esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence
## 3 Experiments
### Dataset  
Eighty Sentinel-1A images acquired in the Cangzhou area between January 2017 and December 2019 were used as the dataset. The multiple Master-image Coherent Target Small-baseline Interferometric SAR method was used [42] to generate the InSAR deformation time series. Figure 3 shows the spatio-temporal distribution of spatio-temporal land subsidence data in the Cangzhou area (for part of the timestamps). It indicates that high subsidence values are mainly distributed in the western part of Cangzhou, whereas the eastern part of the Cangzhou area mainly consists of points with low subsidence values and uplift points. In terms of temporal distribution, high subsidence points are mainly found in the early stages, while low subsidence points are predominantly found in the later stages. This study organizes the subsidence data into a data matrix of (69,852, 80) for spatio-temporal clustering. The three representative spatio-temporal clusters obtained by clustering are selected as input data for subsequent predictions.
# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 1 Introduction
### Contributions
Thus, the proposed scheme not only enables communication between distant primary terminals but saves the total transmit power of the system as well.
## 2 Optimization Model of Massive MIMO System
### User Scheduling
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Types of Services**} & **Transmission** & **Transmission** & \multirow{2}{*}{**Throughput**} & \multirow{2}{*}{**Packet Loss Rate**} & \multirow{2}{*}{**Priority**} \\  & **Delay** & **Delay jitter** & & & \\ \hline Conversational services & Strict restrictions & Strict restrictions & Largest & A certain range & Highest \\ Stream services & Limit & Limit & Larger & Smaller & Higher \\ Interactive services & Loose & Loose & Smaller & Approaching 0 & Lower \\ Background services & Unlimited & Unlimited & Smallest & Approaching 0 & Lowest \\ \hline \hline \end{tabular}
\end{table} 
Table 2: QoS requirements for different services. Figure 1: System model. in reference [18] and combining with the research elements of this article, the dynamic priority of user i is represented as:  
\[f_{i}=w_{1}.\frac{q_{i}}{\sum\limits_{i=1}^{N}q_{i}}+w_{2}.\frac{M_{i}.R_{i}.V_{ i}}{\sum\limits_{i=1}^{N}M_{i}.R_{i}.V_{i}}+w_{3}.\frac{g_{i}}{\sum\limits_{i=1}^{N}g_{i}} \tag{1}\]  
Among them, \(N\) represents the number of users, \(q_{i}\) represents the service priority of user \(i\) (\(i\in N\)), \(M_{i}\) represents the modulation order, \(R_{i}\) represents the coding efficiency, and \(V_{i}\) represents the transmission rate, \(g_{i}\) represents the number of unsatisfied requested time slots of the \(i\)-th user in the previous cycle. \(w\) is a weight coefficient, \(w_{1}\), \(w_{2}\), \(w_{3}\), respectively, reflect the relative importance of service QoS parameters, channel state information, and the weight of the number of unsatisfied user slots in the previous period, and \(w_{1}+w_{2}+w_{3}=1\). The formula is divided into three parts. The first part reflects the urgency of the business. The higher the business priority, the greater the proportion of this part. The second part represents the channel state information. The better the channel state, the greater the value of this part. The third part guarantees the fairness of users from the periodic point of view and makes up for users who are not satisfied due to lower service priority and poor channel quality to a certain extent. Calculate the dynamic priority of all users, sort by numerical value, and prioritize scheduling for users with large numerical values. If the number of accessed users is greater than the limited number of antennas, end user scheduling and update the current user set to R.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 6 Discussion  
### Summary of Key Findings  
The experimental results demonstrate that QSA consistently outperforms other algorithms in minimizing the number of hops across various scenarios. Specifically, QSA achieves an average hop reduction of 0.1 to 0.6 times compared to other methods, indicating its strong global optimization capability. Additionally, QSA maintains stable performance across different user scales, base station densities, and edge server configurations, proving its robustness and scalability.
