esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 1 Introduction
The SS-MAC improves the channel-hopping mechanism of the IEEE 802.15.4-2006 standard by dividing channels into public and private to reduce external interference of nodes. The SS-MAC selects two channels as public channels, which are used for communication between CHs, where one is the primary channel and the other is the alternate channel. The remaining channels are allocated to CHs according to a DMH-LEACH protocol proposed in [22]. CHs use allocated channels to communicate with their members. At present, the channel-hopping mechanism and the similar rate-splitting multiple access (RSMA) technique are important methods for suppressing interference and improving spectral efficiency in large-scale wireless communication networks, such as the Internet of Things and satellite communications [23]. As mentioned above, large-scale MWSNs have broad application prospects. At present, the research on these focuses on either routing algorithm design or MAC layer technical improvements, and few studies combine them together. This paper aims to propose a MAC layer data collection protocol for MWSNs, called SS-MAC, which combines routing design and MAC layer technical improvements. The SS-MAC provides an overall plan for the data collection process of the network, and describes in detail the whole process from member node to CH, which forwards the data to the sink node level-by-level through tree routing. In brief, the major contributions of the SS-MAC protocol proposed in this paper can be outlined as follows:  
1. It uses a hybrid CSMA/TDMA technique and channel-hopping mechanism to reduce inter-node interference;
2. It improves the sharable slot (SS) algorithm proposed in [8] to wake up tree nodes level-by-level according to the network topology, thereby reducing the energy consumption caused by node listening;
3. It employs an 8-bit short address to identify member nodes, thereby reducing the control overhead of nodes;
4. It improves the knapsack algorithm proposed in [3] to determine the number of slots and assignment order for each member node;(5) The whole network adjusts the duty cycle and the length of the sharable slot periodically to better adapt to the dynamic traffic load. Moreover, we derive and analyze the mathematical expressions of the parameters of the sharable slot algorithm, and evaluate the energy consumption, throughput and packet delay performance of the SS-MAC-based WSN. Then, we compare the above performance of SS-MAC and IEEE 802.15.4 MAC using MATLAB to verify the validity of the SS-MAC protocol. The remainder of this paper is organized as follows: Section 2 summarizes related research work about the proposed scheme. Section 3 presents the principle of SS-MAC. The system model and performance analysis are described in Section 4. Simulations and corresponding discussions are presented in Section 5. Finally, Section 6 concludes the paper.
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 2 Related Work  
Research on OTFS signal detection can be broadly categorized into two major approaches: model-driven detection methods and data-driven detection methods. The former relies on physical modeling and probabilistic inference, while the latter leverages data-driven optimization to capture complex transmission characteristics.
# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
## 3 The Proposed SC Flipping Algorithm  
In Figure 4, we depict the frequency of errors caused by channel noise for \(P(64,32)\) at different \(E_{b}/N_{0}\). It can be observed that the probability of one error bit occurring in the SC decoding is about 90% at high \(E_{b}/N_{0}\), and there are still two or three error bits at low \(E_{b}/N_{0}\). Therefore, in order to further improve the performance of the SC decoding, we also consider multi-bit flipping. In this section, we analyze the effect of error propagation. Then, we describe the DLSTM network structure and training process. Finally, we present the proposed DLSTM-based SC flipping algorithm and a mechanism to improve the robustness of the algorithm.  
Figure 3: An LSTM basic unit.
