esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
We have proposed and investigated a cooperative jamming-assisted untrusted relaying scheme, where the Stackelberg game was invoked between the PU and the CJs, while a power control game was employed by the CJs. It has been noted that when an untrusted relay is present, the UR-MCJST benchmark scheme suffers from a reduced secrecy rate, when the number of CJs are increased. Moreover, the proposed UR-MCJTDT scenario can maintain the same utility rate as the benchmark scheme while allowing the CJs to use a lower transmission power. The PU's secrecy rate of the UR-MCJTDT scheme is significantly better than that of the UR-MCJST scheme, while maintaining the same utility rates for the CJs. However, UR-MCJTDT can sometimes be inefficient in terms of resource utilization because the data are divided into discrete time slots, and each slot is allocated to a specific user for transmission. This means that even if a particular user has only a small amount of data to transmit, it still occupies a full time slot. In such cases, UR-MCJST performs well initially, but as the number of users increases, interference becomes a challenge. This creates a trade-off between time-division transmission, which ensures isolated time slots for each user but may use more resources, and simultaneous transmission, which maximizes resource utilization but risks increased interference with a higher user count. Furthermore, the secrecy rate of the benchmark scheme depends on the locations of the relay node and the CJs. However, the secrecy rate of the proposed UR-MCJTDT scheme is mainly influenced by the location of the relay node. The simulation results indicated that three CJs are sufficient to support a secure transmission when one untrusted relay is employed, as investigated in the proposed scheme. The optimum number of relaying nodes and CJs for a given system is worth further investigation in future work.  
This article was prepared through the collective efforts of all the authors. Conceptualization, N.Z., B.A., M.F.U.B., B.M.L. and S.X.N.; Critical review, N.Z., B.A., M.F.U.B., S.X.N. and M.A.J.; Writing--original draft, N.Z., B.A., M.F.U.B. and S.X.N.; Writing--review and editing, M.F.U.B., B.M.L., S.X.N. and M.A.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) under grant NRF-2023R1A2C1002656 and was supported by the MSIT (Ministry of Science and ICT), Korea, under grant IITP-2023-RS-2022-00156345 (ICT Challenge and Advanced Network of HRD Program).  
Data is available upon request from the first author.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Internalization of HB-AuNPs  
The remarkable application of AuNPs in therapeutics is due to their ability to penetrate the biological barrier and target the cells. In nanomedicine, safe delivery of the NPs into cells is imperative for achieving their higher therapeutic efficacy. In this study, dark-field microscopy in Figure 8 demonstrated that 1 mg/mL_HB-AuNPs, 2 mg/mL_HB-AuNPs, and 4 mg/mL_HB-AuNPs were efficiently internalized by Caco-2 cells. The AuNPs appeared as yellow spots surrounding the nucleus of the Caco-2 cells. Uptake and biodistribution of AuNPs are dependent on their sizes, where \(\leq\)5 nm AuNPs can transverse the nuclear membrane and are often associated with genotoxicity and 14-40 nm AuNPs accumulate in the cytosolic spaces [18].  
Figure 8: Uptake of HB-AuNPs by Caco-2 cells. Blue represents the nucleus of the cells stained by DAPI and yellow represents the distribution of the HB-AuNPs within the cells. Scalebar was set at 100 μm. The red circle represent the part that was zoomed in the red frame.  
Figure 7: Cytotoxicity of HBE (**A**) and HB-AuNPs (**B**) in PC-3, KMRT-6, HEK-293, and MCF-7 cells after 24 h. \(p\leq\) 0.05 was considered as statistical significant for untreated vs. treated samples, * \(p\leq\) 0.05, **\(p\leq\) 0.01 and ***\(p\leq\) 0.001.
