esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 2 Comparison of Wireless Performance in the 915 MHz and 2.45 GHz ISM Bands
### DC Power Consumption
#### 2.2.2 Commercial RF Integrated Circuit Radio Transceivers
Figure 4b shows the values for each radio transceiver considering a sampling rate of 1 sample per minute or \(T_{frame}=1\) min. It can be noticed that in this case the situation among the \(I_{avg}\) values for the Z-Star, ZigBee and BLE radio transceiver changes considerably and are now within 140 nA of each other. As for LoRa and ANT, the correspondent \(I_{avg}\) are significantly reduced but UHF RFID and WiFi are approximately the same as previously. In Figure 4c,d, a sample rate of 1 sample per hour and 1 sample per day have been considered. In these two final plots, it can be noticed that increasing \(T_{frame}\) for values greater than an hour lead to a significant reduction of duty cycle \(\delta\) and hence the values of \(I_{avg}\) for each transceiver converge to correspondent value of \(I_{sleep}\). In conclusion, from the above results, a sampling rate of 1 sample per minute or greater is required to minimize the DC current consumption using the Z-Star Protocol. This sampling rate is suitable for a SpO2 measurement system as it represents a realistic interval between measurements. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **ACK Length (bits)** & \begin{tabular}{c} **Data Packet** \\ **Length (bits)** \\ \end{tabular} & \begin{tabular}{c} **Data Rate** \\ **(kbps)** \\ \end{tabular} & \
\begin{tabular}{c} **\(T_{RX}\) (ms)** \\ \end{tabular} &
\begin{tabular}{c} **\(T_{TX}\) (ms)** \\ \end{tabular} \\ \hline Z-Star [33] & 176 & 232 & 50 & 3.52 & 4.64 \\ ZigBee [56; 57] & 88 & 144 & 250 & 0.352 & 0.576 \\ BLE [56; 58] & 80 & 160 & 1000 & 0.08 & 0.160 \\ LoRa [38; 59] & 120 & 176 & 10.9 & 11 & 16.1 \\ ANT [48; 60] & 48 & 88 & 13.8 & 3.48 & 6.38 \\ UHF RFID [40; 47; 61; 62] & 22 & 88 & 19.2 & 1.15 & 4.58 \\ WiFi [47; 63] & 112 & 168 & 1000 & 0.112 & 0.168 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of \(T_{TX}\) and \(T_{RX}\) as function of ACK Length, Data Packet Length and Data Rate.
## 6 Results and Discussion
### Overview of Performance Measurement
#### 6.2.3 Accuracy  
Accuracy calculates the ratio of correctly predicted instances (true positives and true negatives, TN) to the total number of instances in the dataset. It provides a general overview of the model's performance but may be misleading on imbalanced datasets where one class dominates.  
\[Accuracy=\frac{True\;Positives+True\;Negatives}{True\;Positives+True\; Negatives+False\;Negatives}\]
## 3 Validation and Grid Dependency Study  
### Test of Grid Dependency  
In this subsection, the grid dependency is tested by simulating a spring-mounted cylinder in an unbounded flow at the following conditions: \(Re=150\), \(m^{*}=8/\pi\), \(\zeta=0\), \(U_{r}=5\). The initial position of the cylinder center is set to be \((0,\ 0.01D)\) to accelerate the alternative vortex shedding.  
The width of the computational domain is \([-8D,\ 24D]\times[-8D,\ 8D]\). Approximate non-reflecting boundary conditions are constructed to improve the rate of convergence [27]. A uniform background mesh with 100 and 50 nodes in the streamwise and transverse direction is used and then it is recursively refined to obtain three different near-wall intervals, i.e., \(\Delta x\approx\Delta y=0.04D\), \(\Delta x\approx\Delta y=0.02D\), and \(\Delta x\approx\Delta y=0.01D\). These three meshes contain 37,912, 47,761, and 90,223 nodes and are referred to as coarse, medium, and fine ones, respectively. In Figure 5, the local regions of the three different meshes are presented and it can be seen that the finest mesh cells are clustered in the region where the cylinder will pass. The simulations are carried out with dimensionless time step of \(\Delta t=2.5\times 10^{-3}\).  
