esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Community Integrated Energy System Model
### Load Model  
Under the condition of energy price given by the ES, the energy-consuming group achieves the optimization goal by adjusting the shiftable load and reducible load, that is, maximizing consumer surplus. Maximizing consumer surplus can be expressed by the difference between user utility function and energy cost.  
\[\text{max}F_{\text{la}}=f_{\text{u}}^{t}-R_{\text{sell}}^{t} \tag{16}\]  
where \(f_{\text{u}}^{t}\) indicates the satisfaction degree of users, which can be expressed by quadratic function.  
\[f_{\text{u}}^{t}=\sum_{t=1}^{T}{[\gamma_{\text{e}}P_{\text{ue}}^{t}-\frac{ \lambda_{\text{e}}}{2}(P_{\text{ue}}^{t})^{2}+\gamma_{\text{h}}H_{\text{uh}}^{t }-\frac{\lambda_{\text{h}}}{2}(H_{\text{ue}}^{t})^{2}]\Delta t} \tag{17}\]  
where \(\gamma_{\text{e}}\), \(\lambda_{\text{e}}\), and \(\gamma_{\text{h}}\), \(\lambda_{\text{h}}\) are the user's preference coefficient for different energy use, which determines the LA's demand for power and heat energy.  
The power load \(P_{\text{ue}}^{t}\) of the energy-using side is determined by the fixed load \(P_{\text{fix}}^{t}\) and the shiftable load \(P_{\text{tra}}^{t}\).  
\[P_{\text{ue}}^{t}=P_{\text{fix}}^{t}+P_{\text{tra}}^{t} \tag{18}\]  
where \(P_{\text{ue}}^{t}\) is relatively fixed in power consumption and has high requirements for power reliability, half of which is used for life, production, and other activities. \(P_{\text{tra}}^{t}\), the power consumption time, is relatively flexible, and it is determined according to the change in electricity price, such as charging load. The constraint condition is:  
\[\left\{\begin{array}{l}0\leq P_{\text{tra}}^{t}\leq P_{\text{tra,max}}^{t}\\ \sum\limits_{t}^{T}P_{\text{tra}}^{t}=Z_{\text{tra}}\end{array}\right. \tag{19}\]  
where \(P_{\text{tra,max}}^{t}\) is the maximum value of the shiftable load power. \(Z_{\text{tra}}\) represents the total amount of translational load power in period \(T\). The \(Z_{\text{tra}}\) value is constant before and after the demand side response.  
The heat load power of the LA also includes two parts.  
\[H_{\text{uh}}^{t}=H_{\text{fix}}^{t}-H_{\text{tra}}^{t} \tag{20}\]  
where \(H_{\text{fix}}^{t}\) is the minimum heat demand. \(H_{\text{tra}}^{t}\) represents the heat power that can be reduced, which is used to adjust and reduce the required heat power according to the satisfaction degree of heat use. The constraints that need to be met are  
\[0\leq H_{\text{tra}}^{t}\leq H_{\text{tra,max}}^{t} \tag{21}\]  
where \(H_{\text{tra,max}}^{t}\) denotes the maximum heat power that can be dissipated at time \(t\).
