esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 3 Isfl-Sys Architecture
### A Blockchain-Based Smart Contract Incentive Mechanism
#### 3.2.2 Reward Distribution  
Transaction publishing nodes and authentication nodes each receive a portion of the reward for the transaction. Assuming that, after model aggregation by task-publishing node \(i\), the training reward for this session is \(R\), then for a certain transaction \(j\) containing models \(\omega_{j}\), the reward that can be allocated to this transaction is calculated as follows:  
\[R_{j}=\frac{F_{i}(\omega_{0})-F_{i}(\omega_{j})}{F_{i}(\omega_{0})-F_{i}( \omega_{i}^{t})}R, \tag{1}\]  
where \(F_{i}(\omega_{j})\) and \(F_{i}(\omega_{i}^{t})\) are the loss function magnitudes of models \(\omega_{i}\) and aggregated model \(\omega_{i}^{t}\), respectively, contained in transaction \(j\) at task-publishing node \(i\). For the initiating node of this transaction, it will be allocated a reward of \(R_{j}/2\). As for the \(n\) transactions authenticated in this transaction, the publishing node k will receive rewards calculated as follows:  
\[R_{k}=\frac{F_{i}(\omega_{0})-F_{i}(\omega_{k})}{2\Big{[}F_{i}(\omega_{0})- \frac{1}{t}\sum_{t=0}^{n}F_{i}(\omega_{t})\Big{]}}R_{j}, \tag{2}\]  
where \(\omega_{k}\) represents the model in the transaction submitted by node \(k\), and \(\sum_{t=0}^{n}F_{i}(\omega_{t})\) represents the sum of the loss functions of the selected and aggregated models in the transaction. The above reward distribution method takes into account both the cost of node participation in training and the cost of node authentication process. Furthermore, the reward allocation method is related to the node's training results, encouraging nodes to publish higher-quality training results in order to obtain higher returns.
# Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning
## 7 Applications to Federated Learning
### Machine Learning  
Many machine learning algorithms naturally align with the multi-round paradigm considered in this work. For example, Isolation Forest for anomaly detection [31] relies on iterative data partitioning to isolate anomalies. This process can be adapted to federated learning using the shuffle model, ensuring privacy during intermediate computations. Similarly, iterative clustering algorithms, such as K-means [32], refine cluster centroids over multiple rounds of aggregation and adjustment. Differential privacy techniques, including the shuffle model, have been shown to effectively protect privacy in these settings. The shuffle model's privacy amplification effect is particularly beneficial for such iterative computations, enabling secure aggregation of intermediate results like histogram updates or centroid adjustments [6]. This integration extends the applicability of federated learning to tasks such as anomaly detection, clustering, and other iterative machine learning methods in privacy-sensitive scenarios.
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
###### Abstract  
This paper proposes a multiple-lens receiver scheme to increase the misalignment tolerance of an underwater optical wireless communications link between an autonomous underwater vehicle (AUV) and a sensor plane. An accurate model of photon propagation based on the Monte Carlo simulation is presented which accounts for the lens(es) photon refraction at the sensor interface and angular misalignment between the emitter and receiver. The results show that the ideal divergence of the beam of the emitter is around \(15\,\mathrm{\SIUnitSymbolDegree}\) for a \(1\,\mathrm{m}\) transmission length, increasing to \(22\,\mathrm{\SIUnitSymbolDegree}\) for a shorter distance of \(0.5\,\mathrm{m}\) but being independent of the water turbidity. In addition, it is concluded that a seven-lense scheme is approximately three times more tolerant to offset than a single lens. A random forest machine learning algorithm is also assessed for its suitability to estimate the offset and angle of the AUV in relation to the fixed sensor, based on the power distribution of each lens, in real time. The algorithm is able to estimate the offset and angular misalignment with a mean square error of \(5\,\mathrm{mm}\) (\(6\,\mathrm{mm}\)) and \(0.157\,\mathrm{rad}\) (\(0.174\,\mathrm{rad}\)) for a distance between the transmitter and receiver of \(1\,\mathrm{m}\) and \(0.5\,\mathrm{m}\), respectively.  
