esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a Two-Wheeled Self-Balancing Car Using a Fuzzy Kalman Filter
## 5 Conclusions  
In this paper, an attitude estimation algorithm based on a fuzzy Kalman filter is proposed, which uses fuzzy logic to correct the tilt angle of a two-wheeled self-balancing car, so as to optimize the state of the Kalman filter and improve the balance performance of the car. Under the same controller parameters, the effects of the fuzzy Kalman filtering algorithm, Kalman filter algorithm [21], and complementary filtering algorithm [20] are compared by experimenting with the two-wheeled self-balancing car, and the results show that the fuzzy Kalman filter algorithm designed in this note makes the two-wheeled self-balancing car more quickly and precisely return to the balance position. The fuzzy Kalman filter algorithm can effectively realize the balance control of two-wheeled cars, which has certain reference significance for the design and research of speed loops and steering rings of racing balance cars.  
Conceptualization, S.X.; data curation, F.M.; formal analysis, S.X.; funding acquisition, Y.M.; methodology, S.X.; software, F.M.; supervision, S.X.; writing--original draft, Y.M.; writing--review and editing, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation (NNSF) of China under grant number 62103057 and enterprises and institutions supporting scientific and technological projects under grant number S2326061.  
Not applicable.  
The original contributions presented in the study are included in the article.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c c c}  & \(-\mathbf{10}^{\circ}\) & \(-\mathbf{20}^{\circ}\) & \(-\mathbf{30}^{\circ}\) & \(\mathbf{10}^{\circ}\) & \(\mathbf{20}^{\circ}\) & \(\mathbf{30}^{\circ}\) \\ Fuzzy Kalman filtering algorithm & 0.75 & 1.05 & 1.60 & 0.83 & 1.01 & 1.63 \\ Kalman filtering algorithm ([PERSON] 2015) [21] & 0.95 & 1.30 & 2.00 & 1.02 & 1.21 & 2.12 \\ Complementary filtering algorithm ([PERSON] 2015) [20] & 1.45 & 1.80 & 2.45 & 1.43 & 1.85 & 2.39 \\  & & & & & & \\ \end{tabular}
\end{table}
Table 2: Equilibrium time for different angles (unit: seconds).  
\begin{table}
\begin{tabular}{c c c c c c c}  & \(-\mathbf{10}^{\circ}\) & \(-\mathbf{20}^{\circ}\) & \(-\mathbf{30}^{\circ}\) & \(\mathbf{10}^{\circ}\) & \(\mathbf{20}^{\circ}\) & \(\mathbf{30}^{\circ}\) \\ Fuzzy Kalman filtering algorithm & 255.84 & 243.43 & 335.70 & 337.07 & 232.82 & 352.51 \\ Kalman filtering algorithm ([PERSON] 2015) [21] & 302.27 & 298.31 & 398.30 & 356.58 & 271.24 & 473.89 \\ Complementary filtering algorithm ([PERSON] 2015) [21] & 592.61 & 378.13 & 632.18 & 697.28 & 455.38 & 609.13 \\  & & & & & & \\ \end{tabular}
\end{table}
Table 3: Tilt angle integral absolute error.
# Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM
## 3 Results and Discussion
### Construction of the PSO-Bi-LSTM Model  
This study is based on the Bi-LSTM ROP prediction algorithm and PSO algorithm, combining the advantages of the high ROP prediction accuracy of Bi-LSTM and accurate and the fast search of high latitude spatial targets of the PSO algorithm. By designing a coupling relationship between the two, a PSO-Bi-LSTM drilling parameter optimization method is established.  
The design flow chart of the PSO-Bi-LSTM algorithm is shown in Figure 4. First, data selection and normalization are performed, and the data set's standard is unified. Then, the PSO particle swarm initialization is performed to optimize the drilling parameters. The PSO algorithm is employed to search for drilling parameter combinations, with the objective of maximizing ROP predicted by the LSTM prediction model and minimizing MSE associated with that particular drilling parameter combination. After selecting the kernel function, the kernel parameters are input into the Bi-LSTM ROP prediction model and the MSE calculation model. The PSO drilling parameter optimization iteration is performed with the maximum ROP and the minimum MSE, and the algorithm process is completed by obtaining the optimal parameters at the end of the iteration. The goal of this model is to maximize drilling footage while maintaining the highest ROP, which means that the drilling tool works longer underground while increasing drilling speed. This reduces the non-operating time of the drilling tool while increasing drilling efficiency. The on-site personnel can directly apply the drilling parameters guided by this model to achieve the actual on-site effect of reducing non-operating time and improving drilling speed.
