esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 5 Results
### Effect of Group Movement on Evacuation Time  
Based on the simulation results, when pedestrians move in groups, the evacuation time at different slopes is shown in Figure 12. The figure uses color variations to visualize the change in evacuation time for different group combinations in each row. From the figure we can observe the following:  
1. The results show that the color gradient in each row is subtle as the slope increases from 1\({}^{\circ}\) to 15\({}^{\circ}\). This suggests that the evacuation times for different group combinations change very little as the slope increases. In contrast, the color differences were more pronounced when comparing the colors of different group types. Thus, under congested conditions, the effect of pedestrian group movement on evacuation time is significantly greater than the effect of slope.
2. There are substantial color differences across different group types. For group types with a single age composition like \"M-M-M\" or \"J-J-\", the colors are more homogeneous within the row. This is largely dependent on the relatively consistent pedestrian evacuation ability within the single age group. In multi-age mixed group types, such as \"M-J-\" or \"O-M-J\", the colors show more variation within the row. This implies that evacuation efficiency varies with crowd size and age composition, the implications of which are highlighted below.
## 3 Research Methodology  
### Technical Overview  
The VANETs are closely related to Mobile ad hoc Networks (MANETs), but they vary in that their nodes are mobile and that their topology changes frequently, making them more difficult to discern [32]. Furthermore, VANETs are established on their own initiative and have a rather limited lifespan. VANETs are predominantly used by mobile nodes (i.e., cars), while networks can also support ongoing facilities called Road Side Units (RSUs) [33]. In the event where a node is not immediately in range of another, nodes that desire to interact with that node can accomplish this by forwarding neighboring nodes in the network to reach their target. Many different routing protocols are available, but we concentrate on the ad hoc on demand distance vector routing protocol (AODV), which is among  
Figure 2: Classification of DDOS attack.  
the most important and commonly utilized protocols in works today. Security breaches affecting the availability of VANETs and other Internet of Things (IoT) devices are a major source of worry, particularly as the dependence on applications that need intravehicular communication grows [34; 35; 36]. This document discusses in more detail the Black Hole assault. A node during this assault misrepresents itself as having the quickest path to a target in order to gain attention. Following the selection of this route, packets from the source should be relayed to the next hop on the route by the node. However, these packets are intentionally dropped, blocking communication between the source and destination nodes from taking place. In the event of a single node, we propose a detection mechanism for the assault existing in a network, which is discussed in subsequent text. The single malicious node scenario represents the most basic example regarding this issue. In this work, we present two new ways to improve the capability of our defenses to identify and prevent Black Hole attacks involving many malicious nodes, which are currently ineffective. We are able to show the effectiveness of these new methods and our better preventive measures through simulations of the attack and through the use of our solution in network simulator 2 (NS-2). The results of these simulations indicate that our countermeasure is accurate in its detection of Black Hole assaults and is effective in its prevention of Black Hole attacks. In instances when a Black Hole attack was present, the usage of our approach resulted in a proportional boost in network performance when compared to when it was not used.
# Progress and Prospect of Saline-Alkaline Soil Management Technology: A Review
### The Development and Properties of Salt-Affected Lands  
The development of salt-affected soils is influenced by various natural factors, such as climate conditions and topography, and the specific combination of geology and geomorphology. The development and properties of salt affected lands in China vary across different geographical regions due to diverse climatic and topographic conditions. These lands can be broadly categorized based on soil type, vegetation and environmental conditions as illustrated in Figure 1 below. Extreme climatic conditions, such as drought and low rainfall, restrict the loss of soil moisture and salt movement, leading to the accumulation of salt in the topsoil layer. The fluctuation of topography and the diversity of geomorphology also affect water flow and salt distribution, aggravating the process of salinization. Secondary Salinization is another contributing factor, such as the weathering of parent materials rich in salts or the intrusion of seawater in coastal areas. At the same time, human activities play a crucial and integral role that cannot be disregarded. These activities include long-term irrigation practices, the lack of scientifically planned drainage systems, and the over-reliance on and over-application of chemical fertilizers in agricultural production. These activities contribute to the natural imbalance of the soil, preventing the effective dilution and drainage of salts in the soil, thereby forming saline-alkaline soils [7; 8].  
