esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To assess the impact of the relative displacement between machines and subjects, the machine angle and the fine-tuning of the subject posture on the segmentation accuracy of chest X-rays, this paper proposes a Position and Direction Network (PDNet) for chest X-rays with different angles and positions that provides more comprehensive information for cardiac image diagnosis and guided surgery. The implementation of PDnet was as follows: First, the extended database image was sent to a traditional segmentation network for training to prove that the network does not have linear invariant characteristics. Then, we evaluated the performance of the mask in the middle layers of the network and added a weight mask that identifies the position and direction of the object in the middle layer, thus improving the accuracy of segmenting targets at different positions and angles. Finally, the active-shape model (ASM) was used to postprocess the network segmentation results, allowing the model to be effectively applied to 2014 \(\times\) 2014 or higher definition chest X-rays. The experimental comparison of LinkNet, ResNet, U-Net, and DeepLap networks before and after the improvement shows that its segmentation accuracy (MIoU) are 5%, 6%, 20%, and 13% better. Their differences of losses are 11.24%, 21.96%, 18.53%, and 13.43% and F-scores also show the improved networks are more stable.  
Position and Direction Network; cardiac segmentation; X-ray chest radiograph; image segmentation +
Footnote †: journal: Medical Imaging  
of the 20 th century, more than 150 research studies on such issues had been published, and the number of studies has increased to 331 at present [14]. Most of these works have focused on segmenting single organs, with cardiac segmentation being one of the most important tasks. Moreover, the segmentation difficulty of this task is greater than that of other tasks, as reflected in the lower quality of the final segmentation result [15]. The remainder of this article is structured as follows. Section 2 reviews recent state of research on CXR segmentation. Section 3 lists and explains the basic theories utilized in this article. Section 4 describes our proposed theory and method. Section 5 shows the results of our method and a comparison with other methods and Section 6 presents the future research directions.
# Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking
## 3 Materials and Methods
### Tracking Methods
The anchor receives this beacon at a timestamp \(t2\), and then sends an answer at \(t3\), which is received at \(t4\) by the tag. The timestamps \(t2\) and \(t3\) are then sent to a tag in a data frame [80]. Finally, the tag computes the travelling time of the signal both ways and obtains a distance estimation \(d\), which is given by: \[d(TWR)=\frac{(t4-t1)-(t3-t2)}{2}\times c\] (1) where \(c\) denotes the speed of light (\(3\times 10^{8}\) m/s). 5. Symmetrical double-sided two-way ranging (SDS-TWR). In symmetric double-sided two-way ranging, an additional cycle of sending and receiving signals is performed [80], leading to two additional timestamps, \(t5\) and \(t6\): \[d(SDS-TWR)=\frac{(t6-t5)+(t4-t1)-(t3-t2)}{3}\times c\] (2) where \(c\) denotes the speed of light (\(3\times 10^{8}\) m/s). 6. Near-field electromagnetic range (NFER). The NFER method refers to any radio technology employing the near-field properties of radio waves. 7. Received signal strength indication. RSSI is a term used to measure the relative quality of a received signal to a client device [76]. However, there is a direct proportion between the RSSI and the receiver-transmitter distance. This method matches the RSSI measurement to a receiver-transmitter distance. The formula for converting the RSSI measurement to distance is described below: \[D=A\times\mathbb{\{}(r/t)\mathbb{\}}\ ^{\sim}B+C\] (3) where \(d\) is the distance in meters, \(r\) is the RSSI measured by the device, \(t\) is the RSSI measurement at \(1\) m, and \(A\), \(B\), and \(C\) are constants. Figure 3: Two-way ranging and symmetrical double-sided two-way ranging methods.
## 3 The Proposed Method
### Design Variables  
Parameters directly determining the volume or the weight were supposed to be chosen as design variables, because the optimization objective was to reduce the total cost of towers and cables. In the implementation, six variables were chosen for the example in Table 1. These variables covered different variable formats and types and were a suitable example for the optimum design problem.  
