esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
## 3 Proposed Localization Algorithm with Self-Calibration  
In this section, we explain the proposed two-step localization algorithm in details. We start by describing the calibration phase, which is performed by both anchors and blind nodes to estimate the unknown TOA/RSS channel parameters. Such estimates are then used in the localization phase in part to correct the synchronization errors affecting all the TOA measurements (anchor-to-anchor and anchor-to-blind), and in part to compute the \(N\) distances among the blind node and each anchor in the network. In this respect, it is important to observe that, differently from the case where both \(\mathbf{\beta}\) and \(\mathbf{\theta}\) are assumed to be known (deterministic), the accuracy of the range estimates will also depend on the uncertainty affecting the TOA/RSS parameter estimation. More precisely, \(\sigma_{r}^{2}\) and \(\sigma_{l}^{2}\) will influence the ultimate distance estimation in a twofold fashion: directly, in all the anchor-to-blind measurements,and indirectly through estimation of the models parameters. As formally demonstrated in [44] for the case of RSS-only ranging, this injects severe non-linear effects that can dramatically affect the achievable performance. In the final step, range estimates are then fed, together with a coarse estimate of the blind position obtained in the calibration phase, to the ILS procedure which provides the final position estimate \(\mathbf{\hat{x}}_{b}\). In Figure 1, we provide a block diagram illustrating the main steps of the proposed localization solution.
## 3 The Background and Proposed Method
### The Proposed Method  
Therefore, in order to solve the life prediction problem of equipment under multiple operating conditions, this paper proposes a novel similarity trajectory method based on morphological pattern and symbolic aggregate approximation (MP-SAX-STM) by using morphological pattern and symbolic aggregate approximation similarity measurement method. The diagrammatic sketch of the proposed method is shown in Figure 2. For the test sample \(X_{o}\), historical samples \(X_{r}\), where the \(i\)th historical sample is \(X_{ri}\) and the number of historical samples is \(N\), the process of the proposed method can be described as follows:  
Step 1: According to Step 1 and Step 2 in Section 2, the test sample \(X_{o}\) and historical sample \(X_{ri}\) are divided into trend component (\(TR^{o}\) and \(TR^{ri}\)) and detail component (\(DE^{o}\) and \(DE^{ri}\)).  
Step 2: According to Step 3 to Step 5 in Section 2, the similarity of the trend component \(STR\) and the detail component \(SDE\) can be obtained, then the similarity \(S^{MP-SAX}_{o\to ri}\) between \(X_{o}\) and \(X_{ri}\) can be calculated by Equation (8).  
Step 3: Determine the similar life candidate set and corresponding weight. The life of the historical sample is regarded as a similar life candidate set as follows:  
\[SL_{ri}=t_{Ei} \tag{9}\]  
where \(t_{Ei}\) is the time corresponding to the last set of data of the \(i\)th historical sample. And the weight corresponding to the candidate set is as follows:  
\[W_{i}=e_{i}\Bigg{/}\sum\limits_{i=1}^{N}e_{i} \tag{10}\]  
where  
\[e_{i}=\left(\sum\limits_{i=1}^{N}S^{MP-SAX}_{o\to ri}\right)/S^{MP-SAX}_{o \to ri} \tag{11}\]  
and \(\sum\limits_{i=1}^{N}W_{i}=1\). Therefore, the RUL of the test sample can be obtained as follows:  
Figure 2: The diagrammatic sketch of the proposed method.  
Step 4: Aggregate life to obtain predicted life. According to the similar life candidate set and corresponding weight, the predicted life of test sample \(PL_{o}\) can be obtained as follows:  
\[PL_{o}=\sum_{i=1}^{N}W_{i}\cdot SL_{ri} \tag{12}\]
# Just-in-Time Fluid Flow Simulation on Mobile Devices Using OpenVisFlow and OpenLB
## 2 Method
### Lattice Boltzmann Method (LBM)  
The LBM offers several advantages when it comes to both performance and physical accuracy in simulating fluid behavior. One advantage is that the LBM can be easily parallelized, allowing for faster computation times and higher performance. Another advantage is that the LBM inherently models the fluid at a mesoscopic level, allowing for an accurate representation of complex physical phenomena such as turbulence and multiphase flows. This makes the LBM well-suited for simulating a wide range of fluid dynamics problems. In the remainder of this section, we provide a brief introduction to the LBM and the target equations, the Navier-Stokes Equations (NSE) for mass and momentum conservation in fluid dynamics.  