underwater; optical; wireless, propagation; misalignment tolerance; Monte Carlo simulation; multiple-lens receiver; random forest +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 Materials and Methods
### Shopping bags Degradation Experiment  
Shopping bags were exposed to in situ marine conditions by placing them at a 1 m depth in the tourist marina of Chioggia (Venice, Italy) (45.223186 N, 12.281897 E), in order to simulate a dispersed bag floating in the water column. The experiment was carried out in the summer from 21 June to 1 September 2019 for a total of 73 days of exposure. Average physicochemical water conditions of the period were: temperature 26.8 \(\pm\) 1.1 \({}^{\circ}\)C; salinity: 33.71 \(\pm\) 1.03 PSU; pH: 8.17 \(\pm\) 0.05; oxygen: 5.54 \(\pm\) 0.67 mg/l (data available from the Hydrobiological Station of Chioggia, University of Padua (45.22340 N, 12.28434 E; [[https://chioggia.biologia.unipd.it/banche-dati/parametri-laguna/parametri-2019/](https://chioggia.biologia.unipd.it/banche-dati/parametri-laguna/parametri-2019/)]([https://chioggia.biologia.unipd.it/banche-dati/parametri-laguna/parametri-2019/](https://chioggia.biologia.unipd.it/banche-dati/parametri-laguna/parametri-2019/)), accessed on 30 September 2019). The bags were fixed on the surface of polyethylene supports previously filled with seawater in order to regulate their buoyancy, and the supports were immersed in the water column along ropes tied to the piers of the marina. Six samplings were carried out at regular intervals (T1: 4 July 2019; T2: 16 July 2019; T3: 31 July 2019; T4: 8 August 2019; T5: 21 August 2019; T6: 1 September 2019) during the exposure period of about two months. During each sampling, fragments of the different bags were collected, fouling organisms were gently removed by hand and samples were rinsed with deionized water. Samples were successively dried at room temperature and then subjected to isotopic (IRMS) and spectroscopic (FTIR) analyzes.
## 2 Materials and Methods
### Theoretical Modeling of Integral Squeeze Film Bearing Damper
\begin{array}{l}p=-12\mu\omega R^{2}\int\frac{\left(1-\varepsilon\cos\alpha \right)^{2}\left(1-\varepsilon^{2}\right)^{1/2}}{C\left(1-\varepsilon\cos \alpha\right)^{2}\left(1-\varepsilon\cos\alpha\right)}d\alpha+\int\frac{ \Lambda_{1}\left(1-\varepsilon\cos\alpha\right)^{3}\left(1-\varepsilon^{2} \right)^{1/2}}{C\left(1-\varepsilon^{2}\right)^{3}\left(1-\varepsilon\cos \alpha\right)}d\alpha+A_{2}\\ =-12\mu\omega R^{2}\frac{\alpha-\varepsilon\sin\alpha}{C^{2}\left(1- \varepsilon^{2}\right)^{3/2}}+\frac{\Lambda_{1}}{C^{3}\left(1-\varepsilon^{2 }\right)^{5/2}}\left[\alpha-2\varepsilon\sin\alpha+\left(\frac{1}{2}\alpha+ \frac{1}{4}\sin 2\alpha\right)\varepsilon^{2}\right]+A_{2}\end{array} . \tag{11}\]  
When \(\theta=0\) and \(\theta=2\pi\), while \(p\) (\(0\)) = \(p\) (\(2\pi\)), it can be obtained by Equation (11):  
\[-12\mu\omega R^{2}\frac{\alpha}{C^{2}\left(1-\varepsilon^{2}\right)^{3/2}}+ \frac{\Lambda_{1}}{C^{3}\left(1-\varepsilon^{2}\right)^{5/2}}(1+\frac{1}{2} \varepsilon^{2})\alpha=0. \tag{12}\]  
Then, we can get the integral constant:  
