esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Generation of Frequency Beating Laser Pulse  
To illustrate the feasibility of the proposed technique, start-to-end simulations are performed in this section. Here, we adopt a longitudinal Gaussian laser pulse at 800 nm with a duration of 30 fs and a peak power of 2 MW as the seed laser (Coherent Inc., Santa Clara, CA, USA). The laser pulse is sent into an optical pulse stretcher to stretch the pulse length to 300 fs, and the longitudinal power distribution as well as the Wigner function [48; 49] are shown in Figure 3. After passing through the optical beam-splitter and Michelson interferometer, the pulse longitudinal power distributions are shown in Figure 3c, where the two laser pulses are delayed by 900 fs.  
Using the optical combiner, the two seed lasers are sequentially combined as one laser pulse, whose square root of power distribution and field amplitude are presented in Figure 4, where one can observe the wave packets at THz wavelength in the longitudinal amplitude distribution, indicating that optical signal at THz frequency has been generated. To obtain the exact value of beating frequency, Fourier-transform at time-frequency domain  
Figure 3: (**a**) The pulse longitudinal distribution after the optical pulse stretcher; (**b**) The Wigner function after the optical pulse stretcher; (**c**) the pulse longitudinal power distributions after the optical beam-splitter and Michelson interferometer.  
is carried out and the results are also presented in Figure 4, where the fundamental frequency is about 11.5 THz.
# Numerical Calculation of Slosh Dissipation
## 2 SPH Method: The SPH-Flow Solver
### Brief Recall of the \(\delta\)-LES-SPH Scheme
Regarding the pressure and the viscous forces, these are expressed as:  
\[\left\{\begin{array}{rl}\mathbf{F}_{i}^{p}&:=\,\,\,-\sum_{j}\left(p_{j}\,+\,p_{ i}\right)\,\
abla_{i}\,W_{ij}\,V_{j}\\ \mathbf{F}_{i}^{v}&:=\,\,\,K\,\sum_{j}\left(\mu\,+\,\mu_{ij}^{T}\right)\,\pi_{ij} \,\
abla_{i}W_{ij}\,V_{j}\\ \pi_{ij}&:=\,\,\frac{\mathbf{u}_{ij}\cdot\mathbf{r}_{ij}}{||\mathbf{r}_{ji}||^{2}}\qquad \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\where \(C_{S}\) is the so-called Smagorinsky constant set equal to 0.18 (see [33]). The viscous term (5) contains both the effect of the real viscosity \(\mu\), as well as the one related to the local turbulent viscosity \(\mu_{i}^{T}\). \(\delta\)-LES-SPH Scheme: Enforcement of the Boundary Conditions  
The kinematic and dynamic free-surface and no-slip conditions are applied, respectively, on the free surface and on the solid surfaces and thus coupled with the governing Equation (1). More specifically, while the free surface boundary conditions are intrinsically satisfied in the SPH methods (see [34]), it is difficult to resolve thin wall boundary layers (WBL) as easily performed in mesh-based methods. Concerning the latter, if a solution is represented by the use of smaller particles close to the walls, it implies large CPU costs linked to the explicit time integration of the scheme. Indeed, in high Reynolds number conditions, thin WBLs are developed and a simple no-penetration boundary condition (free-slip) is preferred to avoid a too-demanding cost in terms of computational resources. The Reynolds number of the simulations with water, presented in this work, is about 233,000 and an initial estimation of the WBL thickness returns in that the near-wall regions, even with the maximum spatial resolution, are still under-resolved so the free-slip conditions are preferred. On the other hand, the Reynolds number in the simulations with oil is about 4660, and in this condition, the particle size used for water is sufficient to resolve the boundary layer developed by the oil, thus allowing the application of the no-slip conditions. The limit of the free-slip assumption for the water simulations is evaluated in [12], and it returns that the energy dissipation is not substantially influenced by choice of the wall boundary conditions in the violent free-surface flows presented here. This result is also connected to two opposing phenomena: the wall friction slows down the run-up of the liquid jet, decreasing the intensity of the liquid impact against the tank walls, but it represents a further energy dissipation mechanism.
