esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress in the Flight Characteristics of Flyers Driven by Different Technologies
## 3 Research Progress in Flyer Velocity
### Two-Dimensional (2D) VISAR
#### 3.1.5 Microwave Interferometer  
As shown in Figure 16, after the power amplification and distribution of the signal from the continuous microwave source, one channel passing through the attenuator is used as the local oscillation signal of the quadrature mixer; the other channel is transmitted to the flyer surface through the circulator and antenna and generates reflection. The reflected signal is input to the mixer after transiting the antenna, circulator, and low-noise amplifier. The output signal frequency, after mixing with the local oscillation signal, is amplified by the amplifier and recorded by the oscilloscope. A short-time Fourier transform on the collected signal is used for calculating the Doppler frequency shift, and then the flyer velocity is equal to the Doppler frequency shift times half of the electromagnetic wavelength.  
The laser and microwave interferometric techniques are physically similar, without mentioning any technical details. Their main difference is the electromagnetic radiation frequency used. Because the microwave technology employs 3-4 orders of magnitude longer waves than the laser technique, it has several advantages. First, measurements in radio transparent materials are possible; second, the measured target's surface roughness is ignorable; third, the recording system's bandwidth needs are low; and fourth, a broad range of velocity measurement is possible. However, the microwave interferometry technology has clear drawbacks, such as limited temporal resolution and relatively low precision.  
Figure 15: Schematic layout of FPI with optical resonator.  
[PERSON] et al. designed two microwave interferometers in the Ka and X-band. Complete interior ballistic data was obtained by combining the short-time Fourier transform and phase calculation [46]. [PERSON] et al. studied the acceleration process of an aluminum plate driven by explosive detonation using a microwave interference diagnostic technique with a speed measurement range of 10 km/s [47].
# A Cross-Layer Optimization QoS Scheme in Wireless Multimedia Sensor Networks
## 3 Problem Formation and Dynamic Algorithm
### Design Dynamic Algorithm via Lyapunov Optimization
The data buffer sizes of the sink node, non-real-time nodes, and multimedia nodes were \(2\times 10^{8}\) bits, \(2\times 10^{7}\) bits and \(2\times 10^{7}\) bits, respectively, which could also be recorded as 50,000 packets, 5000 packets, and 5000 packets. We assumed that the energy of nodes could only be consumed in data transmission. The available transmission power levels included 2 mW, 5 mW, 8 mW, and 10 mW. The channel bandwidth was 1 MHz. The transmit loss from node \(j\) to \(i\) was \(G_{j_{\mu}}=\frac{1}{\left(d_{j_{\mu}}\right)^{4}}\), where \(d_{j_{\mu}}\) is the distance between node \(j\) and node \(i\). \(K_{j_{\mu}}=5\), \(n_{i}=10^{-13}\,W\), \(e^{\text{\tiny{max}}}=0\), \(V=50\). In the network, the sink node was the destination node of all sessions. There were \(0.2\cdot N-1\) real-time sessions generated at multimedia nodes. \(A_{n}(t)\) of real-time sessions was set to be 30 packets/s. This implies that the data arrival rate of any real-time service in each multimedia node was 120 Kbits/s. The end-to-end delay deadline of any real-time session was 1 s. There were \(0.8\cdot N\) non-real-time sessions generated at non-real-time nodes. \(A_{n}(t)\) of non-real-time sessions was 1 packets/s. This implies that the data arrival rate of any non-real-time service in each non-real-time node was 4 Kbits/s. The end-to-end delay deadline of any non-real-time session was 4 s. In this simulation, \(A_{\text{max}}^{(n)}=A_{n}(t)+0.1\). There were two CLADS-based schemes: CLADS-BCD and CLADS-MAC. In CLADS-BCD scheme, \(I_{c}\in I\) were obtained through enumeration on all link sets, and the BCD algorithm was adopted in power control. In the CLADS-MAC scheme, the distributed media access control scheme proposed in [51] was utilized in scheduling and the transmission power was set to be \(p_{n,\text{max}}^{T}\). In the distributed media access control scheme, \(\varphi\)=10, \(\gamma\)=10, DIFS = 50 \(\mu s\), SIFS = 20 \(\mu s\), Dtime = 50 \(\mu s\), and minislot = 20 \(\mu s\). In one time slot, the maximum allowable number of times for a sending node to send RTS packets to establish links with the receiving node was 3. In both CLADS-BCD and CLADS-MAC, the utility function for real-time services was (17). The parameters of (17) were set as \(a=1\), \(B_{\text{min}}=0\), and \(B_{\text{max}}=30.1\). The utility function for non-real-time services was (18). The performances of the CLADS-BCD scheme and the CLADS-MAC scheme were compared with that of EASYO [41]\(-\)a backpressure-style scheme\(-\)and ACH [24]\(-\)a cluster-based scheme. In EASYO, the utility function for both real-time and non-real-time services was (18). The time slot duration was 40 ms.
