esa-sceva/satcom-chunk-collection huggingface dataset:
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments
### Deployment of the Neutral Host Platform  
A meticulous planning of computing resources and network connectivity design was required to properly instantiate all the components of the neutral host platform in the three city pilots, following its latest release [49]. For the sake of illustration, Figure 4 represents the mapping of the platform components with respect to the infrastructure deployed in each city. In general, each software module of the platform is deployed as a VM placed in the compute nodes of the core and edge NFVI.  
Below, we provide a brief description of the main technologies that enable the deployment of the neutral host platform:  
* The VIM was implemented in the core and edge DCs using OpenStack (release Queens). This cloud platform is currently the most widely deployed open-source cloud infrastructure software in the industry.
* Additionally, to support the deployment of NSs based on containers, we also installed Fog05 [50] as the extended edge VIM of the platform. This open-source project enables the deployment of services in resource-constrained devices, which are close to end-users, thus minimizing the service latency.
* To orchestrate the lifecycle of NSs within the 5G-enabled slices, we deployed OSM as the NFVO of the platform.  
Figure 4: Platform deployed in (**a**) Barcelona, (**b**) Bristol and (**c**) Lucca.  
Finally, as part of the vRAN capabilities offered by the neutral host framework, we also deployed the dRAX Open Interface RAN Intelligence [51] solution. This cloud-native component runs virtualized in the edge/MEC infrastructure to manage the associated small cells as radio units, which effectively unlocks the potential of 5G network slices for multitenant operators. All this while ensuring low latency and processing at the edge for deployed radio services.  
The rest of the components that integrate the neutral host platform (as described in Section 3), were developed as open-source software and released in the GitHub space of the 5 GCity project [52].
# Feasible Energy Density Pushes of Li-Metal vs. Li-Ion Cells
###### Abstract  
Li-metal batteries are attracting a lot of attention nowadays. However, they are merely an attempt to enhance energy densities by employing a negative Li-metal electrode. Usually, when a Li-metal cell is charged, a certain amount of sacrificial lithium must be added, because irreversible losses per cycle add up much more unfavourably compared to conventional Li-ion cells. When liquid electrolytes instead of solid ones are used, additional electrolyte must also be added because both the lithium of the positive electrode and the liquid electrolyte are consumed during each cycle. Solid electrolytes may present a clever solution to the issue of saving sacrificial lithium and electrolyte, but their additional intrinsic weight and volume must be considered. This poses the important question of if and how much energy density can be gained in realistic scenarios if a switch from Li-ion to rechargeable Li-metal cells is anticipated. This paper calculates various scenarios assuming typical losses per cycle and reveals future e-mobility as a potential application of Li-metal cells. The paper discusses the trade-off if, considering only the push for energy density, liquid electrolytes can become a feasible option in large Li-metal batteries vs. the solid-state approach. This also includes the important aspect of cost.  
s +
Footnote †: journal: Applied Sciences  
solid-state batteries; solid electrolytes; lithium-ion batteries; Li-metal batteries; Li-metal anode; excess lithium; anode-free lithium battery
## 3 Results and Discussion
### Statistical Treatment  
A Pearson correlation coefficient study was performed taking into consideration the different domains in order to detect similarities and differences. As can be seen in Figure 3, the elements with significant correlations in the Almaden syncline were found to be those related to volcanic activity (Fe, Ni, Ca, Cu, Cr, Mg), but in the Guadalmez syncline, fewer significant correlations were found in the Almaden syncline, which is caused by a variety of factors. Lower correlations were found for Hg, Pb, Sb, and As in both synclines (pictured in green in Figure 3), all of which are mineral-deposit-forming elements of quasi-monoelemental mineralizations. Moreover, pedological parameters appeared to be related to rock-forming elements and showed higher correlations for pH than for OM and EC in both synclines. Finally, SiO\({}_{2}\) showed very low correlation coefficients with other rock-forming elements but not very low for mineral-deposit-forming elements. Although this change between such low correlations cannot be considered significant, it may be a consequence of the presence of almost all mineral deposits in the quartzite layers of both synclines.  
