esa-sceva/satcom-chunk-collection huggingface dataset:
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques
### Approaches
#### 3.1.2 Threshold Determination  
The specular reflective properties of non-obstructed water have driven many efforts [48; 49; 50; 51] to determine a threshold below which pixels are identified as water. A single threshold may not hold well in large-area water bodies [52] or for the whole swath of a SAR image since it suffers from the heterogeneity of the environment, caused by wind-roughening and satellite system parameters [53]. [PERSON] and [PERSON] [54] demonstrated the temporal heterogeneity of the backscattering of permanent water bodies, implying the temporal variability of the threshold. To address the spatial variability, [PERSON], [PERSON] and [PERSON] [53] applied a split-based approach (SBA) [55], together with an object-oriented (OO) segmentation method [56]. [PERSON] et al. [51] introduced a histogram segmentation method, and [PERSON] et al. [49] generalized the calibration process.  
Essentially, a threshold-based approach needs to have either a bimodal image histogram (Figure 2 in [PERSON], Hostache, Schumann, Pfister, Hoffmann and Savenije [51]) or some sample data to initialize the water distribution. To deal with non-bimodal histograms, manually drawing regions of interest (ROIs) is the most straightforward solution, but it impedes automation. For automation, SBA [53] ensures that only the splits showing a bimodal histogram (of water and non-water pixels) are used to derive a global threshold. On the other hand, [PERSON] et al. [57] loosened the bimodal histogram restriction by initializing the water distribution using a \"core flooding area\" automatically derived from change detection, using multi-temporal SAR images. As change detection using only a single dry reference is sensitive to speckle and location error (see the subsection on change detection), the method may be affected. Furthermore, the threshold globalization of the method can be difficult.  
More recently, dual-polarized (the common configuration of many active sensors) and fully polarized SAR data are utilized for threshold segmentation. After sampling pixels of high water probability from the global water occurrence map [58], and then removing out-of-bound samples by matching the peak, and 99% confidence interval of the sample histogram and the \(\chi^{2}\) distribution, [PERSON] et al. [59] automatically optimized the Wishart distribution and the probability density threshold of water in the dual-polarized domain. Since the water class is initialized before the threshold determination, this method is not restricted to bimodal histograms.
## 4 Node Insertion Algorithm for Determining Viewpoints
### Node Insertion Algorithm  
We now detail our node insertion algorithm. A starting point is chosen on the mesh, its normal is determined and a direction is given as its starting tangent vector (This is given as an input by the user taking into consideration the panel shape). A set of nodes is created which is initially empty. We now insert the starting node into the set, with (i,j) as (0,0) and the binormal vector can be calculated from the normal and tangent vectors. Whenever we insert a node, it is initially set to active (An active node is one whose neighbors have not been fully expanded). We now continue adding nodes until there are no more active nodes. If an active node is found, we create new nodes along right, up, left, and down directions using the geodesic path algorithm, taking care to ensure that there isn't already a node present at that position. The direction vector input depends on tangent or binormal vectors (for left/right or up/down) and the length input is \(p_{h}\) or \(p_{v}\) as described in Figure 14a. We insert a new node at that point with the appropriate position, normal, tangent, binormal vector, and i,j set as active except in the case where the Node stoppage criteria are satisfied. in which case we make it inactive. After all the expansion possible is completed we set this node to inactive. We repeat this until all the nodes have become inactive which leads to a complete coverage of the area. Node propagation stoppage criteria are explained in detail in Section 4.2 and is briefly recapped here. The criteria are that any one of the following are true:  
* The newly inserted node lies outside the mesh;
* The newly inserted node is too close to an existing saved node;
* Either the 'Indirect method' or 'Direct method' is used to check the divergence of the nodes on the curved surface. Node insertion in progress is shown in Figure 17. Currently, 12 nodes have been inserted so far. Figure 17a shows the position of the nodes on the surface of the mesh, the geodesic paths taken to get there, and also the corresponding viewpoints and intersection of viewing frustum with the mesh surface. Figure 17b shows the same node insertion progress in the 2D space of (i,j). The active nodes are shown in black and inactive nodes (which have been fully expanded) are shown in red. The complete algorithm for node insertion is given in Algorithm 7. Figure 17: Node insertion algorithm. (a) Node insertion on 3D surface; (b) Node insertion in 2D space. ```
Input: Starting Point \(\mathbf{p}\), Starting Tangent Direction \(\mathbf{t}\), Surface Mesh M Output: Set S of all nodes inserted \(\mathrm{S}\leftarrow\emptyset\) /* Creating first node.
