esa-sceva/satcom-chunk-collection huggingface dataset:
# Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
###### Abstract  
With the continuous development and improvement in Internet-of-Things (IoT) technology, indoor localization has received considerable attention. Particularly, owing to its unique advantages, the Wi-Fi fingerprint-based indoor-localization method has been widely investigated. However, achieving high-accuracy localization remains a challenge. This study proposes an application of the standard particle swarm optimization algorithm to Wi-Fi fingerprint-based indoor localization, wherein a new two-panel fingerprint homogeneity model is adopted to characterize fingerprint similarity to achieve better performance. In addition, the performance of the localization method is experimentally verified. The proposed localization method outperforms conventional algorithms, with improvements in the localization accuracy of 15.32%, 15.91%, 32.38%, and 36.64%, compared to those of KNN, SVM, LR, and RF, respectively.  
Wi-Fi fingerprint; indoor localization; particle swarm optimization; location estimation 202222225051. [[https://doi.org/10.3390/s22135051](https://doi.org/10.3390/s22135051)]([https://doi.org/10.3390/s22135051](https://doi.org/10.3390/s22135051))
## 2 Materials and Methods
### Data Collection
#### 2.1.1 Pre-Experiment  
Delsys Incorporated Tigno sEMG sensors were used to record the sEMG signals from four shank muscles (tibialis anterior (TA), soleus (SOL), gastrocnemius medialis (GM), and peroneus brevis (PB) muscles) at 1111.11 Hz. Only TA contributes to ankle dorsiflexion movement, while the remaining contribute to ankle plantar flexion movement. The sensors were adhesive at the volunteer's skin above the muscle belly following SENIAM [18] protocol for sEMG sensors arranging and placing. The placement of each sensor was then validated by asking the volunteers to perform movements to trigger maximum the sEMG activity from the targeted muscles, as recommended by SENIAM [18].  
Figure 1 shows the location of thirty-nine reflective markers attached to each volunteer to track the motion using OptiTrack's (NaturalPoint Inc. DBA, Corvallis, OR, USA) motion capture system consisting of eight reflective cameras at 100 Hz. Marker locations were chosen to align with the generic model Gait2392 provided inside OpenSim software [19; 20]. The anatomical markers (red markers in Figure 1) were used to build the volunteer's generic model and the tracking markers (the white markers in Figure 1) were used to track the motion of the volunteer. Pre-experiment the volunteers were asked to do a static reference pose (A-pose) inside the motion capture space to calibrate the location of the anatomical markers for the OpenSim model. The ground reaction force was recorded from a single
# Osteoarthritis--The Role of Mesenchymal Stem Cells in Cartilage Regeneration
## 4 MSC-Based Therapy for OA
### Sources of MSCs
#### 4.2.5 SM-MSCs for OA Treatment  
In 2001, [PERSON] et al. discovered the first SM-MSCs. These cells, much like AD-MSCs, can be separated from some sites, such as the cotyloid fossa or the paralabral synovialum, and they display site-specific properties. Compared to other types of MSCs, it is interesting to note that SM-MSCs have a proliferative solid potential, the ability to differentiate into a wide variety of lineages, and low immunogenicity [249; 250].  
SM-MSCs have a more tremendous chondrogenic potential than MSCs derived from other sources and are predicted to be used more frequently for cartilage repair and joint homeostasis therapies [227; 251]. This is due to the increased expression of type II collagen, aggrecan, and Sox9, which gives them a higher chondrogenic potential [227; 252].  
According to [PERSON] et al., both SM-MSCs and BM-MSCs have a greater capacity for osteogenic and adipogenic differentiation than other MSCs. Despite this, SM-MSCs generate relatively low-density expansions in vitro compared to BM-MSCs [189; 250].  
In an experimental model of OA in rats, SM-MSCs were administered by frequent injections. These SM-MSCs exhibited migratory behavior towards the synovium and maintained their undifferentiated state. Furthermore, they demonstrated increased expression levels of chondrocytes proteins, including BMP-2 and an anti-inflammatory gene known as TSG-6 [253].  
This demonstrates that SM-MSCs control MSCs characteristics and may also limit the progression of OA via genetic machinery.  