# Influence of Impurity Scattering on Surface Plasmons in Graphene in the Lindhard Approximation
## 6 Summary  
We evaluated TM SPs in graphene for SB and TB transitions in the Lindhard approximation for the polarization function, in which the Coulomb interaction and local field factors are neglected, but thoroughly took into account scattering by impurities, see Equations (3)-(5), without making the usual, phenomenological change \(\omega\rightarrow\omega+i\tau\) in the scattering-independent polarization function \(\chi_{non}\). The main results are as follows.  
(1) Impurity scattering changes the spectrum of the TM surface plasmons. (2) The scattering strength \(\gamma\) restricts the SP domains for SB and TB transitions. Importantly, for each frequency, there is a critical scattering strength \(\gamma_{c}\) below which SPs do not exist. The strength \(\gamma_{c}\) decreases with frequency; it is very fast for very low frequencies and much less so for higher frequencies. (3) The quality factor (QF) is affected by the scattering. We found it approximately equal to the square of \(\alpha\lambda_{SP}/\gamma\), where \(\alpha\) is the fine-structure constant and \(\lambda_{SP}\) the wavelength; see Equation (19). For a fixed frequency, the QF decreases with \(\gamma\) for SB and TB transitions and is higher for the SB ones, cf. Figure 9. (4) TE SPs are suppressed by impurity scattering.  
We hope these findings will be tested experimentally.  
Conceptualization, M.B. and P.V.; methodology, M.B. and P.V. software, M.B.; validation, M.B. and P.V.; formal analysis, M.B. and P.V.; investigation, M.B. and P.V.; resources, M.B. and P.V.; data curation, M.B.; writing--original draft preparation, M.B.; writing--review and editing, P.V.; visualization, M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Overall Architecture of DMPN  
We relied on DUT Computer Control Engineering Co., Ltd. (DCCE; DUT represents Dalian University of Technology), which is located in Lvshun District, Dalian, Liaoning Province, China. This manufacturer uses its independently developed programmable automation gateway PAG series as a physical-layer PLC to provide machine agent services and performs unified management and programming in the PLC_Config v2.11.8 software [40]. Figure 3 depicts the overall architecture of the proposed model. In the lower part of the picture is the physical layer, and manufacturing resources can be monitored through RFID, sensors or code scanners. After the real-time status is transmitted to the PAG controller by a wired or wireless network, it is preprocessed, and the processed data is transmitted to the monitoring network of the cyber layer through the network. There is a double-layer extended deterministic and stochastic Petri net (EDSPN) in the upper cyber layer in Figure 3, which is the monitoring network and the control network. The network part is developed in the host computer's DView v2.6.7.05191 software. Herein, the monitoring network is mainly responsible for mapping the information of physical entities to digital signals for real-time display. The control network extracts the conflicting places and their pre-sets and post-sets to establish the main control subnet. Then, through comparison between the operation of the double-layer network and the data captured, the control network completes the functions of task scheduling, intelligent decision-making and network analysis.
# Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment
## 3 Proposed Proxy-Based Adaptive MP-QUIC Transmission
### System Model and Overall Operations
Figure 1: Proxy-based MP-QUIC transmission in IoT environment. 1. _Path Management_: The proxy performs the RTT measurements with the server for all available network paths periodically (e.g., 30 s). To do this, the proxy sends a _Path Request_ to the server (Step 2). The server then responds with _ACK Response Path_ to the proxy (Step 3). The proxy can now calculate and update the RTT value for the path. 2. _Connection Management_: A client who wants to communicate with the server will first contact the proxy (Step 4). For this purpose, it is assumed that each IoT client already knows the contact information of the proxy by pre-configuration. The client transmits a GET message that contains the Hash ID request (this will be described later) and the priority type (0: non-priority, 1: priority) to the proxy. Then, the proxy creates a Hash ID for the client (Step 5) and sends this information to the client (Step 6). After that, the proxy will determine the MP-QUIC path to the server for this client by internally contacting the path manager (Step 7-9). 3. _Data Transmission_: After getting the Hash ID from the proxy, the client can now transmit the data packets. Each data packet should contain the Hash ID information (Step 10). When the proxy receives the data packet from the client, it can determine the MP-QUIC network path for the client, and thus it will update the _'hits'_ information for the path (Step 11). This _hits_ information will be referred to for the MP-QUIC transmission of the non-priority packets later, which will be described in the subsequent section. Now, the proxy will forward the data packet to the server (Step 12), and the data communication will be done between the client and the server by way of the proxy (Step 13).
