esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Engineering of Nanostructured Drug Delivery Systems
From a clinical perspective, LNPs have effectively enabled the transport of mRNA, as demonstrated by their involvement in COVID-19 vaccines [39; 40; 41]. Carbon-based nanomaterials (CBNs) have also been increasingly explored for use in transporting various therapeutics, with their unique features exploited to interfere with the immune system. CBNs have numerous forms (e.g., graphene/graphene oxide (GO) nanosheets, single/multi-walled carbon nanotubes (SWCNTs/MWCNTs), and fullerenes) of interest in nanomedicine, each with particularities concerning generated immune responses [3; 18; 42]. For instance, GO nanomaterials have been widely investigated for biomedical purposes, including drug delivery, cancer therapy, photodynamic therapy, vaccine antigen delivery, and more. It was reported that GO can activate the immune system through the increase in inflammatory factors and the proliferation and differentiation of lymphocytes. Nonetheless, GO displays certain cytotoxicity and can damage DNA due to its lateral size and oxidation state, thus requiring surface functionalization for better-suiting nanocarriers and adjuvants [43]. Differently, MWCNT was noted to induce cell responses by inducing oxidative stress, destabilizing lysosomal membranes, enhancing the cell permeability of 3T3 fibroblasts, bronchial epithelial cells and RAW macrophages, and eventually causing apoptosis in the targeted cells. SWCNTs were observed to act upon malignant cells by stimulating NF-kB and p38 activation and ROS production [44]. Therefore, considering the inherent potential of CBNs, they can be used together with therapeutic cargo to create synergistic outcomes. Recently, DNA and RNA nanostructures have emerged as promising tools in biomedicine, especially in the field of drug delivery [45]. Due to the self-assembly properties of these nucleic acids, they can be designed with various architectures [46], such as polygons [47; 48], nano-cubes [49; 50], prisms [51; 52], nano-rings [53; 54; 55; 56], and other objects including three-way [57; 58; 59; 60] and four-way junctions [61; 62]. Among the wide range of 2D and 3D shapes, tetrahedral DNA nanostructures (TDNs) have attracted increasing attention especially due to their easy synthesis, remarkable stability, excellent biocompatibility, and facile functionalization. These advantageous properties make TDNs suitable for the transport and controlled release of chemotherapeutics, nucleic acids, imaging probes, immunotherapeutic agents, and theranostics [45]. In addition to the above-described possibilities, hybrid nanomaterials, which are composed of at least two constituents with different compositions and properties, typically organic and inorganic, have been deliberately designed to have combined activities and significant potential in enhancing cancer immunotherapy [63]. Hence, a plethora of synergistic systems have been reported in the recent literature, each with different degrees of success and specific immunotherapeutic applications.
# On the Physics of Kayaking
## Appendix A
### Comparison between the Theoretical Drag and the One Measured by Deceleration  
The theoretical total force at \(V=5\) m/s is thus \(F_{D}=F_{s}+F_{w}+F_{a}=51+12.75+3.9=67.7\) N for Athlete A1, which corresponds to \(SC_{D}=5.4\times 10^{-3}\) m\({}^{2}\). This value is 7.5%smaller than the one measured via the deceleration (\(SC_{D}=5.85\times 10^{-3}\) m\({}^{2}\)). Considering the relative importance of the different contributions of the drag, we find here 75% for skin friction, 19% for the wave drag, and 6% for the air drag. Even if we slightly underestimated the aerodynamic force in the last section, this is not the major part of the total drag.  
For Athlete A2, the theoretical total force at \(V=5\) m/s is \(F_{D}=F_{s}+F_{w}+F_{a}=54.8+26.1+4.3=85.2\) N. This corresponds to \(SC_{D}=6.8\times 10^{-3}\) m\({}^{2}\). This value is 6.7% larger than the one measured via the deceleration (\(SC_{D}=6.38\times 10^{-3}\) m\({}^{2}\)). The proportions of the three contributions are 64% for skin friction, 30% for the wave drag, and 6% for the air.  
