esa-sceva/satcom-chunk-collection huggingface dataset:
# Bent and Branched Microstrip-Line Antennas for Circular Polarization
## 1 Introduction  
There have been many studies on microstrip transmission-line antennas (line antennas) radiating a circularly polarized (CP) wave [1, 2]. The line antenna is created by using the feedline itself that is periodically bent [3, 4, 5] or branched [6, 7, 8] above the ground plane. It has the advantage of a simple feed system for a series-fed linear array. So far, line antennas have been designed individually, and there have been few systematic studies on different line antennas [9].  
This paper investigates different line antennas, where a loop shape [5] is selected as the feedline's periodical bend for a systematic study. Loop-shape selection enables us to study bent and branched line antennas (see Figure 1b,c), together with a modified branched one (see Figure 1d). All line antennas are analyzed using the moment method [10].  
The systematic study aids in designing and understanding a line antenna since the study discusses three different line-antennas having _relationships_ and presents _new findings_. The _relationships_ concern both configurations (see Figure 1b-d) and radiation characteristics (Figures 3, 5, and 9a). The _new findings_ include the following:  
1. A slight change \(\delta\) in configurations (Figure 1b,c) results in a critical difference in antenna design (Figures 3 and 5);
2. Loop addition (Figure 1c,d) results in a gain enhancement of 2.0 dB with the same CP wave bandwidth (Figure 9a).  
Note that a finding of (1) has never been presented in the authors' publications [3, 4, 7], including a preliminary conference paper [9]. Moreover, note that in [9], there has been no experimental result to validate a finding of (2). This paper is the only systematic study on three different line antennas to the best of the authors' knowledge.  
It should be emphasized that present elements (Figure 1b-d) are entirely different from the ones in the authors' publications (see Figure 2a,b). In Figure 2a, a line \(A\)-\(B\)-\(C\) is bent inside the loop, while a line \(A\)-\(B\)-\(C\) is branched to a loop corner \(L_{\mathrm{c}}\) (not the loop-side center) in Figure 2b. These traditional elements have never resulted in the systematic study of this paper. In other words, the present elements themselves constitute the novelty of this paper.  
Figure 1: Antenna configurations: (**a**) perspective view; (**b**) bent line antenna having loops with a gap \(\delta\); (**c**) branched line antenna having loops without a gap (\(\delta=0\)); (**d**) modified branched line antenna with additional loops \(\#n^{\prime}\); (**e**) side view with a vertical wire \(F\)-\(F^{\prime}\).  
Figure 2: Antenna configurations in [3; 7]: (**a**) bent line antenna [3]; (**b**) branched line antenna [7].
# Design, Characteristic Analysis and Modeling of a Tailored Soft Robot for Phosphorite Grabbing
## 2 Structural Design and Manufacturing of TSRPG
### Design of TSRPG  
The 3D assembly model of the TSRPG is shown in Figure 3. The device consists of two sets of symmetrically distributed soft body pneumatic actuator units (SPA1 and SPA2) and a base plate. The SPAs are assembled with the base plate through assembly to form a grasping device with symmetric topological features.  
The designed TSRPG has a certain novelty. Compared to existing soft pneumatic grippers reported in [8; 31], the TSRPG has a smaller number of cavities. Specifically, the TSRPG has 6 cavities, and the grippers reported in [8; 31] have 11 and 8 cavities. In addition, at the end of the TSRPG, there is not a cavity, but a solid structure with a sloping surface, which makes it easier for two fingers to close and grab. Compared to the gripper reported in [30], the TSRPG itself is both an actuator and a gripper, without the need to design additional gripping mechanisms. So the TSRPG has a simple structure.  
The operating mechanism of TSRPG is based on the principle of pneumatic synergistic drive (see Figure 3 motion direction labeling). When the compressed air is synchronously injected into SPA1 and SPA2, the two actuators produce a phased bending motion under pneumatic excitation, and the gripping of phosphate rock is realized through the gradual closure of the end contact surfaces. During the process, the elastic deformation of the actuator units can be effectively converted into a soft gripping force, forming an adaptive wrapping effect. In the pneumatic unloading stage, the actuator unit restores its initial shape under the action of material intrinsic elasticity, realizing the lossless release of the ore. By controlling the air pressure injected into SPA1 and SPA2, the timing and strength of the gripping-releasing action can be controlled, which finally forms a complete cycle of the phosphorus ore sorting operation.  
