esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
## Appendix A
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RT** & **Compound Name** & **Abundance Mean** \\ \hline
20.970 & 3-tert-Butyl-5-chloro-2-hydroxybenzophenone & 26,089 \\
21.017 & 1-Benzamido-N-benzyl-1-[alpha.-(2-pyridylthio)benzylidene]acetamide & 43,002 \\
21.020 & Bis[4-acetamidophenylsulfonyl]phenyl methane & 43,423 \\
21.105 & 1,2,5-Oxadiazol-3-amine, N-cyclopropyl-4-[5-(trichloromethyl)-1,2,4-oxadiazol-3-yl]- & 31,391 \\
21.172 & Oxalic acid, allyl decyl ester & 48,664 \\
21.714 & 5-Hydroxy-2,4,4-trimethyl-cyclopentane-1,3-dione & 28,464 \\
21.726 & Tetradecane, 4-ethyl- & 49,899 \\
22.143 & 3-Ethyl-1,5-ocadiene & 65,512 \\
23.217 & 2-Norbonanone, 6-chloro-3,3-dimethyl-, exo- & 32,271 \\
23.230 & 1,2-Dihydrodinalool & 82,631 \\
23.265 & 2-Propenyl-3-vinyloxirane & 35,236 \\
24.021 & 2,6-Octalen-1-ol, 2,7-dimethyl- & 827,705 \\
24.036 & Linalyal acetate & 886,873 \\
24.803 & 2H-Pyran, 2-(3-butynyloxy)tetrahydro- & 25,758 \\
26.907 & Malonic acid, bis(2-trimethysilylethly ester & 52,354 \\
27.985 & 1,8-Dichlorooctane & 25,086 \\
28.235 & Methylylene asparagine & 26,750 \\
28.269 & 2-Pyrroldinolene, 1-methyl- & 85,192 \\
28.271 & 3,3,3-Trifluoro-N-(2-fluorophenyl)-2-(trifluoromethylomethyl)propionamide & 31,186 \\
30.675 & Oxalic acid, allyl noryl ester & 26,882 \\
31.325 & alpha.-Terpineol & 63,064.5 \\
31.374 & 3-Methyl-2-methylene-5-oxopyrrolidine-3-carbonitrile & 31,392 \\
31.400 & trans-2,7-Dimethyl-3,6-ocatedien-2-ol & 26,447 \\
33.244 & trans,cis-2,6-Nonadeni-1-ol & 25,713 \\
33.895 & Propranololic acid, propyl- & 35,831 \\
34.580 & 5-(3,7-Dimethylocta-2,6-dienyl)-4-methyl-2,3-dihydroiophene 1,1-dioxide & 22,011 \\
34.904 & 2H-Pyran-3-ol, 6-ethenyltetrahydro-2,2,6-trimethyl- & 25,268 \\
36.573 & Allyl heptanoate & 22,998 \\
37.752 & Heptanoic acid & 56,211.5 \\
37.880 &.beta.-Myrence & 29,088 \\
38.160 & 2,5-Pyrrolidineione, 1-ethyl- & 62,446 \\
38.341 & Benzene, [(2-propenyloxy)methyl]- & 72,902 \\
38.344 & Benzyl alcohol & 84,220 \\
39.227 & Phenylethyl Alcohol & 46,827 \\
40.794 & Ethanone, 1-(1H-pyrrol-2-yl)- & 29,239.5 \\
42.939 & 2-Butenediamide, 2-methyl-, (E)- & 24,382 \\
45.070 & Benzylamine, N-(3-chloro-2,2-dimethyl-1-phenylpropylidene)- & 25,983 \\
45.373 & Cyclopropapacarboxylic acid, cyclohexymethyl ester & 23,678 \\
46.290 & 2-Amino-4-dimethylaminomethylepentanedinitrile & 30,668 \\
47.859 & 2(4H)-Benzofuranane, 5,6,7,7a-tetrahydro-4,4,7a-trimethyl- & 35,368 \\
48.860 & 2H-Pyran-2-one, 5,6-dihydro-4-(2-methyl-3-methylene-1-buten-4-yl)- & 76,496 \\
48.921 & 3H-1,2,4-Triazole-3-thione, 2,4-dihydro-4-phenyl- & 36,542 \\
49.912 & 4-Piperidinepropanoic acid, 1-benzoyl-3-(2-chloroethyl)-, ethyl ester & 44,931 \\
52.555 & 1,2,3-Butanetitol & 25,538 \\
53.001 & Oxetane, 2-methyl-4-propyl- & 30,545 \\
56.669 & 2,5-Furandione, dihydro-3-methylene- & 29,961 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results for the EBL sample fingerprint determination with indication of the retention time (RT) and abundance mean for every identified compound.
