esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Amendments and Soil Properties
#### 4.1.1 Soil Physicochemical Properties  
Amending soil did not affect IC concentrations. Amendments, however, affected soil pH and EC. It should be noted that changes to EC and pH occurred upon amendment application, with no significant changes to EC after the 22-week pre-incubation. Biochar was the only amendment that affected soil pH by making soil more alkaline. Greater biochar pH is generally correlated with greater ash content, which increases with increased pyrolysis temperatures [46]. [PERSON] et al. (2020) suggest that the greater the pyrolysis temperature, the greater the loss of acidic functional groups, along with increased formation of Ca-, Mg-, Na, and K-bearing oxide, hydroxide, and carbonate mineral phases that can raise pH [46]. Because increased soil alkalinity can reduce the availability of certain nutrients (e.g., Fe), the change in pH in BIOC underscores the importance of selecting a biochar made using an appropriate pyrolysis temperature and feedstock for the intended purpose and soil environment in which it will be used [46, 47].  
The addition of compost and inorganic fertilizer doubled the EC compared to UNAM and BIOC. Lack of EC response in BIOC may be due to the capacity of charcoal to sorb salts [76]. The increases observed in COMP and INF highlight the utility of amendments in increasing EC in low nutrient soils, where plant health and growth may be adversely impacted through nutrient deficiency. However, it also points to the potential for excessive EC levels through over-application of soil amendments. Excessive EC can prevent effective nutrient uptake by increasing the osmotic pressure of the nutrient solution and also promotes losses of nutrients through leaching [77].
## 3 Results and Discussion
### Effects of Stress-Annealing on Magnetic Properties and GMI Effect of Co-Rich Microwires
Figure 7: \(\Delta\)Z/Z (H) dependencies of as-prepared (**a**); stress-annealed at T\({}_{\text{ann}}\) = 300 \({}^{\circ}\)C under \(\sigma_{\text{a}}\) = 80 MPa for 10 min (**b**); 30 min (**c**), and \(\Delta\)Z/Z\({}_{\text{m}}\)(f) dependencies of as-prepared and stress-annealed Co\({}_{\theta 9.2}\)Fe\({}_{4.1}\)B\({}_{11.8}\)Si\({}_{13.8}\)C\({}_{1.1}\) microwires (**d**). Developed microwires allow us to achieve extremely high magnetic field sensitivity (up to 10%/A/m), the highest among non-cryogenic devices; therefore, they are suitable for high-performance magnetic sensors [12; 13; 15]. However, applications of magnetic microwires prepared using the same fabrication method are not restricted to the aforementioned. Recently, other applications of magnetic microwires in medicine, biology, instrumentation, electronic surveillance and the automobile industry, such as magnetic hyperthermia allowing in vitro cancer-cell treatment [51], magnetic shape memory and magnetocaloric effects for magnetic refrigeration [52], and magnetic bistability, allowing magnetic tags development [23], have been proposed. It is worth noting that the use of nanostructured materials, such as thin films and multilayers, can allow further miniaturization of sensors and devices utilizing the GMI effect [53; 54]. Furthermore, thin-film GMI elements are more compatible with integrated electronic devices. However, generally, thin films present a much poorer magnetic softness and GMI ratio. On the other hand, recently, it has been reported that the problem of compatibility of magnetic microwires with integrated electronic circuits was successfully solved [55]. On the other hand, the predicted theoretical maximum GMI ratio is about 3000% [56; 57], which is still a few times superior to the experimentally reported \(\Delta\)Z/Z values [12; 13; 15]. Therefore, it is expected that technology improvement, as well as the development of effective postprocessing methods, will allow for the achievement of a higher GMI ratio.