## Materials and Methods
### Sample Preparation and Characterization  
Composite samples were prepared from a commercial bi-component thermosetting epoxy resin that acted as dispersing medium for carbon nanotubes. A commercial thermosetting resin (Epilox), produced by Leuna-Harze was used as a polymer matrix. It is a bi-component system formed by a resin and a hardener. Resin (T-19-36/700) is a commercial modified, colorless, low viscosity (650-750 mPa-s at 25 \({}^{\circ}\)C) epoxy resin with reduced crystallization tendency and a density of 1.14 g/cm\({}^{-3}\). The chemical composition of Epilox resin T19-36/700 is mainly Bisphenol A (30-60 wt.%), with the addition of crystalline silica (quartz) (1-10 wt.%), Glycidyl ether (1-10 wt.%), inner fillers (160 wt.%). Hardener (H10-31) is a liquid, colorless, low viscosity (400-600 mPa-s at 25 \({}^{\circ}\)C) modified cycloaliphatic polyamine epoxy adduct having density 1 g/cm\({}^{-3}\). A single 4 wt% carbon nanotubes loading was employed. 4 wt%  
\begin{table}
\begin{tabular}{c c} \hline \hline Production method & CVD \\ \hline Available form & Black powder \\ \hline Diameter & 6–15 nm \\ \hline Length & \(\geq\)10 \(\mu\)m \\ \hline Number of layers & 7–13 \\ \hline Carbon purity & 93\% \\ \hline Metal particles (Al, Fe) & 8 wt.% \\ \hline Amorphous carbon & \textless{} 0.1\% \\ \hline \end{tabular}
\end{table}
Table 1: MWCNT characteristics as declared by the manufacturer.  
choice was predicated on previous studies [17] concerning the dielectric properties of MWCNT/epoxy resin composites for different MWCNT amounts. Such an investigation had revealed that 4 wt% is a good trade-off between dispersion and dielectric behavior suitable for shielding applications. The sample preparation procedure is reported in [17; 18]. Briefly, epoxy resin and MWCNTs were mixed by using Ultraturrax for 5 minutes. Hardener was added at the manufacturer-suggested ratio (1:2). Composite solution was poured into a silicon mold and degassed under vacuum in order to remove air bubbles during the polymerization process. Complete polymerization in air took 24 h.  
The complex permittivity was measured by a commercial probe (Agilent 85070D) and a network analyzer (E8361A, Keysight Agilent, Santa Rosa, CA, U.S.). A standard calibration short/air/water was performed before each measurement. Several measurements were done on each sample and the average values reported.
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 3 Methodology
### Proposed Model  
We propose a model that combines fuzzy logic and BiLSTM. This is a hybrid model and falls under the concept of the fusion of fuzzy logic and DNNs. We built our proposed model using an efficient architecture that fully integrates fuzzy logic and a DNN to take advantage of them as much as possible. The proposed structure is a multilayer model combining specific layers depicting fuzzy logic with the already-existing structure of BiLSTM. For the fuzzy logic, we configured two layers, named the fuzzify layer and the defuzzify layer. Each of these two layers performs a specific process of fuzzy logic. These layers were concatenated with three essential layers commonly used when BiLSTM is utilized for text classification. The architecture of the proposed model is shown in Figure 4.  
It comprises five layers located in the following order: the embedding layer, the fuzzify layer, the BiLSTM layer, the defuzzify layer, and the output layer. Firstly, the input text is mapped into the embedding vectors via the embedding layer, which was explained in detail in the previous section. Then, the vectors are transformed into fuzzy values via the fuzzify layer. These fuzzy values then pass through the BiLSTM layer. The BiLSTM layer is basically two LSTM layers. The output from the BiLSTM layer is passed to the defuzzify layer to convert it into a crisp value. Finally, the output is obtained via a fully connected layer.  
The next subsections explain the implementation of the layers related to fuzzy logic: the fuzzify layer and the defuzzify layer.  
Figure 4: The architecture of the proposed model.  
3.5.1. The Fuzzify Layer  
The fuzzify layer is located after the embedding layer, so its input is the embedding vectors obtained from the embedding layer. The fuzzify layer processes these vectors into fuzzy values using the membership function. These values define the membership degree of the input vector to a certain fuzzy set. The type of membership function that we used is the Gaussian membership function, which is defined in Equation (2):  
\[g(x,c,w)=e^{-\frac{(x-c)^{2}}{w^{2}}} \tag{2}\]  
where x is the input vector previously produced by the embedding layer, and the parameters c and w denote the center value and the width value of the membership function, respectively. Figure 5 shows how these two values are represented.  
As defined in Equation (2), it is clear that the Gaussian membership function is determined by the c and w parameters. In our model, these parameters are trainable, i.e., the appropriate parameters can be obtained via the training and learning process that occurs in the neural network. Thus, we can obtain suitable shapes for the Gaussian membership function with which the region of each of the fuzzy sets is determined.
