esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture
### Architectural Overview  
At a high level, the system comprises the following components:  
* Smart home IoT Devices (Edge Nodes): These include smart thermostats, cameras, voice assistants, etc., each capable of local computation.
* Edge AI Module: Each device or its corresponding gateway runs a lightweight anomaly detection model locally.
* Federated Learning Coordinator: Hosted either on a local hub (e.g., home router with edge server) or cloud-based edge node, this component orchestrates model aggregation.
* 5G Communication Layer: High-speed and low-latency 5G connectivity supports timely model updates and federated rounds. In our architecture, 5G is leveraged specifically to reduce the latency and bandwidth bottlenecks traditionally associated with FL systems, enabling faster convergence and more responsive updates across edge devices.  
The overall architecture of the proposed Edge-FLGuard+ framework is illustrated in Figure 1, highlighting the interaction between edge devices, the federated learning coordinator, and the 5G communication infrastructure. The system leverages the federated averaging (FedAvg) algorithm [20] to coordinate global model updates. For privacy enhancement, differential privacy (DP) [21] may be applied to model updates before transmission. We selected GRU-based autoencoders due to their lower complexity and comparable temporal modeling capabilities to LSTM [22].  
While the current implementation does not incorporate 5G network telemetry or signal-level features directly into the anomaly detection logic, we acknowledge this as a limitation and highlight it as an opportunity for personalization and adaptation in future work.  
Figure 1: System architecture of Edge-FLGuard+. Smart home IoT devices perform local anomaly detection using lightweight edge AI models and exchange encrypted model updates over 5G with a federated learning coordinator. The framework optionally incorporates differential privacy (DP) and secure aggregation to enhance user data protection during federated training.
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 4 Evaluation
### Effect of the Hop Count on Network Performance  
We evaluated the effect of the hop count on PRR. A simple linear network with the hop count varying from 1 to 4 was considered. We compared our proposed approach with schemes like ETX and PRR. Each scheme estimates the number of transmissions by itself. We examined the packet reception rate at the sink for each value of the hop count.  
The results are presented in Figure 6, where it is indicated that our proposed method achieved a very high PRR, even at the highest hop count. That is because our proposed method provided a good estimation of the number of transmissions by using the target PRR to estimate link quality. Under other schemes, the PRR decreased since the hop count increased. This is because with the two other schemes, the estimate of the number of transmissions was not very good.  
Figure 5: Relationship between the number of retransmissions and the packet reception rate.
# Dynamic Stability Assessment of High-Speed Railway Bridges Using Numerical Model Updating
###### Abstract  
Numerical model updating using the data measured from the actual structure is required in order to minimize the error between the initial numerical model and the actual structure. Field load tests, which are conducted in order to assess the condition and safety of high-speed railway bridges, are generally expensive and restricted by railway control and weather conditions. Therefore, a method for evaluating the performance of high-speed railway bridges using updated numerical models without conducting field load tests is required. In this study, numerical model updating was performed by using the data measured from the ambient vibration test in order to assess the dynamic stability of high-speed railway bridges. In the ambient vibration test, the measurement point roaming method was applied in order to accurately measure high-speed railway bridges using a limited number of sensors. For numerical model updating, the univariate search method was used, and several measured parameters were updated and converted into the properties of the target bridges in the numerical models. The vertical and torsional modes of the updated numerical models differed by less than 5% from those estimated using the data measured from the target bridges. The responses of the updated numerical models were found to be similar to those measured from the high-speed railway bridges in operation. It was also shown that the updated numerical models could be used to assess the dynamic stability of the bridges.  
high-speed railway bridge; numerical model updating; univariate search method; dynamic property; dynamic stability +
Footnote †: journal: Applied Sciences
## 3 Case Studies of Pure Bending and Springback
### Numerical Study of SUS304 under V-Bending  
To further study the effect of TCA in the springback of the SUS304, the V-bending process was simulated, and the resultant springback was determined and compared with the experimental results. The V-bending simulations were performed on SUS304 plates with 0.3 mm thickness. The bending angle and bending radius were 90\({}^{\circ}\) 5.0 mm, respectively. In this simulation, a 2D sheet was modelled along with two discrete rigid parts as punch and die. Like the pure bending simulation, the CPS4R element type was chosen and thirty elements were used through the thickness to secure accuracy after mesh refinement. The V-bending simulation with three stages-- before bending, bending and springback--is shown in Figure 12.  
