esa-sceva/satcom-chunk-collection huggingface dataset:
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 3 Congestion Control Strategy for the Power Scale-Free Network  
In the power communication network, the traditional shortest path routing is usually adopted to deliver packets. Some hub nodes are needed to bear very heavy load, which may result in communication network congestion. During the period of congestion, the real time data flows may not meet the delay requirements, which lead the critical networks of the power grid into an unstable state. In order to effectively balance the data flow and obtain a larger network capacity, as well as reduce network congestion in the power communication network, an integrated routing strategy based on service classification with the reasonable allocation of nodes' delivery capacity and cache capacity is proposed.
## 2 Methods
### The NHLBVNet Model
#### 2.1.4 Combustion Prediction Module  
The combustion prediction module is utilized for predicting the laminar burning velocity, and this module consists of an attention layer and an output layer. The attention mechanism directs focused attention to key information, thereby conserving computational resources and rapidly acquiring the most pertinent information [31; 32].  
In order to screen the obtained combustion feature embedding vector \(e_{i}\), the combustion feature vectors \(q_{i}\) from the previous module are collected to form the dataset of combustion feature vectors \(Q_{f}\), which is then placed into an attention module. The combustion prediction module employs an attention mechanism to score the features that affect the laminar burning velocity of ammonia-hydrogen mixtures, thereby highlighting the impact of significant features on the model's predictive outcomes. This utilizes the attention mechanism to assign weights that further emphasize the core combustion data. The attention function formula used within the module is presented as Equation (4).  
\[Q =W_{Q}Q_{f}\] \[K =W_{K}Q_{f}\] \[V =W_{V}Q_{f} \tag{4}\] \[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{ \sqrt{d_{k}}}\bigg{)}V\]  
where \(a_{i}\in A\) represents the attention score of the combustion feature vector; \(W_{Q}\), \(W_{K}\), and \(W_{V}\) denote the matrix of parameters required for computing the attention scores; \(d_{k}\) is the length of the combustion feature vector; the attention score \(a_{i}\) for the combustion feature vector \(q_{i}\) is assigned by calculating the similarity between the combustion feature vector \(q_{i}\) obtained from the ith experiment and the combustion feature vector \(q_{j}\) obtained from the jth experiment.  
The softmax function is frequently used as an activation function to achieve the effect of reducing model linearization and normalizing model results. In this paper, the softmax function is used with the computational formula (5) as follows:  
\[y_{k}=\frac{\exp(a_{k})}{\sum_{i=1}^{n}\exp(a_{i})} \tag{5}\]  
where \(\exp\) refers to the exponential function, and \(a_{i}\) is the input to the function.  
Finally, the combustion prediction module employs a linear layer as the output layer to adjust the final predicted LBV for ammonia-hydrogen mixtures. The formula used is as shown in Equation (6).  
\[x_{i} = a_{i}*q_{i} \tag{6}\] \[s_{i} = softmax(W_{5}x_{i}+b_{3})+b_{4})\]  
where softmax is the activation function; \(W_{5}\) is the matrix of parameters required for the calculation; \(b_{3}\) and \(b_{4}\) are the bias parameters needed for the output layer computation. This module multiplies the attention scores \(a_{i}\) obtained from the combustion feature module for the combustion feature embedding vectors \(q_{i}\) to derive the combustion reaction vectors \(x_{i}\). The combustion reaction vectors \(x_{i}\) are then input into the linear layer to yield the model's predicted result vector \(s_{i}\).
# Efficient Low-Complexity Turbo-Hadamard Code in UAV Anti-Jamming Communications
## 3 Low-Complexity Turbo-Hadamard Codes Based on Tail-Biting  
The turbo-Hadamard code is a kind of hybrid concatenation code, which consist of convolutional codes and Hadamard codes [20]. As illustrated in [27; 28], the turbo-Hadamard code performs well in the case of narrow-band interference suppression since it can achieve relatively high code gains at great lengths due to its low-rate structure.However, in the scenario of UAV communications, the payload is usually low, and the turbo-Hadamard code will thus exhibit high efficiency losses, especially at a relatively short code length. As a result, we propose an improved turbo-Hadamard code based on tail-biting in the convolutional process, in order to make it more available for UAV communication systems. In this section, we will discuss the structure of the component codes firstly, and then discuss the overall codes.
