esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Prototypical Implementation for Medical Use Case  
As a representative prototypical implementation, the medical device example has been partially implemented in ROS 2 [63], which uses DDS as middleware, in combination with Gazebo [64] for the use case depicted in Figure 4. A remote control, an angiography system, and an OR table are currently implemented each as ROS 2 nodes. The Supervisor-NIDS also is implemented in ROS2 and listens to the provided services and sensors in the network (Figure 7). Furthermore, the OR table and angiography system are connected to Gazebo models so that the positions of the joints can be simulated (Figure 8).  
**Payload Plausibility Check for OR Table Joint Positions**  
As an example of payload plausibility, the _Provides Position_ service for the OR table is implemented using the ROS2 sensor message _JointState_ published by the Gazebo model. The NIDS supervisor also subscribes to this service of the OR table and generates a warning as soon as the position of a joint is not in a certain position. Each device in the OR relying on this service is capable of checking the payload plausibility this way.  
**Publish/Subscribe Causality for OR table Movements**  
To detect the implausible movement behavior of an OR table, the NIDS supervisor listens to the _Moves_ service provided by the OR table. If the provided positions indicate a moving joint and there has been no movement request by the remote control for a specific moving joint, an anomaly is detected. The velocity is determined by deriving the provided position according to time. The comparison of the time stamp between the last movement call and a joint movement that has taken place provides information about whether the movement could have been triggered by the remote control.  
**Context Plausibility of a Locked OR table**  
A call to the motion service should not occur while the OR table is in a _locked_ state, which prohibits movement of any kind. Therefore, this is taken into account by the NIDS supervisor, which listens to a sensor of the OR table that provides the current lock state as soon as it detects a call to the motion service. The sensor is implemented with a ROS2 topic that has the states true (locked) and false (unlocked). As soon as the position of the OR table changes while it is locked, a warning is generated.  
Figure 8: Setup for the OR use case implemented in ROS2 and Gazebo.  
Figure 7: OR network implemented in ROS2.
## 4 Materials and Methods
### GC-MS Conditions and Metabolomics Detection  
A triple quadrupole mass spectrometer (TSQ 9000, Thermo Fisher Scientific, Waltham, MA, USA) and a gas chromatograph (TRACE 1310, Thermo Fisher Scientific) were employed in the GC-MS study. A TG-5 MS (30 m \(\times\) 0.25 mm I.D., 0.25 \(\upmu\)m film, Thermo Fisher Scientific) capillary column was used to separate compounds. Helium was employed as the carrier gas, with a flow rate of 1 mL/min. The temperature was first set at 50 \({}^{\circ}\)C and held for 8 min using the GC's program. It was then increased to 140 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C per minute and held for 2 min. Finally, it was raised to 270 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C per minute and held for 1 min. A split ratio of 5:1 was used for injecting the extracted volatile chemicals into the front GC injector. The temperature of the ion source was 290 \({}^{\circ}\)C. With the electron impact (EI) ionization mode and a scan range of 33-370 amu, we collected mass spectra at 70 eV.  
In metabolomics detection, for LC-MS/MS, a C18 column is employed with a gradient of 0.1% formic acid in water (A) and acetonitrile (B), coupled to ESI-MS in positive/negative modes (\(m/z\) 50-1500). GC-MS analysis involves derivatization (methoximation and silylation) followed by separation on a DB-5 MS column with temperature ramping (60 \({}^{\circ}\)C to 310 \({}^{\circ}\)C) and EI-MS (\(m/z\) 50-600). Raw data are processed using tools like XCMS or MS-DIAL for peak alignment, normalized to internal standards (e.g., ribitol for GC-MS), and analyzed via PCA/PLS-DA in MetaboAnalyst. Metabolites with VIP \(>\) 1.0, \(p<\) 0.05, and \(\geq\)2-fold change are annotated by matching MS/MS spectra and retention indices to databases (HMDB, KEGG, NIST) and validated with standards. Quality control includes pooled QC samples (RSD \(<\) 30%) to ensure reproducibility. Key metabolic pathways (e.g., glycolysis, phenolic metabolism) are mapped via KEGG or PlantCyc [49].
