esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation of the Proposed Semi-Analytical Solution
### Validation of the Proposed Semi-Analytical Solution Regarding Experimental Results
The laterally mobile structure of the test equipment does not constrain displacements and rotations of the vertical component cross-section at the load application zone. Therefore, in the mathematical model, it is adopted that the vertical component has a free end in the load application zone. Based on everything aforementioned for the mathematical model of the vertical component, the cantilever beam with span h is adopted. The span of the cantilever beam is equal to the sum of the half of the nut thickness, length of the conical body of the vertical component, and the length of the slave cylinder to the load application zone. The mathematical model is simplified by neglecting the cross-section of the nut and the slave cylinder, so the cantilever beam is treated as a beam of circular cross-section linearly variable along the beam axis. Therefore, the diameter of the cross-section at the clamped end is d\({}_{\text{c}}\), while the diameter of the cross-section at the free end is d\({}_{\text{f}}\) (Figure 10). The material input data for the selected steel C45 are as follows: modulus of elasticity E = 190 GPa, Poisson's ratio \(\upgamma\) = 0.30, yield stress f\({}_{y}\) = 430 MPa, ultimate stress f\({}_{\text{u}}\) = 650-800 MPa, ultimate strain 16%. In the analysis, the nonlinear behavior of the steel material is modelled via bilinear stress-strain relation. After the yield point, the stress is proportional to strain by tangent modulus E\({}_{\text{t}}\) = 6600 MPa (Figure 11). With such defined geometric and material parameters of the vertical components, the semi-analytical solution is conducted in accordance with the assumptions presented in Section 2. Force-displacement diagrams are shown in Figures 8 and 9 and compared to the experimental results. It can be concluded that the results obtained via semi-analytical solution are in good correlation with the experimental ones. The values of the shear forces at the displacement of the 45 mm, elastic stiffness, and post-yield stiffness, for the experimental test and for semi-analytical solution as well, are shown in Table 2. Based on the comparative analysis of the results, it can be concluded that semi-analytical solution is  
Figure 10: A mathematical model for the application of the semi-analytical solution for the force–displacement relationship of the vertical component of the energy dissipation device. validated by the experiment, considering that the difference between semi-analytical and experimental results is less than 10%.
## 1 Introduction
However, we found in previous experiments, it affected the reflection information of the detected object when the observation information from a remote sensing platform came from different directions. This resulted in a significant deviation in the input of the prediction model, ultimately impacting the prediction results. At what relative azimuth angle (the angle between the projection line of the reflected light from jujube fruit received by the UAV multispectral camera to the ground and the projection line of the incident light emitted by the light source to the ground is called the relative azimuth angle; the schematic diagram of the relative azimuth angle is shown in Figure 1) does the spectral data acquired by the UAV provide the most beneficial prediction results? Does a model based on multispectral data at multiple relative azimuth angles have better prediction accuracy than a model based on multispectral data at a single relative azimuth angle? With these two questions, this study focused on jujube fruit as the research object to answer these questions. By adjusting the angle of the UAV platform outdoors, multispectral data at various relative azimuth angles were collected. These data were then used as input for prediction models of SSC and MC. The results were analyzed to determine the best relative azimuth angle (characteristic azimuth angle). Subsequently, multispectral data obtained from multiple relative azimuth angles and data obtained from the characteristic azimuth angle were used as inputs to compare the prediction results. This study can provide a reference for fruit quality detection based on multispectral data at multiple relative azimuth angles.
## 2 Results  
### Azolla Cyanobiont  
The isolation of the cyanobiont allowed us to observe the presence of dense agglomerations of cyanobiont filaments together with plant debris. After one week of cyanobiont culture, we collected sufficient amounts of living material for further studies; an example of the living culture is presented in Figure 2.  
Figure 2: UV microphotograph of the cyanobiont culture obtained during passage (Nikon Eclipse 80i microscope, magnification 4x, UV2A filter, Nikon Instruments Europe B.V., Amsterdam, The Netherlands). Photo: [PERSON].  
