esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Affordable Real-Time PPP Solutions for Transportation Applications
## 5 Conclusions  
We evaluated the accuracy of a multi-GNSS real-time precise point positioning solution employing both a cost-effective ZED-F9P device and instantaneous BKG, CNE, and WHU orbit and clock products for transportation applications. A vehicular trajectory was implemented in a suburban environment with a setup using rover receivers including the ZED-F9P device and two geodetic-grade Trimble R4s receivers, as well as another Trimble R4s receiver statically located at a reference station. The collected datasets from the ZED-F9P module were processed using GPS, GPS/GLONASS, GPS/Galileo, and GPS/GLONASS/Galileo RT-PPP processing scenarios as well as RTK. The differential GNSS kinematic solutions formed by the two rover geodetic devices and the base one were used as references. The obtained RT-PPP and RTK processing solutions were validated against their counterparts in the differential kinematic solutions. The low-cost RT-PPP solutions achieved better decimeter-level positioning accuracy than the BKG SSR-based RT-PPP solutions. The attained positioning accuracy is appropriate for cost-effective autonomous vehicles, intelligent transportation systems, and urban transportation applications.  
Conceptualization, M.A. and A.A. (Amgad Abazeed); methodology, M.A. and A.A. (Amgad Abazeed); software, A.A. (Amgad Abazeed); validation, M.A. and A.A. ([PERSON]); formal analysis, A.A. (Amgad Abazeed) and A.A. (Abdulmajeed Alsultan); investigation, M.A. and A.M.W.; resources, A.A. (Amgad Abazeed) and M.A.; data curation, A.A. (Amgad Abazeed); writing--original draft preparation, M.A.; writing--review and editing, A.M.W.; visualization, M.A., A.A. (Abdulmajeed Alsultan), and A.M.W.; supervision, M.A., A.A. (Abdulmajeed Alsultan), and A.M.W.; project administration, A.A. ([PERSON]); funding acquisition, A.A. (Abdulmajeed Alsultan). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Prince Sattam bin Abdulaziz University, project number PSAU/2023/01/26004.  
Not applicable.  
The data presented in this study are available upon request from the authors.  
The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research through project number PSAU/2023/01/26004.  
The authors declare no conflicts of interest.
## 2 Derivation of Amplification Factors (_A_Fs)  
### Introduction  
For a given signal, the amplification factor at given period (T), in terms of spectral acceleration, can be expressed as the ratio of the response spectrum at the surface to the response spectrum at the outcropping reference rock as follows:  
\[AF(\text{T})=\frac{\text{Sa}(\text{T})_{\text{s}}}{\text{Sa}(\text{T})_{ \text{b}}} \tag{1}\]where Sa(T)\({}_{s}\) and Sa(T)\({}_{b}\) are the 5% response acceleration spectra at the free surface and at the reference rock (bedrock), respectively, while T is the period of the structure.  
Because the response of soils under strong excitations is nonlinear by nature, and in order to apply the theory of wave propagation in an elastic medium, the problem needs to be linearized. The equivalent linear method, first introduced by [PERSON] (1930) [60] and extended by [PERSON] (1965) [61] and [PERSON] (1970), is largely used in nonlinear problems of soils and structures [49; 52]. This method replaces the nonlinear behaviour with an equivalent linear spring in parallel with a viscoelastic damper to include the stiffness and damping associated with the hysteresis of the soil or structure. However, given the nonlinear nature of the problem, the stiffness and damping vary with the displacement or deformation level, which is unknown. Consequently, this linearization method adopts an iterative scheme in order to converge at the target displacement, typically the maximum or design displacement. For a wave propagation problem, therefore, the shear modulus (G) and the damping ratio (\(\upxi\)), which are functions of the deformation level in the middle of each layer, are adjusted at each iteration in order to capture the hysteresis of the soil at the target deformation level. A wave equation can be used to solve the shear wave propagation in soils [62; 63; 64; 65; 66]. [PERSON] et al. (1972) proposed an algorithm based on the continuous solution of the wave equation, the main steps of which are summarized in the Supplementary File, section (a) [67; 68].
