esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Bioactive ceramics, such as bioglasses, hydroxyapatite (HAp) and calcium silicate (CaSiO\({}_{3}\)), are accepted as the most promising bone implant substitutes in bone tissue engineering [1; 2; 3]. Recently, CaSiO\({}_{3}\) has attracted increasing attention due to its excellent HAp-forming ability and outstanding proliferation ability of osteoblast-like cells [4; 5]. However, its inherent brittleness causes an insufficient mechanical strength that makes it hardly able to provide structural support for the new bone ingrowth, which impedes its clinical application in load-bearing bone repair [6; 7].  
Some methods, such as second-phase enhancement and liquid phase sintering (LPS), are effective at enhancing the mechanical properties of ceramics. Nevertheless, incorporation of second-phase materials into ceramics, e.g., silicon carbide (SiC) [8], zirconia (ZrO\({}_{2}\)) [9], aluminium oxide (Al\({}_{2}\)O\({}_{3}\)) [10], silver oxide (Ag\({}_{2}\)O) [11] and titanium oxide (TiO\({}_{2}\)) [12; 13], makes it hard to acquire sufficient mechanical strength to satisfy the demand of load-bearing bone replacement. LPS is an approach that aims to rapidly achieve a dense ceramic with a high mechanical strength [14; 15].  
Diboron trioxide (B\({}_{2}\)O\({}_{3}\)) is a well-known liquid facilitator has been frequently used to decrease the sintering temperature and modify the densification of ceramics [16; 17; 18]. [PERSON] et al. incorporated B\({}_{2}\)O\({}_{3}\) into a CaO-SiO\({}_{2}\)-P\({}_{2}\)O system to form liquid phase and improve the strength of glass ceramics by electric furnace sintering [19]. [PERSON] et al. pointed out that B\({}_{2}\)O\({}_{3}\)-containing bioglass could distinctly reinforce mechanical strength of akermanite ceramics as liquid phase via pressureless sintering [18]. Moreover, boron has a stimulatory effect on increasing extracellular matrix turnover, wound healing and bone physiology [20; 21]. Besides, zinc has well-defined biological roles. It can stimulate bone formation, promote osteoblastic cells proliferation and inhibit osteoclastic bone resorption [22; 23; 24].  
In this study, B\({}_{2}\)O\({}_{3}\)-ZnO liquid phase system was developed according to their phase diagram and then they were incorporated in CaSiO\({}_{3}\) powder. The porous architectures of the scaffolds were established using selective laser sintering (SLS). The study focused on the microstructure evolutions and reinforcement behaviors of the scaffolds with the liquid phase. Moreover, the influences of the liquid phase on the biodegradability, bioactivity and biocompatibility of the scaffolds were also investigated.
## 5 Discussion  
ImageNet pretraining followed by fine-tuning on remote sensing image datasets is a classical paradigm for training RSSC models, and a large amount of work has focused on using pretrained CNNs to continuously improve the performance on RSSC tasks. However, this conventional paradigm presents many problems hindering attempts to achieve further improvements. Limited by the capabilities of feature extractors pretrained on ImageNet, there may be an upper bound on the performance that is achievable based on the original paradigm. In this study, we started by considering various aspects of the overall process of CNN training in an attempt to utilize the data more effectively. We propose a general learning framework, GFSC, that allows models to be trained using only remote sensing images. In the design of the framework, we have considered specific characteristics of the remote sensing images. Our work bridges the gap in model performance achievable with and without ImageNet pretraining. Our framework attempts to force various CNN-based methods targeting remote sensing images to extract more discriminative features. Compared to networks pretrained on ImageNet, the method we propose allows the network to focus more on the characteristics of remote sensing images, achieving better performance. Our experiments indicate that this framework successfully helps different CNN backbones obtain more discriminative feature representations using only remote sensing images, thus achieving state-of-the-art results.  
In future work, with the rapid development of SSL, there is still great potential to further improve the performance of RSSC. We will continue to focus on SSL and on extending our approach to other remote sensing interpretation tasks.  
