esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Graphene Nanoplatelets for Strain Sensors and Stretchable Electronics
### Capacitive Sensors for Tactile Sensing  
There are applications in stretchable electronics which require materials with stable electrical features under constant elongation conditions, such as electronic skin and elongating touch sensors. One of the methodologies employed to build artificial electronic skins is deformable capacitors that can identify touch-induced pressure, shear, and torsion [135]. Ensuring the functionality of such capacitive devices under mechanical stress (bent and even more stretched exceeding 100%) is challenging [136].  
Our group [105] recently reported the fabrication of a durable stretchable haptic capacitive sensor using nitrile rubber as template. A conductive elastomeric polymer dispersion containing GnPs or carbon nanofibers (CnFs) was spray coated onto both sides of a nitrile rubber piece, obtaining a parallel-plate capacitive touch sensor. The conductive spray, either GnP- or CnF-based, reached satisfying sheet resistance levels, i.e., \(\approx\)10 \(\Omega\)/sq. The GnP-based conducting electrodes formed cracks before 60% elongation, while the conducting electrodes based on CnFs sustained their conductivity at up to 100% strain level. However, both electrodes were adaptable and trustworthy, considering the motility and elongation level of human junctures (\(\approx\)20-40% strain). Remarkably, structural deterioration due to cyclic stretch-release events could be healed as a consequence of a straightforward heat gun annealing process. We also demonstrated the haptic sensing characteristics of an elongating capacitive device by wrapping it around the fingertip of a robotic hand. Tactile forces could be detected without difficulty by the device over curvilinear surfaces or under elongation (see Figure 14). The experiments performed in this section were performed with Ultra g+ commercially available GnPs obtained by Directa Plus S.P.A.  
Figure 13: Manufacture steps for the fabrication of PDMS-GnPs foams. (**a**) Sugar template; (**b**) PDMS infiltrated sugar template; (**c**) Sample after the sugar removal; (**d**) PDMS/Multilayer graphene (MLGS) foam resulting after nanoflakes infiltration. Reprinted with permission from Sensor 16, 12, 2148. Copyright 2016 MDPI.
# Computational Complexity of Modified Blowfish Cryptographic Algorithm on Video Data
## 4 Discussion
### Performance Evaluation
#### 4.1.2 Performance Comparison Based on Throughput  
The number of data successfully transported from one location to another in a certain time period is known as throughput. The throughput of an encryption algorithm can be calculated using:  
\[\text{Throughput = data (in kilobytes)/(process end time $-$ process start time)} \tag{3}\]  
The graph in Figure 7 depicts the results of a comparison based on throughput with various sizes of video data. The modified Blowfish algorithm has a high throughput, as shown in the graph. A high throughput signifies that the encryption procedure takes less time.
## 2 Background and Related Work
Using a HILPB WPT system and comprising multiple PTXs and PRXs as in an EV, the proposed method selects the optimal PTX from a group of PTXs best suited for any given PRX in a group of PRXs to satisfy the maximum power requirements (MPQs). We modeled the PTX-to-PRX power transmission and validated the model by simulating the operation of a multi-channel WTP system comprising several PRXs and several PTXs that employ a coded laser pilot.
## 2 Results and Discussion
### Isolation of APs from Water-Soluble Protein Hydrolysate (RSH) of Red Stingray Cartilages
#### 2.1.2 Anion-Exchange Chromatography of RSH-I  
Figure 2A showed that four fractions (RSH-I-1 to RSH-I-4) were separated from RSH-I using a DEAE-52 cellulose column. Among them, RSH-I-1 was eluted using deionized water (DW); RSH-I-2 and RSH-I-3 were eluted using 0.1 M NaCl; and, RSH-I-4 was eluted using 0.5 M NaCl. Figure 2B indicated that the DPPH\(\bullet\) and HO\(\bullet\) scavenging activities of RSH-I-4 were \(70.2\pm 1.8\%\) and \(82.4\pm 3.20\%\) at the concentration of 10 mg protein/mL, which were significantly (\(p<0.05\)) higher than those of RSH-I, RSH-I-1, RSH-I-2, and RSH-I-3 at the same concentration. Acidic and hydrophobic amino acid residues in peptides can be easily adsorbed to the anion exchange resins, and the interaction strength is closely related to the number and location of the charges on the molecule structure [25; 30]. The results indicated that RSH-I-4 might contain some APs, with acidic and hydrophobic amino acid residues.  