## Abbreviations  
ADEP4, acyldepsipeptide antibiotic; _agr_, accessory gene regulator; AMP, Ag, silver; ampicillin; \(\beta\)-LEAP, \(\beta\)-lactamase enzyme-activated PS; BR, breast reconstruction; CA-MRSA, community-acquired MRSA; CCS, core cross-linked star; CDs-NH2; amine-terminated carbon dots; Cu-MOF, copper-based metal-organic framework; CuO, copper oxide; DNA, deoxyribonucleic acid; (e-DNA), extracellular doxorubicinic acid; ECM, extracellular matrix; EMA, European Medicines Agency; EPS, extracellular polymeric substance; Fe-S, iron-sulfur; FDA, Food and Drug Administration; GA, glutaraldehydedehyde; Gd(DTPA), Gd(III) diethylenetriaminepenetacetate hydrate; GNPs, gold nanoparticles; HA-MRSA, hospital-acquired-MRSA; HCP, hyperbranched conjugated polymer; ICST, lower critical solution temperature; MCNAG, magnetic carbon nanotubes functionalized with GA; MNPs, magnetic NPs; MRGO, magnetic reduced graphene oxide; MRGOGA, magnetic reduced graphene oxide functionalized with GA; MRSA, methicillin-resistant _s. aureus_; MSNs, mesoporous silica NPs; NIR, near-infrared; NPs, nanoparticles; PAA, poly(acrylic acid); PDF, penicillin-binding protein; PCL, poly(\(\epsilon\)-caprolactone); PDT, photodynamic therapy; PEO, poly(ethylene oxide); PIs, prosthetic joint-associated infections; PMMA, polymethylmethacrylate; (p)ppGpp, guanosine pentraphosphate; PS, photosensitizer; PT, photothermal therapy; PDMAZA, poly(2-dimethylamino)ethyl methacrylate; QDs, quantum dots; QS, quorum sensing: ROS, reactive oxygen species; SCCmc, Staphylococcal cassette chromosome _mcc_; SCVs, small-colony variants; SNAMPPs, structurally nanoengineered antimicrobial peptide polymers; TA, toxin-antitoxin; 3D, three-dimensional; VRE, vancomycin-resistant enterococci; UV, ultraviolet; VRSA, vancomycin-resistant _S. aureus_.
###### Abstract  
Self-stabilization quantum key distribution (QKD) systems are often based on the Faraday magneto-optic effect such as \"plug and play\" QKD systems and Faraday-Michelson QKD systems. In this article, we propose a new anti-quantum-channel disturbance decoder for QKD without magneto-optic devices, which can be a benefit for the photonic integration and applications in magnetic environments. The decoder is based on a quarter-wave plate reflector-Michelson (Q-M) interferometer, with which the QKD system can be free of polarization disturbance caused by quantum channel and optical devices in the system. The theoretical analysis indicates that the Q-M interferometer is immune to polarization-induced signal fading, where the operator of the Q-M interferometer corresponding to Pauli Matrix \(\sigma_{2}\) makes it satisfy the anti-disturbance condition naturally. A Q-M interferometer based time-bin phase encoding QKD setup is demonstrated, and the experimental results show that the QKD setup works stably with a low quantum bit error rate about 1.3% for 10 h over 60.6 km standard telecommunication optical fiber.  
quantum key distribution; anti-quantum-channel disturbance; quarter-wave plate reflector 2020
## 3 Experimental Results and Analyses
### AE Count/Energy Characteristics
#### 3.5.2 AE Energy Characteristics during Sample Deformation  
To better detect the effect of the loading frequency, the connections between the AE energy/cumulative energy and the loading stage were plotted, as shown in Figure 13. In the AE nondestructive detection, the AE energy signal was a parameter related to the amplitude of the signal (i.e., the energy defined for the signal waveform) and not the actual physical energy released by the AE source. However, the AE energy was still of great significance for measuring and evaluating the degree to which the materials were damaged. The figure indicates that, at the start of each stage, the AE energy suddenly increased, and the AE cumulative energy exhibited a periodic jump in which the energy significantly increased. This was due to the sudden increase in the stress during the stage transitions and the structural damage (e.g., the small-scale slips and other internal damage), which released a large number of stress waves, resulting in the rapid growth of the cumulative curve of the AE energy (producing a step function). Throughout the sample destruction process, the AE energy increased as the loading stage increased. The AE cumulative energy changed roughly in two stages, exhibiting a steady increase followed by a rapid surge. In the stability stage, the internal structure was steadily damaged, and the cracks gradually expanded. In the surge stage, the sample experienced deformation damage and cracks throughout the process, the rocks lost bearing energy, and the energy accumulated inside the sample was released. Therefore, at this moment in the AE count peak, the energy surged. In addition, a comparison between the AE cumulative energy curves at the different frequencies revealed that the samples with low loading frequencies produce more AE energy than the samples with relatively high loading frequencies. The effect of the frequency on the AE energy reflects the response of the material characteristics to the loading rate (strain rate).  
