esa-sceva/satcom-chunk-collection huggingface dataset:
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 4 Results and Discussion
### Other Projected Use Cases for 6G in Colombia  
In Colombia, empirical initiatives have begun to emerge which, although still operating within the frameworks of 4G and 5G technologies, already offer a glimpse of the transformative potential that 6G architecture is poised to bring--particularly across key social sectors such as energy, healthcare, and tourism. These cases not only represent local technological progress but also provide concrete, territory-anchored evidence of how the 6G ecosystem--with its massive connectivity (mMTC), ultra-low latency (uRLLC), and integrated sensing and communication capabilities (ISAC)--could enhance, scale, and democratize high-impact social and economic solutions.  
In the energy sector, [PERSON]. [70] designed a Home Energy Management System (HEMS) capable of predicting electricity consumption in Colombian households using LSTM neural networks, trained on real data concerning appliance usage. This model not only anticipates demand peaks but also optimizes energy usage from within the everyday routines of the home. With the advent of 6G, the real-time integration of millions of smart meters via mMTC would become feasible, while ISAC would enable distributed energy diagnostics, effectively transforming each household into an active energy management unit that contributes to the broader stability of the grid.  
In the field of healthcare, [PERSON]. [71] developed a predictive system for vector-borne diseases--such as dengue, Zika, and chikungunya--based on machine learning techniques including decision trees and random forest algorithms. Trained using actual clinical data from the city of Sincelejo, the model achieved a remarkable accuracy of over 99%. The implementation of a 6G infrastructure would allow such systems to scale via distributed biomedical sensors (mMTC), real-time clinical data transmission (uRLLC), and environmental monitoring through ISAC, enabling a more preventive, intelligent, and context-adapted healthcare model, particularly in historically underserved regions.  
As for tourism, [PERSON]. [72] proposed a smart restaurant recommendation system in the city of Riohacha, combining multi-criteria logic (LSP) with contextual variables such as location, pricing, services, and pedestrian traffic. When integrated with 6G networks, this approach could support the simultaneous connection of thousands of IoT devices in high-density tourist zones, provide real-time updates on routes and offerings, and map visitor flows via ISAC. Such advancements would not only enhance the tourist experience but also improve urban management, paving the way for a more inclusive, accessible, and context-aware model of tourism aligned with sustainable territorial development.
# Blind Phase Search with Angular Quantization Noise Mitigation for Efficient Carrier Phase Recovery
## 4 Experimental Setup and Results Validation
Figure 7 illustrates the improvement in the constellation diagram for 64 QAM achieved by the F-BPS scheme (Figure 7b), using the proposed F-BPS for angular quantization noise mitigation, as compared to the unfiltered C-BPS algorithm (Figure 7a), both employing eight test phases (\(\beta\) = 8). In this case, the BER is halved when employing the proposed F-BPS solution. Figure 7c depicts, in frequency domain, the angular quantization noise of the phase estimator before (C-BPS) and after (F-BPS) the angular quantization noise low pass filter in the F-BPS for different numbers of test phases, illustrating the quantization noise mitigation. Figure 5: Experimental setup. AWG: Arbitrary waveform generator. EDFA: Erbium doped fiber amplifier. OBPF: Optical band pass filter. DSO: Digital signal oscilloscope. DSP: Digital signal processing. Figure 6: **(a)** Experimental BER versus number of test phases comparative between F-BPS and C-BPS for a 28-Gbaud 64 QAM at different linewidth levels; **(b)** Experimental BER versus OSNR comparative between the F-BPS and C-BPS schemes for a 28-Gbaud 64 QAM at different linewidth levels.
