esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A high-speed LC-DFB laser array based on a SiO\({}_{2}\) planarization process is demonstrated. Each channel exhibits a low threshold current of about 12 mA and a high slope efficiency over 0.26 W/A. Stable single mode operation is demonstrated with SMSRs over 45 dB. The small-signal 3-dBe bandwidths exceed 14 GHz for all four channels under injection currents of 100 mA. The high modulation bandwidth benefits from high differential gain, and it can be further improved with a shorter cavity. Such results imply that the LC-DFB laser array can combine high efficiency slopes, high SMSRs, high modulation bandwidths and a simple fabrication process, and it shows great potential to low-cost optical communication.  
Conceptualization, J.W. and Y.L.; data curation, Q.W.; formal analysis, Q.W.; funding acquisition, Y.L.; investigation, Q.W. and J.W.; methodology, Q.W. and C.S.; project administration, Y.L.; resources, C.S. and Y.H.; supervision, Y.L.; validation, B.X., Z.H., L.W., H.L. and J.Y.; visualization, Q.W.; writing--original draft, Q.W.; writing--review and editing, Q.W. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program (Grant No. 2017 YFA0205800); the Science Challenge Project (Grant No. TZ2016003); the National Natural Science Foundation of China (Grant No. 51561165012, 51561145005, 61574082, 61621064, 61822404, 61875104, 61904093, 61927811); Tsinghua University Initiative Scientific Research Program (Grant No. 20161080068, 20161080062); the China Postdoctoral Science Foundation (Grant No. 2018M640129, 2019T120090) and the Collaborative Innovation Centre of Solid-State Lighting and Energy-Saving Electronics.  
Not applicable.  
The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 2 Features of the Machine Tool Taken into Consideration in Volumetric Verification
### Structural Configuration and Kinematic Model of the Machine Tool  
An MT is a compound machine that carries out work and generates metal chips. MTs are classified as compound machines that transmit power (thermal, hydraulic, electrical) and machines that do not start making metal chips (cutting, stamping, compression). Within these two groups, the combination of different structural elements such as guides, joints, or screws that define the kinematic structure of the MT shapes its kinematic chain. Assuming that moving cars of the kinematic chain are rigid bodies, transformation and rotation matrices can be implemented by using a mathematical model (kinematic model) based on the MT configuration [6; 7; 11; 13]. Then, the position of the tool with regard to the part to be machined is obtained in relation to the programmed nominal position, the position of the tip of the tool with respect to the reference machine (offsets), and the geometric errors of the axes. To obtain the combined effect of all geometric errors of the MT through its kinematic model, the measurement system (LT) must replace the part and the retroreflector must replace the tool (Figure 1). The kinematic model of the MT to be verified is represented by XFYZCB, where F determines the fixed part of the machine, the \(X\)-axis drives the worktable, \(Y\) and \(Z\) are combined to actuate the tool, and the \(C/B\) rotational axis allows head orientation, providing five degrees of freedom to the MT (Figure 1, Table 1). As a result of studies performed on this MT, its axes of rotation relative to \(C\) and \(B\) were locked. The final structural configuration of the MT to be verified is \(\mathit{XFYZ}\), with six geometric errors per axis (position, horizontal and vertical straightness, yaw, pitch, and roll) and three squareness errors associated. The equation of movement that relates the nominal coordinates of the MT with measured coordinates of the LT is presented in Equation (1). \[\overline{X_{LT}}=\ \overline{R_{LT}^{-1}}\ \left(\overline{R_{X}^{-1}}\ \left( \overline{R_{y}}\ \left(\overline{R_{z}}\ \overline{T}+\ \overline{Z}\right)+\ \overline{Y}-\overline{Z}\right)-\overline{T_{LT}}\right) \tag{1}\]  
where the components of the equations are constituted as follows: \(\overline{T}\) is the offset of the tool. \[\overline{T}=\left( \begin{array}{c}x_{t}\\ y_{t}\\ z_{t}\end{array} \right) \tag{2}\]  
\(\overline{R_{k}}\) represents the rotational error matrix in the axis \(k\) of the tool with \(k=x,y,z\). \[\overline{R_{(k)}}\ =\left( \begin{array}{ccc}1&-\varepsilon_{z}\left(k \right)&\varepsilon_{y}\left(k\right)\\ \varepsilon_{z}\left(k\right)&1&-\varepsilon_{x}\left(k\right)\\ -\varepsilon_{y}\left(k\right)&\varepsilon_{x}\left(k\right)&1\end{array} \right) \tag{3}\]
