esa-sceva/satcom-chunk-collection huggingface dataset:
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 2 Problem Formulation and Tracking Strategy  
### Pursuit-Evasion Problem Statement  
Suppose that there are pursuit USVs and escaping target USVs in a sea area. When the escaping target ship enters the detection range of the pursuit USV, the interceptor will exhibit an interception behavior. The target also has the ability to perceive the pursuer; when the target USV senses the surrounding captors, it will change its route. The pursuer cannot determine the escape strategy of the target and can only adjust its interception direction in real time according to the perceived position and speed of the target.
## 3 Bridge Vibration Response Based on Vehicle-Bridge Coupling
### Bridge Vibration Response Based on Vehicle-Bridge Coupling
#### 3.2.2 Bridge Vibration Response under Different Vehicle Weights  
The loads of four-axle transport vehicles are very high, and the overloading of actual engineering transport vehicles is frequent. In order to study the effect of a four-axle truck with different loadings on a bridge after a single-pier jacking modification, a four-axle truck was set up to drive in the middle lane at a maximum speed of 60 km/h under three conditions: empty, fully loaded, and overloaded by 15%, to analyze the effect of different loads of four-axle trucks on the bridge after a single-pier jacking.  
The dynamic deflection of the nodes in the span of the bridge under different loads of vehicles is shown in Figure 15.  
The peak values of dynamic deflection for the periods when the vehicle is empty, fully loaded, and overloaded by 15% are 0.31 mm, 0.62 mm, and 0.72 mm, respectively, and it can be seen from the figure that the dynamic deflection of the bridge span nodes increases as the vehicle load increases. The variation of acceleration at the span nodes of the bridge under different vehicle loads is shown in Figure 16.  
The peak accelerations at the span nodes of the bridge under the three operating conditions of empty, fully loaded, and overloaded by 15% are 2.58 m/s\({}^{2}\), 2.59 m/s\({}^{2}\) and 2.61 m/s\({}^{2}\), respectively. It can be seen from the graph that the acceleration at the span nodes of the bridge increases with the increase of the traffic weight, but the change is not very big.
## 2 F0 Detection and Refinement
### Adaptive Time-Warping  
In the speech signal, it is necessary that harmonic components are separated from each other so that they can be easily found and extracted. Once F0 rapidly changes, harmonic components are subject to overlapping each other, making it difficult to separate these components; closely neighboring components can make separation through filtering very hard, especially with a low spectral voice (such as male pitch) [39]. To overcome this problem, previous work in the literature has provided methods by introducing a time-warping-based approach [40; 41].  
[PERSON] et al. [42] incorporate time-warping into instantaneous frequency spectrogram frame by frame according to the change of the harmonic frequencies. In view of that, the observed F0 is seen to be constant within each analysis frame. More recently, a time-warping pitch tracking algorithm has also been proposed by [43] which apparently had a significant positive impact on the voicing decision error and led to good results even in very noisy conditions. There has been another approach introduced by [PERSON] et al. [15] based on iteratively time-warping the speech signal and updating F0 estimate on time-warped speech, which has a nearly constant F0 over a segment of short duration, which sometimes leads to inaccurate pitch estimates. To achieve a further reduction in the amount of contF0 trajectory deviation (deviate from their harmonic locations) and to avoid additional sideband components generation when a fast movement of higher frequencies occurs, the adaptive time warping approach combined with the instantaneous frequency can be used to refine the contF0 algorithm.  
We refer to the warping function as \(p\) which defines the relationship between two axes  
\[\tau=p(t),\;t=p^{-1}(\tau) \tag{3}\]  
where \(\tau\) represents a time stretching factor. The first step is to stretch the time axis in order to make the observed contF0 value in the new temporal axis stay unchanged and preserve the harmonic structure intact [40; 41]. As the initial estimate of the contF0 is available, the second step of the refinement procedure is to filter the input waveform using the bandpass filter bank \(h(\tau)\) with different center frequencies \(f_{c}\) multiplied by Nuttall window \(w(\tau)\)[44] to separate only the fundamental component in the range near \(f_{c}\)  
\[h(\tau)=w(\tau)\cos(2j\pi f_{c}\tau) \tag{4}\]  
Figure 1: Structure chart of adaptive Kalman filter-based contF0 (adContF0).  