## 4 Simulation and Analysis
### Simulation Setup  
The fifth-order foot trajectory described in Section 3.1 is used in the simulation. The whole simulation course can be divided into two experimental groups, depending on the initial inflation pressure and the working volume of the actuator. The settings and differences are as follows.  
1. In group 1, the initial inflation pressure of the accumulator \(P_{g0}\) was set to 2, 4, and 6 Mpa. The working volume of the actuator \(V_{g0}\) is constant (1.3 \(\times\) 10\({}^{-5}\) m\({}^{3}\)).
2. Different from group 1, the initial inflation pressure of the actuator was constant (6 Mpa) in group 2, and the working volumes were set to 2 \(\times\) 10\({}^{-5}\), 4 \(\times\) 10\({}^{-5}\), and 6 \(\times\) 10\({}^{-5}\) m\({}^{3}\).  
The actuator with the original structure was also simulated and used for comparison. The parameters of the cylinder are shown in Table 1, and the rest of the parameters are shown in Table 2.
## 6 Results
### Deployment Location Selection  
This research detailed its application in the fire brigade domain. The architecture was deployed in the KALISIMBI building of the University of Rwanda (UR), as shown in Figure 4. The site was selected due to multiple fire outbreak cases noted in the student kitchen in 2018. The KALISIMBI Block site was selected because it contained many laboratory equipment and supplies for chemistry, physics, and electronics, as well as computer labs. At any time, these chemical components could have reacted and impacted students' lives. The deployment for testing was configured on the third floor in one room, to help prevent and forecast incidents.
## 3 Stress Estimation Based on the Monitored Strain
### Analysis Results
Two other parameters were derived based on the inverse analysis: the creep model uncertainty factor \(\alpha_{c}\) and shrinkage model uncertainty factor \(\alpha_{s}\), as shown in Table 5. The mean values of the two factors were both smaller than 1, indicating that the creep and shrinkage model suggested by the CEB-FIP MC90 model overestimated the actual stress levels for the PC beam shown in this study. Compared with the prior distributions, the standard deviation of the posterior was smaller. This shows that introducing the monitored strain can effectively decrease the uncertainty of the model prediction results. On the basis of posterior distributions of the parameters, a series of samples were obtained using the Monte-Carlo sampling method, which were then introduced into the forward model (Equation (1)). The weight proportion of the strain components in the total strain were calculated for a certain cross-section of the spans during different construction stages. Figure 8 shows the constituents of the total strain at different locations. The figure shows that with the increase in the concrete's age, the weight proportion of the concrete creep strain in the total strain increased, reaching up to 71%. After the pre-stressed stretching construction stage, the creep caused by the pre-stress increased and pre-stress relaxation occurred, resulting in the decreased elastic strain. The only exceptions are the results at the end of the cross-section, in which the proportion of the creep strain in the total strain at 368 days is smaller than that at 65 days. This might be because the tension of the pre-stress at that support would decrease the stress at the bottom of the end cross-section, which mitigates the increase in the creep strain. As the total strain at the bottom of the end cross-section was relatively small, it was not the control point in the structural design \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Random Variable** & **Mean** & **COV** & **Distribution** \\ \hline \(\alpha_{c}\) & 0.828 & 0.247 & Normal \\ \(\alpha_{s}\) & 0.935 & 0.246 & Normal \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical characteristics of the posterior distribution of the parameters of the PC beam. Figure 7: Counters of the joint probability distribution at the top of the 1/4 span cross-section of Span #2.  
and structural performance evaluation. The creep strains at the other cross-sections were dominant in the total strain. Consequently, the influence of the creep strain should be the focus of attention in the design analysis of concrete bridges. Figure 8: _Cont._
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Simulation in Simulation Software
#### 4.1.2 Simulation of Routing Protocol  
After the establishment of control channels, the routing protocol began to run. After a series of message interactions--specified in the routing protocol--each node had collected the resources of the whole network in its LSDB. As the LSDBs in nodes are the same, we only need to show an LSDB in any one of the nodes, as follows:  
From the design discussed in routing protocol, we know that an LSDB consists of LSAs advertised by all nodes in the network. We know that the simulation network has 16 nodes. So, as shown in Figure 13, the LSDB also has 16 LSAs in it, each of which describes the link states of corresponding node. Because an LSA has many links and is usually long, the figure only shows one link state in each LSA. In addition, considering the unidirectional link from node 11 to node 7, we can also observe the neighborhoods of two nodes.  