## 2 Principle
### Photon Tracing Algorithm
As shown in Figure 3, the weight of photon after each interaction is used to update as \(W_{n+1}=W_{n}\alpha_{0}^{n+1}\), where \(\alpha_{0}=b(\lambda)/c(\lambda)\)[23]. The new direction is described by the scattering polar angle \(\theta_{s}\), which can be derived using the Henyey Greenstein scattering phase functions (HG-SPF), \(\xi(\theta_{s},\phi_{s})\)[24]. Similarly as mentioned above, by equating \(2\pi\int_{0}^{\theta_{s}}\xi(\theta_{s},\phi_{s})\sin(\theta)d\theta\) to an RV, the new direction is determined as:  
\[\left\{ \begin{array}{c}\theta_{s}=\arccos(1+s^{2}-\left[\frac{1-s^{2}}{1-s+ 2\mathbb{R}_{5}}\right]^{2}/2s)\\ \phi_{s}=2\pi\mathbb{R}_{7}\end{array} \right. \tag{4}\]  
where \(s\) represents the asymmetric parameter related to the water quality, \(\mathbb{R}_{6}\) and \(\mathbb{R}_{7}\) are two RVs in \([0,1]\). Then the new photon coordinates are continuously updated and the tracking of a photon is terminated when its weight drops below a given threshold (\(E_{th}=10^{-6}\)) or it reaches the receiver plane. That is to say, the photon falls within the receiver aperture and its polar angle is less than half of the field of view (FOV). After repeating the aforementioned tracing process, the corresponding attributes of captured photons are recorded for channel impulse response (CIR) plot and further analysis.
## 2 Experimental Setup and Results
Further optimizing the setup in future work can achieve the modulation frequency up to the GHz range.
## 4 Design of a DT System for Multi-Robot Cooperative CBFMS
### DT Model Behavioral Relationship Construction
#### 4.2.2 Bounding Volumes Collision Detection Algorithm  
The types of bounding volumes include the axis-aligned bounding volume, O-shaped bounding volume, and triangular bounding volume, etc. As the carriers and CBs are regular shapes, the axis-aligned bounding volumes enable the model to be enclosed more evenly and tightly, making it easier to calculate. The shape of the bounding volume is shown in Figure 21.  
As a necessary condition for logical triggering, the construction of the bounding volume has a greater role in the operational efficiency of the DT system. A double-layer construction is used in this paper, as shown in Figure 21b. The algorithm is a separated axis detection algorithm. If the projections of two bounding volumes in the direction of the same axis do not overlap, the two bounding volumes are proven to be non-intersecting. If the projections in one direction intersect, a collision may be released. Simultaneously, if the projections in both directions intersect, the two bounding volumes must have collided. Traditional collision detection requires separate detection of the separation axis. However, the double-layered bounding volume only requires collision detection by design in the construction, without having to collide with each bounding volume. The principle of the collision detection algorithm is shown in Figure 22. The traditional collision detection of each bounding volume requires the detection of the separation axis; B is detected with A and B. The collision detection algorithm is shown in Figure 23.  
Figure 21: Model bounding volume handling: (**a**) The directional bounding volume; (**b**) The double-layer construction bounding volume; (**c**) The axis-aligned bounding volume.  
Figure 20: Kinematic control algorithm for robot model: (**a**) Digital twin model; (**b**) Kinematic algorithm; (**c**) Animator control.  
B, respectively, when entering the state, and A is detected with C and D, respectively, after waiting for B to exit detection. The double-layer construction bounding volume used in this paper collides with the two bounding volumes C and D at the same time and only requires eight rounds to complete (Figure 22a). There is no need to calculate the collision between B and C (Figure 22b) and the collision between A and D (Figure 22c). Compared with traditional separate collision detection, this can greatly improve computational efficiency.
