esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 3 Technological Background  
The rise of CVs and CAVs is attributed to recent advancements in the automotive industry, and CAVs are anticipated to be accessible to the general public in the near future. Along with the deployment of CVs and CAVs, communication systems such as the Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) will be implemented in the transportation environment.
## 5 Discussion: Limitations and Future Work
To reinforce this legal foundation, technical measures can serve as complementary safeguards that enhance the verifiability of \(A\)'s actions. For instance, publicly auditable logs, reproducible verification procedures, and fallback mechanisms such as multi-party or community-based dispute resolution can increase transparency and resilience. However, these technical features are most effective when embedded within a governance structure that prioritizes real-world accountability. Sixth, although the framework supports anonymous submission of transaction hashes and encrypted content, the practical burden of this may lead some users to submit unencrypted data to \(D\), enabling potential collusion with \(t_{M}\). This could allow censorship or manipulation based on transaction content. Future research should consider user-friendly submission methods--such as automated encryption tools, independent relayers, or decentralized mixers--that reduce reliance on the honesty of \(D\) and \(t_{M}\). Seventh, in dynamic business environments, it may become necessary to introduce new operational rules or revise existing ones. In such cases, it is the responsibility of the platform manager \(t_{M}\) to manage the registration of updated smart contracts, either by submitting new ones or replacing deprecated versions. As noted in Section 3.2, any contract submitted for registration should be reviewed by a neutral external expert to ensure platform reliability and user trust. When the economic benefits of platform operation are shared among multiple stakeholders, a governance mechanism may be required, to determine which contracts are adopted. For example, voting weights based on a predefined profit distribution ratio can be used to reflect stakeholder influence, while general users without economic stakes would not participate in such decisions. Although this model introduces additional complexity, it offers a flexible framework for contract-level adaptability and is proposed as a direction for future enhancement. Another important limitation is the absence of an empirical or simulation-based evaluation. While this study focused on the architectural and security design of the framework, future work should include a proof-of-concept implementation and performance benchmarking under real-world conditions. Such efforts will help quantify the anchoring latency, transaction throughput, and operational costs, and will provide essential insights into the practical feasibility, scalability, and optimization potential of the proposed method. In summary, while the proposed method lays a strong foundation for secure and transparent transaction execution in decentralized platforms, further research is needed to improve the usability, reduce latency, evolve smart contracts, enforce oversight, minimize trust dependencies, and validate real-world performance.
# Scalp-Implanted Ultra-Wideband Circularly Polarized MIMO Antenna for Biotelemetry Systems
## 3 Results and Discussion
### Circular Polarization Analysis  
(1) _The 3 dB AR_  
A parametric analysis was performed to assess the impact of the gap width (Wg) in the ground plane on the 3 dB ARBW of the CP antenna, as illustrated in Figure 5. In this study, a slit in the ground plane linked the left and right U-slots, leading to an increase in the 3 dB ARBW. The effective ARBW was further improved by adjusting the width of the slit. By analyzing the proposed antenna ground-plane slit, we selected a slot width of 0.4 mm, as this provides a wider effective ARBW.  
Figure 4: Simulation and test results of the antenna in different environments (**a**) \(\mid\)S\({}_{11}\mid\), (**b**) \(\mid\)S\({}_{21}\mid\), and (**c**) AR.
## 3 Methods  
### Overall Framework for Data Collection  
Frequency for geophysical surveying and subsequent groundwater monitoring was adapted to the particular groundwater quantity (flow) and quality (EC) temporal dynamics of aquifer H. In small, high-yielding alluvial aquifers having almost null groundwater exploitation such as aquifer H, the average groundwater turnover time identifies well the infilling-emptying cycles produced from rainy and dry phases, and consequently the minimum monitoring frequency required to characterize groundwater flow and EC responses. The average groundwater turnover time was deduced from preliminary aquifer geometry and hydraulics data by assuming a predominant piston (plug) flow condition [5; 6; 52] as:  
\[G=b\cdot A\cdot S/T, \tag{1}\]  
where \(G\) is average groundwater turnover time [T], \(b\) is a dimensionless flow-path form parameter determined by the aquifer geometry, \(A\) is aquifer surface [L\({}^{2}\)], \(S\) is the dimensionless aquifer storage coefficient or trainable porosity for unconfined aquifers, and \(T\) is aquifer transmissibility [L\({}^{2}\) T\({}^{-1}\)], which is the product of aquifer hydraulic conductivity [L T\({}^{-1}\)] and saturated thickness [L].  