Figure 1: 2,2-diphenyl-1-picrylhydrazyl radical (DPPH\(\bullet\)) and hydroxyl radical (HO\(\bullet\)) scavenging activity of protein hydrolysate (RSH) and its three fractions by ultrafiltration at the concentration of 10 mg protein/mL. All data were presented as mean \(\pm\) standard deviation (SD, \(n=3\)). a–d or A–D, Values with same letters indicated no significant difference of different samples on DPPH\(\bullet\) or HO\(\bullet\) scavenging activity (\(p>0.05\)).
# MPC and PSO Based Control Methodology for Path Tracking of 4 WS4 WD Vehicles
## 3 Control Methodology
### MPC-SQP Method  
#### 3.1.1 Offset Model Linearization  
In order to be applied in the MPC controller design, the offset model is linearized by a feedback linearization method. By defining the state vector \(\mathbf{x}=[x_{1},x_{2},x_{3},x_{4},x_{5}]\), as \(x_{1}=V_{terr}\), \(x_{2}=l_{os}\), \(x_{3}=l_{os}\), \(x_{4}=\theta_{os}\) and \(x_{5}=\dot{\theta}_{os}\), the offset model can be written in the following form:  
\[\dot{x}_{1}= a_{r}-(a_{lp}\cos x_{4}-a_{rp}\sin x_{4}),\] \[\dot{x}_{2}= x_{3},\] \[\dot{x}_{3}= a_{r}-(a_{lp}\sin x_{4}+a_{rp}\cos x_{4}), \tag{25}\] \[\dot{x}_{4}= x_{5},\] \[\dot{x}_{5}= \gamma-\gamma_{ref}.\]  
In this model, \(a_{r}\), \(a_{l}\) and \(\gamma\) are considered as the inputs while other constants including reference accelerations and velocities can be obtained from the vehicle states. The nonlinearity of the model comes from trigonometric terms of \(x_{4}\). For accurate path tracking problems, the yaw error is assumed to vary smoothly within \([-10^{\circ}\ 10^{\circ}]\). Then, the model can be linearized by feedback linearization. Define the input vector \(\mathbf{u}=[a_{r}\ a_{l}\ \gamma]^{T}\); then, the new input vector at time \(t_{k}\)is obtained as  
\[\mathbf{v}(\mathbf{x},t)|_{t=t_{k}}=\mathbf{u}-\begin{bmatrix}a_{lp}\cos x_{4}-a_{rp}\sin x _{4}\\ a_{lp}\sin x_{4}+a_{rp}\cos x_{4}\\ \gamma_{ref}\end{bmatrix}_{x_{4}=x_{4}(t_{k})}, \tag{26}\]  
and the offset model is expressed as  
\[\dot{\mathbf{x}}=\mathbf{A}\cdot\mathbf{x}+\mathbf{v}(\mathbf{x},t)|_{t=t_{k}}, \tag{27}\]  
where  
\[\mathbf{A}=\begin{bmatrix}0&0&0&0&0\\ 0&0&1&0&0\\ 0&0&0&0&0\\ 0&0&0&0&1\\ 0&0&0&0&0\end{bmatrix}.\]  
It can be seen that the model is time-varying but can be treated as a linear model at each sampling step. The output vector is denoted by \(\mathbf{y}_{c}\) and the equation is written as  
\[\mathbf{y}_{c}=\mathbf{C}_{c}\mathbf{x}, \tag{28}\]  
where  
\[\mathbf{C}_{c}=\begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&0\\ 0&0&1\\ 0&0&0\end{bmatrix}.\]
## 3 Network Models and Related Definitions
### Network Models
#### 3.2.3 Network Reliability  
As a crucial indicator for evaluating the network quality of service (QoS) [32], network reliability is the probability that the network can maintain full network connectivity at a high level. This study mainly investigates UWSNs deployment issues. Thus the network should still maintain a high network connectivity rate, which is a crucial optimization goal of our algorithm, as some nodes begin to die because of persistent network operation. Therefore, in this study, network reliability can be described as the average node degree (not in the same depth):  
\[D_{a}=\frac{\sum\limits_{i=1}^{n}N_{i}(nei)}{n} \tag{8}\]  
where \(D_{a}\) denotes the average node degree, \(N_{i}(nei)\) denotes the neighbor node number of node \(S_{i}\).
