esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Optical Detection of Aminoglycosodies
## 4 Other Optical Assays
### Surface-Enhanced Raman Spectroscopy  
A significant Raman signal is observed when Raman probes are located between two metal NPs, which are also called hotspots. Metal NPs such as Au NPs and Ag NPs are employed in surface-enhanced Raman spectroscopy (SERS), which is widely applied for the sensing of small molecules, biomolecules, and pollutants [75; 76; 77]. Both homogeneous and heterogeneous SERS have been developed for the detection of aminoglycos. Typically, [PERSON] et al. prepared an immunoprobe by conjugating a monoclonal antibody against neomycin and the Raman probe molecule 4-aminothiophen (PATP) with Au NPs. The immunoprobes were employed in a lateral flow immunoassay strip for the competitive detection of neomycin. The Raman intensity of peak at 1078 cm\({}^{-1}\) on the test line was determined and used for the quantitative determination of neomycin. The whole process could be completed within 15 min with a LOD of 0.216 pg mL\({}^{-1}\) and good specificity [78]. Homogeneous SERS was developed forthe detection of kanamycin in milk samples based on bimetallic Au@Ag NPs. The complementary DNA on the surface of Au@Ag NPs hybridized with kanamycin-aptamer with Raman reporter Cy3 at one end. The Raman intensity of Cy3 decreased with the increase in the kanamycin concentration. A LOD of 1.5 pM for kanamycin was achieved [79]. In the opto-microfluidic systems, metal NPs can absorb the high thermal radiation during the laser excitation, causing a blinking effect in SERS spectra. The nanocomposite of GO and Au NPs was employed to overcome the blinking effect. The thermal generated by laser excitation on the surface of the nanoparticles was transferred and absorbed by the nanocomposites through the GO matrix. [PERSON] et al. constructed a GO-based microfluidic system and conjugated an aptamer-based switchable probe with GO. The binding of kanamycin to the aptamer changed the structure of the probe, facilitating the hybridization of complementary DNA and Texas Red-modified Au NPs. The microfluidic-based sensor was reusable, and its limit of detection was 0.75 nM [80].
## 2 Results
### Multivariate Analysis to Differentiate Four Processed Ginseng Products  
Next, multivariate data analyses were performed to characterize in detail the distinct composition of various metabolites from WG, TG, RG, and BG. In the metabolite profiling of these four products, the numbers of individual samples were as follows: WG (\(n\) = 10), TG (\(n\) = 10), RG (\(n\) = 10), and BG (\(n\) = 10). After the UPLC-QTOF/MS-based profiling of individual samples, each dataset was processed using UNIFI software. All of the datasets were then subjected to PCA, to visualize the clustering trends among the four products. In the score plot, four groups of WG, TG, RG, and BG were well-differentiated (Figure 2A). Moreover, in the PCA loading plot, we identified the main variables that functioned to differentiate the four ginseng products in the score plot. Each point represented the \(m/z\)-retention time (RT) pairs of molecules. Based on an in-house library, 14 variables of the loading plot were identified, as well as several ginseng (Figure 2B).  
Figure 2: **(A)** The principal component analysis (PCA) score plot and (**B**) loading plot of metabolic datasets of the four processed ginseng products including white ginseng (WG) (\(n\) = 10), tae-geuk ginseng (TG) (\(n\) = 10), red ginseng (RG) (\(n\) = 10), and black ginseng (BG) (\(n\) = 10). In the PCA loading plot, each point was identified as follows: ginseng (a) Rg3, (b) Rg5, (c) Rk1, (d) Rd, (e) Rc, (f) Rb2, (g) Rb1, (h) Rg1, (i) Re, (j) M-Rc, (k) M-Rb2, and (l) M-Rb1. R2X[1] = 0.9443 and R2X[2] = 0.02803.  
Second, OPLS-DA was performed to differentiate TG vs. RG, TG vs. BG, and RG vs. BG, respectively. In the S-plot of TG and RG, five unknown compounds were elevated in TG, while four ginsenosides (Rd, Rg1, Rb1, Re) were elevated in RG (Figure 4A). In the S-plot of TG and BG, two ginsenosides (Rg1, Re) and one unknown compound were elevated in TG, while three ginsenosides (Rk1, Rg5, Rg3) were elevated in BG (Figure 4B). In the S-plot of RG and BG, three ginsenosides (Rg1, Re, Rb1) were elevated in RG, while three ginsenosides (Rk1, Rg5, Rg3) were elevated in BG (Figure 4C).  