# Lossless WDM PON Photonic Integrated Receivers Including SOAs
###### Abstract  
The role of a semiconductor optical amplifier (SOA) for amplifying downstream traffic at optical network terminals (ONT) within a silicon-photonics integrated receiver in a high capacity passive optical network (PON) is investigated. The nearly traveling wave SOA effects are evaluated by considering fabrication and link loss constraints through numerical analysis and experimental validation. The impact of hybrid integration of a SOA chip on a silicon on insulator (SOI) photonic chip using the flip chip bonding technique on SOA design is evaluated through numerical analysis of a multi section cavity model. The performance of the proposed ONT receiver design employing twin parallel SOAs is evaluated experimentally on a 32 \(\times\) 25 Gb/s OOK WDM transmission system considering cross gain modulation (XGM) and amplified spontaneous emission (ASE) constraints. The XGM impact is evaluated through 32 channel wavelength division multiplexing (WDM) transmission and a likely PON worst case scenario of high channel power difference (~10 dB) between adjacent channels. The impact of ASE is evaluated through the worst-case polarization condition, i.e., when all of the signal is coupled to only one. Successful transmission was achieved in both worst-case conditions with limited impact on performance. SOA results indicate that a maximum residual facet reflectivity of \(4\times 10^{-4}\) for the chip-bonded device can lead to a power penalty below 2 dB in a polarization-diversity twin SOAs receiver.  
Keywords:integrated semiconductor optical amplifier; 5G networks; passive optical networks +
Footnote †: journal: Applied Physics Letters
# A Fundamental Study on Circularly Polarized Double-Loop Antennas with Quasi-Two Sources
## 4 Antennas with an Inner Parasitic Loop  
Thus far, we have studied an antenna with an outer parasitic loop. This section further considers the case of a parasitic loop inside a driven one. The effects of parasite location on radiation characteristics are discussed.  
Figure 6 shows the antenna configuration. A parasitic loop is inside the driven loop, and the parasitic loop corners \(C_{\mathrm{n}}\) are connected to the driven loop corners \(F_{\mathrm{n}}\) by lines \(F_{\mathrm{n}}\)-\(C_{\mathrm{n}}\). The other configurations are the same as those in Section 3.  
The loop perimeter and feedline branch height (\(P,h_{\mathrm{B}}\)) were again selected for wideband CP radiation as a function of loop spacing \(S\). The simulated results are shown in the lower part of Figure 7, together with the selected loop perimeter \(P\) in the upper part. It was found that an increased axial ratio bandwidth of 40% is obtained in an \(S\) range from \(0.02\lambda_{0}\) to \(0.05\lambda_{0}\). The bandwidth increases threefold compared with that (12%) of the single-loop antenna.  
The simulated gain and axial ratio versus frequency are shown with solid lines in Figure 8. The loop spacing is \(S=0.03\lambda_{0}\) with (\(P,h_{\mathrm{B}}\)) = (\(0.98\lambda_{0}\), \(0.10\lambda_{0}\)). It was found that the bandwidth of the 3 dB gain drop from the peak value is 34%, whereas the axial ratio is less than 3 dB.  
For comparison, the frequency responses for an outer parasitic loop (Outer-PL) in Section 3 are again shown with dotted lines in Figure 8. The CP wave frequency range for an inner parasitic loop (Inner-PL) was observed to extend towards upper frequencies. A reason for this upper-frequency extension is that the Inner-PL has a smaller perimeter (\(0.74\lambda_{0}=P-85\)) than the Outer-PL (\(1.12\lambda_{0}=P+8S\)). A smaller loop mainly operates at an upper frequency, where the perimeter becomes one wavelength.  
Figure 5: Simulated radiation patterns for an outer parasitic loop with \(S=0.02\lambda_{0}\) at \(f=f_{0}\). (**a**) \(\phi=0^{\circ}\) plane. (**b**) \(\phi=90^{\circ}\) plane.  