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## 3 Results and Discussion
### Gene Delivery Prospectives of 3 LM-hydrogels  
As mentioned in the introduction, 3 LM as a DNA delivery carrier has been evaluated in the previous paper [31], and its stereocomplexed hydrogel has also shown the potentials in our preliminary study (summarized in Table 3) [32]. However, physical properties, structural transformation, and polymer compositions of the stereocomplexed hydrogel would significantly affect biological test results. The relationship between the micelle structures and gelation mechanism, gel strength, and degradation process revealed in this detailed and systematic study demonstrated the reproducibility and potential use of 3 LM-hydrogels for optimized local gene delivery systems.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Sample** & **Evaluation** & **Method** & **Results** \\ \hline \multirow{7}{*}{3 LM} & Loading efficiency [31] & Heparin extraction & \textgreater{}50\% \\  & Micelle stability [31] & SYBR Gold Assay & No DNA exclusion \\  & DNA release [31] & Dextran Sulfate & No release at pH 7.4 \\  & Cell toxicity [31] & MTT Assay & Right release at pH 4.5 \\  & RAW 264.7 Uptake [32] & Farm-modified 3 LM with & Higher IC\({}_{50}\) than PEI-polymlex \\  & GFP expression in RAW 264.7 [32] & YOVO-1 labeled DNA & Highly specific uptake in activated cells \\  & GFP expression in primary spleen & Flow cytometry & High uptake in activated cells \\  & MEP expression in primary spleen & Flow cytometry & Significantly higher expression in activated cells \\  & macrophages [32] & Flow cytometry & No release at pH 7.4 \\ \hline \multirow{2}{*}{3 LM- hydrogel} & DNA release [32] & SYBR Gold Assay & Slow release at pH 4.5 \\  & Macrophage uptake [32] & Flow cytometry & High uptake from pH 4.5 supernatant \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of biological evaluations previously reported for 3 LM and stereocomplexed hydrogels.  
Figure 11: Solid-state \({}^{13}\)C NMR spectra of (**a**) the PLLA-PEG-PLLA polymer, and hydrogels from L- and D-micelles with (**b**) rapid and (**c**) slow dehydration process, and hydrogels after 5 days degradation tests at (**d**) pH 7 and (**e**) pH 4.5.
# Evaluation of a Microporous Acoustic Liner Using Advanced Noise Control Fan Engine +
## 6 Test Results
### Comparison of Test Results Between Baseline and Liner Configurations
#### 6.2.1 Tonal Sound Pressure Level Response
We see that the dB loss is different at different shaft speeds for all 8\(\times\) BPFs, but the liner is effective in all cases at the microphone 21 location. The maximum dB loss is about 9 dB at 1900 RPM shaft speed for the 6 th BPF (shaft order 96-3040 Hz). Furthermore, we noticed that the liner was effective with similar recorded noise levels (dB loss) by microphones 19 to 23 (a highlighted region in Figure 14) at shaft speeds of 1500 to 2000 RPM. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Frequency, Hz**} & \multicolumn{2}{c}{**dB Loss (Aft**} & \multirow{2}{*}{**Maximum dB Loss, dB**} & \multirow{2}{*}{**Microphone**} \\  & **Microphones 19–23), dB** & & \\ \hline
533 (1 st BPF) & 0.2 to 0.6 & 0.6 & 21 \\
1067 (2 nd BPF) & 0.4 to 8 & 8 & 20 \\
1600 (3 rd BPF) & 2 to 5.5 & 5.5 & 22 \\
2133 (4 th BPF) & 6 to 13 & 13 & 19 \\
2667 (5 th BPF) & 1.5 to 12 & 12 & 20 \\
3200 (6 th BPF) & 3 to 7 & 7 & 19 \\
3733 (7 th BPF) & 0.2 to 4 & 4 & 19 \\
4267 (8 th BPF) & 2 to 7 & 7 & 21 \\ \hline \end{tabular}
\end{table} 
Table 2: Tonal BPF dB loss recorded by Aft microphones 19 to 23 [29]. Figure 19: Tonal blade passing frequency dB loss of 19 to 23 Aft array microphones.
## 2 Materials and Methods  
Most existing landslide datasets are taken from a high altitude, and the edges of the landslide area in the image are blurred and disturbed by noise. After the network extracts features at multiple levels, it is easy to lose the details of a landslide's features. To address this problem, this paper uses U-Net++ as the baseline network and makes improvements on and optimizations to it. Some of the sampling methods in the network are redesigned, and the feature extraction method is enhanced to combine multi-scale feature prediction, further enriching the feature expression of the landslide area.  