\[\Lambda_{1}=\frac{24\mu\omega R^{2}C(1-\varepsilon^{2})}{2+\varepsilon^{2}} \tag{13}\]  
Since the oil supply pressure is environmental pressure, when \(\theta=0\), \(p=0\), \(\Lambda_{2}=0\). Then the oil film pressure is:  
\[p=6\mu\left(\frac{R}{C}\right)^{2}\frac{(-2\omega)\varepsilon(2+\varepsilon \cos\theta)\sin\theta}{(2+\varepsilon^{2})\left(1+\varepsilon\cos\theta\right) ^{2}} \tag{14}\]  
The oil film is a full oil film without cavitation, and the integration is from \(0\) to \(2\pi\), then the radial oil film reaction force is:  
\[F_{r}=\int_{-\frac{1}{2}}^{\frac{L}{2}}\int_{0}^{2\pi}pR\cos\theta dzd\theta= RL\left[p\sin\theta\right|_{0}^{2\pi}-\int_{0}^{2\pi}\sin\theta\frac{dp}{d\theta}d \theta\right]=0 \tag{15}\]  
It can be determined that the integral squeeze film bearing damper has no radial oil film reaction force. The circumferential oil film reaction force:  
\[F_{i}=-\frac{\mu\omega LR^{3}}{C^{2}}\left[\frac{24\pi\varepsilon}{\left(2+ \varepsilon^{2}\right)\left(1-\varepsilon^{2}\right)^{1/2}}\right]. \tag{16}\]  
Then we can get the damping coefficient as:\[c=\frac{|F_{l}|}{C\varepsilon\omega}=24\mu L(\frac{R}{C})^{3}\frac{1}{(2+\varepsilon^ {2})(1-\varepsilon^{2})^{1/2}} \tag{17}\]  
where \(\mu=7.03\times 10^{-3}\) N\(\cdot\)s/m\({}^{2}\) (this depends on lubricant itself), \(L=0.017\) m, \(R=0.03\) m, and \(C=0.0005\) m (these three variables are the design parameters of the ISFBD itself). And \(\varepsilon=C/R\approx 0\), this value is very small and can be neglected, so \(2+\varepsilon^{2}\approx 2\) and \(1-\varepsilon^{2}\approx 1\). From this, the damping coefficient of the integral squeeze film bearing damper can be calculated to be 309.8 N\(\cdot\)s/m.
###### Abstract  
Phase-amplitude coupling (PAC) of theta and gamma rhythms of the brain has been observed in animals and humans, with evidence of its involvement in cognitive functions and brain disorders. This motivates finding individual frequencies of maximal theta-gamma coupling (TGC) and using them to adjust brain stimulation. This use implies the stability of the frequencies at least during the investigation, which has not been sufficiently studied. Meanwhile, there is a range of available algorithms for PAC estimation in the literature. We explored several options at different steps of the calculation, applying the resulting algorithms to the EEG data of 16 healthy subjects performing the n-back working memory task, as well as a benchmark recording with previously reported strong PAC. By comparing the results for the two halves of each session, we estimated reproducibility at a time scale of a few minutes. For the benchmark data, the results were largely similar between the algorithms and stable over time. However, for the EEG, the results depended substantially on the algorithm, while also showing poor reproducibility, challenging the validity of using them for personalizing brain stimulation. Further research is needed on the PAC estimation algorithms, cognitive tasks, and other aspects to reliably determine and effectively use TGC parameters in neuromodulation.  
