esa-sceva/satcom-chunk-collection huggingface dataset:
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 4 Experiment and Discussion
### Ablation Experiments
#### 4.2.2 Evaluation of United Bounding Regression  
The original bounding box regression directly estimate the center point offset coordinating with height and width, which treats these points as independent variables. However, this method ignores the integrity of the bounding box itself. In order to improve the convergence speed and accuracy of regression, united bounding regression based on IoU was adopted instead of the original independent regression of size and offset. Focal Loss was selected as the classification module, which is same as the original network. At the same time, IoU, DioU, and CIoU were adopted for the regression of borders to qualitative analyze the influence of the bounding box regression on the prediction network.  
In Table 2 it can be seen that the prediction network reached the best performance when CIoU was adopted. This is because CioU loss simultaneously considers the overlapping area, the distance between center points, and the aspect ratio. From the data, we can see that the mAP improved from 0.942 (ORIG) to 0.964 (CIOU) and mRecall improved from 0.968 to 0.985.  
Figure 6: Selected detection results of the original network and the DGA network. The first column shows the results of the original network, while the second column shows the results of DGA.
## 2 OMA of Linear Time-Invariant Structures Based on LPP
### OMA of Linear Time-Invariant Structures Based on LPP
\[\mathbf{X}(t)\ =\ (\mathbf{A}\mathbf{A}^{\mathsf{T}})\mathbf{X}(t)\ =\mathbf{A}( \mathbf{A}^{\mathsf{T}}\mathbf{X}(t)) \tag{9}\]  
where \(\mathbf{A}\in\mathbb{R}^{n\times d}\) is the transformation matrix, \(\mathbf{A}^{\mathsf{T}}\mathbf{X}(t)\) is the \(d\)-dimensional embedding \(\mathbf{S}(t)\in\mathbb{R}^{d\times T}\) of \(\mathbf{X}(t)\in\mathbb{R}^{n\times T}\). Therefore, \(\mathbf{X}(t)\in\mathbb{R}^{n\times T}\) has the following form:  
\[\mathbf{X}(t)\ =\mathbf{A}\mathbf{S}(t)\ =\sum_{i=1}^{d}\overset{\rightarrow}{a}_{ i}\overset{\rightarrow}{s}_{i}(t) \tag{10}\]  
Comparing Equations (2) and (10), we can make the conclusion that the low-dimensional embedded \(\mathbf{S}(t)\in\mathbb{R}^{d\times T}\) corresponds to the modal response \(\mathbf{Q}(t)\in\mathbb{R}^{d\times T}\), and the transformation matrix \(\mathbf{A}\in\mathbb{R}^{n\times d}\) corresponds to the modal shape \(\mathbf{\Phi}\in\mathbb{R}^{n\times d}\). Figure 1 illustrates OMA of time-invariant structure based on LPP algorithm. Compared with State matrix eigen-decomposition, LPP algorithm builds a graph incorporating neighborhood information of the data set. Using the notion of the Laplacian of the graph, LPP algorithm computes a transformation matrix which maps the data points to a subspace. This linear transformation optimally preserves local neighborhood information in a certain sense [36]. This feature helps retain information about the response signal.
## 3 Experimental Results and Analysis
### Vicon Dataset  
We validated the previously mentioned frameworks on the offline Vicon dataset [58], which spans 324.3 m in a single room over a 300 s collection period. The dataset provides inertial and magnetic information with nine degrees of freedom (DOF) at 100 Hz from an instep-mounted inertial/magnetic sensor during free walking, while a full six-DOF ground truth (three DOF for position and three DOF for attitude) was captured simultaneously using a high-precision Vicon optical tracking system. The estimated trajectories of the different frameworks and the reference trajectory are shown in Figure 5. The heading-error-stride curve, heading and translation error distributions, and heading and translation error cumulative distribution function (CDF) curves were employed to evaluate the proposed method. In addition to our proposed method, four frameworks were evaluated to provide a comparison to indicate the heading and translation performances of different heading error correction method based frameworks.  
Figure 5: Estimated trajectories of different frameworks (colored lines) and the optical reference trajectory (dashed line).  
