esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of Cyber Range Taxonomies: Trends, Gaps, and a Proposed Taxonomy
## 5 Discussion
### Limitations and Unresolved Issues  
Several limitations identified through our analysis offer opportunities for future research. Firstly, we identified a gap regarding econometric considerations within the cyber range literature. Despite its importance in decision-making, cost-benefit analyses remained sporadic and inconsistent in the literature, making the formalization of an _Econometrics_ dimension difficult and limiting comparability across studies. Moreover, the current taxonomy predominantly captures technical aspects, not addressing socio-technical components such as governance structures, organizational cultures, and regulatory alignments. The explicit integration of these policy-related and human-factor dimensions represents a valuable next step toward the comprehensive socio-technical analysis of cyber ranges.  
Additionally, our systematic review may contain inherent biases due to methodological constraints. Although extensive database searches and backward snowballing enhanced coverage, limitations related to language exclusivity and restricted access to pay-walled literature may have excluded potentially relevant grey literature and studies, thereby affecting comprehensiveness.  
Finally, the rapidly evolving technological landscape poses a continuous challenge. Static classifications risk obsolescence as new developments emerge. Consequently, regular updates and re-validation of the taxonomy, ideally leveraging tools like our open-source toolkit, are essential to maintaining its relevance and utility.
## Nomenclature  
\begin{tabular}{l l} PES & Power Electrical and Electronic Systems \\ PAF & Power Active Filter \\ SC & Static Compensator \\ DAC/ADC & Digital to Analogue/ Analogue to Digital Converter \\ DFIR & Digital Finite Impulse Response filter \\ DIIR & Digital Infinite Impulse Response filter \\ MAF & Moving Average Filtering method \\ AVE (_ave_) & average value or function \\ RMS (_rms_) & root mean square value or function \\ \(N\) & number of sliding window points \\ \(C_{T}\) & Clarke transformation constant \\ \(H(z)\) & digital filter transfer function in \(Z\)-domain \\ \(p\)-\(q\) & instantaneous active and reactive power method \\ \(p(t)\), \(q(t)\) & instantaneous active and reactive power components \\ \(u_{a}(t)\), \(u_{\beta}(t)\) & phase voltages in \(\alpha\), \(\beta\)-coordinate system \\ \(i_{a}(t)\), \(i_{\beta}(t)\) & phase currents in \(\alpha\), \(\beta\)-coordinate system \\ \(A_{1}\) & the amplitude of phase current \\ \(P_{1 av}\) & active power of fundamental harmonic \\ \(P_{0 av}\) & active power of a zero-sequence component \\ \(S_{av}(k)\dots\)\(D_{av}(k)\) & discretized power components at \(k\)- time instants \\ \(p_{ac}(t)\), \(q_{ac}(t)\) & oscillating terms of distortion power component \\ \(p_{a,b,c,p}(t)\) & instantaneous active power components in each phase \(a,b,c\) \\ \(p_{a,b,c,d}(t)\) & instantaneous reactive power components in each phase \(a,b,c\) \\ \(p_{a,b,c,0}(t)\) & instantaneous zero - sequence power components in each phase \(a,b,c\) \\ \(x_{a,\beta,0}(t)\) & components of a non-symmetrical system in \(\alpha\), \(\beta\)- coordinates \\ \(x_{a,b,c}(t)\) & components in \(a,b,c\)- coordinates \\ \(q(t)\), \(i_{0}(t)\) & voltage and current zero-sequence power components \\ \(i_{\rm{y}}(t)\) & neutral wire current \\ \end{tabular}
# Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China