# Path Loss Measurements of Indoor LTE System for the Internet of Things
## 3 Experimental Methods  
For the purpose of determining the path loss exponent and the coverage range of the received shadow fading signal in the factory environments, the down link power measurement of small cell eNB was performed in the laboratory. An SDR-based small cell experimental platform was built in the laboratory, which contained an eNB and UE. We use the NI (National Instruments Corporation, Austin, TX, USA) PXI platform as the main hardware, and the NI (National Instruments Corporation, Austin, TX, USA) LabVIEW Communications Design Suite and LTE Application Framework as the main software tools to build this SDR-based small cell experimental platform, so that the transmitted and the received signals follow the 3 GPP LTE specifications. By using the SDR techniques, we could easily modify important system parameters, e.g., carrier frequency, bandwidth, and transmit power, by software. The carrier frequency of the transceiver was 2350 MHz, the system bandwidth was 20 MHz and the transmission mode was single input single output (SISO). The reference signal received powers (RSRPs) were measured at the scenarios of line of sight (LOS) and non-LOS (NLOS). The scenario of the laboratory is shown in
## 3 Experiments and Discussion
### Multilayer Films Measurement  
The proposed system can be further applied to the thickness measurement of multilayer films. The three polyimide films mentioned above were combined to produce four multilayer films. To ensure the signal-to-noise ratio, the integration time of the spectrometer was set to 5 ms, and other parameters remained unchanged. The spectral interference signals of multilayer polyimide films are shown in Figure 7a-d. The spectral period change ceases to follow an intuitive trend with the increase in the number of film layers. Traditional techniques, such as the extremum method, are no longer feasible for the thickness calculation of different film layers. The power spectral density derived by the proposed method is shown in Figure 7e-h. The thickness of the multilayer film can be calculated by fitting the frequencies corresponding to the peaks of the power spectral density curve. However, the amplitude of the power spectral density decreases obviously with the increase in the number of film layers, indicating a limit on the measurable number of film layers. In addition, as shown in Figure 7e, when the signal-to-noise ratio of the spectral interference signal in a certain layer of the multilayer film is low, the power spectral density curve will be disordered and challenging to fit. The uncertainty of the artificial preparation of multilayer films may lead to the occurrence of this phenomenon.  
The measurement of the above multilayer films was replicated 10 times. The results detailed in Table 3 demonstrated that the relative standard deviation of the measured multilayer film thickness was less than 0.16%. In addition, with the increase in the number of film layers, the measurement accuracy of the film thickness decreased. The large thickness difference of the same type of film in different combinations indicated poor uniformity of the artificially prepared multilayer film.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Combination**} & \multicolumn{3}{c}{**Thickness (\(\mu\)m)**} & \multicolumn{3}{c}{**Std (nm)**} \\ \cline{2-6}  & **3 \(\mu\)m** & **8 \(\mu\)m** & **12.5 \(\mu\)m** & **3 \(\mu\)m** & **8 \(\mu\)m** & **12.5 \(\mu\)m** \\ \hline \(8+3\) & 3.2198 & 7.7106 & / & 2.9 & 11.8 & / \\ \(12.5+3\) & 3.2097 & / & 12.4534 & 1.4 & / & 5.5 \\ \(12.5+8\) & / & 8.3303 & 12.2097 & / & 11.4 & 16.6 \\ \(12.5+8+3\) & 3.1804 & 8.1202 & 12.3221 & 4.1 & 14.6 & 19.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measurement results of multilayer polyimide films.  
Figure 6: Measurement results of polyimide films with different thicknesses. (**a–c**) Raw spectral curves of polyimide films with nominal thicknesses of 3 \(\mu\)m, 8 \(\mu\)m, and 12.5 \(\mu\)m, respectively. (**d**) Film thickness measurement results for 20 times.
## 3 SPT-AD: Self-Supervised Pyramidal Transformer Network-Based Anomaly Detection
### SPT-AD Algorithm
```
0: Input time series data \(X=[x_{1},x_{2},\cdots,x_{N}]\in\mathbb{R}^{N\times D_{\text{input}}}\)
1:Step 1: Project Input to Latent Space
2:\(X_{\text{embed}}=XW_{\text{embed}}+b_{\text{embed}}\)\(\triangleright\) Linear transformation using weights \(W_{\text{embed}}\) and bias \(b_{\text{embed}}\)
3:\(X^{(1)}=X^{\prime}_{\text{embed}}\)\(\triangleright\) Normalization or further transformation of \(X_{\text{embed}}\)
4:Step 2: Apply Multi-Scale Convolution
5:for\(s=1\) to \(S\)do
6:\(X^{(s)}=\text{Conv}_{\text{C}}(X^{(s-1)})\)\(\triangleright\) Apply convolution at scale \(s\)
7:endfor
