esa-sceva/satcom-chunk-collection huggingface dataset:
# Biosorption of Heavy Metals by the Bacterial Exopolysaccharideaccharide FucoPol
## 3 Results and Discussion
### Effect of Temperature in Pb\({}^{2+}\) Removal by FucoPol  
Considering that temperature fluctuations occur during the year and that it affects the metal uptake process [45], the effect of temperature in the Pb\({}^{2+}\) removal abilities of FucoPol was explored in a range of 5 to 40 \({}^{\circ}\)C (Figure 5). In literature, temperatures above 35 \({}^{\circ}\)C seemed to enhance biosorption due to an increase in surface activity and solute's kinetic energy. Moreover, higher temperatures promote an increase in the diffusion rate of metal though the external layer into the internal pores of the EPS, since its viscosity decreases [4].  
As Figure 5 demonstrates, the Pb\({}^{2+}\) removal efficiency of FucoPol was statistically the same in the range of temperatures tested, thus showing that FucoPol is thermostable and suitable to be used at these temperatures. The influence of temperature was not investigated in the majority of the metal removal studies using EPS as biosorbents. Nevertheless, [PERSON] and [PERSON] [22] reported a similar trend for the _Paenibacillus sp_. CH11 biosorbent, since Pb\({}^{2+}\) removal by this protein-based exopolymer was not affected by temperature in the range from 4 to 45 \({}^{\circ}\)C. Also, the EPS produced by a mixed culture of _R. radiolobacter_ F2 and _B. sphaeicus_ F6 was reported to be efficient within the temperature range of 5 \({}^{\circ}\)C to 35 \({}^{\circ}\)C [38]. As so, FucoPol could be an effective alternative to be used in the removal of this metal from contaminated wastewaters without temperature control, despite the seasonal variations of this parameter.  
Figure 4: Effect of pH in Pb\({}^{2+}\) removal efficiency by FucoPol (C\({}_{i}\) 10 mg/L, FucoPol concentration 5 mg/L, temperature 21 \({}^{\circ}\)C, incubation time 24 h).  
Figure 5: Effect of temperature in the Pb\({}^{2+}\) removal efficiency by FucoPol (C\({}_{i}\)10 mg/L, FucoPol concentration 5 g/L, pH 4.3, incubation time 24 h).
## 4 The Proposed Authentication Mechanism
### Initialization Phase  
This is like a registration process among the SBs and the CC; it is very important to run the proposed scheme. The SBs are connected to the CC, where the CC is assumed to be a trusted entity. The applied suites cypher, such as a type of hash function, and the cryptography procedure are defined. In addition, an off-line supplier defined the SB identity \(IDsb_{j}\) and the corresponding parameters \(p_{j}\) and \(p_{c}\) for each recorded SB as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(IDsb_{j}\) & Identification of the \(j\)th \(SB\) \\ \(IDsbw_{j}\) & \(ID_{j}\) shadow of the \(SB\) \\ \(IDcc\) & Identification of \(CC\) \\ \(Msbw_{j}\) & Mask of ID shadow for the \(SB\) \\ \(p_{j}\) & Secure Parameter of the \(j\)th \(SB\) \\ \(p_{c}\) & Secure Parameter \(CC\) \\ \(\alpha_{j}\), \(\beta_{j}\) & Random values from the \(SB\), \(CC\) \\ \(Hsb_{j}\) & Hash function of the \(SB\) \\ \(OHsb_{j}\) & Hash function obtained from \(IDsbw_{j}\) \\ \(IDcrw_{j}\) & Replay of the control ID shadow \\ \(Mcrw_{j}\) & Mask of the replay of the control ID shadow \\ \(Hcrj\) & Hash value of \(IDcrw_{j}\) \\ \(OHcr_{j}\) & Hash value obtained from \(IDcrw_{j}\) \\ \(\oplus\) & XOR function \\ \(\parallel\) & Concatenation function \\ \(h\) & Hash function \\ \hline \hline \end{tabular}
\end{table}
Table 1: The notation used.
# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 4 Results and Analysis
Advancing explainability mechanisms, including developing more seamless integration methods and enhancing model interpretability without compromising performance, will be crucial for building user trust and facilitating broader adoption of FML models in cybersecurity applications [85; 99; 100; 80; 101; 102; 103; 104]. Robustness against adversarial attacks represents a significant advantage of hybrid AI models, often outperforming standalone ML and matching or exceeding FL. By combining centralized and decentralized training approaches, these frameworks demonstrate enhanced resilience to common vulnerabilities, including data poisoning and evasion attacks. The decentralized component mitigates the risk of widespread compromise, while the centralized element (or specific hybrid techniques) ensures consistent and accurate model updates. Furthermore, the adaptive capabilities of hybrid models enable effective responses to dynamic and evolving threat landscapes, ensuring reliable performance even in hostile environments. This combination of resilience and adaptability makes FML frameworks particularly valuable for maintaining security and reliability in challenging cybersecurity contexts [62; 73; 74; 76; 78; 79]. In summary, research findings emphasize how FML techniques effectively balance performance and privacy preservation, scalability and explainability, and robustness addressing multiple challenges in modern cybersecurity more comprehensively than any single standalone approach. The comparative analysis presented in Table 5 illustrates these metrics, highlighting how hybrid models combine the pattern recognition strengths of ML with the distributed learning advantages of FL to achieve superior overall performance. While FL and FML models demonstrate high privacy preservation, and FML shows strong accuracy and robustness, optimizing scalability for large-scale and real-time environments and developing more comprehensive explainability frameworks remain areas requiring further development to enhance transparency and build user trust. Addressing these aspects will be essential for facilitating wider adoption and improving the efficiency of FML models in dynamic cybersecurity environments.
## 4 Experimental Analysis
### Experimental Results and Analysis
#### 4.3.4 Comparison with Other Literature Methods  
To assess the reliability of the model proposed in this paper, comparative experiments were conducted with the models presented in references [12; 28; 29]. As indicated in Table 4, reference [28] employs a fundamental Siamese network framework, utilizing Resnet34 as the backbone feature extraction network to address the issue of predicting radiance in sky images. However, it lacks the incorporation of multi-scale contour guidance, resulting in a simplistic network model and consequently lower accuracy. Reference [29] utilizes an enhanced Siamese depth feature fusion method, leading to improved rejoining effects on bone stick images. Nevertheless, this method merely overlays convolutional features of different scales without specifically focusing on edge information, leaving room for enhancement in the context of bone stick images. In contrast, reference [12] combines residual networks with spatial pyramid pooling, employing a two-dimensional Siamese neural network for matching excavated wooden pieces and obtaining a matching probability. While achieving a high accuracy rate of 89.8%, it predominantly emphasizes texture features, leading to decreased performance when dealing with complex bone stick images. Through comparison, the MFS-GC model proposed in this paper achieves an accuracy of 95.5%. This model demonstrates superior applicability in the domain of bone stick rejoining.  
Figure 10: Accuracy under different test sets.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### General IoT Applications
#### 3.4.8 Lotus  
The applications of the popular commercial mote, LOTUS (MEMSIC), include seismic vibration monitoring, video, acoustic sensing and other applications requiring high-speed processing capability. The LOTUS mote is based on the low-power ARM Cortex M3 CPU containing 64 kB RAM, 512 kB program flash memory and the clock rate can reach 100 MHz. The radio module for LOTUS operates in the 2.40 GHz-2.48 GHz band at up to a 250 kbps data transmission rate. The outdoor coverage range is above 500 m. The mote does not possess on-board sensors but is capable of accommodating several sensor data acquisition boards. The LOTUS node can be powered by a 2.7-3.3 V power supply. Concerning the power consumption, the processor of the mote draws a current of about 10 \(\upmu\)A during the sleep mode and around 50 mA in the active mode when operating at a 100 MHz clock rate.
## 4 Proposed Antenna Beam Model for RBS  
In the scenario of an RBS targeting road segments, it is reasonable to assume that the radio equipment is less sophisticated than a commercial 5G BS. In particular, it is assumed that such equipment will have limited power amplification capabilities at the transmitter; thus, a directional antenna with a relatively high gain is required to deceive a UE. The use of a directional antenna will radiate the radio signal in a relatively small beam so that the malicious actors will need to point the beam along the road segment to some extent to maximise the amount of road that is within the \"rogue cell\".  
Figure 3: Received signal strength of a mobile UE in the legitimate model. (**a**) The geography diagram. (**b**) The pattern of signals generated. (**c**) RSS of the serving gNodeB upon handover.  