###### Abstract  
This study addresses the dynamic response control of deep-water jacket offshore platforms under typhoon and misaligned wave loads by proposing a Tuned Mass Damper (TMD)-based vibration suppression strategy. Typhoon loading is predicted using the Weather Research and Forecasting (WRF) model to simulate maximum wind speed and direction, a customized exponential wind profile fitted to WRF results, and a spectral model calibrated with field-measured data. Correspondingly, typhoon wave loading is calculated using stochastic wave theory with the Joint North Sea Wave Project (JONSWAP) spectrum. A rigorous Finite Element Model (FEM) incorporating soil-structure interaction (SSI) and water-pile interaction is implemented in the Opensees platform. The SSI is modeled using nonlinear Beam on Nonlinear Winkler Foundation (BNWF) elements (PySimple1, TzSimple1, QzSimple1). Numerical simulations demonstrate that the TMD effectively mitigates dynamic platform responses under aligned typhoon and wave conditions. Specifically, the maximum deck acceleration in the \(X\)-direction is reduced by 26.19% and 31.58% under these aligned loads, with a 17.7% peak attenuation in base shear. For misaligned conditions, the TMD exhibits pronounced control over displacements in both \(X\)- and \(Y\)-directions, achieving reductions of up to 29.4%. Sensitivity studies indicated that the TMD's effectiveness is more significantly impacted by stiffness detuning than mass detuning. It should be emphasized that the effectiveness verification of linear TMD is limited to the load levels within the design limits; for the load conditions that trigger extreme structural nonlinearity, its performance remains to be studied. This research provides theoretical and practical references for multi-directional coupled vibration control of deep-water jacket platforms in extreme marine environments.  
t
## 5 Conclusions  
As a principal component analysis method of time series, SSA is mainly used in signal de-noising and prediction. When the GREATEM system is carried out on high-voltage lines or near urban areas, the data contains complex noise, which seriously affects the accuracy of the inversion interpretation results. To solve the problem, the SSA method is proposed to eliminate noise. The selection of the two critical parameters of window length and reconstruction order that affect the algorithm's performance was analyzed based on the characteristics of GREATEM data, and using PSO, the adaptive selection of parameters was realized. Through the simulation analysis of a quasi-two-dimensional earth model, the effectiveness of the SSA method in eliminating GREATEM signal noise was verified. Through the comparison with the classic de-noising method, the superiority of SSA in de-noising GREATEM data was demonstrated, and the SSA algorithm was used to process the measured data in the Daxing'an Mountain range area to verify the practicability of the method.  
Conceptualization, H.L. and B.T.; methodology, X.Y.; software, X.Y.; validation, X.Y.; formal analysis, X.Y.; investigation, Y.W.; resources, Q.W.; data curation, Q.W.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y., Y.W. and H.L.; visualization, X.Y.; supervision, B.T.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Natural Science Foundation of the Education Department of Jilin Province (JIKH20200979 KJ).  
Not applicable.  
Figure 13: The detection results. (**a**) Investigation result of survey area provided by Heilongjiang Institute of Geophysical and Geochemical Exploration; (**b**) inversion result of original data; (**c**) inversion result after SSA de-noising.  
**Acknowledgments:** We thank the anonymous reviewers and editors for their constructive comments. We thank the Key Laboratory of Earth Information Detection Instruments of the Ministry of Education for providing the research platform. We thank Heilongjiang Institute of Geophysical and Geochemical Exploration for providing the Investigation result for this study. The authors thank all members of the GREATEM group of Jilin University for their help.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 High-Precision Confidence Interval Weight Assignment Method for Acoustic Ranging
### High-Precision Confidence Interval Weight Assignment Method  
To establish a high-precision confidence interval for acoustic ranging and to assign the necessary weights for subsequent positioning, this study employs two base station nodes, starting from a distance of 5 cm between them and incrementally increasing this distance until the absolute error in the measured range becomes significantly larger, at which point the distance increment is halted. The results obtained are depicted in Figure 1.  
Upon examining the curve fitted by the least squares method, an error threshold of y = 0.03 m is adopted as the boundary for the high-precision positioning interval. The intersection points of y = 0.03 m with the leftmost and rightmost points of the error curve are determined to be \(x_{a}\) = 2.834 m and \(x_{b}\) = 20.153 m, respectively.  
Based on the obtained values of \(x_{a}\) and \(x_{b}\), a formula for the weights within the high-precision ranging confidence interval is constructed. From the curve in Figure 1, it can be seen that as the distance decreases at close range, the error will rapidly increase, while as the distance increases at long range, the error will slowly increase. At the same time, the larger the error, the lower the credibility weight. Therefore, in order to meet the above two characteristics, exponential functions are used as the main body at both close and long distances. In summary, to ensure the continuity of weight assignment, the formula for calculating the weight \(p\) is as follows:  
\[p=f(x)=\left\{\begin{array}{c}\frac{e^{x}-1}{e^{x_{a}}-1}\,0<x<x_{a}\\ \\ 1\,x_{a}\leq x\leq x_{b}\\ e^{\frac{-ln2(x-x_{b})}{x_{a}}}\,x>x_{b}\end{array}\right. \tag{1}\]  
In Equation (1), \(x\) is the ranging distance and \(x_{a}\) and \(x_{b}\) can be obtained from Figure 1.  
