esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Technologies' Risks and Opportunities: Case Study of an RFID System
## 3 X.0 Technologies
### Radio-Frequency Identification (RFID)  
Radio Frequency IDentification (RFID) is a wireless communication technology that, using tags (fully or partially passive or active), antennas and a host computer or middleware, captures data and links it to different attributes such as serial number, position, colour, date of purchase and other information about the entity carrying the Tag, through a data collection process based on electromagnetic waves exchange between RFID tags and RFID readers.  
Communication performances, interaction with other supply chain information systems and level of standardization of this new concept have known an increasing success during the last few years [77; 78; 79]. Indeed, it became omnipresent in our daily life routines such as ticketing, payment, passports, car keys, etc. RFID systems vary one from another depending on the involved frequencies: micro-waves, Ultra High Frequencies (UHF), High Frequencies (HF), Low Frequencies (LF), which define the read ranges [80]. In the industrial  
Figure 5: Cloud computing characteristics and models.  
field, it offers better visibility and tractability in logistical and Manufacturing processes [77], unitary identification and low-cost tags [80], with real-time data collection and sharing [81]. Furthermore, RFID technology, by redesigning the inter and intraorganizational business processes that, could lead to the emergence of the so-called \"smart processes\", which are processes that can initiate business transactions without any human interventions and, therefore, ease the integration of inter- and intraorganizational information systems [78, 79].  
Regardless of the remarkable evolution of this technology, many risks are still threatening its implementation success, such as data security and privacy that can be easily hacked or captured by hidden readers as data transmission is ensured through air interface [77]. In addition to that, RFID requires extravagant equipment, which ends up with a high cost that may not be covered by some companies [82], especially since it is constraint by a limited channel bandwidth [83] and by material to use since communication cannot be ensured efficiently through metals or liquids [84]. This includes a lack of technical and financial support and a lack of skilled experts [85].
# Part-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model
## 4 Experimental Studies
### Comparison with Classic Machine Learning Models
In contrast, on the immunotherapy dataset, which is a typical imbalanced sample dataset, the DNM-FL outperformed the DNM-MSE, achieving values of 0.8654, 0.9265, 0.8755, 0.9101, 0.9165, and 0.7965 for accuracy, sensitivity, specificity, precision, F1 score, and AUC value, respectively. In comparison, the DNM-MSE achieved values of 0.8404, 0.9221, 0.8437, 0.8874, 0.9019, and 0.7770 for the same six metrics. Both the DNM-MSE and DNM-FL outperformed the other six comparison classifiers in all metrics, except for sensitivity. The DNM-FL performed the best among all the comparison classifiers. Importantly, the improvement in specificity reflects the improvement in correctly predicting negative samples. The FL function effectively addressed the issue of the DNM's inability to correctly identify negative samples when the number of negative samples was small. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Classifier** & **Parameter** & **Setting** \\ \hline \multirow{2}{*}{MLP} & Number of layers & 3 \\  & Number of neurons in hidden layer & 100 \\ \hline Bayes & Assumption of distribution & Gaussian \\ \hline \multirow{3}{*}{SVM} & Kernel & RBF \\  & Penalty parameter & 0.5 \\ \hline Ada & Number of estimators & 100 \\ \hline KNN & Number of neighbors & 5 \\ \hline DT & Maximum depth & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The hyperparameter settings of the six machine learning models. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Dataset** & **Classifier** & **Accuracy** & \(p\)**-Value** & **Sensitivity** & **Specificity** & **Precision** & **F1** & **AUC** \\ \hline \multirow{8}{*}{Cryotherapy} & MLP & 0.8071 & 0.000015 & 0.8572 & 0.8693 & 0.7830 & 0.8087 & 0.8084 \\  & Bayes & 0.7023 & 0.00002 & 0.7756 & 0.7633 & 0.7166 & 0.7288 & 0.7943 \\  & SVM & 0.8738 & 0.133519 & 0.8156 & 0.8443 & 0.9429 & 0.8683 & 0.9338 \\  & Ada & 0.8857 & 0.503208 & 0.8863 & 0.8961 & 0.9064 & 0.8914 & 0.9609 \\  & KNN & 0.8119 & 0.000312 & 0.7946 & 0.8002 & 0.8663 & 0.8180 & 0.9004 \\  & DT & 0.8464 & 0.001133 & 0.8865 & 0.8867 & 0.8385 & 0.8555 & 0.8452 \\  & DNM-MSE & 0.8894 & 0.411553 & 0.8651 & 0.8850 & 0.9212 & 0.8900 & 0.9450 \\  & DNM-FL & 0.9012 & - & 0.8964 & 0.8919 & 0.9302 & 0.9068 & 0.9630 \\ \hline \multirow{8}{*}{Immunotherapy} & MLP & 0.7880 & 0.000043 & 0.9930 & 0.8172 & 0.7926 & 0.8797 & 0.6729 \\  & Bayes & 0.7845 & 0.000012 & 1.0000 & 0.8258 & 0.7845 & 0.8779 & 0.5294 \\  & SVM & 0.8107 & 0.002203 & 0.9845 & 0.8561 & 0.8202 & 0.8925 & 0.6922 \\  & Ada & 0.7928 & 0.000294 & 0.8938 & 0.8461 & 0.8552 & 0.8697 & 0.7759 \\  & KNN & 0.7857 & 0.000026 & 0.9324 & 0.8226 & 0.8269 & 0.8722 & 0.7004 \\  & DT & 0.8011 & 0.001825 & 0.8770 & 0.8441 & 0.8725 & 0.8709 & 0.7163 \\  & DNM-MSE & 0.8404 & 0.046729 & 0.9221 & 0.8437 & 0.8874 & 0.9019 & 0.7770 \\  & DNM-FL & 0.8654 & - & 0.9265 & 0.8755 & 0.9101 & 0.9165 & 0.7965 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the results of the machine learning models.
## 2 Materials and Methods
### Reversed-Phase Liquid Chromatography (RP-LC)  
The lignan contents of the selected hot-pressed sesame oil samples were analyzed by reversed-phase liquid chromatography (RP-LC). Sesamin, sesamolin, asarinin, and sesamol were used as the lignan standards (purity \(>\) 97%). The lignan stock solutions (200 \(\upmu\)g/mL in methanol) were prepared individually. The working standard solutions were prepared by diluting the stock solutions. All solutions were stored at \(-\)20 \({}^{\circ}\)C in the dark. Calibration curves were constructed using the working standard solutions prior to the analysis of sesame oil samples.  
The lignan concentration was determined by the external standard method. For RP-LC analysis, 0.5 g of sesame oil was weighed and dissolved in 8 mL of methanol. The mixture was first vortexed for 2 min, centrifuged at 6000 rpm for 3 min, and then filtered through a 0.22 \(\upmu\) nylon syringe filter (ANPEL Scientific Instrument Co., Ltd., Shanghai, China). For each sample, 20 \(\upmu\)L of the filtered solution was used for analysis. RP-LC was performed with an Agilent 1200 HPLC system coupled with a UV/Vis detector (Agilent Technologies Inc., Santa Clara, CA, USA). A Venusil XBP C18 column (5 \(\upmu\)m, 250 \(\times\) 4.6 mm)was used. The mobile phase was methanol/water (in a volume ratio of 8-2), at the flow rate of 1 mL/min. The column temperature was 30 \({}^{\circ}\)C. The absorption wavelength was 287 nm.
# A Sustainable Polygeneration System for a Residential Building
## 2 Materials and Methods
### Economic Analysis
These investment and installation costs for each technology have been taken from the bibliography of similar studies and are also compiled in Table 6.  
