esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Software-Defined Networking
### Protocol Options for the Southbound Interface  
The most common southbound interface is OpenFlow, which is standardized by the Open Networking Foundation (ONF). OpenFlow is a protocol that describes the interaction of one or more control servers with OpenFlow-compliant switches. An OpenFlow controller installs flow table entries in switches, so that these switches can forward traffic according to these entries. Thus, OpenFlow switches depend on configuration by controllers. A flow is classified by match fields that are similar to access control lists (ACLs) and may contain wildcards. In Section 3, we provide a detailed description of OpenFlow and describe the features offered by different versions of the protocol.  
Another option for the southbound interface is the Forwarding and Control Element Separation (ForCES) [5; 6] which is discussed and has been standardized by the Internet Engineering Task Force (IETF) since 2004. ForCES is also a framework, not only a protocol; the ForCES framework also separates the control plane and data plane, but is considered more flexible and more powerful than OpenFlow [7; 8]. Forwarding devices are modeled using logical function blocks (LFB) that can be composed in a modular way to form complex forwarding mechanisms. Each LFB provides a given functionality, such as IP routing. The LFBs model a forwarding device and cooperate to form even more complex network devices. Control elements use the ForCES protocol to configure the interconnected LFBs to modify the behavior of the forwarding elements.  
The SoftRouter architecture [9] also defines separate control and data plane functionality. It allows dynamic bindings between control elements and data plane elements, which allows a dynamic assignment of control and data plane elements. In [9], the authors present the SoftRouter architecture and highlight its advantages on the Border Gateway Protocol (BGP) with regard to reliability.  
ForCES and SoftRouter have similarities with OpenFlow and can fulfill the role of the southbound interface. Other networking technologies are also discussed, as well as possible southbound interfaces in the IETF. For instance, the Path Computation Element (PCE) [10] and the Locator/ID Separation Protocol (LISP) [11] are candidates for southbound interfaces.
## 2 Theoretical Background and Problem Statement
### Lyapunov Function Proof  
To demonstrate that \(e_{1}e_{2}\), and \(\Phi\) converge to distinct sets within the convergence time \(T_{D}\), we derive the following Lyapunov equation based on the preceding discussion:  
\[V=V_{1}+V_{2}+V_{3} \tag{47}\]  
Deriving the derivative of the above equation yields the following:  
\[\dot{V}\leq-A_{11}\sqrt{V_{1}}-\frac{A_{21}}{\sqrt{\lambda_{\text{max}}(M(q))} }\sqrt{V_{2}}-A_{31}\sqrt{V_{3}}-A_{12}{V_{1}}^{2}-\frac{A_{22}}{\lambda_{ \text{max}}^{2}(M(q))}V_{2}^{2}+\frac{A_{32}}{n}{V_{3}}^{2}+B \tag{48}\]  
where  
\[B=B_{1}+\frac{1}{2}\overline{\sigma}^{2} \tag{49}\]Using Lemma 2 and Equation (48), we obtain the following:  
\[\dot{V}\leq-\delta_{1}\sqrt{V}-\frac{\delta_{2}V^{2}}{3}+B \tag{50}\]  
where \(\delta_{1}\) and \(\delta_{2}\) have the following definitions:  
\[\delta_{1}=\min\Biggl{\{}A_{11},\frac{A_{21}}{\sqrt{\lambda_{\max}(M(q))}},A_{ 31}\Biggr{\}} \tag{51}\]  
\[\delta_{2}=\min\Biggl{\{}A_{21},\frac{A_{22}}{\sqrt{\lambda_{\max}^{2}(M(q))}},\frac{1}{n}A_{32}\Biggr{\}} \tag{52}\]  
To ensure that \(e_{1}\), \(e_{2}\), and \(\Phi\) converge within the corresponding compact sets, and to allow V to converge within a specific interval, we eliminate the term B from (50), and set \(V^{2}>\frac{3B}{\delta_{2}}\), which allows the system \(V\) to eventually converge to the set \(\{\mathrm{V}|0<\mathrm{V}<(\frac{3B}{\delta_{2}})^{\frac{1}{2}}\}\). Furthermore, we design a constant \(\Psi\) within the interval from 0 to 1, which ensures that \(B\leq\frac{\Psi\delta_{2}}{3}V^{2}\). Therefore, Equation (50) can be rephrased as follows:  
\[\dot{V}\leq-\delta_{1}\sqrt{V}-(1-\Psi)\frac{\delta_{2}V^{2}}{3} \tag{53}\]  
Let \(T_{D}\) represent the duration required for the system to reach a converged state within a fixed time period. According to Lemma 5, \(T_{D}\) is designed as follows:  