# Biochar from Agro-Forest Residue: Application Perspective Based on Decision Support Analysis
## 2 Materials and Methods
### Decision Support Analysis  
Biochar has versatile physicochemical properties which proposes its use for a diversity of potential applications. Not only the properties, but the conversion process can also be more beneficial for specific utilizations. At present, four potential applications have been studied: the use of biochar as biofuel, as catalyst, for CO\({}_{2}\) sequestration and soil amendment, or for supercapacitor development.  
Although the maturity level of industrial biofuel production is not at a full commercial stage, e.g., issues with continuous yield capacity, the biofuel pathway is still one of the most attributive to biomass due to its energy content [48]. On the other hand, catalyst processes are known to use carbon-based materials, where biochar can have its role. In addition, biochar's physical and chemical properties can be adjusted to best fit a specific application [49]. Similar features of biochar can be applied to other processes, such as CO\({}_{2}\) sequestration and soil amendment, or supercapacitor development due to its large specific surface area, porous structure, surface functional groups, and high mineral content [50; 51].  
In the present work, different criteria were selected for each of the explored application opportunities. Thus, the analyzed criteria were the ones that recurrently appear to have the highest influence in each application, balanced by a less complex/expensive characterization. The decision problem was based on the definition of the process conditions that are best in terms of proposing the biochar utilization as a biofuel, catalyst, CO2 sequestration, and soil amendment product, or for supercapacitor development, given the decision criteria shown in Table 1.  
Among all tested types of biochar, several possible solutions were found fitting to the applications, but there was no one-fits-all solution, e.g., a decision technique is necessary to choose the preferred process in accordance with the variables. The decision analysis was performed with the following methods: Pareto dominance analysis and an approach of compromise programming [3; 52].
## 2 Functioning Principles and General Concepts for Transduction
### Transduction Principles
#### 2.2.3 Electron Transfer to the Channel or to the Gate  
Electron transfer is certainly the main investigated transduction pathway on OECTs. For this purpose, due to their superior selectivity compared to any synthetic probe or synthetic catalyst, redox enzymes are often used for selective oxidoreduction of the analyte [50; 51]. Three classical pathways could be followed: direct electroreduction of one of the enzyme products on the gate (type I), indirect (mediated) electron transfer between the gate electrode and the redox center of the enzyme (type II) or, when possible, direct electrical connection between the gate and the redox active center of the enzyme (type III). There are multiple examples of type I enzymatic OECTs, the most common being re-oxidation of H\({}_{2}\)O\({}_{2}\), a co-product of some oxidase enzymes such as glucose oxidase, on platinum gates [52]. This oxidation corresponds to the transfer of electrons from the molecule to the gate which in turn causes a shift of the effective gate voltage by decreasing the polarization of the gate/electrolyte interface, thus causing a decrease of the source-drain current. Other catalytic materials may be used instead of bulk platinum, for example Pt nanoparticles [53] or graphene [54]. Most of these materials, in addition to catalytic properties, present high surface-to-volume ratio compared to bare Pt, which helps decrease the limits of detection. Type II enzymatic OECTs were also described, for example using ferrocene as a mediator for the transfer of electrons from the active enzymatic site to the gate [55]. Type III enzymatic OECTs have been much less investigated, probably because direct electron transfer from enzymes is only possible for a small number of enzymes; however, it is a well-described configuration in the field of amperometric sensors [56], which could be applied in the field of OECTs.  