Figures 12 and 13 indicate that the AE energy and AE count corresponded well to the trends observed in the cumulative count, especially considering the spikes in the AE rate. The AE accumulation energy also exhibited jumps in growth throughout the damage process. Thus, the AE count and energy were proportional to each other.  
Figure 12: Relationship between the axial stress, AE rate, AE count accumulation, and elapsed time for the argillaceous shale samples subjected to the different frequencies. (**a–d**). The loading frequencies were 0.5 Hz, 0.2 Hz, 0.1 Hz, and 0.05 Hz, respectively.).
## 3 Home Area Network
### Resiliency  
The resilience defines the recoverability and fault tolerance of a network. Due to some intrinsic features, wireless mesh networks are more vulnerable to possible node and link failures when compared with wired networks. Consequently, the resilience to failures has become a very significant issue recently in the design of wireless mesh networks. It has been also noticed that sensor nodes misbehaviour can origin failures as well and thus have driven new challenging and open problems to the resilient wireless network design and can weaken the performance and even the whole connectivity of the networks [53]. When incidents occur, the degree of resiliency defines how trustworthy the HAN can truly be. It clarifies, mainly from a safety and security perspective, the capability to restore and recover from a range of disruptions or malfunctions through the robust fast-response process, especially the vulnerable digital elements in the house.
# An Improved Opportunistic Routing Protocol Based on Context Information of Mobile Nodes
###### Abstract  
Delay tolerant network (DTN) protocol was proposed for a network where connectivity is not available. In DTN, a message is delivered to a destination node via store-carry-forward approach while using opportunistic contacts. Probabilistic routing protocol for intermittently connected networks (PRoPHET) is one of the widely studied DTN protocols. In PRoPHET, a message is forwarded to a contact node, if the contact node has a higher delivery predictability to the destination node of the message. In this paper, we propose an improved opportunistic routing protocol, where context information of average distance travelled and average time elapsed from the reception of a message to the delivery of the message to the destination node is used. In the proposed protocol, the average distance and average time are updated whenever a message is delivered to a destination node. Then, both average distance and average time as well as delivery predictability of PRoPHET protocol are used to decide a message forwarding. The performance of the proposed protocol is analyzed and compared with that of PRoPHET and reachable probability centrality (RPC) protocol, which is one of the latest protocols using the contact history information of a mobile node. Simulation results show that the proposed protocol has better performance than both PRoPHET and RPC, from the aspect of delivery ratio, overhead ratio, and delivery latency for varying buffer size, message generation interval, and the number of nodes.  
d  
olivery predictability; routing; PRoPHET; delay tolerant networks; opportunistic routing protocol; average time; average distance
## 4 Discussion  
The major finding in this study was that detecting landslides is not fully equal to inventory generation. The detected targets require further examination. [PERSON] et al. [34] suggested that the runout area and topological relationship could be considered independent classes in landslide records when producing a landslide inventory. Therefore, this study focused on the separation of landslide source and runout classes caused by different mechanisms. The feasibility of distinguishing between landslide source and runout signatures to improve landslide detections was assessed. The limited training samples extracted from the top five polygons in the landslide inventory map were used to construct supervised data mining models in order to classify other instances. The top five polygon samples were used for training because manually recognizing the larger targets from a regional scale-based remote sensing image is relatively easier than interpreting smaller objects. Based on this scenario, the developed models with RFs and cost analysis and mapping results demonstrated the stability of the proposed procedure. The hierarchical analysis was designed in this study. Section 3.2 explored the feasible models as listed in Table 3. Section 3.3 demonstrated the performances of developed models verified by the samples of larger and smaller inventory polygons, comparing them with the SVM and LR methods as shown in Figures 8-13 and Tables 4-6. The quantitative evaluations of the optimal results as shown in Figure 13 were determined to be higher than 80% in most cases. The results further demonstrated that the developed models outperform the SVM and LR algorithms in most accuracies.  
