esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Compensation for Center-of-Rotation Errors  
Error compensation for the center-of-rotation was an experiment for center-of-location calibration of the five-axis machine tool. A 5 mm ball end mill was used on the \(A\) axis with table angles of rotation of \(0^{\circ}\), \(-45^{\circ}\), and \(-90^{\circ}\) to cut three grooves, as shown in Figure 12. The grooves' depth of cut differences on the \(Z\) axis were measured using 3D-Probe, as shown in Figure 13a-c. The angle of rotation at \(0^{\circ}\) along the \(A\) axis was used as the baseline for the groove depth of cut. The depth of cut differences of angles of rotation at \(-45^{\circ}\) and \(-90^{\circ}\) were \(D_{1}\) = 0.126 mm and \(D_{2}\) = 0.206 mm, respectively. The measured depth of cut differences were substituted into (2) and (3) to determine the center-of-rotation errors along the \(A\) axis; the deviations were \(d_{y_{A}}=-0.047\) mm and \(d_{z_{A}}\) = 0.159 mm on the Y and Z axes, respectively. These values suggested that the actual center of rotation of the machine tool was located at the lower right side of the center of rotation defined by the controller.  
A 5 mm end mill was used on the \(C\) axis with table angles of rotation at \(0^{\circ}\), \(90^{\circ}\), and \(180^{\circ}\) to cut three grooves, as shown in Figure 14. The cut grooves were measured using 3D-Probe. Side walls with \(0^{\circ}\) angle of rotation along the \(C\) axis were used as the baseline to measure the wall differences at \(90^{\circ}\) and \(180^{\circ}\) on the \(X\) axis, and the differences were determined to be \(D_{3}\) = 0.029 mm and \(D_{4}\) = \(-0.008\) mm, respectively, as shown in Figure 15a-c. The side wall differences measured at \(90^{\circ}\) and \(180^{\circ}\) angles of rotation along the \(C\) axis were substituted into (4) and (5) to determine the center-of-rotation error along the \(C\) axis. The deviations on the X and Y axes were \(d_{x_{c}}\) = 0.004 mm and \(d_{y_{c}}\) = \(-0.033\) mm, respectively.  
Figure 11: PLA cutting experiment (spindle speed 60,000 rpm).  
Figure 12: Three grooves cut on a table rotated along the \(A\) axis (\(0^{\circ}\), \(-45^{\circ}\), and \(-90^{\circ}\)).  
respectively. In addition, when the sidewall at \(0^{\circ}\) of rotation along the \(C\) axis was used as the baseline, the sidewall differences on the \(X\) axis at \(90^{\circ}\) and \(180^{\circ}\) of rotation with compensation were \(-0.003\) and \(-0.003\) mm, respectively, as shown in Figure 15d-f. The experimental results revealed that the compensated differences in cutting were within \(5\) um on both the \(A\) and \(C\) axes.
# In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System
###### Abstract  
The Neo Care home monitoring system aims to detect falls and other events using artificial intelligence. This study evaluated Neo Care's accuracy and explored user perceptions through a 12-week in-home trial with 18 households of adults aged 65+ years old at risk of falls (mean age: 75.3 years old; 67% female). Participants logged events that were cross-referenced with Neo Care logs to calculate sensitivity and specificity for fall detection and response. Qualitative interviews gathered in-depth user feedback. During the trial, 28 falls/events were documented, with 12 eligible for analysis as others occurred outside the home or when devices were offline. Neo Care was activated 4939 times--4930 by everyday household sounds and 9 by actual falls. Fall detection sensitivity was 75.00% and specificity 6.80%. For responding to falls, sensitivity was 62.50% and specificity 17.28%. Users felt more secure with Neo Care but identified needs for further calibration to improve accuracy. Advantages included avoiding wearables, while key challenges were misinterpreting noises and occasional technical issues like going offline. Suggested improvements were visual indicators, trigger words, and outdoor capability. The study demonstrated Neo Care's potential with modifications. Users found it beneficial, but highlighted areas for improvement. Real-world evaluations and user-centered design are crucial for healthcare technology development.  
