esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cognitive Experience Assessment
#### 3.3.2 The Proportion of Single Load Index  
This experiment considers the pressure brought by the brain power consumption, physical exertion, teaching rhythm, the satisfaction of performance, effort and the frustration in the teaching task as the total load. To explore the difference in the contribution of the single load index to those of the total load in the control group and the experimental group, the two groups of students were to sort the proportion of the above six load indicators to the total load in order and select the proportion of each group. The first two items with the largest proportion and the last two with the smallest proportion are statistically analyzed, as shown in Figure 7.  
Figure 7: Statistical analysis of the largest and smallest proportions: (**a**) distribution of the maximum two components of the single load index; (**b**) distribution of the minimum two components of the single load index.  
In the control group, the pressure brought by the teaching rhythm and the pressure brought by the mental power consumption became the two items with a larger total load. Simultaneously, no one chose the pressure caused by the teaching rhythm as the two items with the smallest load. The pressure of the teaching rhythm is indeed a load that cannot be ignored in traditional teaching. In the traditional teaching method, the teaching rhythm is almost completely in the hands of the teacher. The students need to follow the lecturer's teaching and while simultaneously understanding and memorizing. No one chose the physical exertion index among the two largest proportions. Simultaneously, in the two load surveys with the smallest proportion, 50% of the answers think that the proportion of physical exertion is the smallest. Obviously, in the traditional teaching mode, students only need to sit still, which brings about the lack of experience and participation.  
In the experimental group, the pressure brought by the teaching rhythm and the pressure generated to achieve the satisfaction of performance became the two items with the largest total load. The frustration index was clearly marked as the smallest of the two, while no one chose frustration in the two largest proportions. According to observation, in VR teaching, the teaching rhythm is freely controlled by the students, and the need for self-exploration is both challenging and expectable; however, when students are unfamiliar with the teaching cases and the operation of VR devices, students will be subconsciously nervous about the teaching rhythm and the operation performance.  
Compared with traditional teaching, in VR teaching, students need to expend significant physical exertion, but self-exploration and participation can improve self-confidence and interest, thus reducing the frustration from the teaching. The grasping of the teaching rhythm should be noticed in both teaching methods. If the students' autonomy is too large or too small, there will be a large load in the studying due to the teaching rhythm.
# Transfer Learning-Based Object Detection Model for Steel Structure Bolt Fastening Inspection
## 4 Case Study
### Results and Discussions  
In the previous chapter, YOLOv3 was considered suitable for real-time inspection due to its significantly shorter inference time per image compared to other algorithms. However, its accuracy was comparatively lower than the other algorithms. Transfer learning was applied to improve the detection performance of the YOLOv3 model. Table 3 presents a comparison of the performance between the YOLOv3 model after transfer learning, YOLOv3 before transfer learning, RetinaNet, and Faster R-CNN models.  
When comparing the performance of the transfer learning YOLOv3 model with RetinaNet and Faster R-CNN after transfer learning, the objectness loss validation decreased by 0.5% compared to before transfer learning. Consequently, the differences compared to RetinaNet and Faster R-CNN decreased by 0.0024 and 0.0279, respectively. The mean average precision (mAP) improved by 22.85% compared to YOLOv3 before transfer learning, resulting in reduced differences of 3.9% and 8.13% compared to RetinaNet and Faster R-CNN, respectively. Additionally, the inference time per image decreased to 0.045 s, which is nearly twice as fast as YOLOv3 before transfer learning.  
Figure 5: mAP value after transfer learning (YOLOv3).  
Lastly, when comparing the transfer learning YOLOv3 model with Faster R-CNN, which exhibited the highest accuracy, the former displayed slightly lower accuracy with a mAP difference of 8.13%. However, the detection speed was 700 times faster. Therefore, the transfer learning YOLOv3 model can be considered an appropriate detection model for real-time bolt fastening at construction sites.
## 2 Materials and Methods  
### Experimental Tests  
Laboratory experiments were conducted to determine the differences in channel infiltration capacity over time as a function of check dam height and spacing. The experiments were conducted at the Hydraulic Laboratory of Universiti Putra Malaysia using a trapezoidal channel fabricated from a perforated steel sheet mounted on a steel frame. The channel had a length of 10 m with longitudinal and side slopes of 0.3% and 3%, respectively, and a bottom width of 0.2 m, as depicted in Figure 1a.  
