esa-sceva/satcom-chunk-collection huggingface dataset:
# A Linear-Arc Composite Beam Piezoelectric Energy Harvester Modeling and Finite Element Analysis
## 3 Finite-Element Simulation
### Finite Element Analysis Mesh Accuracy Verification  
In order to verify the validity of the finite element simulation in this paper and the accuracy of the meshing accuracy, the PEH-C with a curvature of 100 m\({}^{-1}\) was simulated first. In this paper, four different numbers of grids were selected for calculation and the simulated resonant frequency and open-circuit output voltage were compared with the experimental results of Zuo [32]. Figure 5 shows the relationship between excitation and open-circuit output voltage for different mesh divisions. The results showed that under low acceleration excitation, when the number of grids increased from \(1\times 10^{4}\) to \(4.4\times 10^{5}\), the output voltage in the figure kept approaching the experimental results of Zuo. When the number of grids was \(1\times 10^{4}\), the mesh height was larger than the PVDF height, the simulation error was too large. When the number of grids was increased from \(1.7\times 10^{5}\) to \(4.4\times 10^{5}\), the results were not much different. Therefore, we chose the number of meshing elements as \(4.4\times 10^{5}\) for follow-up research.  
Figure 4: Schematic diagram of material coordinate system: (**a**) straight beam part; (**b**) arc beam section.  
Figure 3: Schematic diagram of mesh division.  
However, as the acceleration continued to increase, the simulation results deviated from the experimental results, which may have been due to the obvious nonlinear characteristics of the beam under high excitation in the experiment.  
In summary, in order to ensure the accuracy of the finite element simulation, the number of meshes in the subsequent finite element numerical calculation was set to 1.7 \(\times\) 10\({}^{5}\) and the given acceleration excitation was set to 5 m/s\({}^{2}\).
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 6 Conclusions  
In this work, we propose GeoSaw, a DTN routing protocol that relies on the planned UAV's path to make the message forwarding decision. We analyzed its performance under varying scenarios, showing the advantages in terms of delivery ratio and overhead. However, the protocol still suffers from significant delays. As future work, we plan to tackle this issue on two fronts: (i) introducing an adaptable behavior to the protocol whereby the number of replicas is computed dynamically based on some time-to-destination criteria and (ii) allowing UAVs to compute and follow ad-hoc optimized path(s) that reach the recipient when necessary [16; 25].  
Figure 8: Average overhead ratio of GeoSaW with different parameters combinations when varying the number of nodes.  
Figure 9: Average latency of GeoSaW with different parameters combinations when varying the number of nodes.  
**Author Contributions:** Conceptualization: D.R., C.T.C. and P.M.; Methodology: D.R. and C.T.C.; Validation: C.T.C., C.E.P., A.B. and J.-C.C.; Resources: C.T.C. and C.E.P.; Writing-Original Draft Preparation: D.R.; Writing-Review: C.T.C., P.M., C.E.P., A.B. and J.-C.C.; Editing: C.T.C.; Supervision: C.T.C. and C.E.P.; Project Administration: C.T.C. and P.M.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
[PERSON]  
1 Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece; [EMAIL_ADDRESS] (K.P.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (N.N.); [EMAIL_ADDRESS] (A.M.)  
[PERSON]  
1 Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece; [EMAIL_ADDRESS] (K.P.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (N.N.); [EMAIL_ADDRESS] (A.M.)  
[PERSON]  
1 Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece; [EMAIL_ADDRESS] (K.P.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (N.N.); [EMAIL_ADDRESS] (A.M.)  
[PERSON]  
1 Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece; [EMAIL_ADDRESS] (K.P.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (N.N.); [EMAIL_ADDRESS] (A.M.)  
[PERSON]  
1 Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece; [EMAIL_ADDRESS] (K.P.); [EMAIL_ADDRESS] (A.P.); [PERSON]ADDRESS] (S.B.); [PERSON]DDRESS] (N.N.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (A.M.)  