Colony filaments were not attenuated towards ends. Cells are ellipsoidal or barrel-shaped, size of cells changes from 4-6 \(\times\) 2.5-3.5 \(\upmu\)m, with granules. Heterocysts were ellipsoidal, larger than vegetative cells, size 5-9 \(\times\) 4.5-6 \(\upmu\)m, solitary and intercalary. Gas vacuoles were absent.  
After conducting PCR reaction using _nif_Df and _nif_Dr primers, specific products of 600 bp were obtained. This observation confirms the presence of the _nif_ gene encoding enzymes important in atmospheric nitrogen fixing, i.e., a feature typical of cyanobacteria. In the case of primers targeting a fragment of the 16S rRNA gene specific for cyanobacteria, we obtained two 1500 and 1700 bp products specific for used primers [21]. After purification of the PCR products, the latter one was further analyzed.  
The analysis of the similarity of the cyanobacterium-specific 16S rRNA gene fragments to a homologous gene revealed similarities to the previously described _Azolla_ sp. cyanobionts (Table 1). We found four hits with a similarity of 90-94%, which indicates that the studied DNA fragments are likely to belong to _Anabaena_ sp.
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 1 Introduction
[PERSON] considered that the spectral signal can be freely fitted using empirical line-shape parameters with the Voigt model and that the best fitting result can be selected [8]. These methods provide a more accurate IA but do not quantify the IA systematic error generated by nonuniformity. Other researchers have reported the measurement uncertainty generated by line-shape fitting [22; 23; 24]. Such work can be regarded as a quantification of IA uncertainty but not a quantification of the systematic error generated by the nonuniformity. The latter should be analyzed and compensated for in the measurement. There has been no quantitative analysis of the IA error for a nonuniform flow field. Due to the complexity of the nonuniform flow field and the diversity of available absorption lines, it is difficult to comprehensively and accurately evaluate the system error of IA and judge the applicability of TDLAS technology to nonuniform flow. This paper studies the IA error under a nonuniform flow field. Measurement noise varies with changes in the test system, so this variation is not considered in the current study. We focus here on the IA error generated by the coupling of the nonuniformity, the absorption line features, and the line-shape fitting function, which are inherent to the measurement. The paper analyzes the effect of nonuniformity on the IA error from the perspective of line-shape parameters, removing the limitation of a specific nonuniform flow field and specific absorption line. The IA error generated by the nonuniformity and the isolated absorption line are universally quantified. Finally, considering the effect of the absorption line overlap in combination with practical applications, a general method for estimating the IA error is given. Figure 1: Schematic of TDLAS measurement in a nonuniform flow field.
# Experimental Study of Material Proportioning for Similar Modeling of Brittle Rocks
## 3 Experimental Results and Analysis
### Sensitivity Analysis of Factors
#### 3.2.1 Sensitivity Analysis of Density  
As shown in Figure 5, the density of the specimens decreased with a reduction in the refined iron powder-barite powder ratio. The maximum density of the specimens was observed when the refined iron powder-barite powder ratio was 1:0.5. The density of the specimens also decreased with an increase in the quartz sand content. The maximum density was reached at a quartz sand content of 6%, while the minimum density occurred at a quartz sand content of 12%. In addition, the density of the specimens decreased with increasing concentrations and dosages of the rosin-alcohol solution. However, at a rosin-alcohol solution concentration of 25%, the density of the specimens actually increased. The effect of the gypsum content on the specimen density was not significant. The maximum density was observed at a gypsum content of 2.5%.  
The density of the refined iron powder in the aggregates was the highest, playing a dominant role in the specimen density, while the quartz sand, being a coarse aggregate, had a negative impact on the density of the specimen. Based on the sensitivity analysis of specimen density (see Table 4), the refined iron powder-barite powder ratio had the greatest impact on the specimen density, with a range of 0.10. In contrast, the gypsum content and the rosin-alcohol solution concentration had the least effect, with a range of 0.07. The sensitivity ranking of the factors influencing specimen density is as follows: A > E > B > D = C.