\[T_{D}\leq\frac{2}{\delta_{1}}+\frac{3}{(1-\Psi)\delta_{2}} \tag{54}\]  
Within a time \(\mathrm{T_{D}}\), \(e1,e2\), and \(\Phi\) will each converge towards \(\mathrm{O_{e1}},\mathrm{O_{e2}},\mathrm{O_{\Phi}}\), respectively. These are designed as follows:  
\[\mathrm{O_{e_{1}}}=\Biggl{\{}\mathrm{e_{1}}\in R^{n}|\|\mathrm{e_{1}}\|\leq k _{p}\sqrt{1-\frac{1}{e^{Y}}}\Biggr{\}} \tag{55}\]  
\[\mathrm{O_{e_{2}}}=\Biggl{\{}\mathrm{e_{2}}\in R^{n}|\|\mathrm{e_{2}}\|\leq \sqrt{\frac{Y}{\lambda_{\min}(M(q))}}\Biggr{\}} \tag{56}\]  
\[\mathrm{O_{\Phi}}=\Biggl{\{}\widetilde{\Phi}=[\widetilde{\Phi}_{1}, \widetilde{\Phi}_{2},...,\widetilde{\Phi}_{n}]\in R^{l\times n}|\biggl{\|} \widetilde{\Phi}\biggr{\|}\leq\sqrt{\frac{Y}{\lambda_{\min}\Bigl{(}\Lambda_ {i}^{-1}\Bigr{)}}},i=1,2,...n\Biggr{\}} \tag{57}\]  
With  
\[Y=2\sqrt{\frac{3B}{\delta_{2}\Psi}} \tag{58}\]
# Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach
###### Abstract  
The IoT system has become a significant component of next generation networks, and drawn a lot of research interest in academia and industry. As the sensor nodes in the IoT system are always battery-limited devices, the power control problem is a serious problem in the IoT system which needs to be solved. In this paper, we research the resource allocation in the wireless powered IoT system, which includes one hybrid access point (HAP) and many wireless sensor nodes, to obtain the optimal power level for information transmission and energy transfer simultaneously. The relationship between the HAP and the sensor nodes are formulated as the Stackelberg game, and the dynamic variations of the energy for both the HAP and IoT devices are formulated through the dynamic game with mean field control. Then the power control in the wireless powered IoT system is formulated as a mean field Stackelberg game model. We aim to minimize the transmission cost for each sensor node based on optimally power resource allocation. Meanwhile, we attempt to minimize the energy transfer cost based on power control. As a result, the optimal solutions based on the mean field control of the sensor nodes and the HAP are achieved through dynamic programming theory and the law of large numbers, and \(\varepsilon\)-Nash equilibriums can be obtained. The energy variations for both the sensor nodes and HAP after the control of resource allocation based on the proposed approach are verified based on the simulation results.  
power control; wireless energy transfer; IoT system; mean field Stackelberg game +
Footnote †: journal: Sensors
# Speech Sentiment Analysis Using Hierarchical Conformer Networks
## 6 Conclusions and Future Direction  
In this article, we introduce a new method that aims at a more accurate extraction of local and global features of speech signals. We use a combination of convolution and a transformer to better obtain the spatio-temporal features as well as the long and short-term dependencies of the speech signal. In this approach, we construct corresponding learning groups for local features and global features, respectively. The local feature learning group contains four learning blocks combined by convolution and transformer to learn the spatial contextual relevance of sentiments, using surplus learning strategies in a hierarchical manner. We used these blocks to extract the most salient emotional features. Then, we input these derived features into a GRU network with a fused attention mechanism to recalibrate the global weights using these learned features. We use the fully connected network to further process the feature information after fusion by the attention mechanism, and finally analyze the results by softmax and central loss function. We performed a wide range of experiments on the datasets IEMOCAP and RAVDESS speech libraries to examine and appraise the effectiveness and importance of the suggested model relative to most advanced models. Our suggested system shows excellent results, with IEMOCAP and REVDESS obtaining 80% and 81% recognition accuracy, respectively, clearly showing the  
Figure 8: Recognition of different emotions by the proposed method.  
reliability of the model. The experiments demonstrate that the model is effective on various existing systems in terms of sentiment analysis and sentiment classification.  