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 2 Related Works
### Resource Management in a MEC System
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notations** & **Meaning** \\ \hline \(N_{i}\) & A set of neighboring MECSs of a MECS \(i\) \\ \(b_{i}\) & Task service rate of a MECS \(i\) \\ \(Q_{i}(t)\) & The length of a task queue in a MECS \(i\) at the beginning of a time slot \(t\) \\ \(a_{i}(t)\) & The number of tasks received by a MECS \(i\) during a time slot \(t\) \\ \(e_{i}(t)\) & The amount of energy consumed by a MECS \(i\) during a time slot \(t\) \\ \(\mu_{i}(t)\) & Utilization of a MECS \(i\) during a time slot \(t\). \\ \(\beta_{i}(t)\) & Operation mode of a MECS \(i\) during a time slot \(t\) \\ \(d_{i}(t)\) & Delta activation level of a MECS \(i\) during a time slot \(t\) \\ \(n_{i}(t)\) & Notch activation level of a MECS \(i\) during a time slot \(t\) \\ \(f(x)\) & A function determining the production rate of the Notch activation level \\ \(g(x)\) & A function determining the production rate of the Delta activation level \\ \(\eta_{i,j}(t)\) & Proportion of tasks transferred from a sleeping MECS \(i\) to its neighbor \(j\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations used. by taking the advantage of the strategic queuing approach. Through simulation studies using both synthetic traces and real-world service request traces, OA2 is verified to achieve maximal revenue while providing QoS guarantee in expectation for the accepted requests. In [17], a fine-grained warm water cooling system is proposed to relieve the multiple-level hotspots and save the cooling energy of high-density and heterogeneous edge data centers. The authors remove the hardware-level hotspots by well designing a water circulation method. They also disperse the chip-level hotspots without manual intervention by developing new cold plates with vapor chambers. The authors in [18] scrutinize the VNF chain deployment problem with the latency guarantee and the resource efficiency. They formulate the problem as a mixed integer linear programming problem. To resolve the formulated problem, they devise a two-stage latency-aware VNF deployment method. In the first stage, paths are selected and VNFs are assigned in the second stage. By considering the VNF instance reuse and the latency requirements, the proposed method jointly optimizes the computing resources of the edge servers and the bandwidth resources of the physical links. In [19], a cost-efficient edge resource management platform for the NFV network called Finedge is proposed. To devise Finedge, the authors derived useful insights into the influence of the network function's flow-level characteristics and CPU allocation on the performances through empirical experiments. Using these insights, they designed Finedge, which can automatically assign the most suitable CPU core and the most cost-efficient CPU quota for each network function by taking its flow-level characteristics and QoS requirements into account.
# Underwater Acoustic Target Tracking: A Review
## 6 Discussion, Challenges, Countermeasures, and Lessons Learned  
In this section, we present a comparison between all algorithms mentioned before and the challenges in underwater target tracking, including low target detection probability, sound speed variation, eliminating PS ambiguity as well as the balance between tracking accuracy and energy consumption are illustrated. Countermeasures for the mentioned challenges are also presented. The lessons learned about designing the underwater acoustic tracking algorithms also are introduced for readers' reference.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 6 Performance Evaluation of IoB-DTN
### Impact of Number of Copies  
The number of copies \(N^{0}\), created during the generation of a packet, is a paramount parameter of IoB-DTN protocol. As discussed above, each node replicates and sends to the neighbors nodes only the packets having more than one copy in its buffer. Thus, the first neighbor node stores half the copies, the second one stores one fourth, the third one an eighth and so forth. The larger is \(N^{0}\), the more redundancy is performed.  
For our study, we evaluate three variants of IoB-DTN protocol:  
* \(N^{0}\) = 2: Two-Hop Relay protocol;
* \(N^{0}\) = 8: Binary Spray and Wait protocol; and
* \(N^{0}\) = \(\infty\): Epidemic Routing protocol.  
In _Binary Spray and Wait_ DTN protocol [45], the number of copies is limited to 8. By assigning the number of copies to 2, IoB-DTN gets the behavior of _Two-Hop Relay_ DTN protocol [47]. The operation of this protocol is simple and works as follows: all packets are duplicated to only first encountered nodes, then the source node and the first encountered neighbor node will carry the duplicated packets until a base station comes in range. When \(N^{0}=\infty\) (large value), IoB-DTN imitates the behavior of Epidemic Routing DTN protocol [17] which floods the network. Here, we are interested in comparing these three variants to evaluate the impact of the number of copies sprayed in the network. We only give a comparison between GPP, which provides better performances on loss rate and delivery delay, and NP, which offers the best delivery delay.  
