esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Evaluations
#### 4.2.2 Results Analysis via Threshold Setting
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Category** & \multicolumn{4}{c}{**SSO**} & \multicolumn{4}{c}{**PSO**} \\ \hline
**Threshold** & **Fruitfly** & **Mouse** & **Scere** & **Human** & **Fruitfly** & **Mouse** & **Scere** & **Human** \\ \hline
0.05 & 33 & 122.4 & 168.6 & 223 & 25.8 & 94.8 & 174.2 & 223.4 \\
0.055 & 30 & 114 & 160 & 198.8 & 27 & 92.2 & 163.8 & 202.8 \\
0.06 & 23.6 & 96.6 & 139.8 & 193.6 & 22.6 & 82.6 & 139.8 & 187.4 \\
0.065 & 26.8 & 82.2 & 127 & 165.4 & 18 & 72.4 & 129.6 & 161.2 \\
0.07 & 19.8 & 68.8 & 106.6 & 142 & 16.2 & 66 & 110.8 & 147 \\
0.075 & 18.4 & 59 & 96.8 & 125 & 14.4 & 60.8 & 101.8 & 126 \\
0.08 & 15.6 & 53.2 & 79.4 & 108 & 15.4 & 46.4 & 79.2 & 109 \\
0.085 & 13.2 & 36.8 & 70.2 & 90.4 & 11.6 & 41.8 & 74.6 & 96.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Change of module number for PSO and SSO algorithms. Figure 6: (**a**) Degree of polymerization in the module; (**b**) degree of deviation between modules.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 4 Research Progress in Energy Harvesting from Enzyme-Based Biofuel Cells (EBFCs)  
EBFCs have emerged as the most practical form of chemical energy harvesting technology [33; 256] for living organisms in implantable form and are also referred to as glucose biofuel cells. These EBFCs are the second largest group within the bioenergy harvesters. Similar to MFCs, they also depend on the electro-catalytic activity of enzymes for energy generation [4]. However, by contrast with MFCs, these cells are not intended for industrial energy production and their focus is on powering up various micro-scale [7], biomedical devices [34] that are used for clinical purposes.
## 4 Experiment
Figure 15: The BER performance under moderate and weak atmospheric turbulence: (**a**) moderate turbulence coefficients at 21 different time slots; (**b**) the pre-FEC BER of FSO link without retransmission under moderate atmospheric turbulence; (**c**) 5% OH with HARQ-aided BER performances at \(\Psi=0.25\) under moderate atmospheric turbulence; (**d**) 10% OH after FEC BER performance at \(\Psi=0.25\) under moderate atmospheric turbulence; (**e**) 5% OH with HARQ-aided BER performances at \(\Psi=0.5\) under moderate atmospheric turbulence; (**f**) 10% OH after FEC BER performances at \(\Psi=0.5\) under moderate atmospheric turbulence; (**g**) weak turbulence coefficients at 21 different time slots; (**h**) the pre-FEC BER of FSO link without retransmission under the weak turbulence condition. Note that Table 3 calculates the overall outage probability under each turbulence intensity, while the previous experiments included the worst turbulence coefficient under each turbulence intensity, so that the BER was not zero after one RF retransmission, and retransmission twice was required. In practice, the probability of the worst turbulence coefficient is consistent with the outage probability.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 5 Research Progress in Biomechanical Energy-Harvesting Technologies
### Energy Harvesting from Humans  
Biomechanical energy-harvesting solutions gained much popularity thanks to their easily wearable features. [PERSON] et al. [40] summarized the applicability of biomechanical energy harvesting from various form of human actions and motions including foot, knee, hip as well as upper limb motions and gave special focus to high power density, back-pack like wearable rotary energy harvesters. Similar wearable harvesters have also been reported by [PERSON] et al. [303], [PERSON] et al. [304] and [PERSON] et al. [41] which generate watt level  
Figure 15: Triboelectric nano electricity generation (TENG) principle.  
power density. [PERSON] et al. [305] reported the identification of lower limb joints which would be better suitable for energy harvesting. Many research groups have concentrated on utilizing biomechanical energy from foot strikes through shoe soles [306; 307; 308]. Works are also found on biomechanical energy harvesting from ankle [309], knee [310; 311; 312; 313] and hip [314] movements. A generalized wearable backpack based biomechanical energy-harvesting system is depicted in Figure 16.  