## 7 Numerical Results and Discussion
### Robustness of the Receiver  
The performance of the detector in [8] versus CFO is shown in Figure 9. In Figure 9, the horizontal line represents the detection performance when no CFO is considered, which gives us a benchmark for comparison. As indicated in Figure 9, good performance is achieved especially when \(f\) equals \(\pm 10\) ppm for the detector in [8]. This result follows from the number of the gap between actual CFOE and its corresponding quantified value \(\pi/2\) being small enough, i.e., only \(0.0380\pi\) radians. However, when this number greatly increases to \(0.2340\pi\) radians, i.e., \(f=\pm 70\) ppm, performance is degraded severely by large estimation error (e.g., more than 2 dB at PER of \(1\times 10^{-2}\) as shown in Figure 10).  
Figure 8: BER and PER comparison for the proposed receiver and the receiver in [10].  
Figure 7: Performance comparison for various receivers. (**a**) BER performance of different receivers; (**b**) PER performance of different receivers.  
The analysis result for the simulation in Figure 9 is described in detail in Table 3. The result for the absolute estimation error \(|e|\) is also presented in Table 3. Observe from Figure 9 and Table 3 that the smaller the absolute estimation error is, the better the detection performance. When \(|e|\) is close to its maximum value 0.25\(\pi\) (i.g., \(f\) equals \(\pm\)5 ppm, \(\pm\)60 ppm or \(\pm\)70 ppm), and the receiver suffers from a serious performance penalty. Based on those observations, we see that the detector in [8] works well only for such CFOEs that are close enough to four values, i.e., \(0,\pi/2,-\pi\), and \(-\pi/2\). Otherwise, a large estimation error will introduce unacceptable performance penalty.  
The limitation in [8] is avoided to some extent in our detector as depicted in Figure 11. As indicated in Figure 11, good performance is achieved for most considered CFOs. This is because a simple additive adaptive offset term \(Im(Y)\) is introduced as shown in Equation (19), and the maximum value for the absolute estimation error \(\left|e\right|\) is reduced to \(\pi/2-1\). In addition, \(only\) when \(\left|\varphi\right|\) is close enough to \(\pi/2\) (i.e., the CFO \(f\) equals \(\pm 10\) ppm), large degradation in performance is observed. This is because a relatively large approximation error in \(\sin^{-1}\left(x\right)\approx x\) is now achieved as shown in Figure 5. However, note that no limitation on the CFOE range is required for direct application of the mathematical approximation \(\sin^{-1}\left(x\right)\approx x\), for small \(x\), in our estimator.
## 3 Results
### The Predictivity of Different CECT Phases-Based RFs  
For this experiment, 56 patients who were followed up were used in the analysis, as described in Section 3.1. RFs extracted from each imaging phase were used to predict overall survival in these patients a thousand times independently.  
Over the 1000 repeats, a different number of successful repetitions were achieved for each imaging phase. The C-index values ranged between (0.08-1.00), (0.05-1.00), and (0.07-1.00) for the non-contrast, arterial and late scans RFs, respectively (Figure 3). On average, predictions based on RFs extracted from the arterial phase scans (average C-index of 0.59) and the non-contrast enhanced phase (average C-index of 0.56) were found to be significantly higher than those obtained using the late phase (average C-index of 0.47), with \(p\)-values < 2 \(\times\) 10\({}^{-16}\). There was no significant difference in the distribution of the C-index values obtained based on RFs extracted from the non-contrast enhanced and arterial phases (\(p\)-value = 0.36).