Figure 2: Realistic signal strength of a platoon’s leader from a legitimate BS.  
Here, a real antenna that would be suitable for mounting in a vehicle that can be parked at a vantage point with a good line of sight to the desired road segment was modelled. The radiation pattern of the main lobe is represented by a blue line in Figure 4 which shows the extremely steep roll-off of the beam at the edges. These data are related to a commercially available antenna, the specifics of which have been redacted to avoid educating potential malicious actors. Because the goal of this work is to generate realistic RSS measurements from a fast and simple simulation, it is important to model the antenna radiation pattern in a manner that captures the characteristics that are likely to help identify an RBS. The model used in this simulator approximates the main lobe of the antenna with a single gain value (17 dBi) and a defined beamwidth, as indicated by the yellow line in Figure 4 as a yellow line. Similarity of the steep sides of the real and simplified radiation patterns were clearly captured which validates the use of the simple model in this particular use case.
## 3 Results  
### Experimental Setup  
The experimental site is located in Toulouse (Occitanie, France) at ONERA, which conducts studies and research for the benefit of the fields of Aeronautics, Space, and Defence. In this particular work, the experiments took place in a tunnel with 80 m length by 3 m height, approximately. The measurements were performed on 12 th and 13 th February 2020 and the same micro-lidar configuration was kept for both days. A fog-oil generator was located inside the tunnel to create a controlled aerosol environment for the system characterization. Fog-oil plumes are obscurant smoke screens produced by the heating of a glycol solution and are disseminated in the air as an electromagnetic obscurant for military applications, entertainments, or fire simulation [52; 53]. A fog-oil plume is disseminated by vaporization using a generator and then is condensed as the vapors cool in the atmosphere beyond the exhaust pipe. The efficiency of fog-oil plumes depends on the aerosol concentration and particle size distribution, which are characterized in this experiment using an optical particle counter (Palas, Fidas 200). This instrument is based on polychromatic light-scattering and provides particle size and number concentrations of aerosol particles in the size range from 0.18 to 18 m.
## 3 Results
### Effect of Radical Scavenging Activity and Superoxide dismutase-Like Assay  
The DPPH and ABTS assays are commonly used to evaluate the radical scavenging ability of probiotics, measuring their ability to neutralize stable free radicals (DPPH\(\bullet\)) and radical cations (ABTS\(\bullet\)+), respectively. These assays provide a rapid and sensitive assessment of antioxidant activity against both hydrophilic and lipophilic substances [27; 28]. ORAC analysis complements these methods by quantifying the ability of probiotics to neutralize oxygen free radicals, offering biologically relevant insights into ROS protection [29]. Additionally, SOD activity analysis evaluates the enzymatic antioxidant potential of probiotics, determining their role in mitigating oxidative stress by enhancing endogenous superoxide dismutase activity [30]. Finally, T-AOC analysis measures the total antioxidant capacity of probiotics, reflecting their combined ability to neutralize various reactive species and provide comprehensive protection against oxidative stress [31]. As shown in Figure 5, the THY-F51 CFS demonstrated a significant dose-dependent reduction in DPPH and ABTS free radical formation, while live cells did not show a significant decrease in radical formation. However, both the CFS and live cells exhibited antioxidant effects in the SOD-like activity assay. And the ORAC values for the cell-free supernatant and live  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{Minimum Inhibitory Concentration (mg/mL)} \\ \cline{2-4}  & _C. albicans_ KCTC & _C. albicans_ ATCC & _C. tropicalis_ KCTC \\  & 7270 & 10231 & 17762 \\ THY-F51 & 1.25 & 1.25 & 1.25 \\ \cline{2-4}  & \multicolumn{3}{c}{Minimal Fungicidal Concentration (mg/mL)} \\ \cline{2-4}  & _C. albicans_ KCTC & _C. albicans_ ATCC & _C. tropicalis_ KCTC \\  & 7270 & 10231 & 17762 \\  & 2.5 & 2.5 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MIC and MFC analysis of THY-F51 against _Candidia_.  
