esa-sceva/satcom-chunk-collection huggingface dataset:
# hLSTM-Aging: A Hybrid LSTM Model for Software Aging Forecast
## 6 Results Analysis
### Visualization  
In this section, the time-series models previously discussed are visually compared. Three result plots are displayed for each software aging time series tested for comparison: one for the LSTM-derived models, one for the probabilistic set of methods, and the other for a proposed combination. Those plots make it possible to understand how the models overlap the original time series and show that the LSTM models have a better generalization performance. Analyzing the CPU utilization in Figure 4, all techniques can achieve a good prediction in the training set. However, when comparing the results for the test set, the Conv-LSTM and the hybrid method had a much better performance fitting the data. For the disk usage plots in Figure 5, even though the MA in the training test was able to fit the pattern of the data, the bidirectional LSTM and the proposed combination both surpassed the probabilistic method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Architecture** & **MSE-Train/Test** & **MAPE-Train/Test** & **MAE-Train/Test** \\ \hline Vanilla-LSTM & \(4\times 10^{-4}/7\times 10^{-4}\) & \(1.944\times 10^{0}/2.888\times 10^{0}\) & \(6\times 10^{-3}/2.2\times 10^{-2}\) \\ Stacked-LSTM & \(4\times 10^{-4}/6\times 10^{-4}\) & \(2.098\times 10^{0}/2.605\times 10^{0}\) & \(7\times 10^{-3}/1.9\times 10^{-2}\) \\ Bi-LSTM & \(4\times 10^{-4}/3\times 10^{-4}\) & \(1.938\times 10^{0}/1.351\times 10^{0}\) & \(6\times 10^{-3}/1.0\times 10^{-2}\) \\ CNN-LSTM & \(4\times 10^{-4}/4\times 10^{-4}\) & \(2.066\times 10^{0}/2.012\times 10^{0}\) & \(7\times 10^{-3}/1.5\times 10^{-2}\) \\
**Conv-LSTM** & \(4\times 10^{-4}/3\times 10^{-4}\) & \(1.998\times 10^{0}/1.037\times 10^{0}\) & \(7\times 10^{-3}/8\times 10^{-3}\) \\ AR & \(4\times 10^{-4}/3.8\times 10^{-2}\) & \(1.9\times 10^{-2}/2.30\times 10^{-1}\) & \(6\times 10^{-3}/1.49\times 10^{-1}\) \\
**MA** & \(8\times 10^{-3}/1.09\times 10^{-1}\) & \(3.08\times 10^{-1}/4.27\times 10^{-1}\) & \(7.9\times 10^{-2}/3.18\times 10^{-1}\) \\ ARMA & \(4\times 10^{-4}/1.1\times 10^{-2}\) & \(1.7\times 10^{-2}/1.19\times 10^{-1}\) & \(6\times 10^{-3}/7.5\times 10^{-2}\) \\ ARIMA & \(4\times 10^{-4}/6.0\times 10^{-2}\) & \(2.0\times 10^{-2}/3.04\times 10^{-1}\) & \(7\times 10^{-3}/2.00\times 10^{-1}\) \\ SARIMA & \(4\times 10^{-4}/1.29\times 10^{-2}\) & \(1.77\times 10^{-2}/1.2\times 10^{-2}\) & \(6\times 10^{-3}/7.8\times 10^{-2}\) \\ \hline
**hLSTM** & \(2\times 10^{-4}/1.3\times 10^{-3}\) & \(1.429\times 10^{0}/1\times 10^{-3}\) & \(6\times 10^{-3}/1.8\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of evaluation metrics for memory used with one virtual machine.
# Evaluating the Effectiveness of Natural Carbon Sinks Through a Temperature-Dependent Model
## 2 Materials and Methods  
The approach taken here is not to investigate possible individual sources or sinks and their specific mechanism, that would be the bottom-up approach. Instead, a top-down approach is taken. The sources of information are global measurable data, notably CO\({}_{2}\) concentration measured at Mauna Loa [11], the global anthropogenic emissions, as defined in the Global Carbon Budget [1], and the global average sea surface temperature [12].  
For validation purposes, other sources of information are included. One of them, the \({}^{14}\)C concentration data after the bomb test treaty in 1963, is also a global data set [13].  
All types of emissions, except explicit anthropogenic emissions, are considered here to be part of the unknown natural emissions, in particular, the land use change emissions. This decision has been discussed in [10]. The variability of these natural emissions is constrained by the residual of the introduced models.  