## 3 FMCW LiDAR Research Progress
### Research Progress on Integration
This innovative approach offers a scalable solution that enhances system reliability and stability through an integrated design and optimised antenna array, thereby providing a technological foundation for the development of large-scale solid-state LiDAR systems. Although silicon-based optoelectronic integration technology has made remarkable achievements in improving the integration and cost-effectiveness of FMCW LiDAR systems, there are still a series of technical challenges in achieving high-performance and highly integrated radar systems. Current research progress has demonstrated the great potential of integration technology in system miniaturization, cost reduction and performance improvement. However, the design of highly integrated systems has extremely high requirements for manufacturing precision and materials and is highly dependent on the perfection of the design and manufacturing processes. Future research will need to focus on optimizing optoelectronic integration design, improving the precision and reliability of manufacturing processes, and developing new materials and technologies to reduce costs and improve system performance. With the continued advancement of these technologies, it is expected that silicon-based optoelectronic integration technology will be more widely used in more fields such as autonomous driving, aerospace and environmental monitoring, further promoting the development of FMCW LiDAR technology towards a more efficient, economical, and intelligent direction.
## 2 Related Work
### Hybrid SOSK-Means  
A hybridization of SOS and K-means proposed by [26] found the solution to the automatic clustering algorithm. The proposed algorithm employed the standard SOS algorithm to globally search for the optimum initial cluster centroids for the K-means algorithm. This resolved the problems associated with the random generation of the initial cluster centroid without initial specification of the value of \(k\). The problem of multiple parameter controls required in most nature-inspired population-based metaheuristic algorithms (e.g., GA, PSO, FA) was also avoided, since the SOS required only the basic control parameters for a metaheuristic algorithm, such as the number of iterations and population size, with no algorithm-specific parameters. The SOS as a global search algorithm ensured that K-means returned a global optimum solution to the clustering problem, canceling the possibility of getting stuck in the local optimum. According to [26], the SOSK-means algorithm combined the local exploitation capability of the standard K-means with less parameter tuning and global exploration, as provided by the SOS algorithm with implementation simplicity common to the two algorithms, to produce a powerful, efficient, and effective automatic clustering algorithm. The resulting hybrid algorithm was credited with better cluster solutions than the results from the standard SOS and K-means algorithms executed separately. The SOSK-means algorithm commences by initializing the population of \(n\) organisms representing the ecosystem, randomly generated using Equation (19), and the fitness value of each organism is calculated based on the fitness function for the optimization process. The initial organisms are generated by the expression \(rand(1,K\times m)\) in the equation, representing random and uniformly distributed points within the ecosystem, the solution search space is bounded between specified lower and upper limits \(a\) and \(b\), respectively, for the clustering problem:  
\[x_{i}=rand(1,K\times m)\times(b-a)+a\quad\
i\;i=1,2,\ldots,n \tag{19}\]  
New candidate solutions are subsequently generated using the three phases of the SOS algorithm, described under the SOS algorithm. The optimum result from the phases of the SOS algorithm is passed as the optimum cluster centroids for initializing the K-means algorithm. These processes are iteratively performed until the stopping criterion is achieved. The details on the design and performance of hybrid SOSK-means can be found in [26]. Algorithm 1 presents the pseudocode for the hybrid SOSK-means algorithm. The flowchart for Algorithm 1 can be found in [26].
## 5 Results and Discussion
### Verification of the Improved GA's Ability to Adjust Inter-Unit Distances and Overall Speed
Figure 8 shows a comparison of the overall speed variations when directly using the improved GA and when using the offline training method. When the improved GA is applied directly, the straight-line speed fails to converge to the target speed. At 10.5 s, the overall speed is 0.66 knots (with a relative deviation of 56.00%). In contrast, when the offline training method is used, the overall speed quickly converges to the target speed of 1.50 knots. Both the improved GA and the offline-optimized model (HHO+BP) can complete result computation and output within a single sampling period, demonstrating their real-time capability. The results clearly demonstrate that the improved GA alone struggles to manage the complexity of large-scale systems. In contrast, our hybrid approach (HHO+BP) achieves significantly better performance, with more stable and controlled inter-unit distance variations. Additionally, the overall speed variations are smoother and more consistent in the HHO+BP method compared to the improved GA. This comparison underscores the superiority of our proposed method in handling large-scale thrust allocation problems. While the improved GA is effective for smaller systems, its limitations become apparent in larger configurations, necessitating the integration of HHO+BP to ensure scalability, stability, and computational efficiency. By combining these techniques, our method provides a robust solution for large-scale MATC systems, addressing the challenges that existing approaches fail to resolve.