Development of smart, energy-harvesting textiles with TENG components that would facilitate both electrical powering and wearable sensing applications has gained a considerable amount of interest over recent years. Example of such works include development of TENG-incorporated stainless steel/polyester fiber-blended yarns [302] and washable hybrid fibers with a piezoelectric-enhanced TENG mechanism [316]. Efforts have also been made to implement skin like transparent sheets that could harvest biomechanical energy. Relevant examples include, a textile-based TENG that can be actuated with skin touch [317]; a soft skin-like triboelectric nanogenerator that works both as an energy harvester and tactile sensor [318]; a transparent triboelectric, piezoelectric, pyroelectric combined hybrid nanogenerator with silver nanowires [319]; and an optically transparent, textile compatible TENG fiber with silk protein and silver nanowires [320].
## 6 Conclusions  
The clipping noise in the PAPR reduced system (PA DCO-OFDM) is investigated analytically in this work. This includes the clipping noise attenuation factor and the variance of the received waveform which are determined in closed-form. The SNR of the system is calculated based on the determined attenuation factor and noise variance. The system's analytical BER performance of the clipped PAPR reduced PA DCO-OFDM is verified through simulation studies at different clipping levels. The BER of the PA DCO-OFDM is then compared to that of conventional DCO-OFDM without PAPR reduction at the selected clipping levels. The PA DCO-OFDM achieved a better BER performance than that of the conventional DCO-OFDM without PAPR reduction in the all used clipping levels. The high PAPR values reduction in the transmitted signal in PA DCO-OFDM reduces the clipping noise hence, enhances the system BER performance. PAPR reduction has the potential to reduce the transmitted average optical power by reducing the required bias power, \(P_{\text{bias}}\), which results in increasing the reliability of the LED and hence, its lifetime span. The BER gain can be used to increase the transmitted optical power for longer transmission distance and/or higher achievable data rate.  
Conceptualisation, H.A., M.H., S.S. and W.P.; methodology, H.A., M.H. and W.P.; software, H.A. and M.H.; validation, M.H. and W.P.; formal analysis, H.A., M.H. and W.P.; investigation, H.A.; resources, H.A.; data curation, H.A.; writing--original draft preparation, H.A.; writing--review and editing, H.A., M.H., S.S. and W.P.; visualisation, H.A., M.H. and W.P.; supervision, W.P.; project administration, H.A. and W.P.; funding acquisition, H.A. and W.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jazan University, Jazan, Kingdom of Saudi Arabia.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Comparison between Different PV Technologies  
Our proposed models were examined afterwards with different module technologies, which were deployed in three locations in the USA. The obtained results for all regions are shown in Table 2 (see Appendix A), while a visual presentation for Cocoa is depicted in Figure 5. As we can see, our proposed model with wind obtained the best compatibility of predicting module temperature since it showed the highest correlation coefficient \(R^{2}\) and the lowest \(RMSE\), whilst the other proposed model has the best performance among the models that ignore the influence of the wind, with highest \(R^{2}\) and lowest \(RMSE\) values calculated for all PV technologies and locations.  
Previous studies have reported that it is difficult to apply a single model or a unique formula to precisely calculate the PV module/cell temperature [9; 11; 18; 19]. Moreover, the thermal characteristics of PV modules are slightly different even if they are manufactured with the same technology and materials [12; 13]. However, our proposed models have demonstrated that they can work well with different PV technologies and weather conditions without adjusting the empirical coefficients.  
