esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis of Results
### The Analysis of Downlink NOMA-VLC
Therefore, the proposed algorithm can outperform the original SIC algorithm and significantly improve the BER performance of the weak user. It provides a viable solution to achieve successive interference cancellation detection of two users in free-space optical communications. Figure 14: BERs at different distances with interference estimation optimization. Insets: eye diagrams of **(a)** the first user with equalization, **(b)** the second user with equalization, **(c)** the second user with LS-SIC, and **(d)** the second user with LSTM-SIC. Figure 15: BERs under different power allocation ratios with interference estimation optimization. As shown in Figure 16, the BER performance of the three successive interference estimation cancellation methods under different attenuation values was analyzed at a fixed power allocation ratio of 0.91. The BER of the original algorithm for User 1 was always below the FEC limit, and the BER of User 2 was below the FEC limit only when the attenuation value was 11 dB. After using LS-SIC and LSTM-SIC detection algorithms, the BERs of User 1 and User 2 were below the FEC limit, and the performance was close to the same. As shown in Figure 17, the BER performance of the three successive interference estimation cancellation methods was analyzed for different attenuation values at a power allocation ratio of 0.92. The BER of the original algorithm for the strong user was always below the FEC limit, and the BER of the weak user was always above the FEC limit. After using LS-SIC and LSTM-SIC detection algorithms, the BERs of User 1 and User 2 were below the FEC limit, which shows that the final power allocation ratio has a greater effect on the BER performance. Figure 16: BERs at optimal power allocation ratio of 0.91 with interference estimation optimization. Figure 17: BERs at optimal power allocation ratio of 0.92 with interference estimation optimization. As shown in Figure 18, the BER performance of the three successive interference estimation cancellation methods was analyzed for different attenuation values at a power allocation ratio of 0.93. The BER of the original algorithm for the strong user was always below the FEC limit, and the BER of the weak user decreased to the FEC limit with an attenuation value of 12 dB. After using LS-SIC and LSTM-SIC detection algorithms, both BERs of User 1 and User 2 were below the FEC limit. Since the power allocation ratio was 0.93, the power of the strong user was 0.93, while the power of the weak user was 0.07, and this ratio was suitable to demodulate both users accurately. An aggregate data rate up to 40 Mbps of the two signals on the same link could be achieved in the NOMA-VLC system. A comparison of the reported MISO VLC systems is given in Table 4.
## 3 Experiments and Results
### Application in the 2017 Ms 7.0 Juzhaigou Earthquake
to restore the low-frequency and suppress the high-frequency noise effectively to acquire a broadband signal. (3) Both the SKF- and VC-AKF-derived results have a sawtooth-like power spectrum in the high-frequency band, which is caused by the inconsistent sampling rates of SM and GNSS. In the SKF, an adjustment is applied to the displacement time series when the measurement updates. When the GNSS observation is not available, the system noise variance is fixed to the valuation of the previous epoch by VC-AKF, which means that VC-AKF needs more adjustments. Therefore, the sawtooth problem in VC-AKF is a little more intense than in the SKF, but this is a minor problem because of its small effect on the co-seismic displacement signal.
# Efficient and HRA Secure Universal Conditional Proxy Re-Encryption for Cloud-Based Data Sharing
## 4 Generic Construction for UCPREA
### Security Proof  
In this section, we prove that our UCPRE is HRA secure. The proof is shown as follows.  