## Appendix B  
The proposed MIMO antenna is designed using the formula for calculating the minimum and maximum size of the dipole elements in the antenna [40]. The maximum (\(L_{max}\)) and minimum \(L_{min}\) length of the dipole element can be calculated using \(L_{max}=\frac{c}{f_{min}\sqrt{c_{r}}}\) and\(L_{min}=\frac{c}{f_{max}\sqrt{\varepsilon_{r}}}\). Here, C is the velocity of light, \(\varepsilon_{r}\) is the permittivity of the substrate, \(f_{min}\) is the minimum frequency, and \(f_{max}\) is the maximum frequency. We have calculated the minimum and maximum length of the dipole element to be 7.2 \(\upmu\)m and 144 \(\upmu\)m, respectively. According to this calculation, we have set the dipole's overall length as 2 \(\times\) t = 140 \(\upmu\)m. In this design case, the substrate is not the only material where EM waves become confined. Therefore, the practical permittivity calculation is required to be calculated again for the overall dimensions of the structure. We have to use the microstrip line formula \(\varepsilon_{eff}=\frac{\varepsilon_{r}+1}{2}+\frac{\varepsilon_{r}-1}{2} \Big{[}1+12\frac{h}{l}\Big{]}^{-1/2}\) to identify the size of the structure. Here, \(h\) and \(l\) are the thickness of the substrate and the length of the feed line, respectively. The calculated \(\varepsilon_{eff}\) is \(\sim\)12.22. The overall structure size is calculated by \(L=\frac{\lambda_{min}}{4}\Big{(}1-\frac{1}{B_{s}}\Big{)}\cot\alpha\). Here, \(B_{s}\) is the desired bandwidth, and \(\alpha\) is 12.132\({}^{\circ}\) to achieve an approximate gain of 8 dB. Overall, the value of the length obtained is 310 \(\upmu\)m. These design criteria are considered for single-antenna radiation. The overall dimensions increase to 620 \(\times\) 800 \(\upmu\)m\({}^{2}\), including the spacing between two MIMO elements and a circular patch.
## 1 Introduction
### Related Works  
#### 1.1.1 Leaf Disease Identification  
Conventionally, image-based leaf disease identification involves two steps: image feature extraction and classifier learning. The first step is image feature extraction, which refers to the process of describing the local image appearance for leaf disease detection and the generation of image-level feature vectors. To characterize local leaf spot appearances, popular feature extractions such as SIFT [2], LBP [3], sparse codes [4], and others [5], including color histograms and entropy, can be applied. Subsequently, these features are pooled and aggregated through Bag-of-words (BOW) [6] and Fisher vector encoding (FVE) [7] to obtain image-level feature vectors. The second step is classifier learning to find a hyperplane that can separate image-level feature vectors into classes. Given these feature vectors, a support vector machine (SVM) [8; 9], which is a data analysis tool, is trained to classify leaf diseases. Certainly, other tools, such as decision trees [10], genetic algorithms [11], and dictionary learning [12], can be utilized for leaf disease identification.  
Recently, the DCNN has replaced a series of steps that consisted of handcrafted feature designs, pooling, and classifier learning, because the DCNN can automatically learn generic representations in a hierarchical manner for discriminative feature extraction. With the emergence of the DCNN, a profound knowledge of feature design, feature pooling, and classifier learning is not necessary, thereby rendering it easier for non-experts to handle the leaf disease identification problems. If a new training dataset is provided, good performance can be obtained through transfer learning (TL), which uses pretrained models such as VGG [13] and ResNet [14], and subsequently updates the model's parameters. A large number of studies [15; 16; 17; 18; 19; 20; 21] have been performed based on TL during the past few years for leaf disease identification.  
