esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nanotechnology and Nanomedicine
### Carbon Nano Tubes  
These nanosystems consist of graphene nanofoil and a honeycomb lattice of carbon with a diameter range between 0.5-3 nm and a length between 20-1000 nm. They are crystalline allotropes of carbon with hollow cylindrical nanostructures that have a remarkable strength, stiffness, excellent electrical properties (sound thermal conductivity, semi conductivity or insulation) and mechanical properties [8; 29]. These hollow core nanoparticles are generally nontoxic to humans and can sometimes be referred to as nano-capsules that are highly sensitive to electromagnetic and thermal radiation (heat and light) [43]. Their functionalization promotes easy penetration of the cell cytoplasm and nucleus, increased solubility as well as a good nanocar for peptide and gene delivery [29; 55]. They are characterized into single walled nanotube (single layer) or multi-walled nanotube (multiple layer). The structures consist of hexagonal networks of carbon atoms arranged in a fashion like a graphite sheet rolled at specific and discrete angles forming a cylindrical shape [40]. Carbon nanotubes are applicable in gene silencing, vaccine delivery, gene delivery, cell internalization, cell specificity, diagnostics, drug delivery, peptides and nucleic acids transportation [55; 68]. Unfortunately, the production of nanotubes is relatively expensive and so would be difficult to be implemented in modern day nanotechnology [69]. Further, they are extremely small which usually limits their use, in fact the mechanisms of how they work is yet to be understood by scientists [68; 70]. In addition,carbon nanotubes are documented to have associated toxic adverse effect in vitro to different types of cells such as; human embryonic kidney, rat brain neuronal cells and human keratinocytes cells [70]. Unmodified carbon nanotubes were documented to initiate the formation of lung granulomas in mice when administered intra-traceally [71].
## 5 Experimental Section
### Baseline Method  
A comparison of our proposed RSII with the following currently most competitive review-based recommendation methods was conducted.  
The DeepCoNN model extracts user preferences from the user review documents and project attributes from the project reviews, and then concatenates the user preferences and project attributes into prediction features using vector concatenation. Finally, the prediction features are fed into a factorization machine to obtain the final predicted rating. However, its performance depends on the target reviews.  
The D-Attn model is based on the DeepCoNN model and introduces two attention mechanisms: a local attention mechanism and a global attention mechanism.  
The NARRE model introduces an attention mechanism on the basis of the DeepCoNN to mitigate the impact of harmful comments.  
The TransNets model consists of a target network and a source network. The target network learns the representation of target comments as predictive features and guides the source network to learn the predictive feature representation, thereby, alleviating the problem of difficult access to target comments.  
The CARL model utilizes attention to extract features from comments, linearly combines them with features derived from the matrix factorization of the rating matrix, and finally predicts ratings.  
The MPCN model is a hierarchical neural network that first selects a few comments most relevant to the rating from all reviews, and then construct a user-item matrix to improve the recommendation performance.  
The MPCAR model is based on MPCN and introduces ID information to alleviate the conflicts between the selected comments and the ratings that are not related.  
In our model, we set the maximum length of each comment to 80 and the minimum length to 2. The learning rate was 0.01, the batch size was 64, the decay factor of the learning rate was 0.99, and the L2 regularization hyperparameter was 0.00001.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 5 Research Challenges of IRS-Assisted UAV Communications
### IRS Phase Shift  
The computation of optimal phase shift is essential for IRSs, which are either attached on a fixed wall or mounted on a moving UAV. Since the IRS does not have computing capability, a separate computing node is required for computing the phase shift. This implies that data transfer between the IRS and the computing node is necessary [67]. The channel between the IRS and the computing node will experience fading and delay, especially when the IRS is mounted on a UAV [104; 105]. Therefore, studies on countermeasures against the channel fading and delay are important to establish a stable link between the IRS and the computing node.
# Seismic Response and Damage Analysis of an Isolated Bridge Subjected to Near-Fault Ground Motion
###### Abstract  
In this study, the seismic response and damage behavior of an isolated bridge under near-fault ground motion are studied and compared with the non-isolated bridge. To consider the local damage of the bridge pier and its evolution process, the nonlinear behavior of the bridge pier in the analysis is simulated by the fiber beam-column element model combined with the damage constitutive model of steel and concrete materials. The mechanical behavior of the isolation bearing is simulated by the three-dimensional isolation bearing model that can consider the instability and failure of the bearing. It is found that the isolation bearings do not work, and even amplify the seismic response of the bridge (pier displacement and pier shear) under the near-fault ground motion. This amplification also leads to more severe damage to the piers of the isolated bridge. In the case of the analyzed concrete bridge type and near-fault earthquake data, it can be concluded that the used isolating lead rubber bearings should not be recommended. In general, it can be stated that the selection of the types of bridge bearings in near-fault areas should always be analyzed in detail by designers in order to avoid unintentional amplifying of the bridge's seismic response.  
