esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### M13 Phages Production  
The M13 phages (helper phage M13K07, Kan+) were propagated in the bacterial host Escherichia coli strain TG1 (Kan-, Amp-, lacZ-), grown in modified Luria-Bertani broth (LB) medium (10 g/L tryptone, 5 g/L yeast extract, 5 g/L NaCl) added with kanamycin (10 mg/L). The culture was incubated overnight at 250 rpm in an orbital shaker (ARGO lab SKO-DXL) at 37 \({}^{\circ}\)C. After the incubation, the phage particles were recovered from the bacterial host culture by means of several centrifugation steps in PEG/NaCl solution, containing 200 g PEG-8000 (polyethylene glycol; Sigma-Aldrich, Milan, Italy) and 150 g NaCl (Sigma-Aldrich, Milan, Italy) per liter. Specifically, the culture was centrifuged at 8000\(\times\)\(g\) for 20 min at 25 \({}^{\circ}\)C; the supernatant was recovered and mixed with 25% (_v/v_) of PEG/NaCl solution. The mixture was cooled in ice for 4 h and precipitated by centrifugation at 15,000\(\times\)\(g\) for 45 min at 4 \({}^{\circ}\)C. The pellet was recovered, resuspended in 10% (_v/v_) of TRIS buffered saline (TBS, 7.88 g/L of Tris hydrochloride and 8.77 g/L of NaCl in deionized water), mixed again with 25% (_v/v_) of PEG/NaCl, cooled in ice for 4 h, and the solution was centrifuged as above. The pellet, containing phage particles, was suspended in 10% (_v/v_) of TBS, filtered through 0.22 \(\upmu\)m pore size membrane (Millipore), and stored at 4 \({}^{\circ}\)C. The M13 phage concentration was deduced by tenfold serial dilutions. Specifically, 10 \(\upmu\)L from each dilution sample was dispensed into sterile micro-centrifuge tubes containing 90 \(\upmu\)L of _E. coli_ TG1 culture at OD600 equal to 0.7. Tubes were incubated at 37 \({}^{\circ}\)C in a static condition for 15 min and at 250 rpm on a rotary shaker for 20 min.  
Figure 1: Experimental set-up photo (**a**), scheme of the Au NPs production by laser (**b**), measurement of absorbance vs. wavelength (**c**) and produced Au NPs diameter distribution (**d**).  
After the incubation, 100 \(\mu\)L of each phage/E. coli TG1 suspension was dispensed into LA plates containing kanamycin (10 \(\mu\)g/mL) and incubated at 37 \({}^{\circ}\)C overnight. Plates, containing between 30 and 300 colonies, were counted to derive the number of active viral particles able to infect the host cell and express the transgene. Thus, the active particles were determined by transducing units per milliliter (TU/_mL_), according to the following Equation (1):  
\[\frac{\text{TU}}{mL}=\frac{(\text{number of colonies})}{\text{volume (0.1 mL) \times\, dilution factor}} \tag{1}\]
## 4 EGCIR Protocol
### Data Security with Hierarchical Key Distribution
Let us consider that \((KU_{i},KR_{i})\) and \(\left(KU_{j},KR_{j}\right)\) are the pairs of private-public keys for cluster heads \(i\) and \(j\). The cluster head \(i\) generates a secure packet \(E_{k}\) by encrypting the symmetric key using the public key of cluster head \(j\) as \(E(KU_{j}(s_{k}))\). The cluster head \(i\) also incorporates the nonce \(N_{i}\) at time \(t_{o}\) in the encryption process, while sharing the symmetric key with a cluster head \(j\), such a method increases the security level of encryption in the presence of mischievous nodes and avoiding the chances of a replay attack. Then the encrypted packet \(E_{k}(i,j)\) from cluster head \(i\) to cluster head \(j\) can be denoted as given in Equation (4). \[E_{k}(i,j)=\left[E\big{(}KU_{j}(s_{k})\big{)}+N_{i}(t_{o})\right] \tag{4}\]On receiving the encrypted packet from cluster head \(i\), cluster head \(j\) applies the decryption function using its private key to recover the symmetric key, which was generated by cluster head \(i\) at time \(t_{o}\). Now both cluster heads \(i\) and \(j\) have the symmetric key that was generated by the BS for a particular session. Using the shared symmetric key, both cluster heads can encrypt and decrypt the data packets securely and authentically. Accordingly, cluster heads are provided symmetric keys by the BS and afterward, when the cluster head \(i\) send data \(D_{i}\) to the cluster head \(j\), the XOR \(\oplus\) operation is performed along with the nonce \(N_{i,j}\) to produce the encryption and randomness features, as given in Equation (5). \[E_{i,j}(D_{i})=(D_{i}\oplus Sk_{i})+N_{i,j} \tag{5}\]  