The simulation of the natural channel with a grass-covered boundary was performed by placing a thick topsoil layer (15 cm thick) evenly covered with cow grass (_Axonopus compressus_) of around 100 mm height on the channel base and side slopes (Figure 1b). The texture of the topsoil was composed of 76% sand, 8% silt and clay, and 16% organic matter.  
Erosion from infiltrated water was prevented by placing a permeable sheet in between the soil and the perforated channel steel sheets. A check dam was installed at the end of the channel using a steel plate that could be fixed by an adjustable sandwich panel (Figure 2).  
The water flow through the channel was monitored using different instruments. The instruments used to measure the water movements (inflow and outflow), water level, and flow velocity are presented in Table 1 and Figure 3.  
Figure 1: The trapezoidal channel model: (**a**) perforated steel sheets with steel frame (**b**); the channel with check dams during operation.  
Figure 2: The check dam at the end of the channel downstream.  
On the basis of the capacity of the channel and the capabilities of the laboratory instruments, the experiments considered a total of 15 tests; 3 cases of check dam heights, \(h_{w}\) = 10, 15, and 20 cm; and 5 inflow rates, \(Q_{in}\) = 0.0055, 0.0075, 0.0095, 0.0115, and 0.0135 m\({}^{3}\)/s for each check dam height. The experiment was repeated for different \(Q_{in}\) for a given \(h_{w}\) and vice versa. The duration of each test was 1.5 h.
# Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data
## 4 Results  
### Descriptive Analysis  
Of the 6805 tweets, 2586 (38%) were original and 4219 (62%) were retweeted with supportive content, reflecting the highly interactive nature of users. All Twitter discussions developed around 237 hashtags. The hashtag analysis identified (excluding 51 hashtags used for data mining) 86 key hashtags among them as the most strongly associated ones with the Lychian elements. Among them, the most used 50 hashtags were: #selfie, #blue, #seaview, #skyporn, #Sky, #ocean, #sea, #coastline, #wonderofasia, #view, #train, #viewfromthetop, #cityview, #luxury, #luxuryhotel, #urbandesign, #greenbuilding, #economy, #green, #blue, #SciencisNature, #Cloud, #Rooftop, #arialview, #discovercolombo, #development, #project, #waves, #randomshot, #roadride, #ship, #sand, #beach, #lankebach, #naturalbeach, #Beauty, #weekend, #trave, #AwesomeMoments, #cappuccino #cappuccinoArt, #convocation, #sunset, #shopping, #fireworks, #restaurent, #foodlover, #foodie, #family, #dayout. Most of these hashtags were used in relation to the user experiences abiding with the Lychian elements identified.  
In total, 2190 users contributed to the creation of the dataset of 6805 tweets. About 79% of the tweets were circulated by individual users and 21% by institutions. However, 75% of the top 20 most active users were individuals. There were 576 tweets with informative URLs in the dataset. Within the period of 2015-2016, the total number of places identified for the analysis was 22. During the next two years of time (2017-2018), this number increased to 25. This number further increased to 28 from 2019-2020.
## Materials and Methods
### Adsorption Experiments and Amines Analysis  
Adsorption experiments were performed at room temperature in glass test tubes, adding 100 mg of adsorbent to 10 mL of sample solution. In order to maintain a regular and homogeneous mixing, a MS1 Minishaker was used at 1400 min\({}^{-1}\). Basing on the results reported by [PERSON] and co-workers [41], an adsorption time of 120 min and room temperature were employed. Moreover, the amount of adsorbent material was calculated as a function of the sulfonate groups and that pH value corresponds to the salification one of amines. Therefore, the composite was collected laterally using an Nd magnet, and the supernatant was recovered and treated with a stoichiometric amount of sodium carbonate. The sample was extracted three times with ethyl acetate (3 \(\times\) 10 mL), and the organic phase was concentrated by a rotary evaporator, reprising the analytes with 10 mL of ethyl acetate. The determination of the concentration of the amines was performed analyzing every sample three times using a gas chromatograph Thermo Fischer Scientific Focus Series, USA with a flame ionization detector (FID) (see Supplementary Materials for the chromatograms). The calibration curves were constructed using the linear regression method analyzing 5 samples of each amine in AcOEt at different concentrations, covering a linear dynamic range from ca. 60 to 1200 ppm (see Supplementary Materials for the graphics). The percentages of amines removal were calculated using Equation (2):  
\[\text{RE}\ (\%)=\ \frac{\text{C}_{0}-\text{C}_{\text{e}}}{\text{C}_{0}}\times 100 \tag{2}\]  
where RE is the amine removal efficiency (%), and C\({}_{0}\) and C\({}_{\text{e}}\) are the initial and equilibrium concentration in the solution, respectively.  
