esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Leap from Gold and Silver to Aluminum Nanoplasmonics for Enhanced Biomedical Applications
## 2 Aluminum Plasmonics  
### Nanostructure Fabrication  
Only a few studies of biological systems with nanofabricated Al plasmonic substrates have been performed. Variable shapes of Al NSs may be used to optimize the near-field enhancement and spectral response. Figure 1 shows that the optical response of Al NPs may be tunable over a broad spectral range by varying the NP size [1]. Recently, it was also shown that other substrate parameters may be varied such as the shape, inter-particle distance, and the refractive index of the substrate to control the Al plasmonic response [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Broad tunability of the plasmonic response has been achieved using periodically designed Al NP substrates on a stretchable support [14]. Plasmonic Al nanoantennas have been designed with controllable localized near-field hot spots [15; 16; 17; 18; 19; 20].  
Al NP colloidal synthetic methods provide possibilities for facile scalable manufacturing [21; 22; 23]. Considering stability and agglomeration issues, Al NPs can be synthesized via surface passivating agents for protection and stabilization. Templates can be used for large-scale synthesis of Al NPs via catalytic decomposition [22; 24]. Passivation agents play an essential role in stabilizing Al NPs as well as protecting them from further surface oxidation. Synthesis of highly regular, faceted Al nanocrystals  
Figure 1: Broadband tunable aluminum plasmonics: (**a**) Experimental spectra; (**b**) electron microscope images; and (**c**) simulated spectra of Al nanoparticles (NPs) with varying sizes. (Adapted from [PERSON] et al. [1]).  
with controllable nanocrystal diameters and shapes, including nanorods and nanocubes, has been reported [25; 26; 27]. Al nanocrystals were also decorated with size-tunable transition-metal nanoparticles to control UV absorption and scattering [28].  
Several methods have utilized inexpensive Al foil for Al NS fabrication [29; 30; 31]. We recently developed a method of ultrasonic synthesis of irregular disk-shaped Al NPs from commercial Al wrapping foil, and used these Al NPs to affect bacterial inactivation efficiency (Figure 2) [29]. Another sonophysical method of Al NS preparation from the commercial Al foil showed bacterial toxicity of Al NS smaller than 25 \(\upmu\)m [30]. Nanotexturing of commercial Al foils by anodization has shown another promising route to plasmonic substrate fabrication [31].  
High quality Al plasmonic films may also be fabricated by thermal evaporation [32]. Coupling of Al NS to Al films has been demonstrated with SPR linewidth narrowing and enhanced scattering efficiency [33]. Nanovoids in Al films have been fabricated and showed localized SPR in extreme UV range [34]. These technological advances in the fabrication of Al NSs showed great promise to optimize biosensing efficiency and, potentially, medical treatment.
###### Abstract  
The growing deployment of the Internet of Things (IoT) across various sectors introduces significant security and privacy challenges. Although numerous individual solutions exist, comprehensive frameworks that effectively combine advanced technologies to address evolving threats are lacking. This paper presents the Integrated Adaptive Security Framework for IoT (IASF-IoT), which integrates artificial intelligence, blockchain technology, and quantum-resistant cryptography into a unified solution tailored for IoT environments. Central to the framework is an adaptive AI-driven security orchestration mechanism, complemented by blockchain-based identity management, lightweight quantum-resistant protocols, and Digital Twins to predict and proactively mitigate threats. A theoretical performance model and large-scale simulation involving 1000 heterogeneous IoT devices were used to evaluate the framework. Results showed that IASF-IoT achieved detection accuracy between 85% and 99%, with simulated energy consumption remaining below 1.5 mAh per day and response times averaging around 2 s. These findings suggest that the framework offers strong potential for scalable, low-overhead security in resource-constrained IoT environments.  