When setting up this piecewise function, the following three characteristics are satisfied: the weight value is 0 when the distance is 0; the weight at the boundary between \(x_{a}\) and \(x_{b}\) is 1, maintaining continuity; and when the distance is greater than \(x_{b}\) and less than \(x_{a}\), the change in distance will cause the error to increase rapidly, so an exponential function is used.  
The coefficient before x can be modified according to the actual sound distance measurement error curve of the scene. The weights will serve as the basis for the base station's self-optimization trilateration positioning method, thereby enhancing the accuracy of the final two-dimensional coordinates.
## 5 Conclusions and Future Work  
This study successfully developed a LSTM autoencoder model for stuck pipe detection in drilling operations, leveraging a dataset containing 11,832 normal cases and 112 stuck cases. The model effectively classified stuck and normal cases with an AUC of 0.958, demonstrating strong predictive capability. By analyzing reconstruction errors, loss curves, and classification performance, the study provided a data-driven approach to mitigating costly drilling disruptions. The findings contribute to the industry by offering an automated, real-time stuck pipe detection framework that minimizes NPT and improves operational efficiency. Additionally, this work highlights the importance of threshold optimization and the impact of data distribution on deep learning-based fault detection in drilling operations.  
Future work will focus on enhancing the model adaptability to varying drilling conditions by incorporating additional real-world datasets from diverse geological formations. The further exploration of adaptive thresholding techniques will help refine stuck pipe classification and reduce false positives and negatives. Moreover, integrating the model into real-time drilling control systems will be a key step toward deployment in practical industrial settings. To improve interpretability, explainable AI (XAI) techniques can be employed to provide drilling engineers with insights into model decisions. Lastly, optimizing the computational efficiency will be crucial for scaling the approach to high-frequency streaming data to ensure seamless implementation in real-world drilling operations.  
Conceptualization and supervision, J.T. and H.M.; methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, and visualization, H.N.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available from the cited article [45] discussed in Section 2.
## 2 Materials and Methods
### Coupon Details  
The details of the TRM coupons fabricated in the laboratory are presented in Figure 2 and Table 1. All TRM coupons were a rectangular prism with dimensions 450 mm \(\times\) 80 mm \(\times\) 30 mm. The total length of the TRM coupons was 450 mm, including two clamping regions, each of length 115 mm, two transition zones of length 10 mm, and the central zone with a length of 200 mm.  
Figure 1: Geometrical properties of the carbon fiber mesh used as textile reinforcement.  
Figure 2: Configurations and details of the test coupons.  
The lap-spliced length of carbon fiber textile was chosen based on the study by [PERSON] et al. [36], and was equal to the length of the central zone (200 mm). It was located at the central part of the TRM coupons as the test region. The lap-spliced carbon textile as a reinforcement of the TRM composite was fabricated in the laboratory by two separated pieces of carbon fiber mesh, by means of these overlapping each other to the required length (Figure 2). The weft yarns at the lap-spliced region were fixed by cable ties, and kept straight by the edges of the mold during the casting of the TRM coupons, to ensure that during the loading process, the carbon fiber yarns could properly resist the tensile stresses (Figure 3). As a consequence, there were five warp yarns along the length of the central region of the TRM coupons, and the number of carbon fiber filaments of each warp yarn of the textile reinforcement was approximately 24,000, as shown in Figure 3. In addition, carbon fiber reinforced polymer (CFRP) tabs with a thickness of 2 mm were adhered at the clamping regions by means of epoxy resin (Figure 2), to avoid any damage within the gripping areas, and to ensure an effective force transfer from the universal testing machine.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Coupons** & **Surface Treatment** & **Experimental Conditions** & **Number of Test Coupons** \\ \hline CE & Non-coating & Chloride environment & 2 \\ CE\_E & Epoxy & (for 60 days) & 1 \\ CE\_ES & Epoxy + sand blasting & 2 \\ \hline HTHE & Non-coating & High temperature and & 2 \\ HTHE\_E & Epoxy & humidity environment & 2 \\ HTHE\_ES & Epoxy + sand blasting & (for 60 days) & 1 \\ \hline SL & Non-coating & Sustained load & 2 \\ SL\_E & Epoxy & (for 60 days) & 2 \\ SL\_ES & Epoxy + sand blasting & & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the textile-reinforced mortar (TRM) coupons.  
