esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Industrial wireless sensor networks (IWSNs) are exploited to achieve various purposes, including enhancing productivity and reducing cost in a variety of industries, and they require low-delay and high-reliability packet transmission. To achieve these requirements, a network manager is responsible for constructing a graph, allocating resources, and determining the transmission cycle and path of each node in advance. However, this approach is inadequate for exploiting mobile devices that constantly change network topology because frequent graph reconstruction and resource reallocation are required. In other words, despite the increasing reliance on mobile devices in a variety of industries, existing schemes cannot adequately respond to path failures due to device movement and subsequent packet loss during recovery. For example, real-time tracking of mobile vehicles in mining operations is crucial for safety and efficiency, where path failures and packet loss can lead to significant issues. To solve this problem, we propose a mobility support scheme to prevent packet loss caused by device mobility. In the proposed scheme, we first classify mobility patterns based on the decision tree and then apply regression to predict their trajectories. By leveraging this predictive information, the network manager could preemptively adjust graph construction and resource allocation to accommodate topology changes. Performance evaluation results show that the predicted mobility patterns closely match the actual patterns, achieving a high packet delivery ratio compared to conventional schemes, while also enabling efficient resource management.  
industrial wireless sensor networks (IWSNs); decision tree; regression; mobility pattern prediction; graph construction; resource allocation
## 3 Results
### Comparison to Histology  
Immunohistochemical stainings against amyloid-beta were performed in order to confirm OCT findings. In Figure 6a, a 3D reconstruction and rendering of 180 stained serial sections is shown. Figure 6b displays a zoom-in into the cortical region of the brain sample. Immunohistochemistrystained amyloid-beta plaques appear as brownish deposits. Neuritic plaques appear as red accumulations when labeled with Congo red and were investigated with brightfield microscopy (see Figure 6c). This staining was performed for each brain sample as well. Evaluating the same Congo red stained section using polarization-contrast microscopy, neurritic plaques exhibit an apple-green color with a characteristic cross shape as displayed in Figure 6d.  
For a direct comparison of OCT and state-of-the-art histology, the number of amyloid-beta plaques was evaluated for intensity-based OCT, PS-OCT and the corresponding histological amyloid-beta stainings in five regions of one patient. In the immunohistochemistry stained histological section, 240 \(\pm\) 64 plaques/mm\({}^{2}\) were counted. Of these plaques, 33.5% (\(\vartriangle\) 79.2 \(\pm\) 20.8 plaques/mm\({}^{2}\)) were classified as cored/dense and 66.5% (\(\vartriangle\) 160.8 \(\pm\) 42.8 plaques/mm\({}^{2}\)) as diffuse. In the corresponding OCT intensity and retardation images, plaque densities of 34 \(\pm\) 10 plaques/mm\({}^{2}\) and 148 \(\pm\) 32 plaques/mm\({}^{2}\) were measured, as shown in Figure 7a. The diameters of the diffuse and dense plaques annotated in histological sections were evaluated and plotted in the histogram shown in Figure 7b. Representative images of a dense plaque (diameters ranged from 4.8 \(\upmu\)m to 25.5 \(\upmu\)m) and a diffuse plaque (diameters ranged from 6.9 \(\upmu\)m to 83.6 \(\upmu\)m) are shown in Figure 7c,d, respectively. The histology data showed that significantly fewer plaques are visualized by OCT (\(p<0.01\)), and they appeared significantly bigger (\(p<0.01\)).  
Figure 6: Histological analysis of Alzheimer’s disease (AD) human brain samples. **(a)** Three-dimensional reconstruction and rendering of 180 immunohistochemistry stained serial sections. **(b)** Zoom-in into a plaque-rich cortical region where brown staining indicates amyloid-beta deposits. **(c)** Congo red stained section imaged by bright-field microscopy. Plaques appear as reddish accumulations. Red circles indicate neuritic plaques. **(d)** Congo red staining investigated by polarized light microscopy. A neuritic plaque can be observed in characteristic apple-green color and cross-shaped form.