It should be pointed out that there are also some examples of enzyme-free OECTs where the electroactive target molecules, such as ascorbic acid [57] (Figure 2) or dopamine [58], are directly electrooxidized on the gate electrode. However, this is often at the price of weak selectivity compared to the extreme selectivity of most enzymes.  
Figure 2: Illustration of ascorbic acid oxidation at the gate electrode; PEDOT:PSS is, in turn, reduced leading to a drain current decrease. Reproduced from [57] with permission of The Royal Society of Chemistry.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 5 Localization in Smart Applications
### Smart City Services  
This section emphasizes the positioning-based services in smart cities. Position-based services integrate the location of a moving device into the other information to offer additional standards to the user. Consequently, the objective is to implement the IoT to enhance localization schemes and guarantee user-friendly processing in dynamic situations. Despite its dimensions, smart cities require continuous maintenance and cleaning by specialized machines and trained staff. In this context, the ultra-wideband (UWB) scheme proposed in [76] uses a semi-automatic floor scrubber adjunct component to clean indoor and outdoor tiles in smart cities. The accuracy of this localization scheme is around 20 cm. IoT nodes' detecting and networking capabilities help in optimal scheduling of energy dispersal and consumption in heterogeneous environments. The device that benefitted from these properties determines the location of the faulty parts, disconnects them and applies a swapping assignment to improve the major number of vigorous parts of the influenced energy flow. Furthermore, it uses a self-curing method capable of activating users' involvement and disseminated generation elements. A localization scheme using infrared technology [77] is developed, and it relies on the information gathered by the inactive infrared sensor nodes connected with the poles of light. An algorithm [78] localizes Wi-Fi admittance points and structures to regulate the urban noise sources. It does not require anchor nodes but gradually trusts crowdsourcing information to enhance the localization outcomes, aiming for improved precision.
## 2 Materials and Methods
### 16S Library Preparation  
The library preparation was started from 12.5 ng of total DNA. For NGS (Next-Generation Sequencing) library preparation, the recommended protocol for preparing 16S ribosomal RNA gene amplicons for the Illumina MiSeq system was used (Illumina 2013). The suggested universal bacterial primers were utilized for amplifying the V3 and V4 hypervariable regions of the bacterial 16S rRNA gene with polymerase chain reaction (PCR) using the KAPA Hifi HotStart Ready Mix (Roche Diagnostics Deutschland, Mannheim, Germany). PCR products were purified, and dual indices and Illumina sequencing adapters were attached using the Nextera XT index kit, Illumina. Finally, the libraries were purified once more with AMPure XP beads, Agencourt. The high quality of libraries was ensured using Advanced Analytical Fragment Analyzer and the concentrations of the libraries were quantified with Qubit(r) Fluorometric Quantitation (Life Technologies, Invitrogen division, Darmstadt, Germany). In a second PCR sample-specific \"barcode\"--primers and adapter sequences were attached. Up to 96 libraries were normalized and pooled for an Illumina MiSeq sequencing run using the MiSeq Reagent Kit version (v.) 3 with marginally overlapping 300 base pairs (bp) paired-end reads.
## 5 Results and Discussion
### End-to-End Delay Analysis  
Table 6 depicts the comparative analysis of the packet delay of the four existing methods with the proposed approach. Figure 7 represents the end-to-end delay with respect to the different number of nodes. The proposed protocol reduces the likelihood of path re-finding with the lowest number of re-transference, in contrast to existing solutions, by selecting the security-aware and reliable path for the routing. Such a routing technique eventually reduces congestion issues and effectively utilizes the bandwidth of wireless channels to send data packets with the least amount of network delay.  
