esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Calibration of a Sensory System of an Adaptive Gripper
## 3 Results
### Determining errors for each module (Sequence 1)
The amplitude of this decrease is an indicator of the magnitude of the slip. Variability in amplitude from one cycle to another suggests that either the sliding force or the friction conditions varied during the simulation. For example, if the object has a rougher surface, the slip will be smaller. To evaluate the slip values, the probe was kept in constant contact, and the slip was simulated manually, moving in the gravitational direction, without the object detaching from the sensor jaws. Measurements were performed over a period of 30 s, during which various slips were simulated to establish a specific range in which the slip occurs for each grip module. The simulation of the slip of Module 1 corresponds to the data presented in the graph in Figure 29. During the slip, it is observed that, from the contact position of \(-750\), the value decreases to \(-400\). This difference represents the maximum slip allowed for sensor 1. The slip range for sensor 1 is between \(-750\) and \(-400\). The simulation of the sliding of Module 2 corresponds to the data shown in the graph in Figure 30. During the sliding, it is observed that, from the contact position of \(-900\), the value decreases to \(-400\). This difference represents the maximum allowed sliding for sensor 2. The sliding range for sensor 2 is between \(-900\) and \(-350\). The simulation of the sliding of Module 3 corresponds to the data shown in the graph in Figure 31.  
During the sliding, it is observed that, from the contact position of \(-700\), the value decreases to \(-400\). This difference represents the maximum allowed sliding for sensor 3. The sliding range for sensor 3 is between \(-700\) and \(-100\). The simulation of the sliding of Module 4 corresponds to the data shown in the graph in Figure 32. During the sliding, it is observed that, from the contact position of \(-950\), the value decreases to \(-400\). This difference represents the maximum allowed slip for sensor 4. The slip range for sensor 4 is between \(-950\) and \(-400\). The simulation of the slip of Module 5 corresponds to the data presented in the graph in Figure 33. During the slip, it is observed that, from the contact position of (700), the value decreases to \(-400\). This difference represents the maximum allowed slip for sensor 5. The slip range for sensor 5 is between 700 and 300. The differences in frequency of results over the same time interval are caused by the way the simulations were performed: by applying contact of an object to each trough, while the slip simulation was performed manually.
# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 3 Approach  
### Preliminaries  
Diffusion models [22; 33; 34] utilize a stepwise denoising process to recover data samples from randomly distributed initial samples. This process is defined by a Markovian chain of diffusion forward processes, where the noise level of the data samples gradually increases.  
The forward noise process is described by the equation:  
\[q(z_{t}|z_{0})=\mathcal{N}(z_{t}|\sqrt{\tilde{\alpha}_{t}}z_{0},(1-\tilde{ \alpha}_{t})I), \tag{1}\]  
This equation transforms the data sample \(z_{0}\) into a latent noisy sample \(z_{t}\) at each step \(t\) by adding noise to \(z_{0}\). Here, \(\tilde{\alpha}_{t}\) is defined as \(\tilde{\alpha}_{t}:=\prod\limits_{s=0}^{t}\alpha_{s}=\prod\limits_{s=0}^{t}(1 -\beta_{s})\), and \(\beta\)s denotes the noise variance schedule.  
During training, a neural network \(f_{\theta}(z_{t},t)\) is trained to predict \(z_{0}\) from \(z_{t}\) by minimizing the \(\ell_{2}\) loss [21]:  
\[\mathcal{L}\text{train}=\frac{1}{2}||f_{\theta}(z_{t},t)-z_{0}||^{2}. \tag{2}\]  
In the inference phase, the data sample \(z_{0}\) is reconstructed from the noise \(z_{T}\) using the model \(f_{\theta}\) in an iterative manner, i.e., \(z_{T}\to z_{T-\Delta}\rightarrow\ldots\to z_{0}\). In our setup, the data samples represent a set of bounding boxes, where \(z_{0}=b\) and \(b\in\mathbb{R}^{N\times 6}\) is a set of \(N\) boxes. A neural network \(f_{\theta}(z_{t},t,x)\) is trained to predict \(z_{0}\) from the noisy oriented boxes \(z_{t}\), conditioned on the corresponding images \(x\), and generate the corresponding category labels \(c\).