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
## 5 Conclusions  
This paper investigates the anti-jamming capabilities of OTFS modulation, focusing on detection and suppression methods in an AWGN channel. A frequency-domain anti-jamming technique was proposed, utilizing a time-domain overlapping window strategy to effectively reduce jamming sidelobes while minimizing the SNR degradation caused by the window function. To further improve jamming detection accuracy, a smoothing window method was introduced, providing stable samples and reducing the false alarm rate significantly. Additionally, a low-complexity improved FCME algorithm was developed, enhancing the practicality and efficiency of interference suppression in real-world applications. Through simulation analyses under single-tone and partial-band jamming scenarios, the proposed methods demonstrated robust performance, validating their effectiveness in complex and jamming-prone environments. These contributions highlight the potential of OTFS modulation as a resilient waveform for high-mobility communication systems, providing insights into its practical deployment in next-generation wireless networks. Following the main results presented here, a promising direction for future work is to extend the proposed methods to more diverse jamming scenarios, such as multitone and adaptive jamming, and to explore their applicability in non-linear and multipath fading channels.  
Conceptualization, L.L. and H.L.; formal analysis, P.W. and J.L.; supervision, L.L.; writing--original draft, P.W. and D.Z.; writing--review and editing, H.L.; funding acquisition, L.L. and H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62371037, in part by the China Scholarship Council under Grant Number 202306460096, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515011186 and Grant 2025A1515010082, and in part by the China Postdoctoral Science Foundation under Grant 2023M740223.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Figure 13: BER performance of OTFS with different jamming detection and suppression techniques under partial-band jamming.
## 4 Experimental Verification
### Field Test in Underground Coal Mine
#### 4.4.2 Results and Analysis  
After the training, 1220 images in the testing set were processed to adjust the size of the images to 300 \(\times\) 300 pixel values. They were then input into the traditional Faster R-CNN and the improved Faster R-CNN model for testing. The results are shown in Table 6. It can be seen that the improved Faster R-CNN model increased the identification accuracy and recall rate of pressure relief holes by 2.2% and 6.15%, respectively, and the average training time has been shortened by 35.73% compared with the traditional Faster R-CNN. The identification effect of the relief holes based on improved Faster R-CNN model is shown in Figure 17. In general, the above results verify the superiority and practicability of the proposed relief hole identification model.  
Figure 16: Partial pressure relief hole images in coal mine.  
In order to objectively evaluate the model performance, the identification results of three network models, YOLO v3 [26], YOLO v4 [27], and YOLO v5 [28], are compared and analyzed, as shown in Table 7. It can be seen that the accuracy rate and recall rate of the three YOLO network models are lower than those of the improved Faster R-CNN. Although the training time and recognition time of YOLO model are better than those of the proposed model, the recognition performance of relief holes based on improved Faster R-CNN can meet the practical needs of the field.
# New Advances in Audio Signal Processing
###### Abstract  
The growth in computing capabilities has significantly transformed the realm of data analysis and processing, most notably through the widespread adoption of artificial intelligence (AI) and deep learning technologies [1]. Audio signals, rich in content, have benefitted immensely from enhanced analytical and interpretive methodologies. This advancement is evident in the rapid progress in traditionally challenging areas, such as speaker recognition [2] and sound categorization, while also paving the way for novel areas of study in audio analysis powered by machine learning, like emotional computing [3].  
Consequently, contemporary approaches to audio signal processing are increasingly centered around AI methods that either analyse audio for tasks like classification and recognition or enhance audio quality through techniques like noise reduction and balancing. More broadly, the emphasis, in both the commercial and academic spheres, on signal-as-data involves automation. This prioritizes the development of swift, automated methods for dissecting, segmenting, annotating, and processing audio information as a dynamic area of interest. Furthermore, while AI relies on data, incorporating specialized knowledge within the domain can significantly refine these solutions, making signal processing a critical component of stages such as data enhancement and preparation.  
Moreover, traditional signal processing strategies that delve into new or underexplored areas remain crucial, considering the extensive reliance on acoustic characteristics across various sectors, including AI.  
Thus, the new advancements in audio signal processing inherently embed various fields, which can be summarized as follows:  
* New techniques for processing and analyzing sound signals, such as acoustic imaging [4] or pitch detection algorithms [5].
* Models and methodologies used for the analysis of audio data to derive new information, especially those directed towards AI and Machine Learning-based sound analysis. With the recent growth of deep learning (DL), advanced AI techniques are nowadays employed for tasks such as the preliminary detection of diseases in human voice, sound event detection, speaker recognition or sound classification [6, 7].
* The assessment of acoustic effects and/or relevant signals as indicators of the performance of another system (e.g., sound-based fault detection).  