# Performance Analysis of Two-Hop mmWave Relay Nodes over the 5G NR Uplink Signal
## 2 System Model and Basic Assumptions
### D&F Signal Model
Then, considering the data processing at the gNodeB by means of \(\mathbf{M}_{\text{DR}}\) and \(\mathbf{T}_{\text{BB}}\), where the estimated received signal at the destination can be given as  
\[\mathbf{\hat{S}} =\mathbf{T}_{\text{BB}}^{\text{H}}\mathbf{M}_{\text{DR}}^{\text{ H}}(\mathbf{H}^{2}\mathbf{\hat{X}}+\mathbf{N}_{\text{DF}}^{2})\] \[=\mathbf{G}^{\text{H}}\mathbf{H}^{2}\mathbf{R}_{\text{RT}}\mathbf{ \tilde{S}}+\mathbf{N}_{\text{D}}\quad, \tag{9}\] \[=\mathbf{\tilde{S}}+\mathbf{N}_{\text{D}}\]  
where \(\mathbf{N}_{\text{D}}=\mathbf{T}^{\text{H}}\mathbf{N}_{\text{DF}^{\prime}}^{2} \), \(\mathbf{T}=\mathbf{T}_{\text{BB}}\mathbf{M}_{\text{DR}}\), and \(\mathbf{R}_{\text{RT}}=\mathbf{M}_{\text{RT}}\mathbf{R}_{\text{BBT}}\). In (9), the first term is the desired signal and the second term is the noise, which represents the noise in the backhaul link at the decode-and-forward (D&F) co-operative system, respectively.
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 3 Results and Discussion
### Mechanical Properties and Deformation Stability of Soils
#### 3.2.3 Volume Stability Test  
The volume stability test results of SL, SSL, and SSLC are shown in Figure 12. For SL, SSL and SSLC, the water immersion expansion rates were 1.51%, 0.87% and 0.14%, which meet the requirements of less than 2% in the Chinese specification (GB/T 24175-2009), suggesting they all have good volume stability. By comparison, it can be seen that the volume expansion rate of SSL was 42% lower than that of SL, and the volume expansion rate of SSLC was 91% lower than that of SL. Therefore, it was obvious that the reduction of the volume expansion rate led to a good stability control effect. For practical engineering applications, SL with a shorter aging time or slightly greater expansibility can be reduced by adding intact soil and cement to improve its volume stability.
## 3 Results
### Springback Results  
Figure 16 shows the simulationvs. experimental results of the springback studied in this work. As described in Section 2.3.1, relative springback displacement from a reference plane show in Figure 8 is plotted vs. the position of the measured point in the perimeter of the part. The abscissa shows the perimeter of the edge. The origin is the left bottom corner of the top view (A) shown in Figure 7. The plot is divided in four zones delimited by the background color corresponding with the front, right, back and left edges of the part.  
Experimental data contain the average displacement value of every measured point and the standard deviation for that point in the bunch of parts for the same experiment. Two experimental datasets are plotted, 50 t BHF and 200 t BHF, for the sake of clarity. The 100 t and 150 t results follows the behavior of 50 t and 200 t but with interpolated results.  