## 2 Experimental Section
### Methods  
#### 2.2.1 Production of TEMPO-Oxidized Cellulose Gel (TOCgel)  
In the first step, the oxidation of native cellulose fibers was carried out by the TEMPO-mediated oxidation system [49] and conducted in a 45L flow-through sonoreactor (semi-continuous mode) with a nominal input power capacity of 2000W (262 W/L, 12300 W/m\({}^{2}\)), according to the procedure developed in our laboratory [47,50]. The experimental process based on TEMPO-mediated oxidation used in the preparation of TOCgel was illustrated in Figure 1.  
Before oxidation, 400 g of dried bleached hardwood pulp were pre-soaked for an overnight in 40 L of deionized water at room temperature. The soaked pulp was disintegrated for 5 min in a laboratory disintegrator to obtain a uniform fiber suspension of about 1% consistency. To the cellulose suspension, we added 9.2 g of 4-acetamino-TEMPO and 25 g of sodium bromide. A solution of NaOCl (3.1 mmol/g) was added dropwise to the mixture at room temperature under gentle agitation during the first 30 min. The pH was maintained at 10.5 by adding 0.5 M NaOH. After the pH was stabilized, the reaction was stopped after 90 min by adding 1 L of H\({}_{2}\)O\({}_{2}\) (1%). The TEMPO-oxidized product was filtered, washed thoroughly with deionized water, and stored at 4 \({}^{\circ}\)C. Then, the carboxylate content of the oxidized cellulose was determined using an electrical conductivity titration method [49], and it was equal to \(\sim\)1.2 mmol/g. The obtained carboxylate celluloses were used to make the oxidized cellulose gel. In the second step, TEMPO-oxidized cellulose gel (TOCgel) was prepared by high shear dispersion of oxidized pulp in a wet colloid milling apparatus (MK 2000/4) from IKA Works, Inc. (Cincinnati, OH, USA). Briefly, the oxidized pulp suspension (2 L) was pumped from an agitated tank between a conical rotor and a stator in the MK mill in closed loop for one hour to obtain TOCgel material (3% consistency) [47]. Then,  
Figure 1: Schematic representation of the experimental process used in the production of oxidized cellulose gel (**A**); and the TEMPO-oxidized cellulose nanofiber (**B**).  
carboxylate groups created on the cellulose microffibrils by the TEMPO-mediated oxidation have anionic charges in water. These charges induce electrostatic repulsions between the microffibrils, which consequently help the defibrillation process and contribute to the formation of individual microfibrils [51].
# Internet of Things (IoT) and the Energy Sector
## 3 Enabling Technologies
### Actuators  
Actuators are devices that transform a certain form of energy into motion. They take electrical input from the automation systems, transform the input into action, and act on the devices and machines within the IoT systems [53]. Actuators produce different motion patterns such as linear, oscillatory, or rotating motions. Based on the energy sources, actuators categorized as the following types [54].  
_Pneumatic actuators_ use compressed air for generating motion. Pneumatic actuators are composed of a piston or a diaphragm in order to generate the motive power. These actuators are used to control processes which require quick and accurate response, as these processes do not need a large amount of motive force.  
_Hydraulic actuators_ utilize the liquid for generating motion. Hydraulic actuators consist of cylinder or fluid motor that uses hydraulic power to provide mechanical operation. The mechanical motion gives an output in terms of linear, rotatory, or oscillatory motion. These actuators are used in industrial process control where high speed and large forces are required.  
_Thermal actuators_ use a heat source for generating the physical action. Thermal actuators convert thermal energy into kinetic energy, or motion. Generally, thermostatic actuators are composed of a temperature-sensing material sealed by a diaphragm which pushes against a plug for moving a piston. The temperature-sensing material can be any type of liquid, gas, wax-like substance, or any material that changes volume based on temperature.  