home monitoring system; technology; fall detection; user perceptions; real-world evaluation 2024 16 197. [[https://doi.org/10.3390/616060197](https://doi.org/10.3390/616060197)]([https://doi.org/10.3390/616060197](https://doi.org/10.3390/616060197))
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 3 Methodology
### Datasets in IoT
#### 3.1.2 CICIoT-2023 Dataset Characteristics for Evaluating Selected ML Algorithms  
To broaden the scope of the study and include the latest threats, the CICIoT-2023 dataset [35; 36; 37; 38] was also included in the analysis. It is the most up-to-date dataset of the 44 analyzed, meaning that it contains information on the latest types of attacks and threats that have emerged in the IoT ecosystem.  
CICIoT-2023 stands out for its remarkable comprehensiveness, covering a wide range of attacks divided into seven categories. All attacks are carried out directly between IoT devices, reflecting real-world threat scenarios that IoT systems may encounter in practice.  
The dataset was created in an extensive and realistic test environment consisting of 105 devices. Such a diverse environment allows for a more accurate simulation of real-world conditions for IoT systems and enables a more reliable assessment of the effectiveness of different AI models in detecting threats.  
CICIoT-2023 offers a rich dataset, including both raw network traffic in PCAP format and a set of 47 extracted features in CSV format. These features were calculated from the analysis of network packets and include information on the duration of flows, headers, TCP flags, protocols, packet lengths, and various types of timing statistics. Such detailed data enable deep analysis of network traffic and the identification of subtle patterns that may indicate the presence of threats.  
The CICIoT-2023 dataset can be used to train and evaluate various AI models, in particular anomaly detection models. These models are able to identify abnormal patterns in network traffic that may indicate the presence of previously unknown attacks.
## 4 Current Approaches to Measure Resilience in Smart ICI  
Smart Interdependent Critical Infrastructures (Smart ICIs) currently employ a variety of methods for measuring their resilience. In general, these methods can be categorized as qualitative or quantitative. To evaluate resilience, qualitative methods frequently employ expert opinions, surveys, and conceptual models, whereas quantitative methods employ mathematical and statistical models. Typical resilience measurement indicators include robustness, redundancy, resourcefulness, and speed. These indicators are used to evaluate the system's ability to withstand disruptions, recover rapidly, and adapt to changing conditions. Developing a resilience index that incorporates these various indicators is a common strategy. Given the complexity and interdependence of Smart ICIs, however, there is a growing emphasis on the development of more sophisticated and comprehensive models that can capture these interdependencies and provide a more accurate and holistic assessment of resilience.  
In terms of methodology, as shown in Figure 2, a systematic literature review approach was utilized for this review paper. This required a literature review on the topic of resilience in Smart ICIs, as illustrated in the figure below. Using a combination of keyword searchesand citation monitoring, relevant articles from various databases, such as academic journals, conference proceedings, and technical reports, were identified. The selected papers were then subjected to a comprehensive analysis to extract pertinent information and insights. The review process was iterative, with the search strategy being modified based on the results of the initial literature review. The information gleaned from the literature was then synthesized and classified according to the various examined aspects of resilience, such as definitions, models, metrics, and strategies. This methodical and exhaustive approach ensured that the review captured the scope and depth of current research on this crucial topic.
## Materials and Methods
### ToF-SIMS Measurements  
A TRIFT III instrument (Physical Electronics, Chanhassen, MN, USA) with a gold liquid metal ion gun as the primary ion source was used for the ToF-SIMS measurements. The primary ion beam energy was 22 keV and a typical ion current of 600 pA, respectively. ToF-SIMS spectra were collected selecting Au\({}^{+}\) primary ions. The spectra were measured in bounced mode with a typical mass resolution, m/\(\Delta\)m, around 2000. The spectra were recorded within static conditions keeping the dose of primary ions below 10\({}^{12}\) ions/cm\({}^{2}\). An electron gun was used to neutralize the positive charging of the samples. The _m/z_ scale was calibrated using the mass peaks of CH3\({}^{+}\), Na\({}^{+}\), C2H3\({}^{+}\) and C3H5\({}^{+}\).