## 5 The Simulation Validation and Results Analysis
### Comparative Simulations for Communication Optimization
#### 5.1.2 Group B: Without Communication Optimization  
Figures 12-14 show the trajectories at 30 s, 64 s and 100 s, respectively for UAVs without communication optimization. The algorithm without communication optimization only focus on sensing optimization item \(f_{k+l}^{\text{sensing}}\), and thus guides both \(A_{1}\) and \(A_{2}\) toward the target to maximize the information gain. However, with \(A_{1}\) and \(A_{2}\) flying away from the base station, a significant amount of measurement packets is dropped by the base station. This conclusion is confirmed in Figure 15. As shown in Figure 15, in Group B, the average transmission probabilities of \(A_{1}\) and \(A_{2}\) are only 0.6717 and 0.6276, respectively. It means that, in Group B, there are more packets from \(A_{1}\) and \(A_{2}\) are dropped (not received) by the base station than in Group A. The measurements contained in the dropped packets will not be incorporated into the global estimate result of the target state. As shown in Figure 16, the track of the FIM at the base station shows zero growth and a small downtrend over a long time (20 s-100 s). Figure 17 shows the distance between \(A_{1}\), \(A_{2}\). The distance is always larger than the safety distance (\(d_{\text{safe}}\) = 100 m), and thus the safety of the UAVs is guaranteed.  
Figure 11: The distance between the UAVs during the simulation process (Group A in Scenario 1).  
Figure 10: The track of FIM of \(A_{1}\), \(A_{2}\) and base station (Group A in Scenario 1).  
Figure 14: Trajectories of UAVs and target at \(t=100\) s (Group B in Scenario 1).  
Figure 12: Trajectories of UAVs and target at \(t=30\) s (Group B in Scenario 1).  
Figure 13: Trajectories of UAVs and target at \(t=64\) s (Group B in Scenario 1).  
Figure 15: The probabilities of successful transmission of \(A_{1}\) and \(A_{2}\) to base station (Group B in Scenario 1).
## 2 Deterministic Model of a Dual-Rotor System
### Calculation Model of a Dual-Rotor System
Therefore, the whole transfer matrix method is introduced to build the whole transfer model of this dual-rotor system. In the deterministic modeling of this system, the coupling actions at the coupling element need to be considered. In contrast, such effects need not be considered in the uncoupled element. The mean transfer model is usually used to describe a deterministic dynamic system. From transfer matrix theory [22], it is known that the state vectors of adjacent nodes agree with the following deterministic transfer model. \[\mathbf{Z}_{i+1}=\mathbf{T}_{i}\mathbf{Z}_{i}=\left\{ \begin{array}{ll}\mathbf{C}_{i}\mathbf{U}_{ i}\mathbf{Z}_{i}&\text{ coupling element}\\ \underline{U}_{i}\mathbf{Z}_{i}&\text{uncoupled element}\end{array} \right. \tag{2}\]  
where \(\mathbf{T}_{i}\) is the whole mean transfer matrix of each element, and \(\underline{\mathbf{U}}_{i}\) is that of the uncoupled element. At the same time, \(\mathbf{C}_{i}\) is the coupling matrix.