The amount of springback was reduced as the BHF is increased, as shown in the experimental results described in Figure 16. For 50 t BHF, at the front edge, springback develops a displacement of 4 mm, while for 200 t, the displacement is reduced to 1.4 mm. Simulation shows 4.9 mm displacement for 50 t BHF and 2.1 mm for 200 t.  
Figure 15: Bending moment: 50 t (**A**), 100 t (**B**), 150 t (**C**), and 200 t (**D**).
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 4 EMF Exposure Level and 5G Technologies
### EMF-Aware Radio Resource Management
Deploying additional gNBs increases costs and power consumption, but reconfigurable intelligent surfaces (RISs) offer a cost-effective alternative to enhance mmWave coverage. Despite their potential, most studies on RIS-assisted 5G networks prioritize SE or EE, neglecting EMF exposure. Few studies address EMF-aware designs for RIS-enhanced environments. The few studies investigating EMF exposure in 5G networks assisted by RISs are summarized in Table 5. In [107], passive and active RISs were compared for signal-to-interference-plus-noise ratio (SINR) performance, with active RISs demonstrating superior SINR improvements. However, this study did not evaluate EMF exposure differences, highlighting a significant research gap.
## 3 Results and Discussions
### Effect of Rotation Angle of Square Inclusions  
The effect of the orientation of the square inclusion was investigated by varying the rotation angle of the square inclusions from \(\theta\) = 0\({}^{\circ}\), 20\({}^{\circ}\), 45\({}^{\circ}\), 70\({}^{\circ}\) to 90\({}^{\circ}\) for different inclusion numbers, as shown in Figure 4a. The bandwidths of the bandgap in the phononic crystal with different inclusion numbers were plotted against the rotation angle \(\theta\) for different filling fractions from 0.1 to 0.49 in Figure 4b.d. Due to the square shape of the inclusion, the simulation results show that the bandwidth versus rotation angle is symmetrical, as compared to the results between 0\({}^{\circ}\) to 45\({}^{\circ}\) and 45\({}^{\circ}\) to 90\({}^{\circ}\). For the FF < 0.49, the bandwidth of the phononic bandgap shows a negligible change with an increase in the rotation angle of inclusions. Except when the rotation angle is 45\({}^{\circ}\), the bandwidths of the bandgaps of the phononic crystal are increased owing to the reduced periodicity of square inclusions.  
When the FF is near 0.49, the bandwidth significantly decreases at a rotation angle of 45\({}^{\circ}\) due to extremely narrow gaps among inclusions. In Figure A2a,b of Appendix A, the LBF and UBF versus rotation angles of the four-square inclusion composite, for different filling fractions from 0.4 to 0.45, are presented. The simulation results show that the bandwidth of the bandgap is decreased with a decrease in the FF, mainly due to the reduction in the UBFs. The elastic vibration at the UBF is related to the Bragg scattering [43], as indicated by the eigenmode analysis in the previous section. When the spacings among square inclusions are reduced with an increasing FF, the eigenmode caused by the Bragg scattering shifts to a lower frequency (higher wavelength).
## 6 Conclusions and Future Work  
The optimization results suggest that the combination of VLEO (300 km) and LPWAN technologies, particularly LoRa and NB-IoT, offers a highly effective balance between service quality and economic efficiency. Our analysis, based on link margin calculations, confirms that VLEO (300 km) constellations provide superior performance in terms of revisit time, data transmission volume, and satellite count.  
However, the current study does not fully account for the unique physical-layer characteristics of each communication technology--such as NB-IoT's high bit rate, LoRa's long-range capabilities, and Mioty's scalability for massive IoT connectivity. Future work will address these limitations by incorporating detailed physical-layer simulations and exploring multiple altitude levels. Additionally, experimental validation in real-world environments will be pursued to refine our models further and ensure that the optimized configurations are both practically viable and cost-effective for NTN-based IoT systems.  