## 1 Introduction
Electronic nose devices with metal oxide semiconducting (MOS) sensors are often a preferred sensor type selected to analyze plant volatiles because of their stability, precision, rapid recovery, resistance to deactivation (poisoning) by strongly polar VOC analytes, and effective operating mechanism for plant VOC detection [15]. Several analytical methods have been shown to be effective in identifying VOCs derived from living organic sources, including mass and ion mobility spectroscopy [24], gas chromatography [25], and infrared spectroscopy [26]. However, these instruments are very expensive to operate and require lengthy and complex analytical procedures [27; 28]. In recent years, e-nose devices have been shown to be effective alternative instruments to detect and analyze VOC emissions from commercial plant and animal food products. These devices offer more simplified means of discriminating between VOC emissions from different sources and provide reliable and precise measurements, fast operation, and quick sensor recovery times at affordable operating costs [15; 27; 28; 29]. A recent study, utilizing an experimental MOS e-nose device, provided evidence of the potential efficacy of analyzing differences in VOC emissions from cucumber fruits, derived from plants receiving known urea fertilization rates, to estimate actual nitrogen-based nutrient levels with the assistance of statistical models [30]. The current research is a follow-up of this study to further evaluate these methods using statistical models for indirectly estimating levels of NPK nutritive components of cucumber fruits using data from chemical analyses of plant tissues in combination with e-nose (olfactory machine-based VOC analyses) data for correlating fertilizer application rates to resulting nutrient levels in harvested fruits. We hypothesized that differences in nitrate levels in fruits could be indirectly detected by changes and differences in VOC emissions from cucumber fruits, due to nitrate-level effects on host plant metabolic pathways, as reflected by differences in the e-nose sensor array output (smellprint) patterns. The objectives of this study were to (1) evaluate the capabilities of an e-nose instrument to detect differences in fertilizer treatments based on VOC emissions from fruits and (2) test partial least squares regression (PLSR), principal component regression (PCR), and multiple linear regression (MLR) statistical models for estimating and correlating actual nutrient levels in cucumber fruits relative to soil fertilizer application rates applied to individual treatment plants.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 4 Equalization Algorithms  
Two distinct features that characterize the ISI generated by NLIN must be kept in mind when devising methods for its mitigation. On the one hand, the typical correlation times (tens to hundreds of symbols) are significantly shorter than in the case of most other ISI systems. This fact makes the design of an adaptive equalization algorithm fairly challenging, as it hinders the seamless adoption of readily available adaptive equalization methods. On the other hand the distortion that is generated by NLIN induced ISI usually does not prevent the extraction of the transmitted data even without mitigation, albeit with an unacceptably higher error-rate (This is in contrast to the ISI produced by other effects, such as chromatic dispersion, which make the signal completely unintelligible). This feature facilitates the process of equalization in ways that will be elaborated in what follows. An adaptive equalization algorithm generally consists of two blocks: estimating the ISI coefficients (channel estimation), and attempting to remove their adverse effect (equalizing). All of the algorithms reviewed here are decision-directed equalizers, namely they contain an additional block that performs an initial estimation of the symbols which is subsequently improved by the equalization process (see Figure 3). The performance relies on the quality of the initial symbol estimation and therefore it is expected to degrade considerably in low SNR regimes. We discuss several equalization algorithms in what follows.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 4 Methodology
### Attack Scenarios  
Considering the above-mentioned threat model, we selected a set of attack scenarios for our experiments in this study. The selection of attack scenarios was informed by both theoretical vulnerabilities and practical concerns observed in enterprise deployments of HLF identified in prior studies [64; 65]. Unlike public blockchains, HLF's permissioned nature reduces the node diversity and increases the reliance on specific architectural roles (e.g., orderers, endorsers, peers), which introduces unique threat vectors. We focused on seven distinct types of DoS attacks, each representing a plausible and high-impact method of degrading the performance or reliability of HLF. These attacks include a batch size attack, block-discarding attack, boycott attack, collusion attack, malicious client attack, consensus protocol attack (on Raft protocol), and architectural attack (on CouchDB).  
Each attack scenario selected aligns with practical enterprise deployments of HLF, where access control boundaries are enforced through the MSP, but insider roles (e.g., node operators) may still have limited configuration or API-level access. These attacks are feasible and practical as they are based on documented vulnerabilities and security advisories, such as the 2022 Fabric Gateway DoS advisory [66], as well as misconfiguration risks in hybrid-cloud deployments [67; 68] and real-world CouchDB vulnerabilities [69].  