The time-averaged amplitudes \(A_{ave}=(Y_{max}-Y_{max})/2\), lift coefficients \(C_{L,mean}\), and Strouhal number \(St\) are listed in Table 1. The computational time per time step by using the service with Intel Xeon (R) Silver 4210R CPU @ 2.4 GHz (Intel, Santa Clara, CA, USA) is also listed in the table. It can be seen that the differences in the results diminish with mesh being refined from \(\Delta x\approx\Delta y=0.02D\) to \(\Delta x\approx\Delta y=0.01D\). However, the computational time cost of the fine mesh is almost twice that of the medium one. Therefore, to balance the computational accuracy and efficiency, the medium grid resolution is used for all the simulations in the present work.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \multirow{2}{*}{**Nodes**} & **Computation Time** & \multirow{2}{*}{\(A_{ave}\) (Errors)} & \multirow{2}{*}{\(C_{L,mean}\) (Errors)} & \multirow{2}{*}{\(St\) (Errors)} \\  & & **(s/Time Step)** & & & \\ \hline Coarse & 37,912 & 2.16 & 0.5325 (4.8\%) & 1.415 (19.3\%) & 0.2206 (4.8\%) \\ Medium & 47,761 & 2.98 & 0.5562 (0.5\%) & 1.695 (3.3\%) & 0.2082 (1\%) \\ Fine & 90,223 & 5.91 & 0.5593 (\(-\)) & 1.753 (\(-\)) & 0.2104 (\(-\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grid independence test results at \(Re=150\), \(m^{*}=8/\pi\), \(U_{r}=5\), and \(\zeta=0\).  
Figure 5: Local regions of the coarse, medium, and fine mesh. (**a,a1**): coarse mesh and its local magnification; (**b,b1**): medium mesh and its local magnification; (**c,c1**): fine mesh and its local magnification. The circles in the figures represent the cylinder surface and the arrows mean that Figures (**a1–c1**) are the corresponding local magnification of Figures (**a–c**).
###### Abstract  
(1) Background: Dual-energy X-ray absorptiometry (DXA)-based parameters such areal bone mineral density (aBMD) and Trabecular Bone Score (TBS) are routinely used to evaluate participants at risk for fragility fractures (FFs). We compared the accuracy of lumbar spine aBMD and TBS to that of volumetric BMD (vBMD) by quantitative computed tomography (QCT). (2) Methods: We conducted a retrospective analysis of participants who received both a DXA scan and a chest/abdomen CT scan. BMD and TBS values were obtained from lumbar DXA and vBMD values from QCT (three vertebrae from L1 to L4). T-score values were used for DXA diagnosis; the American College of Radiology ranges were used to diagnose bone status with QCT. (3) Results: We included 105 participants (87 women, mean age 69 \(\pm\) 11 years). Among them, n = 49 (46.6%) presented at least one major FF. QCT diagnosis was as follows: osteoporosis = 59 (56.2%); osteogenic = 36 (34.3%); and normal status = 10 (9.5%). DXA diagnosis was osteoporosis = 25 (23.8%); osteogenic (33.3%) = 35; and normal status = 45 (42.9%). A total of 38 participants (36.2%) showed a TBS degraded microarchitecture. Correlation was moderate between aBMD and vBMD (r = 0.446), as well as between TBS and vBMD (r = 0.524). A good correlation was found between BMD and TBS (r = 0.621). ROC curves to discriminate between participants with/without FFs showed the following areas under the curve: 0.575 for aBMD, 0.650 for TBS, and 0.748 for QCT BMD. (4) Conclusions: QCT detected a higher prevalence of osteoporosis compared to DXA. TBS performed better than aBMD from DXA in discriminating between subjects with and without FFs.  