The XoR is an additive cipher that operates on bits level between sensor data and encryption keys. The encrypted data \(E_{i,j}(D_{i})\) is further communicated with cluster head \(j\), which can be decrypted by applying XOR operation with the same symmetric key \(Sk_{i}\), as given in Equation (6). Accordingly, the encryption and decryption process based on symmetric keys between the set of selected cluster heads are achieved in the chaining mode. \[D_{j}(m_{i})=E_{i,j}(D_{i})\oplus Sk_{i} \tag{6}\]  
In the end, the encrypted clusters' data \(D(C_{i})\), i.e., \(D(C_{1})+D(C_{2})\ldots+D(C_{n})\), is delivered and stored at the BS. After verifying the identity and symmetric keys of cluster heads from the constructed generalized table, the cluster data are decrypted.
# An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks
###### Abstract  
Cooperative routing is one of the most widely used technologies for improving the energy efficiency and energy balance of wireless multi-hop networks. However, the end-to-end energy cost and network lifetime are greatly restricted if the cooperative transmission model is not designed properly. The main aim of this paper is to explore a two-stage cooperative routing scheme to further improve the energy efficiency and prolong the network lifetime. A two-stage cooperative (TSC) transmission model is firstly designed in which the core helper is introduced to determine the helper set for cooperation. Then, the two-stage link cost is formulated where \(x\), the weight of residual energy, is introduced to be adjusted for different design goals. By selecting the optimal helper set, the two-stage link cost of each link can be optimized. Finally, based on the designed TSC transmission model and the optimized two-stage link cost, a distributed two-stage cooperative routing (TSCR) scheme is further proposed to minimize the end-to-end cooperative routing cost. Simulation results evaluate the effect of \(x\) on the different performance metrics. When \(x\) equals 0, TSCR can achieve the shortest end-to-end transmission delay and highest energy efficiency, while a larger \(x\) can achieve a longer network lifetime. Furthermore, simulation results also show that the proposed TSCR scheme can effectively improve both the energy efficiency and network lifetime compared with the existing schemes.  
wireless multi-hop network; two-stage cooperative transmission; cooperative routing; network lifetime; energy efficiency +
Footnote †: journal: Sensors
## 3. Key Supporting Technologies for the WSN
### Data Aggregate Mechanism  
In the integrated monitoring system for underground coal mines, a sensor node is equipped with multiple sensors so as to collect different types of environmental parameters. To improve the transmission efficiency, the collected environment data needs to be aggregated before reporting it to the parent routing node. Therefore, a data aggregation mechanism is employed on the sensor nodes. The frame format of the aggregated data is illustrated in Table 2. Here, the _RFD_ field shall be set to 0X0F, indicating that this frame is environmental information that the sensor node sends; _Limit_ field shall be set to 0X01 if a parameter overruns, otherwise, be 0; _Type_ field indicates the type of parameters; _Para_ field carries the specific value of parameters.  