The primary limitation of this study was that the developed models were more favorable for larger area sizes but may be difficult to apply to smaller areas. The capability of the models based on the supervised strategy was related to the characteristics of the training datasets. The tradeoff between the cost of producing the training data and the area sizes of targets for predictions should be considered. Perhaps a multi-scale model trained by different area sizes of the inventory polygons is a possible solution. This method could also be improved by including more topographic attributes, as described by [PERSON] et al. [61].  
Producing a high-quality landslide inventory is time consuming and expensive. Landslide inventories are crucial for landslide analyses, such as for performing tasks in the framework of landslide risk assessment and management [14]. The potential for reducing the degree of manual interpretation is demonstrated in mapping results. Based on the spatial patterns of predictions, the misclassification for detecting runout areas appeared in the top region. Two possible strategies may solve this problem. First, a threshold based on the elevation attribute could be designed. Second, studies could investigate only extracted landslide sources from the affected areas, and the remainder are assigned the runout label. These runout patterns may be adequate to further assess the related parameters, such as runout distance and damage corridor width [14].
## 2 Materials and Methods
### Search Strategy and Database Searches  
A consolidated search strategy was developed using relevant keywords, and medical subject heading (MeSH) terms related to STIs, AI technologies, KPs, and SSA were used. Electronic databases, including PubMed, Scopus, Web of Science, Science Direct, EBSCOhost, and Sabinet were searched for eligible studies published between the years 2015 and May 2024. In addition, manual random searches of online documents, grey literature, and technical reports on the websites of relevant organizations, including the World Health Organization (WHO), United Nations (UN), and Centers for Disease Control and Prevention (CDC) were also conducted. Online searches of full texts of primary studies conducted in SSA on the use or applications of AI and/or ML in the diagnosis, treatment, and/or management of STIs or STDs from 2015 were carried out.  
Supplementary Material: SM S6 shows the search strategy employing Boolean operators AND, OR, and NOT to incorporate various combinations of keywords and phrases related to the research topic. Additionally, terms such as curable STIs and KP were included, along with a delineation of different types of KPs, such as sex workers, gay men, transgender people, people who inject drugs, and individuals in prisons. The study topic was broken down into short statements using original words, acronyms, and synonyms of the relevant words or terms. For example, (applications OR uses) AND (of 4 IR OR ML OR AI AND ML) AND (diagnosis AND management) AND (STIs OR sexually transmitted infections) AND (key populations OR KPs) AND (Sub-Saharan Africa OR Angola OR Benin OR Botswana OR Burkina Faso OR Burundi OR..., OR Zimbabwe).
## 2 Materials and Methods
### Characterizations of Synthesized Graphene Nanostructures
#### 2.3.1 Raman Analysis  
Inelastic scattering of excitation light by the molecules of gaseous, liquid, or solid material is called the Raman effect. The interaction of a molecule with photons gives rise to vibrations of its chemical bonds; this causes a specific energy shift in the scattered light called the Raman spectrum. All chemical samples can be categorized by this individual  
Figure 1: A schematic illustration of graphene transfer.  
spectral fingerprint. A confocal microscope combined with a spectrometer collects Raman spectra. Raman spectroscopy, which is a strong, rapid, sensitive, and non-destructive analytical approach to provide both qualitative and quantitative information for carbon-based materials, is one of the best tools to investigate the structure and quality of carbon-based materials, including Gr. In this study, Raman analyses were performed for the synthesis of GrNs. This characterization was done with WITech alpha 300R at 532 nm laser, and the Raman spectra of GrNs were obtained by the excitation source of 532 nm. D, G, and 2D peaks of graphene, the carbon-based material, were extensively investigated using Raman analysis. Strong G peak and strong D peak indicate good graphitic structure, and 2D-G peak intensity ratio (I\({}_{\text{2D}}\)/I\({}_{\text{G}}\) > 2), on the other hand, confirms monolayer graphene [19; 20; 21; 22; 23; 24; 41]. Raman analysis was preferred for the determination of the number of layers in the present work.