Figure 9: Visualization results of Grad-CAM [60]. The input images are sampled from the NWPU-RESISC45. Under each image, the prediction and ground-truth labels are shown. P denotes the softmax possibility of each network for the prediction class. We compare the class activation visualization results obtained from a GFSC-trained network (WRN-50+GFSC) and the corresponding baseline model (WRN-50) based on ImageNet pretraining.  
**Author Contributions:** Conceptualization, T.X.; Methodology, T.X. and Z.Z.; Software, Z.Z.; Validation, J.W.; Formal analysis, J.W.; Writing--original draft, T.X.; Writing--review & editing, Z.Z.; Supervision, J.W.; Funding acquisition, Z.Z. and J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is partly supported by the Joint Funds of the National Natural Science Foundation of China (No. U20B2068), the Natural Science Foundation of Anhui Province (No. 2208085 QF192), the National Natural Science Foundation of China (No. 62201008), and the Natural Science Foundation of Education Department of Anhui Province (No. KJ2021A0017).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Novel Dual-Wavelength Method for Evaluating Temperature Effect in Fiber-Optic SPR Sensors
## 2 Material and Methods
### Temperature Dependence of the Optical Fiber for Wave-Interrogation Mode  
In this study, the optical fiber is considered as a step index multimode fiber. The central core of the optical fiber is made of fused silica. Therefore, the dependence of its RI can be expressed by the Sellmeier relation as Equation (7):  
\[n_{1}(\lambda)=\sqrt{1+\frac{A_{1}\lambda^{2}}{\lambda^{2}-{B_{1}}^{2}}+\frac {A_{2}\lambda^{2}}{\lambda^{2}-{B_{2}}^{2}}+\frac{A_{3}\lambda^{2}}{\lambda^{ 2}-{B_{3}}^{2}}} \tag{7}\]  
where \(\lambda\) denotes the wavelength of incident light. The coefficients \(A_{1}\), \(A_{2}\),\(A_{3}\), \(B_{1}\), \(B_{2}\), and \(B_{3}\) are constants with certain numeric values.  
Meanwhile, the RI variation of the fiber core (\(n_{\lambda}\)) with temperature is another factor which needs to be taken into account. The temperature effect on the fiber core can be expressed as Equations (8) and (9) [31]:  
\[R=\lambda^{2}\Big{/}\Big{(}\lambda^{2}-\lambda_{lg}^{2}\Big{)} \tag{8}\]  
\[2n(\lambda)\frac{dn}{dT}(\lambda)=GR+HR^{2} \tag{9}\]  
where \(\lambda_{lg}\) represents the band gap wavelength in units of \(\mu\)m. \(dn/dT\) is the thermo-optic coefficient for silica.
## 4 Experiment Verification  
In this paper, a data set for validating the proposed method is constructed based on the process specifications of aircraft assembly, reducer assembly and valve assembly. This dataset describes the information and relations contained in the assembly processes of different products, including process steps, operation steps, resources, reference standards, components, and their interconnections. The dataset consists of two parts: a triplet-based dataset for the pre-improved presentation learning model, and a quintuple-based dataset for the post-improved presentation learning model. Examples of the two and their differences are shown in Figure 4.  
Part (a) of the figure above is a dataset based on triplet format _<head entity_, _relation_, _tail entity_> where head and tail entity information is expressed using identity document, and part (b) is a dataset based on quintuple format _<head entity_, _relation_, _tail entity_, _head entity type_, _tail entity type_>, the only difference between them is that part (b) adds the head and tail entity type information.  
The data set constructed for assembly process domain in this paper is divided into three parts: training set, verification set and test set, and the ratio is 8:1:1, which ensures that the model has enough data for parameter learning during training, as well as enough data for verifying model performance and adjusting hyperparameters, as well as finally testing the generalization ability of the model. The dataset, which derived from the assembly process knowledge of complex aviation products, contains a total of 3824 quintuples, 1174 entities, 7 relations, and 9 entity types. Therein, 7 types of relations include _'has_subleaf_, _has_step_, _has_operation_, _use_process_equip_, _next_operation_, _next_step_ and _use_device_', and 6 types of entity types include _'know_type_, _process_case_, _operation_, _step_, _equipment_resource_, _device_resource_', which describe the whole assembly process specifications. Given that the sources of knowledge cover a variety of assembly processes and the information of processes, work steps, resources, parts, etc., the data set is sufficient to prove the validity of the proposed method. In the constructed dataset, entity information is processed by replacing it with unique identifiers (UUIDs), which are then associated and mapped using a constructed entity dictionary. This approach avoids the direct use of word vectors for entities, which can lead to significant differences between word vectors due to excessively long texts. Additionally, the completed entities can retrieve their corresponding textual information through the entity dictionary.  