_Electric actuators_ apply external energy sources, e.g., batteries to generate motion. Electric actuators are mechanical devices capable of converting electricity into kinetic energy in either a single linear or rotary motion. The designs of these actuators are based on the intended tasks within the processes.  
In the energy sector, for example, in power plants, pneumatic actuators are traditionally applied to control valves. Electric control-valve actuator technology enables achieving energy efficiency. They are also often used as the final control element in the operation of a power plant [55]. In addition, there are variety of actuators developed for energy industry, e.g., LINAK electric actuator ([[https://www.linak.com/business-areas/energy/](https://www.linak.com/business-areas/energy/)]([https://www.linak.com/business-areas/energy/](https://www.linak.com/business-areas/energy/))) that offer solutions for example for minimizing the energy waste when opening hatches and locking brakes in wind turbines and creating motion in solar tracking panels. In the literature there are also many studies aimed to illustrate the applications of the actuators within IoT. For instance, the research in [56] proposes a wireless sensor and actuator network to provide an IoT-based automatic intelligent system. Whereas, by optimizing the operation of devices and machines in the IoT, the proposed system achieves reduction in their overall energy consumption at a given time.
## 6 Results and Discussion
### Scenario 2 (Charging of SC during Regenerative Mode)  
When the battery's SOC is 40% and the regenerative braking system is engaged, then the speed of the motor is decreased if the battery is in regenerative mode or normal mode. We have decreased the battery's state of charge to 40% to observe the effect of the SC on the system. The battery's SOC can be seen in Figure 21, which is decreasing initially so that the SCs are charged enough to provide power to the battery.  
Now the regenerative braking system is engaged. Every time the BLDC motor accelerates after deceleration, it draws a large amount of starting current. The effect of a large current drawn can be seen in Figure 22, where there is a sudden dip in the battery's SOC at  
Figure 21: Battery’s state of charge (SOC) during braking.  
Figure 20: Boost converter outputs of (**a**) voltage and (**b**) current during Scenario 1.  
2.5 s. The second dip is less noticeable at 3.3 s because at this point the SC is charged up to a point where it can supply the energy generated back to the battery from regenerative braking via the SEPIC converter. The voltage and current supplied by the SEPIC converter can be seen in Figure 23.  
The response of FLC when the regenerative braking system is engaged can be seen in Figure 24. By taking a closer look, it can be observed that in this case, the FLC controller responded to the change in the system at three different time intervals. The first time FLC responds at the beginning when the motor is starting and drawing a high starting current from the battery pack. At time 2.5 s, the second FLC response can be observed and it can be  
Figure 23: SEPIC converter outputs. (**a**) Voltage and (**b**) current for braking mode.  
Figure 22: (**a**) Speed of motor during braking. (**b**) Charging of SC during regenerative mode (Scenario 2).  
seen that the FLC response is relatively larger in the period. This is the same time when the BLDC motor accelerated after almost coming to a standstill. The third response of the FLC can be seen at time 3.3 s and the response period is smaller than the previous one. This is because at this point the SC has stored enough charge to supply the energy back to the battery.  
The same effect can be seen in the output of the boost converter voltage and current; the voltage spike at the time 3.3 s is larger than the spike at time 2.5 s because at that time the SC is relieving the burden of the battery. Observing the boost converter output voltages and current, the DC voltage of the boost converter is constant. The output current of the boost converter is also constant, as can be seen in Figure 25 below, due to the constant speed of the BLDC motor. Since the motor's speed is uniformly constant due to which the output current and voltages are constant. The current and voltage from the boost converter can be seen in Figure 25a,b.  
Figure 24: The response of FLC during braking.  
Figure 25: Boost converter outputs (**a**) voltage and (**b**) current during braking.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 5 Simulation Results and Analysis  
We carried out a series of experiments to evaluate the performance of our proposed protocol, HCEL. Details regarding the performance metrics can be found in Section 3.4. The evaluation involved comparing our proposed protocol (HCEL) with both SIMPLE and ERRS protocols [24; 25] across these performance metrics. Our implementation utilized the Networkx package [69] with different experiment parameters outlined in Table 3. To account for the inherent randomness in the RSSI values generated by our protocol, we conducted 15 simulation runs following a log-normal distribution, each lasting 35,000 time units.
