esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
They used principal component analysis to evaluate the mining economic competitiveness of 10 major mining provinces in China and proposed directions for improving the mining economic competitiveness of Heilongjiang Province. Various existing evaluation methods have played important roles in mining economic evaluation, but in this process, the calculation procedures of each evaluation method may be cumbersome or simple, and the reliability of evaluation results may be high or low. Seeking a mining economic evaluation method that can simplify the evaluation calculation process and improve the reliability of evaluation results can effectively improve the efficiency and accuracy of evaluation work. The Rosenblueth point estimate method [22] (RPEM) is a method of obtaining reliability and failure probability by calculating the mean-variance of the objective function, which is widely used in engineering reliability analysis [23; 24; 25]. RPEM only needs to calculate the objective function at the selected weighted points, which is easy to implement and does not require numerical iteration [24]. However, there is currently no precedent for using this method for economic evaluation. In terms of engineering reliability analysis, [PERSON] [26] used RPEM for slope stability analysis of hydropower stations and compared the calculation results with the results obtained by Monte Carlo method. It was found that the probability of slope failure obtained by these two methods was similar. [PERSON] et al. [27] used RPEM for real-time analysis of the probability of earth-rock dam failure, concluding that RPEM can balance data accuracy and efficiency. [PERSON] et al. [28] applied RPEM to the reliability analysis of tunnel bolt support structures and obtained results that were basically consistent with the actual situation on site, improving computational efficiency. [PERSON] et al. [29] used RPEM to evaluate the uncertainty of groundwater model parameters, believing that it has the advantages of simplicity and efficiency compared to Monte Carlo methods. Using TRIGRS landslide prediction mode and adopting RPEM to solve the uncertainty problem of parameters, [PERSON] et al. [30] studied the relationship between rainfall events and landslide occurrence. Based on the widespread application of RPEM in engineering reliability evaluation [25], the major objective of this study was to introduce RPEM into mining economic evaluation to shorten the cycle of economic evaluation and improve the reliability of evaluation. The development and production processes of mining projects are complex and involve many technical and economic indicators. In order to study the applicability of RPEM in mining economic evaluation, on the basis of establishing an RPEM economic evaluation method, we studied the probability of achieving benefits with different geological grades, conducted empirical research to verify the rationality of the results, and explored the feasibility of using RPEM for mining economic evaluation.
## 2 Materials and Methods
### Transponders or Tags  
A transponder is typically an element that sends and receives the optical signal from an antenna. Transponders are usually characterized by their data rate capacity, size, and the maximum distance of signal transmission. The size of the tag is crucial, as bees can easily be burdened and disabled by their weight and size. In our case of tracking honey bees, the smaller the tag, the better. In this study, we used the Hitachi transponders (\(2.5\times 2.5\times 0.4\)) mm, which are considered among the smallest and lightest tags available on the market within a reasonable price range. If a smaller transponder is desired or required (i.e., monitoring smaller bee species), users can opt for the UHF RFID tags produced by muRata with (\(1.5\times 1.2\times 0.5\)) mm dimensions at a higher cost.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Frequency Bands in 5G
#### 2.1.1 5G FR1 Sub-6 GHz Band  
The Frequency Range 1 (FR1) encompasses the sub-6 GHz spectrum, some of which is used by previous standards. Higher-order MIMO in FR1 bands enables spatial multiplexing and Multi User Multiple-Input Multiple-Output (MU-MIMO), offering a balance of coverage and capacity, making it suitable for wide-area deployments and ensuring  
Figure 2: 5G architecture block scheme with critical interfaces (red text) and its end points. Purple and blue colors represent Control Plane (CP) and User Plane (UP) functions respectively.  
compatibility with existing 4G networks. FR1 frequency bands offer a balance between coverage and capacity. They provide wider coverage compared to higher frequency bands such as those in Frequency Range 2 (FR2), or millimeter-wave bands, typically used in urban, suburban, and rural areas. Additionally, FR1 bands offer better penetration through buildings and obstacles, enhancing indoor coverage.