In Figure 14, it can be seen that the routing protocol has already collected the link states of two nodes, including the states for the unidirectional link. It is shown that port 9 of node 7 and port 1 of node 11 have entered the state of UNI  
Figure 11: Topology of the simulation network.  
respectively, which both belong to unidirectional state. These two states mean that the link from port 1 of node 11 to port 9 of node 7 is available, but the reverse is not true, which is exactly what is set in the topology shown in Figure 11.  
As can be seen, the routing protocol has collected the resources of the whole network and supported the use of unidirectional links successfully.  
Figure 12: Local resources collected by link management protocol in node 4 (0x0004).  
Figure 13: Link state data base (LSDB) in one of the nodes in simulation network.
###### Abstract  
The NASA Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has produced a 17-plus-year time-series of calibrated microwave radiances that have remarkable value for investigating the effects of the Earth's climate change over the tropics. Recently, the Global Precipitation Measurement (GPM) Inter-Satellite Radiometric Calibration (XCAL) Working Group have performed various calibration and corrections that yielded the legacy TMI 1B11 Version 8 (also called GPM05) brightness temperature product, which will be released in late 2017 by the NASA Precipitation Processing System. Since TMI served as the radiometric transfer standard for the TRMM constellation microwave radiometer sensors, it is important to document its accuracy. In this paper, the various improvements applied to TMI 1B11 V8 are summarized, and the radiometric calibration stability is evaluated by comparisons with a radiative transfer model and by XCAL evaluations with the Global Precipitation Measuring Microwave Imager during their 13-month overlap period. Evaluation methods will be described and results will be presented, which demonstrate that TMI has achieved a radiometric stability level of a few deciKelvin over almost two decades.  
TRMM; radiometric stability; TMI 1B11 V8 (GPM05); inter-satellite calibration +
Footnote †: journal: Climate
###### Abstract  
Data authenticated aggregation is always a significant issue for wireless sensor networks (WSNs). The marine sensors are deployed far away from the security monitoring. Secure data aggregation for marine WSNs has emerged and attracted the interest of researchers and engineers. A multi-signature enables the data aggregation through one signature to authenticate various signers on the acknowledgement of a message, which is quite fit for data authenticated aggregation marine WSNs. However, most of the previous multi-signature schemes rely on the technique of bilinear pairing involving heavy computational overhead or the management of certificates, which cannot be afforded by the marine wireless sensors. Combined with the concept of identity-based cryptography, a few pairing-free identity-based multi-signature (IBMS) schemes have been designed on the basis of the integer factorization problem. In this paper, we propose two efficient IBMS schemes that can be used to construct provably secure data authenticated aggregation protocols under the cubic residue assumption, which is equal to integer factorization. We also employ two different methods to calculate a cubic root for the cubic residue number during the signer's private key extraction. The algorithms are quite efficient compared to the previous work, especially for the algorithms of the multi-signature generation and its verification.  
identity-based multi-signature; provably secure; integer factorization; data authenticated aggregation; marine WSNs +
Footnote †: journal: Sensors
## 5 Experiment
### Comparison with State-of-the-Art Methods
#### 5.3.2 Quantitative Comparison  
Figure 6 shows the quantitative results of the proposed DLRSD model against eight state-of-the-art methods. It is known that it perform competitively and is both better than the other methods in terms of the P-R curve, ROC curve and F-Measure curve. Especially, the precision can remain above 90% within a large threshold range.  
Table 2 summarizes the quantitative results of all the eight methods. We can see that the proposed DLRSD model has achieved the best performance in AUC, \(F_{\beta}\) and MAE. Compared with SMF, it increased by 8.52% and 4.05% in AUC and \(F_{\zeta}\), respectively, decreased by 5.01% in MAE. All experiments are run in Matlab 2018a on a PC with an Intel Core [EMAIL_ADDRESS] GHz CPU and 8 GB RAM, the running time of the proposed DLRSD model is slightly slower than RS but much faster than ULR and SMF.  