The NSE is the fundamental equation that governs the behavior of fluids, and it is widely used in CFD simulations. The NSE in full form can be solved only numerically using various discretization methods, such as the finite difference, finite volume method or the LBM.  
The NSE can be written as follows:  
\[\
abla\cdot\mathbf{u} =0, \tag{1}\] \[\frac{\partial\mathbf{u}}{\partial t}+\mathbf{u}\cdot\
abla \mathbf{u} =-\frac{\
abla p}{\rho}+\
u_{m}\
abla^{2}\mathbf{u}+\frac{\mathbf{F }}{\rho}, \tag{2}\]  
where \(\mathbf{u}\) is the velocity vector, \(p\) is the pressure, \(\rho\) is the fluid density, \(\
u_{m}\) is the molecular kinematic viscosity of the fluid, and \(\mathbf{F}\) is the external force acting on the fluid.  
The LBM approximates the conservation equations in its limit (Chapman-Enskog expansion) on a discrete grid of points connected by a set of links that represent the paths along which the fluid particles can move [19]. In the LBM, the spatial and temporal states of these particles are represented by the probability distribution functions (PDFs) that evolve according to the lattice Boltzmann equation.  
\[f_{i}(\mathbf{x}+\mathbf{e}_{i}\Delta t,t+\Delta t)-f_{i}(\mathbf{x},t)= \Omega^{\mathrm{C}}+\Omega^{\mathrm{F}}, \tag{3}\]  
where \(f_{i}\) is the PDF at lattice node \(i\) and time \(t\), \(\mathbf{e}_{i}\) is the normalized discrete velocity in the \(i\)-th direction, \(\Omega^{\mathrm{C}}=-(f_{i}-f_{i}^{\mathrm{eq}})/\tau\) is the collision operator, and \(\Omega^{\mathrm{F}}\) is the Guo forcing term [20].  
In the current simulations, the D2Q9 lattice is used, where D is the number of dimensions and Q is the number of the normalized discrete velocity directions. The corresponding lattice cell is shown in Figure 1.
###### Abstract  
This paper investigates resource optimization schemes in a marine communication scenario based on non-orthogonal multiple access (NOMA). According to the offshore environment of the _South China Sea_, we first establish a Longley-Rice-based channel model. Then, the weighted achievable rate (WAR) is considered as the optimization objective to weigh the information rate and user fairness effectively. Our work introduces an improved joint power and user allocation scheme (RBPUA) based on a single resource block. Taking RBPUA as a basic module, we propose three joint multi-subchannel power and marine user allocation algorithms. The gradient descent algorithm (GRAD) is used as the reference standard for WAR optimization. The multi-choice knapsack algorithm combined with dynamic programming (MCKP-DP) obtains a WAR optimization result almost equal to that of GRAD. These two NOMA-based solutions are able to improve WAR performance by 7.47% compared with OMA. Due to the high computational complexity of the MCKP-DP, we further propose a DP-based fully polynomial-time approximation algorithm (DP-FPTA). The simulation results show that DP-FPTA can reduce the complexity by 84.3% while achieving an approximate optimized performance of 99.55%. This advantage of realizing the trade-off between performance optimization and complexity meets the requirements of practical low-latency systems.  
offshore communications; non-orthogonal multiple access; power allocation; maritime user allocation; joint resource optimization +
Footnote †: journal:  
0021  
[PERSON], [PERSON], [PERSON].  
Optimization Algorithms for Joint Power and Sub-Channel Allocation for NOMA-Based Maritime Communications
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 4 Multi-UAV Collaborative Search Algorithm
### Multiple UAVs Collaborative Search Algorithm Based on Greedy Algorithm  
We describe a basic multi-UAV collaborative search algorithm based on the greedy algorithm called MUCS-GD. We define the initial power of the UAV as \(E\), the moving speed as \(V\), the power consumed per second at the current speed \(V\) as \(SEL\), the hovering  
Figure 10: The structure of the multi-UAV collaborative search algorithm part of the methodology.  
time as \(ST\), and the power consumed per second while hovering as \(STL\). We calculate the energy required for the UAV to move from \(P_{i}\) position to \(P_{i+1}\), the energy required to hover \(ST\) at \(P_{i+1}\), and the energy required to return to the initial position \(S\) at \(P_{i+1}\). If the current remaining energy of the UAV is not enough to reach the next position \(p_{i+1}\) and to return to the initial position \(S\) after hovering \(ST\) at position \(p_{i+1}\), the UAV needs to return to the starting position \(S\) immediately; otherwise, the UAV moves from position \(P_{i}\) to position \(p_{i+1}\) and deducts the energy consumed from \(P_{i}\) to \(p_{i+1}\) and the energy required for \(ST\) to hover at position \(p_{i+1}\). This rule is followed and so on until the last target cell is detected by the UAV. The algorithm calculates the number of UAVs required to complete the exploration of the area under the current conditions and assigns a working path to each UAV. The details of the MUCS-GD algorithm are shown in Algorithm 5.
