esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation and Design of Three 5G Antennas
## 5 Design of an Eight-Element MIMO Array Antenna for Mobile Terminals with a Metal Frame
### Fabrication and Measurement of the Eight-Element MIMO Array Antenna
#### 5.3.1 Fabrication of the Antenna  
To facilitate the fabrication and soldering of the antenna's metal frame, soldering points were designed between the antenna array and the metal frame, as shown in Figure 46, which displays the model for antenna fabrication.  
Figure 44: Simulated S parameters for the three different layouts of the “ring-slot” antenna structure. (**a**) Layout 1, (**b**) Layout 1, and (**c**) Layout 3.  
Figure 45: The impact of the spacing (d) between the “ring-slot” unit modules on the S parameters.  
Figure 44 shows the actual fabrication of the antenna array, including Figure 47a for the front view, Figure 47b for the back view, and Figure 47c for the side view. Soldering the 50 \(\Omega\) SMA feeders directly to the antenna substrate or the metal frame would have a significant impact on the antenna's feeding performance and result in stronger mutual coupling between antenna elements, preventing the antenna from achieving its simulated performance. For the loop antennas (Ant1/3/5/7), a feeder line is routed from the outside of the metal frame, and an SMA feeder head is mounted on the exterior of the antenna. Likewise, for the slot antennas (Ant2/4/6/8), feeder lines are brought in through the metalized holes, connecting to L-shaped feed branches for feeding. This soldering method keeps the SMA feeder heads at a distance from the antennas to prevent mutual coupling and maintain antenna performance.
## 4 Experiments and Analysis of Results
### Analysis of Results
#### 4.4.6 Comparison of Model Computational Efficiency  
To validate the model's effectiveness in addressing redundant computations, the training times and inference times of MD-SERel and the comparison models were evaluated using the pesticide dataset PRD and the DuIE dataset, with a batch size of 4 and a maximum sentence length of 200. The results are presented in Table 7.  
As shown in Table 7, the MD-SERel model demonstrated superior efficiency and accuracy on both the pesticide dataset PRD and the general dataset DuIE. Compared to CasRel, MD-SERel achieved an inference speed 1.2 times faster on the PRD. On the DuIE dataset, this advantage increased to 1.5 times the speed. This was because the DuIE dataset contained a larger number of relation types, and CasRel first extracted head entities in a phased manner and then traversed all relations to extract the corresponding tail entities. As the number of relations increased, its inference time significantly increased. In contrast, the MD-SERel model reduced sensitivity to the number of relations by processing predefined relations in parallel, thereby effectively shortening training and inference times and demon  
Figure 7: The results for different overlapping patterns of triples.  
-strating significant computational advantages. Compared to CasRel, the BiRTE model not only matched the corresponding tail entity from the head entity but also reversely matched the corresponding head entity from the tail entity, finally traversing the relationships to generate triplets. This bidirectional matching strategy increased its computational time and resource consumption. However, by forming entity pairs complementarily from two directions, it reduced the errors caused by relying solely on head entities, achieving higher extraction accuracy. In contrast to BiRTE, the MF-SERel model adopted a single-stage extraction strategy, eliminating the need to traverse all relationships sequentially. Instead, it extracted entity-relation triplets in parallel, improving computational efficiency. Additionally, MF-SERel offered richer feature representation capabilities, capturing the text's complex logical structure. On the PRD and DuIE datasets, MF-SERel achieved F1 score improvements of 1.5 and 1.4 percentage points over BiRTE, respectively, demonstrating its effectiveness in handling complex relationships and addressing redundant computation challenges.
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
## 5 Experiments  
In this section, experiments are conducted to quantify the antenna pattern effect in the image and verify the precision of the proposed correction method. From the analysis in Section 3, the maximum radiometric variation occurs in the azimuth direction of the swath edge, and the minimum at the reference squint angle. Consequently, the experiments are designed as follows: First, point targets are set in the scene, distributed in the azimuth direction along the swath edge and the range direction of the scene center. The RCS of each point target is the same. Next, the echo signals are generated from the simulation of the physical process of the AMAO spaceborne SAR acquisition. Then, the image is focused and radiometrically calibrated. Two antenna pattern-correction approaches are respectively applied, which are the proposed approach accounting for the spatial-variant antenna pattern and the conventional one using the reference EAP. Finally, target image energy is measured by integrating the power response over a sufficiently large area. The relative deviation of these measurements represents the antenna pattern effect and correction precision.  
The main simulation parameters are listed in Table 1. The block diagram of the simulation is shown in Figure 6, and Figure 7 shows partial imaging results of point targets at different positions in the scene.  
Figure 6: Block diagram of simulation processing.  
