esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Dexterous Grasping
#### 3.2.3 Transformers  
The Transformer model exhibited superior performance in predicting grasp forces and finger positions across a variety of objects, effectively overcoming the limitations seen in LSTM and GP models. Unlike sequential architectures, which process data step by step, Transformers leverage a self-attention mechanism to analyze the entire sequence at once. This ability enables them to efficiently capture long-term dependencies and contextual relationships, leading to more accurate and adaptive force modulation. In the experiments, the Transformer model demonstrated improved force modulation across different grasp configurations. For instance, the forces applied to an apple were recorded at 3.2 N for a two-finger grasp and 2.4 N for a three-finger grasp as shown in Table 2. The model achieved an overall efficiency of 95.6%. In the three-finger grasp configuration, the average forces recorded were 1.8 N, 2.9 N, 2.2 N, and 2.7 N, for the Ball, Banana, Orange, and Tomato, respectively, as shown in Figure 13.  
A key advantage of the Transformer model is its ability to process multiple dependencies within an input sequence simultaneously, allowing it to refine force predictions more effectively than LSTM, which relies on sequential memory. By considering the broader context of the grasping scenario, the Transformer model was able to adjust forces dynamically, making it more adaptable to varying object shapes and textures. However, similar to LSTM and GP models, the Transformer encountered stability issues with the ball during five-finger grasps, as the grasp configuration struggled to maintain a constant force once grasped. These findings indicate that, while the Transformer exc  
Figure 13: Force profile for three-finger grasp configuration for the Transformer model: (**a**) Ball, (**b**) Banana, (**c**) Orange, (**d**) Tomato.  
and generalization, certain object geometries and grasp configurations will require further refinement. These findings highlight the potential of Transformers in robotic grasping applications, demonstrating their ability to enhance force adaptation and grasp precision across a wide range of objects while maintaining computational efficiency and robustness.
# ISO 50001 Data Driven Methods for Energy Efficiency Analysis of Thermal Power Plants
## 3 Methodology and Preliminary Analysis
### Definition of the Neural Network Based Analytical Model  
In particular, in designing our feed-forward neural network, the main choice was the number of layers and their proper sizing [26]. In this light, a rich literature gives several empirical equations to better estimate the number of ideal structures of the neural computing engine in terms of neurons in the hidden layer. Since our main application is related to the power sector, we decided to follow the interesting approach proposed in [27], suggesting a hidden layer between 10 and 14.  
The algorithm chosen to train the network is the Levenberg-Marquardt because compared to classical Gradient Descent and Newton methods, we experienced a general best behavior along with the Bayesian regularization algorithm. The characteristics of the simulations are a maximum number of epochs, set to 1000, and a maximum number of validation failures, limited to six. The number of validation failures has been set to this value because it was considered a reasonable standard in our practice allowing us not to lose the generalization capability of the network.  
The dataset has been divided into three parts: 70% of the data is dedicated to the training part, 15% to the validation and the remaining 15% to the testing phase. In order to stress the design robustness of the neural network, regardless of the literature on ANN structure, other attempts have been conducted on three neural models with different numbers of neurons and layers: namely, 2 with a single layer structure (with 14 and 70 neurons, respectively) and 1 with two hidden layers (with 14 and 12 neurons, respectively).  
After this preliminary analysis, as reported in the following, the selected neural model is the single layer neural network with 14 neurons in the hidden layer, since it has been shown to have similar results to bigger networks in terms of the confidence coefficient R and MSE (Mean Square Error) but a lower computational effort on the whole plant dataset. The non-linear relationships are estimated through the sigmoid function. The output value is varied through a linear relationship with the weighted connections from the former layer.
## 4 Implementation and Testing Activities on MDTP
### Specific PDU Tests
#### 4.2.3 Testing Case 3: Discovery of Control Data Terminal Component  
This use case was introduced with the idea of having an accurate idea of the amount of time required to check the existence of available vehicles present in a deployment. The system where MDTP works is based on the usage of CDTs in order to know which autonomous maritime vehicles are present (since an active CDT will mean that a vehicle has been deployed). While the PDU required to make this possible is simpler than the others, as it can be seen in Figure 12, MDTP has been conceived to work with two different versions of DDS when a system is deployed. Consequently, there are several parameters that must be negotiated and will take a longer time to have that process completed. Fortunately, it is a procedure that will only have to be done once during a mission, so it will not be time consuming or disruptive from the bandwidth usage point of view.  
The measurements involving a collection of 50 attempts to successfully prove this functionality have been included here too. These are larger than the ones obtained previously due to the negotiations that have to be done regarding the different DDS versions used in the designed system, but discovery is carried out successfully in any case. Furthermore, no discovery requests or interchanged data were lost during the testing activities. These features can be appreciated in Figure 13, where the results are shown in a graphical manner.  