Cluster analysis (Figure 3) differentiated two groups and three subgroups from each dataset. The group that differed most clearly was that of the mineral-deposit-forming elements (Hg, Sb, Pb, and As) (marked as E in Figure 3b-d), which appeared together with EC, OM, and S in the Almaden syncline, and together with P and Ag in the Guadalmez syncline. In the Almaden syncline, a large amount of cinnabar in its monomineralologic deposits produces a remarkable level of dissemination of Hg and S and significant emissions of gaseous Hg uptake by the soil organic fraction (humic and fulvic acids) [27; 28; 29; 30; 31]. As described by [PERSON] et al. (2003) [27], the soils of the Almaden region are poor in smectites, thus reducing the possibility of cation exchange and increasing the availability of metals from the inorganic matrix, leaving organic matter as the only metal retention agent. However, in the Guadalmez syncline, there are only two new elements: Ag related to Pb due to the Pb-Zn-Ag deposits of the Valle de Alcudia mining district; and P, related to Pb-Ag, which may be due to the more agronomic character of the soils from this syncline, less populated than the Almaden syncline and much less anthropized.  
Another large group was formed by the rock-forming elements with a less clear distribution between the subgroups. In the Almaden syncline, the three subgroups differentiated ferromagnesian elements typical of mafic magmatic rocks (Subgroup C, see Figure 3c) from two other less well-defined ones. In the Guadalmez syncline, there was also a smaller subgroup related to mafic magmatic rocks (Subgroup B) and another with mobile elements (Cu and Zn) typical of Pb-Zn-Ag mineralizations (Subgroup A) (Figure 3b). Finally, there was a subgroup related to the carbonate-rich rocks outcropping in this Guadalmez syncline on which the OM and EC pedological parameters apparently depend (Subgroup C, Figure 3b).
## 3 Testbed Vehicle Specifications and Loading Scenarios
### Braking Forces--Second Loading Scenario  
Using Equation (4) force \(N_{f}\) can be calculated for different deceleration values (\(d\)). In addition, using Equation (10), the coefficient of friction (\(\mu\)) can be obtained, related to different braking distribution factors (\(\rho\)) and decelerations. The brake force acting on the front wheels (\(S_{f}\)) is then calculated based on \(N_{f}\) and \(\mu_{f}\). The same procedure is used to calculate the rear braking force \(S_{r}\). The worst-case scenario of forces applied is considered when only the front brake is used (\(\rho=0\)). Assuming a friction factor \(\mu_{f}=0.8\) and a maximum deceleration of d = 0.8g, results in \(S_{\mathit{flight}}=S_{\mathit{flight}}=S_{\mathit{f}}/2=1251.75\) N on each front wheel. The brake moment (\(M_{br}\)) generated by \(S_{f}\), considering the front tire radius (\(R=0.32\) m), is \(M_{br}=S_{f}\times R=400.5\) Nm. The vertical load is also found equal to \(N_{\mathit{flight}}=N_{\mathit{right}}=1210\) N. These forces correspond to the second loading scenario considered in the simulations. Using Equation (13), the maximum deceleration that can be achieved in order to avoid tire skidding would be \(d=0.8\) g, assuming \(\mu=0.8\).
# Influence of the Slope and Gate Offset on Movement Variability and Performance in Slalom Skiing
## 1 Introduction
SampEn is more robust to variations in time series length and can provide meaningful insights even with shorter data [40], and is one of the most widely used entropies in sports and health sciences [9, 10, 11, 12, 35, 41, 42]. In recent years, MV has been calculated through SampEn from the acceleration time series collected at the lower back of athletes using an inertial measurement unit (IMU) [9, 10, 11, 12, 13]. In alpine skiing, IMUs can offer a number of advantages that go beyond the limitations of traditional or laboratory methods [43, 44, 45, 46, 47, 48, 49]: compact and lightweight, easily portable and positionable, capable of wireless operation, and with the ability to capture a substantial amount of data (i.e., several runs), instantaneous data acquisition, and the optimization of data collection due to automatic synchronization among all the built-in sensors. However, to the best of our knowledge, no research has been conducted yet to evaluate MV through IMUs in the domain of alpine skiing, nor has it been evaluated regarding how the characteristics of the terrain (flatter vs. steeper slopes) and course setting (gate offset: i.e., turned vs. straighter courses) influence skier MV. Additionally, no article has been found that relates skier performance to MV. Therefore, the aims of this study were (a) to evaluate the influence of different slopes and slalom gate offsets on MV through an IMU and (b) to assess the relationship between MVand performance of elite alpine skiers. It was hypothesized that (a) steep and turned courses would increase MV and that (b) best-performing skiers would exhibit a lower MV.