The main difference with Athlete A1 is associated with the shape of the boat, which is larger for Athlete A2 (\(w=61\) cm instead of \(w=41\) cm) and, thus, induces a much larger wave drag.  
We conclude that the theoretical drag is able to estimate the drag measured experimentally at \(\pm 8\)%, which is fair considering the different approximations that we have used, especially concerning the shape of the hull.  
Concerning the evolution of these different drag contributions with the velocity, we present in Table 12 their values in the range \(V\in[1\) m/s\(-5\) m/s]. Equations (A1), (A4) and (A5) are, respectively, used to estimate \(F_{s}\), \(F_{w}\), and \(F_{a}\) using the parameters associated with Athlete A1: \(L=5.2\) m, \(w=0.41\) m, \(H=1.70\) m, and \(M_{t}=76\) kg.  
Even if the different force contributions increase with the velocity, we observe in the last column of Table 12 that the total drag area remains almost constant throughout the whole velocity range \(SC_{D}=5.7\ \pm 0.3\times 10^{-3}\) m\({}^{2}\).
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 3 Fundamentals of Acoustic Emission Technique
### Frequency Characteristics of Acoustic Emission in Soils  
The frequency characteristics provide critical insights into underlying AE generation mechanisms in soils as they are directly related to stress/strain waveforms released over source process durations. However, observed AE frequency ranges vary widely depending on the soil types, test conditions, and analysis methods employed, as shown in Figure 6.  
Several studies have shown that AE signals from granular soils span a 0.1-1000 kHz range in general [29]. Particle scale interactions including sliding, rolling, and breakage produce higher frequency emissions. For example, single-particle crushing tests using silica/coral sands identified dominant frequencies within 100-600 kHz corresponding to particle failure timescales [104]. Similarly, tropical residual soil fracture occurred at 100 Hz-20 kHz under triaxial conditions [105].  
Soil sample shearing experiments indicate that sliding and rolling contacts emit lower frequencies. Results from [PERSON] et al. [106] monitoring avalanching sand flows found dominant 60-160 Hz emissions attributed to grain collisions and friction. Similarly, direct shear tests on glass beads sheared at 60 kPa produced up to 80 kHz AE signals [107]. Lower confined stresses facilitated more grain reorganization, releasing lower energy signals at lower frequencies.  
Unsaturated soil testing is less reported. But results indicate an influence of the degree of saturation on detectable frequency ranges. Frequencies up to 10 kHz were recorded during partially saturated sand flow experiments, presumably owing to localized pore structure reconfigurations accompanying fluid redistributions [108].  
Regarding field applications, typically lower monitoring frequencies prove practical. Landslide analyses employed seismometers capturing 20-30 Hz MS signals from slipping soil/rock volumes [109, 110]. Likewise, excavation slope assessments relied on 5-8 kHz bandwidth detections through embedded waveguides [111]. Lower frequency ranges allow  
Figure 6: Frequency range for acoustic emission monitoring.  
for the monitoring of larger volumes hampered at higher bands by high attenuation in geological media.  
Overall, multi-frequency analysis may better characterize failure micro-mechanisms in granular soils by capturing the co-existence of grain-scale and fluid-pore interactions, depending on frequency sensitivities. Additionally, factors like stress-states, saturation conditions, and soil mineralogy introduce complexity, necessitating systematic parametric investigations for establishing quantitative correlations between frequency contents and specific processes driving slope instability, relevant for monitoring and early warning objectives.
