esa-sceva/satcom-chunk-collection huggingface dataset:
# XClinic Sensors: Validating Accuracy in Measuring Range of Motion Across Trauma Conditions
## 5 Medico-Legal Implications  
In Italy, Legislative Decree 62/2024 redefines disability as a lasting physical, mental, intellectual, neurodevelopmental, or sensory impairment that, in interaction with various barriers, hinders full and equal participation in life contexts. This aligns with the UN Convention's shift from the ICDH health model, which focuses on physical limitations, to a human rights model that emphasizes individuals' rights to shape their life paths with appropriate support. Disability is thus viewed relationally, reflecting the interaction between a person and their environment, with an emphasis on self-determination, equality, and inclusion [42].  
Central to this approach is a multidimensional assessment structured in four phases: identifying the individual's goals and functioning profile, assessing barriers and adaptive capacities, determining support needs, and defining life objectives with tailored interventions and outcome evaluations. This process ensures comprehensive support, considering existential, relational, emotional, educational, and cultural needs, and promotes both independence and social participation [43]. Assessment units are designed to integrate diverse expertise while prioritizing individual choices and expectations, culminating in a life project. This project specifies tools, resources, interventions, and services--such as suitable housing and home care--aimed at removing barriers and facilitating inclusion in education, employment, and social life. Unlike standardized criteria, it offers personalized solutions, ensuring that individuals can choose where and how to live, in line with Article 19 of the UN Convention.  
Given this framework, medico-legal evaluations require strict adherence to both clinical and dynamic-relational criteria, addressing individuals' ability to independently engage in daily, social, and professional activities. Instruments such as rating scales and motion sensors provide an accurate assessment of these limitations, supporting the eligibility for disability benefits. For example, osteaarticular problems that increase the risk of falls or hinder stair climbing highlight the importance of comprehensive assessments, including objective measures of functional limitations [44]. By correlating physical impairments with real-life challenges, these tools offer a holistic evaluation method that aligns with the decree's emphasis on personalized support and complete social inclusion.
## 3 Methodology
### Resilience of Air Flights
#### 3.1.1 Key Performance Indicators (KPIs)  
Based on the independence of airport delays and airport capacity and the KPIs set by the International Civil Aviation Organization (ICAO) for affected and surrounding areas, the key indicators of air flights are divided into the following three categories:  
1. Operational Performance Indicators The numbers of delayed and canceled flights within a period are \(n_{11}(\Delta t_{i})\) and \(n_{12}(\Delta t_{i})\), respectively.
2. Economic Performance Indicators
* Profit loss caused by delays within a time period \(\Delta t_{i}\): \[CD_{21}(\Delta t_{i})=n_{11}(\Delta t_{i})\cdot\overline{d_{31}(\Delta t_{i} )}\cdot C_{31/d}\] (1) where \(\overline{d_{31}(\Delta t_{i})}\) is the average delay time of each flight in \(\Delta t_{i}\); \(C_{31/d}\) is the unit cost of delay per flight.
* Profit loss caused by flight cancellations within a period \(\Delta t_{i}\): \[PL_{21}(\Delta t_{i})=n_{12}(\Delta t_{i})\cdot\overline{pl_{21/ap}}\] (2)where \(\overline{pl_{21/ap}}\) is the average profit loss caused by a flight cancellation during time \(\Delta t_{i}\), which equals the total value of the flight tickets lost due to the flight's cancellation.  
In Equation (1), the profit loss due to delay can be regarded as increased costs. \(C_{31/d}\) consists of direct and indirect unit costs. The direct unit cost includes the aircraft's operational costs and the airport ground service costs, which can be expressed as Equation (3):  
\[C_{\text{direct}}=C_{f}+C_{g} \tag{3}\]  
where \(C_{f}\) is the aircraft's unit operational cost (which includes fuel, maintenance, and crew wages);  
\(C_{g}\) is the unit cost of airport ground services.  
The indirect unit cost mainly consists of the passengers' time value, which can be expressed as Equation (4):  
\[C_{\text{indirect}}=N_{p}\times V_{p} \tag{4}\]  
where \(N_{p}\) is the number of affected passengers;  
\(V_{p}\) is the average unit time value per passenger.  