Table 7 shows the optimal coefficients, \(\Delta T_{ref}^{*}\) and \(W_{ref}\) in Equation (13), and corresponding implementing results which were calculated for various PV technologies in three  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Coefficients**} & \multicolumn{3}{c}{**Implementing Results**} \\ \cline{2-4}  & **Tri An** & **Da Nang** & **Ha Noi** \\ \hline \(\Delta T_{ref}^{*}\) (\({}^{\circ}\)C) & 31.4 & 24.7 & 34.7 \\ \(W_{ref}\)(m/s) & 11.1 & 11.8 & 12.8 \\ \(R^{2}\) (\%) & 98.0 & 98.4 & 98.4 \\ \(RMSE\) (\({}^{\circ}\)C) & 1.43 & 1.40 & 1.55 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Correlation coefficients (\(R^{2}\) and \(RMSE\)) for measured and calculated module temperatures for three regions in Vietnam using optimal coefficients (5-min time step).  
Figure 5: Correlation between measured and calculated module temperature for different module technologies obtained with the studied models for Cocoa, USA: (**a**) correlation factor \(R^{2}\); (**b**) \(RMSE\).  
different locations. As we can see, the operating temperature of CdTe and CIGS modules were least affected by on-site POA, with \(\Delta T_{ref}^{*}\) is \(31.25\pm 0.45\)\({}^{\circ}\)C and \(30.65\pm 0.75\)\({}^{\circ}\)C, respectively. Silicon PV module operating temperatures, on the other hand, were influenced significantly by local POA irradiance conditions, with \(\Delta T_{ref}^{*}\) equal to \(28.6\pm 1.3\)\({}^{\circ}\)C and \(27.65\pm 1.95\)\({}^{\circ}\)C, respectively, for xSi and mSi modules.
# Implementation of a Cryogenic Facility for Space Debris Analysis
## 4 Prototype Implementation
### Cryocooler  
The cryocooler used was from Advanced Research Systems (ARS), with an ARS-4 HW compressor and a DE-104(T) cold head. The cooling capacity of the cryocooler was temperature dependent, as shown in Figure 18.  
Figure 18: Cryocooler cooling capacity.  
During the thermal cycles, the heat load on the cold head goes from about 40 W, when the temperature at the clad head is the minimum (around 60 K), to 70 W at a maximum temperature of about 115 K.
## 2 Materials and Methods  
### Study Area  
Apulia is the most southeastern region of Italy, geographically located between 39\({}^{\circ}\)47\({}^{\prime}\)29.8\({}^{\prime\prime}\) N, 14\({}^{\circ}\)56\({}^{\prime}\)02.8\({}^{\prime\prime}\) E and 42\({}^{\circ}\)08\({}^{\prime}\)25.5\({}^{\prime\prime}\) N, 18\({}^{\circ}\)31\({}^{\prime}\)09.5\({}^{\prime\prime}\) E (Figure 1). Encompassing a total area of 19,540 km\({}^{2}\), Apulia has an average elevation of 565 m above sea level (a.s.l) (range: 0 m-1150 m a.s.l). Most of the region is characterized by flat and hilly landscape while the mountainous areas are limited to the north. It is characterized by hot summers and mild winters, which are typical of the Mediterranean climate. Annual rainfall is concentrated in fall and winter, while summers are typified by long periods of drought. The average annual rainfall varies from 450 mm to 650 mm. The mean annual temperature ranges from 12.0 \({}^{\circ}\)C for inland areas (i.e., Daunian Subappenines and Gargano Promontory) to 19.0 \({}^{\circ}\)C along the coasts.  
The landscape is dominated by agriculture and characterized by the presence of olive groves and vineyards (29%). Conversely, wooded areas cover only 9.7% of the landscape and are mostly concentrated in hilly and mountainous areas which are difficult to cultivate [39]. Oak woodlands and Mediterranean maquis are the most dominant forest types in the region, while conifers (_Pinus pinea_ L., _P. halepensis_ [PERSON], and _P. pinaster_ [PERSON]) are limited to coastal areas where, historically, reforestation has taken place for slope stability. The main tree species are _Quercus_ _ilex_ L., _Q. pubescens_ [PERSON], _Q. cerris_ L., _Q. calliprinos_ [PERSON], _Phyllinea_ spp., _Ruscus aculeatus_ L., _Pistacia lentiscus_ L., _Asparagus acutifolius_, L., _Paliurus spina-christi_ Mill., _Cistus nonspeliensis_ L., _C. incanus_ L., and _c. salviofilus_ L.  