Figure 3: The S-plot of (**A**) white ginseng (WG) vs. tae-geuk ginseng (TG) (\(R^{2}\): 96%, Q\({}^{2}\): 92%), (**B**) WG vs. red ginseng (RG) (\(R^{2}\): 95%, Q\({}^{2}\): 92%), and (**C**) WG vs. black ginseng (BG) (\(R^{2}\): 94%, Q\({}^{2}\): 91%).
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 5 Simulation Results
### Angle Rule  
Firstly, the UAVs path optimization with only a turn rate constraint is investigated to verify the angle rule. The true emitter location is \(\mathbf{x}_{t}=[0,0]^{T}\). Here we assume that \(\sigma_{i}^{2}\) is irrelevant to distance \(r_{i}\). Figure 4a,c show the optimal UAV path, taking CRLB as the rule and the straight-line UAV trajectories, respectively. The red triangle in the figure denotes the true emitter location; the small blue circles denote the estimated value of the emitter position with SDP methods and EKF estimator within each time step.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbols** & **Values** \\ \hline Initial emitter position & \(\mathbf{x}_{t}\) & \([0,0]^{T}\) \\ Fixed flight velocity & \(v_{0}\) & \(150\) m/s \\ Sampling time interval & \(T\) & \(1\) s \\ Signal to noise ratio & \(SNR_{0}\) & \(30\) dB \\ Control vector & \(u_{max}\) & \(15^{\circ}\) \\ Maximum distance from the UAV platform to the emitter & \(R_{h}\) & \(30\) km \\ Minimum distance from the UAV platform to the emitter & \(R_{l}\) & \(1000\) m \\ Safe distance between the UAV platform & \(c_{l}\) & \(200\) m \\ Communication maximum distance & \(c_{h}\) & \(15\) km \\ Barrier parameter for interior point optimization & \(\gamma\) & \(10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in simulations.  
Figure 4: (**a**) Optimal paths without constraints. (**b**) Evolution of angle changes. (**c**) Straight-line paths. (**d**) Comparison of localization performance: optimal deployment and fixed deployment.  
From Figure 4a,b, it can be seen that the UAVs try to fly away from each other and obtain the evolution of angle \(\theta_{i}\). Meanwhile, it is noted that each UAV does not obtain effective emitter information due to the initial deployment, and the localization errors is high. After the 10 th time step, the localization error drops sharply with changes in the \(\theta_{i}\), emitter, as is shown in Figure 4d. Figure 4c shows the straight-line UAV trajectories, whereby each UAV is steered directly towards the estimated emitter position.  
To demonstrate the effectiveness of the proposed path planning algorithm, Figure 4d shows the RMSE of optimal trajectories compared with straight-line trajectories after 50 Monte Carlo simulations. This shows that the application of the angle rule is capable of reducing the location error, while the location error with straight-line trajectories is large and apparently uncertain. However, even after all constraints are considered, it can be seen that minimizing the localization could result in baseline expansion. UAVs fly away from each other only within the constrained scope, which is not desirable in actual situations. Hence, it is impractical to reach the optimal location by relying only on the angle rule.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
###### Abstract  
Intelligent Transport Systems (ITS) are an essential part of the global world. They play a substantial role for facing many issues such as traffic jams, high accident rates, unhealthy lifestyles, air pollution, etc. Public bike sharing system is one part of ITS and can be used to collect data from mobiles devices. In this paper, we propose an efficient, \"Internet of Bikes\", IoB-DTN routing protocol based on data aggregation which applies the Delay Tolerant Network (DTN) paradigm to Internet of Things (IoT) applications running data collection on urban bike sharing system based sensor network. We propose and evaluate three variants of IoB-DTN: IoB based on spatial aggregation (IoB-SA), IoB based on temporal aggregation (IoB-TA) and IoB based on spatiotemporal aggregation (IoB-STA). The simulation results show that the three variants offer the best performances regarding several metrics, comparing to IoB-DTN without aggregation and the low-power long-range technology, LoRa type. In an urban application, the choice of the type of which variant of IoB should be used depends on the sensed values.  
internet of bikes; smart cities; internet of things; delay tolerant networks; wireless sensor networks; data aggregation; data collection; low-power long-range technology; LoRa/LoRaWAN 2018 12 2819 2018 27 August 2018
## 2 Inter-Satellite Communication  
ISL, or crosslink, refers to the capability to transfer data between satellites or a multi-entity network with minimal human interaction. According to the mission purpose, ISL data could be user IoT data from a ground terminal, operational data (e.g., Spacecraft (S/C) relative position data, operation schedule, or housekeeping data), or payload data.  