Figure 6: Antenna with an inner parasitic loop. (**a**) Top view. (**b**) Side view.  
Figure 9 shows the radiation patterns for the Inner-PL with \(S=0.03\lambda_{0}\). It was observed that the antenna radiates a CP beam like that of the Outer-PL (see Figure 5). The HPBWs are \(101^{\circ}\) and \(89^{\circ}\) in the \(\phi=0^{\circ}\) and \(90^{\circ}\) planes, respectively. The gain is 6.7 dBi. The corresponding HPBWs and gain of the Outer-PL are \(85^{\circ}\), \(81^{\circ}\), and 7.2 dBi.
## 5 Results and Discussion
### Modified CAZAC Preamble Structure Performance  
Figure 9 compares the autocorrelation of the OFDM received signal transmitted over an AWGN channel at an SNR of 12 dB, utilizing both the Suparna preamble structure and the modified CAZAC preamble structure. Specifically, Figure 9a illustrates the autocorrelation plot of the received OFDM signal, \(S\), when the Suparna preamble structure is used in the SISO-OFDM system. In contrast, Figure 9b shows the autocorrelation plot of the received OFDM signal, \(S\), when the modified CAZAC preamble structure is employed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Tap-1** & **Tap-2** & **Tap-3** \\ \hline Delay spread (\(\mathrm{\SIUnitSymbolMicro s}\)) & 0 & 4 & 10 \\ \hline Power 30\({}^{\mathrm{o}}\) directional antenna (dB) & 0 & \(-11\) & \(-22\) \\ \hline Rician \(K\)-factor & 2 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters for SUI-5 channel [48].  
Figure 9: Autocorrelation plots of OFDM received signal, transmitted over AWGN channel: (**a**) the Suparna preamble structure; (**b**) the modified CAZAC preamble structure.  
The OFDM system is very sensitive to frequency and timing offset because it can cause inter-carrier interference, resulting in performance degradation. It can be seen from Figure 9a that the autocorrelation plot for the Suparma preamble structure has a sharp peak to determine the start of the incoming signal at the receiver. Hence, it provides timing synchronization beneficial for OFDM system performance. However, the sub-peaks on both sides of the main peak can cause timing ambiguity when considering transmission over frequency-selective channels. On the other hand, there are no sub-peaks on both sides of the main peak in the autocorrelation plot for the modified preamble structure, as seen in Figure 9b. This leads to more precise synchronization and better SNR estimation.  
The performance of an SISO-OFDM system utilizing the modified CAZAC preamble structure and the Suparma preamble structure is also compared in terms of NMSE, as shown in Figure 10. The SISO-OFDM system, which uses the CAZAC-modified preamble structure of Figure 3, outperforms the system with the Suparma preamble structure. It is evident that the modified CAZAC preamble has improved the NMSE by 2 dB at \(10^{-2}\). These improvements highlight how the modified CAZAC sequences may improve OFDM system performance.
## 6 Simulation Setup and Results
### Assumptions  
For the GSO and NGSO links, we consider up to four application flows with different throughput demands, which are enabled or disabled during the scenario. Therefore, the simulation scenario involves dynamically changing application loads for all satellite links. For the two FWA links, we consider a continuous application flow. The application layer packet size was selected to match the size of the PHY protocol data unit (PDU) (64,800 bits) to avoid fragmentation and aggregation requirements, as this was not the focus of this study. Specifically, the UDP/IP/MAC headers have a size of maximum 54 bytes, so the application layer packet was set to 64,368 bits. It must also be noted that each MF-TDMA slot supports sending a maximum of three PPDUs, and that the selected modulation and coding scheme (MCS) for all satellite communications was 16 APSK, 2/3 coding rate, and 0.1 roll-off factor. The selected MCS requires an SINR of 9 dB in order to offer successful decoding of a PHY frame. Therefore, all satellite-terrestrial links are designed to have a reception threshold of 9 dB SINR, below which they lose connection. The nominal level of SINR is set to around 10 dB for satellite links, and around 20 dB for FWA links, offering a safety margin of 1 dB and 11 dB for satellite and FWA links, respectively.  