Theorem 4.2: _If a homomorphic encryption scheme \(\Pi_{t}\) is CPA secure. Then UCPRE is CPA secure._  
Proof.: For our UCPRE, the correctness of \(\Pi_{t}\) implies the correctness of resulting UCPRE because we have re-encrypted ciphertext rct generated by the algorithm \(\Pi_{t}\). ScalMul, where \(\mathrm{rct}=\mathrm{ct}_{t}^{m}\). For any probabilistic and polynomial-time (PPT) adversary \(\mathcal{A}\) with advantage \(\mathrm{Adv}_{\mathrm{cpa}}^{\mathcal{A},\Pi_{t}}\) attack our scheme UCPRE, we can construct an efficient algorithm \(\mathcal{A}^{\prime}\) with advantage \(\mathrm{Adv}_{\mathrm{cpa}}^{\mathcal{A},\mathrm{UCPRE}}\) to attack the encryption scheme \(\Pi_{t}\). We have \(\mathrm{Adv}_{\mathrm{cpa}}^{\mathcal{A},\Pi_{t}}=\mathrm{Adv}_{\mathrm{cpa}}^ {\mathcal{A},\mathrm{UCPRE}}\) because we have re-encrypted ciphertext rct generated by the algorithm \(\Pi_{t}\). ScalMul, where \(\mathrm{rct}=\mathrm{ct}_{t}^{m}\). By the CPA secure of \(\Pi_{t}\), the advantage \(\mathrm{Adv}_{\mathrm{cpa}}^{\mathcal{A},\mathrm{UCPRE}}\) is neglectable. Furthermore, the UCPRE scheme is CPA secure.  
Theorem 4.3: _If the distribution of re-encryption ciphertexts likes the distribution of fresh ciphertexts. Then our UCPRE is re-encryption simulatable._  
Proof.: We prove that the UCPRE is re-encryption simulatable when \(\mathrm{ReEncSim}=\Pi_{t}\). Enc We have \(\mathrm{ct}_{t}^{m}:=\mathrm{ReEnc}(\Pi_{f},\Pi_{t},\mathrm{pk}_{t},\mathrm{ rk}_{f\to t})\), where \(\mathrm{ct}_{t}^{m}\) is the ciphertext of the public key under the scheme \(\Pi_{t}\). Therefore, we set the algorithm \(\mathrm{ReEncSim}\) as \(\Pi_{t}\). Enc such that both the algorithms \(\mathrm{ReEncSim}\) and \(\mathrm{ReEnc}\) are statistical indistinguishability. That is, the distribution of re-encryption ciphertexts likes the distribution of fresh ciphertexts. Consequently, the UCPRE is re-encryption simulatable.  
Theorem 4.4: _If a homomorphic encryption scheme \(\Pi_{t}\) is CPA secure and UCPRE is re-encryption simulatable. Then UCPRE is HRA secure._  
Proof.: Here we first prove that the UCPRE is CPA secure if a homomorphic encryption scheme \(\Pi_{t}\) is CPA secure by the Theorem 4.2. Then, we prove that the UCPRE is re-encryption simulatable by the Theorem 4.3. Finally, by Theorem 4.1, we prove the UCPRE is HRA secure because UCPRE is CPA secure and UCPRE is re-encryption simulatable.
# Educational Escape Rooms as an Active Learning Tool for Teaching Telecommunications Engineering
## 2 Methodology
### Escape Room 1
#### 2.1.3 Modulation  
Signal modulation, where a signal is combined with a carrier signal, is a fundamental communications concept for radio transmission. There are many different schemes of modulation including: Amplitude Modulation (AM), Frequency Shift Keying (FSK), Phase Shift Keying (PSK), Quadrature Amplitude Modulation (QAM) and On-Off Keying (OOK).  
This puzzle presents students with a series of OOK, PSK and FSK encoded waveforms (a subset is shown in Figure 1). Students are not told what modulation method is used for each waveform and are expected to decode each waveform into a binary and convert the string into a numerical character using the ASCII table.  
Each of the different modulation puzzles brings out a different element of how the signal encoding is used. For the top modulation signal (PSK), 0's and 1's are encoded in different phases so participants will need to work out which corresponds to 0 and 1 and will decode the value '00110001' which corresponds to 1. For the middle modulation signal  
\begin{table}
\begin{tabular}{l l l l} \hline
**HEX** & **Decoded Value** & **Binary** & **XOR** \\ \hline
0x43 & C & 01000011 & 01000011 \\ \hline
0x6f & o & 01101111 & 00101100 \\ \hline
0x6d & m & 01101101 & 0100001 \\ \hline
0x70 & p & 01110000 & 00110001 \\ \hline
0x75 & u & 01110101 & 01000100 \\ \hline
0x74 & t & 01110100 & 00110000 \\ \hline
0x65 & e & 01100101 & 01010101 \\ \hline
0x20 & \textless{Space}\textgreater{} & 00100000 & 01110101 \\ \hline
0x58 & X & 01011000 & 00101101 \\ \hline \end{tabular}
\end{table}
Table 1: LRC Computations.  
