esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing and Analysis
All 10 clustering methods available in the package were considered, that is, hierarchical clustering with the [PERSON]'s method [14], partitioning around medoids (PAM) [15], k-means [16], DIvisive ANAlysis clustering (DIANA) [15], Clustering LARge Applications (CLARA) [15], AGglomerative NESting (AGNES) [15], self-organizing map (SOM) [17], self organizing tree algorithm (SOTA) [18], model-base clustering [19], and fuzzy analysis clustering [20]. The clustering performance of the methods was ranked according to seven internal clustering validation measures [21; 22], namely, the connectivity [23], silhouette width [24] and Dunn index (combining measures of compactness and separation of the clusters) [25], average proportion of non-overlap (APN), average distance (AD), average distance between means (ADM), and figure of merit (FOM) [26; 27]. All of the clustering algorithms were ranked based on their performance as determined simultaneously by all the validation measures [28]. There is no generally accepted rule on the ratio between the number of clustering variables and sample size (number of observations, sites in this study). Usually, the latter should be much higher than the former. In this study, the ratio between sites and variables is low (90/48 = 1.875). Thus, principal component analysis (PCA) was also performed on the input matrix in order to reduce the number of variables and account for the largest possible variance of the original variables. The method generates a new set of variables, called principal components, and each principal component is a linear combination of the original variables. All the principal components are orthogonal to each other, so there is no redundant information. Components with larger variance are the most relevant to the clustering and, therefore, removing features with low variance acts as a filter that results in a distance metric that provides a more robust clustering. The PCA output for the selected components was taken as input for a further k-means clustering, and the obtained classification was compared with that obtained by the earlier k-means applied to all 48 variables. Furthermore, the hourly values of the 24 h pattern, corresponding to the cluster membership of the site, were compared with those observed at the site itself and the differences \(\varepsilon\) were evaluated in terms of root mean square (RMS) and probability \(P_{\varepsilon}\) of \(\varepsilon\) being within a specified interval width. Supervised machine-learning algorithms can be applied to classify new roads into the clusters determined by the cluster analysis, but the acoustic data must be known. Unfortunately, very often they are not available and, therefore, an alternative classifier is needed. For this purpose, the road traffic flow is a potential candidate as its value, obtained from transport models or measured, is rather available and linked to the produced noise. In the present study, the decimal logarithm of the day-time (06:00-22:00) traffic flow rate was considered as an alternative road classifier.
## 4 Materials and Methods
### Proposed Variable Proportional-Integral (VPI) Control Methodology  
In this paper, the performance of the proposed multi-agent DDPG controller was compared to the performance of the VPI controller during the considered cyber attacks. Therefore, a description of the VPI controller and its structure is described and depicted below.  
Stator VPI-idqs: The variable proportional-integral (VPI) controller for the stator idqs ref values utilizes the error in the DC-link voltage to scale the Kp-dc and Ki-dc gains if the dc-link voltage changes by more than 5% of the nominal value. The Kp-dc and Ki-dc  
Figure 9: (**a**) DDPG actor network for rotor \(v_{dq}\) reference points; (**b**) DDPG critic network for rotor \(v_{dq}\) reference points.  
gains are adjusted by multiplying the error by the gain value and then adding this value to the original gain value, creating a variable gain value to mitigate the effects of an FDIA. As seen in the plots and the RMSE performance table in the Results section, the VPI can dampen the effects of an FDIA on the idqs PI controller.  
Rotor VPI-vdqs: The VPI controller for the rotor vdqr ref values utilizes the error in the idr and iqr measured values with respect to their ref values. If the dc-link voltage changes by more than 5% of the nominal value, the VPI will scale the gains accordingly. The scaled Kp-rsc and Ki-rsc gains for the rotor VPI controller are found by multiplying the error by the respective gain value and then adding this value to the original gain value for Kp-rsc and subtracting this value from Ki-rsc. As seen from the plots and the RMSE performance table in the Results section, the VPI can dampen the effects of an FDIA on the vdqs PI controller. The mathematical modeling of stator VPI and rotor VPI controllers are provided below. Figures 10 and 11 show the diagrams for the stator VPI and rotor VPI controllers.  
