esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Multiple Input and Multiple Output
M-MIMO systems offer significant energy efficiency by achieving higher spectral efficiency with low power consumption. The increased number of antennas in the M-MIMO system will increase the spectral efficiency, but it will also increase the total power consumption, which reduces the energy efficiency. Numerous studies have been done to develop energy-efficient M-MIMO systems based on the trade-off between energy efficiency and spectral efficiency and also to reduce the power consumption by designing the power amplifiers. Therefore, achieving higher energy efficiency is one of the challenges of future research.
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 5 Discussion
### Impact of IoT Data Gaps on Model Accuracy  
The absence of IoT-generated environmental data, such as temperature, humidity, and CO\({}_{2}\) metrics, significantly impacted the precision of the ARIMA and MLR models in predicting spoilage-related demand fluctuations. These gaps limited the models' ability to adapt to real-time changes in environmental conditions, critical factors in cold chain logistics. Observed MAE and RMSE values for both models highlight these limitations, particularly for short-term demand forecasts. These errors serve as a baseline indicator of potential improvements if IoT data were integrated.  
Empirical evidence supports the assertion that IoT data can enhance predictive accuracy. [PERSON] et al. [5] demonstrated that integrating IoT metrics into predictive models for cold chain logistics improved forecasting accuracy by 15-20%. Similarly, [PERSON] et al. [6] reported a 10% reduction in spoilage when real-time environmental data was incorporated. These findings underscore the potential of IoT data to enhance model reliability and reduce waste in cold chain operations.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 5 Real-Time UWB Indoor Positioning Systems (RTIPSs)  
The urgent need to develop real-time indoor positioning systems has emerged with the great developments in the sciences of automation engineering and robotics in particular, and in the sciences of electrical engineering and informatics in general, to provide the possibility of accurate tracking of self-driving systems such as robots and drones in closed environments, i.e., inside buildings. The development of real-time tracking and indoor positioning systems faces many challenges that affect the performance of the systems and limit the required accuracy.
## 3 Numerical Results
### Effect of Radial Shift
The above figure of radial shift effect indicates that the case of non-Lambertian Z-Power light beam is the least sensitive to the radial shift of the terrestrial user, while the case of non-Lambertian NSPW light beam is the most sensitive to the radial shift of the terrestrial user. The main reason for these phenomena is the apparent divergence characteristic of the Z-Power light beam and the high directivity characteristic of the NSPW light beam. To help readers grasp the overall trends and identify key findings of the radial shift effect more effectively, a summary of the results is presented in Table 3 as follows:  
Figure 4: Energy efficiency performance versus the radial shift of terrestrial users in target area of drone VLC.
## 3 Photodetector Electrical Structures
### Dark Current Criteria for Photodetectors  
An important issue in integrated photodetectors is dark current, which increases the power consumption of the receiver. Most importantly, shot noise associated with this leakage current undesirably degrades the Signal-to-Noise Ratio (SNR) leading to increased bit error rates (BERs).  
Generally, dark currents less than 1 uA are referred to as acceptable value for a high-speed receiver design, below which the transimpedance amplifier (TIA) noise is the main noise source [23,38,39]. Inpractice, a precise value of the required dark current depends upon the speed of operation and the amplifier design. In the recent successful demonstration of an Ge-on-Si photodetector-based receiver, photodetectors with dark current of both \(\sim\)10 nA [38] and \(\sim\)2 \(\upmu\)A [24] were reported. Depending on the receiver design, a higher dark current level is tolerable with certain sacrifices in the receiver parameters. For example, [PERSON] _et al_. [40] have shown that with an increase of the input power of about 20% in comparison with photodetector without dark current, a photodetector with 300 \(\upmu\)A dark current is still able to ensure a BER of \(10^{-18}\) at a frequency close to 50 GHz. The conclusion was drawn based on SPICE simulation taking into account of feedback resistance noise, the shot noise from detector dark current and photocurrent sources, and the transistor channel noise [41]. For the detailed modeling of the precise criteria for the dark current in high speed receiver, the readers are referred to [42] for further understanding.