Conceptualization, C.L.; Methodology, C.L.; Software, C.L. and T.K.; Validation, C.L.; Formal analysis, C.L.; Data curation, C.J.; Writing--original draft, C.L.; Writing--review & editing, C.L.; Visualization, C.J.; Supervision, Z.Y.; Project administration, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2025 Korea Aerospace University Faculty Research Grant.  
Data Availability StatementData are contained within the article.  
This research was supported by the Distributed Space Systems Laboratory (OctoLab) at Korea Aerospace University. The authors would like to express their sincere gratitude to [PERSON] for his invaluable guidance throughout the study and also appreciate the support and collaboration provided by the lab members.  
The authors declare no conflicts of interest.
# Machine Failure Prediction Using Survival Analysis
## 4 Materials and Methods
### Types of Machine Failure
#### 4.3.2 Heat Dissipation Failure (HDF)  
Heat dissipation failure occurs when there is insufficient cooling of the machining process, leading to an increase in the temperature of the tool and workpiece. This can cause deformations, cracks, or even melting of the material being machined. HDF is particularly important in high-speed machining operations, where the heat generated can be significant and quickly damage the tool or the workpiece.
# Antimicrobial and Antibiofilm Properties of Bioceramic Materials in Endodontics
## 5 Potential Supplements to Enhance Antimicrobial Properties of Bioceramics
### Nanoparticles  
The addition of nanoparticles to endodontic materials [89] has been reported to improve their direct and diffusible antimicrobial properties and antimicrobial activity in denitual tubules [90]. A novel synthesis of high-loaded antimicrobial drug-silica co-assembled particles (DSPs) were recently developed through co-assembly of silica and octenidine dihydrochloride [91]. It has been reported to enhance the antimicrobial performance of EndoSequence BC sealer [61].  
Engineered bioactive chitosan nanoparticles have been shown to effectively inactivate bacterial biofilm and disrupt its extracellular polymeric matrix [90]. They were reported to eliminate mono-species and multi-species biofilms [92] in a time-dependent interaction and present a potential antimicrobial/anti Biofilm agent for root canal disinfection [93; 94]. Carboxymethyl chitosan has been reported as a surface modifier of dentin matrix to enhance antibacterial efficacy [95]. Engineered chitosan-based nanoparticles, as a bioactive biopolymer, might alter the host inflammatory response of macrophages and promote healing [96]. Chitosan has been widely incorporated in biological scaffolds for bone regeneration [97; 98] and tissue engineering [99; 100]. [PERSON] et al. [23] incorporated chitosan nanoparticles into the zinc oxide-eugenol sealer and the mixer inhibited biofilm formation within the sealer-dentin interface. Given their high biocompatibility and wide application in bioengineering, chitosan nanoparticles should have further potential to be used as a supplement to different endodontic bioceramics.  
Other nanoparticles, including silver nanoparticles [101] and bismuth lipophilic nanoparticles [102] were also used in endodontics and showed antimicrobial activities against single-species biofilms. However, the contribution of their cytotoxicities to the bioceramic materials is concerning and requires further investigation.
# Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality Assurance
## 4 Privacy-Preserving System Design
### Video Retrieval  
In order to retrieve and display a previously recorded video, it has to be decrypted on the documentation system. For this, all members of at least one group must consent to the decryption of the video by disclosing their respective key share. Figure 6 shows the process of a video decryption in 4 Crypt.  
Figure 5: 4 Crypt video encryption process.  
