esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Method Comparison  
#### 4.4.1 Accuracy Comparison  
In this sub-section, the attributes of the proposed scheme are compared with that of the recent studies, as shown in Table 14. Practically, it is difficult to exactly compare the experimental results associated on different intrusion detection data sets, because the quality of original data set (affected by imbalanced ratio of data set) is different. For example, in [21, 25], the studies achieved very high classification accuracy on both KDDCup 99 and NSL-KDD datasets, but not with the UNSW-NB15 dataset in [14, 17]. As described in Table 2, the serious data imbalanced problems existed in the UNSW-NB15 dataset. In [21], experiments on KDD Cup 99 dataset show that the classification accuracy of random forest algorithm has reached 92.39%, which is higher than other classification methods, such as J48, LibSVM, NaiveBayes, Bagging, and AdaboostM1. Moreover, the accuracy of the RF combined with the SMOTE has increased from 92.39% to 92.57%, after over-sampling the samples for the minority classes including probing, U2R, and R2L. Overall, the experiment reports in [21] that are consistent to that in Table 10 on UNSW-NB15 dataset, i.e., the SMOTE can improve the classification effect of minority classes for imbalanced datasets on both the KDD Cup 99 and the UNSW-NB15 data sets.  
From Table 14, it shows that the multi-classification accuracy of the scheme proposed in this study is higher than that of [14, 17] on the UNSW-NB15 dataset. In contrast to [14, 17], proposed approach has advantages on better multi-class classification accuracy on UNSW-NB15 dataset by incorporating with C4.5 DT algorithm to the model overfitting prevented from inputting additional variables to avoid learning the noise in the training data. Moreover, Table 14 indicates that the proposed approach achieved the binary accuracy close to those of [32] on CIC-IDS 2018. However, the multi-classification accuracy was 96.53% is slightly lower than that of [19].
## 3 Simulation Results
### Measurement Results  
Figure 19c shows the complete setup overview of the measurements. The chip was measured by a DSAZ334A Infiniium Oscilloscope with a 20 GSa./s sample rate and up to 4,194,304 pts memory depth per channel feature. The oscilloscope was well calibrated and  
Figure 19: (**a**) PLL chip mounted on PCB; (**b**) PLL chip; (**c**) PLL measurement setup.  
enabled measuring 6-bit pattern length. Using this oscilloscope, we measured PLL output. Figure 20 shows the PLL output and the corresponding eye diagram. For two rising edges, the measured values of channel 1 (point1) and channel 3 (point 3) are 509 mV with an offset of \(-\)14 mV, and 461 mV with an offset of \(-\)31 mV, respectively. Table 3 summarizes the measured duty cycle and frequency with standard deviation. The eye diagram in Figure 20 also provides all jitter value information, which is summarized in Table 4. Figure 21a depicts the bathtub histogram for the total jitter (TJ) with a bit error rate of (1E-12), while Figure 21b shows a Gaussian profile for the random jitter (RJrms,narrow).  
The bathtub curve slope continuity and smoothness along with Gaussian distribution profile show absence of cross talk in the measured jitter.  
Table 4 includes the TJ with a bit error rate of (1E-12), RJrms,narrow, the periodic jitter rms value (PJrms) and the duty cycle distortion (DCD). RJ is also known as unbounded jitter, while PJ and DCD are known as bounded jitter. PJ and DCD are parts of deterministic jitter. The total jitter TJ comprises deterministic (bounded) jitter and random (unbounded) jitter. RJrms,narrow measured value is 19.10 ps. The low random jitter ensures precise and stable clock generator application of proposed PLL. This is a spectral jitter measurement, which has more accuracy at critical low cross-talk. Abbreviations are defined below.
## 3 Empirical Analysis and Problem Statement
### Research Challenge  
Based on the facts presented in Section 3.1, designing and implementing a static taint analysis tool for Lua applications in firmware is challenging.  
First, the development environment for IoT devices is heavily fragmented, with manufacturers exhibiting preferences for diverse frameworks. Most existing static analysis methods are designed for desktop systems, while their web applications often utilize popular frameworks. These frameworks have comprehensive documentation, which considerably aids in conducting static analysis. However, these methods cannot be directly ported to firmware objects. Meanwhile, as Fact-3 shows, multiple vendors use Lua to develop IoT devices. Furthermore, usually, they have their respective framework but without documentation. Consequently, data-flow analysis encounters challenges in pinpointing the initial input source from users.  
