esa-sceva/satcom-chunk-collection huggingface dataset:
# A Structure for Accurately Determining the Mass and Center of Gravity of Rigid Bodies
## 4 Simulation Results and Discussion  
A simulation was carried out to demonstrate the feasibility of the proposed method. As illustrated in Figure 10, the solution employs a system of 27 equations. The corresponding unknown variables were solved iteratively by using a nonlinear numerical technique [19]. The inputs to the solver include \(B_{0i}\), \(P_{01}\), \(P_{02}\), \(\overrightarrow{n_{p3}}\), \(\overrightarrow{||i_{1}||}\), \(F\), and \(M\). The outputs of the algorithm include \(B_{i}\), \(P_{i}\), \(F_{i}\), \(T_{BP}\) and \(R_{BP}\).  
With the proposed mathematical modeling, we analyze the traditional structure. Setting the offsets between axis 1 and axis 2, as shown in Figure 5, to 0 mm, 0.05 mm, 0.10 mm,..., 1.00 mm, the side forces loading on the three load cells were obtained and shown in Figure 11. The offsets of the loading axes between the designed axes are listed in Figure 12. The inputs are listed in Appendix B.  
From Figures 11 and 12, we can see that the traditional structure provides high accuracy without lateral forces in the ideal case. However, when taking the manufacturing or assembling errors into account, i.e., making the axis of the ball grooves on the base-frame shift the axis of ball grooves on the platform-frame, the results show that the offsets of the loading axes between the designed axes increased, so do the side forces.  
The improved structure was also analyzed. With the offset or not, the side forces loading on the three load cells were always less than 0.01 N, and the offsets of the loading axes between the designed axes were less than 0.001 mm, which means that the accuracy of the improved method is higher than the traditional one.  
Figure 10: The solution for the proposed mathematical model.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 1 Introduction
Escalator generates a conflict-free schedule that allows all nodes in the network to send packets to the sink in every slotframe cycle. Escalator minimizes the end-to-end delay by creating a consecutive timeslot schedule along the packet forwarding path from a node to the sink. We implement Escalator and evaluate the performance with the existing autonomous scheduling schemes in a testbed. We also perform an extended evaluation in various aspects through simulations. In the evaluation results, Escalator shows better performance in the packet delivery ratio and the end-to-end delay than the existing autonomous schemes. In terms of the energy efficiency, the duty cycle of a node is more susceptible to the size of the routing table entries compared to the existing schemes. In other words, the duty cycle of the node in Escalator is generally similar to that of the existing schemes, but the duty cycle becomes higher as the routing table entry increases. The main contributions of this paper are as follows:  
1. We propose Escalator, a new autonomous timeslot and channel scheduling scheme for the convergecast in TSCH-based WSNs with RPL. 2. We prove that Escalator generates a conflict-free schedule. 3. We show through experiments that Escalator improves the packet delivery ratio, the end-to-end delay and the average energy efficiency for the convergecast compared to the existing autonomous schemes. The rest of this paper is organized as follows. In Section 2, we introduce TSCH and RPL, which form the basis of this study. In Section 3, the existing timeslot scheduling algorithms are discussed. Section 4 proposes the overall operation of Escalator. In Section 5, we prove the conflict-freeness of the generated schedules and analyze the packet transmission delay and bandwidth. In Section 6, we evaluate the performance of Escalator using a testbed and simulations compared to the existing autonomous scheduling schemes. Finally, the paper is concluded with future research directions in Section 7.
## 2 Sensory and Communication System Architecture: X-IoCA  
This section presents the Internet of cooperative agents architecture (X-IoCA), which is shown in Figure 1. The purpose of X-IoCA is to serve as a cloud robotics framework for the effective integration of heterogenous sensor networks and robots, multi-edge computing, and 5G communications in field applications requiring the cooperation between human and robot teams. Thus, agents are entities that carry or wear end-devices. Agents can be humans, different types of robots (e.g., UGVs and UAVs), vehicles, and even sensorized animals.  