\[TI_{PS}=C_{PV}+C_{PVT}+C_{INV}+C_{ST,DHT,CWT}+C_{SP,BP}+C_{BB}+C_{HP/SEAC}+C_{RO/ MED} \tag{7}\]  
\[OC_{RS}=E_{D}\cdotp p_{E,p}+\left(\frac{Q_{SH}}{\eta_{Q}}+\frac{Q_{DHW}}{\eta_{ Q}}\right)\cdotp_{NG}+\frac{Q_{CL}}{COP_{R}\cdot\eta_{E}}\cdotp p_{E,p}+W_{D} \cdotp_{W} \tag{8}\]  
\[OC_{PS}=E_{FG}\cdotp p_{E,p}-E_{TG}\cdotp p_{E,s}+E_{B}\cdotp_{E,b}+OM_{PV,PVT, BB,HP/SEAC,RO/MED} \tag{9}\]  
The right side of Equation (7) includes the investment costs of all the devices composing the system: PV field, PVT field, inverters, solar, demand hot water and cooling water tanks, solar and boiler pumps, biomass boiler, selected cooling technology (HP or SEAC), and chosen desalination technology (RO or MED). To focus on the comparative analysis of alternative production systems, the cost of the distribution system to the dwellings is not included, nor is the assembly of the central drive system itself. In Equations (8) and (9), to provide the operating costs of the compared alternatives, the terms \(\mathrm{P_{E,P}}\), \(\mathrm{P_{NG}}\), and \(\mathrm{P_{W}}\) are the purchase cost of electricity, natural gas, and water from their respective grids. On the other hand, \(\mathrm{P_{E,s}}\) is the electricity price of surplus electricity, \(\mathrm{E_{B}}\) is the energy supplied by the biomass boiler, \(\mathrm{P_{E,b}}\) is the purchase energy cost of biomass pellets, and \(\mathrm{OM_{x}}\) are the operating and maintenance costs of the x technology. As an additional economic indicator, the levelized costs of each demand (\(\mathrm{LCO_{x}}\)) have also been estimated. Taking into account that some driving technologies provide energy for several demands, heat (and even electricity) and distribution factors (\(\mathrm{f_{E,x}}\) and \(\mathrm{f_{Q,x}}\)) based on the electrical or thermal energy required for each need concerning the total produced by the system must first be estimated. As an example, the estimation of the \(\mathrm{LCO_{W}}\) is shown. Note that it is one of the most complex demands to assess at the tail end of the integrated system. The total cost, O&M, and energy required for the desalination plant also consider the upstream equipment's fractional costs, with the distribution factors described above. \[LCO_{W}=\frac{\left[C_{w}+\sum_{n=1}^{25}OM_{w}\cdot\left(1+r\right)^{-n} \right]}{\sum_{n=1}^{25}E_{w}\cdot\left(1+r\right)^{-n}} \tag{10}\]  
In Equation (10), \(\mathrm{C_{W}}\) is the total investment cost associated with water, \(\mathrm{OM_{W}}\) is the total cost for O&M associated with water, r is the rate of return, and \(\mathrm{E_{W}}\) is the total energy required for water, depending on the seawater desalting facility adopted. Finally, the internal rate of return (IRR) and the net present value (NPV) of the investment have also been estimated as fundamental economic indicators.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 5 Conclusions  
Our results show that urbanization has been the main driver of LULC changes in the city of Maseru, Lesotho, between 1988 and 2019. Our results have validated the value of Landsat data with spatial variables and the MOLUSCE model, which effectively simulated and predicted the LULC changes in Maseru city. Based on these results, we concluded that:  
1. Landsat products provide satisfactory results in classifying and mapping LULC changes from 1988 to 2019. The overall accuracy ranged from 88% to 95%, with kappa values between 0.84 and 0.94.
2. Remarkable LULC changes occurred in Maseru from 1988 to 2019, with the built-up area increasing from 15.3% to almost half (48%) of the city, much of which consumed pristine classes such as agricultural lands and grasslands.
3. In 2050, built-up areas are projected to increase further, while the area covered by agricultural land, bare soil, water bodies and woody vegetation is expected to decrease.  
Overall, our study provides useful insights for land management authorities in the city of Maseru, Lesotho, so that proactive planning strategies can be formulated to achieve sustainable development. Our results are also valuable for implementing the Maseru 2050 urban plan. Using high-resolution imagery such as unmanned aerial vehicles (UAV)-drone-derived LULC is recommended to provide more detailed spatial data for urban planning. However, the use of UAVs is limited by the large area, big data processing capacity, and legal regulation of UAV operations. Future studies should consider economic factors in the simulation of LULC in 2050 and beyond and also incorporate the latest deep-learning methods.  
Conceptualization, N.E.M. and E.A.; methodology, validation, and formal analysis N.E.M.; writing--original draft preparation, review and editing, N.E.M., E.A. and S.X.; visualization, S.X.; supervision, E.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available upon request.  
The authors would like to thank the United States Geological Survey (USGS) for processing and providing Landsat data. The authors thank the reviewers for their constructive feedback.  
The authors declare no conflict of interest.
# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 4 Experiments
### Baselines  
To evaluate the proposed method's performance in foreign object detection on coal conveyor belts, we selected several baseline models known for their innovative optimizations in detection accuracy and robustness. These models have made significant contributions to the field of object detection.  
* AutoAssign (2020) [18] adopted a differentiable label assignment mechanism, dynamically adjusting the label assignment strategy during training to improve the effectiveness and robustness of object detection.
* Sparse-RCNN (2021) [22] introduced learnable proposals, reducing the number of candidate boxes while maintaining high efficiency in object detection, enabling end-to-end training and inference.