```
0: P, S, E, V, SEL, ST, STEL Output: MovePath, T, UE
1: P: path
2: S: Starting point of UAVs
3: InitE: Energy of UAVs
4: V: The speed of the UAVs
5: SEL: Energy lost per second at the current speed
6: ST: Hovering time of the UAVs
7: STEL: Energy lost per second while hovering
8: LastPath \(\leftarrow\) S
9: StartPoint \(\leftarrow\) S
10: MovePath \(\leftarrow\)\(\oslash\)
11: T \(\leftarrow\) 0
12: E \(\leftarrow\) InitE
13:foreach\(p\in P\)do
14:\(LastToNow\leftarrow\frac{dist(LastPoint,p)*SEL}{V}\)
15:\(NowToStart\leftarrow\frac{dist(p,S)*SEL}{V}\)
16:\(StayLossEnergy\gets ST*STL\)
17:if\(E>=LastToNow+StayLossEnergy+NowToStart\)then
18: p append to MovePath
19:\(E\gets E-LastToNow-StayLossEnergy\)
20: LastPoint \(\leftarrow\) p
21:if p is the last element in path then
22:\(E\gets E-NowToStart\)
23: T \(\leftarrow\) T + \(\frac{dist(p,S)}{V}\)
24:else
25: T \(\leftarrow\) T + \(\frac{dist(LastPoint,p)}{V}\)
26: StartPoint append to MovePath
27:\(E\gets E-NowToStart\)
28: brea
29: UE \(\leftarrow\) InitE - E
30:return MovePath, T, UE
```
**Algorithm 5** MUCS-GD (Greedy algorithm)
# Model Predictive Control Algorithm for Converter Based on a Convolutional Neural Network
## 3 Load-Type Identification Method Based on CNN
### CNN Design  
Based on the efficiency and accuracy of network computation, we choose a 7-layer network structure, as shown in Figure 6, which includes the following:  
1. The input layer contains 4 features (F1-F4).
2. Convolutional layer 1. The layer performs convolution and activation operations. The kernel size is set to 3 \(\times\) 1, with 16 kernels. Padding mode is configured as 'Same'. The ReLU nonlinear activation function achieves nonlinear computation.
3. Pooling layer. It utilizes max pooling to downsample feature maps and reduce the space scale. The pooling window size is 2 \(\times\) 1 with a stride of 2, halving the output feature map size.
4. Convolutional layer 2. It performs convolution and activation operations. The kernel size is set to 2 \(\times\) 1, with 32 kernels, 'Same' padding, and ReLU activation to accelerate convergence. Additionally, batch normalization is applied between convolution and activation to avoid overfitting.
5. Fully connected layer. The layer connects all neurons from previous layers, flattening pooled feature maps into a one-dimensional vector.
6. Softmax layer. It transforms the fully connected layer's output into probability distributions, ensuring the sum of probabilities across all classes equals 1.
7. Classification output layer. The layer selects the class with the highest probability as output, and contains 4 neurons: NL(1), HPFRIL(2), LPFRIL(3), and NLL(4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Principal** \\ **Component** \\ \end{tabular} & **Eigenvalue** & \
\begin{tabular}{c} **Contribution Rate** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Cumulative** \\ **Contribution (\%)** \\ \end{tabular} \\ \hline F1 & 3.1354 & 39.1927 & 39.1927 \\ F2 & 3.0879 & 38.5983 & 77.7910 \\ F3 & 0.6865 & 8.5816 & 86.3726 \\ F4 & 0.6519 & 8.1485 & 94.5211 \\ F5 & 0.2351 & 2.9
# Dialog CIN: Contextual Inference Networks for Emotional Dialogue Generation
## 5 Experiments
### Comparison Experiment and Result Analysis  
To make comparisons, we employed the following models as baselines:  
(1) HRED: The Hierarchical Recurrent Encoder-Decoder Model: this model divides the dialogue into multiple hierarchical representations using a hierarchical design and models them using an RNN. By introducing a context encoder and a generator, the model can capture the semantic information and contextual dependencies of the dialogue, resulting in the generation of dialogue responses.  