In the V-bending process, the friction between sheet, punch and die is inevitable. It was reported by [PERSON] [27], [PERSON] et al. [28] and [PERSON] et al. [29] that the average value of friction coefficient in steel sheet forming is in the range of 0.1 to 0.2, depending on the surface properties, geometrical features, velocity and lubricant condition, etc. Therefore, in this study, the average value of 0.15 was considered as the friction coefficient for the process. The predicted springback from the simulation is compared with the experimental results in Figure 13. As it can be seen, considering TCA in calculations improved the springback results significantly. The numerical springback values with TCA are closer to the experimental results, compared to those with tension. By applying TCA in calculations, the relative errors were reduced from \(-\)7.86 to \(-\)2.11% and 23.01 to 4.32% for TD and RD bending, respectively.  
Figure 11: Comparisons of numerical and analytical bending moment–curvature diagrams: (**a**) SUS304; (**b**) DP980.  
Figure 12: Finite element simulation of the V-bending: (**a**) before bending, (**b**) bending and (**c**) springback.
## 1 Introduction
dentata_ essential oil has exhibited antifungal activity against strains of _Candidia albicans_[21], and antibacterial activity against Gram-positive and Gram-negative bacterial strains [22; 23; 24]. Despite these promising antimicrobial properties, studies specifically evaluating the efficacy of _L. dentata_ essential oil against _Enterococcus_ species remain scarce. In addition, while _L. dentata_ essential oil has been tested against _E. faecalis_, no studies have specifically investigated its activity against _E. fecium_. This is an important distinction, as _E. fecium_ exhibits greater antimicrobial resistance and adaptability compared to _E. faecalis_[8; 9]. Our study fills this gap by providing the first data on the susceptibility of _E. fecium_ to _L. dentata_ essential oil, which could have implications for endodontic infections where this species is present. To the best of our knowledge, only one study has assessed its antibacterial effect against _E. faecalis_ using disk diffusion and broth microdilution methods, reporting that the oil was effective [25]. However, no studies have been conducted to date to evaluate its antimicrobial activity against _E. ficecium._ Considering that biofilms are the primary mode of bacterial persistence in endodontic infections, our study uniquely assesses the antibiofilm effects of _L. dentata_ essential oil against both _E. faecalis_ and _E. ficecium_, providing new insights into its potential as an adjunct in endodontic disinfection. Still, further research is needed to explore its potential as an alternative therapeutic agent in endodontics. In this study, the antibacterial or antiplanktonic effect was evaluated by determining the minimum bactericidal concentration (MBC) values, and the antibiofilm was evaluated by the MTT assay: these methods are widely accepted in the literature to evaluate the antibacterial and antibiofilm action of herbal medicine [26,27]. Therefore, the aim of this study was to evaluate the antibacterial and antibiofilm effects of _L. dentata_ essential oil against standard and clinical strains of _E. faecalis_ and _E. ficecium._ The null hypothesis was that _L. dentata_ essential oil has no effect against the tested strains of _E. faecalis_ and _E. ficecium_.
## 4 Simulation and Experiment
### Span of 200 m PTL Case
Figure 13: FPTLIR Z-displacement and attitude at different landing points: (**a**) Z-displacement; (**b**) pitch angle; (**c**) roll angle; (**d**) yaw angle. The simulation results in Figure 15 indicate the different dynamic responses of FPTLIRs under varying conditions of mass (10 kg, 55 kg, and 100 kg). The Z-displacement variation with different FPTLIR masses is shown in Figure 15a. Specifically, a 10 kg FPTLIR had a maximum Z-displacement of \(-0.56\) m, while an FPTLIR of 55 kg mass and a 100 kg mass had displacements of \(-0.87\) m and \(-1.15\) m, respectively. It is evident that heavier FPTLIRs required more time to reach a stable state. It can be observed that an increase in mass had a proportional impact on the PTL, resulting in a corresponding increase in Z-displacement. Figure 15b-d illustrate that the amplitude of post-landing attitude oscillations increased with the mass of the FPTLIR. Specifically, raising the mass from 10 kg to 100 kg led to a significant rise in the roll angle from \(-1.0^{\circ}\) to \(-7.84^{\circ}\), and the yaw angle from \(-0.09^{\circ}\) to \(-0.65^{\circ}\), while the pitch angle remained unaffected by the mass, consistently oscillating between \(-0.05^{\circ}\) and \(0.05^{\circ}\). The motion patterns of each angular parameter remained unchanged with varying mass; pitch exhibited oscillation, roll showed oscillatory decay, and yaw gradually increased. However, the amplitude uniformly increased as mass grew, attributed to the larger moments of inertia, which amplified the oscillation magnitude.
