esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Application of the WoE-LR Model  
In this case, SPSS 18.0 software was applied to build a landslide susceptibility model with the WoE-LR model. The input table of the LR model can be generated by the determined class values of variables based on the WoE model [54]. In the analysis process, a forward stepwise LR was adopted, and the analysis results are given in Tables 3 and 4. The Cox and Snell R Square (0.245) and Nagelkerke R Square (0.326) are two pseudo determined coefficients that are used to reflect the degree of independent variables explaining dependent variables [102; 103]. According to Table 4, the LR equation and landslide occurrence probability \(P\) can be expressed as Equations (7) and (8), respectively.  
\[\begin{array}{ll}y=&1.122\times\text{Slope angle}+2.157\times\text{Slope aspect}+0.986\times\text{Elevation}\\ &+2.505\times\text{Plan curvature}+0.868\times\text{Profile curvature}+1.764 \times\text{TWI}\\ &+1.427\times\text{SPI}+1.142\times\text{STI}+0.512\times\text{Distance to rivers}\\ &+1.445\times\text{Distance to roads}+0.972\times\text{Distance to faults}\\ &+0.859\times\text{NDVI}+1.392\times\text{Soil}+1.634\times\text{Landuse}+1.03 2\times\text{Lithology}\\ &+1.594\times\text{Rainfall}+0.806\end{array} \tag{7}\]  
\[P=\frac{e^{y}}{1+e^{y}} \tag{8}\]  
Figure 4: Landslide susceptibility map using the WoE model.
## 2 Problem Formulation  
An HRSI is a data cube with two spatial dimensions and one spectral dimension. An HRSI can be expressed as \(O\in R^{h\times w\times b}\), where \(O\) is the original HRSI; \(h\) and \(w\) are the spatial height and width of the HRSI, respectively; and \(b\) is the number of bands. The first \(p\) principal components (\(O_{p}\in R^{h\times w\times p}\)) are retained after PCA dimensionality reduction. The first \(J\) principal components are selected for binarization, and the spatial features (\(O_{p_{j}}\in R^{h\times w\times J}\)) of the binarization data are extracted via MP. \(O_{p}\) and \(O_{p_{j}}\) are concatenated to obtain \(O_{A}\in R^{h\times w\times A}\), \(A=p+3j\). The input patch is \(O_{patch}\in R^{s\times s\times A}\); finally, the created HRSI patch is fed to the 3D-2D CNN.  
The flow of the proposed HRSI classification method is illustrated in Figure 1. First, PCA is used to reduce the dimensionality of the HRSI cube, and the first \(p\) principal components are extracted from the low-dimensional HRSI cube. The first \(j\) principal components are binarized, and the spatial features of the binary data are extracted through MP. Then, the low-dimensional HRSI cube and spatial features are concatenated. Finally, the designed 3D-2D CNN framework is used to complete classification.
# Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks
## 2 System Model
### Communication Model  
In the learning process, the aggregating phase is the bottleneck, because the wireless UL of the IoT node is resource-limited compared to the DL of BS. Hence, we focus on the resource optimization of the wireless UL in FL-aided wireless IoT networks. We assume that each IoT node transmits its local model parameter through frequency division multiple access. The achievable rate of IoT node \(i\) at the \(t\)th round can be written as  
\[r^{t}_{i}=B^{t}_{i}\log_{2}(1+\frac{P^{t}_{i}h^{t}_{i}}{\sigma^{2}}), \tag{5}\]  
where \(B^{t}_{i}\) is the frequency bandwidth of IoT node \(i\). \(P^{t}_{i}\) is the transmitting power of IoT \(i\), \(h^{t}_{i}\) is the channel gain from IoT node \(i\) to BS, \(\sigma^{2}\) is the power spectral density of the background noise. For each IoT node \(i\), the local model parameter \(\mathbf{w}^{t+1}_{i}\) is encapsulated into a packet with a size of \(D\) in the transmission. Thus, the transmission time from IoT node \(i\) to BS can be written as  
\[T^{t}_{i}=\frac{D}{r^{t}_{i}}. \tag{6}\]  
With (6), the outage probability of IoT node \(i\) can be expressed as  
\[p^{t}_{i}=\Pr(T^{t}_{i}>\theta), \tag{7}\]  
where \(\theta\) is the maximum acceptable latency for the \(t\)th round in FL.  
