esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Limitations and Future Work  
Despite the fact that the customized CNN model had promising results in detecting COVID-19 from X-ray images, the current model still has some limitations. First, the model depends on COVID-19 patients' chest X-rays to focus on lungs for diagnosis. However, it is often necessary in clinical practice to classify COVID-19 patients and then segment the infected areas to decide how to further treat the patients. As a result, we'll look at an artificial-intelligence-based automatic diagnosis model that combines COVID-19 detection and lung infection area analysis into one system. Second, the model is based on binary classification which can be used to assist multiclass labelling between COVID-19 and normal X-rays. However, the ability to identify the difference between COVID-19 and viral pneumonia is still deficient. To provide category differentiation between normal, viral pneumonia and COVID-19 X-ray images can prove to be more efficient as COVID-19 and viral pneumonia can result in similarity between the images which makes it difficult to distinguish in medical terms. For future work, we will do further analysis to imbed these tasks to provide more benefits for the medical professionals in hospitals.
## 6 Future Work and Conclusion
### Future Work: A Cross Layer Approach to Increase the Robustness of a MP2P System
#### 6.1.2 Open Problems  
To sum it up, we are able to prevent lookup requests from being dropped or redirected as long as the underlay routing algorithm operates correctly. For this, each node has to monitor all nodes in transmission range and a sufficient density of monitoring nodes is required. Yet, we are still facing open problems regarding the monitoring of neighbor nodes as mentioned in [40].
# GC- and UHPLC-MS Profiles as a Tool to Valorize the Red Alga Asparagopsis armata+
###### Abstract  
_Asparagopsis armata_ [PERSON] is a red alga native from the southern hemisphere and then introduced in the Mediterranean Sea and the Atlantic Ocean, including the Azores Archipelago, where it is considered an invasive alga. Some studies show that the extracts exhibit antimicrobial and antifouling activities, and it is incorporated in some commercialized cosmetic products. (e.g., Ysaline(r)). However, knowledge of this species chemical composition is scarce. The GC-MS and UHPLC-MS profiles of both the nonpolar and polar extracts were established to contribute to this problem solution. According to the results, _A. armata_ is rich in a great structural variety of halogenated lipophilic and aromatic compounds, some of them identified here for the first time. In the lipophilic extract, 25 compounds are identified, being the halogenated compounds and fatty acids, the two major compound families, corresponding to 54.8% and 35.7% of identified compounds (224 and 147 mg/100 g of dry algae, respectively). The 1,4-dibromobuten-1-ol and the palmitic acid are the two most abundant identified compounds (155 and 83.4 mg/100 g of dry algae, respectively). The polar extract demonstrated the richness of this species in brominated phenolics, from which the cinnamic acid derivatives are predominant. The results obtained herein open new perspectives for valuing the _A. armata_ as a source of halogenated compounds and fatty acids, consequently improving its biotechnological and economic potential. Promoting this seaweed and the consequent increase in its demand will contribute to biodiversity conservation and ecosystem sustainability.  
_Asparagopsis armata_; Rhodophyta; GC-MS; UHPLC-MS; dibrominated compounds; 1,4-dibromobuten-1-ol; palmitic acid; brominated phenolics; red seaweed; invasive seaweed
## 4 Advanced Strategies for eNO\({}_{3}\)R Toward Ammonia and Nitrogen
### Copper-Based Electrocatalyst for the eNO\({}_{3}\)R: A Case Study for Ammonia Production
#### 4.1.8 Copper-Based Nanomaterials  
Advances in the rational design of micro-/nanostructures have enhanced their efficacy. Notably, copper nanomaterials have gained attraction toward nitrate reduction to ammonia. Cu-nanomaterials used to date are nanosheets, nanowire arrays, nanocubes, nanoparticles,  
Figure 13: Schematic illustration showing the electrocatalytic nitrate-to-ammonia process over the (**a**) Cu/Pd/CuOx [172] and (**b**) Pd-Cu\({}_{2}\)O [173].  
