esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physical-Chemical Characterization of the Waste Vitreous Enamels
#### 3.1.2 Results of X-ray Structural Analysis (XRPD)  
In order to reveal the main mineralogical composition of waste vitreous enamel, phase analysis was performed by X-ray powder diffraction analysis. Waste vitreous enamels from the process of manufacturing heating devices (sample WEP and WETM) were sampled four times (batches 1-4). The obtained mineralogical compositions are presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Batch**} & \multicolumn{6}{c}{**Content, mg/kg**} \\ \cline{2-10}  & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**Maximum**} \\  & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**Values for**} \\  & \multicolumn{2}{c}{**WEP**} & \multicolumn{2}{c}{**WETM**} & \multicolumn{2}{c}{**WETM**} & \multicolumn{2}{c}{**WEP**} & \multicolumn{2}{c}{**WETM**} & \multicolumn{2}{c}{**WETM**} & \multicolumn{2}{c}{**Disposal**} \\ \hline Mo & **449.5** & 4.5 & **439.5** & 4.5 & **399.5** & 3.5 & **387.5** & 5.5 & 10 *30 **  
Based on XRPD analyses, quartz (silicon oxide, ICSD No. 174) was identified as a dominant mineral in both groups of samples. Furthermore, small amounts of the oxides of metals Sb, Ti, Fe and Zn, appear in the form of supporting minerals such as semarmontite (antimony oxide, ICSD No. 1944), TiO\({}_{2}\) (titanium oxide, ICSD No.657748), ZnFe\({}_{2}\)O\({}_{4}\) (zinc diiron(III) oxide, ICSD No. 24496) [47] and hematite (iron(III) oxide--alpha, ICSD No. 22505), were as well identified in waste vitreous enamel. The presence of metal oxides in WETM samples is more pronounced than their amounts in WEP samples.
## 4 Simulation Experiment Design  
### Construction of Large-Scale Dynamic Traffic Scenario  
The simulation, which was conducted using large-scale dynamic traffic scenarios that closely resembled real-world traffic environments, can effectively reflect the performance of V2X standards. Constructing such scenarios is crucial in ensuring the simulation accuracy of such research. The first simulation scenario that we constructed was the grid road scenario. The parameters of road networks and traffic flows used to construct this scenario are derived from [33], where the hourly traffic flow data are as shown in Figure 5a. Each section of the road in this scenario measures 5000 m.  
Figure 4: Correction effect of partial data transmission modes.  
Figure 5: Road network and traffic flow of large-scale dynamic traffic scenarios.  
To observe the performance of V2X standards in more complex traffic environments, we also created a geometric road scenario adjusted to replicate the city of Sioux Falls. This scenario, proposed in [34], is widely utilized across traffic demand prediction, traffic flow distribution, and traffic network optimization, making it a standard benchmark in assessing traffic algorithms. The parameters of the road network node coordinates and OD demands used to construct this scenario come from [35]. The serial numbers of nodes in the road network are shown in Figure 5b, and each lane is set as a 4-lane dual carriageway.
# Honey Therapy in Diabetic Foot Ulcers: A Promising Strategy for Effective Wound Healing
## 4 Honey's Antimicrobial Properties
### Non-Peroxide Antimicrobial Activity  
#### 4.2.1 _Acidity_ and pH  
Honey usually presents an acidic environment with pH values ranging between 3.2 and 4.5 [97,107] due to the formation of gluconic acid from glucose catalysis by glucose oxidase [108] As with its other characteristics, honey's acidity also depends on its botanical origin [15]. Low-pH environments have demonstrated effectiveness in inhibiting the activity of microorganisms, and in honey this seems to be one of the most important characteristics for chronic wound healing [109], since such wound promote a neutral to slightly alkaline environment that is more favorable to microbial infection [18,110]. In fact, the optimal pH range for microorganism growth is from 6.5 to 7.5, meaning honey's acidity can drastically inhibit the microbial proliferation [74]. The evidence suggests that after neutralizing H\({}_{2}\)O\({}_{2}\), MGO, and defensin 1 (Def-1), honey's low pH was the responsible factor acting against _B. subtilis_[79]. Honey's acidic environment is also related to increases in oxygen release from hemoglobin in the capillaries [67], stimulating fibroblast migration and proliferation and collagen organization, and at the final step accelerating wound healing [111].
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
### The Precipitation Radar (PR)  
TRMM satellite carried three primary instruments, Precipitation Radar (PR), TRMM Microwave Imager (TMI) and Visible and Infrared Scanner (VIRS). It also carried Lightning Imaging Sensor (LIS) and Clouds and the Earth's Radiant Energy System (CERES).  