In a single retrospective study [254], SM-MSCs have been examined concerning human knee OA. Forty-eight months following surgery, they saw clinical improvement and safe defect filling, as confirmed by second-look arthroscopy and MRI.
# Research on Automatic Recognition Method of Artificial Ground Target Based on Improved HED
## 3 Research on Automatic Target Recognition Algorithm Based on Geometric Primitives of Line Groups
### Noise Filtering  
As can be seen from Figure 9, a mean filter, median filter and Gaussian filter were all commonly used filters, but they made the whole image smooth and the boundary in the image blurred. Bilateral filtering was a filtering operation that could not only smooth the noise but also maintained the edge information. Its calculation formula is as follows:  
\[g(i,j)=\frac{1}{k(i,j)}\sum_{i,j}e^{-\frac{[(i-f)^{2}+(i-f)^{2}]}{2\sigma_{d}^{ 2}}}e^{-\frac{[(i-f)^{2}+(i-f)^{2}]}{2\sigma_{f}^{2}}}f(i^{\prime},j^{\prime}) \tag{2}\]  
where \(f(i,j^{\prime})\) was the input image, \(g(i,j)\) was the image after bilateral filtering, \((i,j)\) was the domain pixel of \((i,j)\), and \(\sigma_{d}\) and \(\sigma_{r}\) were the standard deviations of the spatial domain and gray domain, respectively. \(K(i,j)\) was the normalization factor.  
It can be seen from the formula that the smoothness of bilateral filtering was different for different regions. In areas with infrequent gray level transformation, the smoothing effect was greater, while in areas with frequent gray level transformation, such as edges, the smoothing effect was smaller.  
The bilateral filter automatically judged whether the filter core was in a flat area or an edge area: if the filter core was in a flat area, an algorithm similar to Gaussian filtering was used for filtering. If the filter core was in an edge area, the weight of the edge pixels was increased to keep these pixels unchanged as much as possible.  
Bilateral filtering was used to smooth the image. As shown in Figure 10, (a) is the original image, (b) is the image after bilateral filtering, and (c) and (d) are the effect pictures after Canny edge detection on the (a) and (b) images, respectively. The results showed that bilateral filtering could effectively reduce the noise in the image and improve the effect of edge detection.  
Figure 8: Flow chart of automatic target recognition.  
Figure 10: The effect picture of the image after bilateral filtering. (**a**) Original image of overpass; (**b**) image after bilateral filtering; (**c**) Canny edge detection on the original image; (**d**) Canny edge detection after bilateral filtering.  
Figure 9: Common noise filter. (**a**) Original airport image; (**b**) mean filtering; (**c**) median filtering; (**d**) Gaussian filtering; (**e**) bilateral filtering.
## 4 Experiments Results and Discussions  
### Evaluation Criteria  
In this paper, the performance of crack detection is evaluated by calculating the detection accuracy (\(D_{a}\)). It is calculated by _Precision_ and _Recall_. The equations are:  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[D_{a}=(1+k)\cdot\frac{Precision\cdot Recall}{k\,Precision+Recall} \tag{12}\]  
In Equations (10) and (11), \(TP\), \(FP\), and \(FN\) are the proportions of crack detection that are considered true positives, false positives, and false negatives, respectively. In Equation (12), it can be seen that \(k\) controls the weight of _Precision_ and _Recall_. When \(k\) is set as a high value, it indicates that _Precision_ is more important in diagnostic performance, and vice versa. Here \(k\) is set as 1 to indicate that the _Precision_ and _Recall_ are looked as of equivalent importance.  
