esa-sceva/satcom-chunk-collection huggingface dataset:
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 6 Discussion and Open Research Challenges
### From the Evolution of the Nanosatellites Industry  
The enormous growth foreseen for the IoT market is highly related to the rapid evolution of low-cost wireless access technologies. In particular, with the introduction of LPWA technologies such as LoRa, Sigfox, and NB-IoT, to mention some, the massification of connected devices seems more plausible in the near future. Although nanosatellite connectivity is being identified as part of the IoT technologies ecosystem [6; 7; 8; 9], it is still not considered as low-cost as to become part of the LPWA category [72; 73]. The industry of nanosatellite construction and launching needs to keep evolving, and to evolve fast, to reduce costs even more and become another key player in the LPWA market.
## 5 Results
### Direct Comparison for Simple Detection
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Attitude Mode** & **Sensor** & **Classic** & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**ANN**} \\
**Linear** & **Quadratic** & **Cubic** & **Gaussian** & **Narrow** & **Medium** & **Large** & **Bilayered** & **Trilayered** \\ \hline \multirow{4}{*}{Pointing} & GYRO & 93.75 & 88.1 & 91.1 & **94** & 80.6 & 82.1 & 88.1 & 92.5 & 89.6 & 94 \\  & MAG & 93.75 & 86.6 & 91 & 82.1 & 79.1 & 88.1 & 85.1 & 83.6 & 80.6 & 80.6 \\  & SUN & 93.75 & 88.1 & 91 & 91 & 89.6 & 88.1 & 92.5 & 95.5 & **95.5** & 94 \\  & ACTS & 93.75 & 83.6 & 82.1 & 88.1 & 82.1 & 89.6 & 91 & 89.6 & 86.6 & 86.6 \\ \hline \multirow{4}{*}{Slew} & GYRO & 93.75 & 88.1 & 88.1 & 88.1 & 83.6 & 86.6 & 89.6 & 80.6 & 91 & 92.5 \\  & MAG & 93.75 & 86.6 & 88.1 & 83.6 & 77.6 & 88.1 & 86.6 & 86.6 & 85.1 & 74.6 \\  & SUN & 93.75 & 89.6 & 91 & 88.1 & **94** & **94** & **94** & 92.5 & 89.6 & 88.1 \\  & ACTS & 93.75 & **92.5** & 91 & 83.6 & 86.6 & 88.1 & 83.6 & 83.6 & 82.1 & 79.1 \\ \hline \multirow{4}{*}{No Control} & GYRO & 93.75 & 80.6 & 86.6 & 73.1 & 76.1 & 91 & 85.1 & 85.1 & **95.5** \\  & MAG & 93.75 & 83.6 & 86.6 & 89.6 & 83.6 & 88.1 & 83.6 & 82.1 & 86.6 & 88.1 \\ \cline{1-1}  & SUN & 93.6 & **92.5** & **92.5** & 88.1 & 92.5 & 89.6 & 89.6 & **97** & 94 & 94 \\ \cline{1-1}  & ACTS & - & - & - & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Direct comparison: test batch % accuracy \({}^{1}\) on a 4-class classification. Each sensor in each attitude mode is validated and tested on a balanced dataset of 672 total entries. Figure 9: Differences in the two processing methods. While classic detection in the functional engineering simulator works through a moving window scanning real-time signals to extract the variance, feature extraction needs those same windows to splice the signal and extract meaningful features on short time-periods. For a rigorous training and for immediate modularity in the functional simulator, the sliding window used by all methods is 20 s long.
## 2 Materials and Methods
### Bacterial Strains and Culture Media  
The antibacterial effectiveness of spirulina was tested against the following: _Staphylococcus aureus_ (ATCC 29213), _Escherichia coli_ (ATCC 25922), _Pseudomonas aeruginosa_ (ATCC 060127853), _Enterococcus faecalis_ (ATCC 29211), _Klebsiella pneumoniae_ (ATCC 700603) and _Candida albicans_ (ATCC 24433). To evaluate the antimicrobial activity of the AM, bacteria strains were cultured in Muller Hinton broth or agar (Oxoid, Basingstoke, Hampshire, UK), while the fungal strain was in Sabouroud broth or agar (Sigma-Aldrich, Saint Louis, MO, USA). The prebiotic effectiveness was tested against the following: _Saccharomyces boulardii_ (CBS5926), _Lactobacillus casei_ (R0215) and _Lactobacillus rhamnosus_ (DSM20021). To evaluate the prebiotic activity of the AM, the bacterial strains were grown in Brain Heart Infusion (BHI) broth.