In Figures 9 and 10, we notice that Epidemic routing protocol provides the best performance in terms of loss rate in all cases. Indeed, the redundancy and the robustness are maximized by diffusing a large number of copies in the network. Here, it is important to point out that GPP outperforms NP in all simulated scenarios, this is because of its protection to own production. Binary Spray and Wait has similar findings than Epidemic protocol whilst Two-Hop Relay protocol is the worst. This behaviour can have two explanations: one consists of the wrong choice of the neighbor node chosen as relay to forward the packet to a base station, another reason can be the need of more than two hops to send the redundant packet.  
Figures 11 and 12 show the delivery delays of received packets for GPP and NP policies by varying the number of copies sprayed in the network. The first observation we make is that the three variants of IoB-DTN protocol offer similar performances. More precisely, Two-Hop Relay protocol provides the lower delivery delay, whereas Epidemic routing protocol is the worst. This is an expected result since Binary Spray and Wait and Epidemic protocol disseminate more packets in the network so they deliver older ones which degrades the overall delivery delay.  
Figure 8: Average delivery delay.  
Figure 10: _Cont._  
Figure 9: Loss rate for GPP buffer management policy.  
Figure 11: Delivery delay for GPP buffer management policy.  
Figure 10: Loss rate for NP buffer management policy.
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 2 System Model
Therefore, the signal-to-interference-plus-noise ratio (SINR) of the \(k_{n_{c}}\)th user can be expressed as  
\[\gamma_{k_{n_{c}}}=\frac{P_{k_{n_{c}}}\left|l_{n_{c},k_{n_{c}}}\right|^{2}}{ \sum\limits_{k_{n_{c}}^{\prime}=k_{n_{c}}+1}^{K}P_{k_{n_{c}}^{\prime}}\left|l_ {n_{c},k_{n_{c}}}\right|^{2}+\sigma^{2}}. \tag{14}\]  
The rate of the \(k_{n_{c}}\)th user on the \(n_{c}\)th subcarrier is  
\[R_{k_{n_{c}}}=B_{sub}\log_{2}\left(1+\frac{P_{k_{n_{c}}}\left|l_{n_{c},k_{n_{ c}}}\right|^{2}}{\sum\limits_{k_{n_{c}}^{\prime}=k_{n_{c}}+1}^{K}P_{k_{n_{c}}^{ \prime}}\left|l_{n_{c},k_{n_{c}}}\right|^{2}+\sigma^{2}}\right). \tag{15}\]  
Therefore, the spectrum efficiency (SE) of OBON can be calculated as  
\[ \begin{split} E_{\text{total}}&=\frac{\sum\limits_{n_ {c}=0}^{N_{c}-1}\sum\limits_{k_{n_{c}}=1}^{K}B_{sub}\log_{2}\left(1+\frac{P_{ k_{n_{c}}}\left|l_{n_{c},k_{n_{c}}}\right|^{2}}{\sum\limits_{k_{n_{c}}^{ \prime}=k_{n_{c}}+1}^{K}P_{k_{n_{c}}^{\prime}}\left|l_{n_{c},k_{n_{c}}}\right| ^{2}+\sigma^{2}}\right)}{N_{c}B_{sub}}\\ &=\frac{1}{N_{c}}\sum\limits_{n_{c}=0}^{N_{c}-1}\sum\limits_{k_{ n_{c}}=1}^{K}\log_{2}\left(1+\frac{P_{k_{n_{c}}}\left|l_{n_{c},k_{n_{c}}} \right|^{2}}{\sum\limits_{k_{n_{c}}^{\prime}=k_{n_{c}}+1}^{K}P_{k_{n_{c}}^{ \prime}}\left|l_{n_{c},k_{n_{c}}}\right|^{2}+\sigma^{2}}\right).\end{split}  \tag{16}\]
# Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks
## 3 Materials and Methods
### Scenario Configuration  
The simulations were configured to reproduce the characteristics of an industrial plant. The dimensions of 2000 m \(\times\) 500 m correspond to four big, connected warehouses or rooms, forming an industrial plant, during a period of 24 h. The height of the chosen plant was 10 m. This scenario is based on an automotive production plant that is being used for related projects. The devices deployed from a rectangular grid with the GW at the bottom-left corner (as in the x = 0, y = 0 coordinate). There are four additional elements representing the interference sources, aligned vertically and horizontally in each room. This distribution can be seen in Figure 8.  