\[\text{Stator VPI-idqs}\] \[\text{e}_{\text{vdc}}(\text{t})=(\text{V}_{\text{dc},\text{nom}}- \text{V}\text{dc})/\text{V}_{\text{dc-nom}} \tag{22}\] \[\text{K}_{\text{vp}}=\text{K}_{\text{p}\_\text{dc}}+[\text{K}_{ \text{p}\_\text{dc}}*\text{e}_{\text{vdc}}(\text{t})]\] (23) \[\text{K}_{\text{vi}}=\text{K}_{\text{i}\_\text{dc}}+[\text{K}_{ \text{i}\_\text{dc}}*\text{e}_{\text{vdc}}(\text{t})] \tag{24}\]  
\[\text{Rotor VPI-vdqs}\] \[\text{[e}_{\text{idr}}(\text{t});\text{e}_{\text{iqr}}(\text{t})]=[ \text{Idr\_ref}-\text{Idr};\text{Iqr\_ref}-\text{iqr}] \tag{25}\]  
\[\text{K}_{\text{vp\_rsc}}=\tan(\frac{\pi}{2}*\text{K}_{p\_\text{rsc}})+\left[ \tan(\frac{\pi}{2}*\text{K}_{p\_\text{rsc}})*\text{e}_{\text{idr}}(\text{t}) \right]\text{;}\ \tan(\frac{\pi}{2}*\text{K}_{p\_\text{rsc}})+\left[\tan(\frac{\pi}{2}* \text{K}_{p\_\text{rsc}})*\text{e}_{\text{iqr}}(\text{t})\right] \tag{26}\]  
\[\text{K}_{\text{vi\_rsc}}=\tan(\frac{\pi}{2}*\text{K}_{i\_\text{rsc}})-\left[ \tan(\frac{\pi}{2}*\text{K}_{i\_\text{rsc}})*\text{e}_{\text{idr}}(\text{t}) \right]\text{;}\ \tan(\frac{\pi}{2}*\text{K}_{i\_\text{rsc}})-\left[\tan(\frac{\pi}{2}* \text{K}_{i\_\text{rsc}})*\text{e}_{\text{iqr}}(\text{t})\right] \tag{27}\]
# Exergaming-Based Rehabilitation for Lateral Trunk Flexion in Parkinson's Disease: A Pilot Study
## 2 Materials and Methods
### Instrumental Assessment
#### 2.3.2 Tensiomyography (TMG)  
Tensiomyography (TMG) is a non-invasive validated and objective technique that assesses in vivo specific skeletal muscle characteristics like contractile properties through displacement-time parameters [35; 36]. TMG measurements were evaluated bilaterally during maximal isometric contractions electrically evoked in selected trunk muscles: upper trapezius, middle trapezius, latissimus dorsi, and erector spinae [37; 38; 39; 40]. A single 1 ms maximal monophasic electrical impulse was applied to induce belly muscle oscillationsdue to a twitch contraction. To record these oscillations, a sensitive digital displacement sensor (TMG-BMC Ltd., Ljubljana, Slovenia) was placed on the selected muscles [37; 38; 39; 40].  
The amplitude of stimulation was initially applied just above the threshold. Then, it was gradually enhanced to elicit the radial twitch Dm (in millimeters) and did not further increase. The electrical pulses were between 85 and 110 mA at a constant voltage of 30 V. Moreover, a 10-15 s interstimulation interval was used. The final analysis included the average of two maximal responses [37; 38; 39; 40].  
The TMG parameters were:  
Contraction time (Tc): time from 10% to 90% of the maximal radial displacement (Dm);  
Delay time (Td): time from electrical pulse and 10% of Dm;  
Half-relaxation time (Tr): time from 90% to 50% of Dm in the decreasing curve;  
Maximal radial displacement (Dm), or peak radial displacement: the spatial transverse deformation of the muscle, which increases proportionally to muscle trophy, and decreases in the presence of muscle stiffness or tension.  
Sustain time (Ts): time from 50% of contraction time (Tc) and 50% of relaxation time (Tr).
# Cloning and Characterization of Two Iridoid Synthase Homologs from _Swertia Mussotii_
## 4 Materials and Methods
### Heterologous Expression  
The full-length ORF of _SmIS_1 cDNA was subcloned into the _BamH_ I-_Hind_ III sites of pETMALc-H (New England Biolabs, Ipswich, MA, USA). The resulting plasmid was used to express a fusion protein in which a maltose-binding protein was placed at the N-terminus. The full-length ORF of the SmIS2 cDNA was subcloned into the _Nde_ I-_Xho_ I sites of pET-28a (Novagen, Madison, WI, USA) to produce a fusion protein with 6-His tags at both the \(N\)-terminus and \(C\)-terminus. The constructed plasmids and the empty vector (used as a control) were transformed into _E. coli_ BL21 (DE3) cells (Novagen, Madison, WI, USA). Correct in-frame insertion was verified using sequencing. A preculture (5 mL) was grown overnight at 37 \({}^{\circ}\)C in LB medium. This culture was used to inoculate 500 mL of fresh medium to a density with an OD600 of 0.4 at 25 \({}^{\circ}\)C. The cells were then grown for 12 h at 16 \({}^{\circ}\)C. Then, 0.3 mM isopropyl-\(\beta\)-D-thiogalactopyranoside (IPTG) was added to induce protein expression. The cells were harvested using centrifugation and disrupted using sonication. The soluble maltose-binding protein-tagged SmIS1 protein was then purified to apparent homogeneity as previously described by [PERSON] et al. [30].  