Figure 3: Photos of carbon fiber mesh with different coating treatments: (**a**) carbon fiber mesh (without surface treatment); (**b**) carbon fiber mesh impregnated by epoxy; (**c**) carbon fiber mesh coated with aluminum oxide powder after epoxy impregnation.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 2 Related Work
### Faster R-CNN (Faster Region-Based Convolutional Neural Network)  
Based on Fast R-CNN, [PERSON] et al. [8] proposed a new Faster R-CNN algorithm in 2017, which takes advantage of CNNs to enhance the procedure of the candidate region generation for detection targets. Notably, the architecture strategically integrates the feature extraction network with the candidate frame-generation network, employing the same convolutional layer network. This astute design choice greatly reduces the time of the whole-image object-detection process. Such an idea of using CNNs for image feature extraction is also reflected in other algorithms. The operational process of the Faster R-CNN algorithm is illustrated in Figure 1.  
The process of Faster R-CNN is as follows:  
* The input image undergoes processing via R-CNN's convolutional layers to extract image features, resulting in a feature map comprised of feature vectors that capture essential information from the image.
* The feature vectors are subsequently channeled into the Region Proposal Network (RPN) to generate candidate boxes.
* The dimensions of candidate boxes are determined by the pooling layer (ROI pooling), which enables the classification of candidate regions, using a support vector machine (SVM), to identify the target category.  
* A precise regression is performed on the classified target boxes.
## 5 Active Coating and its Control Algorithm
### The Measuring Section of the Active Control Damping System  
The control system tries to minimize the difference between three, generally speaking, physically non-identical values: (1) desired [in accordance with control algorithm (15)] normal displacement of surface \(S_{V}\); (2) real normal displacement of surface \(S_{V}\); (3) measured normal displacement of surface \(S_{V}\). In this section we will formulate the way to measure the value \(Y_{k}(t)\) [see (15)]. Let's direct the axis \"\(z\)\" to the external media (Figure 6c) along the normal of the \(k\)-th (\(k\)=1,2,3,...,\(N_{V}\)) active piston \(\overline{S}_{k}\) with point \(z=0\) (which is identical with the piston's center \(\mathbf{p}_{k}\)) and with square \(\sigma_{k}\). Now we cover the boundaries \(z=0\) (surface \(S_{V}\)), \(z=-h_{c}\), \(z=-2h_{c}\) (surface \(\overline{S}_{V}\)), \(z=-2h_{c}-h_{r}\) (surface \(S_{\bullet}\) ) by thin weightless metal film between the outside media, the piezoelectric layers -\(h_{c}\leq z\leq 0\) and -\(2h_{c}\leq z\leq-h_{c}\) [with relaxed thickness \(h_{c}=h_{c}(\mathbf{r})\) and \(h_{c}=\overline{h}_{c}(\mathbf{r})\) respectively], and polymer layer -\(2h_{c}\) -\(h_{r}\leq z\leq-2h_{c}\) [with relaxed thickness \(h_{r}=h_{r}(\mathbf{r})\)], and outside surface \(S_{\bullet}\) of the shell. So we obtain, respectively, three plane electric capacities:  
\[\overline{C}_{k}(t)=\varepsilon_{0}\varepsilon_{c}\sigma_{k}\ \underset{S_{k}}{\iint[h_{c}(\mathbf{r})+\overline{h}(\mathbf{r},t)]^{-1}dS _{k}}=\overline{J}_{k}(t)/[d\overline{\overline{\varphi}}_{k}(t)/dt]\] \[\overline{\overline{C}}_{k}(t)=\varepsilon_{0}\varepsilon_{c} \sigma_{k}\ \underset{S_{k}}{\iint[h_{c}(\mathbf{r})+\overline{h}(\mathbf{r},t)]^{-1}dS _{k}}=\overline{\overline{J}}_{k}(t)/[d\overline{\overline{\varphi}}_{k}(t)/dt],\] \[\overline{\overline{C}}_{k}(t)=\varepsilon_{0}\varepsilon_{r} \sigma_{k}\ \underset{S_{k}}{\iint[h_{c}(\mathbf{r})+\overline{h}(\mathbf{r},t)]^{-1}dS _{k}}=\overline{\overline{J}}_{k}(t)/[d\overline{\overline{\varphi}}_{k}(t)/dt],\]  
