esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Flow Rate Estimation by Decentralization of Kalman Filters
### The Design of Flow Rate Estimation
\right],\] (26) \[\eta(x) = W_{L}. \tag{27}\]  
The input vector \(u\) consists of the angular velocity \(\hat{\omega}\) on the \(\Theta\)-axis and the acceleration \(\hat{a}_{z}\) on the \(z\)-axis estimated by the SSKFs. Then, the Jacobian matrices used for updating the Kalman gain in the EKF can be given as:  
\[\frac{\partial f(x)}{\partial x} = A_{f}=\left[ \begin{array}{ccc}\alpha_{11}&0&0\\ \alpha_{21}&\alpha_{22}&0\\ 0&\alpha_{32}&\alpha_{33}\end{array} \right], \tag{28}\] \[\frac{\partial\eta(x)}{\partial x} = C_{f}=\left[ \begin{array}{ccc}0&0&1\end{array} \right], \tag{29}\]  
where  
\[\alpha_{11}=1-\frac{T_{s}}{A(\theta)}\left(\frac{\partial q(h)}{\partial h}+ \frac{\partial A(\theta)}{\partial\theta}\hat{\omega}\right),\;\alpha_{21}=T_ {s}\rho\frac{\partial q(h)}{\partial h},\;\alpha_{22}=1,\;\alpha_{32}=\frac{T_ {s}}{T_{L}},\;\alpha_{33}=1-\frac{T_{s}}{T_{L}}.\]  
The derivative of the pouring flow rate \(q\) to the liquid height \(h\) can be derived from Equation (5) as follows:  
\[\frac{\partial q(h)}{\partial h} = cL_{f}(h)\sqrt{2 gh}. \tag{30}\]  
Based on the Jacobian matrices in Equations (28) and (29), it was confirmed that twice differentiability of the model parameters according to the ladle shape was not required. Therefore, this estimation method could be applied to a complicatedly shaped ladle. Following this, the pouring flow rate \(\hat{q}\) m\({}^{3}\)/s could be estimated by substituting the estimated liquid height on the pouring mouth as follows:  
\[\hat{q}(\hat{h}(t)) = c\int_{0}^{\hat{h}(t)}L_{f}(h_{a})\sqrt{2g\hat{h}_{b}}dh_{b}. \tag{31}\]  
In the EKF, the time-update equations can be represented as follows:  
* Predict \[\hat{x}_{n}^{-} = f(\hat{x}_{n-1}),\] \[P_{n}^{-} = A_{fn-1}P_{n}A_{fn-1}^{\intercal}+Q_{f},\]
* Update \[G_{n} = P_{n}^{-}C_{fn}^{\intercal}(C_{fn}P_{n}^{-}C_{fn}^{\intercal}+R _{f})^{-1},\] \[\hat{x}_{n} = \hat{x}_{n}^{-}+G_{n}\{y_{n}-\eta(\hat{x}_{n}^{-})\},\] \[P_{n} = (I-G_{n}C_{fn}^{\intercal})P_{n}^{-},\]  
where \(\hat{x}^{-}\) is a priori state estimate, \(\hat{x}\) is a posteriori state estimate, and \(Q_{f}\) and \(R_{f}\) represent covariance matrices of the system noise and the observation noise, respectively. Furthermore, \(G_{n}\) is the Kalman gain, and \(P^{-}\) and \(P\) are a priori error covariance and a posteriori error covariance, respectively.
## 6 Results
### Scenarios Without Load Balancing vs. Scenarios with Load Balancing
#### 6.1.2 Load Distribution Analysis  
The behavior of server loads over the 600 s simulation period is illustrated in Figures 4 and 5. The comparison between scenarios reveals the critical role of load balancing in achieving uniform distribution.  
