esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Application of Plasma Treatment in Advanced and Emerging Packages
### Wafer-Level Packaging (WLP)  
There are two main types of WLP, namely fan-out wafer-level packaging (FOWLP), where the die is embedded in a moulding compound and the interconnects are fanned out over the compound, and fan-In WLP (FI-WLP), where all the external connections (solder balls) are located directly on the wafer surface within the perimeter of the die. In this case, plasma cleaning is required to address various challenges, such as the cleaning of bond pads and modifying surfaces to improve the adhesion of the underfill and encapsulants [148].  
The reviewed literature indicated that a mixture of Ar and H\({}_{2}\) was selected over other gases, such as CF\({}_{4}\), SF\({}_{6}\), O\({}_{2}\), Ar, H\({}_{2}\), He, and N\({}_{2}\). This mixture was shown to effectively remove oxide layers. Of course, other plasma gases, such as O\({}_{2}\), are used for the cleaning of photoresist residues (scum) after the photolithographic processes [149]. Other plasma processes involved in WLP are used for the stripping of bulk photoresist after the etching process and dielectric patterning [70].  
These processes are beyond the focus of this paper. It was also noted, however, that no single gas mixture can be considered ideal for all applications. A specific recipe must be developed for each application to ensure that it provides the optimal balance for the desired outcome, such as prioritizing adhesion enhancement versus oxide removal [150].
# An Analytical Model for the Prediction of Emptying Processes in Single Water Pipelines
## 3 Existence and Uniqueness of the System Solution  
This section demonstrates that the system (4)-(6), subject to the specified initial conditions (7), admits a unique solution within certain time intervals. To this end, the system is reduced to a set of nonlinear differential equations as follows:  
Combining Equations (4)-(6), and using the notation \(L:=L_{e}(t)\) and \(v:=v_{w}(t)\) (these are time-dependent variables), the system reduces to:  
\[\frac{dL}{dt}= -v \tag{8}\] \[\frac{dv}{dt}= \frac{a}{(L_{T}-L)^{k}L}+b-v|v|\left(c+\frac{d}{L}\right)-\frac{p_ {atm}^{*}}{\rho_{\omega}}\frac{1}{L}; \tag{9}\]  
with initial conditions  
\[L(0)=L_{T}-x_{0}\quad\text{and}\quad v(0)=0 \tag{10}\]  
where  
\[a=\frac{p_{atm}^{*}(x_{0})^{k}}{\rho_{w}},\,b=g\sin(\theta),\,c=\frac{f}{2D}, \,\text{and}\,\,d=R_{v}gA^{2}.\]  
Using \(L^{\prime}=\frac{dL}{dt}\), the system (8)-(10) can be expressed more compactly using vector notation as follows:  
\[\begin{pmatrix}L^{\prime}\\ v^{\prime}\end{pmatrix}=\begin{pmatrix}P(L,v)\\ Q(L,v)\end{pmatrix} \tag{11}\]  
with  
\[\begin{pmatrix}L^{\prime}(0)\\ v^{\prime}(0)\end{pmatrix}=\begin{pmatrix}L_{T}-x_{0}\\ 0\end{pmatrix} \tag{12}\]  
where \(P,\,Q\colon(0,L_{T})\times\mathbb{R}\to\mathbb{R}\) are functions defined by:  
\[P(L,v):= -v\] \[Q(L,v):= \frac{a}{(L_{T}-L)^{k}L}+b-v|v|\left(c+\frac{d}{L}\right)-\frac{ p_{atm}^{*}}{\rho_{\omega}}\frac{1}{L}\]  
The functions \(P\) and \(Q\) do not explicitly depend on the time variable \(t\). Therefore, Equations (11) and (12) constitute a nonlinear autonomous system of differential equations with initial conditions, whose solutions \((L(t),v(t))\) are parametrised curves in the phase plane \((L,v)\), commonly referred to as orbits.  