For the heading performance, Figures 6-8 show that ZUPT-AFM and ZUPT-MED-EC, which have small heading errors (all absolute heading errors are within 9.6\({}^{\circ}\), and 75% absolute heading errors are less than 3.5\({}^{\circ}\)) that hardly change with time, outperform the other frameworks. Both frameworks have similar performances, which indicates that the AFM and conditional EC (MED-EC) prevent long-term heading drift and help the PNS output a greater heading accuracy. By contrast, the heading errors of ZUPT and ZUPT-HDR are greater and increase over time, while the heading error of ZUPT-EC is the greatest. In Figure 8, the 75% translation errors are less than 33 cm, with the exception of the ZUPT-EC framework because a standalone EC is easily affected by magnetic disturbances. For the ZUPT-AFM and ZUPT-MED-EC, the 99% translation errors are less than 47 cm and the 75% translation errors are less than 30 cm, which indicates that both frameworks can localize with the highest accuracy. The primary advantage of the proposed ZUPT-AFM and ZUPT-MED-EC is their complementary correction of the heading error using inertial and conditional magnetic data. In Table 3, ZUPT performs a little better than ZUPT-HDR. The possible reason is that the straight-line path may not be absolutely straight. Improper correction by the HDR may over-correct the heading drift on the small-scale dataset. Considering that the HDR is a component of ZUPT-AFM, this may be the reason why ZUPT-MED-EC performs a little better than ZUPT-AFM. For this Vicon dataset, there are too many nonlinear motions. Therefore, ZUPT-HDR is equivalent to ZUPT because the HDR cannot be triggered frequently. By contrast, MED-EC has more chance to work. Thus, ZUPT-MED-EC outperforms ZUPT-HDR. Finally, it can be temporarily concluded that ZUPT-MED-EC > ZUPT-AFM > ZUPT > ZUPT-HDR > ZUPT-EC. However, more data are needed to fully validate their performances.
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 3 Public Key Infrastructure
### Implementation of PQC Algorithms for PKI
Focusing on three PQC algorithms, SABER, CRYSTALS-Dilithium, and NTRU, a breakdown of the fraction of time spent (as a percentage) in the hashing/XOR and polynomial multiplication components of the algorithms is reported in Table 1. NTRU spends the majority of its time doing polynomial multiplication first, then hashing second [44], but no benchmarks have been calculated thus far. The times spent for the hashing and polynomial multiplication components of CRYSTALS-Dilithium, and SABER are reported as percentages of the total execution time for the key pair generation procedure where the percentages are an average of 10 time trials. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Hashing and XOF** & **Polynomial Multiplication** & **Reference** \\ \hline SABER & \(\sim\)30\% & \(\sim\)60\% & Our benchmarks \\ CRYSTALS-Dilithium & \(\sim\)42\% & \(\sim\)33\% & Our benchmarks \\ NTRU & second bottleneck & first bottleneck & [40; 41] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Breakdown of the fraction of the time hashing and extendable output function XOF compared with the time performing polynomial multiplication. Both SABER and CRYSTALS are constrained by the polynomial multiplication. The light versions considered in the post quantum cryptographic (PQC) implementations of hashing and XOF functions such as SHA3 with SHAKE are extremely effective.
###### Abstract  
Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin's remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.  
ANN; SVM; lithological mapping; machine learning; remote sensing +
Footnote †: journal: Applied Sciences
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 2: Multi-Temporal Input  
Cross-sectional modelling of urban forest attributes and their ecosystem services might be biased by unobserved factors. A useful strategy to mitigate this bias is to replicate observations of the same units over time, thus time-invariant and location-specific unobserved factors can be controlled ([PERSON] et al., 2017). In particular, multi-temporal remotely sensed data carry great potential for capturing local details and have greater potential to improve estimation accuracy based on locally detailed and spatially explicit data input. For example, using multi-temporal image data can increase the success rate of mapping urban tree species composition as phenological information is retrieved ([PERSON] et al., 2017; 2018); this could be of vital importance for urban forests due to their compositional and configurational complexity and heterogeneity. Due to such potential, empirical studies have made use of short or long series multi-temporal remote sensing datasets to estimate changes in urban forest compositional and configurational attributes (e.g., dominant species, forest age, canopy density); hence, a reliable relationship between urban forests and the ecosystem services they provide is established.