where \(\varepsilon_{c}\), \(\varepsilon_{r}\), -relative dielectric constants of piezoelectric and polymer, respectively; \(\overline{h}(\mathbf{r},t),\overline{\overline{h}}(\mathbf{r},t),\overline{ \overline{h}}(\mathbf{r},t)\) are the instant fields of thickness changes of layers -\(h_{c}\leq z\leq 0\), -\(2h_{c}\leq z\leq-h_{c}\), -\(2h_{c}\) -\(h_{r}\leq z\leq-2h_{c}\) of piezoelectric material and polymer, respectively;\(\overline{\varphi}_{k}(t),\overline{\overline{\varphi}}_{k}(t),\overline{ \overline{\varphi}}_{k}(t)\) the measured voltages on the corresponding capacities; \(\overline{J}_{k}(t),\overline{\overline{J}}_{k}(t)\), \(\overline{\overline{J}}_{k}(t)\)the measured charging currents.
## 5 Numerical Results for Specific Error Models
### Uncorrelated Errors
Specifically we consider \(p=0.1\%\) and \(p=0.05\%\). Data was obtained for system sizes from \(L=3\)  
Figure 2: Results for uncorrelated errors. (**a**) Graph of logical error rate, \(P\), against bit-flip error rates, \(p\), around threshold. (**b**) Graph of minimum linear system size required for \(P<p\), \(L^{*}\), against \(p\). (**c**) Graph of \(P\) against linear system size, \(L\), for various \(p\) well below threshold. (**d**) A graph of \(\alpha(p)\) against \(p\). to \(L=11\), and linear interpolation was used to find the gradient. This gives \(\beta=0.637\) for \(p=0.1\%\) and \(\beta=0.643\) for \(p=0.05\%\), with coefficient of determination \(R^{2}=0.923\) and \(R^{2}=0.919\), respectively. These results are consistent with the expected value of \(\beta\approx 0.63\). To determine the values of the decay rate \(\alpha(p)\), the data is fitted to a function of the form \(P=0(e^{-\alpha(p)(cL)^{\beta}})\) using \(\beta=\log_{3}2\). Note that this differs from the method used in [19], but is morally the same.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 8 Discussion
### Potential Future Developments  
Future developments may involve integrating true random frequency hopping with AI and machine learning algorithms to dynamically optimize communication strategies. For example, AI could predict optimal frequency bands to use based on real-time environmental data, enhancing the system's efficiency and robustness.  
The combination of true random frequency hopping with advanced encryption techniques could further enhance communication security. This dual-layer security approach could protect against both physical and digital threats, ensuring comprehensive protection for communication systems.  
The principles of true random generation could be extended into the realm of quantum communication. Quantum key distribution (QKD), combined with true random frequency hopping, could provide unprecedented levels of security, leveraging the inherent unpredictability of quantum states.  
Future systems may focus on creating interoperable communication platforms that can work seamlessly across different types of drones and other autonomous systems. This could involve standardizing true random frequency hopping protocols to ensure compatibility and secure communication between diverse systems.  
Ongoing research will likely focus on miniaturizing RPUs and improving their power efficiency. This is crucial for expanding the use of true random frequency hopping in smaller drones and other devices with limited power resources. Advances in materials science and semiconductor technologies could play a key role in these developments.  
As drones become more integrated into critical infrastructure and services, they will become targets for cyber-physical attacks. Future research will likely explore ways to make true random frequency hopping systems resilient not only to traditional jamming and interception but also to sophisticated cyber-attacks aimed at disrupting drone operations.