esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model  
### Network Model  
The method proposed in this paper is specifically designed for the online monitoring of railway tunnels, and the network characteristics are as follows. The monitoring area is a \(W\times H\) rectangular area (\(W>>H\)), with all sensor nodes used for data collection randomly deployed within this area. The total number of nodes is \(n\), each with a unique identifier \(N_{i}\), \(0\leq i\leq(n-1)\). The sink is located at the center of the edge of the area and has sufficient energy. The monitoring area is illustrated in Figure 1.  
We make the following assumptions regarding the sensor nodes:  
1. All nodes remain stationary throughout the monitoring process.
2. The identifiers of the nodes are unique.
3. Nodes have the same initial energy and maximum communication radius.  
Figure 1: Long rectangular monitoring area.  
The communication radius of the nodes can be adjusted based on the transmission power.  
In monitoring, the first step is determining the number of anchor nodes and their deployment spacing. Then, deploy anchor nodes and sensor nodes within the tunnel area. Following this, the self-organizing network process begins, including topology discovery and network clustering. In the routing phase, nodes collect data and send it to their cluster heads. The cluster heads will fuse the data and forward it to the sink through the routing protocol. Topology discovery is performed only once throughout this process, while clustering and routing phases occur periodically, with all active nodes collecting data in each round. Figure 2 illustrates the complete monitoring process.
## Material and Methods  
### Experimental Model  
[PERSON] et al. (2015) conducted their experiments in a horizontal channel with a 90-degree diversion, which directs the flow in the main and the branch channel illustrated in Figure 1 adapted from ([PERSON] et al., 2015). The length of the main channel is equal to 6.198 m and its branch is 2.794 m long. The widths of the channels are equal to 0.61 m and their heights are equal to 0.305 m. The branch channel is located 2.794 m away from the beginning of the main channel.  
Taking into account that W*m is the width of the main channel and W*b is the width of the branch channel proposed by [PERSON] et al. (2015). It has a width ratio of 1 (W*b/W*m = 1). The discharge entering the main channel entrance is equal to Qu = 0.046 m3/s. The discharge in the branch channel is equal to Qb = 0.038 m3/s. The branch channel discharge ratio to the main channel discharge (Qr = Qb/Qu) is equal to 0.838. All the parameters have been made dimensionless by the channel width (B = 0.61 m) and the main channel upstream critical velocity (Vc) in the experiments conducted by [PERSON] et al. (2015) and so the coordinate axes are defined in a dimensionless manner (x* = x/b'.y* = y/b, and z* = z/b). The dimensionless velocities in x, y, and z coordinates are marked as u*, v*, and w* respectively. The measurement locations in the channels according to an adaptation from [PERSON] et al. (2015) can be seen in Figure 2.  
Figure 1: The plan of the experimental channel used in current research.
## 4 Experiments
### Evaluation Metrics  
We utilize well established evaluation metrics [6; 7; 43], where \(d_{i}=D(x_{i})\) implies the actual depth value at a specific pixel position, \(x_{i}\), and \(\hat{d}_{i}=\hat{D}(x_{i})\) signifies the predicted depth. The metrics are defined as follows:  
Root mean square error (RMSE): \(\sqrt{\frac{1}{N}\sum_{i}\left(\hat{d}_{i}-d_{i}\right)^{2}}\)  
Mean absolute relative error (REL): \(\frac{1}{N}\sum_{i}\frac{\left|\hat{d}_{i}-d_{i}\right|}{d_{i}}\)  
Mean absolute error (MAE): \(\frac{1}{N}\sum_{i}\left|\hat{d}_{i}-d_{i}\right|\)  
Improved root mean square error (iRMSE): \(\sqrt{\frac{1}{N}\sum_{i}\left|\frac{1}{d_{i}}-\frac{1}{d_{i}}\right|^{2}}\)  
Improved mean absolute error (iMAE): \(\frac{1}{N}\sum_{i}\left|\frac{1}{d_{i}}-\frac{1}{d_{i}}\right|\)  
Threshold \(\delta\): This metric determines the percentage of \(\hat{d}_{i}\) that satisfies the condition \(\max(\frac{\hat{d}_{i}}{d_{i}},\frac{d_{i}}{d_{i}})<\delta\), where \(\delta=1.25,1.25^{2}\), and \(1.25^{3}\).  