Second, we need to propose a new interprocedural and cross-file taint-tracking algorithm to compensate for failures of existing methods. As Fact-2 shows, Lua applications in firmware are complicated and usually consist of various files. Therefore, a taint analysis with interprocedural and cross-file support is a prerequisite for accurate vulnerability detection. However, existing tools can only support intraprocedural analysis.  
Thirdly, it is necessary to establish vulnerability models in the IoT field to rectify the shortcomings of current approaches. However, this is a formidable undertaking due to the intricate interdependence between software and hardware within IoT devices. Moreover, the web servers of these devices necessitate intricate interactions with back-end programs and underlying system modules.
## 4 Simulation Tests and Results
### Case Study 1  
The aim in the first case study was to maximize the coverage of a two-dimensional, squared area of \(20\times 20\). The number of the available sensors was equal to 35 and their sensing range was equal to 1.5. The size of the population in the GA algorithm was selected to be equal to 200 while the swarm size in the PSO algorithm was adjusted to 200.  
The results of the simulations performed are synoptically presented in Table 3.  
Additionally, the optimal positions of the network nodes that are calculated with the use of the GA algorithm are depicted in Figure 3 while the best and mean fitness values for each generation are shown in Figure 4.  
Similarly, the optimal positions of the network nodes calculated using the PSO algorithm are illustrated in Figure 5 while the best fitness values for each iteration are graphically presented in Figure 6.  
Figure 4: Best and mean fitness values for each generation of the GA algorithm in case study 1.  
Figure 5: Optimal node positions with the use of the PSO algorithm in case study 1.
## 3 Results
### Identification and Characterization of LA-1  
LA-1 was synthesized via a condensation reaction and the product was identified using \({}^{1}\)H NMR, \({}^{13}\)C NMR, and FT-MS (Figures S1-S3). The purity of LA-1 was confirmed to exceed 98% by LC-MS (Figure S4). In our previous studies, we found that RGD-containing analogues could form nanoparticles via self-assembly [33; 34]. Therefore, the nanoscale properties of LA-1 were also evaluated here. Initially, we measured the particle size and polydispersity index (PDI) of both LA-1 and loganin (Table 2 and Figure 2C). The average particle size of LA-1 was 219.9 \(\pm\) 30.95 nm, with a PDI of 0.421 and a zeta potential of 12.2 \(\pm\) 4.66 mV. In contrast, loganin exhibited an average particle size of 231.5 \(\pm\) 81.235 nm, a PDI of 0.194, and a zeta potential of \(-\)4.16 \(\pm\) 4.40 mV. The positive zeta potential of LA-1 indicates enhanced stability in suspension due to the repulsive forces between particles, reducing the likelihood of aggregation [35], which could also facilitate better interaction with negatively charged cell membranes, potentially enhancing cellular uptake [36].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **Particle Size (nm)** & **Zeta Potential (mV)** \\ \hline LA-1 & 219.9 \(\pm\) 30.95 & 12.2 \(\pm\) 4.66 \\ Loganin & 231.5 \(\pm\) 81.23 & \(-\)4.16 \(\pm\) 4.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physicochemical characterization of LA-1 and loganin.  
Figure 1: Molecular docking results. (**A**) The structure of compound LA-1. (**B**) Molecular docking and enlarged view of LA-1 and the active pocket of \(\alpha\)v\(\beta\)3 protein (PDB: 1L5G). (**C**) 2D diagram of the interaction force between LA-1 and \(\alpha\)v\(\beta\)3 protein.  
As observed through different microscopic techniques (Figure 2D-F), LA-1 exhibited spherical and regular nanostructures, while the nanoparticles of loganin appeared irregular and unevenly dispersed. These observations indicate that LA-1 possesses superior nanoscale properties. In conclusion, the excellent physicochemical characteristics of LA-1 nanoparticles may enhance its performance in drug delivery.
# Nanotechnology, Nanomedicine, and the Kidney
## 5 Nanotechnology for Renal Therapy
### Chelation Drugs  
Vascular calcification involves the deposition of minerals, which increases the stiffness of the arteries. It is assumed that vascular smooth muscle cells can differentiate into osteoblast-like cells or chondrocytes and deposit collagens containing minerals [81]. Hemodialysis patients and patients suffering from chronic kidney disease are at increased risk for vascular calcification, which increases their mortality and morbidity [81]. Removal of free or excess calcium and phosphorus ions from these patients can slow the calcification process.  
