esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and Materials
### Solar PV Modeling  
PV panels regulate power output through series or parallel-coupled solar cells. Two main PV cell models exist: double-diode and single-diode. The high precision requirements of power electronics applications necessitate accurate PV module modeling. This consists of a current source (\(I_{SC}\)) in ampere, diode (D), series (\(R_{S}\)) and shunt (\(R_{sh}\)) resistors in ohm. As shown in Figure 1, [PERSON]'s Current Law (KCL) provides the following expression for the output current (\(I_{PV}\)) in ampere [23],  
\[I_{PV}=n_{p}I_{L}-I_{D}-\frac{V_{D}}{R_{sh}}\ -\ - \tag{1}\]  
Equation (2) provides the calculation for the diode current (\(I_{D}\)):  
\[I_{D}=n_{p}I_{OS}\bigg{[}exp\bigg{(}\frac{q(V+IR_{s}}{n_{s}AKT}\bigg{)}-1 \bigg{]} \tag{2}\]  
In Equation (2), \(I_{OS}\), \(q\), \(V\), \(R_{S}\), \(n_{S}\), \(A\), \(k\), and \(T\), represent reverse saturation current in ampere, electron charge in coulomb, output voltage in volt, cell current in ampere, series resistance in ohm, number of series-connected cells, ideality factor, Boltzmann constant, and cell temperature in Kelvin respectively. With this, light-generated current (\(I_{L}\)) and reverse saturation current (\(I_{OS}\)) in ampere are affected by solar irradiance and atmospheric temperature in Kelvin which are further determined using Equation (3) and Equation (4) correspondingly.  
\[I_{L}=\frac{\psi}{\psi_{sh}}\Big{[}I_{Lref}+\mu I_{sc}\Big{(}T-T_{ref}\ \Big{)}] \tag{3}\]  
\[I_{OS}=D_{f}T^{3}exp\bigg{(}\frac{-qE}{AKT}\bigg{)} \tag{4}\]  
Here, \(\psi\) and \(\psi_{sh}\) represent the existing and reference irradiances respectively; \(\mu\) is coefficient efficiency of temperature (0.004 K\({}^{-1}\)); \(I_{Lref}\), \(T\), \(T_{ref}\), \(D_{f}\) and \(E\) are light-current in ampere at reference conditions, present temperature in Kelvin, reference temperature in Kelvin, diode diffusion factor, and bandgap energy correspondingly [24].  
Figure 1: Single-Diode Model of a PV Cell.
## 2 Materials and Methods  
### Device Fabrication  
Multilayer MoS\({}_{2}\) flakes were mechanically exfoliated from bulk MoS\({}_{2}\) (SPI Supplies, West Chester, PA, USA) through the conventional Scotch tape method and transferred onto thermally grown silicon dioxide (SiO\({}_{2}\)) with a thickness of 300 nm used as a gate insulator. A \(p\)-type doped silicon (Si) wafer (resistivity \(\leq\)0.005 \(\Omega\)-cm) was used as a gate electrode and substrate. To remove the organic and inorganic residues resulting from the transfer procedure, the MoS\({}_{2}\)-covered SiO\({}_{2}\)/Si substrate was cleaned in acetone and isopropyl alcohol for 1 h 30 min and 30 min, respectively. Titanium/gold (20 nm/100 nm) layers were sequentially deposited by electron beam (e-beam) evaporation, and then source/drain (S/D) electrodes were patterned through conventional photolithography and wet etching processes. To improve the contact between the S/D electrodes and MoS\({}_{2}\) active channel, the devices were annealed at 200 \({}^{\circ}\)C for 2 h under mixed gas flow (100 sccm of Ar/10 sccm of H\({}_{2}\)).
## 4 Results
### Accuracy Assessment  
The accuracy assessment was performed on the SVB estuary, as an example, and compares field data with test pixels on the defined features (Table 1). The most recent cartography (2017) and field and UAS data (2018) made it possible to estimate an overall accuracy (\(P_{c}\)) of 0.87 and a k coefficient of 0.85. Incorrectly classified limits were more abundant between beach and embryo dune features, but also between saltmarshes and mudflats (Table 2). The differences in elevation and the presence of vegetation were both difficult to detect in these environments.  