* Deformable DETR [27] incorporated a deformable attention mechanism to greatly reduce the computational complexity of Transformer and enhance detection capabilities for objects at different scales.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Camera 1**} & \multicolumn{2}{c}{**Camera 2**} & \multicolumn{2}{c}{**Camera 3**} & \multicolumn{2}{c}{**All**} \\ \cline{2-9}  & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** \\ \hline Big coal & 330 & 125 & 594 & 334 & 678 & 280 & 1602 & 739 \\ Wood & 81 & 32 & 70 & 39 & 60 & 24 & 211 & 95 \\ Iron bars & 103 & 46 & 330 & 148 & 456 & 192 & 889 & 194 \\ Iron mesh & 87 & 31 & 227 & 74 & 206 & 94 & 520 & 199 \\ \hline Total & 601 & 234 & 1221 & 595 & 1400 & 590 & 3222 & 1227 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Statistics.  
Figure 6: Four types of foreign objects on coal conveyor belts: big coal, wood, iron bars, and iron mesh.  
* VarifocalNet (2021) [10] mainly introduced an IoU-aware loss function, improving the model's performance in predicting the confidence of candidate boxes and thus enhancing the accuracy and stability of object detection.
* CentripetalNet (2020) [21] utilized high-quality keypoints at the center of objects to achieve more accurate object detection, especially in keypoint detection and object localization.
* O2F [17] proposed a \"one-to-one\" label assignment strategy, which dynamically adjusts the number of labels for each prediction, optimizing the label matching process and significantly improving the accuracy and robustness of end-to-end object detection.
* CEASC [19] introduced the Adaptive Sparse Convolutional Network (ASCN), combined with a global context enhancement module, which greatly accelerated and improved the speed and accuracy of object detection.
* AFPN [20] presented the Asymptotic Feature Pyramid Network (AFPN), which gradually fuses feature pyramids in stages to enhance feature representation quality and improve object detection performance, with a significant boost in small object detection.
## 2 Optical Coherent Detection Technique
### Optical Heterodyne Detection Technique  
The optical heterodyne detection scheme consists of acousto-optic modulators (AOM) in order to generate an optical frequency shift. An acousto-optic effect is generated with an acoustic wave into a lead molybdate crystal. The acoustic wave causes a change in the refractive index, which is periodic in nature. This generates an effect analogous to a moving diffraction grating along the material, with the site spacing equal to the wavelength of the acoustic wave. An AOM cell, as shown in Figure 4, is constructed using a piezoelectric transducer to introduce the sound waves into the transparent material. It creates a diffraction grating in the material which has a spacing equal to the order of the wavelength of the acoustic wave. A piezo-electric transducer is used to create the acoustic wave into the lead molybdate crystal. In AOM, the angle of incidence satisfies [PERSON]'s condition for constructive interference. The period of spacing created by the acoustic wave is given by \(\wedge\). In order to satisfy [PERSON]'s condition, the incident angle (\(\theta\)) is given by,  
\[\theta=\sin^{-1}\biggl{(}\frac{\lambda}{2n\wedge}\biggr{)} \tag{17}\]The optical heterodyne technique makes use of modulation from the AOM frequency signal, as shown in Figure 5. The input signal with frequency \(f_{s}\) is combined with the local oscillator signal with frequency \(f_{LO}\) by using a 3 dB beam combiner. The combined frequency of the optical signal remains the same (\(f_{s}-f_{LO}\)) in both orthogonal arms. The AOM modulates the optical signal in one arm with modulation frequency \(f_{m}\). The optical beating of one signal with frequency \(f_{s}-f_{LO}-f_{m}\) and the other signal with frequency \(f_{s}-f_{LO}\) will result in the direct detection of modulation frequency \(f_{m}\) at the photo receiver [30].