With regards to the most prominent figures obtained from the set of run tests, despite been higher than the ones obtained previously (due to the parameters that must be negotiated between the two different implementations of DDS) they still show the same positive features that were shown before. For example, the difference between the minimum and maximum time figures is still contained (the minimum value is 62.9% of the maximum). What is more, as it can be seen in Table 8, the difference between average (25.4436 s) and median (25.885 s) parameters is small enough to confirm the stability of the protocol under the scenario that has been put forward.  
\begin{table}
\begin{tabular}{c c} \hline
**Request Attempt** & **Time (s)** \\ \hline Maximum & 5.71 \\ Minimum & 3.6 \\ Average & 4.4002 \\ Median & 4.455 \\ Standard deviation & 0.59223027 \\ \hline \end{tabular}
\end{table}
Table 7: Most prominent figures obtained from periodic data subscription measures.  
Figure 12: PDU used for CDT discovery.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 4 Results and Discussion
### Improving the Network Architecture  
The model achieved valuable results in regard to the shallow network architecture proposed in this work. Recent deep learning models adopted or built for the purpose of blood volume pulse [57] or pulse rate [61] measurement from videos exhibit significant results, in particular for image sequences that contain wide head movements. The shallow architecture proposed in this work may not compete with these deep models.  
The main objective of this pilot study was to assess the limits and potential of 3D CNN in the context of PPG measurement from image sequences. We therefore envisage improving the network architecture in order to get more promising results. One of the main avenues consists in increasing the number of hidden (i.e., 3D CNN) layers and integrating optimized distributed gradient boosting (XGBoost). XGBoost is a widely used method that achieves state-of-the-art results in many machine learning challenges [84].  
An iterative, grid-based procedure should be developed to assess the impact of network architecture (e.g., number of layers, number of filters per convolutional layer, and number of neurons per dense layer) and hyper-parameters on performance. This procedure could provide an objective and automatic way of selecting the network architecture that achieves the highest performance.  
Figure 10: Beat-to-beat Bland–Altman plots showing the differences in pulse rate between video and ground-truth measurements, plotted against ground-truth measurements. Means are represented by dash-dot lines and 95% limits of agreement (\(\pm\)1.96 SD) by dashed lines.  
Only video patches of a 25 \(\times\) 25 \(\times\) 60 size were analyzed by the neural network. Varying these values or adopting a spatiotemporal multiscale approach [66] should be investigated in future work. In addition, the learned pulse rate values were sampled with a constant 2.5 bpm step. Rising the sensitivity of the model by reducing this interval could be particularly interesting.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### Key Performance Metrics
#### 6.4.2 Isolation  
Isolation refers to how well the switch suppresses signals leaking into the \"OFF\" port. If \(P_{\text{leak}}\) is the power reaching the isolated port and \(P_{\text{input}}\) is the input power, the isolation \(I\) in dB is:  
\[I(f)=10\log_{10}\Bigl{(}\frac{P_{\text{leak}}}{P_{\text{input}}}\Bigr{)}. \tag{54}\]  
Often, in terms of S-parameters, the isolation to the \"OFF\" port from the input port is given by:  
\[\text{Isolation (dB)}=-20\log_{10}\bigl{|}S_{31}\bigr{|}\quad(\text{when port 3 is OFF}). \tag{55}\]
## 4 Network Node Bandwidth Allocation Method
### Motivation  
As we discussed earlier, when relying on the sending or receiving nodes to control the transmission rate, network nodes only being responsible for forwarding inevitably leads to inaccurate network status detection. Therefore, utilizing network nodes to participate in transmission rate control is a feasible solution. The information network nodes provided in the ECN scheme are not accurate enough. The XCP scheme adds rate control information to each packet and processes it during network node forwarding, which increases the burden of forwarding packets.  
Based on the above discussion, we propose a bandwidth allocation method for network nodes which allocates the bandwidth for each transmission based on information such as the current available bandwidth of the network node and the number of serviced transmissions. To implement this algorithm, the transmission rate control scheme proposed in this article is based on the following network characteristics. Network nodes, namely routing nodes, adopt a new network architecture that separates control and forwarding and has sufficient computing resources. In addition to forwarding data packets, they can perform operations such as computing, storing information, and sending control messages using controllers on nodes.  
The transmission adopts a new network architecture that separates the identification and address for transmission protocols. Identification can represent receiving nodes, sending nodes, and transmissions.