Figure 8: Total rates of change (ha/year) per feature (Categories 2 and 3) in the three study sites. To maintain the same x-scale for the three study sites, the total rates of change in the relict littoral ridge, relict dune ridge and overwash deposit in the EBR, which showed rates of change greater than \(\pm\) 2 ha, are indicated numerically on each bar.
## 2 Materials and Methods
### Quaternion Model for RPW Communication (Q-RPW)
If \(h_{HH}\), \(h_{VV}\) were the complex channel coefficients of copolarized links and \(h_{VH}\), \(h_{HV}\) the complex channel coefficients of cross-polarized links, we can write [17]  
\[r_{H}=s_{m,\ell}\Big{(}h_{HH}+e^{-i\pi/2}h_{VH}\Big{)}r_{V}=s_{m,\ell}\Big{(} h_{HV}+e^{-i\pi/2}h_{VV}\Big{)} \tag{18}\]  
where \(s_{m,\ell}\) is obtained by a phase shift \(\varphi_{\ell}\) in \(s_{m}\). It was shown in [17] that the channel coefficients of quaternion model and the conventional model are related by:  
\[ \begin{array}{ll}h_{o}=\Re\{h_{HH}\}=\Re\{h_{VV}\}&h_{i}=\Im\{h_{HH}\}=- \Im\{h_{VV}\}\\ h_{j}=-\Re\{h_{VH}\}=\Re\{h_{HV}\}&h_{k}=\Im\{h_{VH}\}=\Im\{h_{HV}\}\end{array} \]  
This is the significant advantage of quaternion model. The number of real random variable that model the channel are halved by using quaternion model [17]. To recover the transmitted sequence \(\left\{q_{m,\ell}\right\}\), MLD is performed on each \(r_{m,\ell}\) by evaluating the following expression [17; 18]:  
\[\hat{q}_{m,\ell}\triangleq\operatorname*{argmin}_{m}\big{|}\big{|}r_{m,\ell}- q_{m,\ell}H\big{|}\big{|}^{2}=\operatorname*{argmin}_{m}\big{[}(r_{m,\ell}-q_{m, \ell}H)(r_{m,\ell}-q_{m,\ell}H)^{\mathcal{Q}}\big{]} \tag{19}\]Here, \((\cdot)^{Q}\) is the quaternion conjugate. Finally, to recover the transmitted symbol \(q_{m}\), the estimate \(\hat{q}_{m,\ell}\) that has most frequently occurred is selected:  
\[\hat{q}_{m}\triangleq\text{mode}\big{(}\{\hat{q}_{m,\ell}\}\big{)} \tag{20}\]  
Hence, the RP-MPSK transmitted symbol stream \(\{q_{m}\}\) has been recovered as \(\{\hat{q}_{m}\}\) using quaternion model for RPW. A block diagram for implementation of the quaternion model for RP-MPSK modulation is in Figure 5.
## 5 Results and Discussion
### Evaluataion Metrics  
To assess the CF model performance, RMSE and MAE were selected as evaluation metrics. RMSE is defined in (10) and MAE in (11). RMSE uses the square function and imposes a penalty when the residual value is significant. MAE ascertains the absolute average of the error by comparing the predicted and actual values to identify their difference on the absolute scale.
## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland
### Application Prospects Based on Case Study Insights
#### 5.4.1 Urban Noise Pollution Monitoring  
The scalability demonstrated in this case study indicates that the system could be expanded to monitor larger urban areas effectively. Despite the limited number of nodes, the stable communication and accurate sound classification achieved during the deployment suggest that the system is well-suited for high-density urban centers where traffic noise and other environmental sounds are a concern. For instance, this system could be deployed across busy city streets to monitor real-time noise, helping city officials and urban planners develop noise mitigation strategies. Additionally, data from such a system could support enforcing urban noise regulations, particularly in cities with strict policies, where automated warnings could be issued when noise levels exceed acceptable thresholds.