At first, the worker that has been recorded authenticates by holding his smartphone on the NFC-reader and selecting the video that should be displayed. This retrieves the video-specific, encrypted key shares of all necessary group members from the key store. To prevent a modifying attacker from foisting a faked video on the worker (c.f. Section 5), his key share \(k_{w}\) is decrypted and its signature \(\sigma_{w}\) is verified on the smartphone. Similarly, each member of the selected group (usually a set of trusted representatives) must decrypt their respective key share \(k_{j}\) by holding their smartcards on the NFC-reader as well. Once all group members including the recorded worker have consented to the video disclosure in that way, the group access key \(gak\) can be restored by XORing all decrypted key shares. Then the video file key \(\forall k\) is retrieved from the key store and decrypted using the group access key. Finally, the video frames are decrypted using the video file key and shown on the connected display.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Other Signals
#### 3.4.3 Engine Speed  
[PERSON] et al. [44] explored the possibility of engine knock detection based on small variations in the engine speed that can be associated with knock. Their experiments were conducted on a single-cylinder, air-cooled, port-fuel-injection spark-ignition engine at 3000 rpm with wide-open throttle conditions. [PERSON] et al. extracted the speed oscillations using a high-pass filter. The study found that the amplitude of speed oscillations correlated with the knock intensity. Hence, through precise measurement of the engine speed, they found that they could detect knock in the limited operating conditions used in their study.
# Surface Defect Detection of Rolled Steel Based on Lightweight Model
## 3 Methods and Principles
### The Improved Network
#### 3.2.3 The EIoU Function  
In the defect detection process, the algorithm will generate multiple prediction frames for the position of the same defect, so it is necessary to filter the redundant prediction frames to make the prediction output close to the actual value. In YOLOv5, CIoU is used as an indicator to measure the degree of coincidence of two borders, which takes the overlapping area, center point distance, and aspect ratio into consideration, which speeds up the convergence of the model to a certain extent. However, when the width, height, and aspect ratio of the prediction box and the actual box are linearly related, the width and height of the prediction box cannot be increased or decreased simultaneously, which will hinder the effective optimization of the model.  
Based on CIoU, \(EloU\)[37] disassembles the influence factors of the aspect ratio of the predicted frame and the actual frame and calculates the length and width of the predicted frame and the actual frame, respectively, solving the problems of CIoU and improving the model performance. Its definition formula is shown in Equation (10):  
\[L_{EIOU}=L_{IOU}+L_{dis}+L_{asp}=1-IOU+\frac{\rho^{2}(o,\sigma^{8t})}{c^{2}}+ \frac{\rho^{2}(k,kg^{st})}{C_{w}^{2}}+\frac{\rho^{2}(g,ggt^{t})}{C_{h}^{2}}, \tag{10}\]  
where \(L_{IOU}\), \(L_{dis}\), \(L_{asp}\) represent overlap loss, center distance loss, and width and height loss, respectively. _IOU_ represents the ratio of intersection and union between predicted boxes and ground truth boxes. \(o\), \(\sigma^{8t}\) represent the center point of the predicted box and the actual box, respectively. \(k\), \(kg^{st}\) represent the width of the predicted box and the actual box, respectively. \(g\), \(g^{gt}\) represent the height of the predicted box and the actual box, respectively. \(\rho\) represents the Euclidean distance calculation. \(C_{w}\), \(C_{h}\) are the width and height of the minimum bounding box covering the predicted and ground-truth boxes.
## 5 Results
### Vision-Based Versus Prompt-Based Approach: Results  
#### 5.2.1 Perceived Accuracy  
Questions 1 through 2 asked the participants to rate their perceived accuracy via each approach of the automatic hierarchy method. As shown in Figure 6a, for Q1, participants showed no statistically significant difference between the two approaches (\(Z=-1.27\), \(p=0.20\)), with the vision-based (\(M=4.33\), \(SD=1.83\)) and prompt-based (\(M=5.08\), \(SD=1.24\)) approaches being relatively similar. For Q4, participants showed no statistically significant difference between the two approaches (\(Z=-0.63\), \(p=0.53\)), with the vision-based (\(M=4.33\), \(SD=2.06\)) and prompt-based (\(M=4.83\), \(SD=1.40\)) approaches being relatively similar.