Based on the above qualitative and quantitative analyses, it confirms that our proposed method consistently outperforms some state-of-the-art methods and verifies the effectiveness of the proposed structural constraints in separating the low-rank and sparse subspaces.
# Effects of Various Extraction Factors on Protein Yield of Haliotis discus hannai (Abalone)
## 3 Results and Discussion
### Abalone Protein Composition  
Protein composition was analyzed by electrophoresis. On the SDS-PAGE gel, myosin heavy chain (~200 kDa), paramyosin (~100 kDa and ~75 kDa), actin (~48 kDa), tropomyosin (~37 kDa), and myosin light chain (~25 kDa) were detected (Figure 6) [30]. The most abundant proteins extracted using PB were actin and tropomyosin (Line A). The addition of salt and surfactant changed the composition of extracted protein (Lines B and C), but Triton X-100 did not have the same effect (Line D). Sous vide cooking reduced the extraction of actin and tropomyosin (Line E), which could be due to the denaturation of myofibrillar proteins with thermal treatment [14]. The addition of NaCl did not improve the extraction yield of cooked abalone protein. However, SDS improved the protein extraction, achieving a yield similar to that of the raw abalone sample (Line C), while Triton X-100, another surfactant, did not improve the extraction yield (Line H). SDS was effective in extracting myosin light chain in raw and cooked abalone samples. Since myofibrillar protein is soluble at relatively high ionic strength, the addition of salt improved tropomyosin. In addition, since anionic surfactant effectively breaks protein-protein interaction, SDS showed higher overall myofibrillar protein extraction [27].  
Figure 5: Protein extraction yield of sous vide-cooked abalone with various optimal extraction factors, including 0.6 M NaCl, 16 mM SDS, and 0.8 mM of Triton X-100. Bars with different letters are significantly different (\(p<0.05\)).  
Figure 6: Composition of raw and sous vide-cooked abalone protein extracted under different extraction conditions. (**A**) Raw abalone in phosphate buffer (PB) + EDTA, (**B**) raw abalone in PB + EDTA + 0.6 M NaCl, (**C**) raw abalone in PB + EDTA + 16 mM SDS, (**D**) raw abalone in PB + EDTA + 8 mM Triton X-100, (**E**) sous vide-cooked abalone in phosphate buffer (PB) + EDTA, (**F**) sous vide-cooked abalone in PB + EDTA + 0.6 M NaCl, (**G**) sous vide-cooked abalone in PB + EDTA + 16 mM SDS, and (**H**) sous vide-cooked abalone in PB + EDTA + 8 mM Triton X-100.
## 2 Optically Pre-Amplified PPM Receiver  
We considered a pre-amplified OWC receiver, as shown in Figure 1. The optical signal is collected by an aperture and amplified optically in a semiconductor optical amplifier (SOA) or an Erbium doped fiber amplifier (EDFA). It should be noted that both amplifier types are applicable in OWCs, each with their own merits. EDFAs typically provide a higher gain and add less noise than SOAs, and therefore require a reduced optical power at their input, but SOAs are more compact, compatible with integration, which is important in diversity reception considered later, and one may utilize their regenerative capabilities to partially alleviate fading [15, 16, 17]. For the rest of our analysis, we do not differentiate between the two amplifier types and we consider them as components that provide a gain equal to \(G\) and introduce a noise signal with a spectral density of \(N_{0}=n_{sp}hf(G-1)\), where \(hf\) is the photon energy and \(n_{sp}\) is the spontaneous emission factor of the amplifier.  
The amplified PPM signal is optically filtered to reject out of band noise and is converted to a photo-current using a PIN photodiode. Each PPM symbol is transmitted over \(Q\) time-slots and one of the slots contains the total symbol energy \(E_{s}\). In order to identify the slot that contains the optical energy, the photo-current is integrated over the slot duration. The integrator output is then driven to a soft-decision PPM demodulator, where the slot with the highest signal value is identified as the energy-containing slot, and the corresponding symbol is selected as the most probable transmission.  
Figure 1: Optically pre-amplified pulse-position modulation (PPM) receiver. OpAmp: optical amplifier; Fl: optical filter; PD: photodiode; Int: time-slot integrator.