The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential data, obstruct activity, etc. To this end, intrusion detection systems (IDSs) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparisons with previous attacks. However, with the increased availability of computational power and data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time series format and use predictive models to forecast forthcoming malign packets. We propose a new architecture combining convolutional neural networks, long short-term memory networks, and attention. The findings indicate that our model performs strongly, exhibiting an F1 score and AUC that are within margins of 1% and 3%, respectively, when compared to conventional real-time detection. Also, our architecture achieves an \(\sim\)8% F1 score improvement compared to an LSTM (long short-term memory) model.  
intruation detection system; deep learning; time series forecasting; cybersecurity; machine learning
# Comparison of Microbial Gene Diversity in Grassland Topsoil Depending on Soil Quality
## 3 Results
### Genomic Characteristics of Prokaryotes and Eukaryotes According to Soil Quality
#### 3.2.2 Characteristics of Eukaryotes  
Seven eukaryotic phyla, including Opishtokonta, were analyzed at the phylum level, where other eukaryotes were the most dominant (43.50%), followed by Opishtokonta (36.20%), SAR (13.45%), and Archaeplastida, Amoebozoa, Excavata, and Centrohelida (<3%) in high-quality soil. In contrast, in low-quality soil, Opishtokonta was the most dominant (42.83%), followed by other eukaryotes (36.04%); and different from high-quality soil was SAR (11.89%), Archaeplastida (7.23%), and Amoebozoa, Excavata, and Centrohelida (<3%). In high-quality soil, other eukaryotes were relatively higher than in low-quality soil, by 7.46%, along with SAR, Amoebozoa, and Centrohelida. In contrast, low-quality soil had more Opisthokonta (6.63%), Archaeplastida (3.88%), and Excavata (0.1%) than the high-quality soil. At the class level, Nucletmycea and Holozoa of the phylum Opisthokonta were higher in the low-quality soil than in the high-quality soil, by 4.79% and 1.65%, respectively. In addition, the classes Stramenopiles and Rhizaria of phylum SAR were relatively higher in the high-quality soil (by 1.31% and 1.15%, respectively) (Figure 2b). To design a biological index for soil quality assessment, more data need to be accumulated about microbiota identified up to the genus or species level and regarding biological genes that differed according to soil quality. In this study, eukaryotes showed greater differences in rate than prokaryotes in the high-quality soil (Table 2). Among these, _Penicillium_, which was predominant in the high-quality soil, is commonly present in soil and air in temperate climates, degrades organic matter, and produces mycotoxins. It can affect the fruits and bulbs of plants and can be pathogenic to some organisms, including mosquitoes [42; 43; 44]. _Pythium violae_ is a fungus imperfectus belonging to the phylum Oomycota and is pathogenic to plants, including carrots [45; 46]. Other predominant eukaryotes in high-quality soil included _Klebsormidium flaccidum_, a filamentous green algae, _Trinema_ of phylum Cercozoa, saprophytic _Rhizopus delemar_, _Sorsophareula veronica_ inhabiting plants, _Acanthamoeba_ inhabiting water or soil, and the order Chaetonotida of phylum Gastrotricha [47; 48; 49; 50; 51; 52; 53]. Some predominant eukaryotes in the low-quality soil were _Artemisia_, which is contained in mugwort, crown daisy, tarragon, and southern trees [54]; the subclass Acari, including mites and ticks that inhabit the skin of animals such as humans [55]; and Triplonchida, an order of terrestrial nematodes [56]. Other predominant eukaryotes in the low-quality soils included Archaeorhizomyces, which is a symbiotic fungus inhabiting plant roots and rhizosphere; _Pyrenochaeta_, which causes cumycetoma; and pantropically distributed genus _Vigna_[57].