3D nanobelts, and nanoclusters. Cu-nanosheets (CuNSs) have demonstrated significant efficiency in several studies such as [PERSON] et al. [137] who investigated CuNSs with [111] basal planes, Cu nanocubes ([100]), and irregular Cu nanoparticles. The results indicate that CuNSs displayed exceptional NH\({}_{3}\) production selectivity (99.7% FE) at a low overpotential of \(-0.15\) V (vs. RHE). Furthermore, they exhibited distinct reduction peaks in linear sweep voltammetry (LSV), attributed to their specific facets and efficient NO\({}_{2}^{-}\) generation, crucial for high eNO\({}_{3}\)R performance [137]. Another nanomaterial that has exhibited notable efficiency is nanowire arrays (NWAs), NWAs have been effective due to their reconstructed surface during catalysis, which suppresses HER and achieves high faradaic efficiencies towards ammonia production (up to 95.8%) [58]. Incorporating metals like ruthenium (Ru) on Cu nanowires (Ru-CuNW) has led to exceptionally active catalysts, achieving reduction currents of 1 A cm\({}^{-2}\) at low overpotentials (\(-0.13\) V vs. RHE) and converting over 99% of nitrate into ammonia [179]. Cu-nanobelts have been found to substantially increase the specific surface area, thus boosting the catalytic activity by facilitating better nitrate transfer and accelerating electrocatalytic kinetics. These nanostructured copper electrodes exhibit a high FE of 95.35% and a maximum NH\({}_{3}\) production rate of 650 mmol h\({}^{-1}\) g\({}^{-1}\)\({}_{\text{cat}}\) with a potential 0.15 V vs. RHE, outperforming traditional foam copper electrodes. The enhanced efficiency is attributed to their large surface area and high charge transfer capability, which promotes effective nitrate adsorption and reduction [163]. The advantage of using nanomaterials is their operation at low potentials, which minimizes energy consumption. However, scalability remains a significant barrier to the use of Cu-nanomaterials for nitrate reduction to ammonia.
# Semantic Web and Knowledge Graphs for Industry 4.0
## 5 Semantic Web and Ontologies for Industry 4.0
### Ontologies for Industry 4.0 Manufacturing
In another study [70], the researchers proposed the ontology model of RAMI4.0 to exchange information between assets and I4.0 components in a meaningful way. In [7], the authors proposed an ontology by merging five ontologies that are base, product, process, device, and parameter ontology to represent the manufacturing production process beginning from order to completion of the product. The ontology is built on the top of the product, process, device, and parameter ontologies to provide interaction with each other. Additionally, the order concept is modelled as a separate ontology that is linked with the product. Service-oriented architecture has been built on the top of this ontology model to discover, select, organize, and consume semantic web services dynamically [71]. [PERSON] et al. utilized the concepts of manufacturing resource, process, and product from the ISA-95 standard [72]. They adopted the approach of semantic rules to infer implicit knowledge to allow inspecting the machines needed to produce product variants. [PERSON], [PERSON] et al. designed an ontology model for the manufacturing domain and developed a semantic query algorithm to investigate the semantic richness of the queried keyword return by the ontology model [73]. Some of the most renowned ontologies in the manufacturing domain are process specification language (PSL) [45], ONTology for Product Data Management (ONTO-PDM) [74], MAnufacturing Semantic ONtology (MASON) [46], ADAptive holonic COntrol aRchitectture (ADACOR) [75], etc., ontology. MASON ontology has been developed to estimate the production cost of the mechanical components. The design of PSL ontology emphasis to enable the exchange of process information in manufacturing systems accurately and comprehensively. [PERSON], H. el. al modelled the product concepts based on two standards ISO-10303 and IEC-62264 to facilitate the interoperability between software application exchanging product life cycle information. PSL ontology represents the concepts of process modelling, planning, scheduling, simulation, etc. in axioms of first-order logic theories. ADACOR ontology has highlighted the knowledge related to customer work orders, production plans, model operations. These ontologies are helpful to recreate an ontology model to cover the notion of the whole production line from customer order to the product life cycle. There is a great amount of literature available for ontology-based agent system such as CORA [76], ROA Ontology [77], ORArch, and O4I4 Ontology [78] that perform main tasks in the manufacturing industry and is out of the scope of this paper.