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 4 Discussion
While there are more powerful sensors, such as hyperspectral cameras [81], that capture more information from the electromagnetic spectrum than RGB, these are usually commercial, licensed products. The same considerations apply for the software used to create the models. This makes the use of open-source software a necessity. For this reason, this research used python as a programming language for the algorithms and a Linux distribution as the RPi's operating system to deploy the models. While this research focused on grapevine plants and, specifically, diseases affecting their leaves, it is important to note that this approach should be primarily validated in the other disease classes on which the models have been trained, not only in Esca, and can also be extended to detect diseases in other types of crops. Moreover, research can be focused on different types of disease, affecting fruits of the plants or, in the case of grapevines, their grapes. Botrytis cinerea is a pathogen affecting more than 200 crops worldwide. In the case of grapevines, visual symptoms are observable on the grapes. Another possible approach is the detection of pests and insects that can allow for an even earlier warning about crop safety before the disease causes visible symptoms.
## 3 Results and Analysis
### SEM Analysis  
We utilized a scanning electron microscope (SEM) to observe the changes in the crystalline structure of the hydration product within the cement mortar, with a focus on the impact of the substitution amount of the TPSW.  
For a water-cement ratios of 0.5 and a 5% replacement, the structural crystal image changes and pore distribution of the hydration products in the cement paste were observed using scanning electron microscopy (SEM) at 3 days and 91 days. In Figure 15, the observation results show that with the increase in the content of TPSW, the interfacial gap between the cement matrix and the plastic aggregate became larger, which weakened the adhesion of the matrix. Furthermore, the addition of the discarded TV casings resulted in the enlargement of the bubbles and an increase in the number of pores formed in the internal structure. At the age of 3 days, since the material had not yet completed the hydration reaction, the formation rate of the hydration products was slow, which resulted in more obvious pores and cracks in the matrix, which was particularly significant compared with the control group. However, when the age was extended to 91 days, the hydration products gradually filled the internal pores, which significantly reduced the number of pores, which made the structure denser and improved the overall performance of the matrix.
# Evolution and Testing of NB-IoT Solutions
## 5 End-to-End NB-IoT Testing
### Triangle NB-IoT Capabilities
In the TRIANGLE testbed, the Test Automation Platform (TAP), from Keysight, acts as the coordination entity responsible for configuring and running the tests. TAP is based on plugins and test plans. Each testbed component is controlled through a TAP plugin which serves as a bridge between the TAP engine and the actual component interface. The plugins expose a set of commands to configure and control the components of the testbed. The test plans are based on the usage of the default test steps provided by TAP to control the test execution flow and the specific commands exposed by the plugins of the components. Three new plugins have been developed to support NB-IoT testing. A SSH (Secure Shell) plugin has been implemented to control NB-IoT devices. This plugin enables the remote control of NB-IoT solutions, which provides a remote access via SSH network protocol. One of the key components of the TRIANGLE testbed is the set tools used to stimulate the system under test. The testing of NB-IoT solutions covers a wide range of use cases based which can be based on private protocols with security restrictions, so its testing has been based on the reproduction of the traffic generated by the solutions under test. Common tools such as iPerf or Ping are not suitable for NB-IoT traffic generation because of the high latencies reached in NB-IoT cause the expiration of the timers that regulate packet transmission in these tools and therefore the cancelation of the traffic generation. Instead, Scapy, a packet manipulation and injection framework, has been used, which enables replaying the pre-recorded traffic capture and Nping tool, which allows for packet generation and selecting the protocol, the packet length, and packet interval among other options. Thus, traffic can be reproduced using traffic captures provided by the experimenters or can be simulated configuring properly the packet generation in the Nping tool. Figure 6: Testing environment for NB-IoT solutions. Figure 7: Radio propagation and interferences emulation.
## 2 TBM Selection Decision-Making Method
### Determination Method of Single Index Evaluation Standard  
Determining the evaluation standard of each single index is the basis for realizing the comprehensive evaluation of TBM selection decision-making. This paper classifies the evaluation standard of TBM selection decision-making according to open TBM, single-shield TBM and double-shield TBM in combination with the literature research, expert opinions and engineering experience. Open TBM, single-shield TBM and double-shield TBM all have a relatively clear scope of application.  