In future work, we plan to implement sentiment analysis tasks in terms of multimodality. We use this relatively novel convolutional and transformer architecture to better analyze the spatio-temporal features of different modalities. Then, we will perform the fusion of different modal features by deep learning methods to achieve the sentiment classification task under multimodality. The method introduced in this paper can also be applied to more speech-like sentiment analysis tasks to further achieve more effective speech sentiment analysis tasks by finding the interconnections between different modalities.  
P.Z. writing--original draft, writing--review and editing. F.L. conceptualization, methodology. X.Z. software, data curation. All authors have read and agreed to the published version of the manuscript.  
We are grateful for the support of the National Natural Science Foundation of Shandong ZR202011020044. We are grateful for the support of the National Natural Science Foundation of China 61772321.  
Not applicable.  
The authors declare no conflict to interest.
## 4 Experimental and Results
### Expressiveness  
We relied on a 5-value mean opinion score (MOS) to evaluate the naturalness of speech synthesis systems. During the testing phase, 100 test samples were generated for each system, and each test sample was evaluated by 20 testers. Moreover, the text for generation contained a portion of sentences with significant emotional tendencies.  
The listening test results are shown in Table 1. The recordings in our dataset contain rich prosodic variations. Among those baselines, PortaSpeech achieved the lowest MOS, since our dataset is a mix of emotional speech and reading; maybe it is difficult for PortaSpeech to fit training data with such rich prosodic variation. The MOS of FastSpeech2 is also low; its prosody is relatively flat, which may be the cause of the over-smoothing of FastSpeech2. MultiGST improves the naturalness of FastSpeech2, which suggests that leveraging semantic information helps learn variable prosody. However, since MultGST only models prosody at the subword level and sentence level, the rhythm it produces is far from that of recordings. The speech generated by VITS also suffers from rhythm and stress errors. Our model achieves higher naturalness compared to all the baselines. HierTTS is very natural in pauses and stresses and close to the rhythmic performance of the recording.
## 5 Simulation and Discussion
### Simulation Results
#### 5.2.2 Step Change of Load Power  
As shown in Figure 16, the load power increases from 100 kW to 300 kW at 50 s. Figure 17 shows the displacement factors of the pump and the motor. It can be seen that the motor displacement gets immediately bigger from 0.26 to 0.76 for the change of the load power. Simultaneously, the pump displacement rises first to 0.365 in a short time and then decreases to 0.358. Later, it has a creeping increase.  
Figure 16: Load power step.  
Figure 14: Power coefficient and tip speed ratio.  
Figure 15: Wind power and turbine power.  
Figure 18 shows the pump flowrate, the accumulator flowrate and the motor flowrate. As shown, the motor flowrate changes from 400 L/min to 1150 L/min. The pump flowrate remains approximately constant during the change process of the load power. About one third of the pump flowrate 566 L/min flows into the hydraulic accumulator when the load power is 100 kW. After the load power increases to 300 kW, the hydraulic accumulator discharges the high-pressure oil to complement the shortage of the pump flowrate. From Figure 19, it can be shown that the power changes of the pump, the accumulator and the generator is essentially in agreement with the flowrate changes.  
Figure 19: Turbine, accumulator, and generator output power.  
Figure 17: Pump displacement factor and motor displacement factor.  
Figure 18: Pump, accumulator, and motor flow rate.  
Figure 20 shows the pressure of the pump outlet, the accumulator outlet, and the motor inlet. The difference between the pump outlet pressure and the motor inlet pressure is always a fixed value, which is the pressure loss of the check valve. The accumulator pressure gradually decreases owing to the oil discharge after 50 s. Besides, the accumulator pressure starts to be greater than the pump outlet pressure. The pressure loss between the accumulator and the motor becomes greater on account of the increases of the accumulate flowrate and the pressure loss.  
From Figure 21, it can be shown that the pump speed rises first for the decrease of the pump outlet pressure and then falls to the old value for the increase of the pump displacement. The motor speed undergoes a short-term drop to 1499 r/min and returns to 1500 r/min after about 2 s.