Figure 7 depicts the minimum end-to-end delay in the proposed approach. In case there are 100 nodes, the proposed approach acquires 0.015 ms, i.e., minimum delay, as compared to the existing approaches. In the case of 250 nodes, it acquires 0.192 ms for Sectrust-RPL, 0.173 ms for HBEER, 0.141 ms for SEHR, and 0.121 ms for SEAMHR; 0.104 ms has been acquired for the proposed approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No. of Nodes** & **Sectrust-RPL** & **HBEER** & **SEHR** & **SEAMHR** & **Proposed** \\ \hline
50 & 0.152 & 0.141 & 0.120 & 0.0912 & 0.015 \\ \hline
100 & 0.163 & 0.149 & 0.122 & 0.101 & 0.018 \\ \hline
150 & 0.171 & 0.154 & 0.132 & 0.110 & 0.100 \\ \hline
200 & 0.178 & 0.162 & 0.136 & 0.114 & 0.100 \\ \hline
250 & 0.192 & 0.173 & 0.141 & 0.121 & 0.104 \\ \hline \end{tabular}
\end{table}
Table 6: Comparative analysis of proposed end-to-end delay (ms) with state-of-art approaches.  
Figure 6: Packet drop ratio.
## 4 Lessons Learned from Norwegian Case Studies
### Time Aspects  
The most common LCA method (attributional LCA) uses static calculation perspectives assuming that the emissions occurring today have the same impacts as the emissions in the future. Considerations of emission reduction measures today and in the near future are important to limit the effects of global emissions over time. Evaluation of the time it takes for new buildings to become less carbon-intensive than refurbished buildings, the payback time, is thus the way of showing the importance of refurbishment to make informed decisions. However, the length of the payback period can vary based on several factors including depth of energy efficiency refurbishment measures, emission factors for operational energy use, carbon intensity of materials, and systems used for refurbishment and demolition [50].  
Even for the EU factor, which leads to higher operational energy compared to the NO factor, the GHG emissions from the Statens hus Vadso refurbishment scenario were lower than the new building for up to 22 years (for emissions including the technical installation) and 24 years (for emissions excluding the technical installation) of the building lifetime. This means that after 22 years, the associated lower operational energy GHG emissions will make the new build scenario more beneficial than refurbishment. On the other hand, for the NO factor, the refurbishment has lower GHG emissions than the new building (for both emissions with and without technical installation scenarios) over 60 years of the building lifetime.  
In Villa Dammen, the GHG emissions for the refurbishment scenario were lower than for the new building for up to 52 years. This means that in Villa Dammen, considering the operational GHG emissions can only lead to underestimating the GHG emissions from new buildings by up to 40% over 52 years and make refurbishment a less attractive option for emission savings.  
From an environmental goal perspective, refurbishment of the Statens hus Vadso and Villa Dammen buildings will be a better option to achieve the 2030 and 2050 environmental goals. The Historic London study [50] results showed a lower GHG emission from the Victorian terrace refurbishment for up to 60 years and also showed refurbishment as the best option to achieve the 2030 and 2050 lower GHG emission goals compared to demolition and new building scenarios. The results from a Norwegian study [62] noted that refurbishment strategies are in line with the 2030 goals; however, new buildings need to implement more climate mitigation strategies to close the gap. To reach the 2050 climate goals, much more is needed to be conducted for both new and existing buildings.  
This demonstrates that refurbishment of existing buildings with historic/heritage values should be favored over demolition and new buildings to achieve short- and medium-term environmental goals.
## 4 Results and Discussion
### MLP Classification Test Results  
The Weka-MLP tool was used to do the MLP classification test. The parameters were the number of hidden units (2 or 4), the ridge factor for quadratic penalty on weights (default 0.01), the tolerance parameter for delta values (default \(1.0\times 10^{-6}\)), conjugate gradient descent was used (recommended for many attributes), the size of the thread pool (default 1), the number of threads to use (default 1), and random number seed (default 1). Tests were done for four combinations according to the parameters, the combinations were AA (approximate sigmoid and approximate absolute error), AS (approximate sigmoid and squared error), SA (soft plus and approximate absolute error), and SS (soft plus and squared error). The results in Table 5 show that the highest recognition rate was 97.82%, and when number of hidden units was 4, and approximate sigmoid and squared error had also been selected, the calculation speed was 0.17 s.  
