esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mass Yields of PM\({}_{10}\)
#### 3.4.2 PM\({}_{0.5-10}\)  
The very similar yields of various elements except S in PM\({}_{0.5-10}\) from AIR and OXY27 are found in Figure 7b, supporting the similar yield of AIR as OXY27 shown in Figure 5b. Because there is no apparent impact on the partitioning of individual elements, the nearly identical particle-burning temperature between AIR and OXY27 (Table 3) should be responsible for their similar PM\({}_{0.5-10}\) yields. It is noteworthy that the slightly lower amount of Ca in OXY27 is caused by Equations (2) and (4), which inhibits the transformation of Ca, while the higher content of S in OXY27 can be attributed to the above-mentioned retention of ash particles in oxy-fuel combustion.  
Figures 6 and 7b also show that the absolute mass of various inorganic elements in PM\({}_{0.5-10}\) increases with the increased oxygen fraction during oxy-fuel combustion, especially for Ca, which leads to an obvious increase in the PM\({}_{0.5-10}\) yield. The irrelevance with the individual elemental yield reveals that both the fragmentation of char particles and the melting and coalescence of the included minerals are increased by higher char combustion temperatures [31; 33]. The extensive fragmentation of low-rank coal even in the ignition stage has already been observed by [PERSON] et al. [42].
## 4 Presentation and Discussion of the Results
### Effectiveness of the Reputation Scores Calculation--False, Unfair, and Biased Feedback  
Regarding user reputation data, as discussed in Section 2.4, its main concerns are about the quality and honesty of the feedback in the sequence of a commercial transaction. In order to address this problem, there are several approaches proposed in the literature that take advantage of blockchain-based architecture.  
The first one is the sharing of data reputation across the chain, thereby, solving the isolation problem [37] that makes platforms as data silos [18]. By breaking this isolation, platforms can share their data among themselves, increasing the total volume of available data and enabling their algorithms to become more efficient while deriving reputation scores, as well as more resistant to ballot-stuffing and bad-mouthing frauds [19, 37]. Other approaches based on machine learning [45] also benefit from this volume of data. Despite the efficiency of the algorithms, in such a decentralized environment the same data are provided to users avoiding incoherent information about feedback when visiting distinct platforms. Additionally, the calculation of the reputation scores could be more transparent, even if each platform applies different methods and algorithms.  
In [16, 17, 18, 19, 35, 44], a financial model is proposed as a strategy to reduce fraud. The authors stated that one effective way to reduce ballot-stuffing and bad-mouthing frauds, is by making them economically unviable due to required costs. In their proposals, when submitting feedback in the sequence of a commercial transaction, the seller supports a cost, in general, the cost of the transaction in the public blockchain (e.g., Ethereum Gas). Additionally, honest users are rewarded. This approach is not exclusive to blockchain-based reputation systems, because it could also be applied in other distributed environments. However, a public blockchain provides an environment that enables a seamless integration of the reputation system and platforms, as well as in terms of identity management, as we discuss below.  
In order to mitigate the fraud and bias in feedback, in [16, 18, 40] a community-driven approach based on endorsers and voting mechanisms, complemented with rewards, is proposed. According to the authors, if users become motivated to classify and validate feedback from other users, the quality of the reputation data is significantly improved. Again, this approach is also not exclusive to blockchain-based reputation systems, because it could also be applied in other distributed environments in which e-commerce platforms share their data.