###### Abstract  
Swarm electron density (Ne) observations from the Langmuir probe (LP) can detect ionospheric disturbances at the altitude of a satellite. Along-track satellite observations provide a large number of very short observations of different places in the ionosphere, where Ne is disturbed. Moreover, different perturbations occupy various Ne signal frequencies. Therefore, such short signals are more recognizable in two dimensions, where aside from their change in time, we can observe their diversity in the frequency domain. Spectral analysis is an essential tool applied here, as it enables signal decomposition and the recognition of composite patterns of Ne disturbances that occupy different frequencies. This study shows a high-resolution application of short-term Fourier transform (STFT) to Swarm Ne observations in the Papua New Guinea region in the vicinity of earthquakes, stunamis, and related general seismic activity. The system of tectonic plate junctions, including the Pacific-Australian boundary, is located orthogonally to Swarm track footprints. The selected wavelengths of seismically induced ionospheric disturbances detected via Swarm are compared with the three sets of three-month records of seismic activity: in the winter solstice of 2016/2017, when seismic activity was highest, and in the summer solstice and vernal equinox of 2016, which were calmer. Moreover, more Swarm data records are analyzed at the same latitudes for validation purposes, in a place where there are no tectonic plate boundaries that are orthogonal to the Swarm orbital footprint. Additional validation is supplied through Swarm Ne observations from completely different latitudes, where the Swarm orbital footprint orthogonally crosses a different subducting plate boundary. Aside from the seismic energy, the solar radio flux (F10.7), equatorial plasma bubbles (EPBs), and geomagnetic ap and Dst indices are also reviewed here. Their influence on the ionospheric Ne is also found in Swarm observations. Finally, the Pearson correlation coefficient (PCC), applied to the pairs of 3-month time series created from Swarm Ne variations, seismic energy, ap, Dst, and F10.7, summarizes the graphical inspection of mutual correlations. It points to the predominant correlation of Swarm Ne disturbances with seismicity, especially during nighttime. We show that most of the Ne disturbances at a selected wavelength of 300 km correlate more with seismicity than with geomagnetic and solar indices. Therefore, Swarm LP can be assessed as being capable of observing the lithosphere-atmosphere-ionosphere coupling (LAIC) from the orbit.  
s 2023155557. [[https://doi.org/10.3390/rs15235557](https://doi.org/10.3390/rs15235557)]([https://doi.org/10.3390/rs15235557](https://doi.org/10.3390/rs15235557))
## 1 Introduction
To the best of our knowledge, no study to date has investigated the antioxidant potential of BWPH in cellular systems or their immunomodulatory potential. In most cases, in order to exhibit effective bioactivity in vivo, bioactive peptides have to survive gut transit and depending on their target cell, may need to permeate the gut membrane. However, due to the large population of macrophages that exist along the entire length of the GI tract [25], it is possible that bioactive food components may interact with and modulate macrophages as they transit the gut. However, gut macrophage populations characteristically do not induce classic inflammatory responses so the ability of BWPH to modulate inflammation should initially be assessed in the well-established macrophage cell line RAW264.7, which can generate proinflammatory mediators and cytokines. Recently our group generated distinct blue whiting soluble protein hydrolyses (BWSPHs) at commercial scale using various food-grade microbial-derived proteolytic enzyme preparations and hydrolysis conditions (enzyme:substrate (E:S) ratios ranging from 0.005% to 0.900% (\(w/w\)), 50 \({}^{\circ}\)C, 45-120 min, [26]). Although these hydrolyses differed in their DH and molecular mass distribution, all BWSPHs tested demonstrated in vitro antidiabetic activity through dipeptidyl peptidase-IV (DPP-IV) inhibitory and insulin secretory activity. The objective of this follow-on study was to research additional potential bioactivities of the six BWSPHs and their simulated gastrointestinal (GI) digests through noncellular and cellular antioxidant and immunomodulatory assays. The 2,2-diphenyl-1-picryl-hydraz (DPPH) radical scavenging, ferric reducing antioxidant power (FRAP), and oxygen radical absorbance capacity (ORAC) assays were used to assess noncellular antioxidant activity whilst the ability of the BWSPHs and simulated GI digests to modulate redox balance (GSH, CAT, and ROS) was assessed in oxidatively stressed RAW264.7 cells. The immunomodulatory potential of BWSPHs was also studied via investigating their effect on NO production, and IL-6 and TNF-\(\alpha\) cytokine levels in lipopolysaccharideaccharide (LPS)-activated RAW264.7 cells.