(FSK) two different frequencies are used to represent 0 and 1. Hence, the decoded signal will be '00111000' which corresponds to 8. Finally, the bottom modulation signal (OOK) is a basic variant of Amplitude Shift Keying (ASK) where a carrier signal is used to encode 1 and no signal is used to encode 0. Hence, the decoded signal should be '00110101' which corresponds to 5.
## 3 Results and Discussion  
### Volatile Compound Analysis: Component Identification  
As shown in Table 1, the identified compounds are listed with their respective molecular formulas, abbreviations, CAS numbers, and retention times. These results identified a total of 24 volatile compounds, which were categorized into nine structural groups: (2) nitrogen compounds, (2) aldehydes, (8) hydrocarbons, ketones, ethers, alcohols, phenols, esters, and (7) others. These findings indicate that distinct volatile compounds can be effectively separated and identified from large yellow croaker during storage, and the corresponding mass spectra are provided in the Supporting Material.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Super Class** & **R.T. (min)** & **Volatile Compounds** & **Abbreviation** & **CAS** & **Formula** \\ \hline Nitrogen & 8.971 & Nicotinic acid, 2-phenylethyl ester & N1 & 1000308-36-9 & C\({}_{14}\)H\({}_{13}\)NO\({}_{2}\) \\ compound & 9.794 & Pyridine, 3,4-dimethyl & N2 & 000583-58-4 & C\({}_{7}\)H\({}_{9}\)N \\  & 10.549 & Benzaldehyde & D1 & 000100-52-7 & C\({}_{7}\)H\({}_{6}\)O \\  & 13.521 & Nonanal & D2 & 000124-19-6 & C\({}_{9}\)H\({}_{18}\)O \\  & 6.02 & Toluene & H1 & 000108-88-3 & C\({}_{7}\)H8 \\  & 8.447 & Benzene,1,3-dimethyl & H2 & 000108-38-3 & C\({}_{8}\)H\({}_{10}\) \\  & 15.083 & Naphthalene & H3 & 000091-20-3 & C\({}_{10}\)H\({}_{8}\) \\  & 15.251 & Dodecane & H4 & 000112-40-3 & C\({}_{12}\)H\({}_{26}\) \\  & 16.111 & Benzene,1,3-bis(1,1-dimethyllyl)- & H5 & 001014-60-4 & C\({}_{14}\)H\({}_{22}\) \\  & 18.176 & Caryophyllene & H6 & 000087-44-5 & C\({}_{15}\)H\({}_{24}\) \\  & 18.789 & Pentadecane & H7 & 000629-62-9 & C\({}_{15}\)H\({}_{32}\) \\  & 20.467 & Hexadecane & H8 & 000544-76-3 & C\({}_{16}\)H\({}_{34}\) \\ Ketones & 10.539 & Ethanone, 2-(formyloxy)-1-phenyl & K1 & 055153-12-3 & C\({}_{9}\)H\({}_{8}\)O\({}_{3}\) \\ Ethers & 11.991 & Heptane,1,1’-oxybis- & E1 & 000629-64-1 & C\({}_{14}\)H\({}_{30}\)O \\ Alcohols & 12.127 & 1,2-Propanediol,1-phenyl & A1 & 001855-09-0 & C\({}_{9}\)H\({}_{12}\)O\({}_{2}\) \\ Phenols & 19.000 & Butylated Hydroxytoluene & P1 & 000128-37-0 & C\({}_{15}\)H\({}_{24}\)O \\ Esters & 13.181 & Benzeneacetic acid, & T1 & 056143-21-6 & C\({}_{10}\)H\({}_{12}\)O\({}_{3}\) \\  & 13.175 & Benzene,(1-methoxypropyl) & R2 & 059588-12-4 & C\({}_{10}\)H\({}_{14}\)O \\  & 14.009 & Triethyl phosphate & R3 & 000078-40-0 & C\({}_{6}\)H\({}_{15}\)O\({}_{4}\)P \\  & 14.014 & Phosphoric acid, diethyl pentyl ester & R4 & 020195-08-8 & C\({}_{9}\)H\({}_{21}\)O\({}_{4}\)P \\ Others & 17.070 & Benzoic acid, 2-methylpropyl ester & R5 & 000120-50-3 & C\({}_{11}\)H\({}_{14}\)O\({}_{2}\) \\  & 18.438 & Dimethyl phthalate & R6 & 000131-11-3 & C\({}_{10}\)H\({}_{10}\)O\({}_{4}\) \\  & 18.695 & 2,5-cyclohexadiene-1,4-dione, & R7 & 1000396-22-4 & C\({}_{14}\)H\({}_{18}\)O\({}_{2}\) \\  & 20.520 & Sulfurous acid, 2-ethylhexyl & R8 & 1000309-19-6 & C\({}_{21}\)H\({}_{44}\)O\({}_{3}\)S \\  & & tricely ester & & & \\ \hline \end{tabular}
\end{table}
Table 1: Volatile compounds of large yellow croaker identified through HS-SPME-GC-MS during storage.
# Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities
## 1 Introduction
It provides an algorithm to set the medical nodes into decentralized grouping and increases the connectivity for application users with the support of mobility and manages communication efficiently. 2. Secret information is generated and shared among individuals using the computing power of gateway nodes. It achieves authentic association before the communication among medical nodes is initiated and improves sustainability using the 5G network. Figure 1: Main components of the proposed architecture. 3. Multi-secured layers are provided for threat sensing and share the collected data with the confidence of authentication. 4. The architecture is tested and validated along with the discussion using simulations, and its significance has been proven against other benchmark algorithms. This research work is structured as follows. Section 2 presents a literature review of the existing work. In Section 3, the proposed architecture is introduced along with a detailed discussion. A simulation-based evaluation and a discussion of the results are presented in Section 4. At the end, Section 5 discusses the conclusion.
## 2 Overview of SWARM IoT Constellation  
### History of SWARM Technologies' Successful Missions  
Satellite communication systems, particularly those using Swarm Technologies and similar low-cost IoT satellite constellations [34], have been deployed successfully in various remote areas to enhance connectivity for scientific, commercial, and humanitarian purposes. One notable example is Swarm Technologies' real-world deployment in environmental monitoring and disaster response scenarios. SWARM's satellites, which offer global low-power and low-cost communication, have been used for remote monitoring in regions where traditional connectivity is limited. In Alaska, for example, Swarm's satellite constellation has been instrumental in collecting and transmitting real-time data on environmental conditions, supporting wildlife conservation and natural resource management projects. The data collected help researchers monitor remote ecosystems, track climate change, and make data-driven decisions in real-time disaster response, Swarm's satellite communication has been vital in connecting teams during emergencies in remote or inaccessible locations. The system provides first responders with reliable communication links in areas where terrestrial networks have been damaged or are non-existent. In one specific case, Swarm's low-cost satellite services were employed in Puerto Rico after Hurricane Maria, enabling real-time data transmission on the status of critical infrastructure such as bridges, roads, and electrical grids. This information was crucial for relief efforts, as it allowed agencies to coordinate their response and prioritize areas needing urgent attention. Additionally, especially in underserved regions, satellite IoT solutions have been used to provide remote healthcare services. For instance, in rural parts of sub-Saharan Africa, similar satellite-based communication systems have been deployed to enable remote health monitoring and facilitate consultations between doctors and patients, improving access to medical services in places with limited infrastructure [30].  
These deployments illustrate the role of satellite constellations like Swarm in bridging the connectivity gap in remote regions, supporting environmental monitoring, disaster response, and healthcare applications. Further development of these systems could significantly enhance connectivity in the polar areas, where traditional communication infrastructure is often unavailable.