Figure 3: Motion diagram of TSRPG.  
Figure 2: Global sectional view of SPA.
## 5 System Implementation
### Audio Driver
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Kernel** & **Stride** & **Output** & **Activation** \\ \hline DeepSpeech & - & - & \(16\times 1\times 29\) & - \\ Identity concat & - & - & \(16\times 1\times 37\) & - \\ Convolution & \(3\times 1\) & \(2\times 1\) & \(8\times 1\times 32\) & ReLU \\ Convolution & \(3\times 1\) & \(2\times 1\) & \(4\times 1\times 32\) & ReLU \\ Convolution & \(3\times 1\) & \(2\times 1\) & \(2\times 1\times 64\) & ReLU \\ Convolution & \(3\times 1\) & \(2\times 1\) & \(1\times 1\times 64\) & ReLU \\ Identity concat & - & - & 72 & - \\ Fully connected & - & - & 128 & tanh \\ Fully connected & - & - & 50 & linear \\ Fully connected & - & - & \(5023\times 3\) & linear \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model architecture. Figure 7: Network architecture. smoothed using a Gaussian filter, shifting out the acquisition noise. The mouth region is not smoothed to preserve subtle movements [23]. Among voice-4D scan datasets, represented by \(\{(x_{i},y_{i})\}_{i=1}^{F}\), the \(x_{i}\in R^{W\times D}\) is the voice input window, the window center for the case of a video frame \(y_{i}\in R^{N\times 3}\). Moreover, let \(f_{i}\in R^{N\times 3}\) denote the VOCA output of \(x_{i}\). The loss function consists of two terms: a position term and a velocity term. The position term is used to calculate the distance between the predicted vertex and the training data vertex. The formula is expressed as follows:  
\[E_{p}=\|y_{i}-f_{i}\|_{F}^{2}\]  
The velocity term is used to calculate the distance between the differences in consecutive frames between the predicted output and the training vertices. The formula is expressed as follows:  
\[E_{v}=\|\left((y_{i}-y_{i-1})-(f_{i}-f_{i-1}))\right\|_{F}^{2}\]  
The hyperparameters were tuned on the validation set and trained for 50 epochs using a learning rate of \(1\times 10^{-4}\). The weights for the position and velocity terms are 1 and 10, respectively, and batch normalization with a batch size of 64 is used during training. The window size is \(W=16\), and the speech feature dimension is \(D=29\). The virtual teacher in this paper is implemented in Python (Version 3.6) using TensorFlow and trained using Adam. Training one epoch takes about ten minutes on a single NVIDIA Tesla K20. We use a pre-trained DeepSpeech model which is kept fixed during training. Extracting identity from facial motion enables the animation of a wide range of adult faces. Figure 8 shows the static templateand some VOCA animation frames, which are driven by the same audio sequence, and the results show that the model has some generalization ability. The possibility to change the identity-related shape (top) and head pose (bottom) during animation, driven by the same audio sequence, makes the facial animation look realistic despite different shapes and poses.
## 3 Results
### Dynamic Balance (BBS and LOS)  
Dynamic balance was assessed using the BBS and LOS (Table 4) (Figure 3). Dynamic balance significantly improved in both groups following the intervention. Additionally, a significant group \(\times\) time interaction was observed for the BBS (F = 8.767, \(p\) = 0.006), suggesting that the experimental group exhibited greater improvements than the control group.  
For the BBS, the scores increased from 23.07 \(\pm\) 13.06 to 46.60 \(\pm\) 8.63 in the experimental group and from 30.93 \(\pm\) 2.93 to 43.93 \(\pm\) 11.01 in the control group. The mean change was greater in the experimental group (23.53 \(\pm\) 9.94) than in the control group (13.00 \(\pm\) 9.53).  