## 3 Results
### Differential Scanning Calorimetry Analysis  
The endothermic DSC thermograms showed that the melting points of the glyceryl monostearate (solid lipid), poloxamer 407, SBO-NLCs, and blank NLCs were 73.79, 54.45,  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Terms**}} & \multicolumn{1}{c}{\multirow{2}{*}{**Size**}} & \multicolumn{1}{c}{\multirow{2}{*}{**\(p\)-Value**}} & \multicolumn{1}{c}{\multirow{2}{*}{**PDI**}} & \multicolumn{1}{c}{\multirow{2}{*}{**\(p\)-Value**}} & \multicolumn{1}{c}{\multirow{2}{*}{**ZP**}} & \multicolumn{1}{c}{\multirow{2}{*}{**\(p\)-Value**}} & \multicolumn{1}{c}{\multirow{2}{*}{**EE *}} & \multicolumn{1}{c}{\multirow{2}{*}{**\(p\)-Value**}} \\ \cline{2-2} \cline{6-9}  & **Size** & & & & & & & & \\ \hline \%Wax (A) & 27.1940 & 0.0003 & 0.0131 & 0.1083 & \(-\)5.9460 & 0.0070 & 0.0154 & 0.2426 \\ \%Surfactant (B) & \(-\)22.0925 & 0.0013 & 0.0246 & 0.0083 & \(-\)2.2804 & 0.0646 & \(-\)0.0009 & 0.9063 \\ \%PEG400 in Surfactant (C) & \(-\)3.7271 & 0.4733 & \(-\)0.0071 & 0.3566 & \(-\)5.5408 & 0.0008 & \(-\)0.0141 & 0.0212 \\ \%Wax \(\times\) \%Surfactant (A \(\times\) B) & \(-\)7.3729 & 0.2856 & \(-\)0.0271 & 0.0240 & \(-\)0.8950 & 0.5380 & \(-\)0.0349 & 0.0210 \\ \%Wax \(\times\) \%PEG400 (A \(\times\) C) & & & & & & \(-\)0.0817 & 0.0010 \\ \%WaxT \(\times\) \%PEG400 (B \(\times\) C) & \(-\)5.9812 & 0.3816 & \(-\)0.0441 & 0.0017 & 0.3883 & 0.7872 & 0.0561 & 0.0040 \\ \%Wax \(\times\) \%Wax (A)2 & \(-\)13.8050 & 0.0225 & & & & & \(-\)0.0604 & 0.0016 \\ \%Surfactant \(\times\) \%Surfactant (B) & & & & & & 0.6302 & 0.5890 & \(-\)0.0615 & 0.0015 \\ \%PEG400 \(\times\) \%PEG400 (C) & & & & & & & \(-\)0.0316 & 0.0166 \\ \%Wax \(\times\) \%Surfactant \(\times\) \%PEG400 (ABC) & & & & & & 0.0908 & 0.0007 \\ \%Wax \(\times\) \%Surfactant (A \(\times\) \%Surfactant (A \(\times\) B) & & & & & 5.5077 & 0.0343 & \(-\)0.0116 & 0.0023 \\ \%Wax \(\times\) \%Wax \(\times\)PEG400 (A \(\times\) C) & & & & & & 0.0363 & 0.0688 \\ \hline \hline \end{tabular} PDI: Polyldispersity index; ZP: Zeta potential; EE: Entrapment efficiency. * Logarithm transformation was implemented to assess the EE.  
\end{table}
Table 4: The estimated variables for the SBO-NLC preparation and its properties.  
Figure 4: The response surface plot presents the effects of wax, surfactant, and PEG percentage in the surfactant on the entrapment efficiency of the SBO-NLCs. (**a**) Interaction of wax and PEG400 percentage in the surfactant. (**b**) Interaction of surfactant and PEG400 percentage in the surfactant.
## 3 Experimental Design and Implementation
### Experimental Procedure
#### 3.3.4 Path Identification Experiment  
Path identification is the core technical component of this study, with the experimental process as follows:  
1. OD point addition: Adding original OD points at road network edges based on network topology, adding supplementary OD points for anomalous flow segments, and generating a complete node (intersection + OD point) list.