## 5 Analysis of Test and Calculation Results
### Analysis of Measurement Data and Simulation Calculation Data at Different Speeds  
Under the no-load condition, the test and calculation were carried out according to Table 1, and the results are shown in Figure 4.  
Figure 4 illustrates that the calculation results approximated the test results. With an increase in rotation speed, the movement area of the axis orbit became smaller. Thus, the bearing operation was more stable. However, a considerable difference of 0.04-0.06 mm can be seen on the \(x\) axis. This is because this area corresponds to the crank angle of the oil return angle. The pressure changes greatly at this time, which causes the axis trajectory to oscillate, and the oil film that occurs in this area is the minimum for the whole working cycle.  
It is thus shown that with an increase in the rotating speed in the test, the oscillation in the oil return angle decreased. The test bench became more stable, and the correlation coefficient of  
Figure 4: The axis orbits of the tests and calculations at different speeds: (**a**) Measurement and calculation results at 1000 r/min; (**b**) Measurement and calculation results at 2000 r/min; (**c**) Measurement and calculation results at 3000 r/min; (**d**) Correlation coefficient of calculation and measurement results at different speeds.  
the calculation and measurement increased. In particular, the test data of the working condition at 3000 r/min reflected the simulation calculation results.
## 5 Experimental Framework and Results
### Case Study Analysis of CARAG
#### 5.1.3 Smart Prompting for Explanation Generation for Claim 59
By comparison, CARAG integrates climate-specific details with broader economic and governmental insights, offering a more comprehensive reflection of public and policy  
Figure 7: Comparison of generated explanations across retrieval approaches for Claim 59, showcasing the methodology’s application. perspectives on climate issues. CARAG's approach leverages this global perspective effectively, balancing claim-specific elements with thematic coherence to enhance relevance and interpretability. This layered approach, connecting climate change to economic impacts and policy actions, demonstrates CARAG's ability to generate trustworthy explanations for nuanced, high-stakes claims by integrating broader, non-local context. This deeper contextual alignment surpasses RAG's capabilities, producing user-aligned explanations that encompass both thematic and factual nuances. Through this case study, we underscore the dual benefits of CARAG: its proficiency in selecting contextually relevant evidence that deepens understanding and its capacity to translate this evidence into explanations that resonate with user expectations for interpretability and reliability. This analysis exemplifies how CARAG achieves balanced explainability by combining both local (claim-specific) and global (thematic) insights to provide a comprehensive and trustworthy explanation. Moreover, CARAG leverages both textual and visual explanations, two widely recognized forms of XAI representation [59]. As illustrated in Figure 6, Panel (b), visual explanations use graphical elements to clarify decision-making processes, while Figure 7 highlights CARAG's textual explanations, which offer natural language reasoning that provides intuitive insights into the model's rationale. By aligning with these two forms of XAI, CARAG enhances both interpretability and transparency in fact verification, resulting in a comprehensive and insightful explainability mechanism. In summary, CARAG's approach demonstrates superiority over RAG by providing a multi-faceted view that resonates with both the thematic and factual elements of the claim. To further substantiate these findings, in-depth comparative evaluation results of global explainability, focusing on RAG and CARAG across multiple claims, are presented in the upcoming section.