For the LOS, the values increased from 6673.71 \(\pm\) 4430.90 mm\({}^{2}\) to 13091.13 \(\pm\) 6896.00 mm\({}^{2}\) in the experimental group and from 6749.42 \(\pm\) 4393.63 mm\({}^{2}\) to 10014.97 \(\pm\) 5086.36 mm\({}^{2}\) in the control group. A significant group \(\times\) time interaction was also found for the LOS (F = 6.415, \(p\) = 0.017), indicating a greater improvement in the experimental group (6417.42 \(\pm\) 4358.72 mm\({}^{2}\) vs. 3265.55 \(\pm\) 2056.75 mm\({}^{2}\)).  
Figure 2: Pre- and post-treatment comparisons of postural control measures between groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Experimental Group** & **Control Group** & **Group X Time** \\  & **(\(n\) = 15)** & **(\(n\) = 15)** & **F(df), \(p\)** \\ \hline K-TIS (score) & & & \\ Pre & 12.67 \(\pm\) 3.57 & 14.87 \(\pm\) 3.87 & \\ Post & 19.27 \(\pm\) 2.31 & 18.73 \(\pm\) 2.78 & \\ Change & 6.60 \(\pm\) 3.56 & 3.86 \(\pm\) 2.47 & F(1,28) = 5.958, \(p\) = 0.021 \\ PASS (score) & & & \\ Pre & 25.00 \(\pm\) 4.69 & 27.00 \(\pm\) 4.29 & \\ Post & 32.60 \(\pm\) 2.92 & 30.93 \(\pm\) 2.93 & \\ Change & 7.60 \(\pm\) 3.92 & 3.93 \(\pm\) 2.63 & F(1,28) = 9.034, \(p\) = 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pre- and post-treatment scores of postural control measures (\(N\) = 30).  
Given that a change of approximately 6 points in the Berg Balance Scale (BBS) is considered clinically meaningful in individuals with stroke, the observed improvement of over 23 points in the experimental group represents a substantial enhancement in functional balance, likely contributing to reduced fall risk and improved mobility.
# Anomaly Detection for Hydraulic Power Units--A Case Study
### Business Needs  
The industry of hydraulic components and systems (including high-pressure water systems) in recent years has experienced a very large development of new products that are currently used on the market. However, it should be mentioned that the dynamic development of hydraulic devices, such as HPU, is not possible indefinitely, as the pressures used today are sufficient for the current industrial applications to carry out most of the planned processes. Being aware of the above, the current portfolio of products and complete applications expands and develops solutions in the field of Industry 4.0, so that PONAR machines and devices are characterized by the highest possible availability, no uncontrolled downtime, and thus, savings and satisfaction on the part of the customer. The remote monitoring and predictive maintenance systems described in this article make the classic solutions in the field of power hydraulics \"smart\", and introduce devices to the world of industry 4.0, thus building a competitive advantage. An important aspect to be mentioned is that the anomaly detection system presented in the article is not real-time. Nonetheless, a real-time system was not mandatory for the described project.
## 2 OneNet Scenario: Deep Power System Analysis through GIS Server Application  
The scenario, which was related to the project and was envisaged as an aid on the path to achieving the desired targets, was named \"Deep power system analysis through GIS server application (Georeferenced power system modeling and analysis utilizing the geo-server)\". Along with the title, the solution that was developed was depicted as the detailed power system analysis algorithm with GIS implementation that has been fully entangled with the foreseen functionalities of the F-channel platform. The main goals of the adopted scenario could be summed up as follows:  
* Power system analysis on the selected region (Crete Island and Peloponnese), including both TSO and DSO grid simulation models down to the lowest voltage level entities.
* Geographic visualization of simulation results, with exposing results and linking to other services that are developed under F-channel via GIS technologies.  
Out of these desired outcomes, particular attention needs to be paid when selecting areas of the system that must be modeled down to the lowest level entities, such as the individual households or singled out production units in the power plants. Figure 1 gives an insight into the required level of modeling of all participants in the energy market.  