## 3 Materials and Methods
### The Framework  
As shown in Figure 1, the workflow of the attention-based feature pyramid module (AFPM) consists of three parts: feature extraction, lateral attention connections and feature enhancement. In the bottom-up feature extraction structure, ResNet-50 [35] network is utilized to forward propagation, and the output of the last residual block in each stage is denoted as \(\{C_{1},C_{2},C_{3},C_{4},C_{5}\}\), and \(\{C_{2},C_{3},C_{4},C_{5}\}\) is considered to participate in the subsequent calculation. In order to improve the expression of the region of interest, convolutional block attention module (CBAM) is added to the end of the first stage and the last stage. The output of the last residual block in each stage is denoted as \(\{C_{1}^{\prime},C_{2},C_{3},C_{4},C_{5}^{\prime}\}\) after adding convolutional block attention module. In the lateral attention connections, convolutional triplet attention module is included to focus on the cross-dimension dependencies and capture the abundant discriminating feature representation by catching the interaction between spatial dimension and channel dimension. The feature map after \(3\times 3\) convolution operation is defined as \(\{P_{2},P_{3},P_{4},P_{5}\}\). The top-down feature enhancement structure of feature pyramid network only enhances the strong semantic information of the high-level layer but lacks the reinforcement of the low-level features [14]. Based on this, we add branches to strengthen the location information in the network to achieve the purpose of increasing the whole feature hierarchy by propagating low-level features for instance segmentation.
# Study on Local Vibration Control of the 100 m X-BOW Polar Exploration Cruise Ship
## 3 Results
### Local Vibration Control Analysis of TMD
Figure 8: Location of TMDs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mass Ration**} & **Total Quality** & **Total Stiffness** & & \\  & **of TMD (T)** & **of TMD (N/m)** & \(\gamma\) & \(\xi_{opt}\) \\ \hline
0.50\% & 0.017 & 122.8 & 0.995 & 0.043 \\
1\% & 0.035 & 243 & 0.99 & 0.06 \\
2\% & 0.069 & 476.7 & 0.98 & 0.085 \\
5\% & 0.173 & 1124.7 & 0.95 & 0.13 \\
10\% & 0.346 & 2049.5 & 0.91 & 0.185 \\
20\% & 0.69 & 3444.3 & 0.83 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of TMD. Figure 9: Frequency response at node 2566. (**a**) Frequency response curve of node 2566 under various working conditions; (**b**) Zoom in at 12 Hz to 15 Hz. When evaluating the control effect of TMD, the larger \(\Delta L\) is, the better. When evaluating the magnification effect of TMD, the smaller \(\Delta L\) is, the better. The control effect of each working condition is evaluated. Figure 10 shows the control effect at 13.4 Hz. It can be seen from Figure 10 that when \(\mu\) is in the range of 0.005-0.05, the control effect of TMD is significantly improved from 24 dB to 31 dB. When \(\mu>0.05\), the control effect of TMD does not increase significantly, increasing from 31 dB to 33 dB. When \(\mu=0.1\) and \(\mu=0.2\), the control effect is basically not different; therefore, it is recommended the quality ratio \(\mu\leq 0.05\). Figures 11 and 12 show the control effect at 10 Hz and 18.8 Hz, respectively. As can be seen from the figures, at these two frequency points, there is always a mass ratio \(\mu^{\prime}\), which makes \(\Delta L\) reach the peak; when \(\mu=\mu\), TMD has the worst control effect at 10 Hz and 18.8 Hz, and even has the amplification effect. At 10 Hz, \(\mu=0.01\), the TMD amplitude effect is 4 dB; at 18.8 Hz, \(\mu\prime=0.02\), the TMD amplification effect is 3.7 dB; and when \(\mu\
eq\mu\), TMD has a certain control effect on the amplitude at 10 Hz and 18.8 Hz. To sum up, for this local structure, it is recommended to take \(\mu=0.05\); at this time, the control effect at 10 Hz is 31 dB, which has the highest control cost performance. At the same time, the amplitude response at 10 Hz and 18.8 Hz also has a certain control effect, which is 3 dB and 2 dB, respectively. After TMD parameters are selected, TMD is installed on the model loaded with the excitation source, and the vibration velocity response is calculated. The vibration velocity response before and after TMD installation is compared, as shown in Figure 13. It can be  
Figure 11: Control effect at 10 Hz. Figure 10: Control effect at 13.4 Hz. seen from Figure 13 that after TMD installation, the response near 13.4 Hz decreases from 0.8 mm/s to 0.13 mm/s. It shows that TMD has a good control effect.