The time value equals the region's per capita GDP divided by the annual average working hours. People are assumed to work 240 days a year, 8 h per day.  
(3) Airport Capacity Estimation  
The Airport Cooperative Research Program (ACRP) proposed that capacity is the maximum sustainable throughput that can be accommodated per unit of time under varying local capacity constraints [34]. According to the construction stages of airports, capacity can be categorized into pre-planning capacity, pre-operational planning capacity, and operational capacity. This study focuses on airports already in operation, thus aiming to analyze the resilience of their operational capacity. Moreover, the bottleneck affecting flight operations under various factors is primarily the runway capacity of the airport, which can be understood as a trade-off optimization between arrival and departure capacities. For such bi-objective optimization problems, the Pareto frontier method can be applied to estimate capacity.
# A Multi-Segment Expanded Anchor for Landslide Emergency Management
## 3 Field Tests  
### Overview  
As shown in Figure 2, the field tests of MSEAs were carried out at the Beichuan test base, Mianyang City, Sichuan Province, with geographical coordinates of 104\({}^{\circ}\)35\({}^{\prime}\)46.67\({}^{\prime\prime}\) E and 31\({}^{\circ}\)51\({}^{\prime}\)31.42\({}^{\prime\prime}\) N. There exists a smooth interface with a slope of approximately 25\({}^{\circ}\) left after a landslide on the test site. The exposed bedrock is a relatively intact hard, greyish-black arenites of Devonian D\({}_{2-3}\) Geotechnical test results show that the arenites have a density of 2.2 g/cm\({}^{3}\), compressive strength of 110 MPa, and a tensile strength of 2.1 MPa.  
Figure 1: Schematic diagram of the MSEA structure. (**a**) The initial state; (**b**) the lock-off state.  
Figure 2: A schematic diagram of field tests.  
Six MSEAs in three rows and two columns were tested in the field, each with a length of 23 m. The formed hole with a diameter (d) of 120 mm was enlarged using a concentric expansion drill, achieving a final diameter (D) of 150 mm and a length of each underream of 1.5 m. Each MSEA has two load-bearing bodies, with a total design anchoring force of 600 kN. The design value of the initial bearing capacity of each MSEA without grouting was 300 kN (150 kN for each load-bearing body).
## 4 Numerical Results
Using an array of antennas on the base station can be a good choice to  
Figure 4: Bit error probability varying the LoS strength. Figure 5: Bit error probability upper bound. achieve better spectrum sharing in diverse scenarios. It is also remarkable that the result predicted by the formula proposed for the spectral efficiency is very close to the results obtained by the Monte Carlo simulation. In Figure 7, the secrecy outage probability for a Nakagami\(-m\) eavesdropper link with \(\Omega=1\) and \(m=1.4\) is shown. The sum was truncated up to the index 1000, and the number of iterations used was \(10^{6}\) to generate the Gamma distributed random SNR with parameters \(\alpha\) and \(\beta\), the Von Mises concentration parameter \(\kappa=2\), \(K=2\) antennas, unity variance, and Nakagami-\(m\) fading distribution for all channels between the antennas, the LIS, and the user. The larger the number of reflectors or the SNR, then the greater is the SOP. The first-order approximation of the SOP, considering that the Nakagami-\(m\) parameters are \(m=2.5\) and \(\Omega=0.1\) for all the channels in the system model, is also close to the simulated result as shown in Figure 8.  
Figure 6: Spectral Efficiency varying \(M\) and \(N\). Figure 7: SOP for \(K=2\) antennas and different number of reflectors (\(N\)).