PR was the first space-borne precipitation radar in the world [(2; 3; 4; 5)]. It operated at a frequency of 13.8 GHz (Ku band) or a wavelength about 2.2 cm. PR scanned perpendicularly to the satellite orientation with a scanning angle of 17\({}^{\circ}\) and had an observation range between 36\({}^{\circ}\) S-36\({}^{\circ}\) N. The swath width was 247 km with a horizontal resolution at the ground 5 km after the boost while it was 215 km with 4.3 km at the launch. The vertical detection range was from the ground surface up to a height of about 20 km with a high range resolution of 250 m (Figure 1). PR had a minimum detectable equivalent reflectivity factor of 18 dBZ [(6)], and through rigorous calibrations, it had reflectivity error within 1 dBZ [(7; 3; 8; 9)]. In addition, the PR system noise level was also quite stable with a periodic change less than 0.15 dB [(10)].  
Due to the satellite platform, PR had a view of the whole tropical region from the space and did not suffer from surface constraints like the curvature of the earth and beam blocking for ground-based radars (GR). As an active remote sensing instrument, PR could achieve all-weather and all-day observation of rainfall with 3D high-resolution quantitative measurement. With these advantages, PR played a crucial role in TRMM's goals such as measurements of precipitation and latent heating [(2)].
# Study on Surface Roughness of Sidewall When Micro-Milling LF21 Waveguide Slits
## 4 Conclusions  
This paper used a GMDH neural network to predict the surface roughness of sidewalls when micro-milling LF21 waveguide slits. The present work included the estimation of the optimal values for the process factors, such as spindle speed and feed per tooth, while the surface roughness was taken as the output. Experiments were conducted to verify the accuracy of the model; the maximum relative error was 21.22% and the average relative error was 13.92%. The obtained results prove the ability of the GMDH method for micro-milling modeling. In the final validation of the predictive models, through another machining test, the GMDH model helped us to find that the external criterion and threshold selection had a great influence on model accuracy. This paper's contributions are its influence on breaking conventional thinking by confirming that milling is not the only option and that micro-milling can be considered during the machining of waveguide slits. Within a specific range of cutting parameters, we argue that the spindle speed had a more significant effect on the surface roughness of the sidewall in the slit than the feed per tooth. We have confirmed that the GMDH can be used to predict the surface roughness of the sidewall when micro-milling a waveguide slit. It is encouraged to further refine the range of cutting parameters and to seek the optimal combination to meet the requirements of the sidewall when micro-milling waveguide slits, to ensure processing efficiency.  
The shortcomings of this paper are that the effects of the extended length of the tool and the single cutting depth in the axial direction for the surface roughness of sidewall are not considered.  
Conceptualization, X.L. and P.H.; methodology, P.H.; software, P.H.; validation, Y.L.; formal analysis, Y.L.; investigation, X.S.; data curation, Y.L., J.Q. and Y.Z.; writing--original draft preparation, P.H.; writing--review and editing, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the National Natural Science Foundation of China (Grant No. 51875080), the Fundamental Research Funds for the Central Universities (Grant No. DUT20 ZD204) and the Dalian Science and Technology Innovation Fund (Grant No. 2020 JJ26 GX041). The financial contributions are gratefully acknowledged.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Evaluation
### Wilcoxon Test  
After obtaining and visually comparing the results, we performed a Wilcoxon signed-ranks test [62] to assess whether there is a significant difference between the results of the different classifiers or not. We used this test because it is recommended in cases where the same models or subjects are assessed under more than one different condition. It is also recommended when what is being assessed is the definite numeric scores instead of nominal values [63]. The Wilcoxon signed rank test has a null hypothesis \(H_{0}:M_{1}=M_{2}\) and an alternative hypothesis \(H_{1}:M_{1}\
eq M_{2}\), where \(M_{1}\) and \(M_{2}\) are the datasets that are being compared. We first have to subtract the values of one dataset from the other dataset, that is, \(D=M_{1}-M2\). After that, we have to rank the absolute values of \(D\) in ascending order, that is, the smallest value of \(|D|\) is number 1, and the highest value of \(|D|\) is number \(n\), where \(n\) is the number of values in the datasets. We then add all the positive values of \(D\) obtaining \(T_{+}\) and all the negative values of \(D\) obtaining \(T_{-}\). We obtain the Wilcoxon statistic using the following formula:  
\[W_{stat}=min(T_{-},T_{+})\]  
We obtain the Wilcoxon critical value \(W_{crit}\) using the Wilcoxon signed-ranked test quantiles table. After that, we compare \(W_{crit}\) and \(W_{stat}\). If \(W_{stat}<W_{crit}\), then we reject the null hypothesis, meaning that there is a statistical difference between the datasets. On the other hand, if \(W_{stat}>W_{crit}\), we accept the null hypothesis, meaning that there is no statistical difference between the datasets. Table 7 shows the mean of the scores obtained by each model per database. The values used for the Wilcoxon test were the results obtained per partition, for each database. Table 8 shows the results obtained from the test. When comparing the models, we could see that the logistic regression classifier together with the Ensemble of BOWs and Handcrafted features performed better on some databases. Nevertheless, according to the Wilcoxon test performed, there is no significant difference between the F1 score and AUC of the sentiment-based representation together with PBC4 cip. The results also show that there are classifiers and representations with which there is a significant difference. However, the difference proves an advantage of using sentiment-features with PBC4 cip over the other classifiers. Moreover, PBC4 cip provided patterns that explain the decisions  
Figure 6: A comparison plot of the F1 score for the best proposed representation using PBC4 cip, and other classifiers found in the literature. taken by the classifiers. The features that are taken more into consideration by the model to classify a social media post as depressive or not depressive are as follows:  
* The emotions of sadness, excitement, anger, and boredom in the text. * The polarity of the text, especially whether the text is neutral or not. * The subjectivity of the text.