###### Abstract  
Soil moisture (_SM_) significantly affects the exchange of land surface energy and the stability of terrestrial ecosystems. Although some conclusions have been drawn about the effects of _SM_ on the ecosystem water use efficiency (_WUE_), the influence mechanism and the quantitative assessment framework of _SM_ on _WUE_ are still unclear. This study provides an analysis framework for the feedback relationship between _SM_ and _WUE_ based on the dependence of the evaporation fraction on _SM_ and output datasets from remote sensing and the Global Land Data Assimilation System. The results show that the range of _WUE_ of terrestrial ecosystems of China was 0.02-19.26 g C/kg H\({}_{2}\)O in the growing season with an average value of 1.05 g C/kg H\({}_{2}\)O. They also show a downward trend in 43.99% of the total area. In the evapotranspiration (ET) pathway, SM negatively affected _WUE_, and the sensitivity coefficient ranged from \(-\)18.49 to \(-\)0.04. In the net primary production (_NPP_) pathway, the sensitivity coefficient ranged from \(-\)68.66 to 43.19. Under the dual effects of the _ET_ and _NPP_ pathways, the influence of _SM_ on _WUE_ was negative in 84.62% of the area. Variation in _SM_ led to significant _WUE_ variability. Generally, the percentage change in _WUE_ (\(\Delta\)_WUE_) ranged from 0% to 190.86%, with an average value of 28.02%. The maximum \(\Delta\)_WUE_ ranged from 0% to 758.78%, with an average value of 109.29%. The _WUE_ of forest ecosystems showed strong resistance to _SM_ variation, whereas that of non-forest vegetation was more sensitive to _SM_ variation. This analytical framework provides a new perspective on the feedback relationship between _WUE_ and _SM_ in terrestrial ecosystems.  
feedback relationship; evaporation fraction; net primary production (NPP); elasticity coefficient; climatic zone +
Footnote †: journal: remote sensing  
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# Prediction of Post-Yield Performance of Hybrid Precast Beams Based on Strength Degradation
## 3 Analytical Prediction of Post-Yield Deflection
### Prediction of Post-Yield Behavior
**(d-4)** Load-strain relationship for Specimen #9[7]. **(e)** Post-yield deflection of pre-stressed Specimens #10 using the degradation tendency of non-pre-stressed beams from Table 3 and Figure 4d[5]. load-displacement relationship for both positive and negative directions, as shown in Figure 5a-1-a3. In Table 4, a ductility \(\left(\mu\ =\ \frac{\Delta}{\Delta y}\right)\) between 1 and 5 was selected from the load-displacement curve shown in Figure 4. The post-yield deflection of pre-stressed Specimens #4, #5 and #6, #7 (Figure 5b-1,b-2,c-1,c-2) was calculated based on the stiffness degradation, \(\frac{k}{k_{y}}\ =\ -0.61 ln\left(\frac{\Delta}{\Delta y}\right)+1.01\), provided in Table 3 and Figure 4b. The post-yield deflection of pre-stressed Specimens #8 and #9 (Figure 5d-1,d-3) based on the stiffness degradation regressions, \(\frac{k}{k_{y}}\ =\ -0.677 ln\left(\frac{\Delta}{\Delta y}\right)+0.9985\) and \(\frac{k}{k_{y}}\ =\ -0.61 ln\left(\frac{\Delta}{\Delta y}\right)+1.01\), found in Table 3 and Figure 4c-1,c-2, respectively. Figure 5e provide the post-yield deflection of Specim #10 based on the stiffness degradation regression, \(\frac{k}{k_{y}}\ =\ -0.41 ln\left(\frac{\Delta}{\Delta y}\right)+0.834\), shown in Figure 4d. A ductility \(\left(\mu\ =\ \frac{\Delta}{\Delta y}\right)\) between 1 and 5 was selected from the load-displacement curve. It was interesting to note that the post-yield deflection of pre-stressed Specimens #4 and #5, shown in Figure 5b-3,b-4, matched the test data better when it was calculated based on the global stiffness degradation tendency provided in Figure 4e-2, \(\frac{k}{k_{y}}\ =\ -0.45 ln\left(\frac{\Delta}{\Delta y}\right)+1.02\), which represented the stiffness degradation tendency of the thirteen specimens. The post-yield load-displacement relationships based on degradation tendencies (refer to Figure 4) were relatively well correlated with the test data, as shown in Figure 5. However, the use of an inappropriate beam degradation tendency can cause mismatches between test data and estimation. It should be noted that more consistent prediction of the post-yield load-displacement relationships for precast composite beams with various boundary conditions is possible when the predictions are based on big strength degradation data.