This Editorial provides an overview of the most recent and advanced applications of audio signal technology, whether used for their processing or analysis. As shown in the articles presented, deep learning represents a common topic of interest in audio data classification, whereas the analysis of acoustically relevant features such as pitch or reverberation will only become increasingly crucial as the importance of clean and quick data analysis grows.
# Landslide Susceptibility Modeling Using a Deep Random Neural Network
## 4 Results and Discussion
### Robustness to Training Data Size  
To fully understand the advantages of the proposed landslide susceptibility modeling method, the robustness to the training data size is examined (Figure 9). The robustness test is important because the number of landslides may be too small to train the deep learning models fully in some cases. The robustness test was implemented by changing the partition of training and testing data from 80%/20% to 10%/90%. This means that the amount of training data is gradually decreased while the amount of testing data is gradually increased during experiments. We took the OA and K as examples to visualize the variation in the model's performance. It can be observed that the K values of the four network models show a downward tendency with the reduction in training data (from 80% to 10%), with  
Figure 8: ROC curves of different neural network models (**a**) on the training dataset, (**b**) on the validation dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**On the Training Data**} & \multicolumn{6}{c}{**On the Testing Data**} \\ \cline{2-9}  & **OA (\%)** & **K** & **F** & **MCC** & **OA (\%)** & **K** & **F** & **MCC** \\ \hline ADAANN & 88.22 & 0.764 & 0.889 & 0.771 & 86.59 & 0.732 & 0.869 & 0.733 \\ DNN & 88.26 & 0.765 & 0.882 & 0.767 & 86.58 & 0.731 & 0.866 & 0.732 \\ CNN & 92.53 & 0.850 & 0.925 & 0.851 & 88.41 & 0.767 & 0.883 & 0.767 \\ DRNN & 91.16 & 0.823 & 0.912 & 0.823 & 91.46 & 0.829 & 0.914 & 0.828 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation and comparison of landslide models.  
decrements of 10.49%, 30.97%, 13.78%, and 11.02% for DRNN, DNN, CNN, and ADAANN, respectively. The OA values decline by 5.28%, 15.58%, 6.97%, and 5.51% for DRNN, DNN, CNN, and ADAANN, respectively. The DRNN is consistently better than other network models. Note that DRNN still achieves OA values higher than 85% when the remaining training data size is only 10%. The DRNN achieves the lowest standard deviation in terms of K, with a value of 3.06, followed by ADAANN (3.80), CNN (4.91), and DNN (9.23), respectively. Similarly, DRNN achieves the best standard deviation of OA, with a value of 1.53, followed by ADAANN (1.90), CNN (2.48), and DNN (4.63), respectively. These results demonstrate that the DRNN can keep robustness to the training data size effectively. The random strategy of the DRNN helps alleviate the negative influence of limited data on landslide modeling accuracy.
## 2 Description of the Experiment
### Material and Energy Balances  
For easy and accurate calculations, the packed-bed column with a height of \(Z\) is divided into some stages. Figure 4 shows a stage \(j\) of the column, which represents a differential height of the column (\(j\) refers to the stage number, where \(i\) refers to the compounds). The material and energy balances around stage \(j\) are performed by using the MERSHQ equations (Equations of Material, Energy balances, Rate of mass and heat transfer, Summation of composition, the hydraulic equation of pressure drop, and equilibrium relation) presented by [PERSON] et al. (2016) as the following:Material balance for bulk liquid:  
\[F_{j}^{L}x_{i,j}^{F}+L_{j-1}x_{i,j-1}+N_{i,j}^{L}+r_{i,j}^{L}-L_{j}x_{ij}=0 \tag{7}\]  
Material balance for bulk vapor:  
\[F_{j}^{V}y_{i,j}^{F}+V_{j+1}y_{i,j+1}+N_{i,j}^{V}+r_{i,j}^{V}-V_{j}y_{i,j}=0 \tag{8}\]  
Material balance for liquid film:  
\[N_{i,j}^{l}+r_{i,j}^{L}-N_{i,j}^{L}=0 \tag{9}\]  
Material balance for vapor film:  
\[N_{i,j}^{V}+r_{i,j}^{fV}-N_{i,j}^{l}=0 \tag{10}\]  
Energy balance for bulk liquid:  
\[F_{j}^{L}H_{j}^{FL}+L_{j-1}H_{j-1}^{L}+Q_{j}^{L}+q_{j}^{L}-L_{j}H_{j}^{L}=0 \tag{11}\]  
Energy balance for bulk vapor:  
\[F_{j}^{V}H_{j}^{FV}+V_{j+1}H_{j+1}^{V}+Q_{j}^{V}-q_{j}^{V}-V_{j}H_{j}^{V}=0 \tag{12}\]  
Energy balance for the liquid film  
\[q_{j}^{L}-q_{j}^{L}=0 \tag{13}\]  
Figure 4: Rate-based stage model (Reproduced from ASPEN PLUS software manual).  