When it receives environment information from its children sensor nodes, the routing node caches it firstly. After it receives the data from all the children nodes, it aggregates it and then sends it to the sink node. The frame format of the packaged data is illustrated in Table 3. Here, the _ROUTER_ field shall be set to 0X0E, indicating that this frame is environmental information that the routing node is sendsing; _Num_ field indicates the number of sensor nodes in this frame; _Addr_ field means the network address of each sensor node; fields of _Type and Para_ have the same meaning with the corresponding items in Table 2. Because the overrun parameter is sent directly to the sink node by the sensor node, _Limit_ field is not included in the frame.  
In the environmental information delivery process, data aggregation is adopted by the sensor node and the routing node, which decreases the amount of data, reduces data collisions, saves power, and extends the lifetime of the whole network. Meanwhile, for the overrun parameters, the sensor  
\begin{table}
\begin{tabular}{c c c c} \hline
**1 Byte** & **1 Byte** & **1 Byte** & **Variable** \\ \hline _RFD(0X0F)_ & _Limit_ & _Type_ & _Para_ \\ \hline \end{tabular}
\end{table}
Table 2: Frame format on sensor node.  
node directly sends the to the sink node without aggregation, realizing urgent transmission of the emergency data.
# A MIG Welding Clamping Scheme for Power Battery Enclosure's Deformation Restrain
## 3 Simulation Analysis of Clamping Scheme
### Heat Source Model Selection and Verification  
3.2.1. Heat Source Model Selection  
Welding profile of the power battery enclosure is aluminum alloy sheet, and its thickness is between 2 mm and 3 mm, which has led to a shallow molten pool during welding. Therefore, the use of Gaussian heat source in welding can not only obtain more accurate numerical simulation results but also have the advantages of high calculation efficiency and short calculation time [12], compared with the double ellipsoid heat source. The selected Gaussian heat source model is shown in Figure 4.  
Gaussian heat source model is a typical distributed heat source model. During welding process, the arc heat source transfers heat to welding components through heating spots with a certain action area, and its heat flux can be described by Gaussian normal distribution function, as shown in Formula (1).  
\[q(r)=\frac{Q}{2\pi\sigma_{q}{}^{2}}e^{(-\frac{r^{2}}{2\sigma_{q}{}^{2}})} \tag{1}\]  
where, \(q(r)\) is the heat flux density at position \(r\) from the center of the heating spot, \(J/(\text{mm}^{2}\cdot\text{s})\); \(Q\) is the effective thermal power of the arc, \(Q=\eta UI\), \(W\); \(\eta\) is the welding thermal efficiency; \(\sigma_{q}\) is the Gaussian heat source distribution parameter; \(r\) is the distance from the center of the heating spot, mm.
# Axial Compression Behavior of Steel Angle-Corrugated Steel Plate-Confined Concrete Columns
## 3 Experimental Results and Discussion
### Load-Strain Curves of Steel Angle and Steel Tube  
The load-strain curves of the steel angle (or the steel tube) were obtained by extracting the data from strain gauges, as shown in Figure 7. In the figures, \(\varepsilon_{\text{h}}\) represents the transverse strain, \(\varepsilon_{\text{v}}\) represents the vertical strain, \(\varepsilon_{\text{e}}\) represents the equivalent elastic strain, and \(\varepsilon_{\text{v}}\) means the yield strain. The steel angle and steel tube could be considered to be in a bidirectional plane stress state, as the extrusion pressures of concrete to the steel angle (or steel tube) are far less than the transverse and vertical stress of the steel angle (or steel tube). Assuming that the steel is an ideal elastoplastic material, and before the point at which the steel yields, the relationship of stress-strain can be written as Equation (1):  
\[\left\{\begin{array}{c}\sigma_{\text{h}}\\ \sigma_{\text{v}}\end{array}\right\}=\frac{E_{\text{s}}}{1-v_{\text{s}}^{2}} \left[\begin{array}{cc}1&\
u_{\text{s}}\\ v_{\text{s}}&1\end{array}\right]\left\{\begin{array}{c}\varepsilon_{\text{h} }\\ \varepsilon_{\text{v}}\end{array}\right\} \tag{1}\]  
where \(\sigma_{\text{h}}\) and \(\sigma_{\text{v}}\) represent the transverse and vertical strain, respectively; \(E_{\text{s}}\) is the elastic modulus; and \(\
u_{\text{s}}\) is the Poisson's ratio of steel, which is determined as 0.3.  