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 3 About PBFT
### Algorithm Principle and Process  
As a state machine replication algorithm, PBFT ensures that consensus can be accurately reached in distributed systems, even in the presence of a certain number of faulty or malicious nodes, thereby achieving consistency in message replication and verification. The feasibility of PBFT is based on the following assumptions: if the number of Byzantine nodes in the system is \(f\) and the total number of nodes is greater than \(3f\), i.e., \(N\geq 3f+1\), then reliable message transmission can be carried out between nodes. Its key idea is to reach consensus through the collaboration of multiple replicas. At each stage, a sufficient number of messages must be collected to ensure security and correctness. The process of the algorithm, as shown in Figure 1, has the following phases:  
(1) Request. The client sends a transaction request \(\langle Request,o,t,c\rangle\) to the cluster's Primary node, where \(o\) represents the specific operation or command that the client wants to perform, \(t\) represents the timestamp and \(c\) represents the client's unique identifier. If the request is sent to a Replica node, it will be forwarded to the Primary node;  
(2) Pre-Prepare. After receiving the transaction request, the Primary node performs verification, packaging, and sorting operations on the transaction. After confirming that it is correct, it will broadcast \(\langle Pre-Prepare,v,n,d\rangle\) to the Replica node, where \(v\) represents the current view number, \(n\) represents the sequence number and \(d\) represents the content summary;  
(3) Prepare. The Replica node checks the legitimacy of the Pre-Prepare message sent by the Primary node. If the check passes, it broadcasts the Prepare message \(\langle Prepare,v,n,d,n\rangle\) to other consensus nodes for cross-verification. If the check fails, the message will not be sent. Note that if most nodes in the system fail the check at this time, and therefore, do not send the Prepare message, resulting in a transaction timeout, the transaction will trigger a view-switching operation;  
(4) Commit. The Replica node checks the consistency of the received Prepare message with the Pre-Prepare message content in the cache. If a sufficient number \(2f+1\) of Prepare messages that meet the requirements are received, the Commit message \(\langle Commit,v,n,n\rangle\) will be broadcast to indicate that this node can execute the transaction;(5) Reply. The Replica slave node performs a consistency check on the received Commit message and the Prepare message content in the cache. If the check passes, the execution result \(\langle Result,v,t,c\rangle\{n\}\) will be written to the local ledger and replied to the client. If the client can receive \(f+1\) enough consistent replies within the timeout period, then the transaction has been successfully completed.
## 2 Materials and Methods
### Methods
#### 2.1.2 Regional CO Flux Inversion System  
DART [63] is an open-source software framework for ensemble data assimilation research developed by the National Center for Atmospheric Research (NCAR). DART has been widely coupled with the WRF-Chem model [64; 65; 66; 67]. On the basis of DART [63], a regional CO flux inversion system was developed according to the flux inversion scheme implemented in [PERSON]'s CO\({}_{2}\) flux inversion system [40]. The inversion system employed the same profile assimilation strategy as in [38; 41] to estimate CO concentrations and fluxes over western Europe. The Ensemble Adjustment Kalman Filter (EAKF) [63; 68; 69] method in DART was used to assimilate the MOPITT CO retrievals and meteorological observations into the forecast outputs from the WRF-Chem model.  
By applying a state augmentation approach [40; 70] to include non-prognostic variables, the state vector \(x\) of the regional CO flux inversion system was defined as:  
\[x=[Met,COC,OtherC,COF]^{T}, \tag{1}\]  
where \(Met\), \(COC\), and \(OtherC\) represent meteorological variables and the concentrations of CO and 35 other gases, respectively. They were advanced over time by the WRF-Chem model. \(COF\) represents the non-prognostic CO fluxes input to the WRF-Chem model; it could not be forwarded by the WRF-Chem model.  