**Without load balancing**: Server loads fluctuate significantly, with some servers near- ing full capacity (150 users), leading to congestion and latency spikes. Fog nodes also experience irregular activation, exacerbating localized congestion.  
\begin{table}
\begin{tabular}{l l} \hline
**Input Data** & **Quantity** \\ \hline Servers & 8 \\ \hline Capacity of each server & 150 \\ \hline Fog nodes & 4 \\ \hline Capacity of each fog node & 50 \\ \hline Location of players and servers & Random within range (\(-0.1\) to \(0.1\)) \\ \hline Number of clients & 1300 \\ \hline Simulation Time & 600 s \\ \hline Connection probability & 60\% \\ \hline Disconnection probability & 30\% \\ \hline Movement probability & 5\% \\ \hline No movement probability & 5\% \\ \hline \end{tabular}
\end{table}
Table 1: Input data for the simulation.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Metric** & **Without Load Balancing** & **With Load Balancing** & **Improvement** \\ \hline Average latency & 165.25 ms & 53.69 ms & 67.5\% \\ Highest latency & 485.76 ms & 192.69 ms & 60.3\% \\ Lowest latency & 3.57 ms & 1.09 ms & 69.5\% \\ Standard deviation & 82.93 ms & 28.38 ms & 65.8\% \\ \hline \end{tabular}
\end{table}
Table 2: Latency results for scenarios with and without load balancing.  
**With load balancing**: The proposed model ensures a more even load distribution across all servers. Occasional spikes are managed effectively by activating fog nodes, preventing overload, and maintaining system stability.  
**Quantitative Improvements:**  
* Load uniformity improves across servers, with variance in server load reduced by approximately 42%.
* Fog nodes are activated strategically, reducing server overload instances by 55% compared to the scenario without load balancing.
## 2 Materials and Methods
### QuECHERS Sample Preparation
#### 2.4.4 LC-MS/MS Analysis  
A reverse-phase chromatographic separation and determination were used to analyze 231 pesticides using a Nexera X2 UHPLC system coupled with a Shimadzu LCMS-8050 triple quadrupole mass spectrometer (Kyoto, Japan). The UJPLC system consisted of a solvent delivery module (LC-30 AD), a column oven (CTO-20A), an autosampler (SIL-30 AC), and a Kinetex C18 column (150 mm \(\times\) 2.1 mm (i.d.), 2.6 \(\upmu\)m) (Phenomenex, Torrance, CA). The mobile phase employed a time-programmed gradient system using solvents A and B. Solvent A consisted of 0.1% formic acid and 5 mM ammonium formate in water, whereas solvent B was 0.1% formic acid and 5 mM ammonium formate in methanol.  
Gradient elution was initiated with 95% A for 1.0 min. Solvent B was then increased to 15% after 1.5 min and 60% within 2.4 min. Solvent B was linearly increased to 90% within 10 min and maintained until 12 min without a change in solvent B. Afterwards, solvent B was increased to 98% within 12.1 min, then kept constant for 18 min. Finally, solvent B was decreased linearly to 5% over 6 min and equilibrated for 6 min. The total analytical time was 24 min, and the injection volume was 10 mL. The flow rate was 300 \(\upmu\)L/min.  
The ESI-MS/MS was conducted under dynamic multiple reaction monitoring. Lab-solutions software (version 5.72) (Kyoto, Japan) was used to control the UPLC-MS/MS system. Interface temperature, heat block temperature, nebulizing gas flow, heating gas flow, and drying gas flow were set at 150 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 220 \({}^{\circ}\)C, 3 L/min, 10 L/min, and 10 L/min, respectively. Fragmentation and collision energy levels were optimized for each precursor and product ions. For each pesticide, at least one precursor ion with one product ion for setting identification (qualifier) and quantification (quantifier) purposes was determined, with a selection of the most abundant product ion for quantification, and the second one for confirmation. The precursor ion and optimized MS/MS parameters (fragment and collision energy) for each pesticide, using LC-MS/MS, are summarized in Tables 2 and S2.
## 3 Samples and Experimental Methods
### Rare Earth Elements  
The ICP-MS was used in the experiment for the determination of rare earth elements in shale samples. During the experiment, the samples were decomposed using the sodium peroxide melting method. The measured elements were precipitated along with the matrix elements in an alkaline medium. A large amount of melting agent was separated off by filtration. Then, the precipitates were dissolved using acid and then directly determined using the ICP-MS external standard method. Based on repeated measurements of the same sample, the standard deviation of the experimental results is 1%, and the error is 1%.
## 3 RSMA-Assisted Integrated Sensing and Communication Systems
### RSMA-Assisted ISAC Architecture
Further, since RSMA can effectively manage mutual interference between communication and radar functions by dynamically allocating resources in the spatial and power domains, which outperforms ISAC systems with the orthogonal allocation of time or frequency resources [28]. In a nutshell, the performance gain of the RSMA-assisted ISAC systems mainly comes from the three crucial roles of the common stream: (1) intra-system interference management, (2) inter-system interference management, and (3) beam forming to the target.