## 6 Conclusions and Discussion  
In this paper, a novel SF-partition-based clustering and relaying scheme was proposed to solve the near-far fairness problem in IoT LoRa networks. The proposed SFPCR scheme utilized clustering and a multi-hop relay approach with the aid of low-SF LDs to increase the minimum success probability of distant nodes with a high SF. In the proposed scheme, the SF-based network partitioning method was used to determine the best partitioning threshold point for dividing SF zones into an LSFZ and an HSFZ to bridge the performance gap among SF zones by using an optimal SF division based on a heuristic algorithm. Then, by a combination of the DBSCAN algorithm and naive Bayes classifier, the density-based subspace clustering method constructed clusters of arbitrary shape for adjacent LDs in the HSFZ and selected CHs by a binary score representation. To extend the transmissions of CHs in the HSFZ by multihop communications, the relay LD selecting method chose the best relay LD in the LSFZ by using the harmonic mean based on the packet success probability and the remaining energy. The packet success probability of each LD in a certain SF zone was examined based on the hop distance of each hop to provide the optimal zone allocation with connection fairness. Through comprehensive simulations, we revealed that the SFPCR scheme exhibited the highest packet success probability of 29.1% for distant LDs at 6000 m, followed by the FSRC scheme at 1.12%, the mesh scheme at 0.67%, and lastly the cluster-based scheme at a negligible value of 0.036%. Using the default payload size of 20 bytes, the proposed SFPCR scheme also exhibited the highest success probability of 65.7%, followed by the FSRC scheme at 44.6%, the mesh scheme at 34.2%, and lastly the cluster-based scheme at 29.4%. The SFPCR conserved more energy for the LDs in order to prolong the lifetime of IoT LoRa networks in comparison to previous LoRa multihop schemes. Future work will include the development of intelligent clustering algorithms that can maintain up-to-date information on the link quality between LDs and their neighbours and react to sudden topological changes, as well as the responsiveness to changing network conditions through the use of lightweight reinforcement learning techniques.  
Conceptualization, D.M.; methodology, D.M.; software, Y.N.; validation, Y.N., H.C. and Y.S.; writing--original draft preparation, D.M.; writing--review and editing, Y.N. and E.L.; supervision, E.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2022-2020-0-01462) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
Not applicable.  
Figure 13: Variation in the number of packets according to the the distance of LoRa devices from the GW with a comparison between the proposed SFPCR scheme and other protocols.
# Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz
## 3 Path Loss Measurements and Models  
This section presents the details of the environment used for the measurement campaign and the propagation models.
## 3 Evaluation and Corpus
### Human Interaction Assessment  
Final performance assessment of a human assisted system must take into account the cost of human interaction in order to evaluate the quality of the interaction process. Penalized DER (\(DER_{pen}\)), a metric introduced in [10], is used to merge the information about the final performance (after human interaction) with the cost of the interaction required to reach this result. This metric adds a constant amount of error time, called penalized time (\(t_{pen}\)), to the diarization error time, for each question asked to the human expert. Equation (1) defines the \(DER_{pen}\).  
\[DER_{pen}=\frac{FA+Miss+Conf+N\cdot t_{pen}}{T_{total}} \tag{1}\]  
where \(FA\) is false time, \(Miss\) is missed time, \(Conf\) is confusion time of diarization hypothesis, \(N\) is the number of questions asked to the human expert and \(T_{total}\) is the total duration of audio files. For all experiments conducted in this paper, \(t_{pen}\) is set to 6 s. Although fixed empirically, this value has been chosen based on on-going work with human annotators from different companies; this time corresponds to the time required to listen two segments of 3 s each. An analysis of the value of \(t_{pen}\) has been produced in work to be publish soon. Incremental cross-show penalized DER is computed following the same formula in the incremental framework as explained earlier. We also propose to use the number of corrections over number of questions (\(CQR\)) ratio as a questioning performance criteria. It is used to evaluate the early stopping criteria and the question generation module for within-show speaker diarization.