The \(Met\) and \(COC\) part of the state vector \(x\) were assimilated in the same way as [PERSON]'s approach [40; 64]. The \(OtherC\) part of the state vector \(x\) did not participate in the assimilation process. \(OtherC\) provided constraints on CO atmospheric sources and sinks to improve the forecast accuracy of \(COC\) during the WRF-Chem simulation process. The \(OtherC\) were reset to the values extracted from the CAM-Chem outputs dataset [48] at the beginning of each forecast-assimilation cycle. The update of the \(COF\) part of the state variable \(x\) also adopted the same approach as in [PERSON]'s work [40].
# Circulating Current Reduction in MMC-HVDC System Using Average Model
## 4 Average Model of MMC  
The continuous model of MMC shown in Figure 1b can be developed by considering a cell's internal dynamics as an arm equivalent. Then, the converter arm can be modelled as variable capacitances, with their values determined by the insertion index \(n\)(t) i.e., \(n_{u}\) and \(n_{l}\) (upper and lower arm), which turns into a continuous parameter. The capacitor voltages in each arm can be considered as controlled voltage sources. By considering \(V_{c}^{\Sigma}(t)\) asthe sum of capacitor voltages in one arm, the voltage inserted by the arm, \(V_{c}\), is given by:  
\[V_{c}^{p}=n(t)V_{c}^{\Sigma}(t) \tag{35}\]  
where \(p\) denotes number of the arms. Due to the series connection of inserted capacitors (\(c^{p}\)) inside an arm and the flow of the charging current \(i(t)\), the increase in the capacitor voltage in that arm is:\[\frac{dV_{c}^{\Sigma}(t)}{dt}=\frac{i(t)}{c^{p}} \tag{36}\]  
Using Equation (38), the dynamics of capacitor voltages in the upper and lower arms can be written as [38]:  
\[\frac{dV_{u}^{\Sigma}(t)}{dt}=\frac{n_{u}i_{1u}}{c^{arm}} \tag{37}\]  
\[\frac{dV_{l}^{\Sigma}(t)}{dt}=\frac{n_{l}i_{1l}}{c^{arm}} \tag{38}\]  
where \(c^{arm}\) is the capacitance in each arm. The upper and lower arm currents as per Equations (11) and (12) can be derived as:  
\[i_{1u}=\frac{c^{arm}dV_{u}^{\Sigma}(t)}{n_{u}\ dt} \tag{39}\]  
\[i_{1l}=\ \frac{c^{arm}dV_{l}^{\Sigma}(t)}{n_{l}\ dt} \tag{40}\]  
According to Figure 1b, by inserting resistance \(R\) (arm converter resistance), the output voltage in phase \(a\) can be shown as:  
\[V_{c(a)}=\frac{V_{dc}}{2}-Ri_{1u}-L_{arm}\frac{di_{1u}}{dt}-n_{u}V_{u}^{\Sigma }(t) \tag{41}\]  
\[V_{c(a)}=-\frac{V_{dc}}{2}+Ri_{1l}+L_{arm}\frac{di_{1l}}{dt}+n_{l}V_{l}^{\Sigma }(t) \tag{42}\]  
Subtracting Equation (42) from (41) and using Equations (8), (37)-(40) gives the average model of one leg of an MMC in Equation (43):  
\[\left[\begin{array}{c}\mathrm{i}_{diff}\\ V_{u}^{\Sigma}\\ \mathrm{V}_{l}^{\Sigma}\end{array}\right]=\left[\begin{array}{ccc}-\frac{ \mathrm{R}}{\mathrm{L}}&\frac{-\mathrm{n}_{u}}{2}&\frac{-\mathrm{n}_{L}}{2}\\ \frac{\mathrm{n}_{u}^{\Sigma}}{\mathrm{m}}&0&0\\ \frac{\mathrm{n}_{L}}{\mathrm{c}^{arm}}&0&0\end{array}\right]\left[\begin{array} []{c}\mathrm{i}_{diff}\\ V_{u}^{\Sigma}\\ \mathrm{V}_{l}^{\Sigma}\end{array}\right]+\left[\begin{array}{c}\frac{ \mathrm{v}_{dc}}{2}\\ \frac{\mathrm{n}_{u}^{\Sigma}}{2\mathrm{c}^{arm}}\\ -\frac{\mathrm{n}_{u}\mathrm{i}_{1}}{2\mathrm{c}^{arm}}\end{array}\right] \tag{43}\]  
This model will assume an even voltage distribution among the SMs in three legs of an MMC while the switching pattern is neglected.
