esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Different Equalization Algorithms
### Constant Modulus Algorithm  
The CMA [34; 35] is one of the most frequently employed blind equalization algorithm, as it works very satisfactorily for modulation formats in signal constellations with constant radii, like the Phase Shift Keying (PSK) modulation format. If multiple radii exist in constellation points, as in the case of 16 QAM, the estimated error attained with the algorithm found on the constant modulus (CM) measure does not minimize after equalization. This is the reason for the undesirable performance of traditional CM algorithms with higher-order QAM signals [36].  
The mathematical equation of CMA is given by  
\[\text{J(k)}=\text{E}[(\text{y(k)}^{2}-\text{R}_{2})]^{2} \tag{7}\]  
where \(\text{y(k)}\) is the equalizer output and \(\text{R}_{2}\) is a constant which depends only on the input data symbols.  
The output of the equalizer \(\text{y(k)}\) is given by  
\[\text{y(k)}=\sum_{\text{K}=0}^{\text{L}-1}\text{z}_{\text{n}}\ (\text{k})\text{d(n-k)} \tag{8}\]  
Here, d(n) is the signal to be equalized, and zn(k) are the filter taps.  
where the equalizer tap weights vector is  
\[\text{z}_{\text{n}}\ =[\text{z}_{\text{o}}\ (\text{k}),\ \text{z}_{\text{1}}\ ( \text{k}),\ \ldots\ldots\ \text{z}_{\text{N}-1}\ (\text{k})]^{\text{T}} \tag{9}\]  
and  
\[d(k)=[d(k),d(k-1),\ldots\ \ldots\ d\ (k-N+1)]^{T} \tag{10}\]  
where d(k) is the input data vector. \(N\)' is the length of the tap weights of the equalizer; the superscript \(T\)' is the transpose of a vector. The tap weights vector is given by [37]  
\[z(k+1)=z\ (k)+\mu.\ \Delta\ J\ (k)=z\ (k)+\mu.\ d\ (k)\ e^{\bullet}(k) \tag{11}\]  
where \(\mu\) is the step size parameter, and the error signal is given [38] by  
\[e(k)=y(k)\ (R_{2}-\ |\ y(k)\,|\,^{2}) \tag{12}\]
## 3 Results and Discussion
### Effect of Fermented CF Leaf Methanolic Extract on Skin Hydration  
The CF extract exerts moisturizing effects; thus, we explored the role of AQP3, a protein implicated in skin hydration, which is crucial to skin aging [12]. AQP3, which is encoded by the _AQP3_ gene, functions as a water channel in the basal layer keratinocytes of the epidermis and regulates the water content in the stratum corneum. A decreased water content in the skin results in wrinkles and loss of elasticity [45,46]. Given that the fermented CF leaf extract exhibited antioxidative, skin-whitening, antiaging, and antimicrobial activities in this study, we believed that it is also effective in moisturizing. Figure 4 displays the effects of the fermented CF leaf extract at different concentrations on the relative mRNA expression (AQP3) in HaCaT cells, which increased with the increase in extract concentration. AQP3 expression was 2.43 \(\pm\) 0.08 and 4.18 \(\pm\) 0.31 times higher than that in the control group at concentrations of 100 and 250 mg/L, respectively. These findings indicate that the fermented CF leaf extract significantly improved skin hydration and may thus help prevent wrinkles and maintain skin elasticity [11].  
Figure 3: Cytotoxic effects of _C. formosanum_ leaf extracts on HEMn, HaCaT, and Raw 264.7 cells. Fresh medium was used as a control. Data are expressed as the means and standard deviations of three independent experiments (* \(p\) < 0.05 vs. blank control).