According to the European Forest Fire Information System (EFFIS), Apulia is one of the Italian regions most frequently crossed by wildfires, although only 9.7% of the total area (189,086 ha) is covered with forests (INFC 2015). During the 2000 to 2019 time period, the average burnt area was slightly under 100,000 ha, with more than 10,000 recorded fire events (Civil Protection Department). The fire season coincides with the summer period. Typically, the number of wildfires slightly increases between May and June, reaching a peak in August; these fires are mainly related to human activity [40; 41]. Martina Franca represents one of the main towns in the Irtia Valley, located in the central-south part of Apulia. Most of the territory is covered by agricultural lands (olive groves, vineyards, and arable crops), followed second by forests and shrubland areas. In recent years, tourism has caused an expansion of residential patches (i.e., buildings, infrastructures, and educational farms) in wildland areas, which have generated urban interfaces. These areas have subjected the territory of Martina Franca to more frequent wildfire occurrences and spread. For this reason, it has been chosen as a pilot area in this study.
# Joint Deployment of Sensors and Chargers in Wireless
## 6 Conclusions  
This paper explores the novel problem of jointly deploying sensors and chargers on a two-dimensional plane, deploying a minimum number of wireless sensors and chargers under the constraint of continuous monitoring Pols. Considering the interaction between the deployment of sensors and chargers, we derive two progressive problems: P1 and P2. P1 solves the deployment of sensors with the constraint that the monitoring probability of any PoIs is not less than a preset limit \(c_{th}\). P2 solves the deployment problem of chargers with the constraint that the minimum charging power of any sensors is not less than \(P_{min}^{s_{j}}\). We demonstrate that both P1 and P2 are NP-hard problems. We note that the aggregation effect of sensors can effectively reduce the number of deployed chargers, and therefore the GHDSAE solution is proposed. Then, the GH solution and the PSO solution are proposed for the deployment of chargers. The accuracy, scalability, and efficiency of the GH and PSO solutions are verified by changing the problem scale, the discretization accuracy, and the perception probability. The numerical comparative results show that the aggregation of sensors can effectively reduce the number of chargers required, based on which the PSO solution can effectively reduce the deployment of chargers at the cost of a slight increase in computing time. As a result, it is more cost-effective to deploy sensors using the GHDSAE solution approach and chargers using the PSO solution.  
In the future, the joint deployment of sensors and chargers will be applied in mobile wireless rechargeable sensor networks for electric vehicle adoption rates, grid constraints, and renewable energy integration scenarios to further explore the key network performance, such as charging benefit maximization, monitoring benefit maximization, delay minimization, life cycle maximization, network deployment cost minimization, etc.  
H.Y.: Conceptualization, Methodology, Writing--Original draft preparation, Reviewing and Editing. J.L.: Data curation, Software and Validation. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Deployed chargers with the increase of \(L_{c}\). (a) Deployed chargers of GH; (b) Solving time of [PERSON].  
**Funding:** This research was funded by the Science and Technology Commission of Shanghai Municipality grant number 18510745100.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Sampling and Data Collection
In addition, secondary wind and wave data were also obtained from global ECMWF data for calibration purposes. Table 2 displays the coordinates of the sampling locations for each type of data measurement. The classification of tidal types is determined based on the Formzahl number divided according to the following criteria [35; 36]:  
1. Semi-Diurnal Tide: If the Formzahl (\(F\)) value is 0.00 \(<F\leq\) 0.25;
2. Mixed Tide, Prevailing Semi-Diurnal: If the Formzahl (\(F\)) value is 0.25 \(<F\leq\) 1.5;
3. Mixed Tide, Prevailing Diurnal: If the Formzahl (\(F\)) value is 1.5 \(<F\leq\) 3.0;
4. Diurnal Tide: If the Formzahl (\(F\)) value is \(F>\) 3.0. This value can be calculated using the following Equation (1):  
\[F=\frac{(K1+O1)}{(M2+S2)} \tag{1}\]  
Here, \(F\) represents the Formzahl number; \(K1\) stands for the main luni-solar diurnal constituent; \(O1\) is the principal lunar diurnal constituent; \(M2\) represents the principal lunar constituent; and \(S2\) refers to the principal solar constituent [35].
