esa-sceva/satcom-chunk-collection huggingface dataset:
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 2 Proposed Method
### Model Formulation
If the flow is real-time data, we have:  
\[R_{i,k}^{MT}(n)=argmax_{i}C_{i,k}(t) \tag{3}\]  
where \(C_{i,k}(t)\) represents the current channel condition. The MT scheduling guarantees that the resource blocks are used to transmit the maximum data for maximizing the throughput in a good channel condition. However, users with poor channel condition are not allowed to use the channel. The proportional fair scheduler concentrates on both available data rate and achieved data rate while calculating throughput:  
\[R_{i,k}^{PF}(n)=argmax_{i}\frac{C_{i,k}}{\overline{C_{i}}}(t) \tag{4}\]  
Here, \(\overline{C_{i}}\) represents the mean data rate for the \(i\)th user, which can be accurately calculated only for non-real time scenarios. Argmax is used to find the best channel condition for transmission. The transmission rate is computed through the packet waiting delay with the queue length for computing from the user equipment, so the transmission rate is equal to the user. The scheduler provides balance between fairness and the overall system throughput. Different scheduling mechanism correspond to different computations of the metric \(C_{i,k}(t)\). For the concerned channel aware-QoS unaware case \(C_{i,k}(t)\) is set based on the CQI feedback that gives the Signal-to-Interference-plus-noise ratio (SINR) of each user on each RB. The MT scheduling guarantees that each resource block can be used to transmit the maximum amount of data so that it is able to maximize the aggregate throughput. However, for users with poor channel conditions it is difficult to obtain available resources, and MT is unfair. For real time applications, the delay is calculated and multiplied with the utility factor to increase the fairness index:  
\[R_{i,k}^{\frac{EXP}{PF}}(n)=argmax_{i}\propto_{i}\frac{C_{i,k}}{\overline{C_{ i}}}(t)\ \ W_{i}\ (t) \tag{5}\]  
Equation (5) describes the improvement version of the proportional fair scheduling algorithm. At the same TTI, the queue management is taken placed in the time domain. To improve the throughput, the head of delay is included for all users as:  
\[R_{i,k}^{MLWDF}(n)=argmax_{i}\ (\propto_{i}\ \frac{C_{i,k}}{\overline{C_{i}}}(t) \ \ \exp\ (\propto_{i}\ \frac{W_{i}\ (t)-\overline{W_{i}\ (t)}}{1+\sqrt{W_{i}\ (t)}})) \tag{6}\]  
If only one user is allowed to transmit at one time slot, this will degrade performance of the system. Proportional fair scheduling aims to make the tradeoff between multiuser diversity gain and fairness. A scheduling policy \(R_{i,k}^{\frac{EXP}{PF}}\) is proportionally fair, if and only if the sum of the logarithmic average user throughput is maximized after the scheduling decision where \(i\) is the active user and \(C_{i,k}\) is the active data rate. Based on the proposed formulae and notions, we introduce the new ITFDS in the next section.
## 5 Discussion
The experimental evaluation also shows that, at the observational-noise level of 0.005 m, the water-level forecasting error approached zero and outperformed the work from the Federal Emergency Management Agency. Bed roughness could be influenced by various factors, including the size and shape of bed sediments [28], the degree of sorting and the roundness of bed sediments, the flow rate [29] and depth [30] of the river, and the presence of obstructions in the river channel [27]. For example, [PERSON] and [PERSON] showed that Manning's roughness decreased with a higher flow rate [22]. Future work can be carried out to compare the developed model's roughness inversion capacity and water-level forecasting performance at different conditions that may influence bed roughness.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 3 LDPC Decoding Algorithms
### Sma-Msa  
The concept of SMA-MSA is the same as that of the rExMin algorithm. The SMA-MSA treats the probabilistic second minimum as the upper bound and the first minimum as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **NMSA [7]** & **SRTA [21]** & **NPMSA [9]** \\ \hline Architecture of minimum finder & [20] & [21] & Group comparison [9] \\ \# of two-input comparisons in a & 23,424 & 18,432 & 11,904 \\ check node process & (100\%) & (78.69\%) & (50.82\%) \\ Error rate loss @ BER = \(10^{-6}\) & 0 dB & 0.2 dB & 0.05 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of check node processes in different algorithms.  