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
## 4 Proof of Stability  
### Proof of Stability of Velocity Subsystem  
The Lyapunov function is defined as follows for the velocity subsystem:  
\[V_{V}=\frac{1}{2}e_{V}^{2} \tag{45}\]  
By deriving the above formula, we can obtain the following:  
\[\dot{V}_{V}=\dot{e}_{V}e_{V} \tag{46}\]  
Bringing Equations (22) and (26) into Equation (46),  
\[\begin{array}{l}\dot{V}_{V}=e_{V}(f_{V}+g_{V}\beta_{e}-\dot{V}_{d})\\ =e_{V}[f_{V}+g_{V}\frac{1}{g_{V}}\left(\dot{V}_{d}-z_{2V}-c_{1}e_{V}\right)- \dot{V}_{d}]\\ =e_{V}(f_{V}-z_{2V}-c_{1}e_{V})\\ =-c_{1}e_{V}^{2}\leq 0\end{array} \tag{47}\]  
According to Lyapunov stability, the velocity subsystem is uniformly stable. Simultaneously, it can also be proved that the velocity error \(e_{V}\) and its differential \(\dot{e}_{V}\) are bounded.  
According to [PERSON] theorem, if the \(e_{V}\) and \(\dot{e}_{V}\) are bounded and \(e_{V}\) is square integrable, the following formula holds:  
\[\lim_{t\rightarrow\infty}e_{V}(t)=0 \tag{48}\]  
According to Equation (47), Equation (45) is a bounded and decreasing function, which can be derived:  
\[\int_{0}^{\infty}e_{V}^{2}(t)dt=[V_{V}(0)-V_{V}(\infty)]/l<\infty \tag{49}\]  
According to Formula (49), \(e_{V}\) is square integrable. At the same time, \(e_{V}\) and \(\dot{e}_{V}\) are bounded. Therefore, the speed tracking error converges to 0, and the speed subsystem is asymptotically stable.
## 3 Design of Approximate QAM Demodulation Circuits
### Exact LLR Architecture  
The exact LLR (ELLR) method predicts each bit of the received symbol based on probabilities. Assuming equal probabilities \(P(b_{i}=0)=P(b_{i}=1)=1/2\), the LLR for the \(i\)-th bit of the symbol is defined as [26]  
\[LLR(b_{i})=\ln\left[\frac{\sum_{s:b_{i}=0}e^{-\frac{1}{\sigma^{2}}((\tau_{x}-s _{x})^{2}+(r_{y}-s_{y})^{2})}}{\sum_{s:b_{i}=1}e^{-\frac{1}{\sigma^{2}}((\tau_{ x}-s_{x})^{2}+(r_{y}-s_{y})^{2})}}\right] \tag{1}\]  
where \((r_{x},r_{y})\) are the in-phase and quadrature coordinates of the received symbol \(r\), and \((s_{x},s_{y})\) are the coordinates of the constellation points \(s\) with 0 and 1 in the \(i\)-th bit.  
In Figure 1, we present our ELLR architecture. As shown, all the LLRs are calculated in parallel, with the modules retaining the input throughput of 1 data per clock cycle (CC). Our distribution logic module forwards the square distances for the 0 and 1 constellation points in the corresponding adder tree to perform the accumulations of Equation (1). The ELLR method requires the implementation of exponential and natural logarithmic functions; thus, we adopt two different approaches in VHDL, i.e., the hyperbolic coordinate rotation digital computer (CORDIC) function and a polynomial approximate function based on the Remez algorithm [27]. CORDIC is designed in a generic way and is able to function with a configurable bit-width at compile time by the user in order to explore different resource-accuracy trade-offs. As for the polynomial function, we examine various polynomial degrees and evaluate their impact on resources and accuracy. Both functions are designed to be synchronous and fully pipelined, and impose only a small latency, e.g., for 64-QAM, the latency is 43 CCs and 14 CCs for the CORDIC and polynomial functions, respectively.