Figure 4: Sample data sets for validation and comparison.  
The embedded learning model used in the verification part includes RotatE, DistMult and ComplEx. Parameters set during the experiment are shown in the following Table 1.
## 4 Simulation Analysis
### Transmission Power Range  
In Figure 6, the transmission power of each node will be analyzed with the other successive techniques. When compared with the other techniques, the SAARICS performs better than other methodologies. The SAARICS level stands high, so the transmission energy among the nodes will be adequate enough to broadcast the information from source to destination.
# An Overview of Fog Data Analytics for IoT Applications
## 4 Current State-of-the-Art
### Disaster Management  
Currently, many cameras and monitoring devices are available throughout the cities, which can detect disasters such as fire, smoke, or crashes. Processing live streaming data from the sources as a method of identification could be a task that needs immediate analytics generation and decision making. Conventional cloud-based solutions would be scalable but suffer from a lack of processing on edge devices to make quick decisions, resulting in a higher latency. The implementation of fog makes edge computing possible, satisfying real-time analytical needs for taking certain actions. Systems using fog computing to forecast and monitor disastrous phenomena [52] at coastal areas and ensure the safety of the people and infrastructure have also been proposed. The same could be implemented in the smart home environment for fire and smoke detection [53] at any location with the help of sensing devices and fog nodes. Disastrous events such as a gas leakage, fire, and short-circuit could be averted with fog processing and IoT sensing.
# Seismic Response of a Large LNG Storage Tank Based on a Shaking Table Test
## 3 Simplified Mechanical Model of the Storage Tank  
### Comparison of Simplified Models with Different Degrees of Freedom  
[PERSON] et al. [8] regard the concrete outer tank as a single-degree-of-freedom (1-DOF) model and deduce the motion theory of the outer tank. This paper refers to its research method to study the dynamic response characteristics of concrete outer tank under earthquake action. Taking a non-isolated storage tank as an example, it is simplified into single-degree-of-freedom, two-degree-of-freedom (2-DOF), three-degree-of-freedom (3-DOF) and four-degree-of-freedom (4-DOF) models, respectively, and the corresponding motion equations are established. By comparing the theoretical calculation results of the four simplified models with the shaking table test results, it is determined which simplified model can more comprehensively characterize the dynamic response characteristics of the outer tank.  
In order to compare the calculation effects of different models, the Wolong wave (X direction, 0.75 g) was selected for analysis and the acceleration time history curve of the dome was compared. Figure 6 shows a comparison of the results of different simplified models and shaking table tests. It can be seen from the figure that the calculation results of the 2-DOF model fit the results of the shaking table test the worst. The model's calculations amplify the seismic response of the tank. Only the calculation results of the 1-DOF model and the 3-DOF model are in good agreement with the results of the shaking table test.  
Table 5 is a comparison table of the acceleration amplitude fitting of different simplified models. The fitting degrees of the 1-DOF, 2-DOF, 3-DOF and 4-DOF models are 81.24%,  
Figure 6: Comparison of theoretical solutions of four simplified models and shaking table test results: (**a**) Acceleration time history comparison of \(1-\)DOF; (**b**) Acceleration time history comparison of \(2-\)DOF; (**c**) Acceleration time history comparison of \(3-\)DOF; (**d**) Acceleration time history comparison of \(4-\)DOF.  
46.72%, 83.46% and 79.87%, respectively. The theoretical results of the 3-DOF model are the closest to the experimental results, which indicates that the 3-DOF model can better reflect the dynamic response characteristics of non-isolated storage tanks. In the following, the seismic response of the storage tank will be analyzed based on the 3-DOF model.