The SmIS2 protein was purified from the resulting soluble fraction using the His Trap FF (GE Healthcare, Hammersmith, UK). The concentration of the purified protein was quantified using the Bradford method, and the proteins were visualized using SDS-PAGE (Luyi, Beijing, China).  
\begin{table}
\begin{tabular}{l l l l} \hline
**Primer usage** & **Gene** & **Forward (5/-3/7)** & **Reverse (5/-3/7)** \\ \hline \multirow{4}{*}{ORF Cloning} & \multirow{2}{*}{SmIS1} & gcggatccATGACCTGGG & cccaagcttCTATGGAATTTG \\  & & TGGAAAAAG & AAGTCTCCTC \\  & & SmIS2 & ggatccatATGATG & cctc
# Intramode Brillouin Scattering Properties of Single-Crystal Lithium Niobate Optical Fiber
## 4 Conclusions  
In conclusion, this paper presents a new inorganic material lithium niobate as the transmission medium, and reports the Brillouin scattering properties of different pump-Stokes mode pairs in lithium niobate optical fibers, showing nine LP\({}_{01}\)-LP\({}_{01}\), LP\({}_{11}\)-LP\({}_{11}\), LP\({}_{21}\)-LP\({}_{21}\), LP\({}_{02}\)-LP\({}_{02}\), LP\({}_{31}\)-LP\({}_{31}\), LP\({}_{12}\)-LP\({}_{12}\), LP\({}_{41}\)-LP\({}_{41}\), LP\({}_{22}\)-LP\({}_{22}\), and LP\({}_{03}\)-LP\({}_{03}\) pump and Stokes mode pairs. We analyzed the L\({}_{0m}\) acoustic mode in the pump-Stokes mode pair in detail: the gain of the lithium niobate optical fiber BSBS reached 1.7525 m\({}^{-1}\).W\({}^{-1}\), and the frequency shift of Brillouin scattering in the range of 20.73-21.17 GHz was achieved, different from conventional silica. Lithium niobate is an anisotropic material. We plan to study the temperature and strain of lithium niobate optical fibers in future work, and carry out the fabrication of lithium niobate fiber and related experiments. We expect our design parameters to contribute to the optimization of Brillouin sensors and provide a new platform.  
Conceptualization, L.F. and Y.L.; methodology, L.F. and X.C.; software, L.F.; validation, L.F., X.C. and X.X.; writing--original draft preparation, L.F. and X.C.; writing--review and editing, L.F. and X.C.; visualization, W.H. and Y.Y.; supervision, L.W. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2019 YFF0301802); Key Research and Development (R&D) Projects of Shanxi Province (201903D121124); China Postdoctoral Science Foundation (2020M682113); Shanxi Scholarship Council of China (2020-112); Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (2020L0268); Fundamental Research Program of Shanxi Province (20210302124390); Graduate Innovation Project of Shanxi Province (2021Y616); Fundamental Research Program of Shanxi (20210302124558).  
The authors declare no conflict of interest.
## 3 Methodology
### Training (UMCL Dataset)
#### 3.6.1 Dice Similarity Coefficient and Intersection over Union  
Accuracy in classification tasks can be useful if the dataset evenly distributes the instances of each class. However, since the sizes of lesions are very small there are significantly few \"lesion\" voxels to be accurately classified; this phenomenon is known as class imbalance [37]. Thus, if models were to return predictions of only blank space for some slices of the brain, the accuracy can still be very high. Consequently, this research study evaluates a sequence's tendency to yield better performances based on the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU).  
The \(DSC\) is frequently used for evaluating the performance segmentation models. It can be calculated as shown by the equation below.  
\[DSC=\frac{2\times TP}{TP+FP+TP+FN} \tag{1}\]  
The \(IoU\) is another metric used for evaluating the performance of segmentation models. It is very similar to the \(DSC\), except for the fact that it does not weigh the true positives as much as the \(DSC\). It can be calculated as shown by the equation below.  
\[IoU=\frac{TP}{TP+FP+FN} \tag{2}\]  
In general, these metrics are a measure of the extent to which two images are similar by how much they align when they are overlapped; this proves to be useful for evaluating how the input choice can affect segmentation performance. These metrics are able to mitigate the impact of class imbalance because of the greater emphasis on true positives. For this reason, these metrics are used in other research studies.