Figure 1: Types of saline-alkaline lands in China [9]. In the figure; 1—Semi-humid semi-dry early steep meadow saline area in Northeast China, 2—Arid, semi-desert grassland saline area in Inner Mongolia Plateau, 3—Gansu and Xinjiang desert saline areas, 4—Extreme arid desert saline area in Qinghai and Xinjiang, 5—Saline area of Alpine desert in Tibet, 6—Inland local saline-alkali soil coastal saline soil, 7—Arid semi-desert saline area in the middle and upper reaches of the Yellow River and 8—Semi-humid to semi-arid dryland-meadow saline area in the Huang-Huai-Hai Alluvial Plain.
# Inelastic X-ray Scattering as a Probe of Terahertz Phonon Propagation in Nanoparticle Suspensions
## 4 Experimental Details  
### The Measurements  
We utilized the high-resolution IXS beamline Sector 30 of the Advanced Photon Source at Argonne National Laboratory [34; 35] to measure all suspension spectra presented in this paper. The sample scattering was energy-analyzed by nine independent analyzers. These analyzers were placed on the moving extreme of a spectrometer arm. The latter can be rotated in the horizontal plane to select the exchanged wavevector through \(Q=2\pi/\lambda sin\theta\), where \(\lambda\) and \(2\theta\) are the incident beam wavelength and the scattering angle, respectively. The spherical analyzers were placed at a constant angular offset corresponding to a \(Q\) separation of 2 nm\({}^{-1}\). The incident beam energy was 23.7 keV and corresponded to the Si(12 12 12) backscattering reflection from the analyzers, while the energy analysis was implemented through the rocking of the crystals of the monochromator unit while keeping the analyzers fixed. The shape of the energy resolution profile slightly varied for each analyzer, with an average spectral width of about 1.2 meV. More details on the spectrometer are given in Ref. [34].
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.3 Melete  
Melete [60] is based on the Mate virtual machine, and allows for the dynamic updating of TinyScript-based functionality based on dynamic grouping. By joining and leaving groups, and thus executing the relevant code, this allows a sensor network to respond to the current conditions. Multiple applications are executed concurrently, so a node may perform multiple functions as required. Dissemination is based on modified Trickle functionality: code is selectively and reactively disseminated only to nodes in the group. And additional FORWARD state is added to Trickle, creating a multi-hop _forwarding region_. Only code for its associated groups is stored on a node. To minimize network overhead, while minimizing delay, version information for all groups is propagated throughout the network.  
The provision of concurrent applications, and support for dynamic responses to network state, are two features which are of direct interest to an autonomic software update.
## 4 Background  
Several techniques have been defined to optimize energy consumption in applications for reducing data sent to the sink. The most common are compression, aggregation and fusion [1,4]. Such techniques are usually used without taking into account the multivariate spatial and temporal correlation of readings gathered by sensors nodes on field. However, many sensor nodes deployed on field are usually able to monitor more than one variable, and are thus called multisensors.  
This section describes two concepts used by current works found in the literature, which we used in the conception of our solution. To the best of our knowledge, there is no other paper that uses multiple linear regressions to perform prediction and Euclidian distance to check correlation between neighbor sensor nodes readings, but we found papers such as that of [PERSON] _et al._[14] which use a technique adopted for spatial correlated data reduction by Pearson's coefficient (r). Also, we found papers such as the one by [PERSON] _et al._[7] which uses a technique adopted for temporal correlated data reduction by simple linear regression. Next, we present these two concepts and the corresponding equations.