## 3 Simulative Experiment and Discussion
### Experiment on the Relationship between Attitude Error and Maximum Central Angle  
Based on the experiments in Sections 3.1 and 3.2, the EPnP algorithm will have a huge error in the EarM_EPnP model due to the increase of the central angle, and it was verified that the number of solution points of the EPnP algorithm in the EarM_EPnP model is not related to the accuracy. Based on the above experiments, we calculated the maximum center angle that can be obtained when the positioning error is less than 1 m when the positioning error is less than 10 m, and when the positioning error is less than 100 m. The experimental results are shown in Figure 8.  
Figure 7: Relationship between flying height and field angle. (**a**) Relationship between flight height and field of view angle when the error is 1 m. (**b**) Relationship between flight height and field of view angle when the error is 10 m.  
Figure 8 illustrates that at a flight height of 40 km, the field angle varies with the central angle and consistently focuses on the Earth's surface. In the scatter plot, the horizontal axis represents angles in radians, and the vertical axis represents errors in meters. We calculated the error of the EPnP method on the X-axis by manipulating the central angle. The errors within the ranges of 0-1 m, 1-10 m, and 10-100 m were recorded. Figure 8a depicts the central angle range for errors between 0-1 m. When the error is 1 m, this method yields a critical value of 0.057 degrees for the central angle. Figure 8b presents the central angle range for errors between 1-10 m. With a 10 m error, the critical value of the central angle obtained by this method is 0.179 degrees. Figure 8c showcases the central angle range for errors between 10-100 m. Notably, when the error reaches 100 m, significant oscillations are observed. Additionally, for each case, this paper provides the corresponding mean and variance within the specified range.
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 3 Yaw Position Anomaly Recognition Model  
### Parameter Selection Effect of Model SIG-ReliefF  
Select the direct-drive turbine data of a certain wind farm in service. The turbines are labeled F1-F6, and their basic parameters are as follows: rated power of 5200 KW, blade diameter of 165 m, cut-in wind speed of 3 m/s, cut-out wind speed of 24 m/s, with a data sampling interval of 7 s. The recorded operational parameters include time, wind speed, power output, yaw position, wind direction angle, and yaw speed.  
To verify the effectiveness of the feature-mining algorithm, this paper conducts performance comparisons on a total of four feature selection methods: ReliefF, KPCA, AE-ReliefF, and SIG-ReliefF. The experimental results are mainly comprehensively considered in dimensions such as the prediction accuracy and F1 index of the Informer model. The number of iterations of the model is set to six; the learning rate is set to 0.0001; the bath size is set to 64; the number of attention heads is set to eight; the activation function is Gelu; and dropout is set to 0.05.  