# Characterizing Micro-Displacements on Active Faults in the Gobi Desert with Time-Series InSAR
## 4 Results and Discussion
### The Structural Geology of the Jiujing Region
#### 4.2.4 F\({}_{\text{g}}\) and F\({}_{\text{i-1}}\) as Reverse Faults  
Along fault F\({}_{\text{g}}\) (Figure 7), the eastern side JJ1, JJ2, and JJ3 are displacing towards the west by \(-\)0.46, \(-\)0.05, and \(-\)0.26 mm a\({}^{-1}\), respectively; meanwhile, blocks on the western side of the fault (JJ12, JJ13, and JJ14) are moving towards the east by 0.22, 0.22, and 0.29 mm a\({}^{-1}\), respectively. This indicates a crustal convergence along F\({}_{\text{g}}\), showing it is a reverse fault.  
Figure 6 shows that JJ1, JJ2, and JJ3 on the eastern side of F\({}_{\text{g}}\) are uplifting by 0.33 to 0.60 mm a\({}^{-1}\) while, on the western side, JJ13 is uplifting by only 0.04 mm a\({}^{-1}\) and JJ12 and JJ14 are subsiding by an average of 0.05 and 0.14 mm a\({}^{-1}\), respectively. This kind of vertical displacement difference is clearly illustrated by the cross-section BB' in Figure 11 and by the average time-series analyses at JJ3 (Figure 9c) and JJ12 (Figure 9d), illustrating vertical deformation rate variability along F\({}_{\text{g}}\) (Figure 9b). Generally, the blocks on the eastern side are moving upwards relative to those on the western side, indicating they are part of a reverse fault hanging wall and that F\({}_{\text{g}}\) is dipping towards the northeast.  
Similarly, to compare the average vertical (Figure 6) and horizontal (Figure 7) deformation rates at JJ11 and JJ12 on the western side and JJ10 on the eastern side, it can be speculated that F\({}_{\text{i-1}}\) is another east-dipping reverse fault.
## 2 Materials and Methods
### Meteorological Data  
The average daily temperature (\({}^{\circ}\)C), average daily precipitation (mm), and the 1961-2008 normal monthly accumulated precipitation (mm) data for each month were downloaded from the Alberta Agriculture and Forestry website, using data from the Onefour weather station (approximately 25 km East of the Pinhorn grazing reserve) and from the Oyen weather station (approximately 20 km East of the Sounding Creek grazing reserve), which were the closest available locations to each grazing reserve. Cumulative monthly precipitation for both grazing reserves was calculated from the daily precipitation average.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 1 Introduction
### Background and Motivation
Moreover, an NFV/SDN-based energy management architecture can potentially be used for managing virtual resources and networks [16; 17], a capability that can enable energy management in 5G network slicing. In summary, new services and applications demanded by customers impose that the current and future mobile networks have sophisticated energy management and consumption schemes. These energy management approaches must be adapted to finite energy production and the dynamic generation-consumption conditions. Natively, mobile networks lack an efficient energy management scheme for the whole system. In addition, the redundant design of mobile networks (e.g., duplication of access, transport, or core devices) for keeping the reliability and performance in communications has produced a constant increase in energy consumption (mainly for non-renewable sources) and carbon footprint related. To promote sustainable and environmentally friendly development of mobile networks, the traditional network design and operation must incorporate efficient management of energy consumption and prioritize the use of renewable energy sources. In this regard, the evolution to 5G and specifically to network slicing is an opportunity to develop a sustainable and adaptive energy management ecosystem that is capable of working in a multi-tenant approach, encourages the use of green energy, and optimize the energy consumption. In this regard, the main objective of this paper is to meet these requirements, because the existing literature on network slicing is mainly targeted at the creation of network slices by means of the partitioning of the core, distribution, and access networks resources. Thus, our proposal considering the characteristics of network slicing, NFV, SDN, and green energy represents a feasible and sustainable solution that can offer reductions in OPEX and environmental impact and an improvement in service processing.