## 2 Materials and Methods
### Tested UV Sources  
In this study, the mitigation of target gases was investigated using four different light sources (UV-A: 367 nm and UV-C: 254, 222, 185 + 254 nm, Figure 10). Two different low-pressure mercury sources were used, both of which emit strongly at 254 nm, but one additionally contains a small 185 nm component because the bulb is made from special materials that allow transmission of that line. The emission spectrum of low-pressure mercury lamps is well known, and these sources both also contained small emissions at 365 nm and other wavelengths common to all of these bulbs. Nonetheless, we refer to these as 254 nm or (185 + 254) nm light sources. An excimer source emitting at 222 nm was the third source; these three sources constitute variations on wavelengths between 222 and 365 nm. The fourth source was an LED with emission centered at 367 nm, quite near the 365 nm range that mercury lamps commonly were used for, but without many of the disadvantages of a mercury-based lamp. This is considered within the UV-A region.  
Each chamber inside the mobile laboratory was equipped with 5 UV-A LED lamps (T8 LED, Eildon Technology, Shenzhen, China). An additional 100 UV-A lamps (effectively adding 20 times the light intensity, Table S1) were installed on a removable rack in each of the two chambers (#2-#3) to investigate the reduction of targeted gases according to the UV dose (Figure 2). Detailed information on UV-A lamps used in this study was reported previously [13].  
Figure 1: Schematic of mobile laboratory for UV treatment of gaseous emissions. UV dose is controlled by either adjusting treatment time (by controlled airflow rate) or adjusting irradiation (by turning lamps on/off). Brown arrow: untreated gas from the manure drum (blue barrel); white arrow: ambient air for diluting the untreated gas; red arrow: inlet air to UV treatment with reduced particle matter load (due to the filtration unit; pictured on the right); blue arrow: UV-treated air. Yellow: gas sampling ports.  
All UV-C sources were tested inside chamber #2 while all the UV-A lights in other chambers were turned off. For UV-C (254 nm and 185 + 254 nm, American Ultraviolet Co, Lebanon, IN, USA), four lamps of each different wavelength were installed on the door in one chamber (#2). In the case of the 222 nm excimer UV-C (Ushio America Inc., Cypress, CA, USA), one lamp (Care222 Series) and power supply were installed on the door in chamber #2 (Figure A2). The effects of UV wavelength were measured locally in chamber #2 for all lamp types. The targeted gas concentrations in the untreated gas (control) were measured in the #1 chamber's sampling port. The treated gas concentration after the UV treatment was measured in the #3 chamber's sampling port (Figures 1 and 2).
# Adsorption of Coxsackievirus in Sediments: Influencing Factors, Kinetics, and Isotherm Modeling
## 3 Results and Discussion  
### Effect of Different Factors on Virus Adsorption  
#### 3.1.1 Sediment Particle Size  
Figure 3a illustrates CA16 adsorption onto sediment particles of different sizes, emphasizing the significant role of particle size in this process. The figure demonstrates that as particle size decreases, the impact of sediment particles on CA16 adsorption becomes more pronounced. Specifically, fine sand, medium sand, and coarse sand reach adsorption equilibrium in approximately 70 min, resulting in a reduction of virus concentration to 1.83 log, 1.56 log, and 1.30 log, respectively. Conversely, clay and silt complete adsorption within 120 min, achieving an adsorption rate exceeding 99%, and reducing virus concentration to 2.50 log and 2.10 log at equilibrium, respectively. Importantly, Figure 3a indicates an observed increase in the adsorption rate of CA16 as sediment particle size decreases, following the sequence: clay > silt > fine sand > medium sand > coarse sand. This phenomenon is mainly attributed to the larger specific surface area of sediment particles accompanying their reduction in size [52], resulting in a higher availability of adsorption sites on their surface, thereby facilitating virus adsorption.  
Figure 3: _Cont._
## 3 Results Analysis
### Operation and Maintenance Stage  
The operation and maintenance stage of the assembled MEP is a vital part of the whole life cycle to ensure its long-term stable operation and reach the expected service life. This section will analyze and discuss the O&M (operation and maintenance) stage of assembled MEP systems, mainly from the aspects of importance, BIM, algorithms, component life planning, and inspection.