\[w(\tau) =0.338946+0.481973\cos\Bigl{(}\frac{j\pi}{2}f_{\mathrm{c}}\tau\Bigr{)}+0. 161054\cos(j\pi f_{\mathrm{c}}\tau)\] \[+0.018027\cos\Bigl{(}\frac{3j\pi}{2}f_{\mathrm{c}}\tau\Bigr{)} \tag{5}\]  
Next, instantaneous frequencies \(I\!F(\tau)\) of \(h(\tau)\) have to be calculated. [PERSON]'s equation [45] is used to extract them from both the complex-valued signal and its derivative  
\[I\!F_{k}(\tau)=\frac{a\frac{db}{d\tau}-b\frac{da}{d\tau}}{a^{2}+b^{2}} \tag{6}\]  
where \(a\) and \(b\) are the real and imaginary parts of the spectrum of \(h(\tau)\), respectively. \(k\)
## 2 Materials and Methods
### Cell Viability  
For the cell viability assays, B16F10, A427, HT-29, MCF-7, and HEPG-2 cancer cell lines, peritoneal macrophages and bone marrow cells from healthy Balb/C mice, and human peripheral blood mononuclear cells (PBMC) were used. The cells used for the study are adherent cells, except for the murine bone marrow cells and human PBMC, which include adherent monocytes and non-adherent lymphocytes. All cells were adjusted to a 5 \(\times\) 10\({}^{3}\) cells/mL concentration in DMEM F-12 medium (Invitrogen, Carlsbad, CA, USA) supplemented with 10% (\(\upsilon\)/\(\upsilon\)) fetal bovine serum (FBS) (Invitrogen, Carlsbad, CA, USA) and incubated with different concentrations of wet and dry bDLE, in the range of 0.1 to 2.55 mg/mL, in polystyrene tissue culture treated 96 well plates for 72 h at 37 \({}^{\circ}\)C and 5\(\%\) CO\({}_{2}\) atmosphere. After incubation, the supernatant was removed by centrifugation at 250\(\times\)\(g\) for 10 min. Then, cells were washed twice with a DMEM F-12 medium supplemented with 10% FBS. Cell viability was determined by the colorimetric MTT reduction assay, and the optical densities resulting from formazan production were determined at 570 nm. Cell viability was expressed as the percentage of viable cells. The results are given as the mean \(\pm\) standard deviation (SD) of three independent experiments.
## 4 Data Silos and Fragmentation:
### Proposed Framework
**Federated Learning:** The federated learning process begins, where machine learning models are trained collaboratively using the distributed EMR data across the network. The models remain on the local nodes, and only model updates are shared among the participants, preventing the direct sharing of patient-level data [23]. 4. **Consensus Mechanisms:** Robust consensus mechanisms, such as Proof of Stake (PoS) or Practical Byzantine Fault Tolerance (PBFT), are employed to ensure agreement and trust among network participants. These mechanisms validate the model updates and prevent malicious or erroneous contributions from compromising the learning process [28]. We will address the following points: the blockchain network applied Proof of Work (PoW) as mentioned earlier; on the other hand, the blockchain network has adopted Proof of Stake (PoS). 5. **Smart Contracts:** Smart contracts, implemented on the blockchain, enforce the rules and agreements among participants. They define the reward system, govern the validation process, and facilitate the fair distribution of incentives based on the quality and quantity of data contributed [37]. 6. **Model Aggregation and Evaluation:** The model updates from each participant are aggregated using aggregation algorithms, such as Federated Averaging, to create a global model. The aggregated model is then evaluated for performance, accuracy, and generalizability [38]. 7. **Transparency and Traceability:** The blockchain's transparency ensures that all participants can verify the integrity of the aggregated model and the data used for training. The distributed nature of the blockchain provides a transparent and immutable record of the learning process, enhancing traceability and auditability. 8. **Reward Distribution:** Based on the smart contracts, participants are rewarded for their contributions to the federated learning process. This incentivizes active participation and encourages data sharing while ensuring fairness and equitable distribution of rewards [24]. 9. **Iterative Learning Process:** The framework facilitates iterative learning, allowing for continuous model updates and improvement. Participants can contribute updated EMR data and participate in subsequent learning rounds, resulting in a dynamic and evolving model [29]. 10. **Medical Insights and Applications:** The BEFL-EMR framework produces valuable medical insights by leveraging the collective intelligence of the distributed EMR data. These insights can be used for various applications, such as disease prediction, treatment recommendations, personalized medicine, and healthcare resource allocation [28]. The working of the proposed framework combines the privacy-preserving capabilities of federated learning with the security and transparency provided by blockchain technology. It enables collaborative model training while maintaining data privacy, ensuring data integrity, and facilitating trust among participants. By unlocking revolutionary medical insights, the framework contributes to advancements in healthcare analytics, research, and decision-making [30].
# Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles
## 2 Related Work
### Anomaly Detection Based on Deep Learning Architecture  
Recently, the development of machine learning has aroused more attention in regard to its application to anomaly detection for the security of in-vehicle information. Due to its ability to automatically extract data features, real-time learning parameters and high prediction accuracy, the methods of deep learning are used in a variety of anomaly detection tasks. In the literature [16], a novel intrusion detection system is proposed, which utilizes a deep neural network (DNN) to train the weight and extract the feature vector from original CAN data packets. When real-time data packets are extracted, DNN provides the probability of each classification to identify normal and attack packets, so sensors can identify any malicious attacks on vehicles. In addition, in the literature [17; 18] a DNN architecture is established, which is trained on normal data to learn the higher level features, is then used to predict future values.
## 4 Conclusions  
In this study, efforts were made to solve the limitation of experimental observation of binder droplets and powder interaction of the 3D printing process by applying a numerical simulation method with the VOF method. For verification of the VOF model, the simulation and experiments of the impact of millimeter-scale droplets were carried out under similar conditions, and the agreement between the simulation and the experimental data proves the effectiveness of the VOF model. On the above basis, the effects of particle size and wettability of powder on the microdroplets spreading behavior were studied. The results revealed that the powder wettability and particle size could directly influence the droplet spreading behavior. Finally, with the characterization of 3D printed samples under different print resolutions, the significance and effectiveness of the VOF model of droplet impact were verified for the prediction of 3D printing process parameters.  
Although this study involves only the influence of wettability and particle size on binder spreading behavior, our modeling method can be promoted and applied in a more widely used mechanism of study rather than just limited to the parameter optimization of binders and powders. In particular, it can be used in printability tests of new materials, which can also help determine reasonable process parameter ranges and reduce the workload of new material applications.  
In conclusion, this study has an important reference value in improving the accuracy of 3D printing, assisting in the 3D printing of bioactive scaffolds, and optimizing the amount of binder to improve the activity of the cells of bone scaffolds. We are convinced that there will be more applications in 3D printing practice from the perspective of the current study in the future.  
Conceptualization, X.-P.L. and Y.-E.W.; methodology, Y.-E.W. and Q.-H.W.; software, X.-P.L.; validation, Y.G. and K.Z.; formal analysis, X.-P.L.; investigation, X.-P.L.; resources, Y.-E.W.; data curation, X.-P.L.; writing--original draft preparation, X.-P.L.; writing--review and editing, Y.-E.W. and A.A.; visualization, X.-P.L.; supervision, Y.-E.W.; project administration, Y.-E.W.; funding acquisition, Y.-E.W. and Y.-K.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2019 QY(Y)0502), funded by the key research and development program of Shaangi province(Grant No. 2020 ZDLSF04-07), funded by the National Natural Science Foundation of China (Grant No. 51905438), funded by the Fundamental Research Funds for the Central Universities (Grant No. 31020190502009), funded by the Innovation Platform of Bio-fabrication (Grant No.17 SF0002), and funded by the Project Based Personnel Exchange Program with China Scholarship Council and German Academic Exchange Service (PPP) (Grant No.201709920023).  