As can be seen from Tables 1 and 2, compared with the other three feature selection algorithms, the SIG-ReliefF algorithm has a significant improvement in the average accuracy and F1 index on the selected ten data sets. This indicates that the proposed method has better feature selection performance compared to traditional single-feature selection algorithms and is more suitable for the prediction task of the Informer model.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 4 Risk Prevention and Control  
In order to reduce the risk probability of the integrity bottom events, it is necessary to take corresponding preventive and control measures in the system. This section introduces the failure/risk prevention and control measures taken in BDS-3 satellites and ground facilities, which mainly focus on software/hardware redundancy backup and signal/information monitoring and verification.
## 4 Experimental Results
### Ultimate Bearing Capacity  
The ultimate bearing capacity of each specimen is taken as the peak load of the corresponding loading-displacement curves shown in Section 4.2. The ultimate bearing capacity of three specimens in each group is not identical. This may be due to the fact that the thickness and strength of all the U-shape connectors are not exactly the same. Overall, the ultimate strength of three specimens in each group is close. The ultimate bearing capacities of the 12 K-joint specimens are summarized in Table 3.  
By making a comparison among the average of the ultimate bearing capacity of three specimens in each group, it can be concluded that the pattern of stamping indentation has a negligible effect (it causes a maximum 16.9% difference between specimen UTW-2 and UPL-3) on the ultimate bearing capacity of the K-joint. Comparing three specimens in group 1, specimens using the UTW connector have a higher average ultimate bearing capacity and other specimens using the ULW and UPU connectors have a lower average ultimate bearing capacity. This implies that the stamping indentation patterns used in the ULW and UPU connectors may have a negative effect on the ultimate bearing capacity of the proposed K-joint. In reality, the UTW connector is preferred for the proposed K-joint.  
Figure 19: Loading-displacement curves of specimens in group 4.
## 2 Basic Requirements and Primary Concepts
### Nomenclature  
Optical wireless and fiber-optic communication technologies operate in the same band of the electromagnetic spectrum and have comparable transmission bandwidth capacities; hence, optical wireless communication was formerly known as fiber-less optics. New names for fiber-less optics technology arose in the literature as it advanced and was employed in new sectors, such as Lasercom, Optical Wireless Communication (OWC), and FSO. The terms \"OWC\" and \"FSO\" have become commonly used in recent decades, although \"fiber-less optics\" and \"lasercom\" are now regarded as obsolete [65].  
[PERSON] and [PERSON] [27] utilize the symbol FSO to refer to fiber-less optics technology in a recent classification and survey. The authors divide FSO technology into two categories: indoor systems and outdoor systems. Terrestrial Links and Space Links are two subcategories of the FSO system. Because the technique uses an unguided channel in both the terrestrial atmosphere and the vacuum, FSO refers to outside links (outer space). This is also true in indoor and underwater locations where unguided channels are used by fiber-less optical systems. It has been discovered that FSO in the underwater environment is commonly known as Underwater OWC.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 2 Materials and Methods
### Testing Process
#### 2.2.1 Establishing the Law of Focus  
The function of the focusing law is to determine the array of elements involved in beam transmission and reception, as well as to establish the time delay rules and excitation voltage amplitudes for each element involved in beam transmission and reception [30; 31; 32]. The HS PA30-E ultrasonic phased array testing instrument supports both shear wave and longitudinal wave modes, with the shear wave mode being selected for this study. The parameters were set according to the scanning parameters in Table 4 and adjusted based on the actual parameters of the test specimen. The distance between the probe front and center of the weld seam was adjusted to ensure comprehensive coverage of the weld seam and its heat-affected zone, with the probe positioned as close to the center of the weld seam as possible for optimal performance [33].