## 1 Introduction
Recent technological improvements, which have allowed acquisition time to speed up in order to obtain many measurements and develop innovative algorithms for data inversion, have made it possible to test the ERT method and monitor landslides. Indeed, considering the dependence of the electrical resistivity on the water content and the role that the latter plays in the triggering of landslides, repeated resistivity measurements over time along defined profiles (TL-ERT) can be carried out with the aim of monitoring, for example, the water content variations in the first subsoil levels and to try to define triggering thresholds. Figure 1: Yearly variations of the number of ISI articles concerning the topic “time-lapse ERT” spanning the period 2001–2020 (Source data: WoS database). Compared to other fields of application, the use of the TL-ERT in landslide monitoring can be considered recent. The geological complexity of the investigated phenomenon still requires additional experimentations and the development of petrophysical laws that allow quantitative correlation between the electrical resistivity values and the hydrologic parameters directly involved in the triggering of landslides. In this paper, a selected list of papers extracted by the WOS database for the period 2010 up to now, concerning the TL-ERT method and its application for landslide monitoring, have been considered. The main goals of this work are: (i) to perform an overview of the recent methodological advances in TL-ERT data processing and inversion; (ii) to identify the limits and the advantages of this method when it is applied in monitoring hydrogeological phenomena; (iii) to perform a critical analysis with the identification of future directions for the research activity on this topic. The work has been organized as follows. In the first step, the basic principles and the algorithms for data inversion of the TL-ERT method are briefly summarized. In a second step, a systematic overview of the main results obtained with this method in a selected list of field-scale experiments for landslide monitoring is carried out. Finally, some gaps and future methodological and technological trends are identified and discussed.
# A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications
## 3 Antenna Radiation Properties  
The radiation properties must be thoroughly estimated when measuring quasi-isotropic antenna performance. In this section, we discuss the radiation performance of the proposed antenna, including its gain deviation and CP characteristic.
## 3 Comparison with the Literature
### Standard Finite Element  
The standard finite element (FE) method for extracting the dispersion curves consists of performing a series of modal analyses of a representative part of the structure [37,38]. The dispersion relations are obtained by simply changing the length of the representative portion in order to allow the wavelength to change. The mode shapes and corresponding natural frequencies can be obtained by solving eigenvalue problems. Then, the dispersion curves are obtained from the eigensolutions. This method can only be used to extract propagating modes, whereas the WFE methodology is more general as it can extract both the propagating and evanescent modes. The two techniques were compared for a 2.16 mm thick laminate \([0/90/0]_{s}\). The FE model is shown in Figure 5, where a coarse mesh is shown for visual representation. The laminate is modelled with 2D plane strain elements with the plane strain in the z-direction. The element length is 0.09 mm in the propagation direction and four elements per layer through the thickness. The length of the representative part needs to be changed to obtain the result for different wavelengths. The comparison of dispersion curves is shown in Figure 6, where a good agreement can be observed. The WFE periodic section is shown in Figure 2b, which is modelled using standard eight-node hexahedral elements and the composite material properties for each layer is taken from [37]. The WFE periodic section has orders of magnitude fewer degrees of freedom compared with the standard FE approach. Moreover, the standard FE provides discrete output for each wavelength by changing the length of the representative part, whereas a single periodic section is used in WFE, which can be used to extract dispersion curves for a range of frequencies.  
Figure 4: Dispersion curves along the principal direction 1 of \(0^{\circ}\) lamina for \([+45/-45/0/90]_{s}\) laminate obtained using the SAFE (- -) and WFE (–) methods.  
Figure 5: Representative part of laminate modelled in FE.
## 5 Conclusions  
The anomaly detection task involves two major challenges: insufficient data and insufficient abnormal data. This paper proposes the IWGAN network, whose architecture combines WGAN and Skip-GANomaly. The WGAN subnetwork mitigates the issues of training difficulty and mode collapse, while the excellent detection ability of Skip-GANomaly resolves the insufficient data problem. In addition, we found an optimal combination of training parameters for IWGAN: the LeakyReLU activation layer, a decay learning rate of 0.1, five training iterations, and unilateral label smoothing. The proposed model was evaluated using the FID, with experimental results exhibiting significant improvements in performance. The AUC value of the overall network for the GDXray+ discriminant gun samples reached an average of approximately 0.95, which is excellent in terms of a single-generation network. In future work, we will introduce an attention model mechanism to extend the proposed model's applicability for all anomaly detection tasks. In addition, we plan to adopt a neural architecture search to optimize the hyper-parameters (HPO) automatically.  