Energy balance for vapor film:  
\[q_{j}^{V}-q_{j}^{I}=0 \tag{14}\]  
Phase equilibrium at the interface:  
\[y_{i,j}^{I}-K_{i,j}x_{i,I}^{I}=0 \tag{15}\]  
Summations:  
\[\sum_{i=1}^{n}x_{i,j}-1=0 \tag{16}\]  
\[\sum_{i=1}^{n}y_{i,j}-1=0 \tag{17}\]  
\[\sum_{i=1}^{n}x_{i,j}^{I}-1=0 \tag{18}\]  
\[\sum_{i=1}^{n}y_{i,j}^{I}-1=0 \tag{19}\]  
where \(F\) is the molar flow rate of feed. \(L\), \(V\) are the molar flow rates of liquid and vapor respectively, \(N\) molar transfer rate, \(K\) is equilibrium ratio, \(r\) is reaction rate, \(H\) is enthalpy, \(Q\) is heat input to a stage, \(q\) is heat transfer rate, \(x_{i}\), \(y_{i}\) are the mole fraction of component \(i\) in liquid and vapor phases respectively.
# Influence of Variotropy on the Change in Concrete Strength under the Impact of Wet-Dry Cycles
## 1 Introduction
### Background
Based on the results of these studies, it was found that alternating wetting and drying with an increase in the number of cycles contributes to a more accelerated development of sulfate degradation in concrete. In engineering practice, concrete structures are under constant stress. Thus, in works [19; 20; 21], the effect of wetting and drying on the mechanical characteristics of concrete under load has been investigated. In general, over time, as a result of prolonged exposure to dry-wet cycles, concrete and reinforced concrete structures lose their strength characteristics. Many works are devoted to the research of the mechanical and physical properties of various types of concrete, such as concrete of normal density [22; 23], concrete on recycled aggregates [24], and self-compacting fiber-reinforced concrete [11] after exposure to dry-wet cycles and sulfate attack. All these works describe the mechanisms of degradation as well as the dependence of the deterioration of concrete properties due to the considered cyclic effects. Particular attention is paid to the mechanisms of damage to concrete under the conditions of \"dry-wet\" cycles and sulfate erosion in works [25; 26; 27]. The effect of crack opening width and \"dry-wet\" cycles on the penetration of chlorides into concrete [28] and engineering cement composite [29] is also the subject of many works. They studied and described the destructive processes occurring under these impacts and the dependence of the chloride penetration value on the \"dry-wet\" cycles and the crack opening width in concrete. Works [30; 31] are devoted to the study of the fatigue behavior of high-strengthconcrete [30] and the fatigue modeling of the wear of reinforced concrete slabs of road bridges [31] under the conditions of \"moisture-drying\" cycles. The influence of moisture in concrete and a humid environment on the fatigue characteristics of concrete is noted in the form of a decrease in fatigue resistance, expressed in the number of cycles to failure [30]. The influence of the pH of mixing water and various media (acidic, neutral, and alkaline) in which concrete is operated on its behavior was previously studied to assess the properties of concrete and structures made of it, such as strength [31], degradation degree [32], composition of the corroded surface of reinforcing steel [33], compressive strength and microstructure analysis, durability [34; 35], loss of strength, and mass [36]. At the same time, the methods and criteria for measuring pH in the study of cement-based materials [37], as well as the main causes of concrete degradation, including mechanisms, including specific ones, designed for technically complex energy facilities [38], are important factors in this matter.