Not applicable.  
The authors declare no conflict of interest.
# Introduction of a New Index of Field Operations Efficiency
## 2 Materials and Methods
### Generation of Continuous Path  
#### 2.2.1 Fixed Entities  
Headland passes, field-work tracks, and rows, are referred to as fixed entities (Figure 2). These fixed entities are explained as follows:  
* Headland pass (H): A headland pass is a concentric path covering the headland area at the same width as the operating width w, of the implement which is made up of a set of sequentially clockwise ordered points. An inner boundary between the headland area and the work area is created at a distance half of the operating width w/2 from the last headland passes, the area enclosed by the inner boundary is denoted the field body area.
* Row (R): The field body area is covered by parallel rows that transect the area. The width of each row equals the operating width w of the machine.
* Field-work track (T): A track is represented by two ending points is the central line of a row and is used as the guidance line for the machine to cover each row.  
Figure 1: The graphical description of the developed tool.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE
### Forwarder Search and Selection  
Routing is based on a greedy approach, and forwarder selection is sender-initiated. When any node \(u\) (either a source node or any router node) has a data packet to transmit, it broadcasts Beacon messages. A Beacon from \(u\) includes its gateway_distance. Any awake neighbor \(v\) receiving this beacon will proceed as illustrated in Algorithm 3:  
```
1:if (\(v\) is closer to the gateway than \(u\)) and (RSSI \(<\) RSSI_THRESHOLD) then
2:\(v\) sends a Reply message
3:else
4: the node \(v\) stays silent
5:endif
```  
**Algorithm 3** Reception of Beacon message  
The initiator \(u\) will repeatedly send Beacon messages (the periodicity is given by WAIT_REPLY_PERIOD) until the forwarder search phase expires, i.e.,:  
* After a predefined period of time denoted BEACON_PERIOD,
* Or, when a predefined number (MAX_NB_REPLY) of Reply messages is received.  
When node \(u\) receives a Reply message from any node \(v\), it can read the following parameters from this message:  
1. Residual energy of \(v\)
2. Distance of \(v\) relative to the gateway
3. RSSI of the Reply message from node \(v\)  
Depending on the policy (e.g., emphasis on energy, or lower latency, etc), \(u\) will select the most appropriate forwarder. This design allows ODYSSE to support different policies. In our experiments, we used a policy purely based on: (i) the distance of \(v\), (ii) the RSSI and, (iii) the residual energy. Each potential forwarder is characterized by a weighted sum of these three metrics (the residual energy is expressed in terms of total number of sent and received messages). The best forwarder is the one having the best sum. Once the forwarder is selected, the node \(u\) transmits its packet. If no forwarder is found after BEACON_PERIOD, this period is extended, unlike ASSORT [25] for instance where nodes would rather return to the sleep state. However, our approach tends to minimize delays, and we believe that it is more efficient for dense networks.  
The advantages of this design are the low storage required and a light-weight selection procedure. Furthermore, ODYSSE, unlike ORW (Opportunistic Routing in Wireless sensor networks) [24] is loop free. Notice that ODYSSE does not result in redundant transmissions which is a real issue in opportunistic routing. By selecting a unique forwarder on the fly at each hop, ODYSSE guarantees packet unicity in the network (unlike ORW [24] for instance). If a node is not an intended destination of a given packet it simply ignores it. The path diversity and hence the load balancing which characterize the opportunistic routing are not compromised. Nodes alternate between active and sleep state, thus relay nodes at each hop are not always the same (with the detail that nodes close to the source are more solicited) (see Figure 12 for the average percentage of sent data packets). Also, all nodes still profit from good duty cycle allowing them to save energy (will be highlighted in Figure 11). Section 5.5 will explain these properties.
# Three-Dimensional Modeling of a Robotic Fish Based on Real Carp Locomotion
## Three Dimensional Dynamic Model and Motion Equations of the Robotic Fish
### Swimming Motion of the Robotic Fish  
Motions of the carp obtained from experimental analysis according to all joints for forward and turning swimming modes are given in Figure 3.  