Figure 6: Load-axial displacement curves.  
Combined with the criterion proposed by [PERSON] (Equation (2)) and Equation (1), the expression of the equivalent elastic strain could be obtained, which is shown in Equation (3) [26].  
\[\sigma_{\text{e}}=\sqrt{\sigma_{\text{h}}^{2}-\sigma_{\text{h}}\sigma_{\text{v}}+ \sigma_{\text{v}}^{2}} \tag{2}\]  
\[\varepsilon_{\text{e}}=\frac{\sigma_{\text{e}}}{E_{\text{s}}}=\frac{1}{1-v_{ \text{s}}^{2}}\sqrt{(1-v_{\text{s}}+\
u_{\text{s}}^{2})\big{(}\varepsilon_{ \text{h}}^{2}+\varepsilon_{\text{v}}^{2}\big{)}+(-1+4v_{\text{s}}-v_{\text{s}} ^{2})\varepsilon_{\text{h}}\varepsilon_{\text{v}}} \tag{3}\]  
When \(\varepsilon_{\text{e}}=\varepsilon_{\text{v}}\), the steel angle and steel tube are considered to yield. Compared with the transverse strain, the vertical strain of the steel angle and steel tube was much larger, which was close to the yield strain.
## 3 Results and Discussion
### Effect of the Inlet Gas Flow Rate Hydrodynamic Characteristics of Sieve Tray
#### 3.2.4 Effect of the Inlet Gas Flow Rate on Froth Height  
To investigate the effect of gas flow rate on froth height, the absorber was fitted with a ruler to observe froth formation above the tray. It can be seen from Figures 8 and 9 that the froth height above the tray goes up as the flow rate progresses. The trend of this effect is similar for all pressure values studied. This trend can be explained by the increasing of the superficial velocity of the gas into the absorber through the increase of the flow rate, leading to an increase of the liquid holdup, as can be seen in Figure 7. The increase of the tray liquid holdup will increase the height of the froth on the tray.  
Figure 7: Effect of the inlet gas flow rate on the tray liquid holdup.  
Figure 8: Froth height above the trap at inlet gas flow rate (**a**) 12 Nm\({}^{3}\)/h, (**b**) 16 Nm\({}^{3}\)/h, and (**c**) 18 Nm\({}^{3}\)/h.  
Figure 9: Effect of the inlet gas flow rate on froth height.
## 5 Performance Analysis  
The proposed GIC-IGGM-PMBM filter provides an applicable method to combine adaptive newborn density with adaptive detection probability based on target states following a Gaussian distribution. In this section, comprehensive simulations are presented to validate the accuracy of this proposed method, using the NS-3 and MATLAB simulator according to previous analyses. A total of 100 Monte Carlo trials are performed for each simulation, and the results represent the average simulation results obtained. The simulation setup is similar to that of the MC-MPMC [34].  
Target states and measurements can be presented as \(\mathbf{x}_{k}=F_{k|k-1}\mathbf{x}_{k-1}+G_{k}\mathbf{q}_{k-1}\) and \(\mathbf{m}_{k}=H_{k}\mathbf{x}_{k}+R_{k}\), where \(\mathbf{x}_{k}\) is the state vector at time \(k\) denoted as \(\mathbf{x}_{k}=\left(x_{k}\;x_{k}\;y_{k}\;\omega_{k}\right)^{T}\) (vectors are denoted in lowercase bold letters), \(x_{k}\) and \(y_{k}\) are the states of target position, \(\dot{x}_{k}\) and \(\dot{y}_{k}\) are the states of target velocity, and \(\omega_{k}\) is the turn rate. \(F_{k}\) is the target transition matrix, \(G_{k}\) is the control input matrix, and \(\mathbf{q}_{k-1}\) is the process noise which follows a Gaussian distribution with zero mean and covariance \(Q_{k}\), defined as \(E[q_{k}q_{k}^{T}]\). \(\mathbf{m}_{k}\) is the measurement vector at time \(k\) denoted as \(\mathbf{m}_{k}=\left(x_{k}\;y_{k}\;\omega_{k}\right)^{T}\), \(H_{k}\) is the measurement matrix, and \(R_{k}\) is the measurement noise. The process noise and measurement noise are mutually uncorrelated to each other. The parameter values can be presented as the values in [34].  