Internet of Things (IoT); IoT security; artificial intelligence; blockchain; quantum-resistant cryptography; integrated security framework; performance modeling; edge computing; federated learning Article
# Management Platforms and Protocols for Internet of Things: A Survey
## 1 Introduction
Therefore, this survey elaborates on a deep study of the related literature focusing on available solutions, tools, and policies, including approaches for IoT networks and end devices management. Among the available technologies for IoT, an evaluation was performed using features its heterogeneity, scalability, supported technologies and security. Based on this evaluation, the most promising technologies were chosen for a detailed performance evaluation study (through simulation and deployment in real environments). Then, the main contributions of this paper can be summarized as follows:  
* An extensive review of the related literature considering network management protocols and platforms for IoT;
* Requirements analyses for IoT network and devices management;
* A comparative analysis of the available IoT protocols and network management platforms for IoT;
* Identification of challenges and open research issues on IoT management and its importance for further studies on the topic. The paper is organized as follows. Section 2 addresses important background information on network and device management. In Section 3, IoT Network Management and its requirements are introduced. The IoT Network Management protocols and the most relevant platforms available in literature are detailed in Sections 4 and 5 respectively. IoT Device Management and its requirements are introduced in Section 6. Then, the IoT Device Management protocols and platform technologies are studied in Sections 7 and 8. Section 9 presents a performance evaluation study of IoT management technologies and proposes open research issues based on obtained results. Finally, the lessons learned and the main conclusions are addressed in Section 10.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**EU**} & \multicolumn{2}{c}{**Denmark**} \\ \cline{2-5}  & **Directive** & **EC Working Document on** & **BEK No. 1001 of 27/06/2018** \\  & **86/278/EC**[22] & **Sludge, 3 rd Draft**[23] & **BEK No. 1001 of 27/06/2018** \\ \hline
**Metals** & **mg/kg ds** & **mg/kg ds** & **mg/kg ds** & **mg/kg TP 1** \\ \hline Cadmium (Cd) & 20–40 & 10 & 0.8 & 100 \\ Copper (Cu) & 1000-1750 & 1000 & 1000 & - \\ Nickel (Ni) & 300–400 & 300 & 30 & 2500 \\ Lead (Pb) & 750–1500 & 750 & 120 & 10,000 \\ Zinc (Zn) & 2500–4000 & 2500 & 4000 & - \\ Mercury (Hg) & 16–25 & 10 & 0.8 & 200 \\ Chromium (Cr) & - & 1000 & 100 & - \\ \hline
**Organic compounds** & **mg/kg ds** & **mg/kg ds** & **mg/kg ds** & **mg/kg TP** \\
**CONTAMINANTS** & **mg/kg ds** & **mg/kg ds** & **mg/kg ds** & **mg/kg TP** \\ \hline LAS & - & 2600 & 1300 & - \\ PAH & - & 6 & 3 & - \\ NPE & - & 50 & 10 & - \\ DEHP & - & 100 & 50 & - \\ \hline \hline \end{tabular}  
* EC = European Council; ds = dry solids; LAS = linear alkylbenzene sulfonate; PAH = polycyclic aromatic hydrocarbons; NPE = nonylphenol polyethoxplates; DEHP = di-(2-ethylhexyl)phthalate; BEK = Bekendtogerelse; TP = Total phosphorous.  
\end{table} 
Table 1: Regulatory limits in Denmark and the European Union (EU) for heavy metal and hazardous organic compound contents in sludge reused in agriculture [22; 23]. STRB case studies (both pilot and full-scale projects) treating various sources of industrial sludge from different countries and climates, outlining the advantages and challenges of these systems as they arise from practical experiences.
## 4 Discussion  
### Subirrigation and WTD Impacts on Yield and GPP  
The results obtained in this study show significant differences between the daily GPP fluxes on the HWL and LWL plots (Figure 5). Furthermore, in all cuts in the second year of the study and in the third cut in the first year, the cumulative GPP fluxes were higher at the HWL plot than at the LWL plot, which is equivalent to a higher CO\({}_{2}\) assimilation. Therefore, it can be concluded that higher WTD levels positively affect meadow productivity (GPP). A similar trend is observed when aboveground biomass yields are considered. In all cutsexcept the first cut in 2021, the yield was higher from the HWL plot than from the LWL plot. On an annual basis, maintaining a higher groundwater level contributed to a 5.4-11.7% higher biomass yield.  