The modeling process was set to focus on providing the users of the F-channel platform (mainly the SOs) with the option of accessing the information related to the current system state that was previously unavailable to them, i.e., the real-time changes of the behavior of the individual households, as depicted in Figure 1. By providing the operators with realistic data on the system entities of interest for them, it is ensured that they have all the potentially relevant information at their disposal to make reliable decisions and to reach effective conclusions. Therefore, the obtained decisions would be in line with the well-being of the entire system and all the participants in it. The solution named Georeferenced power system modeling and analysis utilizing the geo-server (GEOGRID) was selected after all applications were collected, thoroughly studied, and evaluated.
## 5 Numerical Results
### Effect of Changing the Model Function, Mesh Size, and Mesh Distribution
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Function**} & **Mesh** & **Mesh** & \multirow{2}{*}{**Method**} & **Log10[Norm(Error)]** & \multicolumn{2}{c}{\(\varepsilon\)} \\ \cline{4-7}  & **Distribution** & & & & \(\frac{\partial f}{\partial x}\) & \(\frac{\partial f}{\partial x}\) & \(\frac{\partial f}{\partial x}\) & \(\frac{\partial^{2}f}{\partial x^{2}}\) \\ \hline \multirow{6}{*}{Equation} & \multirow{6}{*}{Regular} & RBF & \(-5.37\) & \(-3.58\) & \(1.43\) & \(1.10\) \\  & & RBF-TPU & \(-4.70\) & \(-3.00\) & All & All \\ \cline{3-7}  & & \multirow{2}{*}{41} & RBF & \(-6.39\) & \(-4.43\) & \(1.35\) & \(1.35\) \\  & & RBF-TPU & \(-5.01\) & \(-3.24\) & All & All \\ \cline{3-7}  & & \multirow{2}{*}{61} & RBF & \(-6.34\) & \(-4.46\) & \(1.33\) & \(1.33\) \\  & & RBF-TPU & \(-5.01\) & \(-3.24\) & All & All \\ \cline{3-7}  & & \multirow{2}{*}{21} & RBF & \(-5.08\) & \(-4.18\) & \(1.18\) & \(1.18\) \\  & & RBF-TPU & \(-4.14\) & \(-3.18\) & All & All \\ \cline{3-7}  & & \multirow{2}{*}{41} & RBF & \(-6.58\) & \(-4.66\) & \(1.35\) & \(1.35\) \\  & & RBF-TPU & \(-4.98\) & \(-3.06\) & All & All \\ \cline{3-7}  & & \multirow{2}{*}{61} & RBF & \(-6.62\) & \(-4.46\) & \(1.35\) & \(1.35\) \\  & & RBF-TPU & \(-4.85\) & \(-2.94\) & All & All \\ \hline \multirow{6}{*}{Equation} & \multirow{6}{*}{Regular} & \multirow{6}{*}{21} & RBF & \(-5.41\) & \(-3.46\) & \(1.35\) & \(1.35\) \\  & & RBF-TPU & \(-3.86\) & \(-2.09\) & All & All \\ \cline{3-7}  & & \multirow{2}{*}{41} & RBF & \(-6.13\) & \(-4.48\) & \(1.33\) & \(1.33\) \\  & & RBF-TPU & \(-4.29\) & \(-2.40\) & All & All \\ \cline{3-7}  & & \multirow{2}{*}{61} & RBF & \(-5.70\) & \(-3.77\) & \(1.35\) & \(1.30\) \\  & & RBF-TPU & \(-4.20\) & \(-2.38\) & All & All \\ \hline \multirow{6}{*}{(21)} & \multirow{6}{*}{21} & RBF & \(-4.56\) & \(-3.14\) & \(1.15\) & \(1.10\) \\  & & RBF-TPU & \(-3.80\) & \(-2.49\) & All & All \\ \cline{3-7}  & & \multirow{2}{*}{41} & RBF & \(-6.05\) & \(-4.63\) & \(1.33\) & \(1.33\) \\ \cline{3-7}  & & RBF-TPU & \(-4.38\) & \(-2.34\) & All & All \\ \cline{3-7}  & & \multirow{2}{*}{61} & RBF & \(-5.60\) & \(-3.63\) & \(1.30\) & \(1.30\) \\ \cline{3-7}  & & RBF-TPU & \(-4.14\) & \(-2.10\) & All & All \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the minimum error for approximating derivatives between Gaussian RBF and RBF-TPU. The results of RBF-TPU are obtained from Figure 8. Note that “All” indicates the error is the same for all studied \(\varepsilon\) value.
