles.  
* Traffic Monitoring: Traffic events can be effectively detected by network edges with high accuracy according to monitored traffic conditions. Vehicles can be warned of a potential crash or a predicted congestion to take proper actions.
* Dissemination Efficiency: Due to the characteristics of proximity and communication coverage, network edges can provide efficient data dissemination with high delivery ratio and acceptable latency to relatively faraway distances.
* Trust Management: Edges have the potential to generate trusted safety and convenience messages in a context of trust management system, which can address many of the existing trust management in VANET.
* Enhanced privacy; since user data can be produced and consumed locally and anonymously without the need to store it or upload it to the cloud.  
In the remaining of this section, we review existing efforts to push computing out onto the edge in the context of vehicular environments. Potential applications include data collection and analysis, vehicular control systems and traffic management.
# Research and Application of Slag-Nanosilica Stabilizer for Silt Subgrade
## 3 Experimental Methodology
### Xrd  
X-ray diffraction (XRD) is a commonly used method for qualitative analysis of mineral composition in nature. By comparing the XRD results of stabilized soil, the mineral composition changes to stabilized soil before and after adding SNS and at different ages can be better analyzed. The plain soil and SNS-S (6%/7 d, 6%/28 d, 10%/7 d, and 10%/28 d) were ground and dried for testing, and the diffraction angle of 20 was set at 5-85\({}^{\circ}\).