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 3 Results
### Future of AV Deployment in Rural Areas
#### 3.3.1 Benefits
The study focused on Suzu City in Ishikawa Prefecture, Japan, a rural area with a high elderly population and limited public transport. Using C++ on the Aimsun traffic simulation SDK platform, the research modeled traffic conditions and AV behavior, incorporating data from person-trip surveys and traffic census reports. Custom algorithms simulated key AV behaviors, including car-following dynamics, vehicle spacing, and reaction times. AV penetration rates between 10% and 100% were tested to evaluate their impact on traffic delays. Field tests were conducted on a 6.6 km route in Suzu City, including urban-like and rural segments, with a fully autonomous 13.2 km round trip. Data from these tests validated and refined the simulation models. The results showed that at low AV penetration (10-45%), delays increased slightly due to mixed traffic inefficiencies. However, once penetration reached 45% to 50%, delays dropped significantly and stabilized, indicating that a critical mass of AVs is necessary to improve traffic efficiency in rural areas. Conceptual assessment-based studies, such as [PERSON] et al. (2021) [34], explored the potential benefits and research needs of AVs in rural America, focusing on safety, mobility, and accessibility. Changes in conceptual assessments and mechanisms for vehicle miles traveled (VMT) were reviewed using travel demand elasticity and preference surveys. Findings indicated that rural areas could see greater safety and mobility benefits due to higher fatal crash rates, longer travel distances, an aging population, and limited transportation options. However, AV sharing models may offer fewer efficiency gains due to low population density. The study emphasized the need for rural-specific policies and research to ensure equitable AV deployment.
# Carbon and Nitrogen Pools and Fluxes in Adjacent Mature Norway Spruce and European Beech Forests
## 2 Materials and Methods  
### Site Description  
The study sites are situated near the Czech-German border in the Ore Mts. (50\({}^{\circ}\)35\({}^{\prime}\)25\" N, 13\({}^{\circ}\)15\({}^{\prime}\)11\" E for the spruce stand and 50\({}^{\circ}\)35\({}^{\prime}\)20\" N, 13\({}^{\circ}\)16\({}^{\prime}\)1\" E for the beech stand) at an elevation between 780 and 820 m a.s.l., close to the village of Nacetin. The spruce stand consists of Norway spruce (_Picea abies_ (L.) [PERSON]) monoculture planted in the early 1930s, and the beech stand consists of European beech (_Fagus sylutatica_ L.) monoculture. The history of the forests can be briefly described as follows: based on maps of the Stabile cadaster from 1842, both sites were dominated by mixed beech-conifer (spruce/fir)forests. However, soon after this mapping (1850s), the current spruce stand was converted into spruce monoculture, which was first harvested in 1930s. Thus, the current spruce forest is a second generation plantation. Unlike the spruce stand, the beech stand continued as a mixed forest of dominantly beech and spruce (based on aerial photographs from 1953). During the 1970s and 1980s, however, mature spruce trees continuously died off due to air pollution (SO\({}_{2}\) emissions causing acid rain), and the stand became pure beech monoculture. In the spruce stand, spruce trees survived the peak acid deposition as the plantation was relatively young. It is likely that before the 2 nd World War, dense settlements in that area caused human exploitation of natural resources (timber harvest) and historical agricultural activities (forest pasture, litter raking, see [3]), which affected the surrounding landscape. After the war, German inhabitants were expelled from the former Czechoslovakia, and human activities largely ceased in this border region.  
The distance between stands is approximately 1 km; annual precipitation averages 1012 mm (1992-2015), and annual temperature is 6.5 \({}^{\circ}\)C. Both stands are underlain by parapneiss bedrock. The dominant soil type is dystric cambisol. Soils at both sites were acidified by acid deposition during the 20 th century, which resulted in low soil pH and low base saturation. More substantial soil acidification was documented in the spruce stand due to the higher acid deposition under the spruce canopy compared to the beech stand [32]. In 2003, the pH of the forest floor was 3.80 in the spruce stand and 4.34 in the beech stand, and soil base saturation was 19% and 70% in the spruce and beech stands, respectively. In the mineral soil (up to 40 cm), soil pH was 4.07 in the spruce and 4.23 in the beech stand, and base saturation was 2.8% in the spruce and 5.5% in the beech stand.