## 1 Introduction
However, predictions based on the perception results inevitably have uncertainties in perception information. Many studies have assumed that Vehicle to Vehicle (V2V) can provide the true values of target states without uncertainty [10; 16; 17; 18; 19; 23; 24]. According to a careful review of the previous studies, various approaches have been introduced to develop the prediction and motion-planning algorithm for intersections. Many studies focused on the maneuver decision of the autonomous vehicle among the pre-defined policies. In particular, MDP-based approaches were frequently utilized to select the optimal policy. However, the computational burden of MDP is an obstacle to achieving real-time implementation. MPC was also employed to calculate the desired acceleration to pass the intersection. When using MPC, the target states were often provided by V2V communication to overcome the limitations of perception caused by blind spots and sensor uncertainty. A robust motion planner that can manage the perception and prediction uncertainty has not been addressed much in previous studies. In this paper, the motion planner based on SMPC with prediction uncertainty estimation is proposed and evaluated. The objectives of the proposed algorithm are ensuring safety, robustness to sensor uncertainty, and minimizing control efforts. Prediction uncertainty of the prediction algorithm is estimated using the recursive covariance estimator to improve the Intelligent Driver Model (IDM)-based prediction algorithm. The estimated uncertainty is used to define the probability distribution of the chance constraint for the SMPC problem, which improves the performance under changes in sensor noise. The desired longitudinal acceleration is decided by solving the SMPC problem. The proposed algorithm has been evaluated via computer simulation using MATLAB/Simulink and FORCES solver.
## Materials and Methods
### Hybrid Technologies: Robocasting Joined with Polymer Processing Routes  
#### 2.2.1 Fabrication of Hybrid Ceramic-Polymer Scaffolds  
An original attempt to join the ceramic robocast sample to a porous polymeric layer, able to provide new functionalities to the scaffold, was carried out. To this aim, the robocasting technology to fabricate the ceramic part was combined with a solvent casting/salt leaching technique for the fabrication of the foamed polymer. In order to demonstrate the achievement of multi functionalities of the scaffold, silver ions were added to the polymer layer to provide antimicrobial properties. The polymeric matrix was made of Poly(\(\varepsilon\)-caprolactone) (PCL, Mw 80000, Sigma Aldrich, Milan, Italy) reinforced by a 70/30 mixture of HA and \(\beta\)-TCP (whitlockite, batch P346S OD, Plasma Biotal, Buxton, UK) particles. The PCL to CaP mass ratio was 60:40 and Ag\({}^{+}\) was added as AgNO\({}_{3}\) compound, by using 1.7 wt% of AgNO\({}_{3}\) with respect to the polymer mass equal to 1 wt% of AgNO\({}_{3}\) with respect to the total mass (polymer and CaP), in accordance with previous literature [55].  
AgNO\({}_{3}\) was dissolved in acetone (Sigma Aldrich, Milan, Italy) under magnetic stirring for 72 h. After the addition of the biphasic calcium phosphate (BCP) mixture, the suspension was homogenized for a further 24 h. Afterward, PCL was added and for its complete dissolution, the mixture was stirred for a further 24 h at 40 \({}^{\circ}\)C. Then, NaCl granules (Sigma Aldrich, Milan, Italy), sieved in the 125-355 \(\mu\)m range, were added to the mixture as porogen agents. After homogenization, the suspension was cast around the sintered bi-layer robocast samples which were already placed in plastic molds (30 mm diameter). Control samples were also prepared under the same procedure but without AgNO\({}_{3}\) addition.  
Drying was carried out for 24 h at room temperature, to completely evaporate acetone. Porosity was achieved by the salt leaching method, by soaking the samples in deionized water to remove the salt. The samples were immersed for 4 days and the water was refreshed each 24 h.
## 5 Simulation Study
### Performance Comparison  
We compare the performance of the two protocols under the default parameters, with results presented in Table 2.  
As we can see, the CED achieves a data delivery ratio of 86.5%, which is much higher than that of the DG protocol. This stems from the effective event delivery strategy where events information of sensors in a community are delivered when meeting with a mobile subscriber. Besides, the queue management scheme also ensures the credible transmission of events. In contrast, the DG protocol performs worse in terms of data delivery ratio, because the sensor nodes here can only transmit events to interested subscribers directly. Thus for sensors never meeting with some mobile subscribers, their events will never be received by interested subscribers.  
In addition to the data delivery ratio, we are also interested in the data delivery delay and average copies. As shown in Table 2, the DG protocol has a longer data delivery delay than CED, for events in a sensor can only be forwarded to interested mobile subscribers directly. As a result, the transmission speed of events is low. On the other hand, due to the efficient event delivery strategy based on communities, the CED protocol outperforms DG in terms of data delivery delay. Moreover, In CED, as mentioned above, both the event successful delivery time and the event survival time are adopted to manage the data queue of sensors and thus invalid events are cleared away timely from the network. As a result, the number of average event copies in CED protocol is much lower than that in DG.  
\begin{table}
\begin{tabular}{l l l} \hline  & **CED** & **DG** \\ \hline
**Delivery ratio (\%)** & 86.5 & 47.0 \\
**Average copies of each event** & 4.2 & 75.9 \\
**Average delay(s)** & 230.6 & 615.5 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation results with default parameters.  