For the convenience of readers, the parameters concerning the formulation can be found in Table 1.
# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 4 Experiment
### Comparative Experiment
#### 4.5.3 Experiment on UCAS-AOD  
As shown in Table 7, comparing different methods on the UCAS-AOD dataset, our method was the best, with an mAP of 90.52%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Backbone** & **Size** & **mAP (\%)** \\ \hline \hline Two-stage: & & & \\ \hline \(R^{2}\)CNN [39] & Res101 & 800 \(\times\) 800 & 73.1 \\ \(R^{2}\)PN [40] & VGG16 & - & 79.6 \\ AOPG [41] & Res50 & 800 \(\times\) 800 & 80.6 \\ Rol-Trans [2] & Res101 & 512 \(\times\) 800 & 86.2 \\ \hline Single-stage: & & & \\ \hline \(R^{3}\)Det [1] & Res101 & 800 \(\times\) 800 & 89.3 \\ RRD [42] & VGG16 & 384 \(\times\) 384 & 84.3 \\ OPLD [43] & Res101 & 800 \(\times\) 800 & 88.4 \\ AR2 Det [44] & Res101 & 512 \(\times\) 512 & 89.6 \\ SDet [45] & Res101 & 800 \(\times\) 800 & 89.2 \\
**ours** & Res101 & 800 \(\times\) 800 & **90.41** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different single-stage and two-stage methods on the HRSC-2016 dataset.  
Figure 5: Visualize the results of this method on DOTA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Car** & **Airplane** & **mAP (\%)** \\ \hline R-RetinaNet [46] & 84.65 & 85.46 & 78.19 \\ R2 PN [40] & 76.74 & 88.66 & 78.63 \\ Rol-Trans [2] & 88.02 & 90.02 & 89.02 \\ S2 ANet [47] & 89.56 & 90.42 & 89.99 \\ RIDet-O [32] & 88.88 & 90.35 & 89.62 \\ ours & 86.64 & 94.28 & 90.52 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with multiple methods on UCAS-AOD.  
Figure 7 shows the detection results of this method on the UCAS-AOD dataset. In the first row of images, the vehicle is obstructed by trees and buildings. This network is sensitive to the features of the target spatial direction, so it can detect partially occluded targets by predicting the direction and width of vehicles. The airplane in the second row of images exhibits characteristics with different directions and a dense layout. We ensured the accuracy of model detection through balanced loss training.  
Table 8 compares the inference time and mAP values of different models. Our proposed method has a significant improvement in model size compared to Faster RCNN and SSD, with significantly reduced Params and FLOPs of 133.9M and 65.1G, respectively. Running on 11 th Gen Intel (R) Core (TM) i7 with hardware conditions, our fps reached a  
Figure 6: The detection effect of this method on HRSC-2016.  
Figure 7: The detection effect of this method on the UCAS-AOD. The yellow box represents detection results.  
maximum value of 8 and mAP also reached a maximum of 86.8%. Table 8 demonstrates that our method has improved accuracy and inference time compared to other models.
# Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System
###### Abstract  
Unmanned aerial vehicles (UAVs) are widely used in wireless communication systems due to their flexible mobility and high maneuverability. The combination of UAVs and mobile edge computing (MEC) is regarded as a promising technology to provide high-quality computing services for latency-sensitive applications. In this paper, a novel UAV-assisted MEC uplink maritime communication system is proposed, where an MEC server is equipped on UAV to provide flexible assistance to maritime user. In particular, the task of user can be divided into two parts: one portion is offloaded to UAV and the remaining portion is offloaded to onshore base station for computing. We formulate an optimization problem to minimize the total system latency by designing the optimal flying altitude of UAV and the optimal task allocation ratio. We derive a semi closed-form expression of the optimal flying altitude of UAV and a closed-form expression of the optimal task allocation ratio. Simulation results demonstrate the precision of the theoretical analyses and show some interesting insights.  