In this scenario, four interference sources, one per room, introduced high-frequency noise during given periods of time, matching workers and process lines turns and shifts. This behaviour was modelled as three periods of 1 h of high frequency interference, with the sources introducing more frequent signals, as described in Section 3.3. The rest of the time (21 h), the interference sources introduced spurious signals at a lower frequency. This test configuration (the combination of high and low frequency noise periods) will be called the \"real scenario\" from now on, as opposed to the saturated scenario introduced by Figure 6, in order to validate the impact of interference sources.  
The chosen payload size is 100 bytes (13 bytes of LoRaWAN protocol headers +87 bytes of data), which is enough to represent a simple binary message with a short text instruction or measurements with timestamp. The preamble length is eight symbols by default as indicated in the standard. For this configuration, the corresponding airtimes and duty cycle restrictions are shown in Table 1.  
The rest of involved configuration parameters for the network operation can be found in Table 2. These include interference signal configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **DR5** & **DR4** & **DR3** \\ \hline Airtime & 189.7 ms & 338.4 ms & 615.4 ms \\
10\% max duty cycle & 1.9 s & 3.38 s & 6.15 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Airtime and duty cycle for downlink packets for each data rate configuration, always considering a bandwidth of 125 kHz.  
Figure 8: Map of the scenario showing node and interferer distribution.
## 5 Conclusions  
The present study highlights horseradishish (_Armoracia rusticana_) as a promising natural source of bioactive compounds, which have been associated in previous research with antiallergenic properties. However, further studies are needed to confirm such potential in horseradish specifically. In this regard, spectrophotometric and chromatographic analyses were performed to highlight the complexity and diversity of the identified compounds. The extracts were obtained with two different solvents--methanol-water (1:1, \(v/v\)) and ultrapure water--and the specific parameters for ultrasonication (time, temperature, and potency) were varied to determine the optimal conditions, allowing maximum extraction of polyphenols and vitamin C, subsequently analyzed by HPLC. The content of fatty acids found in lyophilized horseradishish was also analyzed by gas chromatography, compounds with properties that support its antiallergenic character.  
During the extraction process, a significant sensitivity of horseradishish compounds to process variables was highlighted, suggesting that they are directly influenced by all extraction parameters. The determinations performed revealed a better solubility of polyphenols, flavonoids, and antioxidant substances in the hydroalcoholic mixture compared to the aqueous one. Furthermore, results for total phenolic content (TPC), total flavonoid content (TFC), and DPPH radical scavenging activity demonstrated increased compound instability under prolonged exposure to elevated temperatures. However, it is important to note that the use of methanol, a non-food-grade solvent in the hydroalcoholic mixture, limits its direct applicability in food products. Therefore, while methanol facilitates efficient extraction and compound solubilization, further research should focus on optimizing food-grade solvents such as water, which--although less efficient--offer safer alternatives for potential food applications. On the other hand, higher potency during ultrasound-assisted extraction had a favorable impact, highlighting the importance of optimizing extraction conditions.  
This study successfully identified and quantified key bioactive compounds such as polyphenols, vitamin C, and fatty acids in horseradish extracts. While the identified bioactive compounds are known for their antioxidant and potentially antiallergenic properties based on literature data, further investigations--including biological or clinical evaluations--would be valuable to both clarify their biological activities and to complement the current findings. Such additional studies could help substantiate potential functional properties, including antiallergenic effects, thereby providing a more complete understanding of the health-related benefits of horseradish extracts.  
B.S. and S.A. contributed equally to the collection of data and preparation of this paper. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interests.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 5 Methodology
### Hypotheses
#### 5.1.1 Prompting Technique Effectiveness  
Prompting techniques that break down the task into manageable steps (e.g., two-step conversion) or provide a structured reasoning path (e.g., CoT) are expected to improve the model's ability to accurately map information to FHIR resources. Basic prompting, which lacks such guidance, may result in less structured and less accurate outputs. Thus, we formulated the following hypotheses:  
**H1.1**: _Prompting techniques such as chain-of-thought (CoT) and two-step conversion will yield more accurate and reliable FHIR bundles compared to basic prompting._  
**H1.2**: _Two-step conversion will outperform CoT in terms of resource mapping accuracy and overall completeness of the FHIR bundles._