Assuming that there are 12 targets following their separate trajectory in the simulations, true trajectories of these targets are as shown in Figure 2. The birth time and death time of all targets with the initial states are shown in Table 1. The processing time slot was set to \(T=1s\), \(\sigma_{x}=0.01\), \(\sigma_{y}=0.01\), and \(\sigma_{\omega}=\left(\pi/180\right)^{2}\). The clutter generated in each measurement was assumed to abide by a uniform distribution [16]. The optimal sub-pattern assignment (OSPA) [35] and generalized OSPA (GOSPA) were introduced to comprehensively evaluate the tracking behaviors of different schemes or those of different parameters originating from the same scheme with \(p=1\) and \(c=200\). At first, we performed the proposed GCI-IGGM-PMBM filter, and then compared its tracking performance with the BGM-PMBM [4], IGGM-PHD (CPHD) [20], and BGGIW-PMBM [16] filters based on unknown detection probability. Next, the tracking behavior comparisons between GCI-IGGM-PMBM and BGGIW-PMBM are presented as a function of the characteristic parameters of adaptive newborn distribution.  
Figure 2: Target trajectories: small squares represent locations of target birth, and the line destination point represents the locations of target death, which are not labeled. The labels of \(S\), \(A\), and \(Tn\) are abbreviations of the normal sensor, anchor, and trajectory n, respectively.
## 1 Introduction
Assessing project resilience is frequently influenced by subjective factors; thus, it is difficult to objectively and robustly reflect the level of project resilience [16]. Additionally, most resilience assessment models are model-driven, with fewer studies focusing on data-driven approaches. Thus, this paper intends to propose a more accurate method to assess project resilience, including developing a quantitative data-driven model and establishing objective resilience assessment criteria. Due to the Reference Class Forecasting (RCF) technique offering an objective approach by considering an external project perspective and the Radial Basis Function (RBF) neural network excelling in fitting nonlinear patterns, especially with small sample sizes, this paper constructs a model by combining the RCF technique and RBF neural networks to forecast project performance, illustrate the changes in performance levels during the disruption and recovery phases of a project, and thus quantitatively assess project resilience. Assessing resilience can provide a theoretical basis for project decision-makers, such as adjusting the work plan, improving communication and cooperation between parties, and reallocating resources to ensure construction project success [4]. To summarize, the goal and objective of this paper are to develop a data-driven approach for assessing project resilience. To this end, this paper combines RCF and RBF to construct a theoretical model that can forecast project performance levels and then assess project resilience on this basis. The proposed model is validated with 64 construction projects to demonstrate its feasibility and applicability. The model in this paper can be effectively used to assess project resilience, serve as a bridge for future research, and provide clear guidance for project management. The remainder of this paper is organized as follows: The following section provides an overview of resilience definitions, as well as resilience assessment, RCF technique, and RBF neural network applications. Subsequently, based on the RCF technique, an RBF neural network model is established to forecast project performance levels and assess project resilience. Then, the model is validated using 64 construction projects that have faced disruptions from various sources. Finally, we discuss the theoretical contributions and practical implications of this paper.
## 3 Improvement of DSOGI-PLL
### Dsogi-Pll  
The approach utilizing the DSOGI boasts minimal delay and superior dynamic performance, enabling the acquisition of grid voltage phase under conditions of asymmetric grid voltage. The structural schematic diagram for this method is illustrated in Figure 4 below [20].  