Figure 4: Anti-biofilm-formation activities of strain THY-F51 against associated (**a**) _C. albicans_ KCTC 7270, (**b**) _C. albicans_ ATCC 10231, and (**c**) _C. tropicalis_ KCTC 17762. Data are presented as mean \(\pm\) SD of the result in three replicates. ***, \(p\) < 0.001 compared to control.  
cells were 120.45 umol TE/g and 63.65 umol TE/g, respectively, while the T-AOC values were 5.27 (U/mL) for the cell-free supernatant and 2.34 (U/mL) for the live cells (Table 2). Studies have shown a notable distinction in antioxidant capacity between the cell-free supernatant and live cells of probiotics, with the supernatant exhibiting higher ORAC and T-AOC values compared to live cells. These findings indicate that the antioxidant compounds present in the cell-free supernatant are likely pivotal in the scavenging of ROS.
# Cloud Storage Data Verification Using Signcryption Scheme
## 3 Proposed Scheme
### ZSS Signcryption
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(b,d\) & Security parameter \\ \hline \(G_{1}\) & Cyclic additive group \\ \hline \(G_{2}\) & Multiplicative cyclic group \\ \hline \(P\) & Generator of group \(G_{1}\) \\ \hline \(Enc\) & Symmetric encryption operation \\ \hline \(Dec\) & Symmetric decryption operation \\ \hline \(SK_{S}\) & Sender’s secret key \\ \hline \(PK_{S}\) & Sender’s public key \\ \hline \(SK_{R}\) & Recipient’s secret key \\ \hline \(PK_{R}\) & Recipient’s public key \\ \hline \(m\) & Original message \\ \hline \(n\) & Total number of shards \\ \hline \(V\) & ZSS signature of message \(m\) \\ \hline \(X\) & Randomness of encryption key generation \\ \hline \(Y\) & Encryption of message \(m\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of notations. As explained earlier, this presents a concern since \(r\) can also be used to create a symmetric encryption key using the function \(Hash_{3}(r)\). In this way, the verifier threatens data privacy by being able to decrypt the original message. Different from [14], by removing the \(r\), our proposed scheme prevents leakage of the encryption key to the verifier. Furthermore, in our scheme, the original message \(m\) will not be passed to the verifier during the verification process. So, it assures data privacy and increases verification efficiency because the verifier does not need to download the original message \(m\) beforehand.
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 4 Experimental Results  
### Collection of Thermal Bridge Data  
Thermal images of old houses were acquired to develop a model for classifying thermal bridges. Thermal images were taken when environmental conditions suggested by ISO 6781 [34] were satisfied. The images were taken at dawn before sunrise because thermal images can be influenced by solar radiation. In addition, the images were taken in winter because a high temperature difference between the indoor and outdoor may distinguish thermal bridges from other normal thermal images. Actually, the average outdoor and indoor temperature were \(-\)1.02 and 12.78 \({}^{\circ}\)C, respectively. This satisfied the indoor and outdoor temperature difference of 10 \({}^{\circ}\)C or more, following ISO 6781. Finally, all the measurements were achieved with a wind speed of less than 5 m/s. This study chose the conventional walk-through thermal imaging method to take the images. In this method, a person walks around the target to check the building envelope with thermal images and takes images of thermal anomalies. In this study, the thermographer walked around the target building and took images of all the thermal anomalies in the building envelope, even if they were not thermal bridges. In the conventional method, the audit records while moving around the building and interprets the pattern based on the apparent temperature [13]. Therefore, in this study, the apparent temperature was measured by  
Figure 6: Structure of a neural network.  
using default parameters for emissivity and reflected temperature. This study used a Testo 882 thermal imaging camera, the specifications of which are shown in Table 1.  
After taking the thermal images, the thermal bridges and the other anomalies were classified, cropped, and used as data for training. This study acquired a total of 134 thermal images for thermal bridge modeling. These thermal images contain thermal anomaly areas. A total of 151 thermal bridge cases and 223 other cases were cropped. All of these data were used for thermal bridge modeling. In addition to the thermal images used for thermal bridge modeling, eight more thermal images were acquired and used to validate the methodology proposed in this study.
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 3 The Proposed Method Description  
In this section, we study the bistatic sea clutter suppression method based on compression sensing optimization. Following the fundamental steps of compressed sensing, we introduce three parts: sparse representation of echo, optimal design of sensing matrix, and sea clutter reconstruction suppression.