Another measure to evaluate the performance of the algorithm is the location accuracy (\(L_{a}\)). Crack position is an important parameter to judge the health of a coating. It is defined as following:  
\[L_{a}=1-\frac{\sqrt{\left(x_{d}-x_{g}\right)^{2}+\left(y_{d}-y_{g}\right)^{2} }}{\sqrt{\left(x_{v}-x_{o}\right)^{2}+\left(y_{v}-y_{o}\right)^{2}}} \tag{13}\]  
In Equation (13), \(x_{d}\) and \(y_{d}\) are the \(X\) coordinate and \(Y\) coordinate of the detection crack center point, respectively; \(x_{g}\) and \(y_{g}\) are the \(X\) coordinate and \(Y\) coordinate of the ground truth crack center point, respectively. For normalization, \(x_{v}\) and \(y_{v}\) denote coordinates of the vertices of the coating piece, while \(x_{o}\) and \(y_{o}\) denote coordinates of the center of the coating piece. A large value of \(L_{a}\) means that the center point of the detected crack is close to the ground truth, and vice versa.
# Assessment of Impacts of Climate Change on Hydropower-Dominated Power System--The Case of Ethiopia
## 3 Result and Discussion
### Electricity Supply Mix Drought  
Figure 3 and Table 3 show that the electricity supply in the Drought Scenario increased from 12 TWh in 2015 to 83 TWh in 2030, 308 TWh in 2050, which gradually reached 503 TWh in 2065. This is slightly lower than the New Policy Scenario's 12 TWh in 2015 to 98 TWh in 2030, 305 TWh in 2050, gradually reaching 517 TWh in 2065. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Capacity (GW)**} & \multirow{2}{*}{**2015**} & \multicolumn{3}{c}{**Drought Scenario**} & \multicolumn{3}{c}{**New Policy Scenario [28]**} \\ \cline{2-9}  & & **2030** & **2050** & **2065** & **2030** & **2050** & **2065** \\ \hline Biomass & 0.20 & 0.33 & 2.28 & 5.75 & 0.33 & 0.53 & 4.00 \\ LFO/HFO & 0.14 & 0.07 & 0.00 & 7.97 & 0.07 & 0.00 & 3.72 \\ Gas & 0.00 & 0.97 & 5.69 & 21.82 & 0.00 & 0.00 & 4.42 \\ Nuclear & 0.00 & 0.00 & 5.00 & 8.89 & 0.00 & 0.00 & 1.50 \\ Hydro & 2.36 & 15.94 & 38.53 & 44.01 & 2.36 & 18.48 & 44.01 \\ Geothermal & 0.01 & 2.05 & 5.00 & 5.00 & 0.01 & 2.05 & 5.00 \\ Wind & 0.32 & 4.96 & 10.00 & 10.00 & 0.32 & 0.46 & 10.00 \\ Solar PV & 0.01 & 5.56 & 32.50 & 35.00 & 1.82 & 22.75 & 35.00 \\ Solar CSP & 0.00 & 0.00 & 11.00 & 41.00 & 0.00 & 1.00 & 29.02 \\ Total & 3 & 30 & 110 & 179 & 23 & 88 & 169 \\ RET share & 95\% & 97\% & 90\% & 78\% & 99\% & 95\% & 77\% \\ Djibouti exports & 0.10 & 0.10 & 0.10 & 0.10 & 0.10 & 0.10 & 0.10 \\ Kenya exports & 0.00 & 1.00 & 1.40 & 1.40 & 1.40 & 1.40 & 1.40 \\ Sudan exports & 0.10 & 3.10 & 3.10 & 3.10 & 3.10 & 3.10 & 3.10 \\ Tanzania exports & 0.00 & 0.41 & 0.41 & 0.41 & 0.41 & 0.41 & 0.41 \\ Exports & 0.20 & 4.61 & 5.01 & 5.01 & 5.01 & 5.01 & 5.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Power Generation Capacity of Drought and New Policy Scenario. Figure 2: Comparison of power generation capacity between Drought vs. New Policy Scenarios. In both scenarios, renewable energy technologies constitute most of the power generation mix, from 95% (12 TWh) in 2015, increasing to 97% (82 TWh) in 2030, gradually decreasing to 90% (270 TWh) in 2050 and finally reaching 81% (420 TWh) in 2065 compared to 95% (12 TWh) in 2015, increasing to 100% (98 TWh) in 2030, gradually decreasing to 98% (299 TWh) in 2050 and finally reaching 89% (458 TWh) in 2065 in New Policy Scenario. In both scenarios, the share of renewable would be slightly decreasing over time, which is the same for Hydro. Throughout the modelling period, the percentage of renewable energy technologies in comparison to all other technologies is nearly identical. The most common cause is that when hydropower technology generation is reduced due to severe drought,  
Figure 3: Electricity supply mix of grid-connected technologies in the Drought scenario.