# Stabilisation of Soft Clay, Quick Clay and Peat by Industrial By-Products and Biochars
## 3 Results
### The Characterisation of Natural Soils and Binders
For the Tiller-Flotten peak, a DTG peak was observed between temperatures of 220-350 \({}^{\circ}\)C, caused by the dehydration of the organic content in the peat. A high weight loss in this temperature range indicated that the peat had a low degree of humification, e.g., a large number of intact fibres and leaves [73], which was coherent with the [PERSON] classification of H2-H3. The TGA further showed a continuous weight loss at higher temperatures, which is typical for peat due to its rather complex composition [80]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Material** & **Illite + Mica** & **Chlorite** & **Quartz** & **K-Feldspar** & **Plagioclase** & **Amphibole** & **Calcite** & **Others \({}^{1}\)** & **Amorphous** \\ \hline \multirow{3}{*}{Natural soils} & Tiller-Flotten & \multirow{3}{*}{30.5} & 14.3 & 13.2 & 5.6 & 17.7 & 7.3 & 2.1 & - & 9.3 \\  & quick clay & & & & & & & & & \\  & Tiller-Fotten peat & 1.2 & - & 0.3 & - & - & - & - & 98.6 \\  & Onsey clay & 30.2 & 10.9 & 14.3 & 5.3 & 11.6 & 2.5 & - & 0.7 & 24.6 \\ \hline \hline \end{tabular} \({}^{1}\) Onsey clay: halite (0.6%) and pyrite (0.1%).  
\end{table} 
Table 4: Results of whole rock X-ray diffraction analyses (XRD) of natural soils, showing clay minerals in crystalline phase and amorphous phase separately. All values in per cent. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Material** & **Al2O3** & **SiO2** & **CaO** & **MgO** & **SO3** & **K2O** & **Fe2O3** & **Na2O** & **LOI** \\ \hline \multirow{3}{*}{Natural soils} & Tiller-Flotten quick clay & 16.97 & 51.04 & 3.22 & 5.86 & 0.02 & 4.05 & 9.28 & 2.01 & 5.6 \\  & Tiller-Flotten peat & 1.17 & 1.28 & 0.24 & 0.23 & \textless{}0.01 & 0.10 & 0.38 & 0.03 & 96.4 \\  & Onsey clay & 17.55 & 53.18 & 1.00 & 3.38 & 0.02 & 4.79 & 8.38 & 2.35 & 7.5 \\ \hline Cement & CEM I & 4.58 & 18.79 & 61.05 & 2.40 & 3.94 & 0.87 & 3.30 & 0.34 & 2.6 \\ \hline Sludge & PSA & 6.8 & 18.1 & 42.8 & 2.8 & 6.3 & 0.5 & 3.5 & ND & 16.3 \\ \hline Bioash & FA & 2.2 & 4.3 & 36.5 & 3.9 & 2.2 & 7.4 & 0.4 & ND & 35.2 \\  & BA & 3.4 & 10.9 & 57.2 & 4.8 & 1.2 & 3.5 & 1.3 & ND & 8.0 \\ \hline \multirow{3}{*}{Ladle slag} & LS1 & 6.6 & 33.2 & 47.1 & 7.1 & 3.2 & ND & 1.8 & ND & 0.0 \\  & LS2 & 6.9 & 25.5 & 43.7 & 7.5 & 1.7 & ND & 3.2 & ND & 9.7 \\ \hline \multirow{3}{*}{Biochar} & BC1 & 1.3 & 3.2 & 3.2 & ND & 0.7 & 0.5 & 0.8 & ND & 87.1 \\  & BC2 & 8.2 & 9.5 & 32.8 & 0.8 & 2.2 & 0.3 & 6.3 & ND & 32.2 \\ \cline{1-1}  & BC3 & 10.1 & 16.5 & 4.1 & 0.4 & 2.8 & 0.7 & 24.9 & ND & 32.4 \\ \cline{1-1}  & BC4 & 2.9 & 12.2 & 4.7 & 0.4 & 0.5 & 1.1 & 2.3 & ND & 74.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of major element X-ray fluorescence analyses (XRF). All values in per cent. ND = not determined as abundance was below the detection limit.