osteoporosis; osteogenic; vertebral fractures; bone mineral density; quantitative computed tomography; dual-energy X-ray absorptiometry
## 1 Introduction
### Emergence of New Classes of Hybrid Model  
Modern forest landscape models are the result of five key model development phases, listed in chronological order: (1) Growth-and-yield models; (2) fire models; (3) gap models; (4) physiological models; (5) hybrid models combining design principles from each [20; 29; 39]. Terrestrial biosphere models similarly trace their roots back to early one-dimensional physiological models, with land surface models currently in their third generation and dynamic global vegetation models in their second generation [40]. This latest generation of models was intended to address the lack of explicit representation of vegetation dynamics--a critical source of model uncertainty in future climate scenarios [41]. This inspired the aforementioned forest ecosystem model intercomparisons as well as new terrestrial biosphere model designs based on gap models, bypassing the trade-offs of medium-resolution forest landscape models.  
Collectively, these efforts yielded a number of new terrestrial biosphere models based on the classical gap model, including the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) [42], the Ecosystem Demography model (ED/ED2) [43; 44], and Land Model 3 with PPA (LM3-PPA) [45], based on the Perfect Plasticity Approximation (PPA) [46; 47]. These models represent the current state-of-the-art in modeling vegetation dynamics globally, in what we term 'cohort-leaf' vegetation models. While individual-based global models have begun to merge, forest landscape models have remained in between, focused on spatial processes of fire, harvest, and biological disturbance. Yet, previous research has shown that such forest landscape models are often insensitive to landscape configuration and are therefore aspatial [48], counter to the main assumption and selling point of these models.  
While most forest landscape and terrestrial biosphere models lack individual trees, the SAS [43] and PPA [49; 32; 46] model reduction strategies have demonstrated an ability to successfully up-scale gap dynamics to forest stands. Other up-scaling strategies exist as well. One recent forest landscape model participating in the CoFoLaMo intercomparison scales from individual trees to stands by pre-computing light tables [50]. Regardless of the model structure, it is clear that gap, forest landscape, and terrestrial biosphere models are beginning to merge into new models of the terrestrial biosphere. This trend is also attributable to improvements in computational efficiency with new processor designs and cluster or cloud computing infrastructure. As few, if any, existing models are designed for highly parallel architectures (e.g., general-purpose graphics processing units, or GPGPUs), there remains much potential for future efficiency gains. Meanwhile, a clear opportunity exists to embed machine learning models within simulators for data-driven, pattern-based processes (e.g., from remote sensing data streams).
# DNA Barcoding as a Plant Identification Method
## 4 Chloroplast DNA Barcodes
### atpF-atpH (Intergenic Spacer)  
The non-coding, plastid region _atpF-atpH_ could be used as a universal DNA barcoding marker for species-level identification but its discriminatory power is medium. The genes _atpF_ and _atpH_ encode ATP synthase subunits CFO I and III. The length of _atpF-atpH_ sequences vary from 598 to 613 bp and the alignment of these sequences is difficult despite easy PCR amplification. For this reason, it could be useful only as supplementary marker in plant DNA barcoding, providing better resolution on specific projects and taxonomic groups [33; 47; 55; 56]. According to [PERSON] et al. [55], it is a suitable marker for duck seed identification.
## 1 Introduction
For focal length estimation, different approaches based on images are implemented in the form of convolutional neural networks (CNN). It is possible to deal with the above-mentioned issue by calibration using specific patterns on a single image [18; 19; 20] or the usage of an RANSAC-based algorithm [21]. All these approaches have in common that a specific pattern must be visible on the image, so that the focal length is calculated based on the geometric features of this pattern. In addition, some techniques require more than one image to estimate the focal length [18], so no single shot estimation is possible. There are also newer approaches with deep learning in the form of neural networks to estimate the focal length from a single image without specific patterns [22; 23; 24; 25]. However, all the above-mentioned methods were only applied on pictures of subjects such as landscapes, persons, animals, cells, or synthetically generated images. In the case of microscopy, it is beneficial to be independent from the type of sample. To our knowledge, there is currently no neural network to estimate the focal shift introduced by adaptive optics based on images without a subject on it available [22; 23; 24; 25].