Figure 3: Flowchart of VSG Low-Voltage Ride-Through.  
In the diagram, \(v_{g\alpha}\) and \(v_{g\beta}\) represent the grid voltage in a two-phase stationary reference frame; \(v_{g\alpha+}\) and \(v_{g\alpha-}\) represent its positive-sequence and negative-sequence components, respectively; \(v_{g\alpha+}\) and \(v_{g\alpha+}\) represent the grid voltage in a two-phase rotating reference frame; \(\omega_{n}\) is the rated angular frequency of the grid; and \(\theta_{0}\) is the phase angle of the positive-sequence grid voltage.  
The DSOGI-PLL is essentially a combination of the DSOGI filter and the SRF-PLL. The fundamental design principle of the DSOGI filter is illustrated in Figure 5.  
In the Figure, \(q\) represents a \(90^{\circ}\) lag, and \(v_{g\alpha f}\), \(v_{g\beta}\) are the grid voltages on the alpha-beta axes after being filtered by the SOGI. In practical modulation, a value of \(\zeta\) 0.707 is taken, and \(v_{i}\), \(v_{o}\) represent the input voltage and output voltage of the SOCI, respectively.  
The transfer function of the SOGI is given by the following equation:  
\[D(s)=\frac{v_{o}(s)}{v_{i}(s)}=\frac{2\zeta\omega_{0}s}{s^{2}+2\zeta\omega_{0} s+\omega_{0}^{2}} \tag{4}\]  
\[Q(s)=\frac{qv_{o}(s)}{v_{i}(s)}=\frac{2\zeta\omega_{0}^{2}}{s^{2}+2\zeta\omega _{0}s+\omega_{0}^{2}} \tag{5}\]  
The magnitude-frequency response of its transfer function is given by the following equation:  
\[\left\{\begin{array}{l}|D|=\frac{2\zeta\omega_{0}\omega}{\sqrt{\left(2\zeta \omega_{0}\omega\right)^{2}+\left(\omega-\omega_{0}^{2}\right)^{2}}}\\ \angle D=arctan(\frac{\omega_{0}^{2}-\omega^{2}}{2\zeta\omega_{0}\omega}) \end{array}\right. \tag{6}\]  
\[\left\{\begin{array}{l}|Q|=\frac{\omega_{0}}{\omega}|D|\\ \angle Q=ZD-90^{\circ}\end{array}\right. \tag{7}\]  
It is clear that, when the input angular frequency is zero, the output signal will track the input signal accurately without any steady-state error, while the amplitudes of all other harmonic signals will be attenuated. As a result, the SOGI inherently functions as a filter.  
Figure 4: Schematic Diagram of DSOGI-PLL.  
Figure 5: DSOGI-Filter.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology
### Identification of Full-Field Mode Shapes
#### 2.4.1 Extraction of Modal Parameters Using SSI-COV  
The full-field displacement time history of the structure is first expressed as a Hankel matrix, based on which the covariance matrix is constructed by calculating the elements using Equation (15) as  
\[X_{i}=\frac{1}{N-i}\sum_{k=0}^{N-i-1}x_{k+i}\cdot x_{k}^{T}, \tag{15}\]  
where \(x_{k}\) is a vector with the sampled outputs at time instant \(k\), \(N\) is the number of points of the time series, and superscript \(T\) means transpose. The covariance matrix is constructed as follows:  
\[T_{1|i}=\begin{bmatrix}X_{i}&X_{i-1}&\cdots&X_{1}\\ X_{i+1}&X_{i}&\cdots&X_{2}\\ \cdots&\cdots&\cdots&\cdots\\ X_{2i-1}&X_{2i-2}&\cdots&X_{i}\end{bmatrix}, \tag{16}\]  
The singular value decomposition is then applied to obtain the system matrix, and the modal parameters are calculated by the eigenvalue decomposition.