## 3 Image Acquisition and Methodology  
We first obtained the dataset through field photography and online collection. Then, in order to find the color features that could better distinguish the normal leaves from insect and diseased leaves, the RGB color space was used, respectively, to calculate the statistics on the number of pixel values in the image. Finally, the initial clustering value was obtained by fitting the characteristic curve with a polynomial, and the final segmentation result was obtained by combining them with the gravity kernel clustering algorithm.
# Learning a Hierarchical Global Attention for Image Classification
## 1 Introduction
The HGA hierarchically finds the spatial-wise relations among features with multi-scale structure design and utilizes nonlinear exploration to learn an adaptive attention. * We provide several patterns for applying HGA to exist network backbones, which demonstrates its flexible applications for different structures. * Experimental results show HGA can boost the image classification capacity for different advanced network structures with restricted computation complexity and parameters. Figure 1: Structure of proposed hierarchical global attention mechanism (HGA). There are three modules in our proposed HGA: Multi-Scale Feature Exploitation (MFE), Hierarchical Global Average Pooling (HAP), and Excitation Exploration (EE).
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 6 Bromelain Studies
### Perioperative  
A recent meta-analysis of six RCTs demonstrated that bromelain alleviates postoperative pain seven days after mandibular third molar surgery (\(p\) = 0.002) and decreases facial swelling in the early and late stages after surgery (respectively \(p\) = 0.02 and \(p\) = 0.0004) [79]. Similar results were obtained in previous meta-analyses carried out by [PERSON] et al. [80], [PERSON] et al. [81] and [PERSON] et al. [82], that also showed improvements in social isolation and sleep quality.  
In another RCT, sixty patients with long bone fractures were treated with 810 mg/day bromelain for the first three days after surgery and 480 mg/day in the remaining follow-up period, with the antioedematous drug aescin being used as the control. A significant decrease in post-traumatic and post-operative swelling and pain was observed in patients who were administered bromelain. Healing acceleration was also observed compared to the control group (to whom aescin was administered) [83].  
In another RCT, eighty-two primiparous women were treated with bromelain and benefited from reductions in pain (measured on the VAS scale), ecchymosis and oedema caused by episiotomy compared with a placebo group (\(p\) < 0.05 for all). In addition, wound healing was faster in the bromelain group than in the placebo group (\(p\) < 0.05) [84]. [PERSON] et al. also demonstrated the existence of a trend of reduction in pain and ecchymosis, although the differences did not reach statistical significance [85].  
[PERSON] and colleagues reported the ability of bromelain to decrease perioperative oedema and ecchymosis in 53 rhinoplasty cases [86].
## 3 Results
### Evaluation of the PV-GIS Tool  
Various studies have presented validation results for the solar radiation PV-GIS-satellite-derived data by comparing them with ground-based solar radiation measurements from 30 BSRN (Baseline Solar radiation Network) stations [13; 14; 25]. The reported differences (%) in the form of 100 \(\times\) (estimated values--measurements)/measurements were found to vary between \(-\)14% and +11% (the estimated values refer to the PV-GIS tool and the measurements come from the BSRN). Furthermore, [PERSON] et al. [1] demonstrated this comparison by taking monthly mean H\({}_{\rm g}\) values measured at the Actinometric Station of the National Observatory of Athens (ASNOA, 37.97\({}^{\circ}\) N, 23.72\({}^{\circ}\) E, 107 m above sea level) and corresponding values from the PV-GIS platform for the period 2005-2011. Figure 9 presents this comparison, which shows an excellent agreement (R\({}^{2}\) = 0.99). Nevertheless, the PV-GIS-estimated values seem to overestimate the measured H\({}_{\rm g}\) ones by +10%, a figure that is within the range in the above-mentioned studies, i.e., from \(-\)14% to +11%. Therefore, the PV-GIS data were accepted for use in the present study.  
Figure 6: Intra-annual variation of the solar energy sum on a 40\({}^{\circ}\)-tilted plane, H\({}_{\rm g,t,40/\rho g}\), at the sites of Arar (#6 in Table 1), Jeddah (#16), and Jazan (#17). The values of H\({}_{\rm g,t,40/\rho g}\) are averages over each month in the period 2005–2016 at 13.00 LST. The name and the geographical latitude of each site are shown in the legend.  