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 4 Efficient Communication Model for an SPS with Multiple Data Consumers
### Positioning BR for a Balanced Mesh Formation  
BR is the root node in the mesh that initiates the mesh formation. RPL protocol forms a directed acyclic graph (DAG) that is destined to the BR by sending a DAG information object (DIO). DIO is an advertisement, and nodes hearing it join the DAG. It then furthers the transmission of DIO using trickle timers, and nodes join as in a ripple. Hence, nodes at the far end of the network perimeter takes more time to join the DAG. To have a uniform distribution of DIO along the perimeter of the network, it is necessary for the BR to be at the center of the network. This ensures that all nodes along the entire perimeter of the network have the smallest possible hops to reach the BR. With the number of hops directly proportional to the energy utilization and latency, positioning the BR at the center is the best approach. In addition, congestion around the BR node is quite low for a network having BR at the center when compared to a BR at the top (as in Figure 6a).
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 4 Results
### The Sustainability of E-Health Solutions
#### 4.2.2 Sustainability Dimensions
\begin{table}
\begin{tabular}{l l} \hline \hline
**Aspect** & **Study ID** \\ \hline Functional suitability & PS14, PS13, PS16 \\ \hline Performance efficiency & PS12, PS13, PS14, PS16 \\ \hline Usability & PS12, PS13, PS16, PS22 \\ \hline Security & PS14, PS13, PS16, PS22 \\ \hline Modifiability/changeability, accessibility, reusability & PS12 \\ \hline Interoperability & PS11, PS13, PS14, PS16 \\ \hline Portability & PS12, PS13, PS16, PS20 \\ \hline Flexibility & PS16, PS13 \\ \hline Reliability & PS12, PS16, PS13, PS22 \\ \hline Scalability & PS16 \\ \hline Accuracy & PS13, PS16 \\ \hline Completeness, consistency, data accessibility, confidentiality, relevance & PS13 \\ \hline Timeliness & PS13, PS16 \\ \hline Semantic interoperability & PS1, PS20 \\ \hline Electromagnetic compatibility, power, physical parameters, data exchange, compatible architecture & PS14 \\ \hline Mobility and unobtrusiveness & PS5, PS14 \\ \hline Supporting online social networking & PS5 \\ \hline Attitude & PS9, PS16 \\ \hline Perceived ease of use & PS9, PS14, PS16 \\ \hline Perceived usefulness & PS9, PS14, PS16 \\ \hline User satisfaction & PS9, PS3, PS8, PS16 \\ \hline Confirmation & PS9, PS16 \\ \hline Trust in using e-health, readiness to make changes, human computer interaction, benefit in learning, satisfaction and willingness, users’ technology adoption, awareness & PS16 \\ about e-health & PS8, PS14, PS18 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The sustainability aspects of e-health solutions as mentioned in the studies. Two studies [8; 35] discussed the issue of sustaining the implementation of e-health solutions and identified similar sustainability dimensions, which are social, environmental, economic, technological, and individual dimensions. It is worth noting that the focus of the environment dimension in both studies was different. In the study of [PERSON] et al. [8], the focus was on the sustainability from an ecological perspective, by raising environmental issues such as energy consumption and emissions of carbon dioxide. The study underlined how the e-health solution