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
The 3 GPP defined frequency range designation (FR2) is 24.25-52.6 GHz, and corresponding operating bands are defined in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operating Band** & **Uplink (GHz)** & **Downlink (GHz)** & **Duplex Mode** \\ \hline n257 & 26.5–29.5 & 26.5–29.5 & TDD \\ n258 & 24.25–27.5 & 24.25–27.5 & TDD \\ n260 & 37–40 & 37–40 & TDD \\ n261 & 27.5–28.35 & 27.5–28.35 & TDD \\ \hline \hline \end{tabular}
\end{table} 
Table 1: NR operating bands in FR2. With the digitalization of railways, there are more and more types of sensors installed on trains, and a large number of monitoring data are collected during train operation, which need to be transmitted back to the ground center for comprehensive analysis. The traditional method is for these data to be copies by staff after the train is put into the yard or depot, which has low working efficiency and poor real-time performance. With the wide spectrum resources of mmWave and the application of Massive MIMO, beamforming, spatial multiplexing, and other technologies, the data throughput of mmWave communication systems between train and trackside can reach more than Gbps [2] during the process of trains entering the depot or passing through hotspot areas, such as train stations or marshaling yards, at very low speeds, normally 5-10 km/h, so large amounts of monitoring data such as videos collected during train operation can be transmitted during this timespan to the ground via the mmWave communication system in a few seconds for further analysis. Thus, mmWave is a promising solution for this scenario as a supplementary means of 5G-R and can significantly improve the efficiency and real-time performance of train-ground data transmission.
## 2 Materials and Methods  
The studied images were obtained from the THERMO-WET system, as the first in the world, which enabled automated measurement in a wide temperature range (up to 1800 \({}^{\circ}\)C) in a protective atmosphere [10]. The developed system is able to measure the surface tension of the liquid phase, and the extreme wetting angle of the base by the liquid.  
Several methods have been developed to measure interfacial tensions and contact angles, such as the well-known axisymmetric drop shape analysis (ADSA) method [11], electrowetting-on-dielectric (EWOD) [12]. These methods can only be used for symmetrical droplets. However, in most cases, the specimen is not symmetric. Therefore, there was a need to develop methods that could measure non-axisymmetric droplets. These methods include polynomial fitting [13], sub-pixel polynomial fitting (SPPF) [14], moving goniometric mask [15], and 3D analysis [16].  
The measurement method related to THERMO-WET system is based on image processing and analysis algorithms, and is conducted using the sessile drop technique [17]. In short, the method works as follows. The camera records and monitors images of the melting process of the specimen (in this study, this specimen is copper) placed inside the high-temperature furnace. The specimen begins to melt at 1080 \({}^{\circ}\)C, and takes a spherical shape at about 1134 \({}^{\circ}\)C. The measurements are performed in a temperature range of 1135-1300 \({}^{\circ}\)C with a 20 \({}^{\circ}\)C step. Twenty images are acquired for each temperature. There are two forces that affect the shape of a droplet placed on a non-wettable surface: the surface tension, which attempts to give the droplet a spherical shape, and gravity by which the droplet is flattened [18].  
The specificity of images taken at high temperatures makes the segmentation process particularly difficult and requires the use of specialized, dedicated algorithms. Figure 1 shows an original image of a copper specimen obtained from the THERMO-WET measurement system at a high temperature (1175 \({}^{\circ}\)C). The image shows low contrast and blurry boundaries, which makes image segmentation a challenging task.
## 4 Future Research Directions
### Open Research Direction for DSM
#### Consumers' Preferences and Priorities  
Consumers preferences and priorities are the most important factors in DSM. The existing literature contains various DSM schemes which are cost-effective and can efficiently achieve their required objectives; however, the consumer's preferences and priorities are not considered in these schemes. As in DSM, the consumers' demands are managed at the consumers' premises; therefore, it is very important to care about the consumers' preferences and priorities. Therefore, the design of a DSM scheme based on consumers' preferences and priorities may be a new research direction.
## 3 Description of the Equipment Used  
The tests were carried out on the cyclic simple shear equipment manufactured by the Wykeham Farrance Company of England, which is doubly servo-controlled; full description can be found in [35]. Figure 3 shows a line drawing of the application mechanism of the axial and tangential loads.  
A brief description of the cyclic simple shear equipment used is as follows:  
* The equipment functions with pneumatic action and requires an air supply system that guarantees at least a working pressure equal to 800 kPa.
* The application of axial and tangential loads is carried out by two actuators and two digitally controlled servo-valves. The capacity of the actuators is 5 kN and the maximum working frequency of the servo-valves is equal to 70 Hz.
* To measure axial and tangential loads, device is equipped with two load cells of 5 kN capacity and 1.2 N precision. For the control and registration of axial and tangential deformations, the equipment has three linear variable differential transformers (LVDTs); two interiors, installed inside the actuators and one outside, located above the axial load frame.
* The simulation, control, and capture of data are done by a panel connected to a computer via USB. The data capture module has 13 input 10 V channels with an acquisition resolution equal to 20 bits and a speed of 5 kHz. The control module has two channels: one for the vertical load actuator and the other for the tangential load actuator.