U  
A _Article_  
Altitude Optimization and Task Allocation  
228061. [[https://doi.org/10.3390/s22280861](https://doi.org/10.3390/s22280861)]([https://doi.org/10.3390/s22280861](https://doi.org/10.3390/s22280861))  
23 September 2022  
Accepted: 19 October 2022  
Published: 21 October 2022  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright**: © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).  
**Keywords**: UAV communication; mobile edge computing; delay; UAV altitude; task allocation
# Metadata Quality for Biobanks
## 2 Background
### Metadata  
We follow [16] in defining the metadata as _\"the information that provides the context and additional information about the domain data or conditions on the usage of data\"_.  
Let us look at the above definition in more detail. The context of the domain data includes among other elements:  
* the information on data sources and data collection methods;
* the descriptions of the external data.  
The additional information about the domain data includes, among other elements:  
* the semantics necessary to understand the data, e.g., referring to some ontology [17];
* the information on the units of measurement which are used for data interpretation, e.g., meters or grams.
* the metric values characterizing the quality of data, e.g., percentage values of data completeness metrics, or expert-based estimates of data reliability metrics.  
The conditions on the usage of data include, among other elements:  
* the information on data attributes referring to their meaning, source, etc., e.g., its precision or the collection method;
* the descriptions of the domains which specify the allowed value sets, e.g., the disease code domain defining the set of allowed disease codes;The metadata differs from data in purpose and usage, but not in format or structure, so it can be considered the data itself: \"the data that provides information about other data\". It is possible to distinguish the following categories of metadata:  
* metadata describing the data schema; this is the approach most widely used for databases (_database metadata_); it often assumes that the data is homogeneous, so the schema is centralized and stable;
* metadata describing data semantics, e.g., by ontological means (_semantic_ or _content metadata_); this is the approach most widely used for digital libraries;
* metadata describing data quality (_quality metadata_), the quality metadata elements may hold the values of metrics that quantify data item quality characteristics.  
The most general definition of metadata quality assumes that the metadata always serves a specific purpose (e.g., it can describe the data schema or provide data semantics, support the search for samples, etc.). It is stated as follows: _metadata quality is the degree of success for the metadata in serving its purpose_. Metadata of high quality serves its purpose better than low-quality metadata. This is the kind of quality we will deal with in this paper so we will provide a more detailed metadata quality definition specific to the biobank domain later in the paper.  
In the next section, we provide a detailed review of state of the art in the area of metadata in medical research and the quality of such metadata.
# Device Modeling of Quantum Dot-Organic Light Emitting Diodes for High Green Color Purity
## 2 Materials and Methods  
### Device Modeling of the OLED Multi-Layers  
Figure 1 shows the material and thickness of each layer of the OLED structure with multiple layers investigated in the present study. In order to more effectively investigate the change in the emission spectrum according to the wavelength from a structural perspective, a structure in which a cathode is stacked at the bottom and an anode is stacked at the top was employed. A 150 nm-thick aluminum was used as cathode electrode material, and 5 nm-thick Ca was added between the n-electron transport layer (ETL) and aluminum layers to facilitate electron movement by improving the contact property with the n-ETL layer. For the n-doped electron-injecting layer (EIL), 40 nm of n-NET5 was applied, and 10 nm of NET5 was used as the ETL. In order to create an incident light in the blue wavelength band where absorption occurs most in the G-QD, blue OLED materials were stacked as the emitting layer. The blue, fluorescent layer was a 15 nm spiro-DPVBi (B-Spiro-DPVBi). A 100 nm transparent indium-tin oxide (ITO) rod was applied as the anode electrode. A 10 nm p-NHT5 was employed as a p-doped hole-injection layer, and a 10 nm a-NPD was used as the hole-transport layer. The energy level of the multi-layer OLED is shown in Figure 2. The highest occupied molecular orbital (HOMO) levels of the a-NPD, blue, fluorescent layer, B-Spiro-DPVBi, and NET5 were \(-\)2.33, \(-\)2.55, and \(-\)5.6 eV, respectively, and the lowest unoccupied molecular orbital (LUMO) levels were \(-\)5.33, \(-\)5.6, and \(-\)5.7 eV, respectively.  