# Securing SCADA Energy Management System under DDs Attacks Using Token Verification Approach
## 2 Related Work
### The Denial of Service Attacks on Industrial Internet of Things  
The Industrial Control System (ICS) is the core of IIoT and how to improve its reliability and security has become an important topic. A denial of service attack is common in the IIoT environment and attackers usually send a lot of ineffective data to receivers to destroy the SCADA system's ability to function properly, meaning its devices fail to operate according to normal programs, and the absence of proper defense can result in serious losses. In terms of the threats and breaches faced by the ICS system, ref. [21] proposed that, with the development of IoT and communication technology, the IIoT control system is no longer closed. [PERSON] et al. [22] put forward LIDOR, which is a safe and lightweight multihop communication protocol that uses effective symmetric keys to encrypt and protect the transfer of data packets. The protocol proposed in this study can also defend against the known packet dropping DoS attack model. [PERSON] et al. [23] designed distributed congestion control by duty-cycle restriction (D-ConCReCT) to conduct congestion control examinations and lower the DoS attack risk in IIoT. Ref. [24] introduced fog computing as the medium for the smart microgrids system and used the average consensus-based algorithm for scheduling tasks to reduce the impact of DoS attacks. To this end, this paper proposed a Trusted Encrypted Validator module (TEVM) based on token authentication to lower the probability of the SCADA system being attacked by DoS.
# Phonon Scattering and Thermal Conductivity of Actinide Oxides with Defects
## 3 Results and Discussion
### Temperature Effect on Thermal Transport in Actinide Oxides  
To investigate the temperature effect on the thermal conductivity of actinide oxides with oxygen interstitials, RNEMD has been performed for UO\({}_{2}\) and PuO\({}_{2}\) with different defect concentrations at 100 K, 200 K, 300 K, 600 K, 900 K, and 1200 K. As can be seen in Figure 7, the thermal conductivities decrease with an increase in temperature quickly. This can be explained by Umklapp phonon scattering. In crystalline solids, phonons with large wave vectors are populated at high temperatures. These phonons with large wave vectors fall outside the first Brillouin zone when they scatter from other  
Figure 6: (**a**) The comparison between the effects of different point defects on the thermal conductivity of PuO\({}_{2}\) (\(N_{x}\) = 100). Thermal conductivities are normalized by the thermal conductivity of the stoichiometric PuO\({}_{2}\) (\(K_{max}\) = 17.15 W/m-K). (**b**) Phonon density of states (PDOS) as a function of phonon energy for PuO\({}_{2}\) with different point defects.  
phonons with large wave vectors, resulting in the loss of their momentum. Umklapp phonon-phonon scattering in actinide oxides has been investigated by different researchers. [PERSON] et al. [39] incorporated a Umklapp scattering term in measuring the thermal conductivity of UO\({}_{2}\). [PERSON] et al. [4] showed evidence of Umklapp phonon scattering by estimating the thermal conductivity of ThO\({}_{2}\) at different temperatures. [PERSON] et al. [40] also showed that the Umklapp process causes a thermal conductivity reduction in actinide oxides at high temperatures.  
The simulation structures with large defect concentrations (5%) do not show the temperature dependency that is observed in the simulation structures with low defect concentrations (0.1%). This implies that in the simulation structures with high defect concentrations, phonons are scattered greatly from their defect sites before they are scattered by other phonons with large wave vectors. In these simulation structures with high defect concentrations, the generation of long-wavelength phonons is prohibited by their many defect sites, even at very low temperatures (100 K or 200 K). Therefore, the thermal transport properties of the structures with high defect concentrations cannot be improved by decreasing the temperature, unlike stoichiometric samples or samples with low defect concentrations.