After identifying the design variables, other dependent parameters essential for forming the parametric model were obtained according to the derivations in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Illustration** & **Variable** & **Format** & **Type** & **Symbol** \\ \hline  & Ratio of tower height to span & Continuous & Geometrical & \(X_{1}\) \\  & Cable anchorage distance & Discrete & Topology & \(X_{2}\) \\  & & Longitudinal length at & Continuous & Sizing & \(X_{3}\) \\  & & tower bottom & & \\  & Ratio of cross-sectional traverse & Continuous & Sizing & \(X_{4}\) \\  & & width to longitudinal length & & \\  & Ratio of cross-sectional thickness & Continuous & Sizing & \(X_{5}\) \\  & & to traverse width & & \\  & Cross-sectional size reduction ratio & Continuous & Sizing & \(X_{6}\) \\  & & of tower top to tower bottom & & \\ \hline \end{tabular}
\end{table}
Table 1: Design variables.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Type** & **Symbol** & **Derivation** \\ \hline Length of the side span & Set & \(l_{s}\) & \(=358\) m \\ Length of the main span & Set & \(l_{m}\) & \(=818\) m \\ Distance of standard anchorage & Variable & \(l_{n}\) & \(=X_{2}\) in Table 1 \\ Ratio of densification length & Set & \(\varphi_{e}\) & \(=\frac{l_{s}}{t}=0.65\) \\ Half count of mid-span cables & Dependent & \(N\) & \(=\text{round}\left(\frac{l_{s}-l_{n}\cdot\varphi_{e}}{l_{n}}\right)-1\) \\ Count of symmetric cables & Dependent & \(N_{s}\) & \(=\text{round}\left(\frac{l_{s}-l_{n}\cdot\varphi_{e}}{l_{n}}\right)\) \\ Count of densified cables & Dependent & \(N_{e}\) & \(=N-N_{s}\) \\ Length of none-cable area near the towers & Dependent & \(l_{d1}\) & \(=\left(\frac{l_{m}-l_{m}}{2l_{n}}-\text{round}\left(\frac{l_{m}-l_{m}}{2l_{n}} \right)+1\right)\cdot l_{n}\) \\ Length of none-cable area in the middle & Dependent & \(l_{d2}\) & \(=l_{n}\) \\ Distance of densified anchorage & Dependent & \(l_{d3}\) & \(=\frac{l_{s}\cdot\varphi_{e}}{N_{e}}\) \\ \hline \end{tabular}
\end{table}
Table 2: Determination of dependent design parameters.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 5 Conclusions  
This work designs a readout circuit based on an FPA, which can encode the current signal of the sensor into a spike sequence and supports two different spike-encoding schemes in the same pixel. In contrast to conventional digital FPA readout circuits, which output a digital encoding, our scheme encodes image data as single spikes or spike sequences. Without the need for digital-to-spike conversion, this encoding can be received and processed directly as the input of spiking neurons in SNNs. Furthermore, compared with the single, non-configurable encoding scheme, our scheme can switch between temporal encoding and frequency encoding. The circuit is able to perform both full-array low-pulse encoding and regional pixel high-precision encoding, making it suitable for the various processing modes of SNNs. It is also able to bring the artificial vision system closer to the working mechanisms of the biological vision system when it is matched with different structural SNNs.  
Figure 14: Circuit diagram of the segmented-slope \(V_{ref}\).  
Figure 15: (**a**) The PD signal versus the segmented-slope \(V_{ref}\) signal. (**b**) Linearity analysis of the PD signal and the segmented-slope \(V_{ref}\) signal.  
**Author Contributions:** Conceptualization, W.L.; Methodology, G.C.; Validation, Z.L.; Writing--original draft, D.L.; Writing--review & editing, J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences, grant number [XDA18040400, XDB44000000].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request due to restrictions privacy. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the project containing this study is still in development.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Main Findings
### Ingredients and Their Effects on Gluten-Free Dough Rheology and Bread Quality
#### 3.3.1 Naturally Gluten-Free Flour  
Several alternatives to wheat flour can be used in gluten-free breadmaking. The review found that the most widely-used are rice flour, corn flour, teff flours, and several others that can be obtained from pseudo-cereals such as amaranth and buckwheat. Several authors observed that rice flour has received the most attention, due to its neutral taste, white color, low sodium content, easily-digested carbohydrates, and hypoallergenic properties [3; 15; 17]. Choosing the correct type of rice flour is essential, since different flours can have different impacts on the final quality of products [14; 15; 17]. Corn is another interesting alternative, illustrated by the wide range of gluten-free corn products on the market. Ground corn is used in products such as corn pasta, corn meal, corn starch, masa farina and corn flour, tortillas, pancake mixes, corn chips, and cereal [15].  