Loading factors were calculated using Equation (3) [28]:  
\[\text{Q}=\ \frac{\left(\text{C}_{0}-\text{C}_{\text{t}}\right)\text{V}}{\text{m}} \tag{3}\]  
where Q is the amount of pollutant adsorbed onto a unit dry mass of sulfonic cellulose in mg/g. C\({}_{0}\) and C\({}_{\text{t}}\) are the initial and the remaining concentrations of pollutant in the solution (mg/L) at initial time and time t (minutes), respectively. V is the volume of the pollutant solution in L, and m is the weight of the dry SC@Fe\({}_{3}\)O\({}_{4}\) composite in grams.
## 3 Background Theory
### Structure-Borne Noise Analysis  
To predict the interior sound pressure caused by structural vibration, the finite element models representing the enclosed cavity and the structural panels can be coupled. The  
Figure 1: Mesh-based structure-borne noise model which consisted of the panel and cavity.  
coupling accounts for the structural vibrations forcing the interior acoustic response, and also the loading of the acoustic pressure excites the panels.  
The coupled acoustic-structure equations of the motion are expressed as Equation (3) [14; 15].  
\[\begin{bmatrix}M&0\\ A^{T}&Q\end{bmatrix}\begin{Bmatrix}\ddot{w}\\ \dot{p}\end{bmatrix}+\begin{bmatrix}C&0\\ 0&D\end{bmatrix}\begin{Bmatrix}\dot{w}\\ \dot{p}\end{bmatrix}+\begin{bmatrix}K&-A\\ 0&H\end{bmatrix}\begin{Bmatrix}\dot{w}\\ \dot{p}\end{Bmatrix}=\ \begin{Bmatrix}F\\ G\end{Bmatrix} \tag{3}\]  
where  
\[F_{i}=F_{i}^{E}-\int_{A}p^{E}N_{i}dA\ \ \ G_{i}=\int_{V}\dot{q}iN_{i}^{A}dV\]  
where \(\ddot{w},\dot{w},\) and \(w\) are the matrix of nodal acceleration, velocity, and displacement at the boundary, respectively, \(N\) is the row matrix of interpolation functions for the structural displacements in the normal direction of the surface, \(D\) is the impedance damping matrix, superscript \(E\) indicates external load or pressure, and \(q\) is the volume velocity of any interior sound source. The upper row of Equation (3) represents the structure and the lower row represents the acoustic finite-element equation of motion, respectively, and they are coupled using structural-acoustic coupling matrix A which was obtained by assembling the integrations over the appropriate elements on A.  
In this study, the structure and acoustic response is converted to the surrogate model using the GCN, inference function, CNN, and neural network. Coupling matrix A is represented by the CNN, which is input from the structure and acoustic cavity.
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 3 UAV and IoT  
Unmanned Aerial Vehicles (UAVs) have witnessed an exceptional growth in the market in addition to the huge demand in the IoT area. Installing a UAV or collections of UAVs known as swarms for IoT industrial services is becoming a reality and a necessity for applications concerning public safety, pollution mitigation, etc. [15; 16]. During the upcoming years, UAVs, often called drones, will be used in various fields such as homeland security [17], security and border surveillance [18], and goods transportation [19; 20].
# GNSS Accuracy Under White Gaussian Noise Jamming +
## 3 GNSS Jamming Experiment
### GNSS Simulator  
The GNSS simulator used in the experiments was the Orolia Broadsim, which runs the Skydel simulator software. This simulator replicates the GNSS signals, in our case, GPS C/A and Galileo E1, at a chosen position and time. The simulator allows one to place a jammer with user-defined characteristics, like the signal modulation, bandwidth, center frequency, etc., at a certain fixed location or on a moving trajectory. In the experiment, the jammer was set as an AWGN jammer with a transmission power of 20 mW and with different bandwidths of 1, 2, and 4 MHz. The distance from jammer to receiver was varied between 1000 m and 10 km. All simulated experiments were performed 30 m above a flat terrain, with no obstructions of any kind present. The receiver antenna was set to be isotropic in the simulation.