# Computer-Aided Diagnosis of Skin Diseases using Deep Neural Networks
## 3 Results
### Results on ISIC Archive-2018  
ISIC Archive consists of high resolution clinical and dermoscopic images. It does not provide any ontology information about the diseases. Therefore, the approach used in Exp-2 for DermNet cannot be applied here. We achieved Top-1 accuracy of 93.06% \(\pm\) 0.31% and Top-2 accuracy of 98.18% \(\pm\) 0.06% with 99.23% \(\pm\) 0.02% AUC using ensemble approach. Since this dataset has only seven classes, we restricted ourselves to Top-2 accuracy. Table 4 shows that the ensemble of four classifiers was able to achieve high precision of over 80% for all classes except Vascular Lesions that can be justified by small number of images (157 only) in this class. Confusion matrix showing number of correctly classified and misclassified images per class in this dataset is shown in Figure 2. Table A3 in Appendix A presents accuracy and AUC scores of individual classifiers.  
The ISIC Challenges of 2016 [26] and 2017 [36] have focused on binary classification of skin lesions whereas ISIC Challenge 2018 [37] included seven classes. However, as shown in our experiments, DL has enormous capacity to discern far many diseases with high sensitivity and specificity if given enough data. While reliable and accurate detection of melanoma is of utmost importance because of its lethality, it might also be of interest for dermatologists to use CAD to detect other non-lethal skin diseases.  
Figure 3 shows some examples of correct and misclassified images. We observed that some of these misclassified images had very high correlation with other classes. For example, there is significantly small inter-class variance between Figure 2(a) and Figure 2(e) and  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **Precision (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **F1-Score (\%)** \\ \hline Bowen Disease (AKIEC) & 80.43 & 78.74 & 99.71 & 79.58 \\ Basal Cell Carcinoma (BCC) & 91.85 & 86.96 & 99.79 & 89.34 \\ Benign Keratosis-like Lesions (BKL) & 85.55 & 77.48 & 98.95 & 81.32 \\ Dermatofibroma (DF) & 91.67 & 81.15 & 99.96 & 86.09 \\ Melanoma (MEL) & 84.64 & 66.05 & 98.75 & 74.20 \\ Melanocytic Nevi (NV) & 94.90 & 98.30 & 79.09 & 96.57 \\ Vascular Lesions (VASC) & 66.10 & 74.52 & 99.73 & 70.06 \\
**Weighted Average** & 85.02 & 80.46 & 96.57 & 82.45 \\ Standard Deviation & 09.10 & 09.38 & 07.15 & 08.38 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance Metrics of ISIC Archive-2018 using Ensemble.  
Figure 1: Accumulated confusion matrix of 23-ary classification of DermNet dataset.  
Figure 3h. Therefore, CAD had really hard time classifying those classes. ROC AUC curves for all experiments are depicted in Figure A1 in Appendix A.
## 3 Results
### Surface Analysis and Roughness Measurement  
After loading in the rolling-sliding test bench and the gravimetric measurement, the running surfaces of all samples were examined by LSM and compared to the initial state. The PU samples showed a higher penetration depth of the test wheel into the material (see Figure 7). All samples showed slight scratches and ruts along the direction of movement, as well as scattered pitting. The PU samples also showed partial delamination of the surface. Furthermore, partial smoothing of manufacturing marks could be found on the running surface of the PE samples. Pronounced backside wear was observed, particularly for the PU samples (Figure 7). The extent of the running surface and the strength of the backside wear were consistent with the wear rates.  
To measure surface roughness, the parameters Sa and Sz were recorded. The measured roughness values are shown in Figure 8. The dynamically tested PU samples showed increased surface roughness of the running surface. The arithmetical mean height of the PE samples decreased while the maximum height stayed consistent, which was due to smoothing of the manufacturing marks. The PU1 material showed the highest roughness  
Figure 6: Overall wear of samples after loading in the rolling-sliding test bench. The straight line results from linear regression of the data for estimating the abrasion after 1 million cycles. Different to ISO 14243–1, the zero time point is used in this calculation.  
Figure 7: Worn surfaces (each left) and backsides (each right) of the three tested materials: (**a**) PU1, (**b**) PU2, and (**c**) PE.  
values. The soak control samples showed polished contact surfaces, and thus, reduced roughness compared to the initial state.