8: Combine features: \(X_{\text{CSCM}}=[X^{(1)},X^{(2)},\cdots,X^{(S)}]\)
9: Compute dimensionality: \(X_{\text{CSCM}}\in\mathbb{R}^{1\times D_{\text{embed}}}\), where \(L=N+\sum_{s=2}^{S}\frac{N}{\text{C}^{s-1}}\)
10:Step 3: Compute Attention Scores
11:for\(l=1\) to \(L_{\text{attm}}\)do
12:for\(s=1\) to \(S\)do
13: Compute inter-scale attention: \(H^{(s)}_{\text{inter}}=\text{Softmax}\Big{(}\frac{Q^{(s)}K^{(s+1)T}}{\sqrt{d}} \Big{)}V^{(s+1)}\)
14: Compute intra-scale attention: \(H^{(s)}_{\text{intra}}=\text{Softmax}\Big{(}\frac{Q^{(s)}K^{(s)T}}{\sqrt{d}} \Big{)}V^{(s)}\)
15:endfor
16:endfor
17: Combine attention results: \(H_{\text{PAM}}=[H^{(1)},H^{(2)},\cdots,H^{(S)}]\)
18:Step 4: Compute Anomaly Scores
19:for each time series point \(i=1,\cdots,N\)do
20:\(z_{i}=W_{\text{pred}}h_{i}+b_{\text{pred}}\)\(\triangleright\) Linear transformation
21:\(a_{i}=\sigma(z_{i})=\frac{1}{1+e^{-z_{i}}}\)\(\triangleright\) Sigmoid normalization
22:endfor
23:Output:\(A=[a_{1},a_{2},\cdots,a_{N}]\)\(\triangleright\) Anomaly scores for each time step
```  
**Algorithm 1** SPT-AD Algorithm
# Experimental Verification of the Dynamic Model of Turbine Blades Coupled by a Sealing Strip
## 2 The Test Rig  
The test rig, shown in Figure 1, has particular technical specifications that make it unique in this kind of measurements. The test rig was purposely designed and machined in order to create in the lab loading conditions as close as possible to the real ones, but with the possibility of having all the important parameters under control. The first prototype of the main components of the test rig are described in detail in [30]. Starting from the previous prototypes, the test rig was assembled with the technical features listed below.  
* The test rig is non-rotating; this allows for accurate control on the excitation force amplitude and phase and accurate measurement of the blades' response.
* The centrifugal force acting on the strip is simulated by a loading system based on compressed air, so as to avoid contact with the strip. This system is here preferred to a classic contact loading device, as the latter may induce deformation of the strip itself and introduce a spurious system in the blades-strip system.
* Each of the two dummy blades is machined in one-piece. This solution avoids additional interfaces with friction beyond the strip-platforms contact interfaces.
* A clamping hydraulic machine holds the prismatic bases of the blade with a 200 bar pressure so that the blades become one with the big inertial mass of the rig.  
* The blades' platforms in contact with the strip are equipped with a replaceable contact pad (glued to the platform). In this way, different typical blade materials can then be tested independently from the blades.
* Each blade is connected to a shaker, so that both blades can be excited at the same time with a different phase, as in a real engine. The two-blades system at hand can be excited to vibrate along the in-phase (IP) and out-of phase (OOP) modes.
* Each blade is excited with a step-sine force with constant amplitude. A precise control of the force is requested since the system is not linear.
# Exploring a Multimodal Mixture-Of-YOLOs Framework for Advanced Real-Time Object Detection
## 2 Object Detection Schemes with RGB Images  
### CNN for Object Detection  
Object detection in the field of image processing has been performed by extracting a feature of an object from image data in advance and detecting an object based on the feature. To find such a feature point, a scale invariant feature transform (SIFT) architecture extracts local feature points in the image, and a feature descriptor, histogram of oriented gradients (HOG), shows the direction of the edge of the segmented image as a histogram [16; 17]. Such an image processing-based method has an inherent disadvantage of the need to find a feature that directly affects object detection performance.  
With the advent of CNNs, end-to-end learning of neural networks extracting and learning their own features has led to significant performance improvement in object detection. CNN-based object detection algorithms are largely divided into single-stage and two-stage methods. The single-stage approach typically includes regional CNN (R-CNN), which generates a candidate region of interest (ROI) where objects may exist; extracts features from those regions to detect objects; and regresses the classification algorithms and bounding boxes to detect objects in the ROI. This method shows a large performance improvement compared to the existing image-based object detection algorithms. However, the feature extraction and classification stages are divided, and learning takes a lot of time because the features extracted through input of each ROI to the CNN should be individually trained.  
