esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Evaluation Protocol for the Flocking Task  
Most of the existing studies on flocking [38; 39; 41] have only regarded velocity consistency as the ultimate goal, and the velocity standard deviation is often used as the only evaluation metric for flocking control strategies. This paper has redefined the consistency requirements of the flocking in terms of three aspects: velocity, one-hop distance, and graph importance. In addition, for a flocking task, we have to examine the speed of achieving consistency and the ability to maintain the graph's connectivity and avoid swarm splitting in the flocking process.  
Based on the above analysis, this paper proposes a evaluation protocol for the flocking task, which specifically contains the following items:  
1. Velocity Reward (VR): the mean of the cumulative single-step velocity standard deviation values for all test episodes, which is used to evaluate the velocity consistency of the swarm. To ensure a positive correlation between the velocity reward and the velocity consistency, the opposite number of the mean value is chosen as the VR, which means that the maximum value of VR is 0;
2. Distance Reward (DR): the mean of the cumulative single-step one-hop distance standard deviation values for all test episodes, which is used to evaluate the one-hop distance consistency of the swarm. Similar to the velocity reward, the DR is also changed to the opposite number;
3. Graph Importance Entropy (GIE): the average of the single-step graph importance entropy for all the test episodes, which is used to evaluate the graph importance consistency and is also the metric to evaluate the swarm robustness;
4. Steady-state time (TS): the average of the time consumed for all episodes in which the variation ranges of single-step VR, DR and GIE all decay and remain within \(\pm\)2%. The TS is to evaluate the speed of achieving consistency in a robot swarm;
5. Success Rate (SR): the success of the flocking task is defined as the situation where the connectivity is well maintained from the beginning to the end of the episode, i.e., none of the robots splits from the swarm in the flocking task. The success rate is defined as the number of successful test episodes as a percentage of the total number of test episodes. This metric is used to evaluate the essential ability of the flocking control strategy to avoid swarm splitting, which is also a direct reflection of the swarm robustness.  
Based on the above evaluation protocol, a comprehensive evaluation can be made on the consistency performance and the robustness of the robot swarms in the flocking task. This protocol can be used to evaluate and compare the advantages and disadvantages of different flocking control strategies and optimize the parameters.  
It is essential to note that the calculation of the metrics requires the acquisition of the positions and velocities of all robots, so it is an objective evaluation protocol of different strategies but cannot be used to generate the action policy in real-time.
## 1 Introduction
In [22; 23; 24; 25], the scenario is limited to uplink multiple access, and the authors study the resource allocation (e.g., transmit power, bandwidth and number of antennas) in uplink transmissions under a fixed backhaul bandwidth, but the arrangement of users and the optimal backhaul resource allocation are not mentioned. In [26], a smart grid scenario has been considered to solve the optimal problem, where big data related methods are adopted, but there is little innovation in solving such an integer programming method, and the solution rather relies on the advantages of distributed computing architecture. In [27; 28; 29], the problem of uplink CoMP with imperfect CSI under capacity limited backhaul is studied, but the solutions are mainly about the uplink resource allocation and decoding design, not the backhaul link allocation. In [23], the authors discuss the problem in a centralized computing scenario, so backhaul links become transmission links, and the cooperation consumption will cost no more resources, so such problems could be easily solved using convex optimization tools. In this work, we are trying to figure out the optimal solution of the uplink multiuser CoMP with limited backhaul resources using convex optimization tools. The rest of this paper is organized as follows: In Section 2, we summarize the contributions of this paper. In Sections 3 and 4 the system model and problem formulation are given. After that, the proposed novel algorithm is introduced in Section 5. Finally, numerical results including theoretical and system level simulation results are presented in Section 6 before conclusions are drawn.