Keywords:seismic response; near-field ground motion; damage analysis; isolated bridge +
Footnote †: journal: Applied Sciences
# Multi-Controller Deployment in SDN-Enabled 6G Space-Air-Ground Integrated Network
## 2 Problem Formulation and Solution
### Problem Formulation
#### 2.1.1 Problem Description  
In SDN-based satellite networks, due to the dynamic changes of network traffic and network topology and unreasonable deployment of SDN controllers seriously affecting the performance of SDN controllers, a load imbalance is caused in the controllers. The overload or idle state of the controller leads to a decrease in server resource utilization. Furthermore, the unreasonable network partition also leads to an increase in network delay and a decrease in network throughput. The satellite always moves along a particular orbit, and its topology changes periodically and highly dynamic during the system cycle. In the study of the optimal deployment of multi-controllers, a highly dynamic topology is planned as a system cycle by using the idea of time slice partition, and it is decomposed into several small time slices. It is assumed that the satellite network topology does not change in the time slice. With the change in time slice, the corresponding switch node migration strategy is made.  
In the research of SDN-based multi-controller deployment for satellite networks, the problem that should be solved is how to divide the network reasonably and to determine the relationship between the controller and switch node. That is, the multi-controller  
Figure 2: Hierarchical SDN-enabled 6G SAGIN architecture.  
deployment problem can be transformed into a task assignment problem by assigning a task to a target object in order to achieve the overall optimal goal.
## 2 Background
### Three-Dimensional NAND Manufacturing Process  
Figure 5 shows a detailed organization of a vertical layer in 3D NAND flash memory using a cross-sectional view (along the x-z plane) and a top-down view (of three cross sections along the x-y plane). The stacked cells are vertically connected through _cylindrical_ channel holes. The channel holes are formed at the early stage of 3D NAND flash manufacturing by an etching process [5]. Since the dimension of the flash cells is greatly dependent on the structural shape of the underlying channel hole, the etching process is regarded as one of the most critical steps for manufacturing 3D NAND flash memory.  
Ideally, we expect that each channel hole has the same geometrical structure regardless of its physical location to achieve uniform characteristics between flash cells. However, while the etching process proceeds from the topmost layer to the bottom substrate, it introduces structural variations to channel holes depending on their vertical locations. For example, as shown in Figure 5, the diameter of the channel holes varies significantly depending on the vertical height. These unexpected phenomena are caused by a high _aspect ratio_ (HAR) of the cylindrical channel hole. (The aspect ratio of a channel hole can  
Figure 4: Schematic diagram of the flash cell of 3D NAND flash memory.  
Figure 5: Schematic diagram of a 3D NAND flash cell and its operations.  
be defined as the ratio of width to height. In 64-layer 3D NAND flash memory, the aspect ratio of channel holes is expected to be more than 60:1.) During the etching process, the deeper the depth of the channel hole, the fewer etchant ions it reaches and the less chance of reaction for etching. Therefore, the channel hole diameter in the topmost layer is wider than that in the bottom layer (i.e., the flash cell in the topmost layer has a larger cell size than that in the bottom layer). Furthermore, the shape of channel holes in some layers is an ellipse or a rugged shape unlike the circle due to the variance of etchant fluid dynamics with a vertical position. These structural variations, in turn, cause significant differences in flash cells' characteristics.  
The complex dynamics of the etching process can lead to more severe problems in high-capacity 3D NAND flash memory. As the number of stacked layers increases, the non-ideal process effect gets higher, resulting in a variety of abnormalities in the channel hole profile. The abnormal profile of channel holes not only causes unexpected malfunctions of the NAND device but also becomes the root cause of the high failure rate in flash-based storage systems. Therefore, in order to successfully accomplish mass productions with a high yield and quality, it is crucial to develop test methodologies that effectively detect and screen out the channel hole defects based on the precise analysis of the channel hole abnormalities.