## 7 Experimental Verification
### Test Result  
The standard window lift gear pair was adopted to verify the theory. The pitch of the worm is smaller than that of worm gear. It can be seen from the comparison of data in Table 5, the average maximum destructive pressure with equal and unequal pitch is 447.36 Kgf, and 563.70 Kgf, respectively. The maximum pressure of unequal pitch increased by 26% compared with equal pitch. The results show that the meshing of gears with unequal pitch can improve the root strength of gears, and can improve the service life of gears under the same working conditions, which can provide a reference for the subsequent coordination of worm and plastic helical gears.  
Figure 18: Engineering drawing for the plastic helical gear.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**No. of Helical Gear**} & \multicolumn{2}{c}{**Equal Pitch Worm \& Plastic Helical Gear**} & \multicolumn{2}{c}{**Unequal Pitch Worm \& Helical Gear**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Max Breakage Force (Kgf)**} & \multicolumn{2}{c}{**Max Breakage Force (Kgf)**} \\ \cline{2-5}  & **M = 0.8** & **M = 1.0** & **M = 0.8** & **M = 1.0** \\ \hline No. 1 & 436.36 & 364.21 & 564.99 & 413.85 \\ No. 2 & 430.42 & 352.11 & 568.95 & 420.92 \\ No. 3 & 488.31 & 361.32 & 559.15 & 415.33 \\ No. 4 & 493.26 & 365.65 & 565.19 & 427.62 \\ No. 5 & 444.18 & 341.59 & 560.24 & 411.23 \\ No. 6 & 456.74 & 355.19 & 542.31 & 413.56 \\ No. 7 & 455.95 & 349.53 & 551.23 & 409.23 \\ No. 8 & 460.4 & 355.61 & 538.92 & 407.56 \\ No. 9 & 465.15 & 354.69 & 546.38 & 418.96 \\ No. 10 & 451.89 & 343.58 & 549.21 & 421.55 \\ Average & 447.36 & 362.53 & 554.66 & 415.98 \\ \hline \end{tabular}
\end{table}
Table 5: Breakage test on the fixture.  
Since the contact ratio of m = 0.8 is bigger than m = 1.0, so the force is bigger for m = 0.8. The increased rate of the breakage strength for unequal pitch worm and plastic helical gear is ((\((554.66-447.36)/447.36)\times 100\%=24\%\) for m = 0.8, and the increased rate of the breakage strength for m = 1.0 is (\((415.98-362.53)/362.53)\times 100\%=14.74\%\). It is shown that unequal pitch will be beneficial to the small module worm gear, as the smaller module tooth tends to deform easily than the large module tooth.
## 3 Results  
A total of 60 bacterial endophyte strains, belonging to 29 different species, were isolated from the three plant species grown on different soils: 19 from cucumber, 14 from sorghum and 27 from tomato. In particular, 60%, 16.7% and 23.3% of the bacterial endophytes were isolated from plants grown in pasture soil, in vegetable garden soil and in woody soil, respectively. The complete list of the bacterial strains containing their origin, taxonomic identification, GenBank accession number and qualitative evaluation of their plant beneficial physiological traits are reported in Table 1.  