\begin{table}
\begin{tabular}{c} Rotation \\ \end{tabular} & 0.14 (s) & 0.16 (s) & 0.11 (s) \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Classifier**} & \multirow{2}{*}{**Data**} & \begin{tabular}{c} **First Group** \\ **(Correct Rate/Time)** \\ \end{tabular} & \
\begin{tabular}{c} **Second Group** \\ **(Correct Rate/Time)** \\ \end{tabular} &
\begin{tabular}{c} **Third Group** \\ **(Correct Rate/Time)** \\ \end{tabular} \\ \hline \multirow{4}{*}{MLP (N = 2)} & Original data & 97.74\% & 78.84\% & 75.84\% \\  & Original data & 0.19 (s) & 0.16 (s) & 0.17 (s) \\ \cline{2-5}  & Data after dimension & 97.18\% & 83.12\% & 79.10\% \\  & reduction & 0.31 (s) & 0.36 (s) & 0.17 (s) \\ \hline \multirow{4}{*}{\(\text{MLP (N = 4)}\)} & Original data & 97.76\% & 81.54\% & 78.98\% \\  & Original data & 0.23 (s) & 0.17 (s) & 0.16 (s) \\ \cline{2-5}  & Data after dimension & 97.24\% & 81.44\% & 78.40\% \\  & reduction & 0.27 (s) & 0.22 (s) & 0.38 (s) \\ \hline \hline \end{tabular}
\end{table}
Table 5: MLP training and detection results
## 1 Introduction
This paper proposes a dynamic AES key replacement mechanism based on the concept of MTD, coupled with the D-H key exchange protocol. This combined approach enables the dynamic replacement of AES encryption keys during transmission, significantly reducing the risk of key compromise. To evaluate the effectiveness of this approach, we constructed an IoT transmission system using an Arduino UNO and the ESP8266 Wi-Fi module [13]. During testing, attackers used probes to capture the physical traces emitted by the encryption devices and conducted statistical analysis on the GPU computing platform. Results showed that after analyzing a sufficient number of traces, the attackers were able to extract the AES encryption key. This finding highlights that static AES keys were insufficient for ensuring the security of IoT devices in public environments. Dynamic key replacement is necessary to mitigate the risk of key leakage. To address these vulnerabilities, this study proposes a high-efficiency dynamic key replacement mechanism based on the Diffie-Hellman (D-H) protocol. Building on the Moving Target Defense (MTD) approach introduced by [PERSON] et al. [14], the proposed mechanism dynamically updates encryption keys after each transmission, effectively mitigating vulnerabilities related to side-channel attacks (SCAs). Designed as a lightweight solution, this mechanism is tailored to the computational limitations of resource-constrained IoT microcontrollers, ensuring low power consumption and real-time performance. By enhancing resilience against SCAs, the proposed approach significantly reduces the risk of data leakage in IoT devices deployed in open and unsecured environments. Experimental analysis validates the mechanism's feasibility and effectiveness, demonstrating minimal time overhead (12 to 50 milliseconds) and strong resistance to key extraction attacks, even after 20,000 attempts. The remainder of this paper is organized as follows: Section 2 (Preliminaries) provides an overview of foundational concepts, including SCA vulnerabilities and the cryptographic mechanisms that underpin this study. Section 3 (Related Work) reviews existing research on SCA countermeasures and lightweight cryptographic protocols in IoT environments, identifying key gaps that this study addresses. Section 4 (Research Methodology) details the proposed dynamic key replacement mechanism, focusing on its integration with the D-H protocol and its lightweight implementation for IoT devices. Section 5 (Experimental Results) presents the experimental setup and findings, evaluating the mechanism's performance and security under realistic IoT scenarios. Finally, Section 6 (Conclusions) summarizes the key contributions of this study and highlights its practical implications.