# Integrated Magnetic Management of Stored Angular Momentum in Autonomous Attitude Control Systems
## 2 Attitude Control System
### PID Three-Axis Attitude Control
Indeed, from the reaction wheels' control torque,  
\[\mathbf{t}_{R}=(1-k)\,\mathbf{t}_{R}^{PID}=(1-k)\,\mathbf{t}^{PID}, \tag{13}\]it is possible to compute the actuation command for the reaction wheels, \(\hat{\mathbf{h}}_{R}^{PID}\), assuming that:  
\[\mathbf{t}_{R}=-\mathbf{R}\hat{\mathbf{h}}_{R}^{PID}-\boldsymbol{\omega}\times \mathbf{R}\mathbf{h}_{R}. \tag{14}\]  
Knowing that the reaction wheels are commanded in terms of angular momentum variation as:  
\[\hat{\mathbf{h}}_{R}=I_{R}\left[\dot{\omega}_{R_{1}},\dot{\omega}_{R_{2}}, \dot{\omega}_{R_{3}},\dot{\omega}_{R_{4}}\right]^{T}=\mathbf{t}_{CMD}, \tag{15}\]  
where \(\mathbf{t}_{CMD}\) is the \(4\times 1\) vector containing the torque commands to be directly applied to each single rotor. The actuation commands for the three-axis PID attitude control become:  
\[\mathbf{t}_{CMD}^{PID}=\hat{\mathbf{h}}_{R}^{PID}=-\mathbf{R}^{\dagger}( \mathbf{t}_{R}+\boldsymbol{\omega}\times\mathbf{R}\mathbf{h}_{R}), \tag{16}\]  
where \(\mathbf{R}^{\dagger}\) is the Mooree-Penrose generalized inverse of the configuration matrix \(\mathbf{R}\) to distribute the torque among the 4 redundant reaction wheels with the the pseudo-inverse law [29]. The actuation commands for the magnetic torquers are analogously computed from the PID control torque:  
\[\mathbf{t}_{M}=k\mathbf{t}_{M}^{PID}=k\mathbf{t}^{PID}, \tag{17}\]  
as:  
\[\mathbf{m}=\frac{1}{\left\|\mathbf{B}\right\|^{2}}\mathbf{B}\times\mathbf{t}_ {M}. \tag{18}\]  
In fact, because of the classical controllability issues with magnetic torquers, the control dipole calculation should take into account the surrounding magnetic field to maximize the available magnetic control torque
# Compressive Sensing-Based Coding Iterative Channel Estimation Method for TDS-OFDM System
###### Abstract  
Satellite Internet is the key to integrated air-space-ground communication, while the design of waveforms with high spectrum efficiency is an intrinsic requirement for high-speed data transmission in satellite Internet. Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) technology can significantly improve spectrum utilization efficiency by using PN sequences instead of traditional CP cyclic prefixes. However, it also leads to time-domain aliasing between PN sequences and data symbols, posing a serious challenge to channel estimation. To solve this problem, a compressive sensing-based coding iterative channel estimation method for the TDS-OFDM system is proposed in this paper. This method innovatively combines compressive sensing channel estimation technology with the Reed-Solomon low-density parity-check cascade coding (RS-LDPC) scheme, and achieves performance improvements as follows: (1) Construct the iterative optimization mechanism for the compressive sensing algorithm and equalization feedback loop. (2) RS-LDPC cascaded coding is employed to enhance the anti-interference and error correction capability of system. (3) Design the recoding link of error-corrected data to improve the accuracy of sensing matrix. The simulation results demonstrate that compared with conventional methods, the proposed method can obviously converge on the mean squared errors (MSEs) of channel estimation and significantly reduce the bit error rate (BER) of the system.  