Conceptualization and methodology, K.-W.H., G.-W.C., Z.-H.H. and S.-H.L.: Formal analysis, K.-W.H. and G.-W.C.; Supervision, K.-W.H. and S.-H.L.; Project administration, K.-W.H. and S.-H.L.; Writing--original draft preparation, K.-W.H. and S.-H.L.; Writing--review and editing, S.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research is financially supported by the Ministry of Science and Technology of Taiwan (under grants No. 110-2222-E-992-006- and No. 111-2221-E-992-070-MY2).  
Not applicable.  
No new data were created or analyzed in this study.  
The authors would like to thank Editor and anonymous Reviewers for their valuable reviews.  
The authors declare no conflict of interest.
## 3 Human-Robot Interaction Application for the Reinforcement of ADLs
### Human-Robot Interaction Application: Software Description
#### 3.2.2 Graphical User Interfaces  
Our assistive robot application was developed with a user-friendly graphical interface that includes distinct sections; see Figure 4. It is the responsibility of healthcare professionals to manage the interface shown, but it can also be managed by users who are cognitively capable of making their own decisions and taking control over the application. Any process of reinforcement of an activity of daily living requires the following steps:  
1. **Configuration step**: The healthcare professional selects a user (identified by an ID) and particular action for the monitoring.
2. **Activity observation**: The user watches a video of the chosen action.
3. **Activity monitoring**: The user is encouraged to replicate the observed action, and the AI online action detection module starts the automatic monitoring of the performance. The software also provides the option to generate a report of the performance of the user during therapy sessions.  
The user ID, as well as the action that we want the user to perform, can be selected from the main menu of the application (see Figure 4a). Once the configuration step has been completed, the final users must watch an example video of the chosen action. This phase is started with the button in Figure 4b. For this research work, our team had to record for each of the actions of interest, up to four videos, along with step-by-step sub-action instructions for achieving the final objective of that action. Figure 5 shows some examples of two actors performing some of the actions of interest. All this dataset of videos is integrated into LOLA2 along with the application. When the professional selects an action to work with, one of these sample videos is randomly presented to the user. This option illustrates and instructs the user on the steps involved in taking a daily action. Then, the user may begin performing the chosen action in accordance with the observed video. Once the user is ready to carry out the requested action, the activity monitoring step will be activated by professionals. An AI-based online action detection (OAD) system is integrated to monitor the user's actions, as detailed in the following section. The developed application allows the generation of a report containing a detailed log of the entire work session with the patient. In particular, the log file can be used to obtain the following statistics: How much time has the user spent on certain tasks or actions? What actions are carried out in certain time slots? Have been the actions correctly performed?  
Figure 4: Different options in the developed graphical user interface for action monitoring. (**a**) gives options to specify the user ID and the actions to be monitored. There are options to choose from for observing and monitoring the action in (**b**). (**a**) Main menu. (**b**) Action monitoring options.
## 4 Experimental Verification
### Test Results and Analyses
#### 4.2.4. Creep Curve of Three-Layered CGS  
Figure 13 shows the tested creep strain of the three-layered CGS (L1, L2 and L3) as well as its predicted \(\varepsilon\)-\(t\) curves with consideration of different interface-layer numbers: \(N\) = 1 (L1 + L1/L2 + L2 + L3 or L1 + L2 + L2/(L3 + L3) and \(N\) = 2 (L1 + L1/L2 + L2 + L2/L3 + L3), where each interface-layer height is \(h\) = 0.3 \(H\) (\(H\) = 200 mm for the three-layered CGS, the total specimen height is 600 mm). Similarly, they all have the same three stages as those both the one-layered and two-layered CGS: instantaneous deformation, attenuation deformation, and stable creep deformation.  