## 3 Results
The MobileNet model significantly outpaces both, completing training in just 43.40 s, a testament to its lightweight design. When evaluating images processed per second, MobileNet again stands out, handling 44.19 images/s, making it optimal for real-time or high-throughput applications. The Xception processes 16.83 images/s, striking a balance between accuracy and efficiency, while VGG16 lags at 16.74 images/s. Concerning the overall computational order in terms of the big-O notation [50], training complexity scales as O(_e-N-P_), where \(N\) is the dataset size, \(e\) the epochs, and \(P\) the model parameters, while inference scales  
Figure 4: Comparative analysis of the performance of three deep learning models using the Grad-CAM visualizations for the classification of gypsum. In row (**a**), Aragonite is displayed, in row (**b**), Gypsum is presented, in row (**c**), Obsidian is displayed, and in row (**d**), Pyrite is displayed. as _O_(_N_-_P_). MobileNet (\(\sim\)4.2 M parameters) has the lowest computational cost, ideal for resource-limited settings. Xception (\(\sim\)22.9 M) leverages depthwise separable convolutions for efficiency, outperforming VGG16 (\(\sim\)14.7 M) despite its higher parameter count. The stacking ensemble approach demonstrated exceptional performance across all metrics, achieving consistently high precision, recall, and F1-scores for all mineral classes, with an overall accuracy of 99.71%. By combining the strengths of VGG16, Xception, and MobileNet, the ensemble effectively addressed individual model limitations, such as VGG16's lower precision for Aragonite and Obsidian or Xception's challenges with Obsidian's precision. Notably, the ensemble outperformed all base models for difficult minerals like Obsidian, achieving balanced precision (0.993) and recall (0.999), while delivering near-perfect scores for Quartz (F1-score: 0.998) and Gypsum (F1-score: 0.996). This robust and generalized performance highlights the ensemble's capacity to leverage diverse feature extraction capabilities for enhanced classification accuracy. The most significant gains were observed over VGG16, with precision and recall for challenging classes like Aragonite and Obsidian improving by 8.6% and 6.0%, respectively, while smaller yet meaningful improvements (0.1-0.8%) over Xception and MobileNet for well-performing classes like Quartz and Gypsum underscore the ensemble's ability to refine high-performing models (Table 5).
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 1 Introduction
In [PERSON]'s study, they presented an elaborate introduction to the classification of SI [11], but it lacks the analysis and comparison of related methods towards similar problems. To solve these problems, this paper summarizes the application of SI algorithm in the IoT. Our paper generally classifies the network types into three categories: (1) the application of SI in the WSN which is then divided into five sub-problems in terms of CH selection, routing protocol, UAV application, sensor deployment, and node location; (2) the application of SI in UAV-aided wireless network; (3) the application of SI in other related IoTs. According to the role of UAV in the network, UAV-aided wireless network is classified into three categories: UAV as a base station (DBS), UAV as relay and UAV assisted information collection, respectively. Besides, we presented some widely applied SI algorithms, such as PSO, ACO, and ABC. The main contributions of the survey are outlined below:  
* This paper introduces and summarizes more than 40 applications of SI in IoT--such as the WSN and UAV network, especially in the last three years;
* This paper compares 11 SI-based CH selection algorithms and 13 SI-based routing protocols in WSN applications;
* We summarize the validation platform and parameter selection of each method used in WSNs as well as their merits and demerits;* We survey the state-of-art SI algorithms in IoT applications and discuss potential future development directions. The rest of this paper is organized as follows: Section 2 introduces the principle of SI algorithm. Section 3 analyzes and compares different applications of swarm intelligence algorithms in WSNs. Section 4 presents the application of SI in UAV-aided wireless network. Section 5 summarizes the application of swarm intelligence algorithm in other Internet of Things. Section 6 concludes the verification scenarios and verification platforms adopted. Section 7 discusses the current state of the SI algorithms in the wireless network and forecasts their future development. The conclusion is drawn in Section 8.