## 3 Results
### Clinical Response  
#### 3.1.1 PASI, DLQI, and GEPARD  
For the exact values of the PASI, DLQI, and GEPARD, see Table 1. The reduction in the PASI under therapy was clinically significant when comparing all study visits (\(p<0.001\)) (Figure 4a).  
The improvement of the DLQI in the course of treatment was also statistically significant (\(p<0.001\)) (see Figure 4b).  
The GEPARD remained relatively equal under therapy. There was no difference during the study period.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **V0 Median (IQR)** & **V1 Median (IQR)** & **V2 Median (IQR)** & **
\begin{tabular}{} \end{tabular}** \\ \hline PASI & 18.3 (8.4–30.0) & 8.6 (2.0–15.0) & 3.8 (1.7–7.6) & \(p<0.001\) \\ \hline DLQI & 13.5 (7.8–15.3) & 3.0 (1.8–6.3) & 1.0 (0.0–4.0) & \(p<0.001\) \\ \hline GEPARD & 6.0 (3.8–9.3) & 7.0 (4.8–9.3) & 5.0 (4.0–10.0) & \(p=0.519\) \\ \hline LPSI (SP) & 6.0 (6.0–9.0) & 3.0 (2.8–5.0) & 1.0 (1.0–2.3) & \(p<0.001\) \\ \hline ET (SP) & 290 \(\mu\)m (249–328 \(\mu\)m) & 213 \(\mu\)m (139–256 \(\mu\)m) & 121 \(\mu\)m (90–168 \(\mu\)m) & \(p<0.001\) \\ \hline SCT (SP) & 84 \(\mu\)m (56–94 \(\mu\)m) & 41 \(\mu\)m (29–63 \(\mu\)m) & 22 \(\mu\)m (13–31 \(\mu\)m) & \(p=0.001\) \\ \hline LPSI (CoA) & 0 & 0 & 0 & \\ \hline ET (CoA) & 87 \(\mu\)m (73–90 \(\mu\)m) & 76 \(\mu\)m (66–87 \(\mu\)m) & 77 \(\mu\)m (72–92 \(\mu\)m) & \(p=0.563\) \\ \hline SCT (CoA) & 9 \(\mu\)m (9–13 \(\mu\)m) & 13 \(\mu\)m (11–14 \(\mu\)m) & 13 \(\mu\)m (10–16 \(\mu\)m) & \(p=0.052\) \\ \hline Difference ET (SP) & 202 \(\mu\)m (161–243 \(\mu\)m) and ET (CoA) & 133 \(\mu\)m (76–173 \(\mu\)m) & 46 \(\mu\)m (23–57 \(\mu\)m) & \(p<0.001\) \\ \hline Difference SCT (SP) & 72 \(\mu\)m (42–84 \(\mu\)m) and 29 \(\mu\)m (16–48 \(\mu\)m) & 8 \(\mu\)m (2–20 \(\mu\)m) & \(p<0.001\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soriasis area and severity index (PASI), dermatology life quality index (DLQI), and local psoriasis severity index (LPSI) median values, as well as the median values of subjective measurements of the epidermal thickness (ET) and stratum corneum thickness (SCT), the respective \(p\)-values in the study plaque (SP) and control area (CoA), and their differences in the course of treatment; \(\mu\)m = micrometer, IQR = interquartile range, 25–75% percentile.  
Figure 4: (**a**) Reduction in PASI score under therapy; (**b**) reduction in DLQI score under therapy. PASI = psoriasis area and severity index; DLQI = quality of life index. The thick horizontal line within each boxplot corresponds to the median.
## 3 \(D^{2}\)-SPDM: Faster R-CNN-Based Defect Detection and Classification with Label Enhancement
### Defect Detection Process  
The ResNet-50 model used in the Defect Detection process is pre-trained on large-scale datasets such as ImageNet, which consists of approximately 14 million images spanning 1000 class labels, and the COCO dataset, which contains over 2 million object instances across 80 classes. Training a model from scratch on such extensive datasets demands significant resources and time. However, by employing a pre-trained model through transfer learning, only essential parts are fine-tuned, greatly reducing training time and improving initial model performance.  