For the sake of clarity, Figure 1 refers to the different elements of the architecture with specific colors and shapes. The X-IoCA architecture is composed of sensing and computing elements, divided into two fundamental entities: an Internet of robotic things (IoRT), which is the source of sensor data, and a feedback information system (FIS) for data monitoring, storing, and processing. The FIS is at the top of Figure 1, meaning that it has less location awareness [34; 35], i.e., the processing performed at FIS level is perceived as more distant and computationally complex by the agents. Conversely, the IoRT (at the bottom) has more location awareness. The fog (yellow area in Figure 1) includes all elements, except end-devices (i.e., sensors and actuators) and the cloud. Fog elements consist of local edges (depicted as green rounded rectangles) and cloud edges (green ellipsoids) as well as switches and 5G customer-premises equipments (CPEs) to route the data traffic between their respective PCs or hosts (depicted as incoming red arrowheads in Figure 1).  
Both local and cloud edges are MECs. They consist of computing devices (e.g., PCs or smartphones) that can be used within either the IoRT or the FIS. Moreover, for communication, an MEC has one or more user-equipment devices (e.g., a 5G CPE or a 5G  
Figure 1: Internet of cooperative agents architecture (X-IoCA).  
smartphone). Local edges correspond to local hosts in the operation area, while cloud edges are remote MECs. The MECs inside the FIS are distributed between its two elements: X-FIS and ROS-FIS, each dedicated to a specific type of information flow coming from the two parts of the IoRT: the H2 WTN and the ROS-based distributed network. Similarly, the local edges inside the IoRT are distributed between the H2 WTN and the ROS network. Thus, the information is not necessarily at the physical site where the data are acquired.  
This means that a given agent can use a resource more or less distant from it, which entails complying or not with a series of requirements, such as latency, bandwidth, storage capacity, fault tolerance, real-time capabilities and response time to attend an event [10]. The 5G communication system is represented by a thick red line, to which all CPEs and switches are connected.  
The IoRT and the FIS are discussed in the following subsections.
# A New Methodology for Type Synthesis of Planar Linkages for Exoskeletons up to Five Angular Outputs
## 5 Generation of Multiple Angular Outputs
### Generation of Three Angular Outputs Using Series Connection  
Three angular outputs can be generated from six-bar linkages that have two angular outputs as represented in Figure 6a. The existing RRR dyad contains: angular output '5', cut link '6' and three revolute joints namely: i, j and k. A new angular output can be achieved by adding another RRR dyad using series connection as illustrated in the following steps:  
Figure 6: Generation of three angular outputs using series connection. (**a**) Initial graphical topology having two outputs; links: 4, 5; (**b**) new output ‘link-7’ is added; (**c**) all available topologies of three outputs with series connection.  
1. Link '7' is added directly above the current cut link '6' to present the newer angular output as a hollow circle as illustrated in Figure 6b.
2. Joint 'k' is deleted and reconnected directly with the newer angular output '7'.
3. Link-8 is added at the left of the new angular output as shown in Figure 6b.
4. The joint between the newer output '7' and its adjacent cut link '8' is represented by solid edge 'R1'.
5. The remaining two revolute joints (R2, R3) have some alternatives to connect newer output and its adjacent cut link with other links, excluding the connections that produce rigid sub-chains. These alternatives generate the available topological graphs that can achieve the required number of angular output motions as shown in Figure 6c.
6. Linkage diagrams can be established from their corresponding topological graphs as illustrated in Section 4.4.  
Generally, six-bar linkages having two angular outputs can generate 16 available eight-bar linkages with three angular outputs.
## 2 System Model  
Let us consider a land-mobile satellite communication system with a relay and two terrestrial users \(D_{1}\), \(D_{2}\) as Figure 1. The relay are equipped with a single antenna while the satellite and terrestrial users are equipped with \(M\) and \(N_{i}\) antennas, respectively. The key parameters are described in Table 1. The ground users employ the power-domain NOMA technique and decode the superimposed signal transmitted by satellite. The wireless link from terrestrial relay to \(D_{i}\), is denoted by \(h_{i}\). The channel from the relay to users is assumed to undergo Nakagami-\(m\) fading with fading severity \(m_{i}\), average power \(\Omega_{i}\) and channel gains are \(\Lambda_{i}=\frac{\Omega_{i}}{m_{i}}\). Furthermore, the satellite links are experienced by Shadowed-Rician fading [31]. It is further assumed that the receiving nodes are distributed as additive white Gaussian noise (AWGN) with mean zero and variance \(N_{0}\). The overall communication operates in two orthogonal time slots and it is considered that no direct link exists between the satellite and the NOMA users. During the first time slot, the satellite sends the superimposed signal to the terrestrial relay. The terrestrial relay deploys the time switching-based relay energy harvesting protocol to harvest energy from the radio signal of the satellite. During the second time slot, the NOMA users decode their individual signals with the help of SIC. For the sake of detailed performance evaluation, we consider AF and DF relaying protocols. The working of these relaying protocols in the considered system model is elaborated in following paragraphs.