Figure 7: Partial imaging results of point targets at different positions in the scene. (**a**) Target located at the scene center. (**b**) Target at 0 m in azimuth and 4500 m in range. (**c**) Target at 0 m in azimuth and \(-\)4500 m in range. (**d**) Target at \(-\)4500 m in azimuth and \(-\)4500 m in range.
# Polymer Models of Chromatin Imaging Data in Single Cells
## 3 Results
### All-against-All Comparison between Single-Molecule Imaged and Model-Derived 3D Structures  
As a further step, we aimed to investigate the structural predictions of our polymer models at the single-molecule level. In particular, to assess whether our models do provide a bona-fide representation of the imaged chromatin structures, we used a computational method based on the root-mean-square deviation (RMSD) criterion [39; 46]. In brief, the algorithm performs a roto-translational alignment of two conformations (e.g., experimental and model derived) by minimizing the RMSD of their particle positions; in this way, each experimental 3D structure from imaging is univocally associated to a corresponding best-matching conformation of the models by searching for the minimum RMSD of their coordinates. To fairly compare model and imaged 3D conformations, a z-score is performed on both sets of coordinates. To efficiently run the RMSD comparison, we used the MDAnalysis Python library, which employs the fast quaternion-based characteristic polynomial (QCP) algorithm to calculate the least RMSD between two structures [47]. In Figure 3 we report an example of best-matching conformations identified by the RMSD method: in this case, for instance, the single-cell imaged distance map is characterized by two main TAD-like structures, corresponding to distinct globules in 3D space, which are consistently recapitulated by the different models (Figure 3).  
To check the statistical significance of the RMSD analysis, we considered as control the RMSD distribution between random pairs of imaged conformations (Figure 4). For each type of model, we found that the experiment-model best-match distribution is statistically distinguishable from the control (Figure 4a, two-sided Mann-Whitney test \(p\)-value = 0), with less than 5% of the entries of the former distribution that fall above the first decile of control (Figure 4b) [28]. Overall, the all-against-all RMSD analysis show that both loop  
Figure 2: The genomic boundary probability function of the IMR90 locus is consistently recapitulated by the different models. Adapted from [28].  
extrusion and phase-separation single-molecule conformations significantly represent the ensemble of single-cell imaged 3D structures of the studied IMR90 cell region.
## 1 Introduction
However, the system does not support rotation, as the spatial arrangement of multiple Txs induces error in the demodulation. [PERSON] et al. [17] proposed an RS-MIMO for the IoT using grouped LED arrays as a transmitter to provide rotation support in a flicker-free RS-OCC system. The maximal throughputs of 1.9 and 0.9 kbps were obtained for 0\({}^{\circ}\) and 50\({}^{\circ}\) rotations; however, the throughput was also dependent on the distance between Tx and Rx. LED array-based active marker design was proposed by [PERSON] et al. [18] to provide efficient marker detection for augmented reality and positioning applications under low-ambient-light conditions. In [6], a 2D LED array-based OCC system with rotation support and MIMO was proposed. The system provided BER of 10\({}^{-4}\) for 11 dB SNR; however, the throughput could be further enhanced by using the chromatic features of light [19; 20]. User-centric MIMO provides variable bandwidth depending upon the number of devices under the VLC access point [21; 22]. Figure 1a,b shows the bandwidth distribution for three OCC Rx devices and one Rx device, respectively. OCC-MIMO can be achieved by modulating signals using color mapping techniques. However, few phenomena exist in nature that inherently showcase changes in color on the basis of changes in its underlying composition. Quantum chromodynamics (QCD) is a branch of quantum physics under which the chromatic properties of quantum particles are studied [23]. Recent discoveries in the field of quantum physics have revealed that subatomic particles neutrons and protons are composed of hadrons that consist of quarks held together by the strong force produced during the exchange of gluons. Depending upon the number of quarks in a hadron, the hadron is classified into baryons and mesons. Baryons consist of three quarks, while mesons consist of a pair of quarks and antiquarks [24; 25]. Baryons hold the quarks together by exchanging gluons, keeping the overall hadron stable. Loosely inspired by the baryon gluon exchange model, this paper proposes a 2D 8 \(\times\) 8 neopixel LED matrix for an OCC. The objectives of this paper are as follows:To use quantum chromodynamics principles for data to color map using an \(8\times 8\) LED matrix. To provide rotation support for the OCC using a QR code-inspired anchoring pattern. To build and evaluate the simulator of proposed QCD-inspired OCC modulation with rotation support and MIMO. To build a low-cost prototype of the proposed QCD-OCC system and compare the success of reception (%) with the proposed simulator. To evaluate the performance of the proposed system on the basis of metrics such as SNR, the success of reception (%), intensity profile, throughput, and BER. The remainder of the paper is organized as follows: Section 2 describes the proposed modulation scheme inspired by QCD, and the proposed simulation and prototype design system. In Section 3, the implementation details of proposed system are highlighted. Section 4 discusses the experimental setup, experiments, and corresponding results. Conclusions are drawn in Section 5.