should consider this aspect in order for it to be sustainable. The same conception of the environment dimension was found in study [32]. On the other hand, study [35] focused on the work and organizational environment, such as organizational culture, values, leadership, and resources. [PERSON] et al. [26] concluded that some issues impact the sustainability of health information exchange (HIE). These are financial, political, and organizational issues. In their
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 4 Performance Evaluation
### Comparison of EDC Deployment Strategies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**EDCs**} & \multicolumn{2}{c}{**Serial Version**} & \multicolumn{2}{c}{**Spark Version**} \\  & **Latency** & **Workload (\(\times\)10\({}^{7}\))** & **Latency** & **Workload (\(\times\)10\({}^{7}\))** \\ \hline
20 & 4.63944 \(\pm\) 0.04750 & 2.620 \(\pm\) 0.314 & 4.58038 \(\pm\) 0.08287 & 2.618 \(\pm\) 0.265 \\
30 & 3.68743 \(\pm\) 0.04739 & 1.762 \(\pm\) 0.058 & 3.69201 \(\pm\) 0.04122 & 1.784 \(\pm\) 0.134 \\
40 & 3.18106 \(\pm\) 0.06106 & 1.314 \(\pm\) 0.142 & 3.13490 \(\pm\) 0.02953 & 1.303 \(\pm\) 0.039 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of latency (km) and workload (min) results between the serial and distributed versions. resource for understanding the practical implications of the algorithm's deployment in real-world scenarios.
## The NB-IoT Channels and Coverage Enhancements  
### NB-IoT Design Overview  
The NB-IoT, initially defined by 3 GPP Release 13, was conceived within an LTE framework, employing its multiple functionalities that, on the one hand, undoubtedly determine NB-IoT characteristics and boundaries. On the other hand, the existing LTE authorized spectrum, hardware, and deployed providers' setup may be reused for NB-IoT field implementation without any extensive modernizations, including core networks.  
The NB-IoT can be deployed in three different operational modes: standalone, inband, or guardband. In standalone mode, an independent and dedicated carrier is deployed with regards to, for example, refarning the global system for mobile communications (GSM) frequencies. Inband and guardband modes require coexistence with a wideband LTE carrier, where the inband version consumes at least one physical resource block (PRB) within an LTE carrier, whereas the guardband mode carrier is placed as a new resource block, which utilizes the edge frequency (guardband) of an LTE carrier. All NB-IoT mode carriers occupy a bandwidth of one LTE PRB (i.e., 180 kHz).  
Design targets of the NB-IoT are oriented to cover low-end mMTC scenarios, such as extended coverage, higher latency applications, support of a massive number of low throughput and low-cost devices, minimized power consumption, or simplified network or system architecture, with the ultimate goal of providing support for applications based on smart metering or intelligent environment monitoring. To fulfill the requirements, the NB-IoT design is addressed by a plethora of 3 GPP standards and employs half-duplex operation and, initially, the frequency division duplex (FDD) technique (time division duplex (TDD) support is introduced in Release 15). Access to network services is defined by a set of documents with comprehensive specifications of the physical layer and the medium access control (MAC) protocol [24; 25; 26; 27; 28].