Figure 2: Conceptualization of the hydrogeological functioning of aquifer H during (**a**) the dry phase in summer and (**b**) predominant rainy phase in winter. Scale-out scheme after [7; 46; 51], and direct field observations. SAL, Santo André lagoon; LPS, low-permeability (clay-rich) lagoon sediments; ITL, inland temporary lagoon.  
Aimed at interpreting the ERT surveys properly [23], aquifer monitoring included piezometry and EC measurements in selected handmade open wells (Figure 1d). Piezometry was measured using a level probe from Seba Hydrometric, with a precision of 0.005 m. EC was measured each 1-m from 1 m below water table to 8 m depth using a multi parameter probe from Hanna Instruments, with a precision of 0.01 mS cm\({}^{-1}\); the average value of these measures was calculated for each well.
## 2 Cost Components
### Environment Costs
#### 2.3.2 Work Zone Additional Energy Consumption and Emission Costs  
In the work zone, CO, HC, NOX, and CO\({}_{2}\) emissions are closely related to fuel consumption, which is calculated using Equations (13) and (14). Maintaining consistency with fuel consumption cost calculation, the speed-change-related CO, HC, NOX, and CO\({}_{2}\) emissions rates were obtained from [30], respectively.  
\[\mathrm{No~{}queue:~{}EE_{WZ}=\sum_{i=1}^{2}\left(R_{EE}^{C_{i}}\times t^{C_{ i}}\times n^{C_{i}}+R_{EE}^{R_{i}}\times n^{R_{i}}\times L_{w}+R_{EE}^{D_{i}} \times n^{D_{i}}\times L_{D}\right)} \tag{22}\]  
\[\mathrm{Queue:EE_{WZ}=\sum_{i=1}^{2}\left(R_{EE}^{C_{i}}\times t^{C_{i}}\times n ^{C_{i}}+R_{EE}^{R_{i}}\times n^{R_{i}}\times L_{w}+R_{EE}^{D_{i}}\times n^{D_{ i}}\times L_{D}+R_{EE}^{\bar{a}_{i}}\times n^{\bar{a}_{i}}\times L_{q}\right)} \tag{23}\]  
where  
\(\mathrm{R_{EE}^{C_{i}}}\)--The average instantaneous acceleration/deceleration environmental emission rate of the i-th vehicle type, g/s/vehicle  
\(\mathrm{R_{EE}^{R_{i}}}\)--The change in environmental emission rate of the i-th type of vehicle at its original speed and restricted speed, g/km/vehicle  
\(\mathrm{R_{EE}^{D_{i}}}\)--The change in environmental emission rate of the i-th type of vehicle at its original speed and detour speed, g/km/vehicle;  
\(\mathrm{R_{EE}^{d_{i}}}\)--The change in environmental emission rate of the i-th vehicle type at its original speed and queue speed, g/km/vehicle  
As expressed in Equation (24), vehicle emissions costs (VEC) were obtained by multiplying the user emissions and CO, HC, NOX, and CO\({}_{2}\) price units.  
\[\mathrm{EEC_{WZ}}=\mathrm{EE_{WZ}}\times\mathrm{P_{E,j}} \tag{24}\]  
The additional energy consumption cost of the work zone can be calculated based on Equations (13) and (14), combined with the calorific value of the fuel and the unit price of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Vehicle Type**} & \multirow{2}{*}{**Fuel Type**} & **CO** & **NOx** & **HC** & **CO\({}_{2}\)** \\ \cline{3-5}  & & & **g/kg** & & **kg/kg** \\ \hline Passenger Car & gasoline & 132 & 14.5 & 7 & 3.180 \\ Light-Duty Vehicle & diesel oil & 11.0 & 15.0 & 8 & 3.140 \\ High-Duty Vehicle & diesel oil & 8.0 & 37.0 & 18 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Environmental Emission Factors of Road Transport Vehicles.  
energy consumption, to calculate the energy consumption cost of the additional users in the work zone according to Equation (25).  
\[\text{NC}_{\text{WZ}}=\text{FC}_{\text{WZ}}\times\text{R}\times\text{P}_{\text{N}} \tag{25}\]  
\(\text{NC}_{\text{WZ}}\)--work zone vehicle energy consumption cost (yuan)  
\(\text{FC}_{\text{WZ}}\)--work zone additional vehicle fuel consumption.