## 3 Data Used and Methodology
### Standard Precipitation Evaporation Index  
The SPEI index is based on the determination of climatic water balance (CWB) approach, which uses rainfall and potential evapotranspiration (PET) as input values, wherein CWB is defined as follows:  
\[\text{CWB}_{\text{i}}=\text{R}_{\text{i}}-\text{PET}_{\text{i}} \tag{1}\]  
where R is rainfall, and i is the month counter, which provides a measure of water surplus or deficit for a given month. The log-logistic probability density function was used to transform the CWB series to standardized units at different monthly scales (1 and 3 months). Thereafter, the log-logistic distribution was used to determine SPEI, by using the inverse normal function [15]. The use of PET methods also has a significant effect on the drought index calculation, and Penman-Monteith (PM) has been suggested to be performed [15]. In cases of data unavailability, Hargreaves method has also been suggested to be used for SPEI index calculation [15]. The detailed explanation about the calculation of SPEI index can be found in References [14; 15; 46]. This could be crucial, as a calculation of PET using PM via other climatological, reanalysis or ground-based datasets could prove to be difficult, and due care needs to be taken when defining drought. This could be achieved by statistically validating the historical drought events and its variation with the global drought-monitoring tool, like the SPEI database [15]. The methodology of the present study is depicted in Figure 4. The time series data for SPEI 1 and SPEI 3 for the two major droughts (World War II Drought (1937-1945) and the Millennium Drought (2001-2010)) are depicted in Figure 5a,b. As the figure represents, SPEI was able to successfully capture the historical major droughts and can be considered as a good index, with the ability to capture droughts. The variation in SPEI values depict various drought classes, as mentioned in Table 1[8].  
Figure 4: Flowchart of the work being conducted.
## 3 Results  
### Results for Knowledge and Self-Efficacy  
The findings regarding whether there is a significant difference between the knowledge and self-efficacy levels of the teachers in the application group who were given the mathematics learning disability training program before and after the application in terms of the variable of participation in the study on mathematics learning disability are shown in Table 3.  
Table 3 presents the independent sample \(t\)-test results examining the pre- and post-implementation differences in terms of the knowledge and self-efficacy levels of teachers who received IOL and face-to-face education.  
Before the application, it was observed that the knowledge and self-efficacy levels of teachers who received IOL education (\(M\) = 1.732, SD = 0.176) were higher than those who received face-to-face education (\(M\) = 1.652, SD = 0.144), and this difference was statistically significant (t = 2.250, \(p\) = 0.027 < 0.05). However, after the application, it was determined that the difference between the two groups was not significant (t = \(-\)0.829, \(p\) = 0.410 > 0.05).  
According to the analyses conducted at the factor level, it was determined that the average scores of the teachers who received IOL education (\(M=1.880\), SD = 0.190) were significantly higher than those who received face-to-face education (\(M=1.784\), SD = 0.183) for the personal teaching adequacy factor (pre-test F1) (t = 2.289, \(p=0.025<0.05\)). However, this difference was not found to be significant for the instructional support adequacy factor (pre-test F2) (t = 1.066, \(p=0.290>0.05\)).  
After the application, no significant differences were found between the groups in terms of both personal teaching efficacy (Post-test F1) and instructional support efficacy (post-test F2) factors (t = \(-0.721\), \(p>0.05\)). These findings show that at the end of the training process, the knowledge and self-efficacy levels of teachers who received IOL and face-to-face training were balanced and both methods had similar effects.