\(N\) refers to the total number of pixels.
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
## 2 Sewage Treatment
### With Nanotechnologies
#### 2.2.6 Sepiolite  
Sepiolite is a natural 2:1-type fibrous nanolay mineral [225] whose molecular formula is Si\({}_{12}\)O\({}_{30}\)Mg\({}_{8}\)(OH)\({}_{4}\)(H\({}_{2}\)O)\({}_{4}\)\(\cdot\)SH\({}_{2}\)O. Type 2:1 is defined by two tetrahydric silica layers, which are interspersed with another octahedric magnesium oxide layer. Its shape is similar to that of zeolites because it is formed by ribbon-like composites that, in turn, form an open channel. Thanks to such a structure (approx. 0.4 nm \(\times\) 1 nm), it can be generated by the penetration of organic and inorganic cations. The sepiolite surface has many silanol (Si-OH) groups as a result of a discontinued silica sheet on the outer 2:1 layers [226; 227; 228; 229] (Figures 6 and 7).  
On the sepiolite structure, three modification/adsortion zones are distinguished: Si-OH groups along the component fiber axis; oxygen ions on the tetrahedric silica layers; and a few cationic exchange gaps. Other parameters that affect sepiolite's adsorption capacity are the load of the substance to be adsorbed and its shape and size. By bearing these parameters in mind, some researchers have found that substances presenting low-polarity and large molecular-sized molecules cannot enter sepiolite channels and, despite being adsorbed on the tetrahedric silica layer, they only represent about 40% total SSA [230].  
Figure 6: Sepiolite Type 2:1 two tetrahydric silica layers, and another octahedric.  
Unlike MMT, it is possible to introduce an organic modifier into the sepiolite surface (between 400 m\({}^{2}\cdot\)g\({}^{-1}\) and 500 m\({}^{2}\cdot\)g\({}^{-1}\)) [231].  
Sepiolite is frequently industrially and scientifically commercialized as an excellent absorbent in pet latrines, and its efficiency as an adsorbent for textile industry dyes has also been demonstrated. Some studies have experimentally shown sepiolite's capacity for adsorbing different cationic dyes [232; 233; 234; 235]. It adsorbs cationic substances thanks to this clay's CEC (31.4 cmol\(\cdot\)kg\({}^{-1}\)) [236], which also occurs through neutral gaps that favor the adsorption of cationic dyes [233]. Saturation with Na or Ca increases sepiolite's adsorption capacity for methylene blue dye [237]. However, it does not offer good adsorption results with azo acid dyes [238]. One study employed SiO\({}_{2}\cdot\)Mg(OH)\({}_{2}\) derived from sepiolite to remove the Cd(II) present in dye with up to 95% adsorption [239]. It worked considerably better at a neutral pH than at a basic pH, and at constant temperature (T = 25 \({}^{\circ}\)C) [240].  
The pH parameter is important because it affects sepiolite's adsorption capacity. Due to isomorphous nanoclay substitutions, their structural charge can be changed by pH, which leads to hydroxyl dissociation [240].
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
## 3 Power Smoothing Control Strategy
### Control Strategy for the BESS  
The BESS adopted in this paper was the backup support of CWTs to satisfy the high power requirements of the grid. As analyzed in Section 3.1, the charge and discharge times of the BESS were less than those of the BESS used in the traditional BESS based power smoothing control strategy.  
Figure 5: Rotor inertial power smoothing control by adopting the low-pass filter.  
Figure 6: Control diagram of the power wind turbines.  
Figure 7: Control diagram of the control wind turbines.  