# Frequency-Domain Lifting-Line Aerodynamic Modelling for Wing Aeroelasticity
## 3 Numerical Investigation
### Reduced-Order Modelling  
As stated in Section 2.3, from the rational matrix approximation of the aerodynamic transfer function, it is possible to derive a reduced-order model of the aerodynamic operator to be suitably coupled with the structural dynamics one in order to define the aeroelastic operator.  
As an example of this outcome of the application of the proposed frequency-domain LLT-like solvers, the rational matrix approximation is applied to the matrix \(\mathbf{E}\) related to the NMV shape functions, obtained by the U-T algorithm, for the wing with \(l=5\,\mathrm{m}\).  
The comparisons between the transfer functions directly given by the LLT-like solver, and those analytically provided by the rational matrix expression of Section 2.3 with the introduction of four poles, are shown in Figures 13-15 in terms of the magnitude and phase.  
These results demonstrate the excellent accuracy of the approximation procedure, and hence of the reduced-order unsteady aerodynamic model that can correspondingly be derived for aeroelastic applications.  
Figure 11: Transfer function \(E_{12}(k)\), NMV shape functions, \(l=1.25\,\mathrm{m}\).  
Figure 12: Magnitude of transfer functions \(E_{21}(k)\) and \(E_{22}(k)\), NMV shape functions, \(l=1.25\,\mathrm{m}\).  
Figure 10: Transfer function \(E_{11}(k)\), NMV shape functions, \(l=1.25\,\mathrm{m}\).
## 3 Results and Discussion
### Orthogonal Experiment for Manganese Dioxide Lithium Ion Sieve Preparation
#### 3.1.3 Analysis of Experimental Results  
As is seen in Table 5, through analysis and comparison using the range method, among the six factors, the one that had the most significant influence on the experiment was the roasting temperature. When the temperature was too low, there was an insufficient reaction between the manganese dioxide and lithium hydroxide, preventing the formation of substances with high selectivity for lithium. This is consistent with the research of [PERSON] et al. (2018, 2019).  
The second was the mass ratio of manganese dioxide to lithium hydroxide. A lower mass ratio led to an incomplete reaction between the manganese dioxide and lithium hydroxide, whereas an excessive mass ratio resulted in the appearance of other substances in the reaction products of manganese dioxide and lithium hydroxide, which interfered with the adsorption of lithium. Research by [PERSON] et al. also shows that impurities are produced when the ratio of manganese to lithium is not suitable ([PERSON] et al., 2019).  
The third was the concentration of HCl. If the concentration was too low, HCl could not fully replace Li\({}^{+}\) vacancies in the ion sieve with H\({}^{+}\), thus affecting the adsorption of lithium ions in the solution. Conversely, if the concentration of HCl was too high it could damage the structure of the ion sieve and affect the lithium selectivity, which was confirmed in a previous study ([PERSON] et al., 2019).  
The fourth was the roasting time. Insufficient roasting time led to an incomplete lithium-manganese reaction; while excessive time caused the decomposition of reaction products, generating substances that interfere with the adsorption of lithium ions. This was confirmed by [PERSON] et al. (2019).  
Finally, the soaking time of the ion sieve in HCl and the stirring time had minimal effect on the results.  
Therefore, the optimum level was determined as B5A3D4C1E4F3 (800 \({}^{\circ}\)C, 10:1, 1.5 mol/L, 60 min, 24 h, 18 h).  
Furthermore, Table 6 shows that the average lithium adsorption efficiency under the above conditions was 99.98%, which is higher than that in Table 5. This indicates that the above conditions were optimum, which included a 10:1 mass ratio of manganese dioxide to lithium hydroxide, a roasting temperature of 800 \({}^{\circ}\)C, a roasting time of 60 min, an HCl concentration of 1.5 mol/L, a soaking time of 24 h for the ion sieve with HCl, and a stirring time of 18 h for the lithium removal with HCl.
# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
## 3 Results  
### Evaluation of the Diagnostic Ability of the CNN-Based Models  
The general performance of all models was evaluated using several classification quality metrics, such as accuracy (ACC), precision, recall, F1 score, confusion matrix, Matthews correlation coefficient (MCC), receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) score. Evaluating the classification quality metrics across all developed CNN-based models, as shown in Table 4, it is evident that they all significantly exceeded the 50% threshold, thereby demonstrating robust predictive capability across AD, CN, and MCI classes. Precision, quantifying the proportion of correctly predicted positive observations among all predicted positives, ranged from 83% to 100% on average. This indicates that the models consistently returned high proportions of true positives relative to false positives, with the strongest results observed in the VGG16-based and MobileNet-based models (100% and 98%, respectively). Figure 3: Selected plots depicting loss values achieved by the CNN-based models during training and validation: (**a**) Loss values achieved by Custom CNN 128 model; (**b**) Loss values achieved by Custom CNN 1024 model; (**c**) Loss values achieved by ResNet50-based model; (**d**) Loss values achieved by VGG16-based model. Regarding recall (sensitivity), most models demonstrated satisfactory performance, with average values ranging from 82% to 100%. Again, this indicates that the models were generally reliable in detecting the true cases in each class, including unseen testing inputs. The F1 score, which correlates precision and recall into a single metric, followed the same trend, ranging from 81% to 100% on average across the models. The consistency among these three metrics supports the stability and general reliability of the classifiers explored in this study. ACC and MCC were computed to further account for class imbalance and provide a more objective, reproducible measure of predictive performance. Average ACC values ranged from 81.67% (Custom CNN 128) to a perfect 100% (VGG16-based), reflecting strong overall performance. Notably, all CNN-based models exceeded the 80% threshold, with several, such as the Custom CNN 1024, Xception-based, and MobileNet-based models surpassing 95%.
# 6G Visible Providing Advanced Weather Services for Autonomous Driving
## 3 Communication with Autonomous Traffic  
To export sensor data from autonomous traffic (as well as any other vehicles) and import real-time weather- and safety-related warnings (presented later in this paper), a proper communication system available within the whole traffic entity is required. Cellular communication with 5G is currently commonly available in Finland and, therefore, is considered as the primary approach. However, there are also methodologies especially designed for short-range traffic entity communication, called V2X (Vehicle-to-Everything) communication, based on European ITS-G5, inherited from IEEE 802.11p standard and C-V2X [11]. These approaches can also be used in parallel as a hybrid communication system. Such approaches within the realm of road weather and safety services have been studied in [12].  
Naturally, general Wi-Fi communication can be employed, as well as satellite communication, at least as a complementary communication system for 5G cellular networking. All of these communication systems are available at the Sod5G test track and were evaluated as candidate communication entities for the 6G Visible operational entity. The primary approach is to rely on commercially available communication system with adequate coverage within the public road network, which is, in our case, 5G cellular networking, with supplemental 4G cellular networking in some (rural) areas where 5G is not available. However, alternate solutions with ITS-G5 and C-V2X are studied in the test network area, as  
Figure 2: Example of parallel camera (above) and LiDAR measurement. There are two traffic signs in the middle with infrared coatings, visible as yellow in the LiDAR figure.  
they present alternative approaches if the required service availability can be achieved through specified user density in public traffic.
## 1 Introduction
### Research Significance  
The increased realm of 6G key technologies leads to faster adoption strategies that enable the operation of smart cities. Therefore, it is very important to highlight the key technologies, applications, and trends in the IoT for 6G in smart cities. Based on the research aim, the study results are highly significant for technology companies, government officials, scholars, and smart city management. The report results provide a holistic view of popular IoT 6G wireless communication technologies, which could enable smart cities to perform operations with high-speed connections. This allows technology companies to predict future trends and manufacture communication devices for smart cities accordingly. In addition, it helps government officials and smart city management to maintain the essence of building smart cities by adopting technologies that are relevant to 6G fastest wireless communication. Moreover, the result fills the gap in the literature, where limited information is available related to 6G wireless communication technologies, applications and trends. This shows the significance of our study.