## 4 Case Study and Simulation Results
This is because our objective function is composed of the generators' start-up cost, the generators' expected operation cost, and the expected compensation cost of the ACL reduction. When the compensation cost is very high, the value of the objective function may be increased. Figure 8 shows the load reduction results when the values of \(K_{1}\) in the first- and second-type ACL were 0.06 and 0.04, respectively. Compared to Figure 6, it shows that when the compensation cost decreases, the system may allocate more ACLs to reduce the peak load. However, the cusomer benefits are decreased. Hence, future research should pay greater attention to how to make a balanced compensation plan from the perspectives of both customers and the electric network. Figure 6: Load reduction curves in Mode 2. Figure 7: Load reduction curves under different scenarios (the values of \(K_{1}\) in the first- and second-type air-conditioning load (ACL) are 1 and 0.9, respectively). The front simulation was based on the assumption that 15% of ACL consumers participate in the DR project. With the development of smart grid technology, an increasing number of electricity consumers will join the DR project. Therefore, we analyzed the effect of consumers' participation on the load reduction. Compared to Figure 6, with the same values of \(K_{I}\) as in the first- and second-type ACL, Figure 9 shows the load curtailment in the case of 25% participation of the ACL consumers. As we can see, more loads are reduced when more ACLs are in the control of the power company.
## 2 Materials and Methods
### Preliminary Definition of Land Cover Units  
We focused on defining and mapping vegetation types that were of interest to both the conservation and agricultural sectors, including invasive species, like guava, and valued cash crops, like coffee. Figure 1: Study area. Each of the inhabited islands has a designated agricultural zone. The map above shows the geographical location of the agricultural zones (blue outline), and the panels below show a detail of each agricultural zone with cloudless PlanetScope images. We based the agriculturally-relevant categories on those used by the 2014 agricultural census of the Galapagos [40]. Similarly, we based conservation-related categories on those used by the official classification systems of the MAG and on previous classification studies of the protected areas of the GNP [42; 43]. We used level hierarchies to address the complexity of landscapes, breaking down broader categories into increasingly specific categories (Figure 2). At level 1, categories allow choosing between Land and the Pacific Ocean. At level 2, the 'Land' category is broken down into four categories: 'bare ground,' 'built environment,''vegetation,' and 'freshwater.' Bare ground is volcanic rock or soil surface area with no vegetation cover. This category excludes recently tilled land for crops. Built environment is surface covered by concrete, pavement, or other impervious surfaces. Vegetation represents all plants that cover the land surface, and freshwater describes naturally-occurring or artificial bodies of freshwater, such as ponds or reservoirs. At level 3, the'vegetation' category is further broken down into 'introduced' and 'native' species, and levels 4 and 5 correspond to more specific categories that can be recognized as individual vegetation stands or patches at a mapping scale of 1:10,000. _Introduced_ species (Table 2) are plants that were intentionally or unintentionally brought to the islands by humans and have become naturalized since their introduction, such as _Citrus_ and _Erythrina_ spp. _Native_ species (Table 3) are those that arrived on the islands by their own means, transported by the wind, water, or other animals [44]. Introduced species include food crops (_transitory_ and _permanent_ crops), pastures, and invasive species. The _pastures_ category includes grass species planted by farmers for cattle grazing naturally-occurring species that are dispersed by the wind [40]. _Intvasive_ species are a subset of naturalized plants that produce reproductive offspring in large numbers and over a considerable distance, thus having the potential to spread over large areas and negatively impact native biota [45]. Some of the most common and aggressive species include _Cedrela odorata_ (cedar), _Cinchona pubescens_ (quinine), _Psidium guajava_ (guava), _Rubus niveus_ (blackberry), _Lantana canara_ (supirosa), and _Syzygium jambos_ (pomarrosa) [42]. Figure 2: Category hierarchy used for land cover classification of Galapagos agroecosystems.
## 3 Resources and Demand Assessment of the Case Study
### Sensitivity Variables  
In the rural areas, there is a low load demand which varies on a time basis [4]. On the other hand, solar radiation and wind speed vary depending on the daily environmental conditions. Sensitivity analysis monitors the effect of certain variables. That is, different values are assigned to these variables within a given range in order to assess their influence on the optimal minigrid system [19]. In this study, considering the optimal minigrid system, the sensitivity variables with the variations of GHI from 5.05 to 6.69 kWh/m\({}^{2}\)/day, fuel price from USD 0.62 to 1/L, and load consumptions from 76.94 to 107.72 kWh/day are considered for the analysis.