TDS-OFDM system; channel estimation; compressive sensing; channel coding; LDPC code +
Footnote †: journal: Electronics Letters  
18 April 2025  
Revised: 4 June 2025  
Accepted: 5 June 2025  
Published: 7 June 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON] Compressive Sensing-Based Coding Iterative Channel Estimation Method for TDS-OFDM System. Electronics **2025**, 14.238, [[https://doi.org/10.3390/electronics1412238](https://doi.org/10.3390/electronics1412238)]([https://doi.org/10.3390/electronics1412238](https://doi.org/10.3390/electronics1412238))  
**Copyright:** 62 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 5 Numerical Results
### Energy Consumed by RN1 and RN2 in the Data Period, the Awake and Normal Cycles  
Figures 5 and 6 show the energy consumed by RN1 and RN2 when deploying the APT transmission scheme with \(F_{1}=F_{2}=2\) and \(Q_{1}=Q_{2}=10\). Figure 5 shows that the energy consumed by RN1 is virtually the same as in Figure 3, where C1 SNs deployed the SPT scheme (\(F_{1}=1\)).  
However, Figure 6 shows that RN2 achieves the congestion state with a larger arrival rate than in the scenario of Figure 4, approximately, at an arrival rate twice as large as the previous one. In addition, the energy consumed in the congestion state is larger than the one consumed in the scenario of Figure 4, indicating that the packet throughput in the congestion state is larger when deploying \(F_{2}=2\) (APT) than when deploying \(F_{2}=1\) (SPT). The deployment of APT in C2 SNs implies that more packets are sent when an SN wins the contention for medium access. This reduces the average number of active C2 SNs per cycle, and, in turn, it leads to a higher medium access success rate.  
Figure 4: Average energy consumed by RN2 per cycle during the _data_ period, and in the _awake_ and _normal_ cycles, when deploying SPT (\(F_{2}=1\)).  
SNs, \(E_{d,2}\) increases with the load, indicating that larger PFs are being transmitted when compared to the previous scenarios studied. Consequently, the C2 SNs throughput is now also higher.  
Note also the trend of the energy consumed in _awake_ cycles as load increases. Clearly, \(E_{aw,c}\) decreases as load increases, and this trend is more pronounced as the value of \(F_{c}\) increases. This is because when an SN is in an _awake_ cycle and transmits a PF with success, particularly when transmitting long PFs, it takes the SN longer to switch to the _awake_ mode and activate the receiver. On the other hand, when an SN is in an _awake_ cycle and transmits a PF that collides, the SN will switch to the _awake_ operation mode and activate the receiver earlier than in cycles where the PF is successfully sent. Recall that a collision is detected quite early in the transmission process, when the corresponding _CTS_ packet is not received.  
Figure 8: Average energy consumed by RN2 per cycle during the _data_ period, and in the _awake_ and _normal_ cycles, when deploying APT (\(F_{2}=5\)).  
Figure 10: Average energy consumed by RN2 per cycle during the _data_ period, and in the _awake_ and _normal_ cycles, when deploying APT (\(F_{2}=10\)).  
Figure 9: Average energy consumed by RN1 per cycle during the _data_ period, and in the _awake_ and _normal_ cycles, when deploying APT (\(F_{1}=10\)).  
Then, the trend of the energy consumed in _awake_ cycles as load increases clearly indicates that the success rate of transmitted PF increases with load as \(F_{c}\) increases.  
Note also that in the technical data sheet of the SN that has been used as a reference for the energy consumption values, the energy consumed in reception mode is larger than the one consumed while transmitting [29; 31].
## 1 Introduction
In summary, the distribution of frost heaving forces in tunnels is affected by the geometry, multifield coupling effect, and anisotropy of frost heave deformation. To describe the evolution pattern of the freezing depth and frost heaving force of the rock surrounding a tunnel under the action of different factors, this study adopted the freeze-thaw cycle overall frost heave model and established a thermo-mechanical coupling model for frost heaves in the surrounding rock of tunnel by considering the anisotropy of frost heave deformation via the COMSOL Multiphysics finite element simulation platform. Sequential coupling was used to perform thermo-mechanical coupling calculations under different minimum temperatures, numbers of negative temperature days, frost heave ratios, and anisotropy coefficients of frost heave deformation to obtain the distribution characteristicsof the freezing cycle and the frost heaving force. Finally, the effects of different factors on the frost heaving force of the lining were analysed. The results indicate that the frost heave ratio and the minimum air temperature have significant effects on the frost heaving force. An increase in the anisotropy coefficient leads to a directional concentration of frost heave strain, which in turn amplifies the frost heaving force. In contrast, the shift in the coldest day has a negligible impact on the frost heaving force. This study is important for revealing the size of the freezing cycle, the distribution characteristics of the frost heaving force and the key influencing factors of tunnels in cold regions.