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 9 Signal Transduction in Response to LPS Defects
Furthermore, mutations in some of the conserved early glycosyltransferases encoding genes, like _vaaF_, _vaaG_, _vaaO_ or _vaaP_ whose product phosphorylates Heptoe I, or in the absence of RfaH transcriptional factor whose product is required to ensure transcription/translation and prevent premature termination of transcription of the _vaaQ_ operon also induce the _rpoE_ transcription [11]. Consistent with sensing of correct assembly of LPS by RpoE, such a phenotype was used to identify LapA and LapB assembly factors, since the lack of LapB causes increased constitutive induction of RpoE and Cpx regulons quite like \(\Delta\)_vaaA_ mutants [9]. As mentioned above, \(\Delta\)(_lapA lapB_) mutants produce more LPS due to stabilization of LpxC, accumulate precursor species of LPS with incomplete core, defects in acylation of lipid A and reduced amounts of LptD in the OM. Thus, given the overwhelming evidence, it is fair to conclude that severe defects in either early steps in LPS core biogenesis or when LPS is composed of only Kdo\({}_{2}\)-lipid IV\({}_{\rm A}\) or when LPS assembly is defective lead to the induction of RpoE envelope stress response. Figure 3: Pathways of sensing defects in LPS biosynthesis/assembly: sensing via the Rcs phosphorelay system, which leads to the induction of transcription from the sigma 70-regulated _rpoE_P3 promoter requiring RcsB as activator and the activation of Qse TCS induces the _rpoE_P2 promoter with QseF acting as an activator of RpoN. Severe defects in LPS induce the Rcs phosphorelay cascade leading to phosphorylation of RcsB. Phosphorylated RcsB acts as an activator for the transcription directed from the _rpoE_P3 promoter leading to the induction of RpoE-mediated stress response. The overlapping _rpo_EP2 promoter also responds to LPS defects albeit to a lower extent. Severe defects in LPS also induce the CpxR/A TCS. \(\rm{Eq^{E}}\) is required to maintain envelope integrity by regulating expression of genes whose products are involved in protein folding in the periplasm, assembly of OMPs (_hum_ genes) and certain genes whose products are involved in LPS biosynthesis/modifications. Release of LPS from the Lpt system can lead to association of LPS with RseB, which relieves inhibition of RseA proteolysis and can lead to accumulation of free RpoE without RseA.
## 4 Materials and Methods  
### Gathering of Omics Data from Cnidaria  
A total of 8 proteomes derived from genomic data and 104 transcriptomes from 111 species of Cnidaria were collected, encompassing 78 Anthozoa and 33 Medusozoa (Supplementary Table S1). These transcriptomes were obtained from diverse tissues--whole body/non-specific (\(n=70\)), tentacles (\(n=32\)), and nematocysts (\(n=2\)). Notably, the sea anemome _Heterodactylh hemprichii_ had data available from both tentacles and nematocysts. Proteomic data were obtained from the UniProt Proteome Database ([[https://www.uniprot.org/](https://www.uniprot.org/)]([https://www.uniprot.org/](https://www.uniprot.org/)), accessed on 5 January 2024). Transcriptomes were retrieved from the Sequence Read Archive (SRA) and Transcriptome Shotgun Assembly (TSA) databases at the National Centre for Biotechnology Information (NCBI) ([[https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)]([https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)), accessed on 5 January 2024). FASTq files associated with each SRA entry were then downloaded through the European Nucleotide Archive ([[https://www.ebi.ac.uk/ena/browser/](https://www.ebi.ac.uk/ena/browser/)]([https://www.ebi.ac.uk/ena/browser/](https://www.ebi.ac.uk/ena/browser/)), accessed on 5 January 2024). To ensure high-quality data, we employed Trimmomatic v0.39 [60] for read trimming and adapter removal, followed by quality control with FastQC v0.12.0 [61]. De novo transcriptome assembly was performed for both single-end and paired-end reads using Trinity v2.15.1 [62]. Finally, BUSCO v5.5.0 [63] assessed assembly completeness using the Metazoa lineage (Supplementary Table S2).