(2) WSeq [33]: This model initially computes the cosine similarity between the vector representations of the conversation context and the current message, employing these values as weights to effectively manage the significance of response generation. Subsequently, the conversation context representation is integrated using this weighting scheme. Lastly, a hierarchical sequence-to-sequence model is employed to generate a response.  
(3) DSHRED [34]: A method is proposed herein for generating context-aware dialogue responses, employing a combination of static and dynamic attention mechanisms. The static attention mechanism assigns weights to individual sentences within the dialogue history, whereas the dynamic attention mechanism assigns weights to individual words within each sentence.  
(4) ReCoSa: The core idea of this model is to automatically learn the correlations between contexts using a self-attention mechanism. It utilizes the encoded representationsfrom the dialogue history and computes weights for each context representation through self-attention. This allows the model to focus on relevant contextual information and generate responses by understanding the semantics and context of the conversation.
# Performance Analysis of Ambient Backscatter NOMA Systems
## 4 Energy Efficiency Analysis  
EE is an important index by which to evaluate system performance. This section analyzes the EE of AmBC-NOMA systems, which can be [42] expressed as  
\[\eta_{EE}=\frac{\text{Total speed of data}}{\text{Total energy consumption}}. \tag{24}\]  
In AmBC-NOMA systems, the total energy consumption is equal to the sum of energy consumption among the BS, BD, users, and hardware static power, which can be given by  
\[P_{total}=\kappa P_{S}+P_{BS}+P_{BD}+\sum_{d=1}^{D}P_{UE,d}, \tag{25}\]  
where \(\kappa=\
u^{-1}\), \(\
u\) demonstrates the effectiveness of the transmitting power amplifier, \(P_{S}\) denotes the BS's power transmission consumption, \(P_{BS}=\kappa_{\mu}P_{BS}(d)\) and \(P_{BD}\) represent the total power consumed by the hardware devices of the BS and the BD, respectively, and \(P_{UE,d}\) indicates the power consumed by the hardware device at the users. Based on the above evaluation, the EE of AmBC-NOMA systems can be written as  
\[\eta_{EE}=\frac{R_{d,dl}}{P_{total}}. \tag{26}\]
## 2 Materials and Methods
### Moisture Ratio (MR) and Mathematical Modeling  
To model the drying process, the _MR_ parameter during the kiwifruit drying was obtained using the following Equation (1) [24]:  
\[MR=\frac{M_{t}-M_{e}}{M_{o}-M_{e}} \tag{1}\]  
_MR_: moisture ratio (dimensionless), \(M_{t}\): _MC_ of samples at any given time on dry basis (d.b.), \(M_{e}\): equilibrium moisture content (d.b.), and \(M_{o}\): initial moisture of samples (d.b.). According to Equation (1), the moisture ratio depends on the initial moisture, balanced moisture, and moisture of samples at any time during the drying. For long drying times, the \(M_{e}\) values are very small compared to the \(M_{o}\) and \(M_{t}\) values. Therefore, the equation of _MR_ during the drying can be simplified, as in Equation (2), and to calculate the _MR_, it is not necessary to measure the balanced moisture [25].  
\[MR=\frac{M_{t}}{M_{o}} \tag{2}\]
## 2 Description of the Proposed Approach
### Forwarding Process of the FLRAF  
The specific forwarding process of the FLRAF routing protocol is described as follows. First, during network initialization, the sink node broadcasts a data collection query, which is divided into location-based queries and queries that define event-triggering conditions. The former broadcasts data packets carrying the geographic location of interest to all nodes in the network, while the latter broadcasts data packets carrying events of interest to the sink node (such as whether the temperature in the area exceeds the threshold) to all nodes in the network. Through this step of processing, nodes know whether they need to start the data collection task. This preliminary step significantly reduces unnecessary energy consumption by allowing only relevant nodes to participate in data forwarding.  