# Dynamic Mechanical Characteristics of Horseshoe Tunnel Subjected to Blasting and Confining Pressure
## 4 Dynamic Response under Confining Pressure  
### Uniform Confining Pressure  
The static stress distribution around the hole and tunnel under the confining pressure loading was calculated by LS-DYNA. Figure 12 shows the static stress distribution when \(P_{\text{h}}=P_{\text{v}}=5\) MPa (\(k=1\)). The centrosymmetric compressive stress zone is formed around the hole and tunnel structure. The stress concentration phenomenon is observed obviously in the bottom floor corner of the tunnel, and the Von Mises stress reaches 10 MPa. The confining pressure produces the circumference compressive stress, reduces the tensile failure and inhibits the crack propagation after blasting.  
The horizontal crack length-time curve after detonation with different confining pressures is described in Figure 13. When \(P_{\text{h}}=P_{\text{v}}=5\) MPa, the horizontal crack originating from the borehole stop propagating at 0.6 ms(the crack length 138 mm). With the confining pressure increase to \(P_{\text{h}}=P_{\text{v}}=10\) MPa, the crack propagation time is 0.3 ms, and the crack length is 70 mm, which is half of it under 5 MPa confining pressures. The results indicate that hoop compressive stress is formed by confining pressure and reduces the tensile failure by explosive loading, and crack length decreases with confining pressure increase. With confining pressure increase to 15 MPa, the crack propagation time and crack length decrease to 0.25 ms and 45 mm, respectively. The hindering of crack extension by confining pressure is enhanced with pressure increasing, and the enhancement effect is gradually weakening.  
Figure 11: Fragmentation in specimen’s back surface from experimental results.  
Figure 12: Stress distribution about rock for \(k=1\), \(P_{\text{v}}=5\) MPa.  
As shown in Figure 14, the number and length of cracks in the fracture zone gradually decrease as the confining pressure increase. When the confining pressure rises to 10 MPa, the hindering of crack extension is significant. When the confining pressure increase to 15 MPa, only fewer cracks are generated around the borehole, and the max length of cracks is 46.5 mm. Moreover, the vertical cracks can be clearly observed when \(P_{\text{h}}=P_{\text{v}}=5\) MPa, and the through cracks are not found in the other two conditions.
_Information_**2015**, \(6\), 866-879; doi:10.3390/info6040866  
_information_  
**ISSN 2078-2489**  
www.mdpi.com/journal/information  
_Article_  
**Codeword Structure Analysis for LDPC Convolutional Codes**  
**[PERSON] \({}^{1,2,*}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1,2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Jiangsu Key Laboratory of Meteorological Observation and Information Processing,  
Nanjing University of Information Science and Technology, Ningliu Road No. 219, Nanjing 210044, China; E-Mails: [EMAIL_ADDRESS] (J.F.); [EMAIL_ADDRESS] (P.L.); [EMAIL_ADDRESS] (J.X.)  
\({}^{2}\) Jiangsu Collaborative Innovation Centre on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Ningliu Road No. 219, Nanjing 210044, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.:+86-25-5873-1196.  
Academic Editor: [PERSON]  
_Received: 16 September 2015 / Accepted: 8 December 2015 / Published: 14 December 2015_  
**Abstract:** The codewords of a low-density parity-check (LDPC) convolutional code (LDPC-CC) are characterised into structured and non-structured. The number of the structured codewords is dominated by the size of the polynomial syndrome former matrix \(\textbf{H}^{T}(D)\), while the number of the non-structured ones depends on the particular monomials or polynomials in \(\textbf{H}^{T}(D)\). By evaluating the relationship of the codewords between the mother code and its super codes, the low weight non-structured codewords in the super codes can be eliminated by appropriately choosing the monomials or polynomials in \(\textbf{H}^{T}(D)\), resulting in improved distance spectrum of the mother code.  
**Keywords:** low-density parity-check convolutional codes (LDPC-CCs); structured codewords; non-structured codewords; distance spectrum