## 4 Discussion
### Volatile Aroma Composition
Excessive concentration of C-6 aldehydes (>2430 \(\mu\)g/L) in apple juice has been associated with negative odor impressions and thus led to lower sensory scores and is often denoted by sensory descriptors such as \"artificial flavor, too green, and shampoo-like\" [2]. Thus, it is reasonable to assume the relatively high content of C-6 aldehydes in McIntosh juices might lead to the development of negative organoleptic properties. The volatile compounds identified from McIntosh and Honeycrisp juices are a combination of primary (synthesized by the intact fruit) and secondary (synthesized in response to cellular disruption during juice processing) volatile compounds [25]. The volatile compounds detected from intact fruit are mainly composed of esters and ethanol (Figure 3C,D). These primary volatiles are synthesized by controlled enzymatic reactions mainly from fatty acid metabolism [26]. It is well known that fatty acids are major precursors of aroma volatiles in several fruits, including apple, and the biosynthetic pathway includes beta-oxidation (primary volatiles) and lipoxygenase (LOX) action (secondary compounds) [26]. The beta-oxidation pathway provides alcohols and acyl co-enzyme-A (CoA,), which are the main precursors for volatile ester production. Acyl CoAs are reduced by acyl CoA reductase to produce aldehydes, which in turn are reduced by alcohol dehydrogenase (ADH) to form alcohols that are converted to esters via the action of AAT [25]. Secondary volatiles, which are mainly C-6 aldehydes and the associated alcohols, are formed by the LOX pathway from unsaturated fatty acids (linoleic and linolenic acids) when the fruit is crushed and exposed to oxygen [26]. In our experiment, C-6 and C-5 aldehydes such as (E)-2-hexenal and 2-methyl-4-pentenal were detected only in juice samples but not in whole apple samples (Figure 3). This is in agreement with other studies, which reported the higher content of C-6 aldehydes in apple juice compared to intact fruit [27; 28]. As discussed earlier, the presence of aldehydes in juice samples but not in intact Honeycrisp and McIntosh apples is attributed to the oxidation of unsaturated fatty acids (linoleic and linolenic) during juice processing.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 4 Experimental Results and Discussions
### WER Results
Adding the CTC loss in the hybrid CTC/attention architecture to assess the contribution of the CTC loss further improved the recognition performance, which demonstrated that monotonic alignments were very useful for speech recognition. These results were consistent with more clean encoder-decoder attention weight maps. Above all, our model using the DCM attention and the \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Modality**} & \multirow{2}{*}{**Objective**} & \multirow{2}{*}{**\#Params**} & \multicolumn{6}{c}{**Dataset**} & \multicolumn{6}{c}{**Noisy Reverberant**} \\ \cline{5-16}  & & & & & **LRS2-BBC** & & **LRS3-TED** & & \multicolumn{6}{c}{**SNR (dB)**} & \multirow{2}{*}{**Avg.**} \\ \cline{5-16}  & & & & & & **20** & **15** & **10** & **5** & **0** & **-5** & \\ \hline  & & & & & & & & & & & & & \\ TM-seq2 seq & V & CE & 54.2 M & & & & & & & 67.3 & & & \\ \hline  & & & & & & 9.8 & 21.7 & 23.3 & 25.7 & 33.7 & 47.6 & 68.9 & 33.0 \\ TM-seq2 seq & A & CE & 47.3 M & & & & 10.1 & 21.4 & 23.5 & 26.1 & 33.8 & 48.1 & 69.6 & 33.2 \\ \hline  & & & & & & 10.5 & 19.7 & 19.8 & 23.0 & 25.1 & 34.0 & 43.7 & 25.1 \\ TM-seq2 seq & AV & CE & 84.6 M & & & & 10.8 & 20.0 & 20.2 & 23.5 & 27.6 & 36.4 & 51.3 & 27.1 \\ TM-av\_align & AV & CE & 76.2 M & & & & 11.5 & 18.8 & 19.3 & 22.6 & 25.0 & 31.2 & 43.4 & 22.6 \\ TM-DCM & AV & CE & 86.7 M & & & & 11.7 & 18.1 & 18.9 & 21.8 & 25.8 & 34.1 & 47.1 & 25.4 \\  & & & & & & 8.7 & 17.3 & 17.5 & 19.2 & 22.0 & 29.2 & 41.2 & 22.2 \\  & & & & & & 9.0 & 17.8 & 18.0 & 19.8 & 22.9 & 31.5 & 45.8 & 23.5 \\ \hline  & & & & & & **8.6** & **16.8** & **16.9** & **18.8** & **22.0** & **28.9** & **40.7** & **21.8** \\ TM-DCM & AV & H & 86.7 M & & & & **8.8** & **17.1** & **17.3** & **19.2** & **22.2** & **30.9** & **43.6** & **22.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Word error rates (WERs) (%) for the TM-seq2 seq, TM-av_align, and TM-DCM on the LRS2-BBC and LRS3-TED datasets. The boldface WERs denote the best performance in each condition. Abbreviation to understand the table: A, audio-only modality; V, video-only modality; AV, audio-visual modalities; CE, cross-entropy loss only; H, hybrid CTC/attention loss; As a result that the video-only modality case (V) does not use the audio modality, we have a constant WER for each dataset on the TM-seq2 seq using the video-only modality in the first two rows. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **TM-seq2 seq** & **TM-av_align** & **TM-DCM** \\ \hline Modality attention & & & & & \\ (Query-Key/Value) & None & Audio–Video & Audio–Video and Video–Audio \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Architectural differences in cross-modality attentions between the TM-seq2 seq, TM-av_align, and TM-DCM. hybrid CTC/attention loss achieved the WER of 8.7% averaged over the two datasets for clean data with consistent better performance for all the experimented conditions than the others.