In the publications \"Emissions and CO\({}_{2}\) Concentration--An Evidence Based Approach\" [14] and \"Improvements and Extension of the Linear Carbon Sink Model\" [10], the relationship among yearly data of emissions, concentration increase, and sink effect was analyzed and a robust, simple model of the sink effect was developed that not only reproduces the measurement data of the last 70 years very accurately but also allows reliable forecasts. For example, the concentration data for the years 2000-2020 were predicted with extremely high accuracy from the emissions and the model parameters determined before the year 2000. However, the most recent series of measurements used in the publications ended in December 2022 and annual averages were used, so the phenomena that are currently causing so much excitement were not yet taken into account.  
This article builds on the evidence gained by these previous papers and develops the models further by introducing monthly data resolution and considering time shifts between temperature change and its effect.
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 4 Experiment and Analysis
### Scalability of the Scheme  
To evaluate the usability of the proposed scheme, an analysis of its scalability has been conducted. Scalability is manifested in two aspects: the storage pressure when the volume of stored data increases, and the computational overhead imposed on the blockchain system as the number of users grows. For the first aspect, the architecture that separates data and data ownership disperses the storage pressure both on-chain and off-chain. With only the data ownership proofs stored on the blockchain, the data itself is stored in the off-chain IPFS. As the volume of data increases, the data stored on the blockchain increases linearly, that is, for each shared data, 825 B of storage space is required on the blockchain (see in Table 3). This storage overhead to the blockchain system is minimal. Further, as an extensible distributed storage mechanism, IPFS can be expanded on demand easily to meet the future data storage needs. Therefore, the scheme exhibits excellent scalability from a storage perspective.  
For the second aspect, experimental validation was conducted. We used Capliper to perform stress tests on the relevant smart contracts, simulating the response latency for NFT creating and querying under three scenarios: a single user peer, 500 user peers, and 1000 user peers. The results are shown in Figure 10.  
As depicted in Figure 10, the response latency for both NFT creating and querying increased with the number of nodes. The response latency for querying is more significantly affected by the number of nodes; for instance, when the number of nodes increased from 1 to 500, the response latency increased by 51.4%. However, regardless of the number of nodes, the response times for NFT construction and querying remained within seconds, indicating that the proposed scheme also possesses good scalability in terms of the number of users.  
Figure 10: Response latency of NFT operations.
## 3 Finite Element Analysis of Knee Integration Model
### Model Validation  
A three-dimensional finite element model of the normal bony knee joint was created, as illustrated in Figure 3.  
A. Two types of experiments--axial stress and anterior drawer--were conducted for model validation [46,47]. According to clinical practice and the available literature, applying an anterior thrust force of 134 N to the superior end of the tibia produces a displacement at the midpoint of the intercondylar ridge of the tibia, similar to forces typically used in the anterior knee drawer test [47, 48, 49]. In the present study, the application of this force resulted in a calculated anterior displacement of the tibia of 4.35 mm. Previous studies by [PERSON], [PERSON], [PERSON], and [PERSON] et al. reported anterior displacements of 4.0, 4.3, 4.13, and 4.18 mm, respectively, aligning closely with the simulations of this study, thus validating the finite element model [11,48,49].  
Furthermore, the fluoroscopic image data of the subject's knee joint during landing were collected using a high-speed dual fluoroscopic imaging system (DFIS) [50]. The calculated knee displacement results were compared with those obtained from FEA to validate the finite element model (FEM) [50, 51, 52]. As shown in Figure 3A, DFIS captures X-ray information, utilizing varying levels of attenuation from different tissues and organs, which is processed to generate reliable gray-scale medical image data [52]. To collect the motion data of the knee joint, a jumping platform experiment was conducted, where the subject landed on one leg. Kinematic and kinetic data were synchronized using a VICON motion capture system and a force platform, providing input for the finite element model. Figure 3A illustrates that the perspective system acquired knee images via two orthogonal planar projections, with the knee model calibrated and aligned using 3D modeling software to ensure consistency with the actual X-ray images. Knee displacement was accurately calculated using the Coordinate System Calculator plug-in in Rhinoceros software (version 6.16, McNeel and Associates, Seattle, WA, USA) [50,51]. The results indicated a high consistency between the knee displacement calculations based on FEA and DFIS in three-dimensional space, demonstrating the feasibility and reliability of the constructed finite element model.