phase-amplitude coupling; theta-gamma coupling; electroencephalography; brain stimulation; reproducibility; reliability; intraclass correlation coefficient
## 3 Mesoscale Equivalent Model for UHPC
### Bond-Slip Constitutive Model  
For building the mesoscale equivalent model for UHPC with steel fibers, the failure mode and interfacial behavior between steel fibers and matrixes must be determined. [PERSON]  
Figure 7: Frontal damages of UHPC targets: (**a**) U-1 target; (**b**) U-2 target.  
et al. [21] pointed out that the fiber pullout is the most common failure mode when UHPC is subjected to loading due to the weak fiber-matrix interface and high tensile strength of the steel fiber. Hence, the interfacial behavior between steel fibers and matrixes can be regarded as a kind of bond-slip behavior. [PERSON] et al. [22] carried out single steel fiber pullout tests and put forward a bond-slip constitutive model for UHPC with steel fibers. In this model, the interfacial shear stress \(\tau\) is simplified to be constant at a given relative slip \(S\). For different values of the relative slip, the interfacial shear stress is determined by Equation (2), where \(\tau\) increases linearly with \(S\) in the bonding phase and declines exponentially with \(S\) in the debonding phase.  
\[\tau=\left\{\begin{array}{cc}G_{s}S&S\leq S_{max}\\ G_{s}S_{max}\times e^{-EXP\times D}&S>S_{max}\end{array}\right. \tag{2}\]  
where \(G_{s}\) is the interfacial shear modulus, \(EXP\) is the exponent in the debonding phase, and \(D\) is the accumulated plastic displacement in all the integral time steps.  
The pivotal constitutive parameters for UHPC (150 MPa) are determined according to the pullout load-slip curve, where \(G_{s}\) is 2393 MPa, \(S_{max}\) is 0.00125, and \(EXP\) is 0.2. Then, simulations on the static split tension test and SHPB test are performed, where the numerical results agree well with experimental results, and more details can be seen in Reference [22]. This bond-slip constitutive model is proposed, and the determined parameters are validated by static and dynamic tests and are adopted in the present study.
## 3 Results
### Hydroxapatite Impurities  
[PERSON] et al. emphasized the importance of stringent processing conditions to maintain HA purity, noting that certain manufacturing methods could inadvertently introduce unwanted CaO phases [91]. Maintaining high purity in hydroxyapatite is crucial to optimizing its functionality, mechanical properties, and bioactivity for biomedical applications.  
The presence of impurities in hydroxyapatite (HA) can significantly hinder its bioactivity, impairing its ability to facilitate osteoconduction and support new bone formation. Structural anomalies or unwanted phases caused by impurities can weaken the mechanical integrity of HA, making it less durable and more prone to degradation under physiological stress. These impurities often arise from the synthesis process, starting materials, or production environments. For instance, during wet synthesis methods, the use of low preparation temperatures can lead to ionic impurities being incorporated into the crystalline structure from the aqueous solution [92].  
[PERSON] et al. identified various impurities in synthetic hydroxyapatite/polyvinyl alcohol (n-HA/PVA) composites, including \(\beta\)-tricalcium phosphate (\(\beta\)-TCP), free CaO, calcium-deficient HA phases, and residual polyvinyl alcohol. These impurities adversely affect the material's osteogenic potential and mechanical properties [93]. Impurities can also originate from natural substrates used for HA synthesis. For example, [PERSON] et al. demonstrated that grinding and thermally treating shells can yield smooth, spherical HA particles suitable for bone substitutes, but processing can also introduce impurities such as calcium carbonate [94].  
[PERSON] et al. reported the presence of calcium sulfate hemihydrate and traces of calcium dihydrate residue in HA materials, while [PERSON] et al. identified contamination with calcium carbonate and calcium hydroxide phases during production [95; 96]. Such contamination can compromise the material's biocompatibility and structural integrity, necessitating precise control during manufacturing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
## 3 Proposed Method
### Exploring Inter-Sparsity among Multiple Sources  
Further, in order to investigate the inter-sparsity among multiple sources, and how many active sources are involved in the overlapped TF bins, we proposed a statistic algorithm of the number of the active source at each TF instant when there occur multiple sources. In detail, the source whose energy at a certain TF instant is dominant among all the sources is regarded as the active source at this instant, i.e., its energy occupies a significant proportion of the total energy of all sources. To find the proportion of active source number when different numbers of sources occur, we define a statistical measure which is reflected in Algorithm 2.  