## 3 Results and Discussion
### Aldehydes  
Aldehyde content varied across cultivars and across ripening seasons. A total of six aldehydes were detected (Figure 2, Tables 2 and 3). Benzaldehyde (A2) was the most abundant with a content ranging from trace amounts to 104.9 ng\(\cdot\)g\({}^{-1}\) (Tables 4 and S1). Benzaldehyde is known to act in combination with lactones to shape the basic peach aroma [16]. It is characterized by an \"almond\" and \"burnt sugar\" odor as described in the online database Flavornet ([[http://www.flavornet.org/flavornet.html](http://www.flavornet.org/flavornet.html)]([http://www.flavornet.org/flavornet.html](http://www.flavornet.org/flavornet.html)), accessed on 11 November 2021) and a \"fruity\" flavor as described by the Good Scents Company ([[http://www.thegoodscentscompany.com/index.html](http://www.thegoodscentscompany.com/index.html)]([http://www.thegoodscentscompany.com/index.html](http://www.thegoodscentscompany.com/index.html)), accessed on 11 November 2021). The \"Ruston Red\" cultivar had the highest benzaldehyde content (104.9 ng\(\cdot\)g\({}^{-1}\)) (Table 4). Other cultivars with a high benzaldehyde content (greater than 5 ng\(\cdot\)g\({}^{-1}\)[6]) were \"August-prince\" (43 ng\(\cdot\)g\({}^{-1}\)), \"Flameprince29\" (47.3 ng\(\cdot\)g\({}^{-1}\)), \"Elbetra #2\" (49.9 ng\(\cdot\)g\({}^{-1}\)), \"Early Big Red\" (53.5 ng\(\cdot\)g\({}^{-1}\)), \"Fat Lady\" (68.9 ng\(\cdot\)g\({}^{-1}\)), \"Caroking\" (7.9 ng\(\cdot\)g\({}^{-1}\)), \"Southern Pearl\" (85.4 ng\(\cdot\)g\({}^{-1}\)), \"Majestic\" (87.1 ng\(\cdot\)g\({}^{-1}\)), and \"Red Globe\" (88.6 ng\(\cdot\)g\({}^{-1}\)) (Table 4). As shown in Figure 2, other abundant aldehydes were nonanal (A4), followed by hexenal (A1), benzeacetaldehyde (A3), decanal (A5), and dodecanal (A7), in that order. \"Fiesta Gem\" had the highest of nonanal (A4) with 15.3 ng\(\cdot\)g\({}^{-1}\). Late-season cultivars had higher benzaldehyde and benzeneacetaldehyde (A3) relative concentrations than early- and mid-season cultivars (Table 3). Benzeneacetaldehyde is characterized by a \"green\" odor and a \"honey\" flavor (Good Scents Company database). Hexanal and nonanal relative concentrations were the highest for early-season cultivars in comparison to late- and mid-season cultivars (Table 3). Hexanal is characterized by \"grass\", \"tallow\", and \"fat\" (Flavornet database) odors and \"green\" odor and flavor (Good Scents Company database). The reported low concentration of aldehydes among all volatiles detected was probably due to all peaches being processed at a fully ripe stage, as aldehydes are characterized by green-grassy notes (Figure 1). They are prominent in immature peaches, and they decrease as the peach ripens [8; 17].  
Figure 2: Relative concentration per cultivar (ng\(\cdot\)g\({}^{-1}\)) of aldehydes across 42 peach cultivars. Mean values of 3 fruit pureê samples per cultivar. Standard error (SE) bars are reported.