## 2 Adaptive R Estimation  
### Related Work about **R** Estimation  
The most commonly used AKF (i.e., IAE and RAE), make use of the new information in the innovation sequence or residual sequence to adaptively tune the measurement noise covariance matrix **R**[11; 31]. For a nonlinear system, the two algorithms are implemented as:  
\[\left\{\begin{array}{l}\mathbf{\hat{R}}_{k,IAE}=\frac{1}{N}\sum\limits_{j=j_ {0}}^{k}\epsilon_{j}\epsilon_{j}^{T}-\mathbf{P}_{zz,k}^{-}\\ \mathbf{\hat{R}}_{k,RAE}=\frac{1}{N}\sum\limits_{j=j_{0}}^{k}\mathbf{r}_{j} \mathbf{r}_{j}^{T}+\mathbf{P}_{zz,k}^{+}\end{array}\right. \tag{1}\]  
where \(\mathbf{P}_{zz,k}^{-}\) is the covariance of the predicting measurement error [31], \(\mathbf{P}_{zz,k}^{+}\) is the covariance of the filtering measurement error [32] and \(N\) represents the length of a sliding window. The innovation \(\epsilon_{k}\) and residue \(\mathbf{r}_{k}\) are given by:  
\[\left\{\begin{array}{l}\epsilon_{k}=\mathbf{Z}_{k}-h\big{(}\mathbf{\hat{X}} _{k,k-1}\big{)}\\ \mathbf{r}_{k}=\mathbf{Z}_{k}-h\big{(}\mathbf{\hat{X}}_{k,k}\big{)}\end{array}\right. \tag{2}\]  
where \(\mathbf{\hat{X}}_{k,k-1}\) denotes the state prediction, \(\mathbf{\hat{X}}_{k,k}\) denotes the state estimate and \(h(\cdot)\) is the measurement function.  
As shown in Equation (2), \(\epsilon_{k}\) and \(\mathbf{r}_{k}\) can be affected by biased state estimates that cannot be totally avoided in an INS/GNSS integrated navigation system. Therefore, if the system state vector is not well estimated, a negative effect will be introduced on the filter performance and as such reduce the rationality of adaptive filtering or even cause diverge. The proposed RMNCE algorithm just utilizes redundant measurements to estimate **R**, which is immune to state estimation error and then improves filtering performance.
## 3 Results and Discussion
### Electrochemical Analysis
Very recently, [PERSON] and [PERSON] [30] have reported on the electrochemical performance in the electrospray water desalination of nano-sized spinel-structured AlCrFeCoNi-HEO NPs and their mixture with biovaste-derived activated carbon functionalized with carboxylic acid (f-CSAC), obtained through a laborious multi-step preparation process involving the carbonization of coconut shells at 800 \({}^{\circ}\)C, the subsequent KOH activation at 800 \({}^{\circ}\)C, followed by the functionalization in H\({}_{2}\)SO\({}_{3}\):HNO\({}_{3}\) 3:1 acid mixture. The C values reported in their work refer to a higher NaCl concentration than in the present case (1 M vs. 0.1 M), which would hamper a direct comparison since lower salt concentrations give rise to weaker EDLs which, in turn, results in lower capacitance [51]. Nonetheless, for the sake of completeness, below, those data are compared with the results of the present study. At 100 mV s\({}^{-1}\), in 1 M NaCl aqueous solution, the mixture composed of AlCrFeCoNi-HEO NPs and f-CSAC in mass ratio 1:9 exhibits a specific capacitance of 147.5 F g\({}^{-1}\), which largely exceeds that of the present C/CrMnFeCoNi-HEO NFs (12.4 F g\({}^{-1}\)) in aqueous solution, with salt concentration lower by a factor of 10. However, the main contribution to the specific capacitance of AlCrFeCoNi-HEO/f-CSAC mixture is from its f-CSAC component, which, alone, shows a capacitance of 157.5 F g\({}^{-1}\)[30]. Conversely, the major contribution to the specific capacitance of C/CrMnFeCoNi-HEO composite NFs seems to be due to the HEO component (Figure 4e). The specific capacitance of AlCrFeCoNi-HEO NPs (12.5 F g\({}^{-1}\)) also exceeds that measured here in both CrMnFeCoNi-HEO NPs and NFs (9.1 and 7.7 F g\({}^{-1}\), respectively). Neglecting the small difference in HEO composition, both the higher molarity of the solution (1 against 0.1 M) and the very small size of AlCrFeCoNi-HEO NPs (\(<\)10 nm vs. 240 and 200 nm, respectively) are responsible for this finding. In 1 mol L\({}^{-1}\) NaCl solution, at 5 mV s\({}^{-1}\), the C value measured in SnO\({}_{2}\)/aminated polystyrene co-functionalized graphene oxide nanocomposites (SnO\({}_{2}\)/PPAS-rGO) prepared under optimized conditions is lower than that obtained in 0.1 mol L\({}^{-1}\) NaCl solution, at the same scan rate, in C/CrMnFeCoNi-HEO composite NFs prepared without optimization (-42 against 65.7 F g\({}^{-1}\)) [51]. Finally, a lower specific capacitance (56 F g\({}^{-1}\)) pertains also to commercial activated carbon (AC) in 0.5 M NaCl solution, at 2 mV s\({}^{-1}\)[52].