# Routing Strategies for Isochronal-Evolution Random Matching Network
## 4 Two Routing Strategies  
### Shortest Paths in the IERMN  
The path planning algorithm is the core of the routing decision-making algorithm for vertices in IERMNs. A routing strategy is defined as a routing decision-making algorithm using a certain path planning algorithm. In this paper, we focus on the shortest path routing strategy in the IERMN, not only because the shortest path routing strategy is the most popular in real-world networks [33], but also because of the characteristic topology of the IERMN.  
Since each vertex in an IERMN has, at most, one neighboring vertex in any slot, the IERMN is unconnected at any instant of time. In this case, there are no contemporaneous end-to-end paths between sources and destinations in the IERMN, just like the delay tolerant network [34]. Therefore, many improved routing strategies are unsuitable for the IERMN.  
As a path in an IERMN has both a spatial length and a temporal length, there are two kinds of shortest path: one is the Least Delay Path (LDP), which has the shortest temporal length, and the other is the Least Hop Path (LHP), which has the shortest spatial length. There may be more than one LDP or LHP; thus, we are most interested in the Least Delay Path with Minimum Hop (LDPMH) and the Least Hop Path with Minimum Delay (LHPMD). Hence, in this paper, we studied two routing strategies for the IERMN: the LDPMH routing strategy and the LHPMD routing strategy.
# Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target
## 2 Theoretical Background
### MTF Analysis  
Cameras do not provide images that perfectly represent real objects. object's level of representation is related to the camera's performance; the MTF value, which indicates the object's level of representation, is used for analyzing UAV images. The MTF analysis is based on the camera's lens and performance. The MTF value is expressed as the relative ratio of the actual modulation value of the resolution target to the modulation value of the target in an image. The MTF can be analyzed using a graph based on spatial frequency, which shows how many line pairs (lps) can be included in one pixel when black and white lines form each lp. In the MTF graph, the horizontal axis expresses spatial frequency, and the vertical axis expresses the MTF value [18].  
Figure 3 illustrates the DNs extracted from an image with black and white line pairs. In this graph, the modulation value is expressed by Equation (2):  
\[\mathrm{Modulation}=\frac{l_{max}-l_{min}}{l_{max}+l_{min}}=\frac{a+b-(a-b)}{a+ b+(a-b)}=\frac{2b}{2a}=\frac{a}{b} \tag{2}\]  
Figure 2: Concept of modulation value analysis.  
The MTF value is expressed by Equation (3):  
\[\text{MTF}=\frac{Modulation_{Image}}{Modulation_{Object}}, \tag{3}\]  
where \(Modulation_{Image}\) is the modulation value of the image, and \(Modulation_{Object}\) is the modulation value extracted from the actual object.  
\[\text{MTF}=e^{-2\pi^{2}\sigma^{2}MTF^{2}}, \tag{4}\]  
The DNs extracted from UAV images taken by the iXM-100 sensor, as illustrated in Figure 4, were linearized, and an MTF graph was generated using Equation (4), where K represents the spatial frequency equivalent to the vertical axis of the MTF, and \(\sigma^{2}_{MTF}\) is the variance of the MTF [19].  
We calculated \(\sigma_{MTF}\), the standard deviation of the MTF, and performed comparisons. We also calculated and compared \(MTF_{50}\), a spatial frequency equivalent to 50% of the MTF graph, and \(MTF_{20}\), a spatial frequency equivalent to 20% of the MTF graph, as illustrated in Figure 4. \(MTF_{50}\) is an empirical criterion used in many studies, and it refers to a spatial frequency from which the boundary begins to blur in the operator's eyes. \(MTF_{20}\) refers to  
Figure 4: MTF graph for explaining MTF20 and MTF50 (iXM-100 400 m).  
Figure 3: DNs extracted from an image of a pair of black and white lines.  
the minimum spatial frequency at which the boundary is distinguishable by the operator's eyes. It is also an empirical criterion used in many studies.
# Hardware Design of a High Dynamic Range Radio Frequency (RF) Harmonic Measurement System
## 3 Issues in Creating a High Dynamic Range Harmonic Measurement System
### Creating a Harmonic Free Probe Signal  
In the previous section, it was assumed that the probe signal was ideal, i.e., it contained no second harmonic. This is not physically possible and this section demonstrates how to create a probe signal that is \"ideal enough\" to measure weakly nonlinear targets. In the previous section, the fundamental frequency needed to be attenuated in the receiver to measure the second harmonic accurately. In this section, the second harmonic must be removed from the probe to ensure that the measured harmonic is coming solely from the DUT and not the probe signal. If the second harmonic is not adequately attenuated from the probe signal, the measurement system generates a false, self-created second harmonic. This problem is viewed as co-site interference.  