## 5 IoT Service Provisioning via NDN in the Smart Campus
### Service Provisioning
#### 5.3.1 On-Path Forwarding  
In this first stage, the service interest is forwarded from the leaf node connected to the consumer, which in the following is referred to as Consumer Access Node (CAN), and to the leaf node connected to the IoT data producer, which in the following is referred to as Producer Access Node (PAN). In case the consumer and producer have the same access node (CAN = PAN), then the candidate on-path executor is just this single node.  
Forwarding is guided by the first component of the hierarchical service name, that is, the content name. Specifically, when the CAN or a subsequent NDN-fog node (let us call it \(N_{i}\)) receives the service interest, the following processing is performed.  
* First, \(N_{i}\) looks in the CS for a matching name. If it is found, it means that the same service has been previously computed and the result can be immediately sent back to the consumer.
* If the CS matching fails, it looks in the PIT. If a matching is found, it means that the same service has been requested and it is pending. Therefore, \(N_{i}\) updates the correspondent PIT entry with the incoming service interest face and discards the packet.
* If the PIT matching fails, then the interest is processed by the SDE, which computes the service provisioning time and decides if \(N_{i}\) can candidate itself for the execution. This choice depends on the available computing resources and the implemented service allocation strategy, as it will be clarified in the following section. If the locally computed service provisioning time is lower or equal than the value already included in the SPT field, then \(N_{i}\) becomes the potential service executor: the SDE updates the SPT field and creates a new PIT entry that includes as additional record the service provisioning time. Then, the packet is forwarded to the next node towards the IoT provider (and, therefore, towards the PAN).  
After applying the service interest processing, the PAN acts as follows:1. If no on-path node is available for the execution, it sends back a NACK packet advertising that the service has not been allocated.
2. Otherwise, the node with the lowest service provisioning time is selected as the executor. In particular, if the executor is the PAN itself, then it will take in charge the computation. Otherwise, it sends back a data acknowledgement (ACK) packet carrying the service name and the correspondent SPT. The first node with the correspondent SPT in the PIT will act as the executor.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Chem. Family \({}^{*}\)**} & \multirow{2}{*}{**KI MXT-5 \({}^{*}\)**} & \multirow{2}{*}{**KI MXT-1701 b**} & \multicolumn{2}{c}{**Possible Matched Compounds**} & \multirow{2}{*}{**Sensory Descriptors \({}^{**}\)**} & \multirow{2}{*}{**WG**} & \multirow{2}{*}{**BG-C**} & \multirow{2}{*}{**BG-P**} & \multirow{2}{*}{**BG-S**} \\ \cline{1-1} \cline{5-10}  & & & & & & & & \\ \hline E & 383 & 314 & Methyl formate & Fruity & x & x & x \\ \hline Orth. & 419 & 471 & Trimethylamine & Ammoniacal, fruity, pungent & x & x & x & x \\ \hline S & 446 & 488 & Methanethiol & Garlic, sulfurous, cabbage & x & x & x & \\ \hline S & 457 & 455 & Ethanethiol & Garlic, sulfurous, & x & x & x & x \\ \hline E & 464 & 551 & Ethyl chloride & Ethereal & x & & & \\ \hline S & 482 & 536 & Dimethyl sulfide & Sulfurous, gaseous, cabbage & & & x \\ \hline Ald. & 492 & 577 & Propanal & Earthy, ethereal, nutty & x & & & x \\ \hline Orth. & 525 & 636 & 2-methylo-propanal & Floral, fresh, fruity, spicy, & x & & \\ \hline S & 542 & 600 & Carbon disulfide & Sulfurous, burnt & x & & x \\ \hline E & 557 & 605 & Methyl acetate & Fruity, sweet & x & x & x \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characterization of the main volatile compounds identified in the headspace of fresh garlic, black garlic, black garlic powder and paste.