This paper summarizes the engineering experience, summarizes the characteristics of different types of TBM (see Table 3) and provides theoretical support for the single indicator adaptability evaluation standard [19; 27; 28; 29]. In order to quantify and divide the adaptability of different indexes to different TBMs and improve the efficiency of the decision-making model, this paper uses fuzzy mathematics to divide the adaptability of TBMs into six grades: 0 indicates that any TBM construction is not suitable, 1 indicates that single-shield TBM construction is suitable, 2 indicates that single-shield TBM and double-shield TBM are suitable for construction, 3 indicates that double-shield TBM is suitable for construction, 4 indicates that double-shield TBM and open TBM are suitable for construction and 5 indicates that open TBM is suitable for construction.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Open TBM** & **Double-Shield TBM** & **Single-Shield TBM** & **Single-Shield TBM** \\ \hline It is difficult to construct under the conditions of soft and broken surrounding rock, in case of geological disasters such as rock bursts, the protection ability of with strong convergence, the ability to personnel is good; & It can be constructed under the conditions of the soft and broken surrounding rock, mainly facing the medium hard rock and relatively complete soft rock stratum; & In case of geological disasters such as rock bursts, the protection ability of personnel is good; & When encountering the surrounding rock with strong convergence, the ability to escape from difficulties is poor; & When encountering the surrounding rock with strong convergence, the ability to escape from difficulties is poor; & The construction consists of two processes and cannot be constructed continuously; \\ Flexible and forepiling; & & & & & & & \\ Under suitable surrounding rock conditions, the penetration rate is fast; & & & & & & \\ The flat surface radius adapts to a wide range; & & & & & & & \\ The adaptability to adverse geology is extremely poor. & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of various types of TBM.
## 1 Introduction
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Research Work** & **Summary of Technique** \\ \hline [PERSON] et al. (2012) [20] & At each reference location, several RSS observations were collected and a smoothing technique was used to find a mean RSS representative. \\ \hline [PERSON] et al. (2013) [11] & Instead of measuring aggregated RSS, the RSS ratio coefficient was used. Utilized a 3-antenna receiver system where the RSS ratio coefficient was computed using the log-normal shadowing model. \\ \hline [PERSON] et al. (2018) [24] & The RSS measurements were normalized using the Z-score after the time-series RSS matrix was generated (concatenation of multiple observations of the RSS fingerprint). \\ \hline [PERSON] et al. (2019) [22] & Instead of the aggregated RSS obtained by the smartphone device, RSS measurements from each BLE advertisement channel were used. \\ \hline [PERSON] et al. (2021) [23] & The mean RSS was calculated by averaging two consecutive observations from a wireless AP with multiple advertising antennas. \\ \hline [PERSON] et al. (2021) [7] & Instead of using direct RSS subtraction, the authors transformed RSS to Q-based RSS and used a Q-based RSS subtraction. \\ \hline [PERSON] et al. (2021) [21] & Each AP had N RSS measurements taken, which were then arranged in descending order. The RSS representative is determined by taking the median of RSS measurements above the \(-90\) dBm threshold. \\ \hline [PERSON] et al. (2022) [19] & A non-stationary, non-linear RSS sequence was generated, and then smoothed using the empirical mode decomposition threshold smoothing (EMDT) technique to obtain the RSS representative of the observation. \\ \hline [PERSON] et al. (2022) [6] & Utilizing aggregated RSS measurements from multiple observations, RSS representative was obtained. \\ \hline [PERSON] et al. (2022) [17] & Ten RSS measurements were obtained using ten different parameters from the power-distance equation, which were then aggregated. \\ \hline [PERSON] et al. (2023) [25] & At each reference location, multiple RSS measurements from each AP were obtained and averaged. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of techniques for dealing with RSS fluctuation.