As the GPP indicates the amount of CO\({}_{2}\) assimilated by plants in photosynthesis, the clear relationship between the GPP and plant biomass exists. These relationships, for the LWL and HWL plots, are shown in Figure 14. When all data are considered (HWL and LWL combined), it is evident that the more carbon dioxide the meadow assimilates (higher GPP), the higher is the harvested aboveground biomass yield, although this correlation is not significant (at the \(\alpha\) = 0.05). However, when analyzing this correlation, one should consider that only the aboveground yield of plants is taken into account in our study. This study did not estimate the root biomass; hence, it is hard to assess how much carbon sequestered by plants was accumulated in belowground biomass. However, it is well known that the root system of plants might be stronger and its biomass might be higher at sites with a lower WTD and lower soil moisture [47]. Furthermore, other agronomic practices like meadow management, fertilization, and cutting frequency may also impact root biomass. For example, [PERSON] et al. [48] and [PERSON] et al. [49] indicated a higher belowground carbon allocation and higher root biomass in extensively managed grasslands, when compared to intensively managed meadows. Also, [PERSON] et al. [50] pointed out that highly fertilized grasslands may lose carbon sequestration capacity due to low underground C allocation. Their study shows that only a low fraction (17%) of net primary production is allocated to the roots. Considering the above, it can be speculated that the correlation between GPP and biomass yield would be stronger when both the above- and below- ground biomass of plants is considered.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 3 Proposed Solution
### Instances Classification  
Traffic classes in SG are not yet standardized. In this paper, we use the classification presented in [5] for the requirements in terms of delay and reliability in a Neighborhood Area Network (NAN) as shown on Table 1. The aforementioned classification sorts the traffic into 9 different classes, ranging from delays inferior than 3 s with reliability >99.5% for the most critical class to delays of hours/days with a reliability of >98% for the least critical class. In our model, we have gathered these 9 classes into 3 classes with 3 main instances:  
* Instance 1: critical traffic with an authorized delay ranging between 1 and 30 s and a reliability of >99.5% packets received with \(\alpha=0.9\) and \(\beta=0.1\)
* Instance 2: non-critical traffic with an authorized delay of days and a reliability of >98% packets received with \(\alpha=0.1\) and \(\beta=0.9\)
* Instance 3: periodic traffic with an authorized delay ranging between 5 min and 4 h and a reliability of >98% packets received with \(\alpha=0.3\) and \(\beta=0.7\)  
In this classification, we increment \(\alpha\) for the critical traffic thus fostering the link quality and end to end delay assured by \(ETX\) and \(d\), which results in routing the packets in a reliable and faster path. For less critical traffic we increment \(\beta\) which leads to fostering paths where the nodes consumed less their batteries and then achieving a load balancing. We note that our model is not limited to this classification and for any other one \(\alpha\) and \(\beta\) can be modified or be totally independent depending on the network characteristics.
## 5 Computational Experiments  
### Implementation and Instances  
Our algorithms were implemented in C language and executed on a 3.20-GHz Intel(R) Core(TM) i5-3470 CPU computer with 8 GB of RAM, running under Ubuntu. A set of HHC instances was designed by [PERSON] and [PERSON] [PERSON] and [PERSON] (2017). These instances include most of the particular features of the VRPTW-SP studied here: time windows, synchronization, and preferences. They were specifically extended to fit the additional problem specifications related to the different types of services offered to and requested by customers. The benchmark was grouped into three different categories according to the number of customers and vehicles, which were equal to 18 clients with 4 vehicles in the first group (\(G1\)), 45 clients with 10 vehicles in the second group (\(G2\)), and 73 clients with 16 vehicles in the third one (\(G3\)). In each group, the number of synchronizations ranged from 2-5, and the time windows could be small, medium, or large. In the extended version of instances, the customer demands and caregiver qualifications were randomly generated, but the rest of the information remained the same as in [PERSON] and [PERSON] (2017). Without loss of generality, we only treated the case when at most two services were needed. The extended benchmark can be obtained by a simple request to the authors.  
The file names followed an _n-m-t-nb_ format, with \(n\in\{18,45,73\}\) clients, \(m\in\{4,10,16\}\) vehicles, \(t\in\{s,l,m\}\) referring to the width of the customer time windows (small, medium, or large,) and the last index \(nb\in\{2,3,4,5\}\) corresponding to the maximum number of clients to be synchronized. These values were inspired from literature instances, where the number of synchronized customers did not exceed 10% of the total number of customers. All randomized algorithms were executed five times on each instance.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 4 Relation Tree
### Maintenance of the Relation Tree  
At the end of each cycle, the Relation Tree is updated in real-time, and the main operations are insertion and deletion. The process of updating the Relation Tree is shown in Algorithm 1.  