# Reflection Characteristics Measurements of Indoor Wireless Link in D-Band
## 5 Results and Discussions
### Reflection Coefficients
This is expected, because with increasing frequency, the Rayleigh roughness factor (described by \(\rho_{\mathrm{s}}\)) of the material grows and the energy in the specular direction decreases, leading to the reduction in the reflection coefficient [20]. At a certain frequency, the reflection coefficients decrease with the incident angle (i.e., 20\({}^{\circ}\), 30\({}^{\circ}\), 40\({}^{\circ}\), 50\({}^{\circ}\), 60\({}^{\circ}\)). Additionally, the measured reflection coefficients are generally small and most of them are less than 0.3, implying a high reflection loss. Figure 7: The relationship between measured reflection coefficients of five materials and frequencies in D-band. **(a)** Wood, **(b)** Plexiglass, **(c)** Drywall (plaster), **(d)** Concrete board, **(e)** Red brick.
## 7 Performance Analysis: Reliability and Scalability  
This section covers the performance evaluation of the proposed framework by following the approach and metrics discussed in Section 3.2.4. The evaluation strategy used consisted of a function to generate a huge amount of data ingesting into the system to observe its behavior. The data generator function's purpose was to simulate IoT devices as publishers. The ingested data allowed observation of how the containers would be orchestrated under the governance of the Docker Swarm manager to compete for te available resources. The goal of this experiment was to assess the proposed IoT framework from two different points of view:  
* **Reliability**: the reliability of the system was measured by utilizing all the available resources to ensure that the system was running at full capacity. Those resources included CPU and active memory usage, with how many data were processed in a specific period. CPU usage metrics indicated average OS core utilization for all available CPU physical and virtual cores of the running system. Active memory showed the number of bytes stored in memory during the experiment at the time of the test.
* **Scalability**: the measure of the prediction for extending the system to support hundreds of thousands of IoT devices publishing data with minimum system overhead to help time-critical applications. The measurement of these characteristics was indicated by the system throughput and response time metrics, showing how the system scalability was maintained.  
The excellent performance was shown by the high throughput rate, as in principle, it should have been increased linearly as the number of devices or requests increased. The calculation of the throughput was given by the number of messages received and processed by the IoT system in one second; the lower the response time, the faster the response and processing of the system.
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 4 Experimental Setup
### Hyperparameters of C-SDFormer  
The hyperparameters of the C-SDFormer model are comprehensively outlined in Table 4. Here, the encoder block, input dimension of the encoder, number of attention heads, and feed-forward network dimension were set based on the optimal parameter values obtained from grid search. In the input embedding stage, we adopted an optimal CNN structure, determined through rigorous experimentation. This choice allows us to more effectively extract local patterns for voltage, temperature, and the communication count than a linear layer. We incorporated zero padding and the tanh activation function, notably omitting a pooling layer to preserve the temporal order of the time series data. Additionally, we employed Batch Normalization (BatchNorm) 1D to maintain the model's training stability.  
The encoder stage integrated four self-attention heads and eight encoder blocks to adeptly learn features and patterns. The feed-forward stage enhanced the model's capability to represent various types of information through the inclusion of two linear layers, one dropout layer, and the GELU activation function. The output stage utilized two CNN layers and one linear layer to yield the final results. Only the values corresponding to the first vector in the output of the second CNN layer are used as input to the subsequent linear layer.
## 4 Pointing Errors Caused by Thermal Gradient  
On a clear day, solar radiation induces thermal gradients on the alidade, leading to non-orthogonality between the EL and AZ axes and deterioration of pointing accuracy. Consequently, astronomical observations at NSBT higher than K and Q bands requiring greater pointing accuracy can only be conducted at night. To measure pointing errors caused by thermal gradients, we designed a measurement system and carried out experiments.
## 3 System Model
#### 3.1.3. Energy Level System  
To describe the residual energy of nodes more clearly, we divide the battery into three levels, as shown in Figure 4. Depending on the residual energy \(E_{r}\), we study the following two cases:  
1. \(Case\) 1: \(0\leq Er\leq Level\) 1 In this case, since the node has insufficient energy to maintain more data transmission, the data receiving device would be shut down temporarily. Then, the node confirms whether there are still packets to be sent in its data buffer. If so, continue processing these remaining packets until the residual energy or number of packets reaches 0; otherwise, turn off the sending device and enter sleep mode.
2. \(Case\) 2: \(Er\geq Level\) 2 When the residual energy is greater than \(Level\) 2, the node would be transitioned to active mode if it is in sleep mode.  
Figure 4. Energy level system.