More recently, attention networks [22], feature pyramid networks [23], and vision transformer networks [24] have been actively studied. Attention networks [22] model spatial and channel weighting maps, to emphasize the features in a particular area or channel. Feature pyramid networks [23] utilize multiple feature maps with different scales in the backbone, which refers to general-purpose feature extractors such as VGG and ResNet, to be more robust to the object's scale problem. Vision transformer networks [24] replace the DCNN backbone as a convolution-free model, and employ a pure transformer and pyramid transformer as a unified backbone for various vision tasks. A sequence of patches is adopted as the input, which is different from conventional DCNNs. It has been reported that vision transformer networks can be applied to many downstream tasks, while outperforming traditional backbones.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 2 Fundamentals of Accelerometer Data
### Noise and Artefacts  
The analysis of practical challenges faced by railway monitoring systems is facing signal interference in complex environments and real-time requirements. The noise which affects the signal of an accelerometer has two primary sources:  
* Internal noise;
* External noise.  
Internal noise is generated by the accelerometer's electronic and mechanical system, as analyzed for piezoelectric accelerometers in [41] or MEMS accelerometers in [42]. This type of noise is specified in the device datasheet, and the user must select an accelerometer with internal noise levels compatible with the application's requirements.  
External noise is generated from multiple sources. The user can control and limit some of this noise using proper design techniques, such as shielding and grounding. Some of the most common external noise sources come from capacitive and inductive coupling and ground loops [43], as shown in Figures 11 and 12.  
Figure 11: Capacitive coupling disturbance of accelerometer sensor signal.  
Figure 9: Analogue output voltage of accelerometer as a function of acceleration.  
Capacitive coupling can be minimized by using proper shielded cables for the accelerometer, such as coaxial or twisted pair cables. Magnetic coupling is more complicated to shield. Adequate care must be taken when laying out and installing the accelerometer. The signal cables should not run close to AC power lines or magnetic devices (motors or transformers) and should be as short as possible with no loops or turns. Installing the signal cables inside grounded metal tubing can sometimes improve noise cancellation. Ground loop noise is the result of ground loops in the measuring circuit. Ground loops occur when the system has more than one ground point. Having multiple ground points that are not connected at the same voltage potential can allow current to flow through these loops and cause additional noise. The solution is to ensure all grounding is conducted at one point only.
## 1 Introduction
[16; 17] proposed a projection matrix to solve the problem of matrix singularity caused by unmeasured variables, and it is generalized to bilinear data reconciliation. Then, [PERSON] [18] eliminated the unmeasured variables in the constraint equations using the QR matrix orthogonal transformation and improved the solving speed. In addition, the global test method [19], the constraint equation test method [20] and the measurement test method are all suitable for identifying instrument deviation type of gross error. The generalized likelihood ratio test method [21; 22] is suitable for identifying the equipment fault type of gross error. In industrial applications, data reconciliation technology is usually used for parameter correction and significant error monitoring in chemical process flow in the field of industrial chemistry [23; 24; 25; 26]. By contrast, nuclear power units usually use data reconciliation technology for thermal reactor balance calculations and optimizing reactor power calculations [27; 28; 29; 30], while thermal power units usually use data reconciliation technology to improve the measurement accuracy of feed water flow, electric power and other parameters, and to improve the recognition ability of gross errors [31; 32; 33; 34]. It is very effective to rectify the parameters of thermal system by using data reconciliation technology, which greatly improves the measurement accuracy of mass flow and power, and it is also the case for the waste heat recovery system of a gas turbine. In the field of gas turbines, the researches on data reconciliation mainly focus on equipment breakdown identification and gas-steam combined cycle system at present, and there are no further researches on data reconciliation in the relatively new system configuration. The data reconciliation model for a waste heat recovery system of a gas turbine using an S-CO\({}_{2}\) cycle established in this paper aims to solve the data reconciliation and suspect measurement identification problems in the relatively new waste heat recovery system of a gas turbine. It lays a foundation for the new system performance monitoring of a gas turbine coupled with S-CO\({}_{2}\). In this paper, the model is firstly built based on the heat balance equations of the system, and the accuracy of the model is tested using the original calculated design values of the system as the verification benchmark. Then, the optimization results of the model and the recognition of gross errors under 10 different working conditions are calculated, and the availability of the method is analyzed and evaluated. The results show that the model built in this paper improves the measurement data accuracy and the calculation accuracy of the component performance, and provides a certain reference for the model establishment and application of the simple coupled system based on the data reconciliation.