## 1 Introduction  
In general, there are three primary sources of non-linearity in the structures that are generally identified. Material non-linearity concerns structures made of an elastic-plastic material. The second case, boundary non-linearity, involves structures which deflect linearly until they contact unexpected supports. The last one is geometric non-linearity; in this case, structure's stiffness changes due to large deflections. The last case is the one regarded in this study.  
Nowadays, large space structures are very popular; they are made mostly of cable members, which are geometrically a kind of non-linear structure. A deep understanding of their structural performance yields from their structural analysis. Direct linear methods, such as the Force Method (FM) and Stiffness Method (SM), are linear techniques and cannot accurately cover non-linear structures. Theoretical nodal displacements and internal bar forces show significant inconsistency with the practical results [1]  
The proposed approach is a straightforward technique, simple enough to be studied in undergraduate courses for civil engineering students. The system is adapted from a direct linear method, namely, the Stiffness Method. The modification concerns the load and updates the structure's geometry after each incremental loading. In the conventional linear method, the entire load is directly applied to the joints all at once, so the joint displacementsare given instantly. However, in the current technique, the load is incrementally applied (\(\delta P\)) to the joints to obtain incremental displacements (\(\delta d\)). The problem of direct linear methods arises here; the nodal displacements of geometrically non-linear structures obtained from linear methods are substantially far from practical results. The linear stiffness method involves a single iteration, while the proposed approach involves two or more repeated steps. When the displacements are achieved in each iteration, they subsequently update the corresponding joint coordinates; hence, the structure geometry is updated. When the sum of the discretized loading matches the full load, this will be the last step of loading; then, the nodal displacements are summed up. The novelty of the current work means that the approach of linear stiffness is applied to analyze geometrically non-linear structures with coordinated updating of the structures, which, in fact, is a partial non-linear technique. The advantages of the proposed approach are twofold. The method is straightforward compared to other non-linear approaches; it is an effective routine for geometrically non-linear pin-joined assemblies and flexural structures. Its accuracy has been evaluated in the light of the previously proved non-linear methods. This approach can be easily included in educational courses for undergraduate students to pave the way for other advanced non-linear methods.
# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 5 Explainability in Federated Intrusion Detection
### Use Case Example: DDoS Detection in Smart Healthcare  
Consider a smart hospital where distributed IoT devices (infusion pumps, ECGs, gateways) participate in FL-based anomaly detection. During a simulated DDoS scenario, local models detect packet rate spikes and abnormal source IP entropy.  
Using LIME, a client device explains a prediction by identifying src_bytes, duration, and dst_host_srv_count as dominant features. This explanation is obfuscated and sent to the aggregator, which confirms across multiple clients that these features consistently appear in DDoS-related alerts--providing insight into attack vectors [40; 41].  
A system administrator can then visualize the aggregated explanation as a ranked list of contributing features, enabling more informed threat responses and potential updates to firewall rules or access policies.
# Helicopter Safe Landing Trajectory after Main Rotor Actuator Failures
## 6 Conclusions  
This paper focuses on helicopter safe landing trajectories after swashplate actuator stuck failure occurs in level flights by using the remaining manipulations without introducing other control inputs. The general geometry of a single main rotor helicopter swashplate actuator is analyzed, and new configurations of the control mixer are designed for each actuator failure case, without changing the original geometries of the mechanical structure. With new configurations of the control mixer, the trajectories are obtained with a modified two-dimensional nonlinear helicopter dynamic model by the optimal control method. Although the vertical channel is given up due to swashplate actuator failure, results show that the helicopter is still able to land successfully by proper longitudinal manipulations. Results of the trajectories with various initial conditions for each actuator failure case are shown. The results agree with the characteristic \"bucket profile\" of the collective pitch in level flight. The velocity boundaries of the optimal control problem are found by speed sweeps, which show the safe zones are different with different actuator failure cases. In particular, via the optimal control method proposed in this study, larger safe zones are found for the lateral and aft actuator failure cases, and a tighter zone is found for the forward actuator failure case. Further potential improvements will be introducing a high fidelity helicopter dynamic model, which could be: (1) employing a six-degree-of-freedom helicopter model; (2) introducing more state variables of the rotor induced velocity.  