Figure 7: _Cont._
# Recent Advances in the Synthesis of Graphene-Based Nanomaterials for Controlled Drug Delivery
## 3 Drug Delivery with Graphene-Based Nanomaterials
### Graphene-Polymer Nanohybrids
In a recent study, [PERSON] et al., coated GO nanoribbons with an amphiphilic block polymer, 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)], and found that the fabricated nanohybrids could efficiently load and deliver lucanthone to the glioblastoma multiformae cell line U251 [55]. In another study, [PERSON] et al., synthesized a graphene/tri-block copolymer nanocomposite by coating graphene with drug-loaded tri-block copolymer via \(\pi\)-\(\pi\) stacking interaction [83]. The as-prepared nanocomposites exhibited a well-dispersed stability in aqueous medium and dual release behaviors either under acid environment or via biodegradation of the predesigned disulfide bonds. Figure 7: Schematic illustration of the synthesis of thermo-responsive graphene (GO)-polymer nanoparticles (PNPs) nanohybrids for loading and release of adriamycin (ADR). Reproduced with permission from [35]. Copyright Royal Society of Chemistry, 2014. Furthermore, graphene-polymer nanohybrid-based multi-systems have been used for drug delivery application. For instance, [PERSON] and co-workers [54] modified GO with PEG linker and tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL) by step-by-step site-specific modifications, as shown in Figure 8a. The designed nanocarriers could efficiently release TRAIL and DOX to their distinct sites of action separately, in a site-specific manner (Figure 8b). In addition, this two-dimensional (2D) nanocarriers also showed great potential in programmed-release therapeutics capability. Recently, [PERSON] et al., modified GO with carboxymethyl chitosan (CMC), HA, and fluorescein isothiamate (FI) to enhance the efficiency and specificity of anticancer drug delivery [84]. The obtained HA-FI-CMC-GO exhibited a high loading capacity for DOX (95%) and rapid release rate under tumor cell microenvironment of pH 5.8. In addition, the DOX-loaded HA-FI-CMC-GO could specifically target cancer cells and effectively inhibit the growth of cancer cells. In another case, [PERSON] et al., functionalized GO with CMC, FI, lactobionic acid (LA), and acylation (Ac) for delivering DOX to liver cancers [52]. The as-prepared Ac-LA-FI-CMC-GO had high DOX loading content and efficiency (>96%) and pH-sensitive release.
## 6 Experimental Results
### Comparative Analysis of Different Methods  
In order to verify the performance of the recognition, we selected three papers for comparison; both [10] and study [30] uses similar datasets to those used in this paper, while [31] in the literature use the same publicly available dataset. The outcomes were compared with those of cross-domain Wi-Fi activity recognition reported in [10; 30; 31]. Ten-fold cross-validation was executed for multiple experiments. Finally, the average recognition accuracy of the multiple experimental results was determined (see Figure 14 below).  
The above Figure 14 shows that the average recognition rates were 0.4%, 18.6%, and 3.93% higher than those reported in [10; 30; 31], respectively. As for the cross-domain approach, the recognition method proposed in this study outperformed the other methods.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 4 System Model
### Physical Model  
Given the underwater sound propagation is subject to attenuation, the path loss depends on several factors. Some of them are related to the communication medium, e.g., water temperature, density, and pressure, while others depend on the signal being transmitted. In the last case, the most important parameter is the frequency of the signal that impacts directly on the absorption. As the sound propagates in the water, there is a spreading loss that increases with distance. The most accepted model for the path loss is given by [49; 50; 51], represented by the following equation:  
\[A(l,f)=(\frac{l}{l_{r}})^{k}a(f)^{l-l_{r}} \tag{1}\]  
where \(f\) is the signal frequency, and \(l\) is the transmission distance taken in reference to some \(r\). The path loss exponent \(k\) models the spreading loss, and its usual values are between 1 and 2 (for cylindrical and spherical spreading, respectively). The absorption coefficient \(a(f)\) is an increasing function of frequency, which can be obtained using an empirical formula [52].  