## 2 Research Area and Data Sources
### Indicator Selection
#### 2.3.1 NPP Calculation of CASA Model  
Generally, NPP serves as a key biophysical parameter for quantifying and characterizing carbon storage dynamics within ecosystems. The Carnegie Ames Stanford Approach (CASA) model is a commonly used method for estimating the indicator [23]. The calculation formula is as follows:  
\[\text{NPP}(x,t)=\text{APRA}(x,t)\times\varepsilon(x,t) \tag{1}\]  
\(\text{NPP}(x,\,t)\) represents the NPP of vegetation for pixel \(x\) in month \(t\) (g C m\({}^{-2}\) a\({}^{-1}\)); \(\text{APRA}(x,t)\) represents the photosynthetically active radiation absorbed by pixel \(x\) in month \(t\) (g C m\({}^{-2}\) month\({}^{-1}\)); \(\varepsilon(x,t)\) represents the actual light energy utilization rate of pixel \(x\) in month \(t\) (g C M\({}^{-1}\)). Light Use Efficiency (LUE) is a constant that is typically set based on vegetation type and environmental conditions.  
\[\text{APRA}(x,t)=\text{SOL}(x,t)\times\text{FPAR}(x,t)\times 0.5 \tag{2}\]  
SOL is the total solar radiation = sunshine hours\(\times\)sunshine intensity, FPAR represents the absorption ratio of incident light and effective radiation by the vegetation layer, which has a certain linear relationship with the normalized vegetation index NDVI and the specific vegetation index SR. The constant 0.5 represents the proportion of solar effective radiation that vegetation can utilize from total solar radiation.  
\[\text{FPAR}(x,t)=\frac{\text{NDVI}_{\text{max}}-\text{NDVI}_{\text{min}}}{ \text{NDVI}-\text{NDVI}_{\text{min}}} \tag{3}\]  
\(\text{NDVI}_{\text{max}}\) and \(\text{NDVI}_{\text{min}}\) represent the best and worst vegetation cover, i.e., areas with dense vegetation and bare land, respectively.  
Based on the Google Earth Engine platform, atmospheric correction, geometric correction, and stripe restoration were performed on NPP, vegetation type, and leaf area index. After calculating the annual average values, the raster images were imported into ArcGIS 10.8 software for reclassification, and the NPP distribution maps of Inner Mongolia in 2000, 2010, and 2020 were obtained.
# Effectiveness of Electrode Design Methodologies for Fast EDM Slotting of Thick Silicon Wafers
## Appendix A  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Case-1 (Intended Electrode Leg Height is 0.2′′ = 5080 μm)**} \\ \hline \multirow{2}{*}{**Number of Arrays**} & \multirow{2}{*}{**Sample**} & **Leg** & **Leg** & **Leg** & **Leg** & **High Amid** & **Width Amid** \\  & & **Length** & **Width** & **Height** & **Legs** & **Legs** \\  & & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** \\ \hline \multirow{2}{*}{\(1\times 4\) (Row \(\times\) Column)} & Sample-1 & 3620.94 & 3627.12 & 5082.35 & 3620.69 & - \\  & Sample-2 & 3615.54 & 3631.29 & 5058.82 & 3628.02 & - \\  & Sample-3 & 3611.45 & 3623.38 & 5094.12 & 3628.02 & - \\ \hline \multirow{2}{*}{\(2\times 4\) (Rows \(\times\) Column)} & Sample-1 & 3615.19 & 3644.08 & 5070.58 & 3627.13 & 3606.01 \\  & Sample-2 & 3642.29 & 3613.25 & 5062.87 & 3610.35 & 3631.65 \\ \cline{1-1}  & Sample-3 & 3628.18 & 3614.26 & 5072.15 & 3619.15 & 3621.95 \\ \hline \multirow{2}{*}{\(3\times 4\) (Row \(\times\) Column)} & Sample-1 & 3632.31 & 3614.86 & 5095.38 & 3607.91 & 3615.18 \\ \cline{1-1}  & Sample-2 & 3634.92 & 3625.58 & 5085.02 & 3611.24 & 3625.27 \\ \cline{1-1}  & Sample-3 & 3614.10 & 3630.18 & 5069.86 & 3625.35 & 3614.75 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Dimensional characterizations of arrays of electrode legs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Electrode**} & \multicolumn{2}{c}{**Workpiece (Entry)**} & \multicolumn{2}{c}{**Workpiece (Exit)**} \\ \cline{2-7}
**Type of** & **Average** & **Average** & **Average** & **Average** & **Average** & **Average** & **Average** \\
**Electrode** & **Length** & **Width** & **Length** & **Width** & **Length** & **Width** & **Length** & **Width** \\  & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** \\ \hline Block & 25,463.5 & 25,467.73 & 26,289.42 & 26,326.31 & 26,620.65 & 26,656.71 \\
0.12\({}^{\prime\prime}\) & 25,467.73 & 25,471.97 & 26,253.40 & 26,326.49 & 26,362.77 & 26,399.44 \\
0.08\({}^{\prime\prime}\) & 25,471.97 & 25,467.73 & 26,326.49 & 26,252.98 & 26,362.98 & 26,253.16 \\
0.04\({}^{\prime\prime}\) & 25,467.73 & 25,741.97 & 26,255.85 & 26,252.98 & 26,179.44 & 26,179.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characterization of graphite block and hollow electrode and Silicon workpiece.  