The BESS was connected to the HVDC DC link with a two-quadrant DC/DC converter, as shown in Figure 8. The DC/DC converter can work in both buck and boost modes according to the switch conditions of the two Insulated Gate Bipolar Transistors (IGBTs) \(S_{1}\) and \(S_{2}\). The duty ratios of switches \(S_{1}\) and \(S_{2}\) were controlled to regulate the power flow between output power of the nine WTs and grid demand power. The reference power of the BESS was obtained according to Figure 4.  
When output power of the nine WTs was higher than the grid demand power, the redundant power will be absorbed in the BESS. Then \(S_{1}\) was controlled to be activated and the BESS was charged, while the DC/DC converter operated in the buck mode. On the contrary, when the output power of the nine WTs was lower than the grid demand power, \(S_{2}\) will be in the turn-on operation and the BESS was discharged while the DC/DC converter operated in the boost mode.
# Dynamic Tuning and Multi-Task Learning-Based Model for Multimodal Sentiment Analysis
## 2 Related Works  
### Multimodal Sentiment Analysis  
As a core topic in affective computing research, past scholars have primarily focused MSA research on representation learning and multimodal fusion strategies. In representation learning, [PERSON] et al. [25] introduced the Recurrent Attended Variation Embedding Network (RAVEN), tailored for fine-grained structural modeling of non-verbal subword sequences, dynamically adjusting word-level representations in response to non-verbal  
Figure 2: An example from the CH-SIMS dataset.  
cues. Regarding multimodal fusion techniques, [PERSON] et al. [26] designed the Tensor Fusion Network to deeply model intra-modal and inter-modal relationships in online video analysis, addressing the transient variability of spoken languages, sign language, and audio signals. Subsequently, they proposed the Memory Fusion Network [23], employing attention mechanisms for interactive information integration across different views. [PERSON] et al. [4], attentive to heterogeneity issues, proposed an attention-based cross-modal fusion scheme that facilitates modal interactions through attention mechanisms, promoting the effective alignment of distinct modal features. However, these efforts overlooked the potential impact of the settings of optimization objectives on model performance.  
[PERSON] et al. [18] recognized the significant influence of unimodal sentiment expressions on overall affective states, leading to the construction of the CH-SIMS dataset, which encompasses both uni- and multimodal sentiment intensity measures. Their study employed the L1 loss function for multimodal and unimodal sentiment analysis as a joint optimization objective. Experimental results revealed that incorporating unimodal sentiment analysis tasks enhanced the model's accuracy in predicting holistic emotional dispositions. [PERSON] et al. [5] decomposed unimodal representations into similarity and dissimilarity components, utilizing a text-centric contrastive learning approach. However, when implementing multi-task learning, they failed to adequately account for the potential negative transfer effects resulting from pronounced disparities in task convergence rates and loss scales.  
In contrast, the DMMSA model incorporates a GDWG mechanism, enabling the model to adaptively adjust task weights based on the relative rates of loss decrease during training, effectively mitigating the detrimental impact of negative transfer on model performance. Moreover, DMMSA incorporates coarse-grained sentiment analysis tasks to constrain the prediction scope.
## Data and Methodology  
### SAR Data  
To determine the surface displacements in the research area, SAR images acquired by the ERS-2, Envisat and Sentinel-1A satellites were used (Table 1).  
Figure 5: Relative vertical displacements in the 1930–1998 period [42].  
InSAR stacks from the longest observation period of nine years was selected from ERS-2 data. Observation period for the Envisat data set is seven years, while, for the Sentinel-1A satellite, it is 5 years. Over the past 24 years (1995-2019), SAR sensors (ERS, Envisat, Sentinel-1A) and the method of data acquisition were changed, which resulted in the increase of SAR images quality. For the ERS-2 and Envisat satellites, the spatial baseline is no larger than 800 m, while the time baseline is not greater than 365 days. The frequency of SAR images acquisition by Sentinel-1A is much higher and has a timespan of no longer than seven days. In the research, a time baseline of no more than 90 days was assumed and a spatial baseline no greater than 150 m, which was sufficient to achieve the objectives. The data were made available by European Space Agency.