## 2 Materials and Methods
### Data Acquisition and WiFi Communication System
In this study, TCP/IP communication was used between robot server and robot clients (Figure 4). TCP/IP communication model provides a connection-based protocol and includes all the algorithms necessary for opening multiple connections, ensuring error-free data transfer over the network, and then closing the connections. In the designed system, the server socket that is bound to a port number on the same computer and listens to the robot client's incoming requests is an endpoint to provide a bidirectional communication link between the robot server and robot clients running on the same network. The robot client socket has to know the robot server's IP address and port number. Once the connection is established between the robot server and the robot client, they can communicate through their sockets for sending and receiving messages. After the connection is established between the robot server and the robot client, the robot server listens to the port to check if any robot client request exists. The robot client sends a message containing its spatio-temporal (UtmX, UtmY, speed, and heading) data. Every robot client connecting to the robot server should be sent this message. When the robot server receives the messages from the robot clients, it collects all data, stores it to an array variable, creates a WiFi message frame, and transfers it \begin{table}
\begin{tabular}{c c c} \hline \hline
**Devices** & **Sentence Format** & **Specifications** \\ \hline  & & HHMMSSS.SS: Time (UTC) \\  & & Q: Status \\  & & LLLLLLL: Latitude \\  & & A: N or S \\  & & YYYYYYYY: Longitude \\ AGI-4 RTK GPS & 5 GPRMC, HHMMSSS.SQ, Q & A: E or W X.X: Speed, knots \\ Receiver & LLLLLLL, A, YYYYYY, A, & X.X: Heading, degree \\  & X.X, X.X, XXX, XX, A, A*HH & XXX: Date, ddmmyy \\  & & X.X: Magnetic variation, degree \\  & & A: E or W \\  & & HH: Control \\ \hline  & & X.X: Heading angle, degree \\ HMR3000 Digital & \$HCHDT, X.X, & T*HH: True, Control \\ Compass & T*HH+CR\textgreater{}\textless{}LF\textgreater{} & CR: Carriage Return \\  & & LF: Line Feed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The NMEA 0183 sentence specifications of the GPS receiver and digital compass. to the robot clients via Wi-Fi (Figure 5). The size of the WiFi message frame is a maximum of 2346 bytes and the maximum length of the data field is 2312 bytes. In this study, both the robot server and the robot clients send a Wi-Fi message containing 33 bytes of data. Figure 4: Multi-robot TCP/IP communication system between robot server and robot clients. Figure 3: Multi-robot WiFi communication system.
# Practical I-Voting on Stellar Blockchain
## 3 System Proposition
### Final Voting Protocol and Elections  
After evaluating all possibilities, we construct a highly abstracted final protocol that has been presented in Figure 3.  
1. User proofs its identity to Identity Provider using one of the possible methods, e.g., email/password, one-time code, email confirmation, OAuth2/OpenID.
2. Identity Provider issue certificate that is later presented to the election commission (TDS) proving user identity.
3. User writes a random number (authorization token) on paper and puts it into the envelope with carbon paper.
4. User shows the certificate and the envelope with the authorization token (random number) to the election commission (TDS). The certificate allows the election commission to identify the user.
5. The election commission (TDS) validates the certificate and checks if the user has not already issued a ballot paper. Sign the envelope, and so the authorization token through the carbon paper.
6. User put off the authorization token from the envelope.
7. User waits a random amount of time outside the election local.
8. User put the mask on his face and goes back to the election local--this time as Anonymous. The Anonymous shows the signed authorization token to the election commission (TDS) and redeem it for ballot-paper.
9. [PERSON] mark the candidate on the ballot paper.
10. [PERSON] throws the ballot paper into the ballot box.  
To build an election using our framework, one needs to complete steps:  
* Create an issuer, distributor, ballot-box accounts.
* Create separate channel account for each voter.
* Issue specified number of voting tokens.
* Lockout the issuer account from issuing new tokens.
* Prepare a set of candidates consisting of candidate names and candidate codes.
* Generate key pair for blind signature.
* Generate key pair for encrypt/decrypt transaction memo fields during/after the election period.
