esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Four major peaks were observed in the FT-IR spectra bands of the water samples. All heavy metals, Ag, Cd, Cu, Pb and Zn, affected the water bands to shift within a wavenumber range of 3357 to 1635 cm\({}^{-1}\). Calibration models for PLS and PCA methods had good regression fits for Ag, Cd, Cu, Pb and Zn ions. The PLS models had R\({}^{2}\) values ranging from 0.95 to 1 and the PCA models ranged from 0.98 to 0.99. PLS models predicted Cd and Pb (less than 0 mg L\({}^{-1}\)) in both the river and borehole sources well. Cu was the least detected heavy metal using the PLS models. Significant differences were seen at 0.001% and 0.05% between the PLS and PCA models on detecting only Cd and Pb in the water samples. The PCA models highly detected Ag concentrations (less than 0 mg L\({}^{-1}\) on selected sites) as compared to the other heavy metals. The PLS had a higher sensitivity and accuracy to detect heavy metals in solution (below 0 mg L\({}^{-1}\)) when validated with the chemical results (AAS analysis) below 0.2 mg L\({}^{-1}\) for all the elements. Both the PLS and PCA models detected high Zn ions (mostly above 45 mg L\({}^{-1}\)) in the water samples (River and Borehole). However, the FT-IR spectroscopy demonstrated good potential and can be reliable for heavy metal determination purposes. Rigid and controlled calibrations can reduce costs for water resource monitoring and analysis. To increase its reliability, it is recommended to increase the number of standard spectra scans when constructing calibration models, especially for the PCA method. Future work should aim to further evaluate and verify the accuracy of these multi-component methods using different aqueous solutions as well. Another focus should also further compare the PLS and PCA to other well-known analytical methods such as the Atomic Absorption Spectrometer (AAS).  
Conceptualization, M.M., J.J.v.T and M.P.A.; methodology, M.M., J.J.v.T and E.K.; software, E.K. and M.M.; validation, J.J.v.T, M.P.A. and E.K.; formal analysis, M.M. and E.K.; investigation, M.M; resources, J.J.v.T. and E.K.; data curation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, J.J.v.T. and M.P.A., visualization, M.M.; supervision, J.J.v.T. and M.P.A.; project administration, J.J.v.T.; funding acquisition, J.J.v.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Fund (NRF) and South Africa--Mozambique--Zambia NRF Trilateral Joint research (ZAM180911357528-118479). This project was also partially funded by the Department of Environmental, Forestry and Fisheries (DEFF) under the National Resource Management (NRM).  
The authors would like to acknowledge Chief [PERSON] and the Monontsha Traditional Council for granting the permission for the water surveys; [PERSON] for assistance in the collection and handling of the water samples.
# Optical Channel Selection Avoiding DIPP in DSB-RFoF Fronthaul Interface
## 2 System, Methods, and Network Components  
Modern mobile 5G systems are based, in the radio domain, on the C-RAN architecture, strongly promoted by the Open RAN Alliance. It is highly likely that this type of architecture will be the basic determinant of the development of B5G/6G systems and networks. The study below is based on the 3 GPP model [13], in which eight basic Splits/Options and three intermediate Splits/Options were defined, which enable the split of functions between network devices due to the processing of information and signals at the level of the NG-RAN domain. The assignment of appropriate functions depends on the current network load and the processing load of individual nodal components. It should be noted here that, in the case of real-time services, e.g., from the ultra-reliable low-latency communication (URLLC) family, it should be ensured that as many stages of information and signal preparation as possible are carried out very close to the place where the radio signal is emitted. On the other hand, the simplicity of the network components located right next to the antenna array provides a flexible approach to network reconfiguration, as most signal processing can be performed in an edge cloud.  