## 3 Materials and Methods
### Literature-Based Scenario Planning
#### 3.5.2 Assumptions on the Factors  
This study defines the five scenarios based on previous literature (see Table 2). First, regarding the driver and remote operation costs, the driver cost was eliminated in all scenarios, since this study considered ATs with level 5, which is that ATs can make decisions without any human intervention or control [20; 21]. However, since ATs will require costs for new technologies, networks, and skilled operators, this study considered the cost estimate; for instance, the most pessimistic scenario assumed 40% of driver costs [9]. Second, a change in the acquisition cost of the vehicle was included in the cost estimation due to the distinction made between the components and design standards for ATs and trucks. On the one hand, the driver's cabin is one of the most difficult components to manufacture, and it normally accounts for approximately one-third of the total cost of a vehicle [7]. On the other hand, since the necessity for safety features will be much reduced, it will lower costs associated with the production of ATs [22]. That is, this study assumed that the cost of acquisition will remain the same in the basic scenario, while it will increase by 25% in the most pessimistic and decrease by 25% in the most optimistic scenario. Third, fuel consumption will decrease in all the scenarios, based on the previous literature. For instance, [PERSON] and [PERSON] [3] quantified and valued the effects of ATs and found that ATs will reduce fuel consumption by approximately 10%. [PERSON] et al. [14] found that fuel economy improvement in ATs was around 15%. [PERSON] et al. [23] revealed that the fuel consumption saving would be around 5 to 15%. [PERSON] et al. [24] suggested that the effect size of autonomous vehicles on fuel consumption would be between \(-45\%\) and 30%. Relatively outdated studies have also indicated the potential cost savings of ATs associated with reduced fuel consumption. For example, McKinsey Global Institute [25] anticipated that ATs will have an increased fuel efficiency of between 10 and 40 percent compared to the trucks that are available today. [PERSON] and [PERSON] [10] suggested that ATs will reduce fuel consumption by 15%. In sum, since the previous studies suggested that the fuel consumption saving will range between 10% and 40%, this study used this range to define the scenarios. That is, ATs can operate without mandatory breaks, with greater traffic performance per month, and consequently, the gasoline costs can be significantly higher than those of HDTs. Fourth, ATs will eliminate driver insurance due to the absence of drivers in the vehicle. Also, ATs will lower the cost of auto insurance to some extent, mainly due to their potential to improve road safety [16]. For instance, [PERSON] et al. [9] estimated a reduction in insurance costs ranging between 10% and 20%. Thus, this study followed the findings of the previous literature. Fifth, there is no consensus among experts on the change in maintenance costs of ATs, because smoother driving for DL trucks may lower the cost of maintenance that is required,
# Patient-Specific Dosimetry Evaluations in Theranostics Software for Internal Radiotherapy
## 4 Discussion
Concerning curve fitting, it was stated in [PERSON]'s paper [6] that this is one of the most relevant sources of uncertainties. Many options exist for curve fitting [23], including modelling software, such as SAAMI ( version 2.0 or higher), or Olinda/EXM (version 1.0 and higher), or advanced software, such as MATLAB (version 2020 or more recent), or Python (version 3.12.4 is now available). Preparation of biologic data and choice of the appropriate error model are the starting point of fitting calcuations. In this context, Velocity Theranostics provide complete error metric statistics, calculating the Symmetric Mean Absolute Percentage Error (SMAPE). This is an important tool for estimating the goodness of the curve fitting. In this work a unique set of structures for organs was used in both techniques, so that VOIs could not represent a source of uncertainties, limited to this study. Velocity Theranostics has the advantage of using the Acuros MRT algorithm, based on the interactions of radioactive particles with matter, which considers a source and a radiation transport model applied to IR. Aside from that, Acuros MRT takes into account density and type of material, through the Hounsfield Unit (HU) of the CT images. In clinical cases, patient anatomy provides specific information on materials, which permit to calculate the radiation transport in the matter (e.g., CT tissue segmentation based on density ranges). [PERSON] et al. [24] demonstrated a good agreement between Acuros MRT and Monte Carlo approaches and concluded that Acuros MRT is an effective alternative to Monte Carlo. In this paper we presented the comparison with OLINDA 2.0, which is the reference for dosimetry computation in IR. However, OLINDA 2.0 cannot provide a complete personalised dosimetry, since it uses standard anthropomorphic phantoms and considers only homogeneous distributions of activity. The differences obtained in this paper between Velocity Theranostics and OLINDA 2.0 with TIAs from Velocity Theranostics (OLINDA(\(\mathrm{\SIUnitSymbol{Theranostics\,TA}}\))) may mainly be due to these aspects. Theranostics software, such as other commercially available software, meets the need of a complete workflow for dosimetry from image elaborations to absorbed dose distributions. For this reason, these kinds of software turn out to be more robust and reliable than the use of multiple tools to calculate TIAs for OLINDA 2.0.