Figure 5 illustrates the network architecture of ResNet-50. ResNet-50 is a deep neural network with 50 layers, organized into five stages, each comprising multiple residual blocks. Considering the network's depth of 50 layers, it uses a bottleneck block structure with a \(1\times 1\), \(3\times 3\), and \(1\times 1\) layer configuration to address gradient vanishing issues. In Stage 1, the model employs a \(7\times 7\) convolution filter to extract features from the input, utilizing 64 filters with a stride of 2 for convolution operations. In Stage 2, three layers with kernel sizes of 64, 64, and 256 form a bottleneck structure, and a total of three blocks are utilized in convolution operations.  
Figure 4: \(D^{2}\)-SPDM framework.  
Subsequent stages progressively double the number of feature maps, capturing increasingly complex and abstract representations. To manage computational complexity, the input size is halved, enabling the network to process higher-level features. Curves between each block denote skip connections. Dashed lines indicate skip connections implemented when input and output dimensions differ, requiring a transformation before addition. Notably, this transformation occurs at the beginning of blocks when the stride changes, as indicated by the dashed lines. Following convolution operations up to Stage 5, the network employs global average pooling and concludes with a fully connected layer that outputs predictions for six defect classes.
# CNN-Based Pill Image Recognition for Retrieval Systems
## 6 Conclusions  
The impact of consuming the wrong medication can be lethal. This paper proposes a method for identifying pills from images. The proposed method studies the impact of combining widely-known classifiers, namely k-NN and SVM, with neural networks. The classifier is placed between the fully connected feature layer and the output layer to handle prediction. Experimental evaluation was conducted on a real-life dataset called the NLM dataset, and results were compared with those obtained from comparable models. We have examined three deep learning models for the classification of pill images; two are hybrid models (a combination of proposed CNN with SVM and k-NN classifiers), and the third is the ResNet-50 model. Results show that using the k-NN classifier in a Convolutional Neural Network architecture (our proposed model) increased pill identification accuracy by around 10% while maintaining almost the same runtime as in the compared methods (nearly 1 ms per execution).  
For future work, the proposed method can be improved to account for some inherent drawbacks in consumer-grade pill images. For example, the model may not be able to accurately determine the shape of a pill if the pill image was taken under conflicting lighting conditions. One naive solution to this problem could be constructing a 3D model of the query pill by having more than one image showing the pill from multiple angles.  
Conceptualization, E.A. and R.M.A.; Methodology, K.A.-H. and I.K.; Software, E.A.; Validation, K.A.-H., E.A. and I.K.; Formal analysis, K.A.-H., E.A. and I.K.; Investigation, E.A.; Resources, K.A.-H. and R.M.A.; Data curation, E.A.; Writing--original draft, E.A. and K.A.-H. and R.M.A.; Writing--review and editing, K.A.-H., I.K. and R.M.A.; Visualization, E.A. and R.M.A.; Supervision, K.A.-H. and R.M.A.; Project administration, K.A.-H. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by the DSO-RIT Dubai Research Fund (2022-23-1003) from the Rochester Institute of Technology--Dubai.  
Not applicable.  
All the pill images used in this study are publicly available through the National Library of Medicine [46].  
The authors would like to thank the TDRA-ICT Fund for providing high-end computing machines for our experiments through the Digital Transformation Lab at Rochester Institute of Technology--Dubai (RIT-Dubai).  
The authors have no competing interests to declare that are relevant to the content of this article.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CBIR & Content-Based Information Retrieval \\ WHO & World Health Organization \\ CNN & Convolutional Neural Network \\ ResNet & Residual Network \\ SVM & Support Vector Machine \\ NLM & National Library of Medicine \\ NIH & National Institutes of Health \\ k-NN & k-Nearest Neighbor \\ \end{tabular}