the lower bound. Combining the first minimum and probabilistic second minimum, the SMA-MSA obtains the new second minimum, as shown below:  
\[new~{}min^{2 nd}~{}=~{}\rho_{2}\cdot min^{1 st}+\sigma\cdot min^{2 nd}, \tag{9}\]  
where both \(\rho_{2}\) and \(\sigma\) are used to weigh the first minimum and probabilistic second minimum for the new second minimum. The values of \(\rho_{2}\) and \(\sigma\) are set as \(2^{-x}\) or \((1-2^{-x})\), depending on the code, where \(x\) is an integer. The author of SMA-MSA noted that the new second minimum auto-adjusted the correction value applied to the first minimum by a factor \(\rho\). The definition of \(\rho\) is expressed as:  
\[\rho~{}=~{}\rho_{2}+\frac{min^{2 nd}}{min^{1 st}}\cdot\sigma. \tag{10}\]  
The formula of the check nodes updated with the grouping search is shown below:  
\[R_{i,j}^{k}~{}=~{}\left(\prod_{j^{\prime}\in N(c)\backslash j}sgn \Big{(}Q_{i,j}^{k}\Big{)}\right)\left\{\begin{array}{ll}\rho\cdot min^{1 st}, &j^{\prime}\
eq j_{index}\\ new~{}min^{2 nd},&j^{\prime}~{}=~{}j_{index}\end{array}\right.. \tag{11}\]
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 3 RF Energy Harvesting Application in Medical Devices
### RF Energy Harvesting for Wearable Medical Devices
respiratory rate, and heart rate of the patients and provide real-time data for accurate treatments. Such devices are low in power consumption (ranges from pW to \(\upmu\)W) and can be efficiently powered using ambient RF sources. Low-power devices include temperature sensor devices (power consumption ranges from 113 pW-1.4 \(\upmu\)W) [97; 121; 122; 123; 124], biosensors, voltage, and current sensors (power consumption ranges from 9.3 nW-436 \(\upmu\)W) [125; 126; 127]. Suggestions have been made for ambient RF power harvesting to power these devices instead of batteries [128; 129]. RFEH systems provide a controllable and continuous source of energy. However, the implementation of RF harvesters to power wearable devices faces some issues, such as their ability to supply an adequate amount of energy and the deterioration in power transfer efficiency of far-field RFEH systems. Several innovative methods to design RFEH systems for energizing various types of low-power wearable medical devices are reviewed in the following subsections.
# Wind-Wave-Current Coupled Modeling of the Effect of Artificial Island on the Coastal Environment
## 1 Introduction
For example, [PERSON] et al. [10] used the MIKE21 model based on an unstructured grid to study the influence of the nonlinear superposition effect on tidal currents and waves when artificial island and coastal remediation projects co-exist. [PERSON] et al. [11] used a mathematical model of typhoon winds and waves to analyze wave elements under extreme weather in the presence of the artificial island of the Hong Kong-Zhuhai-Macao Bridge. According to previous studies, it is necessary to use an unstructured grid ocean dynamics model when considering the dynamic nonlinear processes of hydrodynamics, waves, sediment transport, and topographic evolution affecting the nearshore area under the coupling of artificial island and natural driving forces. When a complex coupled mathematical model of wind and wave flow is used to study offshore dynamic processes under the disturbance of artificial island, a large number of basic data-driven models are needed to obtain reliable initial and boundary conditions of the model, which was lacking in previous numerical simulation studies on the influence of artificial island on the offshore environment [12; 13]. Meanwhile, when mathematical models with different spatial-temporal evolution scales are coupled, such as the hydrodynamic, wave, and sediment transport models mentioned above, couplers are usually used for spatial interpolation [8], and interpolation errors will be introduced no matter what interpolation algorithm is used. In this study, the marine area around the artificial island of Qionghai City, Hainan Province, was taken as the research object, and a unified unstructured grid marine dynamics SCHISM simulation system [14; 15] was adopted to complete the simulation of the wind, wave, current, and sediment transport systems. Combined with a large number of field-measured data including sounding, velocity, suspended sediment, and bed sand and quaternary bed sand thickness in the surrounding marine area collected in 2020 and 2021, a simulation study on the influence of artificial island on the coastal environment under a coupled model of the wind, waves, current [16], and sediment transport [17] was carried out. The proposed approach improved the simulation accuracy of inshore environments across multiple scales (transition from small-scale artificial island to large-scale open ocean boundary).