When it comes to the network topology of a multi-satellite constellation, we begin with the optimization problem of coverage, where the criteria are the minimum number of satellites, required revisit time, swath width, and available ISL range. The synthesis of this kind in multi-satellite constellation optimization for Earth discontinuous (periodic) coverage has been thoroughly described in [3; 4; 5], where [PERSON] presented a route theory as a mathematical abstraction for the approximation of an arbitrary satellite constellation.  
Figure 2: ICARUS tag on a blackbird. Credit: Max Planck Society.  
For global coverage constellations, ISL allows for reduction of the coverage rate of the demand region, as ISL links can be used to route the data from satellites out of the coverage region to satellites that are within the coverage region. Additionally, the number of ground stations can be significantly reduced. In [6], the impact of ISL, with respect to the system throughput of large constellations from Telesat, SpaceX, and OnWeb, has been analyzed. It was shown that, for OneWeb, a hypothetical ISL of 5 Gbps could reduce the number of ground stations from 70 to 27, in order to achieve maximum system throughput; furthermore, the total system performance was 10% higher for the scenario considering 30 ground stations and ISL, as compared with the case of no ISL.
# Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires
## 3 Proposed Model  
In the proposed model, the target area (forest area) is assumed to be divided into n equal-sized grids. All the sensor nodes are homogeneous and equipped with temperature sensors and global positioning systems (GPS). The sensor nodes are randomly deployed in the target area. Each node is capable of transmitting directly to the BS by varying its transmission power. The BS is located outside of the target area and is fully aware of the dimensions of the grids. The sensor nodes send data packets to the BS periodically. The BS sends the data collected from the sensor nodes to an IoT cloud. The data can be accessed by the registered Fire Stations. If an event (fire breakout) is detected, it is immediately reported by the sensor nodes to the BS. In this case, the data are immediately sent to the IoT Cloud, and thereafter, to the local fire station.  
The proposed model is divided into two phases: (i) Setup and (ii) Steady-State. During the setup phase, the BS selects CHs and establishes multi-hop communication paths with them. During the steady-state phase, the data collected by the sensor nodes are sent to the IoT Cloud.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & \begin{tabular}{c} **Cluster** \\ **Formation** \\ \end{tabular} & \begin{tabular}{c} **Slot Allocation** \\ **Technique** \\ \end{tabular} & \
\begin{tabular}{c} **Communication** \\ **Technique** \\ \end{tabular} &
\begin{tabular}{c} **Energy Consumption** \\ \end{tabular} & **Application** \\ \hline LEACH [28] & Yes & Self allocation & Single-hop & Medium & General \\ \hline LEACH-C [29] & Yes & Self allocation & Single-hop & Medium & General \\ \hline SLEACH [31] & Yes & Self allocation & Single-hop & High & General \\ \hline Q-LEACH [32] & Yes & Self allocation & Single-hop & Medium & General \\ \hline MG-LEACH [36] & Yes & Self allocation & Multi-hop & Low & General \\ \hline LEACH-B [34] & Yes & Self allocation & Single-hop & Medium & General \\ \hline BEE-MAC [37] & Yes & Self allocation & Single-hop & Medium & General \\ \hline ES-MAC [38] & Yes & Allocation by CH & Single-hop & Medium & General \\ \hline EE-MAC [39] & Yes & Allocation by CH & Single-hop & Medium & General \\ \hline LEC-MAC [40] & Yes & Allocation by CH & Single-hop & Medium & General \\ \hline FFSS [41] & No & Self allocation & Multi-hop & Low & Forest fire \\ \hline E-RARP [23] & Yes & Allocation by CH & Multi-hop & Low & Forest fire \\ \hline LARRR [44] & No & Self allocation & Single-hop & High & Forest fire \\ \hline FWI [26] & Yes & Allocation by CH & Multi-hop & Medium & Forest fire \\ \hline HDRS [46] & Yes & Allocation by Fog Node & Multi-hop & Medium & Detection \\ \hline EEFFL [45] & Yes & Self allocation & Multi-hop & Medium & Forest fire \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table.