The three-layered CGS has two interface-layers. The more the interface-layers are taken into account in calculation equation (Equation (19)), the predicted creep deformation becomes smaller, since it is known from Section 4.2 that the interface-layer with some height would decrease the deformation of multi-layered CGS, furthermore, three-layered CGS has two interface-layers with the height of 30% \(H\), therefore, two interface-layers has the smallest deformation. The tested results agree well with the predicted results of the three-layered CGS with \(N\) = 1 or 2. This proves again the interface-layer really exists in the multi-layered CGS and the established interface-layer calculation model is reasonable and reliable. At least one interface-layer must be considered for predicting viscoelastic parameters of the multi-layered CGS.
## 4 Parallel Multi-Objective Energy Valley Optimizer
### Pareto Selection with Elimination of Homogeneous Individuals  
As the algorithm iterates, an increase in the number of solutions in the solution candidate set slows down the iteration speed. This is especially true when calculating the Pareto front, as the algorithm must consider the dominance relationship between each pair of solutions, causing the solving time to increase significantly with more solution candidates.  
The non-dominated solution identification process follows these criteria:  
(1) Dominance Relationship. For any two solutions \(\mathbf{x}_{a}\) and \(\mathbf{x}_{b}\), \(\mathbf{x}_{a}\) dominates \(\mathbf{x}_{b}\) (\(\mathbf{x}_{a}\prec\mathbf{x}_{b}\)) if:  
\[\left\{\begin{array}{l}f_{i}(\mathbf{x}_{a})\leq f_{i}(\mathbf{x}_{b})\ \ \ \ \forall i\in\{1,2\}\\ \exists j\in\{1,2\}:f_{j}(\mathbf{x}_{a})<f_{j}(\mathbf{x}_{b})\end{array}\right. \tag{58}\]  
(2) Fast Non-Dominated Sorting: Solutions are partitioned into successive Pareto frontiers \(\mathcal{F}_{1},\mathcal{F}_{2},...\) where \(\mathcal{F}k\) contains solutions only dominated by those in \(\bigcup i=1^{k-1}\mathcal{F}_{i}\).  
In the basic EVO, each iteration generates one or two new solutions. When the model expands to multi-objective optimization, solution candidates must be guided by multiple front solutions, leading to a significant increase in the number of new solutions generated in each iteration.  
To speed up the algorithm, this paper eliminates duplicate solutions from the solution set after each iteration, ensuring unique solutions when calculating fitness and the Pareto front. Furthermore, only \(N\) solution candidates are retained in the next iteration, based on the Pareto dominance relationship and crowding distance.  
Next, we will calculate the complexity of the algorithm with the Elimination of Homogeneous Individuals. Let \(N\) be the maximum number of solution candidates per iteration, \(M\) the number of objectives, and \(D\) the solution vector dimension. The computational complexity can be quantified as follows:  
(1) Baseline Approach (without duplicate removal)  
Non-dominated sorting: \(O\big{(}MN^{2}\big{)}\) due to pairwise dominance comparisons  
due to pairwise dominance comparisons: \(O\big{(}MN\text{log}N\big{)}\) for sorting per objective  
Total per iteration: \(O\big{(}MN^{2}+MN\text{log}N\big{)}\)  
(2) With duplicate cleaning  
Duplicate detection: \(O\big{(}ND\big{)}\) via hashing-based similarity check  
Effective solution count after cleaning: \(K\leq N\)  
Reduced sorting cost: \(O\big{(}MK^{2}\big{)}\)  
Total per iteration: \(O\big{(}MK^{2}+MK\text{log}K+ND\big{)}\)  
The theoretical acceleration ratio can be expressed as:  
\[\eta=1-\frac{MK^{2}+MK\text{log}K+ND}{MN^{2}+MN\text{log}N} \tag{59}\]  
When \(K=\alpha N\) (\(0<\alpha<1\)), the dominant term reduces from \(O\big{(}N^{2}\big{)}\) to \(O\big{(}\alpha^{2}N^{2}\big{)}\), achieving quadratic complexity reduction.
## 4 Structural Models
### Inception Time Model
Because of the sliding windows, the m packets produce a sum of (m \(-\) T + 1) windows [13], as shown in Figure 4.  