Figure 1 shows the overall architecture design of the proposed LKN-UKAN. The first three layers (\(L1\), \(L2\), and \(L3\)) of the network use standard Conv2d for feature extraction, while the fourth and fifth layers (\(L4\) and \(L5\), respectively) introduce Tok-KAN convolutions to enhance the modeling of nonlinear features. In the backbone network, each convolutional block \(\chi^{i,0}\) is processed through the DLFFSKA module prior to its skip connection to subsequent layers, strengthening the network's ability to perceive and represent landslide-specific features. In addition, max pooling is employed for pooling operations, whereas bilinear ipsampling is used to ensure smooth and effective resolution recovery during upsampling.  
The architecture follows a progressive relationship between resolution and depth; for every pooling operation, the feature resolution is halved, and for every upsampling operation, the resolution is doubled. The network depth increases from \(L1\) to \(L5\), with the \(L1\), \(L2\), and \(L3\) layers utilizing standard Conv2d blocks to extract basic features and \(L4\) and \(L5\) layers introducing Tok-KAN convolutions to better capture complex features. At the same time, in order to further enhance the network's ability to perceive the spatiotemporal information of landslide images, a learnable time embedding is added between the convolutional blocks in the \(L4\) and \(L5\) layers. This design can capture the dynamic characteristics of landslide scenes, thereby significantly enhancing the expressiveness and robustness of the network.
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 4 Polymer-Inorganic Hybrids as Linkers
### Coordination Polymers (CPs) and Metal-Organic Frameworks (MOFs)
The field of SCC-based metallosupramolecular polymers has expanded rapidly in recent years, yielding exciting results and significant progress. SCCs, including two-dimensional (2D) metallaccles and three-dimensional (3D) metallacages, have demonstrated wide-ranging applications in host-guest chemistry, sensing, catalysis, and drug delivery. These SCCs are characterized by their ability to form complex structures through self-assembly, which, when combined with polymers, has led to the development of two novel types of metallosupramolecular polymers [46]: metallacycle/metallacage-cored star polymers (MSPs) and metallacycle/metallacage-crosslinked polymer networks (MPNs) [47; 48; 49; 50]. In these hybrid materials, SCCs function as either core structures or crosslinking units, enhancing both mechanical properties and responsiveness to external stimuli. The highly connected SCC cores or crosslinks impart these materials with unique mechanical properties and stimuli responsiveness, making them ideal candidates for smart materials that can adapt to changing environmental conditions. Moreover, the well-defined nanopores or nanocavities of SCCs, combined with the processability of polymers, make these hybrid materials promising for practical applications as porous, soft materials, particularly in molecular separations. As shown in Figure 10b, SCCs can be classified into two main structural categories: 2D metallacycles and 3D helicates. These structures have a wide range of therapeutic applications. For example, metallacages (which include capsules and prisms) are used in cancer therapy, drug delivery, and imaging theranostics. Additionally, SCCs play significant roles in DNA/RNA recognition, biosensors, protein recognition, gene regulation, and ion sensing. The unique properties of SCCs, such as their ability to form precise molecular architectures, make them ideal for targeted applications in biological systems [46].
## 3 Results
### Evaluation of Microbial Growth in Atlantic Mackerel Muscle under Different Packaging Systems
However, at the most advanced storage time, all three batches presented microbial numbers above seven log units, indicating that the protection exerted by the bioactive compounds present in the alga extracts concerning the development of psychotrophic bacteria was not as relevant on day 9. The comparative evolution of total aerobes in all three batches was also evaluated in this work, and the obtained results are shown in Figure 1. Similar to the effects observed for other microbial groups, the incorporation of algal extracts into the gelatin films was associated with slower microbial growth than the CTR batch. This effect was observed at early (day 2) and advanced (day 9) storage times, and the greatest differences between the alga-treated and CTR batches were close to 1 log unit. Remarkably, these differences were statistically significant (\(p<0.05\)). The results obtained in this study concerning the ability of lipolytic bacteria to produce extracellular lipases with activity against triacylglycerlecrols (TAGs) and phospholipids (PLs) in fish muscle are also displayed in Table 1. Thus, the incorporation of algal extracts into the gelatin packaging film led to restrictions in the growth of lipolytic bacteria during storage compared to the CTR batch. However, although the greatest difference among batches was 1.30 log units on day 9 (AF-1 batch with respect to the CTR batch), such differences were not statistically significant (\(p>0.05\)). Similar results were observed at intermediate storage times (day 6). These results indicate that the incorporation of algal extracts into the gelatin films exerted slight microbial control over lipolytic bacteria, but the effect was not very intense. Figure 1: Determination of aerobic counts (log CFU-g\({}^{-1}\) muscle) in refrigerated mackerel subjected to different packaging conditions. Average values \(\pm\) standard deviations (\(n=3\)). The packaging conditions are expressed in Table 1. Different lowercase letters denote significant differences (\(p<0.05\)) with chilling time; different capital letters denote significant differences (\(p<0.05\)) as a result of the packaging condition. Time 0 corresponds to initial fish. Figure 2 shows the results of the comparative evolution of proteolytic bacteria in Atlantic mackerel muscle in all three batches. As with other microbial groups, the incorporation of the bioactive alga extracts into the gelatin films was associated with better control of proteolytic bacteria, which was especially relevant in the AF-1 batch compared with the CTR batch. Accordingly, the results indicated statistically significant (\(p<0.05\)) differences derived from the more limited growth of proteolytic bacteria, especially in the AF-1 batch. These differences were especially relevant at intermediate (day 6) and advanced (day 9) storage times, reaching a maximum of 0.92 log units on day 6.