# CFD Simulation of Dry Pressure Drop in a Cross-Flow Rotating Packed Bed
## 2 Model
### Boundary Conditions and Numerical Solution  
The 3D CFD model for the cross-flow RPB is established by using Ansys Fluent 15.0. A velocity inlet boundary condition is applied to inlet, a pressure out condition is applied to the outlet, and the enhanced wall function is applied to the wall. The SIMPLE algorithm is used to solve the pressure-velocity coupling and the residuals for all equations are controlled below \(10^{-4}\). A grid with 3,281,495 meshes is selected in this work after the grid independence test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**RPB** \\ \hline Diameter, mm & 390 \\ Height, mm & 1020 \\ Gas inlet diameter, mm & 160 \\ Gas outlet diameter, mm & 160 \\ \hline
**Packing** \\ \hline Type & Wire \\ Shape & Concentric ring \\ Internal diameter, mm & 190 \\ External diameter, mm & 375 \\ Height, mm & 180 \\ Porosity & 0.95 \\ Viscous resistance coefficient, m\({}^{-2}\) & \(2.35\times 10^{5}\) \\ Inertial resistance coefficient, m\({}^{-1}\) & 42.3 \\ \hline
**Operating condition** \\ \hline Gas type & air \\ Gas flow rate, m\({}^{3}\)/h & 0–1000 \\ Rotating speed, rpm & 0–1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The characteristic parameters of the cross-flow RPB.  
Figure 1: The physical diagram and CFD model of cross-flow RPB. (**a**) Physical diagram; (**b**) CFD model.
# Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning
## 4 Experimental Results and Analysis
### Data Processing and Evaluation Index  
#### 4.1.1 Feature Construction and Dataset Partitioning  
In the original dataset, each sample's features included the coordinates of the measurement points (\(x_{p},y_{p},z_{p}\)) and related scene parameters, namely the equivalent charge \(Q\), detonation velocity \(\mathbf{U}\), and the Cartesian coordinates of the explosion center (\(x_{b},y_{b},z_{b}\)). For deep learning models, higher-quality input features typically lead to better predictive performance. Therefore, to enhance the performance of the blast loading prediction model, this study further constructed new features based on existing features.  
The empirical Formula (12) for peak overpressure indicates that the angle \(\Theta\) between the measurement point and the direction of detonation velocity affects the calculation of the peak overpressure. Therefore, \(\Theta\) was included as one of the input features. Additionally, the scaled distance \(d\), defined by Equation (13), significantly influences the peak overpressure results and was also incorporated as an input feature.  
\[\Delta p_{0}=\left\{1+(0.53 Ma)\bigg{(}\frac{1}{0.27+0.48d}\bigg{)}\bigg{[}0.3 2+0.80\sin\bigg{(}\frac{\Theta+2.42}{3.58}\pi\bigg{)}\bigg{]}\right\}\times \bigg{(}\frac{0.067}{d}+\frac{0.301}{d^{2}}+\frac{0.431}{d^{3}}\bigg{)} \tag{12}\]  
Here, \(\Delta p_{0}\) represents the peak overpressure (Pa); \(d\) is the scaled distance, and its formula is shown in Equation (13) (\(\text{m}\cdot\text{kg}^{-1/3}\)); \(\Theta\) is the angle between the detonation line and the direction of the detonation source (rad); and \(Ma\) is the ratio of the source velocity \(\mathbf{U}\) to the speed of sound (i.e., 340 \(\text{m}\cdot\text{s}^{-1}\)) (\(\text{m}\cdot\text{s}^{-1}\)).  
\[d=\frac{R}{\sqrt[3]{Q}} \tag{13}\]  
Here, \(Q\) is the equivalent of the detonation source, and \(R\) is the distance from the detonation source to the center of the structure.  
Compared to the input that includes only the original features, the input vector presented in Equation (14) is more effective at enhancing the prediction performance of the blast loading model. On the basis of constructing new features, the 360 sampleswere divided into the training set and the test set using a ratio of 9:1, and the model's performance was evaluated using five-fold cross-validation with five repetitions.  
\[\mathbf{x}=\left[x_{b},y_{b},z_{b},Q,R,\mathbf{U},x_{p},y_{p},z_{p},d,\frac{1}{d},\frac{1 }{d^{2}},\frac{1}{d^{3}},\Theta\right] \tag{14}\]
# Routing Schemes in FANETs: A Survey
## 3 Taxonomy  
This section presents a taxonomy of routing attributes in FANETs as shown in Figure 4. The rest of this subsection explains the taxonomy.