###### Abstract  
Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential to detect flood extents in urban areas through clouds during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpasses improve the SAR estimate of urban flood extents. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to urban ones. It was compared to an existing method using SAR returns in both rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas and was more accurate than the existing method.  
image processing; hydrology; synthetic aperture radar +
Footnote †: journal: Article  
* [2][[https://doi.org/10.3390/w13111577](https://doi.org/10.3390/w13111577)]([https://doi.org/10.3390/w13111577](https://doi.org/10.3390/w13111577))
# Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
## 3 Deep Learning Model
### Transformer
#### 3.2.5 Residual Connection  
In neural networks, complex features are learned by stacking multiple layers. However, as the number of network layers increases, the gradient may gradually decrease, making the training process difficult. The idea of residual connections is to introduce skip connections, allowing the network to directly skip one or more layers and add the input signal to the output signal. In this way, even in deep networks, the information of the original input signal can still be propagated directly to deeper layers, thus helping to alleviate the vanishing gradient problem.
###### Abstract  
This study attempts to investigate the impact of structural parameters (volume, shape, and the wall absorption coefficient) in closed space on the auditory perception of three different musical sound types. With binaural audibility technology and room impulse response measurement (RIR), this paper first verifies the reliability of using ODEON software in simulating simplified closed-space auditory scenes. Then, 96 music binaural signals produced in eight simulated closed spaces with different structural parameters are synthesized. Finally, auditory perception experiment is conducted on the synthesized binaural signals by using pair comparison method, and variance analysis is also made on the experimental results. It is concluded that (1) a hemispherical cabin with a small volume and large wall sound absorption coefficient is most suitable for playing a single instrument, such as the flute or violin, and (2) a cabin with large volume is suitable for playing multiple instruments music such as symphony, but the walls should not be totally reflective. The experimental scheme and results of current study provide guidance for designing the inner structure of the concert hall to achieve preferable auditory perception in practice.  
closed space; structural parameter; auditory perception; subjective hearing experiment; pair comparison method +
Footnote †: journal: Applied Physics Letters  
_Article_
# Exploring the Impact of Ambient and Character Sounds on Player Experience in Video Games
## 2 Experiment 1
### Results
#### 2.2.3. Avatar Identification  
The analysis on avatar identification revealed no significant main effect of ambient sounds (\(F\)(1,31) = 1.07, \(p\) = 0.31, and \({\eta_{p}}^{2}\) = 0.03). However, the main effect of character sounds revealed a non-significant trend (\(F\)(1,31) = 3.54, \(p\) = 0.07, and \({\eta_{p}}^{2}\) = 0.10), with higher avatar identification when character sounds were present than when they were absent (\(M\) = 2.07 vs. 2.21 on a scale from 1 to 5). The interaction between ambient and character sounds was not significant (\(F\)(1,31) = 0.60, \(p\) = 0.31, and \({\eta_{p}}^{2}\) = 0.03).
# Data Downloaded via Parachute from a NASA Super-Pressure Balloon
## 3 Descent and Recovery  
We initially intended to release one DRS capsule around mission day 40, 60, 80, and 100, whenever SUPERBIT passed over land, to mitigate the risk of the payload and all its onboard data being lost at sea. SUPERBIT did indeed pass over Chile and Argentina on mission day 40 (25 May 2023). However, in the early hours of the morning, it became apparent that the main payload would need to descend later that day because of reduced communications bandwidth and a weather forecast indicating a southerly trajectory away from further land crossings. After finishing the night's observations and coordinating with local air traffic control, we released (not one but) two DRS capsules over Santa Cruz Province, Argentina. This strategy ensured that independent, redundant sets of data would be available both on board and separated from the main payload.
# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 3 Green Underwater Sensor Network Framework using Adapted Whale and Wolf Optimization
### The Complete Working Structure of the Adapted Optimization Framework (W-GUN)
In step six, underwater node energy acquisition of the next slot is predicted by using a modified eco state network (MESN) model [39]. Here, for \((j+1)\) time slot, energy acquisition is \(E_{aquisition}^{x}(j+1)\). Finally, in step seven, underwater node energy consumption is calculated, and the duty cycle is adjusted accordingly based on energy consumption, the energy acquisition of the next slot and the energy threshold of the underwater network environments. In the underwater network, the energy utilization of \((j+1)^{th}\) the time slot is nothing but the sum of the energy acquisition of the next time slot and the excess energy of the current time slot, which can be referred to as given in Equation (26):  
\[E_{U}^{x}(j+1)=E_{acquisition}^{x}(j+1)+E_{ex}^{x}(j) \tag{26}\]  
where \(E_{acquisition}^{x}(j+1)\) denotes the underwater node energy acquisition of next time slot, \(E_{ex}^{x}(j)\) represents the excess energy in the current time slot. By utilizing Equation (2), the dynamic duty cycle in underwater networking as calculated as given in Equation (27):  
\[Duty\_Cycle_{act}^{x}(j+1)=Duty\_Cycle_{x}\times min\left(max\left(\frac{E_{U} ^{x}(j+1)-E_{T}}{E_{max}^{x_{T}}-E_{T}},0\right)1\right) \tag{27}\]  
where \(Duty\_Cycle_{act}^{x}(j+1)\) represent the active time at slot \((j+1)\) of the underwater relay node, \(Duty\_Cycle_{x}\) is the slot length, \(E_{U}^{x}(j+1)\) refers to the energy utilization of \((j+1)^{th}\) time slot for the underwater relay node, \(E_{max}^{x_{T}}\) represent maximum consumption of energy for a slot, and \(E_{T}\) represent the energy threshold in underwater communication network environments. The active-duty cycle constraint is referred to as, \(Duty\_Cycle_{act}^{x}(j+1)\leq Duty\_Cycle_{x}\). In addition, if \(Duty\_Cycle_{act}^{x}(j+1)=0\), this represents that the underwater node is entirely inactive for the \((j+1)^{th}\) time slot.
## 4 Results and Discussion
### Number of Pixel Change Rate and Unified Average Changing Intensity Analysis  
Other parameters dedicated to quantitative assessment that validate the strength and robustness of the encryption algorithms against minor changes and resistance to different attacks are the number of pixel change rate (NPCR) and the unified average changing intensity (UACI). These parameters depict the disparity between two encrypted images, after performing encryption operations over the original biometric image and the image that suffers slight adjustment by changing one bit in a pixel, keeping the encryption keys unchanged. If a slight modification in the original biometric image determines a significant difference in its encrypted version, the level of diffusion and confusion will be sufficiently high enough in order to obtain ideal values for these parameters.  
According to [60], the NPCR calculates the number of different pixels between two encrypted images, where a percentage of 100% indicates that the images are completely different, while the UACI measures the average percentage of differences in grey levels between the original encrypted image and the encrypted image resulting from the modification of the original image, and both are expressed algebraically by the following equations:  
\[\text{UACI}=\frac{1}{\text{M}\times\text{N}\times\text{O}}\text{$\sum_{X=1}^{ \text{M}}\sum_{Y=  
The same situation is not valid for the NPCR parameter; in this case, the generated values are close, presenting an insignificant difference between them, and both algorithms approach the maximum percentage. Thus, we can notice the superiority of the BGV algorithm in the encryption process over biometric images, being more suitable for integration in the proposed multi-factor authentication system because it enhances the strength and security of the users' database.
## 5 Conclusions  
Using near-miss incident data from vehicles traveling on the left side of roads, we identified the characteristics of dangerous traffic situations wherein vehicles turning right at intersections encounter pedestrians, focusing on two directions of pedestrian crossing: Left-Pedestrian and Right-Pedestrian. The results reveal that the Left-Pedestrian and Right-Pedestrian scenarios had different features for the width of roads going to and through the intersection. The highest rates of incidents for Left-Pedestrian were on medium roads (\(\geq\)5.5 m, \(<\)13 m) to (55.6%) and through (47.2%) the intersection. However, the highest incidents for Right-Pedestrian incidents occurred on wide roads (62.5%) and through (60.0%) intersections. The average of ego vehicle travel speeds for the Right-Pedestrian (17.1 km/h) was significantly faster than that for the Left-Pedestrian (13.4 km/h). The results revealed that the Left-Pedestrian and Right-Pedestrian scenarios had similar characteristics in terms of the presence of other vehicle categories. The total rate of incidents with other vehicles was 69.4% (\(n=50\)) for Left-Pedestrians and 77.5% (\(n=31\)) for Right-Pedestrians. Among the categorized other vehicles, the rate of incidents with preceding vehicles (23.6%) was the highest for the Left-Pedestrian. For Right-Pedestrians, the rates of incidents with preceding vehicles (25.0%) and oncoming vehicles (25.0%) were higher than those for the other vehicles categorized. When an accident prevention safety system such as an AEB system is used for pedestrian detection during right-turning vehicle maneuvers, the test conditions are expected to reflect the features revealed in this study; that is, the road width and presence of other vehicles, such as preceding and oncoming vehicles. The findings of this study will contribute to the development and evaluation of safety systems for preventing collisions between right-turning vehicles and pedestrians at intersections.  