```
Input:\(\mathbf{S}_{a}^{n}=(a_{li})^{L\times K}=[\mathbf{S}_{1}^{n}\ \mathbf{S}_{2}^{n}\ \cdots\ \mathbf{S}_{K}^{n}]\), where \(\mathbf{S}_{i}^{n}=[|\mathbf{S}_{i}(n,1)|,|\mathbf{S}_{i}(n,2)|,...,|\mathbf{S}_{i}(n,L)|]^{T}\). Initialize:\(\mathbf{Counter}=[Count_{1},Count_{2},...,Count_{K}]=\mathbf{0},c\) is used for counting the active source number within current TF bin, \(c=0\), loop frequency index: \(l=1\), loop source index: \(i=0\). for\(l=1,...,L\)do if\(a_{li}>\eta\cdot\sum\limits_{j=1}^{K}|\mathbf{S}_{j}(n,l)|\)  increment \(c\). endif endfor  increment \(Counter_{c}\) (i.e., increment value of the element in counter whose index is \(c\)). Reset:\(c=0\). endfor Output:\(\mathbf{Counter}\).
```  
**Algorithm 2** Statistic of Active Source Number  
Then, we can calculate the probability of active source number (\(PASN\)) among \(K\) simultaneously occurring sources as:  
\[PASN(c)=\frac{Counter_{c}}{L} \tag{12}\]  
where \(Counter_{c}\) denotes the number of TF instants when \(c\) sources are active simultaneously, \(L\) is the number of STFT points in a frame, and \(PASN(c)\) represents the probability of TF bins which contain \(c\) active sources. In other words, it implies the probability of \(c\) sources active simultaneously over all TF bins. In order to analyze the \(PASN\) among \(K\) simultaneously occurring sources, we calculate the \(PASN\) of all groups mentioned above when \(K=6\). The results are shown in Figure 4 with 95% confidence intervals.  
It can be seen that when there are six simultaneously occurring speech sources, most of the time only two or three sound sources are active at the same time over the non-sparse TF bins (occupying nearly 70%), i.e., most of the non-sparse components are involved with two or three sources. This implies that the proposed half-K assumption that no more than \(K/2\) sources are active in a certain TF bin when there are \(K\) simultaneously occurring speech sources is reasonable.
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 2 Related Works
### Real-Time Model  
In real-time applications, the requests submitted by clients are associated with deadlines, and the primary objectives of real-time scheduling is to minimize the deadline miss ratio of requests. EDF (Earliest Deadline First) [25] is a classical scheduling algorithm in real-time systems, broadcasts data item based on the urgency of the request. To achieve the lower request deadline miss ratio and access time, [PERSON] [32] proposed to consider request urgency, service productivity and scheduling fairness in scheduling. Motivated from EDF and MRF (Most Requested First), [PERSON] et al. [9] proposed a real-time scheduling algorithm called SIN (Slack time Inverse Number of pending requests). For time-critical services, [PERSON] et al. [33] proposed an algorithm called SUSC (Scheduling under Sufficient Channels), which creates a broadcast program under sufficient channels. Alternatively, PAMAD (the Progressively Approaching Minimum Average Delay) is used when the number of channels is insufficient. DTIU (Dynamic Temperature Inverse Urgency) [34] is proposed to cater real-time multi-item requests, which considers both data item popularity and request urgency. [PERSON] et al. [3] studied the performance of different scheduling algorithm under strict deadline environment. [PERSON] et al. [35] proposed UPF (Urgent and Popular data item First) for scheduling real-time requests under multi-channel environment.