## 2 Optimal Scheduling Models for AC/DC Distribution Systems
### AC Subnetwork Constraints
#### 2.2.1 AC Power Flow Constraints  
The AC power flow constraints are modeled using Equations (5)-(8) from [7]:  
\[\widetilde{U}_{j,t}^{\text{AC}}-\widetilde{U}_{i,t}^{\text{AC}}=2(r_{ij}^{ \text{AC}}P_{ij,t}^{\text{AC}}+x_{ij}^{\text{AC}}Q_{ij,t}^{\text{AC}})-(r_{ij}^ {\text{AC}^{2}}+x_{ij}^{\text{AC}^{2}})\widetilde{I}_{ij,t}^{\text{AC}},\forall ij \in L_{e}^{\text{AC}},\forall t\in T \tag{5}\]  
\[P_{j,t}^{inj}-\sum_{k\geq j-k}P_{j,t}^{\text{AC}}+\sum_{i\geq i-j}(P_{ij,t}^{ \text{AC}}-r_{ij}^{\text{AC}}\widetilde{I}_{ij,t}^{\text{AC}})=0,\forall j\in B _{e}^{\text{AC}},\forall t\in T \tag{6}\]  
\[Q_{j,t}^{inj}-\sum_{k\geq i-k}Q_{jk,t}^{\text{AC}}+\sum_{i\geq i-j}(Q_{ij,t}^{ \text{AC}}-x_{ij}^{\text{AC}}\widetilde{I}_{ij,t}^{\text{AC}})=0,\forall j\in B _{e}^{\text{AC}},\forall t\in T \tag{7}\]  
\[\left\|\begin{array}{c}2P_{ij,t}^{\text{AC}}\\ 2Q_{ij,t}^{\text{AC}}\\ \widetilde{I}_{ij,t}^{\text{AC}}-\widetilde{U}_{j,t}^{\text{AC}}\\ \end{array}\right\|_{2}\leq\widetilde{I}_{ij,t}^{\text{AC}}+\widetilde{U}_{j,t} ^{\text{AC}},\forall ij\in L_{e}^{\text{AC}},\forall t\in T \tag{8}\]  
where \(\widetilde{U}_{j,t}^{\text{AC}}\) and \(\widetilde{U}_{i,t}^{\text{AC}}\) are the squared AC voltage magnitudes at nodes \(j\) and \(i\) at time \(t\); \(P_{ij,t}^{\text{AC}}\) and \(Q_{ij,t}^{\text{AC}}\) are the active and reactive power flows on branch \(ij\) at time \(t\); and \(P_{j,t}^{inj}\) and \(Q_{j,t}^{inj}\) are the injected active and reactive power at node \(j\) at time \(t\).
# Adaptable and Automated Small UAV Deployments via Virtualization
## 3 System Design
In our design, each of these types of management communications is delivered over an independent virtual network, which is created over the wireless ad-hoc network infrastructure offered by the SUAVs (i.e., the virtual network operates over the FANET routing protocol executed at every SUAV). Thus, management communications are isolated and delivered \"in-band\", through the aerial network infrastructure conformed by the compute nodes. Finally, given that management communications are always necessary for any application requiring the deployment of an unmanned aircraft system, both virtual networks can be pre-configured offline at each SUAV unit and the GCS. Regarding data communications among the lightweight VNFs that conform a network service (indicated as _Data communications_ in Figure 2), these are also isolated using virtual networks that are built on top of the wireless ad-hoc network established by the compute nodes. The number and  
Figure 2: Overview of the platform design. configuration of these virtual networks will typically be application-specific (e.g., IP telephony traffic would require an independent virtual network). Hence, they will automatically be created by the MANO system as required, under the indication of the Mission Planner. In an operational deployment, the VNFs of the network service (e.g., the components of an IP telephony service) would be instantiated and configured at every involved compute node. In addition, the MANO system would also create the virtual networks that are necessary to interconnect the VNFs and support data communications. In our solution, the flight control functionalities required at each SUAV unit can be provided by a lightweight VNF. This VNF will execute the needed actions to position the SUAV at its target position (the position, as well as the trajectory of the SUAV to reach that position, can be configured at the flight control VNF by the MANO system). Management communications (i.e., between the MANO system and each VNF, and between the VIM and each compute node) are still maintained through the virtual networks that have been pre-configured offline for this purpose, with the support provided by the FANET routing protocol that operates at the compute nodes. Finally, as a specific design criterion regarding the deployment of virtual functions, we want to highlight that, given the limited-capacity hardware and software platforms that can be provided and/or transported by SUAVs, we have decided to use container virtualization, as opposed to traditional hypervisor-based virtual machines, to support the deployment of the lightweight VNFs in our design. This is a crucial consideration that has a significant impact on the practical feasibility of the proposed design.