# A Review of Medication Adherence Monitoring Technologies +
## 4 A Review of Medication Adherence Monitoring Systems
### Fusion-Based Systems
#### Visual-Sensor Systems  
Assistive living techniques have been used to track medication intake based on the patient's activity. One example is iMEC, that has been developed by [PERSON] and [PERSON] [88] for medicine timing and pill taking detection. Some home appliances (refrigerator, microwave oven, chair, and bed) have been attached with ubiquitous sensors for predicting the behavior of the patient. A medicine case equipped with a camera has been used for detecting pill removal. Eventually, the blend of data from these devices were used for confirming medication adherence. Another example of monitoring medication adherence for people with dementia is presented in [89]. A pillbox equipped with sensors and LEDs, and a Kinect were used in this system. The pillbox is used such that it detects two actions, opening of the compartment and taking medication, and closure of the compartment. Meanwhile, the Kinect is fixed near the medication compartment to detect two activities, bringing hand to mouth and bringing water cup to mouth. Hence, the pillbox uses magnetic reed sensors to detect compartment opening and closing, while the LEDs are used to notify the patient about the correct medication compartment. The skeletal tracking functionality provided by Kinect is used for retrieving the coordinates of the multiple joint positions of the patient, including head and hands. Finally, the authors evaluated the system using 20 subjects, with which a recognition accuracy ranged from 92% to 100% was achieved depending on the patient's facing angle with respect to the Kinect.
## 4 Experiments
### Pressure Ulcer Segmentation
#### 4.3.3 Results  
The \(T_{sim}\) training for pressure ulcer segmentation used the MWD and AZH datasets. The \(T_{sim}\) row in Table 6 indicates that pressure ulcer segmentation using transfer learning with a similar image performed better overall than the baseline. Because wound images are very similar to ulcer images, they provide increased training data. Therefore, during transfer learning with a similar wound image, the DSC performance was very high.  
The \(T_{ssl}\) experiment for pressure ulcer segmentation performed self-supervised learning using the MWD and AZH datasets with the labels removed. In pressure ulcer and wound images, as well as skin lesion images, it was difficult to predict the arrangement of normal skin parts. When pressure ulcer segmentation was trained using the weights transferred with \(T_{ssl}\), the accuracy was degraded.  
Because the purpose of pressure ulcer segmentation is to segment RoI, the \(T_{roi}\) experiment was slightly modified to distinguish pressure ulcers from the wound and ulcer images. The baseline model for pressure ulcer segmentation was U-Net. The U-Net encoder was trained to classify wounds and ulcers by combining the encoder with U-Net. As seen in Table 6, the performance after \(T_{roi}\) transfer learning was worse because the weight trained to distinguish ulcers and wounds was not helpful in training segmentation, as it learned only local features instead of the overall shape.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Experiment** & \multicolumn{3}{c}{**Metrics**} \\  & **Acc (\%)** & **IoU (\%)** & **DSC (\%)** \\ \hline Baseline & 98.5 & 99.9 & 62.0 \\ \(T_{sim}\) & **98.9** & **99.9** & **93.3** \\ \(T_{ssl}\) & 87.2 & 99.8 & 63.0 \\ \(T_{roi}\) & 91.4 & 91.2 & 91.2 \\ \(T_{multi}\) & 85.4 & 87.2 & 21.0 \\ \hline Ensemble & **99.0** & **99.9** & **93.4** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Transfer learning results for pressure ulcer segmentation.  
Figure 9: Pressure ulcer image examples from the DGU, MWD, and AZH datasets, respectively.  
\(T_{multi}\) for pressure ulcers was also evaluated with pressure ulcer classification and segmentation, similar to skin lesion multi-task learning, but the DSC performance was inferior to the single-task baseline model training. Therefore, multi-task learning may not be suitable for segmenting and classifying lesions in skin images, such as skin lesion images or pressure ulcer images.  
When ensemble learning was performed with different initial weights using \(T_{sim}\), which had a higher performance than the baseline, it had the highest accuracy, IoU, and DSC. Therefore, it was most effective to use transfer learning with similar images in the application of learning a global shape, such as pressure ulcer segmentation.