## 2 Spatial Time Domain Reflectometry
### Spatial Time Domain Reflectometry Two-Way Inverse Analysis  
To extract the \(C\) and \(G\) profiles, the last step is the application of the optimization technique for minimizing the difference between forward modeling and TDR measurement results. Generally, for an optimization problem, the objective function is usually the square difference between prediction and measurement, starting from an initial estimation and finally determining with the global minimum. [PERSON] [19] derived the objective function as Equation (5):  
\[F(C,G)=\sum_{i=1}^{2}\int_{0}^{T}\Bigl{[}U_{i}^{M}(x_{i},t,C,G)-U_{i}^{m}(x_{ i},t)\Bigr{]}^{2}dt \tag{5}\]  
where \(F\) is the objective function of \(C\) and \(G\); \(U_{i}^{M}\) and \(U_{i}^{m}\) are, individually, the predicted and measured voltage (\(i\) = 1,2 corresponds to two-way TDR tracing). This function has been successfully optimized using the conjugate gradient method with the reconstruction of \(C\) and \(G\) by [PERSON] [19]. The optimization procedure is to input one initial estimation of the \(C\) and \(G\) profiles into forward modeling to calculate the cost function and update the input by previous minimization outcomes until the cost function achieves the global minimum. The derivation of the Jacobian gradient of the objective function using the adjoint PDE of Equation (4) can be sourced from the mathematical study [19].  
Figure 3: (**a**) The schematic plot of spatial TDR measuring electrical circuit with previous assumptions; the flat ribbon cable is located between \(x_{1}\) and \(x_{2}\), coaxial cable is between \(x_{a}\) and \(x_{1}\) or \(x_{2}\) and \(x_{e}\)[19]; (**b**) the implementation of two-way TDR tracing using TDR device and multiplexer [17].
## 2 Materials and Methods
### Experiment Data  
#### 2.2.1 Sample Data Set  
The role of the sample data set is to provide training samples for the construction project detector and detail ground object detector. Therefore, it is necessary to label the construction project with the smallest bounding box, and use the smallest bounding box to label various detailed ground objects in the construction project. This data set is manually labeled. Based on the collected scope of responsibility in the soil and water conservation plan for construction projects in Wuhan and the overall layout of the project, the construction project and its six detailed ground objects (bare soil (rock), dust screen, construction roads, moveable slab houses, buildings under construction and constructed buildings) were selected for labeling as shown in Figure 4. The labeling of this data set is based on the Gaofen-1 satellite remote sensing image with a resolution of 2 m, RGB format, a size of 600 \(\times\) 600 Pixels, and labelImg as the tool ([[https://github.com/tzutalin/labelImg](https://github.com/tzutalin/labelImg)]([https://github.com/tzutalin/labelImg](https://github.com/tzutalin/labelImg))) (accessed on 2 June 2020).  
The number of labeled construction projects, bare soil (rock), dust screens, construction roads, movable slab houses, buildings under construction, and constructed buildings is, respectively, 752, 763, 154, 82, 372, 292, and 278.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 4 Simulation and Performance Analysis Results
### System Level Simulation Description and Performance Measures  
In the SLSs, we considered an uplink backhaul link of a UCWN, where there are one UBSC, ten UBSs with multiple UTs, and a few mobile UTs like AUVs. We assumed that each UBS receives the packet from the UTs connected to the UBS with Poisson distribution with the packet arrival rate \(\lambda\). In other words, each UBS receives different amounts of packets with Poisson distribution, but all the UBS have the same average packet arrival rate \(\lambda\). In addition, we assumed that there are a few mobile UTs in the UCWN, which communicate with one of the UBSs in the UCWN, and there is only one mobile node in the UBS. So, the UBS communicating with a mobile UT will receive burst traffic from the mobile UT with Poisson distribution with much higher arrival rate \(\lambda_{b}\).  
The parameters used in the SLSs are listed in the Table 2. We performed simulations for different sets of the parameters. For each simulation, some parameters are used as default values and others are changed to investigate the different perspectives of the performance. We also considered four performance measures in the SLSs which are mean values of all UBSs and defined as follows.  
* Average packet drop rate: a ratio of dropped packets over ingress packets in a queues of a UBS.
* Average resource utilization: a ratio of used backhaul capacity over assigned backhaul capacity of a UBS.
* Average message overhead: the number of messages induced by resource reallocation.
* Average reserved UBSC capacity of the UBSC.