Figure 1: The basic structure of the investigated organic light-emitting diode (OLED) multi-layers with a green quantum-dot (G-QD) layer.  
First, the emission spectrum was investigated in the absence of a G-QD layer. After the device was optimized for the basic blue OLED unit, the effect of G-QD layers was investigated with stacked structure, as shown in Figure 1. In this figure, the G-QD layer exists on top of the indium-tin oxide (ITO) layer. Specifically, a top emission structure in which the generated light is emitted toward the ITO was applied by placing the Al cathode at the bottom and the ITO anode at the top. Through this structure, it was possible to simulate the effect of blue incident light on the emission spectrum through the conversion effect after being absorbed into the QD layer.  
The density and thickness were considered as variables of the G-QD layer. The concentration of G-QD used in the simulation was 1%, 5%, 10%, 20%, 30%, and 40%. At various concentrations, we tried to see the emission spectrum change according to the wavelength. In addition, after fixing the concentration at 10%, the emission spectrum change was observed while changing the G-QD thickness to 2 \(\upmu\)m, 4 \(\upmu\)m, 6 \(\upmu\)m, 8 \(\upmu\)m, and 10 \(\upmu\)m in order to investigate the influence of the thickness.
## Methods
### Sample Selection  
First-year and multiyear samples were manually distinguished in the SAR imagery based on spatial context, texture and backscatter intensity. Multiyear sea ice (MYI) is characterized by high backscatter values in co- and cross-polarization, distinct texture and large floe sizes. First-year sea ice (FYI) has a much larger spectrum of characteristic SAR appearances that are usually lower in backscatter than for MYI especially at cross-polarization. High backscatter FYI, e.g., brash ice or frost flower covered ice, is distinct by its more homogeneous texture compared to MYI. Ice charts were used as auxiliary information in the decision process. Only those waveform samples that show a homogeneous ice type within the footprint area of the altimeter were considered for further analysis. Furthermore, specular returns of the altimeter where rejected based on PP and peak power to avoid the influence of smooth features below the resolution of the SAR images. We also included larger leads, i.e., areas in between distinct floes, with lower SAR backscatter that are large enough to contain the footprint area of the altimeter. These leads could have a high peak return because it is characteristic for the specular return of smooth ice. Table 2 summarizes the selected samples for each ice type and season. Differences in sample numbers between the seasons reflect differences in the available near co-incidental data sets.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things  
Antoine [PERSON] 1  
++  
produce is stored is at the correct temperature in order to preserve the quality of the produce. Smart RFID tags equipped with temperature sensors are then simultaneously used to sense the surrounding environment's temperature and send an alert if the produce is being exposed to too low or high temperatures. Various other smart applications derived from the integration of sensor and RFID technologies can be beneficial to different industries. These include the identification, counting and detection of human presence and the control of the heating, ventilation, air conditioning and lighting in smart buildings, patient and staff identification and tracking [5] in healthcare systems and the design of smart parking systems in smart cities. Separate deployment of the sensor and RFID technologies may lead to a duplication of both hardware and software resources, complex and costly system management and difficulties in system trouble shooting and maintenance.  
Researchers [6; 7; 8; 9] have presented different ways of integrating both technologies with the objective of building an _ad hoc_ network that has similar properties to wireless sensor networks (WSNs). The issue with such an approach is that the resulting network will also present similar energy limitations to WSNs. In [6], on-demand wakeup is used to eliminate idle listening with the expectation of energy savings. While resulting in energy savings, this approach uses the medium access control (MAC) protocol, which is not a natural fit for the mesh model implemented by sensor networks. Other approaches have been proposed for the integration of sensor and RFID technologies. This includes the work done in [7], where an integrated sensor-RFID node was designed to enable sensing of gas levels in underground mines and detecting the presence of miners while providing communication capabilities using ZigBee or WiFi. The designed sensor/RFID more was further used as a sensor prototype in the smart parking prototype described in [8] and as a sensor node in the optimal placement model for smart parking described in [9]. However, despite their relative advantages compared to separate sensor/RFID deployments, these approaches may lead to heterogeneous energy consumption patterns, which need to be accounted for when routing the sensor readings from their locations to the base station of a hybrid IoT system. Otherwise, the network may experience an energy imbalance and subsequent short-lived IoT infrastructures resulting from routing the sensor readings over the most service- intensive sensor nodes, while leaving the least active nodes idle.