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
###### Abstract  
This paper introduces the principle and key technology of single radio frequency (RF) link Multiple-Input Multiple-Output (MIMO) system based on a switched parasitic antenna (SPA). The software SystemVue is adopted for signal processing and system-level simulation with merit of strong operability and high efficiency, which provides tools for the single RF link MIMO system research. A single RF link of a 2 \(\times\) 2 MIMO system based on the switch parasitic antenna is proposed in this paper. The binary codes are modulated to the baseband Binary Phase Shift Keying (BPSK) signals and transmitted with a 2.4 GHz carrier frequency. The receiver based on the super-heterodyne prototype adopts the channel equalization algorithm for restoring symbols, and it can effectively reduce the system error rate. The simulation results show that the MIMO system built on the platform can achieve equivalent performance with traditional MIMO system, which validates the effectiveness of the proposed scheme. The switched parasitic antenna and equalization algorithm provide new research ideas for single RF link MIMO system and have theoretical significance for further research.  
Multiple-Input Multiple-Output (MIMO); single link; switched parasitic antenna (SPA); bit error rate (BER) +
Footnote †: journal:  
_Article_  
Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 3 Methodology
### Direction-Aware Detection Head
#### 3.3.2 Prediction Branches  
Based on the direction-aware features, we implement three specialized prediction branches:  
Classification Branch  
This branch identifies text and non-text regions:  
\[P_{cls}=\sigma(f_{cls}(F_{dir})) \tag{12}\]  
where \(f_{cls}\) consists of the following:  
* 3 \(\times\) 3 convolution (256 \(\rightarrow\) 256 channels);
* Batch normalization and ReLU activation;
* 1 \(\times\) 1 convolution (256 \(\rightarrow\) 1 channel);
* Sigmoid activation for binary classification.  
Regression Branch  
This branch predicts bounding box coordinates for the layout elements:  
\[P_{reg}=f_{reg}(F_{dir}) \tag{13}\]  
where \(f_{reg}\) consists of the following:  
* 3 \(\times\) 3 convolution (256 \(\rightarrow\) 256 channels);
* Batch normalization and ReLU activation;
* 1 \(\times\) 1 convolution (256 \(\rightarrow\) 4 channels) for (x, y, width, height);
* Exponential activation for width and height to ensure positive values.  
Direction Branch  
This branch specifically focuses on text orientation prediction:  
\[P_{dir}=\text{softmax}(f_{angle}(F_{dir})) \tag{14}\]  
where \(f_{angle}\) consists of the following:* 3 \(\times\) 3 convolution (\(256\to 256\) channels);
* Batch normalization and ReLU activation;
* 1 \(\times\) 1 convolution (\(256\to 180\) channels);
* Softmax activation for angle classification.  
The direction branch treats orientation prediction as a 180-class classification problem, covering angles from 0\({}^{\circ}\) to 179\({}^{\circ}\). This approach provides more stable training compared to direct regression and better handles the periodicity of angles.
## 2 Materials and Methods
### Participants  
The study was conducted on a group of patients with NAFLD diagnosed by a gastroenterologist based on an ultrasound examination (USG) with liver steatosis observed, which was confirmed to not be associated with alcohol overconsumption--alcoholic fatty liver disease (AFLD) was ruled out.  
The inclusion criteria were as follows:  
* Patients aged >18 years;
* NAFLD diagnosed and confirmed by gastroenterologist;
* Liver steatosis diagnosed and confirmed by gastroenterologist;
* Daily alcohol intake lower than 20 g--assessed based on the dietary interview;
* Excessive body mass--assessed based on body mass index (BMI), for BMI \(\geq\) 25 kg/m\({}^{2}\).
* The exclusion criteria were as follows:
* Pregnancy or lactation;
* Oncological treatment;
* Diseases potentially responsible for liver steatosis, including hemochromatosis, viral hepatitis B and C, autoimmune hepatitis and [PERSON]'s disease;
* Other factors potentially responsible for liver steatosis, including medications and toxins.  
Participants of the study were recruited and qualified for the study by gastroenterologists, and they were informed about the study either by them or based on the local press advertisement.  