# Seasonal Shifts and Smart Stats: Improving Biodrying in Waste Management
###### Abstract  
The biodrying process is a well-established method in solid waste management for reducing the moisture content of municipal solid waste (MSW), facilitating its mechanical treatment, enhancing energy recovery efficiency, and simplifying disposal. However, challenges such as variability in drying efficiency, seasonal fluctuations, and operational inconsistencies limit its optimization and broader applicability. This study undertakes a detailed evaluation of biodrying operations using Statistical Process Control (SPC) techniques to improve process stability and identify key factors influencing efficiency. Data collected over a one-year period from a waste management facility employing Herhoff Rotteboxes(r) reveal an average drying efficiency of 28%, with notable seasonal trends showing reduced efficiency during summer and fall. A regression model analyzing waste load, operational parameters, and seasonal effects accounted for 25% of the variability in drying efficiency, suggesting additional factors like waste composition and microbial activity significantly impact the process. This study highlights the value of SPC tools in monitoring process stability and demonstrates how targeted optimization strategies--such as seasonal adjustments and refined loading practices--can enhance biodrying outcomes. By addressing gaps in current practices, these findings contribute to the advancement of waste management technologies and support the development of more efficient and sustainable systems for handling municipal solid waste.  
b  
Biologicality performance; process stability analysis; municipal solid waste treatment; Herhoff Rotteboxes(r) technology; seasonal effects on biodrying; waste moisture reduction
# New Vector-Space Embeddings for Recommender Systems
###### Abstract
The method we propose here is applicable in a direct way to both the \"recommendation\" problem (recommending items to the user based on his past purchases) and the \"prediction\" problem (predicting a rating for a novel item based on the users' previous patterns of ratings). Moreover, it is very versatile in the sense that it could take into account content-based features, demographic features and social network connections. All these features could be encoded in the vector representations, with CF being the dominant component. Many other conventional approaches, such as matrix factorization, do not directly model the combined effect of CF, content-based, demographic-based and social network features. In addition, our proposed model has several other beneficial features:  
* For our \"prediction\" problem we have a novel modeling framework. We train an embedding whereby each user maps to a vector and each item maps to a vector in the same space. The users' and items' vectors train side by side, and user-item, user-user and item-item similarities can be taken into account;
* The design is much easier than existing embedding methods. Rather than the existing cumbersome (deep) neural network based approaches that are trained using context data [15; 16], the proposed approach is a simple relaxation procedure that is computationally efficient;
* The proposed method possesses an explicit capability to model sentiment, such as likes/dislikes. This is because the vectors are placed in a spherical space. This allows it to directly tackle the polarity issue, that is, \"like\" (vectors will be near each other) and \"dislike\" (vectors are on opposite poles of the sphere). Sentiment feedback in the form of like/dislike is particularly prevalent in video channels and music subscription services;
* The algorithm has an incremental nature. This makes adding a user or an item require only a small computational load;
* Conventional methods, such as matrix factorization using SVD or otherwise, are computationally very expensive, especially since the number of users and the number of items can be several tens of thousands. Even though using gradient methods for cases of matrix sparsity can reduce the computational cost, this is still a challenge. At the very least, the storage of such a huge matrix is a problem;* Rather than dealing with huge user vectors, such as in the neighborhood-based techniques, our proposed embedding method considers (user or item) vectors of dimensions 10, 20 or 50. All information is therefore efficiently compressed;
* The developed embedding model produces a performance competitive with the state-of-the-art conventional recommender systems.
## 3 Results
### Steady Regime  
Apart from the limited range of \(\Gamma\) where the Hill regime can exist, after the Stokes region of the \(Re\)-\(\Gamma\) phase space we find a steady regime in which stationary separation bubbles appear downstream attached to the torus. It has been observed that for \(\Gamma=2\), already at \(Re=1\) a small recirculation is present while for \(\Gamma=0.1\) only above \(Re=100\) does a separation appear. In the first case, the flow is characterized by a single bubble that grows for increasing \(Re\), and it remains steady up to \(Re=75\) (Figure 9d). As \(\Gamma\) is decreased, two counter-rotating recirculations develop with the inner one (that closer to the axis of symmetry) smaller than the external one (Figure 9c). This difference decreases as \(\Gamma\) is diminished, and for \(\Gamma\to 0\) any difference between the bubbles should disappear since the two-dimensional cylinder case is recovered. Our simulations at \(\Gamma=0.1\) show that indeed the flow approaches the cylinder limit even if differences are still present. In particular, Figure 9a reports the length of the separation bubble (measured as the distance from the back of the torus where the streamwise velocity reverts its sign as in Figure 9b) as function of \(Re\) compared with the analogous quantity for the plane cylinder. It is observed that the results are very close even if the data for the torus are systematically below those for the cylinder. After having verified that this effect is not related  
Figure 8: **(a)** Stokes streamfunction \(\psi(R-a,L_{i})\) as function of \(Re\) for the case at \(\Gamma=1\); **(b)**\(\psi(R-a,L_{i})\) as function of \(\Gamma\) at \(Re=25\).  