Table 2 provides details about the attacker types, their capabilities, access level, and real-world scenarios.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 4 Catalytic Applications of Mesoporous Silica Materials
### Organic Synthesis
#### 4.2.7 Mukaiyama Aldol Reaction  
The heterogeneous catalysts can be highly recyclable catalysts, which is particularly crucial when utilizing valuable metal catalysts. Furthermore, applying metal oxides to diverse supports seems to be a good way of creating, e.g., Lewis acid (LA) catalysts. [PERSON] et al., reported that Fe-SBA-15 and Fe-MCM-41 (Figure 14), different iron-containing mesoporous silicas, could be synthesized through a versatile and straightforward method involving iron acetylacetonate as a metal species. Furthermore, a pH adjustment during the synthesis was found to improve the iron content. The acidity of the surface was tested via a series of Hammett indicators. Using a nonporous catalyst as an LA was monitored in the Mukaiyama aldol reaction. The LA catalytic activities of the materials were particularly fine-tuned, and the corresponding aldol products exhibited good selectivity and yield. The catalysts were stable and reused at least nine times [125].  
The XRD (X-ray diffraction) analysis of the iron-modified mesoporous silica showed there were no iron oxide species [125]. Moreover, FT-IR spectroscopy of the iron-modified mesoporous silica revealed a significant majority on the LA site compared to the Bronsted site. The iron species deposited on mesoporous silica can function as highly selective and active sites for the Mukaiyama aldol reaction of various aldehydes with phenyl-1-(trimethylsiloxy)-propene and trimethylsilyloxycyclohexene. The experimental procedure using a clean solvent system was straightforward, and no harmful organic solvents were used. The catalyst could be recovered and used once again the same reaction without loss in activity. The iron-modified mesoporous silica nanocatalyst is an extremely active heterogeneous LA catalyst and could be applied in many other reactions and numerous environmentally friendly chemical transformations in organic chemistry.  
Figure 14: Mukaiyama aldol reaction in the presence of Fe-MCM-41-LA.  
Figure 13: Asymmetric-hydration through chiral mesoporous nano silica.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Notable Components of DT Implementations
#### 3.2.2 Identification of Entities  
In the context of DTs, each twin is assigned a distinct identifier that facilitates seamless communication with its physical counterpart. This unique identifier encapsulates vital information about the physical object's structure, capabilities, and technical specifications, as well as its expected performance under specific conditions. When creating a DT, it is crucial to consider the unique circumstances of the object to ensure an accurate representation [67]. Even if two physical items are identical, they may exhibit different behaviors when subjected to varying conditions. Hence, each digital twin is assigned its unique identifier, reflecting the individual characteristics and attributes of the corresponding physical object. By leveraging the DT's unique identifier, we can accurately simulate and analyze its properties and behaviours.  
Furthermore, the expansive network of interconnected devices, sensors, and actuators highlights the need for real-time collection and sharing of identified contextual data with digital twins. This data encompasses various parameters such as location, environmental conditions, user preferences, and behavioral patterns. By integrating this uniquely identified contextual data into the DT model, a deep understanding of the user's context and needs can be achieved. Adding to that notion, a Geographic Information System (GIS) as a versatile system generates, controls, examines, and maps various data types and links IoT data to a map. Thus, GIS combines location information, referred to as spatial data, with descriptive details of a DT, forming the basis for mapping and analysis utilized across multiple industries and scientific fields. With GIS, users gain insights into patterns, relationships, and the geographical context of their data, as in [68].