Teff seems to be another promising substitute. Teff contains 11% protein, 80% complex carbohydrates, and 3% fat [15; 17]. It is an excellent source of essential amino acids, especially lysine, which is most deficient in wheat [15]. Pseudo-cereals, such as amaranth and buckwheat, are other interesting alternatives due to their nutritional profile, and the increasing number of papers on the topic suggest growing interest. In particular, these pseudo-cereals are characterized by higher phenol and dietary fiber content, which significantly increased the interest and their use in breadmaking. Despite both phenolic compounds and dietary fiber have positive effects on consumer health, the increase of dietary fiber content could have detrimental effects on gluten-free dough rheology and gluten-free bread volume. Finally, flours have been obtained from legumes such as chickpeas, lupins, beans, etc., although they tend to be blended with the above-mentioned base flours.  
In conclusion, gluten-free doughs and bread formulations based on alternative flours could be improved by hydrocolloids, emulsifiers, enzymes, and other approaches. In this context, choosing the most suitable base flour is essential, and further details of this important aspect are reported in Section 4.1.1.
## 6 Conclusions  
In this paper, we investigated the relay selection problem for UAV-assisted VANETs and proposed a relay selection protocol, namely LQFC. The LQFC protocol takes into full consideration the effect of the LQoS and the NFC on the network performance when making relay selection. For our proposed LQFC protocol, we define a utility function by jointly considering the NEF and the message TTL, based on which the LQFC protocol can effectively delete the redundant copies. Further, we analyze the security of our designed protocol. The simulation results have demonstrated that the LQFC protocol can achieve the significant performance gain in comparison with other schemes, which can be widely applied in smart city and Internet of Things (IoT) domains.  
In addition to those listed in the previous sections, there are many research issues that need further study. In this paper, we investigated the relay selection problem by jointly considering the LQoS and the NFC, and the cooperative secure transmissions are ignored. We ignored the cooperative secure transmissions since some messages in the VANETs can be public. However, for some private  
Figure 6: Network performance vs. mobility model.  
applications, the security of message transmission is very important, which can be an interesting aspect for further investigation.  
Conceptualization, R.Z., D.Z., D.W., and Y.H.; methodology, Y.H., R.Z., and D.Z.; software, Y.H., D.W., and R.Z.; validation, Y.H. and D.Z.; formal analysis, X.T.; investigation, Y.H. and R.Z.; resources, D.W. and X.T.; data curation, Y.H. and D.Z.; writing--original draft preparation, Y.H. and R.Z.; writing--review and editing, D.Z., D.W., X.T., and R.Z.; visualization, Y.H. and R.Z.; supervision, D.Z., D.W., and X.T.; project administration, R.Z.; funding acquisition, R.Z. and D.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Foundation of the Science, Technology, and Innovation Commission of Shenzhen Municipality under Grant JCYJ20190806160218174, in part by the National Natural Science Foundation of China under Grants 61571370, 61601365, 61901381, 61801388, 61901378, and 61901379, in part by the Science and Technology Research Program of Shaanxi Province under Grants 2018 ZDDCXL-GY-03-04, 2019 ZDLGY07-10, 2019 JQ-253, 2019 JQ-631, and 2019 JM-345, in part by the Advance Research Program on Common Information System Technologies under Grant 315075702, in part by the Postdoctoral Science Foundation of China under Grants BX20180262, BX20190287, 2018M641020, and 2018M641019, in part by the Fundamental Research Funds for the Central Universities under Grants G2019 KY05302, 31020180 QD095, and 3102017 QQD091, in part by the Foundation of the State Key Laboratory of Integrated Services Networks of Xidian University under Grant ISN21-06, in part by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under Grant CX202037, and in part by the Program of China Scholarships Council under Grant 202006290166.  