* Optionally split the decryption key among trusted parties (e.g., candidates), so the decryption is possible only if they reveal the secret parts altogether.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 2 Development and Application of Multimodal Interaction Technology in Remote Education
### Development of Multimodal Interaction Technology  
This early research on multimodal interaction included the basics of speech and gesture recognition and proceeded to focus on developing real-world applications only for simple human-computer interactions [11]. However, today, the active use of artificial intelligence, deep learning, and sensor technologies enables bidirectional interaction, which, in turn, gives rise to real-time data fusion in multiple modalities. For example, the introduction of the technologies of emotion analytics, speech tone detection, and gesture interpretation brings quality and efficiency improvements in the human-computer interaction area (HCA) [12; 13]. Additionally, the emergence of the Mixed Reality (MR) and AR has brought about the convenient and instant cooperation of visible, perceptual, and auditory information, and as a result, the qualitative and usability level of interactive systems has improved [14]. Recent technology emphasizes optimizing end-user experiences at its core, which particularly relates to facilitating accessibility to users who may have particular needs. For instance, moving multi-modal interaction (MMI) has generated beneficiaries in smart devices for the elderly, and it was observed that interaction efficiency was significantly enhanced [15]. In addition, analyses show that designers of interacting systems are centering on various user communities and progressively exploring how to customize holistic platforms to be irrefutably user-friendly [16].  
Figure 1: Flow of multimodal interaction in remote education.
###### Abstract  
Devices that measure the presence of instability in the pulse shapes in trains of ultrashort laser pulses do not exist, so this task necessarily falls to pulse-measurement devices, like Frequency-Resolved Optical Gating (FROG) and its variations, which have proven to be a highly reliable class of techniques for measuring stable trains of ultrashort laser pulses. Fortunately, multi-shot versions of FROG have also been shown to sensitively distinguish trains of stable from those of unstable pulse shapes by displaying readily visible systematic discrepancies between the measured and retrieved traces in the presence of unstable pulse trains. However, the effects of pulse-shape instability and algorithm stagnation can be indistinguishable, so a _never-stagnating_ algorithm--even when instability is present--is required and is generally important. In previous work, we demonstrated that our recently introduced Retrieved-Amplitude N-grid Algorithmic (RANA) approach produces highly reliable (100%) pulse-retrieval in the second-harmonic-generation (SHG) version of FROG for thousands of sample trains of pulses with stable pulse shapes. Further, it does so even for trains of _unstable_ pulse shapes and thus both reliably distinguishes between the two cases and provides a rough measure of the degree of instability as well as a reasonable estimate of most typical pulse parameters. Here, we perform the analogous study for the polarization-gating (PG) and transient-grating (TG) versions of FROG, which are often used for higher-energy pulse trains. We conclude that PG and TG FROG, coupled with the RANA approach, also provide reliable indicators of pulse-shape instability. In addition, for PG and TG FROG, the RANA approach provides an even better estimate of a typical pulse in an unstable pulse train than SHG FROG does, even in cases of significant pulse-shape instability.  
ultrafast; ultrashort; FROG; instability; coherent artifact; phase; pulse measurement +
Footnote †: journal: Applied Sciences
# Numerical Analysis of Vibroacoustic Response of Timber Floor Panels Damped with Porous Materials
## 4 Dynamic Analyses and Parametric Study
### Effect of Boundary Conditions  
In this section, we analyze the effect of different boundary conditions on the narrow-band radiated sound pressure of the panel. The comparisons are presented in Figure 8 for the following four different cases: the panel subjected to step load without porous material (Figure 9a), the panel with porous material subjected to step load (Figure 9b), the panel subjected to washing machine load without porous material (Figure 9c), and the panel with porous material subjected to washing machine load (Figure 9d). The results show that the boundary conditions have a noticeable impact on the resonance peaks, both with and without the presence of the porous material. This is consistent with the findings in Table 3, which present the natural frequencies of the system under different boundary conditions. However, despite these differences in resonance peaks, by analyzing these figures using third-octave band representations (not shown here for brevity), we observe that the boundary conditions do not significantly affect the acoustic power radiated by the structure. The reason behind this phenomenon lies in the effectiveness of the introduced porous material. By incorporating the porous layer, the panel gains sound-absorbing properties that effectively dampen vibrations and reduce sound radiation. This sound-absorbing  
Figure 8: Panel submitted to washing machine loading, (**a**) comparison of a radiated sound pressure in the narrow band for rigid connection, (**b**) displacement at the middle point of the rigid panel, (**c**) comparison of a radiated sound pressure in the narrow band for hinge connection, and (**d**) displacement at the middle point of the hinge panel.  
capability is not significantly influenced by the boundary conditions, as demonstrated by the consistent acoustic power radiated by the structure across all cases.