# Congestion Control in CoAP Observe Group Communication
## 6 Conclusions  
In this article, we proposed a new congestion control mechanism for CoAP Observe Group Communication, namely CoCo-RED (Congestion Control Random Early Detection), based on buffer management and backoff algorithms in order to reduce the congestion of transmitted packets. Adapted from the TCP protocol, this congestion control mechanism can replace the previous CoAP. It can detect and predict congestion before packet loss occurs, especially when high network traffic density causes buffer overflow. Three major issues were also raised in this study: (1) RTO timer calculation, (2) average queue (AvgQ) and packet drop calculation, and (3) the use of the backoff algorithm for retransmission. The evaluation of the efficiency of CoCo-RED and the existing mechanism was performed using the Cooja simulator using two scenarios: the continuous traffic scenario and the burst traffic scenario. The experiment demonstrates that the CoCo-RED mechanism can help to predict and reduce the congestion from consecutive dropped packets in the case of buffer overflow. The efficiency of buffer management was higher when dealing with certain congestion levels. It can also help to reduce data traffic in the network to mitigate congestion, employing a wait for the backoff time until the timeout so that the retransmission occurs later. This, finally, reduces the packet loss and response time of network communication.  
Both the authors, C.S. and C.K., equally contributed to this research. The detailed research implementation is as follows: conceptualization, C.S. and C.K.; methodology, C.S. and C.K.; software, C.S. and C.K.; investigation, C.S. and C.K.; writing--original draft preparation, C.S.; writing--review and editing, C.K.; visualization, C.S.; supervision, C.K.  
This research received no external funding.  
The authors would like to thank the anonymous reviewers for their contribution to this paper.  
The authors declare no conflict of interest.
# A Procedure to Prepare Sand-Clay Mixture Samples for Soil-Structure Interface Direct Shear Tests
## 3 Reconstitution Procedure
### Preconsolidation and Sample Cutting  
To reconstitute sand-clay mixture samples with good uniformity for the interface direct shear box, the S1 mixing order was chosen as mentioned above. After the sand, distilled water, and clay were mixed into a slurry with the S1 protocol, the slurry samples were subjected to a preconsolidation process where constant normal loadings were applied on the sample step by step. Once having experienced a preconsolidation phase in the designed oedometer cell (Figures 4 and 5b,c), the sand-clay mixture specimen was consolidated into a solid phase (Figure 5d).  
The whole sample preparation procedure is illustrated in Figure 5 and detailed hereafter. Fontainebleau sand, clay and distilled water were mixed with S1 mixing protocol (sand-water-clay at 1.5 \(w_{\text{L}}\)). After the sample mixing and a vacuum process, it was transferred to the oedometer cell with two porous stone discs lying on the top and bottom, respectively. Each porous stone was pasted with a filter paper on its surface, and both the two stones and filter paper were saturated with distilled water in advance. Then the cell was installed on an oedometer frame, the normal loading steps were 5.59 kPa (weight of frame and piston), 25 kPa, 37.5 kPa, and 50 kPa. A subsequent loading step could be  
Figure 4: The schematic diagram of the whole designed oedometer cell after installation.  
started after the vertical deformation of the sample in previous step is stable. Generally, the consolidation time for each step should last at least 24 h.  
When the final preconsolidation phase ends, a trimming tool was needed for trimming the preconsolidated sample down to the dimensions of the shear box with a minimum of disturbance on the specimen. Thus, a trimming cutter was designed with the same size (100 mm \(\times\) 100 mm) as the upper shear box of the interface direct shear machine. This cutter has a sharp edge, a highly polished surface, and had been coated with a low-friction material for corrosion prevention (Figure 5e,f). Alternatively, a trimming platform or working table is also preferable for cutting the sand-clay mixture sample. During cutting, the cutter must be aligned appropriately to keep the specimen with precisely the same dimension as the shear box. The water content and sample mass were measured at this step with an oven and a balance.  
With the help of a squared aluminum rod (Figure 5g), the sample was moved to the interface shear box, flattening the surface and adding a piece of filter paper, then measuring the initial height of the sample. Finally, the shear box was set within the container, then installed in the interface direct shear machine, and the following test was performed (Figure 5g,h). The S1 order (sand-water-clay), plus the preconsolidation cell that was used to prepare sand-clay mixture samples, is called the S1 protocol hereafter.