###### Abstract  
Aflatoxin B1 is a toxic substance inamonds, other nuts, and grains that poses potential serious health risks to humans and animals, particularly in warm, humid climates. Therefore, it is necessary to remove aflatoxin B1 before almonds enter the supply chain to ensure food safety. Hyperspectral imaging (HSI) is a rapid, non-destructive method for detecting aflatoxin B1 by analyzing specific spectral data. However, HSI increases data dimensionality and often includes irrelevant information, complicating the analysis process. These challenges make classification models for detecting aflatoxin B1 complex and less reliable, especially for real-time, in-line applications. This study proposed a novel hybrid spectral band selection algorithm to detect aflatoxin B1 in almonds based on multilayer perceptron (MLP) network weights and spectral refinement (W-SR). In the proposed process, the hyperspectral imaging (HSI) spectral rank was firstly generated based on MLP network weights. The rank was further updated using a spectral confidence matrix. Then, a spectral refinement process identified more important spectra from the lower-ranked ones through iterative processes. An exhaustive search was performed to select an optimal spectral subset, consisting of only the most significant spectral bands, to make the entire process suitable for real-time, in-line aflatoxin B1 detection in industrial environments. The experimental results using the artificially contaminated almonds dataset achieved a cross-validation accuracy of 98.67% with an F1-score of 0.982 for the standard normal variate (SNV) processed data with only four spectral bands. Comparative experiment results showed that the proposed MLPW-SR spectral band selection algorithm outperforms baseline methods.  
Keywords:hyperspectral imaging; feature selection; dimensionality reduction; multilayer perception; random forest +
Footnote †: journal: _Sciencedence_
## 3 Results and Discussion
### Functional Properties of Fish Burgers  
The functional characteristics of control and _Spirulina_-fortified burgers are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Textural Parameters** & **Fish Burger Types** & \multicolumn{2}{c}{**Burger Formulations**} \\ \hline \hline  & \multicolumn{2}{c}{Control burger} & 1\% _Spirulina_ fish burger \\ \hline \multirow{2}{*}{**Hardness (N)**} & CB & 8.35 & 13.53 \({}^{\text{c}}\) \\ \cline{2-4}  & BB & 6.69 & 7.39 \({}^{\text{a}}\) \\ \hline \multirow{2}{*}{**Elasticity (cm)**} & CB & 0.36 & 0.38 \\ \cline{2-4}  & BB & 0.56 & 0.85 \({}^{\text{c}}\) \\ \hline \multirow{2}{*}{**Chewiness (N.cm)**} & CB & 1.11 & 2.21 \({}^{\text{c}}\) \\ \cline{2-4}  & BB & 0.88 & 2.40 \({}^{\text{c}}\) \\ \hline \multirow{2}{*}{**Gumminess (N)**} & CB & 3.09 & 5.84 \({}^{\text{c}}\) \\ \cline{2-4}  & BB & 1.58 & 2.83 \({}^{\text{c}}\) \\ \hline \multirow{2}{*}{**Cohesion**} & CB & 0.371 & 0.432 \({}^{\text{a}}\) \\ \cline{2-4}  & BB & 0.239 & 0.383 \({}^{\text{c}}\) \\ \hline \hline \end{tabular} CB: carp burgers; BB: barbel burgers; Control vs. 1% _Spirulina_-fortified burgers: \({}^{\text{a}}\)\(p<0.05\); \({}^{\text{b}}\)\(p<0.01\); \({}^{\text{c}}\)\(p<0.001\).  
\end{table}
Table 3: Parameters obtained using texture analyzer for control and fortified burgers with different concentrations of _Spirulina_.  