## 4 Simulation and Analysis
More data will be sensed and more environmental information is formed and delivered to the sink, if the network is alive for a long time, thus making the network efficiency more convenient for long time usage. Given below is the graph of all four protocols data packet delivery to BS. It is easily seen in the graph that proposed the EZ-SEP algorithm's performance is much better than that of the other protocols by transmitting more than 2.4 \(\times\) 10\({}^{5}\) packets to BS. The other three compared protocols show their incapability to improve upon the proposed new algorithm, as illustrated in Figure 5.  
Figure 4: Dead nodes representation. Nodes' energy consumption during network operation in terms of residual energy is illustrated in the graph below. In the graph, we can see that in terms of energy consumption and nodes residual energy, our proposed protocol displays better results compared with the other protocols. For a given number of rounds, the EZ-SEP algorithm provides lower power consumption. At the 1000 th round, the nodes' remaining residual energy is 0.3  joules for EZ-SEP, 0.2 J for Z-SEP and around 0.1 J for the LEACH and SEP protocols. The lowest energy consumption was shown by our proposed EZ-SEP protocol, which ultimately will be reflected in an increase in the number of live nodes and an increase in the number of delivered packets to the BS, as well as in increase of the network service lifetime and, accordingly, the efficiency of the network, as shown in Figure 6.  
For further analysis and comparison of proposed EZ-SEP algorithm with the above-mentioned three protocols, three different network conditions have been observed for parameters like dead nodes, package delivery rate as well as nodes' power consumption in terms of residual energy. The supposed network conditions are as below:
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 4 Implementation and Methodology Overview  
This section details the experimental framework and benchmarking methodology used to evaluate the performance and energy consumption of TLS 1.3-secured MQTT communication over both Ethernet and WiFi in an IoT setup. TLS is leveraged to provide the fundamental security properties of confidentiality, authentication, and integrity through four sequential connection phases, namely, key exchange, server authentication, client authentication, and symmetric encryption. For clarity, we refer to these as Phase 1, Phase 2, Phase 3, and Phase 4, respectively. The first three phases constitute the TLS handshake, where Phase 1 is mandatory, and Phases 2 and 3 may be optionally omitted based on the chosen authentication scheme. Phase 4 involves symmetric encryption of application-layer data, thereby ensuring the confidentiality of MQTT payloads during transmission.  
In our experimental setup, depicted in Figure 3, each of the three primary MQTT roles, i.e., publisher, subscriber, and broker, is statically mapped to a dedicated IoT device. Configuring each device to operate in only one role throughout the experiment enables the precise attribution of resource usage to each role. For our evaluation, we systematically isolate and quantify the computational, energy, and communication overhead introduced by each TLS phase, providing a granular analysis of their impact on all MQTT entities within realistic IoT deployment.
Sensors_**2011**, _11_, 11833-11855; doi:10.3390/s111211833  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Query Result Merging Scheme for Providing Energy Efficiency in Underwater Sensor Networks**  
**[PERSON] 1 and [PERSON] 2,* ***  
* Korean Minjok Leadership Academy, 1300, Sosa-ri, Anheung-myeon, Hoengseong-gun, Gangwon-do, 225-823, Korea; E-Mail: [EMAIL_ADDRESS]
* The Graduate School of Business IT, Kookmin University, 861-1, Jeongneung-dong Sungbuk-gu, Seoul, 136-702, Korea
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-2-910-4559; Fax: +82-2-910-4519.  
_Received: 26 November 2011; in revised form: 7 December 2011 / Accepted: 15 December 2011 / Published: 20 December 2011_  
**Abstract:** Underwater sensor networks are emerging as a promising distributed data management system for various applications in underwater environments, despite their limited accessibility and restricted energy capacity. With the aid of recent developments in ubiquitous data computing, an increasing number of users are expected to overcome low accessibility by applying queries to underwater sensor networks. However, when multiple users send queries to an underwater sensor network in a disorganized manner, it may incur lethal energy waste and problematic network traffic. The current query management mechanisms cannot effectively deal with this matter due to their limited applicability and unrealistic assumptions. In this paper, a novel query management scheme involving query result merging is proposed for underwater sensor networks. The mechanism is based on a relational database model and is adjusted to the practical restrictions affecting underwater communication environments. Network simulations will prove that the scheme becomes more efficient with a greater number of queries and a smaller period range.  