# Tolerance Modeling and Analysis Considering Form Defects for Spaceborne Array Antenna
## 4 Conclusions  
This study proposes an effective and accurate tolerance analysis method for complex electromechanical products. The main findings of this study are summarized as follows.  
1. An accurate tolerance modelling method of surface feature was proposed, the NURBS-surface-based tolerance model can represent the form errors precisely, which will affect the performance of the antenna seriously.
2. The sampling method based on the novel tolerance model was developed by introducing the variance separation method, the traditional double-loop analysis process was then simplified to a single-loop process. This method greatly reduces the computational cost.
3. A new assembly simulation method was proposed because of the characteristics of low stress assembly. The geometric error of the whole antenna can be determined effectively. Together with the sampling method, the whole sample of product geometric error and performance can be generated precisely.
4. The tolerance analysis method was validated by a X band spaceborne array antenna. Simulation results show the effectiveness of the method.  
Conceptualization, Z.L. and G.S.; methodology, G.S., Z.L.; software, G.S.; validation, G.S., C.Q., and Z.L.; formal analysis, G.S.; investigation, J.T.; resources, J.T.; data curation, C.Q.; writing--original draft preparation, G.S.; writing--review and editing, G.S.; visualization, C.Q.; supervision, Z.L.; project administration, J.T.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51490663, 51935009 and 51875517; Zhejiang Provincial Natural Science Foundation of China (grant No. LY20E050015).  
The authors declare no conflict of interest.
## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security
### AI, ML and Network Security
Additionally, the possible difficulties arising from the integration of ML in 5G/network slicing scenarios have been examined in [73]. The study concludes, among other things, that there are more security issues because of the dynamic nature of mobile networks, which are marked by an extensive number of users and services with different requirements and characteristics. While ML algorithms have proven effective in controlled settings with small amounts of data, mobile networks' hyper-dynamic natures expose them to new and sophisticated security threats from expert hackers and attackers. Ensuring ML algorithm stability is crucial to prevent performance degradation in dynamic environments like high-speed trains. Deploying ML in SDN requires visibility and control to avoid bottlenecks and scalability issues. IoT-triggered event spikes challenge ML system latency and capacity, highlighting the need for interdisciplinary research on defensive solutions for 5G and beyond networks. Using ML in these contexts is like handling a double-edged sword; tactics to counteract negative consequences must be evaluated. Furthermore, using AI technologies in network slicing amplifies privacy concerns. While AI can potentially safeguard the networks' privacy, it also introduces vulnerabilities throughout the development and training stages. Some of the challenges presented are [66; 74]:  
* **Security:** ML systems face security threats like poisoning, evasion, API-based attacks, and AI framework infringements, endangering data integrity. * **Privacy:** ML's data analysis and automation can compromise privacy. Insecure IoT devices and model inversion attacks threaten data, making protection crucial. * **Ethical:** ML reduces human intervention, but computers lack human ethical consciousness. ML systems follow training but cannot act against logic in certain circumstances. * **Intelligent Attacks:** AI can be used to identify patterns in large data volumes, potentially exposing network vulnerabilities. Without a doubt, AI, especially ML, has the potential to revolutionise the future of networks. However, it is necessary to consider the challenges it can bring. Addressing these security challenges is imperative to ensure the effectiveness and trustworthiness of AI-driven security solutions in network-slicing environments. Table 1 presents an overview of the importance and role of AI in network security for different applications, objectives, and scenarios, highlighting the strengths and weaknesses derived from including this functionality.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Data Validation: Comparison with Laboratory Data
#### 5.1.2 Bland-Altman Analysis  
The Bland-Altman method was used to evaluate the agreement between system and laboratory measurements [27]. The method consists of three key steps:  
Mean difference (\(\mu_{D}\)) calculation evaluates the systematic bias between the two measurement methods:  
\[\mu_{D}=\frac{1}{n}\sum_{i=1}^{n}(Y_{i}-X_{i}) \tag{5}\]  
where \(X_{i}\) represents the laboratory measurement, \(Y_{i}\) is the system measurement, and \(n\) is the total number of paired samples.  