Figure 7: Scatter plots of the azimuthal vorticity \(\omega_{\theta}\) versus the radial coordinate \(r\) in the region of the recirculation bubble: **(a)**\(\Gamma=1\), \(Re=10\); **(b)**\(\Gamma=1\), \(Re=50\).  
to insufficient numerical resolution or to the finiteness of the computational domain, we have found that this difference is caused by the finite aspect ratio \(\Gamma\) that produces a blockage of the inner flow. In other words, the flow going through the torus is accelerated more than the flow going over, thus pushing the separation towards the rear of the torus surface and consequently decreasing the length of the separation. This argument is in agreement with the recent paper by [14] that computed the flow around a circular cylinder over several domains, and they have observed that indeed the length of the steady separation bubble is reduced for increasing blockage of the flow. These results are qualitatively and quantitatively consistent with those of [7; 8; 9].
## 4 Experiments  
### Implementation Details  
#### 4.1.1 Training Dataset  
In order to make MKD extract robust keypoints from optical and SAR images, the GL3D dataset [42] was chosen for the training data, part of which is shown in Figure 3. GL3D is a large dataset for 3D reconstruction and image geometry learning and contains 378 different scenes. Each scene contains 50 to 1000 images with large geometric overlap and images with weak texture such as deserts and rivers. Meanwhile, the images in dataset are closely linked and cover a wide range of scenes with rich geometric background information. It can provide accurate geometric calculations to establish pixel-level correspondence between image pairs, such as camera pose parameters and geographic 3D information. Therefore, we used the GL3D dataset to train MKD.  
The training data for two-channel matching network was based on QXS-SAROPT dataset [43], which consists of 20,000 pairs of optical and SAR remote sensing images acquired by Google Earth satellite and the Gaofen-3 SAR satellite [44]. To enable the network to learn more detailed image patch information, we randomly selected and cropped this dataset to obtain 80,000 pairs of optical and SAR image patches of size 32 \(\times\) 32 as the training data. Some of them are shown in Figure 4.
## 3 Performance Verification and Results
### Results  
#### 3.2.1 Experiment One  
We tested the usability of the system using five subjects. Figure 7 shows the angle input commands (\(\theta_{est}\)) and the corresponding player position along the \(x\)-axis. The distance, in this case, is the displacement of the paddle from the center position. The approximate angle of the ML model has been factored by five for visibility. We considered uniform game difficulty settings and compared the percentages of objects dropped and intercepted.  
Figure 5: Validation results of the estimated head (neck) rotation using machine learning and equation models: (**a**) raw EMG for left and right neck turns; and (**b**) the corresponding estimation of neck rotation using equation and ML.  
All the subjects reviewed demonstrated accurate game control with little or no training time. Figure 7(a) shows the scores for the subjects. Users controlled the player with average scores of 75.52% and 79.73% for the equation and the ML model, respectively. This confirmed the ease of use as no subject was previously exposed to the game.  
Figure 7(b) shows the average miss rate of the two models. As expected, the equation model had a higher number of missed objects compared to the ML model. Previous studies show the equation model to have higher speeds but lower precision. This is because the non-stationarity of \(\theta_{est}\) makes fine-tuned moves difficult to achieve. In other words, the ML model was more stable than the equation model. Based on these results, we opted to focus on the ML model in a 2D environment as described below.  
Figure 6: Bit EMG for standard input and double-click event showing sequence of trigger \(T1\), \(T2\), and \(T3\): (**a**) raw and processed bite EMG; and (**b**) double-click event.  
Figure 7: Input angle and corresponding paddle position for equation and ML model.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 6 The Potential Applications of Voxel-Based Navigation
### Voxel-Based Modeling for Studying Human-Environment Interactions  
With the continuous advancement of 3D data-acquisition and model-construction technologies [114; 115; 116], 3D city models have been increasingly applied in various fields, such as urban design, spatial planning, facility management, and emergency response [22]. In pedestrian navigation research, the influence of urban environmental features on route choice has become an important focus. Factors such as street width, green coverage, building height distribution, and the proportion of open views can significantly affect pedestrian route preferences. In recent years, there have been numerous explorations into the application of 3D city models in urban studies. For instance, [117] utilized 3D models to extract environmental indicators, such as line-of-sight distance and sky view factor, integrating them into a walkability assessment model. Similarly, ref. [118] developed a spatial index based on 3D building models to measure compactness. These studies highlight environmental features' critical role in shaping pedestrian navigation behaviors. As noted by ref. [119], 3D city models provide an integrated and comprehensive platform for analyzing the impact of natural and built environments on human activities. By discretizing and quantifying urban environmental attributes, voxel-based models offer robust technical support for analyzing how environmental factors influence pedestrians in choosing safer, faster, or more comfortable routes. However, research on developing 3D voxel-based urban models that support human-environment interaction analysis still needs to be completed, indicating a need for further exploration and refinement in this domain.