# 6G Technology for Indoor Localization by Deep Learning with Attention Mechanism
## 3 CNN with Attention Mechanism
### Self-Attention Mechanism  
In image processing, the main idea of the self-attention (SA) module is to build an attention map containing the relationship between two points. The attention map is used to extract features between any two pixels in the image, such as the distance between any two points. With a more powerful ability to represent the detailed spatial structural relationship, it can better learn the high-quality channel responses, as the quality of channel responses from different antennas varies. In other words, we need a SA block to pay more attention to those channel responses. As illustrated in Figure 3, the SA block is composed of four convolutional layers with \(1\times 1\) kernels and two matrix multiplication operations. The input feature map has dimensions \(M\times N\times C\), where \(C\) denotes the number of channels.  
Figure 3: SA module with \(1\times 1\) convolutional layers for feature extraction and attention weight calculation.  
Initially, the feature map is processed by three separate convolutional layers to generate the key, query, and value maps, respectively. Each of these convolutional layers uses a \(1\times 1\) kernel and outputs a feature map with \(\frac{C}{4}\) channels. Next, the key and query maps are multiplied together, and the result is passed to a SoftMax function to generate the attention map. This attention map reflects the importance of different parts of the input feature map. It is multiplied by the value map to produce an intermediate tensor with dimensions \(\text{M}\times\text{N}\times\frac{C}{4}\) that is then processed through a final convolutional layer.
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 3 Mathematical Model
### Electric Vehicles
#### 3.2.2 Across-Time-and-Space Energy Transmission of EV  
In the same MG, the EV is used as an energy storage unit, and its charging/discharging power can be dispatched for the operation of the MG. When the EV is connected to the MG and the power is sufficient, MMGS controls the EV to charge during the low charging price or when the system has excess energy, and discharge during the peak discharging price or when the system is short of power. The EV is charged and discharged in the same MG to realize energy transfer over time, thereby reducing the cost of MMGS purchasing electricity directly from the distribution network. At the same time, it also allows the user of the EV to profit by selling part of the electricity, which enables both parties to obtain a certain amount of economic benefit.  
On the other hand, EVs not only have energy storage characteristics but also can move between different locations. In the case of differences in the electricity price of the distribution network within a region, benefits can be obtained through the cross-space transfer of energy. For example, the electricity prices of RMG and OBMG for electricity trading with the distribution network are quite different. Most of the time, the electricity price of OBMG purchasing electricity from the distribution network is higher than RMG. Therefore, the electric energy charged in the RMG at a low charging price is sold to the MMGS at a high discharging price in the OBMG, and the electric energy is transferred between different spaces and times through charging and discharging.  
EVs realize the across-time energy transmission in the same MG and realize the across-time-and-space energy transmission in different MGs, which can transfer the lower-priced electric energy in RMG to OBMG at a higher price. Under the right conditions, both MMGS and EV users can benefit. This characteristic of EVs for energy transfer between different times and different spaces is called the across-time-and-space energy transmission.