2. Parameter optimization experiment: Determining regular polygon radius candidate values (25 m and 49 m) based on sampling spatial distance distribution, running the path identification algorithm for each parameter setting, counting anomalous vehicle numbers, and selecting optimal parameters.
3. Path extraction execution: Running the routeInferMain() main function, including the following sub-processes:makePolygon2(): Generating regular polygons for each node  
- nodeDigger(): Determining node assignment for each trajectory point  
- routeInfer(): Extracting vehicle path sequences  
- routeDiagnose(): Diagnosing and processing anomalous paths  
(4) Result validation: Checking the reasonableness of path identification results, analyzing the distribution of nodes traversed by vehicles, and analyzing causes of anomalous paths. Quantitative evaluation of path inference accuracy was conducted by analyzing the proportion of successfully identified vehicle paths. With the optimal 49-m polygon radius, 7252 out of 7317 vehicles (99.1%) had their paths successfully identified, while 65 vehicles (0.9%) contained only single trajectory points and were excluded from analysis due to insufficient trajectory information for meaningful path reconstruction.
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 3 Lab-in-a-Box (LiB) Architecture
### Modular System Architecture
#### 3.1.2 Sensors  
Sensors are devices that provide a quantified value of the surrounding environment such as temperature, humidity, pressure, distance, touch, etc. Sensors are the most varied element in IoT systems as different applications require different sensors. A sensor can vary from a simple limit switch to complex micromechanical accelerometers. They are used for detecting events, monitoring changes in environmental parameters and relating changes in physical characteristics to functions. Sensors are used everywhere from farms to rockets sent to outer-space. At the first layer, these sensors are interfaced to a low-power embedded processor to capture and parse raw data. As each sensor provides data in a unique format and the context of application might be different, the sensors first convert the sensed phenomena (e.g., temperature) into an equivalent electric voltage or current. A simple thermistor changes its resistivity with changes in the ambient temperature and with regression analysis, a relationship between the temperature and the corresponding resistance can be developed. Most analog sensors provide either changes in resistance or voltage to indicate changes in environmental parameters. This analog voltage is converted to a digital value by an Analog-to-Digital Converter (ADC) in microcontrollers. Digital sensors have small onboard microcontrollers and/or other circuitry built into them that provides digitized data and communicates via protocols such as Serial Peripheral interface (SPI), Inter-Integrated Circuit (I2C), Universal Asynchronous Receiver/Transmitter (UART), and Universal Serial Bus (USB).
# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
## 4 Defense against Adversarial Attacks on Sentiment Analysis  
In recent years, different studies and researchers have proposed several methods for dealing with the new threats of adversarial examples for text applications models. Such defenses aim to deal with modified inputs, seeking to identify and discard them in order to mitigate their negative impact on the model's results. Until now, defensive methods have focused mainly on implementing techniques such as data augmentation and adversarial training or incorporating methods that identify changes in the inputs; these approaches use knowledge of the attack process to intentionally generate adversarial examples that models can learn from to identify and discard possible modifications. Figure 9 illustrates the general operation of actual defenses against adversarial examples.  
General functioning of defenses against adversarial examples consists of the following:  
1. **Modification control investigates the differences between legitimate and adversarial texts.** Frequently, textual adversarial examples present notable differences compared to the original input texts. For example, adversarial texts may use character-level modifications, in many cases consisting of misspelled words. Consequently, researchers have used spell checkers to identify modified text inputs as a means of detecting this type of adversarial text [40; 41]. However, modifications made by substitution with synonyms cannot be detected by these types of defenses.
2. **DNN models can be improved to strengthen them against adversarial attacks.** This defense includes modifying architectures to improve security and working with a training set with known parameters [42].  
One fundamental purpose of generating effective adversarial examples is to use them to improve the robustness of existing models [8]. There are two principal strategies in text applications models to achieve this goal, namely, adversarial training and distillation. These strategies are briefly described in the following sections. For more comprehensive reviews of defense strategies for text applications, please refer to [43; 44].  