Conceptualization, Y.M.; investigation, S.O.; writing--original draft, Y.M.; writing--review and editing, S.O. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT), Japan, in 2021. The use of recorded data from the drive recorders employed in this study was approved by the ethics committee of the Tokyo University of Agriculture and Technology in Japan.  
**Informed Consent Statement:** Informed consent was obtained from all the individuals included in this study.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# New Methods for the Comprehensive Analysis of Bioactive Compounds in _Camabis sativa_ L. (hemp)
## 2 Results and Discussion
### Validation Data of the HPLC Methods  
Over the concentration range tested, the two HPLC-UV/DAD methods showed good linearity (\(r^{2}>0.999\)) for the reference standards chosen in this study (Table S1).  
The limit of detection (LOD) values for chrysoriol and canniprene were 0.4 and 0.1 ug/mL, respectively, while for cannabinoids it ranged from 0.4 and 0.8 ug/mL. The limit of quantification (LOQ) values obtained for cannflavins and canniprene were 1.3 and 0.3 ug/mL, respectively, while for cannabinoids it was in the range 1.3-2.5 ug/mL (Table S1).  
The low intra- and inter-day % relative standard deviation (%RSD) for retention times (\(\leq\)2.4%) and peak areas (\(\leq\)3.2%) relative to the target compounds (Table S2) and their low intra- and inter-day SD values for content (\(\leq\)21.3 ug/g) (Table S3) indicate the high precision of both the chromatographic system and the extraction procedure.  
By taking into account all the information described above, it can be concluded that this method is a reliable tool for the identification and quantification of flavonoids in fibre-type hep, conforming to the ICH guidelines.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Peak Number** & **Compound** & **\#s (min)** & **UV \({}_{max}\) (nm)** & **MS (sur/3)** & **MS\({}^{2}\)** & **MS (sur/3)** & **MS (sur/3)** & **MS\({}^{2}\) (sur/3)** \\ \hline
1 & CRDA & 138 & 225,269,307 & 359,341 \({}^{\ast}\) & 219 (1000,261 (55),285 (33),232 (22) & 357 & 339 (1000, 331 (9) \\
2 & CRGA & 15.7 & 222,269,305 & 361,340 \({}^{\ast}\) & 219 (100),261 (58),233 (19) & 359 & 341 (1000, 359) \\
3 & CRG & 17.1 & 207,234,273 & 317 & 207 (100),263 (40),280 (10) & 315 & 271 (100),249 (100),247 (51),297 (49) \\
4 & CRD & 17.9 & 209,234,278 & 315 & 259 (1000,233),282 (22),221 (22),207 (24) & 313 & 246 (100),240 (16) \\
5 & CRI-B & 14.5 & 215,273,324 & 300 & 313 (100) & - & - \\
6 & Campine & 16.9 & 204,280 & 342,287 \({}^{\ast}\) & 255 (100),227 (96) (11) & 341 & 326 (100),269 (48),253 (43),257 (12) \\
7 & CRI-A & 21.1 & 214,274,342 & 437 & 313 (100) & - & - \\ \hline \end{tabular} Experimental conditions as described in Sections 3.4 and 3.5. \({}^{\ast}\) Positive ion mode. \({}^{\rm b}\) Negative ion mode. \({}^{\ast}\) Precursor ion.  
\end{table}
Table 1: Retention times, UV, MS, and MS\({}^{2}\) data of the main non-psychoactive cannabinoids, flavonoids and related compounds in hep extracts.