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Electroactive Polymer (EAP)  
EAP is a specific polymer having the ability to change its sizes and shapes by stimulating an electric field. By converting the elastic potential energy into mechanical energy, EAP can enable soft robots to move. EAP exhibits the advantages of large deformation, high energy density, and a lightweight and compact structure. It is therefore a promising driving material to actuate soft robots [57; 59; 108]. There are different actuation mechanisms of EAP, including polarisation, molecular phase transition, and mass/ion transport. The polarisation mechanism is driven by dielectric elastomers and piezoelectric polymers. The molecular phase transition mechanism is activated by shape memory polymers and liquid crystal elastomers. The mass/ion transport mechanism is mainly used to activate some specific materials, such as gels and conductive polymers [109]. From the material perspective, EAP can be generally divided into two categories according to their actuation mechanisms, that is field-activated [110] and ionic [111]. Dielectric elastomers, liquid crystal elastomers, electro-strictive polymers, polymer electrets, and ferroelectric polymers belong to the field-activated category. Ionic polymer-metal composites, ionic gels, carbon nanotubes, and conductive polymers belong to the ionic category [112; 113]. Both of the two categories show remarkable performance in their respective applications. Below are some EAPs that are commonly used in the development of soft robots.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 5 Results
### Simulation Results
Figure 8: The DL total throughput for eMBB UEs versus number of eMBB UEs. (**a**) \(\Gamma_{be}^{min}>\Gamma_{bl}^{min}\). (**b**) \(\Gamma_{bl}^{min}>\Gamma_{be}^{min}\). Figure 10 indicates the DL latency per uRLLC as the minimum DL packet size for uRLLC UE increases. This figure proves that the proposed algorithm performs better than the other approaches. Our proposed algorithm proves that each uRLLC packet can be sent in less than 1 msec, regardless of whether it is associated with MBS or SBS, based on the transmission time (backhaul and RAN transmission time), waiting pairing time, and HARQ time. This figure shows that the proposed H-NOMA approach to all algorithms performs better than DAA-PUNC and OMA. The simulation illustrates promising gains of up to 95% and 80% in latency improvement for uRLLC traffic compared to \"OMA\" and \"Puncturing\" schemes, respectively, as shown in Table 7. \begin{table}
\begin{tabular}{c c} \hline \hline
**Schemes** & **Outage Probability** \\ \hline DAA-NOMA & 98.6\% \\ DAA-OMA & 92.9\% \\ DAA-PUNC & 94.6\% \\ EAA-NOMA & 96.7\% \\ EAA-OMA & 94\% \\ GHA-NOMA & 96.7\% \\ GHA-OMA & 93.9\% \\ MAX-SINR-NOMA & 98.6\% \\ MAX-SINR-OMA & 93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Outage Probability for eMBB Traffic. Figure 10: The DL average throughput for eMBB UEs versus offered load uRLLC. Figure 9: The outage-probability for eMBB UEs versus number of eMBB UEs.
## 5 Result and Discussion
### Performance Evaluation with KU-BdSL Dataset  
We evaluated the proposed model with two variants of KU-BdSL datasets. The proposed system achieved 98.66% accuracy for validation and 98.20% accuracy for testing for the non-segmented dataset. The same accuracy was achieved for validation and testing in the segmentation case, which is 99.60%. Table 6 shows the performance accuracy of the proposed model for training, validation and test dataset.  
Table 7 shows the performance accuracy of the proposed model for training, validation and test dataset. The table shows our system with non-segmented images achieved 98.66% accuracy for validation and 98.20% accuracy for the test dataset. It also shows the performance accuracy of the proposed method using a segmented dataset that is 99.99% accuracy for validation and 99.60% accuracy for the testing dataset.  
Figure 11 shows the class-wise sign word recognition bar chart for our proposed model with USLD and MSLD variants of the KU-BdSL dataset. By observing the class-wise performance, we can say our model produced high-performance accuracy for all the classes, excluding three classes for both variants of the KU-BdSL datasets. Our model produces good accuracy for all classes and correctly classified almost more than 99% except for three classes. The 27 classes of MSLD variant produced more than 99% accuracy for two classes produced 95% accuracy, and one class produced 93% accuracy. For the USLD variant, 27 classes have more than 99% accuracy, but it produced 97% accuracy for 'Ja' class and 95% accuracy for 'Ka' and 'Sha' classes.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Segmented** & **Training (\%)** & **Validation (\%)** & **Testing (\%)** \\ \hline KU-BdSL USLD Variant & No & 99.10 & 98.66 & 98.20 \\ \hline KU-BdSL USLD Variant & Yes & 99.90 & 99.60 & 99.60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance Accuracy for KU-BdSL USLD Variant Dataset.  
Figure 10: Class wise State of the Art Comparison for 38 BdSL datasets [11, 33]  
Table 8 shows the comparison accuracy of the proposed method with the existing model using KU-BdSL datasets. We compared with some relevant work based on the BSL dataset, which shows the proposed model performance is higher than the state of the art model performance. However, due to the new dataset, there is no published paper on the KU-BdSL dataset.