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Back-Pressure Routing  
The back-pressure (BP) algorithm proposed in [45] has been recognized as providing optimal throughput in time-varying networks. The BP algorithm does not perform route discovery of intermediate devices from the source to destination; instead, each packet independently develops its own routing decision by solving the maximum weight process at each time slot. There are two different levels to deliver data, the selection of the flow problem at the device level and the scheduling of links at the network level, discussed below.
Through continuous learning and adjustment from the environment, the DDPG can automatically optimize energy scheduling strategies when facing unforeseen changes, thereby improving the system's dynamic adaptability and robustness. 2. Traditional rule-based and optimization-based methods often struggle with highly nonlinear and complex system models, especially in dynamic environments. By introducing learning-based energy scheduling methods, particularly reinforcement learning, which offers powerful modeling capabilities, the complexities that current methods fail to handle can be tackled. More accurate system modeling enables optimized energy management under various environmental and task conditions. 3. Many optimization methods require lengthy computation times, making them unsuitable for real-time applications. In contrast, DDPG can perform online learning and decision-making in a shorter time, greatly improving the real-time efficiency of energy scheduling. This is particularly crucial for energy management in hydrogen-powered hybrid UAVs operating in uncertain environments. The remaining structure of the paper is organized as follows: Section 2 introduces the force analysis and power calculation of UAVs across different mission phases. In Section 3, the modeling process of the proposed hybrid energy system mathematical model will be elaborated in detail. Section 4 will describe in detail the principles of the MPC and DDPG algorithms and their integration. Then, in Section 5, the power optimization results and optimized scheduling results will be presented for various flight missions. Finally, Section 6 concludes the paper and outlines future work.
# Optimizing the Scheduling of Teaching Activities in a Faculty
## 5 The Development of Theoretical/Practical Contributions
### Comprehensive Approach to Automated Timetable Generation
#### 5.1.5 Database Model for Timetable Generation  
An effective and organized database model is essential for the efficient creation of timetables. The database should be structured to accommodate diverse entities, including student groups, teachers, classes, rooms, limitations, time slots, and users (both administrators and regular users). The primary entities in the database model (Table 3) consist of _StudentGroup, Teacher, Lesson, Room, ConstraintModel, Timeslot_, and _User_. Every entity possesses distinct attributes and establishes connections with other entities, which are essential for the effective operation of the timetable generation system. \begin{table}
\begin{tabular}{l l} \hline \hline
**Input** & **Description** \\ \hline Lessons & Detailed information about courses, seminars, and laboratory sessions. \\ \hline Student groups & Organization of series, groups, and semigroups. \\ \hline Rooms & Name, capacity, and location details. \\ \hline Teachers & Name and availability. \\ \hline Timeslots & Defined periods for scheduling lessons. \\ \hline Constraints & Rules for maximum hours per day, room capacity, lesson type allocation, and the basic constraints that exist in the university/college timetable. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Timetable inputs. To better understand the relationships between these entities, an Entity-Relationship (ER) diagram can be constructed, as seen in Figure 3.
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 5 Improvement in Train Position Accuracy Based on Yaw Angular Velocity
### Position Correction Results and Track Condition Monitoring Results
#### 5.5.3 Position Correction Result  
Figures 13 and 14 show the curvatures of the data from all the trials without and with position correction, respectively. In addition, as a reference value, the curvature information of the midpoint of the curves is shown as a dotted red line, and the ETC and BTC positions used for position correction are indicated as a dotted blue line.  
Figure 12: Satellite photo of the test section.  