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 4 FSM Mitigation Architectures Summary
It must be clearly emphasized that triple modular redundancy is only one element in the mitigation of the effects of radiation. It does not in any way protect the system if an error is injected into the input of the FSM, much less if the error occurs on the clock tree. In the latter case, a circuit based in some way on the SKEW phenomenon and presented in the thesis [57] or in paper [58] can be used. According to the authors, the efficiency of FSM implementations containing TMRs depends on a number of factors, such as power consumption, the number of resources used, and the ability to repair errors. By power, the authors mean dynamic power, which is technology-independent and can be influenced at the implementation stage. The number of resources used in the case of FPGA devices is the number of LUT blocks with which the various functions are implemented. Naturally, the FPGA device also has other logical resources, but the ideas described in the article do not affect them. Error correction capability should be interpreted literally (either the circuit is capable of recovering from faults or it is not, i.e., it does not contain mitigation mechanisms).
# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 2 Preliminaries and Problem Definition
### Lstm  
The algorithms most commonly used for time series forecasting among deep learning methods are the recurrent neural network (RNN) [54], LSTM [31], and gated recurrent unit (GRU) [55]. The RNN has many advantages concerning the processing of sequence data but has more disadvantages in the problems of long-term dependency, gradient disappearance, and gradient explosion. In 1997, [PERSON] proposed the LSTM to solve the long-term dependence problem of RNN [56]. Figure 4 shows the structure of LSTM, which consists of four gates: forget, input, update, and output gates.  
The forget gate determines which information to delete, the input gate determines whether new information is stored in the cell state, the update gate updates the cell state, and the output gate  
Figure 4: Structure of the long short-term memory.  
Figure 3: Deep neural network structure.  
determines which output value to output. Equation (2) is the calculation formula for each state, as follows:  
\[f_{t}=\sigma(w_{f}\times[h_{t-1},x_{t}]+b_{f}),\]  
\[i_{t}=\sigma(w_{i}\times[h_{t-1},x_{t}]+b_{i}),\]  
\[\widetilde{C}_{t}=tanh(w_{c}\times[h_{t-1},x_{t}]+b_{c}),\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C}_{t},\]  
\[o_{t}=\sigma(w_{o}\times[h_{t-1},x_{t}]+b_{o}),\text{ and}\]  
\[h_{t}=o_{t}\times\tanh(C_{t}),\]  
where \(x_{t-1}\) and \(x_{t}\) are the previous and current input values, respectively; \(h_{t-1}\) and \(h_{t}\) are the previous and current hidden gates, respectively; \(C_{t-1}\) and \(C_{t}\) are the previous and current cell states, respectively; \(w_{f}\), \(w_{i}\), \(w_{c}\), and \(w_{o}\) are the weight values connecting the input to the forget, input, update, and output gates, respectively; \(b_{f}\), \(b_{i}\), \(b_{c}\), and \(b_{o}\) are the bias values for the forget, input, update, and output gate's calculation, respectively; \(\sigma\) is a sigmoid function; and _tanh_ is a hyperbolic tangent function.
# Practical Performance Analyses of 5G Sharing Voice Solution
## 4 Voice Services Based on 5G Sharing
### NSA Sharing VoLTE Process
For the communication cost shown in Figure 4, in addition to the regular VoLTE delay, such as the sharer NSA UE anchor access delay and the source-side UE release context delay, the additional handover adds four times of communication between the sharer UE and the contractor eNodeB, one time of communication between the sharer UE and eNodeB, four times of communication between the contractor eNodeB and the sharer EPC, and three times of communication between the sharer eNodeB and the sharer EPC. In addition, SgNodeB is released as a non-essential process. The whole communication cost can be written as:  
\[\mathrm{T_{VoLTE}}=\mathrm{t_{access}}+\mathrm{t_{SSUCR}}+\mathrm{t_{SgNB Release}}+4(\eta_{\mathrm{C}}+\mu_{\mathrm{C}})+\eta_{\mathrm{S}}+3\mu_{ \mathrm{S}} \tag{3}\]  
\(\mathrm{t_{access}}\) is the sharer NSA UE anchor access delay; \(\mathrm{t_{SSUCR}}\) is the source-side UE release context delay; \(\mathrm{t_{SgNB Release}}\) is the SgNodeB release delay, including two times of communication between the contractor anchor eNodeB and the contractor gNodeB if it is configured to require release.