# Investigation of Hydrogen Flux Influence on InGaP Layer and Device Uniformity
## 2 Materials and Methods  
This paper investigates the influence of the total H\({}_{2}\) carrier gas flux on the epitaxial growth of the InGaP single layer and InGaP/GaAs HBT, using a uniform growth pressure of 25 mbar. We meticulously controlled the growth temperature, V/III ratio, and type and flux of source materials to guarantee the precision and reliability of our findings. Specifically, for the InGaP layer growth, we maintained the temperature at 650 \({}^{\circ}\)C and set the V/III ratio to 80, using trimethylgallium (TMGa) and trimethylinom (TMIn) as group III sources and arsenic hydride (AsH\({}_{3}\)) and phosphine hydride (PH\({}_{3}\)) as group V sources. Silicon hydride (SiH\({}_{4}\)) and carbon (C) were introduced as n-type and p-type dopants, respectively. All experiments were conducted on standard 6-inch GaAs substrates with a consistent growth duration of 1000 s. For HBT fabrication, common process and epitaxial wafers were used; the doping concentrations in the InGaAs cap and base regions exceeded \(1\times 10^{19}\) cm\({}^{-3}\), while the InGaP emitter region utilized a lower doping concentration of \(1\times 10^{17}\) cm\({}^{-3}\). In addition, the InGaP collector and semi-collector regions used doping concentrations of \(1\times 10^{17}\) cm\({}^{-3}\)and \(1\times 10^{18}\) cm\({}^{-3}\), respectively. To assess the impact of various H\({}_{2}\) carrier gas fluxes on epitaxial growth, we carried out a sequence of experiments across different fluxes, followed by an exhaustive characterization and analysis of the epitaxial wafers produced. A warm-wall reactor was used for epitaxial growth. Sheet resistance uniformity, a critical quality metric of the epitaxial wafer, was measured using sheet resistance measurement equipment (LEI-1510 EC, Semilab, Budapest, Hungary) across 55 uniformly distributed measurement points on each 6-inch wafer. Furthermore, X-ray diffraction (XRD, PANatical X'Pert\({}^{3}\)MRD XL, Malvern Panalytical, Malvern, UK) measurement fitting provided thickness data, offering invaluable insights into the growth rate of the epitaxial layers. Direct current (DC) HBT devices were characterized using a semiconductor device parameter analyzer (B1505A, Keysight Technologies, Santa Rosa, CA, USA).
###### Abstract
In these approaches, an initial affine transformation is combined with a subsequent deformable transformation within a neural network in order to first reduce large global image displacements by the affine transformation subnetwork and then to correct smaller local image displacements by the deformable transformation subnetwork. In medical image registration, image deformation also consists of larger rigid parts, for example, due to different patient positioning in different scanners or acquisitions. For instance, when planning and performing an image-guided biopsy as in [29], large rotations occur if the patient is supine during a preliminary examination but lateral during the image-guided biopsy due to needle placement. In addition, rigid structures such as bones or tumors that behave rigidly [30] are only or primarily deformed rigidly. Initially, a rigid transformation could be used to roughly reduce the large rigid displacements before correcting affine and deformable deformations. For instance, the NiftyReg algorithm reg_aladin [31] first applies rigid and then affine registration. In combination with the algorithm NiftyReg reg_f3d [32], a multistage registration with first rigid, then affine and subsequently deformable transformation is obtained. However, to the best of our knowledge, no multistage network has yet been published that combines the three transformations rigid, affine and deformable. For this reason, we developed a multistage network for jointly rigid, affine and deformable transformation. This network can be trained end-to-end.
## 3 Verification of the STAR-Derived Excess Phase and Bending Angle Profiles
### Refractivity Comparison with UCAR and ERA5  
The refractivity is a vertical Abel inversion from a bending angle as a function of impact height. All the bending angle errors can propagate into refractivity profiles and be amplified since they involve integration. Figure 9 shows the refractivity comparison for one month for C2E1 between UCAR and STAR profiles (Figure 9a) and ERA5 and STAR profiles (Figure 9b). The relative refractivity difference between STAR and ERA5 between 10 and 35 km is 0.09 \(\pm\) 1.1%, 0.09 \(\pm\) 1.9% for GPS L2P, and 0.06 \(\pm\) 1.2% for GLONASS. The relative refractivity difference between STAR and UCAR between 10 km and 35 km is \(-\)0.02 \(\pm\) 0.83%, \(-\)0.04 \(\pm\) 0.97% for GPS L2P, and \(-\)0.05 \(\pm\) 0.91% for GLONASS. The 10-35 km STAR profiles agree with ERA5 and UCAR refractivity results consistent with bending angle comparison. Compared with ERA5, the standard deviation of relative refractivity for rising GPS 12P signals is larger than other signals up to 45 km. In contrast, in bending angle profiles, the larger relative BA standard deviation for L2P occurs only between 20 and 25 km, indicating the errors at a certain height level can propagate vertically through Abel inversion, which involves vertical integration of the bending angle.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 4 Experiments
### Datasets  
We developed training and test datasets containing information map images and walking data (stride lengths and heading directions). The map images of twelve and eight facilities were obtained for training and test datasets, respectively.  