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 4 Results and Findings
### Framework Development and Implementation  
This part of our interview focused on what the new cyber resilience engineering framework should include so that experts can find it helpful in overcoming their current cyber security and resilience challenges. We also focused on how to measure the success or failure of implementing such a framework to incorporate cyber resilience decisions at an early stage of project design and development.  
\"The cyber resilience framework must be flexible, adaptable, and integrated early into the engineering processes. It is essential from the outset, not as an afterthought, to avoid costly redesigns and rework later in the project. The framework should be fundamental to engineering processes, considering cyber security issues from the beginning. The framework needs to stay up to date with rapid changes in systems and technology, which requires the involvement of skilled professionals continuously assessing and addressing potential vulnerabilities and risks.\"--Cyber GRC Engineer, interview participant_  
The experts agreed that drawing upon various engineering methodologies that have proven successful in the field is essential to achieving a practical cyber resilience framework. They highlighted the significance of Model-Based Systems Engineering (MBSE) and Agile methodologies as the most effective approaches for designing, developing, and deploying critical space systems. Experts also often referred to \"security by design\" as one of the core values of the new framework, which would ensure that security is embedded throughout the system's life cycle rather than being an afterthought.  
Additional themes, such as \"Defence in Depth Approach\" and \"Measure Attacks For Validation\", also emerged as critical factors in designing a new framework. One expert said, \"If the success rate of cyber-attacks goes down, or our services stay operational during such an attack, then I would consider the framework a win\".
# Honey Therapy in Diabetic Foot Ulcers: A Promising Strategy for Effective Wound Healing
## 4 Honey's Antimicrobial Properties
### Non-Peroxide Antimicrobial Activity
#### 4.2.2 Methylglyoxal (MGO)  
MGO is produced from a non-enzymatic conversion of dihydroxyacetone (DHA) found in the nectar of the _Leptsospermum_ flower at a slow rate during the honey maturation process [112]. The rates of conversion and the MGO concentrations found in manuka honey might depend on the storage temperature (usually 37 \({}^{\circ}\)C) when the conversion of DHA in MGO rapidly increases [112; 113]. The concentrations of MGO in manuka honey are up to 100 times higher than in conventional honey [70]. This characteristic confers it a pronounced antibacterial activity, allowing it to be commercially classified as the best product, as shown via microbiological assays, with the marketing description of the \"unique manuka factor\" [114]. The lack of H\({}_{2}\)O\({}_{2}\) does not compromise the bactericidal activity in manuka honey, showing MGO to be the major non-peroxide bactericidal factor [76]. MGO's mechanisms of action seem to be related to changes in bacterial fimbiae and flagella [92], which could explain the better effectiveness of manuka honey against _E. coli_ and _B. subtilis_, limiting the bacterial adherence and mobility [75]. Moreover, MGO's mechanisms of action seem to be different in Gram-positive organisms, where it is able to impair cell division [77], while in Gram-negative bacteria MGO induced a loss of the structural integrity of the bacteria, as well as changes in cell shape and surface [77]. However, in patients with diabetes, treatment with high MGO concentrations [115] is concerning, since MGO is a powerful inductor of the formation of advanced glycation end products (AGEs), which are related to the disruption of collagen matrix remodeling, causing poor wound healing [116].
# Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation
## 1 Introduction
Due to the farthest location, all sensor nodes send data to the cluster head rather than the SINK. Based on the processing of the data, energy will be consumed. Instead of delivering data packets to sink node on a regular basis, data might be sent based on demand to save energy. Geographically close nodes are used to organize groups, and clustering algorithms refer to each group as a cluster [15]. Nodes in all clusters that are only allowed to play certain roles, such as member, SINK, or CH nodes. Data forwarding, data aggregation, cluster maintenance, and intra-cluster transmission arrangements are just a few of the several tasks that a CH might carry out as an organizer for the cluster [16]. CH selection processes vary depending on the application and algorithms since effective CH selection can lower energy usage [17]. Most of the techniques on offer use a two-step process. In the first stage, CHs are chosen based on a primary parameter, namely residual energy, and in the second step, a rotation among cluster members is carried out to balance energy utilization [18]. These selection methods and the other crucial criteria not impacting the CH selection are taken into account by a single node parameter. The CHs near the sink or over key links may result in an earlier mortality as a result of the significant inter-cluster communication delay [19; 20]. Traditional WSN's dependency on insufficient battery capacity is one of their main drawbacks. As a result, to increase performance and longevity, energy-efficient techniques are needed. Numerous factors influence the rate at which energy is utilized [21; 22; 23; 24]. Quite a bit of energy will be used for routing and data transmission. By using intermediary nodes to get to the final destination, routing is a method of transferring data packets from one location to another [25; 26; 27].
## 4 Empirical Modeling
### Model for 1 st-Axis Rotation Motion  
When we placed the velocity profile of the pseudo-impulse input on the 1 st rotational axis, the compressed response showed variation of the longitudinal thrust force in the \(x\)-direction, the lateral thrust force in the \(y\)-direction, and the reaction moment in the \(Z\)-direction. For each PWM, curve fitting was performed for three or more experimental values to extract the results with the highest correlation number, as shown in Tables 1-3. As an example, Figure 9 shows the Bode plot of the experimental values and the proposed model of \(F_{1x}(s)\) and \(F_{1y}(s)\) at 75% PWM.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**PWM** & \(K\) & \(a\) & \(b\) & \(c\) & \(d\) & \(e\) & \(g\) &
\begin{tabular}{c} **Correlation** \\ **Number** \\ \end{tabular} \\ \hline
55 & 1018.79 & 97.21 & 138.87 & 18.70 & 0.48 & 7.30 & 3410.18 & 0.54 \\
60 & 982.72 & 88.35 & 143.12 & 12.27 & 1.00 & 4.15 & 3340.46 & 0.65 \\
65 & 1146.46 & 102.21 & 139.95 & 16.34 & 1.00 & 5.27 & 3418.75 & 0.58 \\
70 & 1006.43 & 80.21 & 124.82 & 11.13 & 1.00 & 3.36 & 3073.84 & 0.69 \\
75 & 1035.34 & 76.51 & 127.9 & 9.88 & 1.00 & 6.40 & 3228.49 & 0.56 \\
80 & 1105.16 & 106.39 & 132.55 & 14.49 & 1.00 & 4.76 & 3375.93 & 0.63 \\
85 & 1365.23 & 96.06 & 141.85 & 9.13 & 1.70 & 5.82 & 3399.70 & 0.58 \\
90 & 1060.66 & 101.21 & 129.96 & 8.24 & 1.00 & 6.69 & 3309.18 & 0.55 \\
95 & 985.19 & 95.17 & 132.58 & 8.24 & 1.00 & 5.32 & 3274.37 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model coefficient of \(F_{1x}(s)\) in 1 st-axis rotation.  
Figure 8: Magnitude Bode plot and slope of asymptote: (**a**) force; (**b**) moment.  
Figure 11 shows the Bode plot of the experimental values and the proposed model of \(M_{12}(s)\) at 75% PWM. The correlation number of the moment model is 0.80 or higher. The empirical model matches the experimental results very well. The variations in each parameter for PWM are shown in Figure 12. The parameters of the model are not significantly affected by the change in thrust force. The structure of the gimbal is a more dominant factor in the reaction moment model than in the change in thrust force.  
Figure 11: Magnitude Bode plot of experimental results and model of \(M_{12}(s)\) in 1 st-axis rotation with 75% PWM.  
Figure 10: Model coefficient of thrust force in 1 st-axis rotation according to PWM: (**a**) \(K\), (**b**) \(a\), (**c**) \(b\), (**d**) \(c\), (**e**) \(d\), (**f**) \(e\), and (**g**) \(g\).