## 5 Analysis of Selected Design Variants of the Considered Structure  
### Solution I: Adding Longitudinal Reinforcing Columns  
Solution I involves mitigating stress concentration issues at specific points of the side wall panels by adding longitudinal reinforcement columns along the length direction of the wall.  
Figure 24: The total deformation and stress for the three thicknesses in the X+Y+Z direction.  
These reinforcement columns are fixed to the inner or outer surfaces of the side wall panels, as depicted in Figure 25a. The material used is ST12, with a column diameter of 48 mm, and the height matches the total height of the box-type substation, which is 2102.5 mm.  
Simulation results after adjusting the structure, as shown in Table 10, lead to the following conclusions:  
1. Deformation Analysis: As the wall thickness decreases, there is a slight increase in deformation, indicating that thinner structures generally have a lower rigidity. The maximum deformation for all thicknesses primarily occurs in the Z and XZ directions, with the deformation in the Z direction being significantly larger compared to the X and Y directions.
2. Stress Analysis: The 2.0 mm-thickness side walls exhibit the lowest stress in the X direction, indicating that the reinforcement by columns is most effective for side walls of this thickness. In the Y direction, the stress values for the three different thicknesses do not vary significantly. However, in the Z direction, there is a slight increase in stress with a decreasing wall thickness, with the 1.5 mm-thickness side walls displaying the highest stress levels.  
In the simulation results after adjusting the structure, taking the 2.5 mm-thickness cloud map as an example (similar optimization levels are observed for the 2.0 mm and 1.5 mm maps), as shown in Figure 26, significant changes are evident: the phenomenon of deformation concentration in the side walls has been effectively alleviated, and stress concentration points at the junction of the side walls and brackets have been significantly  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Thicknesses** & **Response** & **X** & **Y** & **Z** & **XZ** & **ZY** \\ \hline \multirow{2}{*}{2.5 mm} & Von Mises Stress (MPa) & 0.52671 & 0.32637 & 4.0611 & 4.067 & 4.0611 \\  & Total Deformation (mm) & 0.051331 & 0.057588 & 1.1536 & 1.1547 & 1.1536 \\  & Von Mises Stress (MPa) & 0.22718 & 0.31417 & 4.1499 & 4.156 & 4.1499 \\  & Total Deformation (mm) & 0.052718 & 0.055495 & 1.1548 & 1.156 & 1.1548 \\  & Von Mises Stress (MPa) & 0.34629 & 0.32705 & 4.1478 & 4.1541 & 4.1478 \\  & Total Deformation (mm) & 0.054138 & 0.057823 & 1.1577 & 1.159 & 1.1577 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Combination simulation results of three thicknesses under Solution I.  
Figure 25: Model of Solutions I-IV.  
improved. This indicates that Solution I has achieved a significant effectiveness in enhancing structural stability and reducing stress concentrations.
## 3 Results and Discussion
### Protein Content According to Dumas  
In order to see if the protein content of the spinning solution differed from the protein content of the spun and heated fibers, the protein content of all samples was determined. The protein determination according to Dumas involves combusting the sample. The nitrogen content of the burned sample can then be determined from the resulting exhaust  
Figure 1: Histograms of the diameters of the different blends (**A**) (80:10:5), (**B**) (80:10:10), (**C**) (80:10:15) and (**D**) (80:10:20). The different letters represent significant differences (\(p<0.05\)) between the different blends.  
gas. Since the fibers consist only of protein and maltodextrin, which contains almost no nitrogen, the protein content of the fibers can be determined directly from the released nitrogen content. The protein contents of almost all unheated fibers were slightly lower than the calculated protein content in the spinning solutions (Table 2). Only the unheated fibers of blend D showed a slightly higher protein content (20.68 \(\pm\) 0.29 g/100 g) than the spinning solution with a protein content of 20% \(w/w\).  