Chelation is a chemical process used to bind minerals or metals into a complex ring structure via multiple bonds. In the body, glutathione and metallothioneein function as natural chelating molecules. Supporting the natural chelation process using nanoparticles could help to support renal and cardiovascular health [82].  
Ethylenediaminetetraacetic acid (EDTA) is a powerful chelating agent that can bind calcium and iron ions. Clinical studies revealed that systemic infusions of EDTA are efficient in the depletion of calcium ion levels, but that the solid systemic effect of EDTA can cause hypocalcemia in blood and bone marrow. In addition, the necessary frequent infusions make ETDA therapy hard to apply. To optimize EDTA-infusion therapy, [PERSON] et al., developed a nanoparticle that contained EDTA and an antibody that specifically binds to sites of arteriosclerotic damage [83]. Injection of EDTA-loaded DiR (1,1-dioctadecyl-3,3,3,3-tetramethylindotricarbocyanine iodide) nanoparticle in a rat model showed that the particles delivered a minimal dose of that reduced vascular calcification in the absence of bone demineralization.
## 9 NS-2 Simulations  
### Simulation Parameters  
The LocalHeur and the MST scheme were implemented and comparatively studied using the ns-2 simulator [27]. A realistic wireless environment is modelled using the parameters specified in [28]. Nodes were uniformly distributed on a 1,500 m by 300m grid. Instances where the node density has to be varied were obtained by changing the number of nodes (50,100,250) in the above grid and instances where traffic-load has to be varied were obtained by fixing the sending rate at 4 packets per second and varying the number of sources (1,10,20,30). To nullify the effects of congestion, CBR traffic was used and the packet size was kept as low as 64 bytes. Each node adapts its transmit power after the next-hop forwarding decision is made and the receiver sensitivity of all the nodes are fixed. IEEE 802.11 was the underlying MAC, using the Distributed Coordination Function (DCF). In our energy model, to eliminate the MAC effects, we do not consider the energy expenditure for idle listening. Each simulation was carried for 150 s and averaged over 10 runs for different node distributions and connectivity patterns.
## 3 Detection in Receiver
### SVM Detector
#### 3.2.2 Feature Extraction  
The optimal selection of discriminant features is an issue of the greatest importance in EBPSK system. Figures 5(a,b) show the SIF output waveforms of \"0\" and \"1\", respectively. The certain distinguishing features between the waveforms of \"0\" and \"1\" are very apparent. In order to establish the characteristics space that can identify two symbols, we may use the features as follows:  
1. If we define the area below y(n) as the energy, then we may note this energy item is quite concentrated at the left range of the characteristic waveform \"1\" when the received signals pass through the SIF. This energy is relatively dispersed while the symbol \"0\" passed through the SIF with only channel noise. As a result, we define the first feature as: \[f_{1}=\frac{\sum\limits_{n=5-P}^{5+P}y^{2}(n)}{\sum\limits_{n=1}^{M}y^{{}^{ \prime}2}(n)}\] (12)  
where \(M=N\times f_{s},f_{s}\) is the sampling rate and the range of interest in (12) is limited by the key parameter P. Practically, P can be determined by the impacted part of received signals y(n), which have been processed by taking the envelopes, as is shown in Figure 3. That is, P can be immediately obtained once the right value y(n + k) has surpassed the left value y(n) by \(\delta\). A simple and practical strategy is directly set \(\delta\) to 0. On the other hand, if we use short bit duration then we can choose P = M/4, which means the half of most concentrated part of y(n) are used. To make sure there are enough sampling points, we use P = 2N while P \(<\) 2N.  
(4) The total received energy can be also utilized to differentiate the two symbols, Therefore, we add it to our feature set as the fourth feature:  
\[f_{4}=\sum\limits_{n=S\cdot P}^{S+P}y^{2}\big{(}n\big{)} \tag{16}\]  
By taking full advantage of the developed characteristic waveforms, we have constituted a feature set which is dedicated to separating the two symbols. It is noteworthy that we do not need to estimate the channel noise power \(\sigma^{2}\), and choose only four features of SVM for training and testing, which can reduce the complexity significantly.
# Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia
## 3 Data Used for This Study
### Surface Backscattering Model  
In order to understand the amplitude variations of the backscattering coeffcient \(\sigma_{0}\) in SAR images, we used a surface backscattering model with a single layer. The \"Integration Equation Model (IEM)\" was developed by [32] and updated in subsequent years [33]. According to [20], the skin depth of the C-band radar signal for Sentinel-1, with a frequency of 5.4 GHz, is equal to 20 cm on a dry soil (dielectric constant \(\xi^{\prime}\) = 3.22 and \(\xi^{\prime\prime}\) = 0.08)--based on values measured in the field at Gobabeb [20]. As a first step, we therefore consider that the penetration of the radar signal is low, which justifies the use of a surface backscattering model. The IEM model simulates the backscattering coefficient \(\sigma_{0}\) (in dB) of a surface from the physical characteristics of the ground and for a given SAR configuration, depending on the following parameters [32; 33]:  
\[\sigma_{0}=\text{function}(\text{f},\,\theta,\,\text{\&},\,\sigma,\,\text{L}_{ \text{corr}}) \tag{5}\]  
with:  
\(\text{f}=\text{SAR}\) frequency (GHz)  
\(\theta=\text{SAR}\) incidence angle  
\(\text{\&}=\text{Dielectric}\) constant (real part) of the surface  
\(\sigma=\text{Surface}\) height standard deviation (cm)  
\(\text{L}_{\text{corr}}=\text{Surface}\) correlation length (cm)  
(\(\sigma\) and \(\text{L}_{\text{corr}}\) are the roughness parameters of the surface).  
In our case, the frequency is equal to 5.4 GHz and \(\xi\) varies from 3 (dry environment) to 15 (wet environment). We therefore varied the \(\sigma\) and \(\text{L}_{\text{corr}}\) parameters to find the best combination that reproduces \(\sigma_{0}\) variations similar to those observed in the Sentinel-1 radar signal amplitude.
# An Overview of Cube-Satellite Propulsion Technologies and Trends
###### Abstract  
CubeSats provide a cost effective means to perform scientific and technological studies in space. Due to their affordability, CubeSat technologies have been diversely studied and developed by educational institutions, companies and space organizations all over the world. The CubeSat technology that is surveyed in this paper is the propulsion system. A propulsion system is the primary mobility device of a spacecraft and helps with orbit modifications and attitude control. This paper provides an overview of micro-propulsion technologies that have been developed or are currently being developed for CubeSats. Some of the micro-propulsion technologies listed have also flown as secondary propulsion systems on larger spacecraft. Operating principles and key design considerations for each class of propulsion system are outlined. Finally, the performance factors of micro-propulsion systems have been summarized in terms of: first, a comparison of thrust and specific impulse for all propulsion systems; second, a comparison of power and specific impulse, as also thrust-to-power ratio and specific impulse for electric propulsion systems.  
micro-propulsion survey; CubeSat propulsion; cold gas propulsion; liquid propulsion; solid rocket propulsion; resistojets; RF ion propulsion; hall propulsion; electrospray; pulsed plasma thrusters +
Footnote †: journal: Aerospace
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 4 Floods
### IoT Architectures for Flood EW Systems
Furthermore, protocols that work in ISM bands such as LoRa might experience degradation if there is a large number of devices transmitting at the same time [48; 57]. Zigbee is the most used solution when it comes to short range communications, and the Zigbee support for mesh networks is considered useful to survive sensor network faults. Lastly, while many of the reviewed solutions still make use of Cloud platforms or servers to store and process data or to run ML models, Fog/Edge layer solutions are recommended to improve the overall efficiency of the system, both in terms of latency and the amount of data to be transferred on the network. Machine Learning models can enhance the result of predictions and they can be embedded in Fog devices. More simple threshold-based alarms can also be effective to create an Edge solution and generate quicker alarms, and they are more easily implemented on local sensor nodes.
## 4 Electric Vehicles  
### Battery-Powered Topologies  
Fully electric vehicles no longer use the widespread internal combustion engine from conventional vehicles and hybrid drivetrains. These vehicles are equipped with a high-voltage battery package, usually lithium-ion, lead-acid, or nickel-metal-hydride, using an inverter as a conversion circuit. The voltage and current delivered to this motor are adjusted to the predefined values during driving. A basic schematic is shown below in Figure 14.  