## 6 Simulation Results
### Network Performance Parameters: Definitions
#### 6.2.2 Path Loss  
Path loss depends on the distance between the sender and receiver and is affected by wave movement. Path loss is calculated by using the MMPE model. Figure 11a-c depicts the comparison of the path loss of AURP, AEERP, AEDG and AEDG without the GN rotation schemes in three different scenarios. In AURP, the intermediate nodes die out more quickly, and hence, the path loss increases. As there is no restriction on the association of member nodes, so the energy of the GNs quickly depletes. As nodes start dying, the distance between the sender and receiver increases; thus, the path loss increases. In AEDG, due to the balanced energy consumption, the path loss almost remains constant throughout  
Figure 10: Number of dead nodes. (**a**) 10 nodes deployed in 100 m \(\times\) 100 m; (**b**) 100 nodes deployed in 100 m \(\times\) 100 m; (**c**) 100 nodes deployed in 10 km \(\times\) 10 km.  
the network lifetime and increases abruptly at the end due to the long haul communication of the far end nodes. Figure 11a,b shows the impact of node density on path loss; a high node density means a relatively greater number of communication links (distant communication), which leads to high path loss. On the other hand, Figure 11c shows the impact of network area on path loss, _i.e._, direct relation. However, the impact of the number of links is far greater than the impact of the network area, as depicted in these figures.
## 2 Fast Frequency Domain Method
### The Algorithm Workflow  
In order to make the procedure of the algorithm clearer, the workflow of the algorithm described above is shown in Figure 1, which is executed via three steps:  
Step 1: Given \(m\), \(k\), \(\eta\), and \(f_{n}\), calculate the Fourier coefficients \(F_{j}\) from Equation (3).  
Step 2: Calculate \(D_{j}\) from Equation (22).  
Step 3: If \(\big{|}D_{N/2}\big{|}<0.05\text{max}\big{|}D_{j}\big{|}\), compute \(u\) by solving Equation (25), otherwise, solving Equation (23) to calculate \(u\).
# Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot
## 1 Introduction
Compared with other modular robots developed in [4; 9] with joints exposed to the outside, the spherical unit module proposed in this work integrates joints into the interior of the module, with a regular spherical shape that can rotate without exposing loads, and the interior space of the sphere can also accommodate other devices. In addition, compared with the Soldercube module in [22], this work has a symmetrical configuration, making replacement between modules more convenient. The entire system can achieve configuration reconstruction through the connection and disconnection of docking surfaces, as well as the rotation of joints. Each module has six docking surfaces, which can form both chain configurations like PolyBot [4], CONRO [5], and YaMoR [6] and crystal configurations like Molecules [10] between modules and can simultaneously achieve the advantages of two configurations. The innovative works of this paper are as follows. First, reconfigurable modular robots greatly improve their flexibility and adaptability by modularly integrating components and then assembling them according to mission requirements. Second, self-reconfigurable robots are composed of modular units with consistent configurations and a certain degree of freedom, and allosteric rules are formulated according to mission constraints, which results in them being able to perform self-reconfiguration in real time and adapt to more complex mission requirements. The structure of this paper is organized as follows: in Section 2, the configuration of reconfigurable modular space robot is designed, where the space mission is described and the unit module with its mechanism is designed. Section 3 gives the dynamic model of the modular space robot based on the RW method, including the motions of joints, attitude, and position. The simulation results and analysis are carried out in Section 4, and the conclusion of this paper is given in Section 5.