* The control panel computer assembly controls the servo valves that apply the load with the preset speed, magnitude, and wave type and simultaneously captures the information supplied by the load cells and LVDTs.  
Figure 3: Scheme of the mechanism for the application of axial and tangential loads.  
* The equipment can test under undrained conditions (constant volume) and drained conditions (constant axial load).
* The dimensions of the samples are 70 mm in diameter and 19 mm in height. To keep the diameter of the specimen constant during testing, it is confined by a series of sliding rings.
## 6 Conclusions  
The operation of Glidarc plasma installation was analysed from the EMC-radiated and conducted emission point of view. For safety and legal reasons it is necessary to monitor the electromagnetic compatibility of plasma reactors.  
Arc discharges introduce radiated disturbances into the environment and caused disturbances in the power supply system. When a reactor in the laboratory is switched on, it interferes with other equipment in the local environment. This is particularly visible in computer monitors, electronics controlling the reactor systems, lack of correct Wi-Fi transmission in the nearest space, and in the transmission of GSM calls. The results of professional measurements confirm that the permissible levels of electromagnetic emissions are exceeded. The author's research and literature studies indicate that the interference is in many types of reactors and installations with arc discharge [2; 3; 5; 11; 12; 15]. These are high emission levels, for the measured installation exceeding 20 dB.  
Very large conductive disturbances were measured. They are so large that their impact on energy quality and reliability of the power supply system has to be determined. A poorly functioning power supply can cause unstable arc and plasma.  
Understanding the physical processes responsible for the radiated and conducted emission makes it possible to design anti-interference components. This situation forces further testing. The tests must be carried out at many levels. Interference levels must be determined in many different reactor operating configurations, at different gas flow rates, different supply voltages, different electrode geometries, different ignition systems.  
On the other hand, work is necessary to improve compatibility. Thus, the principles of filtration and shielding of such installations must be defined. High levels of interference indicate the need to design filtration systems with chokes that have amorphous or nanocrystalline core material. Such filters have high attenuation.  
This research was funded by the Lublin University of Technology.  
The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Fluorinated Polymers for Photonics--From Optical Waveguides to Polymer-Clad Glass Optical Fibers
## 2 Fluorinated Polymers for Passive Optical Waveguides
The optical losses at 1550 nm were still too high (0.8 dB-cm\({}^{-1}\)), and acrylates have a rather limited temperature resistance. Silicones, some of which are deuterated, have also been used as waveguide materials in the past [22; 23]. The disadvantages were non-optimal optical losses, a tendency to swell in media and a thermo-optic coefficient about 2.5 times higher than that for PMMA [10]. Ormocer(r) hybrid materials have high thermal stability and show moderate optical losses of approx. 0.6 dB-cm\({}^{-1}\) at 1550 nm. Their refractive index is above 1.51 at 1550 nm, even in the case of fluorinated systems [24]. The South Korean company ChemOptics [25] (Daejeon, Republic of Korea) offers a material system that shows low optical losses (0.35 dB-cm\({}^{-1}\) at 1550 nm) in the layer waveguide and good adhesion to silicon. However, the TOC is not low (\(-\)(1.5-2.2) \(\times\) 10\({}^{-4}\) K\({}^{-1}\)). Photochemically active polymers are a broad branch of polymer science and are used as UV-curable thermosets in passive and active optical waveguides, optical cladding for glass fiber, photoresists and composites. From a chemical point of view, two main reactions are the radical photochemical polymerization of carbon double bonds (often acrylates) and the ring-opening polymerization of oxiranes using photoacid generators. Photopolymerization of the double bond is used in the above-mentioned commercial materials from ChemOptics for passive optical waveguide fabrication [25], while photochemical cross-linking of oxirane-containing polymers is the typical reaction in negative photoresists. In general, a planar optical waveguide consists of two materials. The core material (\(n\) = 1.48-1.50) is clad with another polymer material (called cladding, \(n\) = 1.45) [26; 27]. The refractive index of cladding is usually close to that of fused silica (1.444 at 1550 nm). The refractive index contrast between the core and the cladding materials (0.05, 0.03 or lower) is determined by the application. The general design of a channel optical waveguide is shown in Scheme 1. Passive optical waveguides are used in the optical microchip on the polymer platform (Scheme 2). They are shown in dark blue in Scheme 2. A passive optical waveguide serves as a connection between elements of a microchip similarly to wire in an electronic chip. Other elements could be filters, switches, gratings, distributors, etc. An optical fiber is connected to a passive waveguide via a U-nut. Different fabrication procedures can be used for such waveguide fabrication, including direct photolithography, where the core layer serves as a photoresist (Scheme 3). In the case of viscous monomeric mixtures, such as the material from ChemOptics [25], usually more complicated procedures involving reactive ion etching are applied (Scheme 4). It is also possible to apply soft lithography techniques (microcontact printing or micromodling in capillaries [13]).