## 3 Results and Discussion  
### Numerical Simulation Analysis of Roof Cutting and Pressure Relief  
#### 3.1.1 Analysis of the Surrounding Rock Stress Characteristics When the Roof Cutting  
Measure Is Not Taken  
Figure 6 shows the cloud diagram of the surrounding rock stress distribution after 1301 working face mining, and Figure 7 shows the curve diagram of the lateral surrounding rock stress distribution after 1301 working face mining. Figures 5 and 6 show that the original rock stress of the no. 15 coal seam at the 1203 working face is approximately 7.5 MPa. After mining the 1301 working face, stress concentration is formed on both sides of the goat, among which a stress reduction zone (4 m) and stress increase zone (82 m) are formed at the 1203 working face side. The peak value of the stress increase zone is 16.81 MPa, which is located 6 m away from the goat boundary and the stress concentration coefficient is 2.24. If a small coal pillar and roadway are to be set at this place, the roadway will be in the stress increase area, which is not conducive to the stability of the roadway, so roof cutting and pressure relief measures should be taken at this place.  
3.1.2. Analysis of the Stress Characteristics of the Surrounding Rock under Roof Cutting and Pressure Relief  
The stress distribution characteristics of the surrounding rock are simulated under the conditions that the roof cutting height is 6 m (cut to the subkey strata) and the roof cutting height is 12 m (cut to the main key strata) to determine which key strata have more influence on rock mass activity. Figures 8 and 9 show that the range of the lateral stress reduction zone in the goal significantly expands after roof cutting. When the cutting top is 6 m high, the peak stress in the coal pillar is 15.72 MPa and the range of the lateral stress reduction zone in the goaf is 8 m. If the coal pillar and roadway are arranged here, the width of the small coal pillar should be less than 3.8 m. The coal pillar size is small, it is easy to leak air to the adjacent goaf, and there is the risk of spontaneous coal combustion. When the cutting top is 12 m high, the peak stress in the coal pillar decreases to 14.49 MPa and the range of the lateral stress reduction zone in the goaf expands to 10.5 m. If the coal pillar and roadway are arranged here, the width of the small coal pillar should be less than 6.3 m. In conclusion, the pressure relief effect is better when the roof cutting height is 12 m.  
Figure 6: Vertical stress cloud diagram of the surrounding rock after mining the 1301 working face.  
Figure 7: Local curve of the lateral surrounding rock vertical stress after mining the 1301 working face.  
The above numerical simulation analysis shows that the main key strata have a greater control effect on the mine pressure of the working face. Therefore, the roof cutting height is set to 12 m when the roof cutting operation is carried out.
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## 4 Privacy-Based Refinement
### Comparing Mechanisms by Their \"Smallest \(\varepsilon\)\" (For Fixed \(d\))  
Scaling \(d\) by a privacy parameter \(\varepsilon\) allows us to turn \(d\)-privacy (for some fixed \(d\)) into a _quantitative \"leakage\" measure_, by associating each channel to the _smallest_\(\varepsilon\) by which we can scale \(d\) without violating privacy.  
**Definition 4**.: _The privacy-based leakage (wrt \(d\)) of a channel \(C\) is defined as_  
\[\mathrm{Priv}_{d}(C) := \inf\{\varepsilon\geq 0\mid C\text{ satisfies }\varepsilon\cdot d\text{- privacy}\} \tag{18}\]  
Note that \(\mathrm{Priv}_{d}(C)=+\infty\) iff there is no such \(\varepsilon\); also \(\mathrm{Priv}_{d}(C)\leq 1\) iff \(C\) satisfies \(d\)-privacy.  
It is then natural to compare two mechanisms \(A\) and \(B\) based on their \"smallest \(\varepsilon\)\".  
**Definition 5**.: _Define \(A\sqsubseteq_{d_{\mathrm{H}}}^{\mathrm{prv}}B\) iff \(\mathrm{Priv}_{d}(A)\geq\mathrm{Priv}_{d}(B)\)._  
For instance, \(A\sqsubseteq_{d_{\mathrm{H}}}^{\mathrm{prv}}B\) means that \(B\) satisfies standard differential privacy for \(\varepsilon\) at least as small as the one of \(A\).
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 5 Implementation
### Security and Performance Evaluation  
The system's security is evaluated based on its resistance to various types of attacks:  
* Eavesdropping: The unpredictability of true random sequences makes it extremely difficult for unauthorized parties to intercept and understand the communication.