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 2 System Model
The ZTC-C-RAN model comprises a control element that performs the ZTC procedures, including the instantiation, configuration, and synchronization of the UAV and D2D cooperative network as well as the placement of the UFBS in the region of interest \(A\). Furthermore, the proposed ZTC-C-RAN is benefited from the satellite communication as a backhaul relay between the UFBS and the control center, providing ultra-reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB) network slices responsible for routing the control and data plane information to the terrestrial and aerial segments of the proposed scheme.
## Abbreviations  
2D-LC & 2 dimensions Liquid Chromatography  
ACN & Acetonitrile  
AFFFs & Aqueous Film-Forming Foams  
AP & Apex Predator  
CAS & Cryogenic Air Sampler  
CCME & Canadian Council of Ministers of the Environment  
CCS & Collision Cross Section  
CERLCA & Comprehensive Environmental Response, Compensation and Liability Act  
CI-PFESA & Chlorinated polyfluorinated ether sulfonic acids  
CONAMA & Brazilian National Council for the Environment  
CSCA & China Standard Conformity Assessment  
dSPE & dispersive Solid Phase Extraction  
DSF & Differential Scanning Fluorimetry  
DWTP & Drinking Water Treatment Plant  
ECHA & European Chemicals Agency  
EDCs & Endocrine-Disrupting Chemicals  
EEA & European Economic Area  
EFSA & European Food Safety Authority  
EHP & Environmental Health Perspectives  
EMBRAPA & Brazilian Agricultural Research Corporation  
EPA & Environmental Protection Agency  
ESI & Electronspray Ionization  
EU & European Union  
EURL & European Union Reference Laboratory  
FASA & Perfluoroadlane sulfonamide  
FCMs & Food Contact Materials  
FDA & Food and Drug Administration  
FTAB & Fluorotelomer sulfonamidoalkyl betaine  
FTOH & Fluorotelomer alcohol  
FTS & Fluorotelomer sulfonate  
FT-ICR & Fourier-Transform Ion Cyclotron Resonance  
GC MS & Gas Chromatography Mass Spectrometry  
GenX & same as HFPO-DA  
HC & Health Canada  
HDPE & High Density PolyEthylene  
HFPO-DA & Hexafluoropropylene oxide-dimer acid or GenX  
HILC & Hydrophilic interaction liquid chromatography  
HLB & Hydrophilic-lipophilic balance  
HPLC & High Performance Liquid Chromatography  
HPFPECAs & Hydro- perfluoroether carboxylic acid  
HPFLCAs & Hydrogen-substituted polyfluoroalkyl (linear) carboxylic acids  
HRMS & High-resolution mass spectrometry  
Hydro-EVE & Hydro-EVE & Hydro-
## 4 Simulation Results
### Generalized Mutual Information and BER Results
Figure 4: The GMI simulation block diagram and parameters in VPI. Figure 5: GMI in bit/symbol over OSNR in dB for bit-metric decoding and PS-64 QAM. The inset zooms into the region around GMI = 4 bit/symbol, where the parallel distribution matcher of length n = 100 is 0.47 dB more power-efficient than uniform 64 QAM, and is 0.12 dB more power-efficient than PS-64 QAM using CCDM. In terms of system bit error rate, the probability distribution of the final PS-QAM signal is the same whether utilizing the parallel distribution matcher or CCDM; the only difference is the number of PS-64 QAM symbols generated. If n = 50 and the input bit sequence length is 58,128, the parallel structure can generate 33,600 PS-64 QAM symbols, while the CCDM can generate 34,600 PS-64 QAM symbols. When n = 100 and the input bit sequence length is 62,300, the parallel structure can generate 35,000 PS-64 QAM symbols and the CCDM can generate 35,600 PS-64 QAM symbols. Consequently, the bit error rate performance of the two is the same in the back-to-back situation. When the transmission distance is long in the optical fiber transmission link, the amplifier spontaneous emission accumulates continuously and the number of symbols can have a slight impact on the bit error rate. Simulation block diagram and parameters of standard single-mode optical fiber transmission are shown in Figure 7. Figure 8 shows the BER performance under different transmission distances; the fiber is a standard single-mode fiber (SSMF) with \(\alpha\) = 0.2 dB/km, \(\gamma\) = 1.3 (W \(\cdot\) km)\({}^{-1}\), and D = 17 ps/nm/km; each span of length 100 km is followed by an Erbium-doped fiber amplifier with a noise figure of 3.8 dB. Laser phase noise and polarization mode dispersion are not included in the simulation as perfect compensation is assumed. The transmission distance of PS-64 QAM signals using the paired optimized parallel distribution matcher is marginally greater than that of PS-64 QAM signals using CCDM at the forward error correction threshold of \(-\)2.42 for n = 50 and n = 100. Figure 6: GMI in bit/symbol over block length n when OSNR = 15, 16, and 17 dB. When the GMI is the same, the parallel distribution matcher required block length n is only 60, while the required block length of CCDM is 100; so, the block length can be saved by 40%. Figure 7: Simulation block diagram and parameters of standard single-mode optical fiber transmission.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 4 Proposed Protocol
### Link Expiration  
At this stage, the proposed protocol CAEL uses the GPS coordinates of the source and the destination node through which a limited area (in a triangular shape) is calculated, after this calculation, all the neighbor nodes will be identified inside the (limited) region. As a result, the list of neighbor nodes is created and link expiration time (LET) of each node in the list is also calculated. After this, the average of all nodes LET is calculated. Then, the  
Figure 4: Random node T (**a**) lies inside the region; and (**b**) lies outside the region.  