Figure 7: The flow of localization comparison.  
The training dataset contained the synthesized walking data described in Section 3.3.1. We generated 50 walking data for each map image and scale \(mpp\). Then, baseline localization was applied to the walking data ten times to extract the features of localization. Thus, there were 3500 sets of localization results for a map. Two results were selected randomly from the same map at the same scale to generate input features for the comparison model. Consequently, the training dataset contained 42,875 input features for each map. The comparison model was trained using the training dataset.  
The test dataset was actual walking data obtained by a smartphone, a Google Pixel 4a, at eight facilities. As shown in Figure 8, there were two walking data properties in a map. The walking data included the time and position of the encounter, which was successfully detected by the proposed method. The walking paths in the map images were determined manually.
###### Abstract  
In this work, five nanocrystalline first transition-metal (Ti, Mn, Co, Ni and Zn) oxide nanofibers were prepared by electrospinning and controlled calcination. The morphology, crystal structure, pore size distribution and specific surface area were systematically studied by scanning electron microscope (SEM), transmission electron microscope (TEM), surface and pore analysis, and thermo gravimetric analyzer (TGA). The results reveal that the obtained nanofibers have a continuously twisted three-dimensional scaffold structure and are composed of neat nanocrystals with a necklace-like arrangement. All the samples possess high specific surface areas, which follow the order of NiO nanofiber (393.645 m\({}^{2}\)/g) \(>\) TiO\({}_{2}\) nanofiber (121.445 m\({}^{2}\)/g) \(>\) ZnO nanofiber (57.219 m\({}^{2}\)/g) \(>\) Co\({}_{3}\)O\({}_{4}\) nanofiber (52.717 m\({}^{2}\)/g) \(>\) Mn\({}_{2}\)O\({}_{3}\) nanofiber (18.600 m\({}^{2}\)/g). Moreover, the photocatalytic degradation of methylene blue (MB) in aqueous solution was investigated in detail by employing the five kinds of metal oxide nanofibers as photocatalysts under ultraviolet (UV) irradiation separately. The results show that ZnO, TiO\({}_{2}\) and NiO nanofibers exhibit excellent photocatalytic efficiency and high cycling ability to MB, which may be ascribed to unique porous structures and the highly efficient separation of photogenerated electron-hole pairs. In brief, this paper aims to provide a feasible approach to achieve five first transition-metal oxide nanofibers with excellent performance, which is important for practical applications.  
nanocrystalline metal oxide nanofibers; electrospinning; controlled calcination; photocatalytic efficiency +
Footnote †: journal: Applied Materials Science
# Coupled Thermomechanical Responses of Zirconium Alloy System Claddings under Neutron Irradiation
## 4 Results and Discussion
### The Thermomechanical Behavior
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Density** & **Temperature** & **Neutron Injection** & **Young’s Modulus** & **Thermal Conductivity** & **Poisson’s** & **Specific Heat Capacity** \\
**g/cm\({}^{3}\)** & \({}^{\circ}\)C** & **Quantiy n/mm\({}^{2}\)** & **GPa** & **W/ (m\({}^{\circ}\)C)** & **Ratio** & **J/(kg\({}^{\circ}\)C)** \\ \hline
6.573 & 100 & 224 & 95.3 & 7.59 & 0.3 & 275 \\ - & 200 & 899 & 88.6 & 11.26 & - & 309 \\ - & 300 & 3376 & 83 & 12.75 & - & 326 \\ - & 400 & 1,746,000 & 77.2 & 14.52 & - & 335 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Zr-2.5 Nb-0.5 Cu alloy parameters of fuel rod cladding. maximum magnitudes of the N36 and Zr-2.5 Nb-0.5 Cu alloys were about 78.9 MPa and 44.3 MPa on the 1200 th day, respectively. This axial stress distribution led to different axial strains for different cladding materials. Therefore, axial deformation should be considered as an important parameter for selecting a cladding material. Figure 3: _Cont._  
Figure 2: Temperature field of different Zr alloy systems irradiated for 1200 days (unit: \({}^{\circ}\)C).