A comparison between Figures 13 and 14 shows that the position correction method provided a slight variation in the estimated curvature at the midpoint of the curves. Subsequently, we calculated the variance and mean value of the position information (distance) at the midpoint of the transition-curve section (when measuring 50% of the maximum curvature value of the curve).  
The results show that the standard deviations before correction were 12.5 and 8.6 [m], whereas, after correction, they was 7.6 and 3.9 [m], respectively, which indicates that the variation in the data owing to the GNSS error can be reduced. The mean values were 3942 and 4296 [m] before correction and 3948 and 4305 [m] after correction, respectively, whereas the actual values were 3946 and 4307 [m], respectively. This indicates that position correction using the yaw angular velocity is effective for the position information calculated via GNSS-based map matching.
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 4 Developments of the Fast BOTDA Schemes
### Optical Frequency Comb Based Fast BOTDA  
To avoid the point-by-point frequency-sweeping process, an OFC wave, which has already been used in precise metrology [35], is employed to quickly acquire the whole BGS in the frequency-domain. As shown in Figure 2a, generally the probe wave with the frequency \(\
u_{0}\) is single-sideband modulated into an optical carrier and an OFC sideband while an optical pulse is used as a pump wave whose central frequency is equal to the optical carrier, which is shown in Figure 2b. The beating Brillouin signal can be amplified by just injecting a pump pulse when the frequency span of the OFC covers and the BFS of the FUT in a frequency-domain via the SBS amplification. Then, the distributed BGS can be demodulated using a fast Fourier transform (FFT) of the beating Brillouin signal.  
To improve the performance of the OFC-based BOTDA, a dual-polarization OFC-probe wave [36] is generated based on the orthogonal frequency-division multiplexing (OFDM) modulation. Both of the upper sideband OFC in the x-polarization axis and the lower sideband OFC in the y-polarization axis are injected into a 1.08 km standard SMF with a counter-propagating pump pulse, which results in a Brillouin loss (in Figure 3a) and a Brillouin gain (in Figure 3b), respectively. The distributed BGS (in Figure 3c) can be reconstructed by calculating the absolute spectra of the Brillouin loss and the Brillouin gain. Then, the combined Brillouin signal can be drastically enhanced by coherent detection and polarization combination. Without frequency-sweeping, polarization noise and averaging a temperature measurement along a 1.08 km SSMF is through the interaction of the OFDM-probe and just one pump pulse. It can be clearly concluded that the maximum sampling rate of this scheme is only confined by the fiber length. Then, the temperature change can be clearly recognized with a spatial resolution of 20.48 m and the comparison of the BGSs at a different position is displayed in Figure 3d.  
Figure 3: The results of the OFC-based fast BOTDA scheme [36]. The distributed BGS of the polarization axis: (**a**) x, (**b**) y, and (**c**) the combination. (**d**) The BGS at position A, B, and C. Experimental data: scatters. The curve-fittings curves: lines.  
Figure 2: The OFC-based fast BOTDA scheme. (**a**) Frequency component distribution of the OFC-probe wave and (**b**) the frequency component distribution of the OFC-probe wave and pump pulse under the SBS effect.
## 2 Numerical Model of the Tunnel and the Support Schemes
### Support Schemes  
Different from the delayed support of the traditional drill and blast method, TBM support is carried out synchronously with the advancing, thus the deformation of the rock mass after the excavation was considered to be non-existent. In general, the support effect is correlated to the support spacing, thus shortening the support spacing is considered for compensating the reduction in the support effect. Moreover, in the TBM advancing project, the tunnel axial deformation should be roughly equal to avoid the TBM being captured. Hence, tests on different support spacings were carried out. The tested spacings were 0.5 m, 1 m, 1.5 m, 2 m, 2.5 m, 3 m, 3.5 m, 4 m, 4.5 m, 5 m, 6 m, 7 m, and 8 m, respectively. The influence of the bolt type on the support effect was not considered, and the properties of the applied bolt (length of 2.4 m) are given in Table 3[16].  