Figure 1 shows the functional split of NG-RAN work according to the structure proposed by 3 GPP [13]. In order to introduce interfaces from the A-RoF family, two new Splits (Option 9 and Option 10) and the extension of the functions of split number 8 (Option 8) [21] were proposed. When Option 10 is used in the fronthaul link, then the optical signal is in the RFoF format. In Ref. [39], this service is referred to as the virtual radio frequency (vRF), as it provides full virtualization of the site of software building the radio signal to the level of the radio frequency (RF) carrier. Transporting the RF signal over optical fiber is possible only in the fronthaul network link, the length of which cannot exceed 20 km [40]. When we use the Xhaul architecture and the proposed Option 10 split, the RF signal can be transported over various generations of the fiber-optic media. The impact of the accidental transmission parameters of a fiber-optic link, due to the possibility of automatic reconfiguration of the optical path, requires a continuous correction of the use of specific optical resources. In Figure 1, a distributed unit (DU) is responsible for software building a radio signal and inserting it into the optical path, which may be a specialized device or a separate process in the edge computing cloud. On the wireless part, the radio signal is built by the user equipment (UE) as a mobile device. The RU device is responsible for the modulation and demodulation of the optical signal, as well as the amplification of the radio signal and its transmission and reception from the wireless path, by using the antenna array.
## 3 Methods and Results
### Actuator Characteristics
The folded actuator can be controlled by a syringe pump if the relation  
Figure 10: Actuator characteristics. (**a**) The relationships between the water volume injected into the actuator and the actuator’s rotation angle under different payload torques. (**b**) The results of 100 cycles of a repeatability test. (**c**) Th maximum pressures inside the actuator across one actuation cycle at different payloads. (All the hysteresis loops are counter-clockwise and all the error bars indicate standard deviation for 3 samples). Figure 9: The test platform for the actuator characteristics. (**a**) The test setup. (**b**) A diagram of the actuator test platform. between the actuator rotation angle and the water volume inside the actuator is acquired. The mathematical expression used for control is based on the curve-fitting of the experimental data corresponding to the lower black line in Figure 10a, from 1.2 mL to 1.9 mL, with the minimum torque payload. A larger initial torque payload of the actuators would cause larger deformations of the robot's scaffold, which would affect the established kinematics model. The mathematical expression is  
\[f(x)=2.603x^{2}-6.218x+5.251 \tag{12}\]  
where \(x\) is the actuator rotation angle and \(f(x)\) is the water volume inside the actuator. Since the cable is made of plastic film, it is essential to test the maximum tension it can withstand. Figure 11a shows the diagram of the cable tensile test setup. A linear stage and an actuator were secured on the bench. The end of the plastic cable was connected to a load cell, which was mounted on the moving platform of the linear stage. The cable was gradually stretched until it reached its breaking point. Figure 11b presents the test results of three separate actuators. The cables could withstand forces exceeding 10 N, resulting in a stretching distance of approximately 20 mm. When the cable tension was below 6 N, the stretching distances remained less than 5 mm for all tested cables.
## 3 Results and Discussion
### Representing the ABFPs with a Reduced Subset
#### 3.3.3 Visualizing Mining of the METNs  
The reduced subset is also useful for unravelling information from METNs, which are even more complex than similarity networks because they include other layers containing additional nodes with associated peptide metadata. Consequently, counting on a representative subset of the original space aids in analyzing the huge amount of information associated with ABFPs. As previously mentioned, microbial biofilms are responsible for most chronic and medical device-related infections, as well as for microbial resistance to several antibiotic classes; thus, the identification of promising ABFP scaffolds is an urgent task.  
As an illustrative example of the METNs' contribution to the identification of promising ABFPs scaffolds, six relevant ABFPs according to the HC and HB centralities were selected from the representative subset (Figure 7) for visual analysis of their metadata. This time, key metadata for the development/design of ABF agents from ABFPs were chosen, e.g., other associated activities of the ABFPs and the targets that they have been evaluated on (Figure 8). The study cases were starPep_00000 (blue), starPep_00193 (yellow), starPep_00004 (green), starPep_00025 (pink), starPep_00514 (black), and starPep_00522 (cyan).  