## 3 Experimental Results
### The Used Classification Datasets  
The descriptions of the used datasets are also provided:  
1. **Appicitis**, which is a dataset originated in [66].
2. **Australian** dataset [67], which is related to bank transactions.
3. **Balance** dataset [68], which has been used in a series of psychological experiments.
4. **Circular** dataset, which contains artificially generated data.
5. **Cleveland** dataset [69; 70].
6. **Dermatology** dataset [71], which is a dataset that contains measurements of dermatological deceases.
7. **Ecoli** dataset, which is a dataset that contains measurements of proteins [72].
8. **Fert** dataset, which is used to detect the relation between sperm concentration and demographic data.
9. **Haberman** dataset, which is a medical dataset related to breast cancer.
10. **Hayes Roth** dataset [73].
11. **Heart** dataset [74], which is a medical dataset used for the prediction of heart diseases.
12. **HeartAttack** dataset, which is used for the detection of heart diseases.
13. **HouseVotes** dataset [75].
14. **Liverdisorder** dataset [76], which is a medical dataset.
15. **Ionosphere** dataset, which is a climate dataset [77; 78].
16. **Mammographic** dataset [79], which is a dataset related to the breast cancer.
17. **Parkinsons** dataset, which is used in the detection of Parkinson's disease (PD) [80].
18. **Pima** dataset [81].
19. **Popfailures** dataset [82].
20. **Regions2** dataset, which is used in the detection of hepatitis C [83].
21. **Saheart** dataset [84], which is a medical dataset used for the detection of heart diseases.
22. **Segment** dataset [85], which is a dataset related to image processing.
23. **Spiral** dataset, which is an artificial dataset.
24. **Student** dataset [86], which contains data from experiments conducted in Portuguese schools.
25. **Transfusion** dataset [87], which is a medical dataset.
26. **Wolbc** dataset [88].
27. **Wine** dataset, which is used for the detection of the quality of wines [89; 90].
28. **Eeg** datasets, which is a dataset that contains EEG experiments [91]. The following distinct cases were used from this dataset: Z_F_S, Z_O_N_F_S, Z_ON_NF_S and ZON_S_S.
29. **Zoo** dataset [92], which is used to classify animals.  
The number of inputs and classes for every classification dataset is given in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DATASET** & **INPUTS** & **CLASSES** \\ \hline APPENDICITIS & 7 & 2 \\ \hline AUSTRALIAN & 14 & 2 \\ \hline BALANCE & 4 & 3 \\ \hline CIRULAR & 5 & 2 \\ \hline CLEVELAND & 13 & 5 \\ \hline DERMATOLOGY & 34 & 6 \\ \hline ECOLI & 7 & 8 \\ \hline FERT & 9 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of inputs and distinct classes for every classification dataset.
## 3 Bioactive Compounds in Peanut Skins
### Enzyme Inhibition  
The action of phenolic compounds as inhibitors of carboxylases has been reviewed [83]. This type of activity has been determined in peanut skins [84]. Acetone (70% in water) was used to extract peanut skins and the extracts were fractionated using microfiltration and Sephadex LH eluted with increasing acetone levels in water solutions. The structures of the compounds in the fraction with the highest action against \(\alpha\)-amylase was found to be oligomers of polyflavan-3-ols, up to 15-units in size using high-resolution Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectroscopy (MALDI-TOF-MS). These compounds were determined to contain catechin/epicatechin units together with several afzelechin/epiafzelechin units and gallocatechin/epigallocatechin units with both A-type and B-type linkages. A recent review included information about the ability of methanol extracts of peanut skins to inhibit \(\alpha\)-amylase and lipid peroxidase [85]. Alpha-glucosidase and lipase play a role in the absorption of glucose and triacylglycerlycerols from the diet. The inhibition of these enzymes could play a role in the management of diabetes and weight. Peanut skins were extracted using 70% acetone in water and partitioned into ethyl acetate as described in a previous study [58]. This was to ensure the extraction of the soluble phenols. The residue was treated with acid and the free phenolics were recovered in a mixture of ethyl acetate and ethyl ether [86]. Subsequent treatment at alkaline pH by the addition of sodium hydroxide freed the phenolic compounds bound into esters. A final extraction into methanol was done to recover those compounds. All the phenolic compound classes showed inhibition of the enzymes with the greatest effect being from the free phenols and the least by the insoluble bound compounds. Along with white grape pomace, peanut skins were investigated for their ability to inhibit the enzymes, \(\alpha\)-amylase, and pancreatic lipase to propose applications for agricultural waste materials [87]. Peanut skins were extracted with ethanol using sonication, filtered, and lyophilized for analysis. Inhibition by peanut skins was found to be close to 100% for lipase and above 90% for \(\alpha\)-amylase. These actions were higher than those of either red or white grape pomace extracts.