## 5 Results
### Performance Evaluation of DVB-T2 Using MER and EVM
#### 5.2.1 Native DVB-T2 with LDPC Coder Using MER  
As explained in Section 4.3, the MER gives an overview of the modulated signal quality. We present in this part the CFO performance using the MER instead of the BER as previously shown.  
Figure 24 presents the MER evolution as a function of SNR. It is observed that when only Gaussian noise was applied, the MER was equal to the SNR without the presence of a CFO. In the presence of a CFO, the MER performance decreased with the increase in CFO value. In other words, the MER was lower than the SNR value for each CFO value. This result confirmed the BER evolution behavior and demonstrated that in the presence of CFO, above 30 dB, the MER did not increase even if the SNR increased.  
Figure 25 exposes the CFO impact on the DVB-T2 system using the MER when the TU6 channel was applied. One can note the impact of the TU6 channel on the MER performance. Instead of having the MER equal to SNR as in the AWGN case without CFO, the MER was lower than the SNR when the CFO was not considered. At the SNR of 20 dB, the MER was equal to 10 dB, meaning that the TU6 channel induced a loss of 10 dB on the MER performance. Furthermore, when the CFO was applied, the MER performance decreased by up to 16 dB in comparison with the MER curve without CFO.  
\begin{table}
\begin{tabular}{c|c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**OFDM vs. UFMC: AWGN case only for 3 different BERs (\(10^{-3}\), \(10^{-4}\), \(10^{-5}\))**} \\ \hline \multirow{3}{*}{**CFO**} & \multicolumn{3}{c|}{**Native**} & \multicolumn{3}{c|}{**UFMC-based**} & \multicolumn{3}{c|}{**Native DVB-T2**} & \multicolumn{3}{c}{**UFMC-based**} \\  & **DVB-T2** & \multicolumn{3}{c|}{**DVB-T2**} & \multicolumn{3}{c|}{**CFO SNR**} & \multicolumn{3}{c}{**DVB-T2**} \\  & **SNR [dB]** & \multicolumn{3}{c|}{**SNR [dB]**} & \multicolumn{3}{c|}{**SNR [dB]**} & \multicolumn{3}{c|}{**penalies [dB]**} \\ \hline \(\epsilon\) & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** & **BER** \\  & \(10^{-3}\) & \(10^{-4}\) & \(10^{-5}\) & \(10^{-3}\) & \(10^{-4}\) & \(
## 3 Results and Discussion
### Reducing Power  
In this study, the ferric reducing power is related to a compound's electron transfer ability and presents the indicator of its antioxidant activity. Figure 7 displays the result of the assay in reducing power, similar to the result of the total flavonoid content in Figure 6. Peel pulp extracted using DI water or 50% ethanol exhibited the best reducing power (41.9 \(\pm\) 2.2 mg AAE/g DW) at the temperature of 60 \({}^{\circ}\)C. Measurement of the reducing power of the seeds extracted with DI water was difficult, and the seeds extracted with 50% ethanol had the best reducing power at 60 \({}^{\circ}\)C. The reducing power of peel pulp and seeds extracted with 50% ethanol was better than that of DI water, and that of peel pulp was better than that of seeds. Compared to the literature, the reducing power of peel pulp extracted with 50% ethanol in this study is higher than extracted with 70% ethanol in 20 varieties of Chinese jujube [41].  