```
1:\(V=\{v_{i}|1<i<n\}\), set of nodes in the network
2:\(N(v_{i},v_{j})\big{|}v_{i}\in V,v_{j}\in V\big{)}\), the number of times \(v_{i}\) and \(v_{j}\) meet in a memory cycle T
3:\(RT_{i}\), the relation tree of node \(v_{i}\)
4:\(RT_{j}\), the relation tree of node \(v_{j}\)
5:M, relationship threshold
6:if\(v_{i}\) encounters \(v_{j}\) and \(N\Big{(}v_{i},v_{j}\Big{)}=M-1\)then
7: add \(v_{j}\) as a child node of \(v_{i}\) in \(RT_{i}\)
8:for each child node \(c_{j}\) of \(v_{j}\) in \(RT_{j}\)do
9:if\(c_{j}\
otin RT_{i}\)then
10: add \(c_{j}\) as a child node of \(v_{j}\) in \(RT_{i}\)
11:endif
12:endif
13:endfor
14:endfor
```  
**Algorithm 1**Updating Relation Tree
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 4 Pre-Processing of Data and Selection of Neural Model
### Training Net Selection
#### 4.3.1 Network Selection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Code** \\ **LSTM Network** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Neurons** \\ **for Layer** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Dropout** \\ **Train** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **R\({}^{2}\) in** \\ **Validation** \\ \end{tabular} } \\ \hline R02 & 200 & & 0.7661 & 0.6681 \\ R07 & 200 & 0.1 & 0.7611 & 0.6544 \\ R15 & 200 & 0.15 & 0.7599 & 0.666 \\ R23 & 200–100 & 0.1–0.15 & 0.7786 & 0.6554 \\ R30 & 200–100 & 0.1–0.3 & 0.7725 & 0.6567 \\ R36 & 200–100–50 & 0.1–0.15–0.3 & 0.7794 & 0.6682 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Selection of six neural networks that present the highest values of \(R^{2}\) in the dropout selection training group, number of neurons and LSTM layers. The selection of input variables was based on the R36 network considering the value of \(R^{2}\) obtained in the validation set for the prediction of PM\({}_{2.5}\). As a result of this experiment, the input variables were PM\({}_{2.5}\), PM\({}_{10}\), NO\({}_{2}\), temp, velv, hrel and hour, which are detailed in Table 2. To optimize results, only PM\({}_{2.5}\) was left as the output variable.
## 4 Case Study 2
### Identification and Definition of the Functions  
With this goal in mind, following the approach proposed by [PERSON] et al. (2017), the hospital staff in charge of using and managing the MRI room was interviewed to better understand the access procedure (AP), which is characterized by a sequence of activities. Accordingly, each step of the procedure was identified as a function. This allowed us to define the main steps of the AP, which can be described as follows:  
1. REG. To register the patient for an MRI examination (this activity is carried out outside the MRI room).
2. EVA. To evaluate the MRI examination request issued by the medical staff: in more detail, the MRI staff, after having assessed the appropriateness of the diagnostic investigation, must identify any contraindications to carrying out the examination (e.g., the presence of ferromagnetic prostheses, implantable medical devices, etc.). In this phase, it is essential to make an initial distinction between patients who are able to provide adequate information for the safe execution of the MRI examination or not.
3. ANA. To perform the patient anamnesis: in order to identify any contraindications to carrying out the diagnostic test or to the entry of occasional workers into the MRI room, the interested parties must be subjected to an anamnesis by filling in an anamnestic questionnaire. If the information provided by the patient/caregiver is not considered sufficient, or if a patient is unconscious, the MRI staff may request further investigations (e.g., X-rays to verify the presence of ferromagnetic objects inside the patient's body or specialist visits in the case of the presence of medical devices such as pacemakers).
4. AUT. To issue the authorization for performing an MRI examination: once all necessary information is gathered and evaluated, MRI staff releases the authorization to carry out the MRI examination.