Figure A2: (a) Entry and (b) exit side of silicon workpiece after machining using 5 \(\times\) 4 arrays of electrodes with electrode leg heights of 0.2\({}^{\prime\prime}\) and 0.3\({}^{\prime\prime}\).  
Figure A3: (**a**) Entry side and (**b**) exit side of machined features using hollow electrodes with 0.08\({}^{\prime\prime}\) and 0.12\({}^{\prime\prime}\) thicknesses.
## 5 DSS Techniques and Generalized Finite State Machine
### Link Setup Procedure in DSS Techniques  
Here we describe how a satellite-GS link or satellite-ES link will be established using the information provided from the CP and the synthetic interference maps (in DSS1) or local energy maps (in DSS2). As an example, let us assume that the link will be an uplink connection from GS or ES to the satellite. At the time that the link needs to be established, the GS/ES transmitter will alert through SAT-CC or ES-CC that it wants to set up a link with \(X\) slots (\(X\) will be determined from the incoming traffic volume) towards the destination satellite. Then, the satellite will examine its synthetic interference map or local energy map and, based on the interference levels depicted, will choose \(X\) interference-free slots and send them back to the GS/ES through the SAT-CC or ES-CC.  
For DSS1, in case the CP information is not forwarded to the satellite or ES but is kept on the GS (serving the satellite or ES), the GS will decide on behalf of the satellite or ES based on the available information in the synthetic interference map and select the interference-free slots, informing the satellite or ES through the SAT-CC or ES-CC to switch its receiver into the selected slots. In any case, for DSS1, the synthetic interference map will be the main tool for picking up new MF-TDMA slots that are interference-free and can be allocated to the new link. In DSS2, by having a clear view of the channel occupation on the receiver side, all present sources of known or unknown interference can be avoided and therefore links can be set up with optimal link characteristics.  
By defining a way in which transmitter and receiver of a new or pre-existing link can negotiate through a control link and reach a final agreement on slot allocation (employing their synthetic and local energy maps), the proposed DSS techniques can offer a completely decentralized solution of dynamic spectrum allocation and minimize interference.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 4 Discussion
### Differences between FY-4A and Other Satellites  
#### 4.3.1 Differences with Earth Observation Satellite  
It was observed that FY-4A TVDI detected a larger area of severe drought than MODIS TVDI. In the northwestern Huanghuai region, FY-4A NDVI values are lower than MODIS, resulting in higher TVDI values computed by FY-4A. The FY-4A IR channel has a bandwidth of 0.2, while the NIR channel has a bandwidth of 0.15. In contrast, MODIS demonstrates narrower bandwidths with its IR channel at 0.05 and NIR channel at 0.035 [64]. Therefore, the differences in bands between the two satellites result in distinct NDVI values in this area, leading to FY-4A TVDI detecting a larger extent of severe drought areas.  
It is found that the overall spatial distribution of FY-4A TVDI is generally consistent with Landsat-8 TVDI. However, the spatial distribution of the TVDI drought on 21 May 2022, was different. This is due to the presence of thin cloud cover in Landsat-8, which results in lower NDVI values. However, FY-4A has effectively reduced the impact of clouds through the MVC method.  
Comparing the bands, products, and TVDI of FY-4A, MODIS, and Landsat-8, it is observed that FY-4A TVDI exhibits a spatial distribution more similar to Landsat-8. The value ranges for FY-4A and Landsat-8 are closer, [0.02, 0.98] and [0.08, 0.93], respectively, with standard deviations of 0.26 and 0.35. FY-4A retains the data variability which could be a better expression of spatial heterogeneity. Moreover, due to the high sampling frequency of FY-4A, drought monitoring of FY-4A TVDI on a regional daily scale has lower computational costs.