In this study, the results of five antioxidant models, DPPH radical scavenging activity, ABTS radical scavenging activity, total phenolic content, total flavonoid content, and reducing power indicated that peel pulp exhibited better antioxidant capacity than did seeds. A similar trend was also shown by investigation of _Canarium odontophyllum_ Miq. [42] and _Punica granatum_ L. fruit [43].  
Besides jujube, famous plants with antioxidant activity in Miaoli include black tea leaves [44] and _Jatropha curcas_ L. [31]. They show the same finding as jujube fruit and _Jatropha curcas_ L. seeds that extracts by ethanol solution have better antioxidant activity than extracts by DI water. The DPPH  
Figure 6: Total flavonoid content of jujube peel pulp and seeds extracted with DI water and 50% ethanol at different temperatures. The error bar represents the standard error of the mean. The temperatures in the histogram with the same letter were not significantly different at the 5% level, according to the results of the LSD test.  
scavenging activity, total phenolic content, and reducing power of peel pulp of jujube are close to that of seed kernel of _Jatropha curcas_ L. The total phenolic content of jujube peel pulp is also nearly that of black tea steeped at 60 \({}^{\circ}\)C. The higher extraction yield of jujube peel pulp compared to black tea leaves and _Jatropha curcas_ L. seeds benefits jujube fruit for applications.
## 4 Statistical Analysis
### Assessing Means of Two Samples Groups with ANOVA
#### 4.1.3 Assessing Means of Two Samples with ANOVA  
There are two hypotheses in ANOVA:  
\(H_{0}-\)the means of all samples are equal  
\(H_{1}-\)the mean of at least one sample is different from the means of other samples. These hypotheses are tested with the following procedure based on [49]:  
- calculating means \(\overline{x_{i}}\) for each of \(k\) samples (for \(i=1,2,\ldots,k\))  
- calculating the mean for all samples \(\widetilde{x_{i}}\) (global mean) with the following formula:  
\[\widetilde{x}=\frac{1}{n}\sum_{i=1}^{k_{i}}\sum_{j=1}^{n_{i}}x_{ij}\]  
where:  
\[n=\sum_{i=1}^{k}n_{i}\]  
\(n_{i}\) is the number of elements in \(i-th\) sample  
\(x_{ij}\) is the value of \(j-th\) element in \(i-th\) sample calculating \(SS_{\mathit{btw}}\) i.e., sum of squared deviations of the means (of each sample) from the global mean with the following formula: \[SS_{\mathit{btw}}=\sum_{i=1}^{k}\left(\widetilde{x}-\overline{x_{i}}\right)^{2 }\times n_{i}\] - calculating \(SS_{\mathit{tot}}\) i.e., sum of squared deviations of \(x_{ij}\) (all values from all samples) from the mean of each sample with the following formula: \[SS_{\mathit{tot}}=\sum_{i=1}^{k}\sum_{j=1}^{n_{i}}(x_{ij}-\overline{x_{i}})^{2}\] - calculating variances two variances: variance between samples named \(MS_{\mathit{btw}}\) and variance within the samples named \(MS_{\mathit{tot}}\), with the following formulas: \[MS_{\mathit{btw}}=\frac{SS_{\mathit{btw}}}{df_{\mathit{btw}}}\] \[MS_{\mathit{tot}}=\frac{SS_{\mathit{tot}}}{df_{\mathit{tot}}}\] where degrees of freedom (\(df_{\mathit{btw}}\) and \(df_{\mathit{tot}}\)) are as follows: \[df_{\mathit{btw}}=k-1\] \[df_{\mathit{tot}}=n-k\] - calculating the statistics \(F\) with the following formula: \[F=\frac{MS_{\mathit{btw}}}{MS_{\mathit{tot}}}\] - reading the \(F_{\mathit{a}}\) value from the [PERSON]'s tables. \(F_{\mathit{a}}\) depends on: the assumed confidence level \(\alpha\), \(df_{\mathit{btw}}\), and \(df_{\mathit{tot}}\).  