Figure 3: Trilateral zone.  
list of nodes is further shrunken by comparing each LET of the node by the average LET (which has already been calculated). If the node's LET is less than the average LET of the list, then that node is subtracted and only those nodes will be placed intact in the list whose LET is greater than the average of all LET nodes.  
\[if(\mathit{LET}_{\mathit{node}}-\mathit{LET}_{\mathit{avg.}})\geq 0 \tag{19}\]  
The proposed protocol is different from our previously published protocols (DyTE) because it does not only rely on a limited zone but also does not use a clustering technique as RGoV. The link expiration in CAEL helps in the identification of the time duration for which the links would be available. The proposed protocol CAEL makes use of the node's speed and direction to calculate the effective routes for the communications.  
To understand how the reliability factor is calculated and which parameters are required for it, the details are as follows; the reliability factor (RF) is a technique to determine the most reliable route which considers the \"Route Expiration Time (RET)\" and \"Hop Count (HC)\" while choosing a routing path. The selection of a reliable route is based on the RF value; and a greater value of RF indicates higher route reliability than is appropriate for the transfer of data packets.
## 3 Experiment Results and Analysis of Dynamic Characteristics
### Influence of Loading Frequency on Reinforced Grawelly Soil
#### 3.2.1 Effect of Loading Frequency on Cumulative Axial Strain  
Figure 11 shows the \(\varepsilon_{d}-N\) curve of the samples under different loading frequencies. Overall the cumulative axial strain increases with number of loading cycles. At the end of the 100 th cycle, the axial cumulative strain of the specimen increases with the increase in the number of cycles, and the axial cumulative strain is not significantly affected by the frequency. When the cyclic loading is between 100 and 1000 times, the axial cumulative strain of the specimen increases at different rates, and the higher the frequency, the greater the axial cumulative strain. When the cycle numbers larger than 1000 cycles, the axial cumulative strain of the sample increases almost logarithmically. The growth rate of the axial cumulative strain under the loading frequency of 3 Hz and 5 Hz is similar and both higher than that under the loading frequency of 0.5 Hz and 1 Hz. This stage continues to show the law that the higher the frequency, the greater the axial cumulative strain. Based on the observation, we can see that the higher the loading frequency the greater the cumulative axial strain. This suggests that train speed and time intervals between the trains are important factors to be considered in the long-term settlement prediction of the subgrade of railway. The curves of the cumulative axial strain can be fitted using the following Equation:  
\[\varepsilon_{d}=\frac{a}{1+b\times N^{c}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Reinforcement Layers** & \(\eta_{ed}\) & \(\eta_{Ed}\) & \(\eta_{ud}\) & \(\eta_{ud}\) \\ \hline
0 & 0.000 & 0.000 & 0.000 & 0.000 \\
1 & \(-\)0.524 & 0.088 & \(-\)0.188 & 0.010 \\
2 & \(-\)0.643 & 0.141 & \(-\)0.313 & 0.026 \\
3 & \(-\)0.655 & 0.164 & \(-\)0.250 & 0.053 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reinforcement effect coefficient under different numbers of reinforcement layers.  
The fitting parameters of the curves are show in Table 5.
# Securing IoT Devices against Differential-Linear (DL) Attack Used on Serpent Algorithm
## 6 Conclusions and Future Work  
The study measured the memory of both Serpent and Mag_Serpent using the C++ program. The results demontrate a memory of 11,181 bytes and 13,206 bytes for Serpent and Mag_Serpent, respectively.  