We also find out that the performance of CED varies with different values of event successful delivery time \(\alpha\). e.g. the data delivery ratios of CED varying with different values of \(\alpha\) are shown in Figure 6. As we can see, the delivery ratio of CED increases with the increase of \(\alpha\). This is because with too small value of \(\alpha\), events in the sensors would be discarded only after very small times of successful delivery, resulting in the loss of some events before their being received by some relevant subscribers.
# Scheduling Non-Preemptible Jobs to Minimize Peak Demand
## 2 Related Work  
Many variations on modifying demand to improve the efficiency of power grids (generically called _demand response_) have been explored. These approaches can generically be categorized as utility function optimization (commonly cost function minimization) and peak demand minimization (PDM). Utility function optimization techniques use price incentives to encourage consumers to modify their behavior, and are outside the scope of this work.  
PDM aims to directly schedule jobs to reduce peaks in power schedules instead of passively influencing changes with price incentives. PDM has been studied in the context of preemptible jobs (i.e., jobs that can be interrupted partway through execution) [1; 2]. In this paper, we consider the scenario where jobs are non-preemptible, and thus must run to completion once started. The PDM problem has been found to be NP-hard to approximate within a ratio of 2 in this scenario [3]. Heuristics have been developed that show promise in practice, but which provide no theoretical guarantee [4; 5]. Approximation algorithms have been developed for the special case where all jobs have the same start time and deadline [6; 7]. Our work introduces the first general purpose approximation algorithm where no assumptions are placed on the job parameters. Finally, optimal algorithms have been introduced that work on a limited number of jobs [3; 8]. Our work also introduces an optimal fixed-parameter tractable algorithm that is able to schedule approximately 15 times more jobs in practice than previous approaches.  
Non-preemptible power job scheduling is also similar to the machine minimization problem and rectangular strip packing [9; 10; 11; 12; 13]. In the machine minimization problem, jobs are assumed to have unit height, as opposed to power jobs that can have variable height. The main differences with rectangular strip packing are that in the general PDM case, jobs are limited in where they can be placed in the strip, and once jobs are scheduled, they do not need to remain as intact rectangles. Since job height represents the power required, each segment of a scheduled job will drop to lie on top of the job below it, instead of remaining as an intact rectangle in the strip.
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 4 Experimental Study
### Quality Measures  
To evaluate the performance of the discrimination models, six indicators were calculated, including the accuracy of the prediction (\(ACC\)), predictive positive value (\(PPV\)), negative predictive value (\(NPV\)), sensitivity (\(S_{n}\)), specificity (\(S_{p}\)) and [PERSON]'s correlation coefficient (\(MCC\)). The classification confusion matrix used to explain the above indicators is shown in Table 1, and microseismic events were assigned as positive, while blasts were assigned as negative.  
These six indicators are defined as follows:  
\[ACC=\frac{TP+TN}{TP+FN+FP+TN} \tag{10}\]  
\[PPV=\frac{TP}{TP+FP} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**True**} & \multicolumn{2}{c}{**Predict**} \\ \cline{2-3}  & **Positive Example** & **Negative Example** \\ \hline Positive example & True positive (TP) & False negative (FN) \\ Negative example & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 1: A typical binary classification confusion matrix.  