The ratio between the protein content of the spinning solution and of the unheated fibers ranged between 1:0.90 (blend C) and 1:1.03 (blend D) (Table 2). Since both the protein content of the unheated fibers and the one of the spinning solutions were relatively similar, it seems that the proteins were well included into the fibers by electrospinning. The fact that the fibers were spun in several batches and therefore the spinning solutions of the different  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Protein Content of the** & **Protein Content of** & **Ratio Protein Content of the** \\
**Blend** & **Spinning Solution** & **the Unheated Fibers** & **Solution to Fiber Protein** \\  & **(g/100 g)** & **(g/100 g)** & **Content** \\ \hline A & 5 & 4.80 \(\pm\) 0.01 & 1:0.96 \\ B & 10 & 9.79 \(\pm\) 0.03 & 1:0.98 \\ C & 15 & 13.57 \(\pm\) 0.02 & 1:0.90 \\ D & 20 & 20.68 \(\pm\) 0.29 & 1:1.03 \\ \hline \end{tabular}
\end{table}
Table 2: Protein content of the spinning solution compared to the protein content in the unheated fiber for the different blends with standard deviation.  
Figure 2: Scanning electron microscopy images showing defects of the electrospun fibers of the different blends (**A**) (80:10:5), (**B**) (80:10:10), (**C**) (80:10:15) and (**D**) (80:10:20) before thermal treatment. The scale bar represents 10 \(\upmu\)m.  
ratios had to be prepared several times could also be a reason for the slight variability of the protein contents of each blend.  
In Figure 3, the protein contents of the different blends are shown as a function of the heating time. All blends showed a significant difference in protein content during heating. Therefore, it appears that the protein content of each mixture fluctuated a little during heating with the exception of mixture D; here, there is a noticeable decrease in protein concentration between 0 and 6 h. (Figure 3). These variations are most likely due to variations in moisture content of the unheated and heated fibers.
## 5 Simulation Results
Conversely, SUS and SIRUS are not influenced by the CSI availability for user scheduling because the user selection metrics are calculated using the estimated channel. Moreover, the RR scheduler is not affected by the CSI availability for user scheduling because of the random user selection. From Figures 8 and 9, it is observed that the scheduling gain for the MF receiver increases as \(M\) increases, regardless of both the user selection algorithm and CSI availability for user scheduling. Simply, the scaling law for the MF receiver does not change depending on the user selection algorithm and CSI availability for user scheduling. However, the scheduling gain for the ZF receiver shows different scaling laws with \(M\) depending on the two factors. For the ZF receiver, it is observed that the scheduling gains for (i) optimal scheduler with non-ideal CSI for user scheduling and (ii) SUS decrease as \(M\) increases, while that for an optimal scheduler with near-ideal CSI for user scheduling increases with \(M\). This implies that if near-ideal CSI is available for user scheduling, full multi-user diversity  
Figure 8: Ergodic sum rate under imperfect CSI as a function of \(M\), where \(N=100\), \(K=20\), \(p_{u}=0\) dB, and \(\tau^{2}=0.4\). Figure 2:0 Scheduling gain under imperfect CSI as a function of \(M\), where \(N=100\), \(K=20\) and \(p_{u}=0\) dB, and \(\tau^{2}=0.4\). gain from the fluctuation of the sum rate can be achieved under the optimal user selection algorithm for the ZF receiver. However, if non-ideal CSI is available for user scheduling, the achievable scheduling gain is limited, even if the optimal user selection algorithm is employed. If a typical low-complexity user selection algorithm, such as SUS, is employed, the scheduling gain for the ZF receiver decreases as \(M\) increases, even though the variance of the sum rate increases with \(M\). Therefore, for a large \(\tau\), the benefit of user scheduling for the ZF receiver disappears under imperfect CSI provided that the user scheduling is far from optimal, and only non-ideal CSI is available for user scheduling, as analyzed in Section 4.
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 3 5G Antennas as a Candidate for Massive MIMO Technique at Sub-6 GHz
### Single Elements  
Single-element antennas are easy to design, implement, and manufacture. There are two types of 5G designs to reduce the size of antennas: three-dimensional designs and two-dimensional crossed dipole and patch antenna designs, as discussed below.