## 1 Introduction
In these situations, the UGVs are not enough to achieve the desired goals because of inaccessible areas without the assistance of UAVs. Although the proposed protocol is a small development toward saving human casualties, it will be useful for researchers to enhance its performance and scope within several other applications, as illustrated in the proposed future work. The UGV and UAV combination provides the following advantages:  
1. The UGVs maintain a safe distance from the mines, and UAVs eliminate the communication gap between mine-detection and incoming vehicles (IVs). 2. The UAVs provide better connectivity and establish stable IVC links among UGVs through the air-to-ground links where UGVs cannot move together because of rough terrain, irregular trajectories, and hilly areas. Consequently, the network fragmentation in the proposed protocol reduces, resulting in low delay, minute overhead, high throughput, and stable IVC links. The main motivation for developing the UGAVs-MDVR protocol is to save human life in rescue operations, detect buried mines, monitor inaccessible areas, inspect construction, develop intelligent transportation systems, etc. The proposed protocol works by combining the UGVs and UAVs into a single routing scheme that results in strong IVC links and an effective central coordination system, which reduces any likelihood of delay or failure in MDM dissemination in rescue operations. The cluster-based centralized coordinating system is adopted for taking the collective decision of movement within dangerous remote areas to provide safety to mine detection and clearance staff. The remainder of the paper is structured as follows. The relevant VANET protocols are reviewed and analyzed in detail in Section 2. The proposed protocol is developed and designed in Section 3, along with the algorithms. Further, in Section 4, the proposed protocol is simulated and compared with the state-of-the-art protocols using a network simulator (NS). Finally, the designed protocol is concluded in Section 5, while future work is suggested in Section 6.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 5 Conclusions  
The methodology employed in this research effectively integrates traditional statistical approaches with advanced machine learning algorithms to develop a robust model for predicting crime rates in Maryland counties. The inclusion of diverse socioeconomic indicators such as unemployment rates, migration patterns, and income levels enhances the model's ability to capture the intricate relationships between these variables and crime trends. This multi-faceted approach enables a comprehensive understanding of the factors driving crime in both urban and rural settings. The findings offer several actionable insights for policymakers and practitioners.  
Age-specific Crime Interventions: Individuals aged 30-50 are disproportionately affected by property crimes, while younger demographics (20-40) face heightened risks of violent crimes, such as aggravated assault, rape, and auto theft. These findings call for targeted prevention strategies, including economic support and job creation programs for younger populations and enhanced property protection measures for older age groups.  
Gender and Racial Disparities: This study confirms significant gender and racial disparities in victimization, with males being disproportionately affected by violent crimes like shootings and aggravated assault and with minority groups experiencing higher rates of violent victimization. These findings emphasize the need for tailored interventions, such as strengthening community policing in minority neighborhoods and expanding support services for at-risk populations.  
Urban vs. Rural Crime Trends: The distinct crime patterns between urban and rural areas suggest the necessity of differentiated resource allocation. Urban areas like Baltimore City require sustained, long-term interventions to address persistently high violent crime rates, while rural counties benefit more from adaptive, community-specific strategies that address localized crime drivers.  
Modeling Performance and Scalability: The neural network model consistently outperformed other machine learning techniques, achieving an R-squared value of over 90%. The ensemble methods further enhanced the model's predictive accuracy, reaching 0.95 in the 85-15 training-testing split. These results underscore the importance of employing advanced algorithms for high-dimensional, non-linear datasets and demonstrate their applicability in crime analysis across diverse contexts.  
This research contributes to the growing field of data-driven crime prevention studies by combining socioeconomic insights with state-of-the-art predictive modeling. By addressing the complexities of crime dynamics in urban and rural settings, the study offers a scalable and adaptable framework for other regions. Future efforts should focus on integrating real-time data sources and expanding the model's applicability to account for evolving socioeconomic and environmental conditions. These advancements will further enhance the ability of policymakers to develop informed, equitable, and effective crime prevention strategies.