## 5 Proposed Dynamic Cluster Head Election Algorithm
### Proposed Dynamic Re-Clustering and Self-Organization Method  
During the process of the proposed Optimum Cluster Head Election Method, and when the SN checks the CH candidate, a result greater than zero means it is alive and, hence, the SN elects it as a current CH and informs the other nodes. Otherwise means the CH candidate is dead or failed. Thus, the SN deletes the CH candidate from the topology list, sends a report/feedback to the Base Station (BS) to do the appropriate, and re-organizes the cluster dynamically by re-arranging the node's ID accordingly. Furthermore, any nodes that cannot transmit their status are considered as dead/failed nodes, so they will also be skipped and deleted from the topology list; the checking of the rest of the nodes will continue periodically. In short, the new idea of the proposed method is that, when the CH candidate is dead/failed, the proposed protocol skips the node and deletes it from the topology list to address the black holes and routing delay problems which cause the waste of time and energy, due to additional control messages for useless nodes which are not deleted from the topology list. Next, the proposed method reorders the nodes and sends feedback to replace the node or do the appropriate. Figure 6 shows the proposed Dynamic Cluster Self-Organization Method's data flow diagram.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 3 Opportunistic User Scheduling with Information-Hiding Power Control
### Overall Scheduling Procedure
#### 3.2.3 Stage 2 (User Selection)  
After receiving \(N\) transmitter's feedback information, the base station selects the best \(S\) transmitters corresponding to \(S\) receive vectors and broadcast scheduling transmitter index set to all transmitters. The Hungarian method [34] is one of the possible candidate algorithms for assigning the best transmitters to the receive beams.
# Vision Transformer-Based Tailing Detection in Videos
## 2 Related Work
Although capable of identifying abnormal behaviors of high-level semantics (e.g., unusual long-term trajectories), these procedures are both difficult and computationally expensive due to the tracking algorithm. All these techniques use handcrafted features along with tracking algorithms, which make them vulnerable to varying environmental conditions. The main difference between our method and these prior studies is that we do not use any tracking algorithm, rather, the discrete spatial locations of the objects are used to train a transformer network. To the best of our knowledge, there has been no prior research to detect tailing events from surveillance videos. Furthermore, no public dataset on tailing exists. Hence, in this research, we first present a large tailing dataset named, Sogang Tailing Detection Dataset (STD dataset), containing videos of tailing events and normal events. Second, we investigate three methods for the purpose of tailing detection. (i) A handcrafted rule-based method, (ii) a convolutional neural network-based (CNN) event classifier, and (iii) the proposed Vision Transformer-based unified framework. The handcrafted rule-based method detects tailing using trajectory similarity and cosine similarity based on the pedestrian's position. However, the rule base method is sensitive to noise, which leads to bad performance. The convolutional neural network [33] detects tailing using the channel-wise concatenated one-hot encoded frames, based on pedestrian location information. However, the one-hot encoded pattern is diluted as it passes through the series of CNN layers, and the performance decreases as the structure of the network is extended. On the other hand, the proposed TSViT achieved high performance with smaller training dataset than the rule-based or CNN-based method. TSViT [34] uses transformer to solve image classification problems for time-series tasks. Inspired by the way ViT divides images into patches and uses them as embedding tokens, TSViT also uses each fixed-length frame as an embedding token. Furthermore, previous work on anomaly detection that uses CNN [35], treats samples that are different from normal samples as anomalies, thus, they ignore the fact that abnormal events have a smaller probability of occurrence. Many normal samples that do not appear are often misjudged as abnormal, leading to false alarms. For tailing detection, it is highly unlikely that the motion patterns of the suspect and the victim match in random frames. Hence, in addition, to TSVit, we also introduce an ordered random sample method to complement the performance of TSVit when working with time-series data, and fully use the fact that the abnormal events are less likely to occur. The comparison of TSViT against rule-based method and simple-CNN classifier proves the significance of the proposed TSViT method for anomaly detection on STD dataset, and shows that TSViT can be used in many applications for video event detection.