The following result is obtained through direct calculation:  
\[\frac{\partial Q}{\partial L}= \frac{1}{L^{2}}\left[\frac{p_{atm}^{*}}{\rho_{\omega}}+v|v|d- \frac{a}{(L_{T}-L)^{K}}\right]-\frac{ak}{L(L_{T}-L)^{k+1}}\] \[\frac{\partial Q}{\partial v}= \left(c+\frac{d}{L}\right)2|v|.\] \[\frac{\partial P}{\partial L}= 0\qquad\text{and}\qquad\frac{\partial P}{\partial v}=-1.\]  
It is worth noting that \(P,\,Q\), and their partial derivatives \(\frac{\partial P}{\partial L},\,\frac{\partial P}{\partial v},\,\frac{ \partial Q}{\partial L}\), and \(\frac{\partial Q}{\partial v}\) are continuous within an open connected set \((0,L_{T})\times\mathbb{R}\). This holds because, according to the parameters of the system (see Section 2), \(L\) is never zero, and \(L_{T}\) does not equal \(L\) at any point during the emptying process, which begins at \(t=0\) from \(L_{T}-x_{0}\). These conditions on \(P\) and \(Q\) ensure that for \(X_{0}:=(L_{T}-x_{0},0)\in(0,L_{T})\times\mathbb{R}\), the initial value problem has a solution \(X(t):=(L(t),v(t))\) on a time interval \([0,\tau)\) for a given \(\tau>0\), where the solution is unique within this interval [23].
## 2 Routing Protocols Overview
### OSPF Features and Characteristics  
OSPF (Open Shortest Path First) is a link-state routing protocol that enables network routers to maintain a synchronized view of the network topology. By having a comprehensive understanding of the network architecture, OSPF can determine the shortest path to a destination, facilitating efficient and optimal routing decisions. In the context of this study, OSPF is likely employed to facilitate communication between routers within the same autonomous system, particularly in an IPv6 environment. OSPF's role includes load sharing, where it allows traffic to be distributed across multiple paths, thereby enhancing network performance and resource efficiency. Additionally, OSPF contributes to system resilience by supporting efficient link-failover mechanisms, ensuring a seamless transition in the event of network outages. The performance analysis of OSPF, in conjunction with EIGRP and BGP, provides valuable insights into load sharing and link-failover strategies within IPv6-based systems.  
The convergence process in OSPF involves several critical phases and the exchange of specific OSPF protocol packets. The process begins with the exchange of Hello packets between neighboring routers, initiating neighbor detection and verification. These Hello packets are essential for establishing and maintaining adjacencies between routers. For proper adjacency formation, routers must match OSPF parameters such as the router ID, area ID, and authentication settings.  
Once adjacencies are established, the routers exchange Database Description (DBD) packets, which provide an overview of the router's link-state database. If there are missing entries, Link State Request (LSR) packets are sent to request specific Link State Advertisements (LSAs) that are not present in the recipient's database. Upon receiving the necessary LSAs, the responding router sends out Link State Update (LSU) packets. Finally, to confirm the receipt of LSAs, Link State Acknowledgment (LSAck) packets are exchanged.  
This series of OSPF protocol packets ensures that routers share and synchronize their understanding of the network's link-state information, enabling precise and efficient routing decisions. Figure 2 would typically illustrate this convergence process, depicting the step-by-step exchange of packets that leads to the synchronization of network topology information across routers.  
Figure 1: EIGRP convergence process.  
Figure 2: OSPF convergence process.  
Figure 2 shows the stages of OSPF convergence, from the initial Hello packet exchange to the final acknowledgment of LSAs, highlighting how OSPF routers achieve a consistent and accurate view of the network.
# Code Edit Recommendation Using a Recurrent Neural Network
## 4 The Code Edit Recommendation Method Based on a Recurrent Neural Network
### Training a Model
#### 4.4.2 Applying an LSTM model  
To train a model with the training data, CERNN uses the model shown in Figure 8. The model has two layers: the embedding layer and the LSTM layer. In Figure 8, the create_model function obtains the number of files and the number of categories (i.e., the number of edited files) as an input and returns the created model.  
In the embedding layer, CERNN reduces the dimensions of the indexed training data set. The embedding layer transforms a sparse vector into a dense vector. If CERNN directly indexes files in various open-source projects, the training time would be very long. Therefore, using the embedding layer to reduce the dimensions while preserving the characteristics of the data is instrumental to reducing the training time.  