The experiment conducted the Avalanche Effect on Serpent and Mag_Serpent in order to obtain SAC. The results demonstrated that the Serpent and a newly generated Mag_Serpent algorithm had better SAC characteristics. The Avalanche Effect of Mag_Serpent and Serpent on both key and plaintext was approximately 50% probability compared to SAC characteristics.  
The experiment continued on a newly developed 4 \(\times\) 32 S-Box of Mag_Serpent algorithm. The program malfunctioned after five hours before _DLCT_ was executed. No computer or machine could compute the _DLCT_ of 24 \(\times\) 232 = 16 \(\times\) 4,294,967,296 matrix, which is expected to contain 68,719,476,736 entities. Without _DLCT_, it was impracticable to conduct a DL attack on a newly developed 4 \(\times\) 32 S-Box of Mag_Serpent algorithm. On the Serpent, the results revealed that the DL attack was possible. The main building blocks that performed all possibilities of the DL attack were the size of the S-Boxes. The Serpent's S-Boxes were 4 \(\times\) 4, indicating 4-bit inputs and 4-bit outputs. The experiment determined that it was straightforward to build a _DLCT_ utilizing 4 \(\times\) 4 Serpent S-Boxes.  
The C++ experiment showed that a DL attack was possible relative to an original Serpent before new S-Boxes and Blocker approaches were implemented, but after the implementation of the novelty of using new 32-bit output S-Boxes and Blocker Function, the DL attack was blocked on a new modified Serpent called Mag_Serpent.  
The study showed that the Serpent algorithm used to secure data stored on IoT devices was secured against DL attacks by using magic numbers and the Blocker Function. It has been confirmed that it is impossible to draw a _DLCT_ of 32-bit output S-Box. Furthermore, it has been proven that if the construction of _DLCT_ is infeasible on a particular algorithm, then no DL attack will be possible on that algorithm. In this study, a new modified Serpent was generated and named Mag_Serpent.  
Future studies will include measuring the power consumption of Mag_Serpent compared to an original Serpent. Other attacks (such as Boomerang, man-in-the-middle, and Denial of Services (DoS)) will be analyzed using a Blocker Function and 32-bit S-Boxes.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
Figure 18: Encryption and decryption of image by using Serpent and Mag_Serpent.
## 5 Relationship between Contact Forces and Wear as well as New Insights
### Frequencies Converge to Develop Uniform Corrugation  
Equal longitudinal and vertical natural frequencies have been shown to represent preferred conditions under which corrugation can initiate and grow; thus, it is reasonable to infer that the frequencies of the related contact forces and the resulting wear are also equal, such that the five different frequencies converge to one frequency: the corrugation passing frequency. Therefore, the frequency appears to be fixed at a preferred resonance, whereas the wavelength is \"fixed\" in the sense that it can vary only in a small range. This supposition explains the observations in Section 5.2, in which the frequency converges to 1226 Hz, while the preferred wavelength varies because of the mismatch between the given corrugation wavelength and the nominal track parameters. The strongest contact forces are at \(\lambda=30\) mm; the frequencies of the longitudinal and normal forces are equal at \(\lambda=31\) mm; and the frequency of the longitudinal force and the corrugation passing frequency are equal at \(\lambda=31.5\) mm.  
It is also reasonable to infer that when the track parameters that caused the observed corrugation are known and the mismatch between the model input and the prescribed corrugation is eliminated, all of the different wavelengths will converge to the observed wavelength of 30 mm if the traffic speed is 140 km/h, which implies that varying traffic speed can suppress corrugation by changing the wavelength as was shown in [60]. The problem for further research now becomes modeling degraded track to identify the track parameters at which the system converges to the preferred frequencies and wavelengths with a phase angle of wear that promotes corrugation growth at every wheel passage.
## 7 IoMT Healthcare Services and Applications
### Tracking of Depression and Moods  
Tracking depression and moods helps individuals identify potential triggers and inform treatment options to better manage symptoms and improve their overall well-being [102]. This process enables individuals to better understand their own mental health and recognize patterns in their behavior that may indicate a need for medical intervention. Additionally, tracking depression and moods helps clinicians identify any changes over time and adjust treatments accordingly. By using an IoT device, such as a fitness tracker or smartphone, to track individuals' moods, we can identify patterns in their behavior that point to depression or other mental health issues. With the data collected, users work with a mental health professional to develop strategies to manage their moods or seek help if necessary. This provides a better understanding of how depression affects individuals over time and can be utilized to develop better treatments or interventions for those suffering from the condition. These data create personalized care plans, alert healthcare providers to sudden changes in mental state, and provide patients with real-time feedback and guidance on how to cope with their emotions.