## 4 Performance Validation
### Results and Discussion
#### 4.2.1 Case 1  
Let us assume four processes arrived at time t = 0. P1 = [20, 5], P2 = [40, 2], P3 = [60, 4], P4 = [80, 5]. The comparison of the traditional Round Robin scheduling algorithm with RRAPS is shown in Table 1.  
Table 1 compares our simulation results in MATLAB 2022b, comparing the performance of the RRAPS algorithm with other scheduling algorithms, including RR with fixed time quantum without priority, RR with dynamic time slice without priority, and the AN algorithm [3]. The performance is evaluated based on turnaround time (ms), waiting time (ms), and context switching. As shown in Table 1, RRAPS outperforms the other algorithms in terms of turnaround, waiting times, and context switching, achieving a turnaround time of 85.1 ms, a waiting time of 44.7 ms, and only four instances of context switching.
# Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG
## 3 Materials and Methods
### Experimental Protocol
#### 3.1.1 Experiment 1--Touch and Grasp  
In the first experiment, 11 healthy volunteers (11 males, ages 20-38) were included. The hand dominance test resulted in nine right-handed participants, one left-handed and one undefined. During the experiment, two glasses were on the table at the participant's reaching distance. They were equipped with one pressure sensor each, in order to precisely detect the grasping onset. The participants were instructed either to grasp the first glass or to touch the second glass for a minimum time of 4 s. Thus, the total duration of each repetition was longer than 5 s. Four sessions of 20 repetitions of the same movement, i.e., grasping and touching, were included in the protocol. Thus, 80 touching and 80 grasping movements were performed by each participant at the end of the experiment. After each session, the participants could take a break and the glasses were switched. The same number of repetitions was performed in each glasses' positions. On the computer screen in front of them, they could see the remaining number of trials to perform.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 3 Reinforcement Learning-Based Routing
### Control Overhead  
This subsection summarizes the cost of control overhead in clustering and non-clustering schemes. The control overhead in the above schemes can be categorized as a beacon message overhead, clustering overhead, and routing overhead.  
* **Beacon messages overhead**: These beacon messages are sent by the nodes to find the information about their respective neighbors. The number of beacon messages sent are dependent on the number of nodes, N, in the environment. The nodes which are in its vicinity will reply. These beacon messages are resent after every 10 s to renew the neighbor's information. Since there is no mobility in our scenario the neighbor change is only possible due to DNs.
* **Clustering overhead**: To calculate the clustering overhead, we categorize the clustering schemes as centralized and decentralized schemes. The schemes that employ K mean clustering are centralized schemes because they require the location information of all the nodes. We assume that after the exchange of beacon messages, location information of all the nodes is forwarded/relayed to the CC. The CC performs K mean clustering and inform the nodes about their clusters and CHs. Here, we do not consider the overhead of passing location information to CC and K mean information back to the nodes from CC. This approximate overhead can be readily found from the achievable capacity [52]. The schemes that employ clustering energy are distributed schemes. The clustering overhead is calculated when a node broadcasts control packets to declare itself as the CH. The receiving nodes will reply accordingly as discussed in Section 3.1.1. These control packets depend on the number of clusters \(i\) and the number of CMs in each cluster.
* **Routing overhead**: Once the CHs are formed, this step includes the amount of control overhead involved in the discovery of neighboring CHs and the routing path. It is assumed that the routing tables are only maintained at the CHs, which will reduce the overall routing overhead. For route discovery, the CHs will send the control packets to the neighboring CHs which will then forward the control packets all the way to the CC. The CC will confirm the routing path for each CH through a reverse response as discussed in Section 3.2.