Then, CERNN passes the training data reduced through the embedding layer to the LSTM layer. In the LSTM layer, CERNN creates an LSTM model. As shown in Figure 7, CERNN should select multiple output values for the recommendation of several files to be edited for a single context. To construct and utilize such a multi-label model, CERNN uses the sigmoid function as an activation function to derive the output values from the LSTM layer. (An LSTM can be one of the four different models. The first one is a binary category model in which the output value is one of two options. The second one is a multi-category model that selects one of several output values. The third one is a multilabel model in which multiple output values are selected. The final one is a sequence generation model, which shows a continuous output result. To implement a different model, a different activation function should be chosen. For example, to implement a multi-category (or multi-class) model that selects one of several output values, the softmax function should be used as an activation function. In our case, we intend to implement a multilabel model and so we chose the sigmoid function)  
Finally, CERNN constructs the settings for the training process. Regarding the loss function, we chose 'binary_corsentropy' because we classified a context into edited files.  
Figure 8: CERNN model for the learning process.  
Figure 7: Sliding window method for generating training data.  
For the optimizer, we chose 'adam.' We tried other optimizers (e.g.,'sgd' and 'adagrad') to check whether they could improve the recommendation accuracy or reduce the training time while maintaining the same accuracy. However, in our experiment, the 'adam' optimizer showed the best accuracy. Therefore, we decided to use it.
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 3 Different Quasistatic Models
### MQS Model  
Suppose that assumption (13) holds and \(B^{\prime}\gg\ |v|/c^{2}E^{\prime}\): the MQS model has to be adopted. This means that the term \(j\omega\mathbf{D}\) defined in the Maxwell-Ampere's law, as given in Equation (2), becomes negligible, which implies that the displacement current is neglected in the dielectrics. The MQS model is derived as follows  
\[\
abla\times\mathbf{E} =-j\omega\mathbf{B}, \tag{19}\] \[\
abla\times\mathbf{H} =\mathbf{J},\] (20) \[\
abla\cdot\mathbf{B} =0. \tag{21}\]  
From (21), the magnetic vector potential (MVP) \(\mathbf{A}\) can be introduced, such as  
\[\mathbf{B} =\
abla\times\mathbf{A}, \tag{22}\] \[\mathbf{A}\times\mathbf{n} =\mathbf{0}\text{ on }\Gamma_{\text{B}}\text{ and }\Gamma_{\text{E}} \tag{23}\]  
and the ESP \(\varphi\) is only defined in the conductive sub-domain, as follows  
\[\mathbf{E} =-j\omega\mathbf{A}-\
abla\varphi, \tag{24}\] \[\varphi =c_{1}\text{ on }\Gamma_{\text{E}}. \tag{25}\]  
Combining the expressions of \(\mathbf{E}\) and \(\mathbf{B}\) with (9), (10), and (20), the \(\mathbf{A}\)-\(\varphi\) potential formulation for the MQS model reads  
\[\
abla\times(\
u\
abla\times\mathbf{A})+\sigma(j\omega\mathbf{A} +\
abla\varphi) =\mathbf{0}\text{ in }\Omega, \tag{26}\] \[\
abla\cdot(\sigma(j\omega\mathbf{A}+\
abla\varphi)) =0\text{ in }\Omega_{\text{c}} \tag{27}\]  
where \(\
u\) is the magnetic conductivity (the inverse of the magnetic permeability \(\mu\)). Applying the FE method, the MVP \(\mathbf{A}\) is discretized with the edge elements and the ESP \(\varphi\) with the nodal elements, which introduce a symmetric matrix. It should be mentioned here that the resultant matrix is not gauged. The uniqueness of \(\mathbf{A}\) can be ensured by adding the Coulomb gauge [15] or the tree gauge [16].