## 4 Results and Discussion
### Fall Time and Undershoot Analysis  
For the decreasing altitudes we calculated fall time (\(t_{f}\)) and % maximum undershoot to analyze our results. From the fall time, we studied the time required by operators to decrease the UAVs' altitude to the specified set point. The effect of the haptic feedback on the fall time and undershoot is shown in Figures 12 and 13.  
In experiments, when users were asked to decrease the altitude from time interval 100 to 130 s and 130 to 160 s to 1200 ft and 1000 ft, the '\(t_{f}\)' is reduced for the realistic and exaggerated force feedback condition, as shown in Table 6. This was due to the fact that decreasing the altitude caused the nose of the UAV to rotate downward causing the increased speed as explained previously through Figure 3. As a result, users experienced an additional increased force feedback which pushed the control stick away from an operator, hence reaching the set point in less time. This also affected operators' performance in the altitude holding task because the force calculation (i.e., Equation (6)) depends on the square of the UAV speed.  
Just as the increase in '\(t_{r}\)' can be used to control overshoot. Similarly, reduced '\(t_{f}\)', excessive pushing force on control stick and increased values of undershoot as shown in Table 7 can be used to increase situational awareness and immersion in a simulation environment by giving an indication to an operator about sudden altitude loss.
# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
## 4 Discussion
### Limitations of the Study  
Our study has several limitations, primarily related to the dataset and the data recorded in the database. Notably, MIMIC-IV lacks a precise definition for ICU patient decompensation. Similar to other studies, we used the risk of death as a proxy. However, it is possible that certain events leading to patient death may not be directly correlated with health decompensation. While a patient can recover from decompensation, they cannot recover from death.  
Another limitation arises from the exclusion of patients with ICU stays of less than 24 h, which may introduce potential sampling bias. Selecting the right length for the time series is a trade-off between patient inclusion and the number of time points per time series, both of which can potentially affect model performance. It is also important to note that SHAP analysis can only suggest potential associations between input characteristics and predictions, which is not the same as stating causation. This means that further research is needed to identify potential confounding factors if such causal links are to be established.  
Furthermore, clinical data often exhibit high levels of missing values and noise, which commonly limit the performance of any data-driven model, regardless of the ML techniques used. The choice of vitals and other factors is also very important in the development of any score, in our case, a decompensation score. Since the aim of our paper was to propose a methodology that would enable the development of such a score, we used publicly available data from the MIMIC-IV database. However, we recognise that further work will be required, including not only a revision of the choice of variables but also further testing in prospective patient cohorts. Importantly, special consideration must be given to the selection of the missing value imputation method, as it can impact both model performance and the quality of the proposed score. In this study, we opted for the same imputation method as the one used in [11] since both studies use similar data sources and settings. However, imputation methods that could be more appropriate for time series should also be explored in further analyses.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 3 Methodology
### Path Curve Optimization and Path Restoration Optimization
#### 3.3.2 Path Restoration Optimization  
After the robot completes the avoidance maneuver, it must reach the next target point. If the local target point before the avoidance is still selected as the guide after the local obstacle avoidance, it usually leads to path redundancy. By adding a progressive enhanced potential field oriented towards the global path and introducing an attraction function between the robot and each node, the robot can smoothly return to the worldwide path after performing local dynamic obstacle avoidance. The design of the attraction function is as follows:  
\[\mathrm{F_{aqt}}(q)=k_{aqt}\cdot(\frac{d(q)}{d_{0}})^{\psi}\cdot\left[\begin{array} []{c}\cos\theta_{\mathrm{q}}\\ \sin\theta_{\mathrm{q}}\end{array}\right] \tag{21}\]  
Among them, \(q\) is the current position of the robot, \(d(q)\) is the distance from the current position to the farthest node of the original trajectory within its search range, \(\theta_{\mathrm{q}}\) is the angle of the path tangent direction, \(k_{aqt}\) is the gravitational coefficient, \(d_{o}\) is the diameter of the robot, and \(\psi\) is the nonlinear factor.