Figure 9: General defense mechanism within a sentiment analysis model. The aim of the defense mechanism is to identify and discard modified inputs in order to mitigate their negative impact on model results.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## 4 Results and Discussion
### Performance of Dual-Frequency PPP
For instance, the convergence of quad-constellation b1/b2 GIM-constrained PPP is obviously improved by 40.6% from 13.88 to 8.21 min, compared with the GPS-only solution. The performance of the IF and UC PPP approaches are generally consistent for the equivalence of two models. Introducing different  
Figure 6: Comparison of positioning error of b1/b2 dual-frequency PPP with different schemes for GPS, GLONASS and GPS/Beidou/GLONASS/Galileo solutions at station SEYG (DOY 246/2018, Kp \(\approx\) 1.92). The corresponding satellite numbers and PDOP values are also shown. ionospheric models as constraints will reduce the convergence time of PPP schemes in different levels. We can see that the improving average convergence performance for GLONASS-only PPP with ionosphere constraints is the lowest compared with other solutions, where the performances of part stations like the results of Figure 6 may even deteriorate, which is primarily attributed to the existing GLONASS pseudo-range IFBs. The results for dual-frequency ionosphere-constrained GLONASS PPP achieved by [13] is slightly different, which is mainly attributed to different GLONASS code observation precision. In three ionosphere-constrained approaches, the GIM-constrained PPP has the best convergence performance and NTCM-constrained PPP performs better than the BIM-constrained solution. For quad-constellation b1/b2 PPP schemes, the improvement in the convergence time of BIM-, NTCM- and GIM-constrained is 5.2%, 6.2% and 8.5%, respectively, compared with the UC solution. A small but not obvious improvement can be seen in the positioning accuracy with the ionosphere constraint, mainly due to the relatively higher weighting after convergence. The GIM-constrained PPP has the best positioning accuracy in the different schemes. Figure 8: Average convergence time for GPS-only, Beidou-only, GLONASS-only, Galileo-only, GPS/Beidou, GPS/GLONASS, GPS/Galileo, GPS/Beidou/GLONASS and GPS/Beidou/GLONASS/ Galileo dual-frequency PPP collected at ten stations over thirty days. Figure 7: Time series of the estimated receiver DCB at station SEYG (DOY 246/2018).
## 3 Materials
### Signal Segmentation  
To ensure dataset balance, it is necessary to guarantee that the recording durations for each subject used in the study are roughly equivalent. Therefore, all records with durations shorter than 8 min were excluded. Subsequently, we segmented the PPG and ABP recordings into several signal segments using a 10-second window. Blood pressure systolic and diastolic pressures represent the maximum and minimum pressures exerted on the blood vessel walls during heart contraction and relaxation, respectively. The representativeness of these two indicators makes them crucial parameters for assessing an individual's cardiovascular health and developing treatment plans. We processed each ABP signal frame using a peak and valley detection algorithm to extract SBP and diastolic blood pressure (DBP). SBP is defined as the amplitude of the main peak in a complete ABP signal cycle, while DBP is the amplitude of the trough adjacent to and preceding the main peak. To avoid interference from extremely high or low blood pressure in extracting general relationships from PPG-BP, we excluded records where systolic or diastolic blood pressure fell outside the normal range. After screening, we retained record segments with SBP values between 80 mmHg and 180 mmHg and DBP values between 60 mmHg and 130 mmHg. Following these processing steps, we obtained a total of 270,488 sample data points. Each data point contains an 8-second PPG signal and the corresponding SBP and DBP values. The distribution of SBP and DBP in the dataset is shown in Figure 2.  
Figure 1: The processing result of the PPG signal is processed using the VMD algorithm.
## 4 Simulation Results
### Daily Power Profiles  
Figures 5 and 6 present the daily power profiles for a representative day in summer (1 July) and winter (1 December). The graphs show the PV energy generation, battery power, and load power for each hour of the day.  