## 2 Materials and Methods
### Digital Twin of the Ocean Components  
Regional high-resolution ocean circulation and wave forecasting models require accurate atmospheric forcing inputs [33]. WRF-DA outputs are used as inputs in a regional configuration based on the Nucleus for European Modeling of the Ocean (NEMO v3.6) framework, which solves the primitive equations [34]. The configuration's domain covers the surrounding area of the Cretan Island (Cretan Sea and part of the Levantine Sea; 22.993-26.925\({}^{\circ}\) E and 34.4218-36.0915\({}^{\circ}\) N), with a horizontal grid resolution of \(\sim\)1 km and 72 vertical levels, with a decreasing resolution from the surface to the bottom from 1 m to 191 m (deepest point 4695.7 m). For the open boundary conditions surrounding the domain, forecast data from the CMEMS platform were used ([[https://data.marine.copernicus.eu/product/MEDSEA_ANALYSISFORECAST_PHY_006_013/description](https://data.marine.copernicus.eu/product/MEDSEA_ANALYSISFORECAST_PHY_006_013/description)]([https://data.marine.copernicus.eu/product/MEDSEA_ANALYSISFORECAST_PHY_006_013/description](https://data.marine.copernicus.eu/product/MEDSEA_ANALYSISFORECAST_PHY_006_013/description)) last accessed online 28 October 2024).  
WRF-DA outputs were also used as inputs in a regional configuration based on the Wavewatch III (WWIII) wave model [35], surrounding the area of 22.705-27.457\({}^{\circ}\) E, 33.95-36.81\({}^{\circ}\) N.
# Susceptibility Modeling of a Rubber Track for Lightweight Mobile Robots
## 5 Model Validation
### Track Modeling Using Constant Stiffness Constraints  
The angular stiffness coefficients \(k_{z}\) for three track variants (E38, E56, and E76) were selected by adjusting the achieved value of the height Hs with the static test results. With an increase in the stiffness coefficient, the values of the heights Hs and Hd also increased. The damping coefficient \(c_{z}\) was adjusted on the basis of the logarithmic decrement \(\Lambda\) so that its simulation-derived value was within the 10% accuracy range with the dynamic test experimental results. By changing the damping values, it was possible to adjust the oscillation amplitudes. A comparison of the oscillation plots obtained in the dynamic tests and simulations is shown in Figure 7. The values of the stiffness and damping parameters for three variants of the track model and the obtained simulation results are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Simulation Variant** & \(\mathbf{k}_{z}[\frac{\mathbf{N}\cdot\mathbf{mm}}{\mathbf{rad}}]\) & \(\mathbf{c}_{z}[\frac{\mathbf{N}\cdot\mathbf{mm}\cdot\mathbf{s}}{\mathbf{rad}}]\) & **Hs [mm]** & **Ws [mm]** & **Hd [mm]** & **Wd [mm]** & **T [s]** & \(\mathbf{\Lambda}\) \\ \hline E38 & 8600 & 450 & 324 & 1042 & 122 & 1056 & 1.5 & 0.69 \\ E56 & 12,600 & 650 & 322 & 1055 & 124 & 1072 & 1.5 & 0.68 \\ E76 & 17,200 & 950 & 323 & 1057 & 125 & 1073 & 1.5 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulations results from constant stiffness constraint model (I) in variants E38, E56, E76.  
Figure 6: Track links constraint scheme (**a**), and the constraint torque-links of angular stiffness definition methods: (**b**) Method I; (**c**) Method II; (**d**) Method III.  
Simulations indicated that regardless of the number of segments used for the track belt model, the obtained oscillation parameters remained similar. The use of constant stiffness resulted in an oscillation time period twice as long as the experimental one. In addition, the height \(\mathrm{Hd}\) obtained in the simulations turned out to be almost two times lower than the experimental measurements. These differences may indicate that the constant value of the stiffness coefficient is insufficient to describe the susceptibility of the rubber belt.  
During the simulations, the range of angles \(\zeta_{\mathrm{Z1}}\) between the modeled track links was also determined. Connections with the largest amplitudes of rotation angles are marked in Figure 4 with the letters A and C. The angle \(\zeta_{\mathrm{Z1}}\) values obtained under these constraints are shown in Table 3. As the number of links increases, the range of rotation angles decreases.