Figure 10: Accuracy curves of 10 PSO-ELM models with 1000 iterations and various NPS.  
\[NPV=\frac{TN}{TN+FN} \tag{12}\]  
\[S_{n}=\frac{TP}{TP+FN} \tag{13}\]  
\[S_{p}=\frac{TN}{TN+FP} \tag{14}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{15}\]  
\(ACC\) represents the overall prediction accuracy, which reflects the overall discrimination performance. \(PPV\) can be understood as the proportion of examples that are truly positive among all examples that are predicted to be positive. \(NPV\) can be understood as the proportion of examples that are truly negative among all examples that are predicted to be negative. \(S_{n}\) can be understood as the proportion of examples predicted to be positive among all the true positive examples. \(S_{p}\) can be understood as the proportion of examples predicted to be negative among all the true negative examples. These four indicators reflect the discrimination performance of positive and negative examples. \(MCC\) belongs to the index describing the relationship between the observation and prediction in binary classification problems which can evaluate the reliability of the algorithm [1]. Its value is between \(-1\) and \(1\), where \(1\) indicates that the prediction performance is perfect, \(0\) indicates that the prediction performance is worse than that of random prediction, and \(-1\) indicates that prediction totally disagrees with the observation [64]. Obviously, the higher the values of these six indicators, the better the discrimination performance of the model.
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
## 3 Transmission Schemes
### Outage Probability and Transmitted Power
#### 3.1.3 Svd  
In this scheme, all antennas are used at the transmitter and receiver nodes, which increases the robustness to channel fading, but also increases the power consumption due to the number of active RF chains. In addition, a feedback channel is also required for SVD; however, the capacity of the feedback channel must be much higher than in the case of AS. Following [23; 25], the outage probability of SVD is  
\[\mathcal{O}_{\text{SVD}}\approx\frac{\gamma\left(\frac{\xi_{0}}{\zeta},mN_{ \text{t}}N_{\text{r}}\right)}{\Gamma\left(mN_{\text{t}}N_{\text{r}}\right)}, \tag{13}\]  
where \(\rho_{0}=n\left(2^{R/n}-1\right)\) and \(n=\min\{N_{\text{t}},N_{\text{r}}\}\).  
Then, the transmitted power of the SVD scheme  
\[P_{\text{out}}^{\text{(SVD)}}=\frac{\rho_{0}\,N_{0}\,B\,F_{\text{rx}}}{\kappa \,\gamma^{-1}\big{(}\Gamma\left(mN_{\text{t}}N_{\text{r}}\right)\mathcal{O}^{ \star},mN_{\text{t}}N_{\text{r}}\big{)}}. \tag{14}\]
# Improvement of the Sensor Capability of the NAO Robot by the Integration of a Laser Rangefinder
###### Abstract  
This paper focuses on integrating a laser rangefinder system with an anthropomorphic robot (NAO6--Aldebaran, United Robotics Group) to improve its sensory and operational capabilities, as part of a larger project concerning the use of these systems in \"assisted living\" activities. This additional sensor enables the robot to reconstruct its surroundings by integrating new information with that identified by the on-board sensors. Thus, it can identify more objects in a scene and detect any obstacles along its navigation path. This feature will improve the efficiency of navigation algorithms, increasing movement competence in environments where people live and work. Indeed, these environments are characterized by details and specificities within a range of distances that best suit the new robot design. The paper presents a laser finder integration project that consists of two different parts, which are as follows: the former, the mechanical part, provided the NAO robot's head; the latter, the software, provided the robot with proper software drivers to enable integration of the new sensor with its acquisition system. Some experimental results in an actual environment are presented.  
anthropomorphic robot; laser rangefinder; indoor scene reconstruction +
Footnote †: journal:  
0000-0002-4011-1111-1111-1111-1111-1111-1111-11111-1111-1111-1111-1111-1111-1111-11111-1111-1111-1111-1111-1111-1111-1111-1111-111  
In particular, the purpose of this study was to improve the sensory and operational capabilities of the NAO robot, as part of a larger funded project concerning the use of these systems in \"assisted living\" activities. The addition of a laser rangefinder sensor enabled the robot to reconstruct its surroundings, by integrating new information with that identified by the on-board sensors. As a consequence, the robot can better detail its surroundings, enabling it to recognize new objects and detect any obstacles that may be present in its path, allowing the navigation algorithms to operate more efficiently and increasing the autonomy of movement inside a house. For this purpose, a new helmet that houses a laser rangefinder sensor has been designed to be worn by the robot. This device inputs measured distance data directly into the robot's memory, making the data available for other tasks, such as localization and navigation.  
The developed system has been tested within living environments where people usually live and work and that are characterized by details and specificities within a range of distances that are well-suited to the chosen device.