## 5 Discussion and Implications
These improvements not only improve efficiency and scalability by allocating resources to reliable, high-quality updates and reducing overall latency but also maintain realistic testing scenarios through a custom dataset that simulates adversarial and heterogeneous states typical of large-scale IoV environments. Ultimately, Fed-DTB stands as a significant step forward in deploying robust federated intelligence, enabling intelligent transportation systems to manage data integrity, performance, and security in tandem, even under dynamic and potentially adversarial conditions.
## 4 Use Cases
### Results  
We compared the performance of meta-learning algorithms, i.e., MAML and CAVIA, with conventional learning, where the data from meta-training devices were not used and the weights of the demodulator were randomly initialized for the three communication settings. We assumed a binary fading channel for the IoT scenario where the bits were modulated with 16-ary quadrature amplitude modulation (16-QAM). Pilot symbols followed a fixed periodic sequence in the meta-training and meta-test sets. A total of K = 10 meta-training devices were considered and the meta-test device transmitted only one pilot symbol. The demodulator was a neural network with three layers (an input layer with two neurons, one hidden layer with 30 neurons, and a softmax output layer with four neurons). The hyperbolic tangent function was used as the activation function. The learning rate was fixed at \(\eta=0.1\) and \(\kappa=0.0001\) with a single local update and the ADAM optimizer was used to update the shared parameter. In the case of IoT, conventional learning failed to reach error rates less than 0.25. MAML and CAVIA both reached the optimum solutions with MAML outperforming CAVIA by 52%, averaged over the total number of iterations. The difference in performance was due to the underlying algorithm, as MAML adapts to a more significant number of parameters with respect to CAVIA, as shown in Figure 3(a). Figure 3(b) shows the symbol error rate (SER) with respect to the number of iterations during meta-training in the case of VLC. The conventional learning fails to perform adequately for P = 1; it should be noted that the performance will increase as the number of pilots during training will increase. However, despite training over a single pilot, both MAML and CAVIA perform optimally. MAML outperforms CAVIA by 9%, averaged over the total number of iterations.  
For molecular communication, we considered a diffusive channel with K = 10 MCS devices. Figure 3(c) shows SER vs. the total number of iterations. The conventional methods failed to reach error rates of less than 0.25; therefore, they are not shown in the figure.  
We investigated the effect of an increasing number of pilots on the overall system's performance. For this purpose, we simulated for up to 1600 pilots with 1000 meta-training devices, where the symbol error rate was obtained by taking the average of 100 different meta-test devices. In Figure 4(a)-c, it should be noted that CAVIA performs better with a smaller number of pilots as compared to MAML and the conventional methods. It is worth noting that the conventional ML algorithm performs just as well as meta-learning when the number of data points is increased.  
Figure 4: _Cont._  
Figure 4: Symbol error rate with respect to number of iterations during meta-training for an offline meta-learning in three different scenarios: (**a**) IoT, (**b**) OWCS, and (**c**) MCS, with 10 devices where P = 1 pilot is transmitted by the meta-test device.  
Figure 5: _Cont._
## 3 Results and Discussion
### Effect of Crosslinker Concentration
[25] reported microencapsulation yields of 30% of the complex coacervate microcapsules after spray drying with comparable inlet temperature (160 \({}^{\circ}\)C vs. 135-145 \({}^{\circ}\)C used here). Thus, the results reported here compare favourably to the literature. Upon increasing crosslinker concentration, the microencapsulation yield decreases. While only a slight decrease is observed between 2.5 and 5 wt% (54% and 47% microencapsulation yield, respectively), increasing the crosslinker concentration to 10% results in a markedly decreased yield (31%). Taken together with the observed difference in size  
Figure 6: Optical microscopy images of peppermint oil complex coacervate microcapsules with different concentrations of crosslinker before (upper row) and after (bottom row) spray drying. The polymer concentration and Polymer:Oil ratio was held constant at 6% and 2:1, respectively. (**A**,**D**) = 2.5% crosslinker, (**B**,**E**) = 5% crosslinker, (**C**,**F**) = 10% crosslinker. The scale bar corresponds to 50 \(\mu\)m.  
distribution, this supports the hypothesized induced defects or porosity above a threshold concentration of crosslinker, which leads to loss of encapsulated material and increased capsule stickiness from exposure to hydrophobic domains.