# Multi-Attribute Collaborative Optimization for Multimodal Transportation Based on User Preferences
## 3 Problem Description and Model Construction
### Parameter Description  
The set of nodes (\(V\)): \(\{o,\cdots,i,j,\cdots d\}\in V\), where \(o\) and \(d\) represent the origin and destination of the transportation, respectively.  
The set of arcs (\(E\)): \((i,j)\in E\), where \((i,j)\) represents an arc in the intermodal transportation network.  
The set of transportation modes (\(D\)): \((m,n)\in D\), where \(m\), \(n\) represent different transportation modes.  
\(U_{i}\): The set of backward neighboring nodes of node \(i\).  
\(q\): Cargo volume.  
\((i,j)\): Directed arc from node \(i\) to node \(j\) in the intermodal transportation network.  
\(t_{ij}^{m}\): Transportation time of mode \(m\) on the directed arc \((i,j)\).  
\(Q_{ij}^{m}\): Maximum transportation capacity of mode \(m\) on the directed arc \((i,j)\).  
\(d_{ij}^{m}\): Transportation distance of mode \(m\) on the directed arc \((i,j)\).  
\(u_{ij}^{m}\): Transportation speed of mode \(m\) on the directed arc \((i,j)\).  
\(c_{ij}^{m}\): Unit transportation cost of mode \(m\) on the directed arc \((i,j)\).  
\(c_{i}\): Waiting cost of cargo at node \(i\).  
\(e_{ij}^{m}\): Unit distance carbon emission of mode \(m\) on the directed arc \((i,j)\).  
\(r_{ij}^{m}\): Transportation risk of mode \(m\) on the directed arc \((i,j)\).  
\(t_{i}^{m}\): Transfer time at node \(i\) from mode \(m\) to mode \(n\).  
\(c_{i}^{m}\): Transfer cost at node \(i\) from mode \(m\) to mode \(n\).  
\(Q_{i}^{mn}\): Transfer capacity at node \(i\) from mode \(m\) to mode \(n\).  
\(e_{i}^{mn}\): Unit carbon emission coefficient for trans-shipment at node \(i\) from mode \(m\) to mode \(n\).  
\(r_{i}^{mn}\): Transfer risk at node \(i\) for trans-shipment from mode \(m\) to mode \(n\).  
\(T_{i}^{A}\): Departure time from node \(i\) using mode \(m\).  
\(\delta_{j}\): Waiting time at node \(j\) before departure.  
\(T_{j}^{L}\): Departure time from node.  
\(T_{j}^{A}\): Arrival time at node \(j\) from node \(i\) using mode \(m\).  
\(T_{end}\): Arrival time at the destination.  
\(\omega\): The set of weights for various indicators.  
Soft time window (\([T^{e},T^{l}]\)): the customer's desired earliest and latest arrival times for the cargo.  
Hard time window (\([ET,LT]\)): the customer's tolerable earliest and latest arrival times for the cargo.  
The following are the variables in the model, while the parameters in the model are as previously described:  
\(x_{ij}^{m}\): If mode \(m\) is selected to transport cargo on the directed arc(\(i\), \(j\)), then \(x_{ij}^{m}=1\) ; otherwise, \(x_{ij}^{m}=0\).  