## 2 Capacitive Deionization (CDI)
### Operational Mode  
There are two types of electric operational modes: Current control or voltage control (Table 2). For the charging step, the system could be operated by either the constant current (CC) or the constant voltage (CV). Note that the CV mode is widely used in CDI operation due to the practical advantages such as easy to execute and fast ion removal rate, whereas the CC mode results in the consistent quality of desalinated water using less energy [36]. During the discharging steps, there are three types of operational modes, such as reversed current, zero voltage (short-circuit), and reversed voltage modes. Zero voltage operation is the most commonly used operation for the regeneration of electrodes without consuming extra energy. For the complete regeneration of certain electrodes, reversed current or voltage modes could be used [15,17].  
Along with the electric operational modes, there are three conditions for flowing the feed solutions: Semi-batch, single-pass, and batch modes. The system with the semi-batch mode is fed the solution from the reservoir where the treated water is re-entered (Figure 2). It is often used to analyze the equilibrium state for different electrode materials or systems [2,34]. For more practical approaches, the single-pass mode is introduced, in which the feed solution continuously inflows so that the equilibrium concentration is reached at the end of the ad/desorption processes [1,34]. Batch mode refers to the system feeding a certain volume of the feed solution, and thus the concentration of feed solution keeps decreasing [17,42]. Between the three processes, batch mode is the simplest operation mode, while the single-pass mode is the most common for the practical studies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Unit** & **Definition** \\ \hline Salt adsorption capacity (SAC) & mg/\(\delta_{\text{electrode}}\) & Ion removal capacity \\ Salt removal rate (SAR) & mg/\(\delta_{\text{electrode}}\)/s & Rate capability \\ Charge efficiency & \% & Ratio of removed ions to the invested electric charge \\ Energy consumption & kT & Energy consumption per removed ion \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard analysis metrics in CDI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System Operation Mode** & **Current** & **Voltage** \\ \hline Charging & Constant current (CC) & Constant voltage (CV) \\ Discharging & Reversed current & Zero voltage \\  & & Reversed voltage \\ \hline \hline \end{tabular}
\end{table}
Table 2: Types of electric operational modes in capacitive deionization (CDI) for charging and discharging steps.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 4 Experiments and Discussion
### Ablation Experiments on Keypoint Numbers  
We also explored how the number of tracked keypoints affects the odometry performance of SCP-VIO. Table 9 summarizes the results for five different keypoint counts: 100, 200, 400, 600, and 800. We observe that the system remained stable across this range, with best performance typically achieved at 400-600 keypoints. For example, the minimum ATE of 0.838 m and RPEt of 1.820% occurred at 400 and 600 keypoints, respectively.  
However, increasing the number of keypoints beyond 600 did not lead to further accuracy gains and may even introduce slight fluctuations due to higher matching redundancy. More importantly, tracking a larger number of keypoints significantly increases computational overhead, especially in the optical flow and feature matching stages. Therefore, selecting an appropriate number of keypoints offers a practical tradeoff between accuracy and real-time performance.
###### Abstract  
Fault-tolerant control is becoming an interesting topic because of its reliability and safety. This paper reports an active fault-tolerant control method for a quadcopter unmanned aerial vehicle (UAV) to handle actuator faults, disturbances, and input constraints. A robust fault diagnosis based on the \(H_{\infty}\) scheme was designed to estimate the magnitude of a time-varying fault in the presence of disturbances with unknown upper bounds. Once the fault estimation was complete, a fault-tolerant control scheme was proposed for the attitude system, using adaptive sliding mode backstepping control to accommodate the actuator faults, despite actuator saturation limitation and disturbances. The Lyapunov theory was applied to prove the robustness and stability of the closed-loop system under faulty operation. Simulation results show the effectiveness of the fault diagnosis scheme and proposed controller for handling actuator faults.  