## 3 Results
### Geospatial Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Criteria**} & \multirow{2}{*}{**Sub-Criteria**} & \multicolumn{3}{c}{**FUZZY Weight**} & \multicolumn{3}{c}{**Crisp Weight**} & \multicolumn{3}{c}{**Normalized Weight**} \\ \cline{3-9}  & & & **Solar PV** & **Wind** & **Solar PV** & **Wind** & **Solar PV** & **Wind** \\ \hline \multirow{2}{*}{Technical} & Cls Grill & (0.201, 0.319, 0.490) & - & 0.337 & - & 0.316 & - \\  & C1w Wind speed & - & - & (0.192, 0.333, 0.540) & - & 0.355 & - & 0.327 \\  & C2 Slope & (0.135, 0.204, 0.309) & (0.154, 0.265, 0.453) & 0.216 & 0.291 & 0.203 & 0.267 \\  & C3 Aspect & (0.124, 0.190, 0.291) & - & 0.202 & - & 0.189 & - \\ \multirow{2}{*}{Technological} & C4 Electric network & (0.064, 0.097, 0.153) & (0.082, 0.122, 0.189) & 0.105 & 0.131 & 0.098 & 0.121 \\  & C5 Roads & (0.046, 0.071, 0.111) & (0.064, 0.104, 0.173) & 0.076 & 0.114 & 0.071 & 0.105 \\ \multirow{2}{*}{Environmental} & C6 Built up areas & (0.035, 0.055, 0.086) & (0.058, 0.092, 0.142) & 0.059 & 0.097 & 0.055 & 0.089 \\  & C7 Water bodies & (0.021, 0.033, 0.053) & (0.027, 0.042, 0.079) & 0.035 & 0.049 & 0.033 & 0.045 \\ \multirow{2}{*}{C8 Land use/land cover} & (0.020, 0.031, 0.058) & (0.025, 0.042, 0.082) & 0.036 & 0.050 & 0.034 & 0.046 \\  & & & & & & CR = 0.061 & CR = 0.054 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison matrix sum, final criteria weights, weighted sum value, and ratio. Figure 7: Final suitability map of solar PV: **(a)** Araceli; **(b)** Balabac; and **(c)** Cuyo. To produce 1 MWp, 1-2 hectares (10,000-20,000 m\({}^{2}\)) of land is needed for a solar farm, and 10 hectares (100,000 m\({}^{2}\)) of land is required to install a 1 MW wind farm [141; 142]. Areas below 5 hectares (50,000 m\({}^{2}\)) were excluded from the site selection for solar PV farms, and 10 hectares (100,000 m\({}^{2}\)) for wind farms, and those near agricultural farms. Additionally, since this study considered island communities, 500 m from the seashore was considered a buffer zone. Since Cuyo has an airport, a 5000 m buffer zone from the airport was also considered. Then, a visual inspection by georeferencing on Google Earth was performed to obtain the best results of potential sites. The application of the TOPSIS method ranked the locations based on the given criteria. The weighted normalized decision matrix was calculated using Equation (12) by multiplying the normalized decision matrix by the indicator's weight from the fuzzy AHP analysis. The Euclidean distance of each alternative from the positive and negative ideal solutions was obtained, and the performance score was calculated. Table 8 presents the summary of the TOPSIS method showing the number of possible locations and the total calculated area in square kilometers and the ranking of feasible selection sites. Figures 9 and 10 show where these possible locations are situated. The areas stated are for solar PV and wind farm suitability mapping. The ownership of the land and other governmental concerns were not considered.
# A Local Information Perception Enhancement-Based Method for Chinese NER
## 4 Experimental Results and Comparison
### Experimental Settings
#### 4.1.2 Baseline Methods  
Aiming to measure and analyze the proposed method, we compared its performance on various datasets with mainstream models of recent years.  
* Lattice-LSTM [7] is a modified LSTM structure that can take as model input the characters in a sentence along with the lattice of all its potential matching words.
* LR-CNN [8] is based on a CNN model combined with the rethinking mechanism and using attention mechanism to the integration of character-word feature information.
* PLTE [38] is an expansion of the Transformer model that batch parallelizes the processing of characters and their matching words.
* LGN [25] is using graph neural networks to handle named entity recognition as a graph node categorization operation.
* Multi-Graph [37] is a graph neural network-based approach to named entity recognition combined with a multigraph that can automatically learn the features of gazetteers.
* FLAT-BERT [9] is a model based on the lattice structure using Transformer's location encoding to capture lexical information.
* SoftLexicon-BERT [13] is a method of incorporating lexical information directly into character representation at the character representation layer.
* PGAT-BERT [27] is a polymorphic graph attention network that can capture fine-grained character and matching word relationships more efficiently and dynamically.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Type** & **Train** & **Dev** & **Test** \\ \hline E-commerce & Char & 119.1 K & 14.9 K & 14.7 K \\ Weibo & Char & 73.8 K & 14.5 K & 14.8 K \\ OntoNotes & Char & 491.9 K & 200.5 K & 208.1 K \\ Resume & Char & 124.1 K & 13.9 K & 15.1 K \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics on the benchmarking dataset for the experiments.
# An Overview of Machine Learning and 5G for People with Disabilities
## 4 Application Scenarios and Benefits of Machine Learning and 5G for People with Disabilities
### Shopping  
Fortunately, 5G offers greater network speeds and capacity to deal with the biggest challenge, being able to keep up with the real-time demands of the user [47]. Smart glasses (with a camera and a headset) and haptic gloves (with vibration motors and cameras, as well) analyze images to accurately locate and identify objects; they use computer vision technology to transfer real-time visual information into auditory information to assist the visually impaired do their daily activities such as shopping [47; 48], which increases their autonomy and self-confidence. This way, a pair of glasses helps the visually impaired to buy groceries in a market. It is possible to capture images from the products using a camera, classify them using convolutional neural networks, and return the prices.