\(y_{i}^{mn}\): If the transportation mode is transferred from \(m\) to \(n\) at node \(i\), then \(y_{i}^{mn}=1\) ; otherwise, \(y_{i}^{mn}=0\).
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Based on Machine Learning  
Techniques related to machine learning are developed to identify objects, areas, events, etc. Various machine learning techniques apply algorithms to generate personalized video summaries. Based on the algorithm applied, the techniques are divided into supervised, weakly supervised, and unsupervised categories. The methods in the above categories use convolutional neural networks (CNNs).  
In large-scale video and image recognition, CNNs have been very successful. The learning of high-level features in a progressive manner and the obtaining of the original image with the best representation are supported by CNNs [36]. Features for the recognition of holistic action that are more generalized and reliable than hand-made characteristics can be extracted from CNN. Therefore, CNN has overcome traditional methods [45]. The FasterRCNN model was proposed by [PERSON] et al. [25] for face detection. [PERSON] et al. [62] proposed a 3D CNN with 10 layers. The layers are trained in frame features and visual motion evaluation. The way for unsupervised and supervised summarization techniques has been paved by the success of deep neural networks (DNNs) in learning video representations and complex frames [25]. In neural networks, memory networks are used to flexibly model the attentional scheme. In addition, to deal with visual answers to questions and answers to questions, memory networks are used [5].  
Through deep learning based on artificial neural networks, computers learn to process data as a human would. By using many data and through training models, they learn the  
Figure 8: Distribution of the papers reported in Table 1 between the time of the personalized summary.  
characteristics that are their own, with room for optimization. On the basis of the algorithm applied, the techniques are divided into the following categories.
# Big Data Perspective and Challenges in Next Generation Networks
## 2 Major Milestones of Next Generation Networks
### Ultra Dense Networks  
The concept of cell shrinking is the most effective way for improving the network performance and, thus, it has gained a huge attention within the research community [14; 15]. In the early 1980s,  
Figure 3: Hand-off in next-generation networks (inspired from [8]).  
Figure 2: Distinguishing features of 5G.  
the cell size was about hundreds of kilometers. With the growth of new technologies, the cell size has shrunk smaller and smaller, e.g., micro-cells, pico-cells, etc. (In Japan, the spacing between BSs is two hundred meters with coverage area in the range of one-tenth of square kilometers [2]). Cell shrinking has numerous benefits. One of the significant benefits of cell shrinking is the reuse of frequency spectrum. The small cells are away from each other and can use same frequency spectrum without interference. Another benefit of cell shrinking is reduction in the traffic towards base station [16]. Because of network densification, some challenges may arise too, such as _Optimal User Association_, _Mobility Support_ and _Cost_.
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 5 Classifiers
### Classifier Selection
#### 5.1.4 Lstm  
Compared to CNNs, which can work on any grid-structured data, recurrent neural networks (RNNs) specialise in processing sequential data. This specialisation is possible due to their specialised structure. While classical feed-forward networks only feed information in one direction (forward), the RNNs can also pass the information back. As such, they have found their applications [45] in different areas with inherently sequential data, such as natural language processing, sentiment analysis, machine translation, or time series analysis. As such, RNNs are a natural fit for our use case.  
There are different architectures of RNNs, such as Long short-term memory (LSTM), Gated recurrent unit (GRU), or Bidirectional RNN. For a more detailed breakdown of RNN architectures, please see [46].  
For our work, we selected an LSTM. Proposed first time by [PERSON] in 1991 [47] is one of the most popular RNN architectures. It utilises the gating mechanism with three different types of gates:  
* Input gate
* Forget gate
* Output gate  
that are responsible for managing and optimising the cell state/memory \(c_{t}\) and hidden state \(h_{t}\). These states are computed from the current input \(x_{t}\), last cell state \(x_{t-1}\), and last cell state \(c_{t-1}\), passing through the mentioned gates. Utilising these gates, when appropriately trained, an LSTM can remember relevant (and forget irrelevant) information.  
The number of cells of our LSTM is being treated as a hyperparameter. To allow for a classification of the sequence, the LSTM is connected to a layer of 32 neurons (with \"relu\" activation), which is consequently connected to an output neuron with sigmoid activation. In between both these connections, there are also dropout layers, with 20% of inputs being set to zero. The inclusion of dropout layers is performed to improve the network's generalisation. The diagram of this LSTM is visible in Figure 6.  
Figure 5: Schema of a CNN with 32 and 64 filters.