Figure 5: S-parameters of the Mini-Circuits VHF-1200 high-pass filter and RBF-272 diplexer in the high pass configuration.
## 3 Analysis and Design of the Force Control System
### Stability Analysis
#### 3.2.1 Stability in Terms of \(K\)  
The gain margin is computed using the open-loop transfer function \(L(s)\). For the force control system in Figure 3, the transfer function \(L(s)\) is as follows (see [18]):  
\[L(s)=KG_{c}(s)G(s)G_{f}(s)G_{d}(s). \tag{7}\]  
Considering \(s=j\omega\), the gain margin is obtained using the magnitude of \(L(j\omega)\),  
\[|L(j\omega)|=K|G_{c}(j\omega)||G(j\omega)||G_{f}(j\omega)||G_{d}(j\omega)|,\]  
where \(\omega\) is the frequency associated with the frequency response of \(L(s)\). Note that the magnitude \(|L(j\omega)|\) is directly proportional to the gain \(K\) independent of the frequency \(\omega\).  
From the definition of the gain margin [18], the gain margin is the biggest increment of magnitude \(|L(j\omega)|\) that conserves stability. The condition for stability is \(|L(j\omega)|<1\), and this condition can be tested in the following way. First, a multiplicative gain \(K_{GM}\) is added to \(|L(j\omega)|\), and second, \(K_{GM}\) is increased until the stability condition is violated, i.e., \(K_{GM}|L(j\omega)|\geq 1\)[19]. The magnitude \(|L(j\omega)|_{test}\),  
\[|L(j\omega)|_{test}=K_{GM}K|G_{c}(j\omega)||G(j\omega)||G_{f}(j\omega)||G_{d}( j\omega)|,\]  
is used to check the gain margin, and the stability is ensured if \(|L(j\omega)|_{test}<1\). Note that the stability condition will be violated for certain \(K_{GM}=K_{GM_{max}}\) producing \(|L(j\omega)|_{test}=1\), and the value of the gain margin will be \(K_{GM_{max}}\).  
One can observe that the term \(K_{GM}K\) detects the whole magnitude \(|L(j\omega)|_{test}\). Therefore, when the gain margin \(K_{GM_{max}}\) is known, one can use \(K_{GM_{max}}\) to estimate the maximum increment/change in stiffness \(K\) that maintains the stability.
## 3 Results and Discussion
### Volatile Compound Analysis of Black Rice Wine
#### 3.2.2 Characteristic Flavor Substances of Black Rice Wine
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compounds** & **Q1-336** & **Q2-336** & **Q3-336** & **Odor Description** & **T\({}^{b}\) (\(\mu\)g/L)** \\ \hline \multirow{8}{*}{OVA \(>\) 1} & Isoamyl acetate & 201.77 & 125.65 & 66.16 & Banana & 3 \\  & Octanoic acid ethyl ester & 71.45 & 61.48 & 52.97 & Fruity & 5 \\  & 2-Octenal (E)- & 25.80 & 17.72 & 14.66 & Green & 3 \\  & Decanoic acid ethyl ester & 16.76 & 15.31 & 13.58 & Fruity & 20 \\  & 2-Decenal & 6.46 & 8.65 & 14.10 & Green & 2 \\  & Detanoic acid ethyl ester & 5.27 & 2.90 & 5.27 & Flowery & 2 \\  & Butanoic acid ethyl ester & 5.24 & 5.41 & 2.47 & Fruity & 20 \\  & Nonanal & 3.87 & 3.66 & 6.70 & Pungent & 1 \\  & Guicarol & 1.39 & 3.87 & 3.44 & Cooked black rice & 10 \\  & Pentanoic acid ethyl ester & 1.70 & - & - & Peach & 3 \\  & 2-Hepmetal & 1.63 & - & 3.64 & Green & 5 \\  & Butyric acid 2-methyl- ethyl ester & 1.49 & 1.00 & - & Fruity & 18 \\  & Benzenacetaldehyde & 1.21 & - & - & Honey & 4 \\  & Isobutyric acid & - & - & 2.43 & Sour & 20 \\ \hline \multirow{8}{*}{1 \(>\) OVA \(>\) 0.1} & Headacanoic acid ethyl ester & 0.51 & 0.44 & 0.60 & Waxy & 1500 \\  & Acetic acid 2-phenylethyl ester & 0.47 & 0.54 & 0.11 & Flowery & 250 \\  & Phenylly Alcohol & 0.42 & 0.33 & 0.39 & Flowery & 10,000 \\  & Limonene & 0.38 & 0.33 & 0.44 & Lemon & 15 \\  & Ethyl Acetate & 0.28 & 0.24 & 0.77 & Fruity & 7500 \\  & Phenol 4-ethyl-2-methoxy- & 0.20 & 0.12 & 0.35 & Buckwheat & 20 \\  & Isopentyl alcohol & 0.09 & 0.07 & 0.11 & Fusel & 65,000 \\  & Butylolactone & 0.15 & 0.16 & 0.20 & Alcohol & 35 \\  & Tetradecanoic acid ethyl ester & 0.11 & 0.10 & 0.16 & Ether & 2000 \\  & Isobutyric acid & 0.40 & - & - & Sour & 20 \\  & Benzenacetic acid ethyl ester & 0.18 & - & - & Flowery & 73 \\  & 2-Octen-l0 & - & - & 0.36 & Mushroom & 20 \\  & Decanal & - & 0.21 & 0.29 & Orange & 10 \\  & Caryophyllene & - & 0.25 & 0.13 & Woody & 64 \\  & Pentanoic acid ethyl ester & - & 0.86 & - & Fruity & 3 \\  & 2-Hepmetal & - & 0.86 & - & Green & 5 \\  & Benzenacetaldehyde & - & 0.88 & 0.75 & Honey & 4 \\  & Butyric acid 2-methyl-ethyl ester & - & - & 0.74 & Fruity & 18 \\ \hline \hline \end{tabular}  