Conceptualization, Y.W. and H.W.; Investigation, C.J.; Methodology, Y.W., C.J., Y.Z. and H.W.; Validation, Y.W. and Y.Z.; Writing - original draft, Y.W.; Writing - review & editing, H.W. and Y.W.. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Mathematical Optimization Model
### Set of Constraints  
The restrictions of the problem of the optimal placement and sizing of STATCOMs in power systems are associated with classical power flow constraints, voltage regulation bounds, and devices' capabilities. These constraints are presented below.  
\[P_{ht}^{g}-P_{bh}^{d}=\sum_{k\in\mathcal{N}}\sum_{m\in\mathcal{ N}}Y_{km}V_{kh}V_{mh}\cos(\delta_{kh}-\delta_{mh}-\theta_{km}),\ \forall\{k\in\mathcal{N}\ \&\ h\in\mathcal{H}\}, \tag{2}\] \[Q_{kh}^{g}+Q_{kh}^{\text{STATCOM}}-Q_{kh}^{d}=\sum_{k\in\mathcal{ N}}\sum_{m\in\mathcal{N}}Y_{km}V_{kh}V_{mh}\sin(\delta_{kh}-\delta_{mh}-\delta_{km}), \ \forall\{k\in\mathcal{N}\ \&\ h\in\mathcal{H}\},\] (3) \[V_{\min}\leq V_{kh}\leq V_{\max},\ \forall\{k\in\mathcal{N}\ \&\ h\in\mathcal{H}\},\] (4) \[x_{k}Q_{\min}^{\text{STATCOM}}\leq y_{k}\leq x_{k}Q_{\max}^{ \text{STATCOM}},\ \forall\{k\in\mathcal{N}\},\] (5) \[-y_{k}\leq Q_{k}^{\text{STATCOM}}\leq y_{k},\ \forall\{k\in \mathcal{N}\ \&\ h\in\mathcal{H}\},\] (6) \[\sum_{k\in\mathcal{N}}z_{k}\leq N_{\text{available}}^{\text{STATCOM}}, \tag{7}\]  
Note that \(P_{kh}^{g}\) and \(Q_{kh}^{g}\) correspond to the active and reactive power injection associated with the generators connected at node \(i\) during the period \(h\), while \(P_{kh}^{d}\) and \(Q_{kh}^{d}\) are the active and reactive consumptions at node \(i\) during the period \(h\). \(V_{\min}\) and \(V_{\max}\) represent the minimum and maximum voltage limits allowed for all buses of the system for each period of time and \(y_{k}\) represents the continuous variable associated with the size of the STATCOM connected at node \(k\). \(Q_{\min}^{\text{STATCOM}}\) and \(Q_{\max}^{\text{STATCOM}}\) represent the lower and upper sizes of the STATCOMs that can be installed in the power system. \(z_{k}\) is a binary variable regarding the installation (\(z_{k}=1\) or \(z_{k}=0\)) of a STATCOM in the power system and \(N_{\text{available}}^{\text{STATCOM}}\) corresponds to the number of STATCOMs available for installation in the power system.  
**Remark 2**.: _The optimization model defined in (1)-(7) is based on the formulation proposed in [16]. However, a new constraint was added to ensure that the STATCOM can inject variable reactive power into the power system as a function of its daily performance._  
It is important to highlight that [PERSON] et al. [16] presented an optimization model to locate and size STATCOMs in distribution networks considering that, during the operation period (a daily demand case), the injection of the reactive power is constant in all the times, i.e., the STATCOMs work as capacitor banks. After this optimization process, an additional operative scenario is evaluated to determine the daily reactive power injection performance of the STATCOMs. However, in the optimization model proposed in this study, the variations in the reactive power injection from the STATCOMs is directly included in the power balance equations, which corresponds to an integrated optimization methodology. This differentiates it from the sequential approach reported in [16].