Figure 8A is organized in such a way that the desired activities were placed outside at the left part of the METN, and the undesired ones at the right. The six candidates  
Figure 8: METNs visualization of the representative subset integrated by 66 ABFPs from the HC \(\cup\) HB of the subset 7 (cutoff 0.40) using the Fruchterman–Reingold layout. (**A**) Red nodes represent the bioactivities, while the ABFPs under study, starPep_00000, starPep_00193, starPep_00004, starPep_00025, starPep_00514, and starPep_00522, are in blue, yellow, green, pink black and cyan colors, respectively. (**B**) Red nodes represent the targets on which the ABFPs have been evaluated. The ABFPs under study are highlighted with the color scheme used in A.  
under study, in addition to antib biofilm activity also have antifungal and the antibacterial properties, specifically against Gram-positive and Gram-negative strains, which are very convenient for developing next-generation antimicrobial agents able to target both planktonic and biofilm microbial forms. However, four of them were reported as hemolytic, and five as \"toxic to mammals\". The peptide starPep_00522 (in cyan) is the only one that was neither reported as \"hemolytic\" nor \"toxic to mammals\", and therefore its peptidic scaffold can be used for developing antib biofilm agents for clinical purposes. Additionally, Figure 8B also shows that starPep_00522 has been evaluated in _Escherichia coli_, _Pseudomonas aeruginosa_, _Candida albicans_, and _Cryptococcus neoformans_, of which the two first targets are classified as ESKAPEE pathogens, considered the most threatening antimicrobial-resistant microbes [68]. This visual analysis of the METNs can be extended to all 66 ABFPs of the representative set.
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
## 6 Analytical Investigation
### Cost Modeling  
An analytical cost model based on [30] is used to compare the handover cost. The handover cost \(C_{S}^{(\cdot)}\) is calculated by adding the additional signaling overhead that results from transmitting handover-related messages and the cost of sending an existing transaction. Since the mobility of NMP is not reflected in these formulas, the mobility model is considered to represent the movement of the NDN producer. We assume that all NARs have a circular coverage with radius R. The NAR's coverage (denoted by S) is expressed as \(S=\pi*R^{2}\). The NMP moves with velocity \(v\). Therefore, the NAR crossing rate of the NMP (denoted by \(\mu_{a}\)) is represented as \(2*v/\sqrt{\pi*S}\). The handover cost to which the mobility model is applied can be expressed as \(\mu_{a}\cdot C_{S}^{\cdot}\). And the notations used for cost analysis are set with reference to [31]. Table 1 summarizes the definitions of the parameters used for the analytical investigation.
# Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM
###### Abstract  
In the context of increasing cyber threats and attacks, monitoring and analyzing network security incidents in a timely and effective way is the key to ensuring network infrastructure security. As one of the world's most popular social media sites, users post all kinds of messages on Twitter, from daily life to global news and political strategy. It can aggregate a large number of network security-related events promptly and provide a source of information flow about cyber threats. In this paper, for detecting cyber threat events on Twitter, we present a multi-task learning approach based on the natural language processing technology and machine learning algorithm of the Iterated Dilated Convolutional Neural Network (IDCNN) and Bidirectional Long Short-Term Memory (BiLSTM) to establish a highly accurate network model. Furthermore, we collect a network threat-related Twitter database from the public datasets to verify our model's performance. The results show that the proposed model works well to detect cyber threat events from tweets and significantly outperform several baselines.  
event detection; cyber threat; Twitter data; machine learning +
Footnote †: journal: Applied sciences
## Materials and Methods
### Photocatalytic System Set-up  
The photocatalytic experiments were performed in a 1-L cylindrical Pyrex beaker (\(\mathcal{O}\)120, H = 150 mm) with continuous stirring by using a magnetic stirrer. Both the reactor and the magnetic stirrer were put in a box covered with aluminium foil in all inner walls. The photocatalytic process used two UVA lamps (10 W, 28 mm O.D., manufactured by Philips (Amsterdam, The Netherlands)), which emitted light with a wavelength between 315 and 400 nm, were placed at the top of the box [19]. UVA lamps were set at 160 mm above photocatalyst and 65 mm above water level. Figure 2 illustrates the layout of a photocatalytic reactor (PCR).  
Supported TiO\({}_{2}\) was prepared by adsorption according to the impregnation method, with \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) being used as catalytic support. Commercially available P25 type titanium dioxide (TiO\({}_{2}\) Degussa P25 (Tokyo, Japan), with its crystal structure comprising of approximately 80% anatase and 20% rutile and with a BET specific surface area of 50 m\({}^{2}\)/g, was used as photocatalyst [19]. \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) balls (Eikme International Ltd., Hsinchu, Taiwan, size: 3-5 mm, shape in bead) were immersed in a TiO\({}_{2}\) solution consisting of TiO\({}_{2}\) Degussa P25. After that, the balls were washed by RO water until the effluent was clear; this means that all TiO\({}_{2}\) had been adsorbed into \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) surface [23]. Then supported catalysts were placed in an oven at 105 \({}^{\circ}\)C for 2 h for drying and calcined in a muffle furnace at 450 \({}^{\circ}\)C for calcination for 3 h.  