If \(F>F_{\mathit{a}}\) there is no reason to reject the hypothesis \(H_{0}\), so it can be stated that the means of the samples are equal. Statistica software returns \(p\) value. If \(p>\alpha\) there is no reason to reject the hypothesis \(H_{0}\). The condition \(F>F_{\mathit{a}}\) is met if and only if the condition \(p>\alpha\) is met.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 2 Robust Statistics Principles
### Robust Estimates for Regression Problems
#### 2.2.4 MM-Estimator  
The MM-estimator is designed to achieve both high efficiency and high breakdown point simultaneously. Consider two bounded loss functions, \(\rho_{0}\) and \(\rho_{1}\), which satisfy \(\rho_{1}<\rho_{0}\). Then, the MM estimator is defined as  
\[\hat{\mathbf{x}}_{MM}=\arg\min_{\mathbf{x}}\sum_{i=1}^{n}\rho_{1}\left(\frac{r _{i}(\mathbf{x})}{s_{M}(\mathbf{r}(\hat{\mathbf{x}}_{0}))}\right), \tag{23}\]  
where \(\hat{\mathbf{x}}_{0}\) is a consistent estimator of \(\mathbf{x}\) that has a high breakdown point, and \(s_{M}(\mathbf{r}(\hat{\mathbf{x}}_{0}))\) is the M-estimate of scale of the residuals of \(\hat{\mathbf{x}}_{1}\), computed using \(\rho_{0}\) and \(b\).  
The MM-estimate is implemented in three steps:  
1. Compute an initial consistent S-estimate of \(\mathbf{x}\), namely \(\hat{\mathbf{x}}_{0}\), with a high breakdown point but possibly low normal efficiency.
2. Compute an M-estimate of the scale of the residuals \(s_{M}(\mathbf{r}(\hat{\mathbf{x}}_{0}))\) using the high breakdown point estimate \(\hat{\mathbf{x}}_{0}\).
3. Compute the regression M-estimate initialized at \(\hat{\mathbf{x}}_{0}\), considering the robust scale estimate \(s_{M}(\mathbf{r}(\hat{\mathbf{x}}_{0}))\) and using a recursive IRLS approach.
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 3 The Proposed Congestion Control Mechanism
### Detecting and Controlling Congestion  
Initially, the DODAG is constructed through exchanging the DIO messages between neighbor nodes. First, a node \(n_{i}\) generates its parent candidate set \(Parent(n_{i})\) as a subset of its neighbor candidate set \(N(n_{i})\) as follows:  
\[Parent(n_{i})=\left\{n_{j}\in N(n_{i})|H(n_{j})<H(n_{i}),ETX(n_{i},n_{j})< \gamma\right\}, \tag{1}\]  
where \(H(n_{j})\) denotes the hop-count between \(n_{j}\) and the DODAG root, \(ETX(n_{i},n_{j})\) is the estimated ETX value between \(n_{i}\) and \(n_{j}\), and \(\gamma\) is a threshold to eliminate neighbors with bad link quality. \(ETX(n_{i},n_{j})\) is obtained as [33]:  
\[ETX(n_{i},n_{j})=\frac{\text{\# of total transmissions from $n_{i}$ to $n_{j}$}}{\text{\# of successful transmissions from $n_{i}$ to $n_{j}$}}. \tag{2}\]  
Each node \(n_{i}\) selects a preferred parent node \(P_{i}\) from its \(Parent(n_{i})\) for data forwarding. We consider that the initial \(P_{i}\) is selected as the one that has the minimum hop-distance towards the DODAG root [8].  
A node \(n_{i}\) selects a new parent \(P_{i}^{*}\) when changes occur to its \(Parent(n_{i})\). In our proposed method, the new parent selection process is triggered if one of the following two criteria are satisfied: the joint Hop-distance and Link quality (HL)-criterion, and the Load-Balancing (LB)-criterion. Based on these criteria, we also introduce two distinct parent selection mechanisms.