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
###### Abstract  
This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (\(\mathbb{R}^{2}\geq 0.84\)). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available.  
s 2024 16 3705 1
## 6 Results
### Classification Results
Figure 3 shows the aggregate confusion matrix of 10-fold cross-validation for the four models, CNN, CNN-SL, CNN-HD, and CNN-SF, on the DMR-IR dataset. In the aggregation confusion matrix, through 10-fold cross-validation of a true label and a predicted label, each fold had 204 normal and 210 cancer images. For the CNN, the 1626 normal breast class images were correctly classified, with 420 misclassifications, and in the cancer class, the 1895 images were correctly classified, with 205 images misclassified. In contrast, for the CNN-SL, 2036 normal breast class images were correctly classified, and 10 were misclassified, while in the cancer class, 2082 images were classified correctly, with 18 misclassified. For the CNN-HD, 2040 normal breast images were correctly classified with 6 misclassified, and 2085 cancer images were correctly classified, and 15 were misclassified. Figure 3: Aggregate confusion matrix of all folds for each model as: (**a**) CNN model; (**b**) CNN-SL model; (**c**) CNN-HD model; (**d**) CNN-SF model. In the CNN-SF confusion matrix, 2029 images were correctly classified in the normal breast class, whereas 17 were misclassified, and of the cancer images, 2090 were correctly classified, while 10 were mistakenly classified. The four tables of the classification report are shown in Figure 4, which also shows the classification performances of the CNN, CNN-SL, CNN-HD, and CNN-SF in terms of accuracy, precision, recall, F1-score, and support, where support represents the number of samples. To conclude and summarize the results, when looking at Table 3 and Figures 2 and 3, we observed that early diagnosis of breast cancer through thermal images using CNNs with the proposed AMs was shown to be effective. The classification rate of 84.92%, with a low false alarm rate of 15.07% for the CNN represented the highest false alarm among all models owing to the absence of AMs. At the same time, the CNN-SL achieved a classification rate of 99.32% with a low false alarm rate of 0.67%, while the 99.49% classification rate of the CNN-HD with a low false alarm rate of 0.50% represented the lowest false alarm rate among all models. Finally, the CNN-SF had a classification rate of 99.34% with a low false alarm rate of 0.65%. It was evident from the accuracy obtained utilizing the CNN models with/without the three AMs that high results were achieved in all the proposed models. According to the findings, hard attention was the most accurate, followed by self-attention, and soft attention was third, the last CNN model.
# Experimental Study of 6 LoPLC for Home Energy Management Systems
## 5 Results and Discussion
### Network Reliability  
HEMS is generally a low-rate system, but a minimum reliability of 99% [37] is required to ensure that monitoring information from the sensor and control message is delivered as needed. We investigate network reliability in terms of PDR with various packet sizes and different network configurations. PDR here is defined as the ratio of the received packets to the transmitted packets. A packet is deemed to have been received when all of its bits have been delivered to the destination service engine. This stateful approach ensures that the next layer processes only complete packets. The result is presented in Figure 10.  
As seen in Figure 10, all of the network sizes favour small application data. From the design point of view and based on the system configuration employed in this work, we can also infer that in a HEMS comprised of 50 smart appliances, in order to guarantee at least 99% packet delivery, the size of the payload in the message exchanged between the nodes and controller must be 64 bytes or less. This is because as packet size increases and more appliances are connected to the HEMS network, the contention for network resources also increases, and more packets are fragmented, all of which result in lower PDR. Hence, larger data sizes tend to result in higher throughput (Figure 9), but this often comes at the expense of lower reliability, as seen in Figure 10.  
Figure 10: Packet delivery ratio (PDR) vs. No. of nodes.