## 4 Results
### Prodigy Experimental Dataset  
In the aforementioned PICMUS experimental data, our estimated tissue SOS of 1570 m/s does not agree with the nominal SOS of 1540 m/s for the experimental phantom. Since the SOS of any tissue mimicking material has a temperature coefficient, the actual tissue SOS will vary with ambient temperature so that the nominal SOS may not be necessarily the actual SOS at the time of measurement [28]. To validate whether or not the SOS estimated by the proposed DCF method is consistent with the actual SOS of the experimental phantom, another dataset was acquired in our own lab using Prodigy ultrasonic imaging system while the tissue SOS of the phantom has been concurrently measured to be 1427 m/s using pulse-echo method as described in the Method section. Figure 10 shows the minimal \(-\)6-dB LW and the maximal CR as a function of \(C_{beam}\) for the Prodigy dataset. For the Prodigy dataset, the \(C_{beam}\) with the maximal image quality is respectively 1420 m/s and 1430 m/s from \(-\)6-dB LW and CR analyses, which is close to the actual tissue SOS of 1427 m/s within an estimation error of 10 m/s. Note that the estimation error is inevitably quantized by the increment value of \(C_{beam}\) in Equation (9).  
Figure 11 shows the DCF maps of Tx-DMAS beamforming for each FOVs in the Prodigy dataset and the corresponding ROIs at elevational focal depth for tissue SOS estimation. Note that two ROIs containing the wire reflectors has been excluded in the SOS estimation due to the obvious dark-region artifacts in DCF map. For the remaining 34 ROIs, the resultant average value and standard deviation of the tissue SOS estimation is 1426 \(\pm\) 6 m/s, which is close to the actual SOS of 1427 m/s for the Prodigy dataset.  
Figure 10: Plot of \(-\)6-dB LW (**a**) and CR (**b**) as a function of \(C_{beam}\) for the Prodigy dataset.  
Figure 9: DCF maps of Tx-DMAS beamforming with \(C_{beam}\) of 1540, 1570 and 1600 m/s of PICMUS experimental phantom.
# Security Risk Analysis of LoRaWAN and Future Directions
## 5 Security Threat and Risk Assessment of LoRaWAN v1.1
### LoRaWAN v1.1 Threat Catalog
#### 5.2.4 Network Traffic Analysis  
This is a passive attack and also called eavesdropping attack if happens in the physical layer (radio signals). In this kind of attack, an attacker can setup a _Rogue-GW_ by using the vulnerabilities mentioned in Section 4.1, to receive packets and deduce some knowledge about the data being transmitted or the keying material being used. Observe that, without access to key material, it is infeasible for the attacker to be able to decode the contents of the packets received and the usefulness of this traffic analysis will be highly application specific. For instance, a LoRa network deployed in a smart-city application to deduce noise levels in the city might leak information related to the level of activity in certain locations through simple observation of transmissions.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 5 Proposed Method
### Routing Using MOSOA
#### 5.2.2 Fitness Function  
The parameters used in the routing optimization are as follows:  
Queue length  
QL, shown in Equation (20), is considered as a primary fitness value during the routing as it considers the congestion level of each node in the WSN-IoT. Since the developed CAR-MOSOA is required to transmit the alert messages over the WSN-IoT, this QL is used to improve the data delivery performances.  
\[QL=\frac{RP_{k}}{Total\ buffer} \tag{20}\]  
where the received packets at the \(k\)th node are represented as \(RP_{k}\).  
Link quality is used to define the successful data delivery between the nodes \(k\) and \(l\) based on the amount of data packet transmissions and the retransmissions, which is expressed in Equation (21).  
\[Link\ quality=\frac{1}{f\times r} \tag{21}\]  
where, \(f\) and \(r\) define the forward and reverse data transmission among the nodes.  
The details of the communication cost and residual energy are already defined in the section. Subsequently, all the multiple objective's fitness is conflicting with each other, so it is transformed into a single-objective fitness value as shown in Equation (22)  
\[Routing\ fitness=\delta_{1}\times QL+\delta_{2}\times Link\ quality+\delta_{3} \times CC+\delta_{4}\times RE \tag{22}\]  
where, \(\delta_{1},\ \delta_{2},\ \delta_{3}\), and \(\delta_{4}\) are the weighted parameters, which are equal to 0.3, 0.25, 0.25, and 0.2, respectively; \(CC\) and \(RE\) represent the communication cost and residual energy, respectively.