Standard deviation (SD) of differences calculation assesses the variability in measurement differences:  
\[SD_{D}=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(D_{i}-\mu_{D})^{2}} \tag{6}\]  
Figure 16: Label distribution of water quality dataset.  
Limit of agreement (LOA) computation defines the 95% confidence interval for measurement differences:  
\[LOA_{\text{upper}}=\mu_{D}+1.96\times SD_{D} \tag{7}\]  
\[LOA_{\text{lower}}=\mu_{D}-1.96\times SD_{D} \tag{8}\]  
These limits represent the expected range within which 95% of differences should fall, assuming no proportional bias.
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 1 Introduction
### Related Research
The hybrid heuristic consisted of a TS heuristic and a dual simplex method. Recall, a dual simplex method is used in the proposed GA.  
As stated above, BSH, presented in [PERSON] and [PERSON] [18], is used to construct solutions (or layouts) for the proposed GA. More specifically, BSH, as a construction algorithm, selects facilities for placement on the plant floor based on the cumulative flow for each facility with all the other facilities. The facilities with higher cumulative flow are placed on the plant floor first. After the first facility, with the largest cumulative flow, is placed on the plant floor, the other facilities are placed along the boundaries of already placed facilities. [PERSON] and [PERSON] [20] and [PERSON] and [PERSON] [21] presented similar, but slightly different construction methods. See papers for details. Besides the metaheuristics mentioned above (i.e., simulated annealing, tabu search, GA, hybrid GA with simulated annealing and/or tabu search, firefly, particle swarm optimization), other metaheuristics have been used to solve UA-FLPs, without fixed facilities dimensions. For example, [PERSON] and [PERSON] [22] used monarch butterfly optimization to solve a UA-FLP which uses a slicing tree structure. [PERSON] et al. [23] used coral reefs optimization, an evolutionary-type algorithm, to solve a UA-FLP which uses a flexible bay structure. Also, [PERSON] et al. [24] presented a parallel GA based on the island model to solve a UA-FLP which uses a flexible bay structure. [PERSON] and [PERSON] [25] presented a hybrid GA with a linear programming approach to solve a UA-FLP which uses a new encoding scheme, which represents the relative locations (or positions) of the facilities. Once the relative locations of the facilities are set by the GA (i.e., binary variables are set in a mixed integer program), the corresponding linear program is solved to determine the actual layout of the facilities (e.g., shapes and locations of the facilities on the plant floor are obtained). [PERSON] and [PERSON] [26] presented a biased-random-key GA (BRKGA) for a UA-FLP where the facilities dimensions are not fixed. First, the BRKGA is used to determine the order facilities are placed on the plant floor and the dimensions of the facilities. Second, a novel placement strategy is used to place the facilities on the plant floor. Third, a linear programming model is used to improve the solutions (i.e., layouts).
###### Abstract  
Accurately reconstructing the strain field within stiffened ship panels is crucial for effective structural health monitoring. This study presents a groundbreaking approach to strain field reconstruction in such panels, utilizing optical fiber sensors in conjunction with the strain function-inverse finite element method (SF-iFEM). A novel technique for solving nodal strain vectors, based on the element strain function, has been devised to improve the accuracy of strain reconstruction using the inverse finite element method (iFEM), addressing the limitations associated with traditional nodal displacement vector solutions. Moreover, the proposed method for determining the equivalent neutral layer of stiffened ship panels not only reduces the number of elements effectively but also establishes a strain function between the inner and outer surfaces of the structure. Using this function, a layout scheme for optical fiber sensors on the inner side of ship stiffened panels is provided, overcoming the symmetrical arrangement constraints of iFEM for sensor placement on both the inner and outer sides of the structure. The results demonstrate a significant improvement in strain reconstruction accuracy under bending and bending-torsion deformations compared to conventional iFEM. Consequently, the findings of this research will contribute to enhancing the engineering applicability of iFEM in ship structure health monitoring.  