# A Survey on Satellite Communication System Security
## 4 Attacks in Satellite Communication Systems
### Availability Threats
Although not deployed in actual combat, this weapon successfully destroyed the Solwind P78-1 satellite during tests. In response to the AGM-135 ASAT, the Soviet Union modified the Mig-31 Foxhound. However, clear experimental evidence of its effectiveness was not available. Later in the 1980s and into the 1990s, the U.S. began developing new ground-based energy weapons. A notable project was the MIRACL [47] (mid-infrared advanced chemical laser). Entering the 2000s, new nations joining the space race introduced additional threats. China, rapidly advancing in space technology, developed various interception systems, notably the SC-19 missile. This system was a mobile ground-based missile that launched a homing vehicle to destroy satellites via direct impact. China conducted a satellite destruction test using the SC-19 on the FY-1C satellite [48] and successfully developed a submarine-launched ASAT weapon, the 2 missile. The U.S. also started developing various weapon systems in response to these new threats. American ASAT experiments and interception systems, such as the RIM-161 Standard Missile 3 (SM-3), were developed. The U.S. successfully conducted an actual satellite interception test, Operation Burnt Frost, with the SM-3 [49]. After the Soviet Union collapsed and Russia was established, Russia developed the new laser weapon Peresvet. It exhibited significant achievements in the recent Ukraine conflict, although the U.S. dismissed these claims as propaganda. Russia also developed the Burevestnik [50], a new missile potentially ready for deployment, evolving from the Foxhound missile. In summary, various forms of satellite interception systems have been developed, which can be summarized as follows. Also, the overall ASAT types can be seen in Figure 8.  
**Kinetic Energy Attack:** This method involves destroying target satellites by colliding high-speed moving objects with them. There are different types of kinetic ASAT weapons, including direct-rising ASAT missiles and common-orbit satellite interceptors. These weapons use rocket propulsion to accelerate a warhead to high speeds for a direct collision or to generate dense metal fragments to destroy the target in space. Prominent weapon systems include the United States' SM-3, Russia's Burevestnik, and China's SC-19. **Invisible Energy Attack:** These attacks use high-energy beams like lasers and microwaves to neutralize or destroy a target. Directed energy weapons have the advantage of a fast attack speed and can potentially reach the speed of light. Paper [51] discusses high-energy laser beam ASAT weapons, high-energy particle beam ASAT weapons, and high-frequency microwave radio-frequency ASAT weapons, elaborating on their technological aspects and capabilities. Some notable examples include the United States' MIRACL and Russia's Peresvet.
# Secured MCDM Model for Crowdsource Business Intelligence
## 3 Recent Research on Crowdsourcing with AI
### Crowdsourcing for Crop Yield  
Remote sensing imagery data are used to detect the plant health in a crop field. The challenge of this imagery data for creating crop-type maps is the deficiency of ground-truth labels. The work in [26] deployed convolution neural networks (CNNs) to generate the crop-type map using crowdsourced data, Sentinel-2 and remote sensing imagery. The farmer-generated images were used to calculate the crop-type labels which were inputted to the CNN model. However, the data had lot of noise and lacked location accuracy. After this, the data were pre-processed and the imagery data from remote sensing units were deployed in CNN for training to generate points in the crop field. The authors concluded that the CNN differentiates rice, cotton and \"other\" crops with 74% accuracy and provides better results than the random forest method.
###### Abstract  
The aim of this study was to analyze and compare the acidity, microbiological, and chromaticity parameters; fatty acid (FA) and volatile compound (VC) profiles; and biogenic amine (BA), macro- and microelement, and mycotoxin concentrations in nontreated 'Ada', 'Sarta', and new breed blue (DS8472-5) and purple (DS8526-2) wheat lines wholemal (WW) with those fermented with lactic acid bacteria (LAB) possessing antimicrobial/antifungal properties, isolated from spontaneous sourodough: _Pedioxoccus acidilactici_-LUHS29, _Liquorilactobacillus urwarm_-LUHS245, _Lactiplantitobacillus plantarum_-LUHS122). All the fermented WW showed >8.0 log\({}_{10}\) CFU/g of LAB count, and the type of LAB was a significant factor in the WW acidity parameters. Phenylethylamine was the predominant BA in WW, and the wheat variety (WV), the type of LAB, and their interaction were significant factors on the BA formation. Despite the fact that some differences in trace element concentrations in WW were obtained, in most of the cases fermentation was not a significant factor in their content. The main FAs in WW were palmitic acid, all-_cis,trans_-octadecenoic acid, and linoleic acid. Fermented WW showed a more diverse VC profile; however, the influence of fermentation on deoxynivalenol in WW was varied. Finally, further studies are needed to indicate the technological parameters that would be the most effective for each WW, including the lowest BA formation and mycotoxin degradation.  
**Keywords:** wheat; fermentation; biogenic amines; volatile compounds; mycotoxins