# Mobility Prediction of Mobile Wireless Nodes
## 8 Conclusions and Future Work  
In this study, we evaluated the performance of classifiers and mobility models using sixteen distinct scenarios. Four mobility models (i.e., RW, RD, Gauss-Markov, and RSSGM) and six classifiers (i.e., DT, LR, KNN, LDA, GNB, and SVM) were used. We also explored the effectiveness of mobility models by comparing the prediction accuracy of these classifiers. The results show that random models (i.e., RD and RW) varied in their prediction accuracy, with a minimum of 39% and a maximum of 81%. The Gauss-Markov model had good accuracy, reaching 89% with the LDA classifier. The RSSGM model was found to have very high prediction accuracy, achieving 98% accuracy with the DT classifier. Moreover, RSSGM reached a high accuracy and a positive correlation between step count and classification accuracy, based on expanding the learning and understanding between user movement and classifier. Therefore, RSSGM is ideal for the classification prediction of nodes with repeated visits to particular locations.  
This experiment is a modeling type that requires less time and can implement many scenarios to provide an initial impression of the classification behavior over different mobility models. However, as a next step, the experiment shall capture more accurate behavior, and future works should include other ML classifiers (e.g., XGboost) to pick the best classifier for use with mobility models. Furthermore, the simulation should consider more parameters to provide more accurate results, such as an RSSGM to evaluate the theoretical work.  
[PERSON] performed the experiments, analyzed the data, and wrote the manuscript. [PERSON] supervised the research and critically revised the manuscript. [PERSON] critically revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to Researcher Supporting Project number (RSPD2023R582), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors declare no conflict of interest.
# Indoor Acoustic Requirements for Autism-Friendly Spaces
## 2 Materials and Methods
### Determination of the Optimal Sound Field Ranges for Autistic Individuals and Acoustic Sensors  
Ranges for hearing impaired individuals can be found, but research related to indoor sound field distribution is neither available focused on individuals, nor on procedures or approaches explaining how and where to select the speakers and listeners' positions.  
In this view, studies not directly related to ASD individuals are available so far and can be used as reference. The American Speech Language Hearing Association [56] recommends 0.4 s as the reverberation time for unoccupied rooms for children with hearing loss and children with auditory learning difficulties or hearing disabilities. The UK Building Bulletin 102 [57] contains the maximum mid-frequency reverberation time requirements for newly-built environments and refurbishments. For spaces intended for users with special hearing or communication needs, the recommended reverberation time is \(\text{RT}\leq 0.4\) s, averaged from the 125 Hz to 4 kHz octave bands. The American National Standards Institute [58] recommends a reverberation time of RT < 0.7 s for schools in the octave frequency band of 500 Hz, 1000 Hz, and 2000 Hz. For the health care sector, [PERSON] [59] suggested RT values in the range between 250 Hz and 2000 Hz of between 0.4 and 0.7 s. [PERSON] [60] pointed out that the speech intelligibility in a room is excellent, if C\({}_{50}\geq 2\) dB and D\({}_{50}\) > 0.6. Furthermore, [PERSON] [61] clearly related C\({}_{50}\) with vowel receptions. These data are also confirmed by [PERSON] and [PERSON] [62] and [PERSON] and [PERSON] [63], where it is also highlighted that a good clarity and definition are not necessarily related to a room destination, but are valid in any situations. For this reason, in this research, those values were considered the best ones to be used.  
The study of the peculiarities above described suggests that the best approach is not to \"standardize\" acoustic characteristics, but rather to use \"user-centered design\" approaches [64]. Once the space has been designed, taking into account for the customizations for each user, smart technology can support in a non-invasive way, permitting the reduction in anxiety, daily stress, psychological hazards [26], and solving important issues such as autonomy, dignity, security, and privacy [65].