The average speed of a carp ranges from 0 to 2.5 m/s in the ichthyology. In the experimental studies, speeds of forward swimming are recorded for some different ranges from 0.105 m/s, 0.125 m/s, 0.144 m/s, 0.233 m/s to 0.41 m/s and turning speed is obtained as 0.24 m/s, approximately. In order to determine flapping frequency of the carp, motion of the caudal fin is analyzed as shown in Figure 4 during different periods. The average recorded flapping frequencies of the carp are measured as 1.818 Hz, 2 Hz, 2.439 Hz and 2.857 Hz, approximately. In order to calculate one sample flapping frequency for 2.857 Hz, minimum and maximum angles of the caudal fin from the center of gravity are measured and time of the fin motion is determined. In this sample, motion time for one period is measured as 350 ms.  
Figure 2: Trajectory study of two swimming patterns of carp. The fish is simplified as a 5-joint linkage represented by different colors. The light color curves indicate a trajectory of the corresponding joint within a period of swimming pattern: (**a**) Forward swimming pattern, period 350 ms; (**b**) Turning swimming pattern, period 550 ms.  
\(M(\theta_{i})\) is the moment of inertia, \(C(\theta_{i},\dot{\theta}_{i})\) is the Coriolis matrix, \(B(\dot{\theta}_{i})\) is the damping coefficient matrix, \(K(\theta_{i})\) is the spring coefficient matrix and \(\tau_{i}\) is the force vector that includes hydrodynamic forces.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 3 Materials and Method
### Data Preprocessing  
#### 3.2.1 Data Standardization  
The data are first standardized to facilitate rapid convergence during learning for the model. If the mean of the data is \(\mu\) and the standard deviation is \(\sigma\), the data sample \(x^{(i)}\) can be standardized as follows:  
\[x^{(i)}=\frac{x^{(i)}-\mu}{\sigma}. \tag{8}\]
## 3 Results and Discussion
### Morphology and Chemical Composition
#### 3.1.2 EDX Analysis  
The necessary data was collected through a samples-electron beam-interaction detector, which collected X-rays from the samples. The characteristic X-rays generated for elements was typically distinct from element to element, with a few spectral peaks overlapping due to the electron energy levels' quantization. When the elements of one peak were unclear, other peaks or a small background of the sample history could contribute to the fair detection of elements. Even with a single point of 0.2 nm or by shifting the light beam anywhere in the sample, the elemental map could be generated.  
Figure 3a shows the image of the sample where the electron beam was passed. From the image, the formation of 2D MXene flakes could be confirmed. Figure 3b depicts the concentration of different elements in the synthesized MXene nanoparticle. From the peaks of the graph, it was confirmed that titanium (52.85 wt%), oxides (22.81 wt%), and carbide (10.23 wt%) composition was higher when compared to other elements. Moreover, small traces of aluminum (2.98 wt%), chlorine (2.64 wt%), and fluoride (8.5 wt%) were also present.  
Figure 3: _Cont._
## 1 Introduction
This research has made breakthrough progress, but the structure design and experimental method are complicated. Achieving reliable and efficient detection is still a major challenge. In this work, we present THz-TDS studies of a periodic crescent cylinder metasurface for highly sensitive detection of (R)-(\(-\))-ibuprofen and (S)-(+)-ibuprofen. By using the traditional THz transmission experimental system, we found that when the symmetry of the cylinder is broken, the input THz waves can excite higher-order resonance modes inside the metasurface and provide stronger anisotropy as well as enhanced sensitivity for the surroundings. It is worth noting that by monitoring the frequency shift and transmittance at higher-order resonance, we were able to detect different concentrations of ibuprofen and distinguish the slight difference between (R)-(\(-\))-ibuprofen and (S)-(+)-ibuprofen at the same concentration. The experimental results show that our metasurface-based sensor is compatible with drug concentration detection and chiral biomolecule recognition.