## 5 Experimental Evaluation
### Evaluation Metrics  
The following metrics are used to assess model performance and system behavior:  
* Accuracy (Acc): Ratio of correctly classified instances to total instances.
* F1-Score (F1): Harmonic mean of precision and recall, suitable for imbalanced data.
* False Positive Rate (FPR): Measures the rate of benign traffic misclassified as anomalies.
* Inference Latency: Average time per instance for local anomaly detection (on-device).  
Figure 2: Federated learning setup for Edge-FLGuard+. Each smart home edge node runs a lightweight anomaly detection model on-device. Model updates are communicated over 5G to a central FL coordinator using the FedAvg algorithm. Optional differential privacy mechanisms may be applied before aggregation to preserve user confidentiality.  
* Communication Overhead: Volume of data exchanged per federated round (in KB or MB). These metrics jointly capture the trade-off between security efficacy and system efficiency.
###### Abstract  
Several studies show that impedance spectroscopy is a suitable method for online battery diagnosis and State-of-Health (SoH) estimation. However, the most common method is to model the acquired impedance spectrum with equivalent circuits and focus on the most sensitive parameters, namely the charge-transfer resistance. This paper introduces first a detailed model of a battery cell, which is then simplified and adapted to the observable spectrum behavior. Based on the physical meaning of the model parameters, we propose a novel approach for SoH assessment combining parameters of the impedance spectrum by building the ratio of the solid electrolyte interphase (SEI) resistance to the total resistance of SEI and the charge transfer. This ratio characterizes the charge-transfer efficiency at the electrodes' surfaces and should decrease systematically with SoH. Four different cells of the same type were cycled 400 times for the method validation, and impedance spectroscopy was performed at every 50 th cycle. The results show a systematic correlation between the proposed ratio and the number of cycles on individual cell parameters, which build the basis of a novel online method of SoH assessment.  
electrochemical impedance spectroscopy; lithium batteries; State-of-Health; charge transfer; solid electrolyte layer Article
## 2 The Establishment of Individuality Indexes System for DSER
### Selection of Individuality Indexes
#### 2.2.2 The Individuality Indexes of EV and ES  
1. Reliability The reliabilities of EV and ES in a certain region are quantified and evaluated in terms of two aspects: power outage time and number of system failures. Such two indexes are represented by the power outage time in one year in a region and the number of failures in the year.
2. Peak shaving capacity Realizing peak shaving and valley filling is one of the important goals for developing EV and ES. Therefore, Peak shaving capacity is an important index for evaluating EV and ES, and it is quantified and evaluated in terms of two aspects: battery capacity and charging and discharging speed, correspondingly, the definitions of the indexes are shown in Table 5.  
* \(B_{41}\)--Total battery capacity in a certain region
* \(B_{42}\)--The average time required for a battery to be fully charged of battery once
3. Discharge performance A cycle means the ES system undergoes a charge-discharge process. Discharge performances of EV and ES in a certain region are quantified and evaluated in terms of two aspects: cycle life and charge and discharge efficiency, correspondingly, the definitions of the indexes are shown in Table 6.  
* \(B_{51}\)--The number of cycles the ES system can withstand before retired
* \(E_{r}\)--Energy released by an ES system after charging
* \(E_{i}\)--Initial storage energy of ES system
# Study on Molybdenum-Rhenium Alloy Ultrasonic Resonance Temperature Sensor
## 2 Materials and Methods
### System Construction  
The practical application of the proposed resonant sensor under ultra-high temperature conditions was investigated in the virtual environment of COMSOL finite element simulation [27] software to explore the impact of the resonator's dimensions on the sensor's performance. A 3D model was first constructed in SolidWorks and was then imported into COMSOL for further analysis. The molybdenum-rhenium alloy rod in the model was configured with a diameter of 2 mm. The resonator structure shown in Figure 3 was positioned 19 mm from the end of the waveguide to effectively observe the propagation of sound waves.  