5. INF. To provide information for entry to the MRI room: both patients and workers who are allowed access to the Controlled Access Zone (ZAC) are provided with specific information concerning the MRI-related risks and safety procedures.
6. DEP. To deposit all ferromagnetic objects: before entering the ZAC, both patients and workers are asked to leave all hazardous objects out of the ZAC.
7. ACC. To access the ZAC: once the permission to enter the ZAC is given, both patients and workers are authorized to enter the ZAC.
8. CHE. To perform the metal detector check: in the ZAC, both patients and workers are scanned with a portable metal detector to verify that there are no ferromagnetic objects.
9. PRE. To prepare the patient for the examination: the patient is prepared to undergo the MRI examination (e.g., the contrast liquid is administered).
10. ENT. To enter the MRI room.
11. MRI. To perform the MRI examination.
12. EXI. To exit the MRI room.  
Besides these functions, two additional functions must be considered, which are essential for the correct functioning of the whole process:
13. TRA. To train authorized healthcare personnel.
14. MAN. To verify the correct maintenance of the MRI device.
## 2 Materials and Methods
### Cluster Analysis and Euclidean Distance  
Cluster analysis is one of the statistical methods for organizing items into groups, which are named clusters, by computing how closely associated they are. Cluster analysis is realized with data matrices where the relation between variables has not been mentioned. The goal of cluster analysis is to find similar groups of data objects. The similarity between each pair of data objects means some global measure over the whole set of the characteristics of the data objects. Cluster analysis belongs to unsupervised learning algorithms. Before running the model, it is not known how many clusters exist in the data. It gives information about if and where data object associations exist in the data. It does not answer the question of what the cause of associations is. The most common task that uses cluster analysis is classification. Here, data objects are separated into groups so that each data object is more similar to the other data objects in its group than it is to data objects outside the group. It is advisable to use a software tool with this functionality for building classification and regression trees [28].  
Euclidean distance is one of the metrics used to determine the distance of objects. Its value in multidimensional space is equal to the absolute value of the distance between the two points (1).  
\[m_{e}\left(\stackrel{{\rightarrow}}{{a}},\stackrel{{ \rightarrow}}{{b}}\right)=\sqrt{\sum_{i=1}^{n}(a_{i}-b_{i})^{2}} \tag{1}\]  
where \(\stackrel{{\rightarrow}}{{a}}\) and \(\stackrel{{\rightarrow}}{{b}}\) are vectors with the same number of elements.
## 3 Numerical Formulations  
The dimensionless frequency parameters of the system are obtained numerically from the harmonic analysis. The system consists of the beam supported at the ends by the AFG bars. The code used for simulations is written according to the theory described above. The numerical investigations are performed in MATLAB on a workstation computer (2 \(\times\) CPU of 3.3 GHz, 30 GB RAM). The numerical results are given in dimensionless form using Equation (47) for comparison with results in the literature.  
\[\lambda^{2}=\frac{\rho_{L}A\omega^{2}L^{4}}{E_{L}I},F_{B}=\frac{F_{\max}L^{2} }{E_{L}I} \tag{47}\]  
where \(F_{B}\) is the non-dimensional form of force, \(\lambda\) is the non-dimensional frequency parameter and \(I\) is the moment of inertia of the area.  
Equation (48) can be written in the following form using the relations of Equation (47):  
\[\Big{(}([K_{S}]+i[H_{S}])-\lambda^{2}[M_{S}]\Big{)}\{q_{o}\}=\{F_{B}\} \tag{48}\]  
The investigation is carried out by implementing different power law exponent (\(na\)) values and changing the materials at the ends of axial FG bars to investigate the effect of material distribution on the response frequency and the force transmissibility.  
The left surface material of the bar (\(E_{La}\), \(\rho_{La}\), \(c_{La}\)) and the right surface material of the bar (\(E_{R}\), \(\rho_{R}\), \(c_{R}\)) are selected from the materials in Table 1. The material properties and damping values are taken from the literature [8]. The values in Table 1 are ranked from low to high, taking into account the longitudinal loss factor and \(c_{A}\). The physical properties of the materials used in the numerical methods are taken from Table 1.