To address these shortcomings, Fast R-CNN and Faster R-CNN with improved learning and detection speed have been proposed. Fast R-CNN reduces learning time by simplifying the computational process and learning steps of CNN through multitasking, which simultaneously learns the loss of classifier and bounding box in the ROI. Faster R-CNN, as shown in Figure 1, uses the region proposal network (RPN) to generate ROI in the last layer of the CNN for faster training and detection speeds. However, the single-stage object detection algorithm performs the two tasks of generating the ROI and classifying the objects in the area sequentially, so that detection accuracy is good, but detection speed is still somewhat slow [18; 19; 20].
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 3 Simulation and Experimental Results
### Simulation Results of SMCSPO and PDSMCSPO
#### 3.2.4 Sliding Surface Response  
The convergence time of the sliding surface of PDSMCSPO was faster than SMCSPO. In Figure 13, it can be seen that the PDSMCSPO (blue line) sliding surface reached zero faster than SMCSPO (red line), and the starting point of PDSMCSPO was almost half of SMCSPO.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial. No** & **SMCSPO** & **PDSMCSPO** \\ \hline
1 & \(\lambda=30\) & \(\lambda=30\) \\ \hline
2 & \(\varepsilon_{o},\varepsilon_{c}=1\) & \(\varepsilon_{o},\varepsilon_{c}=1\) \\ \hline
3 & \(\frac{k_{1}}{\varepsilon_{o}}=90\) & \(\frac{k_{1}}{\varepsilon_{o}}=90\) \\ \hline
4 & \(\frac{k_{2}}{\varepsilon_{o}}=2700\) & \(\frac{k_{2}}{\varepsilon_{o}}=2700\) \\ \hline
5 & \(\alpha_{3}=3.16\) & \(\alpha_{3}=3.16\) \\ \hline
6 & \(\frac{k}{\varepsilon_{c}}=30\) & \(\frac{k}{\varepsilon_{c}}=30\) \\ \hline
7 & \(\frac{k_{p}}{k_{d}}=30,k_{d}=0.5\), \(k_{p}=15\) & \(c=\frac{k_{p}}{k_{d}}=30\) \\ \hline
8 & Input = step & Input = step \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters.  
Figure 12: Output response of both schemes.  
To understand the mathematics of control theory, a sine wave of 1 (rad/s) with an amplitude of 1 was given to the system with lambda = 30 and a disturbance which was the sum of a constant (100), a sine and cosine wave of different frequencies were given to the system, and the following results were observed.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### Employed Methods to Monitor Snow Cover with SAR Data  
As the development of SAR sensors and image processing techniques progresses, the sensing target of snow also changes. For the following discussion, we defined three different sensing targets of snowpack: Wet SCE, total SCE, as well as wet and dry SCE. Here we must address the difference between wet and dry SCE as well as total SCE, although the overall SCE can be retrieved by both strategies, only the former can discriminate wet and dry SCE, respectively. Namely, wet and dry SCE sensing strategies can provide more information than a total SCE approach.