Equation (1) assumes that the transmission path between the source and the sink is straight and free from interference. However, underwater acoustic signals propagate in a physical channel with boundaries in the surface and the bottom (Figure 1). Usually, part of the signal is reflected in both, generating replicas that arrive to the sink at different instants. This effect, known as multipath, makes that the surface reflects the signal without loss, but in the bottom the signal is attenuated. This attenuation depends on the kind of material present in the floor (e.g., sand, rocks or mud).  
The reflected messages have to cover longer distances before reaching the sink (given by Equation (1)). Given the path loss is proportional to the distance, the reflected signal arrives more attenuated to the sink. Anyway, depending on the distance and the network deployment, multiple copies of a message may arrive successively to destination with enough strength to disturb other messages.  
The initial versions of the UW-ASN model (particularly, those described in [14; 43; 53]) ignored the multipath effect in order to focus on the medium access control protocol, and we have assumed a symmetrical propagation delay between any pair of nodes. In this paper, we extend the model to propose a directed multipath that accounts for the multipath problem, and incorporates the possibility of introducing different delays according to the direction in which a message is propagated. There are several causes for this but, to mention just one, the presence of currents contribute in one direction to the propagation speed, while increase the delay in the opposite.  
Acoustic signals are longitudinal waves. In the case a collision among more than one signal is produced, there is a transitory composition of the waves at the point of collision; but after this, the signals continue their propagation undisturbed. Clearly, collisions should be avoided at the sinks, due to they may corrupt the messages in a way that they cannot be recovered.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 5 The Prototype
### Evaluation and Comparison
#### 5.3.2 Performance Measures  
This section demonstrates how the proposed approach can achieve the recommendation accuracy based on user's context, his preferences and his available devices. The multi-domain rule repository (i.e., _history of users' rules_) of this experiment is used. We evaluate the performance in automatically selecting rules items in terms of:  
* Precision (\(P\)) is the ratio of relevant recommended rule items to the user and the total among the recommended ones.
* Recall (\(R\)) is the ratio of relevant recommended items among the total number of all items.
* F1-score (\(F1\)) is used to evaluate a weighted average of precision and recall.  
These metrics are calculated as follows:  
\[P=\frac{TP}{TP+FP} \tag{7}\]  
\[R=\frac{TP}{TP+FN} \tag{8}\]  
\[F_{1}=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
Figure 12: An example of a user rules repository in three user’s domains.  
where:  
* _TP_ is the total number of relevant rules items (True Positive).
* _FP_ is the total number of non-relevant rules items (False Positive).
* _TN_ is the total number of relevant rules items that are not selected by the proposed approach (True Negative).
* _FN_ is the total number of non-relevant rules items that are not selected by the proposed approach (False Negative).
## 3 Evaluation
### Result  
The quality of the results of the methods is shown in Figure 9. The quantitative results related to the metrics from the database are presented in Table 3. According to the evaluations, the quantitative and qualitative results are promising in segmentation, but the results should help the researchers to improve the results in sensitive application.  
The quantitative results in terms of Dice, Jaccard, Bfscore, Precision, Recall, Iteration and Time measured using the database are shown in Table 3.  