Second, each node that receives the query will determine its one-hop forwarding area based on the predefined geometric constraints and establish a candidate set of neighboring nodes. Using a fuzzy logic reasoning mechanism that comprehensively evaluates factors such as residual energy, link quality, and distance to the sink, the optimal forwarding node is identified. The selected node is responsible for transmitting the data, while other nodes in the candidate set discard the received data to avoid redundant transmissions and conserve  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rule Number** & **Rule** \\ \hline rule 1 & If RE is IRE and AD is sAD and LQI is gLQI then NodePriority is bPriority \\ rule 2 & If RE is mRE and AD is sAD and LQI is gLQI then NodePriority is mPriority \\ rule 3 & If RE is hRE and AD is sAD and LQI is gLQI then NodePriority is gPriority \\ rule 4 & If RE is lRE and AD is mAD and LQI is gLQI then NodePriority is mPriority \\ rule 5 & If RE is mRE and AD is mAD and LQI is gLQI then NodePriority is mPriority \\ rule 6 & If RE is hRE and AD is mAD and LQI is gLQI then NodePriority is gPriority \\ rule 7 & If RE is IRE and AD is lAD and LQI is gLQI then NodePriority is gPriority \\... &... \\ \hline \hline \end{tabular}
\end{table}
Table 2: Partial fuzzy reasoning rules.  
energy. This decision-making process ensures both energy efficiency and robustness in dynamic underwater environments.  
The forwarding node then transmits the data in a hop-by-hop manner, repeating the candidate selection and fuzzy inference at each step, until the data eventually reach the sink node. Finally, the sink node gathers the desired information and forwards it to the shore-based control station via its equipped radio modems for further analysis and processing. The complete forwarding process of the FLRAF routing protocol and the information that needs to be exchanged in each part to complete the forwarding task are shown in Figure 11 and Table 3.  
Figure 11: FLRAF routing protocol forwarding process.
## 4 Discussion
### Design Challenges and Trade-Offs
#### 4.1.2 Managing Token Economics in a Permissioned Blockchain  
In a permissioned blockchain, tokens can be used to incentivize behaviors such as expert participation, mentoring session completion, or contribution to the platform's success. However, managing a token economy in a permissioned environment introduces complexities regarding distribution, valuation, and governance.  
* _Centralized control vs. tokenomics flexibility:_ In a permissioned blockchain, some degree of centralization is often necessary for governance, which can limit the flexibility of the token economy. For example, the entity managing the platform might need to control token issuance, governance, or rewards, potentially undermining the decentralization aspect of token management. In SPARK-IT, we designed governance smart contracts that are open to everyone. Even if we use a permissioned blockchain, any potential innovator or expert can join the platform. Therefore, the community formed around our governance protocol is not restricted and behaves in a similar way as the ones deployed in public blockchains.
* _Token valuation and liquidity:_ In a permissioned blockchain, the token's value may be more difficult to establish without the market dynamics that come with a public blockchain. Users might be less willing to use or accept a token that lacks liquidity or a clear external value. In SPARK-IT, we decided to use wrapped tokens from public blockchains (such as USDT). Moreover, even if we can support any token from public blockchains, we decided to go for stablecoin tokens to assure predictability and to protect our users from excessive volatility inherent to crypto markets. We have developed dedicated blockchain bridges that assist in moving assets between public blockchains and our permissioned blockchain.
* _Governance mechanisms_: Without strong decentralization, governance around tokenomics could become a bottleneck. Decision-making about token issuance, rewards,and penalties might rely on a small group of entities, leading to potential trust issues and reducing transparency. In SPARTK-IT, we employ a decentralized governance mechanism (through a dedicated DAO and governance tokens) that ensures that decisions regarding token distribution and rewards are made in a transparent and inclusive manner. Our protocol is open to everyone, as everyone can register in the SPARK-IT platform as either an innovator or an expert.
* _Fair reward distribution_: Allocating tokens equitably while ensuring they incentivize high-quality contributions is very important of the platform's tokenomics. This process requires balancing several critical aspects, including fairness, transparency, and alignment with the platform's overall objectives. SPARK-IT achieves this through a dual-token system, where reputation tokens serve as a measure of quality and reward tokens (SparkCoins) hold monetary value. This mechanism not only incentivizes meaningful participation but also ensures that the rewards are tied to measurable and impactful contributions.
# Experimental Characterization of the Capacitively Coupled RF-Plasma Thruster
## 2 Materials and Methods
### Diagnostics  
The plasma diagnostics was focused on determining the electric potential distribution inside the plasma plume and the ion energy distribution function. For this purpose, custom-made emissive probes and a retarding potential analyzer were used. An emissive probe is made of a tungsten wire heated to a temperature such that thermionic emission of electrons takes place. It is usually applied for the measurements of plasma potential [10,11]. When the heating is turned off it can be used as an ordinary Langmuir probe, provided the necessary conditions of Langmuir probe theory are satisfied. By comparison of the probe characteristics taken in hot and cold states it is possible to estimate plasma density as shown later.