Keywords:ship stiffened panel; inverse finite element method; strain function; strain field reconstruction; equivalent neutral layer calculation; fiber optic sensor layout; structural health monitoring +
Footnote †: journal: Applied sciences  
Article Enhanced Strain Field Reconstruction in Ship Stiffened Panels Using Optical Fiber Sensors and the Strain Function-Inverse Finite Element Method
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 5 Results
### Reflection Gain Impact on PNFR Level
#### 5.3.2 Reflection Gain Impact as a Function of 5G Frame Number  
In Figures 14 and 15, the PNFR level is presented depending on the reflection gain and the number of observed 5G frames (number of frames taken into account while calculating CIR and RVM matrices) for configurations with one and two PRS resources, respectively. For this purpose, the constant level of SNR at 20 dB was used. The maximum number of 5G frames is 15. This is because [2] explicitly states that PRS pilots cannot be placed on the same symbols as the Synchronization Signal Block with Physical Broadcast Channel (SS/PBCH). SS/PBCH is transmitted periodically, with a periodicity ranging from 5 ms to 160 ms. To simplify, we assume SS/PBCH periodicity of 160 ms and exclude the frame on which SS/PBCH occurs from sensing. This gives us a maximum of 150 ms of observation time (fifteen 5G frames). It can be seen that different PRS configurations with the same number of PRS resources perform very similar at the same reflection gain level. Interestingly, the PNFR does not increase linearly, as before in the function of the SNR level, but it has some local maximums; for example, at thirteen 5G frames. This is because for the different numbers of 5G frames that were used to calculate the CIR and RVM, we will obtain different sizes of these matrices. This implies that for different numbers of 5G frames, velocity sensing will be performed with a different resolution (see Equation (18)). As said above in Section 5, the radar targets are designed in such a way that for ten 5G frames of observation time, neither velocity nor range falls exactly into the bins of the CIR and RVM matrices, but right between the two adjacent bins. The spectrum spreads on them, resulting in a lower  
Figure 12: PNFR for different PRS configurations with 1 PRS resource as a function of SNR for different levels of reflection gains.  
Figure 13: PNFR for different PRS configurations with 2 PRS resources as a function of SNR for different levels of reflection gains.  
magnitude and therefore lower PNFR. As as result, for the observation time of ten 5G frames, we have a local minimum, and PNFR appears worse than on the other numbers of 5G frames. This example is very good at showing how important the sensing resolution is to radar performance and what happens when our target is ideally between two bins of the CIR matrix. Ten 5G frames of observation time lose about 4 dB of PNFR to the thirteen 5G frames of observation time. However, increasing the number of 5G frames is generally associated with a higher PNFR, with the gain reaching up to 3 dB when considering values at five and fifteen 5G frames. PRS-12-1-1-RES and PRS-12-1-2-RES stand out from the rest of the configurations: the first one seems to have a problem with the lowest gain because the PNFR does not improve at all. For these configurations, better radio conditions are needed to detect targets with such a low reflection gain.
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 4 Transmission Efficiency Model
### Transmission Efficiency Model  
This is based on the determination of the coefficient of friction in the mixed lubricating state. Furthermore, it is possible to obtain the exact solution of the friction force of the cycloid system.  
\[F_{f}=f_{\max}F \tag{27}\]  
where \(f_{\max}\) indicates the contact surface friction coefficient corresponding to the mixed lubrication condition.  
The instantaneous frictional power consumption of the contact surface is as follows.  
\[P_{i}=f_{\max}Fv_{k} \tag{28}\]  
where \(F\) represents the contact force between the \(i\)-th pin tooth and the cycloid. \(f_{\max}\) is the coefficient of mixed friction corresponding to the \(i\)-th contact surface. \(v_{k}\) is the relative motion velocity at the \(i\)-th pin contact point.  
The total frictional power consumption of the cycloid and the pin tooth in the transmission process is the sum of the power consumption at all contact points.  
\[P_{z}=\sum_{i=1}^{z_{b}}f_{\max}^{i}F^{i}v_{k}^{i} \tag{29}\]  
where \(z_{b}\) represents the number of pin teeth.  