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 4 Neural Network (NN) Based Elasticity for Fracture Problems
### Numerical Examples
#### 4.3.2 V-Notch Bar in a Tension Test  
The second model problem is concerned with analyzing the brittle failure of a V-notch bar under tensile loading. The geometric setup and the loading conditions of the specimen are depicted in Figure 12a. The size of the specimen is chosen to be as follows: \(H=1\) mm, \(L=0.35\) mm, \(h=0.1\) mm and \(V=0.24\) mm. The mesh size of the specimen is chosen to be \(h_{e}=0.004\) mm in the expected fracture zone. The computation is performed by applying a displacement with an amplitude of \(u=0.02\) mm at the top edge while the bottom edge is fixed in both directions \(x\) and \(y\). The material parameters used in this simulation are similar to that of the single edge notched tension test as shown in Table 4. Discretization is achieved by finite element (FEM) formulations with 3-noded linear triangular elements.  
The evolution of the crack phase-field \(d\) is reported in Figure 12b-d. The crack initiates at the notch tip and successively propagates horizontally from the notches inwards till the final rupture. For visualization of crack surface, deformed regions with a phase-field \(d\approx 1\) are plotted in Figure 12.  
Figure 11: Single-edge notched test (SENT). Validation of stress–strain behavior with NN formulation against FE formulation. The stress unit is \([\mathrm{N/mm^{2}}]\).  
Figure 12: V-notch bar in tension. (**a**) Geometry and boundary conditions. (**b–d**) FEM: Contour plots of the fracture phase-field \(d\) evolution for different loading states up to final failure.  
Next, Figure 13 illustrates a comparison between FEM and NN formulations by calculating the stress-strain relationship using both methods. As in previous examples, predicted stress exactly follows the FE solution.
###### Abstract  
In China, a substantial portion of highway asphalt pavements are no longer capable of accommodating increasing traffic volumes and necessitate renovation and expansion. Prior to commencing such activities, it is crucial to evaluate the performance of the existing asphalt pavements. This study developed a novel normal cloud framework integrating a comprehensive weighted indicator system for existing asphalt pavement. Five key performance indicators including riding quality index (RQI), rutting area ratio (R\({}_{\text{r}}\)), cracking area ratio (C\({}_{\text{r}}\)), patching area ratio (P\({}_{\text{r}}\)), and pavement structural strength index (PSSI) were selected to holistically represent the pavement condition in highway renovation and expansion projects. Subsequently, a method was proposed to determine the weights of these indicators by integrating the analytic hierarchy process (AHP) and entropy. A normal cloud model was constructed to address data characteristics and representation of indicator fuzziness/randomness through digital cloud modeling. The model was applied to 12 sections of the Jingjintang Expressway (Tianjin section). The results revealed only one section where the normal cloud model differed from the pavement maintenance quality assessment (PQI) model. The 3D ground-penetrating radar detection results of this different section indicated that the normal cloud model more closely aligned with the road structure condition. Compared to absolute pass/fail criteria of the traditional PQI model, the cloud model offered enhanced sensitivity to define graded condition assessments essential for reconstruction planning and decision analysis. Therefore, the normal cloud model is more suitable for assessing the performance of existing asphalt pavements in highway reconstruction and extension projects compared to the PQI model.  
ao _et al._ Article  
Performance Assessment of Existing Asphalt Pavement in China's Highway Reconstruction and Expansion Project Based on Coupling Weighting Method and Cloud Model Theory. _Appl. Sci._**2024**, _14_, 5789. [[https://doi.org/10.3309/app14135789](https://doi.org/10.3309/app14135789)]([https://doi.org/10.3309/app14135789](https://doi.org/10.3309/app14135789))
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Investigated Pseudorandom Binary Sequences
#### 4.1.4 Zero Correlation Zone Sequences  
Similarly to APASs, binary ZCZ sequences are PRBSs whose non-oversampled PACFs are only ideal within a limited interval of samples in the absence of Doppler shifts, which defines its usable length \(N_{\text{PRBS,usable}}\). The main difference is that, instead of having only a single extra peak, the PACF of ZCZ sequences present a wider interval with non-zero sidelobes. In this article, the considered ZCZ sequences are assumed to be generated by the algorithm from [33], which provides the highest flexibility regarding PRBS length \(N_{\text{PRBS}}\), PACF usable length \(N_{\text{PRBS,usable}}\) and number of different sequences of same length [17], the latter being only relevant for MIMO applications.