# Design of a 120 W Electromagnetic Shock Absorber for Motorcycle Applications
## 3 Modeling and Parametric Study of EHSA  
### Mathematical Modeling of EHSA  
The output power of EHSA is proportional to the design of the electromagnetic coupling. As a result, parameters such as magnet size, material characteristics, and geometric structure of magnet, coil, and magnetic circuit play an important part in the design process. However, calculating the magnetic field analytically is difficult for ironless systems and even impossible for systems that contain back iron. Therefore, conclusions from publications such as [15; 16; 19; 46] are often based on very simplifying assumptions. Nevertheless, understanding how electrical energy may be retrieved requires a fundamental understanding of magnetic induction theory. The transduction mechanism of an electromagnetic vibration transducer is based on [PERSON]'s law of induction. According to this principle, any change in magnetic flux across a conductive loop of wire induces a voltage in that loop. Assuming that \(B_{r}\) (the radial component of the magnetic flux density) causes the magnetic flux through the coils, the flux \(\phi\) can be written as:  
\[\phi=\ \int BdS=\ \int B_{r}dS. \tag{4}\]  
The induced voltage is the so-called electromotive force (_emf_), \(V_{emf}\), for each coil can be expressed using the Faraday-Lenz law as follows:  
\[V_{emf}=\frac{d\phi}{dt}=v\frac{d\phi}{dz} \tag{5}\]  
The _emf_ can also be written as [15]  
\[V_{emf}=\ v\int B_{r}dl=\ v(t)B_{r}(z)L \tag{6}\]  
where \(B_{r}\) is the radial magnetic flux density in a region, and \(L\) is the circumferential length of the coil. Given an EMF, the current flowing in a short circuit coil (\(R_{L}=0\)) of an \(A_{w}\) cross-section area can be determined as:  
\[I_{sc}=\ \frac{V_{emf}}{R_{phase}}=\ \sigma v(t)B_{r}(z)A_{w} \tag{7}\]  
while the power generated in each coil in the short circuit is given by:  
\[P=V_{emf}I_{sc}=\sigma v^{2}(t)B_{r}^{2}LA_{w} \tag{8}\]  
Figure 3: External circuit of the eight-slot eight-pole model.  
The circumferential length of coil \(L\) can be given by:  
\[L=2\pi R_{c}N \tag{9}\]  
where \(R_{c}\) is the average radius of the coils:  
\[R_{c}=\ \frac{R_{os}-t_{sy}+R_{is}}{2} \tag{10}\]  
\(N\) is the number of turns which is determined as  
\[N=\ \frac{A_{slot}k_{fill}}{A_{w}}\ \ \sim\ \frac{R_{os}-t_{sy}-R_{is}}{A_{w}} \tag{11}\]  
by fixing the slot fill factor \(k_{fill}\) and width of the slot. Thus, by adding Equations (9)-(11) to (8) the peak power will be  
\[P\ \sim\ \sigma\pi v^{2}(t)B_{r}^{2}\ \left[\left(R_{os}-t_{sy}\right)^{2}-R_{is}^ {2}\right] \tag{12}\]
## 6 Benefits and Deployment Considerations of GLI-Split  
GLI-Split improves the scalability of Internet core routing by removing the need for fine-grained provider-independent addresses and, moreover, provides many benefits for edge networks. We first summarize the full set of advantages for communication between upgraded GLI-hosts before analyzing which subset thereof is also available for classic IPv6 nodes in GLI-domains. Finally, we present required changes to the existing IPv6 architecture and discuss deployment considerations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Connection** & **1 st RTT** & **2 nd RTT** \\ \hline \(\mathrm{H_{C}}\) in \(\mathrm{D_{C}}\rightarrow\mathrm{H_{C}}\) in \(\mathrm{D_{C}}\) & 16 ms & 16 ms \\ \(\mathrm{H_{C}}\) in \(\mathrm{D_{C}}\rightarrow\mathrm{H_{C}}\) in \(\mathrm{D_{GLI}}\) & 16 ms & 16 ms \\ \(\mathrm{H_{C}}\) in \(\mathrm{D_{C}}\rightarrow\mathrm{H_{GLI}}\) in \(\mathrm{D_{GLI}}\) & 16 ms & 16 ms \\ \(\mathrm{H_{C}}\) in \(\mathrm{D_{GLI}}\rightarrow\mathrm{H_{GLI}}\) in \(\mathrm{D_{GLI}}\) & 28 ms & 16 ms \\ \(\mathrm{H_{GLI}}\) in \(\mathrm{D_{GLI}}\rightarrow\mathrm{H_{GLI}}\) in \(\mathrm{D_{GLI}}\) & 40 ms & 16 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of initial and second RTT between classic hosts (\(\mathrm{H_{C}}\)) and GLI-hosts (\(\mathrm{H_{GLI}}\)) in GLI-domains (\(\mathrm{D_{GLI}}\)) or classic domains (\(\mathrm{D_{C}}\)).