Compared to controls, the WHC values of 1% _Spirulina_-fortified burgers increased significantly (\(p<0.001\)) from 2.31 to 2.78 and 2.12 to 2.52 for carp and barbel formulations, respectively (Table 4). This was probably due to the addition of _Spirulina_ fibers. The great WHC of _Spirulina_ proposes that it could be used as a functional natural ingredient in treatments of food to modify viscosity and texture, reduce dehydration during storage, and minimize energetic value of food products. Furthermore, significant differences (\(p<0.001\)) of OHC values were also noted for the two types of _Spirulina_-fortified burgers (Table 4). These OHC values could be of particular interest, especially for the binding of fat during food industrial production and preservation. This might be attributed to the high water binding and oil binding capacities of the microalgae fibers [40; 43]. Added to that, the highest values of swelling capacity were obtained by the _Spirulina_-fortified burgers made from carp (3.56 mL/g) (\(p<0.001\)) and barbel (3.43 mL/g) (\(p<0.001\)). The control burgers showed significant inferior values of swelling capacity for carp (3.13 mL/g) and barbel (2.99 mL/g) (Table 4). Furthermore, the difference between the functional capacities of control and 1% _Spirulina_ formulations could be explained by the structural properties of each material after the addition of _Spirulina_ fibers. These results are in accordance with previous studies [37; 44; 45].
## 2 Materials and Methods
### Experimental Setup  
The system for acquisition of biospeckle images is schematically presented in Figure 3.  
The system consisted of monochrome camera (model See3 CAM_10 CUG, USB 3.0, e-con Systems India Pvt Ltd., Guindy, Chennai, India) equipped with a CMOS digital image sensor (model Aptina AR0134, 1/3'', 1.2 Mp, Semiconductor Components Industries, Phoenix, AZ, USA) and CCTV lens (focal length 2.8-12 mm 1/3'', F1.4, LENEX, Poznan, Poland). Samples were illuminated by a He-Ne laser (model 05-LLR-811, Melles Griot IDEX Health & Science, Rochester, NY, USA) of 1 mW power and a wavelength of 632.8 nm. The laser beam was expanded by a microscope objective (PZO, Warsaw, Poland, magnification/aperture 20/0.40). The laser-sample and sample-camera distances were 140 mm and 200 mm, respectively. The experimental setup remained unchanged during the entire study. Samples were placed on a height-adjustable rotary table. Elements of the biospeckle acquisition system (laser, camera, beam expander and rotary table) were mounted on a stable optical bench to eliminate the effect of vibrations from the environment that could disturb the acquisition process. The sample illumination angle was approximately 30 degrees as showed in Figure 3. This angle was established experimentally to avoid the effect of glint from the fruit surface. Biospeckle image sequences were acquired in a dark room to avoid disruptions caused by ambient light. The frame rate was 54 fps and sequences of 256 frames were used for further analysis. The target image resolution, which covered the entire fruit surface lit by by the laser beam was 180 \(\times\) 180 pixels, which corresponded to an observation area of about 7 \(\times\) 7 mm.
## 4 Experimental Validation
### Summary of the Results  
It is worth noting that the current work employed two quite distinct datasets of PD voices (Italian and Lithuanian) for analysis and algorithm building. The Italian PD voice samples were based on sustained vowel phonation, whereas the Lithuanian samples were based on phonetically balanced phrase analysis. However, because sustained vowel phonation is an artificial sort of phonation, that feature defines the same constraint in the meaning of the sustained vowel signal analysis. Connected speech, on the other hand, is more typical of everyday speaking and might be deemed more \"ecologically valid\" (i.e., more representative of daily speech and voice use patterns). As a result, if an objective voice evaluation is to be deemed robust and physiologically valid for screening purposes, the acoustic measurements should preferably be done utilizing both speaking patterns-sustained phonation and running speech (used in our approach).  
As a result, the PD classification findings shown on speech samples from two independent datasets must be regarded with caution. The Italian dataset represented Parkinson's disease patients with a minor incidence of advanced PD stages according to the H-Y scale, and hence more pronounced voice abnormalities. This characteristic may have improved classification accuracy, resulting in the somewhat superior PD classification results presented on the Italian dataset.  
The current study, on the other hand, shows that the suggested Hybrid U-lossian deep learning network technique is successful in analyzing and classifying PD voice samples of various sorts, hence expanding PD screening options. It is vital to note that the recommended algorithm is not currently regarded a medical device or diagnostic tool. Nonetheless, the suggested technique has the potential to be applied in clinical settings as a sensitive tool for the screening of [PERSON]'s disease-related voice and speech abnormalities.