## 3 Results and Discussion
### Defining the Model Parameters from Hardness Simulations  
A finite element simulation allows us to investigate the behavior of a material under the applied load. The simulations were carried out for the indenter with a diameter of 3 mm. The investigated sheet with a length of 5 mm and thickness of 2 mm was virtually subjected to different loads. Both the indentation diameter and indentation depth were investigated as a function of the load. In the simulation of the Brinell hardness test, the Hollomon-type material model was employed. In the present case, the strain rate sensitivity was neglected by setting \(m\) to 0 in Equation (3) while the elastic part was accounted for by employing Hooke's law. This implies that the model is valid for quasi-static loads; i.e., the process is neither time- nor velocity-dependent. This approximation can be used for Al alloys at ambient temperature and a relatively low deformation rate since the strain rate sensitivity is negligible under these conditions [43; 44]. The material properties used for the simulation are listed in Table 2.  
Figure 6: Neck formation as predicted by FEM.  
Figure 7: Effect of elastic and plastic terms in Equation (2) on the accuracy of FEM simulation: the continuous line takes into account both elastic and plastic parts, while the dotted line represents the simulation by considering only the plastic term.  
Figure 8 shows the dependence of a surface pressure \(P_{m}\) caused by the indentation-over-\(d/D\) ratio (indentation size per indenter size). The FEM simulation reveals a quasi-parabolic profile, which can be described by the following relation [20; 23]:  
\[P_{m}=k\left(\frac{d}{D}\right)^{p}, \tag{20}\]  
where \(k\) and \(p\) are material constants.  
As reported by [PERSON] [23], the model parameters \(k\) and \(p\) in Equation (20) are functions of the hardening coefficient \(K\) and strain-hardening exponent \(n\), which can be derived from the tensile test:  
\[k=3K\cdot 0.1^{n}\;\;and\;\;\;p=n \tag{21}\]  
Figure 8 shows an excellent correspondence between the FEM simulated data and counterpart computed by Equations (20) and (21) with the strain-hardening coefficient and strain-hardening exponent listed in Table 2 (\(K\) = 144.56 MPa and \(n\) = 0.3691). This implies that the material model parameters can be derived from the Brinell hardness test, performed under various loads, and the strain-hardening characteristics can be calculated by employing Equations (20) and (21).
## 3 Line-Scan Hyperspectral Imaging Instruments
### Major System Components
#### 3.1.2 Imaging Spectrographs  
An imaging spectrograph is an optical wavelength-dispersive device that spatially separates broadband light into different wavelengths. The imaging spectrograph acquires a line of spatial information from a target via an entrance slit, and it is the core component of line-scan hyperspectral imaging systems. Transmission and reflection gratings are two major types of diffraction gratings used in imaging spectrographs for wavelength dispersion. A transmission-grating-based imaging spectrograph is shown in Figure 3a. Incoming light is first collimated by a front lens and then dispersed at a prism-grating-prism (PGP) component, where light propagation direction is dependent on wavelength. The dispersed light is projected onto a detector through a back lens, creating a special 2-D image: one dimension represents spatial and the other spectral. Figure 3b shows a reflection-grating-based imaging spectrograph, which is specifically based on an Offner configuration. The spectrograph includes a pair of spherical mirrors and a convex reflection grating. The lower mirror guides light from the entrance slit to the reflection grating, where the beam is dispersed into different wavelengths. The upper mirror then reflects the dispersed light to the detector, where a continuous spectrum is formed for each spatial point along a scanning line on the sample. Dispersive imaging spectrographs are commercially available for different spectral regions, such as ultraviolet and visible (UV-VIS: 250-500 nm), visible (VIS: 380-800 nm), visible and near infrared (VNIR: 400-1000 nm), near infrared (NIR: 900-1700 nm), short-wavelength infrared (SWIR: 1000-2500 nm), and mid-wavelength infrared (MWIR: 3000-5000 nm). Imaging spectrographs working in narrower wavelength ranges (e.g., Raman spectrographs in 770-980 nm) are also available for particular applications (e.g., high spectral resolution).  
Figure 2: Excitation sources for a line-scan hyperspectral Raman system: (**a**) a 785 nm line laser for Raman imaging measurement; and (**b**) a 785 nm point laser for spatially offset Raman spectroscopy measurement.