# Research on the Robustness of Interdependent Networks under Localized Attack
## 2 Models  
### Cascading Failures of Interdependent Networks  
For simplicity and without loss of generality, we apply the model proposed by [PERSON] in this paper, as shown in Figure 1. In this model, two subnetworks A and B, with the same number of nodes and average degree, are coupled by random one-to-one dependency links, which means one node will fail when its dependency counterpart fails. Assume that only nodes belonging to the giant component remain functional. We define \(p_{A}\) and \(p_{B}\) as the probability that the node belongs to the larger cluster of subnetwork \(A\) and \(B\), respectively; \(\psi^{\prime}_{n}(\psi^{\prime}_{n})\) is the fraction of the remaining nodes in subnetwork \(A(B)\); \(\psi_{n}\) and \(\phi_{n}\) are the giant cluster components; and \(q_{A}(q_{B})\) is the proportion of nodes in subnetwork \(A(B)\) dependent on subnetwork \(B(A)\). At step 1, a fraction \(p\) of nodes in subnetwork \(A\) are attacked [27; 28], then the general form of the cascading failures can be described as shown in Table 1.  
In this paper, we use two indices to represent the robustness of interdependent networks. The first index is of relative size \(S\) of the giant components in subnetwork \(A\) and \(B\) when cascading failures end. For one-to-one correspondence and fully-coupled interdependent networks we can define \(S\) as shown in Equation (1); larger \(S\) indicates better robustness:  
\[S=\frac{G_{A}+G_{B}}{N_{A}+N_{B}}=\frac{G_{A}}{N_{A}}=\frac{G_{B}}{N_{B}}, \tag{1}\]  
The point where the giant component decreases to zero is typically referred to as the critical threshold \(p_{\mathrm{c}}\). It is also commonly used to characterize the robustness of interdependent networks. A larger threshold means more nodes need to be removed to make the interdependent systems collapse, and also shows that the robustness of the interdependent network is better.
###### Abstract  
The premixed combustion of a gas turbine is prone to thermoacoustic oscillation, which affects the safety of combustion systems. This study experimentally investigated the suppression mechanism of a stratified jet-in-crossflow on the thermoacoustic instability and nitrogen oxides (\(\mathbf{NO_{x}}\)) in an unstable lean-premixed combustor. Two key parameters of the jet-in-crossflow--gas density and jet flow rate--were investigated to elucidate their effect on momentum ratios. The results reveal that the stratified jet-in-crossflow reduces the maximum amplitude of combustion oscillation by 58%, while the \(\mathbf{NO_{x}}\) concentration exhibits a high damping ratio of 48.8%. Higher jet flow rates and gas densities enhance the suppression of combustion thermoacoustic oscillations and \(\mathbf{NO_{x}}\) emissions. The distribution of flame radicals indicates that an increase in the jet flow rate reduces the intensity of the flame heat release rate, thereby reducing the flame thermoacoustic instability. As the argon/helium volume ratio increases, the mode of thermoacoustic oscillation shifts. As the argon/helium volume ratio gradually increases from 0%/100% to 100%/0%, the main frequency of thermoacoustic oscillations gradually decreases from 267 to 121 Hz. Notably, the transient amino-group radicals in the flame increase with the increasing argon/helium volume ratio, indicating that the jet suppresses \(\mathbf{NO_{x}}\) generation. The changes in peak temperature and flame shape after jetting further confirm that the stratified jet-in-crossflow alters the flame structure within the combustion chamber. The effect of the momentum ratio on the suppression of thermoacoustic instability is studied for the first time. This study provides a promising method for suppressing the thermoacoustic oscillations and \(\mathbf{NO_{x}}\) emissions in premixed flames, contributing to a safer operation and cleaner emissions in lean-premixed combustors.  
combustion instability; \(\mathbf{NO_{x}}\) emission; jet-in-cross-flow; premixed combustion; thermoacoustic instability control