# An Effective Beam Width Model of Slab in Thick Wall-Thick Slab Joints Subjected to Lateral Forces
###### Abstract  
Thick wall-thick slab structures are a newly-developed type of structural system comprising solely of structural floor slabs and shear walls, the former of which is thicker than that of slabs used in typical reinforced concrete frame structures. This study looks to develop a method to predict the initial stiffness of thick wall-thick slab joints under lateral loading by adopting the effective beam width method (EBWM). The height and length of the bearing wall, and the span length of the slab on effective beam width have been considered. Moreover, a correction coefficient that estimates the lateral force applied on the wall top, which is another crucial parameter for predicting the effective beam width and considered as the condition providing initial stiffness, is suggested based on experimental data from the literature. Additionally, by comparing values of the effective beam width obtained from the proposed model with those calculated using equations employed by the current code according to three wall-slab joint specimens, it has been demonstrated the usefulness and accuracy of the proposed method, which works better than the current code method for the wall-slab joints in TWTS cases.  
e +
Footnote †: journal: Applied  
1
Footnote 1: College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China  
2
Footnote 2: Earthquake Research Institute, The University of Tokyo, Tokyo 113-0032, Japan; [EMAIL_ADDRESS]  
20221103390/app12094600  
Footnote 2: Correspondence: [EMAIL_ADDRESS]  
Reinforced concrete; thick wall-thick slab structure; wall-slab joints; lateral load; initial stiffness; effective beam width method (EBWM)
## 2 Regional Geological Background  
The Wandashan Block is located at the eastern end of the Xing-Meng Orogenic Belt. To the east and north, it is adjacent to the Sikhote-Alin accretionary complex in Russia's Far East, separated by the Ussuri River and the Heilongjiang River, respectively, forming a superterrane. To the west, it is bordered by the Jiamusi Block along the Dahuazhen Fault, and to the south, it is adjacent to the Xingkai Block along the Dunami Fault [22; 23]. Overall, it exhibits a slightly westward-dipping arcuate distribution trending north-south. The Wandashan Block has successively experienced the subduction and closure of the Paleo-Asian Ocean, the subduction of the Mongol-Okhotsk Ocean, and the subduction of the Paleo-Pacific Ocean [24; 25; 26]. The RSAC is situated in the central part of the Wandashan Block, near Raohe County, and trends nearly north-south. It is the subject of this study (Figure 1).  
Figure 1: (**a**) Tectonic outline of NE China; (**b**) geological map of the Wandashan Block showing sample locations (modified by [14; 15; 21; 27; 28; 29; 30; 31; 32]).  
The Wandashan Block has well-developed Mesozoic and Cenozoic strata, with the Triassic strata being the most predominant. The magmatic rocks in the block can generally be divided into the following: the Late Paleozoic mafic-ultramafic rocks in the Yuejinshan-Dongfanghong Area; the Early Mesozoic mafic-ultramafic rocks in the Raohe area, which are the main components of the RSAC, including gabbro, basalt, serpentinite, etc., among which the basalts are the key subjects of this study; and the widely exposed Late Mesozoic acidic intrusive rocks and acidic volcanic rocks, representative of pluton, including the Hamatong pluton and Taipingcun pluton (with crystallization ages between 114 and 116 Ma). Acidic volcanic rocks are mainly rhyolite and are found in the Early Cretaceous strata [14; 23].  
The basement of the Wandashan Block mainly consists of Triassic-to-Jurassic oceanic crust ophiolites and deep-sea siliceous rocks, interspersed with Paleozoic continental margin landslide bodies. To the south, it is bordered by the northern branch of the Tan-Lu fault--the Dunhua-Mishan Fault, adjacent to the Xingkai Block--and to the west, it is bordered by the Baoqing-Daezhen fault, adjacent to the Jiamusi Block. Additionally, there are developed the Fujin-Xiaojiahe basement faults extending from the Jiamusi Block to the base of the Wandashan Block, Early Mesozoic northwest-trending irregular faults, and Mesozoic northeast-trending irregular faults [23; 28].