Several other assumptions have been made to simplify the simulation scenario without heavily impacting the results:  
* The simulations consider satellite antenna properties (main lobe, sidelobes, back-lobes, beam tracking, co-channel interference, and ACI) that are suggested in International Telecommunication Union (ITU) documents ITU-R S.672-4 [33], ITU-R S.1528 [34], and ITU-R S.580-6 [35]. ACI is modeled at \(-27\) dB attenuated signal power compared to main channel power. Values for transmit power, pathloss, fading and other RF characteristics are adopted from SNS3 models.
* FWA RF characteristics are adopted according to [36].
* The simulator includes models for received signal strength indicator (RSSI) measurements and spectrum monitoring.
* For the reaction time of collaboration and resource allocation protocols, propagation delays are considered.
* Perfect modem synchronization, meaning zero time to reconnect after interference is solved.
* All satellite systems are considered single-beam systems.
* Feeder links are not simulated.
* A single carrier is adopted per PHY channel.
* ESIM antennas track the connected satellite.
* GSO/NGSO focal points are fixed.
* During the baseline scenario finetuning, a satisfactory PER threshold value for initiation of interference avoidance procedure is determined to be above 0.01%.
* The default MF-TDMA superframe consists of five time slots and 3-5 channels.
* The channel bandwidth of each channel is 12.5 MHz, consisting of actual 10 MHz signal usage and 2.5 MHz guard space. Guard spaces provide Doppler Effect protection, supporting speeds of ESIMs up to 2500 km/h.
# Remote Water Quality Monitoring System for Use in Fairway Applications
## 3 Results
Deviations may indicate pollutants and potential risks to the ecosystem. * Temperature Range: 0-26 \({}^{\circ}\)C. Stable temperatures support the maintenance of appropriate water quality parameters. Monitoring detects potential threats arising from changes that could impact aquatic environments. In summary, the analyzed parameters enable a comprehensive assessment of water quality and the early detection of threats to aquatic ecosystems. These parameters are interconnected and provide a holistic view of water quality: Changes in temperature affect dissolved oxygen levels and influence pH stability. Abnormal conductivity often correlates with changes in other parameters, indicating pollution or contamination. Together, they help identify the source and nature of potential issues, from chemical imbalances to biological threats. By focusing on these four parameters, water quality can be assessed effectively, ensuring the protection of aquatic ecosystems and identifying potential risks in a timely manner.
## 4. SoSCO-Based Architectural Steps and Application Example
### Step 2: Identify Temporal Factors with Visions and Goals  
In this step, the SoS is divided into phases of the development plan, each with specific goals that must be achieved for mission success [16]. The required capabilities to achieve the goals vary across different phases, and the _TemporalFactor_ in the SoSCO is used to identify and segment these phases. The number of phases decides the _TemporalFactor_ number. Only the number of phases must be determined in the first modeling process. As the SoS architecture is iteratively developed, the models of the phases will be completed by associating them with capabilities, operational architecture, goals, start and end times, and other elements.  
The Strategic Process (St-Pr) view of the UAF, implemented by the SysML block definition diagram (BDD), shows the relationship between phases, as illustrated in Figure 9. The SAR SoS has four goals, which can be assigned in three different time-span phases, with each temporal factor corresponding to a phase. Each strategic phase can be instantiated and associated with different capabilities, operational architecture, and other elements. These elements will be gradually refined in subsequent steps.  
Figure 8: Identify operational factors by tracing sub-missions.
# Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
## 3 System Model and the Proposed Algorithm  
Unlike the VOD market, which are one-way services, online streaming services allow users to enjoy video services interactively. The interactive actions in videos can be (i) stopping, (ii) skipping, or (iii) returning to desired parts of the video. This section describes the proposed personalized user-based adaptive streaming (UAS) method to overcome the issues with traditional adaptive streaming techniques. The UAS method aims to provide the best QoE through personalized user-optimized streaming service provisioning.