## 3 Materials and Methods
### MAR System Overview  
The proposed system includes a 7-degrees-of-freedom (DOF) robot arm with spoon-chopstick utensil [65], a food tray, and vision SWs for food recognition and user input. These components can be divided into three groups, as shown in Figure 2. The first group is robot HW, such as the robot arm, its controller, and food tray. The second is the acquisition point estimator, and a tablet user interface based on the user's face (eye gaze/nod/mouth shape) recognition is the last. Each group is introduced in turn in the following subsections. The operation procedure of the whole system is as follows (see Figure 3):  
1. The caregiver holds the robot arm and directly teaches the position that the user's mouth can easily reach.
2. The caregiver presses the start button after setting a meal on the plate. (After this, the user starts to eat alone using the user interface.)
3. The robot moves to a predefined ready position and takes an image of the food tray using a camera attached to the robot arm.
4. From the image, the acquisition point estimator perceives the food area and extracts picking points for each dish through post-processing. Then, it sends the robot controller the points.
5. Once a user command selecting a particular food is transmitted from the user interface to the controller, the robotic arm moves to the coordinates of the acquisition point for that food.
6. The robot performs a food-acquisition motion and then delivers food to the mouth position, as taught directly by the caregiver in step 1.
7. The robot waits for the user to eat food for a preset amount of time.
8. Steps 3-7 are repeated until the end of the meal.  
Figure 2: The overall flowchart of the meal-assistance process proposed in this work.
## 2 Proposed Methods
### Inter-Frequency Band Mapping  
General machine learning principles prescribe that the recognition unit should be modeled to recognize an object, after which the search step uses the model to identify the connection information of the recognition units to identify the best object. However, in this case, the recognition unit cannot be expressed effectively regardless of modeling efficiency. Therefore, mapping was introduced to correlate objects and add details that could not be handled by modeling alone. An advantage of mapping is that it can rearrange a series of objects to express the phenomena created by the relationship between objects. Inter-frequency band mapping is used to calculate EEG feature sequences in multiple-frequency bands, which generates more effective attention representations because the complementary and interconnective information is learned from other EEG feature representation subspaces, unlike intra-frequency attention mechanisms.  
In this study, the EEG signal is split into 4 frequency bands, which are then utilized for inter-frequency band mapping. The model can be trained to extract emotion and stress-related features from each frequency band and improve spatial complementary information through frequency-to-frequency mapping. This configuration makes it easy to detect abnormalities in the overall brain function. Without inter-frequency band mapping, when there is a region in the \(\alpha\) band lower than normal, the \(\theta\), \(\beta\), and \(\gamma\) bands should be checked individually to see which wave increases as the \(\alpha\) wave in this region decreases. However, combining the EEG features of inter-frequency bands allows us to apply complementary and interconnected internal dependencies between frequency bands. In other words, it can be immediately confirmed that if the \(\theta\) wave increases in the area where the \(\alpha\) wave is reduced, the nerve cell activity in the area decreases, and if the \(\beta\) wave or \(\gamma\) wave increases, the nerve cells in the area are overactive.  
The inter-frequency band mapping maps all feature vectors to one-dimensional (1D) vectors using a fully connected layer after _flatten_ and _concat_ operations, as seen below:  
\[O(m)=concat\left(band^{\theta},band^{\alpha},band^{\beta},band^{\gamma}\right)W. \tag{13}\]  
Figure 5: Internal computing structure of GRU.  
where \(band^{b}=flatten\left(G_{b}^{l}\right)\), \(G_{b}^{l}\) denotes the intra-frequency gating layer output of the current frequency band \(b\), \(W\) represents a weight matrix of inter-frequency band mapping, and \(m\) indicates the entire frequency range from 4 Hz to 50 Hz, which is the sum of each frequency domain corresponding to \(\theta\), \(\alpha\), \(\beta\), and \(\gamma\). The _flatten_ operation converts a multidimensional input into a 1D vector, and the vector concatenation between different frequency bands is made by the _concat_ operation.
# Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand
## 2 SMA-Based Robotic Hand  
As shown in Figure 1, the proposed SMA-based robotic hand consists of five separate robotic fingers. Each finger is actuated by an SMA actuator to perform a single degree of freedom bending motion. The bending sensor is installed in the back of the finger, which is shown in Figure 2.  