## 2 Experimental Investigation of Column
### Axial Compression Test Results and Discussions
#### 2.4.4 Load-Longitudinal Strain Curves  
C159\(\times\)6-R26.5 was used as an example to analyze the relationship between axial compression load and longitudinal strain (\(P\)-\(\varepsilon_{\rm L}\)), as shown in Figure 10, in which negative and positive value denote compressive and tensile strain respectively. It can be noted that the trends of \(P\)-\(\varepsilon_{\rm L}\) curves can be divided into three stages. Firstly, the \(\varepsilon_{\rm L}\) increases linearly with the increasing \(P\) and the \(\varepsilon_{\rm L}\) is relatively small; therefore, this process is defined as the elastic stage. Subsequently, the \(\varepsilon_{\rm L}\) increases nonlinearly with the increasing \(P\), and this period is termed the elastic-plastic stage. With the increasing \(P\) until the maximum value is reached, the growth rate of \(\varepsilon_{\rm L}\) is greater than the increment speed of \(P\), and the deformation increases significantly, so this last stage is called the plastic stage. Most of strain gauges points enter the plastic state when the \(P\) reaches to the maximum value.  
By comparing the \(\varepsilon_{\rm L}\) curves of C159\(\times\)6-R26.5 samples under different \(H\), it can be found that for an undamaged sample, i.e., C159\(\times\)6-R26.5-H0, the maximum compressive strain at yield load is about 2620 \(\mu\varepsilon\), which is greater than the yield strain of steel. It may be explained that under the synergistic action of core concrete, the compressive strength of CFCST structure is much higher than that of steel structures. In addition, the undamaged sample is almost under compression during the whole deformation process, and before entering into plastic stage, the \(\varepsilon_{\rm L}\) values of all points are very close, which reflects the phenomenon that the sample is vertically and uniformly compressed.  
Nevertheless, for the damaged samples, i.e., C159\(\times\)6-R26.5-H2 to H4, the \(P\)-\(\varepsilon_{\rm L}\) curves increases linearly before the loading reaches to 0.7-0.75 \(P_{\rm u}\). After that, the sample enters the elastic-plastic stage and at the concave side in the middle of the column (i.e., the position where the strain gauge 5 is attached), the compressive growth rate of \(\varepsilon_{\rm L}\) is the fastest. Meanwhile, at the convex side in the middle of the column (i.e., the position where the strain gauge 1 is pasted), the \(\varepsilon_{\rm L}\) is the compressive strain at the initial stage of loading, and after the sample yields, \(\varepsilon_{\rm L}\) gradually decreases to 0 and eventually becomes the tensile strain.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.14 Ultra-Wideband (UWB)  
Ultra-wideband is a short-range, high-bandwidth and energy-efficient wireless communication protocol that can be used for radar imaging, sensor data collection, and precise location and tracking. UWB operates at frequencies from 3.1 to 10.6 GHz, has a bandwidth of 500 MHz, and a data rate of up to 1 Gbps [49; 50]. UWB can be used to accurately measure the distance between two devices. This information can be used for a variety of IoT applications [51].
###### Abstract  
This study presents a novel dual-band microstrip antenna operating at 28/38 GHz, which is designed for fifth generation (5G) and next-generation communications. The objective was to create a high-gain, single-element solution that addresses millimeter-wave (mmWave) challenges, like attenuation and signal loss, offering a more efficient alternative to complex array antennas. The antenna was designed using Rogers RT/duroid 5880 as a substrate, and CST simulations were used to optimize the return loss, gain, and efficiency. Analytical methods and parametric analyses were used to further optimize the design. Additionally, an SMP connector was integrated into the simulated model using Antenna Magus software, followed by further refinement through additional parametric studies. The final compact antenna (33 \(\times\) 27 \(\times\) 1.6 mm\({}^{3}\)) demonstrates excellent performance with simplified fabrication. The antenna achieved bandwidths of 1.12 GHz at 28 GHz and 1.27 GHz at 38 GHz, with remarkably low return loss values of \(-\)53.04 dB and \(-\)83.65 dB, respectively. The gain values reached 7.82 dBi at 28 GHz and 8.98 dBi at 38 GHz--prototype measurements closely aligned with simulations, confirming reliability. This study introduces a high-performance, single-element antenna that is both simple and complex. The meticulous optimization process, including SMP connector variations, minimized the fabrication sensitivity and improved the overall performance, thereby marking a significant advancement in antenna design.  
Keywords:dual-band microstrip antenna; 5G; millimeter wave; antenna optimization; parametric analysis; 28/38 GHz +
Footnote †: journal: Applied Sciences