## Abbreviations  
\begin{tabular}{l l} GPP & Gross primary production \\ LUE & Light use efficiency \\ Vls & Vegetation indices \\ EC & Eddy covariance \\ MODIS & Moderate resolution imaging spectroradiometer \\ MTCI & MERIS Terrestrial Chlorophyll Index \\ OLCI & Ocean and Land Colour Instrument \\ OTCI & OLCI Terrestrial Chlorophyll Index \\ GR & Greenness and radiation model \\ TG & Temperature and greenness model \\ MOD17 & MODIS GPP products \\ GPP\({}_{\text{MODIS-GPP}}\) & GPP values obtained from MODIS-GPP algorithm \\ GPP\({}_{\text{EC-LUE}}\) & GPP values obtained from EC-LUE model \\ GPP\({}_{\text{GR}}\) & GPP values obtained from GR model \\ GPP\({}_{\text{VI}}\) & GPP values obtained from VI model \\ GPP\({}_{\text{EC}}\) & GPP values obtained from eddy covariance flux towers \\ GPP\({}_{\text{MOD17}}\) & GPP values obtained from MODIS-GPP products \\ FAPAR & Fraction of absorbed photosynthetically active radiation \\ APAR & Absorbed photosynthetically active radiation \\ IPAR & Incident photosynthetically active radiation \\ T2 MMIN & Minimum air temperature at 2 m \\ T2 MMEAN & Mean air temperature at 2 m \\ RH & Relative humidity \\ SWRad & Surface incoming shortwave flux \\ VPD & Vapor pressure deficit \\ W\({}_{\text{S}}\) & Water stress factors \\ T\({}_{\text{S}}\) & Temperature stress factors \\ T & Average air temperature \\ T\({}_{\text{min}}\) & Minimum air temperature \\ T\({}_{\text{max}}\) & Maximum air temperature \\ T\({}_{\text{opt}}\) & Optimum air temperature \\ \(\epsilon_{\text{max}}\) & Maximum light use efficiency \\ Chl & Chlorophyll content \\ LST & Land surface temperature \\ EVI & Enhanced vegetation index \\ \end{tabular}
## 2 Methodology
### Economic Evaluation  
In the current work, the total annual cost (TAC) was calculated from:  
\[\text{TAC}=\text{TACC}/\text{payback periods}+\text{TAEC} \tag{1}\]  
where TACC is the total annual capital cost with 3-year payback periods, and TAEC is the total annual energy cost.  
In this paper, for the TACC calculations, only the major equipment's components were considered: the distillation column vessels (including the column internals), the reboilers, the condensers, and the other heat exchangers. Small items like pumps, valves, reflux drums, and pipes were not considered due to their much lower prices. The \"tray sizing\" function in PRO/II was used to calculate the diameter of the column vessels; valve trays are selected in all the columns. The tray spacing for each column in the TCED flowsheets was specified as 0.6 m. The overall heat transfer coefficient and the log-mean temperature differences in each heat exchange were used to calculate the heat transfer area. The overall heat transfer coefficients were taken from Luyben [30]. The log-mean temperature differences (LMTDs) were calculated with PRO/II facilities for each heat-exchanger. The operating time was 8000 h/year.  
The TAEC calculation includes the steam costs for the reboilers and the cooling water costs for the condensers and coolers. The cost of the solvent makeup stream was ignored since the flowrates of the solvent makeup stream are almost negligible. Three grades of steam with different prices were used according to the bottom temperature of each distillation column. High-pressure saturated steam with a pressure of 3500 kPa was used as a utility at the SRC reboiler, medium-pressure saturated steam with a pressure of 1300 kPa at the EDC reboiler, and low-pressure steam with a pressure of 600 kPa at the PC reboiler. Cooling water with a temperature of 305.15 K is supposed to be available for all the condensers and at the cooler. Table S2 in the Supplementary Materials shows the different formulas used to calculate various costs for the equipment, as well as the utility prices.  
Figure 3: Two process flowsheet diagrams proposed for the recovery of the latent heat and sensible heat from the hot streams: (**a**) LH-SH1; and (**b**) LH-SH2.