## 5 The Application of the Strategy on the Selected System  
In this research, we will consider a 4 mm depth as the final state of the magnet's cracks before fracture, as we mentioned before.  
At a time, data coming from torque, temperature, and vibration sensors are collected; their features will be inputs of the HMM model. These data are collected from dynamic FEM of the permanent magnet machine previously mentioned. The machine has a 235 mm outer diameter of stator, 145 mm outer diameter of rotor, and 195 mm axial length. The simulation is performed for five revolutions. The rotating step angle is 0.9 degrees, and the time step is 3.7425 \(\times\) 10\({}^{-5}\) s.  
A decision will be generated from the HMM model; the state of the machine is either healthy or faulty. We mention that a percentage of reading error is examined for these selected sensors. The considered percentage error is \(\pm\)0.5% for the torque sensor, \(\pm\)0.55 \({}^{\circ}\)C for the temperature sensor, and \(\pm\)0.06% for the vibration sensor [49; 50].  
When the detected fault is a crack in one magnet, the model will identify the depth of the crack, and the RUL of the magnet needs to be calculated.  
To perform this calculation, the vibration FEM is performed. Maxwell stress tensor '\(\sigma\)' is computed from the vibration finite element model [35]. This calculated value is replaced in Equation (5) to calculate the RUL of the magnet.  
An illustrative example of RUL calculation in the case of demagnetization due to 1 mm cracks in one magnet is presented in Figure 12.  
In Table 1 and Figure 13, the RUL of the magnet, which is the remaining number of cycles 'Nc' before the magnet reaches its final state, is illustrated for different crack depths: 1 mm, 2 mm, and 3 mm. We notice that, as the cracks deepen, the number of cycles decreases because the RUL, before reaching the final state, decreases.  
Figure 12: Illustrative example of RUL calculation in the case of demagnetization due to 1 mm cracks in one magnet.
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 3 Component-Based IoT Architecture
### End Devices Component  
The end device component represents all the lowest-level hardware and consists of sensor nodes such as microcontrollers and microprocessors that integrates with sensors or actuators. There are two major sub-components under this component:  
* Data string generator: The data string generator is a function that aggregates the data from a single sensor into a standardized data string format. The sensor data collected are concatenated into one string message to ensure the data from the same device is transferred and process exactly at the same time. We define the string format of the data from the end devices to be as follows: \"_Data 1/Data 2/..._\", with solidus (/) sign as the separators of each reading. This is especially useful during the transmission of time-sensitive sensor values, as it reduces the possibility of data mismatch when more than one time-based data are transmitted at the same time. As most microcontrollers operate under different frequency and clockwise, the data sensing operation of more than one sensor under one sensor node will create a time discrepancy between different sensors, depending on which sensor is programmed to sense or read first.
* Multi-level naming system: The multi-level naming system for the end devices under this component is used for the end device ID creation. To support device interoperation, the devices and data should be well segregated despite being stored and processed under the same server. The naming of end devices begins with the categorization based on application domain followed by the services provided by them: \"_APPLICATION/SERVICE/#_\", where \"#\" represents the number order of devices. An example is: \"_E_HEALTH/TEMPERATURE/1\"_. The ID of the end devices is designed to be readable for easy identification and modification. The level definition of each naming of information is essential for data labeling, especially when the node device provides more than one type of sensor readings.
# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 1 Introduction
It is based on the interaction of nodes, i.e., satellites, which can describe clearly how the structure of a satellite network affects its properties, behaviors, and performance;
3. It is able to interpret satellite networks in the way of systems or systems-of-systems and to reveal the nonlinearity of satellite networks. Therefore, this paper aimed to apply the method mentioned above to study ICNS and offers a new course of research on satellite networks and the satellite swarm. ICNS was the subject of this study, and the remainder of this paper is structured as follows: In Section 2, we described the process of modeling a satellite swarm as a complex network through the theory of complex networks and the necessity of this method. In Section 3, we introduced the method to construct an ICNS, which is based on the community structure and the structural entropy of complex networks. In Section 4, we put forward an optimization method to ensure that the ICNS remains as a dynamic connected network constantly. In Section 5, we designed some simulations to test and verify the performance of these proposed methods and analyzed their outcomes.
## 3 Mechanism Analysis for Active Control
### On the resonant frequency of the ribbed plate B  
The exciting frequency is successively chosen as \(f=203\) Hz(Case 4) and \(f=425\) Hz (Case5) for this analysis. The control mechanisms on these resonant frequencies present the following two patterns. The first pattern is also in accord with that in Section (b), where suppression and rearrangement of the cavity modes work together to suppress the base plate modes. This is validated by the plots in Figure 8 and the data in Table 7 for Case 4. The second pattern is rearranging the cavity modes to suppress the base plate modes. The cavity modes are all further strengthened, but their energy contributions to each base plate mode cancel each other due to the opposite phase. This is demonstrated by the plots in Figure 8 and the data in Table 7 for Case 5. It can be found that the mechanism involved in the orthogonal ribbed case is also different from that in the single ribbed case on the resonant frequency of the ribbed plate B. The first pattern is also a new phenomenon and emerges due to the interlaced coupling relations triggered by the orthogonal two ribs. The single ribbed case only involves the second pattern. However, it is only formally analogous with the mechanism involved in the orthogonal ribbed case, and the control process is distinctly different. More cavity modes are excited here in the controlled condition to participate in the control process, due to the interlaced coupling relations of the orthogonal two ribs. Figure 8: Modal amplitudes of the first twenty modes before and after control when \(f=203\) Hz (Case 4), \(f=425\) Hz (Case 5). (**a**,**c**) (**a**) Cavity modes for Cases 4, (**c**) Cavity modes for Cases 5. (**b**,**d**) (**b**) Base plate B modes for Cases 4, (**d**) Base plate B modes for Cases 5.
# Fake Base Station Detection and Link Routing Defense +
## 7 Implementation and Experimental Results
### Faulty (but Legitimate) Base Station Experiment: Varying the Signal Power  
We validate the RRC- and NAS-layer setup measurements from faulty (but legitimate) base station experiments while varying the base station transmission power gain as shown in Figure 6. We select three different transmission powers (80, 85, and 90 dBm) of the base station to simulate the different channel conditions between the user equipment and the base station to measure the impact of received signal power on the RRC setup and NAS setup for the worst case of a faulty but legitimate base station. In this experiment, the user equipment connects to the base station only when the base station transmission power is greater than or equal to 80 dBm.  
Figure 6a shows the RRC setup and NAS setup time duration when varying base station signal transmit power. The RRC setup time stays relatively consistent and only varies from 49 ms to 61 ms for three different transmission power gains of the base station. However, the NAS setup time duration decreases as the signal power gain increases, which indicates that the NAS connection setup time depends on the received signal power at the user equipment level, i.e., radio channel conditions. As compared to the RRC setup time, the NAS setup connection time at 80 dBm power level is 822.42 ms, while it is 474.94 ms at the highest power gain. Hence, within this time duration, the user equipment keeps sending registration requests to the core network until it obtains an RRC release message from the faulty base station, as discussed in Section 4.3.  
We also validate the number of registration requests sent by the user equipment during NAS setup in the experimentation while varying the base station transmission power, as illustrated in Figure 6b. The figure shows the number of registration requests also depends  
Figure 6: Faulty but legitimate base station experimentation measurements at the user equipment level while varying transmission power at the base station. The plots include the averages and the 95% confidence interval. (**a**) RRC and NAS time duration between the UE and base station. (**b**) Number of registration requests sent by the user equipment.  
on base station transmission power, i.e., received signal power at the user equipment level. The number of requests is 60 at the lowest transmission power, whereas it is 11 at the highest signal power. We also observe that the user equipment sends the registration request with an interval of 11 ms, approximately.  
While we vary the transmitter power from the base station, this is equivalent to varying the transmitter-receiver distance and changing the channel state at the receiver user equipment level. In different channels, the \(T\) and \(N\) measurements change. We make our scheme generalizable across different communication channels (i.e., effectively working in different channels) by using the control parameters, which are threshold variables. The threshold values change in different channel scenarios.