Figure 1: SMA-based robotic hand.  
Figure 3 shows the internal structure of the robotic finger. Each finger is actuated by one SMA actuator. The SMA actuator is linked with the joint to achieve one freedom bending motion. Therefore, the finger has an underactuated motion mode. The novel part of the SMA-based robotic hand is the magnet reset device. The magnet reset device is connected with the joint to ensure the finger to restore original position.  
As shown in Figure 4, the robotic hand system includes a PC, a controller (Arduino Mega 2560), a bending sensor (flex4.5\", Flex International Ltd., Mipitas, CA, USA), a driver module, an SMA actuator (MigaOne-15, Miga Motor Company, Silverton, CO, USA) and the robotic finger. The control programs are performed in the PC, which is used to obtain the output data. Then, the PC transmits the control input data to the controller. After gaining the commands from the PC, the controller is instructed to drive the SMA actuator to perform the bending motion of the robotic finger through the driver module. Once the finger is driven to bend, the bending sensor can bend with the finger. Then, it can measure the bending value and transmit the bending signal to PC.  
Figure 4: Hardware system of the SMA-based robotic hand.  
Figure 3: SMA-based robotic hand.  
Figure 2: Position of bending sensor.
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
## 6 Discussion
### Image Processing Techniques' Roles to Address Energy Consumption for IoTSs  
In relation to this, there are several examples that demonstrate successful implementations of associated image-processing algorithms with several IoTSs. These examples show that the energy consumption rates of these IoTSs are superior to those of other applications, resulting in greater energy savings. There are several instances examined in this article that proved that image processing techniques had helped design IoTSs that utilize less energy and more security. When it comes to information processing, volume and energy use are closely related. Therefore, the cost of storing in the cloud will impact energy use. The following are some of the roles that image processing techniques have had in enhancing and decreasing the rate of energy consumption produced by IoTSs:  
Images will be uploaded to the cloud if they include information that is useful. That minimizes energy use by reducing transmission time spent [45];  
Because of advancements in image processing, transmission times have become significantly shorter. If all of the obtained multi-frame images are transmitted, it will be significantly less time than that;  
With the aid of image processing, a mathematical model for the intensity of compressed pixels such as CA has been developed, enabling the development of energy-efficient IoTSs that may minimize energy usage [45; 49];  
Lightweight security measures may be applied before images are sent to the IoT cloud via image processing [57]. As a direct result of this, a significant amount of processing time and available computer resources will be preserved. Thus, achieving long-term energy security is a goal.
## 3 Numerical Results and Discussion
### Sensitivity Analysis Based on Finite Element Model
By changing initial values of initial gap \(g\), the sensitivities to the initial gap with different prescribed \(g\) values are investigated, as shown in Figure 21. Still, s discontinuity exists where separation-contact transformation happens. With the increase of initial gap \(g\), the speed region subjected to rub-impact narrows and the response becomes more sensitive. Moreover, with an increase of the initial gap, the multi-solution region of sensitivities narrows as well. Similarly, as seen in Figure 22, results show that with decrease of friction coefficient the offset of sensitivities, which is more obvious in the 300-330 rad/s speed range when \(\mu\) is equal to 0.4, becomes more severe with the decrease of friction coefficients. In other  
Figure 21: The sensitivity curves of the response of the finite element model with respect to initial gaps at \(\mu\) = 0.5 and \(k_{y}\) = 1 \(\times\) 10\({}^{7}\) N/m: (**a**) First-order sensitivities; (**b**) Second-order sensitivities. Figure 20: The sensitivity curves of the x-direction response of the finite element model with respect to the three investigated parameters of rub interfaces at \(k_{y}\) = 1 \(\times\) 10\({}^{7}\) N/m, \(\mu\) = 0.5 and \(g\) = 0.1 mm: (**a**) First-order sensitivities; (**b**) Second-order sensitivities. words, the multi-solution region grows while \(\mu\) is decreasing. This is consistent with the results obtained by the OAT method, as illustrated in Figure 19c. According to Figure 23, the variation of normal stiffness \(k_{y}\) may also change the stable sensitivity region. Moreover, the variation of \(k_{y}\) has relatively little influence on the sensitivities, but the multi-solution region develops indeed with the increase of \(k_{y}\).