## 2 Materials and Methods  
### Analytical and Spectroscopic Acquisition Data  
Adsorption of N\({}_{2}\) was performed at 77 K in a Quadrasorb Evo equipment from Quantachrome Instruments (Boynton Beach, FL, USA), with a previous degasification at 120 \({}^{\circ}\)C for 24 h. Elemental analyses were determined on a Leco CHNS-932 analyzer (Leco, St. Joseph, MI, USA). X-ray fluorescence spectra were recorded on a Bruker S8 Tiger instrument (Bruker, Billerica, MA, USA). X-ray photoelectron spectroscopy (XPS) analyses were performed on FlexPS-ARPES-E equipment from SPECS (Seongnam, Republic of Korea) with monochromatic K\(\alpha\) Al radiation, 12 KV voltage, and 6 mA current. Binding energy values were internally referenced to the C1s peak at 284.8 eV. Point of Zero Charge (PZC) measurements were obtained following the method described by [PERSON] and [PERSON] [23]. For the determination of acid groups, 0.15 g of sample was mixed with 30 mL of 0.01 M NaOH, and these experiments were performed in triplicate. The mixture was stirred for 24 h at 25 \({}^{\circ}\)C, filtered, and the resulting filtered solution (20 mL) was titrated with 0.01 M nitric acid. For the quantification of basic groups, 0.15 g of sample was mixed with 30 mL of 0.01 M HNO\({}_{3}\), and the mixture was stirred for 24 h at 25 \({}^{\circ}\)C, filtered, and the resulting filtered solution was titrated with 0.01 M NaOH. Phenolphthalein was used as an indicator. Acid and basic sites were expressed as mEq/g. FT-IR spectra were recorded in the range of 4000\(-\)400 cm\({}^{-1}\) on a THERMO spectrophotometer (THERMO, Waltham, MA, USA) using an ATR (attenuated total reflectance) device. Scanning electron microscopy (SEM): SEM images were obtained using QUANTA 3D FEG (FEI Company, Hillsboro, OR, USA) with a secondary electron detector, 15.0 kV acceleration voltage, and at high vacuum. Melting points were determined on an Electrothermal IA 900 apparatus (Electrothermal, London, UK) and were uncorrected. NMR spectra were recorded at 298 K on Bruker Ultrashield TM 500 Plus and Bruker Ultrashield TM 300 instruments. Samples were dissolved in CDCl\({}_{3}\) using tetramethylsilane (Me\({}_{4}\)Si, TMS) (\(\delta\) = 0.00 ppm) as an internal reference. Thin-layer chromatography (TLC) was performed on aluminum sheets coated with 60 F\({}_{254}\) silica from Merck (Rahway, NJ, USA).
## 3 Preliminary Calibration Attempt and Selection of Parameters for Optimization
Figure 2: Preliminary simulation results to identify the optimization parameters. In black: experimental values from Reference [31] as reference; Simulation cases: “Only heat loss” is simulated with the standard parameters at constant permeability. “Permeability” applies a permeability change due to porosity alteration, “Heat conductivity” uses different values for solid heat conductivity for CaO and Ca(OH), “Reaction Param 1” applies a reduced reaction rate constant and “Reaction Param 2” introduces the constant term \(k_{R,2}\) to the reaction kinetics. The parameters for each simulation case are listed in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Case** & \(\mathbf{\kappa}_{HTF}\) & \(\mathbf{\kappa}_{loss}\) & **K** & \(\mathbf{\lambda}_{\text{CaO}}\) & \(\mathbf{\lambda}_{\text{CaOH}_{2}}\) & \(k_{R,1}\) & \(k_{R,2}\) \\ \hline Heat losses & 275 & 30 & \(8.8\times 10^{-12}\) & 0.4 & 0.4 & 10 & 0 \\ Permeability & 275 & 30 & \(8.8\times 10^{-12}\) & 0.4 & 0.4 & 10 & 0 \\  & & & \(8.8\times 10^{-11}\) & & & & \\ Heat conductivity & 275 & 30 & \(8.8\times 10^{-12}\) & 0.3 & 0.5 & 10 & 0 \\ Reaction param 1 & 275 & 30 & \(8.8\times 10^{-12}\) & 0.4 & 0.4 & 0.2 & 0 \\ Reaction param 2 & 275 & 30 & \(8.8\times 10^{-12}\) & 0.4 & 0.4 & 10 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the simulation cases displayed in Figure 2. The units of the parameters are listed in Table 5.