In this paper, two support schemes were studied, i.e., the radial support and the non-radial support, as shown in Figure 3. As with the literal meaning, the radial support was that all bolts were oriented on the extension of the radius, which is considered to be the best support scheme without the consideration of other factors. In this paper, the non-radial support was that the anchorage points of the bolts were the same as the radial support, but all of the bolts turned downwards by 30\({}^{\circ}\), which could leave sufficient space for the conveyor belt installation and would increase the advance rate greatly.
## 3 Results and Analysis  
In order to assess the contribution of BDS GEO, IGSO, and MEO satellites to PPP in Asia-Pacific region, the BDS only PPP in static as well as GPS and BDS combined PPP in both static and kinematic modes are performed. The corresponding convergence time and positioning errors are analyzed and compared in this section. Furthermore, GPS and GLONASS combined observations are also processed, and the corresponding ambiguity-float and -fixed PPP results are compared with that of GPS and BDS combined solutions in order to further compare the different contribution of BDS and GLONASS to GPS only PPP. In the meantime, the three constellations combined solutions are computed to demonstrate the level of precision and the convergence time achieved. In this section, the convergence time and position errors are used as the indicators of PPP results.
# XGBoost-Based Detection of DDoS Attacks in Named Data Networking
## 4 Experiment Analysis
### Analysis of Experiment Result
To demonstrate the performance of the proposed improved XGBoost, we conducted experiments to compare it with XGB [17], KNN [18], ADA [19] and Random Forest [20]. Figure 12 shows the final results of multiple experiments and the average of each experiment. As shown in Figure 12, the AUC score corresponding to the enhanced XGBoost model exceeds the baseline model of other multi-classification based machine learning algorithms. After averaging the results over 10 experiments, the final AUC values for each classifier  
Figure 11: The result of the proposed method through combined features. Figure 12: AUC scores between Improved XGBoost and other methods. are as follows: XGB: 0.9615, KNN: 0.9653, ADA: 0.9695, DecisionTree: 0.9583, and ImprovedXGB: 0.9746. Experimental results shows that the Improved XGBoost achieves higher AUC values across multiple experiments, indicating better classification performance. And compared to other models, the Improved XGBoost exhibits less fluctuation across different runs, proving its robustness. These findings confirm that the proposed Improved XGBoost model is more effective and reliable than traditional machine learning models in detecting and classifying network states.
## 5 Co-Located Atmospheric Disturbances
### Latent Heat Flux and Aerosol Optical Depth  
Further evidence of the meteorological perturbations can be obtained from Figure 13. Figure 13 shows daily latent heat flux (LHF) values from 11 January to 1 February. This figure is focused on the LHF behavior in the more narrow latitudinal range of 40\({}^{\circ}\)-42\({}^{\circ}\)N, where the anomalous SH was observed. One can see that between 21 and 23 January, a pronounced enhancement is observed, with a maximum of 5 W/m\({}^{2}\) on 22 January. LHF also tends to increase at the beginning and end of the 20-day period centered on the day of EQ. This implies the release of additional amounts of energy into the atmosphere due to the condensation of water vapor on ions. It should be noted that LHF exhibits a large and asymmetric diurnal variation of about 15 W/m\({}^{2}\), which maximizes around 6-7 UT and drops between 11 and 03 UT). This implies that daily averaging is dominated by local midday hours. Keeping this source of uncertainty in mind, from Figure 13, it can be concluded that the LHF magnitude appears to be considerably larger over a 3-day period centered on the day of EQ.  
Finally, Figure 13 depicts the evolution of the aerosol load over the area around the epicenter. The aerosol optical depth (AOD) is the degree to which aerosols prevent the transmission of light by absorption or scattering of light. In Figure 13, the 500 nm AODs as measured by the MODIS-Terra satellite and collected in MERRA-2 as daily values are presented. It can be seen that AOD increased from 0.12 on 19 January up to 0.19 on 21 January. This enhancement then gradually decays over several days. The behavior of AOD confirms an increase in the number of aerosol particles, which occurred simultaneously with intense condensation of water vapor and an increase in latent heat flux.  
Figure 12: The “time–latitude” diagram for the hourly means of SH averaged over the longitudes 65\({}^{\circ}\)-95\({}^{\circ}\)E. The EQ time (EQ latitude) is indicated by vertical (horizontal) line.