Figure 1: Structure of constructed wetland. (**a**) Plan view; (**b**) Profile A-A: (**1**) Cyperus involucratus; (**2**) Gravel; (**3**) Mixture of sand and soil; (**4**) Water level; (**5**) Collecting pit.
## 8 Conclusions  
In summary, safeguarding corneal health during future space missions is critical for astronaut safety and mission success. The impact of ocular threats, such as trauma, radiation exposure, and dry eye syndrome, are amplified by the unique environment of spaceflight. Traditional solutions to corneal opacification like corneal transplantation and eye banking remain limited by reliance on Earth-based resources and logistical constraints. However, advances in 3D bioprinting, stem cell integration, and customizable bioinks offer transformative alternatives, enabling on-demand tissue fabrication and reducing dependency on terrestrial support [15, 26]. While the use of 3D bioprinting for replacing opacified cornea during spaceflight or habitation on other planets is a promising long-range goal, there are currently numerous technical constraints that would be prohibitive. These include the need for sophisticated OR facilities and skilled surgical personnel in these environments.  
Furthermore, complementary innovations such as adaptive protective eyewear, telemedicine systems, and AI-driven diagnostic tools are paving the way for safer and more autonomous healthcare solutions. These technologies not only mitigate immediate ocular risks but also showcase the broader potential of space-inspired advancements for improving healthcare on Earth [10, 32].  
As space missions grow longer and farther-reaching--extending toward Mars and other planetary bodies--ensuring sustained visual performance becomes more than a clinical challenge; it becomes an enabler of scientific exploration, planetary research, and the human presence beyond Earth.  
Looking ahead, sustained interdisciplinary collaboration among engineers, clinicians, and researchers is essential to refine bioprinting systems, AI diagnostics, and telemedicine frameworks tailored for the space environment. Compact bioprinting units and modular healthcare technologies will play a pivotal role in overcoming the challenges of long-duration missions.  
By prioritizing innovation in ocular healthcare, we not only protect astronauts during exploratory missions but also unlock new possibilities for advancing medicine in extreme and isolated environments. This dual benefit underscores the profound impact of space-driven innovations on both space exploration and Earth-based healthcare--and reinforces the broader scientific value of investing in life science research beyond our planet.  
Conceptualization, J.S. and J.O.; Writing--original draft preparation, J.S., J.O., R.L. and A.S. Writing--review and editing, J.S., J.O., R.L., A.S., C.R.G., J.B., E.W. and T.H.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We would like to thank and acknowledge National Aeronautics and Space Administration (NASA) Export Control for their review, edits, and approval of this paper.  
[PERSON] served as guest editor in _Life_ at time of submission. All other authors declare no conflicts of interest.
## 2 Passive Systems
### Wireless Power Transfer
Thus, it is necessary to implement and design rectifiers with a wide operating input power range [55; 56; 57]. Figure 6: Different rectifier topologies. (**a**) Series diode rectifier. (**b**) Shunt diode rectifier. (**c**) Shunt diode with \(\lambda/4\) stub. (**d**) N-stage Dickson voltage multiplier. Figure 7: State of the art in RF-DC converters, with different topologies, from 450 MHz to 94 GHz. Adapted from [44]. The work presented in [58] focuses on broadband matching networks and voltage-double circuits, it achieves a peak efficiency of 74.8% at 10 dBm at a center frequency of 1.3 GHz. On the other hand, [PERSON] et al. [59], explore dynamic and static bias compensation to decrease the transistors forward voltage drop, their main result is a sensitivity of \(-\)30.5 dBm at 915 MHz and maximum efficiency (which was not the primary goal) of 42.8% at \(-\)16 dBm. In [44], [PERSON] et al. present a table analyzing this discussion between efficiency and sensitivity. Even though the techniques presented until this point can have a good impact in creating new WPT solutions, probably the big challenge in the topic is how to focus energy and improve the end-to-end transfer efficiency. To achieve this goal, transmitters need to be able to locate receivers in order to focus the transmitted energy. One way to wireless locate devices is to measure the Received Signal Strength (RSS), the method consists in measuring the received signal strength and send it back to the reader so it can calibrate and understand the point with better results. However, this method can easily be corrupted by multipath effects, noise, and other environmental parameters that must be addressed by using complex strategies [60]. Another way is to measure the Time of Arrival (ToA), which consists of calculating the distance between the transmitter and the tag by computing the distance using Time-Difference-of-Arrival (TDoA) estimates. This method is commonly used in Global Positioning System (GPS), nonetheless, for common applications to have an accurate location the synchronization needs to be very precise [61]. Besides these methods, the Angle of Arrival (AoA) is also a widely used technique. It computes the target location by processing its received signal and understanding the AoA giving that information to the transmitter [62]. The main drawback of these techniques is that all of them require the receiver to have RF, and sometimes, computing capabilities which are not feasible for a large number of applications. The solution that has been proposed in this topic is the implementation of WUR [63]. As this technique can serve this and others purposes, it is further detailed in Section 3.