## 2 The Duality of Linear Quadratic Regulation (LQR) and Estimation (LQE)
### Linear Quadratic Estimation (LQE)  
Let us consider a linear dynamic system  
\[\left\{\begin{array}{l}\mathbf{x}_{k}=\mathbf{F}_{k,k-1}\mathbf{x}_{k-1}+\mathbf{w}_{k}\\ \mathbf{z}_{k}=\mathbf{H}_{k}\mathbf{x}_{k}+\mathbf{v}_{k}\end{array}\right. \tag{1}\]  
where \(\mathbf{x}_{k}\) is an \(n\)-state vector of the system (the components \(x_{k}^{i}\) of \(\mathbf{x}_{k}\)are called state variables); \(\mathbf{w}_{k}\) is an \(r\)-Gaussian random process noise vector with zero mean and covariance matrix \(\mathbf{Q}=E\left[\mathbf{w}_{j}\mathbf{w}_{k}^{T}\right]\); \(\mathbf{z}_{k}\) is an \(m\)-measurement vector; \(\mathbf{v}_{k}\) is an \(m\)-Gaussian random measurement noise vector with zero mean and covariance matrix \(\mathbf{R}=E\left[\mathbf{v}_{j}\mathbf{v}_{k}^{T}\right]\); \(\mathbf{F}_{k,k-1}\) is an \(n\times n\) transition matrix; \(\mathbf{H}_{k}\) is an \(m\times n\) observation matrix. Moreover \(\mathbf{v}_{j}\) and \(\mathbf{w}_{k}\) are uncorrelated with each other, i.e., \(E\left[\mathbf{v}_{j}\mathbf{w}_{k}^{T}\right]=0\) at any \(j\) and \(k\).  
In the LQE problem, the optimal solutions \(\mathbf{P}_{k}\) and \(\mathbf{K}_{k}\) are generated by the recursion relations [19; 20]:  
\[\left\{\begin{array}{l}\widehat{\mathbf{x}}_{k}=\mathbf{F}_{k,k-1}\widehat{\mathbf{x}}_ {k-1}+\mathbf{K}_{k}\left(\mathbf{z}_{k}-\mathbf{H}_{k}\mathbf{F}_{k,k-1}\widehat{\mathbf{x}}_{k- 1}\right)\\ \mathbf{P}_{k}=\left(\mathbf{F}_{k,k-1}-\mathbf{K}_{k}\mathbf{H}_{k}\right)\mathbf{P}_{k-1}\left( \mathbf{F}_{k,k-1}-\mathbf{K}_{k}\mathbf{H}_{k}\right)^{T}+\mathbf{Q}+\mathbf{K}_{k}\mathbf{R}\mathbf{K}_{k }^{T}\\ \mathbf{K}_{k}=\mathbf{F}_{k,k-1}\mathbf{P}_{k}\mathbf{H}_{k}^{T}\left(\mathbf{H}_{k}\mathbf{P}_{k}\mathbf{ H}_{k}^{T}+\mathbf{R}_{k}\right)^{-1}\end{array}\right. \tag{2}\]  
where \(\widehat{\mathbf{x}}_{k}\) is an \(n\)-state vector estimate, \(\mathbf{P}_{k}\) is covariance matrix of the estimation errors \(\widetilde{\mathbf{x}}_{k}\), \(\widetilde{\mathbf{x}}_{k}\) is the estimation error and \(\widetilde{\mathbf{x}}_{k}=\mathbf{x}_{k}-\widehat{\mathbf{x}}_{k}\), \(\mathbf{K}_{k}\) is optimal gain matrix. \(\mathbf{P}_{k}\), \(\mathbf{K}_{k}\) are obtained iteratively by minimizing the cost function \(\jmath_{k}\), where \(\jmath_{k}=\mathbf{P}_{k}=E\left[\widetilde{\mathbf{x}}_{k}\widetilde{\mathbf{x}}_{k}^{T}\right]\).  
After that, we can calculate the optimal values of \(\mathbf{K}_{k}\) and \(\mathbf{P}_{k}\) iteratively after setting the initial conditions \(\mathbf{P}_{0}\), \(\mathbf{Q}\), \(\mathbf{R}\).