These endophytes were tested for their physiological activities involved in plant growth promotion, including auxin synthesis, solubilization of di- and tri-calcium phosphate and siderophore production (Table 1 and Figure 3). Sixteen out of the 60 bacterial isolates (26.7%) were able to synthesize IAA: 56.2% were from cucumber, 25% from sorghum and 18.7% from tomato. A total of 18 strains (30%) solubilized phosphate on DCP: 5.5% from cucumber, 38.9% from sorghum and 55.5% from tomato. None of the tested strains were able to solubilize tricalcium phosphate. Finally, 19 strains were positive for siderophore production (31.7%): 21% were from cucumber, 42.1% from sorghum and 36.8% from tomato (Table 1). The proportion of bacterial endophytes able to synthesize IAA, solubilize phosphate and produce siderophore was highest in pasture soil (55.5%, 76.2% and 57.1%, respectively). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Strain** & **Origin** & **Taxonomic Identification** & **GenBank Accession** & \ \begin{tabular}{c} **IAA *** \\ **(Intensity Colour** \\ **Scale 0-5)** \\ \end{tabular}  & \begin{tabular}{c} **Sideprophere** \\ **(HD/CD \({}^{\circ}\) **cm)** \\ **(DCP \({}^{\circ}\), HD/CD cm)** \\ \end{tabular}  \\ \hline CVB2R4 & cucumber root, pasture soil & _Herbospirillum Insitatum_ & KX436986 & 3 & 0.00 & 0.00 \\ CVB2R5 & cucumber root, pasture soil & _Herbospirillum Insitatum_ & KX436987 & 3 & 0.00 & 0.00 \\ CVB3S1 & cucumber shoot, pasture soil & _Acinetobacter jolnsonii_ & KX436989 & 0 & 0.00 & 0.00 \\ CVB3S2 & cucumber shoot, pasture soil & _Acinetobacter jolnsonii_ & KX437624 & 1 & 0.00 & 0.00 \\ CVB3S4 & cucumber shoot, pasture soil & _Acinetobacter jolnsonii_ & KX437625 & 0 & 0.00 & 0.00 \\ CVB3S5 & cucumber shoot, pasture soil & _Stenrophanomas rhizophia_ & KX437652 & 3 & 0.00 & 0.00 \\ CVB0S3 & cucumber root, vegetable garden & _Agrobacteriumrium turbolens_ & KX429474 & 4 & 2.96 & 0.00 \\ COGL1 & cucumber leaf, vegetable garden & _Agrobacteriumrium turbolens_ & KX429748 & 0 & 0.00 & 0.00 \\ COGL1 & cucumber root, vegetable garden & _Agrobacteriumrium turbolens_ & KX429749 & 0 & 0.00 & 0.00 \\ COGL1 & cucumber shoot, vegetable garden & _Rizzizz
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 2 Methods  
This study proposes a grid interconnection calculation method for satellite constellations based on the GeoSOT-3D grid model. Previous studies have shown that GeoSOT-3D has good results in the fields of multi-source spatial data integration and efficient spatial database indexing, and is particularly helpful for improving the efficiency of spatial calculation [32; 33; 34]. A general grid-based calculation method for inter-satellite is proposed, taking the need for the user to obtain remote sensing satellite data in the future as an application scenario. The technical method includes: (1) GeoSOT-3D BigTable, (2) grid modeling, (3) satellite visibility calculation, and (4) satellite network routing planning.  
The main steps comprise: (1) divide the entire aerospace space into discrete grids of different scales, identify each grid cell uniquely, and then create an aerospace grid indexing BigTable with the grid code as the primary key, serving as the link to spatial information (the concept of BigTable comes from Google BigTable, and in this article, it refers to the organization and storage architecture that uses grid coding as \"Row Key\" and associates spatial information) [35]. In \"satellite-to-satellite\" and \"satellite-to-ground user\" transmission, the satellite trajectories and satellite resources are mapped to the grid to unify the organization and calculation of trajectories and resources, respectively. (2) Construct a secondary index mechanism, and replace the traditional and complicated visibility calculation method by querying the BigTable and GeoSOT-3D secondary spatial index. (3) Establish a grid virtual node, create the virtual topology, and then plan an inter-satellite transmission path that meets the coverage requirements by querying the visibility in the aerospace grid indexing BigTable. The research process is shown in Figure 1.