The ultrasonic resonant temperature-sensing system consists of a molybdenum-rhenium alloy waveguide rod, a magnetostrictive transducer, an oscilloscope, a resonator (serving as the sensitive element), a frequency meter, a tungsten-rhenium calibration furnace, and a pulse transceiver circuit. The schematic diagram of the constructed temperature measurement experimental system is shown in Figure 4. The machined ultrasonic resonator coupled with the magnetostrictive transducer was placed in the constant temperature area of the calibration furnace, and the temperature measurement system was adjusted by an oscilloscope and a frequency meter so that the acoustic wave transmitted inside it could generate resonance. The magnetostrictive transducer transmitted the acoustic signal and collected the ultrasonic resonance echo signal to record the resonance frequency at room temperature.  
Figure 4: Schematic diagram of ultrasonic resonant temperature-sensing system.
## 4 Conclusions  
In this study, the interfacial bonding strength between carbon fibers and epoxy resins of the composite test specimens with added HNTs was measured. The added HNT was expected to enhance the interfacial bonding strength between carbon fibers and epoxy resins. The conclusions of this study are as follows.  
Figure 8: Scanning electron micrographs of EP/CF/HNT composites.  
Figure 7: A graph of the relationship between the theoretical tensile strength, the experimental tensile strength, and the interlaminar fracture toughness of CFRP according to the HNT content.  
1. The addition of HNT reinforced the tensile strength of the composite material, and the tensile strength had a greater effect on the bonding force between the fiber and the resin than the effect on the resin.
2. Where the added amount of HNTs was up to 3 wt %, the tensile strength of the carbon fiber-reinforced composite material increased, but for higher content than that, strength decreased. In the case of the addition of higher content, this is considered to be due to the cohesive strength of HNTs decreasing the stress concentration.
3. The addition of HNTs showed a steady increase in the strength until the addition of 3 wt % of nanoparticles, but after that the ILSS decreased with increasing HNT content. This is probably because of the inherent nature of HNTs, where an increase in content leads to agglomeration of the HNTs, decreasing the strength values due to local stress concentration.
4. The size of GIIC due to the addition of HNT increased up to 3 wt %, but decreased thereafter. With increasing HNT content, the aggregated part due to the dispersion of HNT is locally acting as a stress concentration, decreasing the fracture energy.
5. According to the GIIC size based on the interlaminar fracture toughness evaluation of HNT, the nanoparticles with very low cohesive strength are thought to have the highest interlayer energy at ~3 wt %, and the content of nanoparticles depends on the cohesive force. The strengthening effect was insignificant according to the addition amount because of the reduction in the fracture energy.  
This research was supported by the Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology (KIAT) through the IDEA FACTORY Research Grant of N0001410.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Survey of Blockchain-Based Applications for IoT
## 5 Data Protection and Privacy
### Cryptography
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **Type** & **Key Features** & **Applicability to the IoT** \\ \hline ECDSA & ECC & Compact keys and signatures, based on ECC & Commonly used in the IoT \\ \hline EdDSA & ECC & Faster and more secure variant of ECDSA & Faster than ECDSA \\ \hline RSA & Integer Factorization & Based on the difficulty of factoring large integers & Rarely suitable for the IoT due to large key sizes \\ \hline \end{tabular}
\end{table} 
Table 4: Cryptographic algorithm. Figure 7: Digital signature process. uplolds data privacy, immutability, and traceability without relying on external authority. In this setup, a dual blockchain system generates public and private keys for IoT devices, enabling secure and mutually authenticated communication across the network [35]. An alternative approach involves using a lightweight certificate; for example, it can be combined with a self-signing mechanism, which is then stored on the blockchain by an LRA [25]. Quantum computing poses a significant threat to traditional blockchain security, necessitating advanced protective measures. One approach is to use Dual-Factor Quantum-Safe Blockchain (DFQSB) architecture to mitigate these risks. By integrating post-quantum cryptography (PQC) and quantum key distribution (QKD), this model ensures secure blockchain transactions against potential quantum attacks, particularly in IoT environments [36]. [PERSON] et al. proposed the Dilithium-5 algorithm as a robust post-quantum digital signature scheme designed to secure IoT devices against quantum attacks. It is based on lattice-based cryptography. The algorithm is particularly suited for low-power devices due to its efficient key generation, signing, and verification processes. The integration of hardware acceleration further enhances performance, making it feasible for resource-constrained IoT environments [37].