## 2 Structure of Spectrometer  
The CpVMI spectrometer is schematically illustrated in Figure 1. The ultrahigh vacuum system consists of five chambers: (i) source chamber; (ii) the 1 st differential pumping chamber; (iii) the 2 nd differential pumping chamber; (iv) main chamber; (v) jet dump chamber. They are pumped by a 2300 L/s turbomolecular pump TMP, two 400 L/s TMPs, two 700 L/s TMPs backed by 100 L/s TMP, in addition a 2000 L/s SAES non-evaporable getter pump is installed to the main chamber. All TMPs are from Pfeifer. After 5 days' bake-out at 120 \({}^{\circ}\)C, the base pressures reach \(5\times 10^{-9}\), \(2\times 10^{-9}\), \(1\times 10^{-10}\), \(5\times 10^{-11}\) and \(7\times 10^{-11}\) mbar, respectively. In the main chamber, the electron-optical lens system was shielded by a double-layer \(\mu\)-metal barrel to minimize the influence of the external magnetic fields on the trajectories of the electrons. A supersonic molecular beam is created by adiabatic expansion through a 100 \(\upmu\)m pulsed valve (ACPV2-100 from Amsterdam, The Netherlands) in the source chamber. It passes through a Beamdynamics skimmer with a \(\phi\) 0.3 mm hole, two plates with \(\phi\) 1 mm holes, then enters the main chamber, and is finally caught by a dump chamber. With supersonic molecular beam turned on, the working pressures are \(4\times 10^{-4}\), \(4\times 10^{-7}\), \(1.5\times 10^{-10}\), \(5\times 10^{-11}\) and \(9\times 10^{-11}\) mbar, respectively. In case there is a problem with the molecular beam, or the photon beam is too weak, an effusive molecular beam could be ejected from a stainless steel hollow needle with 100 \(\upmu\)m inner diameter. The molecular beam enters the interaction region of the CpVMI spectrometer, where it meets the X-rays beam or the femtosecond laser beam.  
The electron-optical lens projects charged particles from the interaction region onto two VID275 detectors from Photek, each is composed of two multi-channel plates(MCPs) (75 mm effective diameter) and a P46 phosphor screen. Two cameras (Andor Zyla 4.2 PLUS) were used to record the images of electrons and ions. The single-shot TOF signals are decoupled from the phosphor screen, fed in a fast amplifier (FAMP8c from Roentdek, Germany), and finally recorded by an oscilloscope (Tektronix MSO56).
# Dye Adsorbent Materials Based on Porous Ceramics from Glass Fiber-Reinforced Plastic and Clay
###### Abstract  
We investigated the use of waste glass fiber-reinforced plastic (GFRP) to remove dye from industrial wastewater. The dye adsorbent material, based on GFRP/clay ceramics, was produced by mixing crushed GFRP with clay and firing the resulting mixture. Several types of ceramics were produced by adjusting the mixing ratio of clay, crushed 40% GF/GFRP, and firing atmosphere. Adsorption tests with methylene blue (MB) dye were performed by mixing the ceramics into an MB solution while controlling the stirring speed and measuring the decrease in MB dye concentration over time. These results showed that GFRP/clay ceramics reductively fired at 1073 K had a higher MB dye adsorption ability than that of the clay ceramic. The MB dye absorptivity of the reductively fired ceramics increased as we increased the mixing ratio of GFRP. We attribute this result to the high plastic carbide content in the ceramic, which has excellent dye absorbability. Furthermore, these particles had a comparatively high specific surface area and porosity.  
waste GFRP; recycling; ceramics; dye wastewater; methylene blue dye; adsorbent
# Underwater Highly Pressure-Sensitive Fabric Based on Electric-Induced Alignment of Graphene
###### Abstract  
Wearable pressure sensors have received widespread attention owing to their potential applications in areas such as medical diagnosis and human-computer interaction. However, current sensors cannot adapt to extreme environments (e.g., wet and underwater) or show moderate sensitivity. Herein, a highly sensitive and superhydrophobic fabric sensor is reported based on graphene/PDMS coating. This wearable sensor exhibits great superhydrophobicity (water contact angle of 153.9\({}^{\circ}\)) due to the hydrophobic alkyl long chains and rough structure introduced by the Ar plasma. Owing to the network structure created by the electric-induced alignment of graphene sheets, an enhanced sensitivity (\(\Delta\)I/I\({}_{0}\) of 55) and fast response time (\(\sim\)100 ms) are observed. Due to its superhydrophobicity and sensitivity, this wearable sensor demonstrates efficient and stable monitoring of various underwater activities, including pressure, blowing, and tapping. Our approach provides an alternative idea for highly sensitive wearable sensors while broadening the practical application scope.  
wearable sensor; superhydrophobic; graphene; electric-induced alignment; plasma Article
## 2 Proposed MOT Method
### Trajectory States  
Trajectory states are the basis of our framework. When tracking begins, the tracker receives object detections from the detector. It needs to make a corresponding decision for each detection, such as initialization, tracking, or abandon. The aim is to make the objects have different strategies to solve these problems in different situations, such as occlusion, disappearing, and so on, so as to reduce the number of IDS and Frag.  