The authors declare no conflict of interest.
# Holistic Spatial Reasoning for Chinese Spatial Language Understanding
## 3 Methodology
### Task Definition  
We explored three types of tasks in Chinese SLU: abnormal spatial semantics recognition (ASpSR), spatial role labeling (SpRL), and spatial scene matching (SpSM). Given the input textual description, \(X\), the ASpSR task aims to output whether \(X\) has unreasonable expressions, such as self-contradictions and counterintuitive expressions. SpRL is similar to semantic role labeling (SRL) but replaces the semantic role labels with a set of spatial role labels, such as \"Spatial Entity\", \"Event\", and \"Time\". The pre-defined spatial role labels in SpaCE are listed in Table 1. Finally, the SpSM task takes two different descriptions, \(X_{1}\) and \(X_{2}\), and determines whether \(X_{1}\) and \(X_{2}\) describe the same scene.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Role Label** & **Type** & **Description** & **Role Label** & **Type** & **Description** \\ \hline Spatial Entity & Text Span & The entity described in the scene & Direction & Text Span & The direction of a dynamic entity \\ Reference Entity & Text Span & The entity used as the reference object & Orientation & Text Span & The orientation of an entity \\ Event & Text Span & The event related to the spatial entity & Component & & The location of part of an entity with \\ Facticity & Extra Label & Whether the description is true & Part & Text Span & The part of an entity \\ Time & Text Span & The time that the entity is in some spatial state & Shape & Text Span & The shape of an entity \\ Location & Text Span & Description of the location of a static entity & Route & Text Span & The route of a moving entity \\ Starting Point & Text Span & Description of the starting location of a dynamic entity & Distance & Text Span & The distance between entities \\ Destination Point & Text Span & Description of the ending location of a dynamic entity & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spatial role labels in SpaCE.
# Dynamic Energy Efficient Control of Induction Machines Using Anticipative Flux Templates
## 7 Measurements
### Model Predictive Solution  
The model predictive solution briefly introduced in Section 5 is now used for a comparison with a fmincon based solution without anticipation, in order to compare these two optimization approaches. Again, the motor from Table A1 was used. A sampling time of \(\Delta t=250\,\text{\mu s}\) was used as a compromise for the following two reasons: A lower sampling time was not possible because the GRAMPC optimizer became unstable in this setup. A higher sampling time was not possible either because then, the optimal trajectory given by GRAMPC deviated more from the steady state optimum. At \(t=0.2\,\text{s}\), a speed ramp command from \(500\,\text{rpm}\) to \(1500\,\text{rpm}\) in \(0.4\,\text{s}\) was given. In Figure 18, it can be observed that the steady state optimal flux calculated by GRAMPC is closer to that of the offline calculated trajectories. The values come closer on increasing the weightage factor of power loss in the objective functional. The ramp starts at \(t=0.2\,\text{s}\), but GRAMPC shows a tendency to first start increasing the flux and then the speed. This validates the observations made in Section 3.3, that the increase in flux prior to the torque step helps in reducing the losses in transient states.  
Figure 17: Measurement results for anticipative flux increase.
# Investigation of the Pressure Coefficients Correlation Field for Low-Rise Building Roofs
## 2 Experimental Setup  
The experimental study was carried out in the CRIACIV (Inter-University Research Centre on Building Aerodynamics and Wind Engineering) wind tunnel in Prato, Italy, in 2008. The results of these tests have been discussed in detail [29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. In total, sixteen different geometries with four plan shapes (square, rectangular, circular, and elliptical), two different roof curvatures, and two different maximum heights of the roof from the ground were investigated, under sixteen wind angles from 0\({}^{\circ}\) to 337.5\({}^{\circ}\) with a step of 22.5\({}^{\circ}\). The facility is an open-circuit tunnel with a 2.30 m \(\times\) 1.60 m test chamber. For sake of brevity, the present paper discusses the experimental results for four different models with a rectangular plan (denoted by R in Figure 1, according to notation in [30]), two different curvatures (C and F in Figure 1, meaning \"more curve\" and \"flatter\", respectively), and two different heights (L and H in Figure 1, indicating \"low\" and \"high\", respectively).  