Figure 9 shows some binary results processed with six sample images from the CT database. The six images were randomly selected for qualitative evaluation. The authors reviewed the images based on the results of each method compared to the corresponding ground truth.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Initial Contour** & **C-V** & **SBGFRLS** & **MAC** & **ORACM** & **LSACM** & **RSFOLGE** & **ALF** & **FRAGL** & **GLSEPF** \\ \hline Inside & Failed & Failed & Done & Done & Failed & Failed & Failed & Failed \\ \hline Outside & Failed & Failed & Done & Done & Failed & Done & Failed & Failed \\ \hline Cross & Done & Failed & Done & Done & Done & Done & Failed & Done & Done \\ \hline \hline \end{tabular}
\end{table}
Table 2: Success of active contour methods in obtaining infection area in terms of initial contour.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Measure** & **C-V** & **MAC** & **ORACM** & **LSACM** & **RSFOLGE** & **ALF** & **FRAGL** & **GLSEPF** \\ \hline Dice (\%) & 93.55 & 95.94 & 96.30 & 95.77 & 89.88 & 92.12 & **96.44** & 95.60 \\ \hline Jaccard (\%) & 88.31 & 92.32 & 93.06 & 92.01 & 82.49 & 85.70 & **93.21** & 91.77 \\ \hline Bfscore (\%) & 66.82 & 61.40 & **74.13** & 60.46 & 63.50 & 57.05 & 65.55 & 71.96 \\ \hline Precision (\%) & 84.44 & 92.37 & _77.73_ & **96.89** & 73.03 & 93.33 & 91.33 & 68.24 \\ \hline Recall (\%) & 58.15 & 48.25 & 72.41 & 45.67 & 61.44 & 43.34 & 53.18 & **78.81** \\ \hline Iteration & 158 & 8500 & **5** & 200 & 250 & 8 & 10 & 30 \\ \hline Time (s) & 55 & 700 & **1.4** & 12 & 41.40 & 100 & 1.8 & 5.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed evaluation of seven metrics based on COVID-19 CT images.  
To show the effectiveness of active contour methods, we compared the best active contour method (GLSEPF), which achieves the best results among all methods in the database based on Table 3, with deep learning methods. Due to the different structure of the two sets of methods, the comparison is not fair. However, it can show the effectiveness of the active contour methods. Table 4 shows the comparison in terms of recall measure. The deep learning methods consist of 8 popular methods, namely MiniSeg [56], MobileNet [57], InfNet [58], DeepLabv3+ [59], EfficientNet [60], EDANet [61], ENet [62], and ESPNetv2 [63].
# Parasitic Parameters Extraction of High-Speed Vertical-Cavity Surface-Emitting Lasers
###### Abstract  
Parasitic parameters, including electrical capacity and inductance, are the key limiting factors for bandwidth improvement of high-speed vertical-cavity surface-emitting lasers (VCSELs). The traditional parasitic extraction method, which uses a first-order low-pass filter transfer function, is oversimplified, and there are large deviations between the obtained data and the actual measured data. In this paper, we proposed a modified parasitic extraction method that described the extrinsic behaviour of the high-speed oxide-confined VCSELs well and easily extracted the values of all parasitic parameters. This method can also precisely fit microwave reflection coefficient S\({}_{11}\) data even at high frequencies and provide design guidance for high-speed VCSELs. Using this method, a high-speed 850 nm VCSEL featuring a six-layer oxide aperture with \(-3\) dB bandwidth up to 23.3 GHz was analysed. The electrical parastitics have been systematically extracted from VCSELs with different oxide apertures. The enhanced bandwidth based on the improvement of parasitic parameters was discussed. It was found that the critical parasitic factors that affect the \(-3\) dB bandwidth of VCSELs are pad capacitance and inductance.  
high-speed VCSEL; parasitic extraction method; impedance characteristics +
Footnote †: journal:  
Article
# Advancing the Solar Radiation Pressure Model for BeiDou-3 IGSO Satellites
Figure 1: BeiDou-3 IGSOs unfolding on-orbit ([[http://www.csno-tarc.cn/en/system/introduction](http://www.csno-tarc.cn/en/system/introduction)]([http://www.csno-tarc.cn/en/system/introduction](http://www.csno-tarc.cn/en/system/introduction)) (accessed on 15 November 2021)). Overall, currently, there are no published studies on the SRP modeling of BeiDou-3 IGSOs and the POD performance of BeiDou-3 IGSOs during eclipse seasons. The purpose of this study is to carry out the SRP modeling for BeiDou-3 IGSOs and to investigate and improve their orbit performance during eclipse seasons. First, the ECOM, ECOM2 and box-wing (BW) models are introduced. The data collection and POD processing strategies are then presented as the study is to a large extent based on numerical investigation. An a priori BW model is established by estimating the optical properties of BeiDou-3 IGSOs. The performance of the five-parameter ECOM model with and without this a priori BW model, as well as the seven-parameter ECOM2 model, is evaluated. An effective strategy for improving eclipsing BeiDou-3 IGSOs orbit performance is recommended. Finally, the key conclusions are summarized.