**Keywords:** underwater sensor network; query management; query result merging; relational sensor network database; conditional query; periodic query
## 3 Time-Restricted Attention CTC/Attention Encoder-Decoder
### LA CTC/Attention  
In the hybrid CTC/Attention architecture, the CTC objective function is utilized as an auxiliary task with an Attention ED cost function in a multitask learning framework. Denoting \(\mathbf{h}\) as the output sequence of the encoder network, a projection layer employs it as input and transforms it to a particular dim representing the number of CTC output labels. Then, the projected output is optimized with the CTC criterion discussed in Section 2.1:  
\[\mathbf{ph}=\mathbf{W_{proj}}\mathbf{h}+\mathbf{b}, \tag{13}\]  
\[L_{CTC}=-lnp(\mathbf{y}|\mathbf{ph})=-ln\sum_{\mathbf{\pi}\in B^{-1}(\mathbf{y})}p(\mathbf{\pi}| \mathbf{ph}), \tag{14}\]  
where \(\mathbf{W_{proj}}\) and \(\mathbf{b}\) are the weight matrix and bias of the CTC projection layer. \(\mathbf{y}\) denotes the output label sequence. \(\mathbf{ph}\) represents the output of the CTC projection layer. The many-to-one mapping \(B\) is defined in Section 2.1.  
In order to address the conditional independence assumption in CTC, an attention layer is placed before the CTC projection layer. Then, the attention layer output that carries context information is served as the input of CTC projection layer at the current time \(u\):  
\[a_{ut}=Attend(\mathbf{ph_{u-1}},\mathbf{a_{u-1}},\mathbf{h_{t}}), \tag{15}\]  
\[\mathbf{c_{u}}=\sum_{t=1}^{T}a_{ut}\mathbf{h_{t}}, \tag{16}\]  
\[\mathbf{ph_{u}}=\mathbf{W_{proj}}\mathbf{c_{u}}+\mathbf{b}, \tag{17}\]  
where \(\mathbf{ph_{u}}\) is the output of CTC projection layer at time \(u\). \(a_{ut}\) is the attention weight. \(\mathbf{c_{u}}\) is the weighted hidden features. \(Attend(\cdot)\) computes the attention weight \(a_{ut}\) using a single layer feedforward network:  
\[e_{ut}=\mathbf{v}^{\mathrm{T}}tanh(\mathbf{Kph_{u-1}}+\mathbf{Q}(\mathbf{F*a_{u-1}})+\mathbf{Wh_{t }}), \tag{18}\]  
\[a_{ut}=\frac{exp(e_{ut})}{\sum_{t^{\prime}=1}^{T}exp(e_{ut^{\prime}})}, \tag{19}\]  
where \(\mathbf{v}\) is a learnable vector parameter. \(tanh(\cdot)\) is a hyperbolic tangent activation function. \(\mathbf{K}\) and \(\mathbf{W}\) are learnable matrix parameters of the linear layers. The operation \(\mathbf{*}\) denotes convolution. Meanwhile, \(\mathbf{F}\) and \(\mathbf{Q}\) are the convolution and linear layer parameters, respectively.  
Figure 1: The time-restricted attention CTC/Attention architecture.  
In practice, our attention model considers a small subsequence of \(\mathbf{h}\) rather than the entire sequence. This subsequence, \((\mathbf{h}_{\mathbf{u}-\mathbf{\tau}},\cdots,\mathbf{h}_{\mathbf{u}},\cdots,\mathbf{h}_{\mathbf{u}+\mathbf{\tau}})\), is referred to as the _attentionwindow_. It is centered around the current time \(u\). Let \(\tau\) represent the length of the _attentionwindow_ on either side of \(u\). Thus, the resulting vector \(\mathbf{c}_{\mathbf{u}}\) in Equation (16) is replaced by:  
\[\mathbf{c}_{\mathbf{u}}=\sum_{t=u-\tau}^{u+\tau}\mathbf{a}_{\mathbf{u}t}\mathbf{h}_{\mathbf{t}}. \tag{20}\]