The selected wind profile corresponds to the terrain category III as described in [40] and [35]. Results were processed to provide mean, maximum, and minimum pressure coefficient maps [34; 38]. The results were incorporated in [39].  
The geometry of the models is summarized in Table 1, where \(L_{1}\) and \(L_{2}\) are the building plan sizes, \(f_{1}\) and \(f_{2}\) the upward and downward sags of the roof, respectively, and \(H\) is the maximum roof distance from the ground as shown in Figure 1. The number of pressure taps on the roof was 95 and they were connected to the pressure scanners by polymeric material tubes. The pressure series were acquired at a sampling frequency of 252 Hz for a duration of 30 s [34]. The turbulence intensity ranged between 11% and 12%.
## 2 Hybrid Path Planning Based on Improved ABC and BAS Algorithms
### Local Path Optimal Optimization Based on BAS Algorithm  
#### 2.2.1 The Optimized BAS Algorithm  
The BAS algorithm is a novel heuristic algorithm based on simulating the mate and foraging behavior of beetles. If the ABC algorithm generates an excessive number of global waypoints, the BAS algorithm, with its strong local search capability, can obtain multiple locally optimal waypoints. This algorithm determines the optimal solution by comparing the fitness values of the current and the next waypoints. In this section, improvements are made to the optimization direction and fitness value confirmation of the BAS algorithm. The introduction of random reverse learning and a greedy selection mechanism enhances the reliability of the algorithm's waypoint selection and improves the accuracy of waypoint optimization. The beetles' search direction vector \(\overrightarrow{d}\) is defined using the random search formula  
\[\overrightarrow{d}=\left|\frac{\operatorname{rand}(b,1)}{\left\|\operatorname {rand}(b,1)\right\|}\right|, \tag{8}\]  
where \(b\) is the spatial dimension.  
The initial position of the beetle in the \(b\)-dimensional space is shown as follows:  
\[c^{0}=(c_{1}^{0},c_{2}^{0},\cdot\cdot\cdot,c_{b}^{0}). \tag{9}\]  
At the \(e\)-th iteration, the beetle's tentacles determine the left and right directions at the current position as follows:  
\[\left\{\begin{array}{l}c_{1}^{e}=c^{e}+\frac{c_{e}^{d}}{2\overrightarrow{d}} \\ c_{r}^{e}=c^{e}-\frac{c_{e}^{d}}{2\overrightarrow{d}}\\ c_{e,d}^{d+1}=0.95c_{e}^{d}+0.01\end{array}\right., \tag{10}\]  
where \(c_{e}^{d}\) denotes the distance between the two tentacles of the beetle at the \(e\)-th iteration; and \(c_{e}^{d+1}\) is the updated length of the tentacles.  
The beetle position of the next iteration is updated as  
\[\left\{\begin{array}{l}c^{e+1}=c^{e}+\text{step}^{\overrightarrow{d}} \overrightarrow{d}\text{sign}[k(c_{1}^{e})-k(c_{r}^{e})]\\ \text{step}^{e+1}=\mu\text{step}^{e}\end{array}\right., \tag{11}\]  
where \(step^{e}\) denotes the iterative step size of the beetle in the \(e\)-th iteration, which determines the convergence speed of the algorithm's optimization search; \(\mu\) is the step factor; \(sign(\cdot)\) is the sign function; and \(k(c_{l}^{e})\) and \(k(c_{r}^{e})\) denote the objective function values corresponding to the left and right directions at the \(e\)-th iteration, respectively. As the number of iterations increases, \(k(c_{r}^{e})\) continuously updates the convergence value until the optimal objective function solution is obtained.
## 4 Experimental Evaluation
### Ablation Results  
#### 4.5.1 The Validity of CCA  
In the sentiment classification output module, this study uses the CCA enhancement technology to maximize the correlation between text modality features, video modality features, and cross-modal fusion features. As shown in the upper part of Table 5, compared with the model without CCA, the accuracy of the UA-BFET model with CCA increased by 0.6% in Acc-2 and 0.8% in F1-Score. This indicates that using CCA to enhance the correlation between different modalities can effectively improve the accuracy of cross-modal sentiment analysis in unaligned data scenarios.