# Separable Gaussian Neural Networks: Structure, Analysis, and Function Approximations
## 5 Numerical Experiments
### Two-Dimensional Examples  
First, SGNN is used to approximate the two-dimension function \(f_{3}(\mathbf{x})=1/5e^{(x_{1}^{2}+x_{2}^{2})/50}\), which has four sharp peaks and one flat valley in the domain. As illustrated in Figure 2a, the optimizer converges in 400 steps, with the difference between training and test sets at the magnitude level of \(10^{-4}\). Figure 2b-e show that the prediction by SGNN is nearly identical to the ground truth, except for the domain near boundaries. This can be attributed to fewer sampling points in the neighborhood of the boundaries. Better alignment can be achieved by sampling extra boundary points to the input dataset. SGNN maintains its level of accuracy as candidate functions become more complex. For example, Figure 3 presents the approximation of \(f_{4}(\mathbf{x})=\frac{1}{5}(e^{x_{1}^{2}/50}\sin x_{2}+e^{x_{2}^{2}/50}\sin x _{1})\). SGNN can approximate \(f_{4}\) with the same level of accuracy as \(f_{3}\) even with fewer training epochs, possibly led by the localization property of Gaussian function. The largest error again appears near boundaries, with a percentage error less than 8%. Inside the domain, the computed values precisely match the exact ones. As visualized in Figure 3d,e, the prediction by SGNN can fully capture the features of the function. This finding is corroborated in Figure 4, which presents the approximation of the function \(f_{5}(\mathbf{x})=\frac{1}{50}(x_{1}^{2}\cos x_{1}+x_{2}^{2}\cos 2x_{2})\) by SGNN. The function, different from \(f_{4}\), possesses peaks and valleys near boundaries and becomes flat in the vicinity of origin, as illustrated in Figure 4c-e. Interestingly, the neural network converges faster than the network for \(f_{4}\). This indicates that the loss function may become more convex and contain fewer flat regions. One possible reason is that, as the function becomes more complex, more Gaussian neurons are active and have larger weights, increasing the loss gradients. The largest error is again observed near boundaries. As shown in Figure 4, SGNN can capture the features of the target function \(f_{5}\). Due to the gradient configuration of the color bar, the small offset with respect to the ground truth occurs near the origin, but the corresponding absolute errors are very small, as shown in Figure 4b.
# A Blockchain-Based Framework to Enhance Anonymous Services with Accountability Guarantees
## 4 Challenge-Response Mechanism  
At the basis of our protocol, there is a challenge-response mechanism implemented by the smart contract reported in Listing 1.  
It offers the following three security properties.  
* P1: \(IG\), \(SP\), and \(LA\) have to be sure that the users really own the claimed Ethereum addresses.
* P2: if the previous check is satisfied and the owner of an address is able to solve a given challenge, then the smart contract sets, in a verifiable way, the state of such an address as \"Confirmed\".
* P3: the smart contract allows us to notarize some information.  
Even though, at this stage of the paper, it is not clear why we need these properties, we show as the smart contract allows us to satisfy them. Further details are provided in Section 5, where we exploit the proposed mechanism to implement our protocol.  
Our smart contract includes two mappings, called stateMap and challengeMap, and two functions, called setChallenge and solveChallenge.  
stateMap associates an Ethereum address with a string representing the state of such an address. We admit three possible states: \"\" (the built-in default state of Ethereum smart contracts), \"To Confirm\", and \"Confirmed\". By default, all of the addresses are in state \"\".  
challengeMap associates an Ethereum address with a bytes32 variable containing the challenge that such an address has to solve.  
setChallenge can only be invoked by the owner (i.e., the entity who deployed) of the instance of the smart contract (in our application, the owner can be \(IG\), \(SP\), or \(LA\)). It receives as an input an Ethereum address and a challenge. Then, it associates the challenge with the address in the challengeMap and sets the state of the address to \"To Confirm\" in the stateMap.  
solveChallenge can be invoked by anyone and receives the solution to the challenge as an input. The solution to the challenge is a 32-byte word (corresponding to the digest of a cryptographic hash function). The technical detail about how this solution is computed is described in Section 5. First, it checks that the address originating the transaction invoking this function is in state \"To Confirm\". This guarantees P1, since only the owner of an Ethereum address can generate a transaction from such an address. If the check is positive, then the function verifies that the solution solves the challenge associated with this address. In the positive case, the state of the address is set to \"Confirmed\" in the stateMap. This clarifies how the smart contract guarantees P2.  
The mechanism to verify the solution to the challenge is straightforward. The smart contract simply checks that \(\mathit{challenge}=\mathit{Keccak256}(\mathit{solution})\).  
Regarding P3, the solution to the challenge itself represents the information to notarize. Observe that, since this information is published clearly on the blockchain, it should not compromise the anonymity of the user. This will be explained in the next section, in which, the whole protocol is described.