**Inception Model Description**  
**#Function inception_module**  
Input: simple  
Output: staked Layers  
If use_botleneck and input_tensor last_dimmentional > 1:  
input_inception = Conv1D (input_tensor, filters = bottleneck_size, kernel_size = 1, padding ='same', activation = activation, use_bias = False)  
Else:  
input_inception = input_tensor  
Kernel_size_s = [kernel_size//(2** i) for i in range(3)]  
Conv_list = []  
For i in range (len(kernel_size_s)):  
Figure 4: How the input data from the UNSW2015 dataset is turned into window sequences for Inception Time model training using the sliding window technique [13]. Figure 3: The Inception Time model. The first number in each box represents the kernel size, while the second represents the stride size; (s) indicates the padding type. a. conv_list_append (Conv1D)
6. Max_pool = MaxPool1D (input_tensor, pool_size = 3, strides = stride, padding ='same')
7. Conv = Conv1D (max_pool, filters, kernel_size = 1, padding ='same', activation
8. x = Concatenate (conv_list, axis = 2)
9. x = BatchNormalization (x)
10. x = Activation (x, activation ='relu')  
**Function shortcut_layer**
1. Shortcut = Conv1D (input_tensor, filters = out_tensor last dimensional, kernel_size = 1, Padding ='same', use_bias = False)
2. Shortcut = BatchNormalization(shortcut)
3. x = Add ({shortcut, out_tensor})
4. x = Activation (X, activation ='relu')  
**Function inception_time_model**  
1. Input_layer = Input (input_shape)
2. x = input_layer
3. Input_res = input_layer
4. For d in range (depth):  
1. x = inception_module (x)
2. if use_residual and d % 3 == 2:  
1. x = shortcut_layer (input_res, x)
2. input_res = x
5. Gap_layer = GlobalAveragePooling1D()(x)
6. Output_layer = Dense (gap_layer, number_classes, activation ='softmax')
7. Model = Model (inputs = input_layer, outputs = output_layer)
## 3 Envelope Detector-Aided, Machine Learning-Based Fault Type Detection Model  
Figure 7 illustrates the proposed fault type detection algorithm. It consists of cascaded structures to identify fault regions, their presence, and types. As shown in Figure 7, measurement data are obtained using relays and meters in the testbed. Initially, the envelope detector-based fault region detection extracts the area of interest from the recorded  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Devices** \\ **Number** \\ **(Type)** \\ \end{tabular} } & \multicolumn{4}{c}{**Event Characteristics**} \\ \cline{2-5}  & \multirow{2}{*}{
\begin{tabular}{c} **Prelength** \\ **[s Cycles]** \\ \end{tabular} } & \multicolumn{2}{c}{**Total**} & \multicolumn{1}{c}{**Sampling Frequency**} & \multicolumn{1}{c}{**Event**} \\  & & **Length [s** \\ \end{tabular} & \multicolumn{1}{c}{**(kHz)/Sample Rate**} & \multicolumn{1}{c}{**Trigger**} \\  & & **(Cycles)** & & **(Sample/Cycle)** & **Equation** \\ \hline
1 (SEL351S) & 0.1 (6) & 1 (60) & 2 (32) & IN106 \\
2 (SEL700 GT) & 0.1 (6) & 3 (180) & 0.24 (4) & IN304 \\
3 (SEL735) & 0.1 (6) & 5 (300) & 8 (128) & IN101 \\
4 (SEL735) & 0.1 (6) & 5 (300) & 8 (128) & IN101 \\
5 (SEL451) & 0.1 (6) & 3 (180) & 8 (133) & IN106 \\
6 (SEL451 SV) & 0.1 (6) & 3 (180) & 8 (133) & IN206 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Settings for meters and relays.  
Figure 6: (**a**) Event signal circuit; (**b**) digital output card; (**c**) RTS.  
signals. This process keeps the input sample size constant and removed unnecessary parts for the subsequent algorithms. After applying feature extraction to the clipped signals, the extracted features are input into an ML-based fault detection system. This system categorizes the signals as Normal Condition, Motor, Switch, or Fault. If a fault is detected, the algorithm proceeds to a third step to determine the type of fault. A decision block between the second and third steps determines whether the third step should be run based on the output of the second step. The ML models in both steps are trained for different purposes (the first decides the presence of a fault, whereas the second finds the fault type). This two-step structure was shown in previous studies [35; 36] to improve prediction accuracy and decrease prediction time. Finally, in the algorithm's final step, it identifies the fault types as SLG, LL, LLG, or 3 LG.