In summary, the instantaneous transmission power of the cycloid drive system is obtained as follows:  
\[\eta_{z}=1-\frac{P_{z}}{P_{o}} \tag{30}\]  
where \(P_{o}\) is the input power.  
For the cycloid system, transmission efficiency is influenced by tolerances, fit clearances, collisions, and other factors. In this paper, the friction coefficient of the greased teeth is added to determine the mesh characteristics of the teeth. The objective is to compare the effect of the various structures on the transmission efficiency of the cycloid system and to guide the design process.
## 3 Results and Discussion
### Evaluation of Direct Indexes
#### 3.3.3 Analysis of Texture Indexes  
The measured texture indexes, surface parameter \(SP\), perimeter ratio of equivalent ellipse \(A_{I}\), convex ratio \(C_{R}\), shape factor \(SF\) and fractal dimension \(D\), of the selected 9 kinds of FAs based on the high-resolution images were shown in Table 9. It is not surprising to observe that the natural sand (FA1) has the smallest value in terms of the five texture indexes.  
The one-way ANOVA analysis was also conducted to analyze the effect of FA type on the texture indexes and the results are shown in Table 10. As can be seen in Table 10, the calculated \(F\) values for all of the five indexes are smaller than \(F_{0.05}=5.59\). Therefore, conclusions can be drawn that all the texture indexes fail to distinguish the geometric characteristic difference of different FAs at the significance level of 95%.  
To summarize, the geometric characteristics of natural sand are significantly different from stone chips and machine-made sand according to the DIP-based direct evaluations. Among the adopted indexes in this research, only the form index ratio of equivalent ellipse axis \(E\) and the angularity index surface parameter \(SP\) can effectively distinguish the geometric characteristic difference of different FAs.
# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
## 3 The Construction Process of the Synthesis Health Index for Metro Vehicle Wheelsets
### Outlier Removal Based on Isolation Forest for Dimensionality-Reduced Data  
After dimensionality reduction, some data points with low density and relative isolation may persist, potentially affecting subsequent clustering. Unlike anomaly detection algorithms designed for time series data, the Isolation Forest (iForest) algorithm [22] can identify outliers that are sparsely distributed and distant from high-density clusters in the feature space. Therefore, this paper employs iForest to remove outliers from the dimensionality-reduced data.  
iForest isolates and identifies outliers using decision trees by randomly and recursively partitioning the data space to separate the outliers.  
iForest is based on decision trees to isolate and identify outliers. By recursively splitting the data space through random partitioning, outliers are isolated. For a given dataset, the process begins by randomly selecting a feature and then choosing a split threshold between the maximum and minimum values of the feature. All values in the dataset less than or equal to the chosen threshold are assigned to the left subtree, while values greater than the threshold are assigned to the right subtree. This process of recursive splitting continues until one of the following conditions is met:  
1. The tree reaches the maximum depth \(h_{lim}\).
2. There is only one sample left at the node.
3. All feature values of the samples at the node are identical.  
The path length at which each point is isolated in the tree serves as the basis for its anomaly score. Let the number of edges traversed by a sample in the tree be denoted as \(h_{lim}\) and the calculation of the path length be as follows:  
Step 1: If the node is a leaf node, it means the sample has reached its corresponding partition. Return the number of traversed edges as the path length.  
Step 2: If the path length reaches or exceeds the tree's height limit, return the path length as the value \(e+c(T.size)\).  
Step 3: Otherwise, based on the split threshold, recursively search within the subtrees. If the value of the sample is less than or equal to the threshold, move to the left subtree; otherwise, move to the right subtree. Repeat Steps 1 and 2 until the path length is returned.  
The path length where isolation in the isolation tree fails to be found can be treated as a correction value. The calculation of this correction value is as follows:  
\[c(n)=\begin{cases}2H(n-1)-\frac{2(n-1)}{n},n>2\\ 1,n=2\\ 0,otherwise\end{cases} \tag{9}\]  
The number of edges a sample traverses to reach the leaf node is not only related to the sample itself but also to the tree's \(h_{lim}\) and the sample's subset. Therefore, iForest maps the number of edges to the range \([0,1]\) and defines the anomaly score of the sample as follows:  
\[s(x,n)=2^{-\frac{E(h(x))}{e(x)}} \tag{10}\]