# Super-Resolution Reconstruction of Depth Image Based on Kriging Interpolation
## 4 Kriging Interpolation Super-Resolution Algorithm Based on Fractional-Order Differentiation
(**a,d,g**) Original depth images; (**b,e,h**) Outputs of fractional-order differential Kriging interpolation super-resolution model (\(\
u=0.6\), PSNR = 33.819 dB); (**c,f,i**) Outputs of nearest-neighbour interpolation super-resolution model (PSNR = 21.051 dB). Figures 14-18 show the super-resolution results after 25% down-sampling and 400% up-sampling, and it can be seen that the depth images after super-resolution meet the requirements for effective reconstruction and recognition. Moreover, for complex and variable data, such as the multiple bouquets in Figure 18 Kriging interpolation considerably outperforms nearest-neighbour interpolation.
## 3 Results
### Technological Features and Affordances  
#### 3.3.1 Input and Output  
User input is essential for interaction with AR interfaces, and the input devices can be classified into two major categories: natural input and artificial input. Natural input enables users to interact with AR objects in a more intuitive way, often without the reliance of accessory equipment. Common types of natural input devices include voice command, magnetic sensor (i.e., orienting behaviors such as tilt and rotation perceived by magnetic sensors), motion tracker (i.e., gestures, gait, and motions perceived by motion sensors such as Kinect), and haptic sensor (i.e., haptic stimuli synthesized using force feedback or tactile devices). Contrarily, artificial input relies on the use of peripheral devices to achieve unnatural interaction with AR systems. Commonly used artificial input devices include global positioning system (GPS) (e.g., location-based mobile applications), mouse and keyboard, scanner (e.g., image or QR code reader), and button-pressing controller (e.g., gamepad or joystick).  
As seen in Figure 4, natural interaction was applied more frequently in professional training in the first 15 years, especially motion trackers (27%) and haptic sensors (19%). For example, the AR-supported simulator in surgical operation or ultrasonic examination was usually equipped with motion sensors to capture the instrument's motion and control users' operation [25; 26; 27]. In addition, users' haptic stimuli such as pressing or touching were traced by haptic sensors [28; 29]. However, in the last five years, natural inputs seemed to lose their appeal to professional educators as their proportion dwindled substantially. In particular, the use of motion trackers witnessed a sharp decrease from 27% to 10%.  
Contrarily, the recent literature has reported more instances of AR program featured by natural input. For example, scanning significantly increased (from 16% to 27%), accounting for a quarter of the research in professional training. In this way, AR devices were equipped with a camera or webcam to scan the QR code to help students to acquire instructional content, such as texts, images, and videos [30; 31]. Another artificial type, inputting by mouse and keyboard, also showed an increase since 2016 (from 5% to 12%). The reduction in motion-based input and the increase in artificial types suggest the technical  
Figure 3: Research type of all AR-supported research in professional training from 2001 to 2020.  
difficulty in integrating motion trackers into AR and the need to further reduce the cost of AR development.  
Compared with the types of input devices, output devices were simple, including three common types: output through monitor, video, and optical output. The former two types were used mostly in professional training, in which the combination of real images and virtual scenes was output in the form of video to certain display devices (e.g., mobile phones, tablets, HMD, glasses). The optical output type, depending on less artificial devices, was used in K-16 education much more than in professional training [28; 32; 33].
## 3 Proposed Method
### Data Layer  
There are two data sources used in this study, including historical datasets collected from hospitals and real-time data collected from patients via health-monitoring wearable devices. The batch data are loaded into Hadoop Distributed File System (HDFS), a well-known fault-tolerant distributed file system. HDFS is designed to store very large datasets reliably and to stream those datasets at high bandwidth to user applications. The real-time data are ingested into the system with Apache Kafka, a distributed, reliable, high-throughput and low-latency publish-subscribe messaging system. Kafka has become popular when it and Apache Spark are coordinated to process stream data as well as to use both of their advantages. We use Kafka to ingest real-time event data, streaming it to Spark Streaming. The data can be in text format or images, especially CT/MRI images that are commonly used in medical diagnosis. These raw data are collected and fed into the system for storage at the data layer.