* Jamming: True random frequency hopping enhances resistance to jamming, as the attacker cannot predict the frequency changes.  
Performance is assessed in terms of:  
* Throughput: The rate at which data can be successfully transmitted.
* Latency: The delay introduced by frequency hopping and synchronization processes.
* Reliability: The system's ability to maintain communication integrity under different conditions, including adversarial attacks.
## 4 Conclusions  
Based on the experimental research carried out and discussion on the results, the following can be stated:  
1. The use of the CMT hardfacing process enables the production of cladder welds with a minimal-volume fraction substrate material in coatings (small value of dilution), which results in high layer properties.
2. Among the investigated coatings, Stellite 6 layer is the hardest, at about 500 HV0.2, compared to materials such as Inconel 625, Inconel 718 and Alloy 33, which represent a cladded zone hardness about 250 HV0.2.
3. Stellite 6 layer have the lowest wear resistance in the dry sand/rubber wheel test and the highest wear resistance in the erosive blasting test. This proves the existence of different wear mechanisms in the two test methods used.
4. In the dry sand/rubber wheel test, Alloy 33 and Inconel 718 only represent higher wear resistance than substrate 16 Mo3 steel.
5. In the abrasive blasting tests all coatings have a higher wear resistance than 16 Mo3 steel; however, Stellite 6 coatings represents an approximately 5 times higher durability than other investigated (Inconel 625, Inconel 718 and Alloy 33) coatings.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{\
\begin{tabular}{c} **Starting** \\ **Weight (g)** \\ \end{tabular} } & \multicolumn{4}{c}{**Loss of Weight after the Next Machining Cycle**} & \multicolumn{1}{c}{**Total Weight**} & \multirow{2}{*}{
\begin{tabular}{c} **Relative 1** **Ahersion** \\ **Resistance** \\ \end{tabular} } \\ \cline{3-3} \cline{5-10}  & & **10 s** & **20 s** & **30 s** & **40 s** & **50 s** & **60 s** & **Loss (g)** & **Resistance** \\ \hline
16 Mo3 & 348.258 & 347.962 & 347.641 & 347.29 & 346.997 & 346.997 & 346.374 & 1.884 & 0.17 \\ Inconel 625 & 387.205 & 386.929 & 386.636 & 386.337 & 386.045 & 385.75 & 385.451 & 1.754 & 0.19 \\ Stellite 6 & 393.318 & 393.251 & 393.192 & 393.129 & 393.075 & 393.023 & 392.983 & 0.335 & 1 \\ Alloy 33 & 396.202 & 395.994 & 395.719 & 395.453 & 395.174 & 394.898 & 394.628 & 1.574 & 0.21 \\ Inconel 718 & 457.531 & 457.315 & 456.952 & 456.638 & 456.31 & 456.001 & 455.681 & 1.85 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of abrasive blasting wear test.  
Figure 14: Total loss of weight after the abrasive blasting wear test.  
**Author Contributions:** Conceptualization, P.K.; methodology, M.B.; validation, P.K., M.B. and T.C.; formal analysis, T.C.; investigation, P.K.; resources, M.B.; data curation, M.B.; writing--original draft preparation, P.K. and T.C.; writing--review and editing, T.C.; visualization, M.B.; supervision, P.K.; project administration, P.K.; funding acquisition, T.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 4 Analysis of the Performance
### Analysis of the Network Performance
#### 4.2.4 The Stability of the Route  
The stabilities of Algorithms 2-4 are shown in Figure 11. From the figure, the original RPL has the least stability. This is because the original will frequently change the preferred parent node, which results in the instability of the network and excessive energy consumption. In fact, the change of the preferred parent node will cause a reset of the trickle timer, which will make the transfer of control messages more frequent. The more we transfer the control messages, the more energy it will consume. Moreover, the frequent changes will make the network unstable and will worsen the network topology. The above algorithms, except the original RPL, make all parent nodes participate in traffic forwarding without changing the preferred parent node. The preferred parent node will only change if it fails, and then the trigger timer will be reset. This will ensure network stability.  
Figure 10: Network end-to-end delay in different size.