backstepping control; fault-tolerant control; fault diagnosis; quadcopter UAVs +
Footnote †: journal: Applied
## 2 Background
### iPad Pro LiDAR
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Distance to** & \multirow{2}{*}{**Sensor Angle**} & \multirow{2}{*}{**Scanning Time**} & **Number of** & \multirow{2}{*}{**Lighting**} & \multirow{2}{*}{**Resolution**} \\  & **Object** & & & & & \\ \hline
[28] & 5 m & Varied & Not specified & Not specified & Not specified & 1–2 cm \\ \hline
[10] & 5 m & Multiple & Not specified & Several & Standard conditions & 2 cm \\ \hline
[9] & 5 m & Multiple & Not specified & Multiple & Mixed lighting & 2–3 cm \\ \hline
[24] & \(\leq\)3 m & Varied & Not specified & Repeated & Not discussed & 1–3 cm \\ \hline
[25] & 300 mm–5 m & 65\({}^{\circ}\) & Not specified & Multiple & Good in low light & 2 cm \\ \hline
[26] & 5 m & Varied & Not specified & Multiple & Standard lighting & 0.05 mm to 0.1 mm \\ \hline
[29] & Not specified & Not specified & Not specified & Not mentioned & Not specified & Not specified \\ \hline
[11] & 1–4 m & \begin{tabular}{c} Perpendicular \\ (varied) \\ \end{tabular} & 4–20 min & Multiple & \
\begin{tabular}{c} Diffuse indoor and \\ natural outdoor \\ \end{tabular} &
\begin{tabular}{c} 1 cm (max detail high) \\ to 5.2 cm (low) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key parameters considered in the literature. the key scanning parameters affecting the iPad Pro LiDAR scanning process, a design for experiments methodology based on [PERSON]'s orthogonal arrays, as introduced in the following section, was utilized. It is important to highlight that the iPad Pro's \"LiDAR Scanner\" is often misunderstood because, while it shares a name with traditional LiDAR systems, its function and capabilities differ. Unlike full-scale LiDAR systems used in applications such as self-driving cars or large-scale surveying, the iPad Pro's LiDAR uses time-of-flight (ToF) technology, which works by measuring the time it takes for a light pulse to travel to an object and return. While this is a form of distance measurement that can be used in 3D scanning, it lacks the depth and range of professional LiDAR scanners, making it a less advanced and more limited implementation. In professional LiDAR systems, lasers are typically used to emit precise light pulses to generate highly detailed 3D models of large areas. These systems are much more powerful, accurate, and capable of scanning over longer distances, whereas the iPad's LiDAR is optimized for short-range applications, mainly used for enhancing augmented reality (AR) experiences, improving focus, and enabling precise measurements within a limited range. Therefore, while Apple's device is marketed as a \"LiDAR Scanner\", it technically does not match the traditional LiDAR technology in terms of its operational scope or technical capacity, and its usage is more akin to a depth-sensing camera.
## 6 Design of an Intelligent Middleware Platform
### SDN and Machine Learning-Based Multi-Network Management Layer
_Link quality estimation service:_ An accurate estimation of link quality is essential for reliable communication. A link quality estimation service is responsible for using an online machine learning algorithm, such as that presented in [58], to predict link quality on the basis of network-level parameters, such as throughput, packet loss rate, and traffic volume. _Link lifetime estimation service:_ Link lifetime is crucial for communication performance and energy efficiency. Link lifetime estimation service is responsible for estimating link lifetime on the basis of mobility history and network-level parameters, such as signal strength. The mobility history-based link lifetime estimation model proposed in [2] can be adopted. This model only considers the history of user's visited locations. To improve its performance, the model can be extended to consider the location and time that a node spends at the location. _Link energy estimation service:_ This service aims to use a new machine learning-based model similar to that proposed in [58] to estimate link energy consumption. Compared with existing models, the new model should consider mobility history, link quality, number of dropped and lost packets, signal strength, and quantity of data transmission. _Route selection service:_ A centralized SDN-based route selection service is responsible for the selection of single or multiple routes for data transmission on the basis of the application's QoS requirements. In the case of multiple routes, the service is also responsible for determining the amount of data that should be transmitted to each route. Reinforcement learning-based systems or services are used to address decision-making problems. The route selection service should adopt a reinforcement learning-based model, such as that presented in [60], to select routes. Route selection decisions should be based on the application's QoS requirements, link quality, link lifetime, and link energy consumption parameters.