## 3 Application for Failure-Recall Data with DCLNN Method
### Dynamic Time Warping Pre-Processing Before Data Modeling
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Indicator** & **D-M1** & **D-M2** & **D-M3** & **D-M4** & **D-M5** & **D-M6** \\ \hline \multirow{2}{*}{**SI**} & **STD** & 127.73 & 125.34 & 80.79 & 157.41 & 126.12 & 114.32 \\  & \(p\)**-value** & 0.001 & 0.001 & 0.0032 & 0.001 & 0.001 & 0.001 \\ \hline \multirow{2}{*}{**CI**} & **Lempel-ziv** & 0.3196 & 0.3522 & 0.3616 & 0.3434 & 0.3522 & 0.3616 \\  & **SampEn** & 0.3042 & 0.7419 & 0.2076 & 0.4520 & 0.4187 & 0.4055 \\ \hline \multirow{2}{*}{**CSI**} & **CSI** & 0.0124 & 0.0308 & 0.0194 & 0.0244 & 0.0186 & 0.0168 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comprehensive selection indicator after DTW. Figure 10: Cont. As shown in Tables 2 and 3, the CSI is reduced to below 0.1 after DTW with the failure time series as the referenced sequence. A specific comparison of CSI before and after DTW preprocessing is summarized in Figure 9 for a clear description. As shown in Figure 11, the CSI of the other five escalators are reduced after DTW preprocessing with Escalator #3 referenced. They become more stationary and lower-complexity after matching with the failure time series of Escalator #3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Training**} & **\# Of Escalator** & **\#1** & **\#2** & **\#3** & **\#4** & **\#5** & **\#6** \\ \cline{2-9}  & **RMSE** & **Before DTW** & 63.64 & 122.02 & 73.8 & 77.69 & 127.72 & 93.1 \\  & **After DTW** & 119.75 & 117.83 & 73.8 & 150.8 & 123.46 & 107.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Root-mean-square error (RMSE) in training process. Figure 10: DTW preprocessing results. (**a**) Warped failure time series of #1. (**b**) Warped failure time series of #2. (**c**) Warped failure time series of #4. (**d**) Warped failure time series of #5. (**e**) Warped failure time series of #6. Besides, for the convenience of engineering applications, six sigma was applied to the analysis of the CSI of the failure time series after DTW to try to give a possible threshold of the CSI. In addition, the threshold can be used to distinguish whether the data sequences need to be warped before data modeling. The distribution interval of CSI can be given by \([\mu-3\sigma,\mu+3\sigma]\), where \(\mu\) is the mean value of CSI and \(\sigma\) is the variance. For above-mentioned data sequences after DTW, CSI\({}_{\text{mean}}=0.0204\), \(\sigma\)= 0.0064. Thus, the CSI value can be considered in the interval [0.0012 0.0396], and the upper limit of the interval is used as the judgment threshold considering the fault tolerance. That is, CSI\({}_{\text{threshold}}=0.0396\). For data sequences in this paper, if the CSI is less than CSI\({}_{\text{threshold}}\), DTW preprocessing is unnecessary before data modeling, otherwise, it is recommended to be warped with a sequence with a CSI less than CSI\({}_{\text{threshold}}\).
## 2 Materials and Methods
### Protein Crystallization, Data Collection, and Refinement  
The HRas-CRaf_52-187 complex containing RBD and CRD was concentrated to 7 mg/mL for crystallization trials. Crystals of the complex were obtained by sitting-drop vapor diffusion with a reservoir solution of 0.1 M ammonium acetate, 0.1 M Bis-Tris pH 5.5, and 17% \(w/v\) PEG 10K. Drops were prepared with 1 uL protein and 1 uL reservoir solution and crystals were grown at 18 \({}^{\circ}\)C over ten weeks. Data collection was performed on a home source MicroMax007 HF with a Cu\({}^{2+}\) anode, tungsten filament, and R-AxisIV\({}^{2+}\) detector from Rigaku. Data were indexed, integrated, and scaled with the HKL3000 software package [40]. Molecular replacement was performed in PHENIX [41] utilizing the HRas-CRaf-RBD crystal structure (PDB ID 4G0N) as the search model. After correct placement of Ras and Raf-RBD molecules, an additional search was performed for the Raf-CRD utilizing the Raf-CRD NMR structure (PDB ID 1 FAR). Further structure refinement was performed with PHENIX [41] and COOT [42].