# ReDSHIFT: A Global Approach to Space Debris Mitigation
## 4 Software  
One of the deliverables of the ReDSHIFT H2020 project will be a software tool available to the scientific community and the public via a web-based interface. The ReDSHIFT software is thought of as a tool for spacecraft operators, space agencies and research institutions to design a space debris compliant mission, e.g., by suggesting the disposal trajectories and the technologies needed to achieve them, the best shielding opportunities for a given spacecraft and the possibility to produce it with additive manufacturing, etc. It will output safe, scalable and cost-effective satellite and mission designs in response to operational constraints.  
In detail, there will be five interconnected modules:  
* a disposal module (based on the results described in Section 2)
* a flux and collision probability module
* an environment projection module
* a design and shielding model (based on the results described in Section 3)
* a design for demise module (based on the results of Section 3.2).  
All the modules are interfaced and linked, in a carefully triggered processing chain (Figure 14), through the openSF simulation framework properly configured and customised to adhere to the needs of the ReDSHIFT SW tool.  
The core of the software is represented by the modules allowing the computation and characterization of the disposal trajectory for a given spacecraft at the end-of-life. In the disposal module, given the initial orbit of the spacecraft and the spacecraft characteristics in terms of its cross area and mass, the optimal options for end-of-life disposal are given and compared. The end-of-life disposal can be attained either via one or a sequence of impulsive manoeuvres or through the use of a solar/drag sail or through a hybrid sail and manoeuvre approach. This module is based on a study of the natural orbit evolution in the low to medium and geostationary regions that was performed to identify long-term stable orbits or resonance conditions to be used as graveyard or re-entry trajectories. The optimal manoeuvre to reach such re-entry or graveyard conditions is calculated. If the re-entry can be enhanced through a sail, different strategies for sail attitude control can be selected. The optimal disposal by this module is passed to the space environment module so that the effect of this disposal on the space debris environment is calculated. This is done based on precomputed long-term simulations  
Figure 13: The reaction wheel undergoing D4D test in the DLR (German Aerospace Center) test chamber.  
of the whole space debris environment, under different scenarios, to be used for the computation of the collision risk for the spacecraft in the disposal phase. In the case that the disposal trajectory is a re-entry one, the conditions of the orbit at 120 km are used to verify the demisability of the spacecraft. This is done, by default, using some predetermined spacecraft configuration but the external user can also load a preferred configuration.
## 3 Random-Heading-Based Calibration of UAV Safety Intervals
### Design of Other Safety Interval Indicators  
UAV safety interval calibration affects the level of safe UAV operation and the operational efficiency of low-altitude airspace. In order to scientifically and reasonably carry out UAV safety interval calibration, this paper determined a safety interval airspace utilization rate, in addition to the UAV conflict probability, as a UAV safety interval calibration index, which was used to assess and calibrate the UAV safety interval.  
The safe interval airspace utilization rate refers to the ratio of the volume of the UAV's smallest outer sphere in the sphere enclosed by the UAV safe interval. From the perspective of low-altitude airspace resources, an excessively large safety interval can lead to a reduction in the number of UAVs that can operate per unit of airspace, which greatly reduces the airspace utilization rate.  
\[f_{\text{percentage}}=\omega\left(\frac{V_{\text{min}}}{V_{l}}\right)=\omega \left(\frac{\lambda_{\text{min}}}{d_{l}}\right)^{3} \tag{24}\]  
In Equation (24), \(d_{l}\) is the UAV safety interval, i.e., the radius of the UAV safety zone, \(\lambda_{\text{min}}\) is the minimum outer sphere radius of the UAV, \(f_{percentage}\) is the utilization rate of the safety interval airspace, and \(\omega\) denotes the disturbance coefficient that influences the results due to factors such as positioning errors. From Equation (24), it can be seen that the safety interval airspace utilization rate decreases with the increase in \(d_{l}\), and a decrease in safety interval airspace utilization rate means that the airspace occupied by a single UAV operation increases, so it is not possible to be overly conservative when calibrating the safety interval of UAVs.