In total, 21 patients were recruited for the study, but within the study, 1 of them did not consume the provided daily _Silbyum Marianum_ doses, resulting in exclusion from the study. The final sample size analyzed within the study was 20 participants, including 11 female participants and 9 male participants. The general baseline characteristics of the studied group of participants are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & \multicolumn{1}{c}{**Total (_n = 20_)**} & **Females (_n = 11_)** & **Males (_n = 9_)** & _p **_** \\ \hline Age (years) & Mean \(\pm\) SD & 52.3 \(\pm\) 10.9 & 56.1 \(\pm\) 11.9 & 47.6 \(\pm\) 7.9 & \\  & Median (min—max) & 51.0 (36–76) & 55.0 (36–76) & 47.0 (38–64) & 0.0869 \\ BMI (kg/m\({}^{2}\)) & Mean \(\pm\) SD & 36.5 \(\pm\) 5.9 & 39.3 \(\pm\) 6.1 & 33.2 \(\pm\) 3.9 & \\  & Median (min—max) & 36.3 (26.6–55.7) & 38.8 (32.8–55.7) * & 34.2 (26.6–37.0) & 0.0873 \\ \hline \hline \end{tabular}  
* nonparametric distribution (Shapiro–Wilk test;_p \(\leq\) 0.05); ** compared while using t-Student test or Mann–Whitney test (based on distribution).  
\end{table}
Table 1: The general baseline characteristics of the studied group of participants.
# Knee Osteoarthritis Classification Using 3D CNN and MRI
## 3 Results
### 2D CNN Using X-ray Data  
For the same group of patients, we compared the performance of various 2D CNN architectures that use X-rays. Table 5 compares the performance of each architecture for the 5-category classification and the 2-category OA/non-OA classification.  
Based on the above experiments, we selected InceptionResNetV2, given its best performance for the averages of the 5-category and 2-category accuracy percentages. Tables 6 and 7 show the confusion matrix for InceptionResNetV2 for the 5-category classification and the 2-category classification, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crop** & **Slice Removal** & **Dropout** & **Res Layers** & **Acc** \\ \hline \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 86.5\% \\ X & \(\surd\) & \(\surd\) & \(\surd\) & 81.5\% \\ \(\surd\) & X & \(\surd\) & \(\surd\) & 82.0\% \\ \(\surd\) & \(\surd\) & X & \(\surd\) & 80.0\% \\ \(\surd\) & \(\surd\) & \(\surd\) & X & 73.0\% \\ \hline \hline \end{tabular} Note: “\(\surd\)” indicates the feature is included; “X” indicates the feature was removed.  
\end{table}
Table 4: Effects on accuracy for preprocessing steps and architecture of the 3D CNN model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Act.\(\backslash\)Pred.** & **Non-OA** & **OA** & **Total** & **Acc.** \\ \hline Non-OA & 71 & 9 & 80 & 88.8\% \\ OA & 18 & 102 & 120 & 85.0\% \\ Total & 89 & 111 & 200 & 86.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for the 2-category OA/non-OA classification by the 3D CNN model on the validation set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **5-Category Accuracy** & **2-Category Accuracy** \\ \hline VGG16 & 20.0\% & 60.0\% \\ VGG19 & 28.0\% & 60.0\% \\ ResNet50V2 & 49.5\% & 69.0\% \\ ResNet101V2 & 42.5\% & 77.5\% \\ ResNet152V2 & 20.0\% & 60.5\% \\ InceptionV3 & 54.0\% & 80.5\% \\ InceptionResNetV2 & 55.5\% & 80.0\% \\ DenseNet121 & 55.0\% & 70.0\% \\ DenseNet169 & 45.0\% & 78.0\% \\ DenseNet201 & 45.0\% & 81.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of 2D CNN architectures using X-ray data.  
The last columns of Tables 6 and 7 are the results of MR images with 3D CNN models copied from Tables 2 and 3 in order to make it easier to compare the two imaging modalities. Overall, MRI outperformed X-ray in both the 5-category (60.5% vs. 55.5%) and 2-category (86.5% vs. 80.0%) classifications. From Table 6, we can see that MRI has higher accuracy in classifying the categories of KL = 0, 1, and 3, but lower in the KL = 2 and 4 categories. Correspondingly, in Table 7, the accuracy of MRI is much higher than X-ray in classifying the non-OA category (88.8% vs. 70.0%) while a little lower in classifying the OA category (85.0% vs. 86.7%).