There are four trajectory states designed in our paper: Tentative, Confirmed, Vanished, and Deleted. The Tentative state is the preparatory stage before an object is formally tracked. All the object detections will stay in this state for several frames, and only those which continuously appear will be considered as true positives (TP). Thus this state can be used to reduce false positives (FP).  
Figure 1: Proposed multi-object tracking pipeline is illustrated. At the beginning, we load the appearance features of trajectories in the t–1 frame from gallery, and extract the appearance features of detections in the t frame. Then, we predict motion information of the above trajectories and detections by using a Kalman filter. We measure their similarity distance through our proposed equation after obtaining the appearance and motion information. In the end, we match the trajectories and detections with the designed matching strategy.  
The Confirmed state is the state in which an object is being correctly tracked, and only in this state can the bounding box of the object be displayed. The Vanished state is the state in which an object disappears or is lost, due to occlusion or drastic changes in appearance. Here, the trajectory will be hidden temporarily so we can reduce the number of IDS and Frag. The Deleted state is the termination state of a trajectory. When it determined that an object no longer appears in the video, it is included in this state.  
In order to screen out the error detections, we set the threshold \(\theta_{TC}\), which represents the required continuous successful matching frames to transfer from Tentative to Confirmed. Additionally, for the purpose of reducing IDS and Frag, we set the threshold \(\theta_{VD}\), which represents the required continuous unsuccessful matching frames to transfer from Vanished to Deleted.  
Figure 2 is an example of the object state transition. At the beginning, the bounding box for the person on the far right side of frame (a) is not displayed, because he just appeared, so his state was Tentative. In frame (b) his state transferred to Confirmed because the object continuously appeared for \(\theta_{TC}\) frames. In frame (c), his state turned to Vanished as a result of being occluded by the purple object. In frame (d), the blue object recovered from the occlusion and his state turned to Confirmed again. At last, in frame (e), he went out of the screen so his state first transferred to Vanished for \(\theta_{VD}\) frames, and then transferred to Deleted until the last.
The utilization of a lower number of layers facilitates rapid processing capabilities. Among various MUD algorithms, PIC is particularly well suited for the acquisition module of receivers. In addition, the MEO satellite communication system faces the issue of a low carrier-to-noise ratio (\(C/N_{0}\)) due to various factors. One of the main reasons is that MEO satellites are typically located around 20,000 km away from the ground, which causes serious propagation attenuation [21; 22]. Consequently, signal acquisition becomes a difficult task for the receiver. With the development of DSP technology, contemporary spread spectrum receivers have embraced fast acquisition algorithms rooted in the Fast Fourier Transform (FFT). In reference [23; 24], they introduced a rapid acquisition algorithm founded on FFT, which effectively estimates the Doppler shift during the PRN phase acquisition. In reference [25], [PERSON] discussed the performance of weak GPS signal acquisition algorithms from executive time and detection possibility. The findings indicate that coherent accumulation exhibits superior detection capabilities and expeditious processing velocity. In reference [26], [PERSON] proposed a low-complexity acquisition algorithm that is capable of capturing extremely weak direct sequence spread spectrum (DSSS) signals. They discussed coherently accumulating the correlation results across multiple PRN code periods to achieve a reliable signal-noise ratio (SNR) for acquisition. We consider the application of the coherent accumulation algorithm in the MAI scenario and use it as the main comparison object for this scenario. This paper investigates the acquisition algorithm designed for on-board receivers installed on MEO satellites. This study specifically takes into account the challenging conditions of MAI and puts forth an improved acquisition algorithm rooted in coherent accumulation and PIC. Furthermore, we propose a hard reconstruction method based on the acquisition results. Subsequently, the performance of the proposed algorithm is analyzed, highlighting its notable attributes, which include fast acquisition, high detection probability, and effective reduction of BER. Finally, the hardware architecture is presented, and the results of the experiment are discussed. The outcomes of this research hold broad applicability within DS-CDMA communication systems, finding utility in both MEO and Low Earth Orbit (LEO) satellite systems.