* \({}^{a}\) “-\": not detected; \({}^{b}\) Odor thresholds taken from Reference [21].  
\end{table} 
Table 1: OAVs of volatile compounds detected in endpoint samples of black rice wine.
## 2 Numerical Simulation
### Calculating the Critical Angle and Petrophysical Parameters
According to the channel wave propagation mode, we ascertain the time propagation difference between the refracted channel wave and the wave experiencing total reflection (Figure 7). However, under the condition of an extra-thick coal seam, this paper discusses only the case where the incident angle of Love-type waves is equal to the critical angle. According to [PERSON]'s law, the SH-wave first experiences total reflection when the incident angle initially exceeds the critical angle. The corresponding formulae are shown in Equations (6)-(8). The total reflection of a Love-type channel wave in the coal seam along the entire path is expressed as in Equation (6):  
\[L=\frac{d}{\sin\theta}\text{m}, \tag{6}\]  
where \(d\) is the direct detection distance, \(L\) is the length of the total path for critical angle reflection signals, and \(\theta\) is the critical angle. The critical angle is calculated in Equation (7):  
\[\sin\theta=\frac{V_{S1}}{V_{S2}}. \tag{7}\]  
When \(\theta\) is equal to the critical angle, the phase velocity is equal to \(V_{S2}\) and the group velocity is equal to \(V_{S1}\). The time propagation difference between the refracted channel wave and the wave experiencing total reflection is given by Equation (8):  
\[\Delta t_{1}=\frac{d}{V_{S1}}-\frac{d}{V_{S2}}=\frac{d}{V_{S1}}-\frac{d\sin \theta}{V_{S1}}=\frac{\sin\theta(L-d)}{V_{S1}}. \tag{8}\]
# Query Processing in Blockchain Systems: Current State and Future Challenges
## 6 Overview of the State of the Art
### State of Research
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Direction** & **Characteristics and Features** \\ \hline Blockchain Systems Enhanced with Database Features & A database layer is built on top of a blockchain system that is used as an integrity-protected data store. The database layer provides an interface for querying data efficiently. However, verifying the results of a query is an additional step, which increases the overhead significantly. \\ \hline Database Systems Enhanced with Blockchain Features & A blockchain layer is built on top of a traditional database system. Data are queried directly from the database system. However, these database systems are not designed to detect tampered data during query processing. \\ \hline Improvements to the Frontend Query Capabilities & Existing public blockchain systems such as Ethereum are internally modified or extended with a query layer to support familiar query languages such as SQL. However, queries regarding the data history are expensive. \\ \hline Efficiency Improvements in Query Processing & Various techniques such as the parallelization of data processing or novel data structures enable more efficient querying of blockchain data. However, in order for query engines to benefit from this, they have first to be adapted accordingly. \\ \hline Tailored Blockchain Optimizations for Specific Use Cases & Tailored index structures for blockchain systems increase the performance of specific types of queries such as time series queries. However, they are designed specifically for a certain use case, i.e., the blockchain system loses some of its universality. \\ \hline Verifiable Queries and Database Systems & Verifiable queries are enabled over novel or existing database systems. However, these approaches do not necessarily require blockchain systems to be involved. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of key findings regarding the state of the art. research questions to be answered. In the following section, we elaborate on these open research questions.