## 3 Results and Discussion  
### Main Chemical Components of Garlic Essential Oil  
The identification of essential oil components was based on the calculated retention indices (RI) and on comparisons of obtained mass spectra against reference compounds, available in the NIST (National Institute of Standards and Technology, Gaithersburg, MD, USA) library. RIs were determined in relation to a homologous series of \(n\)-alkanes (C7-C30) under the same operating conditions. The relative percentage amounts of the separated compounds were calculated from the integration of the peaks in FID chromatograms. In total, 29 compounds were detected in garlic essential oil, of which 26 were detected by GC-MS and 21 by HS-SPME GC-MS analyses (Table 1 and Figure 1). Most of the detected compounds were sulfur-containing compounds, including cyclic sulfides and chain sulfides. Small amounts of terpenoids and fatty acid compounds were detected. Among them, DADS was the main volatile component (60.12%), followed by trisulfide di-2-propenyl (DATS) (14.18%), disulfide dipropyl (9.13%), and diallyl sulfide (DAS) (5.69%), which were detected by HS-SPME GC-MS. The compounds with the highest content, as detected by GC-MS analyses, were DADS (19.09%) and DATS (17.98%). Other compounds with high content included disulfide dimethyl, tetrasulfide di-2-propenyl, and some fatty acid compounds. Figure 1: GC-MS total ion chromatograms of garlic essential oil. (**A**) Diluted with n-hexane; (**B**) volatile compounds sampling by HS-SPME method. 1, diallyl sulfide (DAS); 2, diallyl disulfide (DADS); 3, disulfide, dipropyl; 4, trisulfide, methyl 2-propenyl; 5, trisulfide, di-2-propenyl (DATS); 6, tetrasulfide, di-2-propenyl. This shows that DADS and DATS were the major components identified using HS-SPME GC-MS and GC-MS analyses, in agreement with previous studies showing that the garlic essential oil consists of a variety of sulfides, such as DADS and diallyl trisulfide [29; 30]. [PERSON] et al. [31] reported the presence of DADS, DATS, DAS, allyl methyl, and trisulfide from the medicinal plants _Allium sativum_, _A. cepa_, _A. porrum_, _A. tuberosum_, _A. ascalonicum_, and _A. schoenprasum_. The Inhibitory Effect of Different Concentrations of Garlic Essential Oil and Dially Disulfide against Phytophthora Nicotiana  
The Ec50 of garlic essential oil against to _P. nicotianae_ was 1108.25 uL/L (y = 10.596x \(-\) 27.261, \(R^{2}\) = 0.984). The chemical compound antifungal test on the main components of garlic essential oil showed that DADS was the main inhibitory active ingredient. Therefore, DADS was selected for further antifungal activity determination. DADS had a good inhibition effect on _P. nicotianae_. The Ec50 of DADS against to _P. nicotianae_ was 150.83 uL/L (y = 5.736x \(-\) 7.9007, \(R^{2}\) = 0.9862). Afterwards, the MIC (fumigation effect) of DADS on _P. nicotianae_ was 20 uL/L (3 uL/dish) (Figure 2), such that the inhibitory effect of DADS on _P. nicotianae_ was greater than that of garlic essential oil.