During the daytime in the summer months, the PV energy production follows a bell-shaped curve, peaking around noon when solar irradiance is highest. The RL-based MPPT method achieves the highest peak power, followed by the ANN-based and traditional methods. The battery power profile indicates that the battery is charged during the morning and early afternoon, when PV production exceeds the load demand. The RL and ANN methods ensure efficient charging, while the conventional method results in slower charging rates. In addition, the load power remains constant throughout the day, with the excess PV energy stored in the battery for use during nighttime.  
During the day in winter, PV energy production is significantly lower due to reduced solar irradiance and shorter sunlight hours. The RL-based MPPT method still achieves the highest energy generation, demonstrating its adaptability to low-irradiance conditions. The battery power profile shows that the battery is discharged throughout most of the day  
Figure 4: Monthly PV energy generation and efficiency gains.  
to meet the load demand, as PV production is insufficient. The RL and ANN methods minimize battery discharge losses, while the conventional method results in deeper discharges, potentially reducing battery lifespan. The daily power profiles highlight the importance of advanced MPPT methods in optimizing energy generation and storage under both summer and winter conditions.
## 10 Conclusions  
In this paper, the statistical performance of the DPD estimator presented in [29] is analytically studied when array model errors are present as well as signal waveforms are not available. The theoretical analysis begins with a matrix eigen-perturbation result, which can express the perturbation of eigenvalues as a function of the disturbance added to the Hermitian matrix. Then, the first-order asymptotic expression of the localization errors is given, from which the analytical expression for the MSE of the DPD estimator is obtained. Besides, the closed-form expressions for the calculation of the probabilities of a successful localization are also deduced, which can offer another theoretical perspective on the study of the localization accuracy. Additionally, the obtained probability formula can be used to provide a new criterion to estimate the radius of CEP. Finally, the CRB expressions for the position estimation are derived for two cases: (a) array model errors do not exist, and (b) array model errors are present and are drawn from Gaussian distribution. Several simulation experiments are performed to confirm the usefulness of the obtained results. The experimental results show that the uncertainties in the model of the array manifold can seriously deteriorate the source location accuracy of the DPD method. Therefore, our future work is to present a new DPD method that is expected to be more robust against the array model errors.  
The author would like to thank all the anonymous reviewers for their valuable comments and suggestions which vastly improved the content and presentation of this paper. The author also acknowledges support from National Natural Science Foundation of China (Grant No. 61201381 and No. 61401513), China Postdoctoral Science Foundation (Grant No. 2016M592989), the Self-Topic Foundation of Information Engineering University (Grant No. 2016600701), and the Outstanding Youth Foundation of Information Engineering University (Grant No. 2016603201).  
[PERSON] wrote the manuscript and [PERSON] helped with the writing, data analysis, and publication process. [PERSON] and [PERSON] was in charge of the experiment and its results. All authors have contributed to the scientific discussion.  
The authors declare no conflict of interest.
# The YouGovern Secure Blockchain-Based Self-Sovereign Identity (SSI) Management and Access Control
## 5 Experimental Evaluation
### Security Threat Analysis and Mitigations  
In addition to role-based access controls and DID compliance, YouGovern incorporates design strategies to defend against several high-severity threats common in decentralized identity systems. These include:  
* Sybil Attacks: Although the current version of YouGovern does not implement on-chain identity weighting mechanisms, access to verification actions is restricted to registered validators and approved smart contract roles, limiting the scope of Sybil vector exploitation.
* Phishing and Smart Contract Spoofing: To prevent impersonation via malicious contracts, YouGovern uses contract whitelisting, ensuring users only interact with signed and verified entities. Smart contract addresses are transparently published and verifiable via BscScan.
* MetaMask Spoofing and UI Manipulation: The front-end integrates security cues such as address checksums, signer origin warnings, and verification badges for known DIDs to help users distinguish trusted connections. Enhanced UX patterns reduce the chance of user consent errors during transaction signing.
* Reentrancy and Input Validation: All critical functions, especially those involving state changes (e.g., identity revocation or access grants), are protected with OpenZeppelin's ReentrancyGuard. Inputs are sanitized, and all authorization flows follow a checks-effects-interactions pattern to mitigate runtime manipulation.