# Machine-Learning-Based Path Loss Prediction
## 2 Theory
### Machine-Learning-Based Predictive Modeling
#### 2.2.6 AdaBoost Regression (AB)  
AdaBoost is a powerful ensemble learning algorithm used to improve the performance of weak learners [48]. The AB algorithm trains a weak learner at each iteration and then updates the weights to minimize the prediction errors of the weak learner. This process enhances the overall performance of the model by combining the predictions of various weak learners. Although the AB algorithm is particularly successful in classification problems, it can also be applied to regression problems. The following formula summarizes the AB algorithm:  
\[F_{m}(x)=F_{m-1}(x)+\alpha_{m}h_{m}(x) \tag{13}\]  
where \(F_{m}(x)\) is the model created at the m-th stage, \(\alpha_{m}\) refers to the weight of the weak learner, and \(h_{m}(x)\) demonstrates the m-th weak learner. AB increases the model's accuracy by giving more weight to misclassified examples at each iteration.
## 1 Introduction
This paper relaxes the hard collisions constraint by allowing some sharing of resources, including space and various services on edges/nodes, by agents. Such sharing reduces the quality of the path, i.e., the satisfaction level of the agent using it, but as long as the quality reduction for each path is below a settable threshold, the solution is acceptable. We call this concept _soft collisions_. Hard collisions are still supported by having a very strict threshold, i.e., a penalty for sharing is very high. The reduction in satisfaction level experienced by an agent caused by soft collisions on resources in its path is quantified using a _collision score_. In this paper, we develop a generalized version of the M* algorithm, called _soft-collision M*_ (SC-M*), for solving the MAPP problem in the soft-collision context. Note that we that we are not simply replacing hard with soft collisions, but instead introducing soft collisions as a generalization that allows modeling different types of collisions. SC-M* extends M* by taking the perspective of soft collision on common resources. Specifically, SC-M* tracks the collision score of each agent and places agents whose collision scores exceed certain thresholds into a _soft-collision set_ for _sub-dimensional expansion_, a technique that limits the search space while maintaining the optimality of the algorithm with respect to the objective. In this way, SC-M* achieves improved scalability to handle a larger number of agents while limiting the probability of collisions on resources to a bound. In this paper, we show that SC-M* has advanced flexibility and scalability for efficiently solving the MAPP problem in the soft-collision context where common resources are considered, and can handle complex environments (e.g., with multiple types of agents requesting multiple types of resources). We theoretically prove that SC-M* is _complete_ and _suboptimal_ under the soft-collision constraints on resources. Experimental results demonstrate the advantages/trade-offs of SC-M* in terms of path cost, success rate and run time against baseline SC-based MAPP planners, such as SC-A* and SC-CBS. The rest of the paper is organized as follows. Section 2 discusses the motivation of soft collisions. Section 3 gives technical briefing of the M* algorithm. Section 4 presents our proposed SC-M* approach. Section 5 evaluates SC-M* in a grid public transit network. Finally, Section 6 concludes our work.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Remote Sensing Data Collection  
One Landsat 5 Thematic Mapper (TM) scene and one Landsat 8 scene for land use/cover mapping were acquired (Table 2). The Landsat Thematic Mapper (TM) sensor carried on Landsat 4 and Landsat 5 created images consisting of six spectral bands with a spatial resolution of 30 m for Bands 1-5 and 7, and one thermal band (Band 6) (EOS, 2019). Landsat 8 consists of the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) sensors, which provide images at a spatial resolution of 15 m (panchromatic), 30 m (visible, NIR, SWIR), and 100 m (thermal). Landsat 5 and Landsat 8 images were selected from cloud-free scenes acquired during the month of June. The two Landsat scenes were geometrically corrected at the United States Geological Survey (U.S.G.S) prior to downloading. The scenes were then georeferenced to the Universal Transverse Mercator (UTM) map projection in zone 36 south, where Harare is located. Atmospheric correction was performed using the dark subtract method on all the bands used in image processing.