Additional features, such as regenerative braking, were typically implemented in electric vehicles. Based on system design, this energy recovery mechanism converts momentum into electrical energy through magnetic circuits, allowing the user to recover all or a certain percentage of this energy.  
In contrast to using a gearbox to increase or decrease the electric motor torque, integrated motors in vehicle wheels also became available to minimize production costs further [66].  
The power controller, in this case, could also monitor the state of health of the electric motor based on transient signal characteristics and pulsed operation measurements [67; 68].  
Onboard electric chargers were developed to convert grid AC into DC at a constant charging rate specified by the high-voltage battery pack manufacturer. This charging system became integrated into the vehicle electric network, and based on the safety standard, it could consist of galvanic separation, delivering charging powers between 3.3 kW and 22 kW [69]. The AC conversion circuit usually consists of a high-voltage PFC (Power Factor Correction) boost converter or a totem pole PFC topology, different for each design, followed by high- to low-voltage conversion circuits, such as a forward converter, a flyback converter, or push-pull topologies. More efficient circuits like resonant converters are more suitable for high-power and high-efficiency onboard chargers.  
For high-energy-density applications, where EMI (Electromagnetic Interference) and switching losses need to be minimized while maintaining a high-efficiency percentage, the zero voltage and current topologies have proven to be a suitable solution [70]. By switching the execution elements during oscillations caused by resonance, zero voltage and current occurs at a certain moment, for which switching losses become negligible.  
Zero voltage and current switching were also successfully implemented for improved PFC circuits, such as the totem pole converter. However, as mentioned in [71], during transistor switching, current spikes may cause certain problems related to electromagnetic interference for this AC charging circuit, with its basic diagram shown below in Figure 15.  
Figure 14: EV (Electric Vehicle) block diagram.  
Figure 15: Totem pole AC to DC converter circuit.  
Interleaving this type of converter allows for the development of even faster charging circuits while also needing improved control strategies to mitigate the switching electrical noise. Coupled inductors were used for this purpose but designs for multiple phases still must be researched to improve performance further [72].
## 3 Materials and Methods
### Design of Proposed Framework
The K number of users is listed as \(k\), local minibatch size is shown by \(\beta\), learning rate is represented by \(\eta\), and local epochs are represented using \(E\). The proposed framework is devised for both client and cloud ends. This proposed framework is implemented into three stages as described below:  
1. Initial Phase: Initially, all the connected IoMT healthcare sites obtain an initial global model \(\omega_{o}\) from the cloud and are initiated with vector \(X_{ni}\). 2. Working at Cloud End: To retrieve the weights \(\omega_{k}\) of the federated model, the cloud first collects only the weights from the clients and performs matched averaging. The clients then train their local model using their local data while the matching federated is kept frozen once the cloud broadcasts these weights to them. Then, we repeat this process up until the final round of communication. 3. Working at IoMT Client Sites: After data collection using IoMT devices, the collected data is fragmented into local minibatch of size \(\beta\). The candidate optimal solution \(C_{n}\) for each \(\beta\) is computed using the M-ABC optimizer and the weights of the local computed solution from every IoMT client site are returned to the global cloud.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 3 Experiments  
### Datasets  
The performance of STFEformer was corroborated by experimental validation on three real-world public datasets, namely, PeMS04, PeMS07, and PeMS08 [23]:  
PeMS04: The traffic data were gathered by the California Transportation Agency's (CalTrans) Performance Measurement System (PeMS) from 1 January to 28 February 2018, utilizing 307 sensors. This dataset encompasses traffic flow information, which is consolidated into intervals of every 5 min.  
PeMS07: This dataset encompasses data spanning four months, acquired through 883 sensors from 1 May to 31 August 2017. It includes traffic flow information aggregated at 5 min intervals.  
PeMS08: This dataset encompasses data spanning two months acquired through 170 sensors from 1 July to 31 August 2016. It includes traffic flow information aggregated at 5 min intervals.  
Details are presented in Table 3. We use the past hour's (12 time steps) data to predict the following hour's (12 time steps) traffic flow. The dataset is partitioned into three segments: 60% allocated for training, 20% for validation, and the remaining 20% for testing.