# Metamaterial Integrated Folded Dipole Antenna with Low SAR for 4G, 5G and NB-IoT Applications
## 3 Antenna Design and Result Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Band No.** & **Frequency** & **Gain** & **Band Name** \\ \hline B2U & 1.85–1.91 & 2.4778 & \\ \hline B2D & 1.93–1.99 & 2.6094 & \\ \hline B25U & 1.85–1.915 & 2.4815 & IoT Bands \\ \hline B25D & 1.93–1.995 & 2.6113 & \\ \hline B1U & 1.92–1.98 & 2.5965 & \\ \hline B1D & 2.11–2.17 & 2.7686 & \\ \hline
35 & 1.85–1.91 & 2.735 & \\ \hline
39 & 1.88–1.92 & 2.801 & \\ \hline
33 & 1.90–1.92 & 2.833 & Low frequency LTE bands \\ \hline
37 & 1.91–1.93 & 2.865 & \\ \hline
36 & 1.93–1.99 & 2.989 & \\ \hline
34 & 2.01–2.02 & 3.150 & \\ \hline
40 & 2.30–2.40 & 3.640 & Middle frequency LTE Bands \\ \hline
41 & 2.49–2.69 & 3.676 & \\ \hline
38 & 2.57–2.62 & 3.677 & \\ \hline
42 & 3.40–3.60 & 4.757 & High frequency LTE bands \\ \hline
43 & 3.60–3.80 & 4.926 & \\ \hline n78 & 3.30–3.80 & 4.843 & 5G NR \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gain for the folded dipole antenna. Figure 12: Antenna radiation pattern in the polar plane. tenna are illustrated in Figure 14. A good approximation between simulated and measured results was obtained. Table 3 provides a comparison of the designed antenna with different reported designs in terms of size and bands. The designs presented in [8; 9] resonate only on a single LTE band, and those of [10; 12; 15; 30] working on the multi-band but not on all of the bands. In comparison, the antenna designs reported in [13; 14; 17] are capable of operating on most of the LTE bands. By comparison, those in [31; 32; 33; 34; 35] are capable of working in 5G NR sub-6 GHz bands in addition to some LTE bands, but are bulky and non-planar in structure. Many of the designs available in the literature considered the infinite ground or active switches for their operations. The folded dipole designed in this research is planar, compact and simple in structure, and resonates for 5G, 4G and IoT bands with sufficient radiation characteristics, bandwidth, gain and return loss. A 97% size reduction was achieved by folding the dipoles in comparison to [31], 96% in comparison to [32], 33% in comparison to [33], 95% in comparison to [34] and 96% in compassion to [35]. Figure 14: Measured and simulated S parameter. Figure 13: Photograph of the fabricated antenna.
## 3 Experimental Results and Analysis
### Sample V-3
From the above description, we could reach a conclusion that five sorts of cracks, namely, elementary tensile crack, posterior tensile crack, sidewall slabbing crack, shear crack and surface spalling crack, were formed in the samples containing inverted U-shaped openings during uniaxial compression. For the samples containing two openings, three categories of hole coalescence were observed in this research: the slabbing coalescence, the shear coalescence and the tensile coalescence. The final failure modes of the above five samples are shown in Figure 7. In the figure, the red line means the failure path, and the black dotted line denotes the appeared crack, which is not easy to identify using the naked eye. To sum up, the failure mode of the samples S-2 and D-1 was shear-dominated failure, while that of the other three samples belonged to tensile-shear failure. To put it differently, the instability of the sample S-2 was attributed to the intersection of the shear cracks (4\({}^{\text{a}}\) and 4\({}^{\text{b}}\)) and the V-shaped notches, while that of the sample D-1 was induced by the coalescence of the shear cracks 4\({}^{\text{a}}\), 4\({}^{\text{b}}\) and 4\({}^{\text{c}}\). For the intact sample C-3, the failure resulted from the connection between the shear crack 4 and the merged tensile cracks 1\({}^{\text{a}}\) and 1\({}^{\text{b}}\). With regard to the samples H-3 and V-3, the coalescence of the shear cracks and the posterior tensile cracks gives rise to the final instability. Since the brittleness of the rock was extremely remarkable, the failure of these samples was violent and rapid. As a result, the collected photos of the samples after the peak were relatively few, and the post-peak failure behavior was hard to monitor. Besides, at the end of the tests, it was found that partial posterior tensile cracks propagated further toward the upper or lower ends. However, when approaching the loading ends of the sample, the propagation direction of the posterior tensile cracks was deflected because of the end friction. The other shear cracks also appeared on the diagonal and propagated towards the opening sidewalls. It is noted that some unmarked cracks are observed in Figure 7, which were formed after failure, and did not take charge of the eventual failure.